diff --git a/Exercise1/exercise1.ipynb b/Exercise1/exercise1.ipynb
index 0d245b5c..15c7333d 100755
--- a/Exercise1/exercise1.ipynb
+++ b/Exercise1/exercise1.ipynb
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -108,7 +108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -126,7 +126,7 @@
     "    Return the 5x5 identity matrix.\n",
     "    \"\"\"    \n",
     "    # ======== YOUR CODE HERE ======\n",
-    "    A = []   # modify this line\n",
+    "    A = np.eye(5)  # modify this line\n",
     "    \n",
     "    # ==============================\n",
     "    return A"
@@ -149,9 +149,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 0., 0., 0., 0.],\n",
+       "       [0., 1., 0., 0., 0.],\n",
+       "       [0., 0., 1., 0., 0.],\n",
+       "       [0., 0., 0., 1., 0.],\n",
+       "       [0., 0., 0., 0., 1.]])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "warmUpExercise()"
    ]
@@ -173,9 +188,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise linear-regression\n",
+      "\n",
+      "Login (email address): waiyen.chan0819@gmail.com\n",
+      "Token: zpKvqojmOUYUxUm8\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "                           Warm up exercise |  10 /  10 | Nice work!\n",
+      "          Computing Cost (for one variable) |   0 /  40 | \n",
+      "        Gradient Descent (for one variable) |   0 /  50 | \n",
+      "                      Feature Normalization |   0 /   0 | \n",
+      "    Computing Cost (for multiple variables) |   0 /   0 | \n",
+      "  Gradient Descent (for multiple variables) |   0 /   0 | \n",
+      "                           Normal Equations |   0 /   0 | \n",
+      "                                  --------------------------------\n",
+      "                                            |  10 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "# appends the implemented function in part 1 to the grader object\n",
     "grader[1] = warmUpExercise\n",
@@ -199,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -236,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -270,6 +309,9 @@
     "    fig = pyplot.figure()  # open a new figure\n",
     "    \n",
     "    # ====================== YOUR CODE HERE ======================= \n",
+    "    pyplot.plot(x, y, 'ro', ms=10, mec='k')\n",
+    "    pyplot.ylabel('Profit in $10,000')\n",
+    "    pyplot.xlabel('Population of City in 10,000s')\n",
     "    \n",
     "\n",
     "    # =============================================================\n"
@@ -288,9 +330,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plotData(X, y)"
    ]
@@ -306,7 +361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -352,7 +407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -377,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -419,6 +474,7 @@
     "    J = 0\n",
     "    \n",
     "    # ====================== YOUR CODE HERE =====================\n",
+    "    J = (0.5/m)*np.sum((np.dot(X, theta)-y)**2)\n",
     "\n",
     "    \n",
     "    # ===========================================================\n",
@@ -434,9 +490,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "With theta = [0, 0] \n",
+      "Cost computed = 32.07\n",
+      "Expected cost value (approximately) 32.07\n",
+      "\n",
+      "With theta = [-1, 2]\n",
+      "Cost computed = 54.24\n",
+      "Expected cost value (approximately) 54.24\n"
+     ]
+    }
+   ],
    "source": [
     "J = computeCost(X, y, theta=np.array([0.0, 0.0]))\n",
     "print('With theta = [0, 0] \\nCost computed = %.2f' % J)\n",
@@ -457,9 +527,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise linear-regression\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "                           Warm up exercise |  10 /  10 | Nice work!\n",
+      "          Computing Cost (for one variable) |  40 /  40 | Nice work!\n",
+      "        Gradient Descent (for one variable) |   0 /  50 | \n",
+      "                      Feature Normalization |   0 /   0 | \n",
+      "    Computing Cost (for multiple variables) |   0 /   0 | \n",
+      "  Gradient Descent (for multiple variables) |   0 /   0 | \n",
+      "                           Normal Equations |   0 /   0 | \n",
+      "                                  --------------------------------\n",
+      "                                            |  50 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[2] = computeCost\n",
     "grader.grade()"
@@ -494,7 +587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -547,6 +640,8 @@
     "    \n",
     "    for i in range(num_iters):\n",
     "        # ==================== YOUR CODE HERE =================================\n",
+    "        h = np.dot(X, theta)\n",
+    "        theta -= alpha*(1/m)*np.dot(X.T, h-y)\n",
     "        \n",
     "\n",
     "        # =====================================================================\n",
@@ -566,9 +661,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Theta found by gradient descent: -3.6303, 1.1664\n",
+      "Expected theta values (approximately): [-3.6303, 1.1664]\n"
+     ]
+    }
+   ],
    "source": [
     "# initialize fitting parameters\n",
     "theta = np.zeros(2)\n",
@@ -593,9 +697,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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vZ/DgwfTs2ZNu3brxy1/+skFyLVq0iDFjxvDXv/4Vj8fDZZddRteuXfnHP/7B8OHDmTZtGgMHDvRLGe2lc+fO/PWvf+Wcc86huroah8PBzJkzyczM5KqrrqK6uhqAyZMnc+DAAYYPH86uXbsQEYqKimjdunWt+/3rX//i6quv5p577qFt27Y88sgjDfpcihILvC6mF513HhvC1B3t8dBnwYKg3kCvffg1Vz7yv1pltw88kdFnHBuwfqLRZHDNiH379pGZmYkxhscff5zHHnus1ubwyYr+1ko8SE9LozLMPrkeIDMtjaoDB2qVP//e51z/eO1p11M6tubZwsTunREuGZyOAJoRa9asYdy4cYgIrVu3Zu7cuYkWSVGSBt/F4GDUzTf0yH8/4S8v1N6Bb0CXn/Hg8B6xETLKqAFoRpxxxhk16wGKotTGuxh8d4iYAG++oXtf+pAZr5bXOndF70785fwusRYzqjRpAyAiSbOYosSGpjBFqaQG4yZMoNe8eQwOEhW8Eph/7jgcWf3AR/nfePbx/KHfcXGTM5rEzAAYY44G5gM/A6qBh0Xkn8aYI4AngBygArhERL6r7/1btmzJzp07adOmjRqBFEVE2LlzJy1bxi4XiqJ4qck3lJ/PaI+H0R4PHbGmfUZcfAdfHNsT3/jcv17QheG9wu1SkNzEbBHYGHMkcKSIrDXGtALWABcAVwLfisgUY0wxcLiITAx1r0CLwB6Ph23bttX4ySupScuWLenQoQOOMKmAFSVauN1uZk6fzuIFC0i78K8c0r62B8/MYd0ZmHdkgqSrHwlbBBaR7cB2+/UeY8wm4CjgfOBMu9o84DUgpAEIhMPhqIlcVRRFiRa5ubk8kzWAlmNqR/wuGv0r+vwiO0FSxYa4rAEYY3KAU4C3gfa2cUBEthtj2sVDBkVRlFCICMdMKvUrf35sH7oe3TrAFU2fmBsAY0wW8DRwg4jsjnS+3hhTABQAdOzYMXYCKorSrPEcqOa42/w3fFlyXW965hyRAIniR0wNgDHGgaX8F4nIM3bxV8aYI+3e/5HA14GuFZGHgYfBWgOIpZyKojQ/9lZW0eXPL/mVPz3mdHp0OjwBEsWfWHoBGWAOsElE7vM5tRS4Aphi/03+UFRFUVKGr3f/yGl3L/crf2XCbzi2bfPaVD6WI4A+wAhggzHGGyN9K5bif9IYMwrLw+riINcriqJEjfKv9/K7+173K//fbb+jbasWAa5IfWLpBfQWEGzCv1+s2lUURfHlfxXfcvEs/+3kP/jLuRzaoknHwjaa5v3pFUVJWco2bGfMorV+5eV/G0BGumbCBzUAiqKkGIEStAF8Mvk8zRpQBzUAiqKkBH9btpHZb37iV14xZWACpGkaqAFQFKVJUzB/Nf/Z6L+ftCr+8KgBUBSlSXL2fa+z5eu9fuWq+CNHV0IURWlS5BQvI6d4mZ/yr5gyMCmUv9vtpqiwkPYuF+lpabR3uSgqLMTtdidaND90BKAoSpMgp3hZwPJkUPpeysrKGJmfzzUeDys8HjoBW/fsYU5JCb3mzWP+kiUMGDAg7H3iRZPdE1hRlOZBU1D8YPX8e+XlsXTfvqAbygxxOlm1fj25uaE2nowe4dJB6xRQGJrScE5RUgnvVE9dkmWqpy4zpk3jmiC7iQH0BkZ7PMycPj2eYoVERwAh8B3OjfIO54A5DgezHY6kG84pSioQSOmf0L4VLxX9OgHSRE57l4sVYTaVdwN9XC6+3LUrLjKFGwGoAQhCMg7nFCVVCZaL//xuP+efl52SAInqT3paGpUiIRdWPUBmWhpVBw7ERaaE7QjW1KnPcO6+GTPiKZqipAw/VVVz/O3+ufgnnH0845vYRuvZWVlsDTMC+NSulyzoGkAQFi9cyCiPJ2Sd0R4PixcsiJNEipI67NrnIad4mZ/y/+dl3aiYMjCo8k/mNblhw4czJ8ze1SUOB8NGjIiTROHRKaAgJONwTlGaOp/u3Mev73nVr/yp63pzapjdt5J9TS4Zp411CqiBNMXhnKIkK2s//Y4LH1jhV/7qTWdyTPahYa93u92MzM/3U665wN0eD4M9Hobk5yd0TS43N5f5S5YwJD+f0R4Poz0eOmLpiRKHgxLbSCXTmqFOAQWhKQ7nFCXZWLZ+OznFy/yU/7t/PJuKKQMjUv7QdFwsBwwYwKr166ksKKCPy0VmWhp9XC4qCwpYtX590nkN6hRQEJJxOKcoTYVZr7uZUrbZr3zzXf1p6Uiv9/2S0cWyKaBTQA2kKQ7nFCXR3PzUOp5as82vvLG5+Hfs3UunMHU62vWUyFEDEALvcG7m9On0WbCAHXv3kp2VxbARI1hVVKTKX1Fshsx4i/Xb/Hve0YrY1TW52KBTQIqiNJh45ekpKiwks6SEu0O4Zk9yOKgsKNC4HB90CkhRlKgT7wRt4yZMoNe8eQwOshC8EmtqdlVRUUzaT1XUACiKEjGJysypa3KxQQ2AoihhSYaUzLomF33CrgEYa+n+NOAoQIAvgHckjosHugagNEXcbjczpk1j8cKFB5XV8OGMmzChySirZFD8SsNp1BqAMeYc4AFgC/C5XdwB+IUxplBE/hM1SRUlhWhqO0PVRRV/8yDkCMAYswkYICIVdcqPAUpF5MTYimehIwClKdGUgwgDKf6cNk5eu/msBEijNJbGegFlAP5RHdZoIHSeBEVppjS1VOLBcvGfd/LPeODyHgmQSIkX4UYAk4BLgMeBz+zio4HLgCdFZHLMJURHAErToqmkLfAcqOa42/xz8f+h33HcePbxCZBIiTaNGgGIyGRjzPPAEKyOi8EaEVwuIhvDNDwXGAR8LSJd7LI7gGuAb+xqt4qIf9dDUZowyZ62YPePHvLu8F++u/firuT36JAAiZREEdYN1Fb0G40xR1hv5bsI7/0oMAOYX6d8uojcWy8pFSXONMaDJ1nTFmz7bh99p/rn4n/sml70zm0TV1mU5CBkOmhjTEdjzOPGmK+Bt4F3jDFf22U5oa4VkTeAb6MmqaLEibKyMnrl5ZFZUsKKPXuoFGHFnj1klpTQKy+PsjL/aRNfki2V+LrPvieneJmf8n/5xl9TMWWgKv9mTLg1gJXAP4AlInLALksHLgZuEJFeIW9uGYkX60wBXQnsBlYDEyIZUegagBIvouHBkyxeQC998CXXLljjV7769t+RndUiZu0qyUO4NYBwG8Jki8gTXuUPICIHRORxoCHdhgexNvHpBmwHpgWraIwpMMasNsas/uabb4JVU5SoEo2NR2rSFjidTHI4cGNtH+rGSlg2xOmMadqCkjc/Jqd4mZ/y33xXfyqmDFTlr9QQbgTwONY0zjxqewFdgWUcLgl58zojgEjP1UVHAEq8iKYHj9vtZub06Syuk7ZgbIzSFtz67AYWv/2pX/nHd59HWlrDc/ErTZdwI4BwBuAQYBRwPlYqCK8X0FJgjohUhmk8h9pTQEeKyHb7dRHwKxG5LNyHUAOgxIv0tDQqRUJ6R3iAzLQ0qg4cCFErfuQ/uILVW/1nUjVqV2msG+hPWNM2Dzag4ceAM4FsY8w24M/AmcaYblg5hSqAa+t7X0WJJcnqwROIX/6xjB891X7lqviVSAmXCygDawRwAbWTwT2PNQIIujuDiAwNUDyn4aIq4UiF5GOJZtjw4cwJs/FIPD14AqF5epRoEW4K6DHge6w1AG9KiA5YawBHiMilMZcQnQKKBN/kY6O8yceAOQ4Hs+1c6cmcfCxZSBYPnkCo4lfqS2PXAD4UkROCnPtIROISL64GIDTJrLSSmWAjppN79mTi+PEhNx6JpzFVxa80lMa6gX5njLnYGFNTzxiTZoy5FIg0IliJMdFwXWxuhAr2mjh+PFPvv5/KggL6uFxkpqXRx+WisqCAVevXx0355xQvC6j8K6YMVOWvRIVwI4AcYCrwWw4q/NbAq0CxiHwSY/kAHQGEo6kkH0sWkn3EpD1+JVo01guoArjUvlEbLIOxI6oSKo0m2ZOPJRvJmq45kOL/+WEtWTGpX9xkUJoXYbeEDHiRMT2B7SLyedjKUUBHAKHREUD9SKbvK1gu/hG9OnHXBWFjJBUlJI3dECYY44E8eyE4Lp5ASnCagutiMpEMI6aqA9X8IkAu/tsHnsjoM46NWbuK4kuDDICIXAFgjGkVXXGUhjBuwgR6zZvH4CDTGiuxDMCqoqJ4i5aUJDLYa99PVXT+00t+5Q9e3p0BJx8Z9fYUJRRhDYAx5jCgP7UDwV4Ske9FZE+M5VMioCb5WH5+SNdFdQG1SMSI6es9P3La35b7lT895nR6dDo8au0oSn0Itx/ASGAtVkoHJ3AocBawxj6nJAkDBgxg1fr1CXddbAqMmzCB2Q4HK4Oc946YxkZhxPT+57vIKV7mp/xfvelMKqYMVOWvJJSwgWBYCdu+r1N+OPC2BoIpTRVv5HSsgr2Wb/qKUfP8n9m1fzybIw49pOGCK0o9aGwgmMGa9qlLtX1OaeK43W6KCgtp73KRnpZGe5eLosJC3G53okWLKbEaMc1bUUFO8TI/5b/xznOpmDIwrPJvrr+HkhjCjQCuAP4E/IeD+wF0BM4G7hKRR2MtIOgIoD7UJyGc5g+KHncs/YBHV1T4lbvvPo/0CHPx6++hRJtG5QKyb3A4cC619wN4qR6bwzeapmwA4pGh09vGI48+imf/fgqB6yCkAkn2aNimwtCHV7Hy451+5fWN2tXfQ4kF4QwAIpL0R48ePaQpUlpaKtlOp0xyOKQcxANSDjLJ4ZBsp1NKS0uj1sZ1GRnSBmQFiAQ4VoBkO51SXl4uIiI3jBkjkxyOgHW9R7HDIUVjxzZaxlTk5D//WzpNfNHvaCj6eyixAFgtIXRrgyKBbcuyQURObqBhqhdNcQQQjx6dbxtPApnA3SHqT3I4qCwo4L4ZM5IqGrYpEas8Pfp7KLGgsemgLwx2CpglIm0bKV9ENEUDUFRYSGYYX3NfhdzYNtoDKyBiBdIUtz5MJLFO0Ka/hxILGmsAPMAiAnsC5YtIXCKBm6IBiEePzreNdKCS0JF9vgpEe5yREa/MnPp7KLGgsbmA1gP3isj7AW78u8YKl8rEI9+MbxvZWAu+kaY30PxBoYl3Smb9PZREEC4O4AZgd5Bzv4+yLClFdlYWW8PUaWy+Gd82hhF+w2VfBRLPaNimRKI2YdHfQ0kEIQ2AiLwpIp8GOde05mTizLDhw5njcISs09genW8b44DZELECqckf5HQyyeHAjTVF5MZamxjidDar/EGJ3n1Lfw8lIYRyEbLXB9oBh9qvM4HbgCnAkeGujdbRFN1Ay8vLJdvpjNgtMxptlIJkgxTb7qY/2X8nZmQEdTstLy+XorFjpb3LJelpadLe5ZKisWMbJVdTIpAr51n3vJoweZr776FEFxrrBmqMeQW4UkQ+Ncb8HWgLbAb6i8hZsTNNB2mKi8AQ+3wzgdrwAPcATwN7gMOdTkZedRVji4q092gTLBf/oLwjmTGsewIkUpTY0KhFYDsVRC5wpjHGYG0P+XdgL9DJzgj6noisj6LMKYM338zM6dPps2DBwUjgESNYFSWFHKyNq0eMUKVfh137PXT9y3/8ykf3PYbbB3VOgESKkljCuYF2Al4CRgCHYcUZ5WPFASwBLgJ2iUhM/dKa6ghASQ4+3bmPX9/zql/53y/K45JTj06ARIoSHxq7KfxWY8w/gRcBBzDSngrqCOyQIAvEipIMvPPJt1zykP+y+OMFveh1bJsESKQoyUU4N1BE5EGsaaAOIvKiXbwTGBpLwRSloSxZs42c4mV+yv81exMWX+Wv6ZeV5kxEewKLyN4673+IjTiK0nAml27ioTc+9itf96dzOMzp75Lrm355hTf98p49zCkpode8eZp+WUl5GpwMLuyNjZkLDAK+FpEudtkRwBNADlABXCIRpJXWNQAlFMNL3uat8h1+5Vv+NgBHeuBBrqZfVpoDjd0RrDE8irWZvC/FwHIROQ5Ybr9XlAZx4h//TU7xMj/l/8nk86iYMjCo8geYMW0a13g8AZU/QG9gtMfDzOnToyewoiQZMRsBABhjcoAXfUYAHwJnish2Y8yRwGsickK4++gIQPElGnl6NPma0hxobDI4700uBKZiRQUb+xARcdVTngEOfPEAACAASURBVPYish3r4u3GmHYh2iwACgA6duxYz2aUVCSaCdrikaxPUZKdiAwAVvDXYBHZFEthfBGRh4GHwRoBxKtdJfmIRWbO7KwstoYZATQ2WZ+iJDuRrgF8FSXl/5U99YP99+so3LNBqPtf8hPLBG31Sdanz4qSqkRqAFYbY54wxgw1xlzoPRrQ3lLgCvv1FcDzDbhHoykrK6NXXh6ZJSWs2LOHShFW7NlDZkkJvfLyKCvzzxOjxI94ZOaMNP1yl+7d9VlRUpaIFoGNMY8EKBYRuTrENY8BZ2LtVfIV8GfgOeBJqMmLdrGIfBuu/WguAqv7X/IS701YwiXrm3r//UwcP16fFaXJEhU3UBG5KsARVPnb1wwVkSNFxCEiHURkjojsFJF+InKc/Tes8o826v6XfCQqF783kV5lQQF9XC4y09Lo43JRWVDAqvXr2bB6tT4rSkoTLhncLSLyd2PM/QTYF1hE/hBL4bxEcwSg7n/JQ7x7/PVFnxWlqdNYN1Dvwm/KOOGr+19iCZaL/5CMND76a3KlXdBnRUl1wmUDfcH+Oy8+4sQedf9LDLt/9JB3h38u/sFdf879Q09JgETh0WdFSXVimQoiKYnHXr3KQbbu/IGc4mV+yn/SgF9SMWVg0ip/0GdFSX2anQGI1P3Pu3m60jBWuneSU7yM39zzWq3yOVf0pGLKQK79Tf29ZuLtj6/PipLqRGQAjDF9IilrCuTm5jJ/yRKGOJ1McjhwAx6sxbxJDgdDnE7mL1mibn0NZMGqreQUL2Po7FW1yv99wxlUTBlIvxPbN+i+iYjd0GdFSXUijQNYKyLdw5XFilgkg3O73cycPp3Fdfbq1X1064/b7WbYA6/zlcNfua+5/Xe0yWrR6PsnMnZDnxWlqRLOCyicG2hv4HTgBsDX2dkF/F5EukZL0FBoNtDk5bhbl+Gp9i+/9J8XMzfNRGVTlaLCQjJLSrjb4wlaZ5LDQWVBAffNmNGothQllWisAfgNVjTvdcAsn1N7gBdEZEuU5AyJGoDE4na7mTFtGosXLqzpAWcWPhaw7idTB2Hs19Hqmas/vqI0jMZuCv868Lox5lER2Rp16ZSkp+62if0mvhiwXsXUQX5lvpGyjemZqz++osSGkIvAxph/2C9nGGOW1j3iIF/caYqZH2Mls9vtZmR+Pkv37WPxjc8GVP57pw5ieQDl72W0x8PiBQsaJUd2Vhbheh/qj68o9SecF9B8+++9wLQAR0rRFLOExlLmGdOmcej4JxkaQPFXTB1ExdRBjAZmhrhHsJ55fYyW+uMrSowQkaAH1v69AFND1Yv10aNHD4k15eXlku10ygoQCXCsAMl2OqW8vDzmskRKLGXuNPHFgEfdNspB2gdpv+a8y1Xr3qWlpZLtdMokh0PKQTx2vUkOh2Q7nVJaWhq3z6koqQywWkLo1nAjgCPtheAhxphTjDHdfY8Y26a4kugsoQ2ZxmmszIHaDJqZ0+7x16UjsANrEbYIaA+k23+LgHsyMmr1zH2nle72eMjFWojKBe72eFi6bx8j8/NrfW71x1eUGBHKOgD5QBmW18+rdY5XQl0bzSMeI4B2rVpJeYiebLDebDQI1iMuzsgQV0aGHJaZKWnGSLtWreSGMWNqerqNkblum8F6/JHcPwvECTLBfu+Vf6JdPmfOnJp2bxgzRiY5HCHvWexwSNHYsX4yl5eXS9HYsdLe5ZL0tDRp73JJ0dix2vNXlCAQZgQQkQIG/hhJvVgd8TAAacaIJ4yy+wkk3Ziaa8rLy+WGMWOkXatWARV0JEQyvdEGZHOdaZI5c+ZIC5B2IGn23xvsOn4yp6UFbTOY4s92OuXKYcPCKusbQQ615YxkeiaRhlZRmhvhDECkG8LcZYwZYoy51z6Cu300UbIyMiLyNMmyFyOjtfgayTTONcBD1J4mGT9qFCOAFUCl/TcT6IU1ZPOVua53TESLux4PacaEzYUzGxhhyxlMft9pKHXpVJTkIdJUEJOB04BFdtFQLMsyKYay1RCPQLDWhxzCGI+HySHqFAOzHA7WbNoUtdQEEQc5AV/6lE3Emge/L1DbwCosg+EbIRssFz/4+/F7A6seefzxgNsmzjKG2fazs8ZuK6T8dpCWBnUpSvyIypaQwEDgbBGZKyJzgf52Wcqw2+OhBEL2ducAe6qqorpgHHGPuE5ZAbA4WNtYrpnebJWXXzeenOJlAZV/yMXdvXuDbps4OyODZ7AWh+rTo1eXTkVJHiIdAawHzhR7D19jzBHAayKSF2P5gPiMANq7XEzes4eJWAp0NAd3ri+xj6nArS4XYk/3eHuxbmAGlkLeAWRjWcelWVns2LMnbLsNGQF4sKZ8qoLU7wG0yOlC5qVT/M5Xf/MJr84d36heeHpaGpUiHIU1/RTpvRKd2E1RmhPRGgFMBt41xjxqjJmHNeq/OxoCJgvDhg+n3OFgFdaceh8sBdvHfr8K2GL3TH177WVY8+6Z1J6Pbw/s37s37FpARD1iYFidsk+xDE0g3uw2gNYTX/RT/tf9JpeKKQO5qMXGRvfCvdG5w7BGRpHeS106FSWJCLVCbI8ODHA0cCTW9PL5wM/CXRfNo75eQA3xzqlPsJHXk6UcJLseHjANbjeAd08xyFW254/XE6jDRX8K6NFTtuGLBn/WYHjdORv6HahLp6LEHqLkBromknqxOupjAOobZRro2mL72p/sa4vrXOtVfjeATArj0hjMpz2Sdm+xlWtpAKXqst1DJ4Vw5fzwy92N/qzB8DUipbacxfY9vPeaANImMzPsvRRFiQ3RMgAzgVMjqRuLI1IDEI2ebSQ90+XLl4srPV0yCe2D7/Vpb5uVFXZEUrfdNk6nuNLT5dqMjFpKdWJGhrQEOSyE4n/5EKe0ycwM25sO9FmvvvxyuXLYsIhGT75GZDnI9SBt7e/ECXLhoEFR79FHI/ZCUZoL0TIAG4EDWFO164ENwPpIro3GEakBaEyUaaSUlpZKm8xMuckYKccK0LrKVsgG5Ig6xuAnWyE2ZEQSzBgFU/wHMI36nA0ZPcVzKqcxoztFaY5EywB0CnREcm00jkgNQKyjTMvLy6X1IYfUjDC8Ux+TqJ0Codhn6sY7R97QEYkvkSZo8/2ckfaYkz3hWrLLpyjJSDgDEG4/gJbGmBuAm7F8/z8Xka3eIzrL0NEjYp/6MK6ZwRg7ejSjfvqJ3lhDoZHAUix3KN+kZpPt8pHAFKxI2UDUjRcIlhCuvgnafD9npNHKiU6GF45kl09RmiLhtoR8AstL701gALBVRK6Pk2w1RBoHEKlPfR6wvry8Xq6Gbrebk3/xCzZgKfkiLNfPUL6wE4EHgXchaMzAEYDH4eC+WbOYOH4813g8jPJ46AT8IsjuW/sfGBrx53yZwGka6vrbJ3uEbrLLpyjJSGPjADqLyHAReQgrM+gZURKqwhizwRjznjEmahFew4YP5yFjQtYpAfKMqXdPcca0aVRyMOp1MTAqzDUFwCEcVP6BYgZWYfVcx48axSQ7RXK/iS8GVP7Lr/klFVMGRhQ7MMsY8oxJmRw9yS6fojRJQs0PAWtDvW/oAVQA2ZHWr48XkJMwPukgy33WASKdI2/XqlUtf/w0e84/1HrDTyDpPnPy4fzlQ83x+y7qRjIf7rQ/Z6TrIcmepTPZ5VOUZIRGZgPtaozZbR97gDzva2PM7hjapQaRm5vLfqxotUlQO8rULp+PNYzZYUfpRjpHvmPv3lpRr9kQUfbQVvbrGVhZPQP1yHMmvhgyMydYvfWHZs4kPS2N0085hT5nnsngzMyg0bT7gV+Hka8p5ehJdvkUpUkSyjrE6gA+AdZipZQoCFKnAFgNrO7YsWPEFq9dq1ayHKQIa6vCdPtvkU/vvRykTVZWvbxKvPf19uIjCQKbmJEhrvR0WYEVK1C3B1sfrx7vaMLX9fHwli3lwkGDArpg1rfHnOxeNskun6IkI0TDDTTaB/Bz+287YB3w61D16xMJHEkswMSMDDnq8MPlpjAK0nfa5YYxY6Q4I6PG9fNarEjccAppzpw5ku101poyCqb4N0HQTV4C7b1bV+nVms7Cik0IFqBW9/OJND46ONYku3yKkmwkpQGoJQDcAdwUqk59DECk8+OuEIoxXA+5HGtE0ZraWyEGU0jl5eVy2CEtQvb4S22DchO1Ywom2QbnYg6OYnzz/xwGcmqXLjWGpm6Q1ESCp5Noijl6kl0+RUkmks4AAIcCrXxerwD6h7qmvsnggvUUJ2ZkiBNkGvVYxPXZTtF734k+6RmWg5wGkom1XaRXIS1fvlxuGDNG2h/ZKaji9ypyYxuScEZrMoEDz0ZHcH0bkE0hDJSmV1CU1COcAYg0HXQ0aQ+8ZYxZB7wDLBORf0ezgQEDBvDECy/w8vHHkwe0wPKJX9iqFSPS07mRyBdxfbdT9G6O8tO119ZsjjLM5aLP2LFsKC+nqrqaL3ft4uyBAxk2/g6edQ2k5ciZfvd9cOogfpg6qMYddLx9hHLZHMvBALO6gWdZEVx/JdANy0++sqCAVevXM2DAgKhtbakoStMjog1hEk19N4QpKytjZH5+raCqrVibpHi3L4wkkMt3O8W6uN1uZkybxuKFC9mxdy/ZWVkMGz6c7zsP4dVtB/zqt6r8gb//41KGAgL8m4MKuw2WJQwX5NQT+C7AufbUb1MW38+gm7MoSuoSLhAsI57CxAO3283I/Hw/pZZL7e0Lx2EFZQ0meKRsicPBqqIiv3O+BmaFbWC6jH6EZw9pCXWU/8g1L3Lny7Nq3l+IpfC9bbqxlHonQtPRlj8QOyK8vm6QVH3SKwQygoqiNG1SzgAEU2plWFNBW7GMQS7WFo/9sfzzx3BwC8gHgPlBdqaqa2BygqRrmPvUHfz2Y/9RyzKs3nqNvIDLR65ghNoBzDudFfZ6n+ksgMULF7LC4wlxlWUA+ixYoAZAUVKQlDMAgZSaN3Hb+ViBXHdjGYSJWFsafoe19eMOrPl0D3D6qady/PHH+93fa2ACBW4BfPHAFezbszPoF/sN8C/gcbu9lsClPnIF4wGsfYYD4Q1QC3V9oCApTa+gKM2blDMAgZSaNwp3FNa0zylAIdaCarDpn9+9/jrdjj+ef86ezdVXX10z5/+sayDc6K+K3X8fQrpU057gvfEyrHUHJ9YooBPWqGQi0JfQ01Gz8N8X2EtDp7Oys7LYGibBWqCRg6IoqUEivIBiinezcl+8idtysVJBjAauIrzXzUnV1YwfNYqioiL6zd5sKf86/DB1EA9OHUS6VAOWki4JcE/vKORlLG8erxdPtv13PsFTWAwGMlq25Dmnk5UB7p1r1/sdUJyREfFG65peQVGaNylnALxKzY3l6dMea9rldPv98YADuDbMfa4FPgbaTnyRZ1v8zu+8N0+PN++/2y4fB8wGP0UdLBeQd/pmAFZm0Eqs6ahM+28lkJ+RwahRo5i/ZAlDnM6A+X8mO53cP2dOLRfVui6fdRk3YQKzHY6ARgUOjhzGBlgIVxSl6ZNybqBut5vuJ51ERmUl12L1/L1uoHOwlPNO4CdCz38FW9wNtAHLJCxFfZ/9/pL0dF4yhkJjGO3x0BH4OZaCrzvd4saavgk1HeXriul2u5k5fTqLFyw46H46YgRji4oa5Krp9Wga7fHUyPopluIvcTiYv2RJQOOhKEryE84NNCUNwKknncSyysrg8/vAC8BvA5yvj+KvaROrt/4lBxX2Ey+8wIvPPFOjqKurq4ManTKsUcQorFFCvJVwtI2KoijJQbMzAEWFhWSWlHB3CPfGCViLsL5TH8EU/6dTB9Uo7rq7eWVjTeFcC3QBbg6hsMPtaOXG8uJ5HKhMS1MlrChKo2nsjmBNjsULFzIqjG97IbAeywDkTHwxoPJfPnUQ+Xa6hvkE3s1rhf2+L5aBCDXfHm7BNRdo53Bw7dixVB04wJe7dnHfjBmq/BVFiRkpNwJIT0ujUiTk/L4HOC7CqZ6VwDlY7povEHyevn9GBms3bw6qsDXtgqIo8abZjQACuYH6kjPxxYDK33f3LV96AycCVxPabXQMhNxnODc3N6QXTzBXTUVRlFiRcgYg0FSLEHyqZ+fUQSwPscAL1vZl4dxGr6mqYvGCBSHreLOJVhYUROyqqSiKEitSbgrI7Xbzq5NP5oX9+/kVhmMnvuBX5/hvKnhw7jhmGcMDIvwD2AgsBL7FSs9wAGuzgpFYqRsqCe026gEy09KoOuCfCVRRFCURNLspoNzcXPqedRYDgL75f6p17vZXSqiYOoj/zB1HLnCPCC8DNwD7OBiItR4raMwAX3AwiVwoNGWCoihNjZTLBQSw8s03eQZ47JUSvsg9lXNKruPhndsC1u2NFb3r4WCQVi5WuoYh9vFb4EHg3hBtasoERVGaGik3BQS1PYEi3iwFK5CrLpOAr4GnsVxBg3nwDM7M5O0NG3QRV1GUpKHZTQFBbU+giDdLCXJuNFZw1h6gH3Aa8AoHPXiKsfL4eKqr+eijjxopuaIoSvxISQPg6wkU8d6/Qc51xFoXqAQ2AGdiZedsiTVq+Alrm8l/V1YyMj8ft9sd8D5ut5uiwkLau1ykp6XR3uWiqLAwaH1FUZRYk5IGwDfLpTfbZihKCJ5r/1PgEOAorDQQ12KldD4C+C9WArhcam+fWBfdeF1RlGQkJQ2Ab9DVXuBh/NMze1mJZQDGBjk/GyjgYNqHXsD3WFNDM+vUHe3x+MUC+G4hebfHU7MPQC5wt8fD0n37Qo4cFEVRYkVKGgA4GHRVffnl7MXKAHoTtTdbKbbLJxF4kXgl1uhhvH3+bqy0zSOALcBDQDrWQnORfd/GbLyuKIoST1LWAHhxuVy0zMykGngT6Aa0AvKAe7B8/P8CXIf/TlxDsBLB+RqH3sCVwOdY8QJ1k8K1atGiVvuRJKcLNHJQFEWJNSlrAHzn3dfs3897QBVQjdVb9yrv/2FlB30Myyi0wFLylViBYYGSM4zBSg9RazoHK1lctcdTazpHN15XFCVZSUkDEGje/XNgM/578nqDvv6NtVUkWNG/3sXdQARzG+2NlRPosvPPrzEC4ZLTgUYRK4qSGFLSAPjOu3v3Bj4fq+ceai7+GiCLxrmNjgHKP/igxrsnlhuvq2upoiiNISUNgHfe3XcTl5ZYyjkU12FNE90apl4ot9GOWEFjXu+ewfn5Mdl4XV1LFUVpNCIS9wPoD3wIlAPF4er36NFD6kOaMbIZJBtkBYiApIF47NfBjp9A0kGcIE8GqbPCvm95kPPlIO3t18UOhxSNHSulpaWS7XRKscMh5XY75fb5bKdTSktL6/X5ysvLJdvprPlsAWV0OqW8vLxe91UUJbUAVksI3Rr3EYAxJh3LhX4A0BkYaozpHM02srOymIo1peOd8qlPRPA4LD//SdT2DLrZGAbg7xnki+/owOvdE+19ANS1VFGUqBDKOsTiwNJPL/m8nwRMCnVNfUcAN4wZI4fV6aXfADIpzAigGKTIvq6t/bq9z6jg6ssvl8Nbtgzd8/Zp9yeQ9LS0eskeCe1atQo6Aqk1EnG5ot62oihNB5JtBICVVeEzn/fb7LKoMW7CBHZTOwncOKyo3kgigjtibQxzH1aG0JvtzdrnLFzIomeeYYjT6RdUFihuIFbePepaqihKNEiEATAByvxyUhtjCowxq40xq7/55pt6NZCbm8vhmZm1pnxysZTz77AigEMpb18vn7qLtN7pnNdPOomeWAvMfQgcNxCrPQLUtVRRlGiQCAOwDTja530HLNf7WojIwyLSU0R6tm3btt6NjLzySkoyau93MwC4BHgdS2kHU96zgYEE36w9NzeXx59/ngynkzexRgl14wYa6t0TCbF0LVUUpRkRan4oFgdW/NXHwDFYiTbXASeFuqa+awAilqfM4S1a+M3Xl9fxDgo0j+8EaZOVJUVjx4b0pIm2d099Ppt6ASmKEg6SbQ1ARKqwpuRfAjYBT4rIB7Fo6wAwiNrePABnYU0FTaDOVJDd419SWsqOPXu4b8aMkDt8Rdu7J1J8s51OcjgCfoa6oxZFUZS6pOSWkABFhYVklpQwyuNhJrAYK31DNpab5iDgVmPY7HCwt6qK7Kwsho0YwdiioiajON1uNzOnT2fxggXs2Lu3SX4GRVFiR7gtIVPWALR3uVixZ0/YvYB/5XSy44cfGiWfoihKMtIs9wSGyF0lv9u3r965czQHj6IoqUDKGoBIXSVbQb0iZjUHj6IoqULKGoBhw4czK0ydEuAiiHgzFt3eUVGUVCJlDcC4CRN4gPCRvzcTecSs5uBRFCWVSFkDkJubiyMzk8H4J3Xzjfx1EHnErG7vqChKKpGyBgDgqiuv5OKMDCoJHvlbn4hZzcGjKEoqkdIGYNyECSw55BAuxkrXUEXttA31TdegOXgURUklUtoARDtiVnPwKIqSSqS0AYDopmsYN2FCTLZ3VBRFSQQpaQDqBmqdfsopSHU1/127lqoDB/hy166weX4CoTl4FEVJJVLOAMQ6UCtRCeAURVGiTUrlAnK73fTKy2Ppvn0BffVXAkOcTlatX6+9dEVRUp5mlQtIA7UURVEiJ6UMgAZqKYqiRE5KGQAN1FIURYmclDIAGqilKIoSOSllADRQS1EUJXJSygBooJaiKErkpJQB0EAtRVGUyEkpAwAaqKUoihIpKRUIpiiKohykWQWCKYqiKJGjBkBRFKWZogZAURSlmdIk1gCMMd9A2BivYGQDO6IoTqxReWNPU5NZ5Y0tTU1eiFzmTiLSNtjJJmEAGoMxZnWoRZBkQ+WNPU1NZpU3tjQ1eSF6MusUkKIoSjNFDYCiKEozpTkYgIcTLUA9UXljT1OTWeWNLU1NXoiSzCm/BqAoiqIEpjmMABRFUZQApIwBMMZUGGM2GGPeM8b45Y0wFv8yxpQbY9YbY7onQk5blhNsOb3HbmPMDXXqnGmM2eVT509xlnGuMeZrY8z7PmVHGGP+zxizxf57eJBrr7DrbDHGXJFgme8xxmy2f/NnjTGtg1wb8vmJo7x3GGM+9/ndzwtybX9jzIf281ycQHmf8JG1whjzXpBrE/H9Hm2MedUYs8kY84Ex5nq7PCmf4xDyxu4ZFpGUOIAKIDvE+fOAMsAAvYC3Ey2zLVc68CWWv65v+ZnAiwmU69dAd+B9n7K/A8X262JgaoDrjgA+tv8ebr8+PIEynwNk2K+nBpI5kucnjvLeAdwUwTPjBo4FDgHWAZ0TIW+d89OAPyXR93sk0N1+3Qr4COicrM9xCHlj9gynzAggAs4H5ovFKqC1MebIRAsF9APcItLQQLeYICJvAN/WKT4fmGe/ngdcEODSc4H/E5FvReQ74P+A/jET1IdAMovIf0Skyn67CugQD1kiIch3HAmnAeUi8rGI/AQ8jvXbxJRQ8hpjDHAJ8Fis5YgUEdkuImvt13uATcBRJOlzHEzeWD7DqWQABPiPMWaNMaYgwPmjgM983m+zyxLNZQT/p+ltjFlnjCkzxpwUT6GC0F5EtoP1sALtAtRJ1u8Z4GqsUWAgwj0/8WScPdyfG2R6Ihm/4zOAr0RkS5DzCf1+jTE5wCnA2zSB57iOvL5E9RnOaKiASUgfEfnCGNMO+D9jzGa7x+LFBLgmoS5QxphDgCHApACn12JNC+2154GfA46Lp3wNJOm+ZwBjzG1AFbAoSJVwz0+8eBC4C+s7uwtrWuXqOnWS8TseSujef8K+X2NMFvA0cIOI7LYGK+EvC1AWl++4rrw+5VF/hlNmBCAiX9h/vwaexRom+7INONrnfQfgi/hIF5QBwFoR+aruCRHZLSJ77delgMMYkx1vAevwlXfazP77dYA6Sfc92wt4g4DLxZ4srUsEz09cEJGvROSAiFQDs4PIkVTfsTEmA7gQeCJYnUR9v8YYB5YyXSQiz9jFSfscB5E3Zs9wShgAY8yhxphW3tdYiybv16m2FBhpLHoBu7zDwAQStNdkjPmZPa+KMeY0rN9qZxxlC8RSwOsNcQXwfIA6LwHnGGMOt6cvzrHLEoIxpj8wERgiIvuC1Ink+YkLddalfh9Ejv8BxxljjrFHkZdh/TaJ4nfAZhHZFuhkor5f+/9nDrBJRO7zOZWUz3EweWP6DMdyVTteB5Y3xDr7+AC4zS6/DrjOfm2AmVjeExuAngmW2Yml0A/zKfOVd5z9WdZhLfycHmf5HgO2Y22rvA0YBbQBlgNb7L9H2HV7AiU+114NlNvHVQmWuRxrLvc9+5hl1/05UBrq+UmQvAvs53M9lqI6sq689vvzsLxE3ImU1y5/1Pvc+tRNhu+3L9a0zXqf3/+8ZH2OQ8gbs2dYI4EVRVGaKSkxBaQoiqLUHzUAiqIozRQ1AIqiKM0UNQCKoijNFDUAiqIozRQ1AEpEGGMO2FkG3zfGPGWMcUb5/lcaY2aEqXOmMeZ0n/fXGWNGRlOOAG3eY2dmvCfAuQHGmNV29sbNxph768plf66f17PNEmNM53rU/6UxZqUxptIYc1Odc2Gzhpog2THtmJmAGXRNgjK+KlEmHv64ejT9A9jr83oRcGOU738lMCNMnTsIkykzBp97N9AiQHkXLB/8X9rvM4DCAPVeI8YxJ1i5bE4F/ub7/RBh1lCCZMckSAZdEpjxVY/oHjoCUBrCm8AvAIwxN9qjgveNvaeBMSbH7hHPs3uOS7wjBmPlLM+2X/c0xrxW9+bGmMHGmLeNMe8aY142xrQ3VnKs64AieyRyhrFy599kX9PNGLPKHMyZ7u3FvmaMmWqMeccY85Ex5owA7Rm7p/++sfKpX2qXLwUOBd72lvlwC/A3EdkMICJVIvKAfd0dxpibjDH5WMFFi2yZBxpjnvVp92xjzDN17uuVuaf9eq8x5m/GSgq4yhjTvm59EflaRP6HFaDlS6RZQ4NlxwyWQTdgpkxjTLox5lGf77EoQFtKEqEGQKkXxsr7MgDYYIzpAVwF/Aqrh3iNMeYUu+oJwMMiE66FeAAAA0pJREFUkofViy6sRzNvAb1E5BQspXWLiFQAs4DpItJNRN6sc818YKLd3gbgzz7nMkTkNOCGOuVeLgS6AV2x0hrcY4w5UkSGAPvt9urmuekCrAn1IURkCbAaK39LN6AUONEY09auchXwSKh7YBmgVSLSFXgDuCZMfV8izWgZLDtmsOuDlXfDSl/cRUROJvxnUxKMGgAlUjKNtdvTauBTrJwlfYFnReQHsRLXPYOVFhjgMxH5r/16oV03UjoALxljNgA3AyFTYRtjDgNai8jrdtE8rM1LvHh72WuAnAC36As8JlYStq+A17GmVKKKiAhWqofhxtrVqTfBU/t6+Ql40X4dTP5gNDajZbDrg5V/DBxrjLnfWPlrdgeopyQRagCUSPH2hLuJyHh7SiFUXt26isb7voqDz13LINfej7UecDJwbYh6kVJp/z1A4BToEeUHrsMHQI8GXPcIMBwrEeBTcnCjj2B4bMMBweUPRqQZLYNlxwx2fcByezqoK9a6x1igpB6yKglADYDSGN4ALjDGOI2VgfD3WOsDAB2NMb3t10OxpnXA2rbOqzgvCnLfw4DP7de+HiZ7sLbKq4WI7AK+85nfH4HVi6/P57jUnsNuizV6eCfMNfcAtxpjjgcwxqQZY24MUK+WzGKl7P0CuB0riVosCZo11Bgz2Rjze7tesOyYwTLoBsyUaa/tpInI08AfsbaPVJKYVNoQRokzIrLWGPMoB5VliYi8ay/YbgKuMMY8hJV18UG7zl+AOcaYW/Hf7cjLHcBTxpjPsTKhHmOXvwAsMcacD4yvc80VwCx7sfljrPn1SHkWazpmHdZI5RYR+TLUBSKy3l70fsxuU4BlAao+asu1H+gtIvuxvKjaisjGesgYFGPMz7Cm5lxAtS1XZ7E2PxmHpbDTgbki8oF92ckcTCE9BXjSGDMKa3rvYru8lIPZKPdhf6ci8q0x5i4sAwNwp13WFXjEGOPtWAba6EhJIjQbqBJ1bAPwooh0SbAoSYmx4h3eFZE5CZThJRE5N1HtK8mBjgAUJY4YY9YAPwATEimHKn8FdASgKIrSbNFFYEVRlGaKGgBFUZRmihoARVGUZooaAEVRlGaKGgBFUZRmihoARVGUZsr/A+9RFpi0o+YoAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# plot the linear fit\n",
     "plotData(X[:, 1], y)\n",
@@ -620,9 +737,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "For population = 35,000, we predict a profit of 4519.77\n",
+      "\n",
+      "For population = 70,000, we predict a profit of 45342.45\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "# Predict values for population sizes of 35,000 and 70,000\n",
     "predict1 = np.dot([1, 3.5], theta)\n",
@@ -641,9 +769,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise linear-regression\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "                           Warm up exercise |  10 /  10 | Nice work!\n",
+      "          Computing Cost (for one variable) |  40 /  40 | Nice work!\n",
+      "        Gradient Descent (for one variable) |  50 /  50 | Nice work!\n",
+      "                      Feature Normalization |   0 /   0 | \n",
+      "    Computing Cost (for multiple variables) |   0 /   0 | \n",
+      "  Gradient Descent (for multiple variables) |   0 /   0 | \n",
+      "                           Normal Equations |   0 /   0 | \n",
+      "                                  --------------------------------\n",
+      "                                            | 100 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[3] = gradientDescent\n",
     "grader.grade()"
@@ -666,9 +817,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# grid over which we will calculate J\n",
     "theta0_vals = np.linspace(-10, 10, 100)\n",
@@ -730,9 +894,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  X[:,0] X[:, 1]         y\n",
+      "--------------------------\n",
+      "    2104       3    399900\n",
+      "    1600       3    329900\n",
+      "    2400       3    369000\n",
+      "    1416       2    232000\n",
+      "    3000       4    539900\n",
+      "    1985       4    299900\n",
+      "    1534       3    314900\n",
+      "    1427       3    198999\n",
+      "    1380       3    212000\n",
+      "    1494       3    242500\n"
+     ]
+    }
+   ],
    "source": [
     "# Load data\n",
     "data = np.loadtxt(os.path.join('Data', 'ex1data2.txt'), delimiter=',')\n",
@@ -773,7 +956,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -816,7 +999,10 @@
     "\n",
     "    # =========================== YOUR CODE HERE =====================\n",
     "\n",
+    "    mu = np.mean(X, axis=0)\n",
+    "    sigma = np.std(X, axis=0)\n",
     "    \n",
+    "    X_norm = (X - mu)/sigma\n",
     "    # ================================================================\n",
     "    return X_norm, mu, sigma"
    ]
@@ -830,9 +1016,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Computed mean: [2000.68085106    3.17021277]\n",
+      "Computed standard deviation: [7.86202619e+02 7.52842809e-01]\n"
+     ]
+    }
+   ],
    "source": [
     "# call featureNormalize on the loaded data\n",
     "X_norm, mu, sigma = featureNormalize(X)\n",
@@ -850,9 +1045,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise linear-regression\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "                           Warm up exercise |  10 /  10 | Nice work!\n",
+      "          Computing Cost (for one variable) |  40 /  40 | Nice work!\n",
+      "        Gradient Descent (for one variable) |  50 /  50 | Nice work!\n",
+      "                      Feature Normalization |   0 /   0 | Nice work!\n",
+      "    Computing Cost (for multiple variables) |   0 /   0 | Nice work!\n",
+      "  Gradient Descent (for multiple variables) |   0 /   0 | Nice work!\n",
+      "                           Normal Equations |   0 /   0 | \n",
+      "                                  --------------------------------\n",
+      "                                            | 100 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[4] = featureNormalize\n",
     "grader.grade()"
@@ -867,7 +1085,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -912,7 +1130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -948,6 +1166,7 @@
     "    J = 0\n",
     "    \n",
     "    # ======================= YOUR CODE HERE ===========================\n",
+    "    J = (0.5/m)*np.dot((np.dot(X, theta) - y).T, (np.dot(X, theta) - y))\n",
     "\n",
     "    \n",
     "    # ==================================================================\n",
@@ -963,9 +1182,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise linear-regression\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "                           Warm up exercise |  10 /  10 | Nice work!\n",
+      "          Computing Cost (for one variable) |  40 /  40 | Nice work!\n",
+      "        Gradient Descent (for one variable) |  50 /  50 | Nice work!\n",
+      "                      Feature Normalization |   0 /   0 | Nice work!\n",
+      "    Computing Cost (for multiple variables) |   0 /   0 | Nice work!\n",
+      "  Gradient Descent (for multiple variables) |   0 /   0 | Nice work!\n",
+      "                           Normal Equations |   0 /   0 | \n",
+      "                                  --------------------------------\n",
+      "                                            | 100 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[5] = computeCostMulti\n",
     "grader.grade()"
@@ -980,7 +1222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1031,7 +1273,8 @@
     "    \n",
     "    for i in range(num_iters):\n",
     "        # ======================= YOUR CODE HERE ==========================\n",
-    "\n",
+    "        h = np.dot(X, theta)\n",
+    "        theta -= alpha*(1/m)*np.dot(X.T, h-y)\n",
     "        \n",
     "        # =================================================================\n",
     "        \n",
@@ -1050,9 +1293,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise linear-regression\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "                           Warm up exercise |  10 /  10 | Nice work!\n",
+      "          Computing Cost (for one variable) |  40 /  40 | Nice work!\n",
+      "        Gradient Descent (for one variable) |  50 /  50 | Nice work!\n",
+      "                      Feature Normalization |   0 /   0 | Nice work!\n",
+      "    Computing Cost (for multiple variables) |   0 /   0 | Nice work!\n",
+      "  Gradient Descent (for multiple variables) |   0 /   0 | Nice work!\n",
+      "                           Normal Equations |   0 /   0 | \n",
+      "                                  --------------------------------\n",
+      "                                            | 100 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[6] = gradientDescentMulti\n",
     "grader.grade()"
@@ -1094,9 +1360,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "theta computed from gradient descent: [340412.65957447 109447.79558639  -6578.3539709 ]\n",
+      "Predicted price of a 1650 sq-ft, 3 br house (using gradient descent): $293081\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "\"\"\"\n",
     "Instructions\n",
@@ -1136,10 +1423,10 @@
     "\n",
     "# Estimate the price of a 1650 sq-ft, 3 br house\n",
     "# ======================= YOUR CODE HERE ===========================\n",
+    "\n",
     "# Recall that the first column of X is all-ones. \n",
-    "# Thus, it does not need to be normalized.\n",
     "\n",
-    "price = 0   # You should change this\n",
+    "price = np.dot([1, (1650-mu[0])/sigma[0], (3-mu[1])/sigma[1]], theta)\n",
     "\n",
     "# ===================================================================\n",
     "\n",
@@ -1171,7 +1458,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1194,7 +1481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1227,6 +1514,8 @@
     "    theta = np.zeros(X.shape[1])\n",
     "    \n",
     "    # ===================== YOUR CODE HERE ============================\n",
+    "    inv = np.linalg.pinv(np.dot(X.T, X))\n",
+    "    theta = np.dot(np.dot(inv, X.T), y)\n",
     "\n",
     "    \n",
     "    # =================================================================\n",
@@ -1242,9 +1531,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise linear-regression\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "                           Warm up exercise |  10 /  10 | Nice work!\n",
+      "          Computing Cost (for one variable) |  40 /  40 | Nice work!\n",
+      "        Gradient Descent (for one variable) |  50 /  50 | Nice work!\n",
+      "                      Feature Normalization |   0 /   0 | Nice work!\n",
+      "    Computing Cost (for multiple variables) |   0 /   0 | Nice work!\n",
+      "  Gradient Descent (for multiple variables) |   0 /   0 | Nice work!\n",
+      "                           Normal Equations |   0 /   0 | Nice work!\n",
+      "                                  --------------------------------\n",
+      "                                            | 100 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[7] = normalEqn\n",
     "grader.grade()"
@@ -1262,9 +1574,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Theta computed from the normal equations: [89597.90954361   139.21067402 -8738.01911255]\n",
+      "Predicted price of a 1650 sq-ft, 3 br house (using normal equations): $293081\n"
+     ]
+    }
+   ],
    "source": [
     "# Calculate the parameters from the normal equation\n",
     "theta = normalEqn(X, y);\n",
@@ -1275,12 +1596,19 @@
     "# Estimate the price of a 1650 sq-ft, 3 br house\n",
     "# ====================== YOUR CODE HERE ======================\n",
     "\n",
-    "price = 0 # You should change this\n",
+    "price = np.dot([1, 1650, 3], theta)\n",
     "\n",
     "# ============================================================\n",
     "\n",
     "print('Predicted price of a 1650 sq-ft, 3 br house (using normal equations): ${:.0f}'.format(price))"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -1299,7 +1627,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,
diff --git a/Exercise2/exercise2.ipynb b/Exercise2/exercise2.ipynb
index 39983d90..449662d4 100755
--- a/Exercise2/exercise2.ipynb
+++ b/Exercise2/exercise2.ipynb
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -151,7 +151,11 @@
     "    fig = pyplot.figure()\n",
     "\n",
     "    # ====================== YOUR CODE HERE ======================\n",
-    "\n",
+    "    pos = y == 1\n",
+    "    neg = y == 0\n",
+    "    \n",
+    "    pyplot.plot(X[pos, 0], X[pos, 1], 'k*', lw=2, ms=10)\n",
+    "    pyplot.plot(X[neg, 0], X[neg, 1], 'ko', mfc='y', ms=8, mec='k', mew=1)\n",
     "    \n",
     "    # ============================================================"
    ]
@@ -165,9 +169,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plotData(X, y)\n",
     "# add axes labels\n",
@@ -202,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -234,7 +251,7 @@
     "\n",
     "    # ====================== YOUR CODE HERE ======================\n",
     "\n",
-    "    \n",
+    "    g = 1/(1+np.exp(-z))\n",
     "\n",
     "    # =============================================================\n",
     "    return g"
@@ -249,9 +266,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "g( 0 ) =  0.5\n"
+     ]
+    }
+   ],
    "source": [
     "# Test the implementation of sigmoid function here\n",
     "z = 0\n",
@@ -275,9 +300,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise logistic-regression\n",
+      "\n",
+      "Login (email address): waiyen.chan0819@gmail.com\n",
+      "Token: UgCSyw8zpcA58nri\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "                           Sigmoid Function |   5 /   5 | Nice work!\n",
+      "                   Logistic Regression Cost |   0 /  30 | \n",
+      "               Logistic Regression Gradient |   0 /  30 | \n",
+      "                                    Predict |   0 /   5 | \n",
+      "       Regularized Logistic Regression Cost |   0 /  15 | \n",
+      "   Regularized Logistic Regression Gradient |   0 /  15 | \n",
+      "                                  --------------------------------\n",
+      "                                            |   5 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "# appends the implemented function in part 1 to the grader object\n",
     "grader[1] = sigmoid\n",
@@ -298,7 +346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -328,7 +376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -373,8 +421,11 @@
     "    grad = np.zeros(theta.shape)\n",
     "\n",
     "    # ====================== YOUR CODE HERE ======================\n",
-    "\n",
-    "    \n",
+    "    h = sigmoid(np.dot(X, theta.T))\n",
+    "    J = (-1/m)*np.sum(y*np.log(h) + (1-y)*np.log(1-h))\n",
+    "                                  \n",
+    "    grad = (1/m)*np.dot(X.T, h - y)\n",
+    "                      \n",
     "    \n",
     "    # =============================================================\n",
     "    return J, grad"
@@ -389,9 +440,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost at initial theta (zeros): 0.693\n",
+      "Expected cost (approx): 0.693\n",
+      "\n",
+      "Gradient at initial theta (zeros):\n",
+      "\t[-0.1000, -12.0092, -11.2628]\n",
+      "Expected gradients (approx):\n",
+      "\t[-0.1000, -12.0092, -11.2628]\n",
+      "\n",
+      "Cost at test theta: 0.218\n",
+      "Expected cost (approx): 0.218\n",
+      "\n",
+      "Gradient at test theta:\n",
+      "\t[0.043, 2.566, 2.647]\n",
+      "Expected gradients (approx):\n",
+      "\t[0.043, 2.566, 2.647]\n"
+     ]
+    }
+   ],
    "source": [
     "# Initialize fitting parameters\n",
     "initial_theta = np.zeros(n+1)\n",
@@ -426,9 +499,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise logistic-regression\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "                           Sigmoid Function |   5 /   5 | Nice work!\n",
+      "                   Logistic Regression Cost |  30 /  30 | Nice work!\n",
+      "               Logistic Regression Gradient |  30 /  30 | Nice work!\n",
+      "                                    Predict |   0 /   5 | \n",
+      "       Regularized Logistic Regression Cost |   0 /  15 | \n",
+      "   Regularized Logistic Regression Gradient |   0 /  15 | \n",
+      "                                  --------------------------------\n",
+      "                                            |  65 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[2] = costFunction\n",
     "grader[3] = costFunction\n",
@@ -459,9 +554,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost at theta found by optimize.minimize: 0.203\n",
+      "Expected cost (approx): 0.203\n",
+      "\n",
+      "theta:\n",
+      "\t[-25.161, 0.206, 0.201]\n",
+      "Expected theta (approx):\n",
+      "\t[-25.161, 0.206, 0.201]\n"
+     ]
+    }
+   ],
    "source": [
     "# set options for optimize.minimize\n",
     "options= {'maxiter': 400}\n",
@@ -508,9 +617,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Plot Boundary\n",
     "utils.plotDecisionBoundary(plotData, theta, X, y)"
@@ -530,7 +652,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -566,6 +688,8 @@
     "    p = np.zeros(m)\n",
     "\n",
     "    # ====================== YOUR CODE HERE ======================\n",
+    "    \n",
+    "    p = (sigmoid(np.dot(X, theta.T)) >= 0.5)\n",
     "\n",
     "    \n",
     "    \n",
@@ -582,9 +706,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "For a student with scores 45 and 85,we predict an admission probability of 0.776\n",
+      "Expected value: 0.775 +/- 0.002\n",
+      "\n",
+      "Train Accuracy: 89.00 %\n",
+      "Expected accuracy (approx): 89.00 %\n"
+     ]
+    }
+   ],
    "source": [
     "#  Predict probability for a student with score 45 on exam 1 \n",
     "#  and score 85 on exam 2 \n",
@@ -608,9 +744,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise logistic-regression\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "                           Sigmoid Function |   5 /   5 | Nice work!\n",
+      "                   Logistic Regression Cost |  30 /  30 | Nice work!\n",
+      "               Logistic Regression Gradient |  30 /  30 | Nice work!\n",
+      "                                    Predict |   5 /   5 | Nice work!\n",
+      "       Regularized Logistic Regression Cost |   0 /  15 | \n",
+      "   Regularized Logistic Regression Gradient |   0 /  15 | \n",
+      "                                  --------------------------------\n",
+      "                                            |  70 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[4] = predict\n",
     "grader.grade()"
@@ -630,7 +788,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -654,9 +812,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plotData(X, y)\n",
     "# Labels and Legend\n",
@@ -686,7 +857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -718,7 +889,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -767,7 +938,11 @@
     "    grad = np.zeros(theta.shape)\n",
     "\n",
     "    # ===================== YOUR CODE HERE ======================\n",
-    "\n",
+    "    theta_ = np.append([0], theta[1:, ])\n",
+    "    h = sigmoid(np.dot(X, theta.T))\n",
+    "    J = (-1/m)*np.sum(y*np.log(h) + (1-y)*np.log(1-h)) + (0.5*lambda_/m)*np.sum(theta_**2)\n",
+    "                                  \n",
+    "    grad = (1/m)*np.dot(X.T, h - y) + (lambda_/m)*theta_\n",
     "    \n",
     "    \n",
     "    # =============================================================\n",
@@ -783,9 +958,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost at initial theta (zeros): 0.693\n",
+      "Expected cost (approx)       : 0.693\n",
+      "\n",
+      "Gradient at initial theta (zeros) - first five values only:\n",
+      "\t[0.0085, 0.0188, 0.0001, 0.0503, 0.0115]\n",
+      "Expected gradients (approx) - first five values only:\n",
+      "\t[0.0085, 0.0188, 0.0001, 0.0503, 0.0115]\n",
+      "\n",
+      "------------------------------\n",
+      "\n",
+      "Cost at test theta    : 3.16\n",
+      "Expected cost (approx): 3.16\n",
+      "\n",
+      "Gradient at initial theta (zeros) - first five values only:\n",
+      "\t[0.3460, 0.1614, 0.1948, 0.2269, 0.0922]\n",
+      "Expected gradients (approx) - first five values only:\n",
+      "\t[0.3460, 0.1614, 0.1948, 0.2269, 0.0922]\n"
+     ]
+    }
+   ],
    "source": [
     "# Initialize fitting parameters\n",
     "initial_theta = np.zeros(X.shape[1])\n",
@@ -832,9 +1031,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise logistic-regression\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "                           Sigmoid Function |   5 /   5 | Nice work!\n",
+      "                   Logistic Regression Cost |  30 /  30 | Nice work!\n",
+      "               Logistic Regression Gradient |  30 /  30 | Nice work!\n",
+      "                                    Predict |   5 /   5 | Nice work!\n",
+      "       Regularized Logistic Regression Cost |  15 /  15 | Nice work!\n",
+      "   Regularized Logistic Regression Gradient |  15 /  15 | Nice work!\n",
+      "                                  --------------------------------\n",
+      "                                            | 100 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[5] = costFunctionReg\n",
     "grader[6] = costFunctionReg\n",
@@ -892,16 +1113,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train Accuracy: 86.4 %\n",
+      "Expected accuracy (with lambda = 1): 83.1 % (approx)\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Initialize fitting parameters\n",
     "initial_theta = np.zeros(X.shape[1])\n",
     "\n",
     "# Set regularization parameter lambda to 1 (you should vary this)\n",
-    "lambda_ = 1\n",
-    "\n",
+    "lambda_ = 0.0001\n",
     "# set options for optimize.minimize\n",
     "options= {'maxiter': 100}\n",
     "\n",
@@ -939,6 +1181,13 @@
    "source": [
     "*You do not need to submit any solutions for these optional (ungraded) exercises.*"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -957,7 +1206,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,
diff --git a/Exercise3/exercise3.ipynb b/Exercise3/exercise3.ipynb
index e37be91f..b1dcad72 100755
--- a/Exercise3/exercise3.ipynb
+++ b/Exercise3/exercise3.ipynb
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -99,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -132,9 +132,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 100 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Randomly select 100 data points to display\n",
     "rand_indices = np.random.choice(m, 100, replace=False)\n",
@@ -158,7 +171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -267,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -348,9 +361,11 @@
     "    grad = np.zeros(theta.shape)\n",
     "    \n",
     "    # ====================== YOUR CODE HERE ======================\n",
+    "    theta_ = np.append([0], theta[1:, ])\n",
+    "    g = utils.sigmoid(np.dot(X, theta.T)) \n",
+    "    J = (-1/m)*(np.sum(y*np.log(g) + (1-y)*np.log(1-g))) + (0.5*lambda_/m)*np.sum(theta_**2)    \n",
+    "    grad = (1/m)*np.dot(X.T, g-y) + lambda_*theta_/m\n",
     "\n",
-    "\n",
-    "        \n",
     "    # =============================================================\n",
     "    return J, grad"
    ]
@@ -389,9 +404,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost         : 2.534819\n",
+      "Expected cost: 2.534819\n",
+      "-----------------------\n",
+      "Gradients:\n",
+      " [0.146561, -0.548558, 0.724722, 1.398003]\n",
+      "Expected gradients:\n",
+      " [0.146561, -0.548558, 0.724722, 1.398003]\n"
+     ]
+    }
+   ],
    "source": [
     "J, grad = lrCostFunction(theta_t, X_t, y_t, lambda_t)\n",
     "\n",
@@ -417,9 +446,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise multi-class-classification-and-neural-networks\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "            Regularized Logistic Regression |  30 /  30 | Nice work!\n",
+      "             One-vs-All Classifier Training |   0 /  20 | \n",
+      "           One-vs-All Classifier Prediction |   0 /  20 | \n",
+      "         Neural Network Prediction Function |   0 /  30 | \n",
+      "                                  --------------------------------\n",
+      "                                            |  30 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "# appends the implemented function in part 1 to the grader object\n",
     "grader[1] = lrCostFunction\n",
@@ -448,7 +497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -526,8 +575,17 @@
     "    X = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
     "\n",
     "    # ====================== YOUR CODE HERE ======================\n",
-    "   \n",
-    "\n",
+    "    initial_theta = np.zeros(n + 1)\n",
+    "    options = {'maxiter': 50}\n",
+    "    \n",
+    "    for c in range(num_labels):\n",
+    "        res = optimize.minimize(lrCostFunction, \n",
+    "                                initial_theta, \n",
+    "                                (X, (y == c), lambda_), \n",
+    "                                jac=True, \n",
+    "                                method='TNC',\n",
+    "                                options=options)\n",
+    "        all_theta[c, :] = res.x\n",
     "\n",
     "    # ============================================================\n",
     "    return all_theta"
@@ -542,7 +600,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -559,9 +617,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise multi-class-classification-and-neural-networks\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "            Regularized Logistic Regression |  30 /  30 | Nice work!\n",
+      "             One-vs-All Classifier Training |  20 /  20 | Nice work!\n",
+      "           One-vs-All Classifier Prediction |   0 /  20 | \n",
+      "         Neural Network Prediction Function |   0 /  30 | \n",
+      "                                  --------------------------------\n",
+      "                                            |  50 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[2] = oneVsAll\n",
     "grader.grade()"
@@ -580,7 +658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -634,6 +712,7 @@
     "    X = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
     "\n",
     "    # ====================== YOUR CODE HERE ======================\n",
+    "    p = np.argmax(np.dot(X, all_theta.T), axis=1)\n",
     "\n",
     "\n",
     "    \n",
@@ -650,9 +729,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Set Accuracy: 95.20%\n"
+     ]
+    }
+   ],
    "source": [
     "pred = predictOneVsAll(all_theta, X)\n",
     "print('Training Set Accuracy: {:.2f}%'.format(np.mean(pred == y) * 100))"
@@ -667,9 +754,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise multi-class-classification-and-neural-networks\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "            Regularized Logistic Regression |  30 /  30 | Nice work!\n",
+      "             One-vs-All Classifier Training |  20 /  20 | Nice work!\n",
+      "           One-vs-All Classifier Prediction |  20 /  20 | Nice work!\n",
+      "         Neural Network Prediction Function |   0 /  30 | \n",
+      "                                  --------------------------------\n",
+      "                                            |  70 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[3] = predictOneVsAll\n",
     "grader.grade()"
@@ -690,9 +797,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 100 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#  training data stored in arrays X, y\n",
     "data = loadmat(os.path.join('Data', 'ex3data1.mat'))\n",
@@ -735,7 +855,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -774,7 +894,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -833,8 +953,10 @@
     "    p = np.zeros(X.shape[0])\n",
     "\n",
     "    # ====================== YOUR CODE HERE ======================\n",
-    "\n",
-    "\n",
+    "    X_ = np.hstack((np.ones((m, 1)), X))\n",
+    "    a1 = utils.sigmoid(np.dot(X_, Theta1.T))\n",
+    "    a1_ = np.hstack((np.ones((m, 1)), a1))\n",
+    "    p = np.argmax(np.dot(a1_, Theta2.T), axis=1)\n",
     "\n",
     "    # =============================================================\n",
     "    return p"
@@ -849,9 +971,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Set Accuracy: 97.5%\n"
+     ]
+    }
+   ],
    "source": [
     "pred = predict(Theta1, Theta2, X)\n",
     "print('Training Set Accuracy: {:.1f}%'.format(np.mean(pred == y) * 100))"
@@ -868,9 +998,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Neural Network Prediction: 8\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "if indices.size > 0:\n",
     "    i, indices = indices[0], indices[1:]\n",
@@ -890,13 +1040,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise multi-class-classification-and-neural-networks\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "            Regularized Logistic Regression |  30 /  30 | Nice work!\n",
+      "             One-vs-All Classifier Training |  20 /  20 | Nice work!\n",
+      "           One-vs-All Classifier Prediction |  20 /  20 | Nice work!\n",
+      "         Neural Network Prediction Function |  30 /  30 | Nice work!\n",
+      "                                  --------------------------------\n",
+      "                                            | 100 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[4] = predict\n",
     "grader.grade()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -915,7 +1092,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,
diff --git a/Exercise4/exercise4.ipynb b/Exercise4/exercise4.ipynb
index ab2e6145..7157b73b 100755
--- a/Exercise4/exercise4.ipynb
+++ b/Exercise4/exercise4.ipynb
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -87,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -128,9 +128,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 100 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Randomly select 100 data points to display\n",
     "rand_indices = np.random.choice(m, 100, replace=False)\n",
@@ -157,7 +170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -215,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -316,7 +329,23 @@
     "    Theta2_grad = np.zeros(Theta2.shape)\n",
     "\n",
     "    # ====================== YOUR CODE HERE ======================\n",
-    "\n",
+    "    \n",
+    "    X_ = np.hstack((np.ones((m, 1)), X))\n",
+    "    a1 = utils.sigmoid(np.dot(X_, Theta1.T))\n",
+    "    a1_ = np.hstack((np.ones((m, 1)), a1))\n",
+    "    g = utils.sigmoid(np.dot(a1_, Theta2.T))\n",
+    "    \n",
+    "    # One-hot encoding y\n",
+    "    y_ = np.zeros((y.size, y.max()+1))\n",
+    "    y_[np.arange(y.size),y] = 1\n",
+    "    \n",
+    "    # Regularized cost\n",
+    "    theta_ = np.append(Theta1[:, 1:], Theta2[:, 1:])\n",
+    "    theta__ = np.append([0], theta_)\n",
+    "    J = (-1/m)*(np.sum(np.multiply(y_,np.log(g)) + np.multiply((1-y_),np.log(1-g)))) + (0.5*lambda_/m)*np.sum(theta__**2) \n",
+    "    \n",
+    "    \n",
+    "    \n",
     "    \n",
     "    \n",
     "    # ================================================================\n",
@@ -351,9 +380,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost at parameters (loaded from ex4weights): 0.287629 \n",
+      "The cost should be about                   : 0.287629.\n"
+     ]
+    }
+   ],
    "source": [
     "lambda_ = 0\n",
     "J, _ = nnCostFunction(nn_params, input_layer_size, hidden_layer_size,\n",
@@ -371,9 +409,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise neural-network-learning\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "              Feedforward and Cost Function |  30 /  30 | Nice work!\n",
+      "                  Regularized Cost Function |   0 /  15 | \n",
+      "                           Sigmoid Gradient |   0 /   5 | \n",
+      "  Neural Network Gradient (Backpropagation) |   0 /  40 | \n",
+      "                       Regularized Gradient |   0 /  10 | \n",
+      "                                  --------------------------------\n",
+      "                                            |  30 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader = utils.Grader()\n",
     "grader[1] = nnCostFunction\n",
@@ -407,9 +466,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost at parameters (loaded from ex4weights): 0.383770\n",
+      "This value should be about                 : 0.383770.\n"
+     ]
+    }
+   ],
    "source": [
     "# Weight regularization parameter (we set this to 1 here).\n",
     "lambda_ = 1\n",
@@ -429,9 +497,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise neural-network-learning\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "              Feedforward and Cost Function |  30 /  30 | Nice work!\n",
+      "                  Regularized Cost Function |  15 /  15 | Nice work!\n",
+      "                           Sigmoid Gradient |   0 /   5 | \n",
+      "  Neural Network Gradient (Backpropagation) |   0 /  40 | \n",
+      "                       Regularized Gradient |   0 /  10 | \n",
+      "                                  --------------------------------\n",
+      "                                            |  45 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[2] = nnCostFunction\n",
     "grader.grade()"
@@ -470,7 +559,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -505,8 +594,8 @@
     "    g = np.zeros(z.shape)\n",
     "\n",
     "    # ====================== YOUR CODE HERE ======================\n",
-    "\n",
-    "\n",
+    "    sig = utils.sigmoid(z)\n",
+    "    g = sig * (1 - sig)\n",
     "\n",
     "    # =============================================================\n",
     "    return g"
@@ -521,9 +610,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sigmoid gradient evaluated at [-1 -0.5 0 0.5 1]:\n",
+      "  \n",
+      "[0.19661193 0.23500371 0.25       0.23500371 0.19661193]\n"
+     ]
+    }
+   ],
    "source": [
     "z = np.array([-1, -0.5, 0, 0.5, 1])\n",
     "g = sigmoidGradient(z)\n",
@@ -540,9 +639,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise neural-network-learning\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "              Feedforward and Cost Function |  30 /  30 | Nice work!\n",
+      "                  Regularized Cost Function |  15 /  15 | Nice work!\n",
+      "                           Sigmoid Gradient |   5 /   5 | Nice work!\n",
+      "  Neural Network Gradient (Backpropagation) |   0 /  40 | \n",
+      "                       Regularized Gradient |   0 /  10 | \n",
+      "                                  --------------------------------\n",
+      "                                            |  50 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[3] = sigmoidGradient\n",
     "grader.grade()"
@@ -571,7 +691,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -609,8 +729,7 @@
     "    W = np.zeros((L_out, 1 + L_in))\n",
     "\n",
     "    # ====================== YOUR CODE HERE ======================\n",
-    "\n",
-    "\n",
+    "    W = np.random.rand(L_out, 1 + L_in) * 2 * epsilon_init - epsilon_init\n",
     "\n",
     "    # ============================================================\n",
     "    return W"
@@ -627,9 +746,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initializing Neural Network Parameters ...\n"
+     ]
+    }
+   ],
    "source": [
     "print('Initializing Neural Network Parameters ...')\n",
     "\n",
@@ -679,6 +806,131 @@
     "**Note:** If the iterative solution provided above is proving to be difficult to implement, try implementing the vectorized approach which is easier to implement in the opinion of the moderators of this course. You can find the tutorial for the vectorized approach [here](https://www.coursera.org/learn/machine-learning/discussions/all/threads/a8Kce_WxEeS16yIACyoj1Q)."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# WITH BACKPROP\n",
+    "def nnCostFunction(nn_params,\n",
+    "                   input_layer_size,\n",
+    "                   hidden_layer_size,\n",
+    "                   num_labels,\n",
+    "                   X, y, lambda_=0.0):\n",
+    "    \"\"\"\n",
+    "\n",
+    "    input_layer_size : int\n",
+    "        Number of features for the input layer. \n",
+    "    \n",
+    "    hidden_layer_size : int\n",
+    "        Number of hidden units in the second layer.\n",
+    "    \n",
+    "    num_labels : int\n",
+    "        Total number of labels, or equivalently number of units in output layer. \n",
+    "    \n",
+    "    X : array_like\n",
+    "        Input dataset. A matrix of shape (m x input_layer_size).\n",
+    "    \n",
+    "    y : array_like\n",
+    "        Dataset labels. A vector of shape (m,).\n",
+    "    \n",
+    "    lambda_ : float, optional\n",
+    "        Regularization parameter.\n",
+    " \n",
+    "    Returns\n",
+    "    -------\n",
+    "    J : float\n",
+    "        The computed value for the cost function at the current weight values.\n",
+    "    \n",
+    "    grad : array_like\n",
+    "        An \"unrolled\" vector of the partial derivatives of the concatenatation of\n",
+    "        neural network weights Theta1 and Theta2.\n",
+    "\n",
+    "    \n",
+    "    - Part 2: Implement the backpropagation algorithm to compute the gradients\n",
+    "              Theta1_grad and Theta2_grad. You should return the partial derivatives of\n",
+    "              the cost function with respect to Theta1 and Theta2 in Theta1_grad and\n",
+    "              Theta2_grad, respectively. After implementing Part 2, you can check\n",
+    "              that your implementation is correct by running checkNNGradients provided\n",
+    "              in the utils.py module.\n",
+    "    \n",
+    "              Note: The vector y passed into the function is a vector of labels\n",
+    "                    containing values from 0..K-1. You need to map this vector into a \n",
+    "                    binary vector of 1's and 0's to be used with the neural network\n",
+    "                    cost function.\n",
+    "    \n",
+    "    - Part 3: Implement regularization with the cost function and gradients.\n",
+    "    \n",
+    "              Hint: You can implement this around the code for\n",
+    "                    backpropagation. That is, you can compute the gradients for\n",
+    "                    the regularization separately and then add them to Theta1_grad\n",
+    "                    and Theta2_grad from Part 2.\n",
+    "\n",
+    "    \"\"\"\n",
+    "    # Reshape nn_params back into the parameters Theta1 and Theta2, the weight matrices\n",
+    "    # for our 2 layer neural network\n",
+    "    Theta1 = np.reshape(nn_params[:hidden_layer_size * (input_layer_size + 1)],\n",
+    "                        (hidden_layer_size, (input_layer_size + 1)))\n",
+    "\n",
+    "    Theta2 = np.reshape(nn_params[(hidden_layer_size * (input_layer_size + 1)):],\n",
+    "                        (num_labels, (hidden_layer_size + 1)))\n",
+    "\n",
+    "    # Setup some useful variables\n",
+    "    m = y.size\n",
+    "         \n",
+    "    # You need to return the following variables correctly \n",
+    "    J = 0\n",
+    "    Theta1_grad = np.zeros(Theta1.shape)\n",
+    "    Theta2_grad = np.zeros(Theta2.shape)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================  \n",
+    "    X_ = np.hstack((np.ones((m, 1)), X))\n",
+    "    z1 = np.dot(X_, Theta1.T)\n",
+    "    a1 = utils.sigmoid(z1)\n",
+    "    a1_ = np.hstack((np.ones((m, 1)), a1))\n",
+    "    z2 = np.dot(a1_, Theta2.T)\n",
+    "    a2 = utils.sigmoid(z2)\n",
+    "    a2_ = np.hstack((np.ones((m, 1)), a2))\n",
+    "    \n",
+    "    # One-hot encoding y\n",
+    "    y_ = np.zeros((y.size, y.max()+1))\n",
+    "    y_[np.arange(y.size),y] = 1\n",
+    "    \n",
+    "    # Cost\n",
+    "    theta_ = np.append(Theta1[:, 1:], Theta2[:, 1:])\n",
+    "    theta__ = np.append([0], theta_)\n",
+    "    J = (-1/m)*(np.sum(np.multiply(y_,np.log(a2)) + np.multiply((1-y_),np.log(1-a2)))) + (0.5*lambda_/m)*np.sum(theta__**2) \n",
+    "    \n",
+    "    # Backprop\n",
+    "    delta_acc1 = np.zeros(Theta1_grad.shape)\n",
+    "    delta_acc2 = np.zeros(Theta2_grad.shape)\n",
+    "    delta3 = a2 - y_    \n",
+    "\n",
+    "    delta2 = np.multiply(np.matmul(delta3, Theta2[:, 1:]), sigmoidGradient(z1))\n",
+    "\n",
+    "    delta_acc1 += np.matmul(delta2.T, X_)\n",
+    "    delta_acc2 += delta_acc2 + np.matmul(delta3.T, a1_)\n",
+    "\n",
+    "    Theta1_ = np.hstack((np.zeros((Theta1.shape[0], 1)), Theta1[:, 1:]))\n",
+    "    Theta2_ = np.hstack((np.zeros((Theta2.shape[0], 1)), Theta2[:, 1:]))\n",
+    "    Theta1_grad = (1/m)*delta_acc1 + (lambda_/m)*Theta1_\n",
+    "    Theta2_grad = (1/m)*delta_acc2 + (lambda_/m)*Theta2_\n",
+    "    # ================================================================\n",
+    "    # Unroll gradients\n",
+    "    # grad = np.concatenate([Theta1_grad.ravel(order=order), Theta2_grad.ravel(order=order)])\n",
+    "    grad = np.concatenate([Theta1_grad.ravel(), Theta2_grad.ravel()])\n",
+    "\n",
+    "    return J, grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -720,9 +972,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 211,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-9.27825235e-03 -9.27825236e-03]\n",
+      " [-3.04978709e-06 -3.04978914e-06]\n",
+      " [-1.75060084e-04 -1.75060082e-04]\n",
+      " [-9.62660640e-05 -9.62660620e-05]\n",
+      " [ 8.89911959e-03  8.89911960e-03]\n",
+      " [ 1.42869450e-05  1.42869443e-05]\n",
+      " [ 2.33146358e-04  2.33146357e-04]\n",
+      " [ 1.17982666e-04  1.17982666e-04]\n",
+      " [-8.36010761e-03 -8.36010762e-03]\n",
+      " [-2.59383093e-05 -2.59383100e-05]\n",
+      " [-2.87468729e-04 -2.87468729e-04]\n",
+      " [-1.37149709e-04 -1.37149706e-04]\n",
+      " [ 7.62813550e-03  7.62813551e-03]\n",
+      " [ 3.69883257e-05  3.69883234e-05]\n",
+      " [ 3.35320351e-04  3.35320347e-04]\n",
+      " [ 1.53247082e-04  1.53247082e-04]\n",
+      " [-6.74798369e-03 -6.74798370e-03]\n",
+      " [-4.68759764e-05 -4.68759769e-05]\n",
+      " [-3.76215583e-04 -3.76215587e-04]\n",
+      " [-1.66560294e-04 -1.66560294e-04]\n",
+      " [ 3.14544970e-01  3.14544970e-01]\n",
+      " [ 1.64090819e-01  1.64090819e-01]\n",
+      " [ 1.64567932e-01  1.64567932e-01]\n",
+      " [ 1.58339334e-01  1.58339334e-01]\n",
+      " [ 1.51127527e-01  1.51127527e-01]\n",
+      " [ 1.49568335e-01  1.49568335e-01]\n",
+      " [ 1.11056588e-01  1.11056588e-01]\n",
+      " [ 5.75736494e-02  5.75736493e-02]\n",
+      " [ 5.77867378e-02  5.77867378e-02]\n",
+      " [ 5.59235296e-02  5.59235296e-02]\n",
+      " [ 5.36967009e-02  5.36967009e-02]\n",
+      " [ 5.31542052e-02  5.31542052e-02]\n",
+      " [ 9.74006970e-02  9.74006970e-02]\n",
+      " [ 5.04575855e-02  5.04575855e-02]\n",
+      " [ 5.07530173e-02  5.07530173e-02]\n",
+      " [ 4.91620841e-02  4.91620841e-02]\n",
+      " [ 4.71456249e-02  4.71456249e-02]\n",
+      " [ 4.65597186e-02  4.65597186e-02]]\n",
+      "The above two columns you get should be very similar.\n",
+      "(Left-Your Numerical Gradient, Right-Analytical Gradient)\n",
+      "\n",
+      "If your backpropagation implementation is correct, then \n",
+      "the relative difference will be small (less than 1e-9). \n",
+      "Relative Difference: 2.41486e-11\n"
+     ]
+    }
+   ],
    "source": [
     "utils.checkNNGradients(nnCostFunction)"
    ]
@@ -736,9 +1039,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 201,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise neural-network-learning\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "(16, 4)\n",
+      "(4, 4)\n",
+      "(16, 4)\n",
+      "(5, 16)\n",
+      "(4, 3)\n",
+      "(4, 5)\n",
+      "(16, 3)\n",
+      "(16, 4)\n",
+      "(15, 4)\n",
+      "(16, 4)\n",
+      "(4, 16)\n",
+      "(5, 16)\n",
+      "(4, 3)\n",
+      "(4, 5)\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "              Feedforward and Cost Function |  30 /  30 | Nice work!\n",
+      "                  Regularized Cost Function |  15 /  15 | Nice work!\n",
+      "                           Sigmoid Gradient |   5 /   5 | Nice work!\n",
+      "  Neural Network Gradient (Backpropagation) |  40 /  40 | Nice work!\n",
+      "                       Regularized Gradient |   0 /  10 | \n",
+      "                                  --------------------------------\n",
+      "                                            |  90 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[4] = nnCostFunction\n",
     "grader.grade()"
@@ -778,9 +1116,64 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 226,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-9.27825235e-03 -9.27825236e-03]\n",
+      " [-1.67679797e-02 -1.67679797e-02]\n",
+      " [-6.01744725e-02 -6.01744725e-02]\n",
+      " [-1.73704651e-02 -1.73704651e-02]\n",
+      " [ 8.89911959e-03  8.89911960e-03]\n",
+      " [ 3.94334829e-02  3.94334829e-02]\n",
+      " [-3.19612287e-02 -3.19612287e-02]\n",
+      " [-5.75658668e-02 -5.75658668e-02]\n",
+      " [-8.36010761e-03 -8.36010762e-03]\n",
+      " [ 5.93355565e-02  5.93355565e-02]\n",
+      " [ 2.49225535e-02  2.49225535e-02]\n",
+      " [-4.51963845e-02 -4.51963845e-02]\n",
+      " [ 7.62813550e-03  7.62813551e-03]\n",
+      " [ 2.47640974e-02  2.47640974e-02]\n",
+      " [ 5.97717617e-02  5.97717617e-02]\n",
+      " [ 9.14587966e-03  9.14587966e-03]\n",
+      " [-6.74798369e-03 -6.74798370e-03]\n",
+      " [-3.26881426e-02 -3.26881426e-02]\n",
+      " [ 3.86410548e-02  3.86410548e-02]\n",
+      " [ 5.46101547e-02  5.46101547e-02]\n",
+      " [ 3.14544970e-01  3.14544970e-01]\n",
+      " [ 1.18682669e-01  1.18682669e-01]\n",
+      " [ 2.03987128e-01  2.03987128e-01]\n",
+      " [ 1.25698067e-01  1.25698067e-01]\n",
+      " [ 1.76337550e-01  1.76337550e-01]\n",
+      " [ 1.32294136e-01  1.32294136e-01]\n",
+      " [ 1.11056588e-01  1.11056588e-01]\n",
+      " [ 3.81928689e-05  3.81928696e-05]\n",
+      " [ 1.17148233e-01  1.17148233e-01]\n",
+      " [-4.07588279e-03 -4.07588279e-03]\n",
+      " [ 1.13133142e-01  1.13133142e-01]\n",
+      " [-4.52964427e-03 -4.52964427e-03]\n",
+      " [ 9.74006970e-02  9.74006970e-02]\n",
+      " [ 3.36926556e-02  3.36926556e-02]\n",
+      " [ 7.54801264e-02  7.54801264e-02]\n",
+      " [ 1.69677090e-02  1.69677090e-02]\n",
+      " [ 8.61628953e-02  8.61628953e-02]\n",
+      " [ 1.50048382e-03  1.50048382e-03]]\n",
+      "The above two columns you get should be very similar.\n",
+      "(Left-Your Numerical Gradient, Right-Analytical Gradient)\n",
+      "\n",
+      "If your backpropagation implementation is correct, then \n",
+      "the relative difference will be small (less than 1e-9). \n",
+      "Relative Difference: 2.30858e-11\n",
+      "\n",
+      "\n",
+      "Cost at (fixed) debugging parameters (w/ lambda = 3.000000): 0.576051 \n",
+      "(for lambda = 3, this value should be about 0.576051)\n"
+     ]
+    }
+   ],
    "source": [
     "#  Check gradients by running checkNNGradients\n",
     "lambda_ = 3\n",
@@ -796,9 +1189,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 227,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise neural-network-learning\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "(16, 4)\n",
+      "(4, 4)\n",
+      "(16, 4)\n",
+      "(5, 16)\n",
+      "(4, 3)\n",
+      "(4, 5)\n",
+      "(16, 3)\n",
+      "(16, 4)\n",
+      "(15, 4)\n",
+      "(16, 4)\n",
+      "(4, 16)\n",
+      "(5, 16)\n",
+      "(4, 3)\n",
+      "(4, 5)\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "              Feedforward and Cost Function |  30 /  30 | Nice work!\n",
+      "                  Regularized Cost Function |  15 /  15 | Nice work!\n",
+      "                           Sigmoid Gradient |   5 /   5 | Nice work!\n",
+      "  Neural Network Gradient (Backpropagation) |  40 /  40 | Nice work!\n",
+      "                       Regularized Gradient |  10 /  10 | Nice work!\n",
+      "                                  --------------------------------\n",
+      "                                            | 100 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[5] = nnCostFunction\n",
     "grader.grade()"
@@ -920,7 +1348,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,
diff --git a/Exercise5/exercise5.ipynb b/Exercise5/exercise5.ipynb
index c5e5c679..9a852352 100755
--- a/Exercise5/exercise5.ipynb
+++ b/Exercise5/exercise5.ipynb
@@ -18,10 +18,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 1,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# used for manipulating directory paths\n",
@@ -99,9 +97,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Load from ex5data1.mat, where all variables will be store in a dictionary\n",
     "data = loadmat(os.path.join('Data', 'ex5data1.mat'))\n",
@@ -121,6 +132,23 @@
     "pyplot.ylabel('Water flowing out of the dam (y)');"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(12, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X.shape)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -140,13 +168,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 3,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "def linearRegCostFunction(X, y, theta, lambda_=0.0):\n",
+    "def linearRegCostFunction(X, y, theta, lambda_):\n",
     "    \"\"\"\n",
     "    Compute cost and gradient for regularized linear regression \n",
     "    with multiple variables. Computes the cost of using theta as\n",
@@ -191,9 +217,12 @@
     "    J = 0\n",
     "    grad = np.zeros(theta.shape)\n",
     "\n",
-    "    # ====================== YOUR CODE HERE ======================\n",
     "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    J = (0.5/m)*(np.sum((np.matmul(X, theta)-y)**2, axis=0) + (lambda_)*np.sum(theta[1:, ]**2, axis=0))   \n",
     "\n",
+    "    theta_ = np.append([0], theta[1:, ])\n",
+    "    grad = (1/m)*np.matmul(X.T, np.matmul(X, theta)-y)+ lambda_*theta_/m\n",
     "\n",
     "    # ============================================================\n",
     "    return J, grad"
@@ -208,9 +237,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost at theta = [1, 1]:\t   303.993192 \n",
+      "This value should be about 303.993192)\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "theta = np.array([1, 1])\n",
     "J, _ = linearRegCostFunction(np.concatenate([np.ones((m, 1)), X], axis=1), y, theta, 1)\n",
@@ -232,9 +271,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise regularized-linear-regression-and-bias-variance\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "Regularized Linear Regression Cost Function |  25 /  25 | Nice work!\n",
+      "     Regularized Linear Regression Gradient |   0 /  25 | \n",
+      "                             Learning Curve |   0 /  20 | \n",
+      "                 Polynomial Feature Mapping |   0 /  10 | \n",
+      "                           Validation Curve |   0 /  20 | \n",
+      "                                  --------------------------------\n",
+      "                                            |  25 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[1] = linearRegCostFunction\n",
     "grader.grade()"
@@ -264,9 +324,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gradient at theta = [1, 1]:  [-15.303016, 598.250744] \n",
+      " (this value should be about [-15.303016, 598.250744])\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "theta = np.array([1, 1])\n",
     "J, grad = linearRegCostFunction(np.concatenate([np.ones((m, 1)), X], axis=1), y, theta, 1)\n",
@@ -284,9 +354,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise regularized-linear-regression-and-bias-variance\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "Regularized Linear Regression Cost Function |  25 /  25 | Nice work!\n",
+      "     Regularized Linear Regression Gradient |  25 /  25 | Nice work!\n",
+      "                             Learning Curve |   0 /  20 | \n",
+      "                 Polynomial Feature Mapping |   0 /  10 | \n",
+      "                           Validation Curve |   0 /  20 | \n",
+      "                                  --------------------------------\n",
+      "                                            |  50 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[2] = linearRegCostFunction\n",
     "grader.grade()"
@@ -312,9 +403,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# add a columns of ones for the y-intercept\n",
     "X_aug = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
@@ -358,10 +462,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 60,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def learningCurve(X, y, Xval, yval, lambda_=0):\n",
@@ -442,8 +544,14 @@
     "    error_val   = np.zeros(m)\n",
     "\n",
     "    # ====================== YOUR CODE HERE ======================\n",
-    "         \n",
-    "\n",
+    "#     theta0 = np.array([1, 1])\n",
+    "#     LRCF = linearRegCostFunction(X, y, theta0, lambda_)\n",
+    "    for i in range(1, m+1):\n",
+    "               \n",
+    "        theta = utils.trainLinearReg(linearRegCostFunction, X[:i], y[:i], lambda_=lambda_)\n",
+    "        error_train[i - 1], _ = linearRegCostFunction(X[:i], y[:i], theta, lambda_=0)\n",
+    "        error_val[i - 1], _ = linearRegCostFunction(Xval, yval, theta, lambda_=0)\n",
+    "        \n",
     "        \n",
     "    # =============================================================\n",
     "    return error_train, error_val"
@@ -462,9 +570,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 61,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# Training Examples\tTrain Error\tCross Validation Error\n",
+      "  \t1\t\t0.000000\t205.121096\n",
+      "  \t2\t\t0.000000\t110.302641\n",
+      "  \t3\t\t3.286595\t45.010231\n",
+      "  \t4\t\t2.842678\t48.368910\n",
+      "  \t5\t\t13.154049\t35.865165\n",
+      "  \t6\t\t19.443963\t33.829962\n",
+      "  \t7\t\t20.098522\t31.970986\n",
+      "  \t8\t\t18.172859\t30.862446\n",
+      "  \t9\t\t22.609405\t31.135998\n",
+      "  \t10\t\t23.261462\t28.936207\n",
+      "  \t11\t\t24.317250\t29.551432\n",
+      "  \t12\t\t22.373906\t29.433818\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "X_aug = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
     "Xval_aug = np.concatenate([np.ones((yval.size, 1)), Xval], axis=1)\n",
@@ -491,9 +631,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 62,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise regularized-linear-regression-and-bias-variance\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "Regularized Linear Regression Cost Function |  25 /  25 | Nice work!\n",
+      "     Regularized Linear Regression Gradient |  25 /  25 | Nice work!\n",
+      "                             Learning Curve |  20 /  20 | Nice work!\n",
+      "                 Polynomial Feature Mapping |   0 /  10 | \n",
+      "                           Validation Curve |   0 /  20 | \n",
+      "                                  --------------------------------\n",
+      "                                            |  70 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[3] = learningCurve\n",
     "grader.grade()"
@@ -527,10 +688,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 180,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def polyFeatures(X, p):\n",
@@ -560,15 +719,38 @@
     "    \"\"\"\n",
     "    # You need to return the following variables correctly.\n",
     "    X_poly = np.zeros((X.shape[0], p))\n",
-    "\n",
+    "    \n",
     "    # ====================== YOUR CODE HERE ======================\n",
     "\n",
-    "\n",
+    "    for power in range(p):     \n",
+    "        X_poly[:, power] = X[:, 0]**(power+1)\n",
     "\n",
     "    # ============================================================\n",
     "    return X_poly"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-15.93675813, -29.15297922,  36.18954863,  37.49218733,\n",
+       "       -48.05882945,  -8.94145794,  15.30779289, -34.70626581,\n",
+       "         1.38915437, -44.38375985,   7.01350208,  22.76274892])"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X[:, 0]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -578,9 +760,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 183,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Normalized Training Example 1:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.        , -0.36214078, -0.75508669,  0.18222588, -0.70618991,\n",
+       "        0.30661792, -0.59087767,  0.3445158 , -0.50848117])"
+      ]
+     },
+     "execution_count": 183,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "p = 8\n",
     "\n",
@@ -602,7 +803,7 @@
     "X_poly_val = np.concatenate([np.ones((yval.size, 1)), X_poly_val], axis=1)\n",
     "\n",
     "print('Normalized Training Example 1:')\n",
-    "X_poly[0, :]"
+    "X_poly[0, :]\n"
    ]
   },
   {
@@ -614,9 +815,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 184,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise regularized-linear-regression-and-bias-variance\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "Regularized Linear Regression Cost Function |  25 /  25 | Nice work!\n",
+      "     Regularized Linear Regression Gradient |  25 /  25 | Nice work!\n",
+      "                             Learning Curve |  20 /  20 | Nice work!\n",
+      "                 Polynomial Feature Mapping |  10 /  10 | Nice work!\n",
+      "                           Validation Curve |   0 /  20 | \n",
+      "                                  --------------------------------\n",
+      "                                            |  80 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[4] = polyFeatures\n",
     "grader.grade()"
@@ -653,9 +875,55 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 185,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Polynomial Regression (lambda = 0.000000)\n",
+      "\n",
+      "# Training Examples\tTrain Error\tCross Validation Error\n",
+      "  \t1\t\t0.000000\t160.721900\n",
+      "  \t2\t\t0.000000\t160.121511\n",
+      "  \t3\t\t0.000000\t59.071640\n",
+      "  \t4\t\t0.000000\t77.997856\n",
+      "  \t5\t\t0.000000\t6.449508\n",
+      "  \t6\t\t0.000000\t10.825774\n",
+      "  \t7\t\t0.000000\t27.922419\n",
+      "  \t8\t\t0.025083\t9.256265\n",
+      "  \t9\t\t0.000182\t31.381205\n",
+      "  \t10\t\t0.033367\t22.879956\n",
+      "  \t11\t\t0.035666\t26.797570\n",
+      "  \t12\t\t0.029686\t43.849641\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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3R7SMESNGsHTp0hhHaopiN+WDIiMTvnsXVMEuWZgk1aBBA+bPnw/A7bffTq1atbj++uv3m0dVUVVSUoo+Hx43blzM4zRFsxJKUGQ0h7ydsH2935EYE3XLly+nQ4cOjBo1im7durFq1SpGjhxJjx49aN++PXfeeWfBvMceeyzz588nLy+PunXrMnr0aDp37sxRRx3F2rVrS1iLqSgroQRFeFuUmg39jcUEwh2Tv2XRr1uiusx2Tetw26nty/XeRYsWMW7cOB5//HEAxowZQ/369cnLy6N///6ceeaZtGvXbr/3bN68mb59+zJmzBiuvfZannnmGUaPHl3h7TBFsxJKUFhbFBNwrVq14ogjjih4Pn78eLp160a3bt1YvHgxixYtOuA91atX5+STTwage/furFy5Ml7hVkpWQgmKDK9vTUsoJkrKW5KIlZo1axY8XrZsGQ899BBz5syhbt26nHfeeUW2t6hatWrB4ypVqpCXlxeXWCsrK6EERY36kFrdEoqpFLZs2ULt2rWpU6cOq1at4r333vM7JIOVUIJDxKs6bI0bTfB169aNdu3a0aFDBw499FCOOeYYv0MyxHBM+XiwAbYKeX4o7NoCf/jA70hMklq8eDFt27b1OwwTRUV9piIyT1V7RHtddskrSKxxozHGR5ZQgiSjOWxdA3m7/I7EGFMJWUIJkoKBtn7xNw5jTKVkCSVIrC2KMcZHllCCxBKKMcZHllCCpE4z998SijHGB5ZQgiQtHWo2srYoJqmtXr2a4cOH06pVK9q1a8egQYP47rvvYrrOlStXkpmZyd69e/eb3qVLF+bMmVPs+5599lkuv/xyAB5//HGef/75IpfdoUOHUtf/0ksvFTyfO3cuV155ZVk2ISFYQgkaqzpskpiqMmzYMPr168f333/PokWLuPvuu1mzZs1+8+3Zsyeq623RogXNmzdn1qxZBdOWLFlCTk4ORx55ZETLGDVqFBdccEG51l84ofTo0YOHH364XMvykyWUoLGEYpLYjBkzSEtLY9SoUQXTunTpQu/evZk5cyb9+/fn97//PR07dgTggQceoEOHDnTo0IGxY8cCsG3bNk455RQ6d+5Mhw4dmDBhAgCjR4+mXbt2dOrU6YAxVgDOOeccXn755YLnL7/8Mueccw4AkydPpmfPnnTt2pWBAwcekODAjd9y3333ATBv3ryCLvMfffTRgnlWrlxJ7969Czq1/OSTTwpimzVrFl26dOHBBx9k5syZDB48GIANGzYwdOhQOnXqRK9evVi4cGHB+i6++GL69evHoYcemhAJyLpeCZqM5rD8fRtoy1TcO6Nh9dfRXWaTjnDymGJf/uabb+jevXuxr8+ZM4dvvvmGli1bMm/ePMaNG8fnn3+OqtKzZ0/69u3LihUraNq0KW+99RbgurDfsGEDkyZNYsmSJYgImzZtOmDZZ599Nl27duVf//oXqampTJgwgVdeeQVwY6x89tlniAhPPfUU//znP7n//vuLjXPEiBH861//om/fvtxwww0F0xs1asS0adNIT09n2bJlnHPOOcydO5cxY8Zw3333MWXKFABmzpxZ8J7bbruNrl278vrrr/PBBx9wwQUXFAxCtmTJEmbMmEFOTg5t2rThsssuIy0trdi4Ys1KKEGTkQm522HHRr8jMSbqjjzySFq2bAnA7NmzGTZsGDVr1qRWrVqcfvrpzJo1i44dO/L+++9z0003MWvWLDIyMqhTpw7p6elceumlTJw4kRo1ahyw7CZNmtC+fXumT5/O/PnzSUtLK7j3kZ2dzYknnkjHjh259957+fbbb4uNcfPmzWzatIm+ffsCcP755xe8lpubyx/+8Ac6duzIWWedVWSX+4XNnj27YBnHHXcc69evZ/PmzQCccsopVKtWjYYNG9KoUaMiS07xZCWUoAkfaKtGfX9jMcmthJJErLRv355XX3212NfDu7Avrh/Cww8/nHnz5vH2229z8803c8IJJ3DrrbcyZ84cpk+fzssvv8wjjzzCBx8c2Odd/mWvxo0bF1zuArjiiiu49tprOe2005g5cya33357sTGqKlLM1YEHH3yQxo0bs2DBAvbu3Ut6enqxyylpO/OXX61atYJpidA9v5VQgsbaopgkdtxxx7Fr1y6efPLJgmlffPEFH3744QHz9unTh9dff53t27ezbds2Jk2aRO/evfn111+pUaMG5513Htdffz1ffvklW7duZfPmzQwaNIixY8cWXDIq7IwzzuDtt99mwoQJDB8+vGD65s2badbMVct/7rnnStyGunXrkpGRwezZswF48cUX91vOQQcdREpKCi+88EJB5YLatWuTk5NT5PL69OlTsIyZM2fSsGFD6tSpU2IMfrESStDYQFsmiYkIkyZN4uqrr2bMmDGkp6fTokULxo4dyy+/7N+lULdu3bjooosKamFdeumldO3alffee48bbriBlJQU0tLSeOyxx8jJyWHIkCHs3LkTVeXBBx8scv1169alV69erFmzpuDSGrgb4GeddRbNmjWjV69e/PDDDyVux7hx47j44oupUaMGJ554YsH0P/3pT5xxxhm88sor9O/fv6DE1alTJ1JTU+ncuTMXXXQRXbt23W/dI0aMoFOnTtSoUaPUhOYn674+aFThrsbQcySccJff0ZgkY93XB491X2/Kr2CgLSuhGGPiyxJKEFlCMcb4wBJKEGU0t4Riyi2ZL4Ob/cX7s7SEEkQZmZCzGvJ2+x2JSTLp6emsX7/ekkoAqCrr16+PqGpytFgtryDKyAQUcn6Fei38jsYkkczMTLKzs1m3bp3foZgoSE9PJzMzM27rs4QSROFtUSyhmDJIS0vbr7qsMWVhl7yCyNqiGGN84EtCEZFrRORbEflGRMaLSLqItBSRz0VkmYhMEJGqfsQWCBn5A23ZuCjGmPiJe0IRkWbAlUAPVe0AVAGGA/cAD6pqa2AjcEm8YwuMtOpQo6GVUIwxceXXJa9UoLqIpAI1gFXAcUB+r3DPAUN9ii0YrC2KMSbO4p5QVPUX4D7gJ1wi2QzMAzapan5XmdlAs6LeLyIjRWSuiMy1miglsIRijIkzPy551QOGAC2BpkBN4OQiZi2yIryqPqGqPVS1RygUil2gyS6/caO1JzDGxIkfl7wGAj+o6jpVzQUmAkcDdb1LYACZwK8+xBYcGZmweyvsPHBkOmOMiQU/EspPQC8RqSFulJgBwCJgBnCmN8+FwBs+xBYcfo6L8vMcWPpO/NdrjPGVH/dQPsfdfP8S+NqL4QngJuBaEVkONACejndsgeJXW5TtG2D8cJh8VXzXa4zxnS8t5VX1NuC2QpNXAEf6EE4w+VVCmXYrbF/vHm/fYMMQG1OJWEv5oKoZgipV49u4ceXH8NULcFAX9/y37+K3bmOM7yyhBFVKCtRpFr8SSt4umHI11D0Yhj3upq1dHJ91G2MSgnUOGWTxbIvy8UOuRHLuq9CwDaTVgHVL47NuY0xCsBJKkMVroK3flsNH90H7YdD6eFc6CrWBdUtiv25jTMKwhBJkGZmQswr25MZuHarw1jWQmg4njdk3PZRlCcWYSsYSSpBlZILudUklVhZOgB8+goG3Qe0m+6aH2rj17rCGlcZUFpZQgizWVYe3rYf3/gKZR0D3Efu/Fmrr/tt9FGMqDUsoQRbrxo3TboWdm+HUh9x9k3ChNu6/XfYyptKwhBJksRxo64dZMP+/cNTl0Lj9ga/XPQRSq1sJxZhKxBJKkFWtCdXrR7+EkrcLplzjkkbfm4qeJyUFQofDOmuLYkxlYQkl6GLRFmX2WFi/DE55AKrWKH6+UJaVUIypRCyhBF2026L8tgxm3QcdzoDWA0ueN5QFW36BnVuit35jTMKyhBJ00SyhqLpLXanV4cR/lD5/KMv9t1KKMZWCJZSgy8iEXVtcbayKWjAeVs6C42+H2o1Ln99qehlTqVhCCbpotUXZth7e+ys07wndLorsPfVauBb0llCMqRQsoQRdtNqiTPs/V9IZPPbANifFSakCDVtbQjGmkrCEEnQFJZQKtEX54SOY/yIcfSU0ble294ba2j0UYyoJSyhBV6sxpKSVv4SSu9PdiK/XAvrcUPb3h9q4ZLYrp3zrN8YkDUsoQZeSAnWalj+hzH4Q1i+HU+4vuc1JcQpqetnojcYEnSWUyqC8bVHWfQezH4AOZ8JhpbQ5KU6j/E4ircW8MUFnCaUyKE9blPw2J2nV4aQI2pwUp14LqFLNbswbUwlYQqkMMjJhy6+wJy/y98x/EX6cDcffCbUalX/dKVWg4eF2Y96YSsASSmWQkQm6B7aujmz+bb/B1FugeS/oekHF1x9qA2uthGJM0FlCqQzK2hZl6i2wayucWoY2JyUJZcHmn9wyjTGBZQmlMihLa/kVH7ouVo65at8N9Ypq5NX0+s1qehkTZJZQKoNIB9oqaHPSEvpcH731F1QdtstexgRZqt8BmDioVhvS65ZeQpl1P2z4Hs6f5Gp3RUu9llClqiUUYwLOSiiVRWltUdYtdY0YO54NrY6L7rqrpEKD1nZj3piAs4RSWZTUFmXvXph8tRsy+MS7Y7P+UBsroRgTcJZQKouMzOLvocx/EX76BE74G9QKxWb9jdrCpp9g97bYLN8Y4ztLKJVFRqYbZKvwcLxb17lqwgcfDV3Oi936Q20AtZpexgSYJZTKIr/q8JZf9p8+9a+u1DD4wei0OSmODQdsTOBZQqksimrc+P0MWDgBjr16X1uRWKl/qOtG3+6jGBNYllAqi8IDbeXugLeudQf63tfFfv1V0qDBYVbTy5gA8yWhiEhdEXlVRJaIyGIROUpE6ovINBFZ5v2v50dsgVW7CUiVfSWUWffDhhXuUlc025yUxGp6GRNofpVQHgLeVdUsoDOwGBgNTFfV1sB077mJlpQqUKeZSyhrl8DssdBpOBzaL34xNGoLG1fC7u3xW6cxJm7inlBEpA7QB3gaQFV3q+omYAjwnDfbc8DQeMcWeBmZrurulKuhWi048e/xXX9+Ta/1y+K7XmNMXPhRQjkUWAeME5GvROQpEakJNFbVVQDe/yIH4RCRkSIyV0Tmrlu3Ln5RB0FGJvz0Gfz0KRz/N6jZML7rD+WP3mg1vYwJIj8SSirQDXhMVbsC2yjD5S1VfUJVe6hqj1AoRo3wgiojE1A45BjoGsM2J8WpfyikpMJaGw7YmCDyI6FkA9mq+rn3/FVcglkjIgcBeP/X+hBbsB3UCarWgsFjQST+60+tCvVbWQnFmICKe0JR1dXAzyLSxps0AFgEvAlc6E27EHgj3rEFXvthcOMKCB3uXwyNsqymlzEB5Vf39VcAL4pIVWAFMAKX3P4nIpcAPwFn+RRbsKVW83f9oSxYPNmNvZKW7m8sxpio8iWhqOp8oEcRLw2IdywmzkJtQPe6ml5NOvodjTEmiqylvImv/Jpe1mLemMCxhGLiq0Er12Lf7qMYEziWUEx8pVZzScUSijGBYwnFxJ/16WVMIFlCMfEXynIdU+bt8jsSY0wUWUIx8RfK8mp6Lfc7EmNMFJWaUESkiojcG49gTCWRP3qjdcFiTKCUmlBUdQ/QXcSPvjpMIDU4DCTFumAxJmAibdj4FfCGiLyC68wRAFWdGJOoTLClpbuOItdZCcWYIIk0odQH1gPHhU1TwBKKKZ9QlpVQjAmYiBKKqo6IdSCmkgllwdJ3IG+364XYGJP0IqrlJSKZIjJJRNaKyBoReU1EMmMdnAmwUBboHqvpZUyARFpteByue/mmQDNgsjfNmPIJeaMXWANHYwIj0oQSUtVxqprn/T0L2HCJpvwatraaXsYETKQJ5TcROc9rk1JFRM7D3aQ3pnzSqkO9FlbTy5gAiTShXAycDawGVgFnetOMKb9QWyuhGBMgpdbyEpEqwBmqeloc4jGVSagNLHvPanoZExCRtpQfEodYTGUTyoK9ea6jSGNM0ou0YePHIvIIMIH9W8p/GZOoTOXQyOvTa92SfY+NMUkr0oRytPf/zrBpyv4t540pmwatAbGqw8YERCT3UFKAx1T1f3GIx1QmVWtAvUMsoRgTEJHcQ9kLXB6HWExlFGoLay2hGBMEkVYbniYi14tIcxGpn/8X08hM5RBq47pf2ZPrdyTGmK70SkIAAByrSURBVAqK9B5KfpuTP4dNU+DQ6IZjKp1GbWFvLmz4AUKH+x2NMaYCIu1tuGWsAzGVVEGfXostoRiT5Eq85CUiN4Y9PqvQa3fHKihTiTT0koi1mDcm6ZV2D2V42OObC712UpRjMZVR1ZpQ12p6GRMEpSUUKeZxUc+NKZ9QltX0MiYASksoWszjop4bUz6hNrB+GezJ8zsSY/yzbilkz/U7igop7aZ8ZxHZgiuNVPce4z1Pj2lkpvJo1Bb27IaNP7hxUoypbPJ2w2uXwta1cNUCSEvOw2uJCUVVq8QrEFOJhY/eaAnFVEYfjoHVC2H4S0mbTCDyho3GxE5DGw7YVGI/fQazH4Su50PWKX5HUyGWUIz/qtWCjIPtxrypfHblwKQ/QkZzOOkffkdTYZG2lDcmtkJtrC2KqXze+wts/BFGvAPVavsdTYVZCcUkhkZZ8Nt3sHeP35EYEx9L34Evn4djr4ZDjvI7mqjwLaGISBUR+UpEpnjPW4rI5yKyTEQmiIiNCVuZhLJgzy7YuNLvSIyJva3r4M0roHFH6PcXv6OJGj9LKFcBi8Oe3wM8qKqtgY3AJb5EZfwRChu90ZggU4XJV8HOLXD6E5AanHNnXxKKiGQCpwBPec8FN/rjq94szwFD/YjN+CS/6vDaxSXPZ0yy++q/sPQtGHArNG7ndzRR5VcJZSxwI7DXe94A2KSq+U2ls4FmRb1RREaKyFwRmbtu3brYR2rio1ptqJNpN+ZNsG34Ad4dDS16Q68/+R1N1MU9oYjIYGCtqs4Ln1zErEV27aKqT6hqD1XtEQqFYhKj8UmjLLvkZYJr7x6YNAokBYY+BinBqxPlR7XhY4DTRGQQrvuWOrgSS10RSfVKKZnArz7EZvwUyoKVs90PL8U6aTAB88nD8PNnMOwJqNvc72hiIu4pUlVvVtVMVW2B6x7/A1U9F5gBnOnNdiHwRrxjMz4LtYG8nbDpR78jMSa6Vi2ED/4O7YZCp7P9jiZmEqnMdRNwrYgsx91TedrneEy8hdq6/3YfxQRJ7k6YOBJqNIDBD4IEd+QPX1vKq+pMYKb3eAVwpJ/xGJ/lDwG8djG0OdnfWIyJlg/+5oa4Pvc1qFHf72hiKpFKKKayS8+A2k2thGKC44eP4NNH4IhLofVAv6OJOUsoJrE0ynJnc8Yku52bYdJl0OAwOP5Ov6OJC0soJrGEsmDdd7B3b+nzGpPI3r4Rcla5Wl1Va/odTVxYQjGJJZQFeTtg809+R2JM+X07CRa+DH1ugMzufkcTN5ZQTGLJ79PLxkYxyWrLKphyDTTtBn2u9zuauLKEYhJLyEZvNElMFd74s6sqfPoTUCXN74jiygbYMomlel2ofZDV9DLJ6Yun4PvpMOg+aNja72jizkooJvGE2lhNL5N8flsOU/8PWg1w1YQrIUsoJvFYTS+TbPbkwsQ/QFo6DHk00K3hS2IJxSSeUBbkboPNP/sdiTGRmXU//Pql61qlzkF+R+MbSygm8RSM3mj3UUwSyJ4HH/4TOv0O2g/zOxpfWUIxicdqeiW/HZsgb7ffUcTe7u0waaSrSHLyP/2OxneWUEziqVEfajW2hJKsduXAv3u5v1++9Dua2Jp2K6xfDsMeczUUKzlLKCYxhdpYQklWnz7quhzZvRWePh5mjw1mBYtl78MXT0KvP0PLPn5HkxAsoZjEFGrr7qFokSNBm0S1dR188i9oexr8+XPIOgXevw1eGAJbAjQI6/YNrgFjKAsG3Op3NAnDEopJTKE27gx3c7bfkZiymHUf5O5wB9nq9eCs5+C0RyB7Ljx2NCye4neEFacKU66G7etda/i0dL8jShiWUExiamSjNyadjSvhi6eh63n7WomLQLfz4Y+zoO4hMOFcmHy1u5mdrBb+Dxa9Af3/Agd19juahGIJxSSmgqrD1mI+acy4G1KqQL/RB77W8DC4ZBoccxXMGwdP9HPjrCebTT/D2zdA815uW8x+LKGYxFSjPtQM2Y35ZLH6G3fm3nMU1Gla9DypVd1AU+e/7gafemqAu4GfLDfs9+6F1y8D3QPDHnfJ0+zHEopJXKGsynPJ67dlMP1OyNvldyTlM/0OSK8Dx15d+ryt+sNln8BhA+G9v8CLZ0LOmtjHWFGfPwYrZ8FJ/4D6Lf2OJiFZQjGJKz+hBL2mV+5O+N8FrvuOWQ/4HU3ZrfwYlk2FY691N+IjUbMBDH8JTnkAfvzY3bD/bmps46yINYvg/TugzSDoer7f0SQsSygmcYXawK4twapuWpQZd8HaRXBQF5dUkmlwMVVXLbh2U+j5x7K9VwSOuARGfgi1m8BLZ7lhc3N3xibW8srdARNHQrXacOrDlbbjx0hYQjGJq6CmV4BvzK+cDZ88Aj0uhnNfhWq1YPJVyXNfYclbkP2FuxGfVr18y2iUBZdOh56XwZz/wJPHuRKBn3J3uCrOr10K97aGNV/Daf+CWiF/40pwllBM4gp6J5E7t8Cky9z1+BPucgerE++Gnz9zNaES3Z48d9+n4eHQ5dyKLSstHU4e45LqtrXwZH+Y82R8L3fu3g6L3oRXL4Z7D3NVnJdPhw7D4KK3IGtQ/GJJUjZio0lcNRtCjYbBren17mjYkg0XT4WqNd20zufAgpfh/dvd9fpE7gp9wXj4bSmc/QJUidKhpPXx7ob965fB29e7A/qQR9x3IRZ2b4Nl02DR6+4eTu42qNEAOp4J7YZCi2Mr3TC+FWEJxSS2UFZy3VOI1OLJMP9F6HMDND9i33QRN6bGY0fDOzfA7/7rX4wlyd0BM/8BzXpA21Oju+xajeD3r7jLX9Nudfti2OPQ6rjoLH/3NvjuPZdElk2D3O2uinrn37kkcsgx0UuQlYztNZPYQm3g61fdpY+g3AzNWePukxzUGfredODrDVq56dPvcNfx2w6Of4ylmfMkbPkFhv0nNp9LSgr0usyVEF67FF4YBkdfAcfd6tqzlNWurfDdu14SeR/ydkDNRtDl914SOdralUSBJRST2Bq1hV2bIWd1Yl/+iZQqTL7SnSUPe6L4yylHXwHfvOZaZbfs49p4JIodm1xttMMGQsvesV1Xk47whxkw9RbX6eSKD+HMZ/Z17VKSXTmw1Esiy9+HvJ1Qq4nrCqbdUDi4lyWRKLOEYhJbwWBbi4ORUL583p0pnzTG1W4qTpU0V0X1qQHuxvcp98UvxtJ8/BDs3AQDbovP+qrWgMEPwGED4I3L4T993P7rdsGBpaOdm70k8oZLInt2ucGvul8E7Ya4LlNSrC5SrFhCMYktvKZXtK6h+2XDCnj3ZmjZF46MoM1GZnfXtuPz/0Cns6H5kbGPsTRbVsFnj0HHs+CgTvFdd9Yp0LQbTPqjK+Utfx9OfQgkBZa+45LI99Nhz26o08y1cWk3BDKPtCQSJ5ZQTGKrGYLq9WFtkrdF2bvHVRFOSYWh/478AHfcLe4+yptXwh8/Kt/9g2j68B7Ymwf9/+rP+usc5PoC+/RfruS2cra7tLU3FzKaw5EjXRJp1sOSiA8soZjEJhKMPr0+fsi1Lzn9ScjIjPx91WrDKffD+N/BJw+5WmF++W25u2R3xCX+9mWVkuJ6+m3ZB2be4yoxtB8GzboHp+JGkrKEYhJfoyz4ZmLy1vRatdB17d5uqLtUVFZtTnIHzA/vhXbDXFfwfvjgTkhN9zephWvaFX7/st9RmDBWJjSJL5TlbgJvTYIeaQvL3en6garRwLUvKW9CPOke15p88lX+dJb5yzx3j+Loy107EWOKEPeEIiLNRWSGiCwWkW9F5Cpven0RmSYiy7z/EXZbagKvoKZXEjZw/OBvrobakEfdGC/lVbsxHP83+HE2fBXnxo6qruV+jQZw1OXxXbdJKn6UUPKA61S1LdAL+LOItANGA9NVtTUw3XtuDISSdDjgH2a5AaR6XAKtB1Z8eV3Pd624p94CW9dWfHmR+v4D+OEj6HNjYrWHMQkn7glFVVep6pfe4xxgMdAMGAI85832HDA03rGZBFWrEaTXTa6aXjs3u/6o6h8KJ/wtOstMSYHBY11XIe/G6Xxr715XOql7MPQYEZ91mqTl6z0UEWkBdAU+Bxqr6ipwSQco8kKtiIwUkbkiMnfdunXxCtX4KRlrer0z2o3jcvoT+zp+jIbQ4e6m+DevxWdAqm8nwuqF0P8WSK0W+/WZpOZbQhGRWsBrwNWquiXS96nqE6raQ1V7hEI2NkGl0SjL3YtIhtEbF70JC16C3tdBZo/oL/+Yq12Cfeta10dVrOTthg/ugsYdylc7zVQ6viQUEUnDJZMXVXWiN3mNiBzkvX4QEMeLxCbhhbJgx0bYluCl0oKOH7tA3xtjs47Uqq5bls0/w4y/x2YdAF8+Bxt/cF2sWCNBEwE/ankJ8DSwWFXDB9B+E7jQe3wh8Ea8YzMJrKALlgSu6aUKb17u7nGc/mRsx9E4uKe72f/5465Kb7Tt2gof/tNVAmh9fPSXbwLJj9OOY4DzgeNEZL73NwgYAxwvIsuA473nxjj5CSWRx0aZ9ywsmwoD73D3OmJt4G1QqzG8eRXsyY3usj97zI2cOPCO5GxManwR95byqjobKO4bOiCesZgkUrsJVMtI3BLK+u/hvb/Cof1cf1LxkJ4Bg+6FCee56snHXh2d5W5b77qKyRq8/+BfxpTCLoya5CDi3ZhPwJpee/Jg0ig3yt+QMnT8GA1tT3UH/pljXG/G0TDrfjcU7oBbo7M8U2lYQjHJI9TG1fRKNB+Phew5MOh+yGgW//UPutf1YjzlmorXgtv0E3zxpBvJML+HAmMiZAnFJI9QFmxfD9t+8zuSfVYtcGOrtz8dOp7pTwx1mrr7KStmwsIJFVvWjH8AAv1ujkZkppKxhGKSR8GN+QQppeR3/Fgz5LqY9/PmdY9LoHlPN4BXeRPumkWwYDz0HFm2LvaN8VhCMckj0aoOT7/TxTLkkYp1/BgNKSlu9MJdOa5yQHlMvxOq1YFjr41ubKbSsIRikkedpu6Alwg35ld8CJ89Ckf8AQ6LQseP0dCoLRx7DSx8GZZPL9t7f/wUvnsHjr3K/+RokpYlFJM8RLwb8z6XUHZsgtf/BA0Og+Pv9DeWwnpfBw1auxv0u7dH9p787ulrNYGel8U0PBNsllBMckmEhPLOTZCzCoY9AVVr+BtLYWnpcOpY2PSjqywQie/edcMT97sp8bbHJBVLKCa5hNq6/ry2rfdn/d++7i4p9bkBMrv7E0NpWhwL3S5wjR1XLSh53r174P07oH4rN96KMRVgCcUkFz9vzOeshilXu7HM+1wf//WXxfF3uhEW37zSJY3iLJzg2vYM+L/Y9j1mKgVLKCa5NPIpoajCG5e7qsKx7vgxGqrXg5PvgVXzXQeSRcndCR/83SXIdjaenak4SygmudRpBlVrxT+hzBsHy6e5M/+GreO77vJqPwxan+jGNNn444Gvf/EUbMmGgbdbB5AmKiyhmOTiR02vgo4f+8MRl8ZvvRUl4hpcIvDWdft3y7JzM8y6D1od5zq0NCYKLKGY5BNqG7+2KBtWuNbwVarC0Dh3/BgNdZu7+yPLp7lhg/N9/LAbsGzg7X5FZgIoyX4dxuBKKFvXwPYNsVn+jk0wdxw8fSI83BV+/dK1Qq/TNDbri7UjR0LTbvDuaLfPclbDZ/+GDmfAQZ39js4ESNzHQzGmwgpqei2FQ46KzjL35MH3H7i+rJa8BXt2QcM27gy+49n+9CIcLSlV4LSH4T99Ydr/QZVqsGc39C9nFy3GFMMSikk+4TW9KppQVn/jksjXr7hST/X60P1C6HyOq/0UlJvVTTrCMVfC7AdBqkCPEdCgld9RmYCxhGKST51MSKtZ/hvzW9e6BLJgPKz+GlLS4PATXRJpfQKkVo1uvImi702uYebWNdDnRr+jMQFkCcUkn5SUstf0yt3pOj+cPx6Wvw+6x5VATr7X3Uuo2SB28SaKtOpw4WQ3pkztxn5HYwLIEopJTqEsd8+jJKqQ/QXMfwm+neiqytZuCkdf4Uoj+ZfOKpO6zd2fMTFgCcUkp1AbWPCSq/pavd7+r236CRZMcJe0NnwPqdWh3WnQeTi07OtuUhtjos4SiklOjdq6/+u+g4N7uoGlFr3pksjKWe61Fr2h97XQbghUq+1frMZUEpZQTHIKtXH/F06AuU/D4smQux3qHwr9b4FOZ0O9Q/yN0ZhKxhKKSU4ZB7uaXnOfhvQM6PQ76PJ7yDwiOFV9jUkyllBMckpJgTOecg0QDz/ZDSxljPGVJRSTvLIG+R2BMSaM9eVljDEmKiyhGGOMiQpLKMYYY6LCEooxxpiosIRijDEmKiyhGGOMiQpLKMYYY6LCEooxxpiosIRijDEmKhIqoYjISSKyVESWi8hov+MxxhgTuYTpekVEqgCPAscD2cAXIvKmqi7yNzJjgkFV93suSdCJpqqSH7bmPy94DfY9SwxC6fu0pN0uQGqVhDrPL5OESSjAkcByVV0BICIvA0MAXxPKMWM+YNP23X6GYCqJ8AOle77/wTT8Qf6BtPDBtqjlRCr/QCcFz6Xg+b7X9p9JDniv7Pc8/6C/33bo/vHvi1e97Shb3EGSWa86s286zu8wyi2REkoz4Oew59lAz8IzichIYKT3dJeIfBOH2OKlIfCb30FESZC2BYK1PUHaFgjQ9vwIDWV0XLYlJoMFJVJCKaogeMC5iqo+ATwBICJzVbVHrAOLlyBtT5C2BYK1PUHaFgjW9iT7tiTSxbpsoHnY80zgV59iMcYYU0aJlFC+AFqLSEsRqQoMB970OSZjjDERSphLXqqaJyKXA+8BVYBnVPXbUt72ROwji6sgbU+QtgWCtT1B2hYI1vYk9bZI4aqExhhjTHkk0iUvY4wxScwSijHGmKhI2oQSlG5aRKS5iMwQkcUi8q2IXOV3TBUlIlVE5CsRmeJ3LBUlInVF5FURWeJ9Rkf5HVNFiMg13vfsGxEZLyLpfscUKRF5RkTWhrc9E5H6IjJNRJZ5/+v5GWNZFLM993rftYUiMklE6voZY1klZUIJ66blZKAdcI6ItPM3qnLLA65T1bZAL+DPSbwt+a4CFvsdRJQ8BLyrqllAZ5J4u0SkGXAl0ENVO+Aqvwz3N6oyeRY4qdC00cB0VW0NTPeeJ4tnOXB7pgEdVLUT8B1wc7yDqoikTCiEddOiqruB/G5ako6qrlLVL73HObgDVjN/oyo/EckETgGe8juWihKROkAf4GkAVd2tqpv8jarCUoHqIpIK1CCJ2nqp6kfAhkKThwDPeY+fA4bGNagKKGp7VHWqquZ5Tz/DtcdLGsmaUIrqpiVpD8L5RKQF0BX43N9IKmQscCOw1+9AouBQYB0wzruE95SI1PQ7qPJS1V+A+4CfgFXAZlWd6m9UFdZYVVeBOzkDGvkcTzRdDLzjdxBlkawJJaJuWpKJiNQCXgOuVtUtfsdTHiIyGFirqvP8jiVKUoFuwGOq2hXYRnJdUtmPd39hCNASaArUFJHz/I3KFEVE/oq7HP6i37GURbImlEB10yIiabhk8qKqTvQ7ngo4BjhNRFbiLkMeJyL/9TekCskGslU1v8T4Ki7BJKuBwA+quk5Vc4GJwNE+x1RRa0TkIADv/1qf46kwEbkQGAycq0nWUDBZE0pgumkR10f408BiVX3A73gqQlVvVtVMVW2B+0w+UNWkPQNW1dXAzyLSxps0AJ+HU6ign4BeIlLD+94NIIkrGXjeBC70Hl8IvOFjLBUmIicBNwGnqep2v+Mpq6RMKN5Nq/xuWhYD/4ugm5ZEdQxwPu5sfr73N8jvoEyBK4AXRWQh0AW42+d4ys0rab0KfAl8jfv9J01XHyIyHvgUaCMi2SJyCTAGOF5EluEG5xvjZ4xlUcz2PALUBqZ5x4LHfQ2yjKzrFWOMMVGRlCUUY4wxiccSijHGmKiwhGKMMSYqLKEYY4yJCksoxhhjosISiikXEVERuT/s+fUicnuUlv2siJwZjWWVsp6zvB6EZxSa3kJEfl/OZX4SwTxPBaAD0P2IyFa/YzD+s4RiymsXcLqINPQ7kHBeT9SRugT4k6r2LzS9BVBkQvE6VSyWqpba8lxVL1XVZG4gaUyRLKGY8srDNYq7pvALhUsY+WevItJPRD4Ukf+JyHciMkZEzhWROSLytYi0ClvMQBGZ5c032Ht/FW+8iC+88SL+GLbcGSLyEq7BXuF4zvGW/42I3ONNuxU4FnhcRO4t9JYxQG+vYdk1InKRiLwiIpOBqSJSS0Smi8iX3nKHhK0rfFtnyr6xVF70WqfjTe+RP7+I/F1EFojIZyLS2Jveynv+hYjcWVwJQETO8/bffBH5j7ePDhE3PkhDEUnx9uMJ3vyvi8g8cWOijAyPW0Tu8V57X0SO9OJcISKnefNcJCJviMi74sYiuq2YmG4I+4zu8KbVFJG3vO38RkR+V9R7TZJTVfuzvzL/AVuBOsBKIAO4Hrjde+1Z4Mzweb3//YBNwEFANeAX4A7vtauAsWHvfxd3wtMa16dWOjASuMWbpxowF9fRYT9cx40ti4izKa7LkRCus8cPgKHeazNxY4MUfk8/YErY84u8GOp7z1OBOt7jhsBy9jUSDt/Wzbh+5lJwLaKPLbxeXKemp3qP/xm2fVOAc7zHo/KXWyjOtsBkIM17/m/gAu/xpbhW8TcA/wl7T/42VAe+ARqExXGy93gSMBVIw40BMz9sP6wCGoS9v0eh7T4Bd6Ih3nZPwQ0BcAbwZFgcGX5/h+0v+n9WQjHlpq5X5OdxgzZF6gt1Y8DsAr7HHbjAlSxahM33P1Xdq6rLgBVAFu5gdYGIzMd18d8Al3AA5qjqD0Ws7whgproOEfN7b+1ThnjzTVPV/LErBLjb647lfdzQCY2LeM8cVc1W1b3A/ELbl2837qALMC9snqOAV7zHLxUT0wCgO/CFt08G4LrcR1WfwnXhMQqX7PNdKSILcGNtNGff/tuNS+LgPosP1XUgWfhzmaaq61V1B65zyWMLxXSC9/cVrouXLG8dX+NKnfeISG9V3VzMNpkkVuL1YGMiMBZ34BgXNi0P73Kqd5mnathru8Ie7w17vpf9v4+F+wRS3IH8ClV9L/wFEemHK6EUpaihDsojfPnn4ko83VU1V1zvykUNpRu+rXso+veWq6payjzFEeA5VT1gVD8RqcG+wZlqATnefhoIHKWq20VkZljc4XEUfC6qurfQfaOiPpfCMf1DVf9TREzdgUHAP0RkqqreGdlmmmRhJRRTId5Z+/9wN7jzrcSdOYMbfyOtHIs+y7v+3wp31r0U1xnoZeK6+0dEDpfSB7z6HOjr3U+oApwDfFjKe3JwZ/fFycCN+5IrIv2BQyLYnrL6DHeZCIofpnc6cKaINIKC8dXzY7kHVxq7FXgyLO6NXjLJwg05XVbHe+upjhsd8eNCr78HXCxufB9EpJmINBKRpsB2Vf0vbpCvZB4GwBTDSigmGu7H9f6c70ngDRGZgzvoFVd6KMlS3IG/MTBKVXeKyFO4yy9feiWfdZQy5KuqrhKRm4EZuLPnt1W1tC7OFwJ53qWhZ4GNhV5/EZgsInNxl7KWlGXDInQ18F8RuQ54C3c/Zj+qukhEbsFVFEgBcoE/ixv58wjgGFXdIyJniMgI3KWzUd6luqW4pFVWs4EXgMOAl1R1bqGYpopIW+BTrw7CVuA8b/57RWSvF+dl5Vi3SXDW27AxCci7ZLVDVVVEhuNu0A8p7X0xjuki3E34y0ub11ROVkIxJjF1Bx7xSmKbcOOLG5PQrIRijDEmKuymvDHGmKiwhGKMMSYqLKEYY4yJCksoxhhjosISijHGmKj4f1CjtHYbEfPiAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "lambda_ = 0\n",
     "theta = utils.trainLinearReg(linearRegCostFunction, X_poly, y,\n",
@@ -731,10 +999,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 204,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def validationCurve(X, y, Xval, yval):\n",
@@ -804,7 +1070,11 @@
     "    error_val = np.zeros(len(lambda_vec))\n",
     "\n",
     "    # ====================== YOUR CODE HERE ======================\n",
-    "\n",
+    "    \n",
+    "    for i in range(len(lambda_vec)):\n",
+    "        theta__ = utils.trainLinearReg(linearRegCostFunction, X, y, lambda_=lambda_vec[i])\n",
+    "        error_train[i], _  = linearRegCostFunction(X, y, theta__, lambda_=0)\n",
+    "        error_val[i], _ = linearRegCostFunction(Xval, yval, theta__, lambda_=0)\n",
     "\n",
     "\n",
     "    # ============================================================\n",
@@ -825,9 +1095,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 205,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lambda\t\tTrain Error\tValidation Error\n",
+      " 0.000000\t0.029686\t43.849641\n",
+      " 0.001000\t0.112683\t9.840830\n",
+      " 0.003000\t0.170995\t16.312957\n",
+      " 0.010000\t0.221495\t16.946156\n",
+      " 0.030000\t0.281826\t12.831959\n",
+      " 0.100000\t0.459323\t7.586845\n",
+      " 0.300000\t0.921767\t4.636793\n",
+      " 1.000000\t2.076202\t4.260597\n",
+      " 3.000000\t4.901374\t3.822909\n",
+      " 10.000000\t16.092273\t9.945554\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "lambda_vec, error_train, error_val = validationCurve(X_poly, y, X_poly_val, yval)\n",
     "\n",
@@ -850,9 +1150,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 206,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise regularized-linear-regression-and-bias-variance\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "Regularized Linear Regression Cost Function |  25 /  25 | Nice work!\n",
+      "     Regularized Linear Regression Gradient |  25 /  25 | Nice work!\n",
+      "                             Learning Curve |  20 /  20 | Nice work!\n",
+      "                 Polynomial Feature Mapping |  10 /  10 | Nice work!\n",
+      "                           Validation Curve |  20 /  20 | Nice work!\n",
+      "                                  --------------------------------\n",
+      "                                            | 100 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[5] = validationCurve\n",
     "grader.grade()"
@@ -896,9 +1217,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
@@ -919,7 +1238,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,
diff --git a/Exercise6/exercise6.ipynb b/Exercise6/exercise6.ipynb
index cdc43975..90afb27f 100755
--- a/Exercise6/exercise6.ipynb
+++ b/Exercise6/exercise6.ipynb
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101,9 +101,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ciZ9yt9fVCF2KkpZl+VqtKkEKsr2uErqclJZEp9Wq6RTWNT+DrNtXQpeT0pLo4twLR4Ym7Gt+VuLar7kooctJaUl0Wq2aLmFf8zPIun0ldDkpqEQX9oKeCRMm0Ny8niVLhrN6dQ379kF3d+ZE6OrVNSxZMpzm5vUqWRyCsKY18gmymVih14cK1+27eyi3yZMnu0TP5s2bfdSo4T5rVo03N+NPP403N+OzZtX4qFHDffPmzWV9/l/8orzPPxi7d+/2RYvu9NrakV5dXeW1tSN90aI7fffu3YHFkCRbt271D553gZ878Xq/tn6K9/T05HwsaK+//rpf81+u8wv+YqL/5//+fb/sa5tOu425+T7/4HkX+LZt2yr++hfe0OgfPO8CX7t2rV9bPyXz2Gczj23durWo5wPafIC8qoQuZ6hUotu9e7ePGjXcV63Ct20787ZqFT5q1HAl1BjqTdxjvnifj/vqk37+h672OXPnnvHYsuX3hxJfd3e3z5k718+75PIzEvq5tWN97dq1FX/9Zcvv94svvezkD0ffx4pN5u75E7rq0GOio6ODlSuXs25dM11dRxg1agQzZ85m4cLFsZkaSEudexqVu9a63JLUTEx16DGXlL4jaalzT6Mga60HK03NxJTQIyTXycK5c2cxc+bnE7EcPy117mkU5R7paWompoQeEQONwtvafsJf/uV/JKLvSFrq3NMqqFrrwYrSNT8rrWBCN7NhZtZqZi+b2U4z+2aOfc4xs5+a2W4ze97Mxlci2KTK1xTrrbecm27K/+/jMk2Rljr3NIrytEaamokVM0J/D/i0u08CPgZMN7Pr+u1zO/Dv7v5h4AHgO+UNM9nydf87eJDETFOkbUFP2PX2QYr6tEZ1dTWL72nkzT/uOTnt0/exJCRzKCKhZytlerNFTfbWvzTmc8Da7P31wDQzs7JFmXD5Thaedx6JmaZI04KepJzILlaapjWirKg5dDOrNrOXgP3A0+7+fL9dxgJ/AnD3buAgcGGO55lvZm1m1tbZ2Vla5AmS72ThtGmweXP+fz+UaYqwRo9BXn4urPeYlr7yfaVpWiPKBlWHbmbnA/8CLHD3V/s8vhP4jLu/kf27A6h39wMDPZfq0E/J15973z6480741rco29V10nBVojDfo+rtpZLKVofu7n8GngGm99v0BnBp9sXOAs4DugYdaUrlO1k4dix84xvwta/B979vJU9TpGH0GPZ7LHe9fZrm4qU0xVS51GZH5pjZB4Drgd/1220jMDd7fwaw1cNaghpDhU4WjhgBZ501jGHDZpY8TZGGy6+F/R7LWW+ftrl4KU0xI/SLgW1m1g68QGYOfZOZLTWz3oK6HwAXmtlu4B7g65UJN5mKOVn4k59s4Ic/bGb//oN0d/ewf/9BmppWDfoEYhpWa4b9HstVbx/2kUZSRLEDZKUUU+XS7u7XuPtEd7/K3ZdmH1/i7huz94+7+9+4+4fdvd7df1/pwJMmqJOFaVitGfZ7LFe9fdhHGkkQ9oUtgqaVohEyYcIEmppWlTwKzycNqzXDfo/lqrcP+0gj7sK+sEUYlNBTJg2rNcN+j+Wqtw/7SCPuwr6wRRiU0FMmDas1o/AeyzGFFvaRRtxFuQNkpagfegr1r9EeMyaTGJJch977HjdtMp54wnnvPbjwwnMj3VNe9eyl6+np4Uu3386TW37D+bNOP9fQ9eiXWbXsPubMmRNSdEOjfuhymiBXa4al/3v8zGeM22+H9vbMIq2nnyby5X9RONKIu6h2gKwUjdAl8To6Oqivn8jSpcfKtto2KGk4mqqUQh0g/f0eDq2/l3vvuo3F98TnR1EjdEm1OJf/peFoqlKi3gGyEjRCl8TL1yun17590Ng4kv37DwYXmFTUrl27+MKsOew5cIyqj0zj+K/X8L2VTax46GH+8O5Rqq6YxvHta2LXNEwjdEm1IMv/1HclOtLYAVIJXQIVRsILqvxPfVeiJy0XtuilhB5TcRwJhpXwglhopL4rEgVK6DEUx5FgmAkviPK/OJ94leRQQo+ZuI4Ew0x4QVz6rpS+K3E82pJoUkKPmbiOBMNuNFXp8r+hnngN82hLPyTJo7LFmIlrCV51dRVPPeVUVw+8T3c3TJ9eRXd3T3CBlclQ/ruEueApDZchTCqVLSZIXDvwJb3R1FBOvIZ1tBXXaTspTAk9REM55I1rYgy7pW2lDeXEa1jTUHGdtpPClNBDMtS507gmxqQ3mhrKidewjrbCPp8hlXNW2AGkUd9D3r6jpN5D3ilTTjB79oycc6cLFy6mvn4tU6bkHmH1JsbW1mglxt6EV6jRVNSaYw1G74nXBx98gMbGH9HVdYRRo0Ywc+YttLY2nvHeMkdb+efdK3G0FddpOylMI/QQlHLIG0QJXqWkodHUYC4jGNbRVlyn7aQwVbmEoByVKh0dHTz44AOsW3f6SHDBgjNHghJNYVW56MIZ8ZavykUJPQRJL+GT4oXR7zzO/eFFZYuRo0Ne6RXGNFScp+0kP43QQ6BDXokCTdvFU0lTLmZ2KfAYcBHwPvCIu6/ot89U4EngD9mHNrj70nzPm+aErkNeERmqfAm9mLLFbmCxu79oZucCO8zsaXf/v/32+5W731hqsGmQhhI+EQlewTl0d3/L3V/M3j8MvAbkqc+QYqShhE9EgjWoOXQzGw/8ErjK3Q/1eXwq8DjwBvAm8BV3P2NNoJnNB+YDjBs3bvLevXtLCF1EJH3KUuViZiPIJO27+ybzrBeBy9x9EvAg8ESu53D3R9y9zt3ramtri31pSSG1dhUZvKISupnVkEnmP3b3Df23u/shdz+Svb8ZqDGz0WWNVFIjjldkKjf9oMlQFFPlYsBaoMvd7x5gn4uAd9zdzaweWE9mxD7gk6e5ykUGpgog9SqX/Eqdcvk4cAvwaTN7KXu7wcz+1sz+NrvPDOBVM3sZWAncnC+ZiwwkyNauURwFq1e5lEILiyRSgroiU1RHwVp0JoVo6b8UJQoj1iBau0Z5FKxe5VIKJXQBonMiMog+N1G+Yo96lUsplNAlUiPWIHqER3kUHLfGbVE4qpNTlNAlUiPWIC5VF+VRcJwuMRiVozo5RQldIjViDaK1a5RHwXG59mqUjurkFCV0idyItdJ9bqI8Co5Lr/IoHdXJKSpblMBKBaMiDouXot6rPG3/z0SJLkEneaWx9jmMS78liS6jGB7VoUtecZm3LadyTeuktcojyuch0kwjdAE0Yh2KqK42DUIaj+qiQlMuUpSoz9tGSRzm4Ssp7e8/TEroImWmEaqO6sKiOXSRMotS7X5YdBnF6NEIXWQIVOUhYdEIXaTMVOUhUaSELjIEUV5tKumlhC4yBGms3ZfoOyvsAETiqLfnSqEqD5XsSZA0QpfECWr1pqo8JGpU5SKJEvfVmx0dHaxcuZx165r7LO6azcKFizXaF0BVLhKSoPucxL1Hty4YIaVSQpeKCCM5xblHd9x/jCQaNOUiZRdWn4849+hWKwEplqZcJFBhjZSjduWlwVArASmHggndzC41s21m9pqZ7TSzRTn2MTNbaWa7zazdzK6tTLgSB2Elpziv3gzqxyit/dvTopgRejew2N2vAK4D7jSzj/bbpwG4PHubD/xDWaOUWAlrpBzn1ZtB/BjppGvyFUzo7v6Wu7+YvX8YeA3oP0v5OeAxz/hX4Hwzu7js0UoshDVSjvPqzUr/GOmkazoMag7dzMYD1wDP99s0FvhTn7/f4Mykj5nNN7M2M2vr7OwcXKQSG2GNlHtXby5ZMpzVq2vYty/T8XDfvswJxSVLhkd29Walf4ziXAEkxSs6oZvZCOBx4G53P9R/c45/ckb5jLs/4u517l5XW1s7uEglNsIcKcd19Walf4x00jUdiipbNLMaYBPwc3e/P8f27wPPuPtPsn//GzDV3d8a6DlVtphsuprN0FTqMoDq354cJV2CzswMWAt0ufvdA+zzWeAu4AbgvwIr3b0+3/MqoSefrlEaHXGu0ZfTlZrQ/xvwK+AV4P3sw38PjANw94ezSX8VMB04Btzm7nmztRK6SHC0cCk58iX0gu1z3X07uefI++7jwJ1DC09EKm3hwsXU169lypTcJ0Z7z2u0tkavAkiKp37oIimg/u3poKX/IikR1wogKZ6ac4mIxIiac4mIpIASuohIQiihi4gkhBK6iEhCKKGLiCSEErqISEIooYuIJIQSuohIQiihi4gkhBK6iEhCKKGLiCSEErpIDh0dHSxadAe1tSOprq6itnYkixbdoYsoS6QpoYv009LSQn39RA4cWE1T02GeesppajrMgQOrqa+fSEtLS9ghiuSkfugifXR0dDB79gyWLj122oUgxo6FefNOMGXKCWbPnkFra7t6h0vkaIQu0sfKlctpaMh9VR+AK6+EhoYTPPjgA8EGJlIEJXSRPtata6ahYeDrbkImoa9b96OAIhIpnhK6SB9dXUe46KL8+4wZk9lPJGqU0EX6GDVqBG+/nX+fd97J7CcSNUroIn3MnDmblpaavPu0tNQwc+YtAUUkUjwldJE+Fi5cTEtLDTt35t6+c2cmoS9Y0BhsYCJFUNmiSB8TJkyguXk9s2fPoKHhBA0NJxgzJjPN0tJSQ0tLDc3N61WyKJFUcIRuZo+a2X4ze3WA7VPN7KCZvZS9LSl/mCLBaWhooLW1ndGj59PYOJLp06tobBzJ6NHzaW1tp6GhIewQRXIyd8+/g9kngSPAY+5+VY7tU4GvuPuNg3nhuro6b2trG8w/ERFJPTPb4e51ubYVHKG7+y+BrrJHJSIiZVWuk6JTzOxlM2sxswHW2IGZzTezNjNr6+zsLNNLi4gIlCehvwhc5u6TgAeBJwba0d0fcfc6d6+rra0tw0uLiEivgnPoAGY2HtiUaw49x757gDp3f7fAfp3A3gJPNxrI+zwhiWpcoNiGKqqxRTUuUGxDVWpsl7l7zhFxyWWLZnYR8I67u5nVkxn1Hyj07wYKqN9ztw00+R+mqMYFim2oohpbVOMCxTZUlYytYEI3s58AU4HRZvYG8L+BGgB3fxiYAfwPM+sG/gO42YsZ9ouISFkVTOju/sUC21cBq8oWkYiIDEnUl/4/EnYAA4hqXKDYhiqqsUU1LlBsQ1Wx2Io6KSoiItEX9RG6iIgUSQldRCQhQk/oZjbdzP7NzHab2ddzbD/HzH6a3f58tiY+KrHdamadfRqTzQsorkIN08zMVmbjbjeza4OIq8jYQmnmZmaXmtk2M3vNzHaa2aIc+4TyuRUZW1if2zAza82uBN9pZt/MsU8o39EiYwvlO5p97Woz+62ZbcqxrTKfmbuHdgOqgQ7gL4CzgZeBj/bb5w7g4ez9m4GfRii2W4FVIXxunwSuBV4dYPsNQAtgwHXA8xGKbSqZRWpBf2YXA9dm758LvJ7jv2con1uRsYX1uRkwInu/BngeuK7fPmF9R4uJLZTvaPa17wHW5frvVqnPLOwRej2w291/7+7/D/gn4HP99vkcsDZ7fz0wzcwsIrGFwgs3TPscme6Y7u7/CpxvZhdHJLZQuPtb7v5i9v5h4DVgbL/dQvnciowtFNnPovcCqjXZW/9KilC+o0XGFgozuwT4LLB6gF0q8pmFndDHAn/q8/cbnPk/8sl93L0bOAhcGJHYAD6fPTxfb2aXBhBXMYqNPSxFNXOrlOzh7TVkRnR9hf655YkNQvrcslMHLwH7gafdfcDPLeDvaDGxQTjf0Sbg74D3B9hekc8s7ISe6xep/y9sMftUQjGv+zNgvLtPBH7BqV/csIX1mRWj6GZulWBmI4DHgbvd/VD/zTn+SWCfW4HYQvvc3L3H3T8GXALUm1n/nk6hfW5FxBb4d9TMbgT2u/uOfLvleKzkzyzshP4G0PcX8xLgzYH2MbOzgPMI5pC+YGzufsDd38v++Y/A5ADiKkYxn2so3P1Q72Gyu28GasxsdBCvbWY1ZBLmj919Q45dQvvcCsUW5ufWJ4Y/A88A0/ttCus7WjC2kL6jHwduskyjwn8CPm1mzf32qchnFnZCfwG43Mw+ZGZnkzk5sLHfPhuBudn7M4Ctnj2TEHZs/eZXbyIz9xkFG4E52aqN64CD7v5W2EFBpplb71yhDaKZWxle14AfAK+5+xrhvdsAAADlSURBVP0D7BbK51ZMbCF+brVmdn72/geA64Hf9dstlO9oMbGF8R1192+4+yXuPp5M3tjq7rP77VaRzyzUi0S7e7eZ3QX8nExVyaPuvtPMlgJt7r6RzP/oPzKz3WR+wW6OUGwLzewmoDsb261BxGaFG6ZtJlOxsRs4BtwWRFxFxhZWM7ePA7cAr2TnXAH+HhjXJ7awPrdiYgvrc7sYWGtm1WR+RP7Z3TdF4TtaZGyhfEdzCeIz09J/EZGECHvKRUREykQJXUQkIZTQRUQSQgldRCQhlNBFRBJCCV1EJCGU0EVEEuL/A16AFLXSsQG6AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Load from ex6data1\n",
     "# You will have X, y as keys in the dict data\n",
@@ -147,13 +160,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# You should try to change the C value below and see how the decision\n",
     "# boundary varies (e.g., try C = 1000)\n",
-    "C = 1\n",
+    "C = 100\n",
     "\n",
     "model = utils.svmTrain(X, y, C, utils.linearKernel, 1e-3, 20)\n",
     "utils.visualizeBoundaryLinear(X, y, model)"
@@ -180,7 +206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -212,6 +238,7 @@
     "    \"\"\"\n",
     "    sim = 0\n",
     "    # ====================== YOUR CODE HERE ======================\n",
+    "    sim = np.exp((-1/(2*sigma**2))*np.sum((x1-x2)**2))\n",
     "\n",
     "\n",
     "\n",
@@ -228,9 +255,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gaussian Kernel between x1 = [1, 2, 1], x2 = [0, 4, -1], sigma = 2.00:\n",
+      "\t0.324652\n",
+      "(for sigma = 2, this value should be about 0.324652)\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "x1 = np.array([1, 2, 1])\n",
     "x2 = np.array([0, 4, -1])\n",
@@ -251,9 +289,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise support-vector-machines\n",
+      "\n",
+      "Login (email address): waiyen.chan0819@gmail.com\n",
+      "Token: TbhdNt76ZWcvdXrp\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "                            Gaussian Kernel |  25 /  25 | Nice work!\n",
+      "        Parameters (C, sigma) for Dataset 3 |   0 /  25 | \n",
+      "                           Email Processing |   0 /  25 | \n",
+      "                   Email Feature Extraction |   0 /  25 | \n",
+      "                                  --------------------------------\n",
+      "                                            |  25 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[1] = gaussianKernel\n",
     "grader.grade()"
@@ -272,9 +331,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Load from ex6data2\n",
     "# You will have X, y as keys in the dict data\n",
@@ -298,9 +370,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# SVM Parameters\n",
     "C = 1\n",
@@ -326,9 +411,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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s9xURF0+ZmZl1rw2LiqaIjj2pS7fbqaqqSvEYzxj0Xqf2/FYPzWw05tieo6hDp850c/KtivMbfU1HatHHcczIzwAPhw4dookTx1FcXCTZ7TaKi4ukiRPH0aFDh+rO2bx5M8XEhFJYGCgrC1RQ4PorKwsUFxdp4TvybgDsJhd2Vbp0JJqo3Ynb2nVFUshF7PpyBz799NNGx4hIUdt9ZdurcTGmPVqkThTqZnE1rtT0AbC364ryEz+i9SML4Sjx5KBWLz975gvod/8DunXtIvTxvJLPZx8bDpvNJkw7bwXObpyMDGDLFkeClisqK4HevW2orKwyb5A+hNThS4QiwqAZkUPgalwffJSNqLTJCE7ogN/WTEXoH/6EyFt+r+BdazyDys7icux1un39RrQvNDM2ZDbOCVoDBwKLF0O3dr8pI334EqGIKMFghGTT1bii0ibXjSu8Uy+c37Ve0ddN1VVC8gZExAyefeYpVPy4C+c+eNb02JDZOJdB7tED2LzZ/fmyfo52pMGXqEZUQpVoySbPuM7krUBgdami8Xx/TZaQRUjNYuYquWr0YxNga9sZIeVn6wLobdq0wcWLJRjzlz6WVigVjXOCVv/+wKZNkPVzDEIafD/FyAqVInbnRuQveBrXuX8tQERYCHI3fuxSffTZZ5/hg4+yFRehgBt7Yfb8BVyLEM9ilpeXp5hclZo+AFFpkxGb+jjOVofgiYkTQERIG3A/jtpbYUvedhT9dNgvjD1QP0ErPh6YOhWYNg144w3UU/YsX45Gyh6JOqTB90NEVFD0tGDo3Z0bVZbA3bjCu/ZD4rXtcddddwFoXIq4oKCgztgqLUIRyf1w6LcLmP+qe0kg72I2eNhfG7mg1m3ZUd8F1SUNf//Hi26TxLQs7t6UydowQevWW4GlS4HycmDCBKBXL+BvfwMKC2/Ezp17ZbE0HUiD72eIKJnsacHo228g+vYfqGt3bkT+gt6nhkdGjob9qvqL0LHX/opzO9fVLULhXdPwwsxZbsfBu5gpxQxaDFtY3wWVvxIsOMxlfETL4q43k1X0YqFUBjk+Hhg3Dli3Dli0CAgKCsX772fLnb1OpErHz9CrEOFR4Pz29hOALQCths7RpSARXY5Xr7Ilsf11OPLrKQREtUR4x144k78Swc1CURkYCltQM4R36oUz21ageWQ4Th13XSRGTcbyXXfd5bI6adFrI9D8jsEIv7FH3TFn9VKtm0eNWkpvJmvDcsVXXAH8+qv+csWyDLI4pCyzCaFX7si7YJwveAPhra8RUoJBFHpLQxw8eBB/Hvww9v/4C8orKhDbZxJC2nYEVVfhwp6NOL9rPQKrS5G78WOPgVvexczdU8m5netw+eBXaDV4Vl3egHOJai2Lu54a9UaXPZBlkMUgDX4TQ09Ne94FY8NHH+K7/+w1JeHHXU34hvV29D41iOwHwPO+PCVXNcwbcG6qUlhYqHpx11OjXkRDE4nxSIPfxNDbhclMo+cJ3oxeUR2lzOxgpeSCurBlEYI6pSIyuV+dC+rc5+8ifsxKAI0Loqn9W9ntNmzZQpoyWUU1NJEYi0y8MhAj5Y9ax6NX7mhlfX9nzO4XrHXutH4GlALXUc0CcWHPx/jt3ako+T4PZ/LeQCBVulQvqf1b6alRL6qhicQ6pMHXgdENurWgV+5oZX3/hhjRVJ33fgDf3Gn5DNQuBgDqqpPSrjVo0aIFMt9chZvaXwPb5bM4s301guzAa4vmKaqXtPyttNaoB/QtFhLvQBp8jZi9++RFr9zR6LZ9Wt+LGS381M6dls9Aw8WAMYYunW9GWXk5zoRfhSnP/R07v/g3Xnj2KbSKiXSbJKblb6UkgXTGXSarY7EIcDuHsuyBl+OqjKbVX95eHvm6GztSRMd7qe0zOdRu8ka6ctTrFJWQVK+UbYvUSdS6TTtDx+GqFHBc6yupZesE1SWT3ZU0tqLkbmVlJQ0bPpyiEpLcljUm8lyumfd+vOWm1X4GnMsdiygJrfVvVVuKeMiQQMrKAm3d6ig5PGRIIMXEhNLmzZsV77dq1SoKCQEtWaJctnjJElBMTGi9sscS84Gb8siWG3ZXX95u8J3/s1056vVGxqjVgzMpLCq6zmgYgaja7Q0RXWNfjyH2VBPe3ft2NRciFgYi9Z8BIzYJWv9WPDXqG54fExNKY8eCoqJAgwej3mLx4IOgkBDQqlWrFF+bkTGWYmMjyGZjFBsbQRkZY90uDFpeY+R1fAl3Bl+ISocx1hvAQgB2ACuJaFaD3z8BYCSASgAnADxKREfcXdMXVDpWqllElCg2Az0qG96a8A/2vhNvr3mXay66dL5ZqOpHzWfAiJLQZuEsySwuBrKzgbw84Nw5ICrKUeWyvDwAV189pp4kU0uilqjkLqOSxLwdQ2WZjDE7gAMA7gVQBGAXgIeI6H9O59wN4GsiusQYGwugOxH9xd11fcHgmynha4gv9GnVuyjxZs6ez1uBkPZ/9DgXtGsNysrLNY2nsLAQixbNw5o1WU5JQUNxyy1/xN/GZ3B/BszYJLgaa0bGk5oTmLRIMq3saduUe+MaLcvsBuAQER0monIA7wHo53wCERUQ0aWaH78CkCDgvpZitZrF7ICmFh4ZORpo06VRcTBelQ1vEHXViuVccxEeEaFJ9eOq9szJkyswctTDCLw5nfszYLTkVW+dHFdokWQuWjQPKSkVqvrTanmNEqKu42+IMPjxAH5x+rmo5pgrRgBQ/NQxxkYzxnYzxnafOHFCwNCMw2o1ixENRERSUFCAE8ePo/TIf3D87SddFgdztyjxNlV/+OGHueZi2yebVS+ShYWFGDp0EGbMuISRIysQH+9ovxcfD4waVYV5c4HSPe+h4swxj58BozcJ7sY6cmQFZsy4hKFDB2kqcqZFkunc2MQVKSkVWLPmbU2vcVfETcu9mwIiDD5TOKboJ2KMDQWQDGCO0u+JaAURJRNRclxcnIChGYcR1R7doZTIs3DhQmS99wECbk5rdL7IBCm1iUW1hi0ybQquHL0C1RdO4vSGmY3OO/evBR4XJecSxrXnNSxrDPDtnBMTE1Uvkjw7xbTUMpzPW+LxM2D0JsHIXa0W/b6WpwLe15w6dcHtk4xMElNGhMEvAtDG6ecEAEcbnsQY6wlgGoB0IioTcF9L4d19iqgno5TIM3/+fDw1ZRqaJf0RJd99AqL6hl2US0lLYpGzYSsv/j9UVlYiqseYRtcOubmvkEVJzc5ZrUuFZ6fYLx2oLN7r8TNg9CZBz67WU8ljLfp9LU8FPK/ZuxcICiK3TzKBgSSTxBQQEbQNgCNo2wNAMRxB28FEtM/pnM4A1gLoTURc9QZ8IWhrBq4Cn1nvfoC4+5+va8wd2PJqsKLvhDa51hp0rVWjHCw6jktnTiBuwFS3Khu9SiLe4C7t/D1oyzsePbVnlBBdEtoZrWPlVbOoLWGspdgaz2vGjWPo2BEYM8a17eI5x18LvRkatCWiSgDjAfwLwA8APiCifYyxGYyx9JrT5gAIB/AhY+w7xliO3vs2FVyVF4i7//l6jbkv7csT7lLSWtqg9unHXn4Bza7tVs+FUfz6yHoNRXhdGO7cSovmz+XaOTsHbWvH48mlIrqcAK+LSs37rz2mZaxq/P4pKSnYuXMvYmNHY9KkSPTubcOkSZGIjR2t2IlKy1MBz2sKCwl9+7rfqI4YQcjOJtkbtwGyWqaXw6PdPrFuBua98jIef/xxoSWK9ejGCwoK0Ce9PyqbtYAtuJmjoUjBKkTfMwLnv/wA9uBQhHXpy1U3n0fL/9WOT7Fo8RK3O+eEhATV9fLNKAnsqfxzUVERV+7ApEnjDdlR63l/WhqbeHrN2bOXuZ5kevViaN68WZNrquJuh295Rq2rL2/PtDUTNeUFPKE2y1TLvRuWEIi+ZxQFNL+iLqO0zZPrKKTFlRQZE+sxa1d0OQK1mam1GaZGlRPwlCHc/vobKTSyOdf71zLW2NgIyspSPr/2KysLFBcXqen91c6hmqxeT69RM2Yt9/Z1YHSmrRHIHf7viErw0pL1quXeelsNOuMNCWa1O85evcrQp09VvZ3ixo12RDS/Avlbt9RrxMKDlnaSnt6/2h216BiFGchGLO6R9fB9GFHabT2VHdXeW6QaxRsSzFJSUrBw4etYvyEAI0bZcd99jozSsrJUlFcF4VzUtZpKYfPESMK7pKP6TDH3+1frZzej5LEZTc+daar+eR78yuB7WzMSEYjSbmsJwGq9t0jJqjckmBUUFOBv4zMQ1e/vaDV+HaKuugkpqQOQu/UzRKZN0VwKm3cxy/04W9X7T0xMxIIFS3D8+DlUVlbh+PFzWLBgiWIJAT318XkwIvM3MTERWVlrMX16KFauDERxseMppLjYsbOfPj0UWVlr/a5kggj8xqVjdis8s9DbmFvpOrwBWFH31ouVNYsAY91KPLV12rRpY9j7N7LmjGx6bg1+79Lx1mYkIhC1W9ayUzYzuUyJgoICtL06EX37DbS0A5eRbiVPiWAvzZqDvv2Ne/9G7paNrmej5klG4sAvdvjeENjzBazeKauhdqyllUDI1V3QInViXQD45Kb5iEzuh4jkdCEJZjwYUeWSp/xzw6CtngC4O4zYLcum59bg9zt8bwjseTtWV/dUg/NYWz40E2U/7sGJd55CyX/zcCJ7JqLuGIILOz/C6fcmC0kw44n9uNuJszY3Y8z4DOzfv9/tNRvCEyMJ7/p70NbImk1G7JbV1rMRHdyVNMYvDL43BPa8Haure6rBeaxBLdog7sGXQRfP4Oy/30bcgGcR0fFeRN41HLYLx3W7lXhqBeXl5blcLEuP7EXJD/+GvW1X9OzdR1Uje1410+ac9Za51fSgRgFkVFlnSX38wuADxtcZd4UWZZAVaiKzq3uKGmvF6WIExbZBqzFvIuGx1Qhp2xGlR/ai7Iu3sGH9R27LEXiCN/bz0MPDFBfLM1uX48SGWYjrPwUtUifi+GWoih3xxkh69uxZV3qhTZs2mDjxMVxxRTSeefpJVFw+jezsD71yF8yrAEpNTTesrLOkPn7hw+dthSe63Z+eRCYr1ERGFu4SjRmdoXhjP/ZvP0R8QgIOFh1H6eVLCKy8hNeWLsaY8RlAi2tQdekc4vpNBgBDY0e+1rKPV6WTnj4AFRUfyEQqQfi9D98Kd4XeRCYr1ERaC3dZgbsntqCOvz+x6Xky4o39vL/mbcye+QLKz51AcPwNaNO2LYYOHYrXFi9E+dEfEBR3FU5+PBcB0a0Nix3xFjnLysoy5OlRi3+dVwG0cWOO6rLO0t+vDb8w+Fa4K/QmMqmpPtnU8PTEFtbFEWDuP2Agevbug2Lm3lfuCt7YDxGh/6A/I7rf1BrXDcNfH30U4yZOQtz909EidSJA1biw++N6rz+fOx9RkZGId5KpaDW+PBLHXr3KMGrMSFW9C3jQ41/nyfxVG9yV/n7t+IVLBzDfXaE3kUlt9cmmhFItnhMfz0HkrQMR2fV3KebpLcvQctDfPTZDdwePVPXixRIctV/h0fVz7ot3ET9mJYCaKqbrX0KzpFvxh2aXsOurz3W57ngljiPHBKPlYx+obhjvCjOagauRb3755TdNtjk5L37v0gHMd1foSWTyVTWRkcFm5+vUPrGdfX8qSv6bh+Mf/QMB0a1x+cCX+O3dqTXGfjma3zlU15MRr1R1QHpfj66fMwWr0CIlo+7nExtmIW7gNLRImYj9x87qdt3x7oLLLpcLfXo0oxm4mvIOsjm5PvzG4FuBFmWQVWoivWhpdaj12omJiZg98wWUHv8JZ/JXgtmDgepqEBGCEzrg3OfvouX90xF5ywAANQZ323LVvnLe2E9m1hq3C/WpTxYh+p4RADkCyqdyF9Q1fmE2O6JSJuk2vrwSx6AgJjQXxYxm4GqKocnm5PqQBl8jWhKZrE5+0rpDNzLY7Ora/Qf9GdH9pyFhQhYCm7eErew87GHNUfrjN4gfsxIh7X6fv5Mfz8EzTzyu+slITezH3UId3jkV575aiwubZ2PJ3Jlo3+5KlB3ejV+znhZmfHl3wddemyT06dGMZuBqyjvI5uT6kAZfI1qUQVYmP+nZoRsZbOYrEZwGKr2A0h+/deykG9D81vuxJf/TRuP3tMDx6uABuP+pW48AACAASURBVF2oI5P7wRYQhOFDB2PYsGHY+81uzPz/noP9XDHOb5zd6HwtxpdnF7xxox2Hfzom9OnRjPLJAH9ZZ7PG469Ig68RLcogq5Kf9O7QjSxdwXPtC9tXoqy0FC0HTVdW7XRNc/k05WmB44n98CzUEcnpyP54U93rk7t2gS0gEGHdGy9QWoyvp13wtGkhKK0IQGTaFKFPjzxPFjk5QJs2bXRLInnKOxhdztnfkQZfAR7Xh5ZKklZVn9S7Qzcy2Ozp2he2LEJFeSnCbrirUTP087uUn4xEu6DULtR6XHfu9OXudsEtWrZD0LW3C3965Hmy+OQTICFhvymSSNn8RB9+I8sUhbfV1ffU4JqnrZ4IOaiRlTbdXfv8znU4v2sD7JFxYDYbwjv2wumtyxGWdCsqzp0AGBDeqRfObF2ObZ9sxt13321I9VQ1sl+tLR71ZNIa2bugdly9e5cjNbWyrm3i5s3Apk3A1KnArbeaJ4nU0hi9KSGbmHMiumG2qPG4anBde4wHPY3Qneel4WvbTd6oa17y8/MpJCycAmLi6cpRr9dds9WDMymoVSK1Hrmcgq+4lkJaXEmh13cnW1AITZkyhbp0u52aX30Thd7gODZv3ry6ax44cIA633IbRV/Tsd41na8dFhVd18RcNM73b5E6icKioikzM5O6dLvdcayP45hzw3QRzdJ5G7QfOnSIMjLGUmxsBNlsjGJjIygjY6zHpuJdunSg8HCQzQaKjgYNGoRGzcSHDAmkiRPHiZlINzTF5uS8QDYx58Ob6urzNLhWk0ijZ4cusil5Q9penYiio78itP3tqDx7DFF/GowLnyxAWellNEu6DZVnjyGs432o/CoLYWFh3El1ZtTicYfaRECzGnPreYowu769iKfbpoi7Hb4Qg88Y6w1gIQA7gJVENKvB74MBvAWgK4BTAP5CRD+5u6YVBt+bMmFFLj56i8sZ5S4oKChA334DEdH3GQQndMCvWc+g+tRPsAcGoXn61LpjOFuETzZ9rPraolxQZhgeM4yp3qxZu92GLVsIdrvre1RWAr1721BZWaVpjLV4m2vVlzA005YxZgewFEAKgBsAPMQYu6HBaSMAnCGiawG8CuAVvfc1Am/KhBWpjNErBzUq2PxYxuOwX31LXTA5tu8TCIlNQPP0qXXB5IjOqYiIjBRm7AF1ihUjE86ccacvLy4Gli4Fxo8HTp48r7lQmN4sVbMkkVYXGfRnRCyP3QAcIqLDRFQO4D0A/Rqc0w9AZs33awH0YIwxAfcWjrdkwopcfETIQY0oXaG0qLV4eGGjRW3te2tUXVdUvoOZhseVMf36a2DcOCAoCFiyBNi6FZoLhenNUjVLEiki70NW01RGhMGPB/CL089FNccUzyGiSgDnALRoeCHG2GjG2G7G2O4TJ04IGJo6rM6EbYioxceoHbre2jpGPVGJyncws7qpkjEtLgZefhl46SVg1CjobgyiN0vVLEmk3qdbWU3TNSIMvtJOvWFggOccENEKIkomouS4uDgBQ1OHiJ2hqAJjohcf0Tt0Ua4O3kVNzbzyLHAzpj+HxzIed3s9M3slKxnT7GygTx8IKxSm1yWjpgSCHvRsBHj7BjTVnb4Ig18EoI3TzwkAjro6hzEWACAKwGkB9xaK3p2hSH+vN/egFeXq4F3Uxo0fr3pe3S1w72S+iekzXvR4vaSkJMye+QKqzhThwuY5jcZXsm2JsJiOkjHdtg1ITXX/OjWFwkS4ZHhLIOhF69OtrKbpHhEGfxeAJMbY1YyxIAAPAshpcE4OgOE13w8CkE9eqAfV4/qwOrvTTES5OngWteqWf8CKVW8Km1c1f6eCggKk9R+ACxdLEXrXo/WuU3pkL0rLK/HK3Pl1hkdvqeiGxvTcOQgtFCbKJcNTAkEPep5ujaqmqSYm4M3xA1GyzFQAC+CQZf6TiF5ijM2AIwEghzEWAuBtAJ3h2Nk/SESH3V3TqkxbrVid3WkmouSrPHLPM9teQ2j7PyEmJUPIvPL+nbDnPVwsuYDyKiBuwLP1DE9tvftmibeg9PBuvPyP55HctYtwyaAoqWZhYSEWLZqHNWuycOrUBQQHA/37M/TtS16Zpaon78MI6aia3AVv6DtsuA7fCHzN4HuTht8MRCU2eVrUXnnpBby6eJmweeX9O8VER+PoiTMIuaYrWtQsNqVH9uJEzmxQVQVaDpxWlydgP18MW0Cgrs5SSohIxlIyQP/5D7BqFUNhIaGigqFFiwgMHvwwJkyY5BVdovTkfYjOZ1CTuwDAK7pxSYNvElZld1qRkWhkbZ2GiJ5Xnuvdfvvt6Hf/Azh4+AjszVsjvFNvnClYBVtwKIITOqBF6sS6J4TzG2cjrPsI4dnYehOlzGhPKBLnJ5HTp0sQHh4E2AKxbMlrGDJkCNfTreiMZTXXI6o2JVvaE02ixaE3YIWG36zEIKV7miVfFT2vPNdLTEzEf7/dgwcHpoFdPIlzX7yLuP5T0PKBf6DyzFH8mjnp99yBYY1zB0Qod/SqYswOYOrxXStJKZctK0NanzJkZIxGbm4ul6pMtHRUTUzAF7pxSYMvCCs0/FZlJJqpIOJX8kzgkm2q+Tt99tlnWJ+zEVEpkxxdttp2RGBMPFoNngVms+Pk+hcbvV50NrYeVYyZBkiP9l2klFK0dFRN7oIvdOOSBl8QVsgozUwMcsYIBZEr45yS1h+2dl09zuuKVf/kesrh/Tu9PHuOy4Wh7Jd9qDj7K6LvHdvofSg9cejZ+da6Od555y2cOnUB0dFheOihIVz+drMMEK/Bzs/PV5yHF1/8u9AnEXeL5IcffoxPPvmY+2+hJnfBF7pxSYMvCCtklGoTg0QlhYnO3HXnlgpIuBFVR77BhbXTXM7r6S3LEXXHEK6nHN6/U2izZooLw5mty3FiwyzE9efrLKVn56s3Y9QsA8TjOurYsQz9+vVSfC/vvvuO8CcRJelor1598MADaarmU03ugi9045JBW4FYIaPkDWhaVX3QU0C5qKjIYxnocx9Ow50dE7Hn2+8azevUac8j4MrrEDfo79yyTZ6/U0JCgqJSZMz4DNjbJdcFbd1JBv9dkKc5aCoi4GpWyWVPypjiYuCxx4CZM5Wzhu+5x1EjyMgqnFrnU6p0TMIXDb4V8KhlZs98Af3uf0BYbX21Y3O3yFy8WIKj9is05y8YKYdVWhj+7//+Dz1798GJMhsiuqS5lQxmZ3+o2eCKMNZmqXQ8ad+XLnUUfxs1Svn3AwcCixfD0NLQeuZTTYctb+jGJQ2+n8Jb5z6w9BRK4zqY2tiFt4HL3x7si39tK9BlsM2Ww/I+yenRhIvSk5thgDyN1ZNBX7oUCAwERo92fQ+9TyJ657OwsBCLF7+KNWvexunTJYiJCXeZu6DmXCOQBt9P4c1ItH/zIeITEkxNClOTefzLj4W6DLaZOQFq0JP1KTJj1GgD5Gn33KMHsGWLa5dNcbGjBPRLLym7fEQ8iZjZvMVqpA7fBEQFRNXAG4B87523TG/soiagrEdnn5WVhT5pKaisvITj703Db8sewPmCZag4cwyA+SWtndETNBUZcHVV++bnn38W8pn1pH0PD4fb9xIf79jdP/UUDKvC6QsKGjOQBl8AViQ/AerUMmYnhfGWuCUizfkLubm5GD16GNL6lmPV62XYupWwcvll3Hv1ZpzOGotLh3bqksNqkVM6G0w9qg2jFR8iP7OetO/V1XZs3uxmaw3g6NFA/OUvQw2rwukLChozkC4dnYhuNm70GN35+h/sfQe2//tzYSUanO9rj4jD5e/W4/IPBSgrKUVQswDERMcgJDQKJ5u1U10oiycg+dRTDCG3PYKKb7NVN3jRUgSrYZD6vaxM3HJLR7z0UqklKh1XGPWZdeU6Sk8fiAceSLNUveJrZSb0IH34BuLOVw0CzhSsQuh1d6LZ4QIc/fknw2vc8IxRyaie2b4aJd9uRNh1dwiRbTobFaoox/lPXka/vpVI61tVZzxzPgY2bwpEy9bX4ExVsKpCWTyqi9dftyH3k1CsX5ejythrMQ6ujOj7H34ExsqQnlaN9L5VqoKmRgVcjajs6glvUK94wxjMQBp8A3ElC6xXQvfQV9iyKQeMMcN1757GqGRUq+La48I3G9Fy0HRhO75aoxJ+6yCceTcDc2eVuTWeo8c8jsysd7jzF0RXRXRGi4TPnRF1PN1k49K+rSi/VI7Y2EjuoKkRAVerKrtarV7xljEYjTT4BtNQFlhr7OP6T0FwQgecfm8KBt7TzVI3jzsp4dTnnkfwtbcLqzkP/G5UDhQeQJ97z2Ls31z7g7VI7oxUXWhZTHytPLZVlV2NpmHFTYdBH4qMjCf9xqB7Qqp0DKZhQPRMwSo0u7ZbXY2byN6PI9ugGje8gUV3Lf/2/edbJIWUCO3dWhu0tVdeRHqa++CflgJeRqoutNSgMaohu1FYUdnVaGTzcs9Ig68TpYBobPozqDx9FL+tmVJnQKMNaH4t6gNulLGy2+0oKSk3pICXkaoLrYuJSCMqqk2e0nUGDeqHPun9Ta3sajRqKm56cwtCo5EGXwPO0jul6osXsmcg/LrbEdD8CpzMmd3o9SJ2eyJLygLG7fiM2omLrnvujJbFRGR5bFELuavrBATkgKpKUF1RBsCcyq5Gw1v7/6mnHrfsKcAbFhrpw1dJQ+ndmrfeRNqA+3HkdCkiuqbjbN5rWPnaMsx8ZQ4OHf4RLe9/3pDsT5GFsXhlm1piDkYW8DJKdZGfn49+/Xph1qxKbpWOnj6szoiSD3LLVm99BBXfZXO3EPRWeOMuI0YA8+YZl9HrCjN73UofviCUGo7MfPllFBcXI+jK63Du83cREn0Fvt65E0ePHVM09oCYR2aRzS1E1PJ3lWm8MTcfmzYFaN6Ju8tgvvbaazU3B3FFbm4uHnggDcnJhGefBVasQL1EohUr7IqZn6LKY4vqUsVznf79Geg/H+oub+0N8MZdKiqUjT0gvgNYLaKfxvUgd/gq4NUvny9YgZCkP+re7blDpEpFT9NowHNVzJalRThz8mfVO3GzSzo33BUXFwPZ2UBeHnDuHBARAVRW2pGTswX33HNPo9eLKI8tSm5qpGzVG+F9v+PHA+vXuz9H9JyYVaa6FrnDFwRvfZh/rnjN8GYoIn3jehqa8LRZPFURiNFjHle1E7eifWPDXXF8vKOo17p1DqOfnQ30729DTs46xde7U0K568PqjKguVXqu4w2+ZrXwxF02bHAUcnMHz9yqnR9v6nUrd/gq4dUvG90MxexdgyuMytq0IhvUG3bFVu/wzfQ1i4QvZuGoyNmli+vreJpbLfNjdqVOw3b4jLEYxthWxtjBmn+jFc65mTH2JWNsH2NsL2PsL3ruaTW8ahYRuz13GKlSUVP5k/ep5+lJE1VVZlTbvlEE3tCEWpTcVMt1vMnXrBae5uX33ZeKb77RPrdapZ9EhPvvd9T9Ly5Wvq9ZlTr1unSmAMgjoiQAeTU/N+QSgGFE1AFAbwALGGPNdd7XEkRK7/TC8wHXUlJWbRVFHg3/hLFj8Pw/XlBVmdGKRCZvKKEraiHXch1RAWPAGreQu+blO3fuxfz5i3TNLe/8PPlkfenn1q2/d/0aNw74+uvGrzWrUqdeg98PQGbN95kA+jc8gYgOENHBmu+PAjgOIE7nfS1BhJpFJJ4+4GofvbX6zd099QR1TMWcBYsRlvKkal+82dmg3lBCV8tCrmRcFy2ahzlzFqu6jihfs+iMVzWLh6va/4mJiW7n9tVXAzBlih3V1YT27ZMU78EzP126VOBf/9qo+BQwapTDpfTyy/V3+nqextWiy4fPGDtLRM2dfj5DRI3cOk6/7wbHwtCBiBr9T2WMjQYwGgDatm3b9ciRI5rHZgR61Szejha/ee0iEdR1IC7u247Y9GfqFZA7nb8SqK5CWMeeiLplALcvXnRuAE+NFW8qoctb5MuTT3nOnMXYu/cbrmJhInzNoufQiJhCw7kNCwtGdXUF+vUDUlIqXd6DZ34WLwYCAoCxY12fs2IFUF4ODBhgvg7fo8FnjG0DoOTZnAYgk9fgM8ZaA9gOYDgRfeVp0N4ctDUyGGslWgqAXX9TJxy5FIiyYwfRLLEbyo8XIri6DLa2nVHyw7/RLLEbyo7tR9WFU2g9/FXuomKiEpkAdUZDSzKXUslrM8pgizauIgLGIsUEZizAau5x222dPc5P//4O9w1PAlhkJH/VVDUYVi2TMbYfQHciOlZr0InoDwrnRcJh7F8mog95ru2tBt/fUVtF8e2338ZfR41B7MDnEZzQAb++NQm3JF2Jr3btqXfsquZB+O1iFXdlRlFPU1qMhpoSumbnCjgjWqkl4noilU5mKNHU3IOo2uO599wDbN3qun8vYHzvXCN1+DkAhtd8PxzABoWbBwFYD+AtXmMvsQ61fvOZs+ci9A931FUGjU2fjO9/+g2xA5+vqwwa0bUfDv34kypfvJ7cAGe0BCLd+YGdsSJXwBnR+m4RAWORSicz9Otq7sEzP0FB7vv3Atb2ztVr8GcBuJcxdhDAvTU/gzGWzBhbWXPOnwHcBeARxth3NV8367yvxAC0qJB45JOnty5H1B1DVSubREhbjTQazkF8ZrMjtOd4bDCoDLYSomWkIpRfIpVOZshk1dyDZ35SUvpYHvh3hy6DT0SniKgHESXV/Hu65vhuIhpZ830WEQUS0c1OX9+JGLxELFpUSJ7kk6c2zUPwFYmISE7nvmZD9Ej8jDQaVuQKOGOEjFSv8kuk0skMmazae3ian7lzFxqWHyMCWVpBUofWAmDu3EBR3QbCdv4Yzn84TVOZCb0SPyONhtVNT0TLSGsX1ttu64zFi5eBiDBu3Bh8+eU3ii4tJUQmBJohk9VyD63STz35MaKQBl9Shxa/uSc3UFiXvgiKiccdN12j2hcvIvPTaKNhZecokcZVlHZepMET9f7cPSEakbEuOj9GJLKWjkQXIuWTDRGh0jBS2mdkHwFeRPQEMGKORDUL1/v+eCS5AAzpq2AVsom5xDCMTEYTJfEzqlGKkYudGvQaV28pxOcKre9PzUIGQMgC5Q1Igy9xiYikIaOS0URWGRS143TGXzKvvaFKqBF4+0JmFLIevkQRtYXSXGFUZVCRAVd3gTatKiBRuQLuMKMImTdUCTUCb6pDz4MZf2tp8JsoVicN8WCGSkNvsJJnsdP6H1l0ETJXeEOVUCPwlYWssLAQAwb0RYcO12Lx4uWoqLiA/v0JU6eK/1tLl04TxYoGI2oxupaKGbVatBb/MrOQm7+6PnzBVZWbm4vBg+/HffddRno66j4fmzcDmzYBU6cC4eHq/tbSpSNpBE/S0KXPVuFPt3bi2pka8TjKI/GbM2cxFi2ap+m+Iuu/K6FHVmr02JwxspmOlWhtAmNWHf/az8eLL17G2LFwWUq5eXNxf2tp8JsonpKGzm+eh0BbKYKDcz26E4x0PbjTNM+ZsxhPPz1B832N9vHqMdpm+p+tSBYyw7CqXcjMcqHVwvP56NPH0UtZ1N9aunSaMK505BVnjuF01ljMnV3JJWezooa8XpdHYWEhkpKuNbSyoR6Xgtl9UAFjlExKmNk3l1eSa0UvBN7Px4QJwAcf8P+tpUtH0gh3SUOXv1uPfunEtTM10/XgjJ771u7kQkONrWyoJ2hoRSCVt0qoHszum8ub9WrF55j383HunLi/tTT4TRR3hdIu/68AaX3d7yRqHzGtkr5pva+zwUlJcQTH3KFHBaTHaHtDu0VAvOvFCsPKs5BZ8Tnm/XxERYn7W0uD30RxVyit7OJl7p2pVdI3rfd1Njj9+zuUEEYFK/UYbW8IpBrh0/ZWbbwVn2Oez8fmzUDnzuL+1tLg+zEFBQW4/qZOOHjwYKNjAFwmDUVEBHPvTK3ScGu9r7PBiY93yN6mTQPeeAP1gpXLl0N3sFKP0ba66qJRrhdv1cZb8Tnm+Xxs2ADs2RMi7G8tDb6fwpNFyxhTTBp65JFHuXemVrketN63ocG59VZHD9LyckdwrFcvx7/r10N3ZUO9RtvKqotGuV70GFYjlT1WfI7dfT6WLQOeegro2bMv9uz5XlyDc6nS8T+cA7LBCR1wfu1zSL+rCz7Kzql3zFUVR7VFp3xJpWNFMo5Z6heRGDVPWpO8jFb2WKHScb63yM+HLJ7WxBCRRaumwqRR1Sg9oeW+/ppVKhqjZKFam8qbYYzdfZ42brTj7rt7YMeOz5yM8lBkZDzpdYu2lGX6OO588c7HahHRek+NO8Eq14OW+1odDDUzk1MPRvm0tbi5zFL2uPo8lZT0AmMMYWFbTEnIMhQi8sqvrl27koQoPz+fwqKiKaJjT+rS7XaqqqpSPNaQyspKGjZ8OEUlJFG7yRvrfUXExVNmZqYF78bBoUOHKCNjLMXGRpDNxig2NoIyMsbSoUOHTLn/5s2bKSYmlIYMCaSsLNDWraCsLNCQIYEUExNKmzdvNuW+27YZc18R85uRMZaGDAmkggK4/BoyJJAmThyneYwTJ46juLhIstttFBcXSRMnjlMcY2xsBGVluR5HQYFjHuPiIjWNxdM4Y2JCackS5fsuWQKKiQk17bPLA4Dd5MKuSpeOF6PHF+8usering1oe/577PpyB2w2cx/yzMyydIfZfnWr3BJa59dKn3ZDrMg6rsUXXYDSh++jaPXF6229J6IpihLeZETMRpThKCwsxKJF87BmTVYjXzIgNoBuVWymIVZWvfSFipsNkT58H0WrL95dFi1VV4HZ7LDf0BPzFGrdi2qKooRVZRi8AREJR54SoZ54IkPo/HpLM24rs469NW9AK7oMPmMshjG2lTF2sObfaDfnRjLGihlj3vHc4wN4qmh5KW8pli1aUKehr8VdFu2Fj55Hyfd5KN2xGu9kvlnvdUY3RfHWLEs1aA266jUcPIlQW7ZsRpcuYufXjPo6nrAy0C4qeO0twXq9O/wpAPKIKAlAXs3PrngBwKc679fk+Oyzz/BRdg5C7vxro98Fd0rFwqWvNdpxa2295/xkwGx2hPYcjw15X9S5hpjNDvv1yk8GPPj6bklPqQG9hoPn6SgtDfjiC/f3ED2/ZhgyK7OORTxdmF122R26fPiMsf0AuhPRMcZYawDbiegPCud1BfA0gE8AJBPReE/Xlj58/b54tTg35W7WYxwCY+o7LkuP7EVJ7hxszF7X6KmCB6P9oe7823qNgd74g14fPu/cjR/vyBJ2PpadDeTlOaouRkQA1dWB+PbbH3TPidkBeCsS2ESU4TY7bmWkD78VER0DgJp/Wyrc3AZgHhwGX6ICPb54LWh1IfFihC+2docZHR2KpKRrsWrVciQnX8Dq1WJ3UXrjD3rdErxPR+fP//7z118D48YBQUHA4sXAli2OMhJ9+lTqnhOzyxzXQlTdQGqoLZ7Ei96nC2+LW3k0+IyxbYyx7xW++nHe4zEAm4noF457jWaM7WaM7T5x4gTn5f0Xrb54PWhxIfEmhon2xf7+qPwGliy5jK1bgddfB2JiHPVwiorEGR+98Qe9hoPXJRQY6JjH4mJHe7yXXnK0y3M2yGPGkO45MduQWekW0RO89ra4leEuHcbYOwDuBFANIBxAEIBlROTO3y9dOjVUVVVhwcJFmLdgIda8tRrdu3evd+ydzDcb+eJ5UJJZzp8/H08/+xyi7x2L8A73NHqNkgup1u1ka9cVSSEXsevLHfj0008bHavV+4uS+vE8Kk+b5tjRxsfr10qL0oJrdUvwuoQuXuyFzz7LxxVXXEanToTRo12PV8+cmClX9GU5rxU5BIbp8BljcwCcIqJZjLEpAGKI6Bk35z8C6cO3HFdGumfvPmjW/nZUnv0VrQbPQtnP3+NS3lIEd0pFaOe+YDY7Sr7PQ9Beh+5fa2KYCF8sjwF84w1HFcxx4/QbH6v12GoL2t188/V47bUKw8ZrpiHzxeSnWqz43Bjpw58F4F7G2EEA99b8DMZYMmNspc5rSwzAnfQy7v7n0SJlIqiyDCc/nu/RhaRG1eOs5mjfPgnvvPMWHnpoCPbvP6BJ6sfzqJya6ghWAvrVKVZ3oFLjEkpMTMSlS5WGKqLMrB/vbW4RNVj9uWmILoNPRKeIqAcRJdX8e7rm+G4iGqlw/mqe3b3EOHiMdESXdJQd3ulRzsmbGDZx3Fjh/lc1/UAB/cbH6qJrgDpfstEG2UxDZqWcV6/s1Bs+N87ITFs/gTdwymukt32yqV5TlNpGKc7xAh5Vz9+nTcXs2TOEqznU9AMF9BsfqztQOY/DVSKUs3E6deoCxoxxxDCKi5WvlZMDtGnTRlPg1kxDZlVXNRGBYm/53NQiDb4foKYcgmjppSdVz6sLjVFz8PYD7dFDnPHxllIDSjQ0TrWKpcBARwzj66/rn79vH/DJJ0BCwn5NT1lmGjIr3CIiZafe9LmRxdN8HC2BU1GVNHkSw35bPACrVlYLD1rxBDGffRbo3j0AO3YENVL/GJmkZTa8c7F4scNobd7saN4+daqjxaMelYsZyVBWqHR8OVAsi6f5MWrLIXgy0qGd+3LXy+FJDCsvqzbE/+puh7l8OfDkkwDQDFdfPabRLsqbUt3dwes/5tHE9+oFjBwJjBgBfPghQATs3u2YLz2aeTNq7VjhFvHlQLE75A7fx1FbDqFhyeXSI3vdSi957227rgdKP1+NZYsWYOHS1/DjyYuwXd8DJdtexapVMLScgpodpq9outWULeCV/o0Z43Dz1F7LeaefkOBdJX6VMLO0gpU1+PUi6+H7OVVVVXh0xAhsyPsCzYfU36Wd/ucYLJk7E8OGDQPAZ6RLd6xWLK7m6t7uEsP+2K0TQkJyvebR2Bce1dUuSrzGqVev32WqzteaNg1YtAh49FHvM15WYXXehR6kS8cLUNuXVg1qyiForaTpCmcFj5Kq55VX5nuVLM3qR3UeN43asgVqFUsNr9WnD/Dee+JVLr6Mt+nnRSENcdIFtQAADBpJREFUvgkY2VREi0/ek5HWUqrBFd4mSzNC083ra+eNHahdlNQolgDH3C9dCgwc6Di2aROwfTuQmpqu+j2ZjVnj8jb9vCikwTcYo5uKqKmoaeRThju8SZYmWtPNa8TVyPzULko8xmnTJqB/f9cVNNPSgA0bPkJubq7uoLZRRtnMYLu3bVREIX34BqO1Ly0vvD75GdOfw/QZL3IXOVPCH6SMIn34anztixbN477vO++8pdp/7KooXU6OQ2//7LOOwOy4cY4Kmq4LzoWAMYYXX7ysKahtVI18q4LtVtTg14sM2lqI0U1FAM+B06cez8D0GS+qLnLmjNnNLoxCpOFQs3ioMeIPPTSk0XUbNjJp1gz4wx9uxAcfZNeNU8k4tWnTBgkJ+zFpUiWWLnXs7EeNcj2GceMYOnZ0lFD29J4aLohGGmVfCLZ7C9LgW4waFY0R6H3K8BUpY0NcPZF06pSMp5+eoLtEsxolx6lTF7hlfvv3H6g3319/7aht36ePoyDc74ttAHJzGyeVNZyD2ms9/7zDjeNuvP37/15S2tN7aqhOMdIo+7JqxmykSsditDQVEQlv/Zw1b61WfL23de3hwZ2/9+mnJ2DOnMW6YwpqfO1qYgfO/uNXXw3AzJnKjUxGjqz0mOLvfK2zZ+FxvBcueD7HVVDbSAWUr/dD9hakwTcYkZmtWtFbP8eo/8hGBfd4AqRPPz0BEyZM0pUhqsaIq5X51Qa6i4quQ0qKss8d4Ftsa68VFhbocbwREdAc1DbSKFtVQM3fkAbfYMzuS+sKPU8ZRvxHNlJxYdYTiRojzqOk+eijCixatLRu4QOAn38+grQ09+PgWWwTExPx6KMjPY43Pp5h40bm9hxX+nMjjbK/6uLNRhp8g7GiL21D9D5liP6PbHQDbLOSq9Rotd3J/JYtAyZPdvTh3boV9RY+kYstz3iLi4PxySch2L69vlZ/4EDHz9u3u9afG2mU/VUXbzbS4BuM6MxWLeh9yhD9H9noHbhZ/l61Wu3G+QgMI0YAp045atykpjZe+AIDSdhiyzPed99dh0mTpuCVV4CAgN+1+osXO35+5RVg4sTJiq4vI42yv+rizUaqdJoAeuvniFbpGK24MFvRYWRj8rFjgU6dGP72N/UySS3jBaDrby2qSb2WsUtj70DKMiUetfrvZL7p9ilD5H9koysR+opmm2dh+uYbR9LUvHmuk6VESmJFzJ00ytYiDb5ECKL+Ixu9Ay8sLERy8k2as0XNgnfhu+8+ICwsAP36MUN2zc5Ivbvv487gB5g9GInvUtvsQu+u2BETcL+L1KO4OHDgACorqzF5MpCe7khYqjWSGzYAW7YE4913rff3OoLh7o1rbZVLmy0EsbHDMWlS/cV2506xu2apd/dvZNBWYjpGBvdqFUCzZpXh9deBigqH+qVXL8e/p04BAEP79u11vQcRDB48FDk57s+prXJ57twlwztLAVLv7u9Igy8xHSMVF84KoPh4R7GwdescNWjWrQOefx7o27dKaFaw1gSyjIwnkZMDj1Uu//hH8wys1Lv7N7oMPmMshjG2lTF2sObfaBfntWWMbWGM/cAY+x9j7Co995VYi4gMWaNKJpvd4ERPAlliYiLuuy8VkycDb7yBegvfG284OlFNnQp88415Blbq3f0bXUFbxthsAKeJaBZjbAqAaCKarHDedgAvEdFWxlg4gGoiuuTu2jJo6514e9VMM3uRipCrFhYWomvXG9G1aym+/dZRCTMqyuHG6d8fOHvW/ACz0dJKibEYWTytH4DMmu8zAfRXuPkNAAKIaCsAEFGJJ2Mv0Y8RzU6MzpAVgZk+aBEJZImJiXj33XX47rtQpKYG4q23gA8+cBj73FxrEoq8qWGNRCx6d/hniai5089niCi6wTn9AYwEUA7gagDbAEwhIrfbK7nD105tKQU9zU6U8AV9u5ljFClhlNp1iSh06fAZY9sAKAm1pgHI5DD4gwCsAtAZwM8A3gewmYhWKdxrNIDRANC2bduuR44ccTs2SWOc6+ZobXbiCl/QaJtZu99M95FEwosulw4R9SSiGxW+NgD4jTHWuuYmrQEcV7hEEYBviegwEVUCyAbQxcW9VhBRMhElx8XF8b4/iRPOdXOYzY7QnuOxIe+LusJpzGaH/Xpt1Tl9QaNtZs0VKWGU+Bp6ffg5AIbXfD8cwAaFc3YBiGaM1VrwewD8T+d9JS7Q2+zEHb5i4MzyQVslYTSqj4BIfGGMTRG9Bn8WgHsZYwcB3FvzMxhjyYyxlQBQ46t/CkAeY+y/ABiAN3TeV+ICvc1O3OFLGu3arGAjE5WskDAa2UegKY2xqSJr6fgh7urfX9yzAW3Pf68paOurvW2NxEwJo5Hz76r/b0bGk6quJT8j1iN72jYhjGypKGuSN8ZMCaNRfQRE7sh9sf9xU0Lu8P2M62/qhGJbK0T3ngDGbCg9sheX8pYiuFMqQjv3BbPZUfJ9HoL2rsPRn3/SdA8pIbQGI1RSvtbrQOIZWR65CaG32YnEezFCBio6b0FKVa1HunSaEN7QUlFiDEaopETXHvIVJVdTRRp8P8Rut+PJJybh6M8/1alxnI9JY++bGKGSEp1b4UtKrqaINPgSiY9ghAxU9I5cVtv0bqTBl0h8BCNUUqJ35FLJ5d1Igy+R+BCiZaBG7MhltU3vRap0JJImjqx/719IlY5EInGJ3JE3HeQOXyKRSPwIucOXSCQSiTT4EolE0lSQBl8ikUiaCF7rw2eMnQCgtcdhLICTAofj68j5qI+cj/rI+aiPr89HOyJSbBnotQZfD4yx3a6CFk0ROR/1kfNRHzkf9fHn+ZAuHYlEImkiSIMvkUgkTQR/NfgrrB6AlyHnoz5yPuoj56M+fjsffunDl0gkEklj/HWHL5FIJJIGSIMvkUgkTQS/MPiMsRjG2FbG2MGaf6NdnNeWMbaFMfYDY+x/jLGrzB2pOfDOR825kYyxYsaY54alPgrPfDDGbmaMfckY28cY28sY+4sVYzUSxlhvxth+xtghxtgUhd8HM8ber/n91/76/6MWjvl4osZO7GWM5THG2lkxTpH4hcEHMAVAHhElAcir+VmJtwDMIaLrAXQDcNyk8ZkN73wAwAsAPjVlVNbBMx+XAAwjog4AegNYwBhrbuIYDYUxZgewFEAKgBsAPMQYu6HBaSMAnCGiawG8CuAVc0dpHpzz8S2AZCLqCGAtgNnmjlI8/mLw+wHIrPk+E0D/hifU/DEDiGgrABBRCRFdMm+IpuJxPgCAMdYVQCsAW0wal1V4nA8iOkBEB2u+PwrHZkAxW9FH6QbgEBEdJqJyAO/BMS/OOM/TWgA9GGPMxDGaicf5IKICJxvxFYAEk8coHH8x+K2I6BgA1PzbUuGc9gDOMsbWMca+ZYzNqVnl/RGP88EYswGYB+Bpk8dmBTyfjzoYY90ABAEoNGFsZhEP4Benn4tqjimeQ0SVAM4BaGHK6MyHZz6cGQEg19ARmUCA1QPghTG2DcAVCr+axnmJAAB3AugM4GcA7wN4BMAqEeMzGwHz8RiAzUT0iz9s4gTMR+11WgN4G8BwIqoWMTYvQemP3FCTzXOOv8D9XhljQwEkA/h/ho7IBHzG4BNRT1e/Y4z9xhhrTUTHav7DKvnmiwB8S0SHa16TDeA2+KjBFzAftwO4kzH2GIBwAEGMsRIicufv91oEzAcYY5EANgF4joi+MmioVlEEoI3TzwkAjro4p4gxFgAgCsBpc4ZnOjzzAcZYTzg2Df+PiMpMGpth+ItLJwfA8JrvhwPYoHDOLgDRjLFav+w9AP5nwtiswON8ENEQImpLRFcBeArAW75q7DnwOB+MsSAA6+GYhw9NHJtZ7AKQxBi7uua9PgjHvDjjPE+DAOST/2ZmepwPxlhnAK8DSCci/xB4EJHPf8HhZ8wDcLDm35ia48kAVjqddy+AvQD+C2A1gCCrx27lfDid/wiAJVaP28r5ADAUQAWA75y+brZ67ILnIRXAAThiE9Nqjs2Aw6ABQAiADwEcArATwDVWj9ni+dgG4Denz0OO1WPW+yVLK0gkEkkTwV9cOhKJRCLxgDT4EolE0kSQBl8ikUiaCNLgSyQSSRNBGnyJRCJpIkiDL5FIJE0EafAlEomkifD/A1dOYIjfWAM/AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Load from ex6data3\n",
     "# You will have X, y, Xval, yval as keys in the dict data\n",
@@ -356,7 +454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -410,7 +508,21 @@
     "    sigma = 0.3\n",
     "\n",
     "    # ====================== YOUR CODE HERE ======================\n",
-    "\n",
+    "    C_list = [0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30] \n",
+    "    sigma_list = [0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30]\n",
+    "    error = 0\n",
+    "    min_error = np.inf\n",
+    "    predictions = np.zeros(yval.shape)\n",
+    "    \n",
+    "    for c in C_list:\n",
+    "        for s in sigma_list:\n",
+    "            model= utils.svmTrain(X, y, c, gaussianKernel, args=(s,))\n",
+    "            predictions = utils.svmPredict(model, Xval)\n",
+    "            error = np.mean(predictions != yval)            \n",
+    "            if error < min_error:\n",
+    "                min_error = error\n",
+    "                C = c\n",
+    "                sigma = s\n",
     "    \n",
     "    \n",
     "    # ============================================================\n",
@@ -426,9 +538,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Try different SVM Parameters here\n",
     "C, sigma = dataset3Params(X, y, Xval, yval)\n",
@@ -451,9 +583,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise support-vector-machines\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "                            Gaussian Kernel |  25 /  25 | Nice work!\n",
+      "        Parameters (C, sigma) for Dataset 3 |  25 /  25 | Nice work!\n",
+      "                           Email Processing |   0 /  25 | \n",
+      "                   Email Feature Extraction |   0 /  25 | \n",
+      "                                  --------------------------------\n",
+      "                                            |  50 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[2] = lambda : (C, sigma)\n",
     "grader.grade()"
@@ -555,7 +707,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -654,8 +806,10 @@
     "\n",
     "        # Look up the word in the dictionary and add to word_indices if found\n",
     "        # ====================== YOUR CODE HERE ======================\n",
-    "\n",
-    "        \n",
+    "        try:\n",
+    "            word_indices.append(vocabList.index(word))\n",
+    "        except ValueError:\n",
+    "            pass\n",
     "\n",
     "        # =============================================================\n",
     "\n",
@@ -676,9 +830,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------\n",
+      "Processed email:\n",
+      "----------------\n",
+      "anyon know how much it cost to host a web portal well it depend on how mani visitor your expect thi can be anywher from less than number buck a month to a coupl of dollar number you should checkout httpaddr or perhap amazon ec number if your run someth big to unsubscrib yourself from thi mail list send an email to emailaddr\n",
+      "-------------\n",
+      "Word Indices:\n",
+      "-------------\n",
+      "[85, 915, 793, 1076, 882, 369, 1698, 789, 1821, 1830, 882, 430, 1170, 793, 1001, 1894, 591, 1675, 237, 161, 88, 687, 944, 1662, 1119, 1061, 1698, 374, 1161, 476, 1119, 1892, 1509, 798, 1181, 1236, 511, 1119, 809, 1894, 1439, 1546, 180, 1698, 1757, 1895, 687, 1675, 991, 960, 1476, 70, 529, 1698, 530]\n"
+     ]
+    }
+   ],
    "source": [
     "#  To use an SVM to classify emails into Spam v.s. Non-Spam, you first need\n",
     "#  to convert each email into a vector of features. In this part, you will\n",
@@ -708,9 +877,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise support-vector-machines\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "                            Gaussian Kernel |  25 /  25 | Nice work!\n",
+      "        Parameters (C, sigma) for Dataset 3 |  25 /  25 | Nice work!\n",
+      "                           Email Processing |  25 /  25 | Nice work!\n",
+      "                   Email Feature Extraction |   0 /  25 | \n",
+      "                                  --------------------------------\n",
+      "                                            |  75 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[3] = processEmail\n",
     "grader.grade()"
@@ -738,7 +927,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -798,9 +987,10 @@
     "    x = np.zeros(n)\n",
     "\n",
     "    # ===================== YOUR CODE HERE ======================\n",
-    "\n",
-    "    \n",
-    "    \n",
+    "#     for i in range(n):\n",
+    "#         if i in word_indices:\n",
+    "#             x[i] = 1\n",
+    "    x[word_indices] = 1\n",
     "    # ===========================================================\n",
     "    \n",
     "    return x"
@@ -815,9 +1005,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------\n",
+      "Processed email:\n",
+      "----------------\n",
+      "anyon know how much it cost to host a web portal well it depend on how mani visitor your expect thi can be anywher from less than number buck a month to a coupl of dollar number you should checkout httpaddr or perhap amazon ec number if your run someth big to unsubscrib yourself from thi mail list send an email to emailaddr\n",
+      "\n",
+      "Length of feature vector: 1899\n",
+      "Number of non-zero entries: 45\n"
+     ]
+    }
+   ],
    "source": [
     "# Extract Features\n",
     "with open(os.path.join('Data', 'emailSample1.txt')) as fid:\n",
@@ -840,9 +1044,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise support-vector-machines\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "                            Gaussian Kernel |  25 /  25 | Nice work!\n",
+      "        Parameters (C, sigma) for Dataset 3 |  25 /  25 | Nice work!\n",
+      "                           Email Processing |  25 /  25 | Nice work!\n",
+      "                   Email Feature Extraction |  25 /  25 | Nice work!\n",
+      "                                  --------------------------------\n",
+      "                                            | 100 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[4] = emailFeatures\n",
     "grader.grade()"
@@ -862,9 +1086,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Linear SVM (Spam Classification)\n",
+      "This may take 1 to 2 minutes ...\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "# Load the Spam Email dataset\n",
     "# You will have X, y in your environment\n",
@@ -880,9 +1114,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Accuracy: 99.85\n"
+     ]
+    }
+   ],
    "source": [
     "# Compute the training accuracy\n",
     "p = utils.svmPredict(model, X)\n",
@@ -899,9 +1141,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Evaluating the trained Linear SVM on a test set ...\n",
+      "Test Accuracy: 98.90\n"
+     ]
+    }
+   ],
    "source": [
     "# Load the test dataset\n",
     "# You will have Xtest, ytest in your environment\n",
@@ -933,9 +1184,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top predictors of spam:\n",
+      "word            weight         \n",
+      "----            ------\n",
+      "our             0.49\n",
+      "click           0.47\n",
+      "remov           0.42\n",
+      "guarante        0.39\n",
+      "visit           0.37\n",
+      "basenumb        0.34\n",
+      "dollar          0.32\n",
+      "price           0.27\n",
+      "will            0.27\n",
+      "pleas           0.26\n",
+      "most            0.25\n",
+      "lo              0.25\n",
+      "nbsp            0.25\n",
+      "ga              0.24\n",
+      "al              0.24\n"
+     ]
+    }
+   ],
    "source": [
     "# Sort the weights and obtin the vocabulary list\n",
     "# NOTE some words have the same weights, \n",
@@ -964,9 +1240,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Processed Data/emailSample1.txt\n",
+      "Spam Classification: not spam\n"
+     ]
+    }
+   ],
    "source": [
     "filename = os.path.join('Data', 'emailSample1.txt')\n",
     "\n",
@@ -1021,7 +1307,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,
diff --git a/Exercise7/.ipynb b/Exercise7/.ipynb
new file mode 100644
index 00000000..87ed5047
--- /dev/null
+++ b/Exercise7/.ipynb
@@ -0,0 +1,1226 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Programming Exercise 7:\n",
+    "# K-means Clustering and Principal Component Analysis\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "In this exercise, you will implement the K-means clustering algorithm and apply it to compress an image. In the second part, you will use principal component analysis to find a low-dimensional representation of face images. Before starting on the programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics.\n",
+    "\n",
+    "All the information you need for solving this assignment is in this notebook, and all the code you will be implementing will take place within this notebook. The assignment can be promptly submitted to the coursera grader directly from this notebook (code and instructions are included below).\n",
+    "\n",
+    "Before we begin with the exercises, we need to import all libraries required for this programming exercise. Throughout the course, we will be using [`numpy`](http://www.numpy.org/) for all arrays and matrix operations, [`matplotlib`](https://matplotlib.org/) for plotting, and [`scipy`](https://docs.scipy.org/doc/scipy/reference/) for scientific and numerical computation functions and tools. You can find instructions on how to install required libraries in the README file in the [github repository](https://github.com/dibgerge/ml-coursera-python-assignments)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# used for manipulating directory paths\n",
+    "import os\n",
+    "\n",
+    "# Scientific and vector computation for python\n",
+    "import numpy as np\n",
+    "\n",
+    "# Import regular expressions to process emails\n",
+    "import re\n",
+    "\n",
+    "# Plotting library\n",
+    "from matplotlib import pyplot\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "import matplotlib as mpl\n",
+    "\n",
+    "from IPython.display import HTML, display, clear_output\n",
+    "\n",
+    "try:\n",
+    "    pyplot.rcParams[\"animation.html\"] = \"jshtml\"\n",
+    "except ValueError:\n",
+    "    pyplot.rcParams[\"animation.html\"] = \"html5\"\n",
+    "\n",
+    "# Optimization module in scipy\n",
+    "from scipy import optimize\n",
+    "\n",
+    "# will be used to load MATLAB mat datafile format\n",
+    "from scipy.io import loadmat\n",
+    "\n",
+    "# library written for this exercise providing additional functions for assignment submission, and others\n",
+    "import utils\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "# define the submission/grader object for this exercise\n",
+    "grader = utils.Grader()\n",
+    "\n",
+    "# tells matplotlib to embed plots within the notebook\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Submission and Grading\n",
+    "\n",
+    "\n",
+    "After completing each part of the assignment, be sure to submit your solutions to the grader. The following is a breakdown of how each part of this exercise is scored.\n",
+    "\n",
+    "\n",
+    "| Section | Part                                             | Submitted Function                | Points |\n",
+    "| :-      |:-                                                |:-                                 | :-:    |\n",
+    "| 1       | [Find Closest Centroids](#section1)              | [`findClosestCentroids`](#findClosestCentroids)  |  30    |\n",
+    "| 2       | [Computed Centroid Means](#section2)             | [`computeCentroids`](#computeCentroids)      |  30    |\n",
+    "| 3       | [PCA](#section3)                                 | [`pca`](#pca)                   |  20    |\n",
+    "| 4       | [Project Data](#section4)                        | [`projectData`](#projectData)           |  10    |\n",
+    "| 5       | [Recover Data](#section5)                        | [`recoverData`](#recoverData)           |  10    |\n",
+    "|         | Total Points                                     |                                   |100     |\n",
+    "\n",
+    "\n",
+    "You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "At the end of each section in this notebook, we have a cell which contains code for submitting the solutions thus far to the grader. Execute the cell to see your score up to the current section. For all your work to be submitted properly, you must execute those cells at least once.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1 K-means Clustering\n",
+    "\n",
+    "In this exercise, you will implement K-means algorithm and use it for image compression. You will first start on an example 2D dataset that will help you gain an intuition of how the K-means algorithm works. After\n",
+    "that, you wil use the K-means algorithm for image compression by reducing the number of colors that occur in an image to only those that are most common in that image.\n",
+    "\n",
+    "### 1.1 Implementing K-means\n",
+    "\n",
+    "The K-means algorithm is a method to automatically cluster similar data examples together. Concretely, you are given a training set $\\{x^{(1)} , \\cdots, x^{(m)}\\}$ (where $x^{(i)} \\in \\mathbb{R}^n$), and want to group the data into a few cohesive “clusters”. The intuition behind K-means is an iterative procedure that starts by guessing the initial centroids, and then refines this guess by repeatedly assigning examples to their closest centroids and then recomputing the centroids based on the assignments.\n",
+    "\n",
+    "The K-means algorithm is as follows:\n",
+    "\n",
+    "```python\n",
+    "centroids = kMeansInitCentroids(X, K)\n",
+    "for i in range(iterations):\n",
+    "    # Cluster assignment step: Assign each data point to the\n",
+    "    # closest centroid. idx[i] corresponds to cˆ(i), the index\n",
+    "    # of the centroid assigned to example i\n",
+    "    idx = findClosestCentroids(X, centroids)\n",
+    "    \n",
+    "    # Move centroid step: Compute means based on centroid\n",
+    "    # assignments\n",
+    "    centroids = computeMeans(X, idx, K)\n",
+    "```\n",
+    "\n",
+    "The inner-loop of the algorithm repeatedly carries out two steps: (1) Assigning each training example $x^{(i)}$ to its closest centroid, and (2) Recomputing the mean of each centroid using the points assigned to it. The K-means algorithm will always converge to some final set of means for the centroids. Note that the converged solution may not always be ideal and depends on the initial setting of the centroids. Therefore, in practice the K-means algorithm is usually run a few times with different random initializations. One way to choose between these different solutions from different random initializations is to choose the one with the lowest cost function value (distortion). You will implement the two phases of the K-means algorithm separately\n",
+    "in the next sections."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section1\"></a>\n",
+    "#### 1.1.1 Finding closest centroids\n",
+    "\n",
+    "In the “cluster assignment” phase of the K-means algorithm, the algorithm assigns every training example $x^{(i)}$ to its closest centroid, given the current positions of centroids. Specifically, for every example $i$ we set\n",
+    "\n",
+    "$$c^{(i)} := j \\quad \\text{that minimizes} \\quad \\lvert\\rvert x^{(i)} - \\mu_j  \\lvert\\rvert^2, $$\n",
+    "\n",
+    "where $c^{(i)}$ is the index of the centroid that is closest to $x^{(i)}$, and $\\mu_j$ is the position (value) of the $j^{th}$ centroid. Note that $c^{(i)}$ corresponds to `idx[i]` in the starter code.\n",
+    "\n",
+    "Your task is to complete the code in the function `findClosestCentroids`. This function takes the data matrix `X` and the locations of all centroids inside `centroids` and should output a one-dimensional array `idx` that holds the index (a value in $\\{1, ..., K\\}$, where $K$ is total number of centroids) of the closest centroid to every training example.\n",
+    "\n",
+    "You can implement this using a loop over every training example and every centroid.\n",
+    "<a id=\"findClosestCentroids\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def findClosestCentroids(X, centroids):\n",
+    "    \"\"\"\n",
+    "    Computes the centroid memberships for every example.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The dataset of size (m, n) where each row is a single example. \n",
+    "        That is, we have m examples each of n dimensions.\n",
+    "        \n",
+    "    centroids : array_like\n",
+    "        The k-means centroids of size (K, n). K is the number\n",
+    "        of clusters, and n is the the data dimension.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    idx : array_like\n",
+    "        A vector of size (m, ) which holds the centroids assignment for each\n",
+    "        example (row) in the dataset X.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Go over every example, find its closest centroid, and store\n",
+    "    the index inside `idx` at the appropriate location.\n",
+    "    Concretely, idx[i] should contain the index of the centroid\n",
+    "    closest to example i. Hence, it should be a value in the \n",
+    "    range 0..K-1\n",
+    "\n",
+    "    Note\n",
+    "    ----\n",
+    "    You can use a for-loop over the examples to compute this.\n",
+    "    \"\"\"\n",
+    "    # Set K\n",
+    "    K = centroids.shape[0]\n",
+    "\n",
+    "    # You need to return the following variables correctly.\n",
+    "    idx = np.zeros(X.shape[0], dtype=int)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    dist = 0\n",
+    "    min_dist = 100\n",
+    "\n",
+    "    for i in range(1, X.shape[0]):\n",
+    "        for k in range(K):\n",
+    "            dist = np.dot(X[i, :] - centroids[k, :],X[i, :] - centroids[k, :])\n",
+    "            if dist < min_dist:\n",
+    "                min_dist = dist\n",
+    "                idx[i] = k\n",
+    "    # =============================================================\n",
+    "    return idx"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you have completed the code in `findClosestCentroids`, the following cell will run your code and you should see the output `[0 2 1]` corresponding to the centroid assignments for the first 3 examples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Closest centroids for the first 3 examples:\n",
+      "[0 2 1]\n",
+      "(the closest centroids should be 0, 2, 1 respectively)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load an example dataset that we will be using\n",
+    "data = loadmat(os.path.join('Data', 'ex7data2.mat'))\n",
+    "X = data['X']\n",
+    "\n",
+    "# Select an initial set of centroids\n",
+    "K = 3   # 3 Centroids\n",
+    "initial_centroids = np.array([[3, 3], [6, 2], [8, 5]])\n",
+    "\n",
+    "# Find the closest centroids for the examples using the initial_centroids\n",
+    "idx = findClosestCentroids(X, initial_centroids)\n",
+    "\n",
+    "print('Closest centroids for the first 3 examples:')\n",
+    "print(idx[:3])\n",
+    "print('(the closest centroids should be 0, 2, 1 respectively)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise k-means-clustering-and-pca\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "grader[1] = findClosestCentroids\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section2\"></a>\n",
+    "### 1.1.2 Computing centroid means\n",
+    "\n",
+    "Given assignments of every point to a centroid, the second phase of the algorithm recomputes, for each centroid, the mean of the points that were assigned to it. Specifically, for every centroid $k$ we set\n",
+    "\n",
+    "$$ \\mu_k := \\frac{1}{\\left| C_k\\right|} \\sum_{i \\in C_k} x^{(i)}$$\n",
+    "\n",
+    "where $C_k$ is the set of examples that are assigned to centroid $k$. Concretely, if two examples say $x^{(3)}$ and $x^{(5)}$ are assigned to centroid $k = 2$, then you should update $\\mu_2 = \\frac{1}{2} \\left( x^{(3)} + x^{(5)} \\right)$.\n",
+    "\n",
+    "You should now complete the code in the function `computeCentroids`. You can implement this function using a loop over the centroids. You can also use a loop over the examples; but if you can use a vectorized implementation that does not use such a loop, your code may run faster.\n",
+    "<a id=\"computeCentroids\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def computeCentroids(X, idx, K):\n",
+    "    \"\"\"\n",
+    "    Returns the new centroids by computing the means of the data points\n",
+    "    assigned to each centroid.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The datset where each row is a single data point. That is, it \n",
+    "        is a matrix of size (m, n) where there are m datapoints each\n",
+    "        having n dimensions. \n",
+    "    \n",
+    "    idx : array_like \n",
+    "        A vector (size m) of centroid assignments (i.e. each entry in range [0 ... K-1])\n",
+    "        for each example.\n",
+    "    \n",
+    "    K : int\n",
+    "        Number of clusters\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    centroids : array_like\n",
+    "        A matrix of size (K, n) where each row is the mean of the data \n",
+    "        points assigned to it.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Go over every centroid and compute mean of all points that\n",
+    "    belong to it. Concretely, the row vector centroids[i, :]\n",
+    "    should contain the mean of the data points assigned to\n",
+    "    cluster i.\n",
+    "\n",
+    "    Note:\n",
+    "    -----\n",
+    "    You can use a for-loop over the centroids to compute this.\n",
+    "    \"\"\"\n",
+    "    # Useful variables\n",
+    "    m, n = X.shape\n",
+    "    # You need to return the following variables correctly.\n",
+    "    centroids = np.zeros((K, n))\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    for i in range(K):\n",
+    "        count = 0\n",
+    "        sum_ = np.zeros((1, n))\n",
+    "        for j in range(1, m):\n",
+    "            if idx[j] == i:\n",
+    "                count += 1\n",
+    "                sum_ = sum_ + X[j, :]\n",
+    "        if count > 0:\n",
+    "            centroids[i, :] = (1/count)*sum_\n",
+    "   \n",
+    "    # =============================================================\n",
+    "    return centroids"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you have completed the code in `computeCentroids`, the following cell will run your code and output the centroids after the first step of K-means."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute means based on the closest centroids found in the previous part.\n",
+    "centroids = computeCentroids(X, idx, K)\n",
+    "\n",
+    "print('Centroids computed after initial finding of closest centroids:')\n",
+    "print(centroids)\n",
+    "print('\\nThe centroids should be')\n",
+    "print('   [ 2.428301 3.157924 ]')\n",
+    "print('   [ 5.813503 2.633656 ]')\n",
+    "print('   [ 7.119387 3.616684 ]')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[2] = computeCentroids\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2 K-means on example dataset \n",
+    "\n",
+    "After you have completed the two functions (`findClosestCentroids` and `computeCentroids`), you have all the necessary pieces to run the K-means algorithm. The next cell  will run the K-means algorithm on a toy 2D dataset to help you understand how K-means works. Your functions are called from inside the `runKmeans` function (in this assignment's `utils.py` module). We encourage you to take a look at the function to understand how it works. Notice that the code calls the two functions you implemented in a loop.\n",
+    "\n",
+    "When you run the next step, the K-means code will produce an animation that steps you through the progress of the algorithm at each iteration. At the end, your figure should look as the one displayed below.\n",
+    "\n",
+    "![](Figures/kmeans_result.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# Load an example dataset\n",
+    "data = loadmat(os.path.join('Data', 'ex7data2.mat'))\n",
+    "\n",
+    "# Settings for running K-Means\n",
+    "K = 3\n",
+    "max_iters = 10\n",
+    "\n",
+    "# For consistency, here we set centroids to specific values\n",
+    "# but in practice you want to generate them automatically, such as by\n",
+    "# settings them to be random examples (as can be seen in\n",
+    "# kMeansInitCentroids).\n",
+    "initial_centroids = np.array([[3, 3], [6, 2], [8, 5]])\n",
+    "\n",
+    "\n",
+    "# Run K-Means algorithm. The 'true' at the end tells our function to plot\n",
+    "# the progress of K-Means\n",
+    "centroids, idx, anim = utils.runkMeans(X, initial_centroids,\n",
+    "                                       findClosestCentroids, computeCentroids, max_iters, True)\n",
+    "anim"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.3 Random initialization \n",
+    "\n",
+    "The initial assignments of centroids for the example dataset in the previous cell were designed so that you will see the same figure as that shown in the cell above. In practice, a\n",
+    "good strategy for initializing the centroids is to select random examples from the training set.\n",
+    "\n",
+    "In this part of the exercise, you should complete the function `kMeansInitCentroids` with the following code:\n",
+    "\n",
+    "```python\n",
+    "# Initialize the centroids to be random examples\n",
+    "\n",
+    "# Randomly reorder the indices of examples\n",
+    "randidx = np.random.permutation(X.shape[0])\n",
+    "# Take the first K examples as centroids\n",
+    "centroids = X[randidx[:K], :]\n",
+    "```\n",
+    "\n",
+    "The code above first randomly permutes the indices of the examples (using `permute` within the `numpy.random` module). Then, it selects the first $K$ examples based on the random permutation of the indices. This allows the examples to be selected at random without the risk of selecting the same example twice.\n",
+    "\n",
+    "*You do not need to make any submission for this part of the exercise*\n",
+    "<a id=\"kMeansInitCentroids\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def kMeansInitCentroids(X, K):\n",
+    "    \"\"\"\n",
+    "    This function initializes K centroids that are to be used in K-means on the dataset x.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like \n",
+    "        The dataset of size (m x n).\n",
+    "    \n",
+    "    K : int\n",
+    "        The number of clusters.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    centroids : array_like\n",
+    "        Centroids of the clusters. This is a matrix of size (K x n).\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    You should set centroids to randomly chosen examples from the dataset X.\n",
+    "    \"\"\"\n",
+    "    m, n = X.shape\n",
+    "    \n",
+    "    # You should return this values correctly\n",
+    "    centroids = np.zeros((K, n))\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "\n",
+    "    \n",
+    "    # =============================================================\n",
+    "    return centroids"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.4 Image compression with K-means\n",
+    "\n",
+    "In this exercise, you will apply K-means to image compression. We will use the image below as an example (property of Frank Wouters with permission to this class).\n",
+    "\n",
+    "![](Data/bird_small.png)\n",
+    "\n",
+    "In a straightforward 24-bit color representation of an image, each pixel is represented as three 8-bit unsigned integers (ranging from 0 to 255) that specify the red, green and blue intensity values. This encoding is often referred to as the RGB encoding. Our image contains thousands of colors, and in this part of the exercise, you will reduce the number of colors to 16 colors.\n",
+    "\n",
+    "By making this reduction, it is possible to represent (compress) the photo in an efficient way. Specifically, you only need to store the RGB values of the 16 selected colors, and for each pixel in the image you now need to only store the index of the color at that location (where only 4 bits are necessary to represent 16 possibilities).\n",
+    "\n",
+    "In this exercise, you will use the K-means algorithm to select the 16 colors that will be used to represent the compressed image. Concretely, you will treat every pixel in the original image as a data example and use the K-means algorithm to find the 16 colors that best group (cluster) the pixels in the 3-dimensional RGB space. Once you have computed the cluster centroids on the image, you will then use the 16 colors to replace the pixels in the original image.\n",
+    "\n",
+    "#### 1.4.1 K-means on pixels\n",
+    "\n",
+    "In python, images can be read in as follows:\n",
+    "\n",
+    "```python\n",
+    "# Load 128x128 color image (bird_small.png)\n",
+    "img = mpl.image.imread(os.path.join('Data', 'bird_small.png'))\n",
+    "\n",
+    "# We have already imported matplotlib as mpl at the beginning of this notebook.\n",
+    "```\n",
+    "This creates a three-dimensional matrix `A` whose first two indices identify a pixel position and whose last index represents red, green, or blue. For example, A[50, 33, 2] gives the blue intensity of the pixel at row 51 and column 34.\n",
+    "\n",
+    "The code in the following cell first loads the image, and then reshapes it to create an m x 3 matrix of pixel colors (where m = 16384 = 128 x 128), and calls your K-means function on it.\n",
+    "\n",
+    "After finding the top K = 16 colors to represent the image, you can now assign each pixel position to its closest centroid using the `findClosestCentroids` function. This allows you to represent the original image using the centroid assignments of each pixel. Notice that you have significantly reduced the number of bits that are required to describe the image. The original image required 24 bits for each one of the 128 x 128 pixel locations, resulting in total size of 128 x 128 x 24 = 393,216 bits. The new representation requires some overhead storage in form of a dictionary of 16 colors, each of which require 24 bits, but the image itself then only requires 4 bits per pixel location. The final number of bits used is therefore 16 x 24 + 128 x 128 x 4 = 65,920 bits, which corresponds to compressing the original image by about a factor of 6.\n",
+    "\n",
+    "Finally, you can view the effects of the compression by reconstructing the image based only on the centroid assignments. Specifically, you can replace each pixel location with the mean of the centroid assigned to it. The figure below shows the reconstruction we obtained. \n",
+    "\n",
+    "![](Figures/bird_compression.png)\n",
+    "\n",
+    "Even though the resulting image retains most of the characteristics of the original, we also see some compression artifacts.\n",
+    "\n",
+    "Run the following cell to compute the centroids and the centroid allocation of each pixel in the image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ======= Experiment with these parameters ================\n",
+    "# You should try different values for those parameters\n",
+    "K = 16\n",
+    "max_iters = 10\n",
+    "\n",
+    "# Load an image of a bird\n",
+    "# Change the file name and path to experiment with your own images\n",
+    "A = mpl.image.imread(os.path.join('Data', 'bird_small.png'))\n",
+    "# ==========================================================\n",
+    "\n",
+    "# Divide by 255 so that all values are in the range 0 - 1\n",
+    "A /= 255\n",
+    "\n",
+    "# Reshape the image into an Nx3 matrix where N = number of pixels.\n",
+    "# Each row will contain the Red, Green and Blue pixel values\n",
+    "# This gives us our dataset matrix X that we will use K-Means on.\n",
+    "X = A.reshape(-1, 3)\n",
+    "\n",
+    "# When using K-Means, it is important to randomly initialize centroids\n",
+    "# You should complete the code in kMeansInitCentroids above before proceeding\n",
+    "initial_centroids = kMeansInitCentroids(X, K)\n",
+    "\n",
+    "# Run K-Means\n",
+    "centroids, idx = utils.runkMeans(X, initial_centroids,\n",
+    "                                 findClosestCentroids,\n",
+    "                                 computeCentroids,\n",
+    "                                 max_iters)\n",
+    "\n",
+    "# We can now recover the image from the indices (idx) by mapping each pixel\n",
+    "# (specified by its index in idx) to the centroid value\n",
+    "# Reshape the recovered image into proper dimensions\n",
+    "X_recovered = centroids[idx, :].reshape(A.shape)\n",
+    "\n",
+    "# Display the original image, rescale back by 255\n",
+    "fig, ax = pyplot.subplots(1, 2, figsize=(8, 4))\n",
+    "ax[0].imshow(A*255)\n",
+    "ax[0].set_title('Original')\n",
+    "ax[0].grid(False)\n",
+    "\n",
+    "# Display compressed image, rescale back by 255\n",
+    "ax[1].imshow(X_recovered*255)\n",
+    "ax[1].set_title('Compressed, with %d colors' % K)\n",
+    "ax[1].grid(False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You do not need to make any submissions for this part of the exercise.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.5 Optional (ungraded) exercise: Use your own image\n",
+    "\n",
+    "In this exercise, modify the code we have supplied in the previous cell to run on one of your own images. Note that if your image is very large, then K-means can take a long time to run. Therefore, we recommend that you resize your images to\n",
+    "manageable sizes before running the code. You can also try to vary $K$ to see the effects on the compression."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2 Principal Component Analysis\n",
+    "\n",
+    "In this exercise, you will use principal component analysis (PCA) to perform dimensionality reduction. You will first experiment with an example 2D dataset to get intuition on how PCA works, and then use it on a bigger dataset of 5000 face image dataset.\n",
+    "\n",
+    "### 2.1 Example Dataset\n",
+    "\n",
+    "To help you understand how PCA works, you will first start with a 2D dataset which has one direction of large variation and one of smaller variation. The cell below will plot the training data, also shown in here:\n",
+    "\n",
+    "In this part of the exercise, you will visualize what happens when you use PCA to reduce the data from 2D to 1D. In practice, you might want to reduce data from 256 to 50 dimensions, say; but using lower dimensional data in this example allows us to visualize the algorithms better."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the dataset into the variable X \n",
+    "data = loadmat(os.path.join('Data', 'ex7data1.mat'))\n",
+    "X = data['X']\n",
+    "\n",
+    "#  Visualize the example dataset\n",
+    "pyplot.plot(X[:, 0], X[:, 1], 'bo', ms=10, mec='k', mew=1)\n",
+    "pyplot.axis([0.5, 6.5, 2, 8])\n",
+    "pyplot.gca().set_aspect('equal')\n",
+    "pyplot.grid(False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section3\"></a>\n",
+    "### 2.2 Implementing PCA\n",
+    "\n",
+    "In this part of the exercise, you will implement PCA. PCA consists of two computational steps: \n",
+    "\n",
+    "1. Compute the covariance matrix of the data.\n",
+    "2. Use SVD (in python we use numpy's implementation `np.linalg.svd`) to compute the eigenvectors $U_1$, $U_2$, $\\dots$, $U_n$. These will correspond to the principal components of variation in the data.\n",
+    "\n",
+    "First, you should compute the covariance matrix of the data, which is given by:\n",
+    "\n",
+    "$$ \\Sigma = \\frac{1}{m} X^T X$$\n",
+    "\n",
+    "where $X$ is the data matrix with examples in rows, and $m$ is the number of examples. Note that $\\Sigma$ is a $n \\times n$ matrix and not the summation operator. \n",
+    "\n",
+    "After computing the covariance matrix, you can run SVD on it to compute the principal components. In python and `numpy` (or `scipy`), you can run SVD with the following command: `U, S, V = np.linalg.svd(Sigma)`, where `U` will contain the principal components and `S` will contain a diagonal matrix. Note that the `scipy` library also has a similar function to compute SVD `scipy.linalg.svd`. The functions in the two libraries use the same C-based library (LAPACK) for the SVD computation, but the `scipy` version provides more options and arguments to control SVD computation. In this exercise, we will stick with the `numpy` implementation of SVD.\n",
+    "\n",
+    "Complete the code in the following cell to implemente PCA.\n",
+    "<a id=\"pca\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pca(X):\n",
+    "    \"\"\"\n",
+    "    Run principal component analysis.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The dataset to be used for computing PCA. It has dimensions (m x n)\n",
+    "        where m is the number of examples (observations) and n is \n",
+    "        the number of features.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    U : array_like\n",
+    "        The eigenvectors, representing the computed principal components\n",
+    "        of X. U has dimensions (n x n) where each column is a single \n",
+    "        principal component.\n",
+    "    \n",
+    "    S : array_like\n",
+    "        A vector of size n, contaning the singular values for each\n",
+    "        principal component. Note this is the diagonal of the matrix we \n",
+    "        mentioned in class.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    You should first compute the covariance matrix. Then, you\n",
+    "    should use the \"svd\" function to compute the eigenvectors\n",
+    "    and eigenvalues of the covariance matrix. \n",
+    "\n",
+    "    Notes\n",
+    "    -----\n",
+    "    When computing the covariance matrix, remember to divide by m (the\n",
+    "    number of examples).\n",
+    "    \"\"\"\n",
+    "    # Useful values\n",
+    "    m, n = X.shape\n",
+    "\n",
+    "    # You need to return the following variables correctly.\n",
+    "    U = np.zeros(n)\n",
+    "    S = np.zeros(n)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "    \n",
+    "    \n",
+    "    # ============================================================\n",
+    "    return U, S"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before using PCA, it is important to first normalize the data by subtracting the mean value of each feature from the dataset, and scaling each dimension so that they are in the same range.\n",
+    "\n",
+    "In the next cell,  this normalization will be performed for you using the `utils.featureNormalize` function.\n",
+    "After normalizing the data, you can run PCA to compute the principal components. Your task is to complete the code in the function `pca` to compute the principal components of the dataset. \n",
+    "\n",
+    "Once you have completed the function `pca`, the following cell will run PCA on the example dataset and plot the corresponding principal components found similar to the figure below. \n",
+    "\n",
+    "![](Figures/pca_components.png)\n",
+    "\n",
+    "\n",
+    "The following cell will also output the top principal component (eigenvector) found, and you should expect to see an output of about `[-0.707 -0.707]`. (It is possible that `numpy` may instead output the negative of this, since $U_1$ and $-U_1$ are equally valid choices for the first principal component.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#  Before running PCA, it is important to first normalize X\n",
+    "X_norm, mu, sigma = utils.featureNormalize(X)\n",
+    "\n",
+    "#  Run PCA\n",
+    "U, S = pca(X_norm)\n",
+    "\n",
+    "#  Draw the eigenvectors centered at mean of data. These lines show the\n",
+    "#  directions of maximum variations in the dataset.\n",
+    "fig, ax = pyplot.subplots()\n",
+    "ax.plot(X[:, 0], X[:, 1], 'bo', ms=10, mec='k', mew=0.25)\n",
+    "\n",
+    "for i in range(2):\n",
+    "    ax.arrow(mu[0], mu[1], 1.5 * S[i]*U[0, i], 1.5 * S[i]*U[1, i],\n",
+    "             head_width=0.25, head_length=0.2, fc='k', ec='k', lw=2, zorder=1000)\n",
+    "\n",
+    "ax.axis([0.5, 6.5, 2, 8])\n",
+    "ax.set_aspect('equal')\n",
+    "ax.grid(False)\n",
+    "\n",
+    "print('Top eigenvector: U[:, 0] = [{:.6f} {:.6f}]'.format(U[0, 0], U[1, 0]))\n",
+    "print(' (you should expect to see [-0.707107 -0.707107])')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[3] = pca\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3 Dimensionality Reduction with PCA\n",
+    "\n",
+    "After computing the principal components, you can use them to reduce the feature dimension of your dataset by projecting each example onto a lower dimensional space, $x^{(i)} \\rightarrow z^{(i)}$ (e.g., projecting the data from 2D to 1D). In this part of the exercise, you will use the eigenvectors returned by PCA and\n",
+    "project the example dataset into a 1-dimensional space. In practice, if you were using a learning algorithm such as linear regression or perhaps neural networks, you could now use the projected data instead of the original data. By using the projected data, you can train your model faster as there are less dimensions in the input.\n",
+    "\n",
+    "<a id=\"section4\"></a>\n",
+    "\n",
+    "#### 2.3.1 Projecting the data onto the principal components\n",
+    "\n",
+    "You should now complete the code in the function `projectData`. Specifically, you are given a dataset `X`, the principal components `U`, and the desired number of dimensions to reduce to `K`. You should project each example in `X` onto the top `K` components in `U`. Note that the top `K` components in `U` are given by\n",
+    "the first `K` columns of `U`, that is `Ureduce = U[:, :K]`.\n",
+    "<a id=\"projectData\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def projectData(X, U, K):\n",
+    "    \"\"\"\n",
+    "    Computes the reduced data representation when projecting only \n",
+    "    on to the top K eigenvectors.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The input dataset of shape (m x n). The dataset is assumed to be \n",
+    "        normalized.\n",
+    "    \n",
+    "    U : array_like\n",
+    "        The computed eigenvectors using PCA. This is a matrix of \n",
+    "        shape (n x n). Each column in the matrix represents a single\n",
+    "        eigenvector (or a single principal component).\n",
+    "    \n",
+    "    K : int\n",
+    "        Number of dimensions to project onto. Must be smaller than n.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    Z : array_like\n",
+    "        The projects of the dataset onto the top K eigenvectors. \n",
+    "        This will be a matrix of shape (m x k).\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the projection of the data using only the top K \n",
+    "    eigenvectors in U (first K columns). \n",
+    "    For the i-th example X[i,:], the projection on to the k-th \n",
+    "    eigenvector is given as follows:\n",
+    "    \n",
+    "        x = X[i, :]\n",
+    "        projection_k = np.dot(x,  U[:, k])\n",
+    "\n",
+    "    \"\"\"\n",
+    "    # You need to return the following variables correctly.\n",
+    "    Z = np.zeros((X.shape[0], K))\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "\n",
+    "    \n",
+    "    # =============================================================\n",
+    "    return Z"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you have completed the code in `projectData`, the following cell will project the first example onto the first dimension and you should see a value of about 1.481 (or possibly -1.481, if you got $-U_1$ instead of $U_1$)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#  Project the data onto K = 1 dimension\n",
+    "K = 1\n",
+    "Z = projectData(X_norm, U, K)\n",
+    "print('Projection of the first example: {:.6f}'.format(Z[0, 0]))\n",
+    "print('(this value should be about    : 1.481274)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[4] = projectData\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section5\"></a>\n",
+    "#### 2.3.2 Reconstructing an approximation of the data\n",
+    "\n",
+    "After projecting the data onto the lower dimensional space, you can approximately recover the data by projecting them back onto the original high dimensional space. Your task is to complete the function `recoverData` to project each example in `Z` back onto the original space and return the recovered approximation in `Xrec`.\n",
+    "<a id=\"recoverData\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def recoverData(Z, U, K):\n",
+    "    \"\"\"\n",
+    "    Recovers an approximation of the original data when using the \n",
+    "    projected data.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    Z : array_like\n",
+    "        The reduced data after applying PCA. This is a matrix\n",
+    "        of shape (m x K).\n",
+    "    \n",
+    "    U : array_like\n",
+    "        The eigenvectors (principal components) computed by PCA.\n",
+    "        This is a matrix of shape (n x n) where each column represents\n",
+    "        a single eigenvector.\n",
+    "    \n",
+    "    K : int\n",
+    "        The number of principal components retained\n",
+    "        (should be less than n).\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    X_rec : array_like\n",
+    "        The recovered data after transformation back to the original \n",
+    "        dataset space. This is a matrix of shape (m x n), where m is \n",
+    "        the number of examples and n is the dimensions (number of\n",
+    "        features) of original datatset.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the approximation of the data by projecting back\n",
+    "    onto the original space using the top K eigenvectors in U.\n",
+    "    For the i-th example Z[i,:], the (approximate)\n",
+    "    recovered data for dimension j is given as follows:\n",
+    "\n",
+    "        v = Z[i, :]\n",
+    "        recovered_j = np.dot(v, U[j, :K])\n",
+    "\n",
+    "    Notice that U[j, :K] is a vector of size K.\n",
+    "    \"\"\"\n",
+    "    # You need to return the following variables correctly.\n",
+    "    X_rec = np.zeros((Z.shape[0], U.shape[0]))\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "    \n",
+    "\n",
+    "    # =============================================================\n",
+    "    return X_rec"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you have completed the code in `recoverData`, the following cell will recover an approximation of the first example and you should see a value of about `[-1.047 -1.047]`. The code will then plot the data in this reduced dimension space. This will show you what the data looks like when using only the corresponding eigenvectors to reconstruct it. An example of what you should get for PCA projection is shown in this figure: \n",
+    "\n",
+    "![](Figures/pca_reconstruction.png)\n",
+    "\n",
+    "In the figure above, the original data points are indicated with the blue circles, while the projected data points are indicated with the red circles. The projection effectively only retains the information in the direction given by $U_1$. The dotted lines show the distance from the data points in original space to the projected space. Those dotted lines represent the error measure due to PCA projection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_rec  = recoverData(Z, U, K)\n",
+    "print('Approximation of the first example: [{:.6f} {:.6f}]'.format(X_rec[0, 0], X_rec[0, 1]))\n",
+    "print('       (this value should be about  [-1.047419 -1.047419])')\n",
+    "\n",
+    "#  Plot the normalized dataset (returned from featureNormalize)\n",
+    "fig, ax = pyplot.subplots(figsize=(5, 5))\n",
+    "ax.plot(X_norm[:, 0], X_norm[:, 1], 'bo', ms=8, mec='b', mew=0.5)\n",
+    "ax.set_aspect('equal')\n",
+    "ax.grid(False)\n",
+    "pyplot.axis([-3, 2.75, -3, 2.75])\n",
+    "\n",
+    "# Draw lines connecting the projected points to the original points\n",
+    "ax.plot(X_rec[:, 0], X_rec[:, 1], 'ro', mec='r', mew=2, mfc='none')\n",
+    "for xnorm, xrec in zip(X_norm, X_rec):\n",
+    "    ax.plot([xnorm[0], xrec[0]], [xnorm[1], xrec[1]], '--k', lw=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[5] = recoverData\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.4 Face Image Dataset\n",
+    "\n",
+    "In this part of the exercise, you will run PCA on face images to see how it can be used in practice for dimension reduction. The dataset `ex7faces.mat` contains a dataset `X` of face images, each $32 \\times 32$ in grayscale. This dataset was based on a [cropped version](http://conradsanderson.id.au/lfwcrop/) of the [labeled faces in the wild](http://vis-www.cs.umass.edu/lfw/) dataset. Each row of `X` corresponds to one face image (a row vector of length 1024). \n",
+    "\n",
+    "The next cell will load and visualize the first 100 of these face images similar to what is shown in this figure:\n",
+    "\n",
+    "![Faces](Figures/faces.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#  Load Face dataset\n",
+    "data = loadmat(os.path.join('Data', 'ex7faces.mat'))\n",
+    "X = data['X']\n",
+    "\n",
+    "#  Display the first 100 faces in the dataset\n",
+    "utils.displayData(X[:100, :], figsize=(8, 8))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.4.1 PCA on Faces\n",
+    "\n",
+    "To run PCA on the face dataset, we first normalize the dataset by subtracting the mean of each feature from the data matrix `X`.  After running PCA, you will obtain the principal components of the dataset. Notice that each principal component in `U` (each column) is a vector of length $n$ (where for the face dataset, $n = 1024$). It turns out that we can visualize these principal components by reshaping each of them into a $32 \\times 32$ matrix that corresponds to the pixels in the original dataset. \n",
+    "\n",
+    "The following cell will first normalize the dataset for you and then run your PCA code. Then, the first 36 principal components (conveniently called eigenfaces) that describe the largest variations are displayed. If you want, you can also change the code to display more principal components to see how they capture more and more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#  normalize X by subtracting the mean value from each feature\n",
+    "X_norm, mu, sigma = utils.featureNormalize(X)\n",
+    "\n",
+    "#  Run PCA\n",
+    "U, S = pca(X_norm)\n",
+    "\n",
+    "#  Visualize the top 36 eigenvectors found\n",
+    "utils.displayData(U[:, :36].T, figsize=(8, 8))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.4.2 Dimensionality Reduction\n",
+    "\n",
+    "Now that you have computed the principal components for the face dataset, you can use it to reduce the dimension of the face dataset. This allows you to use your learning algorithm with a smaller input size (e.g., 100 dimensions) instead of the original 1024 dimensions. This can help speed up your learning algorithm.\n",
+    "\n",
+    "The next cell will project the face dataset onto only the first 100 principal components. Concretely, each face image is now described by a vector $z^{(i)} \\in \\mathbb{R}^{100}$. To understand what is lost in the dimension reduction, you can recover the data using only the projected dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#  Project images to the eigen space using the top k eigenvectors \n",
+    "#  If you are applying a machine learning algorithm \n",
+    "K = 100\n",
+    "Z = projectData(X_norm, U, K)\n",
+    "\n",
+    "print('The projected data Z has a shape of: ', Z.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the next cell, an approximate recovery of the data is performed and the original and projected face images\n",
+    "are displayed similar to what is shown here:\n",
+    "\n",
+    "<table>\n",
+    "    <tr>\n",
+    "        <td><img src=\"Figures/faces_original.png\" width=\"300\"></td>\n",
+    "        <td><img src=\"Figures/faces_reconstructed.png\" width=\"300\"></td>\n",
+    "    </tr>\n",
+    "</table>\n",
+    "\n",
+    "From the reconstruction, you can observe that the general structure and appearance of the face are kept while the fine details are lost. This is a remarkable reduction (more than 10x) in the dataset size that can help speed up your learning algorithm significantly. For example, if you were training a neural network to perform person recognition (given a face image, predict the identity of the person), you can use the dimension reduced input of only a 100 dimensions instead of the original pixels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#  Project images to the eigen space using the top K eigen vectors and \n",
+    "#  visualize only using those K dimensions\n",
+    "#  Compare to the original input, which is also displayed\n",
+    "K = 100\n",
+    "X_rec  = recoverData(Z, U, K)\n",
+    "\n",
+    "# Display normalized data\n",
+    "utils.displayData(X_norm[:100, :], figsize=(6, 6))\n",
+    "pyplot.gcf().suptitle('Original faces')\n",
+    "\n",
+    "# Display reconstructed data from only k eigenfaces\n",
+    "utils.displayData(X_rec[:100, :], figsize=(6, 6))\n",
+    "pyplot.gcf().suptitle('Recovered faces')\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.5 Optional (ungraded) exercise: PCA for visualization\n",
+    "\n",
+    "In the earlier K-means image compression exercise, you used the K-means algorithm in the 3-dimensional RGB space. We reduced each pixel of the RGB image to be represented by 16 clusters. In the next cell, we have provided code to visualize the final pixel assignments in this 3D space. Each data point is colored according to the cluster it has been assigned to. You can drag your mouse on the figure to rotate and inspect this data in 3 dimensions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this allows to have interactive plot to rotate the 3-D plot\n",
+    "# The double identical statement is on purpose\n",
+    "# see: https://stackoverflow.com/questions/43545050/using-matplotlib-notebook-after-matplotlib-inline-in-jupyter-notebook-doesnt\n",
+    "%matplotlib notebook\n",
+    "%matplotlib notebook\n",
+    "from matplotlib import pyplot\n",
+    "\n",
+    "\n",
+    "A = mpl.image.imread(os.path.join('Data', 'bird_small.png'))\n",
+    "A /= 255\n",
+    "X = A.reshape(-1, 3)\n",
+    "\n",
+    "# perform the K-means clustering again here\n",
+    "K = 16\n",
+    "max_iters = 10\n",
+    "initial_centroids = kMeansInitCentroids(X, K)\n",
+    "centroids, idx = utils.runkMeans(X, initial_centroids,\n",
+    "                                 findClosestCentroids,\n",
+    "                                 computeCentroids, max_iters)\n",
+    "\n",
+    "#  Sample 1000 random indexes (since working with all the data is\n",
+    "#  too expensive. If you have a fast computer, you may increase this.\n",
+    "sel = np.random.choice(X.shape[0], size=1000)\n",
+    "\n",
+    "fig = pyplot.figure(figsize=(6, 6))\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "\n",
+    "ax.scatter(X[sel, 0], X[sel, 1], X[sel, 2], cmap='rainbow', c=idx[sel], s=8**2)\n",
+    "ax.set_title('Pixel dataset plotted in 3D.\\nColor shows centroid memberships')\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It turns out that visualizing datasets in 3 dimensions or greater can be cumbersome. Therefore, it is often desirable to only display the data in 2D even at the cost of losing some information. In practice, PCA is often used to reduce the dimensionality of data for visualization purposes. \n",
+    "\n",
+    "In the next cell,we will apply your implementation of PCA to the 3-dimensional data to reduce it to 2 dimensions and visualize the result in a 2D scatter plot. The PCA projection can be thought of as a rotation that selects the view that maximizes the spread of the data, which often corresponds to the “best” view."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Subtract the mean to use PCA\n",
+    "X_norm, mu, sigma = utils.featureNormalize(X)\n",
+    "\n",
+    "# PCA and project the data to 2D\n",
+    "U, S = pca(X_norm)\n",
+    "Z = projectData(X_norm, U, 2)\n",
+    "\n",
+    "# Reset matplotlib to non-interactive\n",
+    "%matplotlib inline\n",
+    "\n",
+    "fig = pyplot.figure(figsize=(6, 6))\n",
+    "ax = fig.add_subplot(111)\n",
+    "\n",
+    "ax.scatter(Z[sel, 0], Z[sel, 1], cmap='rainbow', c=idx[sel], s=64)\n",
+    "ax.set_title('Pixel dataset plotted in 2D, using PCA for dimensionality reduction')\n",
+    "ax.grid(False)\n",
+    "pass"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Exercise7/exercise7.ipynb b/Exercise7/exercise7.ipynb
index 2dbde786..f62497cb 100755
--- a/Exercise7/exercise7.ipynb
+++ b/Exercise7/exercise7.ipynb
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -142,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -185,9 +185,24 @@
     "    idx = np.zeros(X.shape[0], dtype=int)\n",
     "\n",
     "    # ====================== YOUR CODE HERE ======================\n",
-    "\n",
-    "    \n",
-    "    \n",
+    "    ## Pythonic implementation\n",
+    "    sq_dist = np.zeros(centroids.shape)\n",
+    "    min_dist = np.ones(centroids.shape)*1000    \n",
+    "    for i in range(X.shape[0]):\n",
+    "        sq_dist = np.sum((X[i, :] - centroids)**2, axis=1)\n",
+    "        idx[i] = np.argmin(sq_dist)\n",
+    "\n",
+    "    ## \"C-like\" implementation\n",
+    "#     sq_dist = 0    \n",
+    "#     for i in range(X.shape[0]):         \n",
+    "#         min_dist = np.inf\n",
+    "#         best_k = -1\n",
+    "#         for k in range(K):\n",
+    "#             sq_dist = np.sum((X[i, :] - centroids[k, :])**2)            \n",
+    "#             if sq_dist < min_dist:\n",
+    "#                 min_dist = sq_dist\n",
+    "#                 best_k = k\n",
+    "#         idx[i] = best_k\n",
     "    # =============================================================\n",
     "    return idx"
    ]
@@ -201,9 +216,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Closest centroids for the first 3 examples:\n",
+      "[0 2 1]\n",
+      "(the closest centroids should be 0, 2, 1 respectively)\n"
+     ]
+    }
+   ],
    "source": [
     "# Load an example dataset that we will be using\n",
     "data = loadmat(os.path.join('Data', 'ex7data2.mat'))\n",
@@ -230,9 +255,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise k-means-clustering-and-pca\n",
+      "\n",
+      "Login (email address): waiyen.chan0819@gmail.com\n",
+      "Token: W7WlpQiG2o9zSs71\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "           Find Closest Centroids (k-Means) |  30 /  30 | Nice work!\n",
+      "           Compute Centroid Means (k-Means) |   0 /  30 | \n",
+      "                                        PCA |   0 /  20 | \n",
+      "                         Project Data (PCA) |   0 /  10 | \n",
+      "                         Recover Data (PCA) |   0 /  10 | \n",
+      "                                  --------------------------------\n",
+      "                                            |  30 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[1] = findClosestCentroids\n",
     "grader.grade()"
@@ -257,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -302,11 +349,21 @@
     "    # You need to return the following variables correctly.\n",
     "    centroids = np.zeros((K, n))\n",
     "\n",
-    "\n",
     "    # ====================== YOUR CODE HERE ======================\n",
-    "\n",
-    "    \n",
-    "    \n",
+    "    for i in range(K):\n",
+    "        count = 0\n",
+    "        sum_ = np.zeros((m, n))\n",
+    "\n",
+    "#         centroids[i, :] = np.mean(X[idx==i, :])\n",
+    "\n",
+    "# centroids[i, :] = np.mean(X[j, :] for j in range(m) if idx[j] == i)\n",
+    "        for j in range(m):\n",
+    "            if idx[j] == i:\n",
+    "                count += 1\n",
+    "                sum_[j, :] = sum_[j, :] + X[j, :]\n",
+    "        if count > 0:\n",
+    "            centroids[i, :] = (1/count)*np.sum(sum_, axis=0)\n",
+    "   \n",
     "    # =============================================================\n",
     "    return centroids"
    ]
@@ -320,9 +377,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Centroids computed after initial finding of closest centroids:\n",
+      "[[2.42830111 3.15792418]\n",
+      " [5.81350331 2.63365645]\n",
+      " [7.11938687 3.6166844 ]]\n",
+      "\n",
+      "The centroids should be\n",
+      "   [ 2.428301 3.157924 ]\n",
+      "   [ 5.813503 2.633656 ]\n",
+      "   [ 7.119387 3.616684 ]\n"
+     ]
+    }
+   ],
    "source": [
     "# Compute means based on the closest centroids found in the previous part.\n",
     "centroids = computeCentroids(X, idx, K)\n",
@@ -344,9 +417,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise k-means-clustering-and-pca\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "           Find Closest Centroids (k-Means) |  30 /  30 | Nice work!\n",
+      "           Compute Centroid Means (k-Means) |  30 /  30 | Nice work!\n",
+      "                                        PCA |   0 /  20 | \n",
+      "                         Project Data (PCA) |   0 /  10 | \n",
+      "                         Recover Data (PCA) |   0 /  10 | \n",
+      "                                  --------------------------------\n",
+      "                                            |  60 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[2] = computeCentroids\n",
     "grader.grade()"
@@ -367,11 +461,4390 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<link rel=\"stylesheet\"\n",
+       "href=\"https://maxcdn.bootstrapcdn.com/font-awesome/4.4.0//n",
+       "css/font-awesome.min.css\">\n",
+       "<script language=\"javascript\">\n",
+       "  function isInternetExplorer() {\n",
+       "    ua = navigator.userAgent;\n",
+       "    /* MSIE used to detect old browsers and Trident used to newer ones*/\n",
+       "    return ua.indexOf(\"MSIE \") > -1 || ua.indexOf(\"Trident/\") > -1;\n",
+       "  }\n",
+       "\n",
+       "  /* Define the Animation class */\n",
+       "  function Animation(frames, img_id, slider_id, interval, loop_select_id){\n",
+       "    this.img_id = img_id;\n",
+       "    this.slider_id = slider_id;\n",
+       "    this.loop_select_id = loop_select_id;\n",
+       "    this.interval = interval;\n",
+       "    this.current_frame = 0;\n",
+       "    this.direction = 0;\n",
+       "    this.timer = null;\n",
+       "    this.frames = new Array(frames.length);\n",
+       "\n",
+       "    for (var i=0; i<frames.length; i++)\n",
+       "    {\n",
+       "     this.frames[i] = new Image();\n",
+       "     this.frames[i].src = frames[i];\n",
+       "    }\n",
+       "    var slider = document.getElementById(this.slider_id);\n",
+       "    slider.max = this.frames.length - 1;\n",
+       "    if (isInternetExplorer()) {\n",
+       "        // switch from oninput to onchange because IE <= 11 does not conform\n",
+       "        // with W3C specification. It ignores oninput and onchange behaves\n",
+       "        // like oninput. In contrast, Mircosoft Edge behaves correctly.\n",
+       "        slider.setAttribute('onchange', slider.getAttribute('oninput'));\n",
+       "        slider.setAttribute('oninput', null);\n",
+       "    }\n",
+       "    this.set_frame(this.current_frame);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.get_loop_state = function(){\n",
+       "    var button_group = document[this.loop_select_id].state;\n",
+       "    for (var i = 0; i < button_group.length; i++) {\n",
+       "        var button = button_group[i];\n",
+       "        if (button.checked) {\n",
+       "            return button.value;\n",
+       "        }\n",
+       "    }\n",
+       "    return undefined;\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.set_frame = function(frame){\n",
+       "    this.current_frame = frame;\n",
+       "    document.getElementById(this.img_id).src =\n",
+       "            this.frames[this.current_frame].src;\n",
+       "    document.getElementById(this.slider_id).value = this.current_frame;\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.next_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(Math.min(this.frames.length - 1, this.current_frame + 1));\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.previous_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(Math.max(0, this.current_frame - 1));\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.first_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(0);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.last_frame = function()\n",
+       "  {\n",
+       "    this.set_frame(this.frames.length - 1);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.slower = function()\n",
+       "  {\n",
+       "    this.interval /= 0.7;\n",
+       "    if(this.direction > 0){this.play_animation();}\n",
+       "    else if(this.direction < 0){this.reverse_animation();}\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.faster = function()\n",
+       "  {\n",
+       "    this.interval *= 0.7;\n",
+       "    if(this.direction > 0){this.play_animation();}\n",
+       "    else if(this.direction < 0){this.reverse_animation();}\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.anim_step_forward = function()\n",
+       "  {\n",
+       "    this.current_frame += 1;\n",
+       "    if(this.current_frame < this.frames.length){\n",
+       "      this.set_frame(this.current_frame);\n",
+       "    }else{\n",
+       "      var loop_state = this.get_loop_state();\n",
+       "      if(loop_state == \"loop\"){\n",
+       "        this.first_frame();\n",
+       "      }else if(loop_state == \"reflect\"){\n",
+       "        this.last_frame();\n",
+       "        this.reverse_animation();\n",
+       "      }else{\n",
+       "        this.pause_animation();\n",
+       "        this.last_frame();\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.anim_step_reverse = function()\n",
+       "  {\n",
+       "    this.current_frame -= 1;\n",
+       "    if(this.current_frame >= 0){\n",
+       "      this.set_frame(this.current_frame);\n",
+       "    }else{\n",
+       "      var loop_state = this.get_loop_state();\n",
+       "      if(loop_state == \"loop\"){\n",
+       "        this.last_frame();\n",
+       "      }else if(loop_state == \"reflect\"){\n",
+       "        this.first_frame();\n",
+       "        this.play_animation();\n",
+       "      }else{\n",
+       "        this.pause_animation();\n",
+       "        this.first_frame();\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.pause_animation = function()\n",
+       "  {\n",
+       "    this.direction = 0;\n",
+       "    if (this.timer){\n",
+       "      clearInterval(this.timer);\n",
+       "      this.timer = null;\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.play_animation = function()\n",
+       "  {\n",
+       "    this.pause_animation();\n",
+       "    this.direction = 1;\n",
+       "    var t = this;\n",
+       "    if (!this.timer) this.timer = setInterval(function() {\n",
+       "        t.anim_step_forward();\n",
+       "    }, this.interval);\n",
+       "  }\n",
+       "\n",
+       "  Animation.prototype.reverse_animation = function()\n",
+       "  {\n",
+       "    this.pause_animation();\n",
+       "    this.direction = -1;\n",
+       "    var t = this;\n",
+       "    if (!this.timer) this.timer = setInterval(function() {\n",
+       "        t.anim_step_reverse();\n",
+       "    }, this.interval);\n",
+       "  }\n",
+       "</script>\n",
+       "\n",
+       "<style>\n",
+       ".animation {\n",
+       "    display: inline-block;\n",
+       "    text-align: center;\n",
+       "}\n",
+       "input[type=range].anim-slider {\n",
+       "    width: 374px;\n",
+       "    margin-left: auto;\n",
+       "    margin-right: auto;\n",
+       "}\n",
+       ".anim-buttons {\n",
+       "    margin: 8px 0px;\n",
+       "}\n",
+       ".anim-buttons button {\n",
+       "    padding: 0;\n",
+       "    width: 36px;\n",
+       "}\n",
+       ".anim-state label {\n",
+       "    margin-right: 8px;\n",
+       "}\n",
+       ".anim-state input {\n",
+       "    margin: 0;\n",
+       "    vertical-align: middle;\n",
+       "}\n",
+       "</style>\n",
+       "\n",
+       "<div class=\"animation\">\n",
+       "  <img id=\"_anim_img412ad1c0a7e447ffbd1dc1a9d213975b\">\n",
+       "  <div class=\"anim-controls\">\n",
+       "    <input id=\"_anim_slider412ad1c0a7e447ffbd1dc1a9d213975b\" type=\"range\" class=\"anim-slider\"\n",
+       "           name=\"points\" min=\"0\" max=\"1\" step=\"1\" value=\"0\"\n",
+       "           oninput=\"anim412ad1c0a7e447ffbd1dc1a9d213975b.set_frame(parseInt(this.value));\"></input>\n",
+       "    <div class=\"anim-buttons\">\n",
+       "      <button onclick=\"anim412ad1c0a7e447ffbd1dc1a9d213975b.slower()\"><i class=\"fa fa-minus\"></i></button>\n",
+       "      <button onclick=\"anim412ad1c0a7e447ffbd1dc1a9d213975b.first_frame()\"><i class=\"fa fa-fast-backward\">\n",
+       "          </i></button>\n",
+       "      <button onclick=\"anim412ad1c0a7e447ffbd1dc1a9d213975b.previous_frame()\">\n",
+       "          <i class=\"fa fa-step-backward\"></i></button>\n",
+       "      <button onclick=\"anim412ad1c0a7e447ffbd1dc1a9d213975b.reverse_animation()\">\n",
+       "          <i class=\"fa fa-play fa-flip-horizontal\"></i></button>\n",
+       "      <button onclick=\"anim412ad1c0a7e447ffbd1dc1a9d213975b.pause_animation()\"><i class=\"fa fa-pause\">\n",
+       "          </i></button>\n",
+       "      <button onclick=\"anim412ad1c0a7e447ffbd1dc1a9d213975b.play_animation()\"><i class=\"fa fa-play\"></i>\n",
+       "          </button>\n",
+       "      <button onclick=\"anim412ad1c0a7e447ffbd1dc1a9d213975b.next_frame()\"><i class=\"fa fa-step-forward\">\n",
+       "          </i></button>\n",
+       "      <button onclick=\"anim412ad1c0a7e447ffbd1dc1a9d213975b.last_frame()\"><i class=\"fa fa-fast-forward\">\n",
+       "          </i></button>\n",
+       "      <button onclick=\"anim412ad1c0a7e447ffbd1dc1a9d213975b.faster()\"><i class=\"fa fa-plus\"></i></button>\n",
+       "    </div>\n",
+       "    <form action=\"#n\" name=\"_anim_loop_select412ad1c0a7e447ffbd1dc1a9d213975b\" class=\"anim-state\">\n",
+       "      <input type=\"radio\" name=\"state\" value=\"once\" id=\"_anim_radio1_412ad1c0a7e447ffbd1dc1a9d213975b\"\n",
+       "             >\n",
+       "      <label for=\"_anim_radio1_412ad1c0a7e447ffbd1dc1a9d213975b\">Once</label>\n",
+       "      <input type=\"radio\" name=\"state\" value=\"loop\" id=\"_anim_radio2_412ad1c0a7e447ffbd1dc1a9d213975b\"\n",
+       "             checked>\n",
+       "      <label for=\"_anim_radio2_412ad1c0a7e447ffbd1dc1a9d213975b\">Loop</label>\n",
+       "      <input type=\"radio\" name=\"state\" value=\"reflect\" id=\"_anim_radio3_412ad1c0a7e447ffbd1dc1a9d213975b\"\n",
+       "             >\n",
+       "      <label for=\"_anim_radio3_412ad1c0a7e447ffbd1dc1a9d213975b\">Reflect</label>\n",
+       "    </form>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "<script language=\"javascript\">\n",
+       "  /* Instantiate the Animation class. */\n",
+       "  /* The IDs given should match those used in the template above. */\n",
+       "  (function() {\n",
+       "    var img_id = \"_anim_img412ad1c0a7e447ffbd1dc1a9d213975b\";\n",
+       "    var slider_id = \"_anim_slider412ad1c0a7e447ffbd1dc1a9d213975b\";\n",
+       "    var loop_select_id = \"_anim_loop_select412ad1c0a7e447ffbd1dc1a9d213975b\";\n",
+       "    var frames = new Array(10);\n",
+       "    \n",
+       "  frames[0] = \"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAbAAAAEgCAYAAADVKCZpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\\\n",
+       "AAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0\\\n",
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+       "CIgUxQJ2RYoIli8oij8sqHxRBEFQLFhQbKhYEMsXBBQIvRlI6DX0mlASSCPJ5X5/bNrlyu5dLrls\\\n",
+       "8rx98Upud3Z2EvCem9mZZwwWi8WCEEIIoTNGbzdACCGEcIcEMCGEELokAUwIIYQuSQATQgihSxLA\\\n",
+       "hBBC6JIEMCGEELokAUwIIYQuSQATQgihSxLAhBBC6JIEMCGEELokAUwIIYQuSQATQgihSxLAhBBC\\\n",
+       "6JIEMCGEELokAUwIIYQuSQATQgihSxLAhBBC6JIEMCGEELokAUwIB4KDgzl69Ki3m1EkDRo0YOXK\\\n",
+       "ld5uhhDFQgKYKJUKvvF+/fXXdO/evVjv16tXL7744gurY8nJyTRq1KhY76t3r732Gm3atMHHx4f/\\\n",
+       "/ve/3m6OKGckgIkyLysry9tN0D1Hv8MmTZowc+ZMbr/99hJukRASwEQpt3//fp588kk2b95McHAw\\\n",
+       "lStXBuD69eu88MIL1KtXj+rVq/Pkk0+SlpYGwJo1a6hTpw4zZsygRo0ajBw5kitXrjBo0CDCwsII\\\n",
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+       "DeT3EF944QVCQ0Np2LAhy5Ytc/gzNWjQgFmzZtG2bVtCQkJ44IEHSE9Pt6qroILtGDFiBE8//TQD\\\n",
+       "Bw4kODiYm266ifPnzzN+/HhCQ0Np0aIFMTExVtf/+++/REREEBoaysiRI/PuBbBkyRLatWtH5cqV\\\n",
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+       "bm7295AChgSwDmjrFL1lyxZKS0t55513KC0t9fewAkJoaCgvvPACZWVl7Nq1i9dee03uTavVq1eT\\\n",
+       "kZHh72EEnOXLl5OXl8fhw4c5cOCA3CNsvRDXrFlDcXExhw4dwmKxsG7dOn8PK2BIAOsAx07RJpPJ\\\n",
+       "3ilaQN++fcnOzgYgOjqajIwMaUKKrRXQ5s2bWbJkib+HElDq6+v5/PPPWbx4MQAmk4kePXr4eVSB\\\n",
+       "oaWlhaamJlpaWrh69apiK6mblQSwDpBO0dpUVFRQUlJCTk6Ov4fidytWrOA3v/kNISHyT8/RiRMn\\\n",
+       "iI+P57HHHiMrK4slS5bQ2Njo72H5Xf/+/Vm5ciUpKSn07duX2NhYpk+f7u9hBQz5V9QBWjtF38wa\\\n",
+       "GhqYO3cuL7/8MjExMf4ejl9t2rSJhIQESYdW0NLSwr59+/jhD39ISUkJ3bt3l2fKQG1tLRs3bqS8\\\n",
+       "vJwzZ87Q2NjI22+/7e9hBQwJYB2gtVP0zeratWvMnTuXhQsXMmfOHH8Px+8KCwv54IMPSEtLY/78\\\n",
+       "+RQUFPDQQw/5e1gBISkpiaSkJPssfd68eezbt8/Po/K/Tz75hPT0dOLj4wkLC2POnDkUFRX5e1gB\\\n",
+       "QwJYB0inaM+sViuLFy8mIyODJ554wt/DCQi//vWvqaqqoqKignXr1jFlyhT5a7pVYmIiycnJHDly\\\n",
+       "BIDt27czbNgwP4/K/1JSUti1axdXr17FarWyfft2SW5xoJuGloFIOkV7VlhYyFtvvcWIESMYPXo0\\\n",
+       "AM8++6w0JRUevfLKKyxcuBCz2cyAAQN44403/D0kv8vJyWHevHlkZ2cTGhpKVlYW+fn5/h5WwJBK\\\n",
+       "HEIIIXRJlhCFEELokgQwIYQQuiQBTAghhC5JABNCCKFLEsCEEELokgQwIYQQuiQBTAghhC5JABNC\\\n",
+       "CKFLEsCEEELokgQwIYQQuiQBTAghhC5JABNCCKFLEsCEEELokgQwIYQQuiQBTAghhC5JABNCCKFL\\\n",
+       "EsCEEELokgQwIYQQuiQBTAghhC5JABNCCKFL/w8DJcbaiy5AdwAAAABJRU5ErkJggg==\\\n",
+       "\"\n",
+       "\n",
+       "\n",
+       "    /* set a timeout to make sure all the above elements are created before\n",
+       "       the object is initialized. */\n",
+       "    setTimeout(function() {\n",
+       "        anim412ad1c0a7e447ffbd1dc1a9d213975b = new Animation(frames, img_id, slider_id, 500.0,\n",
+       "                                 loop_select_id);\n",
+       "    }, 0);\n",
+       "  })()\n",
+       "</script>\n"
+      ],
+      "text/plain": [
+       "<matplotlib.animation.FuncAnimation at 0x7faa903499e8>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Load an example dataset\n",
     "data = loadmat(os.path.join('Data', 'ex7data2.mat'))\n",
@@ -422,7 +4895,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -453,6 +4926,8 @@
     "    centroids = np.zeros((K, n))\n",
     "\n",
     "    # ====================== YOUR CODE HERE ======================\n",
+    "    randidx = np.random.permutation(X.shape[0])\n",
+    "    centroids = X[randidx[:K], :]\n",
     "\n",
     "\n",
     "    \n",
@@ -503,13 +4978,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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9mp5/jbISwRTrxzeIHbbSPtiSOJBzBbUIgnKaM0HXJUOCnggptJ2qKrCBrIutJVNlNa8DwkSy8uVG0ufWRUEWifAejhbI1ldl+7L8zeox40CE3aAKGI/lusq6JozkO9ofqTCMDUUh4zWeTgg0Re4w0usL51hZK7TdFBeJcI5MhEnkOz1Jpa1+so/JuvSxKlapnQyeynBCVnFO7l3tKgikL5FOpJwroZZ1WWGJUA05CqgqOa+pZWAqVzAsZQxKykagB2N5KQejETaR5TybMiplQmfrKdbJ8mxf7vPb7zvAsCdjkCSWU+ePSx+DXPsaNBp8XRf4V8Jqn4IQapXeRkSubMcSennrt4cRhWr2GAhU1tVe8Do2jiFohLDBNGLRWyZs7YgCWWmdxd68AkE7vstw8fv8wLF9t5Yacwm0hclXKp4+eYH7Di68bv1D6RPN8nYC9Xpju/vhBfe7eIKHUsnI+kufPM6Tp8PX7euxWVgDJElyywntK8GNEtifAo4ZY96E5HP9diT71dZwYG1EhCXTwTRU5KV8sKJYPr5Bk88DqmxJtDbAhj0malKJdV0Ypxi9vDKYx6X6UU1FKJQuagRB6GKs3vf+rn30piKIq6lOCGrohSK4XOLIcjlXiWhzpClUqkGzSlrJR3utEi1zvYJIJw/RIKS20q7LAwonx/UStRyUPaqJTA5WXcm0Vm1cteOorAhCFabOkVX68NUWanlAJ1aEZJJEjdY7MTPUuVyD6cvkYeXsGKNC3KR9cp0M5RVEtQjngQruKlrEJtJu5M5S57I964vg7rkUW8l4FhiqTNrSOQuDyJD2RAgXJqbS60mChNybvF2k7c9SZnJdUThsrrEcnwEgTvcz2iUyZLI+ZqCCPigz0AnMoCeTh5n5GR64R56J15bHxI/Vel3SsfVpwK4ZtW7UIStL+pKrFaWu68Yd4HBEOp4VlrqRuBumfGO8O8RgUFO9d+MYS6DH1DQeGQK3MRHwJvkgMGwo3eZmBmBe2bt8m+GNENZXok0/ffLC69ZtJUzb+263fSc0GvTBJy7Zlt8PgOfkz7uOXX4SxP6Rey8S+H65vW4rDRw2NGsvsP3+APefvMCHn9xceBAO7RbL59Jk2mjbWwnv2xE3RGA75yqtzvKbSCjIv9T0lx06dLiN0L3LHTrcOrhRGjbOud9AConvCGthMrUE1jX+1bqGqKfmR9Uoy7xqCEIm6YESDqjHoNpZvi4zrqg3R6RadVWNqUM9MBTjd88klKoJ2yKnmIrGaF1Ez2gq4IFsj8OQopQZWllMcEZndWNJeWuJsKlorUGUNISpuD6nzRQMZ4RXEDkIak1NbNYw3j+qJvPcZQyVeLG+tgaFaPuFEqdKm2PVd7qeO4yazyubEav2F6vNPc/PE6pWHIZg1V99dkm00MTklOrbr8qMOJF+j8uKUSbWgTiStup4hUhN6WUVUGma8VC1z6ntUauf34YRxkq7WSV9HbuaYaA+3Xg/rhZteZ0ZrN7UYaC+ezcmKeS4XhiRqeUgqlK9B2AiGaOQdTK93jCJMLVoCoWmc+7NzpH05brvPTrmyCFxGT31wrreI8vBw/sBmGQrTFfP6TWqdYUNc7QxhlKvNyAkUmJeZUsdY4O3bVtnqLw/GoFxAaVXm8O21dji6wl4yqGDhhkXBEHjYrgZuJJ3+XbGdv7Va8V22iNcbI7eSru+1PrL3d5GW4P2Ju+tjr9Iq94Cjz6XcvDQXc3vkydeAuDgobua5TYeSi+7i1tiK00b4IHHfg+AJ7XY59JkynDeVyM9c5E/+8sBN0xgXwmss+TFOkmSEimpzJgYE4lZ0taekJUR98RHXU0LrJJ6zCDFTsXH61nieb5KbeRDHTpIU1muCvWphq4hBVkzpE7lvLYYU6ofsvKkt9A0E4KIhCBSYpF+RMtsDVuIADDRAlaZysTS/9RmVGeFJLVuDZUXPCYnSaTdQoUV4RxTNa+vlzmDngiDOfX/rk0cBtkeRzGntXJs7hL2zMi+VShm7rByZBMVJkEPF0gfp5mM55iaRIXNtB6TFiIEe6M+kREhuT7xAqqg7usYBSmZ+sYjHcMqiRrhH472U+ZqRq7knMPZWaYrMtFxs7MNq7Nwe+jpGKyreb4qS4ahHJ86R1bpBCiS9otsQtrTCUU8IvZ9yGsmyPmskgjn5vYS66Tlnjv38Y3vFVP6maVn9bp67NLifkkY86IKUqfPIZUlUcdzZV1j3jYEWBXePkIBp2QxIAgdgTqv6ybawRGFft0G2792Fi/WE312Sls1kQNC9u8qlN5otAXrTj7bnczclyvw7zu4cEUC91px2cL/MorrtoV0G/53W3D7CcC7tqgndC08gm98m7z7T+p0ctegz9GeCun9I06cvzhTa1EUJEnSLN9u6FKTdujQoUOHDrcBbgkNOzCGfhzjkoi6ViJZ5Ahr0Yq9eTKIAnI1lVZltRHvzCxOzdBlIeE81o5BTcSlK6EnJtLQay11SOHDiGyBU4IQbgYTy/kSNeFOx2vUakrH9YicaIqpxmHbtCK1oh26coVa1+eltJmvlY3puDKGrNTj+zGhmtLrXPqVV2dIdd9xbRlEysaO5LqSfkhRiYZsiwnzfVGxi2gvGap15tLvqK4bhvPYrviIOJRMzjjv4QYy402qlCxXM7A1TENZrzwyIjcgVYtDEQVNaNt0LMdMCDGRMLTjImNVtcu+jmHiQsbKeu5lZRMiVpt1ysFIr12jAiJLrRplYUus0+tVT8LqZI0ZNaPHxlBpiBhVRqL3tFISYlWMSYdCDkxGe3jHw/cB8DufPwXAU09NOXanmAmf+5IjQs4V+pK3QdCww6yrCfzjU9cbMe5N/LjXlgEbNBqyJ5pJvP+GeT1QpqNkH/BavJLPnLQsx9WYVtz4VyLeSDb3Tue5lHa9+djLIZztZBK/1dHWpE+eeKnRsLfStLfD1YS5tc3jH3jlowB8+MmQN735GAAvPv/c647x2vXtik7D7tChQ4cOHW4D3BIatgMKoF8klDqFiIgoM6+ZiLZnicgmGoZUb6w3RQ2Bxm+r39oZu+GjCCIi70fUEKDapGA3CFtO/aSmgtqIpjrOxC9e5xW9QLTitfUxRklnke6X9GtQ36S1FUElfUx8YozE4jR2uqxC0kiOj22EmWqI2FQ13dEcq2dEmzYuZ3ldM44Vcl3D/hBTiFYdBgGBamSD+gJTJ1aEvJBrzauM2m74262GUgWqJpo4o/B+VkqcVV/yFALvB0fjxKOAutC+ZOvkDelLMxeVE+pQ9q3KirkDb5LtqolPKksv1uQ2azk2ln2z9dPktWgYZSpkkd2RY3lF/PQTKkK9TxGiYuclJBrHzSAgshoiFm7Mnqvca9pT4p5YKYKox+KiEMzece8eAFYvnGTfgvAiXhqeJRjINQ5CubdVXTCeyL0JjCFq/NUO1CrjrTuupXXjDM6rxU1yPdfKiAfW+AQ3YLxG76kMbGRCqzEE4Vf23PpGaddbaXZbJfS42vbaYUy3E15+4TR3Ht1/Vcdu9m23iWiPPpeSnr/6cLRTzz1xkWbtl+8//AkAjp8Nm36/+PxzDDSBk/87mU4uCvG63fzZt4TABodxJREZzttgrcVO9YOmX7zSTCHwcbItIo5dp8KbzZVUVvWplLUcYrHGtyGH1JSNeTM0Ibma3Yu6plpVU/ZEBLats4ZgVJuC4awIgEzjmqdZhkm9ST2nVvZ5T7OfzQ4GlEpaG5PQU0Z4WE6ZlmrWV4F/4dyYC96kbWdIjRw3jGVdnjsm3lob9kgSb6p3xOsi5MaZN6mHGHULhDZqGOmRUpWLqqRUs2yU9DHaRxMHFLmMo9FMaTUhyxpUndRjcn3QfYa5Se7QMGuGwz1UExmjZWXXj1dWKVaEEJZnBfN7xQwemJq+dy2sCSPezhqWMxmPmWFIbGTfnpHt5DU+UWpgIdaJWxQOyFXg+QSHdV5Ra1rDqL+LIJZOPvimA9J/evRm5PwL/RkWZ2Xf5bEeY0N8LpRpNm1yfBlDk6ksx2c/CzbSwwatdLdq/g/MRopZa10jkF1A8yzaJhXsBlktCMOGcd7h2rGVUN28/VIThCshok2PP3ORqXsrIXU9TeFPvSLv2P2HRzvu++yKuIfumXu6Wb4SvPzC6WZ5K+G+nSk83/0+XdpgqV9tLLnHV7/r3br0iYbWdmRxhV2n5fvoM6F5wb0Vbgfh/ZU9be/QoUOHDh1uE9wSGnYUGnbP9sGVTQLGoKqxsWYiKzRUpo6o1URrTY2pfZhR2ZgiLT51KTjNKGbCBGrNVIZPn1kSaKywsSWBZtbK1881hTCKQjTWIDKUkcQ5TJJdVNpWpqbYOqpJBhrCRUWeicnaBKKl9aOUshTTcd+s4TTt5noBuZHleqJ9dRdY0JCls+MSW8h8sdcUtbAkRtoqTIDVlKhZ5ijV+pBo+FVdBOQ6omtZ0GSG04yvVNZQZmJmzqqcQeizwUVN8Q9vPqpZYlndDYP+gCBQDVn71Zvfh1PLwPmVhNPHJWzqlM6ys8lSE17VSxc4l8t5FxYGzGt/plOJa5/UM7Au1zgzv59SXQhZ7Yut5IQa4hXkBXUq9zQv60aD9dpvUZSkPrOcs0SJmMLvPPYWAOL+cY6f1gxxU8Md+8WtYF/TcLdywHRcNeelpeg624qZ1iUf6hc4g6+XslHOZqPQSRBslLkJjGmKEjR/MUQ6ts5B1M2trwrbmaO9lvzocykHJ3cBbY3wwSb86HqY4tuksmvNTubhNWnYWpt+6pX1bddv4NMAPMvve91+mzXmX/mMfMvOn3zqdfvuPng/n9PtD+9abY71GvidR/dftOyxnWXhatKgts3kjyrX7PjZOR7Q0z25YQy4rWOzbwmBDQHO9Skw4DQlpAkwPmVlIIIzqQtKNZlP2YhjDYwhdK0qXEAVRDgjpmfrNkyKTfy+NZSlF5KGoCcfx2EdN8UzRql8vMvpmPUViefLBjGrlcZ367lqFzQFP+ZGEWUkrOTZkQjeOjBMpzqhqGpCK9djQkOggivTZCq7Z2eppuq/7QUYTVaS67qVPCRQn65xJUbbKm1NpRUknMat96kxVuOpi6hxC1gdqzqKcJoYJa9yAl/xIg8oBtp3NQ+lLiRWq38WzzQThXlNxlKOB7z0nLwpJ089y+rSso6BHDTqjxjskvsRBSkjFUBuKWd1Td6maF7amvZGlOeExb1gh8Qq2rJIPnJRdorST5aMw5WeYV01ArHUZ6eqCmqNYQ+qnDCRPgwXJB57frLGifNSjMrEETNzIvwH+l2r1x2h0utDY6jNhmAtfSEQn1glcE31MzAYNfWHLba3f2atbVXVcBsJV7yL3NgNroGz9uJqah0uG1t99Ntx1vDglqbbR5+T5/tdx3KOT6SNI4Othf/l+qjbQvqNYIS3zeMbJu9PX/KYtkC9lJA+2puwsvD25vfDu1ab5bap/FLwpvH0/COXtf/lwieE2a5Vbxa/2sIgN9N03k3bO3To0KFDh9sAt4SG7QipzAxV6bCaLas0eaOtWOfN3BZjZNaXEFBoScqAlKbKUpOJCgKN6y3LHNS0nGglLWuDJhNa0usRqEYWkWI04DfybUVrmOxFAPrrLzCT7gNgGsiM+bWqJlHLwKA0XNDYZFv5KtCO8ZosD0xAGmphijCjUvrU4pxeowsb7XEhSSnV3Hv6nMxgy2oNqwzmrKqZDZURVefUhWqdvkqYNZ7ITL9XMNW48KlnjtsI1MWAyal8hrc8I58T0zGq4QfFGoHGxddBHzuQa58qmePVV1/lleOSbtRRc/CQHL+4IMzv4XCGdCjXkoQxo5FnmQcUmmLWVx6LliDbfw8ASw5mrKZ9nRE2d1KukJeyb2oGTPU+p85SKXkw0pStjqipBka2QqDx9P7ezywsMDcUjWA2rVjT3LjF1Ou/BqfZz6yDWjPShcZifcWYRvkNW6U0HdZX3mqbu30J01Yx7KC1T1M5PDDU3mLibBO/3eHa0WZutytSbZWO8/jk3kYDP3JsY73XwNvH3yxcbObeeruvTBjOivm7Xv10s9yG144/tzR7kWbts4d5rfpNu1YB+SaJVv56MtdOpDSPfPf7ePrkI8DrXQVbmce3S1Pq153S5Te9eWnLWOytcDlVvG4FUnBoYjEAACAASURBVNotIbCts0yLCYEF6xOQhECkQkyTqRCk1E1pS0uo3TfBgCDQ5BreZGkMRv3GZZVTVuqn9L5iExFqitEotAT6cTSRYayCduKTuAQJVW9R+zVmoS9m6Kn/6KYjcuRBW50U5Jm8QCfXRYCuZSGF1lm+a77G12GqEtfkqU79RIUplfrW09Ay0A954ofAGUIV0tOpxaqv2RRgM59sRFN4ugCjAqjXM1R+vKxPrBKRGelrGLhGAFWuaEK4UjUfjW3J2rqMcZIGhDPiLnjqGcnNXa6vkaSy/fC+o4SaVnZWa1Uv7tlLpD7swXCuSUeapFFj9i8mEs526tWXWb+gFboOzlP0ZILkGfN12CcsdQxqMJWMbRgNqTRN6VRD22ZtROUTsmBxtU6mdHITJUP27ZZ7d99dK/wXrZ1dWOU8BAFOJ1W1dZLXG6nA5Znq7apc/n467EbZTrexLlTqeEi4IYTdRvFMn9jHp2QBcdl0iUm3hjdXw/Ym663QrvXs4ZfbgrttLvdCGlrC6OjGvjdaeG/2W28W1Fut2w6bhbUXqFuZwXcfvJ8VXfam77YA3pyadPP6kyde2tKHvRParoar8WvfM/c0L3qNpYXL9WFvFuK3Anu8M4l36NChQ4cOtwFuCQ1bOLEFYZQQRZpeM7DUmsKz9qoMG9ppXYeEqnERQRir5tNYMg29WItZxDGFEsxcE/tqsKphl1VCaHT2FPaI8cVGtEIXjmigNqV6hsyoxqVEsXm7xJomQCmCtGlrbsbXf45YXdeKY85wZlk1wt0xs6qnFaXMXIvQEKhWXFemmQ0mqWraSY+pJgWJXc50zV9Q3RDr6maMgkZzMzYh0eploSZLKYt11tSingaJpNsEyjDGaQrYSU9Z+YmlPihm7iAacvp5IWqdPHESgEO7F5mbU7LdrkVGqTBU5/eKZWJmfkSgZLcw6TM7FJN03Esa9rjbJZpumvaYPv1Z6dhygV2U+7j6mhRQCWPHjDLgbBiCrxLmKmI9b5ErGa8sKHVmHARgfdUr/VNlY/bskz7OzT7PW45Iv86tSV9Xzm88Z70gwahJvKhLjE9A4+OwjQEfL23MFrPhoGGBix1dTe1GWpH/fWIV2yRpqYKAi+jpHRp4rfr45N4r0rZ9PedHn3uwWW6byXeqVtVGQ5q6Rub3dthOa97MAr9c7XorbNas26SyNvPbm76306r3H3jLVZ3fE9Aezy+2VFyq0tnmJCoeft30+DM8uyIuuRcy+XaMl89sSzq7FUzeO6HTsDt06NChQ4fbALeEhm1wJGEJ1E32pyAwG9OJRMNyrCFQQlYcZTgtAYmbYDRG12pcszERVlNthiYm6vlMZFo8JCixShTL8pLCa2l5TaQhWpGvkplG1Fru0ZUBq5lqfKLEMWRK30jY16BOWC5FY5wq12lXkuEW5GL6owHzGj41NwxxmS8ZqeFbNsX6TGmMGY/lJKES7Oowoa41a08cUqiWX9mS0IcRaehbEFVkWjjDVCVG/a8Z/lxgdHs2dTgj58rLWQolmM32lejVn8dq+c0Lry5z5oUXANg/Jz7qvXuPMLcgGnicGGZVWx7NL+oYxo2pZJD2iXuiHQxHfQb9Gb1G2b5r1wKmJ3394hc/g0N82Evq5+8FA0aprx8dgF53bStiTQEbqCacZRm9oXIRqhCUcGd94ZcqJ5mRfr/pzl2snJNY8K99UK77t35rTKAWlyAoNjRo1yokY3y8dItU5gLxY0sn9ZhWvWzjNrKiOdOQ0GxjEtnYF2M7H/YOaGvUV6Jtv+tY3vJNb/i1dyKjtX2xmzN3bUY79vp6hXO146yvRrNux0a3CWZtctmV+KsvpVm3t7U5AVfj174S9I/cyz0nJWUpb5Wwtt/6AixNhHeT9NLXadlwaxcIuSUENhhqE1MLDRcAZ8LGNGy8sHQOq+boMLZE/utmS5yaz31u7QhHpWko43hErZea+4+vC4g0oUZsp4RqBRkXGWuaLrTwxKU+DNR8niQVpX70M181yw6o9OM7oKC3LsJ7uRTTSziaY5cSxaJwhjTVPNUhVD4ftTLHE5expA9R1LNoFzGpXN/qei45q5GQc6vXOylK4kTaCCJlMhOAXkuZO5wS6zLr+x1gNMb4XJkxq4x0m9UEi7LPdEXO9dSLx1m9IAlhXF0wNycC/eid90tfopT+jAje2dEMsQrhotR63GFEpDHQ69OaSpPiDOd6rGhiksXDIjiTYcKBfWJenzt6hC+9KpODopKPSXb+VEO2A0ulAjtyIdb55C8az0/ZlBxzLsKVG+RCkFznJpD7cfTInTz3hVcBeO/dBwF45Hcfo1bCojUWpzm/wyjA6fPpjdW1KZvUomFAwxj3KXQDY/BltypLM/ahFeKl9EfdIc425ntjJW67w+Vhs4Buk8Vgwxy++bff7/H8wYsE9uY44Q0BfTF2yht+JcK6LZC3E8zXagI/f/KE/jrRCGpf6QpWdxTUTy9+MwD3nf0Ij1R3AvC+6OVm+90PzrT2FqG9/8BbOH3qi5fs21aEwKtJYHPg2IN89lER2H/kfolg+dXfOdO4GbcS1tthKxb55TDLrzc6k3iHDh06dOhwG+CW0LCNgTQMqcIcX2nBumqjDrGalitjcY3JO6UOvFl0SlgpEUzN2YWLwQlRrKSPrbXCVSV/gyCm9mE3pSEwGnIUVswqwWtdz1vXsKqxwgMXMxiKxjQ/1P65tIlxjl3emFuL82JiXl6LqDVEbX6UsbomM+3E7eH0CZnt7d+d6vElaeILSDgKNWObTP5OigllpSabIsdqFTGHoSqVoNaT7ZFL8UWexhmY2pOcfEGPgtJn3Yxi1tVyMOwFrObS31NLYiLOq5zxymsA7Nm9m0P7jgIwO39AzxkRxkrMM0NqDbuKNdRrnJWUWnklHt3B8li0hvMvGvYcFLP6+ROyruwbAo11L3sh8/vlRjgrGsXZyTmmhY5XDE04dAW5Wmh0CDCxYarPVBRYao3JDkIfEhhSF1Mdgx53HhET/uJQtIM3v2nIMy/IGBiCJhWZw1L53KNqzo4xVN7kjZi623COxgKAczhf6MOICV1Wq6YdBL5+Fy5wxN3U+qqxWaO+vP02NLr7EK3Za8jp+UcaLbttUr/WSlTbadBtTftatGoQ8zcIuWy8LFrncH5vo1m3terfW/v9Gwc+LX++7b4TG6s+phrwe7+5Wec1bYBHHmufuTUei7LPfXwE2L5IyOP5RorYq8VGURDBBx44zocekeXtTOKXi5tBTrslBLazJWV2CqoA09O0n6GhthtJJEDKK9aND7JHodIoCYettny60SUM8qF1bje1G/odpK0yo1R2cpCkhJqIw0W2SSXp8zdHASjBmiSqMPFQz6upUW1GGmvcbt0jWJAXLKvk4bYrY1bULLt6IeOuGZEwp7LzTanOtBazrDU1PTVNB4EjGYmAmaxouUtTUSlrednGDHRs4ihrWNh1oZOaMEGtruQmo/TlNTUdaUhCpgllqmABUhn7dQzT8YqOgYxRuWSZX5R86gcO3c3C/jt1bHSsTJ9K87GPTc3MvLz48fBu6f90hldOnZMxOlVx6JBc48LCAi9/QdafM5KcppceptA0pr1Zy/17xZ/cVwZ4kiRMp3JhfQeBuhXCOiNxyuz3Rd2ChJmemvrrqklH21PzfBDmrC8L0z3pBxy8Q67Rrsu5vuFt+/jYx8TMt1y5JsrA5hJ3DRspco0xTZw2dYvX7VOM4ho7uXGmqcSJAeNnVjqJxIBRgR6ZsGH+d3jj4c2xbcHtzeSfPQ8PqXDejll+tSlJL6fi1nY4fnbuot8r9ezr9hnOaznbg/dz59Htq1jBhqD2ZnD5odW2PvYE97339dfuBfpW224kLpVY5f7DIwZ9+QZMphOSnnzfthPcW/mz2+u2E9o3inHezds7dOjQoUOH2wC3hoZtYspoHyasiGPR8ky1SlAIS9Fr2i5ICZzaqTnHyMgspqKHjWWm5MllQb2LqBCt2dhV0lA01LpWjbLqsar1tksLxsd/m5rQyDlin+HKJI1mX9Y1UaEFHHy2riTA2y+LOidSDXl2pHWvqwnRxMdbj8nG0tZ8GNEbKTGur+e0DmO16Eixhk+nNfAmXJMSR1p0JAqb6y1dn8humGtl3Ca4WsZlNuyhl0vt64RHAS7SGaBdpxyJeTsKE4ZWtIITz8rMuhetsn/fWwHYu+8ugkrnemo6XsYwmJFjDux7kOU1ufbHXhSNIq9P8Ja7d+sY9zl9RrTqlx7/OKNdWlw+U4Jcf0x9Qdqa+5q9rGsa0/lIzXX1MbLXhGFqy1NU6W4dg5ep9fmY1HLMviilzLWueRjRUytDpSp42o9wml2vHAf0tQrZqlpi3n70CB/8eiGi/V8fPtlEGYRxQKguhkLvQe1coyEHYdCk1nVKNHNW4v8BjDXUeo6gNo0lxJPprHEE3vzuXJNNrcPNx3Zs74fSJ7zl+LrgWszfRxZXeLL+g4DPTibvwG99YcOkvfugEEY/+PYN7foiM7jivvc+yNPKoG/M4FysObfXb8Z2x2zHHm/Dk/i89WKze+NS2rTf1sZXv+vdfNcrHwXgQ49cWdWuq2GPX29Nu9OwO3To0KFDh9sAV61hG2MOA/83sB9x133IOfdTxpgF4N8DdwEvAX/KObe0Q2s4epS2gKmqlGFMEInv0lQy0wyqCZGGP9k4BlW2k8RCLMeVofqS84jpusyCnbHEPdFandZ8rqsxMxpovWp6jDUPt6tzSq/lqNadRglWfYyrRU6wJqUj5weiHTOMKZTsFJqYvmbhihIlXKVDItXiwtqxtOq1u5JYc6A7zXedBCEvHZcwptFCwkDXL2lcTx0GBBp7HZqQ8UTLhg5houfI1b9bBAF14H3rlhLVrHUMwyCi0lh0enP0Z4VI5saOpWelD3PqE144dIzdc6LhDkZ7iEcSgnVhVctr2nkGkWinL51MePblzwNwWOtL7zELjbben3WgmdTmdx+ARB6P/lSu9dkvnebhr5byl/aEI9l7RPowI6FeJp2wHIhf2VE1lpDZ/gijYxCpyurKkkIzu43mBtSexKcmkbIqmS5NdF2Pmd3qN4yEnDjsL/Idf/BhAP7zZ05x5rSPYa82SGGqVTtME+vuXICvVmr8gjWguc4DaMpvig/cMyzlT2hM4xvHQHCD59bX932++WiXx9xpPx8+tFPoUDvr1pX4pd+IUpqbcfzsHA+/WbTqzy3NMnfhMwAclVf/ojjr31v7hi3b2EmDvpRWfam2NmP/gbdsqWHLvdN7sk0Nj6006622t7Xt7/iTcr0ffvL/48T585c8/nKxnV/7evuwr8UkXgE/4Jx7zBgzA3zGGPNR4M8Dv+Wc+3FjzA8BPwT84CVbcjWmWiEM+9hIiWDRLEYZgqZQc3C2TqGxry7ZD30l+xQTrJrPo0BjmI3BqKWnmFhWlPIdhz7JxYheKISvOWeZn5UneexSsrEv2iA3Yd2FVGNNUJKtEBXevC43I69DtM4HLgzYNZDj4qEI4/lyhnVlt08nGS5RgeqmTDLZd6pkKItlbV0EyHA+IPPEJi3YMSmgVEZ8mILRmOqsMNjQVxeTvto8I/dM5iBtkqSUKLPcGnIrZBTbG1Fnsv7s86corTzI87GYm4ezB6nV5E3vnTz3vLR7dllq4xx+84gXhTPG6Zc+ze79OhlaVaGTTpmuyzV+6cWXSEaa+vPVs+xSIXn0q94GwIMH9/D0k4/KeLPEsy/KvbnnDrmhs/FZKr0Hdy4eZfe8XENwZo3xst7nwE9kesSpbO/15jB6/wNNRJOtjrFa2Wt+/wH6iVbz0ljN116bsEtN9h943xH+6b+TiUKNI/Gvj0Yr1NRUtWeM2ybu3ZPOTAShr5TmNlwXOEvtCWh+X7NBanPWNZOCG4jr9z7fAthKULeTqWwlINoFJi5VbGKneGu48phruD4scE82W1l4O59rTateyOQZ9mbwh3et8triH79kW5crkE89+1EO3PMNzbKHX9duq72vdx+8Y+Z3mv0ujvd+ppUu1q+7/HjsnYT5Bx6oG8Y4sCMBbStsZybfKmZ7u21XgquetjvnTjnnHtPlNeAp4BDwQeBf6W7/Cvi2q+5dhw4d3hB073OHDrc+rgvpzBhzF/DVwCeBfc65UyAfAWPM3m2O+V7gewEO7p2jZAR2SlhrFpp1RzoUE2iQKAnLjCDXbFv5UqMZ2SAlQjTBQolm/djgEtHc0sCxV7X1aS0aVsYchcYKz8Y9lnzN7WSW+VT2PbeupLbVZS4sybp+OsvUiiZaZtLXY4OQyvn0mIay8AyiWvsSUJaiuY1Di621MEWWY1RLT2e1tvaZE5SeX1QPmlKdaHiVcRkmF0JXWVtMoGZwl5NWcr0R2m9jyAslYTFtsntFlbR1oVinGt0h+1YJ659/XsZgssrCvkMAjIZy+6rkLbz4kszvTn/qd1k8KH04sO/NALz45NOUgRQEmT2wh7mF+7Ut6cvjjz3KayuinYZpSlHK9P/uw4dZXZa2PvorvwrA0BSkcxqGMhMwWZGxn1XLRZRa3vTWdwAwlzjSXKwBVW8XM/PyTAxitc5EjmAg2kWQRuS5xu6rC8WYkt13Su3tOExwGqdfanrYsl4jnpfn8H1vuYNfXxAC2tL5kHVvCm+FagU+xaipN0pl+nhrXJOC1Bm0hiyAJVBt2ym5rKwsUbAR91U1ZVxuPK75fV6c2WqXm4Ljk3tfl/lss3a9ObPW5rKOV5Nlq43N8dQ77Xe12BzKtblEZhtt7bqt9bbhteVLbfP4/CM//brzbN5n8/o22c1r1pszoV1q7A8ce/B1pLPtCoJshT/6ziN8+El5306cP39NMdk74XqZxq9ZYBtjRsAvAt/vnFv1H6Sd4Jz7EPAhgK+695CrYzBuROhTfM6uMl0Xc6vTJBtmZoFAqzy5bEytsdN1tUYdy0ciMmI+La2jCGQ5CmucfvwGPRG2cWWJR9KuszGRJiaZ4gjUrLmYiGB9NajJ1VSZTy2zPg92X33ddhm8+bPss65+5Zldfb3YgFKTmuR5xmgkArNa7/PqBRFck1URdutVwu6+9HtaFEw0f3bg/eJhyKqVGGlXpJSelZynFGqanaiAmNoQp6z7nqnJ8Kx4MdfVdUoey7Usn5zAWNwK/cEso33CGI/TdwLw+UdfJpqXMbrnyN1MNHHJ8edFCO8aOCalTDrWTltefUlM2kUhfuvdCz3uv0f80ouLCwyUQZ+7ESunxZY+Wu3p+Rc5clBe1P7cQaq9IjCjWPY7fEfFnj0y0ehNz1O9Kgld4jBkMFT/fCwfgzpwDPoyBkEYE+jYxkNJkNKbO0hxQY4P54dURvZdG2syldiSZ9LXY/c9yNe9U673qSfP89grMlmyjenaNnH+DtewwI3PNd5ihhNseKXDIKbWPOvG5wkIzEblOQJ6+l6tcmNxPd7nB47te+NmF1cAL7hPku6w58W4lHl8s+l7s3Bupxh9I+ArbLWFdRs7ma63EtLbCd72PjsJdb+97ctuP8tva6U03QltgbxZOF+usAa5nx944DgAH35yd+PPvhLB7QVx2+S9U8rSa8lVfk1MFmNMjLzc/9Y59x919WvGmAO6/QBw5lrO0aFDhzcG3fvcocOtjWthiRvgXwBPOed+orXpV4E/B/y4/v2VndpyBFR2QBQmVIGv5ZyCssSDTDKWFUuvYmY0FWZ/H6FPY1qtUVVj35jAhqDaUulijGqiq6i2FkWEWkwiDGpGqi0XRU0+1llTIJrsgfkBan3nZHGEPhJDPLTSr6wIsKVoW5MiI9SdM23HRIZYR9q5nFRjqvMqYHZWtLcVjSuuwoy8lONXygmDgY6Hk/bTZBbTk37ZuqYYK8vcWabqLrDKlDfxLE7j08Oooq+a95qa7HMTMF4RTbK6cJ4ZTTN66PB9VFa0iSd/5yUA5vcY0kXNWhZkHDykplur9a57hmRGriUeDHBqpTh/QjO5jQL2Lmqq2IGhUrfDqJfz0LvfL2OvvoDzy2s89cyzAJx+7PfYfae4O+4+Ise4Q19FGIg2X5iStK9Z0Zgw1CItdSrbY5sR6BjkVclgoLHx/h6tvULgg92dwanLZKqZ5Rb2zXLhvIzB/n0HeefXiMmvOPd5nlHNfJppjewglqoeAK4mUsZ4k0cARxh6lvpGwQ+Js25YZ7I9EEM5ANY0mveNwvV8nwvbawheO9WlfiOwuS/vOpY367aL/90K2xHNPGP86ZMXrqmC1vXAVpr17oP376hZb7Vuq+1t7JTBbCuW+X/5F//4de1eTia0K9GcrwRHH/4WAB44+7FGw74S8tnlasvXi3R2LSbx3w/8GeBJY8zndN0PIy/2fzDG/AXgOPAnd2rIAbUNMKEhn6jpOg6JU00nOhLBO3QOk8uHtJqsUGv+b2yPUBNKh1oiMnCWUk2V8SCG2vuA5ZCVbEimCTWGc3M4rR41F8FrExWYKth6JmfXREK5bBwxnsqAl5rbOo4stQ8xq10TumPUDD4YDTmvDOmQAdmSCNbInsMqA96pIAnWV1kNRID0RzFxLR8An7d6lBpGmlbzwhScnnjqKuiLD2vQFwGXW7ATGa9xnpKpTz1vyMsDGEtf4t0DDh15DwCnX+3z/Iv/VdYvyBge3v+NHLlX8nvvXdiD0aQ1/QU512AY05v1yVQg1vCRqVZRCxPHXQuychhFLGkY3WpWMnlNXB9zWqL06NEBh+79GgA+9phlRsPjjrz1Afl7xwHmNa1smFrGSxq2lfRxvmKZuhDKSUSmGWP6gwFxKMe5qdzP8ThjNCeTOBvCa6/IR3miVcYG2TpuqolX3Az33yn7fiS0vPU+mYD87qdk4obbKA9LWG9MHnVdbVpMb2dwfoMzG2U5fQMblTqxxjV+8huI6/Y+t9EWlpeb3OJqsJWv2p+3zQ73WDwnfImTXLsg2IkRvpMP+2qE/GZ/9VZo+5O9j9njq973l5vlq00d+j1ve/11/9xjG+Fu7cpdbHEOf15p5/WVvQ7svTLXBbzeh+2fr+0iAHw0wSMfhW96q6Rb/tXPPHtZ5yqKYkeBfb1LdV61wHbOfZyNSr+b8f6rbbdDhw5vPLr3uUOHWx+3RGpSSWMRUOaGtKepRdMR01xmP3WusbOBJQh80o+c2IqWVNKjDjR+VhNmRKbETMV0nFV9op5qgoloW+s5mKGYgMM0IFDzepzUHNoj361zSoIqXM7MomiP/Ze+SIWY6oczvp6xodI4a0tFqmZ9p+p8tp6RKiFsMAyZaizudAUiNY/0nFoA5gz1uvZxvY9NZN99MzIu/WHKykSIuuX0NQot3pEDuV7D2kSIbEEcUCkTuS4KSrVClEqaW16dMKeVc8bpe/j8M2IRmEZP8Y5vFrLZwQPyd3Z0gHWtJR3EMTPKBE53q4UgdQwPyPWmYc3yWSVtqbl4uprz5JeEWLfQj+hp1ptdsz2qXPr7xO/8GgBVMqRaEILakX37OXpMmOj37JFkLX0SYh/PbBLSSO79pF5noMtWNXvCkFItMb0oodD4al+LerTrAGgFL8qEQo+zmuJ0bQIDJY3l6xP6qum+6fBBDuspHjUbbl1f+9q4uCGgeVU5NBHWq91mg4BmoUlD6kklDotTk3pUQx3e3qlJDxx7kE8+vpWJ8fqZzr0m/c6HNmlmqmW1Ne0jkouHh44/sW3RjsvBdmlK29q015zbGvSVENG8Nn1kceWSmvUL2eB1bHCPtkYN16cgx1ba9Ps04RDAY5/+jWb5aU152j7vVhr6teJqTecbdcCBy9SwLxWDfSnN+qaRzjp06NChQ4cObwxuDQ3bAXVI4TIqzfxV5eOGLBRYT9Jao9Z0onGVEBbir47SjNDJ7NWqNl7FMwShaIFBvURQa4iVFnfYPXKsqia7Vlhi1cLCMqSfiN93pNm4bF1RTaSthd3zpKsSi5vlEjoUBhHrE/FzprFpsmlVGupVZgVJX65hlPaw3ica9nCalrPORaMb5zVRLMv5yhrhovhM96t2uTY21IVYFqoqaGo9Q4JTbb2MNWtaMQK1SDg7odASo7WTFKPh7ju4kMvMMjvzPPd81V1yrgPfzXAk5/WJ0twgYN9dMqMO5xz7FqWPqnwyCAuOn5Treu3ZlymWhJA1Uh931I8Zr8q605/9LPE50bbjsOZ8oJrzolgOJoOAfVoj+z1vfzvTJWn31SclN1LQH7L7oIR1UZf0jJLOkpyslH0T47OQJQw1e1lIRaClUSPlAdh8rdGKJ2WGiaQvo3mtwZ1nxDPSl+lyTdwTDfnt9x/lqS+Jz33XSJ6D5XFFoBp0bV1jYPZ1sSUTndYkd66Jw05wvupm4/Yua0uocV1VuxTnbYqttetrh9eoDrTO0T7XOx9Km32O6/q2Nt8/ci8PHb82P3qbdLYZ2/mlL3WMx2Ztejvt2ody7d60vk3uulKN+nveduEiDfpSpTJnP/ez/NILp1+3vl1bu+2v/tITooX/3GMbtbO9tu23XW+0Q/K2Cs+78+h+XtZrmN8lFtTlpaXrlv2srXXfLNLZdYO1lkm2BiYmsvKhzAY9zMTnhdY83FHUfPxKEkIVRuVkQhB50pmaN/MzFKiZusroWYm3c4hQcMkcMz2RRrlLcFpLOnMJVpNmTJXg1gsyZgZaS7qapZ5q3K7Ww54yoNKEFwPjSJTwVCvZqS5rykjsp8Wy47Wz0u5MWhHFcj1pLS/26jhtclCnZpk5Iyzs1VXp39rqa5S5MrvNhgvAVuFGAg/NJW76IWEi11K5HmUgL1C0V17kYGWeu/aK8H/T295CryfCe6WoCZUEN7tfPgOTeUN6WMY2sQWvPitM+X1D6eyZl7/A2dfENLy0fI66kmtcf0oIZcW55zfM91Hc5DB3ScTcIZkczC/KB2JkKg7PiUB+4nOfp1wVwdibkXuw6/AD1Iksx+GINJAqYsWJzxNWyiT3edP7I8Jk4zGvnFaDU4FuoxlsKdfwkx6quAAAIABJREFU/IsnWD4u93bYk/tRFWuE8aIuj5lPRDjvm+2zqvXB9x+QSeDK8+eamuQYsHpvGoZ3bRvTNziUSE/lHD5His8f7oxrYrMDGxPGX971sLcjje2ENmntSKukszd/X+1EwQvSds7wnfa9HHhTePuYq2WUe0G9E5v7avDx3/wkTz99qPnt04e+/B9/sVn34vNbJ/j25uV2mlEfc/1zjy1w39lPAnAfkiClXWP77gc3yGdfeuLNTRKV17k5LgOffDzn5Ak5zifFgYtzwvtlIZ/J93HXQL4Ry0vXN2V+W1BfrVm8M4l36NChQ4cOtwFuCQ3buQqXL1MxxGklqbi0lFrgIbdKzEoTop4S0EwMkcbiklKr+TnS0KW4OI1RUpohwKqZOsw1BCcwWCPmz9BOiJD1rhoyruW8WCEoLVdT9s9ovHOckc7IVL6cZk3/Q4197icVgRXNKAq9VmSJNbb65PllCtWcstoQ1Bq6Fqr2mtRkevqamrOnJLvXIFItr5xQ5TLPSsKSgcYgF4Ul1exeTgtbuCBiUojqlrs30595t/S7kGPuvXcvu4YHATh/Zp3pRNYP5ucYHJDluQfUJr6SceozL8l1Zav0A9EKnnn+UwCsnnycalU07LxYw2jIm/OZu4YBi/vEpBfN74e+mJ3G5wNMrtrsCbGCmIP7WDol5u/9B3Zx+KslDel8T6wjrxUlqVpUbA1ZJvP38fIqqWqwvURCrvpl2YSx2Toh03SyPjPd+njKuBQN/5XlGbI1ud43p0pqqwNWNevZbC+m0sIr/STn0G6Zvd+3X8b95RMpeS7PqrNBy4wtqnQQmQ3TtwVvADeEGK2Z7Qt+xGHcVBRzGAp7+9jEkyDbNpzqUthq/7bWfamMY+3jjwyeaY67iGh2BRr85WjWcOWVuC43TSkIyWwz2sU9bgRmP/ezAHzihdPsWf10s/6FLfsnf8PZ37dlW+0Y9/v4CCCpR+/+xndetN/XcvEYerN4O0Xpy2eEeHrn3udft66N9jFttNPPbvccNSFe++XenDh/5Vr9dtisUV+tWfyWENhhGDA3mzId50xqZYZnYROTWnu2dblOT9MKWhtR6NevmIwpxyKcyUTwRdGYXl8+eL3+LLkvq6hs8tCt+SJLrLILU8lHeRScbZJ+lMo4rrMpEzVDp2VAECXabzXLVjmjvrQfpwmhmkJjdQAPwimhVtIqTIz/gE+KVfq6ry/PGURFY94v8yH9YapjIG0VVcp5dRXEUUClJuAKmphuqwljlvOSM4W84DMLD7BvRkw+s1qmMjYJ587IGMa7jrG4R4RovNhn730yNuMlOe/yK8fhFXmBl575NC+c+Lj0sRJ26NxwyCH1K++74yDzu2TiNdL+90ZDhrNy/rmF/czvlfzdvZkDHD9xEoAvPiWfhQvrcOfeNwGwe99dBIjwrdXcPB/H4iMGLoxzzp3SydbyEnFP9pnTe7S6XFBFYmYz0agRuGsXxIe+fN6R68TQBQVrE60iNpb2Z+iRacnXPcMeRpOshKFhoC6V3kDu1+zIEGjOgDNnssbkTbtMZuPSoSEAhM42ArtS9rtr+bUxNZG5/VjiVyO0PfzH3qcRbZs0L3U+uNi8fjlCum0ivRXR9l03Vbe49sQm3i+97+wvNubt9iShPanYPIFpC7sDxx4k3v9w83srgbv/gMRWt03eVwLf1ulTG21ttR0unih4v3Tbn74T/BgM+oPGPP6ifqOuBZ1JvEOHDh06dPgKwS2hYTtnKKuIdNSnVjbe6nSKtyibQMyYMY7JWM3kpYOxFs5YeRnXEzPwcI+YeOfmDzHsK7HJVkxK0YacEsqioKIXCMu8b0KmaIrQ3FJlsn73gpx3bmFArrMjG9WEVlN/pjpjKqckqQxl1Isac2uoGlISBWReWwohiuRHaEMmGmtOpRneopBSCVVJ6cjVxF9ZmZmeOH+q0cwm45JMzaolKZn2cXUqJqa1YDfpLmXR7j3CaEYJV5q+c7yeEw1Fq0727WN0l2rb/ZrynLQVFTJbrl76LBcel7jKqHqOw3dIv3bvE1X88IHd7FUm+2DQI9JcrLHexCAwBJrqNR5PmJ4Wcle9fISDs0JSuevr/4BcV7XKF58Szf2VL54hPSQZzmb2i0k8HaSs5dLX59fXWVPXxO7kAD0t/jGdiqVlJVjEBXfJ8UTs3Sv9WrXS13J8gkDTha6dfwmj/V4vlAwYGtC0s9MyoD8Qja/MLjAdy3OwZyTax8GFVQ5ooZLfOPMMgVZNoyGaVc1yEG5YkKyrNydFIzQ1VSsTWnD7WMQvG20NvK0Vpecfacpz5LvfB1y6atOl2r3ZqVF3qnG93batGOHtOOvroV3vOysEsnr1041522vV9x1cuOwxP/XcExxo/b77wYf174bp22vdH//NT15S2xZmurxPnpR2Ub8Xv5mnq43fPv7bE9fuO/uRhux28sRLr9OsH88fJD3/iLZ/cSU2b2HxJv5dg8/ygJrHXzyx0cZ2zPGrYZRfKToNu0OHDh06dLgNcGto2Bhym2DqlN5Aq2yYFaYTX1tYVp1fGvPaKZmRrq9PGQ5Ei5rdu8BA/bODWdEY00FKpH7j2i7jCvH9jDWrVWRXyTXvdD8pSCJZX5YpYw0Hc5VqicmETOc2NQmhDptVPSAKc4z15TUdLvLFHjyBKGR10jCfMPXGDGxdy3rOqjLWj1POr2oYm7GNL/WCr98cWKz6u8e1I8s0NK2qcKq9pX3JEpbMfQ279stMOwlTqv+fvTeNtey60sO+vc905/vum6d6r+YqFsliFUmRlCUqVHdsKupGS7Lb3U53Og7cgeH8SBqIAztBgCABYsAJECQOEMho2DKcxLCt2O6WeoiUbknsptQsioPIIlmsiTW8qjfPdzrz2fmx1j73vPvuq+FVUarqvgsovFv3nnGfYe31rW99K+BmKFzfbpeHEOQ4n101scWR6kjFh7NOBLJbb3yHxv7Sv8PkGCEP09NljAxRhJpzaAwd0YbB5W7KS+DryJ9V3wzThu3QuEo7B+UycuDMQblXAQBBm5qLmMVJPHWcounVzSZ+cO5PAAD1W2cBAJOnn0KLEYlAFZA4lOOuyxHUmYRX57r2+pYJd4nOJWxs48AE3R9DOY02jMPI8XVyPeQGmFQWEdnPr69D9wZpN0PkHG7bGQXwPeY9MNfh4HAZ05Ncx12+gjaXlGrFOwgDksv/DKiUkKeEgOBrx7cOgshI67CTOOls4zGwbPMPbb3y2eeuOLh57dyu9WcPv5J+1tHSXHtntNxr+3v9/16j7YeRy37Q3td3UjLLNvHYr2lS2fAnvw+t4PDEgVJPkt3dxiEbne7QhO/qUW2Nn0mj6dlRB2++T5Hza1GnnKuXZcu9dnz/emZfXUhCdp2LF8+n5Wh3s17n+vR4KS1Rsz+6mUbO+q+OqLXdLbL+c1OHrZRArHIwhQnJ/tqKyrjBHaw+vkp/b99YQcEgmPrQbAn5UYK/ZXkaFr9I8zZLk5oxEm6MIRISzQCAFkPQeQMI2HEaqgLLImdVLUaIDHZsgslIMaBYoERFCWItVclSn0LmYOj6cT8CQpZX1SW5MoZl0ncOAgQMz9fDBvyQHJ4oaTJdDBXTsmHSRsIdrLbWuYmG5aTwfKSICQ4Ads6CKhJRKz/0efqucgzFPDmQKLEguGFGvkz1la6w4UyzgIgTYZAlNje+/wauf0COuiiIuf30CRMH+eY1owT+FjGztxstPtYk7YhmKwHFJDkwpO+YLopFcjqlagFOns7BKmwjX2SxEyb2KXcDokDHXTVNzI4Tqexfvk4P1VrxJEZHdP25DZUQILfpKWw3uKnIEm1/5aMfA00is40NSthDhwEA7YCvV1hCHJDjLJaqGJw4CAAosLiMu3gO7hIR1NqhQplr803HQsT3T54nfocnq8gx6e3k4TG8c57rONkJG0KkwipKUH9sgJnhTKJTGvQyCBZPTd1bX+pH1ebaJ1LYWxOBsnW8h44c2wFfZmt4gb0d8n5JbZ+GfVoduu6n3roXFJ6Fv9eY+b2Xk74f1vtJ9Hbo2pFrJz7Rc6k7i7HsZRdfP7+jZ7fexldPEmb9u5nacQCon/nPaB+rxFLfqztbtimItqxc6ZeenMV3Prpzz+57hcT70qR961vf+ta3vv05t0ciwhZSwMobiCOJyxcpgjn/9kW8d4lmPDETnybGDRw6SpDmiSemYedphhjGAUwunYkiio6bLYlIR2wJ0hKcAitGJVECrsqCGyhEDGvmlAvHZJlSVk0LPCDgyFwIH1FM0ZvHzTBMZaaNHMIwAhhsKjrcr1vZCBnCtXISIROaWptthFz6FUY1Ho0Y0NFpICE4ct9ifDWRCiaTxgyZh83NUqLcIagaCezbFZrhKmXBjSgSLZYH4XLk7yYEY7SHDQwP8v4Xb+PmO79L6136Q1RqFB2eOEXygdNDI0hWuMe1G6IZ0Lw59ui4Y8sCI/UoGAa4ZwkkR9hB6MLdptI71w3gSBrPSq2EtqBZfbFOkbA9UIczQK08USxiwGb1Mpf2OdBUKNdoB+sbIbYW6TovLzZg8nVcv/wujXfzPF58jsZjqlbEaIWguUKREBlRrGBtg/Z/7q0PMH3oqzQeiqDJ6olBtBrfpOOOmvC5xt4WFmK+provR6lgo5Sn/T939Cg+ufkRAKDOREkhYiRcvmXATNtnChlBZ1R05b4lheYuQggFoR7PuXU2qv7eRxQFtbYIyXl6vJSW0HSTg/R6HUh87/aZd7N7JZ79NMu6stB3r3pr4P6VzLoj1R/80/8FAFDdeAcx7yNLKtN2cWEjJfft0je9g11ceG3XtoDd47iY+Txx7HTmeuyMhndt//XdpXyLl/8oHY+vnpzPlHjReP6dTDj/j3/QGVcNlb8ycRPvcnn5+z5SWdpeBLvj1Yud/xw5icI1Qvra3EDoZ2GPhMMO/QTzVz28du4Srl7il7a1jmOnKY95ZJQe5tFSguowfQezBrDGuG0nEJzjC3RdcmzCUPR7o+2hFZLjyHNe27GAokkv3zYUJNfnekLAYFnLhAtpvYaLIKHtmoYLyZ4pcDmnoUJYsZYhDRAwNOwwdB7HAk3ONUvDgtB5TOkgx7Bn4utOWBI6L+B5ASKfjsEPOLdakJCsQR0JGw1BEK8sP4tKkVoQJayL7sYCkU/rNd0NyAn6XQzQPmtDNtb+jKBv98oPYDV+AgAYG5WYnCCmZzVH9FHfzcPX9dDlKvIs0SkkC8bkbNgF+lyyHFic2tB5WpHESEKedLTqUMzE9wwTVkwPQnOboNJc5CFcJfjJmqhpTRkM5tmDLS5ge5vOccuzEcXksC1EaK6RM4g2yFmeGFYwV+iVcevWFtosgDMyQ0+2WcyhtUU7ODU1Cgmqt5y7TnKoo1OHceILv0br/+ifIeF7aqsegrMR2GrQ/s2CwJEBmhA0gwpGB8lB1eu0TWWojli4VDCY+h2rTvPrTDtsWNA12cbOXtqPuIVBgIX5G/jWO22sL3wMgJz0LzxN52A8SSzc49WL8Id+sec2uiFxAD0Z5VrsYr+2Xyd9v4Ipe1nWWWed+Pbgc/vKV2snt3j5j1DdeCfdh2Y++0P0Lt2h2DrUe7y19ap7BoClRVpntet77ZB7je1++59rGPxMrY5ZzksvzO+e2GXtqyeBi9zvPHX+L5/GK89/GcDObmK9jvXy9snORHJ7Ka3J/lk67Mdz2t63vvWtb33r218weyQi7LW1bfyTb/wezEqEs58jhvPTJz+D8TFiMLfmKeqO/DaKNSKaIREIGO5tJiXETAayGWotOQlyFitjVS0EAX3e0gxsR8CpcAct20vZuQ3PRsHi6I+Zxm7iQ4dGtrQAruVOQvq93doC9/CAVIDgZhPtNi3XDlsIOYIXiUTE+KeTM2Fw3W/BomVFKGGDiWhJAxaHqqW033aHnb4ejkENUB10pTAF6dAM3U9o/aYXwGf4e+DAKEaOHqTfPYp0t/7k9xF8+NsAgLLRwtg0zSbHxp5ErUSqZfkaRdiVUh6FMiu8mQoOj61lMUvcBCxmgVu2ndYzK0YTlLShQ9IwChAy6SsMAMmNQtprxBb3Vy/BYLW3zcU1tA2C0v1NipSjgQUM5Amql8Uc6m2K9t3mJrzbxEAtC1q2vd7GUptmxJ/53CkcnCFEwqkQUmNYFfgRXbyFWwtYukm9cCdGCJK/dOE95MpfBAAc/Eu/jOVzvwNtkqsJ3BbX7ZdyyLN0rh0HqGrYnZdPlAFD3ycQKf4tlUqh8A61LEnrtCEEjOTxmVuvbbfwjd+j6O7nnyTYc/bwczvqYwHAx/iekV03OWhy6mBHvSzTuvjclQ5Tdz/Rdi+yUXf03A353im6vtc+13eKrIH9Ecwuvn4e7XP/EAABxJo0tReZb6/Iudv2UifrVWcNAAFou6vD9CzOdJHTOuP9c+lx3w/xLKte9ixHy71safFCSjY7ycqNv/s6Umb5K89/OYXHgd5qevq7mxhFcYDR3fX1Ox6fnXM+tVrsx+ct0Le+9a1vfevbX2B7JCLsQkni7OfzeOLkE5iapgio6JjYmKeZjFsnAlRhtArJ5VsGEhgxRb3ttkQz4t7Z3BdbCRORyaVejocyR70x53+bfoIWl2qZiY+Y5y5SKaiQcpIJR16mZSDHudrY3UYc8uyYCWG+K2HYXHecJDA5XK8z4UqIDvlKqAQ5VjKTMocwYrIaa4o3vBYiLkeTSR7NSMv6UDQX+ArLER2LHDiKSoEQByGL2HZZc71BEamyHBw4QXnriSNHsXWT+ngvvfl/0fqr38Vkmc576uCLKI8QujFUmcRkjfLVI2OUt6lWBfLcvlDIKI2Apa1zrwKK69ikZQO6fWXIhLEkTPs/J7GHmPP0angUCdcglyfpWLdujWD9KpX8RGEezTZFNIZFkXKp1IIfUHtPSxkAEwK3Fi/D5FrwEtdWK/canj5BxLhDowVUBEVAgmvSHcPHECvijTx/FvkrVH+9PEf7rJhreOOPKdf1K7/5NxCP0u9i7S1cXSZkQHL53/RoFV6bom237cNkqTuTldSESKA4ao7iTj7bkDLtna61xiEEAl5AApDi8SnrKudt/PyTU5g9vDOC1lGzVppKiU73aL3qqbuj9vu1+8lh3y1v3Su6vp966/tp6HHy5dM78tUA0pw1sLNMrldUTU00Oo00tGU1we/HekXhOgK/ev7UDq3vEY64dalZ7a/+D3tuV5+btrj+djrOZ4/V0KuiWR/L0dMv7tI1f6H8I/z4dVruP/2t6TQPvzB/A9/+eDTdBwB87cWZHds9nKP3z+U9j/bTt0fCYRdLBbz00lnkpAOPm3e06i7qy0Q8ikK6LEVnBO0mOTDHksgzDj1VaaIKcrJLq+Tc130LA0zUMQ0LtkGnOlxmkQwRwGMnL4IINr2/YSsfLRY5yefIQ1mIEbE8ZaOxiiJvSzLj17YVuEEWXLeNoEXnEDHQWbZNRIF+USewbBbMiCLUm/R9vsYOLk4QRNybWwo0uWbb5knA/HYecuwp2plVg8f1u2HLgA9yFg7XdM+eegLFEjn0m2+9gfm3vgEAGGx8CAA4fHgGB49SL+nBiQmUi1STPVY1MVGj7RZrul7ahnD07aI6ReaSGWFIoMCfVZCq3Qg+fggJIXiiYQpIk7taiW0obsiiuL95eWAGrWladuniB2jVab8vv0STuQOHZ/Hmn9G90RZFJAY57Hb9OsQGpU+Kk3Ttp0c3MMy+bunCVdxmOVqpJ2hxgCpPWg4c+wyOTR/TZwgAmLt8BSKiieMffuscnnnhS3Q6WMHtN2gCdKBCF//QgUFsrdFkTggftTJNepwcnYvrtRDHTERU6FQDxDJliafguEhg8EQmUgJR9Pj0w3Zsa5ezvh+bKVwCpnYSzBbmb/TcXuq8p05grt313UOy+yGY3U04pdtRf7DUTKHWeyFoa+g4W4+sHfVeThrI9pPe7aSB3Y46XHqvp/MOl97rufydjBwoTRSWFi+knbM++zId47h5M2V0Z6HxrLM+UyOthuPVTv04pUM6xDFt9jaTgatfy0wkaP8L8zfSicJ/9Y/+Gk6+zJKm+H9x7b3fB9CRJs3P1HpO6AocuXSTzz5NSVJtfUi8b33rW9/61rfHwB6JCJtkn0g5KnAJ6kzcEAipRKc0RJCmoWz4LkEcQjkwFEU2Ts7EUI1mtMUCndLNZR8LLSaHWYByuOUlT1GKxRAGlxx5Xg6SG3pAbkHapLKlm1UEfgIVUURoWHkkXDtts+xmzlDY3uZoyTIRMwyccJ1uqGKE3FvbiyRijrYb7Rg+X4JRVmhbayndIRRCehhwKOq9tMztIkuzGDIJui7mjiGx6fcwsmEX6HyfPMvQWjKID1+nWePKB/8WIyYpdp06RRH6zLEnMTlB5U3DgzZGGPIuVxUsRhxMi4uMhUijPwGlGzoDXOYkYHbUuhIXSLj2OKCIFlEbQmhMvQjBym9CCHTgDSawlV0MR0R2q49MYmWVBuTsUbouTrWBYomg75WbLYwepKi4YAdoK7pOJYuuwZjpor5CY1/PS7x3jQCta9dJwW12IMKzB4hgtrX8FsbH6PORL/w9AMD7eYWS0DXf7+Djj6j5QtgeRaVAEXQp3+K/eUQ5ugajlQSTPOOuFBkSD/NoMFFRSIFEabnbBIaeO0stV5og4TSKKQwkxuM3t+6GqXtB4b3Kcna0x2SC2bkrTmd7U7t7XP+sm3wA96Z0pslmf/AB6yMMjKYNPYDeZLNs1Klh8A9f+99TiDZLLtsb/r6zdUfO2Qha/9Zr+W67l8hb3wfjz//PACgC/ypD1r/7+j00LtGIx1CHgKZr/A/n2nDWdQnkG4g/oGj8xK//AwDAu293SHjLjc54Vso30si6U3/92bQ++/jCG8ARYq59sNS5zj/tEq8HdthCCAPA2wDmlVK/KIQ4BOBfgVquvAvgN5RSdxRPbbYivPHmGixHYWaIoJuBYgujDH0EFr281xoGbJsc8/CQkcKIQQgodhx2nl6ihyZbWN2i3bY9gcini1hlpKpkS+Qt1oLO2Ug0C1c4yGmkMqAJQc4OEHJtdRLHWloaNr9QY8+HF0e8LQdNbs1tsnqISmJESve7NuA36QUfeC0EPIPwXHJa800fOS15WhjD4iqdw1JCjnnAmYBRJPZ8aEcw+X6JlIuZoyRN6i1S/nf55hzq198CAEwPNHBilh4m/YAfGp/GGI/3SEXBKmgGvQHBOXWdd0ZsA4GW1QwgYtqxivhv7EPEOk8fQoPKisdFhE2AJ1iwq5DspJPYh4zoAYhzdL2NGCgxnGzaOSieIBkeOVuzlmCSia8ry+tYuMFjKwHpcK9wLt62HDOd1Hx8ewlvnNd9sAnmviASKFbQeVYo5Is3aOx++N/R39uDOHCEHuSKY+DCh+8DAFbXL+KFo+TcT0wy58DzwOg3quUcBhs0AZks06RoTTXgJ1S1GkZJOsExhIGEHbUW4JHChtC114kBKTMtij5FexjPsx+EPfsQayjz/XtADrslR1861ruzl3bej4LDvhfrQOJ3du5ZgZCsgIiGiQ/n2rtY4JNTB++ao76b7eWI97N+1nlraHopo6KiJxLh0s5tZM83zcvXjqHbbl5bwrd/RNoRm216l1OSivzFq8/GHef+L/5bXmc0Ha8Xyj/CjxskNvWtd9o4w9pVvSRbT04O4jJTl1K2OFZ6OuxHnSX+WwA+zvz/fwLwvyqljgHYBPCbD2Effetb33461n+e+9a3R9QeKMIWQkwD+AUA/wDAfymocPTnAPwaL/LPAfz3AL5+p+002wF+9O48YjPAeImoFzPTEi+coUhymPs4l0yJjSaFMAsNB+M1gliDOEDbZRJTnf7aIkDFoCmRrRx4MS0b+vR7iAQ2Q7CQAUyT65wTB6HBikwsQ5kYEjaTfhSStAuYlos0HGCUg8d26MJladIBm740pIQdafavwiIT52IYqBVptrayxtGWl0AV6XyXltuY36J95KoEB9cqI4j496bbRomJXMdOnwWYJX5zjghZmysfYtykGebM2DRmxyhaPlCl/R8/VEAxx32vjQjKZFKYcpDSrjTy7W1ChTxbTbYhmISHREPeCaKAm6VEHsD114Lrx4VSELpY3V6D4JpxaRVTglcc8/ooInGofreAZTiRvo60fzOex8wBmkVHahx/9iZFzZGX4PYCQeWHGEmJSxGigGa7m/VteIE+bhpXNwLmtul6HR4zUqhfNLhb11ob/sAR2u/ENBavk+Tp7JmXcfA4z6636DsvlIh1OgMmDK6hPzRG5zpzoIrgKkcCt7Y6ddZQSBSNgmCynkoSKKllzwIg+vQh8Yf1PDfcIIUohzYJKTlTqwOHCercD7O7GybX9dePQmSdhcI12awXPD63Wk3h1JRoloHDAexobNFtWfWyXgQzIpbdf1S9Yx/7VCLTnbmydrdofTHT1UsjA39nYqek6DWP3tszzNxOIVLsbB6jLRvxfnyrmV4PHWlf/2R7B+qTHe/PcgMRrH07/V2TzogoR1CATkVgPHMs8wvp50+TfPagkPj/BuDvAdBUvCEAW0pjmIRQ3FkwFuQaAiiYsUIz1JCgQIlf8F6TnEq1kmCoRhez4eewvUZQZ9mKETPkvdamwZImMFym3yuOjyFFue8W07ktqeBz+ZZlOgi5HCyHIcQsPWoyK1q0fAQ+OSYR+AgUi7Pk6aVRLRURRnrCIFE2aVmTWeRJoiBYYEQpAy2P8vQDIzU01ujir3l0Q5UnhtHiSceNzQa8HAmYTA0epH3lprFYZ8a5tDH1FLWcbGzFcLcoD9RapxzMsL+M4wfJsZ164ihOccvKwVG6XKVKFTKi/QooiLRjVAB4LJvpM76feFAeO7tQpEIwuiOaCgMkDG0nXhsxPzgqoetlGBZEnm+TnAnHpM9GvgZZpmM08/QAtNevIvAJ/i4kk5CcvzVtWseOljBUonMpPjmONh/r+tIxNw36AAAgAElEQVQSRodonIQkVqmPNUiTrvPBson1CZrsXODWmLUccGCCXgqDQwr5KjnX9jbzD0IPbkCVB0Y7n6bui6aNwsnP0hidI9Z9IizEvmbF26gUaMJ5YIzGKC6auLhI27qNrVT6luZ9XQ5ZRYCi+ytWSPPZn7I9lOc5a1qaFLXOatm8tXa45670ZoHvBX9/mpKk3Trb92pPHCjdMY+dzX12O2ptezlqgODhXvlqzQC3xs/sC9K+m5O+l9K3br3wvbY/UwDWeyyn4XFr/Eyaz/765c44GRV9/3T0vYkPQM/rn16j57mQL6Q8gWxHsux17JWuATo5/zjjsLPWvTwAXPPYyWcc9r3Yflts7nvaLoT4RQArSql3sl/3WLRnI18hxN8WQrwthHhbl231rW99+9nYw3yegyDstUjf+ta3B7QHibA/B+CXhBBfBpADUAHN0AeEECbPyqcB9Jx6KKV+G8BvA0CpXFVmEqDgGDh7jKKsF56RGGSxE59pXovbOVQsioZyogmbo6hYlpHkaL0Kz0HiyEXA0U6Qy6Hg0Iy8KDpiFoLxXi/w4XDk3fbrKaNcE9XCSCBpcB/k0AUYvlTcjctxnBQ6bgbNtMZXcLewODHQ2qKou+01YTl0rK0GwCqiKEoiMDW3EqzXadn1uIqBoeO0nkG/tzeWgRyxI55+7lkEdZpNbs9fhLdCM8eBiGaop05N4cxxql1+cnYUA1zrbbOIvQzbUHqmF0TpecEwkPgcYXMqwA8VFEP58FzEER1jwr/LWCJKnPS7hFEKZVCwZirA8Pm7qI2YG34YYg1CELSlOIWgnKG0I1pr452U4OYwWxvt23DKNBuWZgPPPUMoxPLtZSzc+AAAsNaga9NSNiardO0G2h6ePUL3zzOHKPVimWVUByjymT7iIOD75NINOtdcYQaBFoTx22hs0z138ud/AaEmdlcokpZ2AunytZd55FiaNOfReC9ursBQdC5Fx0TL1cI6Mm2Sovj+TCCRMKtSIoYhP/WCjof2PFcqldSpd6RJx9OoWMPZC/M3sICOtGivHtdZ+FxH2zO7+Uc/M8tG1HcTSdlsr2J6cnbPbWWj62wNchYG/+zLLwGgaLCb/X2/0fV+4e9elo3Cr3VF5NlOWHs1BMlG259/lQRXdAMZoAN/H3+2sx7B3fT++49G7K7ve5PHsj2ugQ4C9MXf/Lt4jV9vfyVzvOmx70bfd1iWaHYvpLP99sTed4StlPpvlFLTSqmDAP4GgO8rpX4dwA8A/DIv9jcBfGu/++hb3/r207H+89y3vj369mlM2/8+gH8lhPgfAfwEwD+92wpxHKFRX8eTz83ii1+giDIPA35AeQfLoPzzAdHCJ1sUhfmtEON5msUcqHmARZFPg3OjbVMg1ophfgCfCT6myXBdEsHk2uc4seBzeVIUShgaoeeI021vwZJch23GcOucv+Ww2qzELF0FlIw8zDw3BWnT/pteDFbyhO1YuHGL8h7V8jhCjrYbTcpthrGFt27T7KtQm0CiKA/vN+j8lGHgqRepGYXpl7ByiwoZgsUPUVM0e33hFEnqnT0+hiNTtP3ykAmjonPI9FdtLEBwzbhIrLRRh0gkEPMMMSYIIPFiMF8PXjuGatB4Bj5LwUZR2j/clAZUjqJKwdKlsBwkFp2XAKBML10/adO5JQ1CCIzwOqIBUmgLW3VIQdefuYBobTSQH+bxQIgK57Nf/NxRXP6EStsWr9CU+CfXKzAVje3sjMJEyFwElpotygAVhyVdZRvX5ujaXr1GEfj0VA3FKRrPH3zne5g88QIAoF0cRGuF8mljjHgYZhPgXuWNlouI+4/7LiEtTRd46jjXyOcu4Z3zLHKYRCnpTJtSqtNSM44h5M+sveZ9P89JHKG1tYKnx0tpRAggVSJ76Vgnyso279Cfs793l3fp5R4kh71Xs49eEdnd7G55awD4U+6jvFd0vVejj2xkDQCfffmlFIWYGO2Mmy6fup8Ie6/o+l6lWnv1j95r/fuRf80qrD37/Jdx8xqpM74Hem6+++47ePXZzrPQS1GuFwdBXyOj0slFf+P33tmTS3CvphEkAPjwIxr/u0XX+81fAw/JYSulXgPwGn++BuCF+1m/mFN49kSAL/2VJ2GyYkeMJO3rrKHtnFrDk6PkQJa3CthkYtBcI8TYIA2SVs+0ZQHb0AzlED7Di2FI6+RMQLKTTaIAsa8hyQYMJvusbpCj8KM2Blnj2W2GiKA7TTFM3ioidMnZOTmFSCufaMavirHdohsmChVqrF3t+i7yZRYTGaQb7w9+3IDkWnI4kwiY/W44tM0jx74Iv05jtLF1Ge0l6g07Ki/glWN083zmBDnk6YMV5Mbp5pWGmUL0yufCRz+ACjUE22EoxzKBTMo8djQGUhoAC5+EeRMeO7ywqTXaE8iA3sgyasHg/t2mT+PiwYMwaAxE2E47nkmTCGkAkEjuPh9vQd0mtre0q8gXSCgGih605nwVpQoxw8XgAGSetjUxWMMzZ8nRLt8mZ5orHsT3L9DYDd/cxLNHyKGOlZkIJnxcZenThU9M3LpN41Wo0st14tRz+OM/JvHh7bUGnvjlrwAAblyfx0Gu+Racjgn9+TQFoESIqMXCO03elweMDRHbf6i0BcfkOtQghsmOPmbSpRACip24YZpQ3Jnup2EP+jznTYmnx0t45S+/3PP3jmP2d8Hk+rP+vhczfL/OupfjyJKRso671+es3YtICkACKbqPci/rdta9CGa/+ioXCOPSvtjYPfd77HRPp60d8bkrTs/uVdr220ccuPvESJ/P7GjHub73Tkck5rvv0mR8ZmS7p8POOmmdjjjEWgqfffklfP3bHV3zL/7m3921fi/NeqBDVutlh3NtXM6xv/J82JnP3bZfOBzoS5P2rW9961vf+vZY2CMhTVoqFvD5F8/i9o0GWkxMClt1RFwXDG62kRdl1BhijU0fW8zYGhYCJZuiGJsba0RoIgea2eZMBSPh+muuTJIFwAs5IjRsxHofpgPJddbhNv21kwhhQFFckHiprKapaHa3ve7BsnTDEBsWQ+VVjqTbvgufy3Lml9rIcR/uclWiVKLo8sLHdPyLzRgDwxQlus2tTqR3mKBFPwSaHkWXhfn3cERQSdFLTwzgM6doW1MniIRljR2ArodWsQR0lBZQpI0kQcxSmVEQIDHofAxDIjG5U1SejsWujcMIaTwMJRAwYtHeJoh5Y2sZ7SanMCIPjtB16wxfiRBRkwo6gqgFxeV3ht+EzXKeJkeUdr4An1GKOApRHqFjqId0fL79JDZu0+y34DeRn6RoPm9H+Pe/QBCX36bv3v7Ty3j+878EAAjDDfx/PzlH21qjQpSybSLn0H1SyQ9ifJKiGmeKyHpv/dlPsHiTovVk6AQWWcJ2YGsDw88QbC83KaoOVg20tugGC71NhAkhJW0mVkfKh46TY5XA4nvOkx04XJdexyT2SvuNOmpsj4OVK+U0utZEsV7Ryc1rO0tlsuU23dF0NgL/tO1+Srm6TUd8332XrvT8+k1MDe1u63G3OutXn41xcpKkmSeO/Xv7Pp47WVD92p6/TU5dwCoO7vo+G3Vmo+zsmPWKoHulHW6ukL7BUtsHMtF+FkX42pc19E77+tY7wFcyyM13/4jQL10y9/R4NuKu7lKD+/q3L6REs6HJJ3aoqvWSRNXn6KxvADi547frn1zZRWLT9mnVYj8SDrvelvj+uyUoI4Iw6aUulQnf4k5RnGelelTNnBUw2UkemzAwyshynmHuRhgB7CxGSgkE6EWaMLW37fnwOcdoOoDDddKWqRAmtL+qw3Xg7RiM8MIUFhKWDt1g5riQMUrsbJrtBmKW9TRYhztvSbgWM7QdgVhSXtrOTeCTeTrHy7fJiVbzVbhtglKdagm1oaO035BfWHYIuUY32aD/Hbz6NL0MnjsJDBwiR20Oc65M5KGYxZ34AcCsZH38KhAIPDrvCAKKO0kZMkSO2z0KQ9fFWzB4ImELM92GPUBjYE9NQUU0bmGjjtglR66adC4ybEHZdJFUvYhWssDXoYncFu3DMlw+ljxUbph/dzE5Qh3FXO6CNvbsq9i+QeOxOf8hlKBccH7iMEos2/rFv0x66asrC3j7rY8AAEeOn8RfevmrdI4G5bUb9TpM5hEIVcQ2S8R+/P5VAMCN2x9heYPO4ZnPPANsUV367Nkagm2qp/d07T+AFpc0qSSBEnRtBeuqR6122iZz5sAMLlyhF4frb6X13Qk7byMxEDBT3jIkoqhnNdUjaVvtJGX3dqzSc9n3dizHy1xbSl+wvRz13eDavfKr+7H7dd4aKv9giZ6PQr6QymYWB3rXX2dhcJ2fPTk5uK/z0E6w27oZ5TdXjvRsianbUeqa5G6byxRcz3D3SXfu0o565+4xOzk5uOP3sy+RfoHO5H7+1Rfxw+/yNguXdgiqaHv2+S/TcV/7Br7FhYdnavV0YvhKz6PtmL4f1xc+xu0FEpZ66pX/onOMe+iX685iDmWI9rRDR45hiicN1++zJvt+rA+J961vfetb3/r2GNgjEWFDSkT5IlQUw2A2toKDUDeO4CgxkkbaF9rMAXmuya6HebTaFO0M1ShyiqIIXsynFyaoN2mGlXCda1FFaIcc6SYKMTeLGFQJwgbD8gzLRqKAgNWuVJwAHCUFLZpJGpaj+WWwhALzhuAyMmCJGBvMqm6GMYoObX9t3cWffEiM8VhSZBgnQL5MJJNSbhBWgY5R5ShKk1tzGFmmHrD/wbM2nj9BM/Ly7CTMQWJIIyZoPA4UPK7/DgMTsaIIJuCGHioRCHjKZhoSZW5Eki8KWDn+oUTrGLadssth2xAGRT45Tg8kQiJmKdew1YDgCD1hNTl//RYES4QOu9tobNPYLS1ex9Ylmv0nGzTzHXBXgeEyj5eLgVmCopavUqQcTx6HM0oRQN2PsXGbpEHH8ouQBt0T49WDAIBf/MqL+HftHwAArly+gEtX6NynJ4nINjQ8gzpLk85dvYYFZvBvuxRKNFsBjh7+DB334BOoTND6iQtMThIKkGwRShErU/fzgAoVSpxWqBW4CU0tj8QhSH1icAilKp3j4soKTMkESX4kY4FULjdOEhjyp0c6+1nb8shfwyyITKkjaR3pdFsvqPVeWOQPAnvfyf7vN3czgO9EOtsNg9N55GdO9CSYadsrkr6baQb20d2iXfR9j6h75+9UI331fAMB95g+/POf4E3u6OL7N9JuXNq6x1qf1+KV3+FtfS2N6OcWO7B7tj5bIwRf+5W/BXyTmeOblRSh0f2ysymWb73TTuHv1halFzbbLp55/ks9z+0Vk94/e1HL9LZ16iYrDwsAfMvusDuRz/Zj/Qi7b33rW9/61rfHwB6NCBsKBjyYIodN7iWBYAu2zcpZBs1QzbwFh6OOomnC5Jlr3lGQrIalFbiqZQcG17sFcYxEUQQrDPq90dxEzFmUQEkoJrOFlkKd9bETbgcpzToMbm8YC4W2S9FuqUrR7/Z2C0GO87+mDcUa0brdZCQSBFxSZckiIlbDCu0GnBxFZ6ucyhsoWvA5WjdLFSCmnNL2JxRdHqi/hZdPURT51KyP/DBF42Z1CshTxBe1KYpb34yxCqpR9u08LC1zzVfdUQmKHLhVrRh55gEYjgNh6MYTXKIWSSiXdcXjIqDr2c0cj6sJk6+XVRsGYl0rSdspThyASoUuE9Q41zsrQyzeoDKNS39Imhz1yz+E3XL5vEZgGnSQOZsRl9Yt2Dkae6N6HOsbVIue3LyKmiIOhBPxjHv8JP76r70KAPjmv/kRLn9IM+02z3jXb17F0jVq9NH2FeKAy3UE3VszT57E8y/T+mpAoGjRhRodsOG16Rzr80QCnM4BDqMUxWoVplnlz3Rd7GKMqEjjNTI+hZEafX/dnIPBzUgSRm+EDNP2sQYAZTw+Oez92l71yABF2tkoO/2c8rnO7+iTnCWtPUgJ0v2ajqZ13jobXWfz13vlrbX1iq7vN6rO6nMDd+9TfT+2MxI/g8+ngeaLuHqe8s3vvk1IYHfEfScbnziV5sl75bNnRz+hKBsAvvkNvMfNZdK/maga6ETW2p55/ks77rNeueteinrZHu69NMUnpw6mDV3s9fVdvz8seyQcdhAmmFt2YcJNX3hSSphSy4hyHbaQMJjRa5lG2jVrytlAXhExaGODXqiFoouxCjmuplFBPaAX5SA/P16jjGJEMPdm00JxkIZiedmFb3BNN7PQY9dAzCSrxI9RYGDC9dhJGxKG4N+VRMzEN82kjqIYCRPgTKMAp0zH7TcUfIavE4apYzOHvEkXPh/ZSBbphp1xiVh1dho4PUPOKFcdhlkkoplwZtH26YHfcGmb80mCgK9wxbFQZCnVIk9IilaEoibi5wyAJ0OATFVKBZ+LStoQmqgc+lAWOUxhMPHONIBAb8zuCE5nMRweI2GZkCaTiYwSpo4RzDb0G/SEnPs/Wki4kYk9fgzNFXKotQo1CbGED7tGjJfy2ABiRS/Hzesx4uv0kqpNM7sTFmam6WX1la89i39ZJ9zqrT8jCdN2q4Uiw/+ekLAGifl99BixxKdPnECxzFK1yTrsHDPWmy0MsBMNfd19rYzIp4fVylVhWPQSCZlZbiBA0+O0QTvGyBidT66YT2u1pdANWEytwYIEMZKest5/Pu2FcgdbzELeWaJZL4i8Q2x6LSU2/TSdNdX87q7P7vRP7khh6o5PMyPbODk5k/6eJZrtB/b+NJ30vZp25kdP/yoAQrC10z45OdiDVPbJjt7ZYKgd25fS66e5bhPHTqfb/xo68LjuDtftoLu7ot1pUtht91uZoPdRWLi5q0+2hsaBB4PH+5B43/rWt771rW+PgT0SETaSCKq9CZm3gYhnIk4egiFpn+FoMxCApJmLCwtTNYpmhpw6BJe9CINgSKEkVEBwckGuoWYS/K3LumJXAlxqJUQbVkzQ8txmA/mc3i9Fj5aRIDIpWi7li2hyFJ8oht+LAkUu4fJ8CT0PUnwuQRjD4pImwxLIcZvJy59sYsOj7RocgTuGhRyfQ967ibFwDgBweIhm7senIhRslsIsTCOyuHxpcxgr3LBjK3J5WEMMc9A7aVqoleh8LYdDZWlkpmwGVBrFZeZxWspTJSk6jiSAMAgeVxZfLzMPFOgchVOAYBhbSZmuA9/n7wSg1X6cKhST1SwmGQ4eP43lHxMBZPjwYfjLBJmPPvFztL4joVyKuh0xialpIpD5jdNYvcVtPblsa9C8RiptAI5Pn8Cv/TpF8wHDzR+8v44kpOtdHhuDOU7IgVXjsVyZg7dNSEu1MgCzSsddyw8gWWI1togGxjUctAOKqlVuEBaHyEpS2ZjpbaPC95zyfIwMUkQ4kCtgs0X3asz3mUgMKO5il6gEUjwaj+q9WBS6O2pd79XGVv8tfSh3IEcNRb7vY0eE3V3WdXFhYwekrCOzu9UHP0z7YKmJeYZDC5xf6o6us5E1sFNaM0s06xVdLy1e6FlutZ+oei91tJ2R7sOxyamD0Cjx6vAvYQa7UQ99PNb4mTSC/uF3T2DB59QG6N2xeOU8WA8Rs6PISN+SvsL3Pupsc68Sur2i7KXFCz2/73U+wM7WrwvzN1Li21whvyvCBh4O8eyReAuoRCHxI7SjCHmbXpR5CDQSlsX02YEKwG7ofI+DI0P0gndMCenQi7bAjqAkAui0nwpaqLKQx1qLa5HDCB47iNGKxOI86f1urLVQG+Q8Oddmu0LAEFo+U8HM8due3/qmISDYYftuE8qih9BhGGRhdRXFIr0sQg+4scAiKnUPBtee2iw27iQDcEJyNsfUhxjmvPLsEOdxpQnkCErdts5icZU+b7h1SJPWq5VovyN5G2PsYEoVAaGdBY+7EApKJ5YTAcF9vhE0ARZUgdvgQRREYQeA0IXSTp07bEFaAOfj4ZSgbP7M+uGwLKSC6koA3I8a7TbQpH0IzoFPjA7hEz6uJ0dnYPrkGGFoJrWCNIt83JuoVEmSdfLgDDaXKZ+8OE/bVMYmRg16oRnKwkEWVvj1/5BejL8/oXDpBu2rbUdIhJ+OJwAkpoEqpwXq/jqqa/SiFSULIwNcBcD5g1acwGdxmcBbgszTtZElOu8wBCLFKZ9cHseeotZD733wAVpN3h/TzGMDYLVciMSAkI9nDvteHfeZWr1nblDbXnXXd5MW3WvZB7FuWdJsNy7tqHvZ4Vy7p6POmmZbA7udR9ZZz45mYeQ7O9n7kS79NBz5+MQprPPle/EZB4s9Ol/12n72fM9duQGAOAlZSF33AgfIcS+PTO3odNbLev3+QvlHvdvQ9bAdPdrZJqcOpo78ex/No8AchqzjfhiM8T4k3re+9a1vfevbY2CPSIQdwW2uQNkFCO59HQYhLFvDqTRbyecinDpCrLHnDtnI2dxAQthQNrHAQ67jduQ2ZMjRtGUjx5FTTmiGdgLFRGbbstDcIMwmUQla27RsxCSsUFgYLnOUDwMGf59wnbZUJuKEI83YRL1Ns6pqlaJn35cwFJ3XemjjJ0yDbAcWBHeKMhxmxEsXMyHBwZVchHKZIlTFVyqRVdwOiaSydNNB26UI5sRkAQeHKKIbGqFZvl0sABZH0IYCEo6aNZEsVhAMR6skgop42TACWLYVXkdaNJV7i9ppYxM09DaTVEIWht2JrB0daVudaNyyASbZIYmh6lRDHzP8Lt0WKgXuEraxjMowwYMy5HXCFhRHoqpWgUiafN5lzB4nZbhL79O2bl66iMSlusmJgyHsCkXjR7kD11//paM4d5UguHXXwO11isJWtonE4rW2UY8pwh+OQgxx4xen7aPF42XqHthOAwkT4EIXkJy6SLjTWpQkcPh6emEToyUito0PjeO6JJhQd+2SKoGtx9OPIOxH4lG9J9PduoAOJJxl7mYt2y97P5ZV0PpZme7G1XbbaYSdZYd3w+DdlhLorgDADQAUsekIswN572Rmd9t+moB0W6+GIBPHTj/Qtu3tzja7e19ry0Li2ggapzHQUe1Pzr2RXvNFdIhrOtIen6jitZc7DT2y0qPpMWQi7F5pmF52M6O+18sW5m+kEfbQ5BOpmtpeliWh3Y89Em8BpWJE4TZMI0HMmt86fwcANucCB0oOpofppd9sKyxu0+dcoQjJuVIrppe3Y7VgsbSo41ioctnWyABtNxck8HyCpVqui1qB1m9FQJthc0/SixayhnXONecLgKOZvKZus6kgWNYTUYI4on3Y3GHLkgomM7Tnb21ig52gNKoIuIynxac77M+hXKPt+lYBCec06yE9rNutMWzfoP1Plq7gpaN0DkeeGIc1TGVC4I5nBFtzTjT2objMTYS6ZEt28HGki0IoBaVnM9rJR24KkwsVgudVUNyhC20filuUKpVAaNVshsYTy0g12GHn07x1HHrk3QD4XIrV3lxDmdtvRrc+gDxI0qRR4xqNmwwh+BqJYARqnBy6VR7H5Cw5yXbrIADg/I+3cJ6lSf3GJg6coNx7waKH/uC4BUvScX2wMoCJA7TeVkjOdGNzGeuLtP7ItR+jynNIqxHDZedqMdcBXgStIGoZlbTEsOXqlp4W6rokcGgU83PET3DbLgzO+Ydc3iVUmFYOKKlgJI8PSzxKEmy23TsKhgAdR6YtqyV+v3an/HQ3hL0XHL0fm1utou3SyzkLh+uyLmAFGN+9v067x+eB7c55a8GXiWMOgGxueqc9DOesba9Wm/f6+90sK13qzl0CUuGUHpMD9HLawNLiQQDAAl7BxYXXAAAncWkHezxd/jxdj9ei2T0lR4GMs0Zvrfvj1Yu4vE2iTXtp3mdNTyq0EA7QuSd65bT3Y31IvG9961vf+ta3x8AeiQhbADCVAPwGXEURWcGI4WniE9exNrZN/Pg893QWHiTLY+bzbVg2zVhNxc00pA2bxUyKjsJYibZVznGnLDNEYhNxKWh4kAx1FnMCMLmnsUtRoBt6mK8T7JV38xgu03p5JqXFJhBwYw0FAcnyqZbBjTUSAxHD86ZlpoIsEWKIhI7HAZ3XASOGI+m8HDPBhqIopb5N8Ho+tnFqis71c09XMHWCOZOVGhQT39JpWCKRqm9ECoLZ0lokBonZIZeFMQSjFNxbiq6N0tFjA0pH1SoCJEdPNv+NI6iAkQev3omwQ80MV1DcsSwGAL5OoQpT+D2AbhgSwszRdqt5E8EawdOSt2UaFsCNX6S3CjBJDxNPI1eiaObQLN0zrcYRvHmOoup33j6PyCeRlYOcGimagxhjGdOlRhur3PNbOmMAgJlCFVeXqWbb8VaR4zESoZ0SDcHoCaQFwbC9lA4iZu1LFpEJwwgxoxtxvo3NBkGkA7aNapHuKX+L+6arGAkfo5ASkYY/HiOjKHNl1/caJr/mFYBPiIF0/ZMr2B58DsDOhiCaeXsTozu2cbx6ccf/sxF2NzzeHVF/WhF3FhLX9nSP6BroENUOVTrncvZY7Y5ypA/T7idqztay79WQ5F7r3fMzJ+647ywLHOhE2xryfhMH8cY1ino/vvU2dL+xNNIGcPQ0R+jnb+K1aPaejitr+nr4Q69gdnejtdSyUXk22t4efA61pT8BkEVaOrZfOBzoR9h961vf+ta3vj0W9khE2BAC0pFQfkKEJwCJpaBYGYtbUWO7HaDls1qWIeBwJCuDOuwC97MGRVaBcmC06DvDUChsUCTJi2HAUnBsbhGpHBgRl3LlPNhMjpI+98uGB8ltJltNF1XOd+eZUCUiBz7nJmMZw+Q+3prE5Scmtrk6KokdGDo/GwfIsVpbgXP304MOdBvwUA5g3ado3+ZjfXFa4sXTRC4bPHoAqBFpRzmDgOQVNXksDtJGJcouApw3TjtUCCPNYQsloVgBDSqk7hNAKieqVAShr42KIDUJqkg5Y1RqEDVWPdtaR8K9r5XP2wwSKI7aY78JxT25E3hIAm5Qwse37eUxdIrqy4tTQ/AvvcfL8v5FCbC1AlPsozgAACAASURBVJuCGV7lfdUhpqh8qDRE8/RTpwtw+Rh+8mMTH3/8EwBA3nkfADApC7AnqRnAocEpLDNZ5NIVut65hfeADW464puIOZo3YwMyTyiAAM2YC6UiuJ07PM9DwtfUAd1PtozhcnnW2sY8kOuUIpb5+q7zfZbEJhLoVrImEjw+ZV2mlKgV8thsuz0lOrOEtGteJiLtUQKmJSe77b3NFwB0IvDLO/hcnWi8OxL/WViWbKYja23HqxdTVbZsdH23Mqq9fn/Q3HavCH+xx3L3qyCnI/OJY6dx7Xv/T8/f0v1le2Pz3zTSxnvQJVxvvA5cXKDrexIdRbROhN6pZc+Sz7K5616W5q0z0XWvHHfWsr+fqQFzd1x6//ZIOGwhANMU8BKZwoAmBKJYC5vQ3yiMEDN0bAgHYcRiIz4A7oxkGxn5S4NFUlSCVsgQKTuzRmhBsfN3jCGImBxjIYhgaOjVo/VLuToMltI0xTos1tHWAiZRAPgMViSGkXYRc306bj+U2HLp+LwohmCBEgmFmMl1R5kRX3QS1BPa11ZURYnfZ184Si/vzz49ioHD3JWrOg1lDfAo2kgZTxoGl4BiYpQKfUh2viJiZngokTZiRpw6dBEFUDy2kc96624A2aaXo+HVkYIzedZoLw4BVRoPMXkEMmSIaJNezvHGMhS/vJXfROyTkw78Lei+5b5HE6Rm4QimiyQRatRGYBj0sEVKT4QiSD2hCJFONIRcgmJ2umTp1NroDM5+hpxFiDzO/4iWvfIREUPswvsYsui4neqLyLt0zVxvKD2mMk/cDD9CyCQ85QfY4omk4/I9GXgQOh0CG4onYyqiY/L9AHl20rF0UOfZkHJycJW+f5gkKBQsdtKxiCAeH399V8uKifSy9YWP71lwZS+H3rGT6afrDL/PjNzTpu9o2vF+sLQTXtfkoqmhId7X9i4nDSDVDz/70mfvy1Fre5ikM3fuUuo0dzhLPq7s8enf8zMn7uq0s6mJsx311XRfev3s/veyLIucnDYAvIQ3Xk/3BmAnEW32GHDy/Qv861TP7cb1t9PPRuX5Hb/t1EA/iUfB+pB43/rWt771rW+PgT0SETYASAVEoYDiDlpeEkPqOlTuCBXBgIjpd0OGMG2OdhKBmIlpbW4ekuQS5ARFqiIGIpY3NRzaZqgSGIp+r8cJ8jbNghMpYPKyCCl6XG2vwRJcLmbWUI2IxFTizk5eIIEik0uiCFaO+3RvUeTV9NpoeBxlJRIi5mNMEhQYHj9UpmOJ8+PY4v3mSsDzoxSJPneE5DerMyeA0aM8LpVO66046eQOIg1txxT+AxChlzbf0AXoIhKAhsENA4LhcxTzUEzYUxzVbtQllEnnVTAFHJ+gYaNOUJC9vQyxxnBAeRAoURQluFmGHM0DDYq2E28JYYPGLmg2oQYp6owMKksrzMyicICALRmHkDX6HK2wYplT7pSb+QESVreTgYDUqmkrNLNWpo3a5EEaw884aNTpmlx/g2q/yxcX4JSJVJaHibxH+3IYGi/7KwgbLIfrbcBmNTdpBHC5pM1lIppMEgwWGf42Y/icZjEF6wHYJURMqixZOTicUnFkFZOjhEhscO7Egw9LaWW6GEpqXdjH37obNAC7o+79SJv2sh0ROJPaPoh3lt48cSDuXu2ONrda3RVZd1uHbLaz9rq7M9e9RNcPM5rWNtc+kfad3ita7gWFT2RKsroj5W67W/cxbd37z0bb3Y1CwqX30nEaX2lg9vANAMDla3qNizvgcWBn5J6Fw+P622lUHdff3hFtA8BlPJ+mVLIlXr2su+xLX/8/vbabdPYg9kg47CSJ0XTraLcVBMOAvmnA4Ryhw/Rkw3DSnGuACCHnCGNEENyW0xD0gChbwuHa6EjESHgbJrObpQwhuP42thwUuYVnMW/CYUlJLa0tnAGECV2AQI7j/Dq9VBe2yFkN5rZRztMBFE0HHjPC61x/6wYKLrOHE0MgYVFuCQMzPIEoDNI+11FDhXMnxwZDPHuYbvqRcaoLRmUMirXGYeYhDIaJAwFIlrzTEw7fg/DZOcNImdXg81YqBCJdbx1BaO3rXBEC7ERZjtT1y2iGNLZmM4cq13pXPGLPJ34T2GA29/JtCIdSDAbnLmWhBMXpCmfoKKKQnrCw7aEZ0zlwWhrTR47DGpvhY1QwWbBFLZF4YBRuwhJaElUC6cTMhtBCL3WC7xXegobcR8YO4eyZgwCA5Rv0IN+46WJkkqA7WbwIO6Dcu3Bp8hG7AusrdN7SiiE26EVddoACy91Krsff9MKUBV7IOZCCHHJLy43aAgkL+0jVQtmha1scqEJyzn5unsbQ9zwkXHstRILHyV3rOuxs7eleoiLaejnxh2F7OXzNSAeAb96gv08b30+/68Uc77DLd0Pc2rJQOLAzZ/3qs3HqxLJO6X7y1Q/qvLVG+fgE0jaWADDDkHXWcfZyxNfmOk5e293g8fzMiR3noHPM1zKQ+N0s67j1GBAbnFp53rxGXbs+vtXcMVHQk5Kx1XPpdzo1AlSB1b3TJHH9bXzMUhx3qyTICqtknbee2PXrsPvWt771rW99+wtkDxRhCyEGAPwTAE+BYt+/BeASgH8N4CBIZ+9XlFKbd9pOFCtsbvqIQh+SmVHKAAKtgMVNKwwrB9OiyMqIJdCmZSNDIVZaYpNmtpZlocVRWBwHsCR/VjTjEXEMxTXZBiRaHF3mbBtSUuScVwznmirtolQsF2FzT+6GpGnpotvGtEfbHbEXkbMpsra4ntq2TbQbDI2YJcRM7jKQ4GiNILs4zz2wLROzVYoIn54YwPgIweNGSXcxk4DUkmQRFBPbVGgCzJAHKLILVR4+KKoJAhfJFs3yig4hBPmyAgZo/8KyofI8i8yVAIf26zBJb2SkBSxSDfOGH2F1laaeYxyJTjgeBPeVTsIQguFvs8mELNOB0qmNXB6xTfuS5UEIlk8VzKYuD9XSTm0wBeQkyRNai7RNd+kqJEgu1BIKQmmilgPFErGC69cRhlAbxAwXpo3ZwwS7H3uSptQXloaxcpXumcrQBnIx3aoOd0FbWm3Dj2ncIj+AZBLfuuejwuhGRTcsE21EbUIRtuwQZYvJiyxlW/cF2qxkZuRKaPM9OzBso1aluOPA7EEAwGbDRcDpjEQKKK2k9ynaw3qe4yjG1uYmgiCArbuyZUxHG3dqlKFts92ReNSReS8VtXtRVutl2Qj8g/jn0s/GNt3fcf3tNLrSf+dW995eLyj80BHq835ycuWu5Kq7Wa8uXlq6tNt6Re5Hd4hz0XO1tHgh7f2c9tK4i90PS7wbDtfHlZ/pENzudXvd0qZaCe2zi3szx8no/freZqUnPtKLGJhl9xO6shMy7yapZRXQdJT99Cd0P3QjTvu1B42w/xGA7yilTgJ4BsDHAP5rAN9TSh0D8D3+f9/61rdH3/rPc9/69gjbviNsIUQFwBcA/CcAoJQKAARCiK8AeIUX++cAXgPw9++0LZUk8AMXpgQSXa6TAFGsS4sog2faEYrcc9m2Ckg46IgTI428hS6LUWZa3ZQoC55OfnPZjSkNJNxeUwgbBudqvTiCycuGCZHLRODD56YOjaaNgkWzsXKVZk9RMY8rLTqYOiIcAa2X1GmGZhsmtlgsXMRtKCaKjTrA0DCVG/gWkc/GqgZmBmnWOFmxkK/QZ8ElUygMAdwohHpNsy7Zpo/6NpdFcXOPyLLgcg/q9tYWRiYIMXAqNIaqoGDk9ZS60wxFBD4UIxEGowmVkQEUx2mWOn36LJrblPdtXP6Q/l56AwUmTMliDrFuBxnomvAwJYpFbgOR1oyPDUByv+lTROowBicBLo1DqQLBkWbuMEVDW1sGwlXud131YDL5TxVqkAUmGcV0vURoQjGnQG28D3uEZvfPPsUtSm8egb/Imt+bCzAKrD8viJRmJQLLHkVbNaeErW3ud2zFWOcyNDdinXmRR7XA4yk7Sn0lwfcvFPLcItQLFbY36bzqW5vw2zSeJf4rhY+Yc+RRHMMUn2726qE+z0ql0XUQBHdadM8o+27RcreClK777l43mxvXxLbsd62tlV2Et6HJJ1KyWnWjim7iGFDtqWBVyBd2Nfg4dORYphZ8NwnrTvnrq+cbe/6W7Y2to24dbd6r6eWPnn4RwIu8T8oJ29u/c9+11t12NzRBj8Gb7/sYwe7c+b2UenUroS3MjwPre9fen6nVcf0e+8Rko+5ejVv2Kgm7eW0pkyd/uPYgkPhhAKsA/pkQ4hkA7wD4LQBjSqlFAFBKLQoh7lx0ySYgoBKRlhJDxVBMFjL4ZRX7IUJmkUtHIpfTsKkB5WiJTHo5elGQnlwuyiMwyCHaQte7okNciiUSobtOAQZDs6FNzi5UIUIWDYlUiMhlWU2uz63Uyumx3EqKsPm5mSrTcsV2hCigbTXaCQImeh05UIGoEfRcYHi0WjQwwMopQ1ULcoIecjVGsJqwBqCYWBU1Q7RXCBr2VCslNJksfedvNhCsEVFr+uwLiLgxSnOThEYaVy9gcJRuynwegMukNVGEcIiVjuI0jbddgbTpJWbaVeQqtF7tpVfo+F54EWLrBg3hlbcRXCJH7i8SsyUM4zRdIS2FhMlw1tQpWB5BSZXPfYV2bzhAixyyMi2IEu3LPEBj4Gy4WObJgVX/ENUBcrjG9mXAIAa91BMRvwXJ11ypTahNcoJlk8bi4KyF6xs0kQkaq7AMrgZQJH2wuGIj8uneaVoWTGbSx4lCkVMbSZsnQBZg2Nz4RVqIeNKim5zJJITp0H1iRT7itqaSebC4P3g5oXMpWAGaHt+fQkAT5z5Fe6jPM4Bdzlr/X8Pk2b7AWcfdy1lnHeReznkvJ3+3uu87dRar5nYvPzOyjfZHHXhTn8eXnpyFUTm2Y9nj1Ys7iGZ3c9RZJ62hbu2QlxY7PbK7e2MDwNzrO2uouxnWWdtr/x2n/x+n34VL76Xb2g8Uvte+smxvLTsysvbt9Pe96rN7Catoe+mYj3/N0qUnJzsTMz1p+u67RkoE20s2Nmt7dVjrtjvVcz9Me5BpuwngWQBfV0qdBdDCfcBlQoi/LYR4Wwjxthb36Fvf+vYzs4f2PCepGE/f+ta3h2kPEmHfBnBbKfUm///fgB7wZSHEBM/GJ9BL/R+AUuq3Afw2AJiWqYSgtoyadEaCjGQGK0ZFQiHUbTdFBMGlWI4wkDQ5ymI5xyj0EejPKkyjrIDbZCohIRyCsWPDRYHJWZBArGj2nPMJhlZoweA6b6UieNyf2/UY1t0IYQzRLNqxytjmJhiD3N5TChcWE8JafoSxEZrZHRifgMGqY5U8Hd+QMYgpJp2ZMwPA1NO0XpNmvivnfojmFkG0dW8OgyO0bGnAgcnqXorbWA4dOo3xJ79A5711E6UaH+NRgr/QPg7vwu/QGDUXEbZoNikaa7BZUS5XpFmyOf4FqNpx+t1oQMummbpW3ioCOYL3jReOwn6RxqYwxzPzt/4QwQ2KusN2AHDde3v5E5RfeZW2VaDjF7EB4VFaQdVFp8FIjsatcvQAlm8R82dhbgDmFh13sRhCbXIEEHOdSjGftgKFl0DFBFXJPF2PqarEOiMaoZuHUSTIWzdjqW/4qFXpuNYbLoa5pG7LXcEgN49xOVI2zSI2A/o8nMsj4hLDtRbXY6sEJS4/TGSCkMloprTgWLoPNp1j2cxjmyFxSu186hH2w3ueTbPnDLwXAW2vKLsX5Nzrt+7IW3+3lxTqnbanCW61Qj5ddnvwOcytUs12NtrSx7q1uYmBWm3X79r8oVcA7N3s4offfTP9vDB/A884HSWxRd6czeu/+MzpNCrNlnfp72bGz2Du9f8TAEWhOhq+1qPGuVeDjb3MGj+Dma79ZrffbXofvcrRehLhTpex1KvoG3eHx3s1EtGEr4sLF3e1Xf1gqZlG1nf6DFAErmHxO8nL7rLVK7vK+rL344OQz/btsJVSS0KIW0KIE0qpSwB+HsAF/vc3AfxD/vutu21LKEDGQJAoSIOedQIB2eEpLUwBBJohHScwAoIqlWEj4frXiOFmQwK6mRKMBAmLWMQMG4dRDCF1bhEIJL1JY+VARwiBQS9vGRsIOCEewUy7Tklezk9cBJwrtgoFBFw1K7SmOHKQXCguEeMkC4VURitI+GVf4ZrcA7U8yiMMtVafw633WXSj9T0AQPlEDcMG3VjW0BdhlFhgJFeFYAcCSS8TYVRSGDoaqKG9RJBa1KRtFmozMF74Ddp+WIfNTPegfh3+ddLa3rpKD4Sz8H2Uh8jR22OfgyrQQyFZ1lNFKs07i2IZKNCNKifpAS3+1bPIX3sLANB+8w/gSpqA+J+8iYkqX/OIO4MFCRIWfxHePMDOUwzRhMGujGDi6EEAwLyfYOvmLV52Dvkc56sDht+TGciYIetGEzDp5W2M0riZ9jYGBlmO1A2QRFwrLmjCgLaJ/DCfa7SEkDW/A1fA4+srTbqnltsexvj29NEGitzL3GN43y/BrDCkHnpIGpzqMQZhlLQuPd1Hhyo5LDdpPPxIwMD+O/zciz3M53kv64bEd/yWcdz7tV6OfK989sO0LxwmPoRROZZCoxoWfcY5v0NHW8Pb9hVyrC8+k4GrnzkBa/xX72mf1viZ1AlmneHMyzuhbG3dkPZOJ37+jsImWSebnRxknbe2u0Hm2bxz1jTEv4SdsHj3drsdd6/v9aTnXlTkO1r2u4Vwsk58brV6z/C4Xj5rT4+XHoqIyoMKp/znAP7F/8/emwVbdl7nYd/6/733Ge/U994e7m30iMZIgAAIEYBEhJAoa6AZyrIjOw+JVY4dVR5cieKqlP3mlzz4IVVJpVLllKqisuQ82JYcleQUbVnFokhKIShzAEiCGJrd6G703H37jmfYe/9DHtb6/7P79LlzN9hNnlWF6osz7Omcs9e/vvWt7yOiDMB5AH8PDLP/GyL6+2AN9N/Y5z7GMY5xfDwx/j2PYxwPcewrYXvv3wIwqsP+uV1vizzSRMH5UCGn0SnKCevZEeCkgi49oS6Qcy1RMAgVNldmlgAIe9ejgBV5SLIBXnfI+sE0IkEhVRIZA4jPthE2t9cFhHyOxNtodhVV15xBVwhddddAUyBpLYYehcuQTHFldqBlMTkrxI71KdQTeVx8nF2jiStW2Kz/8a+Q6L8EABx85kUAQPuJzyObXJDr0ofKBqwYEhIdyRy2hwcEsUgmp9D2vIrtbzDFw3pCkk3LOTZgBaZOm9NIJrkC1YtM4lr94DtYfZ+r7smP/hPqNa6ssyP88SezL0E1+bjQuQHI3LtqMVHNpy0kbSZ3tT/5C1j6LisPNc6cRHriRfkchFLRuwzVF/SEHLAhBiZ1hsHV1AImTzEZ7s6PLsIc/hk+xss3Ybs8K14zDL8nWEPiGB63qoSHOLBlTBVN/QamhXhX/EDDC0hhg2QrTSNf5Wp7IqmjFMZ6c6aN2+tcFR4RQ49ObxU3rUjMFl1Mi7LbjMyEb9gConIKSgsY4W701zUawsqfmRAWuXNoiOPHmnLofgx94fv5ex6OUVU1sHXVDYye2R7lOz3qMWBv89nL3R5a4qmzdPXdkcSzzbYZKusAy76dH8ZC9wQA4M0vvY9P1sSpagRsvNvYjUJahMClkq46ZvUuvX+Pglm14t5MXS1s/whGm4Zs9v5R8Hggu12/dq85SDW2q+CHnx/2Rq/Gc4fbOC+gXmv6IL5//WZ8HOAKexQ8/uOMh0Ka1CvA1xSs8dBO5EJh4UPClRs5OVaiBACtUxhJvjbxqGn+VQXmrbUOXiBxbQlahFGCOEemFWqak7h1GZSMezUUoZS+bJoOeogUZDVtgp4S2FV0za136Envcm2th/Pr/P5zl3lfXT0NzPMP/BBS3CpFSGPNoCZM9IvSIz+w3sH0Ve6zPv7YHZx56W/xMUxzUig7Z5Gvfk+uAUE3ObmrNIFORIwkEdesbCY6nXl0oSY4edbrIhuq+gCCCAtLpQKAgoMVuFfPMePywMQcykWGpLsXzmP1Ivf1kncZIW1nf4Sszc83Dv4CkoMneL+G++3U76Es+cfjdRtJyS2CbHIZVDCTHcJM99kE/Bqz31F0gK7og4tUJ/I1pEd4IbH4qeO4IveKtZVXUN7mz2TCcT+yiRuAaHJTewZe5ELLZR7bUlmJZou/G4ZyoLciu5UFXHoAQReUmhb9ZU4g84cPYbnDx5hLMrUuAzry/fMefRHbmW2K2E9ax7K0TsgV8KHVk2wglzbFKvgzbKUKEy0+3zs9gqYHL5xyv4KI7hnp2m68azi2SrKjEvPwY7tJ0tXed4hRFqDnPxrsIyT01jTiTf/8O1dwqs7fjzAW9sLMWhTUeGLqPTSGrDSHk9l+Evhu4tTnfmMk83uzvvcoyDz0y4e3EWJUoq8Kn4xK3K98soZvvs0Jex5bJ+dqbJfIB7Kyd/+Owuf83OE2vi+PbacTP0p2dicRvmf76WGPpUnHMY5xjGMc43gE4qGosAPpjFQCI/PIcA4qVBVSWKkkQZZSfMyRrNqLAlkiJCaptHP0UBd2sQPBZ1zlNCK8ThG2TQuLnrC1U2h44pV20efLo4yHF4MH6BKJQJlWSnifeqRShmnbx7oSiNXyMlwB0Il4QRuPTpALbSqUiivnIudq7JbvQYsIy9vrk5j5kOeBD4s5yOljBsdOchV29MzjmK4z9JY2pkAqGF6IpCX1EGEGpUAiDqMyMcsgH72zPXVhHK/8fNkF5UK4k+PzzYPIHuOqQc8dRW2Gt9u7yfTOO5duw9/gVe702nk0bzBknc28xPtvPgaSWXabAstLXAG30w6mb/8V72uSq27qe3iZR6Y7H4KUGKcUQhpq/GcgmYVvnn4Os+u8rfeuzKCjWaKw7MjqV51DnfgaJnYdqAtJTkhvlDmkOVfwE60D6K0yQcyIaI4FkIurW6pS9Es+X+1bmBKCWiHfw4bWaNbE13xqCs0wc53yeddbBnSHy7F+j5CE73rNoCHXmaQPlOczOKh4Jf8RbaCwger+6MR2wilZliGr749Mt1klXSWdbcU438kc905jduHpeyRWqm5hby1/Gh/I9+v42UFFuLB4AgDwmV9+ZV/73yw2g7Rj1VyZ2a5WqgFOfgrv31VxA1uLmoya/65C5sPM7iMYVNlVv+s/+tLg/YFItpt4auHASEg8ICYBDdkqtprV3szrfLttXVla2vF7hmNcYY9jHOMYxzjG8QjEQ1Fhew+UFki0R5bKTGzRi7PTJDO/mhw3vAF4ryHj2SgLA9eQRmNNRoR6QN7jakZpitKjHRu8iQ28VJc1q0FSxq8hRSqEI9USslOWQAlprfAWIBnnkeK1lSikYtu5UjoUmivdupDPUmi4UnrCigDHx5UWCUgqsjzj1zpNaIqaVuky9Dt8jFdzruzevngF9S/zscxOfB9PP8l0kk++8iROPsVVeHOSq8gkNdz457OAN9ILTrjyd4mGEuacNx7eS38VayJ9BlAwFNHN+DlQegj6CbYnpEkmoqn2QfRvn+RruHwVqze56s2u/SEfU3sBdoKlRfvtp6N5yORcArvOPexUJEphJkBrfKy+1wFkJhs9MW7xXwGJhCc99gqmn/0lAMDBja/i/VJsQW/L96SX4IDicba6XwXJyBtSrrAztQhNzBnQtS6KG/y+fk/6y1kNScpVYLfIkcmsoO/1QOK9LmqjSEnjoMjGLs4exfxJJrs1ZY67m3dQu8DV/sXL76KUz6PoOHjD59az/N2Yn1/ApTWZ888IvvfoiJFUpUmjqtkmRiBhnGu7Srva99vpvPZun69W2KNkTLdTTFu6+u62/t2h4n7r25Xq7tv8/fzG19+MZLWXXv78rqVGdxPVsTCg4nNdeU3VPGO4Uq0+1zj25KAar8w9j5qRHuV7fe3s9yIpLj38Qqy2FxZzXL1yAQDwds7Ht12lvZ3VJzCorLnS3rpfHeLk6TN32WaG0MJPqEqRjuqBP3e4HR9vNpp77mM/FAmbk0kJBx1lSBtpC6UkTi9JxUEhIINJ4uHNQAylEOQiFTnInjOA/G18AiuCK0kgrZUEb/n0czh40bbWWQojghZaZrtJE0jAiBwaNYG/gwNYzdWReGFmuxypaFerLEDnKbzMf1uqI5VFh+s5rAU4VU7MoImuJHRbJ9TEK9pk/FjTKXSEAHe7dwfnv8WJ72vfew+nFzkRv/Yq3zSeevEpzB7im0yaNeBN8MuWhQymmIUNQCsNEnF2l8zDqTAPLzdHXwAyt66Vhp84LMfDXzztLgBt3ldx6ElsXBZZxbM8DZncuILJpa8AAD688VdY/ISQ2V7+LGwpPtfrLAmZlDOgnM+bNq4CRm4WTkyi7E04WXjBF9AnXgcAzB09hovf/pCvjZC37J0UaU8WfLoP3eTjTRqhnQKo4A+erKMvC7tCxG9M6dCSdkpZrMNJu6FAgbrI2XppO8zPzGHxBC9aTpw4gcUTLFM5e4zJdLnpY+I9vpm4NzUufsTnm/s76AuhzsvC8KOl21jPSY7FIfGjWdQPc1ST9GYs8Pjafh6TdrfX3RKe3ix57/T5BxlVWVNgcz/uUfHW8mRM5H/87T/ECzP8237tdW7z7BUy3w2RLSbus9+7C/YOCTok5moCf/ebf4Zff4UXp9X3bCd6Uk2sYaFQhccPH3kmJuwQ1f0Oi6Lc73jucDs6rR0/dTi2LqqysAuLA7nYrfTDq0l8TDobxzjGMY5xjOMnPB6SCptAWsF5h76MStV1TUwPBEaOIdW2BSDViDcepUBrKhh6ALDyWvIeJD7ESon6mDMoZeabPEDiXa3LEqWMc5HhqjnxFrmoLTZgoGRMKIyYUZqiZ8UoxBWAVMBO3KmoAJAI0cwbJDLLqxMNdAN6wJHAxsoc1qArYwi1gvdZgEJXAJ6AEuLGZTW+d5ZHld45z7PbZ77yFn7h59nX+YXPvIY6luR6SrWjGnzyALzKAGLoVukSLjD9jKiMkYUXEqCzBeIoR2xplwAAIABJREFUuoyVpUdKtJZFwn+pg/oBfu3cMYbpb1wnvHuJ9z915CRO/zIrOtXmACPOW+Ud3pcyXdBGmJvvR1VOkrl5eAPcvsCPuR7Cd2J68WWceukEAOAv/h2PnRXZY5gR9bPJ4h2kYthipZL2/RxORup0vgEjRDIjkLiFR14T5blCoS1kNQUHLYYvMzX+ntTak2hPsN/21NwiWvOHAADJBFcCzUYdz9YZEu10bmFtjY9lqQsYw7B/TUYZV9BFPci/KgWvg1HIoxXbzVnf9doKPL4dlD0q7ldl3Zo+OFIVbTfweIjhintUVKvw8PqqY9hbfzKAzPdbbVdjlMxpiKp5SDVCVfve1TtxVOrpx9o49bmBns7w+3ZiGBJecw2DmfHHn38B16+dAMDnLkeALz69uWLdKNLcTmMrgtnC4om7KusQ4bHjpy48MIeuajwkCVvyhqIoQ0reRQlQJ49pBcSE7UxMIKW30JIEG9ITVkhgJPmTcjHJhQQHZaHDHKxChLyVMjF7hnVC35YRxjbaoy7J20k/cyXvIBdLJqcUUoFKg8yqVnl0EbNpG6mwzEtbIpOk3hesvqEbyIm3VSMLLYmzK5Ksde9hRB4zBcEJq7hHDkRhxpef/86FPi783tcBAL92o4+f/xWGpqbr4ebpQXIszpeA9LCdW5cV0eAaOEpBod+tLKIXZ7DRTBVoQhZYvSXoDU5AyYQc02oTi5/mOe2XvvAbqB9kCM31b8IRw9iFZWEVJF2kTma2fRdabCqVCpm7BcrkwDob8Jf+PzmdAkeO8T5OPsnbvHDVooswm38L6DOPV4s1JtISyOWzKx28LAo2Sj6vqTRBLiIuLu0jkdn/NhHqYqXZnuRWRGtmHkcPcsKeOTATH6+lMgdOHtk0s0pPPvNp3Fjia9Pp93GtZPgzaOV3CVEm1XsLlzxwLfEHHjtN3kU/33XC3U+yHobfOys3Ry4YdqNPvpsYldRHPfblq8CH5/4lANYd//XP71x8ZdS892bs8Z1GgMFPfe43tkz+u43IMseQbWaMvUvM8lz93f/PcS9MvVnfelS89PLn47w9cPaePvZeFqCjYgyJj2Mc4xjHOMbxCMRDU2GDFDwcQgXtYSPZTAkm6pwKKDm897A+VIE+KHDGqjnRNpp4GPKRLV0XiLl0YGgV7NwV/LAbTsEE8pVA01rrWF0qTUjCLHjJ21xf68EJZT1JEphQ+ctyKLNlLESTkmCkbDXaRznORCQtezpHXaB4UoBJuQrTWajKNZRUf5QoEMI1sICcb4Dsm4qQS9X9wfs/wgs/w/DN1GwwL8nhg/6rN2BHRVFFU4EEJeftSngflOfyyC53cg0p00iEUV5rd+H6QdWMIeSJ3gSOv/ZZPq75I7CCiBR3LmP9+ge8DYGFta/Ba0E8dA4SOdpAx1ZuFV5IemTrQCFs2htn0ZR5/KeeYfKXd7eQiqua6x6C0eKnLhK2KTSMeHMX1kKI/SjD90iVyKSdYlUdE0JmrtWA44unAACNJu9zcvowpqa5BZCpBpyQD0txiMvSDEWfr3G9PYljT/D7V5Zu4eqda3LtQzvGohOQngTw7qd7bb0bZu1mMqU7jc2qod0YityPyns4ZheeBu5wq+fi+eu4ePPzAIDHtykCR1W9+62En1o4EGHw4Qp/N57ZwzFMRAskuIAmfPPtHG9f4ef3Mptdnb0+32/e9f+BYBZiM6JZiMefn7jLvzy0KwDg+9e/e8/r90M2C/HTfRcYxzjGMY5xjOMRiYemwvbEGsRaD5S5EHvYQi7zHiQltocHBUMPKCDMV5dcUVpPEFEzECmoUDXLe2AJTghq1np46RH3rIMN+5DeKaUKJFrjTico+zI6JgpZhTVw4ghibIpUzEOcF/tOrZCRlGaJAaUyDpZqNEM1LhVrpjMkMuCdeoVSKlwto0NGWxTS4yZPUf+bvIaW+fCAQpAC2qIb7iZm8IPv86hVa4LPdX7xGCjMuhMBQqhKVAoK+tiQeWjKI6eAyAHCBfBiXqLUNFCGOe8M2TQTrvp93v5jL72M+VO8SlXJJHrLPO619P5fgPq8DV3jUsE0W9AZG30UegMukwp1WWbD+2uA2F9ScgAkym1YvQmvxaAkYSOSI2oFhcxR69YReMtVkjNc0dre5Wgok9oEviHz0FK1l85jQhTLPBFma4lcwxoWDrOaW7vFVXV7agYNmcO2vkQuc+N9+W62k7nIj9hYW0W7zuezeOwxvC99u75omSdWlOgAWFJw9GiurUfOXlfUz6pz2tXHRpl+jIrNqpatqpnhbQ5X1MPvHfX6UWNn97uqHjUStnrgU/LXGq5fYzLa8YOixLdJL3uUL/VOYtSIV3VUa9R2q6YiIXYyG71ZDKumHWu+j2NSCPcu7Xw7QZGsapk5XF0P96tDdQ0A16/98J4qu1pdh88C4Mq89c69c/zhe/Rj8cO+3+GdAykVSMtQRLByo0uCVZYHXGB+k4qZyVfIV4UJAicmEqaU1/ABVpftG2eghNDllEFqA1RuYTQnxCwYfihA6eCDXGC5Fwhm4bAswurAJ0AqoiOZOFqliUKayI9KN6GykPgSJBTkLSXxYgDl940BGRFUiUYjCmkwOklyeBHagK5B13gbVqD6whrYnBcNb333HK6evQAAODDJN6jpuQOoidOU9xlneABEtQiv67DQ8GvwJA5a0LCRCEjxMU/8Be51prB0jd9XFwewudMvIxXnrmL9NjZuMWSUTGfI9HE+7i4TwjbWbkC1+byzuVNIukzIMp7hZCxPQ4uYil9dBwVBf+cAcS9LhBg4P6Ww0Zebay+FtpNynfkalKYHk4d5f4c1IZ2lYqTSyNooRSClRoAu+BwXp47j0Cz/KCeFBa6a07DSeqk1GsgmBKoXFrmuaaSyuCjyHlbviAvYxAwOH+MFxvm1m3JcRfRbtwlBm0fH/GM3MSp5j4r7ASfud9ujhFUeZGzFMP/yVeCt5ZAkOJF8Zgf8qL0m7xCj3LyAQWIdNXPdu/T+yPns7aL6vlGM9Z3EwPSD70NbMcFDVBN1iFGQ+HBUZ8bDYuD7I163n3bNo7lsH8c4xjGOcYzjpywemgqbPIEUxQrY2gH8baMBdRwbBpfb/LxKEElQxgiM6Fw8OyIHiM1kgJZdmkOJNCk5FWbGUHiLREhbxgcykkYtjGJ1c5RhukhGyOpZI8pXZlkDTYE6KRGpzdRBybH2igIuFyMQm0SoPpFzNalFrQyVfx9IeWXoExlHQw1W1lkGDmFIuUYGR2Z4v6cOMxztrMLSbYaWb95axcoyVzPXPmJlsby/jLQWKvsWfLSe83Gei7zAwWiCIFC/K6KMaUAuPACVMqKwtrGI65d55vrpn+WVaX1qPsLRZu0D1NvSbphfgFuTbQWWnk3hPVcwyWSBtC6qZaL2RqqAuyPXuHsdWuw3oRi2BgBKef81PY1czoESi0SuJ8S/3BQ5bMmr4b5NcPsqj5Y1Glw121odPWEMJmu3MTfN53jgwARaE4xOZE2xNU1rsEIi1DpBomXmX9AZUsDkFG//1FNP4851tufsrKzh1InTAIBzF7mq8kVeMQLUMOrRM//YbYwa+9qs+h3lh72X6Pa698ijVre9k6p6FPFs1Gs7Kzd3VZkHSDwonlXjw3NnIwHt6hU+3vJ6bVeqZruptkNVPIpoNixxul01PFyFb1VxDz+3lenIXiJYZVYJZiF2UlUP5GPvVmULBLZR5DNg79/XhyZheyJ465BI4nTOwrng1Rz0wx0UBcnMgX+zJw3jBz7YAGCshZPE67VBIlBlgLEdfOxxQzm44GQFDxLms+1zMsq1BeQmQjqBCux06Xs7beHkuPIyR9ETAZBwctpHDXSjCE25getEQ0sSJGGZW5VASV/Ypgo6yIGKXKpLClhhYyeeImSdKoIVCLU2zY+dObqAdoNnJctuF7dvMAS7OM8/cF2ZPye46IoGpPA+QJWBtU+RJe58N/azw2cDZ4G8kHdrnPjkpwEAh574hLy/H73Ik2YT1FrktycteLknJiSQt6mjv8EJc+pkDX6DZ6p1m5Od8dcA0YlP7Dx8yT86v7bCwvQAaIL/TWtt1DS/trQJqBb64OG470hPHuj361jpcBJ2Kb+uJEDLl2ZWG8xNM6Q+d2gBWZOTtwuLQQ1k0vrwNBDOCex55QgkX7qJA7M4/UmGFb/7tb/AhG7Kdvm8b631YMKCkgYciZ/WqEqXAthxj3tUDLPIt9Ixr/a3l7sXR75mVD971Jz2XmH00FutJpTXXn81JohREO5uYifQ+K25LwIAntxkQVCFyO+By4deN5zQd9vjHqVHvtn7t3PT0pMv73i/WwUnbmbtf+dbX4qf2XPneDH//ev3ZxZ7DImPYxzjGMc4xvEIxENTYRMcvPfwEEKXA7xUFRSGrH2o9+RfqYxSlaCUaqQMphAOIKkCLbhKqbwTzjl4qZS11pHo5S1FFa++bDPzCkWQiUw0XMkVmxEPa5+X0F5WT6SgxHEskao5pQxJgEp9BoTKXFE8h8DszmyKbhja9gQvKl1kG7JNjWBT5mGRCNNdQeH6bT6utW9cAABcnFvBk09wVfrsU4t4/ZnHAQDTs6zA1WgdiYQt50uQD8S8AqHcjnPBsekAALrC1pdDLdeQh6riwAKmTv8Mv1IUyZzxcAVXwiprgJJjcrkUvDiV+WlWCvK3Stw8xyzyqWMvoXGIt4U1Pi99eAJQDPW7SzfhlkMfxUPnMks+wedNtTYSw9VYkfdgRJo0OL156kVyYqc/AzMphgLE7QxjO2gI4W8ya2D6CCMD09NHIuoRIG/owffMGAeSFXUiVb0pSmiZCVeNDDPicX7o9ClcOvsDAMDJ40w+O3fpOnqltE5go3f7oxpb+WJv+b5+fs/foyrtnW5jOJqNJlaWlzd9/ajq++MyFAmks9/lDhY+9+z1u6rtMJscYjdw+E6iytA+ts9txwr4zPMjCWvbVcrVqL52PzPfx+ZXR6IX+42FxRMR/dgOGt9tjCvscYxjHOMYxzgegXh4KmwPOFKwLvSwHZRUmrHSxsDmEvBItJhwkIIRLe9oGELEfVkAxtlIogpy1EiAzIhtorMwYrrgvY/63cHqs3QeWrTAjQdKE6riYNlB8KFMIwfnZBY8EVMJeNgwT+3yOILmbQKS/mri+FxqNND3Jk+RROXFhpNcDan87ZM0jhHVlYoG3aWc99Wba7h5k6uH9753Bb/4Wba0/PkvvsHHkjTgXagmDLyoonlfAk5U4AJyADc4X7s6cD6RPq3r3QSICRjTJ15HrSHjYlLdkgO8ldlp3wORjEKlk1FxDhNiojHTAk2yUtq5b53FM2/w7KmaZJ1w5W/CHxZS2HofLuiDF5OAl9GzoAanG0ja0le+s4yizwQeyvhfizSM8KPTraEmFfZUnc/b3smQrfL2Zw7PYG6BV8yJzkA6eJwHVbYERtAT5QCImloZDEeUhgskvZ7G2h3+bFKdoJ7y9WpmTBj0SKECEZEAekR72LuprKMVZ722ZVW8XQU8/JqtYrsKfbh3/uOML79zBafEYOLk6etRX/tv/zd/Z8fb2Msc9maV+6htbUdkO//lP7jLKKS6H4D73fupmodjWL1s2KCjWlnvhGQ2HNVZ7I8jHpKE7eHJQ/mBX3VuLIJqZsBdlVYxiSoi1ARqtMSJFuBEHd4TfI5B7AYGMDkLAAo3uJkY2EhWciCQLARC4lbKQafCFNYJit7d7HWldJzpZg+TkMwk8XsfRWBADk5ISOTKmMi9zJobFKgHGVSvkQiLPECt1jLZDAAypWPCLn0R5Ty94L1ZLcHiLCfBY9MpFg4KrC+LBPgNBAEUhRw+nK93UXIUAsl70wOE2Ae3DoQWg1mXazWF9LHX+BrXm3C5EMFCYvcGSubHvbFAGSRNs5jokzqfS+OIwfxp/gwu/rDAlW8zG/bop1iohBpHQH2G3/XBo9DCule5hxf4OiRG8hY+CNk4lrcFACVMfEVNlOK0ZpzCYYG8+9e/BgBoptPI2jJLfvwZtCeYOKTqDWSZfP+CK5vSsa2glIoLNyU5gZotFLJIzChFXRLUVLuBtSluUzgh+2W1Bvw6w/sWiIu8Ry1GCaNs+todJsb9JulqNBvNPb2vGqMkS0MMM8j3M789u/A0TlYY4/cTxh0V+4XYw/uPYEA8e/NsDY0hMZT7HcGl692PNqCZIwq79i15dkBCO/XCF+Lfe0nWm8XIbf3l/dn2GBIfxzjGMY5xjOMRiH1V2ET0PwL4B+DC8vsA/h54QfWvABwA8B0A/7UfzAiNDA8WCks1YAxXXkoBzgcYUIhLHpEoVq/XYwVeFMXAFEReSwqR0KVIgShInsrcs3OwTa522j6DCWV8aWBdGBcT6FspOCEYpWkDRFwyxcODj6Q1AkVDDxJ4tHQW8nY4B+hC0ICkRCnHkwkk3qMCWRj1ghv4VYv9Ww4LL6NDtlBoiKJXlgJtsXFsZ7z/hQOElz7Bo0fPvvQ45mXWN2vJfLDvgRA8pj2i8bSycSVnAl6MnKFyAFA5lGHSl7dcHfvJF6BldtmXNyKU7p2cCxGgW3JetdiuIFPE66zEj7s29QwOPHUCAHD75rv44XtMQJts83jXgTMtOFE0U3oVaE/I9S4AJyS8kqtT6i5HKVhjNVZX+BxastBWSRG/ZzqdwvFjXOlu1J8DACxdvgKvmcDWnDoSvcSztB7Jg1q+W1ql0DLzjSSB0wO/dQDQpY8qetRQmDl0UM63CRPg8x4f9/T8FG6viH+5IVi1N9LWbuJ+/Z6rsVNIvDp7vZuKtyr3uF2FPoq0dr9jq2q7Gtt5a1dlM0OcnFmLJKlhwtn9jp1U1zu11EwPvxA9rhe6Od4UxcVXMbrS3osq2qh4+rE28tkg/ybV9Fv/730b5RqOMJM9CiZvTR/cdCxwN7HnhE1EiwD+ewDPeO97RPRvAPyX4GG0/9V7/6+I6P8E8PcB/PNtN+gBHoUeMH6DU5QPEDMN5rSVVlEkhaBizzXojgNJ9MDW8CCBSsPtIyGKPUKvFVLR4U4zB604GeShZ9svI3vdmH5kkQdHK00UWeBOAU7el0ky1lpDBb9tJFDhce+itnQp89beeZCTo0wJeehnB/1wEJyVBUGyjpZIi54+3cLpg3yTOD3PSfrEE4uYO3ECANCYXQQFKyormt/eASRSfUpFyJugAAouYMGhK4t9a+fX4TVnPN36WX6+dgwwt/lv3x1c27Cq8fxJAAClNcTVlstBCBC9fGDKQcliau7UDC5dZij8h29x4n45+SqyaZYzJZXBC2SNVAEigoKuJMuyB50zjFj0C2yUnJAzSMI3N6ITVm2ygd5HPwIAHH2GGdyd1ixu3hCP6sLExZiDjd8TEpY7n2OQ0TVIJZEHFjySFKm0aXrLywD4WKwpo9tca2IqXiMvDyqroenBdq/u++95D7HTRF3tK+9UWGWzxzaLvSb14UQ9rDu+mbtXiJCoR2lbv3omx5EzO/fA3kvsZbu7Se6v4C38kbhthWR8bbM34d456+EEHuDvpxYOjHx/benPAQAvvvqaPPKFim/1/Y1RiXrQtvj2XY/vddJgv5B4AqBBRAmAJvja/wKAP5Tnfw/A39jnPsYxjnF8PDH+PY9jHA9x7HnZ7r2/QkT/C4BLAHoA/iN4GbHifWQsXQawuN22CMQmEt5XZn1xFyMcAOCAVJyTNCiShaxz8KIglQQ2eGLh5P2asuitTQJ3l2ShrChvIUEpKJ9PG6gJ1NlOpQprToHEZGN1+Q6snJ4Wf+ZUq1hRUqKhBP+ui2mEhQW5oE6WIhFY1JGK6mENH6RRAdK5vLaBvsCqiVzRBB4642OtGYWGGHI8t3gEb/wCs6lnjrEXdNqeBMX5XYIP5KjgfuYcEIxVqBZnruFcnM8OanLQHnChmtGgOs9GU5NZmGRX4X037IkNRPgoZZsr8CVXul7VoZIZ2VQ7FtvKbQxeK/7k04uHcPggS3ieP38CAHD05lU81h5ItUJWq15nUOvyXRGlMnI5vMDjWarRnBKpV5EutfkakDBgN398Ht/9KsPu2QSjBfWFX8KRlly3lQKr63yMh+YOIGuI0Uidf0Zlt0TUrdUKLnidy6w8eYIRcxpTdlF0RK7W9XCnx9dmo+BreGLuID46y4zWvi2jK9qDivv5e95NjGKGj4LRq5D5dtVv1Tt7M5nREFeWlu6RRB3e/nbV0Fbw93bQ+GZx/NThBz5nvd9tV6Hwrd4/DJmHqjP6Wl/63raSo5vNaY+qrKuP/dE3g6XXN/if2TfuQS4eZIR57M89u4g/EXRlP4pn+4HEZwD8GoCTAFYA/AGAXx3x0pHzKET0WwB+C4DAjAzDeklcnnxs/gW2NiVplMLs5T0UZRjl0gMINsiVOgcfmNVOQ+vAGA+QOSERuNYrCysa46RLKCdCF1YySVqi1uY+04SiaJtZ2IFzWJATTROFVCDvmrDBc11AhSQPHUe1AA8jPeRwO2qShZGPJfVAFhjMcvzWAVqSglcG81MMiT/z/BkcOsXQMRoCESetiNDC9UCi5Y3AnoeKfX6AQCIWAioi25qirWMByKJG1V8D1eQH5pfk304UUyGfxIVIfDs1ERjnKDfgpZ2B5AhI8Q3RuUBEMEi0LKAmCK15vvlO91hs5cPlDAdX2Sq0PrUML8xrQhtuQm6ussKxnatwadAST9BoiL2mE+EVb6CbfN6Hpyex+AJD/GsbvEhotZuo1/i41q9dQHuSFxr1Rgs10RLvdUSXvMzj+KDO0ijIEjRwjbJRE9yZAuurgSmvUW7wOa7e5OM6MX8E3wi9cV0glbHCBxX39/e8e0b7xtro8ZiR9pz3YdQq9I0XAVzZ4nWjEn71hjsKAh/1+FZRlTEN2tavnhm0Bx5kot7P9qs97JCUR21r+LFjZ3+f/1jke8jbV4BXsT+mfoj3rt6JCbtx7Emcyrk//oHA4K89fyK+tqr9/aDj+KnD94Upvh9I/BcBfOi9v+WZjfT/APhZANMCqQHAUQBXR73Ze/873vuXvfcvK9r9D3wc4xjHfY37+HseD5+MYxwPIvbDZLkE4FUiaoIhtM8B+BaArwD4L8DM0t8E8Mfbb8qDlAWMrwhg+sj8TpJgrpCgKMUX2g7gWkVAIIHrcK9wOs5sJ1pBueAqFV8AE6pti0gGIgv0xYkqFUg7RQ1a6Grt1gGkAjOv3+LqklIPFbQuVQalQgXMD9V9C1pgduMtlEDp3jvMyyx3T6r9lBpQUoWRMmhCoF2p0maawGtPMio5c4AwfYgZzMeeOgmaZGi3Wqki+mwTEPcbREWSivSoHcxZE0A6SLUGP9kJoP2zcpEnQMGbOoyXowYKwiu6hniPF+QC1EC87XsM5rjLZXiZC3dOmN0o46x69/J3YW6zq2zv6ncAALfrn8btO0wKO7pQB5EgChsroCJ8znJ8UHDynTG2gdVlMVOJ884J6rM8rNlz6yBpuRjH17LZnkIpErQzU7OAQPWWHEoxO4FA3l4NWP1lv4yfuZXvU7Hu4JLwPQE6Aq+bhODkuxrm8lPTxLGFOQDAu+euou4F/XhwcR9/z1vHXYzwPUiWblddD5t7hBhV/bamD0aMf6dQ5X4g8FDZh6r6ucPtKO7xxNR7yGeZzXzkzJMPbWW92yivv3VXNR5g7aWrA5g6xF0iKrvwwA7ks2rwfnh7HwcMfv3aD7d8Pnhxf+383iHxPS+FvfffBJNRvgMeAVEAfgfAPwbwj4joRwBmAfxfez66cYxjHB9LjH/P4xjHwx/7mhXx3v9TAP906OHzAD69641ZD68A8mGGeWCVGarEfpHD2TBuRLEWN87FattJJU1E0DKKldsk+mD7oOylsii1mSqFUPiUQByxiZS30sDJ2NhGU6ExxfN2E7KvlZsr6IaKkfpIpFo30odVKkFdCs0D9RqShPfbswQvx6VSUSczPvaYvQIem+LK6o1PcSX9xhdew8HnnpUjqyhrNedgcEDOPXysXQQlMygFErtRJX1xLq6l968oSoQSBqNrpB7j5xtPR3lU7TbgReEsvA6UwGuuHjwZEIKUqvSiXY7BnHcGL0wzggWEbBbG3fKehRUlM6WamDrBFciBZV7Bdi+/jYsT/NhCrwtqyXjV1Dz8kiC28nFYRzCCYqyuruHCWZnjb3P/+eTzizj4wn8OALiz3sbaB2y4cOgZ9iJePPkkbn7IxBVaPIiDT3Af3bkUPlR64udNiuK8vlYEL0RF1+eDKXoFcuFdqIxQ9vm8yyQBiZRqS2w2LQFHZ/kcf3RhCT334CUQ7+vveQexk+p6V9Kmu+hrV/vGgzlofuzK0tLI9+yUyLZVhLGtX/48v+/FVz+NY6//3V1tYy+xVY95v7GTbVaJZ4FA9hS40n7v6p/jTbwBgGezQ5W9E2/t4cr6qYUDUfr0m2/nuPj1NwGwHSnAKmShEl5YPLFvhbPtqupqBCTla+dv7Xl/D4U0qZcZbAcbpUW988hq/AM0Agd740ByU/ceA0UKr6I0qKYgMaqjSIoiwEvChTgrORjU5OYITzCBnWVshGNNLn7GqcaGQMC+tPACwWYtfj817gAd0Z4G4AQSJyFWKZ2hlPNaMgbTM/y+LLPILH8EmUCizdRgTrSvf/0LL+Czv/YZAEDjsMwd19pxHpqvQWAg66jrHRY4ntrRg9orPYC8lej1ORNbBORyxB5EaeAzdvZCyv96rLIkKQBfLCNwjwIbnCgdSMmqBhC8yCsuaUShVaAQaXa2DxhJ7hkTuvzaCiCzy9mhaUw0OWGeTvmHXG/fgnN8Dl03i7ZImlK5Ht/naoF86FH0ePvXVnv40QZf+0XP1/O5uRdhZVGxUS6hm/N5zR87BYAZ3pOiL066DSPXO1/rI+0KvC7tEFVTsCKAYroFvJADS2GG52WOvjDWy06JvkD1aLZY+QZAIkS2YrWPhQUGa5Pv9YH8XvLVoxa7kSnd6v07iep89jA8fjeOx8WYAAAgAElEQVT0fTMm7PBvs9u7x8Frq9guaQco9I2/9npkfn9ccHSIjxP+3uk+q4n71twJAMCRM1svuqre2e9dvYN3P+JFbxBDefHVmZjkjzUjN/yuxHy/ZEh3k6yrMdNs7JkpPmaHjGMc4xjHOMbxCMRDUWETgUlbTg9GuZIEuQnGE2EUKwxtsVBWUA/TiuIoSagYvXIDQxAkcb4oVOgKHkYgS9gseleTMnCJeE97qbTLIs6EN3KDxPDqW9W5ImzXD6Hf5SrQuIF6WVgPkafo6W28C4qW0GmKRFTLTkzwv3/951/B63/7FwEAkyefBHRbzku8qtEBCXTN7HohpSGteGcLREuKK2tIBR5ttoVgRymrnQHwKIBSoPLkSSA7Ko8HVbQeIKNe3nShMzGrkGrdq2wg/2q6g5luUZAj6kcCGkEBAbFQNFBbk+PLDj0J5y/I9dqArvH5JpNchbZmNFYvM9R158YsWjOn5bhyADwW40o+bleUkNFmrHZb6DX5M6s9zsiBmzyNZI7bDTcvfB2FEA0nD3Dbo9QeeSkz4Y0G8tuyok+TOIffk9aINi7Oy5fOw6jwN3/PynwDPTEq6eU5BClH984qjywCaLQZ3u/oVcyImtyRmaM4t/Fg1Jk+zthrZb1VVGHwvUqPVuFxgCugUKFvp77W7XVHVtgPQ1X944jNxrqqlfc9ftgA5i/9Cf999sktTUGG57BDZR1IZZe6J/DKJ2uyrZ2T1nYToyrrnYyIhWNsvbN3mdKHImGDCEoTNCH2G50xcDbAqaHfqeJNneBhBWIleE72ABJpRidqwBI3atDjJgxmoJUkmFpWi+5UiUpRlnyD7YtOd115OCNSiNRBX3S/kwDDV5yu4AawfWCOW7KglB9rKIWkzj/wJyc1funn2PLyV//OrwMAJk6/AIimtoePfWVgI14KHxKjV1GT21EBCpC3POYpi897SuKZh3lq50tQfkc+gjp8U3rjagJMFAa858SnTMFCK3LGLgiy6ApMGSB51YiLCgrsfJfBWWGBmwIucBW0gpVZ7tCKSOvPgWQ61vXW46LCWj7WZEKjPiXnoK9h+SKLnEwcPAxb8musLDQK08Oa3HTXMIf6HEPhB59k4ZeLb/8Al8/9GQDgTn8O03P8o6qL3Onlcx/igLjCJYlC6YMYj0K37Mk58vH1jYfPZW4+72C94O9RV9olZb+EFZZ5L+9hXWCxbr/AhvRNqcYJu3HsEOotvukfPXEcH177CD+NsRsYPCTqYeGUUb3nEMPw+E62X43qNp873MYbf+11APipStLDMPiwUMpm12BUQu1deh/nJSlvpyn+wepT8e8gxnL1ygWcv83bfTt//r6xwzeDv0cl6lHSp9XjmF14Gpev7i1hjyHxcYxjHOMYxzgegXgoKmzvPcrSQYFgXagIKVbAIYgG5h6kFLLgW610JGIFRTNPFElQaaqhgrCWPJ/UkmiioQnwcinIl6BUJArD7LTWUIF57SyMUJBTqcS7eT+y17X2cSZcqaCkRiCB5yfqwN/9LEO4f+s3/zrmnmBReodJ+TeBkrldUBlV0VxgXYOAAGOTYig7Xh/5O5DeMJBi5SHoANFL1V6sgxJe+VH2BCgQ9nwHoaJXRvx3bQmInzXp2Wj+ARUqIBtV5MhaZn8DFUORBpCECr0LX4ghx+r1SJxDgyHJMr8DED9vqI9gi1bL+FrktRQdOa2l25fRFSUzn7joMZ3L59x3JdYMP+YaM5iePgQAmJ1lyPvQz3we3/hDVl66cfsOnvjUq3KM0lrpG6wVwtCutZEl4ulNjnVkAVhheJMpUUrboOsNSqns+ysM06+vrMKJr7pNPEwhDl1FAS1Md9vjffXOG0ycYuLb0dmDsD9Fa+tRVfVO5Eg3+//dGi0sd3t3QeFb7Xum2cAXf+5FAMBv/8PP/lRU1MNRrap3QjrbKVTdu/T+tpKloXINRLLDR57Bd8T6+uL565EdHuL6tR/uinS2G/h7K1ORi+evx2N9YWYNb+/4CO6On567wDjGMY5xjGMcj3A8FBU2PI/fOLhYYSc6QxwdiiwuglKD3iei0YeG02Gci19b1w1AFL8MeWh5XkvZbfsGOuFtlUgHVp46i37UVqpXpQFdBuMMHY8nF31nW9poQ2mci7aIFHvcQF2O5b/7m6/hv/pH/4D3VVg4K6QxHVbxvagCxvvJ5bhlXAkGYciYYAd9abg4NjWopCujWh7RgjRoW1PtCUAqZQ8L7xkx8H4FKlh8hvdQBp8IOQwDcqCPs1wUUQyCHzwuVTk8QF7OUTtAlMjKjT7KjfMAgPqRYFQC+JSf16WLo2OuJWYrkx6+xkSxtcsXsFxy/7ez2sGR40yWUzKy16MGVh3/PXXwBLyQzt7/9pcAALfO9tFxfIyrRYb5Zxj96HR5/zNzU/A9vga1ZhtalNQ6a+twJvhoizpa4uClsq8ljTjOpWTUTLdqg/FCawaojc/R7wuCUudjNZ5w9QIbkcwvzqOe/eTL9+6mXx2i6oe9V8vCEHsZtfniz72I/+l//h/2td+ftAgV9JFNnr+HdFapuKtz1U8tHBjZuw6vOX5qsJ3vfOtLI/c1XE3vdqQrvH4nI1yhgt6s0g6P76ev/lAkbA8Pay2UVpyIASjYQZqO89R+4CJFCbQOzO+U3b4AJAE08EAZ7JXVIHlaF5J8OoDXaTD/jVLBJwOjDoAdwKgWkjvBC1zW666FXSEVIhmcg/aBrMYP5cbhl372BADgi//tb8BsSDIse8BskH+RZOssjF3hR0wPRY/h1N7NG3LcCaghCTcZQHVKISYALbPmWrkBkz6tQ2f8E6L6CXlXDWFBYNEFed6vdr2w1gFpYYE7E9nn0CnggqRpILrpgewrWUA8roPXNPnkbmIe8fWaeOw5eLAYiZEFA6GAsXxtiRpRhEUJ4zyb0mge5nNYurWK9Us/AABs3LyA3jq/b+44s8Cz1kGoFsPgrtfC/FH+sWxc52v4o3e/ilyEW44/8xIKaW3UZEZaJ03MnWL4fOX6ehTQqTeaMMHoA8GVC7ACiff63bggbE3y+2sz03H7t5duoLPKn61KMzSkDWP6Qe7UoydM9+5yH8cX+Bq9/Z0P8ZMYu3HjqsZWhLLNosrqribp6uz2dsfwK88yefG3/+FndzV7HGKYmLXTeFgh9+pxbZaoN4sjZ56PnthP4f2YkIeTd4i8ImVaJZsBdyfLzRLj48/z73GUf/VmMSy4MhxXr1zYsc/2xfPXcXSBvz8fvPfBjo8BGEPi4xjHOMYxjnE8EvFQVNiEUAUjzjA78gO7xiCfCYBkVMqTjSYXCj4Sk5CE2eyKD6BPYAMkLksUpwFteVWvkhRJMMFoJChkfw0wVKqzOryMmDUM0JVK0AaZSQ34jF+b1RtIBAqNz6scT7/KsnSdtS5+9M2vAwCe/NVfQXGbR0k6GzyatHrzKm5dvcB/31rCjQu8artw/iM51hrShpiSNFqAGKOoxCNrcFUw1eRqY2IiweQ0w9gHj30Kc7JKnZjmar3RqCOtBR3WHiiMe6kMXqw2HQ0gb4RK1xmEq+vDnHV1YI5SgLgyp2ALWfYAJ+YgZi0S84xPQQKPW3tDtuWgpC3h0UVwDUm0WFuiick2r76nFp9Avs7v7/f7WLkts/UJS5TOn34MNL0AAMhNA1c/ugwAWF/i671RNpGIveZjT30CC0cYUs83eNZeI4vfOeUcnKiXUVaLq93CcJVW5HkkkvV6XZjQRmmIXWtZxCZGa2IKpViyrq11URNNgNAa0RsOTmbCb3eXMD/38Xn4/rhjO/vMvULfobJuTR+Ms9dV1alRc9zVfVUr8DC+BQwg3arEaKjeqlBqtQqza9+65/jCTPFw3F0pMkRcldUMFeOjHAEmvwYAwRSkEqHafmrhwF1V9Vbzzy+9/Pk9VdPV2I2aWfVz2q7anl14Wv76s10dz0ORsGNv2nu4mJxV7FeHTKAUxf9xniILmyFYJY9Lf9kDlFTcvASadQJjOgPI08iAyML11kRv6r7sv923SBK+US9vrKLT4wSRRinOJMKiaZJCZdKHlBtAXdfwx/+ef9Tff/MyVI2P8RO3N7D0EUO4V28yKHTjUhfXVhmaLlwCE447tH+pAx3kSNMNaHFxooTYQBtALcicEiGp8Uc81byEg7NfAQAcP8793xOPH8CR4+x6dWRxHnNzzNJOWvPxOgetck8eFPvVgx52WCj5+A75N7w2LLpUCi/QM2wf3vLNz6tmlItNRFjFkR5YnZkkzn/7KFvbRW2CXzs7P4POrbbs4xl0u/zDvhNGwjdS5ALrd20fK1dZ4Mb3+Fj63Tt48nm++Z56+mlAmP+uYLi6OXcMKx/yjy9f7SAVlnjR78PIjHl0kMv7KGWBYvp5ZIxHLoRKore7VxnSGs/bqySHFZGeRPgLZa1EreCbTa/sYKr+wN26fqxRFMXdLl57FEHZKgbw9827HgsypJsl6lGP/e6/+zYA4M//7OvxsTfO1uKN+sNzZ+Pj37++Ef/e2i/7u3f9X9Q4f+dex+5T9a9Hberj3+JEUYVqX/lk7cciR3o/IsDfoxy4hmOrmedRC5nHn5/YcfLeTbJeWDxx1+Jhu372CzN83/+DHe+BYwyJj2Mc4xjHOMbxCMRDUmEz21iBZ6IBAOThBHZNYrFG0Cr4Bg9QcOdcLMjCYwoWLkiaqgSJD8pc/E+iAWXDrG4epSHJOmRh9lmqpTw1UKU4huUFbCAkpUHi0w4kPgsfPZXbDf637Hdw8Ryv6s7hNlKZk/4PX38fXv5uTgnc61Pkjt9X6BI6CQQyIZr5NJqawANKZsVJIZK6tKABufboy6x5p7OC64IMfO8CV/ONv3SYFLnPJ09N46WXWOnsyeefxuJxJjllDZm39hqRGAc/UJwLxD3vKqC4ZpU2YACD+xzeS4WjHSjgGFSHt+EceFXq6RYgMpYeKVTClY0LHx55kHwP6hMG86d5VnNjtUSzw4zx7k1GKXplDR3H1Xx3Yw1ljyto0xdjl8Y0Tj5+PJ4DhB1fn2DkIe/00BOWeJLWACXfiX4ZWzJeroG1JUzYfp6jlwvMHVzndAYT4PXEI5P2zcz0dERtblzj6q/mLJRYvGmn0Uym8ZMYe2GGb2XssZNY7vZihVvd1mYxXA0vd3tRqepy5fH/8L/93/HvxdnZu14/6hhGbXuncb7fxPlQeccK/NvRDewbp8/gtdf5t3f4yDMfG2w+SulsN1EloL14DHcZfQCsXoYRBLNqVJGG4Wp6r9A4cO/89Sjy2YOOhyJhEykkug7rcgwyAUVpUUWDbBzgUUU6JgiigXy3c4ORKqf4RuudhZeEp+SVDVuDIUkmrhyMKZFDLgk7E1EQpzz6XfmgrRENb94LwE5jYcQrcevwInyi23wzUZlGKQnIasCrIPRBwUkTOmiCa49euMFDRbERVYhlpypg5Ho0kMHIvhQsrA1CM0FfnJDIgiDxCl5ETJywyzegkW/w80vfWsF33/4aAODpU2/jjZ9/DgDw/KufBADMLxyHVxXNb19J3hB52AiZW3jph0e5VN8H5Fg9VGx9JOkkTF/OV/FNhcx61CL3qRr0xm34qxww+GsJ6lPTcj1zNKYZZk5rDINfP9/DrTVxXspTZDJmluf8eR5+7CicCJ/0lm4gDQImssBbu7EBZQZ+q1GdFRpW4HMjGuzWmAiT27KA7fDNM7DF660MmXAOCuNiyydJajCS/Gt1Pq5ybQ1pU1oytRSaHn23rr1EFRofBZPvhSUObD/CNSqRh+S6mdvSXhcPw9sPMaxxvpM43+djOP/OFXz5nX8LAPjcs2/itWssIBJ0tj8OuDzsYz8JPAinvMj1A948u8WLcXcfebfJea/uWyGqCXwn2uJ7iTEkPo5xjGMc4xjHIxAPRYUN8lCJh3I1GCHqkAK8wMyhcvMAChENSSoz2d5hAMFK9VsaHatXqnKkZI3SdSW0mHs4Z6BtGl+bBD9pqSJ7az0UBa/unbZxLtzI8Tk/MALx3sNHyUlePeu0FpdG1pigaIlMpTBCW+8VQoIyDi5UpYblRwHRGgEAyqCDqInWIGFQOz8g5Blhp9dsBiXVHaWEpsDruUD6JaXQ4v+cAuhIxfhX7xlcvsZuWJ/+gNnpn/3FV3DmBWaxptk0vMwTKzlWvlZh5tpHKdbYo4CPpiXO9eGkAlc4BCckuuBiBq9AAoMTXPTL9kYEY6yPc+JWWSj5PFyZY1nY38ZxtW6RoRMq3XQCucySNwSKPXr8JCakqjYbd9CYYOEU2xcv624PiQ9z8wnKMIuOJM69O5GETVSCImdo2/RzIMzrB4nclsaMMNJXMUCFlHVRR6AlXugbJkVnnT+P6elp2CC48hMWwcFrO2h8J65Z1diq2q2+trrdzeRIt4LNN9vvXv2OR8VeKu1qfPmdK/jw3L8EAFwVdvvCYo7P/PIr9+cAh+JBVO9vnh18HlsxsKvseWB3M9dbiaSMqpivXrmwLSy+HflstzGusMcxjnGMYxzjeATi4aiwPeCsRZoksb9rbR5He2xUJKPYwy4dBWVSgAaKXqGeU0A09yD4KFnqKLzOIRESlUrqyKQ67RoDiAUiGX6s19uAETnPtJYgFYJaGeasSUHJpfQKKMNBSGFmvIt9zrzXDdbcyHU9moaESrpwhEYWoAEP8qGPLwgAqXg9SleA9ABlcCKPqqXqtd6jJs9nSYqJCV79ZxtcBa6aPnpyxQqvEVvgqo/ra3xuf/pVlg29fHkVn/81ft/Ln/k51OpBAa1S+cQRLxeRDh8rbZaf5b+LSB70UNG7moQsB0IkHBIS2EC4E4tSpwm2z6QyMhYNMQ1x0y10+1ypWWlbu/4Kyo4YmCQlCrG8XDxzEgBw7IlTyGS/fZPhQIv74d3eLX5Pbz1+Z1xpYMKsuunHfrYTVOjAbBOJ4kpgbWUpfueUyMr2NhxsISp2iuBqotymM2ixJg2gEnmN5pQorK13cfjQFB61yLJsxx7Y271ueORrp3PaVcnSzTyuw7Z2M0o2altZvbYtiW0/8qmdlZt3VdmBYBYi9K9HRextyzja554dVHz3u9Le75hXVbo0zLgPK5oNx7AJyE5jq751Vd1ss6gez25IaHuVJ30oEraHh/UOZC1SYV5bQ4i+zm5ALgtENOuDrjbD3UG7msI8tjIRSiebgARvdUIKIo1INjKJghOY2XoHLzfdwIB28CzOwgcbCVEqDR7bCqkKc8segRsVji/xhEzg6EInsDY4WOXwMntsQrIC4MRhS2uCsmFul5/jOV0hrTk38PHWA9nw6HxdDnjdlBZYE+JcYMyTWUddCyPdOoR8mhYWZWR5843y3bO3UP7rv5RjKPCp11+Wv4PAiRrA394irkqidnYXEHEQwMZrDKehgnZ69NaeAOzteO1JjtFLC8R2bkY99DSZhBWRl6SeoDERVh18I20e9Jhc5e2vLHWRNvj5I6dOAAAaEy2YDVnopA2s3xEoM7ivkYn7z3s5etIa8dZgaoZJeAFen2sT6rKQuXL+KlwuSRjhO5fBCuHPKECFBY4xyMLsvMD7vURHsp0t88Fi5xGK6mz1ThP3VtsKkWXZpkkXuBem3gkTfHgb20VIvDuB1IffU40qga3KXt9JbJWgt4svv3MFwJvx//eStO/3/PWouNTd2q0rxGbJchgKr7p1bZaMq0k//P2gSGS7jTEkPo5xjGMc4xjHIxAPRYUdpEmhBm5bihJYqXpD5Wicj1AqgeIcLJSPCmcqVLqkkAQjEVLR6MMWAYKGzBYDqrQowvsSwAm5Kcxbe+eiTCrcQE1N61DVDxy8SAEqyKfKNq0xcWWU1LIw3cTvkG1JQYdEJXFkyrnaQO1NTDYKeBACAuBgBHfPfIbMBVhetkkFTMnVYWddodfnSqMuMHnNe9Sm+D15rw9tpII1KbwQ8qzIsJbQuHiZZyH/9N+/iWaD9/HMJ3l2W+k2ot6oN4h+0oFA5zqAZ5zaow+FOTlHRMMXUPi8W1Au+IN34jy8M4IMeEBnB+SzmYGT456aXURhWN70+jWG/nrWoNYSqVYArVmGFE+I13SqkkgOVCjhOnyO9Qmu2vutGnpi1mKcjfPfKQhaDDuCotmVpQ30pA2T1RuwMutdiPNX0VuLc/Oq3UYiyACUixKwiWx/otHGhhi/NOopiiKYrPx0xl5JadVKdzvi2maxFYxdHTcbdTzbVe27JacFAlo19kpGq6qxHWvyvHNVXnWr+Diq6/TwCzh8816J1yqRa7dQ+FYw9/A29jvmVY3jpw7fRTzbKwntoUjYHtzDThIdkyHBxZtjkKTkFm3obSL2rYmiuiUCjE6kYv/UwsYBWi1JRSkFFbRJnQNKfr6f57If3gbAPcbQR1WKogypkm1RklX6tyYmWSU09bIoUEqPMlUJ6m2GUsu8RN9wEo0MaYVoMaqVgZePyEgflJyFIU6ida2hgoWoMTChbxzY76pALn32FFnUU7eS4EgprHWlf2sdtMx/18gjkdd4LUIhHvDy2HvnbuCbX3kHALC4yInzwJEz0c7UgyKZIExRW1h4galJ1QDRBTedVSgtCyN5j07nQYqFS9C/CG+X5DqzGAXVOyDNP1SyB9A+FNy+6lh9/0cAgJWbDKnfuFzA5Xyzf+KF53B4gXvXB2p8E25NtDBT57716tISUpnd3xChhjRrYGVlWa6ngjJhCqFAb5l74zX5PG/c3IjyrWm9BhzgRYcS27aiMJGD4Xod9MPfWQonCTnv8veBALRSkZ1tJSh7j17C3k0P+77sb0Ry3li7lx28XfLfrs88DLFPz8zEx7daFNwPC9BRMYpFXk3smyX0CKmfO4t//acsc/rbZzgR75bpvdXrq89tl+iHtxOS5ig3rtdef/WeJFsViNnrHPao5D8sPToqtnv+fjDGt4XEieh3iegmEf2g8tgBIvozIjor/87I40RE/zsR/YiIvkdEL+35yMYxjnHc9xj/nscxjkc3dlJh/wsA/weA36889k8AfNl7/8+I6J/I//9jAL8K4Iz89wqAfy7/bhukNUpjkagwA02RQBYIN0SRawRNgJKK0SuC0nIqUqlab5HI+ywhqpMFGBuEwWy1MVEe03oLHd2hROkMDiqwvpSHCpKkgcHtbayqnashsM6sMKiNMaAwCE4KmUiWJgmhWJOqM5iauBIpBudiQuWtw/GrKNVqzeB8QQ5W8co/VMdKaTghmFkk0ZHMCnKQ+wSmP1CGS6U6LJMEWohggbRW0KDa3rDAlatc5d/4iFfys4fPxHYGnAcJqz2gHER1kBZ5zWQCRuRXbX4eVJOqJOVKQNEMjBD2oJogxUQcF5Rm1Uz06abGDC5eOAcAOPv2dyJiMXWYySq9/Has/BeOLeLgQfbGbjSEMDY9FUmN1vTRuczVNMnge75ugDBXDwcdPltrsS4GISocV6cHV4TrRvHDS8XJTTezyDgvvY3fP7PehZE2TCkV9lqvg9ljj/G1X7uDieZ9q8z+BT6G3zOwP6LZcBU8bA4ycn9S3W6231Hv36+5SLVibjaa91TYw4z27ZTZqpKl+5Uv3Wmc7zdxUv6OzmPbVNhVCdLdVNBbbS9E1eksVK2jSGWj5q3vRwxX9fuJzfy598oS37bC9t5/DcCwbcqvAfg9+fv3APyNyuO/7zneBDBNRLv1Mx/HOMbxgGL8ex7HOB7d2GsP+5D3/hoAeO+vEVFokiwC+Kjyusvy2LWh94OIfgvAbwHcF05IwcLH0SLvPEiHvrFU3XCxJ0pEUd1MgeBMIH3xKaVq8DyB4MTPuh8qID8Y1bLWIwmmDKSg9aA3DkAIa7J9Oxi0djpYehIyqbwS8jDSBDeFzIxbg5ogAMx1E91oX4P2vOJ2sQLP4ggXV/CBlCZjZwVQS8OMVx1aZrYT55AmXEF4HdTRNILbSQkHK5rYgcznyKNeSn+1QVh3of/fgyNe1Sfi/+yzBiCfx1SqgIy3UYQ+rB8Ygnjv4eS8guEH+RJKsc63V1OwvTAovQpvRXPd8/N5/w5sIe+3q9F6Mnw5Njo9fPTDPwcALC330FnhFfl6fx0TM9z7romO+8zkNJxcUCotJlq8DxvG7PqdaHnZmqkjBfcjS/k+LZ27iolJruZtrwct/uFmtY9EkJZCqvHu2jp8R3TFiyKawFCbr2Wt3kSSCboCgpLPaWV5FU3NiEUffL2SooSS78R0cxJpPQx5PZC4r79nUvs/1mEFtM2q632Pi20z012N6uz1qPfMNBs7HiHbrNKuVtO7raxHEdLC41sR04bnuXcSw5X0qMp6N33wS19nwGdY0Wy7SnSryno3Vpp7ieGqf6cV+V6ra+D+k85G/VJHDpB6738HwO8AQJomXmkNbx1cdG5iWhnAs9GAmICEjK6okrzVIKnL86UrYAVDdb4CzQYYW0UAF0oRlJgrpHrg7BzgB53WIyNKpSnKkHwrRLaesLizhAasZzkW5XyUCAUoMsKtpQG7XVYH1jsE74/cezTkfUlDEmiikMqixHsFHYl1Ou4vnDd5By2Mc6NclFqtiwFGQUCaymMqQVeHufI+tA3z4bxNbR1act7PPDuJTywyvD1pg+ZqNxLkvDPwhos4HwxYdAtOnrf9Jbg8EMksvMDnNjhddd4FhIjWX8uh6pxEN3KGsc//8Ie4fv6qXK8GjJKbtiOYDl+85WWGoiYPHsLUHDPSO2s51tf5B9zI+LjXr2+g7LIsrE4UascOyXHx84eeO4Vb5zhn1csu1leZ8Z13e1FG113jc3TrfZQ5J1ybOLiewKMCbVqToDbNAih6po10ij/TBhyOz/KN5/A0H/+1611cu8mLmsVnjuPKjfsjbbjL2OPvOfX7IZyNSs73c1ubGYmE2Ksb2HK3d892N0vu1e0+CMh7MwLaqDh5+kwliYwmzW0FdW/23E7h8T/60vuoLX2D/2f2jZGvGSVQwtA1w+I7lSDl1907h71dst2OKLaweOIucd7tUmMAACAASURBVJdREH7Yxn4S9l7nsG8EaEz+Dd+IywAeq7zuKICrez66cYxjHB9HjH/P4xjHIxB7rbD/BMBvAvhn8u8f///tfWuMJFlW3nfujcjMqsp6dXf1e7qne6Zndmcf2h2vV4PxwEor7PUKdkG2JZAlrwAJWQLZyLIEiB/whx8YmR+WbCMsVmCLh0E28toyghXyGmTtgIdldmeHefdMv7uruuuRla943eMf59yb0dmZ9e6prFV80kxnRcbjRmRGnjjnfuf7Sst/ioh+D0JO2fCltq3ADGSFyHAOyFlArmVs33vtchesCoko9Ea7okCeqxynL+syl0rajDiU1T1rzQxaj0zse8bgyILJGy3I5THghzyorbfiNL6lKg3l+4IMXOb9kb2yF5B5u0kXhe17eRt+EEEkzDBqdcmAGyZGrTGt78uYEi7C/skRuJAMggyBI29QUlJd02eyuKjBRL4sr3KlROCaHLhDhIaedhpbFN72U69LhBRZJpnixorFE9/7NADgzHMX9VxzbabXyoPz5+6vFYO91GxnI/hkm/o8WI1X+q235FhuBYWV97u9NtJMMpBVJbqtrRXAjGSqLsuRKXFvbfk+5mYls19cOAcAmJ29iLOnJWuOjcH1l8VCdGparlvz+DmQMhmTTUavkKdgM6eltnw1kPRqDUL/dcm2HTu4nrYCKmGM03YgHOYdh0wlbn2vfNbbRNKRaoHpLCJelJa46Zk51Js6tVGTz+Yi30Wto9+Tfo7FYwN/5ceAA76feU8Z8UGpom2HvZTB/Xaj3t9pFr4d0Ww32C4r364MXkbRehkvXPk+AANZ0OzuK9vaY+7UPtMT2YZRVjFLSpm1z0SL1su4dlUUFX1WOmzu4bPku/qtHH5vuEVrOAPfKrPeLqsul+zLWXU5uy5n3fvJrD22DdhE9LsAPgPgBBHdBPALkBv794noxwFcB/CPdfX/BeDzAN4B0AXwozsZBBmDRm0KvW4rBGECBlrhKAmVkA/IQOYDI+cgGgRUHXhw1bLgEKjD20zBlatghjUl6VHjdbBVsAMRDDz7tw6thMJouTi2A3/nPCmQqziLj/vGOBhlRTvKw/ZF6lBo8K6r/KWdimH8fDxc8OF2GtFjsgM9dGtRGPkRqZsYTs8n8rKgyg0AgHo9BqmwykB11AmbGUDGcSiP18kgCiVx+RHNHRBrD3Ke5phVv+moKf8izcFO5+NdD+Q6eqXl4YMLizzx/dYEG8tcctZZA/w8t3mgY+ljsy1a3r3+Eu5pafj+itxASR8gKwHVFl2kyoSfOr6ImXmVKU0kcBe3b2DqCekxXTy9hGxdgvfau9fkWN0ctYaU2vMCaKQy7mmdtli+38PpS+cBAO17d3D+0x8CALzxjdfgOlJK72mvL3MG0vn2wjBSP7cRyPMbYYpgygB5kLg9hZWuBJAnz0tCu5p0cGJKrmfSaGIzPxj3pw/kfibaUw/2QQbqnTLKdxK4dyq4Mmr+emFxceTy3fh476VkXg7W2wXu6ysDnfpyEN4uEO83UG9XhrZznwpBrv7ga7INPhOC8NMfnw2B2mNY7GS4t3o4iJcD6l76o8tl7lGM9t1qm2+HbQM2M//ImLc+O2JdBvCT+x1UhQoVHg+q+7lChaOLiVA6AzNcnsMiQqHpn+MiZIfGG364fFBupjjQuK2xIWv025MZZOMGMciXvEMZPAJpA21MNTgtX0bMajwC1KbqelwDNmrYwTlIJU1ZCVccmeDdXfQyKQ8DgGbtFgZxrEQ2Y9Fva19vngZlLF8+pT4PzK+LOJDVvPeysTVATUfqyAFPSqNo4BAS/MNdMOHgPAdHeg1CqT9G5MluFuC6ViSyaeQ6BlICmykyzEzJvj763CksndOKQa6ZdJHBaF8yYQMcNfRY6rAFA1av6AJrYdrB5RmYJbNOU/m3095Ary1P/autBu7euw4ASJbVoSs+jfiE9jNngNGslbIe5heldHxyQbqPzp69hMUzWt6Ociyek+XNpbNyzLu30FiQDKaz2sK9Ncny7nVuAQCW760gmtM+6uNTSLpyjp31FupQVr5WVJK0H6RxzXQ9uMH572TzzEksnpdspzbVhJ2Wc2xlKe7flmnjpRnJKOJahPUHUlmouyZOnJLyeYXx2I5J3pwbMIpDz/Y25fGdZNejnLu2K5+Py6z3Q0DbrUSpZ4d/5vtexJkrkvV+EJKjPhP1WXMyhmj2zPwbSCD3wydf+C4AwJkrzyI+PfgcR8mJjjL3KPdWj8p696o+Vi7VlzHOP3vUurtBZf5RoUKFChUqHAFMRIbNEOUn8bWWzM2QCfPOuTceBofe17xwgagFh2CiEWvr0aA5C2AuQltXMNNwg/lba/LgQ+ysQ6QZbKKmEzVTR+atOskh8m1ddqBOlvW1DSlLQv+sb7+qxVEgwGV5iiLz/c5F0ElnJT5FEeC8iUejjqY+U+X+0cpYcOHJTEWYt05NDtJ5cvLz9S4P6mNxnRD15X1YzYpdHQ09LxM1QitX0nDh3KY1s+fYYvGYbP/MM7PQKWg4nVulrAOnhC1YA3Y6d63tco4JLpfslLNlJFrqSNMMvVwz69Z9/dcic0LOunP9PjaXZaIqMzLXbGvr2LwmOt79/n2QETLayXMnUdMEpaNEgZXlv8H0grR1TUXPINY+6DyRbTb6QHJPFctqDtOabd++Juey+v5NfP09IZo9+9xHcPppIbM99fEP443/K3N0ccjsUnQTGZft1jwHD3ZG5vmPnzuB0/pEniBCyyvSdQATy3jeuXoTAPDRj57BUzPaerbBaPHO5lEnAXslne0X5d7t8utAZtvDvPWwreeo7Xc6x72T+eqdEtBGZeI70Q8fhReu7P+7NW6+GgB619/E1AXJ4F96ux4y67c2hA+CjUF2W7ReHmw43wwvfX/2C/gWvHLPuD7vUaYdZS3ya1e/DOBhLfLvehH4+p+/9Mh2W+Hi5dPbZsv+uM9/6vO72vc4TEbA9iVxouCNnLmB+YdniVsbhX7pRl09mAHkeYE8lx+8wBIv+TNbQ0Eq04tt5sYg8uSyIoPVHuTINkK/sjU+IOdeMwQ2MqDQSC03c1K4QIAjmEFZVAlbLo6DD3fSSxAHYh2X/LuVAEcmnG+cGyS+p1pjgs1y5PpAkBkK/dvEOWwQMdGpAEuIfF971yJSghqry1TqDNLawHPceY3NXoqpuq6jD1DTVEcRy7qbvQzdngT9KUjpmCgR5j0AmOlQCmeV9czTVeRpWz+vHH3tuW5t3kB7XUrd3Y6cS9ddwMoNkRtNNlbhEp0CmJf9dzd7cCyksvaDO6ipkUc6vwSrTmZJW13GGvO4f0+C4PRyCtOXc9zckMC6cn0d+hyAjIC16+L29aCrDO1eAtOQc1jeXMO1PxZXoxPnz+Nv/+CLAIBOSx441m7exawG/N79HNdfk1L+qYtSfl84exzrG/KD3E5rKJSpzlxgfkn62t97XZjyN27HOO8rf9km+rcenwDEYWG4hF3uXd4u4I9ilJeXbSVDup1v9fDxtyKw7dUBbL/Yzju7zBLfjjH+0tt1/NCVvY9lOFj3rr/50N/fTD6O+ktfAwDUAbx+Q3UP5gbrPBSot8FffFOu+enlzUeC8zgi26iS9+1b74cgffHyaXzXiy9sua/hPuqtgnWZzAY8+hCxV9nTqiReoUKFChUqHAFMRIZN+h/DDew1yQyeJpTklbkcRpV4CjJoaDvO7NQskkSWb6aSieRZ33OvAJhAOvPkrYhcKB0bEwd1sAQ5Iu15Nl65i22w78zAYG/q0fdZNeAzd7IRbFzXYyhZLukh9xm4IWR+XK5AXXujvcmGcxli4zPgNJDRplMtVyNDpLKacRGDvGkJGbCuC+NL5hFYM90IBVKtHMR6rg1yqGt5PqnFYKcVAVfAZb40J/tatzmwKnXwP/yjNdxblgz2039LStenT88h8oYfaQu5EtDyVDPdvI6OypHm2QbSTclKN3obWGk1dF0pXT+48QD97orui3H8rDz+90mz57tvoNeT8U03n8D0kqSi9SiGU+/qYyclY2ViLK9KC1e0eRvTRshb3VuS1bsu0PbZeJtg9LOnruzHRrPorkk2f6N/C7PnZb933r2KlW8KQWfxlKQKzSfOI81kX1MXj+HJY1KyWzwj16jT72GlJdnFzVs30JyVfS2cmcPtd6UKkHbler/9xjVMfVR63aeRgtd8m9x3DsZlseVWqHGGHsPSpWUMl8TDNiMy64eIYttl9SO22Q3GyZbu1XJzuHy+W8JasNf86p+HZb487vuxPXwWXV5ezqzLWfUbak3ryWTXrt5Fod97ALj8ie9/eByv/M+H/v7wE00Mo1y2997d2HgTwM5axEah3EctpfLxSmRlJbMyyqX2cuY9bAd67erdkMFfmH4Tt7E305mJCNiAaG4Q2SCuEZlB8u+DeIwIzoc2BlKdh6QagZThHLNcCJc7GBqUx9nPaJPXHweg87/OFIPgnRdgPbavfKNIQV4CdCArHmRQmQg2VulQy4Exnms5OTYR4CvyrkDqJUtNDRn7krSKmUQWuY+7EaOm51OQ9jDDoAhBOg+CLbEzYPZ92F57O4c1XhCGB8ZeOlftIoe+zp3HeR+5al9bpMgSZZL78jwX2GxLkH07aeDGPbn2b/6N3Ih/57ub+Mhz8gMwZRro6f2ZaEm82++in0qQ7Oc9dPsymk67htaGnGO7rcEyamDq1IflXBJGqnPA7RsSeDudFTQWZdn8yRMwVo/bbGJd+7cL5SFExmBmWq9xlGPjnsyHpzfk4WEtNYgvSOA0UylaD3ROvpCHk7zoon5cfkDi2gzu/vV7cmI2g9UJ83ok23durqLRl/NdujSDOZVvbbfk4eXu2joS7Rk/NjuLtVUZw73bN5Fpif6Eyqhi3uIbr7wBAHj+42dRn/rOC9jDvdq7CYTb9VnvtA8bGB2ot9q+LG16ENirFOpuMMoz2+NqfxpX/8dfAQCufUQ4Gl+4/gdh3rmMcUHawwdr4OEytJ371CPrjipTf/iJ5kOscT/f/RIGy8rBe5hx/tbGhx4KuDsRP9kJRsmNlh8SLl4+/VBwHsbFy6cHc+QvvgDg/UfW2QmqkniFChUqVKhwBDAxGTYrAcuREr4IyL1Rh5cCpSL0GDtXhGw86SeI6p6gJvuzlkKfNIjBTsvcth62R0k9LPeGH4bg1B8ZVglqrkDfk7ccgjd2rKVpsjaU34uiAJyXqlR1NOLwvlQOlOyWI/SCe4cwVziQpuN5ngcZ0zxIhRbhfTJ58NGW6ruMp+cZ8WzgG9QjG4eebC9XWmOHzPh+6ggWMp1QGA4kOZ+X51QHaeqfs4Hry7l9/U3Z170HHXTWJaN87tk59DZku7b2LXfdJvrKpE/yAr1NGetGu49OT07OKglr2hogl+x1Ld9A993XZF9rkj07BzROSA9zf8NhVslom/0eEs3Sez0hj83NTWH+uJSWG3PHsfLOOwCAjvpOszNYS6RkPbs4h/SOlhnTll6rBMWqZPPm1DSWnpIMZPP2GkgZ564hJXlDDh2tdHXurAFq2LG5otM0mEbjlGQ4cycXsKhVgHytwKy+nj8rbmN2bhp/+fLXAABPnl7AwjEZ43cqykSxcm90mRT2UDa+RUl8J8cahcflCHZYGGX6Uc60R2Xdf/qakEjfe7eLv/+8GHL4HmhgdOl7GIH9XcKo0nOZaOYz8Lc2gIsjVHhH9WyLYtr7I8fwODBc5gZGe1yPe/+9d98Oy/baiz0RAZvBcAWDoyzoXDsi1JQBnQd/oCiwotm4oNHMxgGs880qYsHMA21rYKA77oNpSRM8g4i2yMoG5F9q6RhwQbyFDAdbRB8M0zQbWH0SQhD1sqMxGL4zLXE5yPeQmSjIjBZeppIsCh1jzQCpU0EVFybkg2arQwTjfHlcgjkwaCdzhOD8FVEU2tVYf4B6fQujv4cGDlki+40jwlRNAqI+c4DSDqC2nmmRo9CHlhnnJVkZ6w/kuMsrBqv3pEWrpWX0zDik+hmlXEPalWDXvteF03J/PVMxFcpxf03Y0knNYuWWOnOtSlm4vrSIxj0pr5uTDRQtCeTd5Tuoz0vwnJmX8TdQw433pLQc5THQ0uuloicZpWjod2Z1uYO0ocxtfy2zOfS1VWu+WMLZExI4Fz72FDbbUvevz8oxXT/HrTV5EFhtE3o3dc6+K9MHtWMRWKdxkvVVNHQKgpMMRi081zdkn40kQV2/fxt32zi+9Fi1xD8QbMXsLi+v1WqPlJyHmd9+3Z2yuXcyruF9D2MngXu/kqi7KY/7OWs/l70Vc3y4FF4O5uPawXzg/dD1N8cG5+F1JQhLIC6Xwcvz1F4K9dW78v3+2OkmLpS2GRXQy/PaAwvO9x9tESthJ+XuUfPO5WA6KkiPOsbwvPdw8D4IHXGgKolXqFChQoUKRwITkWETCDaKwIZgNTuNmLzxka/kwjAFuU9DBpZ9emhD5uwzUi4A8oSrEkvca5OStUEW1DGQewY1MUKKrdtHth5IZ4YoZNC5ujTZyMEpQ40dB8nUyPuJkA1jYTm4npcJBiTkJUQZgGZ3eW4QWW9E4kv2BrEntRUGuW5vmQaPX/pvnS0KL0fqHDjVa6Cyo2wJrL3kXKsF1yqX5mBIBluPhVU9EzeRQ8vFJsO5WTnI05fUXep8EzaWa//u2++iq5lkqmIuKRnASBbZbwFZW7LWXtqDVQZ9pizyJFlHnkt2sdntor8smbXRfmuT9LDe1orH7CyOa7ZiY0Km2WwxLRnp1PR51Kfl80xam8hJGax9Ob/GsdNonpMyNNZaaG9IBrTBM3otUtQ1W4+mLGauyLq1qIaTi0IQK9QYprPZxcabUvbqPdhEmmlGNi9ZV7Qwg3pTxnr1G28E2de4Rqgdy/Qc5HqdP3cRa5dk/8eOz4LzoyOcUsY4FvdO1x1VBt/JttuxyB/6e4j9vVcv75HjH6oU7NQlbCcYJbLily1OTw29/3D5e9gv+2OnJYO9sCSkTMlopTL1RslQ1fdQAw9n0KP6qMvLygYjr95tP7Kuf/8CXh5JUPMZ9KhyOSAypuX1gIeJYKMwXJreysN6q30AwNdKTPtLT10Zud2lp/bR7K6oMuwKFSpUqFDhCGAiMmwAABNiRyg0E82NDQSzyHi5URZvakiPs58jZuaQOfvpYQLLJC4AGA5z0D5jLVwBdt5QY2AUkrkCfm660Ey3YSjMG+dciM0ngPq0Pi1zhCxTP+48HZh/8MD8wbL38R54dJChMGAKBLsIpBlbTga5t8/UVq0IAGtrm8sN1JUTFLTFQtcZHBjwbW5MwWvcqrRqnBEKNc6wGUKvOXEdTtdNi1W99lNYmJa52oVpwoc+LhnIEydl/71Oitbt+3qNIxS6s0yvhUMN7U2Zn+tubMIlSnDLGDCyvOjJNu0UWF9TVbS+Q60h83I1nYN3LR70xfcSJNOSedv504j8VdCWq25eoNDKgkuzoISXRzLuuYvnoF4v4H4nGIl0NuVcmo05RDpHHZsalm/K9Th3bgnr92WdxSXJVu6srKKmd1Te6SP1HYjapjddFGhpT3c/7SDdlGtQt4T0nrSskZPMfn5hFs9+8hnZf7MBmx69tq5x/dAj191lq9SojLq8bJTq2bixjJpX3o3q2pbjHCNjepCZtsdwVr1T+dKPnW6G7M9nqsDD2fQo+AxasuP5LdcdlVWX3/MZfhmjMu1xCBn4LueKy/PWw3PYw73dnjRWzpRHZdJ+vTIuXj69I4W07TAxAZvBcMSB5c3EoRbuvajZ2MEGBohVSCNNEuSFJ2f5FShIfDIjBP/CKRubQ2c2UJIIJXKl8reyeItiUEonEwhixL4kDsRTGgQLg1xv8ExFQyJG6P+GGfh0OzKoe5FxPVbGDlZPIkIOp0x3Ug11djnywjPDHSJP+rJA7sXVvda4IVhdlgOIvEZ5pp7NzJ7QDrYJ4LXIKQ9e5E6fCCKkofQ704ix3NJz8LroWRc2FgGUzERIEi1za9TqdTvoP5DAnPc6sCp9amKH1AqRa0XFWFrtNvpt+bGZnUmQ5v7z0Gtcs+jmco3bq+uoaQ/+VByj739cN4Sd3l5ZwflLTwIAmraBVkfHoN+NzaSDQmVtG80GEhVUmdKHmjztwHZlrN2UvL037q8YPLgjZLfWspdcdVg4Jizy/jqQ3Zb3m8poj+qEG68JmW5zZQNRQx8YG7OAlrxzliB+/dU3ceLUp+XaTRWIo4FD0VHBOInQcdhNkBwpjDJi2bjXI/e5ReAu7/+hMY8prw/v84NAOVhvJ106DB9838KnHvobkMA5quRdLnOPQjlIl0v1W+H6ynwgoI3C1VceFl4ZRTbbKcpiJ+XgPNxf7dcdhVHBeRS+9tU/x2e+78W9DbSEqiReoUKFChUqHAFMRoZN2i5l4kD0gmHPL5OmbIgZRwTvQW2D+YdLHXzf1KDhigZKZKZUHfdKaeTg+6GJBhk2Spm3T1iJHAZCqYM+7b6TJ+6IC9Q126/bGiLN+Pzw8ywLx4pAodwMLlDAk87Y7x25DjG2HHrNWVueMnIDH3BipD5FJgPnx6i9XEyA09qxMRRcvlI9scgUMD5DJ4vCt7MxI9JrV9OxuJoDYiGK5baJB/e1xSuSJ+fmAqHu5dyyFJkadmxuyH66mxuY15L6idOnMePLzNMLuKuqajfXXwcAtHtdTBnvfoZAzOvqZzw3O4uZWXlST2kTnbZkzdGxMzBT2v/dUknL1Tb6qfSWPn3lWeSQ7WhWUuWCU6Cvn1dcQ31JZUZ1n61uL1RUXNHF2ZMX5bzudlCflc+c254wZgKR8eSlU+gPlbHXHqyj29exNmLUa1LKZwtkfmpC280eLN9Ca12qBHMzNbTUIOUoYSdl8ElEuQ98t8pjW5W8d1v2HyVlupvxbFceLyNkyyuSMV5YGpSkx5HHPC4sbYwsX1/ty3138/a1LY8/qhwOjCa1XV+Zhz3gVqmd4KCPtVfzj8kI2ABABpGNpacafl5Zg41nbcOCjA9sBZzOEUdkQuArAht7IP4RmTj0YfsoTM4iVNjZBjlRxwNjTs8GB0Whz5q4CHPExgdbZ5Bow3JeI9R0IrNmpvXUUhRaTnPg0EsOEHJ9gPDz5VFh4UO9YwqlfN/7nTgOZX8u3GAOmyk4jplwYgYUe0Y6A9rXO8WeBW9A/hqzCex06xis6+aup9fVwrTlwDfSArH+ICV6/H5Sg1WhGceMmo5ltik/2pefeh4Xrsic7MmzZ9BSEZW7d9aw8r7MqxWqE9/NeSBK4xi5fuas8+HXl9dxSh9wTizNIFdVmSRh1JoqalOXwNvqtLHakXPotlOkyiKvz+k2XQfoOVIaoXlWfpDy+zqfbpuwNTlWq5vg9tX39RzOIU3ls5k6o9twHsrkUzPTOH3pAgBgc0MC9/2376JWk3HVmxaZMvS7vQ30E334VC3yZrOBe9dlX+fOnMKb35I57qOKgwzOe2WZ76Y8Xw6so4LkdoF31PvjthnnHva4ZEo9/Hz2qxjMZ19uyEPC9ZX5ELxHzU9feurKyDnZ27fef6R8PE5D3ePVuw8Hbf9QcKnk5hWC98rbDwmQDGP42NuVtHfTJz28r52Uw71e++VGN2x39tyTO5ZEHUZVEq9QoUKFChWOACYiwyYixFEsZXBNGWOyIdv2PcgEwPmslwsUyszOiiL0bIeMlAjexJqIArvc6b8WZpA1kwvlb2tMWCd4VbMLTGJyXCqv+1R3wCkTLpQy0oP6mUFaytqdNxUhB6fZtvF93GyDyhacgbW+j3qQCXu3LhhGXclbFNURhb51zXRhQ1leByLLtd/aFIzEZ/ZUAJnvZTeh370ea8m8sGh3lADXK+DakhXESt6atYxI/b/npmfwzLOnAACfeF5kQU9fOAWOtCe7nyJL9FhFgkx753P9PK0hRJGyyy3B5Vqy1vPLHeHObVFQS7tTOLE4o+eTw7E0ataVsDi/sIDC95p3W5ieVaOQJekvz5wJ5zg1V8PapmTD97pCmHHxPDbvq/JSDqxmQgpbWDqB2Vkhy6VKGOusbiBpS7beb2fgKZWYVfKhzV1wlEl6OXL9HJDFaHiTlTkpHfZzgJXQl+U5XKJU8yMAIkKtVvtAS967ybrL2C4DP2jS2LhSucfjyqq3I32Vs23gUWW0z6opyG6Yzn6f01PTDx13VP/4n11dCePz2fZ77779SO/ypaeuhMx2nAToXjGKHb7XTNjDVyzK2Gs5HKgy7AoVKlSoUOFIYCIybIBgyMDBwXptbeKQjfi5aDJWepcBuLwIWZorXCBtMQ8yab8v5/JBpqnztzkxopD12oEVZzQwzLCa2eWmCJrf5OjRfufChRYwAweXe41z2SbPUzB7cpgNDDhjHKBtZuyVzpCHcyADsJ6vz5WtiWF0XrqIipB1cpEj8QYlWpkoOEPk1dyIQzZckM9YKfhwR9ah8HagFkFb3Wmm24AJZLnIWbAft17DlnNY1PdPLs3hmY9IZn3sjDypFxlQqGc5g5HA95Nl4fO1XNNzJS8DD8sFrG3o9ZQs07AN5Kx+u8AaNvTzWERzSZ7Oj5+SebBOr4XuqmzXbq9jbkEyBW99SukmMu3zbi33cH9Z9tXSlpRopoW4JsePsp5cHAAry7dANcmMSCsD660NFJpt9zcSdPQb0pyVMZ27cgZ37ohs1O2bt2BIsqmZ2alQRfJEtnR9DXMLMq6pqRn0tW/9KKFM3trp+sD2mt27Ne/YDrtt9yq3ne31mGU87rlqYPde2Z315ZBlHz/7YVy8LGMcl1nvJmscNZbyMt8ONo6MtpVi2LDHtceoHurh7XaybHgcO23rKuPsuScfUkbbDbYN2ET0ZQDfD2CZmT+qy34FwA8ASAG8C+BHmXld3/s5AD8OoADwz5n5j7cfBuvqEcDaw5xzsJAOQdoQOFfhiySDywcEs0EpPKh/DPqp2QxKz6HvmUIQtwQ4FWchItjIKuXB0wAAFw5JREFUi7No0GKE3mYyFGREjWehY1BqJyaRCQVgQ2/1QM4UyOG8coozod/Z/2CDKHiBRzEFAxPng6zlgYFJv0BC3jSkQOT7u/3+mYS8B8BaRqRX1GpJPYHFtJ8qMBnq3smscKipb3OmDxdMGViDpGMTHMt01gEJMbghf8yenEV9RoKc/4ycc6EUL6fqTTgYhtRJreGNXWKkKvs6ZSJQTYO7k32SJcwGy28L/yyUt3vINLBxLLKecVZgZk6267YSFKsiXDJ3XM6vMd1ER8vgK2/dQ6ZExhkVxXnQuofZGfnhMsYEEZYH799GR2VM51WiNM9zrN25qa8TOO2dzqalZD918RiO62CTgsH6Yz/ViNBRVnu3J+O//OGn8MSzl+Ucsw7668LQ3y8+mPv5UWwXvHcrULKX/u7ysXazn5Fl7G36v3e6H2A8KWungbxsAjLq9X7xicUWgN0/VPiAv9a9tuM+7GF4dvh2Iipl8tgoUpgP1KOWDe+jjFEB2W83yonLvz+8XZmktx/spCT+mwA+N7TsqwA+yswfB/AWgJ8DACJ6DsAPA/iIbvPvydOQK1SoMAn4TVT3c4UKRxLbZtjM/GdE9OTQsj8p/fkSgH+kr78I4PeYOQHwHhG9A+DTAL6+9UEkA4ssBxtJ4wCKvE2kSoXmBaDlR1fkIdMlolLPdMkf2kub2sgXYOG0fcswBSW11CWAkafjGkcwPjOv+Uw6BmvmRY7A3vZT9ypkL58JM6yXJFWmGjuEpmxLquIGlSzVFJu8/6ZYocgrtvAtZoWXMHUc3reIwD6DJg7n7nurC2ZknvhW0KB1zY/VFOHautQgj+V9a+tIdR91b7xCNbAS3wpOwZoV93Nf3mfMNCRrrU3F6GfewlOymcggqNE5Z0WSFEDBEcin6doKRrGBK7TkzRkce7tReTqfiRiqLIq+ycHaQJ5YYG1dyGizLcl6F2emkal9Z6cfg7Vtqt2VrLqoLQBaeWDEWF1VK815GevShYvIe35qJUN/TfaPnkXnvvSZrh+THunmbB3GK9JlCYwSBvtd2eeD1RimLln3dNxGL5esI0sMqC7XcWFWyHrNhePoaUVi4/41ZOnWrTE7xQdyPyu2K4eXM92DLnNvNYbd9kOP6q0uZ9UHURYfxl77rUe9PqhMez/Y6xhGtXjtFOVMd1xJfKuS9qWnrozMzD3KGfm48vioEvx+SGcHMYf9YwD+i74+B7nhPW7qsq1BPtAyci3xxlEc5ldz75NcZCiKEJlDqRyOH2GBg02YBy1UmAUYlGitHcxbFw5B0zvnHDWV6PTzynFkEOquGZCpOHTINYgCo9zYgSa3Vdq24YFHNTEj1Z5tYgb7BxRlUBvmUpl8IOIS+Y+KcyA8fhCsG0jF+LJ6KK+zBHg5F4fc763w8+2EVAMvkUFeyE3lbI5Cy+axSqPmkQ1z4NY2gEICn9fetjGjoV7SJopRaHDua1Bq2Dj0zRfMYWqjznXYSG9mkmvQdyyuaRBHsppKqQYVl4hAXkY1GniVT0dTQa62pR7Zs1NnQq95xl3MLgg7vOb9vnsGRvu4zz9zCd1vyzE2WlKapqkE0ZwE2XxtE1zocadjWG1NcF0Jpm4mRnNBHhQ4iYKsbKIl97X3e5g5qWWxOEJXHw6yegNOpwAKnRaYujIFFLLf1155J3QRfADY//1cwshgNyZQj/LL3q7cvO37Y0rxww5dW2G7knh5LAcVvLu97iNBu7xs1PvjsBsBlTK2YjjvVg97uzH4+epxmuO7LY0DUrLeKiDvdv55J2X14ff967Is6V7nr4F9Bmwi+nlI9Phtv2jEajxiGYjoJwD8BDAQBalQocLh4cDuZ1PdzxUqPA7sOWAT0Zcg5JXPsqc1yxP4E6XVzgO4PbwtADDzrwP4dQCIazEzDBwDESu5yiCYaORaXiVCKOsaUHDDcjxgbNvA/AbYl5bBwes5VmKVsMoHbl2sRK7IWBTOO0zJD08tYtQiIS7lLoH1Gbhm2saEFmcAUSiZk5K0YkMojHfzciE1jwxC1kuBdDYo2xMXQCbr1kh7zmnQ9E1g5JqhOwfUzCBzlvEzjC+1E4df2tQT1WyJDOcYgLpmsQ3uYptKvCPHaGjpIKcpxEbL38oMrNkGoKznfuLQVkWxWP24KR4oxIEyP7MBl+WAlr/9+Aq2SDQDb3AONko203K2qdcRaUxo2Dqg7PKp5hzOnjurY0z0tCxiq5UDtHBfS+HHNasuEoeppvRTuzphZl4lS/W6bdy+j+aU1ONMzSJRNbe6YxhvYKLTKVmeYW5ByXZ1g3RDsoVCZVZ7SYbcVxbiOmBluzxPkPd1yiVX0hwsatqHvbG8inxE7+pB4kDv5zhm4NHsdjsW+H49qMdlzXuRCx217naKZzthm++GNT+KjFZeNvz+TjLunWbbQhgbPw2zn7LuVhhmhu/XQ3o4Ky73du+G5T1u3e3Y5weNPQVsIvocgJ8B8L3MXP5UvwLgd4joVwGcBXAFwF9utz/hiDMsW6Ta4lP0M5ggFzoI0r4MzjBgrx9uEHKB4LoFGyKA4xxWf1S98IohIPK+h4zgoMUFhbYoL4ySk0Ws5e0Y0yKAAaAIl8+BeBAYg4aLnouBQeE1wfM82IJaYxD7+XAvZmIpPGjEKIJWuE9aGCbMVTNMEGGxZMN0QOHb4Qxgdd2ITbD1ZC3VGiawnyogwIQWM0Luf7JVWIVA6Oq0gKEOjDK2We070ZjGuupv31m+C9tIdNzCkC7iBoxvG0OBvOfZ0ilSlQbNtQRsmLHp/DUGFrXsTrGML3M5ImVeH6/Po63b5TaB02pNc1aFUdIiaNFPmQZ6Km+aphoYNxI4DdiGChyblx+MXl91wF0TXdXxXlicw3RTdeSTBKYn47ba9pXcbAGZOorNTKO7uq7XSB40XJaiyOS6rNf6OL6oWuJxDZsdWbevQbpfrOP2XZ0bTwtk81s7I+0HB30/l7Ff4ZG9yoluh720Um0nsbkTDI/xIMVldlMmB7YP3l5Wc2NtDtinfre/dh9EC9t22GmQHSXcstN1h4P7fgVYPHbS1vW7AD4D4AQR3QTwCxAWaR3AVzV4vMTM/4yZXyOi3wfwN5DS2k+yZ2tVqFDh0FHdzxUqHF3shCX+IyMW/8YW6/8SgF/a1ShYCFYp53DZoCzmS9KhdRocREccXCj9ggcmX17VRBjkSjCDVVMPDERPXAYX+ayXA3EpJ1lfjuE9tk3IsNMYqMX+uJIx9ooCrBZbLs1hY1nuxVbYZODMM88peH7nCUCawfpeZOcsYl0hY8DoR+SlX5gAJXOrQZnP1h1c4VngnngHGCVJZeQwmDjQ4zszYL8bAzbqPsUM45nsKhsas0GusrE2B/qkpiAqmZmgj8yf7/sdRFoGbtaEhBXVsyBUkxQWmmCjn3RBWU+P4asNLnyeXcuo6bWN9fOYtTVkiXpQz89gqibZdJH10elI5lxT5nmr3ULkH+pdgsL3lRtv0JKisyIEtblLJ8AN+eyXFmWf6fEErb4MZvbUIu69KutS2glTJ1bd2aJZgxktia/cW4NTcqDrKmu/yNCblj5w14lQzEqVwKWMC09Lz/XGA3nfFhGu3RBS2hoY3BlNxtktPpD7uYS9lLkfF8aVxkeRu8rbjMqCD0qydNy0wV4z7/LYR2Wz4zLdR+VClx/6673Vh6sL4zLt7TLJUVWKvTLIhz27t8J+S9Y7KZ3vhoy2V1TskAoVKlSoUOEIYEKkSRmcp2BnQ6bLcMjYewQP7Ch977RBESQ8y3PQwbXaOVjNsjIaSJ56iVBLBKMZsiEnfdmQDJa9fyW8LCghd35e2QK+4cz3eTuLQufDHYrQskRWSVxFBudbjbMCZH2LloGjh32QgRyFHitDgZrOjRe6z9gYFMG0ZGBwkoCDAQkFi1ETWtAsDVTLSLNLgwJw3n7TwKm9ZUGAdV7CVbNqTpFrG1PKwJTuK/X2oL00tDG53MHclME0p7SH+vgSrMrCZmSQpLJuv58g0fni3CueWQrz7OQSrBaSbRxT4t+DXo5MP+j51GBGCX2pidDXbH1de5/zJEOmHtiGC0DniJ3RefN6jnzjfQDA2oqBrcm6zZOSYePuKiLNPopWD6S95iimoG3W4bxgHJx+0O3le2jU9Hr7LrxM5HcB6fFPlDOwcv92MJR54pKQ5pqz81i7eQcA0O0lQH50zD+Y+VAy68eZCT/OfW5nxXkQ2bbHqLawUXPfD2fcy7iqqmWXdMlu52TLxxqV5Y+TI/VZ6YWljR0dp5x1X7u6ddb9ODE8T15uMRvXurYTTEbAZinvWsuBSFYwh/K1D0QZM6wnbMGArA/CwglH+L9ocPuwK5rZ+r4Gy5qNxaEKQL9wQYQldhH6euC6ul5xXqCvpeGaHfhNs8qokmW4TNdFAS966mf7anEE1gCYZhli+DJ1FuRTB4qpNvR8N2wc+rR9GT1zHFY21oKCzGkWes1ZWewGGDwAORpIvSpTLbOEKJhrR4CeI8MgkMutF3lxcNpbHbEFa5+2l0ktEHtVWTBSOPWTvlmTHUVmPTiLRbBIlSbe7zn0enIzFk49w4uBT3jNTcMp6ayl2vG2FmN9UwPfzXVcOH1Kj9tCx+ulq1d1mm+CEyF9NaImpq38IDgrPxYNU0Od5cdp4+ZtRE+LZ3dfpUKn6jXYRX+Nc1y6/BQAoLNyG+stEVHppgMvc+7IsU41GHku27X1AapnG+Fh7tzF88g9Aa7bwkJTtNebx4SRvrr+AG1lmfddF6YhDyvfydhPD/O4Pu8yC7y8zm5IUFsRxcol7X0T7Macw0FiK5b5OEjwlhL5n74my7x71yj4wNRZHwSm8vX2r8vH92XxV++2R2qIewGVYQyXnu3cp0LQLlov76pUvp+SdZlF/urdNr7w3Z8E8LCH9n4CtUdVEq9QoUKFChWOACYjw4b0IXNBsNr3K43UD5Oo4siCVRGK7cDRiXlg6uGNL6yhQauXi8RvGwBrxleLCZrQIbIE44lmFqjpGFhzdIINcqUWjDz4WUvWY4sE4CScR2QG/d0AEJEJ7Vkud0hVKhNEKLxRiB20T0WQJ+1+lqHuqwia8loyKHxWnDsY46+XKT19+XY4g9w7bMEGpbGBKcqgWoCMgykIGzdwxso9cc6iZvx0BYs5NICa9kAXlMFpD32es6+044F6VjSXmzi+4L3OIxSplv2LBB0lGvqs2FERSGPOFMEX2qvZpRlDFUBxr9WC0/cvnz+BGb0Inf59OVYUoYaZcA3957+hzmHt/gqOa9XFbrbx4F0x7zhz+bx8Bp0cxy9rBr/WBS3I0//s9EXMZVK+Xrsl2QffuwerX6rmE+fR31yT5feltN2Im6gtPinnkHewcV+2u3T+aUyfE3LevRXJ2q+9+zpYCX2OYnT6I/VKJh6j1MvK7+21dL5VmXj4vf22Tu1FYnUn/eGj9uPX/SC8xMtl6t1iXMvTqCx1uPy+Fda6Pbyq1fZxbl1boVwSL4+nPNZy1rtVm9owiczve1S2X163PO5RRiT7wcQEbDYGEXHouQa5gfCJnwBmLqkomRA4GUXQ1A4VYooQeZ1uQ4FG7mNdljgYDcxx3QShjJimgz0m5b50ncPLfsRcoK43Yz/3TGrRKwfE9Soverq9zulG9TAua21gDzNscPQKsiY8KHNHJoLTAXOQGM1LgZnA8NKiDrnOifrea8OETJflyGFM8CfTYyVhbp4IIJ0CcMWg1ztzcoPFcQzf9G3JwD8eFPqgEtkYhR6ryBipOp71NdC02xmMFzOxCCXvIiWs6TpdtaZsMCPV4F2gQK4PM0Y1wzvs0IV8RguwWO7IGPu37+LCk9JTvTQnpevp2TkU2qe9udaFURlUUtctlxh0tTw/U4/gNt8BANy+I+udWTqJTZUprTlgTqcN3KlZ8NvCGF86c1zeP3MK9rjcrCu3ljE/J4F+/pwwwHtJhk5XzrFuLOYvXZBr24xw+44cd/OmBPn1jQfYiJWfkDLSfHLY1rvBVgF5lCypXz6qPL6XIFYuwT507FLw3C6Y7KZ3er/SpFsF9YMO4ruZFhjMaSt7/HRzbBDy/dtAO+x/q0A9PK/tj/Xq3UHwO0jW9U7n38tyoteu3g3l9d3omo+bt36Ulb8zVCXxChUqVKhQ4QiABiqEhzgIohUAHQD3D3ssW+AEJnd8kzw2oBrffjA8tovMvHRYg9kJiGgTwJuHPY4tMMmfNzDZ45vksQFHb3y7up8nImADABG9zMyHx8PfBpM8vkkeG1CNbz+Y5LGNw6SPuRrf3jHJYwO+88dXlcQrVKhQoUKFI4AqYFeoUKFChQpHAJMUsH/9sAewDSZ5fJM8NqAa334wyWMbh0kfczW+vWOSxwZ8h49vYuawK1SoUKFChQrjMUkZdoUKFSpUqFBhDA49YBPR54joTSJ6h4h+dgLG8wQR/W8iep2IXiOif6HLf5GIbhHRK/rf5w9xjO8T0as6jpd12TEi+ioRva3/Lh7CuJ4tXZ9XiKhFRD99mNeOiL5MRMtE9O3SspHXigT/Vr+L3yKi5w9pfL9CRG/oGP6QiBZ0+ZNE1Ctdx1973OPbLSbpfq7u5X2Prbqf9z+2g72XmfnQ/oNIbr0L4DKAGoBvAnjukMd0BsDz+noWwFsAngPwiwD+1WGOrTTG9wGcGFr2rwH8rL7+WQC/PAGf7V0AFw/z2gH4HgDPA/j2dtcKwOcB/BFE1PYFAH9xSOP7ewAiff3LpfE9WV5v0v6btPu5upcP/LOt7ufdj+1A7+XDzrA/DeAdZr7KYn31ewC+eJgDYuY7zPwNfb0J4HUA461pJgdfBPBb+vq3APzgIY4FAD4L4F1mvnaYg2DmPwOwOrR43LX6IoD/xIKXACwQ0ZkPenzM/CfsfWCBlwCcf5xjOEBM1P1c3csHiup+3sPYDvpePuyAfQ7AjdLfNzFBNxQRPQngkwD+Qhf9lJY2vnxYZSoFA/gTIvorIvoJXXaKme8A8kMF4OShjU7wwwB+t/T3pFw7YPy1msTv449BsgSPS0T010T0f4joxXEbHRIm8foBqO7lA0B1P+8f+76XDztg04hlE0FbJ6ImgP8K4KeZuQXgPwB4CsAnANwB8G8OcXjfzczPA/gHAH6SiL7nEMfyCIioBuALAP5AF03StdsKE/V9JKKfB5AD+G1ddAfABWb+JIB/CeB3iGgXVgSPHRN1/Tyqe3l/qO7n/eOg7uXDDtg3ATxR+vs8gNuHNJYAIoohN/hvM/N/AwBmvsfMBTM7AP8RUv47FDDzbf13GcAf6lju+XKP/rt8WOOD/Ph8g5nvAZN17RTjrtXEfB+J6EsAvh/AP2Gd9GLmhJkf6Ou/gswXP3MY4xuDibl+HtW9fCCo7ud94CDv5cMO2P8PwBUiuqRPcT8M4CuHOSAiIgC/AeB1Zv7V0vLy3McPAfj28LYfBIhohohm/WsIqeHbkOv2JV3tSwD++2GMT/EjKJXPJuXalTDuWn0FwD9VdukLADZ8qe2DBBF9DsDPAPgCM3dLy5eIxI+ViC4DuALg6gc9vi0wUfdzdS8fGKr7eY848Hv5cbLmdsis+zyEvfkugJ+fgPH8XUjZ5FsAXtH/Pg/gPwN4VZd/BcCZQxrfZQj79psAXvPXDMBxAH8K4G3999ghjW8awAMA86Vlh3btID80dwBkkCfuHx93rSAltH+n38VXAXzqkMb3DmTuzX//fk3X/Yf6mX8TwDcA/MBhfMbbnM/E3M/VvXwgY6zu5/2N7UDv5UrprEKFChUqVDgCOOySeIUKFSpUqFBhB6gCdoUKFSpUqHAEUAXsChUqVKhQ4QigCtgVKlSoUKHCEUAVsCtUqFChQoUjgCpgV6hQoUKFCkcAVcCuUKFChQoVjgCqgF2hQoUKFSocAfx/DqMLe33Q3L4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 576x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# ======= Experiment with these parameters ================\n",
     "# You should try different values for those parameters\n",
-    "K = 16\n",
+    "K = 20\n",
     "max_iters = 10\n",
     "\n",
     "# Load an image of a bird\n",
@@ -586,9 +5074,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Load the dataset into the variable X \n",
     "data = loadmat(os.path.join('Data', 'ex7data1.mat'))\n",
@@ -627,7 +5128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -673,6 +5174,8 @@
     "    S = np.zeros(n)\n",
     "\n",
     "    # ====================== YOUR CODE HERE ======================\n",
+    "    cov_matrix = (1/m)*np.dot(X.T,X)\n",
+    "    U, S, _ = np.linalg.svd(cov_matrix)\n",
     "\n",
     "    \n",
     "    \n",
@@ -699,9 +5202,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top eigenvector: U[:, 0] = [-0.707107 -0.707107]\n",
+      " (you should expect to see [-0.707107 -0.707107])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPUAAAD8CAYAAACvvuKtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAc+klEQVR4nO3dfXBU93no8e9P4kVrajskJo5Tg2RsKzQOBYMSQ1WSOqQtprYH+TY1iLgXw1y1M00uacV4+uIZt+m0/SN2xm7GOOOY3IttgR1ji8qODQkvvrbpQKLFQeAaZJAQEGpDrkkanEUY6ekfZ1faXe05e87uOXvOHj2fmTOw2rdHsM/+3n8/IyIopeKjJuwAlFL+0qRWKmY0qZWKGU1qpWJGk1qpmNGkVipmXCW1MeYvjTFvGWMOGWM2G2Pqgg5MKVWaokltjPlN4H8DTSLyGaAWWB50YEqp0ritfk8AEsaYCcBlwOngQlJKlWNCsQeIyM+MMQ8CJ4AU8EMR+WH+44wxbUAbwJQpU+bPmjXL71iVUmnJZPLnIjKt0H2m2DRRY8xU4HngbuAXwHPAFhF52u45TU1N0t3dXXrESilHxpikiDQVus9N9ftLQL+InBWRD4EXgN/xM0CllH/cJPUJYIEx5jJjjAEWA28HG5ZSqlRFk1pE9gFbgP3AwfRzHg84LqVUiYp2lAGIyAPAAwHHopTygc4oUypmNKmVihlNaqViRpNaqZjRpFYqZjSplYoZTWqlYkaTWqmY0aRWKmY0qZWKGU1qpWJGk1qpmHG1oEOpapdM9rJ580nef7+Gj350mBUrpjN/fmPYYQVCk1rF2oULF1izZhudnXNIpRaP/Hz9+n5aWrayYcMS6uritTmuJrWKtTVrtrFp053ktzRTqevYtKke6KKjY1kosQVF29QqtpLJXjo752L/Ma+hs3MOb775TiXDCpwmtYqtzZtPkko1OD4mlbqOjo4TlQmoQjSpVWy9/767j/e5c/FKg3j9Nkpl+ehHh109bupUd4+rFprUKrZWrJhOItHv+JhEop+VK2dUKKLK0KRWsTV/fiMtLQcAu5J4mJaWA9x8842VDCtwOqSlYm3DhiVAV3qc+rqRnycS/bS0HEjfHy+a1CrW6urq6OhYxv79vWzatJNz52qYOnWY1tbpzJsXr/HpDE1qNS7Mm9fIvHnxnBaaT9vUSsWMJrVSMaNJrVTMFE1qY8ynjDE/zbr+yxjz9UoEp5TyrmhHmYgcAeYCGGNqgZ8BnQHHpZQqkdfq92LgmIgMBBGMUqp8XpN6ObA5iECUUv5wndTGmEnAncBzNve3GWO6jTHdZ8+e9Ss+pZRHXkrq24D9IvJeoTtF5HERaRKRpmnTpvkTnVLKMy9JvQKteisVea6S2hhzGfD7wAvBhqOUKperud8i8mvgYwHHopTygc4oUypmdJWWUiUqdkBAaAcIiIjv1/z580WpuEqlUtLa2imJRJ+AjFyJRJ+0tnbKuXPnHO9PpVJlxwB0i03+aUmtlEfFDgjYs+dRBgb+wvb+oA8Q0Da1Uh64OSBgYGApcMz2/qAPENCkVsoDNwcEwPWA/QEBQR8goEmtlAduDwgollpBHiCgSa2UB24PCLDfltgS5AEC2lGmPBtPZz3nW7FiOuvX9+dsNzzWMcD+gIDADxCw6xYv59IhrXgqNpTjx1BNNWht7RQYyvk3GL2GpL7+Xx3vb23tLDsGHIa0NKmVa8U+zH58WKtB1MepjXW/v5qamqS7u9v311XhSSZ7WbRokmPPbyLRz549l2J3jI0d64CAk3kHBDS6vr8cxpikiDQVuk/b1MoVayhnseNjrKGaneMmqYsdEBDWAQLa+61cGa9nPVcj/R9QrozXs56rkVa/lStuhnLieNZzIZEf0rPrQSvn0t7veBrvvd9RGtJDV2kpP0TprOcwSstiq7OCXn3llg5pKc+CHKop5sKFC6xZs83xi6Wurs73943akJ4OaSlfhXnWc1ilZTUN6Wnvt6oabtYyB7VWuZqG9MKPQCmX3KxlDmqtcu6QXi+wE9id/rN35J4oDOlpUquqEWZpuWLFdOrqDgNbgYlYZ0Xemv5zIrCVurrDkRjS0za1qhrlTIApt7d8/vxGrr762wX3HoPrgHquvvpRbr75a65fMzB2Y13lXDpOrYLQ3X1kzBhx/pVI9Mn+/b0jz/FrbNndex/Lee8g4TBOrdVvFbpkspd163ayevVu1q3bSTLZW/Bx8+c30tJyAPtdRYZpaTmQ0/uc6S3Pnwln9ZbfyZo121zFaLXnnTZGgFRqZqB7j7lml+3lXFpSKzdKKUW9PMcqXfs9lex27r13l+PrZK7Vq3f5+m9kB51RpqKolDHnuro6OjqWpSfA7MyZACPyae6/f89Iu/ndd98nlfqyYwxux5arakGLXbZnX8BHgC3AYeBtYKHT47WkVsX4WYrald41Nf/mW+laSns+SPjQpn4E2CYis4A56cRWqmR+jjnbtZuHh6e4isVN6VpKez4sRavfxpgrgM8DqwBE5CJwMdiwlN+itlzQ3ZhzL9u3n+D993fbxuw8y2w60I815FSYl+WiUVrQ4siuCM9cwFzgx8D/Bd4EngCmFHhcG9ANdM+YMaMiVRBVXJSWC2Zrb9/hUJVNCXQKFI/Z+XUk/Tr+LhdNJo9Ie/sOWb16l7S375Bk8oif/zSuUM5uokATcAm4JX37EeAfnZ6jberoiOoaaOc2qvuYi/dKW18QtbW9kfpSK5dTUrvp/T4FnBKRfenbW4C/9qWaoALlZQFEpduCVht1a7qXOzu+XqzKobuYi/dK1wHLWL78KT7xiRN5y0XDX/scCLtsz76A14FPpf/+98A3nR6vJXU0FK+aWld7+45Q4ivcNPAWc9R6pSsFH3q/vwZ0GGN6sL5G/9n/rxflt6gvF8yMOb/xxofMn/89rFVP/+nquZmYq6lXulJcTT4RkZ9ita1VFamWCRPz5jXy27/9M5LJW7GWMhaXHXPV9EpXiM4oi7Fq2gF09AvI+zCU0yyzctvNURsKdMWuXl7OpW3q6Ihq73e+3Laxc8yzZj0m3d3BDiNFdSgwAz0gb/yK+ocz2+gXUEpgo0BvXkL3pRM+FXj8Uf8ydEpq3U10nAhzB1C3Lly4wKpVXTz33CDDw78LfAjsAjKdXNOB7JiHaW31f6PBqO0cWojuJqpC3QHUrbq6OmprJzE8/MdY49S9wBKgweYZwYyzV9POoYXoJgkqMsZOljmJfUJbgthoMOpDgcVoSa0iY2wJGU5y+TEUGGavuSa1ioyxJWQ44+zlDAXmniAy+gW1fn0/LS1bAztBJJsmtQpNfml28eLpvEf4u3TSLft56RmZWWpjO+gicd6WXbd4OZcOaSkndsNskycflZqaJ9NDWpmfhzO0VMpQoJ+7uRSD7lGmglJK29GuNBscvB6rVN4K3JX+qTUF1Npwp3JTQEuZpRaVXnNNalWSUtuObpaD1tbOxpgOLl36RPpxlzNhwh5uuGE7t9xyPVddVVOxpZNehgKj0muuSa1KUmrb0U1pNjR0IzU1B7COtLHU1h5j3ryDfOc7iwLvaCpVVBbQRHOgTVXcsWPHeO2110ilUkUfW87pk25Ls+Hhj+XcHhy83tPm+2FYsWI6iUS/42MqsYBGk1oB8MEHH/CFL3yBK6+8kgULFrBu3To6Ozs5c+bMmMeWsxOo29Ks8HBWcEfV+iEqa7s1qRUAN910E1dccQUffvgh+/bt46GHHuKuu+7i6quv5sYbb2TVqlV897vf5eTJk2W1Hd2UZtYwVuHSLKijav2yYcMSWlu7xvyOiUQ/ra1dFVnbrW1qBUBtbS0LFy5k+/btY+47evQoR48eZePGjdxxxx00Nq519ZqF2o5uxoDhAGDfCRbV6ZkQ7NputzSp1Yjm5uaCSZ3t+PHjPPBAeZsv2O1UMmHCO1y69BbWMJa9sHdqcSPMBTSa1GpEc3Oz4/0NDQ10dXXR0NBQ8owrsC/N5s4dpq1tDqmUfe92VHZqiTS7WSnlXDqjrDqdP39eamtrBRhzTZ48Wfr7+0ceG9TmC1HfnCAq0Bllyo0pU6Ywd+5cksnkmPsGBwd57733aGhoAIJrO+omguXTpFY5mpubc5J66tSpnDt3DoAFCxawd+9eJkyYWtayQqeppVHoaCpFpDYotCvCy7m0+l29nn322ZEqd0NDg/T398vRo0fzquIv5FSLa2t7Zdasx2TPnh7H166m/dLcCut3Qjce9K672zoE7d57rUPQgt69MirOnj0r99xzj3z2s5/NaUPnJzbsHdPeral50vGDHMf2cli/kya1B3EsTfzQ3X1EJk9+tUhi9wkcLvhBruSyxEoJ83dySurojuKHxO4Ac2uhQrTnHgdp8+aTDA5+ATia9dMFwL6s29cBpwpO5fTzkPmoiOrvpEmdpZyFCnE3OjX0epwTu6bgBzkqyxL9FNXfyVXvtzHmOPArYAi4JDb7DVe7qCxyL1cQPbG5CzEyiX1D+vYCYC9wC5nFDJkPciaWH//YXWlVDbPFMqKy1HIMu3p59gUcB65y81ip4jZ18QPMrWv16l1hh1pQkP0BhY+Mze88e1wyp2qsXftyXixH0m3uuLWpw/md0Da1O5H95nUpyP6AwssK86vibcD/J5Ho58iRs3mxNGIt1IjPkbNRWWo5hl22Z19Ya+H2A0mgzeYxbUA30D1jxgzfv5kqoZoPMK9ET2ymJlBbm33G1cG80nq//OEffscmlpRYGwnGZ2ShasepgU+m//w41tft550eX63Vb5HqHUttb9/hqunQ3r6j7Pf693/vkVmzHhNjHslJ6EmTOqS1tVPWrt1WJI4jAjvkppuekvb2HZJMVv8cgGTSmtewevWuivxOTknt9tD50+k/zxhjOoHPAa/5UlWImCjNPfbS4VVuT6yX91q4cDbPPWeYPXv2yM/+9E8fZ+3aJubNa2T16t1FomgEGrnllt08+OCtruKOuiidVVY0qY0xU4AaEflV+u9/AHwj8MhCEoW5x6Xs1Flqf0Ap73Xo0KGchO7p6cm5Xe19E1XPrgjPXMBMrCr3AeAt4O+KPaeaq99RUEoToNT+AK/vdfBgbhu6p2fsfO9q7puoFug00epRToeX1wT1+l5uErrUWJQ3TkmtQ1oRU87UQ6+b3nl5r2JVbnex9FJb+zwzZ25k2rQEyWSv43ur0uh66ogpp8PLa3+A2/fq6xtg9uwvjdwultD5sTz55Cu8+OIAJ04s5tKl/0FfHzzyCDz+eOVOgiwku3Pw4sVTQB2TJl0V/nroMmlSR4wfnUxue2LdvdchOjvXjNxyk9D5sTz00H/Q19dGqCdBpiWTvTz1VD8vvniagYErGBpqBI4Bv0v2WV2VPHrWd3b18nIubVOXrpKdTMXfy30b2vk9Susj8JPdJBF4sirb/mibunq4mXr48Y+/xG/91vSA3+sQ4L4NbScqyxMLT6HtBRYRt1V5mtQRtGHDEurrHwX68u7pB7oYGFhT0jzuZLKXdet2snr1btat20ky2WvToeVPQkM0lifaL6k9CTQ4Prfa1niDtqkj6a23TnDmzB3ARWAn1odxGJhO5uSKTAniZrGA8wSTbWzYsIT29hNs2rSTvr6BstrQ+aIwEcV+SW34XzhB0KSOoNwPYeEOLy/rut0eOztp0kXPvdzFppeuWFHeaR5+sK8thP+FEwi7xnY5l3aUlcfPdd1uO6q+//2XPHWKeVmdFPZEFPvFLtW7xhvdzL+6lFNlzS853333NKnUPY6vk0p9wJ/8ye0jt92U0F4OnQ97kYx9baER2ArUU8rxQVFlrKT3V1NTk3R3d/v+unGVn4jWmVI3FK2y7tlzaaT6ndtuHn1ebe07DA1lDp0rNN7qvVMsmexl0aJJjr3a+fEB6UkxJ/MmxVRmgsfKlVsLfgnBBeBlrH+D0Vizv3CiOE5tjEmK3bZidkV4OZdWv91xqsLW1/+rpyprsSqutTmBP+PQlVy77Re7f+sJE3pl5szHZPny78lXvvL9iq2HLhda/Y4mpyrswMAa6usf5cyZ24tWWd3sggpzgHcYLY1yS+hnn33RdS93FIapvHKeQvvnYYfnK03qkBRPxMs4c+Z2nnjiGPv39znO43azC6o1BXInVlLnJvTSpQ/ntKmLicIwVamitJlBUDSpQ+J2O+L9+/t48EHnx7ktOWtqzjM8PDahn3/+z1w9PyMKw1TKXnTqR+OMn1VYtyXn0qVHyU7oZ57p4gc/WOu5Iyiyu2gqQJM6NH5WYVesmD5mDXW+yZO38dJL60Zu9/T0cPfdd7iKoRCva7dV5eiQVkisYaGJnoatnNgP2QD0YHWUpW+VOfUzW5jDVOOZDmlFlJ8zreyGbCZPfqXs5ZMqetAhrWhyM9PK7da9hYZshoaOsXHj/xp5jJ8ltIourX5HQKEq7Kc/PaPgDDG3M5287immqotWv6tQOVVzL7t+quqE7nxSXco5J1tLaKVJHUGlbgGkCa1AkzqSSpmYogmtMjSpI8jrxBRNaJVNkzqC3MwQmzRpF++9d46Wlu9pQqscrpPaGFNrjHnTGPNSkAGpYnOrLwAvMDQ0naefnsXWraObBC5d+jA33qjzrcc7LyX1WuDtoAJRuezmVtfUPAcsY2hokOzFGdDDyy9/raStg1W8uJp8Yoy5FtgI/BPwVyLiuPhWJ5/4J3tiyuDgKbZsaWZw8NfkJ3Tmtpf54qp6OU0+cTtN9GHgPuByhzdpA9oAZszQdbR23E77zMhe1L9u3U7HhAZrqOtb33qOp57SpB637GalZC7gdmB9+u+/B7xU7Dk6o2wsL1vq2lm2bEPOTDHoKTjjrKZmq+vXVNWJcg6dB/4FOAUcB94Ffg087fQcTeqxyl2RlT/10y6hrWtHRfbTVuFxSuqiHWUi8jcicq2INADLgV0i8hX/6grxV860Txg7Dp1f5c7VD8wo+poqvnScugJKnfaZTPayatUTOQm9dOnDwE02rzIMHCCzY2g1Hu6myudpPbWIvAq8GkgkMVZ82mcvcJKurneBndx118d59NFjPP98HYODo+uhJ09+mcsv/xV33/0CW7bMYWgouzOsHyuhc7cRitI2vaoy9H+8AuynfV7AOvZlIrCYd95ZyUMPLWbRosls2nScwcHbsh7bw+DgbTz77B9TWzuB5cv3Ym35uzv954dYJ2LmrrGO4ja9Klia1BVgP+1zG3An1p7co4aHLwJ/mfWT7Da01Va+7bZrSCRmArcCiyl0OqZu0zs+aVJXQOFpn71Aoc6z3H254UXyO8VSqet4802j2/SqgnSPsgoZux/ZSawSNlt+QvcAZwq+3rlzNWWdJul1EoyqHprUFZK/MWBX17u8kzPaVCihZ2O1mceaOnW4yPlQhY9fzT0dc/RLZf36flpatkb2lEflgd0AdjmXTj4pLvfkSKeJJWNPmCznIPSwD4BX/kD3KIue0c4zuxIaRieSZCu9rVzuJBhVHTSpQzJ/fiO33tqFfUIPA2+QfRA6HGPJkqdLPtKm1Ekwqrpomzokhw4d4uWXv571k/wS+gBW7/hOrO/eSyxZ0scrr3g7oTJbNZ4rrbzTpA5B/lzuZ57p4ic/OcPPf/4j9u07Rl9fIxcvjnZ0jfZm/8+y3reaz5VW7ukJHRXmZpPAoA6d8/tQPhUePzZJUD5wu+tn9sYIfquv387hw20U7k7JdMIVHg5T1UEbTxUS5ja+Fy5cYOXKrSxaNJHDh1cBXVjt9lF6rnR8aEldAWHvy71mzba8s6uXYU1T3QnArFnv0NHxRdsJK6q6aFIHLOyEth+bbiSzCGRgYCbGXKpYTCpYmtQBCiKhvc7Ztsam8+eY57LGpndq51hMaFIHxO+ELnXOto5Njz+a1Gl+rloKooQe2y62pFLXsWlTPdBFR8fYNrGOTY9DdpPCy7mqaUGHH1v3ZgviwPfu7iOSSPQ77B5qv8jDem5fSc9V0YUu6LCXKQHzJ2RYJeCdBY+xOXv2LBs3buT111/P+XlQnWLlzNl2PpcLdDOFGLLL9nKuaimpSy0Bv/zlLwsgiURCdu/eLSLBlNAZ9967yzHGzLV69a6Cz/e7NqLCh0NJPa7b1KX0DIsIe/bsSd+XYunSpXzzm9/kq1/96shz/B62KrddXMpmCqp6jeukLqVneGBggNOnT4/cTqVSgSY0WGuv16/vLzpnu9gmg0FOP1XRMa7b1KWUgJlSupBvf/vbgUws0Xax8mJcJ7X91r2j8ktAp6S+7777ePXVV/0KL4fdedU6Z1vlG/dLL1eu3Fpw/NcyTGtr7vjvnDlz6OnpsX29RCLB9u3bWbRokf/BEtyyTFVddOmlAy/b7P7yl7/k4MGDjq+XSqXo6+sLLKm1XayKKZrUxpg64DVgcvrxW0TkgaADqxQvPcN79+7FqWbzyU9+kubmZpYuXRp02ErZclNSDwJfFJHzxpiJwBvGmFdEZG/AsVWUmxIwuz1tjGH27Nk0NzePXPX19Rhjgg5VKUdFkzo90H0+fXNi+vK/IV4l7r//fpqbm1m4cCFXXnll2OEoNYarNrUxphZIAjcAj4rIvkCjshH2UTHf+MY3KvZeSpXKVVKLyBAw1xjzEaDTGPMZETmU/RhjTBvQBjBjhr8nLepRMUq552mcWkR+gXXo/JhBURF5XESaRKRp2rRpPoVnKWXRhVLjVdGkNsZMS5fQGGMSwJeAw0EHlqFHxSjljZuS+hpgtzGmB/gJ8CMReSnYsEbpUTFKeeOm97sHuLkCsRSk2/Eo5U3kM0G341HKm8gndSmLLpQazyKf1LrsUClvqmJBh5dFF0qNd1WR1Lodj1LuVUVSZ+iyQ6WKi3ybWinljSa1UjGjSa1UzGhSKxUzmtRKxYwmtVIxo0mtVMxoUisVM5rUSsWMJrVSMaNJrVTMaFIrFTOa1ErFjCa1UjGjSa1UzGhSKxUzmtRKxYwmtVIxo0mtVMxoUisVM5rUSsWMJrVSMePmKNvpxpjdxpi3jTFvGWPWViIwpVRp3Oz7fQloF5H9xpjLgaQx5kci8h8Bx6aUKkHRklpE/lNE9qf//ivgbeA3gw5MKVUaTyd0GGMasM6q3lfgvjagLX3zvDHmSLnBAVcBP/fhdfyi8TiLWjwQvZj8iqfe7g4jIq5ewRjzG8D/A/5JRF7wISg379ktIk2VeC83NB5nUYsHohdTJeJx1fttjJkIPA90VCqhlVKlcdP7bYANwNsi8q3gQ1JKlcNNSd0M3AN80Rjz0/S1NOC4Mh6v0Pu4pfE4i1o8EL2YAo/HdZtaKVUddEaZUjGjSa1UzEQyqY0x3zPGnDHGHIpALJGbJmuMqTPG/NgYcyAd0z+EHROAMabWGPOmMealCMRy3BhzMN0H1B2BeD5ijNlijDmc/iwtDOy9otimNsZ8HjgPPCkinwk5lmuAa7KnyQLLwpwmmx6RmCIi59PDjW8Aa0Vkb1gxpeP6K6AJuEJEbg85luNAk4hEYuKJMWYj8LqIPGGMmQRcJiK/COK9IllSi8hrwPthxwHRnCYrlvPpmxPTV6jfzsaYa4E/Ap4IM44oMsZcAXwea2gYEbkYVEJDRJM6qpymyVZauqr7U+AM8CMRCTumh4H7gOGQ48gQ4IfGmGR6CnOYZgJngf+Tbp48YYyZEtSbaVK7lJ4m+zzwdRH5r7DjEZEhEZkLXAt8zhgTWjPFGHM7cEZEkmHFUECziMwDbgP+It2kC8sEYB7wmIjcDHwA/HVQb6ZJ7UKUp8mmq3GvAktCDKMZuDPdjn0Ga6LS0yHGg4icTv95BugEPhdiOKeAU1m1qS1YSR4ITeoiojhN1hgzzRjzkfTfE8CXgMNhxSMifyMi14pIA7Ac2CUiXwkrHmPMlHSnJulq7h8AoY2kiMi7wEljzKfSP1oMBNbR6mnpZaUYYzYDvwdcZYw5BTwgIhtCCiczTfZgug0L8Lci8nJI8QBcA2w0xtRifTF/X0RCH0aKkKuBTuv7mAnAJhHZFm5IfA3oSPd89wH3BvVGkRzSUkqVTqvfSsWMJrVSMaNJrVTMaFIrFTOa1ErFjCa1UjGjSa1UzPw3enxZ4UFYcu4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#  Before running PCA, it is important to first normalize X\n",
     "X_norm, mu, sigma = utils.featureNormalize(X)\n",
@@ -735,9 +5259,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise k-means-clustering-and-pca\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "           Find Closest Centroids (k-Means) |  30 /  30 | Nice work!\n",
+      "           Compute Centroid Means (k-Means) |  30 /  30 | Nice work!\n",
+      "                                        PCA |  20 /  20 | Nice work!\n",
+      "                         Project Data (PCA) |   0 /  10 | \n",
+      "                         Recover Data (PCA) |   0 /  10 | \n",
+      "                                  --------------------------------\n",
+      "                                            |  80 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[3] = pca\n",
     "grader.grade()"
@@ -763,7 +5308,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -807,9 +5352,12 @@
     "    Z = np.zeros((X.shape[0], K))\n",
     "\n",
     "    # ====================== YOUR CODE HERE ======================\n",
-    "\n",
-    "\n",
     "    \n",
+    "#     for i in range(X.shape[0]):\n",
+    "#         for k in range(K):\n",
+    "#             Z[i, k] = np.dot(X[i, :], U[:, k])\n",
+    "    Z = np.dot(X, U[:,:K])\n",
+    "\n",
     "    # =============================================================\n",
     "    return Z"
    ]
@@ -823,9 +5371,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Projection of the first example: 1.481274\n",
+      "(this value should be about    : 1.481274)\n"
+     ]
+    }
+   ],
    "source": [
     "#  Project the data onto K = 1 dimension\n",
     "K = 1\n",
@@ -843,9 +5400,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise k-means-clustering-and-pca\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "           Find Closest Centroids (k-Means) |  30 /  30 | Nice work!\n",
+      "           Compute Centroid Means (k-Means) |  30 /  30 | Nice work!\n",
+      "                                        PCA |  20 /  20 | Nice work!\n",
+      "                         Project Data (PCA) |  10 /  10 | Nice work!\n",
+      "                         Recover Data (PCA) |   0 /  10 | \n",
+      "                                  --------------------------------\n",
+      "                                            |  90 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[4] = projectData\n",
     "grader.grade()"
@@ -864,7 +5442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -912,9 +5490,10 @@
     "    X_rec = np.zeros((Z.shape[0], U.shape[0]))\n",
     "\n",
     "    # ====================== YOUR CODE HERE ======================\n",
-    "\n",
-    "    \n",
-    "\n",
+    "#     for i in range(Z.shape[0]):\n",
+    "#         for j in range(U.shape[0]):\n",
+    "#             X_rec[i, j] = np.dot(Z[i, :], U[j, :K])\n",
+    "    X_rec = np.dot(Z, U[:, :K].T)\n",
     "    # =============================================================\n",
     "    return X_rec"
    ]
@@ -932,9 +5511,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Approximation of the first example: [-1.047419 -1.047419]\n",
+      "       (this value should be about  [-1.047419 -1.047419])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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U5S9nqTGyYNasasu4fPlyOXjwoPd+CUFCw0x5TMV/zHBcYG9ALa9xrqn9LBAjcIVASZUMcvVd7GHemy1yihgpBXkE5BOQoyZO9s58p3zL6wpVwfJk/Pjjj2XHjh0uy1ZaWir33HOP1tBqUF2YaZ+ZcpvzxoWbgUFY/VAWf15eU7Xzvj1W2SOAm4E/OL3e1XcZMAB+fddZVpqRtCYHA9xOX34REk+cHOPSRbPhN7+xXjx4sHXa0tGj5UORYWFhDB8+nK+//rrKtY0x3HzzzQwbNozMzEzPfOkmQsNMuc15hDAZa+PCwdgDzZ+X11TdDvsj4CywAWgNPIf1fSzVfZcXrk8lTo5xPLILxUC7qHPkrv6HY4nSihXWCydNggkTnOaRjRkzhjfffJPhw4fzzTffVLn2uHHjeOONNxg2bBhHjx5tyNdtUjTMlNuqjhDehRUA//L75TVVt8M+BSQAJ4C/AHHAM8Afa/4uZUuQ2k0Zy7bzzqPrmW/o9/gvrWkXAD/9ZO0cm5zs8u1jxoxh0aJFdOjQweXz48aNY926dbRv355z58658xWbLA0z5TbXy2vuIiZmHNddt5DY2L3VvdXnqm6HPRV4HCvQsoAMoDkhIe9zzz1F1U9StS9BSk/nhlWr2NmmDc9mZiJbt1qPR0dbG43VMCF22LBhtGnThtGjR7tscl5++eVkZWXRo0cPbXLWhauONG/fdAAgOFQeIczIEFm0aJF06NBB9uzZ4+viuVT9SOzH0qxZtlx9tcjw4cfkqqv6y5VXXinPPfec9cb8fJEPPxSZM0ckJUXk5EnHFtfx8ZL1m99IfFiYzAIpjY62nq+jpUuXSlxcXLWDAjptwxk6mqkaiz3Q9u3b5+uiuFTdHLlHHimUpKQkOXjwoJw5c0YGDx4s0dHRMvfuu0UiI53fEBcn8t57Tnv2Z4HEh4bKnF//2u0yLV26VNq3by/Z2dkun1+yZInccMMNVTaCbIqqCzPdNlt5xcqVKxkwYADR0dG+LopL1S3a/uMf/8jcuS9y5ZUbKM1vwfYvu5Av51gH3Fj5InFx8O23sHp1+Z79JwcM4GhODj169MAY49YZAYcOHaJLly7k5OSQmRlTZdvt668vorS0lEOHDnHppZd68tcRUHTbbNXoioqKZNiwYX7b5HRl+nSRiIg3pDkd5QDny88gbUBuAdlvjEh4uHMNLSXF5XWeeeYZmTlzpts1qcOHD0vLlhdIq1Y7XK5IWL9+fZNvcqLzzFRjCwsLY8KECQwePJi9e/1zUKDimsz+/eHtt4Fzk/iCs3Qli2jgEHCMUDaJWNW4iAjHBarZWPHhhx8mLS2NJ554wurPqaMffuiIyBvk5Q0Hx2rO8v3XmjUbWD5tQwcFKnGVcN6+ac2saVm0aJF07txZ8vPzfV0UJ676zkBkNr8XAdkD0hXkIEghzeRwi0usF3TtWmvNTEQkKytLevXqJampqXUuk2N1xVKBYdWuSFiyZIm8/PLLDf0VBCS0z0z50k8//UTnzp3Jyckhxg9m1WZkwKhRVWf4R3CW47RjF3m8STOuo4TLgJuAUkz5houANe3i5Mkap1/k5ubSqlUrDh8+TKdOnWrtQ0tIgA0b7PdKgGZVnl+3znF/9erVXHDBBcTHx9fyjYNHdX1m2sxUjaJz587s27ePK664wi+anFVXAgiRFPASjxFFHtcAR4jkOgw3AQLOQQbWLP9aNlaMjo7GGMNdd93Fk08+WWuT03keXLMqz1f+O5CXl6dNTjtX1TVv37SZ2XT5yzw052181klrEuQQ7aq0OY/QTkoqt/WMEXn7bbc+LysrS+Lj42XWrFk1DgrUZ0cS+zy0zMxMd38NAQmdZ6b8xaJFi+SWW27xaRkq7vzRnNMyjOYyGuQ7OouAnKXSqKX91revWxNiK8rKypKhQ4fKkSNHanxdffaKW7FihRw6dEhErEBMSrICOynJuh9MNMyUXykpKZGjR4/K3r17ffL5FWtA40mRQpCBREsYD8tRrImw50IiRECKW5a9sF07lxsrustms8lHH31UYw3N1eqK2pSWlkqPHhOlVauv3QrCQKNhpvzO0qVLfdrktNeAZjNHBGQuMyQq6qS8MmGL08z+8hn/W7Z45HNPnTolvXr1qrXJ6a6NG0VatPhEIE6gaqD546aZ9aFhpvySt/vQqm1yla21PDh5jqzqOlUE5EB0vGxaV2Q9n5Mj0tlqcsrUqR6pkVVU1z40dziazvZAOxZQm2bWVXVh1mhHzSnlyl133QXAt99+y2WXXebRa7s6Du6rdQW8e8V8hn/zAuTncxFwEUBICF1zM+k6rQ8MHQppaY5tfF56qU7nWrqjbdu2pKens2DBAo9d07G90Vis06faOT3vz5tmeoSrhPP2TWtmypW33367Sg2tvp3ZrkYFe7NFfq48YtmypUj37tZ/h4R4rWlZk23btskzzzzT4Bqa83bmVW/BXjPTMFN+o3KTsyEnQDn/wy6VMJbJzxVOUMoLjRZbz55SAJLdPE72t7hSBORw0lTHNj8eblpWx75SoD5rOSsKxINm6kPDTAUEe6ClpHzfoH+YzvPICuQCLpBpIKUVLvRqWJRcT6Tkg6zgZhGQOc3n+GTkzx5oc+bMadB1AukIwPrSMFMBY+XKlXLzzbmVQsz55KTamkwVa2aDSJMskGuhPNAmgmwFGU8zSQDZXzYdYzwpPqvFZGVlyZ49e8RmszWohlafaR2BRMNMBZQBAwoFhgjsEes4uKsFfioPqISEmt9vb3L1I0NKMCIg2SAzQQpB/gmSD1IEcifI6yA/EycRFPi8f+mpp57y+LSNYFJdmOnaTOWX2rQJB+7EOvXpNDARa5/+fwO1nwBlPw5uFSPL11S2JJR5WMu3+wDNgf7AJGAssYzkM85hjVr6cuTv0UcfZfXq1XVay6kcNMyUX7JOgLKf+pQITME6fOR2oqOlTidAvXB9KjFYqVQIHGtxEQtaRDEC63C5ZsBcYDTNuYjFbKNP+Xt9ubGHfdrG6tWrWblype8KEmA0zJRfcpwAdRfWQcORwCSioj5j0qQSfvGLY7VfpMLGic8Yw9yC/dzTtTMdW7ViJFAA9CaKsyyliEnAGQC/OC6vbdu2bNq0iREjRvDjjz9qDa0ONMyU35o/3zqtLSnpQvr23UtERE/efPM4Q4asoV+/fvz73/+u+QL24+BCQ5ktwm7god27WZCXR0fgT8BtLVZRzM3ALiCKqKh/M3ky1R8x14iioqIAuPvuu93esbYp0hUAyq/1728Plst4//3/x6xZg0lPT2fq1KkkJCSwfv16OnXq5PrNycnWDP5jx4gKDeVzm43ZQBGwEDBr1hB7rC3hfy0gPz+G0NADbN16PcnJn+Li+BKfMMbw6aefMmTIEACef/55tw5JaVJcjQp4+6ajmaq+Fi1aJLfddpuIiMyfP99xruWJEyJTpogMHmytpbRv07Ol6qLx41FRMmfyZCkpKZGHHnpIBg0aVL6ld1pamsTGxsqmTZt88fWqlZWVJYmJifLzzz/7uig+h07NUMGipKREjhw5Inv27JHSvDz5KilJfqo8szYkxDrXUsSayZ+SUj6zP/fYMenXr5888MADUlRUJHfeeWeVQLvzzjt9+A2rV1xcLIsXL27S0zY0zFRQWbp0qXSIjZU9550nr4FcDFaghYY6joMLCal2I8Xc3Fzp16+fTJkyRWw2mzz88MPy7bffOr3m4MGDsnnz5sb4OnV26tQpj++2EWg0zFRwKSiQRdHR0gHrFKWXQkLk8ubNZTmIxMaKRFgbK8rUqdVeIjc3V9asWSMi1saGJSUlMn/+/PIa2rp16yQ2NlYy/GwKvTe2Dwok1YWZjmYqv1PxLMvkZOt+Famp3JWby7zmzfkemNG7N++sXcv5l1wCJ05wtk0HALYv/q7aa0RFRTF06FA++eQTpkyZQklJCTt37mTkyJEUFBSQkJDA4sWLGTt2LJs2bfLqd3aHfR5au3bWFj91+n01Ba4Szts3rZmp6rhaKB0WJnLrrZVeOMfaHVauvtr6GRFhbbj4+OPyHMjrWNv5vM7UWhdb25uclfvQzp49KyIia9euldWrV3v3i9fThAlbpHnz3wmUBu3C8srQZmbjCfYDJbylpi1soCzQynaIlXHjrAd79HDsQxYRIbbmkfIDSGeQNzDSmpN12m3DHmgvvfSS2Gw2WbhwoZSUlDi95g9/+INfjXJu3CgSFZUlEC8wq0qg+Vnr2GM0zBpJU9iCxVtq21zw+mZbpLBNXNUn2lU9Im4fRlrSVmB1nXfbyM3NlZycHMnOzpaSkhL5/vvvZezYsX47bcPx+7IH2nNu7SwSqKoLM+0z86CMjKrbNIN1f8EC8KNuF7/k2PYZYCXwKvA/wGHO4ySfl9xEePYx6NLFWm/Uvbv10uPHy99VZML4K+PoSxb5ZGItVHeoaQF5VFQU0dHRPPbYY/zmN7+hS5cutGjRorwP7aabbiIlJYXk5GR27tzpke/cEI7fV1sgHRjn9HzQb5NdiUfCzBiTaIz5zhiz3xjzhCeuGYiqnpK9EWu+ufX4yy/7olSBw/k076uA14EdRHA9X3EhrcsWjZ87dIisDz6ARYvgyiutl48bBykp3Dkih9tYwmnaAB2pvMilLgvIX331Vfbs2cODDz7IggUL6NixI/fccw8AN910E3/729+47LLLKCkpadgXbiDn31db4BdOz/tysbxPuKquuXPD2nzgB6AbEA5kApfX9J5gbWY6725aKjBGIFmgsE57cDV1Gzdanf2O3+EhiaCDjAO5BOR42ROLQkKkJ8ix2FiRadOsF5ft0OqpraNzc3Nl4MCBsmXLFrHZbHLgwAGx2WxSULaVdnFxsfTp08enTc6msk12ZXixmdkX2C8iB0SkCPgYGO2B6wYc57+UBvgIsAG3A0VN7y+lmwYMsKYW2J1HS3ZykteBP2BoA5SER3BnaSkTQkL4z4kTsHQpAC8v60ZCglU7Hjasaq0kJga3FpBHRUWxbt06+vTpw8aNG+nSpQvvvfceI0aMoKCggNDQUJ577jnGjBnD5s2bPfL93eXYWcT5cXe/a9BwlXDu3LAa6u9UuH8n8EZN7wnWmpnrv5SFAtMlKioroP9SNuYI7a23Wp39p4gu/0XeB3KwbMdY+4TYUpDfg+witnyHWHut5NZbPbN1dHFxsdx44401Ln3q3Lmz5OXlefA34J5g3ya7Mrw1mgnc4iLMXnfxugeAbcC2zp07N9LXbnzVjWY++GCOzJ49WwoLC31dRLc1+ghtQUGVUctzYc2lfIlS2WOlIDNpLhF0k8oH3nqymeVqHtr//u//lj+fk5MjpaWl8uOPP3rmA1WNvBlmNwBrKtx/EniypvcEa83MztVfysLCQhk1apQkJSUFVKD5pF8mJcX6gC5d5CzIX0JCrFOV7EuUmjUTATkbEikxZAn8TuAKgWyvTU3Izc2VRx55RPLz88Vms0lJSYns2rWrvIa2f/9+v1z6FIy8GWahwAGgK44BgCtqek+wh1l17IGWnJwcMGvqnOd+5Qqs8v5cJvvs/mnTJDs2Vq4BmWmMc5KGhMhDvTIqDLakCticXuKNAZejR4/Ks88+KyUlJfLggw86NTnXrl2rgdYIqguzBg8AiIgNeAhYA+wB/ioiuxt63WAUHh7OkiVLuO+++zDG2P8Y+DXnuV9HgfuBD8sf8cpcJvsOsevWcd7y5ayNjWVIxd9VZCRs2MDhi+w93AZIwhpYd/DGgEuLFi1IS0tjypQpvPLKK3Ts2LF8HtqQIUNYvHgxCxcu9PwHq9q5Sjhv35pqzayi9PR0GTduXKM1OevbgV91Vv5ugQ4CH3ivZlZQ4NhQMT5e5NFHRTp3lkUg08PDpTQrq/w7+WJqgr0PberUqWKz2eS3v/2t7N271+k1P/zwg99tHxQs0OVM/qWwsFBGjhwpycnJXg+0hnTguw6M3QJ/qVNg1HsU1MUOsdmxsXLNZZfJ9OnTy5vpvlo+lpubK2vXrhURa/sgm80mL774YnmTMz093a+WPgUTDTM/ZA+0xx9/3Guf4YnaS3WBMXLkUvnggw/cfl+dg6bSDrFSUCDZ2dlyzTXXyFtvvVX+Ml9OTfj4449rnLahgeZ5GhiscDoAABpZSURBVGZ+qrCwUE6ePCk5OTleqaHVtni7rs1EV4Gxe/du6dChg8tA82YTMDs7W/Ly8uTUqVM+H0ipbfugtLQ0+eKLL3xaxmCjYebnnnzySa9M23BeYvWCwBqPjvjZA+3TTz91erxqiBaX/3ck+fLytR861bjq49Zbb5UZM2b4TaDNnz9fbDabvP/++1XKNG/ePB3l9BANMz/nrXlozqGyWSDWKdA80YG/Z88eOXr0qNP+X84hahO4ViBNbiRDTlGpyhYXZ/WRuenkyZNyzTXX+E2gnTlzRk6ePCklJSXy3XffyZgxY3TahhdomAWAwsJCSUpKks8//9xj16za3LMH2j89PuI3duzY8iZn1ZrZJgmjtXxOpflioaGOQKtHDe3kyZNy/fXXy65du8q/ry83xrznnnuq7UOzB9rOnTsbt1BBRsMsQNhrGOnp6R6roVXtiM+U6Ohz8uijxR65vl3FPrTKIRpBgfyNFtIF5BgR8tNtM6xpFxUDLSWlXp9rrxGOG7dOoqNLnbKysTfGdNWHZj/nU0Rk69atcu7cObHZbD4P3kClYRZASktLJSkpyaPTNip34K9fb5PevXuXn07kKbt375aOHTvKvn37nEJ0PNYSpTyQddfOEBGRjenpUnLVVY7kKdvGpz7Wrj0nISHXCMyQittHe3vOmSv27YO2bdsmNptNPv74oIwaVSzx8QVywQUi8fHFEh5+jURGbvJp8AYqDbMA4415aJVrAm+8sVliY2M9HminT58WEZGffvpJNqdZnf3bWg92/KuNj5eSc+ckISFB7r3iCimxP17PmpmIvVl7UsB1oDX2FtL2GvbYsWkSHV0i8GeBQQL5ZWVKK2vuVw007VarmYZZACosLJTHHntMsrOzG3yt6uZ83XbbZunSpYvk5OR4oMQO+fn50ikuTj6Ijnb+0AqBlvfww/IrY+RekJLo6HqPaopUHHA4KfCwwDmPjtrWR3p6sTRrdqPA/QJFAne6CLQuAnk+Dd5Ao2EWwE6fPi1PPvlkvWtotc35WrMmT0pLS+X777/3XKELCmR327bSAeSDzp2tNLX3jVW45YE8CJLTwEEPT82n8ySrTLkClQPtjQply61SVt2RuGbVhZkeaBIAIiMj+de//sXtt99OUVGR2++vejaBQ04OvPVWS3766Sd++ctfkpaW5ta1qz2ANjWVy0+eZO1ll/HBpZdS/Pzz1pOhznvyt4yO5o2MDGx9+zJnzpx676s/bVr1C8tjYqzzTxqbtUg/Cvhb2U8bsBCYWuFVUVXepzsS15OrhPP2TWtm7rP3oY0ZM8btOVXOc77+KXCb2M8lqFgT2LzZvT60Gpcr2bfxmTnT+U32PfsHD3aaMJuXlye/+tWv5N57761yXmVd+dsxf7XVFqurKWufWc3QZmbgKywsLD9Z251Ac/5HVSgwSiCpPNAqNsE2b94s9957b63XrNx0jSRfJvChzGaOTI5Mkb1PLBR735gUFVlvKipyTMdw0dlvD7TJkyfX+btV5k9bSNd2qLGrINPRzNppmAWRtWvXypgxY+rch1b1H5U90B6qtiawf//+GtcUVgzI3myRH3E+iPdU83Yibdo4Au3xxx1BVsME2by8PFm1alV5uQN9Hpar2qL91qKFyMUX+0fwBhINsyBSn6VPVf9RFUpU1BF5+OECl9f4xz/+UWOT0950jaBAtnG+XAyyjB7yPDPla8pCq02bqqeN13Hp0ujRKyQsbLJAScDXXOy1xauvFrngAuunhlf9aZgFGXugzazcJ1UDV02wOXPmVBuKNfWh2Wtm9smwT9NJ4EKBfRJKkRyILgu0hQurbONTm40bRaKj8wR+JXBvlUDTEGjaNMyCUGFhoWRnZ8upU6fqPW2jtlreG29sln790qs09exN19lYHf3PM1PgHYE0iYkR+Wn849b/XvWY1e9owtoDba7Pp1ko/1FdmOnUjAAWHh7Oeeedx7x587j99ttJTy9yPU2ilmssWbKE0tJSVqxY4fTcjBkwe3Y//v73QWzYMI9ly9IYNcp63H4A7dFIa7/+YawhlLuIibmJ++8pptO3ZVM87Pv5u8Fx7kBLYBXOUxm8dO6ACnyuEs7bN62ZeVZhYaF06zZSQkOTnaZcuNPHZLPZRETkq6++ksLCwhp221gj7aPzZc9T1n5ke59YaHX2gxyIjrdqZHXo6K+JP06AVf6DampmxnqucfXu3Vu2bdvW6J8brDIyYOTIInJzb8OqxdxU/lxMDHz2GfTvX+3bndx6660UFxdTWvp/rFgRXuGZM4SzhVBGs4oSBnLO8VSbNtbP7GzHY3Fx1gf36VOv7zNqlOsamLvfRwUfY8x2Eeld+XFtZgaBV16B3Nxw4FOsIFsDWCsFcnLg5Zfrfq0PP/yQ0tJS/vGP28qvATmE052PGcdX5PNLzlEM0LIldO9uhVhoKCxcCHPmQEoKHDxYryADRxO28kz4mBjrcQ0y5YqGWRBw9DEZ4CfgNmA0FQOtrux9aFFRFwJWTSuCcD6kkF9zmlNAFrFc36wFp/PzITcXrrwSjh+H8HB46imYMME627IB5s+3amBJSVYfYFKSdX/+/AZdVgUxDbMg0Lp1xXudgRnAJuyB5u5av/DwcN5773Wio2OAuYzjQ27hNAuIYCbwSdgdzFtxmNbx8XDsGHTubL3xwIGGf5kK+veH1FRYt876qTUyVRMNsyBQdZH108As4Fuiowvqtch6wAC4995mtG22ltNMoQgYyTn+Djxoe4Oh5+2FoUOtF2/fbv2sx8ilUp6iYRYEXPcxPU10dCbjxxfy178+XK/dNl6dV8KP0XsJoYTbgFNh52NCmtFMbFY16b33rBcePWp1+Ccne+DbKFU/GmZBwlUf08qVrRk4cD0LFy4kKSnJ/UBLTaXVqeMs6dmTiTExnFecRcjF3cAYKCmBEyes17VrZ314A/vJlGqI0NpfogJF//6u+pVu5/vvv+eFF14gOTmZlStXYoyp2wXL+sDC/+u/GDtuHIwcCd9/73je3uH/2GMaZMrnNMyagKeffhpjDIcPH8YYY00wrEug2fvA1qyBuXOt6RZLlsCsWVbT8s9/hnvu8WrZlaornTTbhGRkwNSp8/n553XceGMqM2aEM2BADW84exa6drVGLOPjrQ7/tDTIzLT6yA4e1BqZanQ6abaJmzHDmlW/e3cx2dkbWbEiiZEji5gxo4Y3RUZafWFxcVaAvfiiI8i0j0z5GW1mNgEZGbBggX3y7BNAMfACubkzWLDgdcYkFtDveKpV0+rWzRqVtAdVnz7W46mpVh9a5eeV8hMaZk2A84EmghVoUcBAeuT8jcuHj4eSXMcbKq+rjIy0ZvUr5ce0mdkEOJY7ASwAxgJTGMYhCrmZCSW55aswCQ21+shGjrT6zJQKEBpmTYDzcqe7gVC60JtlJJGMtfDpVvvTNpsj0FJTG7mkStWfNjObgGnTYP16q6kZSTFPcAk7SeXPWAufmgFOiwdsNuunh9daKuVNGmZNgH25044/b+Wj/BG05zjFWNXy/cCdYWF0Ki6u+kZda6kCiDYzm4j5c8+ypvlI2nMcgDCsMc33gf7FxRwID3d+Q0yMrrVUAaVBYWaMucUYs9sYU2qMqTKJTfmR1FTCs49B+/YAlAI7gXBgJjCoqAh7o1KAnXN1HpkKLA2tmf0LGAPU4egM5VP2/q/e1t+c+4DdwFNAT+AWYCtWkA1nJQOf6l/zhFql/EyDwkxE9ojId54qjPIie//XoUMQF8eTgH3qWQGwCOgE3MTdrKEHOTnWRNtNm3xSWqXcpn1mwaygwNqPf+5cKCqyturZtQuio+neogXTgS+AI8DbhDOQ1qQTCwwCDrh9foBSvlTraKYx5gugvYunZovI8rp+kDHmAeABgM72bZaV92zdak18PXbM8VibNtatwjY+7UwkYySMszyPjUuAi4CuWIG2gZycixq12ErVV61hJiJDPPFBIvI28DZYu2Z44ppNWUaGtUzp9GlrUuy0aTh2wDh5EoYMsQ4b6dIFxoyxNtLPzLRqZwsXwuHD0K0bz32czJnPDmOF1wtYpzt1B84HWrt9foBSPuPqME13b8AGoHddX6+HADfM9OmVD+itcODvli1OT9pAfh0ZKYeXL3cczpuSUn4tx2G/+wQOCpSWXzMkpFDatBktH330g+++rFKVUM0hwA2dmpFsjDkM3ACsMsasaXC6qho574DhUJRTwNk3F1Lyq0GOJ/v2pVl8PBedPUvCmDEcsS8crzCz33F+QHesJub9WOs3obQ0nOzsoUyYMIjJk3U1gPJzrhLO2zetmdVfUlLFGplN4A9yNRvlZ9o5VdXOgvxf+/Yi+fki8fEyD6RHaKgUVaqZ2WVkiNx4o0hIyD6BCwXeqXC5P4oxXWXt2gIffGOlnOGNmplqfM47YEAI39CSwUSVzewvfx3w1NGj/OHiiyE8nCeAT202wuLiOJuYWOW6/fvD+edDaWl3YB3wP8DXZc9ORWQNf/xjJPn5+R7/Tkp5goZZgHHeAaMZs+nCxdgYCZyr8Ex7YD1w+dGj1sgmcGFoNOPOe4l2XW5g6dL/VLm2Iyi7A5nA1RWe7c6RI3vo2bMnB3QBuvJDGmYBpuKBvxGc5UleYgFwJyG8E/ZbJhqDfZ5rR2BEZCQLgWeJoJPtIEv3TiQvbxK33prAAw84B5pzULap8tkdOvTg8ccfZ9CgQRpoyu9omAWYigf+JpNKJIU0A0bQhn2Tn6bfQ6/yLIbyuS9nz9KL85lHLKf5S9mDsygtncx77z3pNMO/6snoDjExMH06TJ06lZkzZzJr1izvfUml6sNVR5q3bzoA0ED5+bLnqQ9lZ+zg8g7/QpCuIeFyb+goOUpbOQWyBeQtHpAICgQOC3QX+LBCx36hJCYelf/85z/ll65x2kcFxcXFcuLECTlw4EAjf3nV1FHNAICGWaDZskUkLs45bcpuO0DiQD4B2QxyPkg4ayu85IjAGae3XXrp29K9e3enQMvIsEZNExKsnxkZrouyePFi6dKli/zwg85DU42nujDTzRkDydmzjiVKvXrBoEHw2mtgsyFY3fWfA/cCmzHEMZ8sJgBLgf7ABVUu2aPH/Vx33UkGDhzIhg0b6NChQzUno1c1fvx4Tp06xaBBg1i3bh3ddDNH5UMaZoEkNdURZFu2QFgYjBsHAwZgyra6vhpIJ5pEVrKb/sCVwPdYYebM3g/Wv/8ThISEsH37djp06OBWkaZOnYoxhh07dtCtW7eal1kp5U2uqmvevmkzs57mzLHahjNnioiIzWazmofTpomArGWwjCelrI/MZUu0xn4wEZH33ntPDh8+XK/iJSYukqioH+r0OUrVFzppNgjYm3Fr1kBxMevXr+eGG27gwOefA/Bx5CQ+YgLnqH6H2GbNrCbkZ5/B/PlVnz969CgJCQn85z9V56HVJCMDNmzI48wZa/sgO90XTTUWDbNAkpxsHdCbmQl9+jAkLY0nSkpI2LuXA23b0va+5Bp3uYiJgUcesYKnuj6xWbNmMXnyZBISEjhy5Eidi/bKK3Du3FTKNuEGfix/TvdFU41B+8wCSWSkVaUaOdIKtMxMpgBERfHBmDG88FokI26xgiMnxzoxTsTqWnP0j9X+MbNmzaJjx45ERUXVuWiO1QNTgVjgPKfnKy+MV8rTNMwCTZ8+cPCgNRhw4AB068aU5GSIjGTLli107Hg+qakNH1WcOHEiBQUFjB49mjfffJOOHTvW+Hrn1QO3VHle90VT3qbNzEAUGQkTJsBTT1k/y05RyszMJCEhwWNLjVq0aMEvf/nLOvWh1WX1gFLepDWzIHL//fdjs9lISEhg/fr1Hpn3ZV+2NHToUHbu3ElYWJjL19mXWVXeay0mxnq8Ls1bpRrCWCOdjat3796ybdu2Rv/cpuLdd9/luuuu44orrvDYNfft28cvfvELCgoKaNGiRbWv27TJ0WfnTj+dUnVljNkuIlXO6dUwC1IiwmOPPcaDDz7osZn527dvZ8KECaxfv97tybVKeUp1YaZ9ZkHKGMMll1zi0T60a6+9lnvvvZeBAwe6NW1DqcagfWZBbMqUKQAkJCSwdetW2rVr1+BrPvHEEwDMnj2bhQsXNvh6SnmKNjObgE2bNnHjjTdaSz5CPFMZLy4uJisri3/8o5QPP+yoazFVo9FmZhPWv39/srKy6NWrl8eanGFhYdx110puvTWBZcv+w4YNsGwZjBoFM2Z45COUcouGWRMRGxvLlClTPNaHlpEBW7feT2npZCABsOah6VpM5SvaZ9aE2PvQxo8fz1dffYUxpt7XeuUV+3yyWVh/EzOxTh1wrMXUKRmqMWnNrImZMmUKaWlplJSU8OmnR0hOhoQEaw17Rkbdr+N85N3jwH85Pa9rMVVj05pZExQTE8PYsWmkpt6PyHrAmoe2fr01W9/V1kCVOa/FdPUZDS+nUu7QmlkDZWRQ79qNr2RkQHr6UESewOrvsvrQ3Onv0rWYyt9omDXAjBnW6N2yZQTUaJ6jv2sK8ASQUv5cXfceq3jkXUW6FlP5ijYz6ykjo+qianDUbpKS/PcftHN/15Qqz9e1v2v+fOt76lpM5Q80zOrJUbuxOwGcDxi/H83zZH9XXU9yUsrbtJlZT861G4DfANOIIJ8JpDAycy4sXmwdD+dntL9LBSMNs3qqWrtZQAvSmEg7PmQikw4+DXfcAV27wtatvihitbS/SwUjDbN6qly7iaA5O8hiHwV8Zi7j3+NnQny8dc7lyJF+V0ObP986TiApyRqJTUqq/sQmpQKBhlk9Va7dJJPKpZzgNRPPpt9+Q6fFL1g1Mnugpab6tsAu9O9vFWvdOuun1shUINMwa4CKtZshXa25Wm1uH8aLr5ZtLR0WBkOHWv/toQXeSinXNMwayF67mTTXmkXf6VvrgF7A+pmWZv23h3Z7VUq5plMzPKXSAb0MHWoFWWam9Xhysq9LqFRQ0zDzFBcH9AJWkH32WflxcEop79Aw8yQXB/RSdkCvUsq7NMw8zX5Ar1KqUTVoAMAY86IxZq8x5htjTKoxppaFMkop5R0NHc1cC/QUkauAfcCTDS+SUkq5r0FhJiJpImIru/sVcGHDi6SUUu7z5DyzScDnHryeRwTi5olKKffVOgBgjPkCaO/iqdkisrzsNbMBGxV3+at6nQeABwA6d+5cr8K6a8aMqnuOubM1tFIqcDT4EGBjzN1Y+98MFpGCurynMQ4Bzsiwdn11tdFgTIw19UvXIioVeLxyCLAxJhHrrLFRdQ2yxuK8eeIZrIqjpa5bQyulAkdD+8zeAKKAtcaYncaYP3mgTB7hvHniHOAOKgeaUip4NGjSrIhc4qmCeJrz5om/B5KxAi0FCNWj0JQKMkG7a4bz5okRQCpWc3Odbg2tVBAK2jCrujV0BLCSmJihDBuWwQ032Gp4t1Iq0ARtmIGrraFDWLFCyM//A3fccQe23FxISYG5/nv4iFKqbho8NaM+GmNqRk3OnTtH8qBBRO/YQUphoaPj0L5dT58+PiubUqpmXpmaEagiREj94QcuKCzkTM+eMNO/Dx9RStWuSYYZqalEHD/Oq7160eLLL8n/3e/8/vARpVTNmmaY2Q8XGTqU5q1a0bJlSz18RKkA1zTDzH64yBo9fESpYNE0d5rVw0eUCjpNM8z08BGlgk7TDDPQw0eUCjJNN8xADx9RKog0zQEApVTQ0TBTSgUFDTOlVFDQMFNKBQUNM6VUUNAwU0oFBQ0zpVRQ0DBTSgUFDTOlVFDQMFNKBQUNM6VUUNAwU0oFBQ0zpVRQ0DBTSgUFDTOlVFDQMFNKBQUNM6VUUNAwU0oFBQ0zpVRQ0DBTSgUFDTOlVFDQMFNKBQUNM6VUUNAwU0oFBQ0zpVRQ0DBTSgWFBoWZMWaOMeYbY8xOY0yaMaaDpwqmlFLuaGjN7EURuUpEegErgd95oExKKeW2BoWZiORWuNsSkIYVRyml6ie0oRcwxjwH3AXkAAkNLpFSStWDEam5MmWM+QJo7+Kp2SKyvMLrngQiROSZaq7zAPBA2d2ewL/qVWLfOx/I8nUh6imQyw6BXX4tu+d0EZHYyg/WGmZ1ZYzpAqwSkZ51eO02EentkQ9uZFp23wnk8mvZva+ho5ndK9wdBextWHGUUqp+Gtpn9rwx5lKgFDgE/KbhRVJKKfc1KMxEZGw93/p2Qz7Xx7TsvhPI5deye5nH+syUUsqXdDmTUioo+CzMAnkplDHmRWPM3rLypxpjWvu6THVljLnFGLPbGFNqjPH7ESoAY0yiMeY7Y8x+Y8wTvi6PO4wx7xpjjhtjAm4qkjGmkzFmvTFmT9n/M4/4ukw18WXNLJCXQq0FeorIVcA+4Ekfl8cd/wLGABm+LkhdGGOaAX8EhgOXA+ONMZf7tlRueQ9I9HUh6skGzBCRHsD1wIP+/Lv3WZgF8lIoEUkTEVvZ3a+AC31ZHneIyB4R+c7X5XBDX2C/iBwQkSLgY2C0j8tUZyKSAWT7uhz1ISI/i8iOsv8+A+wBOvq2VNVr8HKmhgiSpVCTgP/zdSGCWEfg3xXuHwau81FZmixjzEXA1cA/fVuS6nk1zGpbCiUis4HZZUuhHgJcLoXyhbos4zLGzMaqiqc0ZtlqU9claAHCuHgsYGrxwcAY0wpYCjxaqUXlV7waZiIypI4vXQyswo/CrLayG2PuBkYAg8XP5re48XsPBIeBThXuXwgc8VFZmhxjTBhWkKWIyKe+Lk9NfDmaGbBLoYwxicAsYJSIFPi6PEFuK9DdGNPVGBMO3A6s8HGZmgRjjAEWAHtE5GVfl6c2Pps0a4xZCjgthRKR//ikMG4yxuwHmgMnyx76SkQCYimXMSYZeB2IBU4DO0VkmG9LVTNjzH8BrwLNgHdF5DkfF6nOjDEfAQOxdp44BjwjIgt8Wqg6MsbcCGwCdmH9OwX4bxH5m+9KVT1dAaCUCgq6AkApFRQ0zJRSQUHDTCkVFDTMlFJBQcNMKRUUNMyUUkFBw0wpFRQ0zJRSQeH/A2v2A97LOadzAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "X_rec  = recoverData(Z, U, K)\n",
     "print('Approximation of the first example: [{:.6f} {:.6f}]'.format(X_rec[0, 0], X_rec[0, 1]))\n",
@@ -962,9 +5562,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise k-means-clustering-and-pca\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "           Find Closest Centroids (k-Means) |  30 /  30 | Nice work!\n",
+      "           Compute Centroid Means (k-Means) |  30 /  30 | Nice work!\n",
+      "                                        PCA |  20 /  20 | Nice work!\n",
+      "                         Project Data (PCA) |  10 /  10 | Nice work!\n",
+      "                         Recover Data (PCA) |  10 /  10 | Nice work!\n",
+      "                                  --------------------------------\n",
+      "                                            | 100 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[5] = recoverData\n",
     "grader.grade()"
@@ -985,9 +5606,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 100 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#  Load Face dataset\n",
     "data = loadmat(os.path.join('Data', 'ex7faces.mat'))\n",
@@ -1010,9 +5644,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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LYh824ncIvxspAOM3UcWpJB3X5Py0fWffdfq17bdkPHlGulCBST1mrt+BDz74YGZmvv3tb8/Ma2si19sfjC945nFfETOQVh/2FeM+ldlyXEvz+TasRrlYLBaLxQH7Q7lYLBaLxQFPBfO05EzX4OP1ZHptDn47tFvY/F36QSMTsKO6VTxxZYw0GWB6cDpAIyVIPEMmwJjMcp99wMTQmP1xtDucOs1ttGlzdZsrkxu05GSbuRvnpvlk38Rz6yCcNv/ma2xpKSZlSOc984jJlddMtnf9Pu6Ze5iwdqc/pLnbromWQO8AGO/B/L7718gObHamPzkHfN+m15b+8rnPfW5mHivJ5/2ZA/OAzlxzRz84D1pwn83GLRXMaVmtFm7Ca9CCv8xD2/ap3STZf9b+GfOdTbh55nF/TK/cI88qB9g1rmWn3bVKTi2wjv3OWZkBbj6rXRc23zuVJk3InFG0zdmVc2fiEO6ZJlNSZ+gXwUFZH5j9w5o7ZSq/b3dVcthuPcrFYrFYLD4hnqpHicTQwoh5RVJPaYDr7XRuFT2QYBtrv7WjplEi+SDltrqQ/h9/pxRu7ZX+tQodDSc6LmsMpxSSpsUzx/QXSaoFN5jKq2nxd+QE2Q8nwT8z9jcF85iAISVpa5ItYMcSKBLkaX8i7eY1Tunh7wypR7v0Psh0G/Yq68LctQAwjz3XzuvoFKHU8vyc0H5eY6tEq/XKOJwG1ELlTf3WqAddTSgldgdvtLQXW3BcF9bvPVbukc+079eILjxeW3hmLu2I/cZ8nCgYXQEm78XcNw2ZfeeKMi3NhLHTv5yfRjjg5y61PMbrvZdWlmdIFeiD1yX3eKMS9DX0n/lEM/z+97//cs1dhZV8jvmff0M2mGexWCwWi/8DOGqUtm+n5INUhQTSatQ1P9tDB+QDa0nrJ0nwrp0TnZpDrVNqeyaRu2mNTmdoYdnte8A1DBstlKXLRgB9127TbuyjbET0Hyfloc15mwePsdEe2i/Xasu5Hc9P3r/5hU2ldyKLsBSekrr9jqd9772X97Lv21p9q8Zu0oy2F6y5NX9v84G5X/6sWQ7urEj53nur+ertw83noFlQnBbWfNFO60gNwmcK90tNitQiWyeyHe+ldn4w52jx+IcbOYP9540UnWuaFtz2g8/E9kz7/Mt2WiyC+2brYXs2/f12VvhMcY1V93/mfK60dL+7dl714/aTxWKxWCwWz/koQf7iI8WYcu0UwXjSFk2G/KbIyZmeXG0bdIN9lAlHpj6jFZ/ukbAGc6Iva9qZx9aIF+6ib3Osz2g+Ttxu2uubqOrucNJe7Oc0yUS+995p5PaODEzN4Y5MuUnt9pm6tE+iReW6H+6n3888Uh5mVKEJ6U8E844gTe2M59XtND8g33c07cwj8Yjbzb4xl+05dCSyNcyZriXZp58aBxqW+5Rr6Oee8yy1M9qkj8/EIzTLgbWb9hxbo2wWEd7TL5ON3MH7opGIWBNsZQb9TOXzl37w/E7eyzESzXrkyGiTCsz0mIAcX7bTiFOewWqUi8VisVgcsD+Ui8VisVgccDS92tzWGPzNQN9Mr1bRm+nVane7lxO6m+mVdvw681jj0QE0eY2d8HdcnR8HNuM1s5RNai29BTRyBpteTwFLDuQ4VZ5o5lLPyTPBRa2tbMfpBYyx1XZ0SkP2jTHa1JJJ8qRSOJw8+2OTTatZyXgxAZnnM+9/Vx3F/c/23Jd8z3yxdi3lwfPceFDNsZomRweomCt05ppvPiMtp9U49TnQ9iZr0oIuTmazZs7FPWRCjfzbXNGu+Zp9A35WEw6ia3v8FKh2F6jTiBe4pvHctvk7BXe1ikq+r9vheUwSANph/hhrEhdwDxNBNAIK88rmuU5KolPrcl5NaNNcdCdX0mqUi8VisVgc8LE0yoSl1BY04nDspkFYym00d9YkLUXPPJfM7ORst5/9d6BLzsEpPQScCAqaE90acBuj63K6vt/MY8J40ygt5Vlr9H3v+uPPnoU1+SatOmUlAy9cc7OFfPMZUm4bo5PbW30+rueeTeL2utDe6ZpnKj04raHtZSe2N+3kjvBi5ponP8etX9zLtVPzPdI9GmXuO0vxzVJ1F+iVWtvJunMK9KGP9OmUOtaIDmjnlCBP3ziPrOVkHx0wk/2x1sxnLUXIqSgtWCvhoMoWPMO+aPsJ2BqR5BK24Pg5bGgBb816ONOJAkxfmvB5YktRu8ervt1+slgsFovF4jnCgSZ53EmHLTHV/pm0L9tPgZT0DCn6yRZvf0jevyXhG3fJ9Hc4SUp3yftNgrSm0IgC7lIWZu5JpVOyt9+k+Y5dLb31x+M5WR4SbqtZIUAL576TCvNeaNnvvPPOzFwE1C2Vxhpl+gZNe9j8Y/abNBJzz3HT8jx2+tNqnDIOE+6f9mHTxNg7aILWGmceNZdGE2eLTktn8DN0SjB3ukru8fYsnlKm/AygfbeE9Ls0nkTzgXlM9nunP9RWn0Yld3dGNauP/cNtXRKn1CueKdputV69v9E+m9UHmj771GcetUVTU85cc2KK1JY2Z79otuNrXIzD82CsRrlYLBaLxQFHjdJS3SnBv13zchNpkimlIPnbv3MqJ9M0Qfs2Txol1zrqM7/vsbck9IS13lOZn49Tjint7Uil9pM1Ym2TPOQ8OJK0lR26o8t70/9yfH7vMTa/tsujPeMTbQTy1rSaP9fRnPYrzVxJ/ki0rRQZfhhbVZqv5W4v5GfAZcjaXJ40SlsymnZO2xBqM84kN7C156Qt4q+y73PmkRbtRDhBH5uvrBGFeGyNpu+kzVvLalHzTmxv2ufdXsoo4rtxnGj6TuQM+OZYs4xGbvv1pFEy3ru9075He9l/FzOApi+1Ts91I7ugXBfj5u88F2jHe6URsN/5KvP+DatRLhaLxWJxwP5QLhaLxWJxwFOm12aycbJsU3ftIG+mipb0OnOu/H4yfd71vf3PFbbzs5PptZmXPcYWhHPiOHW6gKsn5P8c8JTmjKxHOPPocJ+5zI+YR2ySyrY9Ry2Q4uOkxuR9WoUUpx60xHObWp0IP3OZnByU0GofOqgoTVdUVCfdoZl5HZTQqhfcpV+0gBJgE2WbS7sRmunV6URpTmReHFCSpkLm1QQIOZcEb9i01VK4XKMw952fTXOw5nhaXxrvqIk5bHLP79sk14ITmasT0YDnM/emXR2tOoxTHLg2rzHxA4EuuX9bPUq7p1rQmc+qU+1O7tGeYz9jn/nMZ16uSdKOvFeai6nYwisugpYK5uDG5pK640jOvjasRrlYLBaLxQFPaZSm/5m5D9FvEvup/hmShzWWlAYsobd2LeU01vu7EPAmNVpCbUFBCWuiLQnZWkTTkkwU0IJZHDDQggCcstC0iDvJNt9bo2y0eycn+Kn/TWt2OH3TZpzEzT1aArw19NQick7yXh9++OHL/1yDsO092kFjcwWMHKODlVrQGnCfGzHDiajjrrpDjps+0w4WiUZP532f1gs0SmtvrdqMNcqmKXv/vSlAjD3YCCroJ6+NqvDOKnKq7/lMrVsHZM08WoaaRceBPq3yC3uSABesHhmIddIoW6Af77332r5ybdbUBOnDqU4oe4b9yJzlNdQDxbJDu416jvaa9cvBpy2NaCnsFovFYrH4hDhqlNammlZiCbIleVo7SymtaQMzPbGW11ONQiMlBmsQzWd4N+b8btOgnqnofapRZ625hWXbb9JIld23NkZTXTWfjds70Rl+XB+l00NSSkbaR3JkXzSN0j7BE6mCk83zvvTRftH8/p3FYubSvu78U9mm/XInasRTWsgdWhqTn7u2hqYcy76b9N31FGcuDe6OiH3mcY83P63n+VmN0kQQmQaEpsEr69WILuwbbFqe/XYft1btXVGGlq5yRx84c/kk0ShbCtOJwu4Ux+G+tr656EWjHWU+6VvOORow80hfs/+NROHuXrZetXPoLm7G743VKBeLxWKxOOCoUaLBnDTKu+jXfI/k2cpCGU26uqNxapKo/W55L6QZrrGvL2HNslWOT9jm/Qzh8okOzuWmsg+OYszoMUeAMcaURC2dNeIFo0mNphJs0ZmtzTs/zswlTfKK9J99ZizWdlsU411Sc37PWkv6t2zFsA84rzHlXI7LGmVr5y7qtVkg7DtqpBF3sQGNos9jaD5Y968RlTtquZHxn/xfRiOPP8UIgOajRKPkuWnRoe15MZ6hubN1oe0F5ro9Wzz31uiyHRPQ83c+Ky1GwEQBzf/o7zf/uCkbXZorx9GePz+/ja7Q/2t7xuvRiP29x04E7A2rUS4Wi8ViccD+UC4Wi8ViccDR9GqTaat7eKrSDVBpMYdkWLnV76bqW5U+pSo4cCFND05QP6WZ2NzcyBYSvn/Oh02SbYyuOWjOxYS5DVtFcFdlaQnPNgWfzBBtzu9Mey3tpaGlPTjU/GR69dqcqrG0VBYHLtjVkPdwUMEzaTvN7Og9eApksZm2mV7vEuXzM0B/Mvz+rs+n6iHPBBe1AAubkJ8JDDsFviVYe86lnFdcE7ye+GNtgs1raNvt5fNns2YLNGOufXamydGuE/ZNPis8G6wnezTNs6f0lhOZivufc06/MWUzDzlXPi/Mt5ufuY+n+p5tP/AZAYDm5J25r65zMju/uu72k8VisVgsFmeN0vRnKTn5f0iyqbk8U4fRVer93XxvSbZJAHeh1zOPQQQtqdjpIE1SP2mUfJYS5B3RQNOarQWcau+1NBUn+DYtycEvjdrsLi3kFMxzGlfrv1M3Zi5p0EE9ua+4jyXiUyCLx97+1ypP3FkDMo3Ae+5EzXe3B1vfPJ9NO3OwQ6vMY+2kafzWJJtG6fSObOeZAJc7ApJnqqy0sbc2GiUm83B3Zp36nc8Y37MmlRayOwtGjvnuecm94OCxFohF8I5JOXJ+jhUxyjlyp22etHjmMYMKXRe31TT2nnkmzaalkEBr52ta2t9djeTsT8NqlIvFYrFYHPCUj9IUUDOP0lSrYG1pqJGQG00C+zgSxynpm/cO1W9+CI+5+WcTDoNOP4H73TQph4qftCT7H5vWfPd69728Z7tXkz6tmTa/cCNesLZwqrHXiJ65xpXiT8TNTYPzvvTc5/tT+s9dHcnUgk2p1/yO1mrsw21zyXca6Xuzhri/Jn0wxd7Mo6bc9sIzCd0e1wnWulqRgQb7xGYeYywce5Hv/UygPc7M/NRP/dTMzPz0T//0zHRr2B0FZkuQN7l6e45tIeJ5yPdoV02jPJ21Tau6i7Volg+n7rR0t2esEHepG3mvO3rHmUf/ZRvzXczCiWzhVZ9uP1ksFovFYvEc4YATdvP9T/zET7y6JqUrRwg2smj7UUyBlThRcAFH5rUyPyYYSImbcSA9/uRP/uSr/3uMwP6tHONdVfsmQYJG2mtf06ls10myv7PTt2v4X4v+9T2aj/LkTwKNJgwpmUi2JHpGuzRRQt7rjqy/+WH9/aZZn3xmtl644vzMY0TiM1qVNfYWDQga1aIl7SZFu+1TErpJDZrmfdIkT1G8wPP8LD2cn4nsm4nS/ZrvrQG1CGjHblhjz340642fCea1+b35H6QC6Y+ztaXtrWesd41IhtempfKeZ7SRup8oSd0376e293hlPtLCxHPGXLcI9zvSm1MU/Ku+3n6yWCwWi8VifygXi8VisTjhKdOrw6JnrmAeTJOYKtPcZFML6vgpJLcRFzRHsGETEip69gdTAe0xvhzXpz/96Zm5KnFjWs5r0gzrfjdu2Duu2lNFc1+b7djB3upp2hTcUnucUtDMkh5XWzubvVpqTOLO5DnzGP6NyYkKCTPXXrurqznTTWfuv6910nn2A9Mp65Rrx/W8YhZqXKItcRz4WTjxBnuuab8FsdjclHPigAyubVy2DtpqvM53Cev5/Y8TzHMyzyZsdmvkE+x79k9bH9d9bMGJXOugupn7hPYTN2nbk3Y9UCOV+qgz1zNhF1f24bTfGx+294xrv85ce5/P/Pzk908VVny2nCrF+FxvfMWg1U/mzGYPtH1ywmqUi8VisVgc8FR6iIN6Zi5NC+kMDSw1OH71LY0nHLzR0kyegaWaJvkDU0ahReZ7NGVeU6NM7cxttqCRO024SWl3Du78n8OxUwKz5HYiCnDAUKMEPIW5OzACMUsAACAASURBVL2ipTGcNMq2xk7Wd829mWuvOYAs++85b4FgnptW6cEpRa2Gpvc5mkomRbudpindVed4RrOkXylp0w/TTZ7moCWGG41c4M7qc9qbDV6nU4BFwoFkLUiDeeDsIs1j5rE2KXPXKuFYk8r9Ylq6FmR1p23lnuL+BO/88Ic/nJmZH/3oRy/XcNY6wf5NdXpP6VDWxnhtz7SDNFuwGRpco7BrAYuG93ULCGU8tOPgrZnr/DYRwptq54LVKBeLxWKxOOApjbKRmaNpoYEh+TcfJWg16mz3b2G74K7a/Mx9WHlLcEW6YQxoKDMzn/vc52bm0pQZX6v5mDCFXUqQd6TSjdjXWkW243SBE02fX5u057lq0p41y6ZFnDTKliR/8rE6LJ79lBolUrap97L/fGbJMefc++hEbGGfYCM8dypLapQmHHhGu7ojO2jjanuBPcW9eZ5PVd1b/cdW32+m+8ZNvXZ3faL1x+2lZtTacfL+iTTCvsrWX65NDd1zzT1PaVVNQ7cft1kpIDpHk2waJRYMJ9qnz7HV/r0j2sj+24rYCCgca9GsJNYs0294R0rQzjP7Jptf+BSXcUep1/yhDatRLhaLxWJxwFNRr6Z1m7k0LDSuVg6pkdPOnAmXnfjs93ewNNCizdA8kCQ/+9nPzszM22+//XINfguucVX0mU44cFeeK8fWokLdf8bffIvWxJqWZK2ukSxYw32GyNr9bP9r92pjZV+diJYBbSV1F9K1tfdGR2Vts+0ra+jNr+b1yTHaN8lrUtghETsy8bTPrdXnnnK/Gn2g/b08m80X7XueysQ1y84z/lRbLhrRhSNz29o2v5u13tRK7Pek7Yw7uCMByMhYx1i0eASvWbN+eV2cwD9zWU2IckWTzP64FBj3zPOpaZSn595E5yeyC2vW+azfWb2yHVup2vnoqO6mxd+Vg0zLX/PP381Bw2qUi8VisVgcsD+Ui8VisVgccDS9OvglVXqTEBAYczK98koS+cxjGLbTJPL9yQltU1arS0dfMRdjZs1gHj4jhLzVc2zmjFPwAdc7kKOZMe9IBdq4T7UVP841DXcm1xZ4Y3Pkm+pRMqc2M8881vpkPtKMyf5xkFmrGcqrSQ7yvh5HM1+eqtKYlKCFsHseTvX5/PepTqbnMOfS5i4HRsw8mkxbYNRdtZnEncm1VSqx6bSZgu+4etv3Z86m8TtkO+Z6tVlz5lpfm//yPLhLeWiBew5QyUBITK2YXnE95Bp4zp0GM3PmoQXtHPD5mWZMm1OZj1PN0BZceZcW0ubqLthq5votMqlAC4Q84XTNapSLxWKxWBzwqWfZ+ReLxWKx+L8Rq1EuFovFYnHA0Uf5cz/3cx/NXPbgTA8xJVALI7YtntfmJ7K9/US91BLk72rENaJv+2My9QPfJL5KE3Dn/f/Un/pTL0btv/W3/tZH2XaO0WkDJsieefQDnUKuPdeNZgs846s8Udg9Q5zuezQrxd/+23/7pQN/4S/8hY+yrUY1xfybACLfk6LEa4b7O7m+pSvc+XNbPTz70NMX7/SQRuSOz4lXfE65B/Df/NIv/dKnZmY+/PDDj2YuX3r6d0iRgSybv0lUn7l8pyf6PI+57ZNn6p/6ub0Lx8/vt8IFnjvTtM1ca/t3/s7feWn8L/7Fv/jRzDVHLUXJqUon36h9pDmmltYC7NM+4S6ZfuZxTzEfub6851rWp8VlfP/733+Zq7/+1//6RzPdb3iX6nVKt/F383rP/al2LjjRbTYfuuk1W3yKSSY4M9KfS////J//8w+b9vhD6YG0grYObsigCz+oLSjgrtpFywXyNXnAurg0P345We0HwX/7ATnxWja0w9ZjbJUtHHjBw5MPN5uW/7XqG3d9fKaAcqsYcSroC04HR4N/6FuOluexFbB2wM6JR9IBYQkHZeS6eEysS9sPjOOU1+mqGqcACLMiNRYRB1K09k7BVne8nzk+r/0pJ5U+t8P0Ll+13csCSjuEEw5+a4W1HYST/Xew1inH0Pv9FFzUzgPvl/b88NmJGcv3ajy3Dd7TOVd351Be4/6e5tyc2y1H+cRAZZyEWY8nhSXabIFHHlfDml4Xi8VisTjgqfSQVovQEhgmkmRQsanxZPqxlpi/+GiJqMvmYc3/kabiumN5L0vjH4eX8g53knB+hqZx4l+0RJfmGNc9BCk53XHetr6eUlruaka2e52qEZw4e1u1jjvex1YVw+bQNKEjydqU1iTRUyi/WVRaZRBzu5rzNb/vPZemV5vN3a9Wt9Pt5V6+Y4TK/WO2maYlmTvT5vjs613KTd7/xENsba+10ywX3N+sU/k/a1zPcEafNH5XZcn/nViVAGvIPoGNJz9j3/P9nHPeN60PnFwu7fnDIsgeyecO+Lw4uVyMtj+BLW95Dz8bOZ93Z11LlTpxcrdzGaxGuVgsFovFAftDuVgsFovFAR+LmaeZMe3IPUXUuaxMXm+y6MYo4ULLb7311stnZtRpanRjVbmDTT5vClQ5RQ16Hk/lXGyiyaCmFiTi9jyPoJlDva6nMk7PMPy0aORTSaR2fWMpmXlNBg1biYN6cs+wD3jFfN+Ikh2UkKZJl/ki0jD7g8nVwTLNzMT4GnH63R5z6bFsxxHl7Z4OrEizMf1wRGjuMVweNmOnqZv94kj3nCfGeirLdMc09KZgHvawCzBnGzbtncjDT6XwTgUcmBP2HXOX+47+2Kza7sU17N/2rJ8Ch04E8twj96DdCC2y3ufHM8F8zSzrfrQAMN/L5bJmHgPZmkuF+XNx61z3jJo3VqNcLBaLxeKAo0ZpjteUIC2F80ufkgPfszSdGp35YJsmiITg/jSJEOmq5SoSaIS0RL9O+YMe35v62KSiu/DlFip9yk26y3s8hYy39Iq78lSn/MFTWShrpC2NKOHggxaEgLRJXl0r++Tv5RoR1OWyaZmP6YAv7plBFc5X5LPUlLyPWvFi56e5kHP2w3PgMlkz1x52Oy2FxAFRqa2jIdMOY8m5tFbXUricN8f8ZHAf92U8p5SjO8tI3ivhXLpTypdL6M08anUt8MmFkvmsccbSXiv87qAi2m1cxT6HEj5HmhbcgohOKX3O+eaaFkzFuFtwl/NoW1Fm2nZwYjs/uKYVXDZHc+O89ThOJdIaVqNcLBaLxeKAo0aJdITkldL4Xdhz/orf+S9TGjBriKtn5Pu7Sg4zl4SNJNTs7yZAaNUrbBNnzPgcZl5r1h5r+5t+urpE86Oc2rFN36kU+d7VD04h7E6Cz3t5zVpIPfvE7bbxzDwWYW0+M1sxcl/Rh7YPAJofr02jdIUY1gftceaxcC59bVK7k95TenZaSdOK7COx3zGr7thX6hSs/N5dod38npmCmr/6K1/5ysxca5J7inZcSSW1BftVWzqAff1NQzwRDrRKR4wfUhLYnrA65Gfch76mRsz8M+fNF83a0x7F4bM/9k+3eXC6XNO67Xtu1W/SNwrsJ0/rCPsK7d/P6szjWdAsDIyf/zXGMveDNc92wDPF6u0zzvm0huwC1TP9XAerUS4Wi8ViccBRo+QXFjv75z//+ZfP+CUmGhCJoUUD3kVJzTz6L5vv5i5BNn2d9n/aDznzqDmdfKZci0SJFDpz+b0SlnTSN2p/TfMn3fkx2z3uyAmy/6coYkt5nt98b37e1CKsLZpf1X0DnvdWO5D7oGWltmWqK1NhzTzWDmySMd/jXuzlDz744OUatAjzaTYNxxpTjsuSOuuT0rO1BvrMPdFqZy5NmT1lX7/fz5zpv+gz98h1c3Qw50G2w/zY79h846ekb0ceM6cnn3+2wT5vdGq2TOVe4H+OUTjVvGW/NAIHzk40ytRuuBfnAv3KPX5HU5lzZWtLO1eadmbiktSa77TTbNOEA80iZb8jmm2zFPo8yz1jS1uzZNrCdDoXef54rnPsaW0yVqNcLBaLxeKA/aFcLBaLxeKAo+kVtR2V9Atf+MLLZ4Q7Y4bEHJOBGU7EdvWP/MzmySxRZJMW16aqj+mH7/Pagi5sVmyJ3JgFMJ2kCaaFXFvtT5We9/SxJdo7vaWZhYHTD1riNu21YJi7YJ4E47bpJa91WHhzhp+SytsYHeKNqSTbdvBAM+G5v+yZNIGxZ7kn+5WUkJnLFMmecRWQmcvMRF+dWJ//ox36keYem6lddaelWjg4qAVkmRc2YbOZTc3ZR8bengnm1+bwnCfMb6zNiQeVthlfq76RcDBPM7E73abxfJ4INpyC0kgV2FPszc985jMP7Xkv+lmdeeQYbmWq+F+mnnjsJ47ZVvbQ32vlxpwO1niLfabwdwsuor+4J/I35K6yTzNB20yb/TFxggMsZx7Nxa/6f/vJYrFYLBaLs0aJNOFQ55mZd955Z2auX2hLzDOPGpuTmmcew7AJUkiHLtolbROynxK7NVIk7pb0bYm/Of5xrPP9DOBpkjnSTJPs7Zhv4eAOn24Jz9ZqkKoaVROSGykQJ7IIkP0heIl2mqR9VyMv+3yS0kBzulvKzTlnTE54bvvK92iBF3yPPZPFcW2ZcEJ5tuPqIa3yi9MHEpbCrRE2wgGndTQSCyeYt2us5aek7bk8UVr6+/ldU6212oAOzDilQCX8TLRnlGtcHHzm0spct7ClflhLy2tc5J7nrhWStkUl+2zaS15zrriHrT55TQvm8Zy2SjHPFOD2M+VKNDlu+pEapbVL+pVBaz5PTxWf/Py0Sis+QxtBR8NqlIvFYrFYHHDUKB2um+HLaBxINdYsZx5D4pv/w2gJ7UgKJIIjcaTkj4R9l9aQ9zWVVkpCrnGIFt0k1ITpwlLqdRh0I164801m3+xbaYm+/A8JmaTqlP6tofB3Sr1IwvYNNF/nXbszfd6cJpQSrSVXxpOED6456vSF7JtJslPDcWoT+7P5eKy95nzeaeZJFWdNyQnZeQ9gKbql7/j15KNsaStOK2nk/pa+Gxn43b1yvv1sOLl/5lFzailEJwq7lpDu5H00yfQPsx6M+1SP0RSaaa1xGkTbd267WY+s3TC+XF/TIjpOYqb7KG0JynW0j5kzs1kYfG60lD7mA4tc7nfPlc/AmesZtcUyx2WaSz5LjZX1dVxGI3RpWI1ysVgsFosDjholEk6jo+KX3VJJi8q6k0Dyen7pkQKSXgr/I5pkk1ZNS9ckS5P5egwzj76OEwF74lTd29rEqSQSc2zKpew/fUFKSk2f+UOjRBPLMToa2YnYM5c2xLjsF5y59zm1KOIE89fouWjT854+vRNlHrBk3Hxv+MPpP2NuviL7WFoVdmvdjZSZ/vBZ7nNT2N2VUcp+mYi90SIC+7/zHn7+sh2TxjcavkaQ4WtsXWiV6R2x2Px3zZdksoumJTUt/tRmfjfb5H9t/3oNTe030+M5Zl7Pg8/MRqpx55dtJA8JPy9tjRzRm5G9tkw0Sk7mmHOoaXlo9nzGvOS9/Nw2DZfrfY8TtSDPxskylFiNcrFYLBaLA/aHcrFYLBaLA46mV0xRpGMkByYqtRPEm9PXVTqaumvTTWPStznt1UCUhN8CIOysPfGg4nwmaCnTQ5zgO3OuCXcXlNE4Zp1Y3PrmShjN9OqAlwYHYLRak57zVrEFNNOJr5l5NF23NBlXqEgTktMTWgCYg1ts2s4xOrk9XQM25TW+Yl/jwJ9s07VVT+HyNk02ggq7PNq+a3sS2ETGnmopA5jcSEnJ/t65PJpZ1evVUrjoe0sXagQZTmM4cbR6nRqae4V+8hlmvHxGnfZj7umZ6/lw8FjOleehpZ15XI1ApJGAnNxTTsRvZmL3raWOsR64bDiP8uzkPd/jmkxDNAf4KZXmBJ/9zUR/5z6YWY1ysVgsFosjjholkg+UXu+99971xf9XekG6RHJJqcjpFyfpqiW/AiQ4hzWnBONEWSTRvJcDF+hzSrhINVRKgVghNcqWLG7G+hbGDVo6g8O/XSkkv4eUhiaZUiP/4xo0hla/zcQLTSL0nDciBUuhmRZxCq9vUjtzwphY16ZRnvrmiiRNIzYJwB3ZQd6jJU7TZ1N5tTQMa+q591Izaf1pZA8nK4s1Dj9HOQ6uda3F/J4rMDRaOT+bp4TulopiNO2vnRFuq2kHtn614KjWb+CxOYhr5lrDUz+Y27u6lDOPVgReW8UhB3QlWuCSz6hca5/ZJwKVk6WQcwjt0GfWzDVX7CvO1UzbsaWN8ecZc7ceOR+mnryr/3uH1SgXi8VisTjgqFEi6WBLTx8l4DOkgRbqb99GVmpHYrAtO3HnY2h19eznyb8bWfXMa8kDzfHtt9+emSuEObVIS/4zjxpgC9O35HPSKJtWYgnS7WXfTtRX/A+JkKTevAbJj3GfUgtMNNDIiBPN+gAYm5O5U2tmrk4kyNbYTNWW8PdzHiydNgJrS+32k+d7+4ealnj3naadnfwz9qu2eqFo/DyT3Kv5YE1zl88E97Df7pSK0dbfnzVt6ZSe1eon+rOT1ceaW/bR6W2tRqv3i1M4Zq69Y8L+hM+YNmZrv01Dbz5KPzdtn57827b+Mcb0P2KJe+utt2bmivVI4hDTyrF3mj/Ur7kf2I9+1rPPXpf23Gx6yGKxWCwWnxBHjRLJyZrlzPULjQ+wEWTffT/9ho7ObNKAJSZH/OV7R/HlNfaVNukETQoJiP6lZHYqC3SqIg+a1ulIWKRMa78zjxRibR5O/TEBAj7opARkvPgL2jzcRcSm1tY0OPtxEo4Ybcn7d1GTLXrS2nejrLK2nBKttdem4VjbbBXf7Wtp8+L5cNR47gVH84JcZ1MENo3yBz/4wcxc1oBGT2dCCV+b9+D5aZYh5q4VI2j9zz63xP+E56Mlm3sN8jl25KmT6fO9iUpS++P54DwzxefMY6ECl3nL8T4T4e34jOaDTtwRUeT71hbw/7hHRqtSkpFzlHJjSUTvWAPmqEXB+3ch+2C/uq1SM9cc+RltsRsNq1EuFovFYnHA/lAuFovFYnHA0fTqYI3GgG+TR17jdAGCedJ84FqCrWal65w5KX3mUe3GHJDhyK6Z17heUdH5Hv1LM00zfzic+1QRwwE7M4+1DF0HMdvGjOFUg7zeKR/N5Eg/MF185zvfeWiHe2FWyTQZ5siBB28KuXaodjMXniqs33HLNu7Ru5SE7IcDR1oFDt+jmZcdQJR99pw086H3iTlVG9fyqcapq2aYOCDbOQW4Ae6PiT6fP5u2nEyen/kZP9XH9Hdn+tzZpN1Mr8wR42/pIXfrne/5nlMgZq7nBfNqSyXDLI0ZkrHl2cl4TG7QXBA+B/J5PBGOtBQlB+h4XmYe00E4Exj7zBUMiQnWQT0z1/5hP9DXTP1gHm0yzTESjEg77Ke8xoQUrXbnqULUapSLxWKxWBzwVD1K16GbeZRokcBSGnA9Sr6fWh6/6FyLVNWCgiwppCTkYAskkCZVuOp4ShXWaFrgUIPD/ZvW6UT/Nle8ok1kO0jpDpJKOFWD/qfUzhy7LidabL4nAItrU+pFSrTUd6oUntc14oimZbpNa+QOepl5dOi3dAze8z3XsEywL/gstTtr7Y08425fnLRvNP1Wp485sISd+53njH3DvamakvdvkrrhoLzUbnimHDiU62aN51RD00FFp0CLbKOl0txRtmVaxjMpJLY2oUHxHMw81ultFW7QKE0Kklo9z5+tYJle4XqO7cw7pdI4ONLfzb9zPrmfaQ/T2vTuu+/OzMzXv/71mbk0y9Q6advnYZ5r/I85a2lH9P8u/S/77Gf1VN0msRrlYrFYLBYHHDXKO2LefO/UgFaLkO8jdaZUhObmZO+WgmFpIrUDh743P+Cd1tlqvNlGn2gak/0XjeDYmhu29ZnH1BnmM7Vvz0mToEw00OoUOg2iSaLMH/1qxNy+B99PKbylADQpF3j9WsoG42U+m2ZiYgAk0qblea+ktmj/I/PQqtFzrxbCfpcilPA1thg0rcppBOkTQ9Lnf+3ZYl7SdzTTyeOB00RmHlO4WnqB6Qlb2otTOBjfm+pR2uqVzz3v7avM+T4RYgNbKZply/5knvW0BvDcU2yCZ6wVE/DZ0c5g1rcR9rdzFLQYhzvChNwDjJv7otmmz5r3aJ34cTM9xPVbrWHm/5ib73//+6++M3NpsvTDtTxn7gkp2t5rWI1ysVgsFosDPpZG2TSF5osDfIYGyS9/Rom5ZJUlmeyHqcFSsrQvCqkkk+hNaN3IycFJA2jagKX9JvkhFTrCNd8jDTEPqSEwf/hEGj3YnUbZ5pNrkNbSd8W88Rl9bn5MR9Y17TXhiMqW8Oy913ysbrsl+NsKkX11abZTpKfv2fxqtrw8U4rrlEhvMo8cr7UjU/7NPEb6gbQcfPGLX3z1PXxISS7CPdh/LZKTvp38ZXeE1E37M2F9278Ja6eplbhUVPONNx+270v/eTYaraPvzzzkfH7jG9+YmZlf/dVfnZmZb3/726/G3sZjrbj1uRV5OFlvGpm5rSGOZp55pClkXtLC8K1vfWtmrmcC7Tn9ud5HjdiGM+n999+fmZnvfe97M/Na+/ZZyb7P/nj+WnnHJRxYLBaLxeITYn8oF4vFYrE44GOlhzSTh1MC0tTiGn6uTTbzyKTfgk9sjsR81lRlBxqk6dWVNKyqt7E2XtYGm0FaOsOdOSXfO8AmzXf01/PYeEdPSf02Xbfq48yxTRZpZnKFk2a6bHuGvrXPHMJ/CuZxAEb+jRmHOWdf5hj5310ARrZDv9ivzfzo4LWWyO3w+1Yzz/duaTcmNbDJPeF9l+ZFgivYSySKp2mL79GP5rJwqkEzBd+5M07cns2Mfdo3LUDIdR+bOdd9c+WV/J7N+rmnmEe+xz0xHc5cxB6YEelz3svnSEtpsckVnGqcZlvs01bBxib0dFm4nmwjm+HcJfjm137t12bmda1J3tMO50maXu0Ccl3XmcdznM9OvM6gBV02rEa5WCwWi8UBT2mUTgWZ6VRtM53Wyq8p8SCNWGNLbdFhu00jcXpKC7owFV8LRLLW2erSNe3S2k2j5TJOQSwt3YaAH6QrU5PlmFgXh5fPPGrbaFCtGoQl/FOl+TZXp5SGBkt1rgGYbXrOU+tEkicghj4RVJD9d6h4Su0mI/B3czwOOmt1Qq1ZnuosMoZT0JnrjzaqN2tCOW93wUBp9THNXQvQMpmAq2fkNbawtP3gAMI3pRzdpbDMPAbPtfveVYfJdfc8ck3ulw8//PBVH7k3FXpmLo2Jc6it79152MhRTM6Qe6AF/HmummXA98g2bTU41e40cQnzM3PtNVtMWloH68N+av3x/mrBWk47yvncepSLxWKxWHxCHDXKE9HwSwOyaeevsqnAGtWYJbmmLVpSbzUr3VfXp8y2m/8P3EkVb/JRNoJ1YH9HSwx28jvtpbT6wQcfvOo/EnKSEriCd9Mo0VSQcvGfZHpIhlZnn1NDwDfjmoFvSg6/09qzDWuSOf+2QjQ6KmsdLRHdIeItZNzXNh/YaT95XFxrf9/Mo/Rv8onmf7yr1Zr9+TiaW6sA72f7tKauH9qKCbS0EGB/Xavf+SY6SffRcQvct7VjbSlxV+u1jcMWoUbywHPbzk6nJbXnwMQrXoO7cZzIVO6o/BJYDeyPbbSB7OFGmGBfcdNe0TpbLVXg9bUVaeaaR8e75L1OpBOrUS4Wi8ViccBRo0QKaNKAE1MbrZx9gvzit2hAv55oqhpJu8sNIa21BHMTFackYd+rpciZrl1acsxrLKWiiaW2iJbHXNHej370o5drXBaLiLI2RmsPqQGZjAFNMqPNWCvaQwpOsgj+x7haZFmT0kx117RF74NG4GCNNNfI/o+TtMj3TJ8381z5KUe5NgJ0a+SWlP1+5lqnpnnT16ZxAfs86UOOyVHFLarQfqIWYWtih5aoznzY6pPw89cS5pvVxs/rM5G0DbbstOfeZ02umy05JlSZeZxrrm0J8vS1xVW43GDzCzeN0nPbynvdEVrk94hahaQi72vrR7PWsL8Zv/dOtuOzOgklOLewkPkMnbm0TPuF0/q1hAOLxWKxWHxC7A/lYrFYLBYHHE2vNp2mim6TVOOVxJTguodpvsJsYHNKM3k4qOhkCm7h8u5jMxU6yKIFNzTTz4l53sQLmCxbgAJmB0yuLeXBJqw0i9gsZlNQ9pX15LMWco3pCE7Qd9555+UaJ93T7ptqvDkJupk8vJ4nc7fnN9/bzJ4mIQfW2Jw5c+1hzN28nkw2LUmbNXeSdc6FzbomMGimV6coNK5XB0ukiY89ZfKInG8HIrWgIq9T41O9S/loqVR87xQglfBaNrO3zfl5X6ewsXZpOsWdQxAL15yC6VwHceZaV9dhzBQSm2N5zWeOtXa/Wp8Tds+0s9bcxrkO7HfuD0kFNSdnHit6NDcXY/I9WgqaA8DSTfTNb35zZi5XEv1rvNR3rp28R8NqlIvFYrFYHHAU10xNhEM03/OZUwVmLinaQSipnSFxWPLPX3qkiBa0AZyo34Jw2v9muvT5cTVKa2dNEnY9vqz2jaSIxmbarWyTeeU1NRdLxk2iZY6QZJHEUtMwPVZbXycIO7XF74E1+1PAhqvC5HsnDbcadbzS/5SwXf2Ae6Z1gH2OBIumn7VEXTuQe2XtPd47JeBEvuB0qqbl0Q59yL3gtJWWtM6aMx5L+TOP2hHznHPJfRnnKXDGmuSJOqzt3xMt4ilFws90rrMDuhrxAs8mtTtd73Pmvp5j9plnGi2HtK/UkpzE36wU3J9X9l32x5V9sk9+bf11OsXMdW6w55jHbIe+OJgprZJ3gVOt+skpNYl+QCYCqUEGQjLnPpfzXD89i6tRLhaLxWJxwFMaZavxZm0EySu1A6cPIE3krziSm8nRW4I8r07GzT6aaq2FuTstoWmUJ42swRpl+nHuKKNS2kK75L6el3xvaS3bsfTctLU7EvC06SPd2nKQe8BExaYPnDlL/03Dv5PEm6+L7zdfLdoO6SzMb2oIdyHsua9MmdUkW0ui3BttduaS+h2uf6JGPFEtqHow2QAAIABJREFUWhtg3nIN7fdm3hoZv9cwtU779N2HvH9anWZ66oRfn/FRvkmjdMpD3het6o4yLfvic6PVo7zTsPO991ZLT+L5aTEgTkFhDTM9i/fsLfZ2oxFNnIj0rRE3a+IPfvCDmbnm1T7+mWsf0ZdGfNJoOg33hzMrqfD++T//5zNz1fVEQ896v6auA23sDatRLhaLxWJxwFGjtJ8hJR6kIdurU5pB4rCdPKVDSwzcs1Ff3ZWeaW23cld35ZlatKej595EOODSVa199y3vi1aI5vP5z39+Zq6IsvwMjcV9bf87+cJM3JyE4d/97ndn5pIeITdIP6b3RUv0bXNlLaj5JBotFrB0SJ9aArsjLFMr8tq25H2TZTTp1/51+45mHn2TjUjB+/LjaJTMW0r+9re1+eHZ5N5N27LlpVkrrPk5cT3vb0234STdN/+/NdA8Pxyd6ujI1jeQFhSIOfgeZ2BaKVrhh5nuF+Z5o9xWakCMhz5j2cjzAF+p/d+p4ba5svad68D+5n+MP61E+P681lnSkFJizL19nvk9F7Bo54H7w7k0M/Pee+/NzDWPzGuuHfDvS+6TU4T1apSLxWKxWBywP5SLxWKxWBzwlOm1VbC26bWp6Db9tKTTu+Tjxu3pwI4WhAOcLpLvncLRUkjuTDHuv+8HGgGBTX2tHfM3ZqCOnfYtlcZzdAq5NnJ9uS/BIe1edwEgb+J6tem1mT9slmtBWXyPPdjWmqAkpyhlP9zXnAfGj1mpratTnGwKy/HYJdB4bj2GVlvVgVDNjO30mVMyPn11ZZe8l4NY2p5yoNYpWIm/W6USB889UzEkv59mNPPssl65lg4salVi2Aush1OHZu5TDPI8c5UL16fM/jOvuF2S8MOmV/MI5/cTNn3nc0MfzN3bSCowcTJnaTrGVcM5Qj9a9RD/3daaezBX6SbCDEuKk9O18v6e11bBqWE1ysVisVgsDjhqlPzCujr0zGOQQ6s1CRxwkFKXAyiQApomcqpU4uCBU2AG92jJuOBEZ3RKkG5BDO6/E8Czb8ynU2HyPRKj6zHOPAattGT1u4Cn0zWN/f9u7XJdTkm8TvKeeUwKb1RowIFLLdDIAUupIdxplE16NvVVSupI30j0SNEp2bv/be28r6zd5OfW6lqIvjXKkwbv72Q7rnnZEtWtBfg7+d59z3buqCxP+yjRxuiUL9YpNTjPcZvzVtPUfb6rftSq95hUJM9XxktfSTVCi5y5NEnXt8y92Z4bz2labzhb6S97uFlrTCeX55mtTbYqJpyS06r3MFdojVk712k2Tq2ZeY5IZgkHFovFYrH4hDhqlLbbt6rhTkRPyckaRiO4tgTnhOPsh6WRU+qAq17ne/tP2r1OaNquNcg2V56Ppp1Z2sxwf9cHdbh+tn2SGk2qftIW7xLS8xqP/aQhJJpPwv1va4/Ua+0n72vLhiXJ/N7JisH3rKE24makf5NV5D38eiJXd79OCfImwU/wfRMP5PcsTZ9IIJrWabRny3vSaVMJ9+dE8ODPs+2ZR38bmlejKjQtY47RVppWT9WaSkuL8JnZUo7oM33FR5m0l6bQY3zNn9/QrCPWIJt/nH63tEHDRAMnKwvI58DE8Y0cpWnkOb6ZxyIJzUe5GuVisVgsFp8QR43S1eHzF9eSl31rM4/RkM3fZGqwpt3Y1wKaREt/kBBTo7TtukW2us3Wnxb5eOdfyvs90ybSGRplJvHaB9G0RSc8Nz+QpTxHMOd75pHXlH69Hk0iOyWOt3kwPd2J3L7RlQFr8U1zuvOrpYbOupgisUV8mnigWU7sQ08p2JrFXVRgtmfNv2nn9LURlTsitpW3utu/p/GdCLdPGmmzDtxdk/BebBqxy1Llc2y6zkbwD7wueW+vfSsJ6HJ0jgKfuSLb8Uk2CkZbMBxP4L65/80i5X3VzjruYy28aZaONs8x+vlvpCC2XPqcnLnOePphC8LMY0TwyZrRsBrlYrFYLBYH7A/lYrFYLBYHHE2vDmRo5i8HoaT5DtUcUwcqceNxtYO/EQ64ckNeg+nJlUZayLWDE1qAh81Vb6qxeGcePqEl79NvQq7TdOl7tPqCNi14HNl/zCFwN2b9NsKv6QfXNtPriaTBRBDZlxZEYzN9Cwqx6asFJfgep1qmz5h+HFx0SrY/mZRt0su9dGc+bGk0DghrfXc6Q+M/thm1he/fmYCbWfWu79n/Z8yqDrh7UyX6luLkvnH+kBbWqg+5vUaGYHdAe469B1ranF0G+fdbb701M5fplfSQJLHgWT8RmDxzHuV6uHpR67fbZh5z7p0ueCIRsNsq+3wXQNUC2+6IJXI8jK+ZXk+BT6tRLhaLxWJxwKdOv6KLxWKxWPzfjqPp9a/8lb/y0cxlukmzjIvTusDtzCO3aTNJnfInwclUZzgisjF00C/MXmkuJj/HeaEt1+hv/I2/8dLZX//1X/8o+5omNdo/mTHvTAsn3tRWruuuKHJrh7Gd2IT4n6OJZx7nyLyhM5c56a/9tb/2Mld/+S//5Y9mziw3NpNlZG/Libzrf+McNbyvWlToifvXJvxWdNt8sDaRJ/7En/gTn5qZ+eVf/uVXnT5xojbWGKIBzZ6Vkekt/znb9fuZc5k7l0VqhceZg5ZvemfibmxCf/bP/tmXjv3xP/7HP8q2kyOZuffz3yKg/VmbB++BFsXv9c79exdZf8pd5Tu5TpwncJ3+8Ic/nJle5u6Xf/mXXxr/xV/8xY/yfu2scpR7y0IwM1aajhmv2XaaO+KOnzrhM6u5R9zX9ntF1LD5srPNX/iFX3jowPGH8pSO4Zp0jYz4LqG7UYQ5vP1EAtAOdq73YZ2L5z7z4J4S5NuPc7Pbm2A4x+gUmpa6wiF2SqUBPpizb/YfnSisvNHbj4BrP+a9WDOHlOeD13xep7QSpye0h5B1fKY+KWhCmj9rofz2fbV72O/Y6M984Fugy//d4US3dSK/PxUccLg9f5/mssHr1fz/d0UBWnqW16nVEU2Ypi79UwjxPltOQvQpbeAUj+D1dSpWfub2nqnBmd81ZSI/nC3tJeGzpZ1tJhpp/TWlaM65E/ubAvHMD6W/x72zX3fPb0u98jPafq8a1ke5WCwWi8UBR43S2lT+4lpyQ6ppCaWmXkvpCjMkr04UTyCdcE0zh/rejaDbUl+TaE2o0CJdE7TZNFBHBEMikCWf0C5N63aq9n3SFt3fNg8nsnrDiente46Avuuj+9bMojadP6P1N2nVWmeuy91nrTTUKdLYkaOsXc6DzUGNkP9NJrmcA5NvN2m8EVJ4fF4z+tn6bktEM4ei5WDaynvfldBqEa1N+wVtT9EX5pV+zDyeTW0NTvSD7puT4BsZvzX1Zl0wAULTcO/cDDOPzzoaZaPES9gdkc+NKTRZ+xa97vbybLCWebIsnawXpsnjHuku829GI0swaY413pkts7VYLBaLxSfG/lAuFovFYnHAU6bXlozs6L2mWoMWZQZQrVH5CXRpph87kdN0Ql9dq7KZVU9Ra67GwN9pnjiZCpt5meT9999/f2ZmPvzww5l5bXp1EI7nN/tp03Gry2lTYXOQn2r92QzhyLKZR15MJyDfte0k/pY87KCKFonq9vK+Nse0oIa76L2EAwPM/ZrXeK6bSY7xOQov+wPMTdwS2x1Alf26m4MWpckr17ZAM/rRzKqYN/nMNVOzb94vLUjJe/PEx5z3a/NKvUabXrP/DozhtfEIMx+Y/7I2IqQd1E00H2r2lTHa7Jv351qe+ZxP+sY8MvasrHFC42hlLL/xG78xM9e+zXlwJQ762ipH2UyfEfHmxW3BgA72bMGSmJxZD15zXN7fHsPMo4sisRrlYrFYLBYHHDVKax4piTqvpTlkHQjRrrXm17SSlB5melqHgyNaUALS0Ukjc5h8C944pWww5rwveU7f/e53Z2bmgw8+mJkeMo6Eg0TcAiacm9U0SjvlU0pzWHgLT3e4v4Obsv/WFNK60IKgrF21NB/QHP7Oy21aHnvmlCvK2DzXbR5OASi2IrRUFL7f6NN8L4/ddJE5LjSWVi/UgTAOVMvv+VnP7yKh07+mbbEXaRutJvNuee/c6RaU52CQE13ezKWVobFkHiVtcV/6kQEhaIDcj1qP5Iln256HRt3GM889Wmqdg09yv9ia17RvW9aerUfp/ZRrhEZMTuYpFY/7u2bmzMzbb789M9d+hJKvBVDRXqs1ybpghePspH95jXNAcz4zuGvmeuY//elP316TWI1ysVgsFosDjhql0cK4XQus+ZKcENqSWE3C2wiLrbG0OoxcYxacmUfN2JJY3suaZWq1zR9rKTf7TzqIpeycTxPQNyaLO62zaZTcA8kr18Wh0a0GIW1bo8z5ZG6cHtJYMxLWKBtbh/3iJ/9hk7bt/2wal1MarM3k97yHTwxSLcXJKSPWPmce95BJOLI9S9+8ntanVXW3JmlGlWyTa5HCWzt+7lI7QOJnHO055plk/zVmnrYX+B7+yNQoGRt9QjuhrzPXHqIvtPPZz3725RoIyhk/mlRjH6I9nsMcI+vKfDRWJc4M+15T67E/t83LyTfffJSOEfHZm98DzCOa3czjfqTd3DOsEXPDvdDGZ664Dl6xyuW9eO8Uu3ye6D/7ijHnc3xMgbn9ZLFYLBaLxVmjPCWGW0o9+ZIcsZTtOMG3lT/hPRJHsy/bD8G12Q7SGd9HOmtlv04UWs0f68jeNg98hnTYtBLmAx9JRu85mZrxp7RKv//lv/yXM3NJezlG2rREl5Ix13DPRvkEHHH5JqJ9z1+LwrQ/NrU877n0NQFrSs0yYB8p/qjURpCIaaeVFPI4GoGDy/w0eD9YI0w4GheJHU0kP3MEYmoe7Avm1/6zmWsPMfYvfvGLM9P3QuPiBNaATlRyXps37SnmF02Q15lHTcN7K+/nUk25JtasuWf6Me2HRdvJubJvknlNzY7n3xGyzVfoaP7cLyfiFkf4z1x7nzObPqWFwf7Ttrf5LDW/mdfr8qUvfWlmrrnmXlnuj++zrzJq1vcyL3Sjh+T+zRqwUa+LxWKxWHxC7A/lYrFYLBYHHE2vmeIwc5kDZi41F/NfC4nHtIA5qIVB2+Rqs2heg1mjmSXvKsen6cEO/xYOjIqOGaAluLaEZ5s9WomXd955Z2auMOqWRE0fv/CFLzyMkTlizgnBTtMJa2YzRpqHMDFgZmEeWnK4zWvNlE2fm9ntVJaKz/IergLRTE7Apqt0Ffge3osz1575yle+MjPXvH7+859/uQazY9sHwNUpGmes0x2aGdRzZaKAvNZr1sgEzDPaCCbMkdrMVg5Co510fTjR36W98r3Nsrl/XfWnBfw0U7Q5p9NEx/jddu4XE4xwvwyw8XrQjwz4Mc8q+6elt3j/51z5HODMalWVbPLP56kF+JjUJNM6GBtr6yCnmcf9xNmSZkyeM0ynJmmY6ZzdeW2OEXM/+yufFdqkrzwbub60yTg4O9MUvKbXxWKxWCw+IY4apWsk5i89UqGDV1Ki4nt2DCdMcYR0k7/0SA9IKVyTGq6TTlufTTnVAjMcSs53Msy9BTFYmkvpCokTCc7h8TPXPCJJon02SZowdaTN1NC/9a1vzcwl0dPvlLBp05UeEk6DMGnEzCWxWQpvydWJJvkCB280Oi7aZF+xVqmp+Pu8pkTr4Bm0kdS+TeHVpE4TWbR5AHcVbNr1rkaSwSf01ZpgBiLdFTjPeXehbObkm9/85sP4XMQcTXzm0sKtOWQyu9e29Y/xMGbXPL0De7mlfFnDp48tVYixMdZMIbF1g2cs74Umxtl0qhJj61MLanI91nZmOI3oVNg+wZqnRsz+sbab+wpYU0+tGQq873znOzPzeC7NPBLQMI68hnF/9atfnZlLE2wpXLbK5Vlla0gL5DzN22qUi8VisVgccNQoTY+V5L+8RypxOPTMfYhxIwpAS8IW3dIr+PW3ry/bsa8y4XptrmGZsI8vx9W+x/0YY/o/0Y7pf/PlOeSaaxpRMhoP7TY/iinsMhHd6QItqZtreGUv5L28P5pE1rRGkyHnPFhLbL5mp/C0dpBK0WxajT6TZLPPcn2tsTWKQhPG04/cA7RjTSHH5fljXlv4Pf/za0rRTi+xxpt95H/s89QOTMLBa87Tl7/85VfXmmRj5s01XWce54U91gjmE/S/1W+0P5i1y7m3BYb7NoJ/F3JIsM5oaVh90lrAc9soHAFtZ6pEtp9jNLlDasHNp27ilkbM73qYudb2jTZ6S1sGmMc8R3k2fVblmFkzLBbsoZwH+28bSYmfdRO8uP/GapSLxWKxWBxw1Cgt3TeKMyQFfqFPfkxHSWY7aIlolK3UCpIXEkRqlEguptRLP5TLr7g8Ut6Xe0KnlJJZ85dYS2y+Jydcn5KoHVk3c/kCnHCfc2VfImNN6ddSpqXH7JuJBhoVF+1Zq86xJhy12KJtabNVT7fm1Ugq+L59PNl/U8O1/WCfM+3kGOzLb/SDbq+N3RoXY2Bd8nP2As8SfzdiePs407/jBG7o3XKPeC80cgOu4Xlu5c1o8+QLcjmpRvXXvm/t7OTTtK8y+2atOfeC9601zJlrD3JGYSnLOed/3JNns1l0+AzNP8fFfTmjIAxPjaxp3/YN5rh4XjyfLVPB1JVptYI68/vf//6rPub+RPNFE2asqRGz/uxzk9jkZ3eEFjkO+6Db89KwGuVisVgsFgfsD+VisVgsFgc8ZXpFNW4VKFDJMSe0CtY2X7XUCZy1hFVnWDnmHPrDvRuTPup/C95wyLk5aGcu9ZvwZl7T9HCqZMH3W5qMTUaN45S5cX29mWseHUTQakQ6sKRd43abuZhX+nUy8zL2NOU006vnL/92Mjam0mzzLjG4mZmcnpJmnbuqNqd2MKm1wBSbCVuggFMTsh2bZfkO653r7gCfZj73PNkMndfz3GBOzefP9SNdmWbmCtJwtYuEg9kcwJb/M3FISx5P2CyaJsq79IM0tfm58fOT/WSMLSALMMecZznnnHU8G8xjng+YE9kTbR5wJX3729+emZn33ntvZq4zy/cFrHXrt4NdfIbPPKY/sZ4ZuEWVD9JCMMFmO/TfAXd5PuEK4J5OsZu59ppTrlqgnN1grcZrw2qUi8VisVgccNQoHQadv/TWeEhZaCHspsVqVSpIfbAzeeaRZstO22wTCQ7JOKUES7CMJyU5xuow8TdpSa4pl/d1ZZWTE90pGynROiXHEn7226kT2R/XBWx0andVGxr9mauPvEn7835ILchUZK1qu4OhmmbC9x0yf1o71r4FsvieOVfely1J3vXvWIMk1rjTKJslwXPY6OnutPPsu4NG6FebA2sQGcyDRsl4mibipPlWw9bBZ60/zVrk1ICWluRgpAw+cf1ZXvPZMp0aZ2Dey6kXLV2FdeDMZJ0yCMfpMa4POXPNOdobATM5rjYPfO4x531Nc5d7hv85sCb3A5okWm7OI+D+PKOtRiRtMzae67yGtk1k0vaVgwNbzdiG1SgXi8VisTjgqFECk0/PPCayI/k1rcRIu7ml3lbt22TqXJP9sd8SySMlGVM9uSL9zKNWYb9ofj9hf11KM7y3ZpnSnpPem7ZrDY520zfAeyfqt9Btxmpy9BwH/XBycb43IXde0yjs7G9o9fBca/OUStMSjFk/NMmWuA7sn0vJGIuJpdTcV05f8j6duebEWlkjiwCe+6ZRen5zT1nLavfhvVMVUsuw5u/vzFzzwTnQfED20bf0Jmt79Kc9xwlr+o0OzvfN/tyRKuQY2ZNogk7XyrGYnq6RavA92ssxYj3yuZIUjOxTNFEXRLiD16oRlYNGOMD/2F8tPQTfIv9z3dP8vp+N9vvAuYZmmdd4vI0AgWtOdW6bPxesRrlYLBaLxQFHjdLJ1fnLbd9PI811aSI0lqaduVJ7RtTxHukE6SYlRUtw7RqTBiPZpaRuWrZGXNC0JD5v2u5dVGWLyrJkmz4+IotNtJwSIetiX0v2B6ns/ffff9WfJEdmPk0vlWvHfa2xpPTcIoQdoZl7Bx8zvq72fZMfMB85Rki9kbaR0JNCiznnnkjB0I7NPEZq058co6vaN+2X+TwlxNt/6sjLFq3q56eVoLK/qUn11iTzGog9bK3J/roIgX1sOR776ht5tZPQm88/cecbnLn2if2Xubfss2I+W4Q+mqXLq2U/bTVqxQS4xqQRM9e+5blrJA8uZdWiWZv1y8T1GSXrQhEu1pD9ZI5NKZl9YF7bGO9KsyWRDH3jd8ZUg9lX7tlIVmxN5N4ZI3DCapSLxWKxWBywP5SLxWKxWBzwsUyvpzBuVOFU9Z1i0JylmCQwH2BOgLdw5kpWdW3FVPVtnroLZJh5TN5Nk50rYbh6+N04bPJKp7gTaW36nXms/3YKK6dPzFWaTphHTBSNvxSTC3PM9999992XazBJ2ITd0itswm5BSgnXQEzzFuYsXpnXnF+bcehTmrfYK5iXPdYE+wtzbdY5NYdpq+RhMyN/p/mP/eN0kxMvroPA0ozpvdACOLx3mdO8jyu5ME7q/+V4uIZ9kiZq1oJnoyX1Y060CTb3pgNSbMrMfiScUJ57yudPS0HzPmvBJ+wL5qhx8J7cKwb9cvpbvnet1Twz/NyZ29fvgd1caaLkvTmec88wJ+buznthnqcd9gec3vmefUS7mSbDmHwO5bniILoTSYUJB3J9G9EIWI1ysVgsFosDjhqlk75TqnCQCNJJ/kI7Ab0lpCM9mOoITWDmCj4hIIN7kPg7c9XD43/cK/vsVIPGIG+N0sFBM6/TKIA1tyZlWqNskrEDDVqoNPPxve99b2auOZt5DIOmry3c3wEYrVKCpeYWdm8N8ZTKkfc/aZRO4m9VVLgGqb1VBgGNQMFpDgTzsBfzGjQstIkMOECzR1NvVFjej03St/bh9JuU/B18ckqgtlafa4jlgAAV5jnnkrHzGc/YV77ylZdrCDTje6bfm7msHdZaWvUeB7flurWAJQfItEA/zjP2TQbBOU3NVJIzj3VgHVCV/2N/OCVs5ppr2qE/aSFzOpOJRGYerXqt4lDbZ06Pa+efg5EawQZj5O888+gDWuNbb701MzN/+A//4Zdrvv71r8/MRelHwE7OFW1jEeIMbXvAFJLZn1M1lGewGuVisVgsFgccNUrb5JNQ2tKuk0ZnHgmSXeV95pKGoDpCmsdfNHNJE9jraSd9NvQDDexLX/rSqz7MXBKY+3EiA0eSyjDiFlJsH0L6Ep7RKK05NeJ3JCT8bE7unrmkPKT95iNBI0CiQxvIdtzH5vO1tIk28SZ/ktNDUmt2wnij0GpUbDOvQ8+/9rWvveoLki1a+MwlrbO/mbO333775RqnAnCP1IJMCZbPCTAFXpNs7SNxqkL6+5zq1GpyAn+Wa8L//KxnO+x37olmxbXZJlYfW4FmHgnX7YNt/+Pv5iNLMPfMVdsvJtbIs8E+Rfs8EyY3SN+iiSCY13y2SDPh1RSBM9e82feb47pL2E9rgFPJZh7TZLJvLpDAGqffHu2Q56TFjHA9Y+PM+dmf/dmXa37f7/t9M/P4jLXav6wdZ17ei3PVfsiEC2K0YgstnuLl+7efLBaLxWKxeE6jRIJMSZlfbxOdp1RvwnNeUzpBA3IydEq9vEdypx0kkpmZP/AH/sDMzPzMz/zMq3ZTOkEKcTspddnvRn+Yg5lLAkqYDD3va40DtOhD5pG5S+nQvkT6kUQB/h6+25TCiTIjehGNsvlIHPHc+mzNsiUeJ+4I4GceKbOc6J/3M4VWSoTsjd//+3//zFwSemqUpl1kz6RGaS2ZeWiJ6KbHS9+PpVxri75+5jGRurXHXJ+sI/bfpd8QMg/76JrfCrCX8rmhr8xBKzVGPxzf8EyZrTdFnXOdozWzn5w7vDbid5OD5JnnyPpG+G/Cd65NKxTPH/3gfGiE9vaV5r7jvZ/DPIMbEYwtQGm1clvtmeA9mmU7s9mPnCOsT84nljG+1yj1/Ew17dWxDrYK5pjvfreynYbVKBeLxWKxOGB/KBeLxWKxOOAp0yumpeTJdGANKnGq+ubysyl25jJpYiL7o3/0j87MFWgycwX2oMbTbiZFO3wfU2hyxrqvmEPSHEAYNuYAzBIZuJDvAaaaFnhBEIPDtxt3J3PjavDZDn1sicqYDwi5xjyS1zBursXUkaZh3nMPJ8zPPJppnLyeY05gEjnVULQJNq8xpyPzktULGBt7GPNymqUwQToYIs2FzLUTv9O058CHVk3BaSEt2d6VG1x3Nc3YNoU3c5PNgI0Dk7XnGeC1JX2zri2tx/VgCZ5qgW93Zuh876CeNL2e0kOaOZdn2GkZuT6cFyZKSDOgSSsIPMTdM3PtL+7PGqTrhnMMsyZ9z3s5PYPnN82kdlc1/tLc74D55xnLNp3YT7+z/64d3Pann4G2rwjgZM+w5pnuluf3TA82cyBkq08LXBM1za0bzLNYLBaLxSfEUaNECkeCSvovfr3vtMb8zMnjCScDo71y75mZP/gH/+DMXFKKGeDzHqZ1SqnC4dymS8vPAP1KKa05yF0RodH98dqqODgsG6kzw+uRaPnfKVCGcSAhNioxa3Y5LqRdJ0WnZG86qFOF8IRTGJrk16gDAVo2muSJeMEVDpr26grtWaGBfcS17O8MoHIKAOvaKl44QKelEQFbFU4VRxq5gNeFz1qNSPYbpAsZ9IQ24NqsSbqABYP90hLVwal6SAsaczvtHEFzwQKQ88o80jde89lyYBn9SO2bucHC9au/+qsz8/q5oW2fG3meEQzjoJ58Dpw87+CemcegKqfvzHTrl+s+5jnKvJtUINsxqUSz6KAdY4VkD+XeY86tyaVly5Y9rAJ5dvKZAzCb1ukUkva8NKxGuVgsFovFAUeN0ukHKUWbNqyFjNse3Mid7xJBW607+3fSZ4q2a99CSqsmIW6J66aOaxpl87s4kTylQ6QYpN1WXxBJCwnWvqOZy6aP3b75FhkTEmDT5r35RrgQAAAgAElEQVR2SJSZ9kIfrXXmulgzeBN1HXCyfUtFMDlDaqusMZKsSc3zf+y15rsCXp+UjAF7HyKLpE80qXMjlDCZgJOkZx7rnLI+rks58+jL9WvCJA6plTT/Ut575nGfmaZt5poPtCW0pEYraO0+tT/uYX9+I5hPsBd4XrJeIW05TS3bMZ1b9sn9N/HHt7/97YdrOCOYozw7mSPiKtDGW5oGME1kXmMLVWqvqV0CWz4a7SFzxDmSfWPPsC84j1JDZ/65hr/z7L8jjG+0o449aWewte6cKz67K07wJqxGuVgsFovFAR9Lo0yNo/kAZ15LQpb8kMRSWjPdV4savSt/khGV1nxapXNXmW8E1Y7cdFX3mR5JZq2q+SgdOZkapZN2LbXle+YMKTrv5RJctNtKCpkwPSWwuzJBjZTZ2l9e0yQ2z1GutSvcN03Je4TvpIUBfy6apcuPZTv031GnM5dG7jlqviKkXpOtZ9umGcs5dyI9+6xpObZ80M+WjO/IybQuWPNBM0yNjL7bX5bPARok92gRhC40YJrI/J/3Us5TSwy3Tyy1G/apYyZyb3ovuMxefu9kSfHebHSGbdwzr5P6WQ/2WSM8tx+ftU9Cl3wPrF3ls+W+NUshcFGF3O/sWdrjrGnPsaPO21nlCNt2rjRCf8B9TY34JrpNsBrlYrFYLBYH7A/lYrFYLBYHfCzCgXRI21nrOo4zj4nOqNpp1iHkGse4k+rz+6dKFuZzJNGe1/zslPAMUPUbP20zvTo5vPFXYiJkzprJ1w7pvAbzBc56zGY55zZnNPOyzcmsK7ywM4+h662aux305rLN8SS8jtl/TPo2rbQ5Z46YhwxhZ4zmg80+Ou2ipaTQjmsRpumVuaEdJ1nnvVwVJcdlF4NTnhIO5KCdDLpwJQvaSzOaq1OwFifXRzNxmWuW8WZglOt23nEg5z1PbooEc805wrmS92UNWbtW6xW0ShxOqzDJQvaXa5nrDKrBrIori78JFJu5AnzoFy6ETIFyUA57M8/pUzDPM9zM7ay1OZUx5pltN1fjdeZe/K8Fwbmyis/ZmUd3CJ81Mg9Xh3m2LuVqlIvFYrFYHPCURokEmJIKIcFoAE6qn3kM7W8aJaHV/+yf/bOZuerYZdI3ErUl/5QIkZzQipBgUqJyEIEduwkHaKTUeEriteST9zWtUwsUQLpE0k+NA+34lI7RNHvfy6kvrhKT/3PSeyZgMw7+16p4tKAOJ8K3qiV3le5nLkmWPlI5JtcI6ZwqIgR3ZP+tUTLWnHMHu/CamhvrYG2mVdtxmHuO3VoNErJTJmYeE6bZ79l3S/yNZpI54zMTNCROwQ6uDNJIMVzhg2vz+XMQXiMuaCkwnBcE8yT1Ge+h1UMDSc2auXbwVkulabVEDfrNemegDuco/UF7zOeP9WBczGezQvnsyLSdVunIeyefUZ8ffg5nHquOsJ9yv3OOQ26Szx24s8K1eTVpRDtXGFdrx2e9Ncw3YTXKxWKxWCwOOGqUSEP4xFIqwlaOtmn/zMwlNVtzSakI7RJJkF/8lCBp0wTJKRnTNzRKwtyR1mYuKY32rKnOPIZYt9SYluxqW35LDEbSQWtKiYe5RhOir83fQB+b1G+t2zb5/J9DwVsiN3Pk8PuZa+0cCp5oYdyuJ5kahWnNmtbBNfatZn1Sxutk/UYZZ40ytUVrWLSb0vM3vvGNmbkIIfgsUxRYeyd5nyqs27fYyOatHeS+cy1PxptUlKTRmIQ7tTb360TTeEcYMfPoL27tm4QftAICCZ4ptEc0mvwf2llL+XLF+1av0IQFba7sg2bt8vzAMvSVr3xlZq59nM8PfcU64dSymWs+OTvQUFOjbHSb1uSyTa8f902iAMZm/31LheF/Hofvm/1p1HPMH/PbrBun4gZ3qWyNQKVhNcrFYrFYLA44apSuJp0aJVKRo19TYncZIySdFomK5tf8O7TjEkfpj0GqoR36mtoBkXz2raVU4UhQ+ppSWtMogauH5/+4j0v5zFxSIeNHs8x2HC3HZ6fyS42ijPV0Nfn0L7OuvCLhZpkc5s8Rgm8iR39GouUaV7rP99ZkM2qXdXPpndyfjrJrUa98j7GiqaCJzcy8//77M3NZWVjD1L7RTBzF1+gTQVtfwDjsw0sN1T4b9n1qlMQImPKv+Ym8brnOjghlP6ffjeeGaxwpm2O11SQ1ypZQTr/Zn6lR+kw5+ShB8wtbA3JJvLzepOYnywr7OdeO5w6rBGdFPgemEmwaZSMKYPwmwUgwVuY1Y0YcZcsr9595TZQyc+2H5re3JtiyGXie33333Zl5/VvEOpwI7U0ZaYKamSUcWCwWi8XiE2N/KBeLxWKxOOBoerVKm+quOSEdRDLzWDXcyaczl4nT6RypovN9mxXTrOAUB3MkzlxmBJuX0qzDmBkf5oRWf7ChBVXQvk1G6SDHVOTUh5ZAjsmEz3I+7aQ+BRfZHJFmVebf3KtpUvH8NdPJyeTUKoMAB/WkGYX9gGmTvqVJEXOQqwW02od+bSkNzAPzkukHBPG4ukuaeV0jr62LYdKJnFfMqK5Wn/PtezkJPMfM/DKnjQQAmOxg5jGdib43Tlzvl7Ymvle201IDbF7O9cE0jgkW10W2yVliYo3cU/zvVH3IJA8OfJm5zij6yLma/XEwjU2GM49EBZCEpGurBfx5X+U+5XrWxJV6coyuC5xVS1gjxsYzms+xz3MHUs08BkU1c7eDxHhW817mKwZLOLBYLBaLxf8BHDVKftmRhlpFc7QQh4fne9dYbHRId7XqZh4ljpYoi6RAP5zUPHNJGqaZSmkPqciaZGqRzUHuvjVp25UV0jGP096UZDkP9J91QLpqdewsnZ1SP5rDnrVyAFRKn76XCR3cf3/PgS35mROCU8OhT9aMM/TcKQQOXJp5TEFpKQ185oCn1L5dj4/93qqnWxvJ+bG0a8m9PRN3wVt5ve/VUoWcNJ7z5GoZbY+zB08apfvhaiQz1z69q2Lh6w3WK9fH9G+kZeQzwfPtgKNcE7QiB+hkf+40oDyHuJ79wp5oZBjek/msoxlbo8xzupGpeB2yb06jcFDPzGV18DrkfFrzI5gnx2gr5Mn6ZO29BTDSNudYngc+a7yn87OG1SgXi8VisTjgqFHyK8yveWpVJh1HA0kfFr/QriDfEoztvzzVP7NfZuaRYLulA5hgoJFEe1ymlPN9jeZTQQr0a0q9SD/4UZjzlAiR9tF6m0bJfflf86M4IdekAtkftDeHu888pgs0a8BJ+m+akn2JTfJzX1qdU5MagEYgz/daXUpr3ezl9KEjYSPRNt+3ibR5bVoicMpH01BdF7bV1zNBd0rOfra93jPXPrE/p5ElOPWjpb/4WW0UdneahO8L3GZqE2j8vPLcpeaFxsNzj18zny3TdVrDbvNgGsEcE+tDu5ku5r1Nu+kHpK8mV8l7pSboPjbidxNsOM4kvwccM5HfZ22b9aARV8z0lI1W49X3P5Ht2/rVtMdND1ksFovF4hPiqFE60bn53ZDKsLe3at/2HzaCXKSIlrTuMkbgROGGlJISCNIR90IjS3opU9YhnTWNLGGpKKU05gqpndfUKK1181ney/PYNEr6a40ytWD7iFpJpOZznnk9D3dRlS0RPWEfz+maplE6IraVzHGfTDww80hc4CjgmWuvIpkzR0375vvMee4B+/Ac+TlzH/HZiBDso2S+Gs0dry3Stn0v25+55sPk1acycS1S1xHIJ4IHPmsk7U3y57pGS8laEVmOrzKfCfrAeYZ21ijXsqjDTD8XWW8+a3EZ7Dv2VnvWfY8kcOA9r2jFea8WveznLq1f7HdbM/IcMKVoiwcxWUYjIW9rlf3Ksfj7+axY6/WZ1cbcrF9LYbdYLBaLxSfE/lAuFovFYnHAU6ZXm3dmHsN+MQOmWdXBCS15vIVYG3Z+N1Ob6xY2JzT/c3BDMuzb5Gq+zJlHztmZx7lqIePMFeadTGZmTugjIc5plnKitWvGzVxmHMxNzXTsuT7VErV5LNf0jmf0TcE85p898S020yvXuLZcC8F3ukEL5nFQTwZA2PTKvOY1zJt5STOowkEdrQqCTa/mo2zpUB5DC5s3J26atmxObaYtf9YCM+5wShVyakteczoXmun1VJXFNTIJ6snn3q4KXDBpYrdJj78bwYbdA/n8sc+crtPGajN+mklJZXEqWc5PMyf6upbgz/3Y2y2tyuuQe8bnZ0tT8X5qlY5MAtLMvK1mZt575vGsaqkox8DD208Wi8VisVjMp04hsYvFYrFY/N+O1SgXi8VisTjg6KP8m3/zb3408xjaPvOYMG3qo5nHROVnagv6Nb8PWni67e4t5eAu2TRt0w4Bx1eYPin6/Pf+3t97MXT/g3/wDz6auWzf6du4S0hP3IUmp8Z/579r/iT7ePK7ts+3FAGHU7f6bfjZTP+V+4Tr//Sf/tMvX/z5n//5j/L6TPyGRJmQd3wKrc4p/mf+bsTpDkHPuTItHd/PvWdfU/MFMm9Ov8i6pdCMffWrX52Zmd/ze37PzFx0ajOXX+ezn/3sp2ZmfuVXfuWjnIN8bpySYmL37OOJIsw+teYnuvtO8y2aHKHVx3TqSLvG6V25/lzzcz/3cy8d+Pt//+9/NHOlgPzjf/yPX67/lV/5lZmZ+Rf/4l/MzJV6lfvFftdGZk6MAa+t2IHTQdpcsZfsb8sxek+xN3JP8dzQn/ass46/+Iu/+NKBP/bH/tirs6qlMXG+t5qh7Mc7kolss62x+2Y0P7t/S1qswV192RyXU2nyGu7xC7/wCw8PyvGH8m5jzzxyh/JZO4j8mtd4AlzEMwfjwINmNnZf28Ps4JNWONZMEPlDecq3cZDFzONB3A5tPzxe9OznmwojzzyOv3F3mommcYCeODfNnmQ+0/xegvlwsdy8nnEjcGTgE0IM7bTDlP8xplZdwwFgJ75i+uEi4jOPuVmMIQ9i1gOhItfD1wAHtrQ9xQ/mqWKC93QTME8Hu5870HJbHaDRxsP8sP65j+74kN9U5cFBdzlX3h+Mp/HQ+scv97sP2RPrzumscvAca9cC7hwY1gpYNyEWtHVw8Fn7ofcYG/sQaMFm/hE9sXAZbV/52hawZKE2n1EzRTWu5ZbfD9b0ulgsFovFAUeNEqnAtdXy/alKBd+zWTYldpv4rFG0axqjiyXjxhHpEOGWMkAfXZkiNcrUXIDNKMneQVg6Zlwk3EzHsDbjCicz13wiXSIRtzHa3J39px+YoNCWGleqmX6QNGcuMxCSXNsnTWtEC+KzxtbBHKFJfve73325hvlkTIyxWQ/SdDbzWnq+q0aTKUVel8YHe4eWWsB8UukBns6Z1ybrBGuZ/YJrFpYZUh6yAoyr5dicN3PPSdqqdTgtJ/eUNVvmLZ9j5p60DPZPckjzP/aZqz1kfxLWplpVCOaXe+TepE9mTmrpGFzLa2qU1syb9cgpF2YhyjZ5bXVL7Tpgb+bZ0zRxp7flM40r4N13352ZK02mjdHm8TzPnA5ySlOxttk007vX/B57jXlo1Vh8j5NLKrEa5WKxWCwWB+wP5WKxWCwWBzxVuLmZ1DDV8JlLNuV7vtcYYOy0b2ZVB7HYIZv3sNO2kd46UKZFWfF9/k5y5Ob0dQQmka4zj4V9HRiS33fwQpoYMPVgqrJ5dObRnNlKaFHKi1fGlvNAO8w585tmc/qMua+ZolrgkU01jYnDgQ5p5sO0xKuZTrL/3g+tFBBz7mi+mcfoPfZni+72a/YZEylzRPQr0bAz17wRNGLmodxTlIH6zne+MzNXtGeus/eUx533xPzmCOacA5Pyp4mPPeCC37mnfA40ExnfhyGrFTBvplcXxM6zwdGh7I00+fL+VHiaz0x83vaUzdQtUI5rWYPsj8277L+cK+acfcEeeO+9916u4exJOJgp9+DXv/71mZn52Z/92Zm5XAMtkt0l57L0nPcB50ZzjziythXy9uupPJ2D9GYe16HtvS3cvFgsFovFJ8RT6SEO9Z25pCg7rZ8pkdKK//pezVnrFIXsj8P/W76NeQM9zmyT/zmoZOZ1wAQwJ2hK2/zPWmI67+2Yb5yxDsOmr5mHyHvGiCaTkhPSIdcgveecO3cW6bM5vOkz85LBAa3ItTWvDEZyHlcLdCDXEk3YOYUJF7J+RlNKzY15szafVpE7a0BLM2GOPvjgg5l5LYVnCaWZx5QPLBLZR4f6N/5S7/d8/hz85oC3mUfrQguC43vcv2kHrKG1/FMJKuY2911LOaKP7NfcgwRO8T2KH1P4eObaFyftxoFO7RxyeakW/GWO28Y57fQM+tV4qdk3tNMsZAmu46xIjfL3/t7fOzOXxYN7pGbN/nGB5BZU6OCZfI5p078TTRN0IGTjnmXNThZQnwe599JqaKxGuVgsFovFAUeN0pUjUqpoPivDPpZGOGAtqzE53KVFNP8jElxLRQG+V6uIwavD1XPsCRMTZCg/fXPbafenfWuUTQJzeD0S88wV1s09+E72B1CctrFv3CWZpxbhiif4JVKbbtK/0xXSp0k4On1B6m0Jxk4lSU3QKS/sldzDTkFokrFZPlq4vy0VTTPFf8T8MUfpQ0ptN6+x5J7jQPJvmhT71IQF6cdEUrffPTUXtGjmhX61YuBoIK7Ck32+07pOY87+NS3J2t2pcPqXv/zlmXmtUTplrLG73LHMNCsaYA9g2Zm59qs1y1x/swAxhtybrgzUmK6yOLzbRhPNwvW0yZp873vfm5nX/nb2D69YRVq6G2COmh8W7c7t5XteGU8rMs+6MA9poXnnnXdm5pH5K/fwifd8NcrFYrFYLA54SqNs1GmOWEICaRx8TtpPCdKUWa6DmO/tI0g7NdIIErI5MLOvp5p3SDmOom1kCwnu1zhvHcHZ/B9I10iHSIVOmM8+IRmmj+EP/aE/9Ooe3Dsl2ruk29S+kYSZ17YuJkVgXlL6bHNlLb/5Xehj8/GYlq6RS1j7Aa1GpH14eY19360WH9fQDuNvc46fkX5lhGJqg9kO2lTuF6wI+NtIFE/rAu3RL6RytNuZK3oWQgfXMc17OMoztTbmG4md11xbU9YxnkakQB/pX+6pk5XIz3aCeWRdUnO5q3Wb+9eEA7bw5Lh5JrAY/Nqv/drDGM1L3c6hO9q8mcv6wjNjGtC7eaBt2kwtmL7Z4kGEfH7m+sLtvo6rSIIN9hX9aPWK0Wjph+M9Zq7nj/VhXtJid6L58zX1s9tPFovFYrFY7A/lYrFYLBYnHE2vqLctrcJOazumZy6VmHZwxKaj1wz2qOHp2HYwD9/JYAmr6KjxaY5z8EYzJ2LqwzyBeaSZ9RIOIjpVD2mECcwnphYCDtJ84DQbrk2ntVMMMNWkg9/mXMaYJg9X5mhmHaf7mO8220m49FW26eudyJ7XOCgozTEOUmEPNTIB3zNNaZjeMDeRJpT94RrPQ5r2nD7EWiaHbQaX5HdYgzQNsYZ8h2CFfG54Tmwuzv2L2c6kBLk3CcBwAEQLfjABSQZd2D3Bta2yjU2ojY85wdzQVqZwYb5lHrhH3pd14TPWNINPmGvMh8wdcz/z6LphD/z6r//6yzWQRLg0WT5/mHVtXs3Am6997Wszc50VzEGaqVvgk1O+ch3Z38x34xB2KUT2Z7Zj4pDGK8sewwRrcoLsh1OLGqEE8+CyYzOPJAv0Nc3+ba7AapSLxWKxWBxw1ChdOaGF/7reWkp+SN0EMFjby+8hDSEVpGbkwBrukRolQRFIj7Sb4b+W9tz+zGMS9KkAdKJJQ74eKQgptQU6EIzBa0pO1upaoIyLQ7faj3eadUq09Bnps2nKTg9plV+a9G+JNEPYXbWFvdPosYCDafJ6xs+cI73m+E3FlUDKJJjhm9/85sz0cH9ryvksWENHwk0J26H8Lkide9F1OlvhZmuJTZMy5VpLgWAPsodaf1wHEq2maRmn4uquA2sqNL/3OJygPnOROzBXrRi7x4SWmMFRrroBWs1Ka/NJWPKtb33r1b14xlrAGnsCS0FqlLb0YWVolUoSLjyd1zO3PFOupjJz7V3mzME9+T9XSsk1N21gI6DgjHQKW1pFTHLBvVrA3YkUZOtRLhaLxWLxCXHUKPmFRQJLrcRalbXGmUsaQqJrBL0AOzP3aDUNLTG0VAUkmBZyzXj4fqOgMnmuCdRnuo/SfqBGi2fy4/R/mGLtlJLDvfD/pLRKO4yRdWkUdoyjSWB8ZlLq1NrQNLwebX4SXE//W1I0mgR7JqVt+9pYo0b8fpISGQv9ICUhNQ0keBNQZ5+5FxqpU2qyz0jLtJs+vLzvzGNaVc4Bkj/9sEaVMPVjowwE7K1GD2mpPrUVp2M17cDX8pr7zrU3m/Z3qgfb/OSsS0s1Aabgw/KAj3DmMSaA9eB8m7meF5OzpybIe/pqerX8zFpnWsjYO1g3aDfPjKZROr0l27T/kjOqEX6wNnw/59zED/QjLSjsEb7HfDYrIK8tDZFn3HPWyNXpT/Ozt30FVqNcLBaLxeKAo0aJVGcf28xjiapWasXlm0zGPXP9shNJRsJuRnva9+UIpplL8rHP5iRVNLora0Ot7FfTmNy3plFammmUcZaI2zXMsSM6871LM6U/1367Flnoe7o8TfbNxOenxN1sgzFmm460br5ma9uOvpu5tAZTAebasQ/ta8l58FrbXz7zqDU1ny99ZM82InonQ5soP8fHs2XyiOwv+w1Lhgk3Zh5jC0w7mX23dSWvoT9O1G/r5mjZRh5vsoXcIyaIyH47anbmkc6x7QVHEUPg0GjQ0G6ItE+tzQTn9CujkSEIsQaU/XEhiFbS644KMJ+/piXRt1aSzJ81P6bjMFySMNthrrGyZN9YY5chTJi2tJW5Y4/cRcPn/7g/7TW6yobVKBeLxWKxOGB/KBeLxWKxOOBoekW9bUEaNn9ghklTEmqzqxfkdzH/YerAiZ4mm5aY7v7QDs53zJPNZOrQ5zSLOBwalT1V/VZj0WgmX5skczyYrFxhJe9r531zfmMycYBLml4xMdI2ZsBTAAdrmMEnNsmBNEWlWQfYNNLmyqHu2aYT8QkqSNMN/XQ9v9yfbtupNTPXHjY5RZr9HUTBPZJnlr7SH/ZrBnl4rmxObBVL/N0W/OVgnrzG5l32RPaFvjslrAXTMe+025K+78y9bcwNJ75O+p3PNGfCqdYkzz2mVsysSQLBZ5wtBPGkiZ1gFZ63FnjIvWwSzzGbS7sFHrrGo+s6+r3v38yPrv/a9pX7aHKWmWsf2L2SzxbX8L1GbsD9WzoVMJkH+zXXxWkhzHVyGre0P7Aa5WKxWCwWBxw1Sqc8pDRuadX/n3mUkFu1DodhI+W0xGkz0qeU43DmBqQkV7ludSGRNFrqhBPe8zrQpBPTcjU6OBMFZHAU4e1oTiYwmHkMSEFqS03QIfh8dqpl2OjeHGjh9fF7oznPua+ppnKunOYDUjo0ZRbaQO5hxmJijRZ67lSaRn3Ffsx+AKRu9hoaS2qUqaUmHLCT/Tgl7zsAi3XOa1uqh69pGu3Ma0uGg9FcmSb77HYaccEJbU+5gk2mdRAoSApaIxNxehbnUhJUMA88fwQO5j50GhGf5VnoNDHucQrmMTHEzKOV4xltPMdqq1Hex8FheZ7x3pakRj13OhcdfNOsX/TDlUVaqqKDxXJP07atWM+eVatRLhaLxWJxwFGjRDJpFHZISM1fAUwThHSREjcSv31reS9LxkgZKdE4VQHJMqVwpBBeXUV85pIokbroe6trmeA6Ex3PPEr9po6beZS80AwzsR3fJHODlpTaDRIt7blifPbVGmUSF7BG9hGkz9S0dpbsZnoCfNM2gMkZQM45nzF+7pE+QaR+E3rnPU2S3cglGBuaIFpJzpV9vh5L9pnvQ43WSMwB/WK+sj1TRzLnqU2wvvbxp3bj77WUAadT2OeZfWQO+TutNdYy2nxbezU94v9u78yRpTuzqr0ZAg5RKvUlqVRVVBB4TILAwcHEYgI4WBi4cmgc5kIAI6AzCEoN6okKmIR+h+eedZ9c5/2uFHj/Xk7em3nyvO15c7dre4zAVqdslznnGscqzFzrw3PfCg64Rqv3T/bXqRo5VyYRd+3J/J7rKLZiCyeyhdMz1p5Xa5T2lc7cW1fyjOEcMqF/W0fHLOSZYTIByD1yzn3W8520xt1R861GuVgsFovF/wGOGiVovoUWxTTzXIJxJB3SSdqXHRnLtakduFQLUklKFZawG+GAJXPaTu3FlElIItmfRstl6bglkltzaVGvvLqs08yjb9TE4TOPc25ateyjE+5TEjXJBK85n3zfklhL9E20+QOsjS0Nba2tSWbJKiRYNIQW8WmpuRFJ8B7SPhpqavHMsbWpTA7n+2g3LdLY0r+jzVvfQfMjmrSCucxxmljf1IX5N88tbacfkH3HfmVO89kyGYYtTTkOWz1yjzV/LBGPprCcuZ5l1sfEHzk2riXaNeeHKFeT32f//Rnzmf137AfPX5bCY968zo2cw0UGGm1dwn3K6+8shDmfbgerFxSQM49WFsaR9/XvyonsHtAmmuXMIyVhiwEB3guNZKVhNcrFYrFYLA7YH8rFYrFYLA54kenVXIIzj2bQVt/LwQ2Ym1oYP2o3Zqw0J9r80kKW3UebN9xuopm0bJZr4dEJq/tpArPJtZmOnPzOPGSotE2vNn1kv7kfZolcCydlY17KtXPKQzNX2fHv15nuIHfqSc7nHRdqI6DAZIVJrFUh8XeyP9yTfcF8tJqi5vDM+WRd6McpaM3my1aDEDjdJufVKQ6t3p/H6UCGHLPJEjLlyMESPH95jQNLWn+cNM81OZcO7DBJgf8GnBEmR2h9aXuKtbLJP/vm4KhGmGCzMjgFIHHW5X45VV/xPZ1wn2237/uMaBzCjLuZQ11zFJNr45N2MFO6sNw+90nCAVeTwp2R4+L545V5aRzYfI/+5TO3wTyLxWKxWPxAfC+NsgV7IN25Hl0CaQAJND3YCLIAACAASURBVJ3WSAxIMpaO8p6WopuUeWKHd2Kuw9TzPg64aYE3CYfZt3v6XnlPBy+YVm3m0liQ3PhOC922hp3rgkZBIva77747M88lWqd+NEnddFcnmq2E62HmGplKjD7lNfSFviFJpoOffqb2NPN8zl07Ee0q1851AOlXS4Fh3FgDcn96fzQ6R6ORLQBr7/Q554nPmEO09EyH8vPbtGngtIbUDvjbJBTZH1eCaNVMrMk07fEUzMM5ktYX/rZGnPvUAVNN23I6SAtO5J5eu5wH9pmD0tKKZsIMWw5yzJwHaGL5rLveaN677S/mqqVYAO7PNcx5ng13JB6kuM1c88eaYxHKtWPPkE7lKlPZlgMY8+y8s2a01KSG1SgXi8VisTjgqFFa4klJxX66pkna34QUQD22fA/JA60TgtyZS1IwBV3iLq0iJTD3uUlUDht2AvFMD79uNdCMO80yP6OPaDJ5P8ZmmrrUgKzpt3QVa22EwqemYYmy+TqdwN0SyFvKBVKuk6lnrjXGF9hIkGmHPkHt98033zxdwxpZom4+J0uXqWnQPn1kn2a/3nzzzWffR/NPX4v3kxPKG14Sum4/b0t5MOlC3peaijxvrHuuCWNlLvl+I6HnOeDa3OMuOHAi3LZ/9lUxArZEZfI777WUFd/TaQx5rpnwvfkRTQ8HGlE569PoIXnurL3mfdAkOetazcf2/J38ni4M0FIt7mrF5rzynDAOtMWWrsf6sJ/SUuH0JTTKt99+++k95tFad8Ys+PlrFp3Ts7ga5WKxWCwWBxw1SvsCm08NNIkWWzyJyU62nrmkcaSC9957b2aeJ9qnZD7TJUKuQUJ2CaYGa105RifD5njT9g0cJdrs3dYQWsQVEilzltIfvsRmgwd3pbxyztAgX3vttZm5NIbUtpDu7nyeM/dV5VOKbVKaCedzbk2Hh7Sd1yA5owWx5kk4cCdtpyRKG46aa1XPHdXdSrNBbu3SQjOP0YRNW7T07yjppgnYYpASuGMC+CytNWjj//Ef/zEzM19++eWz/s48+qDZqxlHQB/ZS+zV9BGz79g3ppTM8TiyvRVkSJjgPrUzzgJHPDZtwnR9rZQY+wTNJ888zhvmmO/knPPcsoeSus7XeMzpM+WeRIu6tNzdvRudHLDfv1nIfFaxz/JZ5xrug3aX57GfP8611JrZc37GKMs4c+05Zw40ghzT9p20yMRqlIvFYrFYHLA/lIvFYrFYHHA0vbbEfuCAFMwnaeJDNbepNBNTMZu5Snaq3w4qQlVP0xYmNswQqPEtpaVxxbotm89a6HP73ksqs7fQZMw3zJnrYs488q1iSspKDw6CcKXyfI95aJyxNjlj1klzm3ktG6FC2zvsFeYhzXysI6/0Meeczwg1Jy0k99XXX389MzNffPHFzFxm2Q8++ODpGkxGNtnkumAywtTj0PiZa++eOFW99q524XYTLaDFgVA2Vc9ce8ch8Wm+4z3GzhwSkDRz7QETfmRAE3Npgo58Rs0p7Ooo+feJxKKZDP2ctufVJAKnVJg25yaJwIyYa2hzYqv6wXPioETX7515DCDK5w/TK2tmTmz3zWhVVBy01s4zJ++zF9PMboIS9iLn/cw1f6xHO/MI/MSUzz7LtmgDUzhnfzO/833M3jn2FpAKVqNcLBaLxeKAo0Zp6quUihxggASRUpJDpJEoU6JNyXWmM+C7JmS7xpJxS/R1qLFp6mYew6GbFH7SKH2fE1ottFPCsxPjCYpKKjHXkTwlKiP1ttQPEzg0MgGvS6MGa7U7XeMyNUGCSwhAaXUw74IYTtRm3DeTmZFKTYDQJHI0ffZK7gHmj+9Z086+2RKT2rel91Pdzrv0ibwf36N/Ti/K69lDSPD53PB9Wyly3/F3o6kEDoLhmqbRnIIumkbpChoZLGUqzZPWybV+HmcujYcAmZbOgAWCfefnMft/CvwzEQR7PINh+Dvbn+kpQg3sr3Zm2xKUzwRnANc2ikX2AWcVr2mFcBoV85DaIumDBPFwTT7HtnAQNNZq53KutGBJ75PEapSLxWKxWBxw1CiRopHSWjqCw9JTcuIzpAAkn5TkeA+pAv9Tq07thPaUelyhnFf8R9k3S62N9gvQZva52bKtITS/xQn2DbjG2sw1J8wZUlX2DWkXKZF+pR/QxPOuHTfzWI2++cCsgXkNZrpGaVq6lIghDbDPOrUqNEprOikZmwKvpWOYbB+pt6VhWGNJTcNk2aZxm7n2zIlEwJoFc+7k72zf0nzOt60stiAk0ACYQ9K2sn1bGVpf7zSSmcdzxL7D7PNL2ky4GEGjt3SSec63qfPac08f0EqcdpJjY6w+c2YeNWKubfvOPt9mGaLvLd2mafae2+yP78G1aUni2UTzY2y5t/1M8P2sYcqYmM9G6s7fnFUtRsD+cf5vRDLWdBtFYcNqlIvFYrFYHHDUKLH5miA8/3Yyckow9gkiMaS2SMQi/ibs1kk4gPTgKLmWXG1fSUqi9NUE0nkfrjFt36skD2uULZLPGkLzT9EOc9XKdTFHzHUmFTN+kzO3CDAn5ub63mnB+b61xUZE3zQBJNJGgszYuFejlXMytCOGZx59xCfaLkftJZmA/VJc04i82ae8pvQPvk/5KGsebS87ErXNPeuLdpM+IEfqghaN+xJS/7tzIf+mTeYtNVyX3mradIOpG9v13KuRGNiX35573jOVZvrdHFHfzsW7OIJcf69rs8zw/NJn9v9LNcqmfbu/3DufCZ4FnzF5H9bPhB055z6rGGtav+78uM26wr05+9ucmRo0rVnNYvrUxu0ni8VisVgs9odysVgsFosTXhTM06qH3FVBSLOOKy80NdqJ15gjWiULp3ykWYd2HcyT5jjMCU4LSPOEQ/xdc22mV/0GjDFNc/TXprU0pTnVwmbrvKdTNbL/nmNXI5l5dHbb3JR/Owk/zToOCnCS8Uw3eWLGYW3SFE+qCEE9tJH9Z/5t4mxVSOgTY8595Xk0r2Re772cZlVXc2l1TnnvVLfUplb/35LubUZsPLV2VaQZzWa4Zo6zObRVXnDd01a1xuQBjT/YQTWNlKClh7QgOn92Cgbkbz8TuT70gYAU9klLm/P/bV0c4NX67jMjn1Gb9l33d+Z57V/gakwJu4kclJT35LWloJhjmr61KiTegzku14xtrhyvGWdWmvSdTkVwULp9nGaTWI1ysVgsFosDXkQ40CQnO83txJ95TIjns1aJ4y4gIj9zG9mWnfmtMoW1VacQ5Pdpn7SV1CJbYupLqOss/bfAGPrINandkHRLv03HlGN5Cf0gc8MYM5mZQBuCrSytZV8dHNW0iASSqNNUZq4gLjRL+p/BXawFFHaQCeT+ZM5Za+YxJWOHinNNhrDzni0NqQVb028aApKx04Ban31f1is/t8TekscdJNLoDN2/ts5OwwGNoMI1UpvVhO+1epS2rDg9YqZrlKCRNDjYxNp93tP7tT3XrDP7OJ9RB+ydtG+0GVvMsq93dWFnHisWuVpTvpfg+WE/5Dx4/K16EtdjEeI8yvvwPfana0YmTpa2uwpWLXCIuTKhRX4PyyD7O7XIljb11I/bTxaLxWKxWLyMFL1V+25+S/+PNEA6AJJHaoKWMpud2uHojTLpjjg9fYv2s/Ga0irSFt/jNTXKljRuSTolHvspGt3Yna8pfWpoQEiMzGdeYw0D5LrYt2dy4/zbycSthp/JCHJ9mwSJZszcZq0+tMuktZt5Pp+sBdouKSXpb3ByuCucz1xSurVONNQcyx2RfLZlCb1plA7pb0TnHvOJxs1EH6lRun5k89tzT5MBpMbsNk0unu2eEv+tIds3lZ+5f6+isPPZ0J4/+0TbGnpsuQ/52z6+1FxswaHN1NDdvikg8z7s9eZTc+oUe53na+Y54QowLWfGQ1jLN1n+zONzYyvYzKWp/epXv5qZ6xnP/jdte+b5+nLm/ehHP5qZiwjjxz/+8dM1nIeMp9VGdtod42qWy4bVKBeLxWKxOOBFPsoWtemyL01yQnrA99W0EiQV2+Dzl/6UIGs48T+lJTQw+zHyfkjWjqzMcTWN0lJy66N9ky0CjPaZF/xwMzOffvrpszEhybUSWtYEGy2U/UjNt2gC6RZZah9dS+RO4FuFvDgJE1w6C0k670NfTCfXruH7/J8k/GhBrd+A8SLRQhqOpJufMa6WXG0fSyvp5bk6kVhYmzdR9cyjv5prUstwCS76ldd4vzSCCvsWeW5Sg+A9+2lzDuzLbVGjTaO0ZasVOgD2HyaYM8aGr37mOsewOGAROdFVOtoy/2b/sZfyrLL224jcuQbN7rXXXpuZaz/OPI+ABc0P7b6ZnCXjF3hGXRKwkUuwZ1mfpqEzj1yTVhFI0WmrlV2zRctrOPMYsd0Ka5ziTFajXCwWi8XigP2hXCwWi8XigKPp1Vx+qe46TNdJyTOXuo5qzP+ZDvD555/PzMwnn3wyM1eiOdyvM5f5xonlaYJxaD9miLfffvvpmvfff//ZtYwnzSI2cTj4wtcDm+2aSc1pInkfV6dopleqg2OmtsmiteX0nZnLrOK5SpMN5lCbiVtqgc1jr6r0wL0x62CCnbnWn3QQxthqRGIeZDyNi9hoSebeTxnsQhuvv/76zFz7KftMEAXPC+6DlvbAeFrazktqmAKnZ7EuLTDDCf7NreFgsgwQc4CMA9dmHsPtW4DKHdlCWzcHZLwq5ch9bSkP7ne6I3zGYQ5tQXCcY5xLGeh3ZxLPFBL2P/uGtnJcPP+clTZt5/XsRQJd8jnOdQTmqm5BfH6WMygStwgmUp6RNPO+9957MzPzy1/+8lm/04TLPW0WbX1jHCbBmbn23Cn4zcF8rVLJ6dxajXKxWCwWiwOOGqW1h5YQ67ptKRW7OrcDZWYenf5IUplg7rDsRstmzadVlOB6pDy+06RGSzstLSLh9vOeDv5p6SGW4E40fcxVk4wdAOJKBzNXErIrruR63wWSNGnen7W0l4Ql0aTZQrp2wEQjjnDCc0rPJmNodIWe85Y+YZIK7tNqmDJnaPwZKOC0H6de5feBrQGtuoRTpVq9UMDezmfrTrs4aYLs7bQeOfiN//P+TvQ/JaGbaOCURpNodTltZWEe8xyydYZ9k9qZE/tJeUhtizHSPns8g7+w5LDP6FcGPrGHmqUOsMcJeOE1NbtmATpRxtnK0khiCHC6qzwzc80tqSo842mJYR5on3lsAT9osa7KMvP4/DcLk6vBmCJx5hwkuhrlYrFYLBYHvCg9pNVLs5SJ7bn9QttfkVI9vh/uh5SRUgWSFlINUk/ex3Uoka64/8wlzTgJNyVC7N1OTE2t8FU+OF+PNOjaciklmzS4+XiQnLD/u2Zfwlpeq7mJJNi0G6cznCjx7N96VXoI92L9UgJm/XmPfZB+Nc8Na95SNkhMxg/Lvsg2XMevEVhbe2+E50ipJ4o10J4ppzi47aZ9Oh0p59tEIY1+Dx/YyYJiwo+WXmGttxUHsHXD/tH8++SvbZ/dtZ9/+zxKjRIt27R0aV3AB8g5cqIhtM+41YWkfdYj+2NaSdrK5xhtjb3eSMrb8+o0u1ONWVvfZh5jRugbMRQzj8QFrR8mm+G+qVlzjf2x77777tM1v/jFL5610Sxt1iQbjWE7R8FqlIvFYrFYHPAiCruTD8HSatrSLdHyi51SC1IRkhsaQN7HJW+a38K+CROfZxv0Cy0lk4pNfNAkjwZLuacyRc3/aFJrvt/osUzgnPNpH5gTfrM/LkeTEpiTytucO4q3VY5vPk1HbKYEzHqxL/BVNuorJ2On1smY0JgaET1wmZ6cT/sJWzQt47GEnJqGpW8/E61v3lPN723puV1zKkFFvyzNNw3+jnIs+27Kv0aG4e+0PXVHvjDTfUmej1NZLsaTJNjsE6LM+U5aKUzA0qjSHBvAePI8Y62wXqHNZoQ7Uf+572eek5yjSWIt4czLuTvNVSsZ57V1acKZR1KYb7/9dmaek3nY78i5mpZCF5zgNfcpY4LoA+0xrYLMEX1s5DetuMXMOYI6sRrlYrFYLBYH7A/lYrFYLBYHHPXOFipumG80TQwOQ+b/NMfY3NTa4vq7ygL5twNlGo+rTS8Z3OB6ZTY9tj67L+3/mcf0lhbybgd7miHuqo/knDlBu5nb7kKlW2VxB221Pttc1gJKEieHOqYWAhMwNWXAlZ3uzGsmM2PWdnX7NMdg8sK81FKKHODjgKy8N+234AwHfvB/I4IAfhZy3A76oq18/rxfWgrJ3fPbkr+9lm1vOnijpdHYDN64Uh1MlmhBF82U7b7x2kz1mEExf7Yzh/ccdJVtmn/YAUR5jSu1ZNUaTJT0lb2RriRMrwTAMa8tXSxxV58z33MKVgbcOaWvpcSxj7wvc3/adeK6qTPXc0w/XBEnx+tUpzRb87fTbBqRScNqlIvFYrFYHPAbpyTLxWKxWCz+f8dqlIvFYrFYHHD0Uf71X//1dzOd6sjpDC0x9S5lIm36TkM4VSg/pWC4zUYTd+cbaXXLTKmX9m6+99FHHz116K/+6q++m7n8Xpl8azqqEz0dr/jmMj2EubYftvl8PY/N/m7/yUson1pVe/sTW8L97//+7z914KOPPvqujTn7ib+BuUs/Mj6SUzqGE9CdLJ9/m1Sh7RnT8532OWhUfvb5Nmqyv/3bv/2NmZl/+7d/+27mmvuknoPSy8Tc6Rc1oX+rwXdHWtF88o45SJ+UqfRa6pKJHVj3pDA0GQi+w3z+6POf//mfP038n/zJn3w3cz0LmdZhEnJemy/avv1G/O4atTmf9j2far2easYSP3H3mnMDlR6v+ayAf/3Xf32aqz/4gz/4buY6f9LvSVqWz58WlwFOCf7eX+3M9hw1v7ZjA/LsZB3pK695LvIMkAZE+g3P0cw1b3/5l3/58EC/KInkNLhTfqEPa+erzTw+zI3X8u6HMjcx1zOhTFLm6LnP7cfUVTLaod8CBlqAjq83q0sr7Ov/W3CGq0I0Jhxvvpy7u9y75mjnlcMhr2GuOPBoM3/cT8EVjCMPU6+/C1rPXHvFARyNC9VrnetjAaFxVjonq1W8cFvOt8t7n56b049Vjjv7w8FptpQcn+cwf+AsBJ/m0s9vOxcYM3ui8e+aRSuv4fv+UW6FxxMOHmq5jc53betj9qB8biw8Mvf8YM08BgG6f/k311jgy/v4B7Llt/oHtwlfiTvlZObxh6nlD9+569p9vJ/avjoJV/ztH7/GNMb3T/vS67zBPIvFYrFY/B/gqFHaVJpSxYkRxN+32auFSptZo0nsoJl+zJKR5jzjrj5kvse9Gy9r05Jsosj+W1q3+S0/M29oSk5Iis1EAWxqbW0hlWJWJq0hUzBoH8mW/3PO0QToB+aaZA9p6T5mb2lauCXQZsZkrk6a9Uv4bL13U2NxulBK9B6j0zsSd5pt0/SBtdk0P5rNBY0j7+EKI82tcbdPTlon/6e1BvMXWiJMKrzOXDy77A+bYrNvrkOaa9LWwPOa5lDm7csvv3x2r5wrmyFJvchqF/Tb1SkaQ5i1u+wz7bNmPH+pmXI9ry1dx2k/eVaAdi57HRuvr/m1m0n/xJRmja1Z76wdNmuAXR70I2sauxII90kzPuvqs7+ZeRtWo1wsFovF4oD9oVwsFovF4oCj6dWO9BYxaPU9TQQu2XMqQPsS2CyUJgM7cltEK31D5XeUVMJBF6cCuzOPJMjNrGoS8FYOxwwyba4a64mvsSktTVeYLTDfYfpJ054jS5vpB/MYbRGkAVPIzPPIwrs+5jg83y2iDhMV+8uFgGeuNbV5LwN2PEcOoMhrXNw594ODEloJLRNQnwjGAf3ARA759MzMr371q2eftfu5Py2AwfPdAqzYC1yDqSzZWjBRUorq7bfffvZ+Xk8bbnPmMZIbs9qJzSvHxH5Jlhsi0Jk//k8mJ8D6YjL+6U9/+vTZhx9++GxsmPNyPn0u8kylqZD9a7N+M5OC5iZy4YNWGiz3svvYyrfxTNFHIkFzrnw2tOh7+sIcsfb5jDrq3S6V7AdRqqzhN99883QNRS2YP0yuFNiYmfnZz342M9e6tiDHU2TvapSLxWKxWBxw1CjvSgPNPEpwTfK/C99v0rhxykF7SeHkpuHe5Q02vk0Hk7QyVa2/Lc/O6RxIUC3E+VSmyE7vxtVoLb713xJh09AdyOWApGyLcVHuB61i5rnWYbS5cuCXeSVnLkkcbaNp/WiULhKdgUa2JCB9Z1ATOXcu7XWS/l3GLN/je60guEF/kJ4//vjjp894r6VKAKcI0XbuO/5uPLDA2r338cylxdAmmm4GqHjO2ONpgSCPj3689MzgOuYsS+eRK4cFxRpygs+Y30y14DP2B/s801u8T9ijuX9zX8w8ps3k32hgPBeNP5i2WMu8fxujLR5p+TjlTYM7Hul8ju+4etN6xJ5hHzl3euYq3fXJJ5/MzPUMZJ46c0v7BJTluHgPbZM1a2mIDatRLhaLxWJxwFGjNLv7sy8q7JfXFup9Sg+xRN0kSNuOkUTbfV7CXWtfZ2pkDqVvUkZr41S1xJKXfWszj35TpK30N/C3NdSWiI7060TbmctvwP1an53Ibn9Z/s33kEJPWs7MtVbW9nIsZm1JSRSt0CksTXpmrVmflNq5N/OI9pP+GPsvGXNqnXd+x9zbDrcHJymW/uBvw0+Tn9n/mPA+996auSRtay7ZT1dg4f/UNpDwv/7665np6TRcT1+R7rFEzFyFefEDtvSgBvvwUsv74IMPZmbmd37nd561RwpIXs/40WpSM2UN6Qt7oFl02Jues2wL7fn111+fmZm33nrr6Rr8a/bVprZF39iv7O1mRUswV7ZezVxaqdNl8vy40yhTe7T/kr2XKRu8Z3KHlq7Ctfgaf/7znz9dY3KZxvjFXDOepvFueshisVgsFj8QL9IokUhTqrA04OTXhCXbVi/tlBTN9ywdNX/ZiYLuzh/aIg89nlNE1Myj/zBh/yVzRwL2zCXpuDZiS+rmlbZSaneNOOYh23KNRWuWM5e07KTojDbjbyRY+3ezHwlTb6VEa+oskNKhk7kZc6P3cq26bAvNlH5wv9znpivD3wWv5swjcQP9aD5fxneibwNoD2iPqd1ANEDfmZ/sO2NlnEjVGYmKdsX3WcvUXBgf88NY0u9GVCeapGthZhtoPow914R2c15mOhlGgucPjYWxZnvvvvvuzFxaa/rSeSYYG+sMScHMzBdffDEzl2bPnmi+aFtXWo1W++Qa1zLjaVaKPBvyO2ioM8/XyGDO0qLDXnEka2qC1gA95vzbfu2W4M/508482iX6GH92at9YBrAQ2b+df7OHsdJ89dVXT9ec5mo1ysVisVgsDtgfysVisVgsDngR4QBorO4uTdTCuO2ITxODzZHcr3E7mp8vzRCYZhwC38rbmGczzQHNdGo0cyL9b+YMO6QxG2RCLOYH+ohZJ+eOvvGKiaKZoJkPzBEZgo9Zy2WXmumEazBdNFOwOVcTLf3BKScZfMDf9B+zYyZOOwirEWL4M+a+ldByUFALjsLUirk5zaCYcbiGNnM+HLrOPkmTsgNy2PeY5jKYh7YcAJGBOuwTmx7T9MrfmCq5H0E5M49mK6ebJNhbmPozYAaTIHvxFJRH3xlnS3hPmMQjTbf0gXZZ7zSr2pzPvkuzqt0Zbb/4mbL5PMdG+8xD7l9zzvp8yfE4yC/Nzhl8B0y0ks8093e6Xwte875o1zjwJ8/Odo7n/WautaKPPL9t73E/1j7dEPxNv+hPuhg2mGexWCwWix+IF9WjdHj4zH1BztQonfDsenAzj5RZTj5t97aDO/uB5uGAkZlHGjMHWMw80iiZUMHtAidst+olvEffMjDGY7QkNXPRL5lKLKUrB5+4WHQCrYgghdSSnOjfAqpYI8bcKLGaRukQ+pS2HTSDttYKN/veTSLkPbSq7D9jY9w5fuDUmVMhXvZlCwCzVaGl7Zjuz+lZKWnTH1cuyX2HVoLW6NeZy6qBlYM1zYRu1t6pF7lu1kCs0c88Btwwl43Aw4QZGWjRChM7RaBZdNhL7J/UUp2+Q19bupvJVlLDdUCX04pmrn3v+cw9RX9oE0tEWh0cDNdSMLIoNuBzJ/znveivg+Gy/yboaAXBvS7NGmCLY+4rSCJYQ4LGICKYmfnss89m5ppHxtz2OZ81spkT+cdqlIvFYrFYHHDUKO2LSKnCCb6tJplJmU+k6PZVppSBFOLk1ZQAXAeS15TSkFybPxU44ZrX5ldNWLLPeWD+8DF9/vnnM/Pc7+babkh5GcJu6RCpNwmXke6QbGmjUerRJtcm6TbSN5odEmJK6mhFaC74TFpYdoL+8v3UKKxRtlqZ9h/ZX57tMo+uZ5fjxR9HWymJMiY0UqTx7DPS6ongHIkaKb5J/w73t9+ReZ651o77kKDfUo4IrX/vvfdm5rn/juvRWEg7Yf5nLqne/tV8tpg7Xlmb5uPjftynJX3zbNqykN9P2N/WnnvOEZNpZD/9bKcW75q3oGlbXEu6Rd7HBC7cLy0K7Fun9uQe8Z5s52LTKH3vJOGg3yaHyHmxHxc/co6LvxkTbWXf/LvA/zmfPqPavmKP8Pzz/Kblgb6y39kLzYrWsBrlYrFYLBYHHDVKpAs0ovSFIbEjFbYSPtYoQUr+TjLl/5T8acsSXWor9MNRUScy85bcbr8QrydtIftEP7Jv2Pt5bVRv1kiZj4xyQxNAU+CanCtTzjV/Hvf0/VIzRdKyzzalNFNgNb9Ui07jHiZ3z35b+8895L3Ga9NMkLZpK/1SaKkkHdN2+g3RKLkP0ne2hdRs7Tm1eH+/+bDtI2GcaBNohO0+77zzzrP3Z671ReNtZPym78M3mT4g1guN0nSL+R77jb3U/MZccyInQaNkjdB0Z3okJ+03y5YJNpp/2InxTqZv77VCAY57YA3yzOOZYGymWczv+VxMawDPLeva/NRZBMDzYQKI7Cfz0MqE8bcj0lMjY05MvwLAqAAAIABJREFUjpLziWWA86I9EyauaETu7GdbFRuhPeeaiVTcf2M1ysVisVgsDtgfysVisVgsDjiaXq0up/nr+9SGdGj9qVZjq+hh0yv9avUH7YzO+7ivLaXFavtLg3kYh6u3zzyGU7d70n/MIDjh07yF+S4DQGaemwxoyzURM3CBNpgrTBcZTOC2HPZ/wqtqB2KysUls5rGGaau+0OY4r817pplx5rmpBRMewQisDybGmWu8rEer6YgJzX1NUxL3xFxG/zKgyxymJgxIDk9XVWGdGkeyzaAtwINrSYLPYB6H+mPOy77TR7tOcm/atOVgjnyPtT1VrWn3cq3FvJf3RO4pu1ba2WB3TksncMoGptLG6Yt5u7knfA41Uo9W/cPjamlhJmXI/cDcsNaNzMNECyZAyX5yb56FPIc4B5x61s4Pk8Q007rTj9K0fsdLm89cq70JVqNcLBaLxeKAo0ZpaTxDcq1VIRWcKnE07cKpJ80Ra0kOqSAlBksVLQjHdGxOycj3PI7G/p9wbcXUBJ0y0pj0HUbtkO/8m7E2J7o1YYdyzzyGwrewdyRANCDmpe0B+mpp9A5Oxs65Nq2ZiRyyXSe5J7iPySVSC0IzIaigaZRoY6wPwT0tOdwBIalFWLJuz4u1dVftyX7xmbWkvIeDG5wwn/dBg4QEI/eL79fqiDI+a5utVqMD11L7s5bWtLamgQHG2lKOTKPWqruYli3PId+7aTdO9WhVXVyZg7nOeXBQUrOi3VVqafdJ+IxKSxLrxzgavSXzQLuuWJT3dvBbC7CxZprnkPfsKcXQwVqnYDGfjzPnfbUa5WKxWCwWB7zIR3mqMu5f+KbB2RfR7OyW4NKW7TDqloLhPjdSAYeQtxpvwNpf9rldz+eN+gqJzWHMKZGjfTgZOJPf8VFxDaHOjToKKa1JxswJbdJWarjMnyn5ch74jPu0RPS2RicyB/rmeW8Ugl6HlJ6tafGd3Ff4JiGCQGrOkHo+s6SdY2R9TS2YPsq7Ku4nbQpNw2uR97OVoUnaTizP55g2TDSQmrctBCbFmLlSWNgL7Mkck3247TxpcQw5vpnud/OeaD41W5JaupstJ9lH0w/yWWpA7IW79Zl5tEg5FSP7Y59arovJWmwBzL6299rZZtILrBjNauO1yvk0sQb7rBGeM9fNcuLfB2uEeR/PQzt7XJAh+3OKv1iNcrFYLBaLA15EONBIni3xuPTKTCfS9v9IBkgF/MIntRCRikgn3DcpvYD9D9mWfYVN2vK42rUnP2wrs2Wpkn5k9Kp9k4wNarKZixSd8TNXrQSVfS2N3AA6Jz5Dw5p59Nu5zE2OuUmmoGnf9g+kJGfthfG06DRrKI3IgnnAH5JjhBaN6EP6letCAj4+ODSm9AvTR+bI89LG07Rp+0jY9/ZpzVwSu4kHmi+d77E3cy+YIo51T+3GvjnmPTUp5od9i//3VEzAfuSZR/892kbOd4t6fck5dCrL5vPChAEz1/pyP/rUqBNtYco9xbitWeb8sN/ZA1ybe4S+Ma4W13CKp7jrR77XzjPes2advw9839p7jtFWgGaFMIF+7kvgM6aREgBnA6Q1rmnNYDXKxWKxWCwO2B/KxWKxWCwOOJperVK3qhA29TXn/d39Zh6d1KjWWSGA95wEmxUlTDTAa5qinPpxFziQaBUO2vU2vaZT3KYv5qpVFrdJLYN5MH9gKsQElWYph96DNDHgWMeUSxX1rEaPKY70iBasZdMJ5pZTgMrMowksTa8287VajE7zaWkprC17BL7SNL1i3neicc4V33PSfpq7nYDdAsk8R6xZmnscTMC+5zstyIE9gem+pSF4v6b5inERtISpz+QH+RmvzYVic+opzauRN7D/TeCRHMMt/cjnUZoozd/cgjacYmDO5ewn93NgVuLklnDwSzMD8rfTbHLfufYuaCb/xCkA00Eujb+ZvcuzdapQ5D7ludRcX+4Pc+w0m1ZhxOd7nj1cb1dO7nNXdUmsRrlYLBaLxQEv0ihbdQZLyH7N7zmMOqUKM+kjOTZWd0utKUm1ummGJQ6HV+ffSEftfieqo0Ym4AAhJ9PPPNbhREPIVAVXNnCax8ylgSKBEbCTc45GQSAHARipvfIemgbr0qQuO+pzXdr8OT0k72mpvWkBDrhg7nKMTgEyHdzMtR8cVp+aNXPisPmURJkbV3rPaxzo0yrHp7Ywc2n+9D0Tw5kDJGzWK68BDpBrgXLsJfrZLCImeMj9Sx/ZdydKvRM5iZPGW+rDKQiPPjYLRAsW8T0dGNbqWjJnPEctsMvEJ62qkgN9cv1NkMEeyz1+lzaXaTttrmylaSknjIk9mBo9+weLFvOSe4/njGeTvuXZ4MpEjazFqVF8luvL3x5rrrM1SdcmndlgnsVisVgsfjC+l4+ySWJOHm7h6ZagUiriM/wvpvqaeZSskVZS27IkzXdajTSPo0nGwL6+HFfCqQqNoMCacd6HMbmafdr6rUm1FBLTSnFfqNeyjfTxzjzXIlgP1oH7Nh+00wYaAULC6SFN+rfvu/k/6C//t5qV9J99leviCu30I0miSaRnzuhXpijYV9QIwVt6TY4vvwf4Pt/JPY4WYUk7pXHGw32wIGAlyDbZJy2dgbl/8803Z+bybbdaqYyHuU0p/S7NJOH3TM/mv4E1yqZN2DeXa2GqRBdpmLniJvDfo2XlOvscdErbzKPmZzKKNh6e57yP63ryjKf1pWmUrAn3TG2Rz0xGn/VAXUTAcRUz1x4xAUWe2dzbpPt5DjGPXMP4Wy1UWy6br5a54exLqsatR7lYLBaLxQ/EiwgHWvRcK2dz9337AtM3gfTgskGpJSH1OgIqfXPcG/s5EkNqK9aQ7TeaeUzCbQTKDY48O0lypmzK69HkkNLShm4fUdMiXGm+EYe7MjmvTUNASkSKbnD0XEpmzadpH15qUrYMtKjBO99ki8rmGny1zUeJz4l+5Xyano65y37SN0eM5tqZ3u/kAwP0p0nR1tRb4QHWnH6hOWS/7F9i/6E9zlx74cc//vHMXNpCSv5cg5Zin3v2h/mxHy/747PiVX5vR1yfIlHZL60ogannEvSbM4aI4ey/td1TrMEdNWAbh0kOErxnmrf8XhtH83vSJ9axaZR8j3ZMljJzPW/4+Lk2LTFYOO58jDOPZxRz3aKRHWnc/OOMnX6kNn3CapSLxWKxWBywP5SLxWKxWBxwzkzlIqUlzNzXmGzVCxpf510bqO9pqiBc3472NFVZlW6h2za9Ym5pTn2nF7TUmMTJ9Or0g8ZHyfWMkfbSNOAkfCfD53vMB6+ZEsDcYmYjCCADqJgTTGptrmwqpM9pinLKw8xjwEDuCz6zub+Z+G1ey71I3zCVwtma+4Exee81zkqnDeT+ZP7Mz9kqtjjVI/eGx2ie3rbONrs1U6fXqaUu2VSfPMqumtEqOPC8saaNdMFBNc0Eybw4leqlVR4YT7Z7Vw+2mdhNeJDtmtzBfNA5bp9jGSDGe6w9e7QFAPqsafPZ6uqClvLA80m7LVCKz3yO5D1NetFSirxncr9zPfNoIoOZ65lif9mlkv1wIFU7D5xu09wjDatRLhaLxWJxwFGjtMSekqg1yuZERxpzuHxqX0gKSDBIGandoNXwWQsaQWMidNkBGtlX+o7Ul5KHE49dtd5/AySWRjjAWJB8TMGW3zM9X0q0aBSEqbcEciQl5tz9mrnmhHG3KgJ3dH95DZ95L7Qq5gnG3SpDsDfcXqN6c5WAnHMH2CARN7o09gHrlNeYJKPRDxK4YLqvpGHkvVON1zuyDOYkpV+nKrjqzMxjIFUGMrlf7Btec0+x70iVQYPO/tIfgj68jvmex57zTR+depRnRgvUsSaZ+9RWAbSdtAo4Mb61wR5gjk/EJ2iQb7311szMvPbaa0/XuAqJn7GZx7SYdq44KOkUQJgwKUMLaPE+SKsPfTG5REtlMxVeaos89+wdgnuSbAa4BmqzGJxShHyu8toIEBpWo1wsFovF4oCjRskvcrP72/bc/IZOTWg26DsbfGpALZx75rk0YPLgpuFyH0uLTar/vukhjK1pJbyH1ErfWpVuNGyTcM9cWhESGNekROiUglNSt2sjpsbCeO1HzDm3X7VVOm/pIaZEzD3D3yZ6yPm/I+RP2C/etHj7WLxOM5fW6eTwlD6druSq7DOXxYRrG42bNUpLuLlfWHsI8ulzXmO/Khph7imnLJmMO99jn9Fm7nFbB+zjzjGjMZh4YuZxv7bnrhF+gLYXTKVpkob8289mIyzhfuyNPJfQUk0g0uBnNNeffWYqwGyLcXBNoxpt6SG003y99u1z5uQ90Zad+pJr7RiPVsiCNkz9mHvgjlKwWbacsphnj89X/s9nzDSVidUoF4vFYrE44KhR2j+Qv/SW6lzReuax2ncjReeX3RRFzSdherhGl4eE0Oz2pudqUU7WohvdVtPSkLYdNTtzSatImUguKUk7idlRYzP3ZWByrrxWHs/Mo3RFG41Kjn4h/eW6I0nis2paTaOws+TWfHP22SZMyO8yZu3etoDk38wZa92usV+tRR+abDr7bp9d28P2fTNPTdLmPbQz1invgfbKa4v2xN/GvXk2k9rLtIKOkM35cER5Ai3V/qu8j4sRtFJWTWu8i5nI795RH+b3TYaRGpAjez1n+bcj9BN3vtpcl6ScbP3LNtDkmlXvZNGxH3jm0cLAWrfSeY7gzjW39c/PUf7tvdOKG/h5aeeKyxg2iyPr6ZiBmeeECcZqlIvFYrFYHLA/lIvFYrFYHPAi0ytqbjMjOI3g2c3/93rMO3BFtirXNpnm/VCTnVjbzF8OEGgmGJty02Fvc0DjmmwOckymLfAIk6vD61uoNCY0B0nNXKYFmxzzGgfIOIVi5jKruCJBqwrvOpSZYsB68orpKOezEQ64EkdL8HcSce4HB4yA/N/J4Y0z1ma6VoPUATXMQ5pVqcbxzTffzMzjGs5ca+c0hGa2A3fJ9zPX2vOeTf8z19yTQuKUp5nHChKnmo1O6G78u64xmHucZ4RXTKCtjiHv0Z9XEX44resU0EIf0/Rqd4C5RWcuc7TJHXLdWhUl/293QOuPg8fYL6xXfp897rl3/wH38rmasCst78PzR194zXQorneATfaN/ZNBQDPPzwy7MVrlJcYDgYpTsLLPJsTIvZepVcZqlIvFYrFYHPAijdKvM4/JnU3qBUhK/OLDKJ/vOa0iJTC3YYfzzCUpIPXxmomySBW8Nu3Ajn+P038DpHakuqSsQmpBo4SSL5O6XQnjlI7iAKMWxMLYmmObub1z3Oe9eUXqI7l+5kqmZlzW6me6RslYGUdLomZMfNYquzvgqFU4cSBZSu22kDRtxJaOJrWjUdIPS60zj0QarfKLA+SY80bk4OeE/iTVGOvBvKDJtXQcV+3IwAbXf/WczjymA9B2akAOyW+VSlz/k2ClXJOW1mCigdQmbGU6peY4DSH3iLXFZj2621O5tq7Py7xmn1l77t36zF42kUk+64021Gdbwvude+d9XH0Eesh87tkz7lujp3Pd1FbP1oFfaelxkCTIc9oWNuY+tcjVKBeLxWKx+IE4apQOm2+al69t1/ArjgQBVdHMI51SC623pOTk6PwMCQ7pIqWMO2kvNbJWUT3v38Y+M/Ptt98+u2cL+0eCRitLKRPNgmsavZ6JfU/15xyCntqIfYPN12JaKjQhtOH8m76jWaXUmH8DpGX6nXSF1ri4Nq+hb2hPJMCzBvmZtf9GRegQ9Nwz1gTpT/N/2BqRe8B0X/ZV5veBNbhW95D72Gc5c2lj+I6Yp7Qc2E+EJtfiCLwX0l9tIgXmJ+nIaJc9Ts1ZLBMzzy0WM92vevKptTQ1W1eY8zYP9umnBcL1Mxs9I7BPrZ0ZPqsaoYv3aKMRtR8x22oWHftEW/I+Y2VftFQnk5C0/tOGz66ZR8IBPmsxDrRBv/I88DnG/9lnEzY4bmbmce8lVqNcLBaLxeKAo0bJL3JL/m6295mu3dgPkn4L7NtffPHFzFyk5knLZqniqfMluRotB2k1/aGWlnnN+9hu33x8TaL98ssvn12fkh/+SqQrJPG8J33jWiSeRvyOdugE6Lzekn2TyBk/mkva6OkHfW1aBJqBJcwsB9UIjvleK5mDxoUky7VNSrYfM+/DPJgQOzU4Rw03jdIlhLgm5xwfIHu3RVzbJ9m0ToM54NpGMej7Na2TtUNizn3Her7++uszc0Vkp3TNM2SqstQ6GSvaK2dFrj99o68/+clPZmbmjTfeeGgLbaNFnTfyamsTLUGe/ekCDPk9R882Pyz3a0UeHEnatC2fMe77zKVlWWtrfkX7bPP/k9bbshisdTv6OMdiCsgkmedva8ZtHT3+VvbL+z0jZU0gc4o1MAnHO++883QN/vGG1SgXi8VisThgfygXi8VisTjgexEOpEpsc5XDq2cu1ddpDJnkiYnvpz/96cw8JrFmP7hPqyfn9JJmisJ0ZfNI45G0yfVVptevvvrq2b1zrrjean/ek36bjzbnyoE5Lb2FcfPqNZi5TCf0A5Nr8ktiwnb6Tpo36SvBWa6XOdNNr5j7Grejg3eYh5xPVyvhNU0nfJ/3MOlhYsyx2ayae5i9R6AS64ypPa+xayH3lYN3WqUVw66BNJWZb7OZCmnTc9jSEJgnXBaZsmHTK/3IZ5Q9QD1K9kK2xX3Ybx988MHMzLz33nsPY8Zk6CC/HGuCsfn5n3msLITptQXxnVInbMa3CTbh5znh9Wz1E5nbVjfR8PezP41wwPPXCBNc9aO17zOhpcnYRZfz4TQxfzevZ2w8qzy7M4+pNOaCzjFyxhHEk4Fky/W6WCwWi8UPxFGjtFSRUr0pxkCj/7I0kUEjSJkED7Tk0btQ6VYRA8mWICFoxWau8HhXpGiOdktSrwpPR5J2wEL2Hy0NLTHnwXNF+3kfJHoH46Rj24m5tgokHGrd6jAiuVkLzHsioRNslIFYLT2EezsYZ+YxrYc+ZbtON2oJ+R4j0mKrT5rjnnm+P9CO0SRJQckUJxMMNC2COabNU7UdYI07JW1L+K1GI99jLltCN3PIZ6TV8PzMXHuAeaKtrDDCmnsP5FygzRNAgSb52muvPV1jDajVEW04pfi4PqiDerKfbqetj1/zGtNj0o+0fpmmj75nUr+fY+Y8nxXTfZpQIT9LuKZj7h0HmbWzjjPF92k0gw6kasQFrlPcahqbpCEtbbbStN8rB9OxFzNoLc9aYzXKxWKxWCwOeJFG2SijkGZOib6WuJxGkH/b55IJpdYquLYltiMR85o+MkssHkP20Zrkq0iZTSqdkrTTUlqFdYdqMy+tXVOItYr1JoBP2LfncO+ZRx8fWmuroUlahCncfD1wHbuW1H2XupHX8F4jsnBfGGtK9k6/MLVf3tvaa4MTuVuaCXPcKMm8r1ynr/lpvT+bX8bjy+cPjdkk1o10wTVeU/tyTUXmNqkciUN4//33Z+byg+azQqqNiUfauBL2H+aeshbfkt85L/iMaxsF5l193ByLqRhz7azxtOIMjqNoMSB3NXNTo2yauM+aE9FBo4xjr3gf5P41qUkrONDoBg37nk0Sn/2wfzXnivPMPsr0dba0I7Aa5WKxWCwWBxw1StvHU/KwBHoqj2VS4la6Bt+Py8vMPPokbfeeubRFJ+Gn1unyOLaxzzxK+k17bO9ZAkbLmrn8l0jZ+CiaJmVJLjUy+4wtSWb/mWP+b1KvSQTSZ4rt3onDKfWiRRMRyv+NOiphzST9Daw7EnmTaPne3ZzNXFGXH3/88cxc69FKqnkPt8g85gGJNDUlPnPZqByX/R8maZ65J8JvCel35b+axM56sN753PD88dyw35Jc3dpRs4gwB7wSPYv2ODPz4Ycfzszlo+Ta7A/jwBLE/j35j2bO/ly/1yxb1jZN5j1z7S/vmxbj0KxegHaZv6bpW3tt2o7PoVZSr0UIu/RVI37nWWgxI5ytfja/b2kzl55rpbhMStLK09kyyPezzzyvnGucdbmHT4UoVqNcLBaLxeKA/aFcLBaLxeKAo+nVZrNmVnWwRatWb5NhOtFtzmhwgE0LhHClknY/m5D4P/tsTsUTx2ID/UgTH2YkTJOYu9IM4eAVm80SNjPlfeinzdXN9OqAi1ZD04nxGRxlogGbi3IcCZsSWz3KU6AAYzE/bu4r5hoTi4PGZh5rMDKPmW5jzllz8eY1d3yu2QamzWY2v6vI01Kx7twHaap30EbjITb/cEsxcGh+49tkPkj1IJH73XfffbqGkHxMrvQ5iQvYS+wzk4zkewnvqZam5kCqnCubE3OOjFPtS9YVE3K7xueYTYb5t8fa6vTaBNzO4MTJdGzTK3OWfWMtmL+T2+1ElODn30Qi2Tc+a+tyd+YngQD7k/faM9XOWrAa5WKxWCwWB/zGSzWlxWKxWCz+f8RqlIvFYrFYHHD0Uf793//9dzOXnflUebpRLLX0i5leKw4buGtY+u+ZR7qq9l6rtWYfEPdtdTadeNvq0v3hH/7h08D+4i/+4ruZTlTuVI1TGLLba5RVtum35Gr3P1MesNNjtydtINMZuAZ/kmneZh5JHkjxIV1k5vIV/vEf//HTXP3N3/zNd/n9pBmEOg2/Fb7FXE9T4BEGniQVrt7Onsu96CT1llrAPHp/5X28jxqRPt+DshH6tqywzr74vd/7vd+YmfmjP/qj72auuU/SepL1ncbTErFNlp2+3Lt0qpZWxfjYzy0tiWsaXR5/m/Aj141xuMZgS4/46KOPnhbhH//xH5+ZxnKMkE+wLxlznismbGiE/aZXdF3bmUe/n/dYwqQguV+YK/Y/a9D8/6aCbClHf/qnf/o0V3/2Z3/2XfYp+88+5Xs8v7kfTMrQCOB9NjkGY+aR8IT/86zhb5OTZFt31Ka5B0gP+/zzz2fmKmqQJCXM9d/93d89OHaPP5Sg5bfwIJixJCei5V/6f/+gtfzFO/7F5rAHfnCzr2xCP7gzj/yf+ePhaxKnwCc/CK2Yq/t/Mon7fk0oAO0BNZ8tr/mjzNz4wcncpLsggmyzjcPBFPkQEujkfZUPjyua+Iczx2LOyrYuoDHq3OV1tiAFPnOe68w1n9yHwzp/SO4CSJpgSL9c6LqtD321oJWwMJxz4wAV1isPIt5j/QjGyWscLEFfUwBgv/iAbQEnCebIAne2y/ecG5zfB43Zyly+L2GJaew9ZoCycJHvuT+NJc3nawv6Ol2fYA/zA4ngmhWBeDa9VnlWusi3BaCZx6o9DrbMv08501ZE2pnDPNBH2m5VnhrW9LpYLBaLxQFHjRLJg1/qVn+OcO5mbgKndABLDKcQ4ZMJ1zUBkWyTYYS+Iv2as3HmsT4f6QCvktKMZv44VXywybhJ/+bsbJLhXRstLYJ5aFU8rFnbFJvXu9JBjr310Sa83Ff0yVpuuyef0cdWccYaZaYEWNNokjHz4Fqg2Wf2ms2NqRV4zKRj8DrzPOUk+9XgZ5K2mzmU/ddMhYzLnzWrAGNHW+R5mrnMm8yF08dmrr3DmP3/zD0DS54Hp3lp2oS1b57prF/Kc+8KFI1PmnlwmkX+bXNgM9V7XlNbNJuZuV/znn7uTgxO+b2W0ud0slY71/VI2Qe5Hzh3cTU4FSv7YWaeZCjy/ua+2ZbPc9Y70938bDUWrla7E6xGuVgsFovFAftDuVgsFovFAS8iRUcVTnWXCLJ01s90k6lNFC2iFdOCS/rMdBPBTI8So484o7OIsB3kLbKUMTMus5H4emDzciPxtqk3TWA2fdkUnffxvJ4Ik0/lutz3FlxE+5hgko2F9/gec5Z7opnizbaT/bkzXeWewRzjgIk0u90VSG7mLcbIHKXph/3De4wtTbguydaixNmftEFwBKap7IfhIKGZK4oPF0grK0ebzAumxwyewezJfDUSbgeLtWA09gX3xqwJAfrMVWYLhh72eM4la2tzdu6pFtBiNJMv82uWpfybPrWzij44UrgVXDYaC9DdWNt9WvSvXVmO0M5xJBzZn880+8FRx3l+YOpkD1LUvEWQmuEn++NnlHnBjJ/3/uKLL2bmilbN4CL6xhrCBvWTn/zk6Rr2LGN1UfOZ+7WbWY1ysVgsFosjjhqlC5ymYxvJAOmQ15SQ0cL4xec+SFD5nvOETmH8d8E9M5ckhGTU8gcJ1kDaSa3HYe0tLLtJtA46SG3RqSotX8/FRls5GfMdulBptnFX+HXm0jCcO9ZyRa3RpVWBuXK6Se6TFlzhfKdsF+mfuXFwR46F+7RgCEvSLXTcZeS8X2ceOWIbzykaCvuLNnKuyDF1WkhqEflcZD+cB5njYZ86uC7bctmhDGL50Y9+NDPXc8sYUithzE57ySAc1of7UVLrF7/4xdM15I46sKKVbvv1r389M1d6wtdff/10jedp5nEv5zozJ2gh7M9mgWBdyW/N8wxt2TmBOVfmtQYZKMK63K3PzDW3jUfVfXYKV7bVLDpcxzkIL+/MVRaN85y9nWctzwTrQBnBXCMKcHNGtPQy2ndpwsyr/vTTT5+9h+UkzwP6isXClouZa11Zc+6TZ5Wto4nVKBeLxWKxOOCoUTrMPaUKpELswL/92789M1fB1plLKkLiQnJM+zKSBz5PPkvfov1sTl2YuezTaCRItsl8gkSIVIOknhIqEiwSLf1pBX8T9m2kH/OuoGhL6nbqwymV5qQRt+R/YJ9eY9/3ey3lgXWxxpzXNHIGJGC0l5Roud4pBJmWApAAkQ7TR2LfXSts3Hy7d9cgkbLv0ZhmLp+I91Vqd4wHn4u14Znnvq6ZRy24hfHTFvdPTdfsLi2FhPlx0nfex0n/bW+5wDX7Gd/SzPWMm90oLStOgULrSO21WXRsJUk/17/8y7/MzMw///M/z8ylpeQ1zDXjwLf6y1/+8uma3/3d3332Hpp589/xDDBXzQ/PHPM8p6bNecbcsE9aIXefL6klZRoToE/Mbfqs0SDZK6wf+3bmWkfEkP1OAAAetklEQVSucTWf/NuELS3mgv6yHjlXrMfPf/7zmbkYqdAaZy5LBWd+K8pMu5z1//mf/zkzz7XI9O8bq1EuFovFYnHAUaM0d2VLoEbiQPNqGpATwvOX/i7KM6UKJBdrcskRyb35PtJ0SkJor7TV/BnWPHztTJdokZxaUrfJBOhrq1F3gqX+VisO7YH36H9qZEii+BZ4TQ0Ijd5RydnWXR25xo+ZsEacvgQkaRMcpIZDH5CW06cBHP2M/yPnvPk/Z56vhf3atIlEOnNJqdaUsi1L9ibzmHmMRrYlp1HroQ3Qz1a3ES3EvrqZS5pn33Ntann2gTXLAd/DT8V8s7dmnpN/zFxzmmuLJscr48u5vLMEZD9SW6Qv7Bs0j4zIZS9wpqCVZN98b76TZwP7jTOrRa+bPq1RULIvTIKRe5VrPB+NxjBha0RqVezrjz/+eGZm/uEf/mFmZv7pn/7p6RrOVOYTP+7Pfvazp2v4G58nc55nNnuW91inHA8aLpZBrE9pYWBd2GtohnkNdVJZM1usZvpZBVajXCwWi8XigP2hXCwWi8XigKPpFZUWs0wG2GBiIegFB2+aPPge6jMO2QyEIPgH1Ry0MlW8h+nAJrOZy6yEOSwDPDD9YCpB5U8VHQc9ZgEntef3EzYnZt9ox4EtzcFvvsM0zWH6Mmt/mru5D6YfzAlpQmJuMLthksu1w5zJZ6xBjp0xsr7MY5pXGjkDn7fyPJhemSvGj1lm5pob3vvss89m5jKtzzwGhWGWaWXLmEdMUBm4hRnIZAoEhMzcc3+eqiCw31/Fi5vv5/5hDpn7VgaNvt+Vl5t55NJt5fL8nDViBv5mDzHfGczDnmLfsyfz+cNsxn4jUCr3bwum8xizSgXnDq+Y7zIgBPMhc0Y/0nTMe3ccqzPX3smAmplePYTnsFU84RrWuRGx2IxoN8HMY4BYtkdf0+XCe//+7/8+M9eZmc80rhL66NSibNdl8loJLdaOa/KsdZAk64FpOPvI7wN9zUBOAn7eeOONmelVgE5VVVajXCwWi8XigKNG6eT/1IAITnBaR4YvOwiBX/qUGJDSkOqRHF9SoaOlV5heKjVTkwi05GAkWQritiCJBoeBtwRh5gYpKwM8XLCaaxuVn2v1tTqSrEerq0lfCZTgf0KvZ2Y++OCDmbm0NlO5zVzz5jZaTdKE6fFOycxoIZnMjJWA+UOiTE3JFS74P0PwrY2xP7L/7FnTg6V2533E/6mZ0lfmvNW+856hH63otCtP2PqTffS6pHZmqb4F0/G3Cy2nloEmaY059x3SPH03JePM9bzZotCsLwnfk+CNmUuzoL9YLZLCjjnxOZJtMX8m1mg1DV25Je/DHucZZc0yqMYJ+k2jZC/dUXO6XeD+p/bLWLD6kRKT62iKRgcQ5jU8b6xrXsOz6bSl1DptFXlJqhT9SWuAq+0wnkwNXAq7xWKxWCx+II4apX0tKYGhcWDv55qUPFyBvlGEWZK11pT3cVJwqz+IpMFn2R+0RJOAp7R6V+n8Vekb9lekdII25irw2S5SnskVcoz2zSIF5xiRmGgTSS6lTuYTf2yr9s06mMYwNRYTjTdNudV4s9+iaZRI22iSjZqNezcyaObfxNHpDyM9gM+af4k9z7WunZdtWUtsqR8mMEj/n31vaAb2zee92UvNl+6k90bGb9Jr+plryPq6tmLzmbKnWMc8MxrBgMdlEu3m424+SvrtFKq8B9fgN8UPN3OvmTct3pp5tuU0Kr6fzyj3cepVtmVN0DUsZ659ylnZiFialmTLVp4NnJHWBPOeJnVAc2vPOnPPfsrUJJ+p3DetPvTRey7nMwkTss1cF9OeunZs609iNcrFYrFYLA54kUaJxpFRRPxtza2RTiPB8CueGqV//ZGO8tfdfgrabLZ4E2Y3qcJV75u/yGPIPje7P31skihwlFujWHIies6DiRcaEQNSprW71BBMtNwibB2B1jQyknWZI6JOiYTONhKOxGsJ1wBpEX9itsdcM7bma+EzvkMU5cxFv8j84Y9tFHy0z5hPe+9EJuDI2LScWKJ1xF/uO9M5mug/27I03bQD5pLX9CU5YvdUMol+tNJg1gpalGMrUzfTYx8STvDPebDfkXXO/WJtyHOfY3L7OUZrfoyjWQ6YB/qee8FaosvPZT/sM8y1Sz85sE8959Z+Pl5P1ozmz6X/tME1aRnyOWhClmzXmQ+NwIE5br8Pth7SnxzX6RxajXKxWCwWiwP2h3KxWCwWiwOOpldUbMwYjWvSvKXJwG71HbU5zSJOdWgmU1Rqh6mnqm9zqM2s+Z6rIKTp1U59M/TPnLkZm4PcAUHcK809Nuc4nHnmMRy9mXnvgooy9BzzoRPucx58b65NJ7ornLAXcs6bg9wBD80UT58IBmkmMOampfnQTwID2ENpeiVdgX5wbYaM039Xs2hcvl7DUzoHyLH7M5uk0txkDlE/j62NZs53jVjXD8x73pmPZx73ACa/NP059ckBfPk3c9dM98314aCPNKm5Xb6fJmibWmm3BcPYPJzPjc3dLbiIv9nbzHkjBzBPdDM7+xnP+WkBNg4mShO0CVv8nfzbLrHmLnPgD9WZsn0ChlrVD/PptvPEwV2tGhLwfkoTfyOwAatRLhaLxWJxwFGjREJxjbiZx5BzfqkbRZhTHRrlmincWluWppq0eboGycfBPCk1OhimjSupmtyuk19nHimqLJFlO3bMp0ROv5G8TFKQ/WDO+CwlRAfhuD7dzGPIequPeacFp2TcJFrG1GrsmbqrVU0A9J/9mWHl3mv0KRPRkei5D/8nXZ61EfqRWpBTAfyabXgvpBZsKZ70A65pKQ9+/px8np8xhrRSkEZkQokcn60q3mP5Ht8j+CmfAwI5PAeN3u2UltUsOoybz7JvThmjjXxu+Nvrne0z/06najRorgzSqOeggmPdM0HeSfhN+6YNpw/lPjkl0dPvpCbleaW9luBvbb+RQnA932fts4IMfWPOWzUZk1K089UWBgf7ZRvuawtAaliNcrFYLBaLA44apSX/lMCsAbaq50g2dxrhzKNW1CRJp280Sj230cLT+dufpeSBRG0fXdJ1tXQSa1w5V5ZeWj0/axgtLNv1Ey19zzzWqqTt9EOYRBjLQaPLc1h6SrTWjLlPttXC03mP61tyu9NRMi3F4d8kSackjU+EMSKtQuA8c9GscU3zkbjPjDX3gyV7aycz13q6/mPuARNp00ZLNgcm0U9f9F16VmquzL1ThHIvWKvz3sq/rb1lMjjPEu17r2dbp9QtFxfIsZnKbuYxdcY+/vyeyRXS52tfMffLNbQvsaW7cE/2rf2rM5dVw+kazdLmczbRaucaqXWaOpN5aZY5n+fNZ801rZ4t8+8YlByHtTz2d0tXcQxI4s5veWrr2dhuP1ksFovFYvEyH2WjerOPxATO+dldYmy2YUm2JVebGLeVpWnJ0MCkANZsZh4l60aA0CR7S5utIrv9VAlLh80PYypAJyXnZyaAaJG99o1kmyYMN/VTgrmxf3em+8xMeZcaDvewHzmlPTQ/JPLUEgFrzLy6JNHM5WtmX6H9pBZh4mj2St6HPcu8cm2WLSOy0dpU7gXPFWvY/GXACdmpHbg0mMeQ37MfMvty59PPa6yBgJxLfMDsM/xWbf29b1NLOBUcsG8s27vzreU1Pj+aRYdxNEuZrV1tXmgLi0E7T2gfjZ+5bpGlPl8bAULC1r/sv8/Iptk7jqTRDZoCs1lQ7vrVsiJAI/+/s7i0M9ukF43koWE1ysVisVgsDtgfysVisVgsDjiaXlFpWyitHdAmDJi5zBDmWkxTC6Y8B2+0xGmbq5o50UQDeR+bPk9mSY8h0ZzFmMmaeYw+OAm3cRGCNkabOJxCkdebszOvcbqPzTv5N6kkrEtLPOZaB3/lmBNOXG81Q+kb5tGcH8LHMbliCstgGAfUMGdpsiEs3oEcLfUD/lrmo9V0dJBYS8MgkKrV8PNeY98w9hzfnRsizd7m+bWZNWEXSjNJnQLlHCjjmqN577vqGTOPZt3W1xbgYzKT/J5NpI0EwEErLS2CtT8F07VAvZkeMEj77HEqr+T3mT/OzGZydM3GUyDkzKN7q6WpeT7y+bOJlHMoz1qbcEGeQwBXCu6eTPNijkwA0ypQ+cxsAZU2hbfqNA2rUS4Wi8ViccBRo7TGlVKSw5Vb+K3TD1yXceb6RUda4zWT+u2EbxUOnODb6Lpcr61JYE5TcbpFtpFwKH+rjG7puCUqOzk8JTIHpjhMPfuJdNcqNHBPh+Jn8IkDnRhX1hd0cEdLIzrVeLMknN/1fDSqKcbIPqN25cwVoOQAlkz9YK6Yj88++2xmntNsZYL0zCPpw8yVOH5H7TXzWAOwaQi2Klg7atYa04e1JHrQyBKsbZkSL7/n17zGgRCm/svxNMIOwPWsmxPvs88Ja5Qt9eoueT3vb2tHtuWAKQc5zVz7xWdeBsERxMSZwZmXc5XpR3e4IzfIcTWN3ME8LQATtLPOgTCsZ7PEgJZy5dQ7yEAypYh7moClWfW8Hq2+pwlUmsWxYTXKxWKxWCwOOGqUJ5+EKYEahZYTnJGkUsqhriWh/tipG2UZ32tV5q1JEo7f6NGQhFqfLVkiPWV/ml2bPjViY/s0mr/sri5gSjl3KSgtwd8aaaO5c19zXZCE7c9tvkL7A7PPTaJ1pfjU1i3tMtaUSJECuTd1MHmdeUxAbyTzaNDMA/sz0yfs6+Y+Wakd/y3jaBqLrReNrN6wLyi1G75vjTLnm/k1DWCrg+p93jRvEz20tAie9UYm4fSmk9bkOcznuGmUtm7k3jYdZdO+TdnY0rx4JhhbI0zg3LFvPGkRGTevWCfSWnO3/08kK6zLq1LZjGb9As1q5T3C/zmftMsZ0Qom2GrXzkWnmbS0KvskWw1ha92urZr3bliNcrFYLBaLA44apdF8eY5qSukK6RK7PZpP8/c1CjxgTayRGtOuS9a06ChLjU2y5VquyWitFpHLe80Wz/0tHabfwr405i6vSb9Y9jElIdbDlGaNJNr9yyR6pGf7mVNDsO/VkWkzfT3tc8v5d9QrEmnzWSOlf/vtt8/6nNdndOrM5Q+auSR45owSXNkfJGNrXrkHmCMnYKffztGgJ/84cMGA1OAsWTeN0v519nD2y36uRofmNWwaGXPofZzngyPTrZHMXHPP+F66p04aJe81Hx6wRaqV2XJEfLNS8NwyN/br5t+sQ7MqsFb0q50rtmyxZqZCNEyq0PywfjZzz9A3nrFWYpE5oS/NKmKfIGNs1gz7Kpt180RP52fT1hHf01iNcrFYLBaLA/aHcrFYLBaLA46m12byAQ7wwVSRJg9MCiYnSNOWHdCtegHg+6jaqeoTiIGq7+Ti7KNNDmlWdch0M500TkCbuVpFAQcz5LyaC5RAkzRd8X3MDzbh5DXmqm1VXVx5oiUVm0MzcedET7Twctf4TPOozSgtpYF9xZz993//97O+5j0xtbLnMvQcUzNzRcL3//zP/zxdw3o48Cnnw0QFzVxmQo6WGH+XSmMSjfyeE/TT/OQAHfqQ68S4nF5xqufI+F5i/krYLdD2Ju3b5NbMrQlz1J64QFsSvckDHJSUfcLkaDKOmev8YS3ZA1Sqmbn2JuuAy6BV0eH7LU2t7YuZ53v1lEbh9Zy55p/3GGszj/OeA6Fmrn2J242xNTISt5VBTbib7B7LtpxSBk7BPK028qaHLBaLxWLxA3HUKF2du1WVfpWkl99HAmlSvTXIFnqOBoEElgnySH4OFGlpHSfaPVfdaEFGJymtJdGbPol2U4J0ugCf5ZwjrSLtIhU1jdKaenOim0or14DrHWjQArGcXJ7XNMuAQ+dTWrRlgmtS22L8DtdvZBcmgMj9wPydKPXoh6vK5DWm/WrPhjXJVuXG2pg11JxLa1AOlMl7O2Au95Tp9lr4veeuVbixxsOezPm+qwLUtNe71/x+wtam1CitqTcCEFspXM905jqr+H6rX+o1a6k09I39y9plMJotDjwj+Wz5/GrBKKezqtEVWoNvifmsqSu1nIgOTK2Z/fWz0SwMrgCT+woL24ma1BrlqUpVw2qUi8VisVgccNQo7dNIKdN0cE58nnlMtG/hv8AJ983/gdSL5JUSmH00TQs24Xqzv7vNVgfuVGm+kTNYkm+pJJZ0uCbbshZhQvm8p6uGN18Y92v+3DYn2a+8xhp6frdJtA7Xz3ngb/rYEs6bHyq/M/MorTdpkf3kvdd83xAMmIg6++zq8Ln3LAk337c1Aq9L8/d6LrJfTvVo/j5rdS2tw/EDzbdvf1dLM2GerZHkHnG6irWfvE/ilDpmHxp9y2sYN5okvrVG8mBy+dy/nDGuY5tnFZYh9plJ67Nv3iftGbVG+SoKO5M5nHy1zW9uy0CjbPRc45fNPeNr8PlCQpPvNb+4++OUupZ6ZWvPq8hRwGqUi8VisVgc8L00ypREkWYcjdSSrJ30nr/0fGZpNaUiS3Ku3D7zKOm36D1HreErTaneVeqtfeV8JJyEnUQB9Btpiqiu9NW+8cYbM3P5wpqd3WTUjdTY/kJTuM3cl8VKidaV5mkz15vxmG6rUdIl7H/MuWWMTizO+aQvnge0v/wbcvQvv/zyWZvZlgnGM2rQVH6MP7UI0w42v5Qj+1rkdo5x5vGZyPs5gpP7peZtX5yjV3McJvVvPnZL43kf+xLtW8p7+j7NWgC4T6MsSzCOVkrMY2qauWMEmoaO5uex5bPFNdbyGs2dI0EblZwtVPls3cWJ5Py0uTpFh95lM7RYA89j3pezAA27/YbYn21Kv5lHy1aLlHbZNvsssy1bVU5FBBKrUS4Wi8ViccD+UC4Wi8ViccDR9Op0hmYaMBdqqvqou046dRBGopmtHDzTkkWd9G2G/pnL5Ends0xsBU40bhW1mzmD9s3VOnOZXTDHUCmF15mZ9957b2YezcNZpcIpL4y/pXU4IT3NW5gmWA/MlJluQ59xwjOPSQ5gszvmrzSftfQh2m8BS05VoR+thint0ac0k7meJvdJ8+ZdWkeCfWh+y2zLnJ/uX/bRc5b1LtMMO/O4p1tglE3rLeWBfrUEc1dTaEQIDtQzcUD2w6bXxtt5t0ezDe7XKmK084N1aVygDhB0ENvMy+oVuspHC0pkrn1m5ZnhuWqVNfxstzQZu6kcgOh278aa4D2vdZolWS/z0abbzdV2mkncJB5wN2cVIPYlbfH8NPM5bTjVaeYxkLOZ/c3lm1iNcrFYLBaLA74X4UCDJe6EJRwkiJQILfG0UHg7fV0zMu9pDTClNKQJpAukk+yPJfSWgN1Cpi2JJZkAkhL3hs4qndbQpzlgJyVI7uPKKDnGu/qgLYAKDQattWmvSM9NI6KPzB//t7p0CQdXtBqKDurJ+fRYmc+kCaOffL8FE4BWRcGf2fmfUi/VS0zN1tIGTHGYUrifM+b1VIn+FMYP/Ly0IKNTigzavNci14TvmVwk6d1cCcbX5nuuB9vSPRKst/d29o1x0I8MjOG93/qt33rWRlpQSFsgCI82c0+ZAML1R3OM34cooJ15fn7as35C0yy915iztOgwx6wjr7mXnTLFXskAzLv+5nxyH84hp3nMXNY7p4fkNawVv1NOtZs5WzpXo1wsFovF4oAX+ShbQqnrv7lu38xjKDxSSZOgHJbcQnV5D8m9+QGd1JyaqdtAumn+O16bjb5pSdZG0hdGigLjRyLN9JB33nlnZq45a1XP8SVmGsTMcy3PldkbWYQlOaSrlJ75Gy2NfrXw9DvyCbcLWuoL4B5OIk5/BnPLPCCtZs1KE4I3kgenmTSfDf3Hl/jNN9/MzMx//dd/PV3zySefPBvX66+/PjPPtSm0EeaMvXCq+8g4m+Ztv2qjpzNaegh7sKXYAFP8tXm6szqlD9b+YvZYfpe+mcziVUTf9iE3qjT2CfOae5nvWTtLbdd+sUau7r657ZlrHtgDLRXF8SEN9h37LG79mXncZ83K4tSX1ARZU1vokiiA96ypt5Q++6VbcQany+TZjyUMKwL3y+fP59eJyKRhNcrFYrFYLA54kUbZyqjwy26/RSOCNjlBK3/iqLtGVmuJIyUwl5xphOeMA8mjRYQ6QhdJthHsJk6SPe3hY0TKaom1UDZxn9TykMqcWNt8TvY1NCoxJMLf/M3ffBiPJfQWmWcf6Uuk4Ly+lQkyBV8rzwMJAGuDltd8XfaD5TW05ei9BHuGiGB8k59++unTNZT5ws/cqLjefPPNmbnmDCm4kcwDk140Yn5HZ+Y8WZswKUb+zTozhrRkOLagEVwj4bPXeY6Sus37lvlv0fTWaJpmlLD1Ki069MVEJa28lCOxc664D2Nt54DnuBFBOHbD/tjsj33krU37/F9VwMEUdgnuzzPMHJ2i5ptPkLFwxnGONVo5l+lr/ed7PDepUVIWj3Wl77mHad/RruujXCwWi8Xi/wD7Q7lYLBaLxQHfi3CgpUigrmKaasnjNi2kk5V7m10+zSJO5jf7/sxj/cZmesVEgDmINtJMwz1po4UjNxOdg0da1QGCejAnZBAOc00giIMZZh6d565+nn0zB2iui00eJye6TUAnhn073l91XUv8tpm+mUhsSjMP68xlTnY19lxHV6VpvLiYHTHvYOZNcgbuybrA2/v+++8/XfPWW2/NzLWvWqUVp1g5eT7n1VVyGimIzXaNA5O5pG0HOOX3T9VmHMxzCvzhs5ao7qob/o7/BnmmzPR0BptlM/jL7gL6fTLHNVO2v98CqJxGZRNs3tum51N9V9BICVq/uWdzK5kwIufHKVf0vwXYmCv2lN7SUp1Ya55D3Fe85mdc21xbJiVpBCQbzLNYLBaLxQ/EiwgHWvUChyYjwaWUiTRiCbJJPJaaWz1K2rATOa93Ym6TsK0lZug597a21SolJKwhpOTHHDmIqNHB0R4Scgtzd2pOzsMdk36GZTv1BaRGc0eh1YJ5HJST89PSFRxwlNfwHnNDcEmmGdBPLAPMb64RgTQffvjhzFxpLql50A+nHWUAyq9//etnY0STbHU5sQa8++67M3NpkTmOk4bgSgn0p1WJsHbnILYcnzX8DMwwjWGrWen7gbQuuC5mk84dMGMtdubaO9aSTikYeS/2f2qLPHfMZ6uQ4qBCn1l5ja1Vba6AiUvybwfF5DnUrAjZr7y3n7HcU2lt8r2blchpRw54m7nWmPabNcPjbtqi09QctJlj833yjLEVjyAegupyHjz3eXYuhd1isVgsFj8QZ2fS/6L5kqy58Mt/8t+1+m1OyMUXlFLSXa24lNLoj8mUE0gjtmmnJMe9T/c5pYc0+jKHWFt6n7kkYN5DG8lrLLWj3TQqMV6bjw8geZpwON9zukaOvfmKZl4dnt5SgHxP5h1NLH2CpGOYQi81ZMZN+2iSaH05RvYF0mXS03FP5oHX9H8gwUIagUaZpPdOO2BdW0oAwILS0mh8bZtna5SuH5pjN6lAI0U/+R3tn24pQnyG/xiJP7UVa5RNaztpu6xPapS21jQydmtFTo84ffYSTaqlV6Dp8ppatH2ETpPI8TgFrNXgTdCXE0mFfaxNo3T91ATt2mqQc3VHrdjOfqeg5Dz4WeLZzD3gs960jDPro1wsFovF4gfje2mUKcW69A+/1M2XYC2hSaSmjmqUZby2kkKOxkTyaFKCfZWpiThKy6WB8rOEfU/pb3JpGKLDvvzyy6drkK6RIGkvyQAcbWj/YY6N90won+85wjS1JCcKc20r4XP3egdLgE3qtW8458EEx/gvM9IRonLa4rPUFl3yij2Hxjpz+ShNbN/oBz/44IOZucgF0kfiKPH2THkefG2L2LUm1Pxu9g3muB0lDRpdpakHG1VhroHHRF+ZFyR++2azz4w9+3eiZWtEHbRjovL02/uZalaPVsYq3882OKvYU3me+ZpGYcd+Z87amWPrRIuUb9YvxzjkGjk63H7ZmWsf+TnPNfIZbetV3tP7Ks9j1sgWy9wzfN/+1BwXa2aS93b2N6xGuVgsFovFAftDuVgsFovFAUfTq8P/W4Kxq2ynKusgnlbfstXam+nJtydV32HNzaRkB7vDnHM8Nv2cqjzMPDLh5/WYgTCnuJrIzMxXX301M1fQCaaFHLOJBkh5SHPGXQh8zrkDL7hfqx6CmaWFd7tqQUu8bqYfJ8m3RHr3O9M6nDLCaxJQYCpljfk/7+NUAMaTDn7M5KwVa5+m4LfffvvZK8E9uU/sPmjpS56rFi4PTALQAktsomzVLvwct4AKm8ZagNidGTDXlvXG9Mv50AI8/P+rCAcc2JLtui9OB8q+0F4zw9n02txE5o/ms5wr9pl5pNNUyDPJ82dXV/7tgKdX8eIyjlaTkc+cytfW0absvA8Bhnfm1Zlr7/FZqxyF6dVVhPIan2OuEDJznQPcj7ZajdeG1SgXi8VisTjgN+5C/BeLxWKxWKxGuVgsFovFEftDuVgsFovFAftDuVgsFovFAftDuVgsFovFAftDuVgsFovFAftDuVgsFovFAf8PdAPl0hxAbacAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 576x576 with 36 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#  normalize X by subtracting the mean value from each feature\n",
     "X_norm, mu, sigma = utils.featureNormalize(X)\n",
@@ -1037,9 +5684,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The projected data Z has a shape of:  (5000, 100)\n"
+     ]
+    }
+   ],
    "source": [
     "#  Project images to the eigen space using the top k eigenvectors \n",
     "#  If you are applying a machine learning algorithm \n",
@@ -1068,9 +5723,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 100 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 100 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#  Project images to the eigen space using the top K eigen vectors and \n",
     "#  visualize only using those K dimensions\n",
@@ -1099,9 +5779,802 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support. ' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        fig.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>');\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option);\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>');\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"data:image/png;base64,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\" width=\"432\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# this allows to have interactive plot to rotate the 3-D plot\n",
     "# The double identical statement is on purpose\n",
@@ -1146,9 +6619,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Subtract the mean to use PCA\n",
     "X_norm, mu, sigma = utils.featureNormalize(X)\n",
@@ -1168,6 +6654,13 @@
     "ax.grid(False)\n",
     "pass"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -1186,7 +6679,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,
diff --git a/Exercise8/.ipynb b/Exercise8/.ipynb
new file mode 100644
index 00000000..78a275ce
--- /dev/null
+++ b/Exercise8/.ipynb
@@ -0,0 +1,1402 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Programming Exercise 8:\n",
+    "# Anomaly Detection and Recommender Systems\n",
+    "\n",
+    "\n",
+    "## Introduction \n",
+    "\n",
+    "In this exercise, you will implement the anomaly detection algorithm and\n",
+    "apply it to detect failing servers on a network. In the second part, you will\n",
+    "use collaborative filtering to build a recommender system for movies. Before\n",
+    "starting on the programming exercise, we strongly recommend watching the\n",
+    "video lectures and completing the review questions for the associated topics.\n",
+    "\n",
+    "All the information you need for solving this assignment is in this notebook, and all the code you will be implementing will take place within this notebook. The assignment can be promptly submitted to the coursera grader directly from this notebook (code and instructions are included below).\n",
+    "\n",
+    "Before we begin with the exercises, we need to import all libraries required for this programming exercise. Throughout the course, we will be using [`numpy`](http://www.numpy.org/) for all arrays and matrix operations, [`matplotlib`](https://matplotlib.org/) for plotting, and [`scipy`](https://docs.scipy.org/doc/scipy/reference/) for scientific and numerical computation functions and tools. You can find instructions on how to install required libraries in the README file in the [github repository](https://github.com/dibgerge/ml-coursera-python-assignments)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# used for manipulating directory paths\n",
+    "import os\n",
+    "\n",
+    "# Scientific and vector computation for python\n",
+    "import numpy as np\n",
+    "\n",
+    "# Plotting library\n",
+    "from matplotlib import pyplot\n",
+    "import matplotlib as mpl\n",
+    "\n",
+    "# Optimization module in scipy\n",
+    "from scipy import optimize\n",
+    "\n",
+    "# will be used to load MATLAB mat datafile format\n",
+    "from scipy.io import loadmat\n",
+    "\n",
+    "# library written for this exercise providing additional functions for assignment submission, and others\n",
+    "import utils\n",
+    "\n",
+    "# define the submission/grader object for this exercise\n",
+    "grader = utils.Grader()\n",
+    "\n",
+    "# tells matplotlib to embed plots within the notebook\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Submission and Grading\n",
+    "\n",
+    "\n",
+    "After completing each part of the assignment, be sure to submit your solutions to the grader. The following is a breakdown of how each part of this exercise is scored.\n",
+    "\n",
+    "\n",
+    "| Section | Part                                             | Submitted Function                | Points |\n",
+    "| :-      |:-                                                |:-                                 | :-:    |\n",
+    "| 1       | [Estimate Gaussian Parameters](#section1)        | [`estimateGaussian`](#estimateGaussian)      |  15    |\n",
+    "| 2       | [Select Threshold](#section2)                    | [`selectThreshold`](#selectThreshold)       |  15    |\n",
+    "| 3       | [Collaborative Filtering Cost](#section3)        | [`cofiCostFunc`](#cofiCostFunc)          |  20    |\n",
+    "| 4       | [Collaborative Filtering Gradient](#section4)    | [`cofiCostFunc`](#cofiCostFunc)          |  30    |\n",
+    "| 5       | [Regularized Cost](#section5)                    | [`cofiCostFunc`](#cofiCostFunc)          |  10    |\n",
+    "| 6       | [Gradient with regularization](#section6)        | [`cofiCostFunc`](#cofiCostFunc)          |  10    |\n",
+    "|         | Total Points                                     |                                   |100     |\n",
+    "\n",
+    "\n",
+    "\n",
+    "You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "At the end of each section in this notebook, we have a cell which contains code for submitting the solutions thus far to the grader. Execute the cell to see your score up to the current section. For all your work to be submitted properly, you must execute those cells at least once.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1 Anomaly Detection \n",
+    "\n",
+    "In this exercise, you will implement an anomaly detection algorithm to detect anomalous behavior in server computers. The features measure the throughput (mb/s) and latency (ms) of response of each server. While your servers were operating, you collected $m = 307$ examples of how they were behaving, and thus have an unlabeled dataset $\\{x^{(1)}, \\dots, x^{(m)}\\}$. You suspect that the vast majority of these examples are “normal” (non-anomalous) examples of the servers operating normally, but there might also be some examples of servers acting anomalously within this dataset.\n",
+    "\n",
+    "You will use a Gaussian model to detect anomalous examples in your dataset. You will first start on a 2D dataset that will allow you to visualize what the algorithm is doing. On that dataset you will fit a Gaussian distribution and then find values that have very low probability and hence can be considered anomalies. After that, you will apply the anomaly detection algorithm to a larger dataset with many dimensions.\n",
+    "\n",
+    "We start this exercise by using a small dataset that is easy to visualize. Our example case consists of 2 network server statistics across several machines: the latency and throughput of each machine. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#  The following command loads the dataset.\n",
+    "data = loadmat(os.path.join('Data', 'ex8data1.mat'))\n",
+    "X, Xval, yval = data['X'], data['Xval'], data['yval'][:, 0]\n",
+    "\n",
+    "#  Visualize the example dataset\n",
+    "pyplot.plot(X[:, 0], X[:, 1], 'bx', mew=2, mec='k', ms=6)\n",
+    "pyplot.axis([0, 30, 0, 30])\n",
+    "pyplot.xlabel('Latency (ms)')\n",
+    "pyplot.ylabel('Throughput (mb/s)')\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.1 Gaussian distribution\n",
+    "\n",
+    "To perform anomaly detection, you will first need to fit a model to the data's distribution. Given a training set $\\{x^{(1)}, \\dots, x^{(m)} \\}$ (where $x^{(i)} \\in \\mathbb{R}^n$ ), you want to estimate the Gaussian distribution for each of the features $x_i$ . For each feature $i = 1 \\dots n$, you need to find parameters $\\mu_i$ and $\\sigma_i^2$  that fit the data in the $i^{th}$ dimension $\\{ x_i^{(1)}, \\dots, x_i^{(m)} \\}$ (the $i^{th}$ dimension of each example).\n",
+    "\n",
+    "The Gaussian distribution is given by\n",
+    "\n",
+    "$$ p\\left( x; \\mu, \\sigma^2 \\right) = \\frac{1}{\\sqrt{2\\pi\\sigma^2}} e^{-\\frac{\\left(x-\\mu\\right)^2}{2\\sigma^2}},$$\n",
+    "where $\\mu$ is the mean and $\\sigma^2$ is the variance.\n",
+    "\n",
+    "<a id=\"section1\"></a>\n",
+    "### 1.2 Estimating parameters for a Gaussian \n",
+    "\n",
+    "You can estimate the parameters $\\left( \\mu_i, \\sigma_i^2 \\right)$, of the $i^{th}$ feature by using the following equations. To estimate the mean, you will use: \n",
+    "\n",
+    "$$ \\mu_i = \\frac{1}{m} \\sum_{j=1}^m x_i^{(j)},$$\n",
+    "\n",
+    "and for the variance you will use:\n",
+    "\n",
+    "$$ \\sigma_i^2 = \\frac{1}{m} \\sum_{j=1}^m \\left( x_i^{(j)} - \\mu_i \\right)^2.$$\n",
+    "\n",
+    "Your task is to complete the code in the function `estimateGaussian`. This function takes as input the data matrix `X` and should output an n-dimension vector `mu` that holds the mean for each of the $n$ features and another n-dimension vector `sigma2` that holds the variances of each of the features. You can implement this\n",
+    "using a for-loop over every feature and every training example (though a vectorized implementation might be more efficient; feel free to use a vectorized implementation if you prefer). \n",
+    "<a id=\"estimateGaussian\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def estimateGaussian(X):\n",
+    "    \"\"\"\n",
+    "    This function estimates the parameters of a Gaussian distribution\n",
+    "    using a provided dataset.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The dataset of shape (m x n) with each n-dimensional \n",
+    "        data point in one row, and each total of m data points.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    mu : array_like \n",
+    "        A vector of shape (n,) containing the means of each dimension.\n",
+    "    \n",
+    "    sigma2 : array_like\n",
+    "        A vector of shape (n,) containing the computed\n",
+    "        variances of each dimension.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the mean of the data and the variances\n",
+    "    In particular, mu[i] should contain the mean of\n",
+    "    the data for the i-th feature and sigma2[i]\n",
+    "    should contain variance of the i-th feature.\n",
+    "    \"\"\"\n",
+    "    # Useful variables\n",
+    "    m, n = X.shape\n",
+    "\n",
+    "    # You should return these values correctly\n",
+    "    mu = np.zeros(n)\n",
+    "    sigma2 = np.zeros(n)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    mu = (1/m)*np.sum(X, axis=0)\n",
+    "    sigma2 = (1/m)*np.sum((X-mu)**2, axis=0)\n",
+    "\n",
+    "    \n",
+    "    # =============================================================\n",
+    "    return mu, sigma2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you have completed the code in `estimateGaussian`, the next cell will visualize the contours of the fitted Gaussian distribution. You should get a plot similar to the figure below.\n",
+    "\n",
+    "![](Figures/gaussian_fit.png)\n",
+    "\n",
+    "From your plot, you can see that most of the examples are in the region with the highest probability, while\n",
+    "the anomalous examples are in the regions with lower probabilities.\n",
+    "\n",
+    "To do the visualization of the Gaussian fit, we first estimate the parameters of our assumed Gaussian distribution, then compute the probabilities for each of the points and then visualize both the overall distribution and where each of the points falls in terms of that distribution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#  Estimate my and sigma2\n",
+    "mu, sigma2 = estimateGaussian(X)\n",
+    "\n",
+    "#  Returns the density of the multivariate normal at each data point (row) \n",
+    "#  of X\n",
+    "p = utils.multivariateGaussian(X, mu, sigma2)\n",
+    "\n",
+    "#  Visualize the fit\n",
+    "utils.visualizeFit(X,  mu, sigma2)\n",
+    "pyplot.xlabel('Latency (ms)')\n",
+    "pyplot.ylabel('Throughput (mb/s)')\n",
+    "pyplot.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise anomaly-detection-and-recommender-systems\n",
+      "\n",
+      "Login (email address): \n",
+      "Token: \n",
+      "You used an invalid email or your token may have expired. Please make sure you have entered all fields correctly. Try generating a new token if the issue still persists.\n"
+     ]
+    }
+   ],
+   "source": [
+    "grader[1] = estimateGaussian\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section2\"></a>\n",
+    "### 1.3 Selecting the threshold, $\\varepsilon$\n",
+    "\n",
+    "Now that you have estimated the Gaussian parameters, you can investigate which examples have a very high probability given this distribution and which examples have a very low probability. The low probability examples are more likely to be the anomalies in our dataset. One way to determine which examples are anomalies is to select a threshold based on a cross validation set. In this part of the exercise, you will implement an algorithm to select the threshold $\\varepsilon$ using the $F_1$ score on a cross validation set.\n",
+    "\n",
+    "\n",
+    "You should now complete the code for the function `selectThreshold`. For this, we will use a cross validation set $\\{ (x_{cv}^{(1)}, y_{cv}^{(1)}), \\dots, (x_{cv}^{(m_{cv})}, y_{cv}^{(m_{cv})})\\}$, where the label $y = 1$ corresponds to an anomalous example, and $y = 0$ corresponds to a normal example. For each cross validation example, we will compute $p\\left( x_{cv}^{(i)}\\right)$. The vector of all of these probabilities $p\\left( x_{cv}^{(1)}\\right), \\dots, p\\left( x_{cv}^{(m_{cv})}\\right)$ is passed to `selectThreshold` in the vector `pval`. The corresponding labels $y_{cv}^{(1)} , \\dots , y_{cv}^{(m_{cv})}$ are passed to the same function in the vector `yval`.\n",
+    "\n",
+    "The function `selectThreshold` should return two values; the first is the selected threshold $\\varepsilon$. If an example $x$ has a low probability $p(x) < \\varepsilon$, then it is considered to be an anomaly. The function should also return the $F_1$ score, which tells you how well you are doing on finding the ground truth\n",
+    "anomalies given a certain threshold. For many different values of $\\varepsilon$, you will compute the resulting $F_1$ score by computing how many examples the current threshold classifies correctly and incorrectly.\n",
+    "\n",
+    "The $F_1$ score is computed using precision ($prec$) and recall ($rec$):\n",
+    "\n",
+    "$$ F_1 = \\frac{2 \\cdot prec \\cdot rec}{prec + rec}, $$\n",
+    "\n",
+    "You compute precision and recall by: \n",
+    "\n",
+    "$$ prec = \\frac{tp}{tp + fp}  $$ \n",
+    "\n",
+    "$$ rec = \\frac{tp}{tp + fn} $$\n",
+    "\n",
+    "where: \n",
+    "\n",
+    "- $tp$ is the number of true positives: the ground truth label says it’s an anomaly and our algorithm correctly classified it as an anomaly.\n",
+    "\n",
+    "-  $fp$ is the number of false positives: the ground truth label says it’s not an anomaly, but our algorithm incorrectly classified it as an anomaly.\n",
+    "- $fn$ is the number of false negatives: the ground truth label says it’s an anomaly, but our algorithm incorrectly classified it as not being anomalous.\n",
+    "\n",
+    "In the provided code `selectThreshold`, there is already a loop that will try many different values of $\\varepsilon$ and select the best $\\varepsilon$ based on the $F_1$ score. You should now complete the code in `selectThreshold`. You can implement the computation of the $F_1$ score using a for-loop over all the cross\n",
+    "validation examples (to compute the values $tp$, $fp$, $fn$). You should see a value for `epsilon` of about 8.99e-05.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "**Implementation Note:** In order to compute $tp$, $fp$ and $fn$, you may be able to use a vectorized implementation rather than loop over all the examples. This can be implemented by numpy's equality test\n",
+    "between a vector and a single number. If you have several binary values in an n-dimensional binary vector $v \\in \\{0, 1\\}^n$, you can find out how many values in this vector are 0 by using: np.sum(v == 0). You can also\n",
+    "apply a logical and operator to such binary vectors. For instance, let `cvPredictions` be a binary vector of  size equal to the number of cross validation set, where the $i^{th}$ element is 1 if your algorithm considers\n",
+    "$x_{cv}^{(i)}$ an anomaly, and 0 otherwise. You can then, for example, compute the number of false positives using: `fp = np.sum((cvPredictions == 1) & (yval == 0))`.\n",
+    "</div>\n",
+    "<a id=\"selectThreshold\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def selectThreshold(yval, pval):\n",
+    "    \"\"\"\n",
+    "    Find the best threshold (epsilon) to use for selecting outliers based\n",
+    "    on the results from a validation set and the ground truth.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    yval : array_like\n",
+    "        The ground truth labels of shape (m, ).\n",
+    "    \n",
+    "    pval : array_like\n",
+    "        The precomputed vector of probabilities based on mu and sigma2 parameters. It's shape is also (m, ).\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    bestEpsilon : array_like\n",
+    "        A vector of shape (n,) corresponding to the threshold value.\n",
+    "    \n",
+    "    bestF1 : float\n",
+    "        The value for the best F1 score.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the F1 score of choosing epsilon as the threshold and place the\n",
+    "    value in F1. The code at the end of the loop will compare the\n",
+    "    F1 score for this choice of epsilon and set it to be the best epsilon if\n",
+    "    it is better than the current choice of epsilon.\n",
+    "    \n",
+    "    Notes\n",
+    "    -----\n",
+    "    You can use predictions = (pval < epsilon) to get a binary vector\n",
+    "    of 0's and 1's of the outlier predictions\n",
+    "    \"\"\"\n",
+    "    bestEpsilon = 0\n",
+    "    bestF1 = 0\n",
+    "    F1 = 0\n",
+    "   \n",
+    "    for epsilon in np.linspace(1.01*min(pval), max(pval), 1000):\n",
+    "        # ====================== YOUR CODE HERE =======================\n",
+    "        cvPredictions = (pval < epsilon)\n",
+    "        \n",
+    "        fp = np.sum((cvPredictions == 1) & (yval == 0))\n",
+    "        tp = np.sum((cvPredictions == 1) & (yval == 1))\n",
+    "        fn = np.sum((cvPredictions == 0) & (yval == 1))\n",
+    "        \n",
+    "        prec = tp/(tp+fp)\n",
+    "        rec = tp/(tp+fn)\n",
+    "\n",
+    "        F1 = 2*prec*rec/(prec+rec)\n",
+    "        \n",
+    "        # =============================================================\n",
+    "        if F1 > bestF1:\n",
+    "            bestF1 = F1\n",
+    "            bestEpsilon = epsilon\n",
+    "\n",
+    "    return bestEpsilon, bestF1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you have completed the code in `selectThreshold`, the next cell will run your anomaly detection code and circle the anomalies in the plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best epsilon found using cross-validation: 9.00e-05\n",
+      "Best F1 on Cross Validation Set:  0.875000\n",
+      "   (you should see a value epsilon of about 8.99e-05)\n",
+      "   (you should see a Best F1 value of  0.875000)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pval = utils.multivariateGaussian(Xval, mu, sigma2)\n",
+    "\n",
+    "epsilon, F1 = selectThreshold(yval, pval)\n",
+    "print('Best epsilon found using cross-validation: %.2e' % epsilon)\n",
+    "print('Best F1 on Cross Validation Set:  %f' % F1)\n",
+    "print('   (you should see a value epsilon of about 8.99e-05)')\n",
+    "print('   (you should see a Best F1 value of  0.875000)')\n",
+    "\n",
+    "#  Find the outliers in the training set and plot the\n",
+    "outliers = p < epsilon\n",
+    "\n",
+    "#  Visualize the fit\n",
+    "utils.visualizeFit(X,  mu, sigma2)\n",
+    "pyplot.xlabel('Latency (ms)')\n",
+    "pyplot.ylabel('Throughput (mb/s)')\n",
+    "pyplot.tight_layout()\n",
+    "\n",
+    "#  Draw a red circle around those outliers\n",
+    "pyplot.plot(X[outliers, 0], X[outliers, 1], 'ro', ms=10, mfc='None', mew=2)\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[2] = selectThreshold\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.4 High dimensional dataset\n",
+    "\n",
+    "The next cell will run the anomaly detection algorithm you implemented on a more realistic and much harder dataset. In this dataset, each example is described by 11 features, capturing many more properties of your compute servers, but only some features indicate whether a point is an outlier. The script will use your code to estimate the Gaussian parameters ($\\mu_i$ and $\\sigma_i^2$), evaluate the probabilities for both the training data `X` from which you estimated the Gaussian parameters, and do so for the the cross-validation set `Xval`. Finally, it will use `selectThreshold` to find the best threshold $\\varepsilon$. You should see a value epsilon of about 1.38e-18, and 117 anomalies found."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best epsilon found using cross-validation: 1.38e-18\n",
+      "Best F1 on Cross Validation Set          : 0.615385\n",
+      "\n",
+      "  (you should see a value epsilon of about 1.38e-18)\n",
+      "   (you should see a Best F1 value of      0.615385)\n",
+      "\n",
+      "# Outliers found: 117\n"
+     ]
+    }
+   ],
+   "source": [
+    "#  Loads the second dataset. You should now have the\n",
+    "#  variables X, Xval, yval in your environment\n",
+    "data = loadmat(os.path.join('Data', 'ex8data2.mat'))\n",
+    "X, Xval, yval = data['X'], data['Xval'], data['yval'][:, 0]\n",
+    "\n",
+    "# Apply the same steps to the larger dataset\n",
+    "mu, sigma2 = estimateGaussian(X)\n",
+    "\n",
+    "#  Training set \n",
+    "p = utils.multivariateGaussian(X, mu, sigma2)\n",
+    "\n",
+    "#  Cross-validation set\n",
+    "pval = utils.multivariateGaussian(Xval, mu, sigma2)\n",
+    "\n",
+    "#  Find the best threshold\n",
+    "epsilon, F1 = selectThreshold(yval, pval)\n",
+    "\n",
+    "print('Best epsilon found using cross-validation: %.2e' % epsilon)\n",
+    "print('Best F1 on Cross Validation Set          : %f\\n' % F1)\n",
+    "print('  (you should see a value epsilon of about 1.38e-18)')\n",
+    "print('   (you should see a Best F1 value of      0.615385)')\n",
+    "print('\\n# Outliers found: %d' % np.sum(p < epsilon))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2 Recommender Systems\n",
+    "\n",
+    "In this part of the exercise, you will implement the collaborative filtering learning algorithm and apply it to a dataset of movie ratings ([MovieLens 100k Dataset](https://grouplens.org/datasets/movielens/) from GroupLens Research). This dataset consists of ratings on a scale of 1 to 5. The dataset has $n_u = 943$ users, and $n_m = 1682$ movies. \n",
+    "\n",
+    "In the next parts of this exercise, you will implement the function `cofiCostFunc` that computes the collaborative filtering objective function and gradient. After implementing the cost function and gradient, you will use `scipy.optimize.minimize` to learn the parameters for collaborative filtering.\n",
+    "\n",
+    "### 2.1 Movie ratings dataset\n",
+    "\n",
+    "The next cell will load the dataset `ex8_movies.mat`, providing the variables `Y` and `R`.\n",
+    "The matrix `Y` (a `num_movies` $\\times$ `num_users` matrix) stores the ratings $y^{(i,j)}$ (from 1 to 5). The matrix `R` is an binary-valued indicator matrix, where $R(i, j) = 1$ if user $j$ gave a rating to movie $i$, and $R(i, j) = 0$ otherwise. The objective of collaborative filtering is to predict movie ratings for the movies that users have not yet rated, that is, the entries with $R(i, j) = 0$. This will allow us to recommend the movies with the highest predicted ratings to the user.\n",
+    "\n",
+    "To help you understand the matrix `Y`, the following cell will compute the average movie rating for the first movie (Toy Story) and print its average rating."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average rating for movie 1 (Toy Story): 3.878319 / 5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load data\n",
+    "data = loadmat(os.path.join('Data', 'ex8_movies.mat'))\n",
+    "Y, R = data['Y'], data['R']\n",
+    "\n",
+    "# Y is a 1682x943 matrix, containing ratings (1-5) of \n",
+    "# 1682 movies on 943 users\n",
+    "\n",
+    "# R is a 1682x943 matrix, where R(i,j) = 1 \n",
+    "# if and only if user j gave a rating to movie i\n",
+    "\n",
+    "# From the matrix, we can compute statistics like average rating.\n",
+    "print('Average rating for movie 1 (Toy Story): %f / 5' %\n",
+    "      np.mean(Y[0, R[0, :] == 1]))\n",
+    "\n",
+    "# We can \"visualize\" the ratings matrix by plotting it with imshow\n",
+    "pyplot.figure(figsize=(8, 8))\n",
+    "pyplot.imshow(Y)\n",
+    "pyplot.ylabel('Movies')\n",
+    "pyplot.xlabel('Users')\n",
+    "pyplot.grid(False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Throughout this part of the exercise, you will also be working with the matrices, `X` and `Theta`:\n",
+    "\n",
+    "$$ \\text{X} = \n",
+    "\\begin{bmatrix}\n",
+    "- \\left(x^{(1)}\\right)^T - \\\\\n",
+    "- \\left(x^{(2)}\\right)^T - \\\\\n",
+    "\\vdots \\\\\n",
+    "- \\left(x^{(n_m)}\\right)^T - \\\\\n",
+    "\\end{bmatrix}, \\quad\n",
+    "\\text{Theta} = \n",
+    "\\begin{bmatrix}\n",
+    "- \\left(\\theta^{(1)}\\right)^T - \\\\\n",
+    "- \\left(\\theta^{(2)}\\right)^T - \\\\\n",
+    "\\vdots \\\\\n",
+    "- \\left(\\theta^{(n_u)}\\right)^T - \\\\\n",
+    "\\end{bmatrix}.\n",
+    "$$\n",
+    "\n",
+    "The $i^{th}$ row of `X` corresponds to the feature vector $x^{(i)}$ for the $i^{th}$ movie, and the $j^{th}$ row of `Theta` corresponds to one parameter vector $\\theta^{(j)}$, for the $j^{th}$ user. Both $x^{(i)}$ and $\\theta^{(j)}$ are n-dimensional vectors. For the purposes of this exercise, you will use $n = 100$, and therefore, $x^{(i)} \\in \\mathbb{R}^{100}$ and $\\theta^{(j)} \\in \\mathbb{R}^{100}$. Correspondingly, `X` is a $n_m \\times 100$ matrix and `Theta` is a $n_u \\times 100$ matrix.\n",
+    "\n",
+    "<a id=\"section3\"></a>\n",
+    "### 2.2 Collaborative filtering learning algorithm\n",
+    "\n",
+    "Now, you will start implementing the collaborative filtering learning algorithm. You will start by implementing the cost function (without regularization).\n",
+    "\n",
+    "The collaborative filtering algorithm in the setting of movie recommendations considers a set of n-dimensional parameter vectors $x^{(1)}, \\dots, x^{(n_m)}$ and $\\theta^{(1)} , \\dots, \\theta^{(n_u)}$, where the model predicts the rating for movie $i$ by user $j$ as $y^{(i,j)} = \\left( \\theta^{(j)} \\right)^T x^{(i)}$. Given a dataset that consists of a set of ratings produced by some users on some movies, you wish to learn the parameter vectors $x^{(1)}, \\dots, x^{(n_m)}, \\theta^{(1)}, \\dots, \\theta^{(n_u)}$ that produce the best fit (minimizes the squared error).\n",
+    "\n",
+    "You will complete the code in `cofiCostFunc` to compute the cost function and gradient for collaborative filtering. Note that the parameters to the function (i.e., the values that you are trying to learn) are `X` and `Theta`. In order to use an off-the-shelf minimizer such as `scipy`'s `minimize` function, the cost function has been set up to unroll the parameters into a single vector called `params`. You had previously used the same vector unrolling method in the neural networks programming exercise.\n",
+    "\n",
+    "#### 2.2.1 Collaborative filtering cost function\n",
+    "\n",
+    "The collaborative filtering cost function (without regularization) is given by\n",
+    "\n",
+    "$$\n",
+    "J(x^{(1)}, \\dots, x^{(n_m)}, \\theta^{(1)}, \\dots,\\theta^{(n_u)}) = \\frac{1}{2} \\sum_{(i,j):r(i,j)=1} \\left( \\left(\\theta^{(j)}\\right)^T x^{(i)} - y^{(i,j)} \\right)^2\n",
+    "$$\n",
+    "\n",
+    "You should now modify the function `cofiCostFunc` to return this cost in the variable `J`. Note that you should be accumulating the cost for user $j$ and movie $i$ only if `R[i,j] = 1`.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "**Implementation Note**: We strongly encourage you to use a vectorized implementation to compute $J$, since it will later by called many times by `scipy`'s optimization package. As usual, it might be easiest to first write a non-vectorized implementation (to make sure you have the right answer), and the modify it to become a vectorized implementation (checking that the vectorization steps do not change your algorithm’s output). To come up with a vectorized implementation, the following tip might be helpful: You can use the $R$ matrix to set selected entries to 0. For example, `R * M` will do an element-wise multiplication between `M`\n",
+    "and `R`; since `R` only has elements with values either 0 or 1, this has the effect of setting the elements of M to 0 only when the corresponding value in R is 0. Hence, `np.sum( R * M)` is the sum of all the elements of `M` for which the corresponding element in `R` equals 1.\n",
+    "</div>\n",
+    "\n",
+    "<a id=\"cofiCostFunc\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cofiCostFunc(params, Y, R, num_users, num_movies,\n",
+    "                      num_features, lambda_=0.0):\n",
+    "    \"\"\"\n",
+    "    Collaborative filtering cost function.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    params : array_like\n",
+    "        The parameters which will be optimized. This is a one\n",
+    "        dimensional vector of shape (num_movies x num_users, 1). It is the \n",
+    "        concatenation of the feature vectors X and parameters Theta.\n",
+    "    \n",
+    "    Y : array_like\n",
+    "        A matrix of shape (num_movies x num_users) of user ratings of movies.\n",
+    "    \n",
+    "    R : array_like\n",
+    "        A (num_movies x num_users) matrix, where R[i, j] = 1 if the \n",
+    "        i-th movie was rated by the j-th user.\n",
+    "    \n",
+    "    num_users : int\n",
+    "        Total number of users.\n",
+    "    \n",
+    "    num_movies : int\n",
+    "        Total number of movies.\n",
+    "    \n",
+    "    num_features : int\n",
+    "        Number of features to learn.\n",
+    "    \n",
+    "    lambda_ : float, optional\n",
+    "        The regularization coefficient.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    J : float\n",
+    "        The value of the cost function at the given params.\n",
+    "    \n",
+    "    grad : array_like\n",
+    "        The gradient vector of the cost function at the given params.\n",
+    "        grad has a shape (num_movies x num_users, 1)\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the cost function and gradient for collaborative filtering.\n",
+    "    Concretely, you should first implement the cost function (without\n",
+    "    regularization) and make sure it is matches our costs. After that,\n",
+    "    you should implement thegradient and use the checkCostFunction routine \n",
+    "    to check that the gradient is correct. Finally, you should implement\n",
+    "    regularization.\n",
+    "    \n",
+    "    Notes\n",
+    "    -----\n",
+    "    - The input params will be unraveled into the two matrices:\n",
+    "        X : (num_movies  x num_features) matrix of movie features\n",
+    "        Theta : (num_users  x num_features) matrix of user features\n",
+    "\n",
+    "    - You should set the following variables correctly:\n",
+    "\n",
+    "        X_grad : (num_movies x num_features) matrix, containing the \n",
+    "                 partial derivatives w.r.t. to each element of X\n",
+    "        Theta_grad : (num_users x num_features) matrix, containing the \n",
+    "                     partial derivatives w.r.t. to each element of Theta\n",
+    "\n",
+    "    - The returned gradient will be the concatenation of the raveled \n",
+    "      gradients X_grad and Theta_grad.\n",
+    "    \"\"\"\n",
+    "    # Unfold the U and W matrices from params\n",
+    "    X = params[:num_movies*num_features].reshape(num_movies, num_features)\n",
+    "    Theta = params[num_movies*num_features:].reshape(num_users, num_features)\n",
+    "\n",
+    "    # You need to return the following values correctly\n",
+    "    J = 0\n",
+    "    X_grad = np.zeros(X.shape)\n",
+    "    Theta_grad = np.zeros(Theta.shape)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "    J = (1/2)*np.sum(np.sum(R*(np.matmul(X,Theta.T) - Y)*(np.matmul(X,Theta.T) - Y)))\n",
+    "\n",
+    "    X_grad = np.matmul(R*(np.matmul(X,Theta.T) - Y), Theta)\n",
+    "    Theta_grad = np.matmul((R*(np.matmul(X,Theta.T) - Y)).T, X)\n",
+    "    # =============================================================\n",
+    "    \n",
+    "    grad = np.concatenate([X_grad.ravel(), Theta_grad.ravel()])\n",
+    "    return J, grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## WITH REGULARIZATION\n",
+    "\n",
+    "def cofiCostFunc(params, Y, R, num_users, num_movies,\n",
+    "                      num_features, lambda_):\n",
+    "    \"\"\"\n",
+    "    Collaborative filtering cost function.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    params : array_like\n",
+    "        The parameters which will be optimized. This is a one\n",
+    "        dimensional vector of shape (num_movies x num_users, 1). It is the \n",
+    "        concatenation of the feature vectors X and parameters Theta.\n",
+    "    \n",
+    "    Y : array_like\n",
+    "        A matrix of shape (num_movies x num_users) of user ratings of movies.\n",
+    "    \n",
+    "    R : array_like\n",
+    "        A (num_movies x num_users) matrix, where R[i, j] = 1 if the \n",
+    "        i-th movie was rated by the j-th user.\n",
+    "    \n",
+    "    num_users : int\n",
+    "        Total number of users.\n",
+    "    \n",
+    "    num_movies : int\n",
+    "        Total number of movies.\n",
+    "    \n",
+    "    num_features : int\n",
+    "        Number of features to learn.\n",
+    "    \n",
+    "    lambda_ : float, optional\n",
+    "        The regularization coefficient.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    J : float\n",
+    "        The value of the cost function at the given params.\n",
+    "    \n",
+    "    grad : array_like\n",
+    "        The gradient vector of the cost function at the given params.\n",
+    "        grad has a shape (num_movies x num_users, 1)\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the cost function and gradient for collaborative filtering.\n",
+    "    Concretely, you should first implement the cost function (without\n",
+    "    regularization) and make sure it is matches our costs. After that,\n",
+    "    you should implement thegradient and use the checkCostFunction routine \n",
+    "    to check that the gradient is correct. Finally, you should implement\n",
+    "    regularization.\n",
+    "    \n",
+    "    Notes\n",
+    "    -----\n",
+    "    - The input params will be unraveled into the two matrices:\n",
+    "        X : (num_movies  x num_features) matrix of movie features\n",
+    "        Theta : (num_users  x num_features) matrix of user features\n",
+    "\n",
+    "    - You should set the following variables correctly:\n",
+    "\n",
+    "        X_grad : (num_movies x num_features) matrix, containing the \n",
+    "                 partial derivatives w.r.t. to each element of X\n",
+    "        Theta_grad : (num_users x num_features) matrix, containing the \n",
+    "                     partial derivatives w.r.t. to each element of Theta\n",
+    "\n",
+    "    - The returned gradient will be the concatenation of the raveled \n",
+    "      gradients X_grad and Theta_grad.\n",
+    "    \"\"\"\n",
+    "    # Unfold the U and W matrices from params\n",
+    "    X = params[:num_movies*num_features].reshape(num_movies, num_features)\n",
+    "    Theta = params[num_movies*num_features:].reshape(num_users, num_features)\n",
+    "\n",
+    "    # You need to return the following values correctly\n",
+    "    J = 0\n",
+    "    X_grad = np.zeros(X.shape)\n",
+    "    Theta_grad = np.zeros(Theta.shape)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "    J = (1/2)*np.sum(np.sum(R*(np.matmul(X,Theta.T) - Y)*(np.matmul(X,Theta.T) - Y))) +(lambda_/2)*np.sum(Theta*Theta) + (lambda_/2)*np.sum(X*X)\n",
+    "\n",
+    "    X_grad = np.matmul(R*(np.matmul(X,Theta.T) - Y), Theta) + lambda_*X\n",
+    "    Theta_grad = np.matmul((R*(np.matmul(X,Theta.T) - Y)).T, X) + lambda_*Theta\n",
+    "\n",
+    "    \n",
+    "    # =============================================================\n",
+    "    \n",
+    "    grad = np.concatenate([X_grad.ravel(), Theta_grad.ravel()])\n",
+    "    return J, grad"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After you have completed the function, the next cell will run your cost function. To help you debug your cost function, we have included set of weights that we trained on that.  You should expect to see an output of 22.22."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost at loaded parameters:  22.22 \n",
+      "(this value should be about 22.22)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#  Load pre-trained weights (X, Theta, num_users, num_movies, num_features)\n",
+    "data = loadmat(os.path.join('Data', 'ex8_movieParams.mat'))\n",
+    "X, Theta, num_users, num_movies, num_features = data['X'],\\\n",
+    "        data['Theta'], data['num_users'], data['num_movies'], data['num_features']\n",
+    "\n",
+    "#  Reduce the data set size so that this runs faster\n",
+    "num_users = 4\n",
+    "num_movies = 5\n",
+    "num_features = 3\n",
+    "\n",
+    "X = X[:num_movies, :num_features]\n",
+    "Theta = Theta[:num_users, :num_features]\n",
+    "Y = Y[:num_movies, 0:num_users]\n",
+    "R = R[:num_movies, 0:num_users]\n",
+    "\n",
+    "#  Evaluate cost function\n",
+    "J, _ = cofiCostFunc(np.concatenate([X.ravel(), Theta.ravel()]),\n",
+    "                    Y, R, num_users, num_movies, num_features)\n",
+    "           \n",
+    "print('Cost at loaded parameters:  %.2f \\n(this value should be about 22.22)' % J)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grader[3] = cofiCostFunc\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section4\"></a>\n",
+    "#### 2.2.2 Collaborative filtering gradient\n",
+    "\n",
+    "Now you should implement the gradient (without regularization). Specifically, you should complete the code in `cofiCostFunc` to return the variables `X_grad` and `Theta_grad`. Note that `X_grad` should be a matrix of the same size as `X` and similarly, `Theta_grad` is a matrix of the same size as\n",
+    "`Theta`. The gradients of the cost function is given by:\n",
+    "\n",
+    "$$ \\frac{\\partial J}{\\partial x_k^{(i)}} = \\sum_{j:r(i,j)=1} \\left( \\left(\\theta^{(j)}\\right)^T x^{(i)} - y^{(i,j)} \\right) \\theta_k^{(j)} $$\n",
+    "\n",
+    "$$ \\frac{\\partial J}{\\partial \\theta_k^{(j)}} = \\sum_{i:r(i,j)=1} \\left( \\left(\\theta^{(j)}\\right)^T x^{(i)}- y^{(i,j)} \\right) x_k^{(j)} $$\n",
+    "\n",
+    "Note that the function returns the gradient for both sets of variables by unrolling them into a single vector. After you have completed the code to compute the gradients, the next cell run a gradient check\n",
+    "(available in `utils.checkCostFunction`) to numerically check the implementation of your gradients (this is similar to the numerical check that you used in the neural networks exercise. If your implementation is correct, you should find that the analytical and numerical gradients match up closely.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "**Implementation Note:** You can get full credit for this assignment without using a vectorized implementation, but your code will run much more slowly (a small number of hours), and so we recommend that you try to vectorize your implementation. To get started, you can implement the gradient with a for-loop over movies\n",
+    "(for computing $\\frac{\\partial J}{\\partial x^{(i)}_k}$) and a for-loop over users (for computing $\\frac{\\partial J}{\\theta_k^{(j)}}$). When you first implement the gradient, you might start with an unvectorized version, by implementing another inner for-loop that computes each element in the summation. After you have completed the gradient computation this way, you should try to vectorize your implementation (vectorize the inner for-loops), so that you are left with only two for-loops (one for looping over movies to compute $\\frac{\\partial J}{\\partial x_k^{(i)}}$ for each movie, and one for looping over users to compute $\\frac{\\partial J}{\\partial \\theta_k^{(j)}}$ for each user).\n",
+    "</div>\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "**Implementation Tip:** To perform the vectorization, you might find this helpful: You should come up with a way to compute all the derivatives associated with $x_1^{(i)} , x_2^{(i)}, \\dots , x_n^{(i)}$ (i.e., the derivative terms associated with the feature vector $x^{(i)}$) at the same time. Let us define the derivatives for the feature vector of the $i^{th}$ movie as:\n",
+    "\n",
+    "$$ \\left(X_{\\text{grad}} \\left(i, :\\right)\\right)^T = \n",
+    "\\begin{bmatrix}\n",
+    "\\frac{\\partial J}{\\partial x_1^{(i)}} \\\\\n",
+    "\\frac{\\partial J}{\\partial x_2^{(i)}} \\\\\n",
+    "\\vdots \\\\\n",
+    "\\frac{\\partial J}{\\partial x_n^{(i)}}\n",
+    "\\end{bmatrix} = \\quad\n",
+    "\\sum_{j:r(i,j)=1} \\left( \\left( \\theta^{(j)} \\right)^T x^{(i)} - y^{(i,j)} \\right) \\theta^{(j)}\n",
+    "$$\n",
+    "\n",
+    "To vectorize the above expression, you can start by indexing into `Theta` and `Y` to select only the elements of interests (that is, those with `r[i, j] = 1`). Intuitively, when you consider the features for the $i^{th}$ movie, you only need to be concerned about the users who had given ratings to the movie, and this allows you to remove all the other users from `Theta` and `Y`. <br/><br/>\n",
+    "\n",
+    "\n",
+    "Concretely, you can set `idx = np.where(R[i, :] == 1)[0]` to be a list of all the users that have rated movie $i$. This will allow you to create the temporary matrices `Theta_temp = Theta[idx, :]` and `Y_temp = Y[i, idx]` that index into `Theta` and `Y` to give you only the set of users which have rated the $i^{th}$ movie. This will allow you to write the derivatives as: <br>\n",
+    "\n",
+    "`X_grad[i, :] = np.dot(np.dot(X[i, :], Theta_temp.T) - Y_temp, Theta_temp)`\n",
+    "\n",
+    "<br><br>\n",
+    "Note that the vectorized computation above returns a row-vector instead. After you have vectorized the computations of the derivatives with respect to $x^{(i)}$, you should use a similar method to vectorize the derivatives with respect to $θ^{(j)}$ as well.\n",
+    "</div>\n",
+    "\n",
+    "[Click here to go back to the function `cofiCostFunc` to update it](#cofiCostFunc). \n",
+    "\n",
+    "<font color=\"red\"> Do not forget to re-execute the cell containg the function `cofiCostFunc` so that it is updated with your implementation of the gradient computation.</font>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-0.6501744  -0.6501744 ]\n",
+      " [ 1.39030506  1.39030506]\n",
+      " [-1.80626078 -1.80626078]\n",
+      " [-0.25760131 -0.25760131]\n",
+      " [-1.83200275 -1.83200275]\n",
+      " [ 0.5263574   0.5263574 ]\n",
+      " [-1.90605916 -1.90605916]\n",
+      " [-5.94190932 -5.94190932]\n",
+      " [13.81374328 13.81374328]\n",
+      " [-1.30630646 -1.30630646]\n",
+      " [ 0.86412669  0.86412669]\n",
+      " [11.04263635 11.04263635]\n",
+      " [ 0.49154787  0.49154787]\n",
+      " [ 1.31162016  1.31162016]\n",
+      " [-1.87683757 -1.87683757]\n",
+      " [ 1.00644226  1.00644226]\n",
+      " [ 2.23451441  2.23451441]\n",
+      " [-3.95206928 -3.95206928]\n",
+      " [ 0.07545801  0.07545801]\n",
+      " [ 5.66760115  5.66760115]\n",
+      " [-3.09282714 -3.09282714]\n",
+      " [-1.65952828 -1.65952828]\n",
+      " [-5.74472932 -5.74472932]\n",
+      " [ 1.44360993  1.44360993]\n",
+      " [ 1.49194316  1.49194316]\n",
+      " [ 1.82488548  1.82488548]\n",
+      " [-6.21885088 -6.21885088]]\n",
+      "\n",
+      "The above two columns you get should be very similar.(Left-Your Numerical Gradient, Right-Analytical Gradient)\n",
+      "If your cost function implementation is correct, then the relative difference will be small (less than 1e-9).\n",
+      "\n",
+      "Relative Difference: 1.3529e-12\n"
+     ]
+    }
+   ],
+   "source": [
+    "#  Check gradients by running checkcostFunction\n",
+    "utils.checkCostFunction(cofiCostFunc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "grader[4] = cofiCostFunc\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section5\"></a>\n",
+    "#### 2.2.3 Regularized cost function\n",
+    "\n",
+    "The cost function for collaborative filtering with regularization is given by\n",
+    "\n",
+    "$$ J(x^{(1)}, \\dots, x^{(n_m)}, \\theta^{(1)}, \\dots, \\theta^{(n_u)}) = \\frac{1}{2} \\sum_{(i,j):r(i,j)=1} \\left( \\left( \\theta^{(j)} \\right)^T x^{(i)} - y^{(i,j)} \\right)^2 + \\left( \\frac{\\lambda}{2} \\sum_{j=1}^{n_u} \\sum_{k=1}^{n} \\left( \\theta_k^{(j)} \\right)^2  \\right) + \\left( \\frac{\\lambda}{2} \\sum_{i=1}^{n_m} \\sum_{k=1}^n \\left(x_k^{(i)} \\right)^2 \\right) $$\n",
+    "\n",
+    "You should now add regularization to your original computations of the cost function, $J$. After you are done, the next cell will run your regularized cost function, and you should expect to see a cost of about 31.34.\n",
+    "\n",
+    "[Click here to go back to the function `cofiCostFunc` to update it](#cofiCostFunc)\n",
+    "<font color=\"red\"> Do not forget to re-execute the cell containing the function `cofiCostFunc` so that it is updated with your implementation of regularized cost function.</font>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost at loaded parameters (lambda = 1.5): 31.34\n",
+      "              (this value should be about 31.34)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#  Evaluate cost function\n",
+    "J, _ = cofiCostFunc(np.concatenate([X.ravel(), Theta.ravel()]),\n",
+    "                    Y, R, num_users, num_movies, num_features, 1.5)\n",
+    "           \n",
+    "print('Cost at loaded parameters (lambda = 1.5): %.2f' % J)\n",
+    "print('              (this value should be about 31.34)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "grader[5] = cofiCostFunc\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"section6\"></a>\n",
+    "#### 2.2.4 Regularized gradient\n",
+    "\n",
+    "Now that you have implemented the regularized cost function, you should proceed to implement regularization for the gradient. You should add to your implementation in `cofiCostFunc` to return the regularized gradient\n",
+    "by adding the contributions from the regularization terms. Note that the gradients for the regularized cost function is given by:\n",
+    "\n",
+    "$$ \\frac{\\partial J}{\\partial x_k^{(i)}} = \\sum_{j:r(i,j)=1} \\left( \\left(\\theta^{(j)}\\right)^T x^{(i)} - y^{(i,j)} \\right) \\theta_k^{(j)} + \\lambda x_k^{(i)} $$\n",
+    "\n",
+    "$$ \\frac{\\partial J}{\\partial \\theta_k^{(j)}} = \\sum_{i:r(i,j)=1} \\left( \\left(\\theta^{(j)}\\right)^T x^{(i)}- y^{(i,j)} \\right) x_k^{(j)} + \\lambda \\theta_k^{(j)} $$\n",
+    "\n",
+    "This means that you just need to add $\\lambda x^{(i)}$ to the `X_grad[i,:]` variable described earlier, and add $\\lambda \\theta^{(j)}$ to the `Theta_grad[j, :]` variable described earlier.\n",
+    "\n",
+    "[Click here to go back to the function `cofiCostFunc` to update it](#cofiCostFunc)\n",
+    "<font color=\"red\"> Do not forget to re-execute the cell containing the function `cofiCostFunc` so that it is updated with your implementation of the gradient for the regularized cost function.</font>\n",
+    "\n",
+    "After you have completed the code to compute the gradients, the following cell will run another gradient check (`utils.checkCostFunction`) to numerically check the implementation of your gradients."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 1.38627974  1.38627974]\n",
+      " [-0.40479542 -0.40479542]\n",
+      " [ 1.49882701  1.49882701]\n",
+      " [-0.97699281 -0.97699281]\n",
+      " [-3.08099949 -3.08099949]\n",
+      " [-6.06615485 -6.06615485]\n",
+      " [ 3.68551784  3.68551784]\n",
+      " [ 0.07013897  0.07013897]\n",
+      " [ 4.05792549  4.05792549]\n",
+      " [ 1.1191331   1.1191331 ]\n",
+      " [ 7.57401055  7.57401055]\n",
+      " [ 3.92056053  3.92056053]\n",
+      " [ 1.09356827  1.09356827]\n",
+      " [ 4.68671403  4.68671403]\n",
+      " [ 3.46609574  3.46609574]\n",
+      " [ 2.08795863  2.08795863]\n",
+      " [ 7.84426567  7.84426567]\n",
+      " [ 7.35135377  7.35135377]\n",
+      " [ 3.11538984  3.11538984]\n",
+      " [-0.9712843  -0.9712843 ]\n",
+      " [ 4.62129555  4.62129555]\n",
+      " [-0.51989226 -0.51989226]\n",
+      " [ 4.68033532  4.68033532]\n",
+      " [ 4.20324629  4.20324629]\n",
+      " [-1.79884504 -1.79884504]\n",
+      " [-0.45163974 -0.45163974]\n",
+      " [-1.47509134 -1.47509134]]\n",
+      "\n",
+      "The above two columns you get should be very similar.(Left-Your Numerical Gradient, Right-Analytical Gradient)\n",
+      "If your cost function implementation is correct, then the relative difference will be small (less than 1e-9).\n",
+      "\n",
+      "Relative Difference: 3.09122e-12\n"
+     ]
+    }
+   ],
+   "source": [
+    "#  Check gradients by running checkCostFunction\n",
+    "utils.checkCostFunction(cofiCostFunc, 1.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You should now submit your solutions.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "grader[6] = cofiCostFunc\n",
+    "grader.grade()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3 Learning movie recommendations \n",
+    "\n",
+    "After you have finished implementing the collaborative filtering cost function and gradient, you can now start training your algorithm to make movie recommendations for yourself. In the next cell, you can enter your own movie preferences, so that later when the algorithm runs, you can get your own movie recommendations! We have filled out some values according to our own preferences, but you should change this according to your own tastes. The list of all movies and their number in the dataset can be found listed in the file `Data/movie_idx.txt`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New user ratings:\n",
+      "-----------------\n",
+      "Rated 4 stars: Toy Story (1995)\n",
+      "Rated 3 stars: Twelve Monkeys (1995)\n",
+      "Rated 5 stars: Usual Suspects, The (1995)\n",
+      "Rated 4 stars: Outbreak (1995)\n",
+      "Rated 5 stars: Shawshank Redemption, The (1994)\n",
+      "Rated 3 stars: While You Were Sleeping (1995)\n",
+      "Rated 5 stars: Forrest Gump (1994)\n",
+      "Rated 2 stars: Silence of the Lambs, The (1991)\n",
+      "Rated 4 stars: Alien (1979)\n",
+      "Rated 5 stars: Die Hard 2 (1990)\n",
+      "Rated 5 stars: Sphere (1998)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#  Before we will train the collaborative filtering model, we will first\n",
+    "#  add ratings that correspond to a new user that we just observed. This\n",
+    "#  part of the code will also allow you to put in your own ratings for the\n",
+    "#  movies in our dataset!\n",
+    "movieList = utils.loadMovieList()\n",
+    "n_m = len(movieList)\n",
+    "\n",
+    "#  Initialize my ratings\n",
+    "my_ratings = np.zeros(n_m)\n",
+    "\n",
+    "# Check the file movie_idx.txt for id of each movie in our dataset\n",
+    "# For example, Toy Story (1995) has ID 1, so to rate it \"4\", you can set\n",
+    "# Note that the index here is ID-1, since we start index from 0.\n",
+    "my_ratings[0] = 4\n",
+    "\n",
+    "# Or suppose did not enjoy Silence of the Lambs (1991), you can set\n",
+    "my_ratings[97] = 2\n",
+    "\n",
+    "# We have selected a few movies we liked / did not like and the ratings we\n",
+    "# gave are as follows:\n",
+    "my_ratings[6] = 3\n",
+    "my_ratings[11]= 5\n",
+    "my_ratings[53] = 4\n",
+    "my_ratings[63] = 5\n",
+    "my_ratings[65] = 3\n",
+    "my_ratings[68] = 5\n",
+    "my_ratings[182] = 4\n",
+    "my_ratings[225] = 5\n",
+    "my_ratings[354] = 5\n",
+    "\n",
+    "print('New user ratings:')\n",
+    "print('-----------------')\n",
+    "for i in range(len(my_ratings)):\n",
+    "    if my_ratings[i] > 0:\n",
+    "        print('Rated %d stars: %s' % (my_ratings[i], movieList[i]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.3.1 Recommendations\n",
+    "\n",
+    "After the additional ratings have been added to the dataset, the script\n",
+    "will proceed to train the collaborative filtering model. This will learn the\n",
+    "parameters X and Theta. To predict the rating of movie i for user j, you need to compute (θ (j) ) T x (i) . The next part of the script computes the ratings for\n",
+    "all the movies and users and displays the movies that it recommends (Figure\n",
+    "4), according to ratings that were entered earlier in the script. Note that\n",
+    "you might obtain a different set of the predictions due to different random\n",
+    "initializations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Recommender system learning completed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "#  Now, you will train the collaborative filtering model on a movie rating \n",
+    "#  dataset of 1682 movies and 943 users\n",
+    "\n",
+    "#  Load data\n",
+    "data = loadmat(os.path.join('Data', 'ex8_movies.mat'))\n",
+    "Y, R = data['Y'], data['R']\n",
+    "\n",
+    "#  Y is a 1682x943 matrix, containing ratings (1-5) of 1682 movies by \n",
+    "#  943 users\n",
+    "\n",
+    "#  R is a 1682x943 matrix, where R(i,j) = 1 if and only if user j gave a\n",
+    "#  rating to movie i\n",
+    "\n",
+    "#  Add our own ratings to the data matrix\n",
+    "Y = np.hstack([my_ratings[:, None], Y])\n",
+    "R = np.hstack([(my_ratings > 0)[:, None], R])\n",
+    "\n",
+    "#  Normalize Ratings\n",
+    "Ynorm, Ymean = utils.normalizeRatings(Y, R)\n",
+    "\n",
+    "#  Useful Values\n",
+    "num_movies, num_users = Y.shape\n",
+    "num_features = 10\n",
+    "\n",
+    "# Set Initial Parameters (Theta, X)\n",
+    "X = np.random.randn(num_movies, num_features)\n",
+    "Theta = np.random.randn(num_users, num_features)\n",
+    "\n",
+    "initial_parameters = np.concatenate([X.ravel(), Theta.ravel()])\n",
+    "\n",
+    "# Set options for scipy.optimize.minimize\n",
+    "options = {'maxiter': 100}\n",
+    "\n",
+    "# Set Regularization\n",
+    "lambda_ = 10\n",
+    "res = optimize.minimize(lambda x: cofiCostFunc(x, Ynorm, R, num_users,\n",
+    "                                               num_movies, num_features, lambda_),\n",
+    "                        initial_parameters,\n",
+    "                        method='TNC',\n",
+    "                        jac=True,\n",
+    "                        options=options)\n",
+    "theta = res.x\n",
+    "\n",
+    "# Unfold the returned theta back into U and W\n",
+    "X = theta[:num_movies*num_features].reshape(num_movies, num_features)\n",
+    "Theta = theta[num_movies*num_features:].reshape(num_users, num_features)\n",
+    "\n",
+    "print('Recommender system learning completed.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After training the model, you can now make recommendations by computing the predictions matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top recommendations for you:\n",
+      "----------------------------\n",
+      "Predicting rating 5.0 for movie Aiqing wansui (1994)\n",
+      "Predicting rating 5.0 for movie Someone Else's America (1995)\n",
+      "Predicting rating 5.0 for movie Santa with Muscles (1996)\n",
+      "Predicting rating 5.0 for movie Prefontaine (1997)\n",
+      "Predicting rating 5.0 for movie Star Kid (1997)\n",
+      "Predicting rating 5.0 for movie Saint of Fort Washington, The (1993)\n",
+      "Predicting rating 5.0 for movie Great Day in Harlem, A (1994)\n",
+      "Predicting rating 5.0 for movie They Made Me a Criminal (1939)\n",
+      "Predicting rating 5.0 for movie Entertaining Angels: The Dorothy Day Story (1996)\n",
+      "Predicting rating 5.0 for movie Marlene Dietrich: Shadow and Light (1996)\n",
+      "\n",
+      "Original ratings provided:\n",
+      "--------------------------\n",
+      "Rated 4 for Toy Story (1995)\n",
+      "Rated 3 for Twelve Monkeys (1995)\n",
+      "Rated 5 for Usual Suspects, The (1995)\n",
+      "Rated 4 for Outbreak (1995)\n",
+      "Rated 5 for Shawshank Redemption, The (1994)\n",
+      "Rated 3 for While You Were Sleeping (1995)\n",
+      "Rated 5 for Forrest Gump (1994)\n",
+      "Rated 2 for Silence of the Lambs, The (1991)\n",
+      "Rated 4 for Alien (1979)\n",
+      "Rated 5 for Die Hard 2 (1990)\n",
+      "Rated 5 for Sphere (1998)\n"
+     ]
+    }
+   ],
+   "source": [
+    "p = np.dot(X, Theta.T)\n",
+    "my_predictions = p[:, 0] + Ymean\n",
+    "\n",
+    "movieList = utils.loadMovieList()\n",
+    "\n",
+    "ix = np.argsort(my_predictions)[::-1]\n",
+    "\n",
+    "print('Top recommendations for you:')\n",
+    "print('----------------------------')\n",
+    "for i in range(10):\n",
+    "    j = ix[i]\n",
+    "    print('Predicting rating %.1f for movie %s' % (my_predictions[j], movieList[j]))\n",
+    "\n",
+    "print('\\nOriginal ratings provided:')\n",
+    "print('--------------------------')\n",
+    "for i in range(len(my_ratings)):\n",
+    "    if my_ratings[i] > 0:\n",
+    "        print('Rated %d for %s' % (my_ratings[i], movieList[i]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Exercise8/exercise8.ipynb b/Exercise8/exercise8.ipynb
index 8cd68244..ada24198 100755
--- a/Exercise8/exercise8.ipynb
+++ b/Exercise8/exercise8.ipynb
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -97,9 +97,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#  The following command loads the dataset.\n",
     "data = loadmat(os.path.join('Data', 'ex8data1.mat'))\n",
@@ -144,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -183,6 +196,8 @@
     "    sigma2 = np.zeros(n)\n",
     "\n",
     "    # ====================== YOUR CODE HERE ======================\n",
+    "    mu = (1/m)*np.sum(X, axis=0)\n",
+    "    sigma2 = (1/m)*np.sum((X-mu)**2, axis=0)\n",
     "\n",
     "    \n",
     "    # =============================================================\n",
@@ -205,9 +220,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#  Estimate my and sigma2\n",
     "mu, sigma2 = estimateGaussian(X)\n",
@@ -232,9 +260,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise anomaly-detection-and-recommender-systems\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): n\n",
+      "Login (email address): waiyen.chan0819@gmail.com\n",
+      "Token: vOltOwwJoWFoVPDs\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "               Estimate Gaussian Parameters |  15 /  15 | Nice work!\n",
+      "                           Select Threshold |   0 /  15 | \n",
+      "               Collaborative Filtering Cost |   0 /  20 | \n",
+      "           Collaborative Filtering Gradient |   0 /  30 | \n",
+      "                           Regularized Cost |   0 /  10 | \n",
+      "                       Regularized Gradient |   0 /  10 | \n",
+      "                                  --------------------------------\n",
+      "                                            |  15 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[1] = estimateGaussian\n",
     "grader.grade()"
@@ -286,7 +338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -329,10 +381,17 @@
     "   \n",
     "    for epsilon in np.linspace(1.01*min(pval), max(pval), 1000):\n",
     "        # ====================== YOUR CODE HERE =======================\n",
-    "\n",
+    "        cvPredictions = (pval < epsilon)\n",
     "        \n",
+    "        fp = np.sum((cvPredictions == 1) & (yval == 0))\n",
+    "        tp = np.sum((cvPredictions == 1) & (yval == 1))\n",
+    "        fn = np.sum((cvPredictions == 0) & (yval == 1))\n",
     "        \n",
+    "        prec = tp/(tp+fp)\n",
+    "        rec = tp/(tp+fn)\n",
     "\n",
+    "        F1 = 2*prec*rec/(prec+rec)\n",
+    "        \n",
     "        # =============================================================\n",
     "        if F1 > bestF1:\n",
     "            bestF1 = F1\n",
@@ -350,9 +409,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best epsilon found using cross-validation: 9.00e-05\n",
+      "Best F1 on Cross Validation Set:  0.875000\n",
+      "   (you should see a value epsilon of about 8.99e-05)\n",
+      "   (you should see a Best F1 value of  0.875000)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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T7g2p2aqqzftydClphN+JYN/KI6ybuZWUhFSKV/Kjea9GNO/1IsUq+GXZbktJT9OjS9aRrtNnOhzo0/To0wwYDUbCbkdw9eQNrp26xa2zd9Cl/NXxQNM0s/NFHmdc8pg96ZzzOGNnb4fJaMJkzMgRZTCiTzOQGJNEUmzSX5w5wJx6o1jFIpSsUowSlYtRpkZJ3Arkw8HRLGz2jg44OpuDwLrkdcHZ1emprq8lxyezdc5uVn+1ibjIBGr5v0DAG50pW7MUnj62PZyA2elj7bdbWTFjPfo0PV3GtqP7653UWpXCJnKtQGmaVhI4AFQFXgcGAwlAIPCGiMT+W/3nVaDmTw+jz5FxuGxbDyZT5nGxs+NmtW6U3f49+Ppa1WZCdCKrvtzIyZ1nuX3+HiJCg461GfnlAJvEQ0Q4u/ci677bytWTN4gNj898r1G3evR972XK1Sqd7Tfc1GQdwVdCzG7aQfcJvhpCXGQCSbHm1BZJsUmk6/RPbMcljzNla5WibM1SFC5VyLw3qmhBvIsWpICvh9WjOpPJRHJ8innfU2QCwVdDzSOKoPvcvRRMxL2oJ7ahaRqu+VxwdXOhgK8HXkUL4uVX0Lxnq2hBytcpTfFKRXPkM93y8++s+nIjcRHm/8dCJbypWL8sleqXp1KDcpStWcrqtaWYsFgWfLCSHfP+QESo16EmAa93okaLqv9pRxeFdeRKgdI0LR+wH/hMRNZpmlYIiAIE+ATzNODQx9QbCYwEKF68eO27d+/mqJ3ZzfzpYTR9rxFluIU4OqJ17gwVKqA7fxX7LRtxxEC8V2ncLx6BQoWe2J7RYGTrnN0s+GA5yQmpVG9ehWpNK1O7TXUq1bd+7cFoNHJo3QlWfbmRa4E38SzkTv0OtfAtXYjCpXwoU7MUJSoVteXS/5F7V0LY9utuDm84QfidyMzcTg+jVBTw9SCfZz7yeeTFzTMveT3yZmy2NQeTfZhE0MHJnOOpYBFPilfye6rTb8kJKdw+f5eURB0GvTkQrEFvJF1nHu3pktPQJenQJZsDwkaHxWVGvXiYPgTMKUTqtqtJvfY1qdmqaramDnkYVf3e5ftcOXGdK8dvEH7XvOTr6OxIh+Gt6PdBgNWjq+CrIfyx7BBbft5FXGQCpauXIGBiJ5r3fhFHJ+Wqrvh3cp1AaZrmCGwBdorIN495vySwRUSq/v29R3keR1C6TgG4bFnLKWrxVrmNrDhkvtm3aAHxQffZ6dKFKrrT0L07rFnzr22d23eJHybM4/aFe9RoWZUx3w2hVNXiNtmlT9ezY95eVn+1iQe3wvErV5ieb3bGf0DTHPHaStelc3DtcbbO+Z0LBy/j4GhP3fY1KV+7DCUqF6V45aL4lfV96lEqngW6lDQi7kVx4YB5r9Tp3RdITdLh4GhPTf9qdHylNfVfqpUjghv9IJYrx69zbMspdi3ch7OrEz3e6Ez31zuSx806cUzXpbNn6UHWfruFu0H3KVDYk/6Tu9NhpP8zX6tT5F5ylUBp5rH/QiBGRF575HhhEXmQ8fNEoL6I9P63tp47gbp3D0qVQuztaVXyFnuvF8U7IyxaZCRUrgz7lwTjVa+0eervzp3Hrkmd23eJxR+v5ty+S/gU92LU14No/HJ9q6ZVkuKSuRZ4k6snb3L15HWCjl4jNjyeivXK0vPtrrzYpU6WbyqpSakE7jxHyI0wc+6nsFhiMnJARd2PQZeSRpGyvrw0wp/Wg5pnaV3kfwl9up5Lh69ycvsZ/lh+iKiQGPJ55MXT1wN3Lzc8C7lTskpxytY0J2r0Ke6VLVNqwVdDmP/BCg6uOUZe9zxUqFeWJi83oO2Q5laNhESEwF3nWD59HRcOXKZYhSK0GdScVv2bqnUqxf8jtwlUY+AgcAGzmznAe0AfoAbmKb47wCsPBeufeO4E6vvvYfx46N6diB/XULWqWZgAvL3h4kXw8QECAmDtWvP5Y8dmVo8KjeHXtxfzx7JD+BT3otOoNnR9tYNV+17C70Yyd9JS9q88kjmV5leuMBXqlsG/f1PqtK2RpZtdarKOE9vOsH/1EU5sPZ2ZjNA1n0tm3qcChT0o4OvJi13qUq1Z5eciGsSzwqA3cHRTIKd3nychJomEqASiQmIIuR6W+f/n5pmXMjVK0qJPE1oPbJrlabXLx6+z7dfdXD52jbtB9ylUwpt+k7vTemAzq0a0IsLBtcdYP2sbFw9dQdM0arSsSucxbWnUtZ5ap1IAuSwflIgcwhyW8+/8656n/wni4syvFZ7g7lu+vPk11uwjYtAb2DB7O4s/Wo0+3UD/DwLo/W5XqzzyUhJTWT59PWu/3YKdnUaPNzpRq3U1ytcpg5tnPluu5i9cPn6ddTO3cmxTILqUNDwLudN2SAua9mhI+dqln1ka9ucdB0cHmnRvQJPuDf5yPDVZx52Lwdw4c5ubZ+9w6fAVvh35M0s+Xk2PNzvTfngrmzfsVqpfjkr1y5lHQjvPsmDKSr4Z8TMrZqyn/5QetOzb2KLRtaZpNA1oSNOAhoTeDGPPkoP8vng/H3X/ilqtqzF+9jCKli9ik42K/zb/OoLSNM0F6Ag0AYoAqcBFYKuIXHoqFvL8jqB0HbtT+9YagoL4f1N8e/eCz5g/R1CnKzblp4nzuXMxmLrtazJ25hCr9p2E3gxj7/LDbPxhO7Hh8bTq14Sh0/riU8zL5ssw6A3cPHeXkOsPCLn+gAsHL3NmzwXcCuSjWY+GNO/ViKpNKj7zdQcRISUxlbSUNNJ1Znd0c1TydAx6458u5RmviICmoWkamgZoGnZ2WmZGXwcnBxydzK8ueZ0zoqO7PPPrfHitgbvOsXzaOi4cvIyHjzvNejSkdLUSVGlcMUsx/ESEo5sDWTh1JbfO3aVE5aJ0GduOhl3q4lWkgFVtGY1GNv+0i/mTl6PX6ek+sSM93uqsNnD/h8nWKb6MiA+dgH2YN9lGAC5AeaBFxs9viMh52022jOdOoO7dQ0qVwmCypxS3cK9clL17zW+1aAFBQdCyXDC7b5cm1ujIz50ns3fTGXxL+TD6m8E07FzH4ptMckIKyz5dy7qZWzHojVRrVpkRn/e3OaoAmJ/at8/dw5qvNxN53xzBQNM0fEv50PGV1nQa3eapjZRMJhNRITEZEddDuX81lPC7kSTEJJEYnUhiTBIJMUmZqS9yEpc8zri6ueDunR/vDNdx76IFKehXgMKlfahYv1y2RSS3hAsHL7N8xnouHrxMapIOAHcvN6o2rkiVRpWo3boapatZH7XcZDJxaN1xFn24irtB9wGoWL8cjbrWo/HL9SlazvIHp5iwWH59Zwl7lhzENZ8L3SZ0oNc7XZ/q56TIHWS3QL0kIlv/pTMfoLiI5LhyPHcCBdyoGUDZs2u55FKLQkc24FXT7AQREQF9Ggcz43pXQolnnmMN9HaO9Jn0Mr3e6WKxJ53JZGLPkoPMfXcJMWFxtB3cgkEf98rS4nRibBIbv9/B+lnbSIhO5IUmleg0ui0lqxajSJlCOZ6U7yFhdyI4tO44hzec4Mbp23/ZgJvHzRXf0j64e+U3x6bzzEf+gubXhyktnFwczVlxXRyxd3TA3t4Ou0eKppkHUWL+JzMnlEFvRJ9uwJjxqk/To0tOIzUxlZTEVFISzJl646ISMqNVPIwODmBnb0f52qWp2rgSLzSpRNXGFclfMOdHDCKSOcK9ePgKFw9ezowu8WKXugz5tI9NAWdFhHuX73No/QmObDzJtcCbALQe1Izh0/tRwNfT4rZuX7zHkk/WcGD1UQqV8Gb898Oo/1Jtq21SPL/kuJOEpml2QD4Rydn0pX/jeRQowsKIf6ER7lG3wMHBHD2ifHm4do1rG/Yx01ida1oBajWtwPg5Y6yao798/Do/TZzP5WPXqVivLGNnDbV5xPTw5rZz/l42/rCD1CQd9V+qRe93u1G1UUWb2rS0X12yjrhIcxLA+KhEbpy5zaF1x7h++jYApauVoFqzyhSvZM5aW7RCkWyNvJ4dGPQGYh7EcjfovlkgDl3hyokb6NP0aJrGC00r0bzni/iVL0I+jzy4eeajQGGPHBf7qNAYdi3Yx8ovNpCaqMN/YFO6jGlHmRolbXbpjwiOYvOPO1nzzWacXJ0YOLUnHUe1tupaLhy8zMzRc7gbdJ8m3eszZuZQq6cPFc8nOSJQmqYtA0YBRsxTfe7ANyLypa2GWstzKVAAYWEwbhysN0eSSMWeRVRhnVYOd2cY/d0Qmo/sYPENN+JeJL++s4R9K4/gWcidYdP70XpgM6u94+5evs+ZPRe4cPAyFw4EERsej6ZpNOvZkN7vdsv2CORJcclcOXGDy8euceXEdW6fv0dcZAL6tP8fKaJSg3I07lafRt3qPbex39J16VwLvMnp3RfYt/LwXzIBP8TLrwCFyxSiSGlzrL/ydcpQy/+FbF/nSohOZMWM9Wz4fgf6ND0ueZ3pPLotfd57mXweeW1q8/61UH58bT4nd5wlf0E3Oozwp/vElyyO+ahP17Pm6y0s+WQ1Ti5OjJs9jJZ9G+eqBw9F9pNTAnVWRGpomtYPqA28A5wSkWq2m2odz61APSQ4mBPT5zNrxRXC4/S81K8hw2e/YvENIjVZx8rPN7D6q00A9HyrCz3e7Gz1BsvQm2H8+s4SDq07DoB3sYJUa1aZak0qU7PVCxQu/eSIFpYSERzFxtnbObb1FPcuhwDmdawSlYtStlYpCvh64u7lRn6v/Lh7ueHu5Uahkuasr/9LPByhxobHkxibZA5oGxxN6K0wHtwMJ/RmODEPzF6cPsW9aD+sFe2Gtcz2UUVsRDzn913i6OZA/lh2CLcC+RgwtQcdX2lt04hKRDi/P4j1s7ZyZGMg+TzyMHRaPzqMaGXxA1PIjQd8MfgHgo5cpUGn2oz/fniWnHoUuZucEqhLmPctLQO+F5H9mqadE5HqtptqHc+zQMWGx/HT6wvYu/wwxSv5MfGXV6jauJJFdUWEP5YdYu67S4gKiaF570aMmNEPn+LeVtmQHJ/M0k/XsmH2duwd7On5dhdaD2yGb0kfWy7pX7kaeJO1325m/6qjAJm5jCo1KEeFumXI627bU/v/MqnJOgJ3nGXLnN85/ft57OztaNi5Dh1faUPt1lnPbvx3rp++xZy3FnF27yWKVSjC8M/707CT5Y45f+duUDCzx/3GuX2XqFivLBN+GknZmqUsqms0Gln33TYWTlmBnb0dQ6f1pdPoNrnCY1KRveSUQL2KedR0DngJKA4sEZEmthpqLc+jQIkI+1cdYfa430hNTKXPey/T652uFqfXDr4awnej5nB+fxDlapdmzHdDrF4TunnuDmu/3cLhDSdITdTRZlBzBn/aO9uezlOTdQQducq5fZe4cPAy9y6HkBCdSB43VzqM8Kfr+PYUKmGdmP7XCbnxgG2/7mHXgr3ERSaQ1z0PxSoUwa98YYqV96NohSLUaVudvPnzZKkfEeHYllP8+vZigq+GUrpaCdoOaUGrfk1w98pvU3t7lh7klzcXkRCVQMdRbRj8SW+L9949uB3OzNG/cmrXOeq0rc6UNW8qT7//MbLbi68hcEz+dkJGCCN7ETHYbKmVPG8CFRUaw6wxv3J0UyDl65Th7QVjKVHZMi8qfbqelZ9vZNlna3HO48yILwbQbmgLq9aZEmISWfDBSrb+sgtXN1cad6tPl3HtKFertK2XlElEcBTbft3N2b0XuXriBga9ETt7OyrULUPpaiUpU70ELfs1yfIN9L9OepqeQ2uPcfHwVe5fM7vXP3T5d/PMS/fXO9F1fPssf84GvYFdC/ax9dfdXAu8iYOjPQ271KXdkJbUblPN6pFMUlwy8ycvZ8vPu8jvlZ/R3w6mRe9GFo3ORIStc3Yze+yvVKhXlk+3TFL7pv6HyG6B+hmoB1wDdgA7RCQsy1bawPMiUCLC9t/+YM5bi9Cn6Rn8SR9entDB4pQPQUev8u3IX7hzKZjmvV5k9LeDrXLlNRqN7PjtD+a9v5yk2CQ6jW7LoI97ZUsEiegHsayYvp6tc37HaDRRvk4ZqjerTPUWVanaqIKKIPEUSE3WceP0bVZ9uZFjW05lq1AB3L5wl53z97J7yQHioxIpXLoQo78dTMNOVt1TALhx9jYzR83hyokb1GlbnY99lzsAACAASURBVFd/HEHhUpatcR7ecILPen+Lp68Hr/3yCnXb1rC6f0XuI6em+CoC7YG2mD349mIWrMMiYrTRVqt4HgQqNSmVD7t/xenfz1OtWWVe/3WUxV5oqUmp/DZpGZt+3IlX0QJM+HGE1XtETv1+jt8mLeX66du80LQS42YNs2mT5qMkRCdydHMg5w8EsX/lEfTpBtoMak7/DwJyzdSd0WA0526KSkSXrCMtJR1dijl7blpKOga9AaPhr8kJMwJJmCNJ2JkjStjZ22VGj3B0dsTR2fyaN78red3zmEtG+o/cEFPwauBNlny8OlOoWvZtQp22NajevHKWHxb06XqObT7FwqkruRt0n7rtazJsWl+rvTuNRiObf9zFvPeXYTKaGPnlQDqNbmPRaOry8et8NfQH7l0O4aWRrXn1x+G54nNX2M7T2AflijmKRHugobWd2UpuF6igY9f4auiPhFwLZeysYXQc1driL9P5A0F8NfRHwm5H0GVsO4Z81scq77yokGh+en1h5gbIYdP70bzXi1laWE9LTWP9zG2s+HwDyfEpuHnmpUHnOvR7v/szcf1OiE7k1vm73L5wj1vn7xJy4wFxEQnERyaQGJOENX/DWUXTNDwLuVOopDeFSvrgW9IH35LeFKvoR6UG5Z56XqSrgTdZPn0dgTvOkpaajoOjPVUbV6R2mxo07FTb4qnlx2HQG1g/azvLPltLUlwyTQIaMOjDnla3GREcxXej5nBy+xnaDWnBmJlDLBLRdF06Cz5YweqvN9NlbDvGzhqqXNGfY3JMoDRNqwU0xhyB/LCInLbNRNvIrQKVrktn4dRVrPl6EwX9CvDmb2Oo5W+Z970uJY157y1j/axtFC5diDfnjaFa08oW9200GNn4ww4WTlmJPt1Av/e70+OtzhY7YTy2TaOR3YsPsHDKSiLvR9OgU20GTOlB2ZqlnurTa2pSKse3nubAmqMEHb1GdOifCZfdvdwoVtEPT18PPLzy4+HjjoePO+5ebrjkc8EljzPOeZzMkSRcnXB0dsTO3g57B3vsHeywt7fjYSiJh9EkxCQYjSaMegP6NAPpaXoM6QbSdXpSE1NJiksmOT6F5PgUkuKSiQ6NJfxuJGG3I4i4F4XRYJ5IcMnrTI2WVanTpgZ129WgSBnrsiVnhXRdOhcPX+XUzrME7jrHrfPmBJ8t+zZm6Gd9szTiTYpLZs03m1k/cxu6ZB2dx7Rj8Ke9rZpWNJlMLJq6imXT1lGohBev/fIKtVtb5gj88xsLWfvtFoZ82oe+771s62UonjE5NcU3BegBrMs41BVYLSKf2mSlDeRGgbp++hbT+88i+EoIHYa3YuRXAy3+wl4+fp3PB84m5PoDuoxtx7AZ/azyWLpy4jrfjZrDzbN3qNuuBuNmD8vSzdBoNHJ8y2kWTFnB7Qv3qFC3DCO+GED1ZlVsbtPivg1Gwu9GEnIjjKj70ZzceTYzfUcBXw9q+r9AmWolKVWtBKWrFcezkEeueoo2GoxEh8Zw48wdAnee5eTOs4TdjgCgSFlfmvVoSLnaZfDyK4CXXwGb0tDbQvSD2MyoDyaT0O3VDnQd3z5LobASohNZOHUlm3/aRYHCHoydNYwmL9e3qo2Lhy7zzYifCb4aStvBLRg768mjKZPJxBeDvmfP0oNMnDOKDsNb2XwNimdHTgnUZaCmiOgyfncFTouIZZt5soHcJlDJ8ckMrTwROzuNN34bQ502lj0JGvQGlk9bz9LP1uLlV4A3542hRot/TSb8F1KTUlnwwUo2zN6Gp68HY74bQpPuDWy+YUeFRLN97h9s/20PkfejKVKmEEOn9aNpgO1t/hsiwrl9lzi6KZD710MJuR5G2O2IzBEIgIePO026N6B5rxep0qjCc7cfRkQIuRFG4M6zHNtyijO7z2My/fkds7PTKFm1ONWaVqZa8ypUa1rJJrduS4m8H838ycv5fdF+NE2jZquqDJjSw+K9eI/j0Qek1oOaMW7WMKumpdN16Sz+eA2rvthATf9qfLLpnSdOjerT9XzQ+XNO7TpHp9FtGfFFf+WG/pyRUwK1HegjInEZv3tg3gfV0WZLrSQ3CVRaahpfD/+JfSuOMPvYNCrULWtRvVvn7/LlkB+4ceY2Lfs2ZuysoVa50J7ceZaZo+YQfjeSTqPbMmx6X5s8tx6ma9jy8y6ObTmFyWiidpvqvDSyNQ071c6R1OuxEfH8vnAf2+buIeT6A1zyOONXvjBFyvriV7YwfuUK41fWFy+/AviU8HruROnfSIxNIvxuJFH3Y4gKiSEyOIorJ64TdORaZhDcklWLUb9DLbqMy9oI598IvRnG7sUH2DZ3N9GhsbTq14Thn/e3eU+cQW9gySdrWD5tHYVK+vD2grFWi96O+Xv5etiPtOrfhLcXjHviNHJaahrzJ69g3XdbKVymEG8vGEeVF5+Qc02Ra7BFoMxz8I8pwGxgFrABCAEWAPOB+8CKf6qXE6V27dqSG7gaeEOGVp4g/lqALPpolUV19Ol6Wfzxamnn1EsCCg2TQ+uPW9VnXGS8zBg4S/y1ABlSaYJcOBhki+kiInInKFje8v9I/LUACSg0TOa+u0RCb4bZ3N6/YTKZ5PyBIPm09zfSzqmX+GsB8lqTyfL74v2iS9HlSJ/PE+lp6XLx8BVZ+tlaebvNx9LGoae0dewlMwbOkhtnb4vJZMqRflOSUmXe+8ukvXNv6eTWX1Z8vkHSdOk2t3fhYJD0LzVa/LUAmTnmV0mKT7aq/pJP14i/FiDvtvtEIoKjLKpzdt9F6VdytLSx7yFzJy3Nkv2KpwcQKFbe+/9tH9SgJwjbQquUMAvkhhHUuX2XeLftJ3j4uFs8rSciTOv7HftWHqF570aMmzXUqumci4cu82nvb4mLSKD3u13p+97LFqfjeJSE6EQWf7SazT/vwiWvM4M/6c1LI/2z1eMsNVnH1RM3CDp6jcvHzCU+KhE3z7y0HticDiNaZcmj7H+dsDsRrPtuK9t/24MuOQ0PH3cq1i+Lf/9mvNilTrZ7B4beDOOn1xdwbPMpfEv5MGxaX5r1tM378+HU8/pZ2yhaoQhf7J5i8chMRNj80y5+fXsxTq5OzNg52aIN5ckJKfz8+kJ2zPsD/wFNeWfheKvtVjxdctzN/FnxrAXqwe1wxtWbhLt3fr479IlFU3Ox4XF8M/Jnjm0+xaCPetH/gwCL+9OlpDH//eWsn7WNQiW9mbr2TcrWsCy22aPo0/VsmL2DZZ+tJSUhhfbDWjHok954+lgWddoSkuKSWfP1ZtbN3JqZNK9YRT8qNyjPC00r0aznizanJP8vkhCTyP6VR7hy8gZn9lwgMjgaDx932g1pQYcR/tka0BfMU8e/TVqa6XAz/vvhNvdxdu9FpnT5nAKFPflyz1SrpiuDr4Ywqd1nJMUlM23be1RuaNnU3S9vLmLdd1uYtHQCzXs1ssluxdMhW6f4HhbMKd/PADFAApAIJFg7VMtKeZZTfCmJKTKy+hvS1XOQBF8LtajO4Q0nJMBnqLR36SNrvtksRqPR4v4uHLosg8qPz5wySUlMscnu8weCMqcjJ3X4TG5fvGdTO/9ESmKKLP1srXT1HCT+WoB80utrObb1lMRHJ2RrP/9lDAaDHNt6Sj7oMkPa2PeQ1nY95N12n8jZvRezvZ91M7dKJ7f+0sG1jyyfvk706Xqb2rp4+Ip0zj9ABpQZK+F3I6yqG343QgaVHy8d8/WTM39csKhOfHSCjG84Sfy1AJkxcJbVU4yKpwc2TPFZIlA3gGpkjLaeRXmWAjV9wExpY99DTuw4Y9H58ycvF38tQF6p+abVorDyiw3S2q6H9C81Wk7vOW+LuZKmS5fZ4+aKvxYg/UqOlmNbT9nUzj8Rfi9Sfn5joQT4DBV/LUAmd5ou18/cytY+FP+fiOAoWfThKunlN0L8tQB5s+VU+X3xfklJSs3WPj7s/qX4awEy/IWJcuPsbZvauXz8mnTxGCgBhYZZ/L15SPSDGBn+wkTp4NpHLhy6bFEdg94gC6eulDb2PaR/6TESGRJti9mKHCanBGovYGdtw9lZnoVAGfQGWTBlhfhrAfLLmwstqhN8NUTaOPSU6f1nSnqadQu3vy/eL/5agHzU4ytJTrBt1BQRHCXjGpifJn+YMC9bb15JcUky990l0sG1j7Rz6iWTO0+XS0evZlv7CstIS02TVV9tynRM6Jivn8wYOEsuHbmSbX0c2XRSehYZIe2cesmST9bYNJq6ffGeDH9horS26yELp64Ug8Fgcd34qATpW2KUdM4/QPYsPWBxvQuHLksH1z7yYfcvrbZXkfPYIlCWuJnXBT4B9gNpj0wNfmPVXGIWeNprUBHBUczoP4sLBy/TemAzXv1xhEXrKJ/1/Y5jmwJZdPN7PAt5WNRXckIKP742n10L9lG1cUU+3/WBTY4QZ/64wLS+M0lLSePNeWNoGtDQ6jYehz5dz5aff2fJJ2tIiE6kVb8mDP6kd47kkspu9Ol6EmOSSIr7MwpEclwy+jQDDg/j7mW8uuRxwqtoQbz8CuSIq312YzKZuHT4KrsX72f/6qMkx6fQok8jhs/ony1J/+Ii4/nh1XnsW3mEMjVK8ua8MVavg+pS0pg15ld+X7SfWq2rMWnJqxZn3Q27E8GMAbO4dPgq/gOaMm72MIu2Vaz4fAO/TVrK1LVv0ribdZuIFTlLTu2D2gUkARcA08PjIvKRLUbawtMUqMBd55jW9zv0aXpe/XEErQc0s6je1ZM3GFd/Er3f7cawaX0tqxN4k097fk3EvSj6THqZ/lMCrL45pqfpmfPmIjb+sINiFYowdd1blKhU1Ko2HoeIcGj9Cea+s5jQm+HUaFmVkV8MyJaUHVnFZDKRFJdMfGQCcREJxEUmkBCVQEqijoSoBO5dCeHe5fuE3gzHZDQ9ucFH0DSNgkU88SnhjU9xL4pX8CO/lxv5PPKSz8McMLaArwfexQo+9bh7/0Rqso5VX2xk1Zcb0TSNXm93JeCNjtkSYf7Q+uPMGvMrCdFJ9JnUjQFTe1gV9krEHOH/+/G/4e7lxofr36ZCnTIW1TUajCz7bB1LPllNoZI+fLjurScGQDboDYyt9y5xEQnMC/pWJcjMReSUQAVa22hGvWLAIsAXs7DNEZGZmqYVAFYCJYE7QE8Rif2nduDpCVR6mp4BpcbgViAfH65/m6LlLAuMun/1Ub4Z/hNOrk78FvStRV5+yfHJjHjhDTQ7jfeWvWbThsPkhBQ+fPlLzv5xka7j2zN0Wt9s2V1/48xtfnp9Aef3B1GySjFGfDGAuu1qPPUQQyJC+N1Ic6DY8/e4deEut8/fJfRm+F+iTzyKvYM9fuV8KV6pKMUr+lGwSIFMYXkoMg5ODhj0Rox6I/p0A4Z0A6lJOiKDo4i4F0VEcBQRGbH2wu5EPrYfOzuNgn4FzMFiS/lQrIIftVpXo1ytpxu38FHC7kQw5+3FHFxzDA/v/PR9vzudx7TNcmilhJhEfpq4gN2LD9BpVBvGfT/M6mu8ceY2U7t9gaZpzNj1gcXfLYCLh6/wccBXFK1QhG/2ffzE84OOXWPCi+/T+91uDP2sT64KjfVfJqcEagbwh4jsstKYwkBhETmtaZobcApzHL/BQIyIzNA07V3AU0Te+be2npZA/b54P18M+p7pOyZbtM8pXZfOL28uYtOPO6nUoBzvL59ocVDOb0f+zI55fzDzyGdUrFfOalujH8TyXofPuHvpPm/8Ntrikd6T2pz//nJ2LdxH/oL5GPRxbzoMb/VUYsc9yv1roexcsI/di/cTFRKTebxw6UKUrlacouWLUMDXE3fv/Hj45M8MFuvq5opLXudsjUSRnqYnOS45M2BsYmxGsNg7EZkBY8PuRBAZbE4m6O7lRq3W1ajbtia121SzKp9XdhF07Brz31/G2b2XKFOjJK/9PNKmv7FHERF+m7SUlV9spEaLKrw1fyw+xa0LQBt07BqTX5qGQW/k1R9H4N+/qcV1V36xkbnvLmHupW+fOEMgIkzp+jnHNp+i9aBmvPqDZVP0ipwlp9zMEzGPgFLJgps5sBFoDVzFLFwAhYGrT6r7NJwkTCaTjKr1lgytPMGiXfwhNx7I6NpvZTpRWLOQfOr3c+KvBcictxbZZOu9K/elf6nR0jFfP6u9pB6HPl0vy6evk475+kk7p17yy5sLJTE2KcvtWkNcZLxsm7tbJjR+X/y1AGlj30Pe7zhNNv+8Sy4dvWqz48jTIjYiTvYsPSAzBs6SgELDxF8LyHQLP7zhxFOPnmEymeTAmqPSs8gIaW3XQ2aPm5tlF2yTySTbf9sjndz6SxePgbJ7ieUODA8JvxshrzWZLP5agHw+aLbF/68x4XHSzqmX/DRxvkXnGwxmz77Wdj1kRLXX5d6V+1bbqsheyAkvvuwomKfz7gH5gbi/vRf7pPpPQ6AuHAwSfy1ANv2084nnmkwmGVhunHQrMEiObDppVT9XA29IzyIjZHCF8TbdtC4eviI9Cw+XAJ+hcuXkDavr/53Qm2Eytt474q8FyJSun8v965bt9coqyQkpcmxLoPz42nwZUe118dcCMsM5rfh8g0SFxjwVO3ICo9Eo10/fkgVTVmS6hbd17CWj67wts8b+Kmf+uJBjoYz+TlJcksweN9e8faH0GLkamD1/Mw8fJHbM/8Pq+o+6hX/c82uL633a+xtp79xb9q8+YnGdkzvPSnfvIdLLb4QKsfWMyVaBAkr+a0XQgKJP7ADyYZ7eeznjd4sEChgJBAKBxYsXz6nPTETMsb0CfIZKF4+BEhsR98Tzr5+5Jf5agGz/bY/FfZhMJtn88y5p79xb+hR/xeq9Q0ajUZZNWydtHHpK/9Jj5NaFu1bVfxx7VxySzu4DpIvHQKu+9LZi0Bvk98X75bUmk6Wtozk+X3uXPvJ2649k2bR1cvn4tad2435aGPQGOb7ttPz23lJ5s9WH0smtv/hrATK6ztuyd8UhMegtd7/OChcOXZY+xV6R9s69ZdVXm6xy+34cBoNBXm8+RTrnHyBhd6zbkPuQ+R+Y9wxaut8qKjQmc1PunLcWWfzZnd13Ufy1AFk/a5tNdiqyh+wWqNXAWmAgUAXwAYoDLTG7nR8BWv9r4+AI7ARef+RYrpniM5lMsn7WNmnr2EuGVHxV7l62bBpg0UerpLVdD4kJi7Xo/JSk1MyAr++2+0TiIuOtsjP6QYy83doc5PXT3t9IUlzWpt9Sk3Xy9fCfxF8LkFdffE8e3A7PUntP4qEwDa5gjpAxrMprMnfSUjm1+/x/7qk2LTVNtvyyK/Oz6F9qtKyftS1b96z9E3GR8TKl6+firwXIhMbvZ3m0HHorTDq59Zc3W061KlrKQxJiEqWLx0Cr9i2l6dJl5ug55s3KrT606IFSRGRi0w+kd9GRKrDsMyTbp/iAysBnwL4MYTkDLAP6Ay5PqKth9uL77m/HvwTezfj5XeCLJxmZUwL18AlucufpFt/0Lx6+Ip3dB8irjd636Hyj0Shj6r4jre16yKKPVln9RY4IjpIevsPkpTx9Zeuvu7M8wrh35X7mBsq5k5baHNLGEmLCYmX59HUysOxY8dcCZGSNN+TQ+uM23cz+1zAajXJo/XF5tZF5qqy79xBZMGVFjk9tmkwm2bVon3TxGCgv5emb5Ugj2+buFn8tQGaPm2vTaHDh1JXirwXIuf2XrKq3c8Fe6eDaR/qVHG3ROlbgrrNWz3oospdctQbFnynizwNnM0oHoCCwB7ie8VrgSW3lhEDFhMdJB9c+8kmvry2+YR7ZdFI6uPaRQeXHW/z0eXaveXphyy+7bLLz6+E/SXvn3jaHnXnIwwXujnn7SbeCg7PFueKf0KXoZOlna6Vjvn7irwXIxGYfyMF1x5Qw/QMXDl2WyZ2mS2u7HtLWsZdM6/edBB27lqN9Rt6PktG135KX8vSVi4dtj0JhMpnkhwnzMsMvWTqieUhCTKL0LTFKXsrTV/auOGRV3ePbTou/FiD7Vj15etpkMkkP32HyxZDvrepDkX3kKoHKzpITAjXv/WXS2q6Hxd492+buljYOPWVsvXes+hJ+Pfwn6eTWX1KTrZ/KCjp2Tdo49JTvX/3N6rqPkhibJJ/0+jrzJhJ537K8O9ZiMplk36oj0q+kOQzP1Je/UN5TVnD/eqj8+Np86ew+QPy1ABnXYJKcP2B7/q8nERMel+nsc+v8nSy1tXPBXmnv0kf6lhgl107dtM6OsNhMz765k5ZavD5mMBjkZa8hMr3/TIvOf7fdJ9LDd1i2rN8qrEcJlIUkJ6RIV89B8lGAZXPfq7/elLl+ZE108ZTEFOniMVBmDJxltY2/L94v7V3MUxiWrnU9jpvn7piTuzn0lGXT1mV5cfyfuH76lkxs+oF5Kq/6GxZHo1b8f5ITUmTD99ulT/FXxF8LkOkDZlo9MrGU0Fth0rPICOmYr5/8sdy6EczfuRp4Q/oUf0U6uPaRA2uOWlU3PS1dvh35c+aUu6VTz58Pni1dPQdZdP6Ns7elZ5ER0jn/ADm127ZgzArbUQJlISd3muejT/1+7onn6lJ00jFvP3m/4zSr1msSYhLl9eZTxF8LkLP7rEuPcGj9cWlj30PebDnVaoeKR0lN1smg8uOll9+IHAvsqkvRyZy3Fkkb+x4S4DNUtvyyK8dE8L/Go9lvX/YaInuWHcwRL8fI+1GZDxebftyRpbZiI+JkdJ23pWeREVavSZlMJln1lflh0FKv0r0rDllld0RwlAyvOlG6FRhkdToQRdbIEYEC9lhyLCdLdgvUpp92ir8WYFGK6WNbT4m/FmDVms2D2+EytPIEaefUy6pozCLm/VgdXPvIuAaTsuTZFRMWKxObmW86tqbueBLn9l+SgeXGib8WIN+M+Ompb+79T3D3rtx+b4aMKzpQ/LUA+aDN1BxJJ5GWmiaTO08Xfy1AVn25MUttHVp/XPy1AKv3CIqYp+36FHtFJnX4zKLzdSk6eaPFVPHXAmTlFxssEvD710Olc36zo1NOOgkp/kp2u5m7AAWAc4Bnxs8FMjbdXra2o6yU7Baon15fIO2de1u0aP/dqF+kY75+FrunXjt1U3oWHi5dPQdZnVjuzqV70tVzkAyuMD5LI6fLx69J76Ij5aU8fa0WSEtISUyRmWN+NbtJlx6TYwL4X2betAeS2rG7iJ2dCIgBZDXlpIPWTTo7dJfdP2zK9j716Xr5tPc34q8FyMKpK20erenT9dLDd5iMbzjJpugV8ycvlzb2PSx6gBQxu54/XGP9YcI8i77Xe5YdzFzzUjwdslugJgC3MafYuP1IOQeMs7ajrJTsFKjrp29JZ/cBMq7+uxadP6j8ePmgywyLzjUYDNK3xCjpW2KU3LlkfQbbN1pMlYBCwyT0VpjVdR/y4Ha4dHYfIP1Lj8mRRILh9yJlVK23pLVdj2zPOZXTfP+9SPhjtnyFh5vfyy3Mm/ZAblBaBMTk6CjSvbvIe+9Jasfucgt3eZXm4q8FyPyJc7N9ys9gMMiXQ34Qfy0gSy7oe5YdlLaOveTVF9+zOit0yI0H4q8FyLqZWy2uYzQa5cfX5ptdyedZFt3iyyE/SGu7HhL94PmNWvI8kVNTfOOtbTS7S3YJ1I2zt6VbwcHSp/grFolAbESctHHoKb+9Z9lT1sO9Fpa4vf6dhJhEaePQU+a9v8zqug8xGAzyWpPJ0tl9QI5svj22JVC6FRwsndz6y/Ftp7O9/Zzk++/Nf+2VK/9VpMLDzccg94hUasfuIiCB1JIW5YIlPPxPO/0IlvPOteRraom/FiA/vjY/20VKn66X/qVGy/AXJkp8VILN7RxYe0za2PeQt/w/krTUNKvq9in2ioys8YbER1vev8lkkpE13pChlSdYNIq6dPSq+GsBcmDtMatsU9hGTgnUwMcVazvKSskOgbp1/o687DVE+hR7RUJuPHji+dEPYmR4VXPqaUtj3k3r9510KzDI6i+jyJ+LvVnZk7Lk0zXirwXI74v329zG49Cn62XOW4syN9sGXw3J1vafBo8K0UORetyxZ87duyJ2dmJydJQW5YIFRLy9zeWhnZGn74nJ3kF+0GqIvxYgXw//KdsdU45tPSXtnXvL4ArjJfSm7SP6XQv3mdfOusywar3n2JZAae/cW0ZUe90qL9Y9Sw9YvP6VpkuX9i595Oc3LMuYrcgaOSVQsx8pvwK3gDXWdpSVklWBSklMkR6+w6SX3wiLNtim6dJlRLXXpWPefha7S6cm66SDax+ZOXqO1fYlxibJ8KoTpbv3EJtvNPeu3Je2jr3k0z7fZusTtdFolEkdPhN/LUC+G/WLTeKbW3hUkP5+088V4iQiMnu22aju3SU8/E8bH9qcaWf37mICmdfOHGh3Wr/vJD0te8P4XDgYJN0KDpYevsOyFOFi4w87bIref+r3c9Ixbz8Z/sJEi6/NoDdIv5Kj5c1WH1p0/mtNJlscFUaRNWwRqCdmHROR8Y+UEUBNwPqc5M+Qo5tPERsez7uLX8Wv7JMTpa3+chO3L9xj8sqJ1GhR1aI+7l8NJV2np0ZLy85/iC4ljcmdpnP/WijvLH7V5lxG2+fuQUQY/c2gbE3QtubrzZzcfoaxM4cy4aeRNqWjf1aIGBBjBKK/woZVR3GSrRz9YzFT35pLz06L6NJmJaMGb+DTD7bh7b4fMdxFxMAPP0BExP9vLyICfvghh42OizO/VnhCAsvy5dGAIY08GfJpH/5Ydoh3235KQkxitplStXElvt73EcnxKXw36peHD6xW03lMW5oENGDXwn0YjY9PNPk4avlXY9KyCdy5GMzmnyxLR2fvYE+DjrW5dvKmRfYWLlOIyOAoi21SPF2syy9uJgXIWvazp8y+lYfx8itAtWaVn3hu6M0wlk1bS7OeDan/Um2L+wi5/gAAPysyherT9Xzc42uCjlxj8sqJ1G1bw+K6j/L7ov2s/XYLTQIaZGuCC3QtLQAAIABJREFUvKuBN5k/eTmNX65Pl3Htsq3dnEJMcZB+BtGfgvTToL+A2ccHOjfFHHgLmPL6Y+pm5HQ2Gh1pWrUEZ/aVplHT0uT1rA9OdYmMdKJFCwgKMp83dmwOXYSHBwC681dp0QIiI8E7Iy9gZCS0aAF794LPtWvmg56e9B37MoVKePP1sB+Z8OL7fLJ5klUZa/+NUlWLM+TTPvzy5iJ2Lzlgc2LM5j1f5OCaY1w8eIXqzatYXK9hpzrUal2NJR+vxn9AU4uyVRevVJSUxFSiQmLwLlrwX8/18MpPQlT2iboie3niCErTtM2apm3KKFsxB43dmPOmZQ9Jccmc3H6GZj1ftChN9Q8T5uHg6MCobwZb3IfRaGTbb3twcLSnSFlfi+t9+8ovnNx+htd+HknTgIYW13uUPUsP8uWQH6jeoipvzsu+u+bFw1d4p/XHePp6MPGXV3Jl2mwRI2uWnyEp7GtMUS8hEfWQuFcgeR56fTrnrvdByz+VBG0mQ15fzAvNtlKi9lHylzlDhUbHKVnnAOUa7KaW/zbuxK9Ayz+DNPvBhEeVoFiR6zga5iKxQzCG1+f03ldpWncpndpfp0eAKecuqnNnxM4O+y2biA+6T+XKcPGiuVSubBbIPo2DkY0bwc4OOncGoFW/JnyxewoJ0Um82vA9bpy9nW0mdZvQgSqNKvDjhPnEhsfZ1Ebd9jVxdnXiwJqjVtXTNI1RXw0kOT6FZZ+utahO8Up+ANy7fP+J5+b3yk9aajqpyTqr7FI8JZ40Bwg0e6Q0woIcUNldsrIGdWr3eYujRsRFxou/FiALpqywqo+HzgnrZ1uebyb0ZliWsuqKmPPjdM7/f+ydd1xT1/vHPzcQ9l6C4sKNu+69ELWiFgngRtx71Lq1bq1Vq9ZV9957VOusC/dCQEWQKXtvEpI8vz8uQaRo7k2C4/vL+/U6LzCcc+4Jkvu55znPGExT28/XaNmKyNfvyd3Kh3xqTlS51o+mUbiIy+X5JM+7TLL0WZQV3pJkcTVIHF2b8uIGkzxrM8nFDykhPvcjzzxPT6IzZ4j09dnXGIbI0pL9vjDMiJydiQID2f4rVhDVrElkaJhLA0TXaefauRT5tC3J4mqwLbELybP3UUJ8dpl4/oU0Yr34Ag1+oKRnH8IVEhKIOteIoif4oeicqiQxoXHkaT+c8xkMV8JeRqidaWJW9yU0uvEvKo1dJFpF/RxHceobH5FILoyILu64prTvnVMPyIUR0aHlp1RalxbuoKxSHQGwB9AbQC8A9nwvom5TR6D+3naVXBgRpxutIgI+4A73BJ3+t4LIVceTlg9cx8s5QVFTKiEqifOYkiwbsJZ6GPTXaBXc1Pg0GuQ0jo3HUsN7S5Ns3EgkFIppwYxDJIkrFIr4JpQaMZUGeZ4nC/N0srcv3TOvVy/2e13dD+KkcDpQNBMT9qu9/YfXatUiMjb+8G8dHTl1bBdJw/ofoyh/T5LF1aC0t01oxdzfafdODVchjoujdBunDwv38CCaPZt1jFC8EScnovjS/38UuSPV8QgtiVwupwGVx3DOX1kaW6bupp5GA1Qqy3H6z4ucs7/kZuWSCyOiIyvPKO0rl8tpifcactXxpMeXX/BelxbulIlAARgBtlz7HgB7AUQAGMb3Quo0dQRqx6wD1F3Pm5N33Japu6mHQX/OWSPSEtPJu8JI8qk5kVNNGgVyuZx8ak6kXzov4DymJIqd4d4FR1WeoyR5Ofk0ocUs6mk0gN48CtHYvOogl8soPf4kRT7rRLK4GvT4ijelxt+ihHhJkRApdkbFPfPs7IjmziW6devDz0trurpEFhYf/l2tGnvv/1T/ypXZPi2bPKMjWyeR5H0tksTUptcPp5FcqkGhiotjhUmxxVM0gYB9/RPiRMTm8POw9aVZ3Zdobj3EBra6Ww9VuWyK39lH5MKIaMvU3aw7/YYNREuWsF+jPh/Y/uZRCK/SGv0rjaYhNSZQZmqW0v652Xk0ssHP5G7l8808lP0vUlYCFQzAuti/rcGhCq4mmzoCtXzgOhpcbTynvlPbz+flcrpj1gFy1fXinbEh6s17tWpEERHN6LqIBlQeo1HT3rFVZ8mFEZHfmUcam1Md5JI3JEv2IllcDcqPc6eRPrcIkH8kRPb2RFOmEFlbly4odnasSAmFnxadkgJUsSK3vjo6RJUdo2nVguWUFVafciKa0MlDl4rMkRox/0VFsRMtWcJ+VXIjV3D09zNq/42V5Or+W2yGiQtPVJ5jXOsNbH5IptxHv0y5QMCaNuNKj1GUiCXUTejNOWg+4O5r6ib0prWj/uLUPyY0jnqbD6al/f7g/F608EMVgVLuNQC8B1DczSULQLSqZ15fmuz0HJhYGnPqm/Q+BfZVbDn1JSLcOfUQjTrXQ/VGVXmtKep1DACgemN+4xRI8iUIvPsGbd1bQN9QX6U5SkJEuLL3Jpxb1UTrPs00Mqc6a6GcfaCUvoAsEoz5SuiVO4mlv7eHrS2DpCTWo83EBIiPB9at++CdDbC+AwoSE4Hu3QGplNu1IyOBaI5/3TIZEPneEdMXzUYH9wsIflcFP3WaBN28eWjeLA8TJmjALb1iRdZlcN489mvFipyGefzshmbdG2HjxJ0I9Huj5iJY2vZtgYq1K+CPkX8hLTGD9/jdK+Ix8d462FM2tqABZO4ewJw5yHfzgFQuQPUXJ5FRvw2QkPCfsUI9ISzLmSM1jpuTRr02tdFrjCsu7byO8MAopf3LV7NHlwHtcO/sY+Rk5vJ+b1rKBi4CFQPgIcMwCxmGWQDgAYBQhmF+ZhimFIfdb4ucjFwYmxsp7UdESIlNg015K07zRr56j5iQOLR1b8F7TdFvWIFyrFWe91gACLoXDEl+ARp3qa/S+NJ4+zQMEUHRcPXpqLE5VYHkGaD08aCspYB+GzA2F8EYupfqRaivD1SuzH6vCK8RCAB5CSe7vDz2Mb0seeZfCc27HcZvG0Zh+IDjuLC/L+rXeYOsr+TBrKOjg9kHJ8Ousi0Wi1Yj6X2K2nMaGOlj3pGpyErLwSrfTZCX/EUrof+9CaiNd+iCHITDHEdbjkTi5GVoEnYCVRGOIIMfYJ4c9kkffkt7C6Ty8CIc9KsIhqaG2D7zAKf+LoPbQ5JfgDsnH3K+hpayhYtAvQNwBkVRJDgLIA6AaWH7psnJyIWxmaHSfllp2SgQF8DKgVsc0YPzTwBApd3G+7dxsHKwhLGZcuEsDf+bQRDoCFC/fR2VxpfGv4fuQKgvRAev1hqbky8kzwKleAHiW2BMZ4Gx2ApGwD4wJCYCDRt+iAuytQVSUoD3JTyJed4zNYpUKsTc5b+ge79dsDTPxMNLIoz29ftq6zG1NMHiMzOQnyPG6uGbNTKnU4PKGLPGB48vPce1/be5D4yKgsHF0yChEPuqH0QaOWP77KOoV1eGV68Ac2dHlLt3BtDVBU6fLnUba2VvgdS4NM6XNLcxw8B5Ijy+9Byv7gcr7V+nZU2Ur26PW8e+3v+Zlo/hkkli0efal1ikOkjyC6BnqDz7QU4Gu63nag4MD4xCucq2sOYoaMVJT8qAtYMF73EKYkLjUa6yrcoCVxIiwvMbgajbuiZMLLi9f01DJAdlzABk0WAsd4IxHla0a1KIU3w8oKcHjBrFBquamn7YOX1LXL/TBl29z0FHvyrMMQ4kefLV1lLZuSK8Z/yEZ1dfIi78v6YzVeg11hUVa5XH5T3/ch907hwgl4Pp3RtH/Coiw6w7dCkLkuRQ2Nqy/582jSsCffqwTxnnzv1nChMLY+Rm5vFa648jOkMgYPD4nxdK+zIMgwbtnRH6PILXNbSUHVwCdWsyDLONYZgrDMPcULQvsThNUCAugFBfqLRffjYbqGdoYsBp3piQOFSoqVq0fmZKFkytTFQaCwCJUckoV9lG5fEl+ffwXYS9jEQHrzYam5M3OdsA8XUwpjPB6LcEgKKUQxMmsOIEABIJsGwZm1Fhw4avt1xlvA62xoELuwGBPShtJKgg4KutxWVwewDgt+P5DAzDoFP/tgi4/RqJXNMElUjhlC1ks0lYIfDjfjVrsl/T/rtTMjA2QF4WP4EyNjdGjSZOePFvoPLOACo7OyI9MQMZyZm8rqOlbOBi4jsO4DmAeQCmF2vfBVKJFEI95QKVx0OgiAjv38ZxyutXGlmp2eoJVGQSbCtpRqDysvPw17S9qNWsGnqM6KyROflC4tug7HWAQU/AaAgAVpwmTGB3TsePA3Z2H86bANbUN3ToV1kuJxgGaNHKBozVXkBgCUodBpIqP6wvC8pVtkWjTnVxbf8thSeu2nQe0BZEhFtH73EbUCKFU3yKGXJ1qqKc8GVRCqfERADFUjiVpFrDykhPyuQsNgoadaqHNw9DOGWLqFyXdUKJCPpu/MD+p+EiUFIi2kJEj4joqaKV+co0gFwuR36OGEI95SkHxXkSAOCUDFWcJ0FORi7sKn4+z1dpSPIlSH6fCks71Ux8RIT0xAxYlVPdRFgcRSLdkSsHq5yoVh2IJKDMRYBuNTBmy4rMep6ebGqf+HjWGSIxkb3pC5U/a3wTEAEeHkBSij0Yyz0gykPAw11fbT2tejVD7LsEpKvgfVcaFao7wLGmA149eMttQCkpnNwGVoe9WfRnUzgVp5tvJxibG+H6AX47wZpNq0FaIENsaLzSvg5V7QAAye9TeV1DS9nwSYFiGMaKYRgrAOcZhhnHMIyD4rXC1795Xt1/i7zsfNRpqTy3rbSAPczQ5SBm2WnZAAATS/67oEeXniM/V4yWvbgnoi1OTGg8pAUy2ChJgskVvzOPYGVvoVGHi8/xn0zhufsBWTQyZHOwecuHMzU7O/Zcwt4eELP5XhERARQUfJFlaoTgYGDXLiAptRJOX+qNqg6nsX2bZgSCL1XqaX5nUNnZkVO+OwBApUp418AdQhTgskEf3DoQjSq1bJGdmoVLF/LRuUY0fg/5CYxUCri7l+pOr2egh+Y/NsaDC095ZUW3rchaGxKjlJsjzaxZvy+tie/b4HM7qKcAngDwAWvSu1f4muL1b567Jx9AqKeLFm7KxUDGQ6Cy0nIAAKYcHSqKc/OoHyxszTiX8SjJjYN3wDAM2ro3V2l8ccR5Yjy6+Ayt+zTjlEhXXRRmO4U5h+RpoOwtkKA92nZu85+4oePH2R3UV9jYqY1esY14p07A4lVDYGyUh359jn+V9VRydgTAhkdoioq1KiA2NB7SAm5BZtUvbUSGjRPq5j+DTXMnlDtzkP3BmEG4Fu6EJngGODl9NnisVa9mSE/KRPCjUM7rtC20dCRFK3e1N7YwAsMwyEzRZjj/FvjkXYmIqhKRU+HXks1J2cQMw+xiGCaRYZjAYq8tZBgmhmGYF4XtR029kdJ4efsV6revw8nbTfEh09FVfqNWHNQacXBfL4n/v0Fo0bMJdHRVu+s+veqPOq1qwqaC+juody8ikJ8jRrMejdWeiwsKs92rV+xNOzPpCkCZGDDqZ7x6xf7M05Ptu2kTEBcHVKv2bXrqfQ49PdaZw8QE+OMP9v3KmNooQCMY6179KmuydrCEgZE+4sM048kHAA7V7CEtkCEllqPrt709zAP8WNunXA7rR6ypLuXKbTByOfv6vXtAuXKfnKKJawMAQOBd7sHHVvYWYBiGk4u6jo4OjMwMkZOuDdb9FlC6XWAYpm8pL2cACCCiUsq6FbEHwEYA+0q8vpaIVnNeoRokx6SiemOlWgoAIDl7eMxlJ1EgYcWMy26rOBJxAdKTMuHg9OkPoDISo5LR2EUzAbophVH5dhpyuFCGwmynqKt0eH8oBnkY4syF2nB2BgYPZvspdlrAx1khvhckEnbXl53NNoUbtVC/OiC+9VXWxDAMjMyNkJulubISivhChYMRJ+ztgRMngOho6P95AFjzDJKxE4DZvpyyZJhZmcLASB9pCdxNpQKBAHoGQkjyJZz6C/WFnHeFWsoWLh//4QB2ABhY2LYD+BmAH8Mwgz81iIhuA/hqJ40yqQzpiZmwLs8tTknh3cQIlNc9kknZaFBdIT+BUjzBcV3Tf68rQ2pcGuwcNSMoafGsQFnZa8bhojRKnjkpRMraGqji+A6v3laDjY0A1asDs2ez4tWxI7tzAr5u4K06lLbrY3QcAXkSiL5O7SFDEwON1j0yKPR45SVQCipWhN7gAQAASRdXzimcAMDCzgxpifzqUrECxe0AU1eoA6lEK1DfAlwESg6gDhF5EJEHAGewZUpbAJipwjUnMAzzstAEqLnyryXITMkCEcHc1oxTf4X3LZfCfDIpe/cR6PB7vFc89VnYmfMapyA9KRNyOXHOdqEMxUGw4mBY05Q8c1KQlMSGxThVjkJYREXk5bFxmfr67M7KywuoXr1MllRmmJn996xMkfFC4UadmVNYzFL2OcND2WFgrI98TQqUEZsHUpwrVmm8ngHrkslVOBSYWpvyroIrNNDjJ1DS78yu/D8KlztsFSIqbrhOBFCTiFIB8PWp2gKgGoBGYNMlrflUR4ZhRjEM84RhmCdJSUk8L4Oi/HtcI88VusQlTkRQuMsino/3JhbsmhRZK/iiGJ9V6EWoLoqzuVyewY9cKXnmlJgIBAUBTZqwO4yEJBs4lk9GdjYrTmLxB5G6fLlMllQmMAyQmfnxrsne/r+VcP/aUngGIii7HevnKBAXcAqj4IpEzH78uQTClwYfz9ni5GXl8T7/lRVIOV9HJpV/lZALLf+Fi0DdYRjmAsMwPgzD+IDNxXebYRhjALz22USUQEQyIpKDNRV+0hWNiLYRUVMiampryy3DeHH0DPRgYmHMOXeXYuekOIv6HArTnuIDxhXrwkS0KbGqWT71DfVhbmPKyRuJCxbl2J0cH3s+HxTmPMUN2tmZDbxVCFHdBk5o2ewdnJ0/vCYWf3hY+F4o+UxjZwf4+7Nfi/8ODIUxEEtMwQi47eo1TV52PgyNuWVK4UJ+DrtzMjBWLaP+h9hDfgKXnpgJC1t+VghJfgHn60gLpNAVagXqW4CLQI0H6/DQCEBjsE4P44koh4g68bkYwzDFUy+4AyXznGgWKwcLJHMUA8XZE5cMzTqFf7x8BcrI1BAGxvpqZZa2cbRGYhT/HWVpWBYKFJ8EnHxR3KAVyV1lMtYU9vQpYGlbDQKk4uaNpCKRMjIq+8zjmsKshM4oHrqnTmXftwLF76DXj9HQN6rw5RZYgrysfJXFpDQU6cFUnVNSKFD6HHJlFo0RFyA7PYeXmZyIIM6TcBcoibToM67l68IlWSwR0QkimkpEUwq/V3oLYRjmMID7AGoxDPOeYZjhAH5nGCaAYZiXADoBmKr2O/gMNX5wQsDt10VnRp9DcZ4klykXKMUHUpXD4RpNnPDs2kuVU87UblYdAXdea+Swu0q9ShAIGLy89UrtufhgYcEKFvTaAxDA2nAH/v0XmDv3Q1Du90B+sf8CHR1WfO3tgWHD/tvX1joFlR3uAXqqBWirS05GDrLTc4p28ZpAkYdP1TPR9CT+Z6AK93KnBpWV9PxAdnoO5DI5p+sQEXKz8mFkyj+ERIvm4ZIsNothmMzCls8wjIxhGKVh1kTUn4gciEhIRI5EtJOIBhNRfSJqQES9iShOM2+jdNq4t0BmShZe3lZ+A1akQ+KyKzItzCCRk57De00dvdog6nUMIjgUUSuNTgPaIj9HjPvn1I+VtrQzR902teF35pHac32KxET2/KlkmYxOnYCktBqAYV8g9wDSU6KwejV7k/8eXMsZhnUn19P7EKtlb88GFpd0CgEA5B0BUADG6JOOr2VKZGGRzMp1HTU2Z3RwDOwq2ahsNkwqzOzAJ8zhwfknEOoLeYVaKEziiowSnyM3Kw8yqazMHIe08IPLDsqUiMwKmwEAD7DxTd88zbo3glBPF08u+yvtq1N4riTjIFAfnBX4C1Q7jxYQCBjcOn6f91gAqN+uDhiG4Z5iRgltfmqO8IAoxIRq/llBUSZDcf5U0mmgYUMgNX8yCLoIfroKYjFBXx94+ZLdTX2LQmVoyIosEVu6SCIBRowANm5kz50U7+14sYQRRGJQ7mFArx0YXW5xeZomqjCDRGVnDQrUm1hUrK26yTIxKgkGRvqcEycTER5ceIrGXerxEsWkwp2eLYfcmYoMEqZagfom4H0LIKIzAL5O2mueGBjpw9jcCLkcSjjr6bMCxSWYz8jMCEJ9IdLi+Z/dWJazgF1lW8SHq+ZqLBAIYGplgvREzeQKa+fRAjq6Oji0/JRG5iuOokyGvj5w7NgHp4Fjx9jX4uOBcRPL4VHgGPR0uYxZkw/gl1/YsadPszFQCpH6Vhwn8vLYs6devYAXL1hhmjWLLQKrOGvauPFDUViiAlD6FECeCMZ4xFdb94ubgTCxMIZ9VTvlnTmQlZaN8JeRqMbD1FaS8MAolK9uzym0AwCeXH6BuLAEtPNoxfM6bP7B8tWUB8inxLBn1mUZG6iFO1xMfH2LNRHDML/hQ3Xdbx5dPV1OQXeKoEOFZ9LnYBgG5auVQwyH7MilYWploparuF0lGyS951iHR+lctvCc1gtX9txEwJ3XGplTwcaNH5K9enmxO6rERPZ7sZj92caNQMsuoxER2xmLZ6zAjcvP0Lr1h13Xv/+yKYO+BccJhRPEu3dsIlhb2/9WJ7ezKy5OMrYIo/g6GLMFYPT53Vg1RW5WHvxOPUIHr9Yac5++e+ohCiRStFexArMkX4LAu2/QsGNdzmMe/v0MRqaG6DKwLa9r+d8MRKU6FTh5/kUVmkIr1fl6zixaPsBlB9WrWOsGIAtAn7JclCYR6guLUhN9DkUdKK4xQRVqOCAmRDWzmKmVCbJS1ROoxEjNCBQADJwvgpWDJU6uu6CxOYEP7tYKs1e9emxTiI/CFZthBKja6HdAUB7Hd06CoX4SdHWBLVvY1GzZmgn7UhlDQ3adMtmHJLBv335sxisJkRyUOR/I/xuM6QwwRgO/zGJL4e6ph8jPFaPrkA4am/PG4buoUMMBNZuoZrJ8df8tJPkFaNyF+1lSbnYeTK1MONV3UyAtkBYKIbfkzBFB0TAw0v9i6b+0fB4uZ1C+xdpIIlqmJAffNwXXHFwKrx2ugb2ONcsjNjQe+SpE0ZtZm6plorOvYof48ESN5QszMNJH4y71EHT3DSePRz4UdzNPSvrgLPHvvx+7YjMCM+hab0Q520zcPjsA9esEoXNnYHVh1sbiD/5f6mxKYXnKywNGjmRFVSJhSxUVN+OVhOSpoPSxQN4JwHj8VzXt5Wbl4cjKMyhf3R7OrWpqZM6ge8Hw/zcInfu35WyeK8nNI37Q0dVBgw7OnMfkZuZxrnit4NHF58jPEaNxZ24C9fbpO1Rydvwi2f21KIeLic+RYZjThZnJExiGOckwjOZOWssYQ1NDTgkyzWxMIRAwRfnplNHEtSEKJFI8ufyC95oqVLNHQmRSUSQ+X5xb10J+rhhvn7xTaXxptPdohfSkTJXej6ZghLWhY70Ltrb58DvvhdFD9iMlhcAwH7I02NiwThR2mjlKKZWOHVkRio8vqlAOB4cP50tnz35GnMQPQMm9AfFdMKbzwJhMKruFKkEul+N3nw2ICYnDlL9GqSwmxclKy8byAetgX9UOHj+7qTRHeEAkLu28jl5jXDlVGlCQlpBRFFzOBZlUhh2zD6JCDQe06t1Uaf/4iEQE+QWjdZ9mnK+hpWzh8piwG8A5AOUBVABwvvC17wIjM0NOqYV0dHRgaW/BOctDg/Z1YGplopKLtmOt8pDL5Ih7p9oZVqNOrN3++Q3NxTk369EI5jamuLJPs9m2S3Mz/6jEdwk2b2+KGi3O4tqdNtiwfAnO7RuNihViYGnJilJyMrBo0YcKu+rwKZFLTAS2b2d/fvv2h91S8fOlkhBJIc9aB0rzARhjMNbHwRgP0YgoqMqhZafgd+YxRq8agsad1c+AT0RYO+ovpMSmYc6hybzEpfgcW37eC2NzIwxe6MlrbFp8Oi/nhYs7riP6TQxGrhzEKbHzjUN3AbDl7LV8G3ARKFsi2k1E0sK2BwD/3ENfCWNzI87xStblrZAUw02gdIW6aNmrCe6fe4LMVH6JKxWuuRFBqrmKm9uYwalhZTz8+6nKAb8lEeoJ0al/W9w/+xixKgpnSRTi9Ck389JEqmNHIDvHCr0Hb8XkefPQofUjBN76EVNHrYVEnAFnZ2DBAlYs1H3rimszDGBcWHtSkQtQsbbPiRLA3nBJ7AdK7QfkbAYM+4KxPgVGyN10VRY8vvwC+xYeQ5dB7eA+WTNl155de4k7Jx9i6JJ+qN1ceZXq0vj3iB+eXw/AkIXeMLPi7spNREiNS4MlxwwSaQnp2LfgKOq3q8NpR5SfK8bl3TdQr21tOFRVvRyOFs3CRaCSGYYZxDCMTmEbBEAzyeC+ANYOlkiOSeV0I69SryJCnoZxSncEAH0n9YQ4V4w1w7fwEoqq9SvB1NIY98895jymJD2Gd8HrByG4c/KBynOUpO+UnjA0NcTcnss1UlH0+PGPvfFK5qYrGS9U3MOPYRhs3DkE9TpcxIUrnTB3yha8e9gZF45uxuVL2aXuvooj5JHe7eZNICwMH+UELLm2krCxTSdBKb1Bab6ALA6M+R8QmK8AI+BfaVmTEBF2zDoAh2rlMHXraI3t4p5e8YdQTxc/Teyh0vjwgEj8MWILnFvXgtuYrrzGxoTGQ5wnQaU63E4Xlg9cj7zsfEzYMFzp+ycirBu9FXFhiRgw14PXurSUMUT02QagElgTXxLYTOZnAFRWNk6TrUmTJqQqJ9ddIBdGRGmJ6Ur7Xtp5nVwYEUUERXGffy07/6n1f/Na19rRW8nNeCDlZuXyGqdAWiCl0Y1/oX6Oo1SeozQC7ryiHvr9aGr7+STOl6g938aNRAkJ/309IYH9Wcm+AJG+PvtVR4f9ChDVr/OaTu8eQ7K4GpQe2oiObptIg0SnycoytahPyWZrW/rrJduKFR/W5OzKuFewAAAgAElEQVTMvubpWfr7kUvjSJ71J8kSWpIsrgbJktxInnOc5PJ8tX9XmuLeucfkwojo8p5/NTrv2CbTaVqnBSqP3z3/MLnqeFJqgvLPYkmu7LtJLoyIwgIilfaViCXkwoho19xDnOZWfIYPLD3Be11auAPgCfG89392B8UwjA4AD2LTEtkSkR0R/UREkWWsmxqjfDW2Bk/sO+Wlruu1qwMAeHmbezyQ++Qf0bJXE2yfsR/Bj0M5j3MZ1A75uWLcPqHaDkhHVweTNo1Ackwq9sw/qtIcpVGvbR1M3zMBAXdeY9XQjWp79SnObkpSmuls/Hi2RIdYDNSsyebsU/A+vjZGz9yClj2O48jpnmjd7Bn2bpiB+IBW+Pf0AEwfvw0NnF9DIPiwXisroFu3j69RrRqbpcK6lKQCxQNtjx1jXyN5Dij/CuQZ8yBPcgUltQdlbwCE9cBY7gZjfQ6MkQgMo7kkrOogERdg/+LjsK9qp9GzlMyULIQ+j0CjTty84UojOjgWDtXsOZvpivP6/lsYmRpyik/KTGHjEmwclWeOePv0HbZO34c27s3Rf7Y773VpKVs+K1BEJMN3FPNUGlXrVwIABHIIQq1Q3R4Va5XHqXUXOHvYMQyD6bvGw8rBEr/2Wcn5hu7cuhacGlbG5im7Efo8nNOY/8zRqhZ6je2GU+v/xsGlJ1WaozQ69WuDEb8Nws2j97DQYxVkpZWHLSOOHQNWrGDPhVJSPjhWpKUBlpZAX++GkBouxdYTt+Ex4gSWrx8Lc9MsLJ21Fs+v90FqcFMEP/TFnys2oGI5P7yPjIaxUQ4AQq1abFzV0qWsCa9mode1qWmhJUGWBFuLRxjnewTyzOWQp/qCEluA0icA+RcBXScwpjPB2PwDgeV2MPptvqoTREkyU7Iwy3UJQp6GwXdJP94Vnz/Hnl/ZhyBVPdxkMhkC77xGtUZVeI/NTMnCv0f80LhLPU6BxorURtYcktgG3H4NuUyOCRuGa13Lv0WUbbEALAObe68dgB8Uje9WTZ2mjomPiGhcs5k0tukMTn0fX37BbveX8NvuX9p1g1wYEb0PieU8JiEqifpXGk2icsN5jSuOVCql34b8SS6MiI6sPKPSHJ9CYR49/Ntpjc77OYqb2Zyd2X+XfC0w8MO/FWbAihViaJDoLG1csYCC7vQiaWxN1gRX2LLD65Ekrj3Jkn4iWbI3yZLcSRzXk1JCXEiW0J5k8Y0+6i+Lb0CypN4ky1hG8vx7JJerb+4sS6KDY2hIjQnUw6A/3Th8V6NzP78RQC6MiLb8vEflOV7efkUujEiltW2ctJNcdTwpPJCb6f3KXtYcGPXmvdK+O+ccJFddL5LL5bzXpYUfUMHEx+URS5HLZHFxXcN3ko8PADp6tca2GfsRExqHCtUdPtu3qWtDtPdshUPLT6LzgLZwcOLm0eNYo9CUGBqv9BoK7Cra4LfL8zG13XzMcl2CtXeXwoZnOQQdHR38smscstNycHjFKbhP6qGxqqnuk35E0L1g7Jl/BI271EetptU0Mu/nKM2xAmC/V3gELlrEflUUOLS1BfIl5XHgRG8cP98bYjGwZnU2/G76w9wkDjbWqajkmIZhQ1Oho5MKkBgQmEJXRw8WBvoAowcwRmB0qgC6TmwT2INhvo8n6pe3X2Gh++/Q0dXB6hsL4NyqlsbmzsvOw5oRW1C+uj2GLumn8jx3Tj6AUF+IFj1/4DXufUgczm+5gh7Du6BK3YqcxkS9fg8dXR1On93M5CyY25h+UzthLcXgq2hfo6m7g0qITCRXXS+a0XURifPESvsnvU+m3maDaUS9qZQan8bpGmmJ6eTCiGj/4uO81/fmUQj1Mh3EeZdXGk+vvSyTXVRmahb1rzSafrL0obunH2p07k+hzLEiIYHI3v7Tuyw7O6JatT44SiicJRR9/5d4etWf3IwHkm+dyRT7Ll6jc2ckZ9LktnOpq8CTXt5+pfI8ir/vBe4reY1Ljk2l4XWnUG+zwZw/h0REk1rPoZENfubUd/aPy2hUw2m81qVFNaDCDoqLiU8fwAAAcwD8qmh8L6ROU1egiIj+2X2Dugo8afaPyzh5pz2/EcB+8GtP4vzhmNphPg2tNVElc8HxNefIhRFR9FvVTH1yuZwWeqyi7nreFPoiXKU5PkVsWDyNazaTXBgRbZq8iyTir2vuUnj7lRSchASimjWpyDvvUybC/xWRenDhCfUw6E8jG/zM6wbOhdiweBpaayL1MOhPN4/6qTxPRFAUuVsPpUFVx1LS+2TO4+IjEmlI9fHkZjKQXvwbyHlc0L035MKI6OS6C0r7yuVycrceSquHbeI8vxbVKSuB+gfAUQAzAExTNL4XUqdpQqCIiP7edpVcGBHN672C001WYTc/tPwUp/kVbupB997wXlvsu3iV3NWLk56UQV4OI2hEvamcdop8EOdLaNPkXeTCiGh885m8bjZlwad2WStWfFq8FCJV0r39e+T2ifvUXc+bxjWbSRkpmRqff1qnBfSTpQ8F3H2t8hyxYfHkXWEkeTmMoJjQOF5jxzefSX0shlDQ/WBe4xZ6rCJ3Kx9OoRdRb96TCyOiizuu8bqGFtUoK4EK5DupppumBIqI6Oymf8iFEdHvvtzuUl4OI2ilzwZOfXMyc8nNeCDN6LqI8nL4x8X41p5EY5tMVyv+6NGlZ+TCiGiRaBXlZuepPM+nuHPqAfUyHUTeFUbSg7+fanx+TcAn9up7o0BSQPsXHydXXS+a1GYuZadna/waCicDLruQTxH5+j0NqjqW3K18KOxlBK+xmalZ1FXgSfsWHuM17v75J9RV4Ek75xzk1P/YqrO84x61qE5ZCdQ2APX5TqzJpkmBIiJaNmAt9bEYwqnvItEq6uc4irPZ7sLWK9RV4EkTWsziHZB486gfuTAiWuK9hmQyGa+xxTm+5hy56njSyAY/835y5ULI8zAaXncKuTAi+m3In5SRrPkneC3/5Z1/BI1tMp1cGBEtG7BWowHaCi7uuEZdBZ70S+cFau3CxzaZTiK7YfTmcSjvsee2XCYXRsRr7N/br5GrjieNazaTMlOzlPYPC4ikHw3706zuS7QefF8IjQoUgEAALwG8AlAAILjw3wEAXvK9kDpN0wJ1bDV73sPlD1nhPh7yPIzz/H5nHlFPowE0uNp4ig6O4be2wqc6dVx6iVh3eXfroeRuPZQSopLUmqs0xPkS2j3/MHUTepOo3HC6dfyexq+hhUUiltDeBUeLfte3T9xXPigykmjDBqIlS9ivUcp3Cac3XCQXRkSzeyyl/FzVM2O8ffqOXBgRnd5wUaXxE1rOphH1p3ISDrlcTvsWHitaNxfRzsvJpxH1ppKn/XCNn91p+TSaFqg0AJU/1fheSJ2maYG6c+oB5ye01Pg0cmFEtH3mfl7XePXgLYnshpG79VBeuxi5XE4bJ+0sisVS5+kuOjiG3EwG0tQO88vEFEREFPoivOipfl6vFSo7eWgpnUC/NzSi/lRyYUS0YtB6Sk/K+Gz/XcvjKM/Ng0ggoOL5nOQCAYU08iCK++/fokwmo/2Lj5MLI6Jff1qplolZJpPRikHrqYdBf04PgCVRODkcX3OOU39FjNaKQeupQFKgtL9UKqWVPhvIhRHRkysveK9Pi+poWqCe8Z2srJqmBSohKolcdb1o6y97OfVfJFpFLoyIjv7Oz4U78vV7ctX1om0z+ImbVCqlFYPWkwsjoqX9/uD0wfsUV/ffIlddLxpUdSzvA2euSAukdGzVWeptNpi663nTlp/3UFZa2Qji/xdiQuOKbqT9K46me+ceKx2za3kchcKJFSShkMjDg2jOHMpz8yAJdIkASrdxIor/4I4uzpfQvF4reN3kP0VOZi4t9GA/K1w/W8W5eeweuRkPpP4VRysVYgXbZuyn7nrelJ2Rw2l9c92Wkwsjon2L+J1vaVEfTQvUewA/f6rxvZA6TdMCRcSeQ/U2G8zpRirOl9AS7zXkwoho2/R9vHY1c3ouowGVx/A+U5LL5XRg6QlyYUR0dtM/vMaWJOjeGxpUdSy56nrRgaUnSCqVqjXfp0iJS6XVwzdTV4Enedj60rktl0laUDbX+l9ELpfT06v+NK/3Cuoq8KTuet60Y/ZBzmdNeW4eRAA9wQ/UqUb0Ry72FRBNgQY/sB95Dw8iYnc7S/v9wZrj/ryo1m79fUgsjag3lVx1POnEH+d5zSWTyWj3vMPkwohoUpu5lBKXynmsb53JNMN1Mae+K4duIFddLzq35TLn+bVoDk0LVFxhzNOC0hrfC6nTykKgQp6FkQsjooPLTnLqL5VK6c/x24s8ALk+aV47cJtcGJFKgY5yuZymtp9PXuVHqu2Rl52eTUv7ryUXRkTTOi3gdRPgS8izMJraYT65MCIaUn08HVx2kvMT8f9HxPkSurD1SpHjichuGO2ef5iSYlK4TxIZSSQQkFwopE41oksNUk56FkWkq0skEJAsIqLo7/nYqrNqrf/RP8/pJ0sfcrceSk+v+vMaKy2Q0gL3leTCiGj18M28zIvRwTFF4soFn5oTaaHHKl7r06I5vikTH4BdYMtzBBZ7zQrAVQAhhV8tucxVFgJFxO5uugm9OT89yuVy2rfoGC839dysXPrJ0oe8yo9USaQC7r6mrgLWIy/iVTTv8cWRy+X0z+4b1NNoAHk5jKDnNwLUmk/Zte6cekDTOi0gF0ZEbiYDacesA1qhKkZmahYdXnGKvMqPJBdGRGObTKcre2+q5j23YUPR7igh4eNSI7a2xdzuPTwoFsb0c43hKpviivP26TvqJvSmUQ2nUWwY/0wWijIah5af4rXrinz9nnzrTKbuet6UEJmotH/021jqKvDk/ECqRfNoWqCe852sxPj2YBPLFheo3wHMKvx+FoCVXOYqK4HKTs+m+X1+IxdGRCuHbuB8Y/jddyO5GQ/k3P+dfwT51JxIrrpedGzVWd6mlEeXnpHIbhi5GQ+kSzuvq+0W+84/gnxrTyJXHTbWpKxMfgoigqJo2YC11FXgSW4mA+mvaXvJ/1bQV89I8bWIDYunjZN2kpvJQHJhRDSz2xJ6etVfvf/XJUvYj/OcOZ8UKLlcTue7jyM3/ES9Dbzo0q4bal1TnCem4XWnkHeFkSoFC0ulUhpaayKNajiNlwn89skH1NtsMInshnHOMrF62Cb60bC/1mvvK6JpgbLiO1kpc1QpIVDBABwKv3cAEMxlnrISKCLW/q1wUx3ffCYn0Xn0z3NyYUS8AlWzM3JokedqcmFEtMB9JW8ngqSYFPqly8KiGJicTPViYHKzcouyoP/SeQE/c5KKRLyKpmUD1pKrjie7qzIeSLO6L6EjK89Q8JNQ1c+rVHCp/pLk5eST/60gOrLyDM11W06uOuz50kqfDfTOP0IzFyncQeW5eRRlzChu4qtbK5tmu7EODDPQjhIWr1b7klt/2UsujIgeXXqm0niF+ZuT2zyx5sDtM/eTCyOiCS1mUWI0t2wm8RGJ1E3oTRsn7lRpnVo0Q5kE6qrTShGo9BI/T+MyT1kKlIJzm9kME35nHyntK86XUC/TQTS++UzOHxIi9gn25NoL5KrrRXPdlvN+epVKpXRg6Qly1fGkKe3macRT7p/dN8jNeCD1tfGlSzuvqxUgzJWstGzyO/OINk7cWXTuojh72TJ1N+fMA6q4VH8p5HI5vXkUQquHbyY344FF73FI9fG0bcZ+zaeKiowkuUBAEgipAqI/ykPYuOITaoMx1IXxomOC2iRnBGqJuLRASvsWHqOuAk9aO3qrSnO8vP2KTctVfyrnv7mdcw6SCyOitaO3cj6rSo1Po6kd5rOmwDKIB9TCnf8pgQIwCsATAE8qVapUFr+vj5CIJeTlMIJm/7iMU//bJ+5TL9NB1NfGlx5e5PcEeeKP8+TCiOj6wduqLJVuHvWj7nqs3T85Vn1nh4hX0TSpzdyicxB1MlerQnJsKl0/eJsWidhkty6MiMY2nUFnNl6ihMjEUoVcFZfqsiY9KYNuHL5Lv/tuJO8KI4t2iauHb6YHF56U+flbSCPWiy/Q4AdKehZFSTEpRbv2Lvrj6SRaUXEvPlWIfhtLE1rMKnJL55vSSyplxc1Vx5OG1JjAeQcpzpdQXxtf+vUn7hnR/W8FkVf5kfSjYX+6uv8Wr3Vq0Tzfg0B9cya+4igcILhmf4gOjqFRDaeRCyOiHbMOcDZRSaVSmtByNvW18aW0RH7pkBQ8vvyC3EwG0iCncSoXOyyOXC6n64fuUP9Ko8mFEdFirzUqHXqrS3pSBp1a/zeNbvxL0a6jj8UQmtJuHq0fu43ObblMz28EUHAHD0qDHj3i6FKtKaRSKSW9T6aAu6/p2oHbdGDpCVozYguNazaTugpY06W7lQ8t8V5Dl3Ze/7LxYHFxlG7jRPkQ0FFBbeot9KAfhZ50sK4HiXWE7O/DSXXRfnjxGbkZDyR3Kx+VMpynJqQXeXeuGLyel5n6xuG75MKI6PFlbsG1J9exlgqfmhM1Z0bVohaqCBTDjisbGIapAuACEdUr/PcqAClE9BvDMLPAnnPNUDZP06ZN6cmTJ2W2TgVpCekYWHksWvZuitkHJkGoJ1Q6RpwnxpYpe/D39mto2LEuVl6ZDx1d5WWpI4KiMfaH6Wjs0gBzD02Gsbkx7/W+eRSCuT1XQKAjwOwDk/CDSwPec5QkP1eME2vO4+jKM5DJ5PCY0hP9Zv2k0vrUJexlJIL83iA8IAphAZEID4hCbmbeR30IDArIFHJdU8gYQ+QWWMDApgKmTzRCncVjYCvPhcmrFzCoVe2zRemICBnJmUiMSkZSdAoSo5IRH56I8MAoZCRlIjs9B9npOf+5PgBYljOHY63y+KFLAzRxbYiaTZ04lSbXNHK5HFc3nMPu2QeQks+gGcVhPF6gAnIAgQBwdwc2bQLKcSvCqYCIcOPQXawZsQVV6jpi8dmZsKlgzWuOvOw8LOy7CoF332DKX6PRdUgHzmOz0rIxq9tSZCZnYm/oRqWl2ZNjUjCg0lg0cW2AeUd/hrGZEa+1aikbGIZ5SkRNeY0pK4FiGOYwgI4AbAAkgI2fOgPgGIBKAKIAeBJRqrK5vpRAAcD+xcexb+Ex1GxaDXMOTeZcHffspn+wceJOTNs5Dt19O3Eac27zZWyatBO2FW0wc99E1G9Xh/d6o97EYKH774gOjkWvsd0w5g8f6OkrF1ZlJMekYOecQ7i2/zbMrE0xcK4H3Ma6amRuVSEiJEUn4/2qv5CxcRsyGrVAbGcRtm/JhCwvE7rIh7FuCowFcZAVyD4aK9ARwNjcCCYWxhDq60IqkUJaIINUIkWBRIr8HDEKxAUfjdE31EOVehVhXd4KxhZGMDE3homFMSzLmcO+qh3KVbGDXSUbGBjpf8lfQ6kE3QvG5im78fbJO9RpWQMjfnZFg4QgIC0NsLQEevcGKnKrSFuc5JgUrB+3HQ/OP4Vzq5pYcm4WzKxNec0RHRyDRR6rEf0mBlO2jkaP4V04jw248xorBq1Halw6ZuwZj84D2ikds3zgOtw9+RDbA//g/PnVUvZ8UwKlSb6kQAHA3dMP8ceILZBJ5Zi8ZSSnDwURYXzzWchKycLu4D+hK9TldK1XD95i5eA/EReWiMG/emLwAk/e6xXnibF73hGcXHsBtVvUwK/Hp8HWkd8T7qcIfR6OHbMP4ukVf9hXscXQJf3RqX8bpU+xZcrSpcD8+cCcOUicvAz16gFJSeyPbG2BF8+lkGUnIHb+70g5fh7Z3dyQ3aQlcjJykZORC4m4AHr6QugKdaEr1IGuni70DfVg42gNu0o2Rc3M+tsuBS4RF+DRxWe4svcm7p97Auvylhi5cjA6D2ir9rqJCJf33MRfP+9BgbgAvkv7w33yj7x3hndOPsDqYZuhZyDE7ENT8EOX+pzGyaQy7F98HIeXn4K9UznMOTgZtZpVVzru8eUXmNNjGQb/6okhC714rVVL2aKKQJXpGZSm2pc6gypOQmQiTWk3j1wYEf2z+wanMQ/+fqpSFufcrFxaMZjNvXdm4yVVlktEbHxIL9NBJCo3nHdEvzKeXvWnMT+wSWFHNZxGZzZeUivjtVoocakuKlbowToNfPdFoEoQ8Sqa1o/dRu5WPuTCiMjTfjjtnn9YY+U3UuJSi/LzTe0wX6UEwBKxpMgNfULL2by8XWUyGc3+cVlRQDzXs6rs9Gwa5DSOfGtPUivhrZayAd+ak4Sm2tcQKCLWnXZ041/Iw9aX4iOUR6vL5XKa2mE+dRV40paf9/DKCCCVSml2j6VFcU6q1liKeBVNvnUmF324NVmrSSaT0fVDd4ocQ7zKj6QzGy99+YDbz7hUKwSrc40okhem9fnW4qJUJfhJKP3uu5FcdTypp9EAWjZgLT269Exj+Q5lMhmd/+sK9bEYQj30+9HJdRdUCjvITs8u8gpdP247L7GQSqX0u+9GNgXTam4ZzYnYjCuKfJMvbnIvEa/ly6EVqDIgOjiG+lgMoTE/TOfkUpubnUfrx24jF0ZEI+pN5VVHqkBSQPsWHaNuQm/ytB9Od08/VGnNeTn5tGPWAeom9Ka+Nr50ZuMltbJUl4b/rSCa2p71yBpYZSxd2nVD42XmP0dJl2oFCQmsOD1B2XjxfWmyM3Lo3JbLRSVNehoNoM1Tdqvs/fkpwl5G0KTWc4oCt6PevFdpnqy0bJrQYhZ1E3rTv0fu8horLZDS8oHrirKNc4kTlMlktHv+YXLV8aRBTuMo6N4bldatpezRClQZ8eACW0p6xaD1nINrH158Rl4OI6i7njfvujOhL8KL3KxXDFqvsukm7GUE/dKZzYXnW2cy3Tv3WKPVQ+VyOT2+/ILGNZtJLoyIuut504SWs2nzlN1086hfmSakVbhUE8AmQPXwIJo9m8jDg905qelS/TWJfRdP1w/doVW+m4qCfEc1nEZnN/2jcbf1jORM+mvaXuom9CYPW1+6su+myn8jGSmZNLbpDOqu580p4L04BZICWuzFVgw4vOIU53F/b7/Gpirz2cCp5IaWr4dWoMqQA0vY0heLPFdzNptlJGfSiHpTqbfZYLq44xqvD36BpKAooNGn5kSVzRZyuZz8zj6iobUmkgsjolndl/Cu8svlGo8vv6BtM/bT1Pbz6UfD/uTCiMhV14uW9l9Lz28ElE1Z7bg4VphKZJIggYB9/TsSJ7lcTk+uvKAZrouL4r/cTAbSmhFb6PXDtxr//UkLpHR6w0X6ydKHugo8afXwzWqZg59ceUGDqo6lHvr96MGFJ7zGRr15TxNbzSYXRkQn/jjPedyNw3fpJ0sfGt98prZs+3eAKgKl9eLjiFwux7Hfz2LvgqMwszbFrAOT0Lizco+khMgkrPLdBP+bQWjWvRGmbhvDy8Pu+Y0A/DHyL8SHJ6LnSBeMWDkIJhb8Y5KkBVKc23QZexcehSRPgg7erdFzZFfUa1tb455q0gIp3r2IwL+H7+LynpvITs9B+er26DGsM1yHdoSVvaVGr4foaODcObVdqr80MpkM4QFReHnrFa7svYl3LyJgZW+Bnyb+iOY/NkZlZ0fO3qB8eHn7FTZN2oWwl5Fo3KU+xq4diqr1Kqk0V05mLrZM2YPLe/6FY00H/LJrPOq2rsV5fMCd15jdfSn0DPUwadMIdPRuo3RMZkoW/hy/HbeO3UftFjUw5+BkODjxi+3S8uXRupl/AUJfhGP5gPVIT0jHztfrYWlnrnSMXC7Huc2XsXPWQegIdTDmj6HoNrQjZ2HIy8nH/oXHcHLtBViUs8DEjcPR1r2FSutPjU/DwaUnce3AbeRm5qFi7QroOdIFXYd04B3fwgVxnhh3Tj7ExR3XEHD7NQQ6AjRxbYiq9SqhQnV7lK9ujwo1HGBd3vLruq6XMRJxAVJiU5EUnYLgR6F4efsVAu++QXZ6DgCgSr2K8Jjihs4D25VJrBkR4fWDtzi94RJuHvGDXSUbjFnjg7Z9W6j8gBJ0Lxi/Df4TiZFJ8JreB4MXeELPQI/z+IigaExtNx+W5cyx6sZCWDsof3B5ePEZ/hixBZkpWRi8wAveM/pwCozX8vXRCtQXIvJVNMY0no4GHeti9oFJsLBVLlIAEPsuHquHbUbAnddo27cFpm4dzUsU3j59hzUjtiDMPxJt3Jtj3Dpf2FW0Uek95OXk49ax+7i4/SpePwiBUF8IV5+OGLzAk9ONQhXev43FpZ03cP/8E8SHJaBAIi36mVBfCOvylmjQwRkdvVqjcZf6ZbJ7+FIkRifj4d/PcPOoHyKDopGRnPXRzx1rOqBBe2fUb++MBh2cVf5/VAYR4daxe9i74Cjev42DgZE+RNN6wXvmTyoHGOfninFwyQkcW3UWdpVtMWv/JF67JgDwvxWE3wb9Cbmc8Oe9ZShX2VbpmINLT2LPr0dQpV5FzNw3EdUbVVVp/Vq+DlqB+oL8ve0qNk3aBWNzI0zcNALtRa04jZPL5Ti++jx2zzsMc1szTNsxFs17NOZ8XWmBFCfWnMeBJSfACBgMWegN90k91LqZh72MxLnNl/HPrhsQ6unCY6obPKf3LtMUMTKZDMnvUxETEoeY0HjEvYtHfGQSnl7xR25mHkytTNCmTzM06FAXNZtVg2MNh2/6STkvJx8vb73C0yv+eHrVH1GvYwAAlZ0dUa9tHdg6WsO6ghVsKlihav1KZfYQUJyge8HY+stevH4Qgqr1K6HvFDe0F7WEkamhynM+uvQcGybsQHx4Irr7dsKYtUN5/Z3kZOZix8wDuLD1KspXK4cFJ6fDqUFlpeMu7riOtaP+QpeB7fDzjrFfNaOJFtXQCtQXJjwwCqt8NyHkaRg6eLXChA3DOe+mQp+HY6XPBkQERqPnSBeMXjMEhibcbxxx4QnYNGkXHv79DE4NKmPylpFwbsXvKbYkMaFx2D3vMG4duw8LWzMMnCdCz9EunHISagqJuABPr/jj1rF7eHL5RdHOQ6gvRGVnR1StX7dNQtQAACAASURBVAmONcvDws4cFrZmMFc0G1MYmhhofNcll8uRl52PnPScokwUKXHpSIhIRHxEEhIiE5EYmYyYkDgUSKTQMxCiQQdnNOnaEE1cG6JK3YpfLBuFJF+CQL9gPL3ij2fXXiL0eTisHCzhu6Qfuvp0UCs/4OuHIdjz6xE8u/oSFWtXwOQtI9GwQ11ec/jfCsLKIRuQEpMK98k9MXRJP067uIcXn+HXPivRpGsDLD4787veWf9/RitQXwFpgRRHfz+LA4uPw8zGDL9dnsf5wFmSL8HeX4/i+JrzsK9qh2k7x/L60BMR/M48wubJu5H0PgVdh3TA0MXesKuk3FzyOYIfh2L7zAPwvxkEy3LmaNDBGe08WqGdR4svek4kLZAiIjAa4QFRCA+IRHhgFMIDopASm/bJMTq6OtA30oO+oR70jfQh1NOFQEcAHV0d6OgKINARgBEIQHI5irxY5QS5XM7m5RNLUSAugFQihURcgLysfHzqM2JqaYxyVexQrootHGs4oHGX+qjfrg6vcxh1keRLcHnPTfideYSA268gyS+ArlAHzq1roWXPJnAb05XXg09JIl9FY8esg3hw4SnMbUzhPdMdfSZ057WDkcvlOLPhErZN3w8HJzvM2DsRdVrU4DTW/1YQ5vVcgYq1y2PNzUVqvRctXxetQH1FQl+EY3a3pcjJyMWAuR7oN+snzk96AXdeY5XvJsSFJaDH8C4Y+fsgmFqacL52XnYeDiw+gTMbL4FhGPSf3Reev/RS60ZJRHj8zwtcO3ALAbdfIzkmFVXrV4LPIm+07tPsq+aoE+eJkZGUibTETGQkFbbkTIhzJcjPFUOSJ4E4V4z8PDFkBTLIpDLIZXLIpHL2ezlBIGAAhoFAwIBhGDACBkJ9IYR6uhDq6UJXTxdCfSGMTA1hbGEMY3OjomZZzhz2VWy/SoZ3BZJ8CS7uuI4jv51GSmwaKtaugKau7K6tQfs6at/Is9NzcHDpSZz+8yIMTQzg+UtvuE/qwXveiKBorB29Fa/uBaOlWxPM2j+R0+8tLzsPu+YextmN/8ChWjmsvb1Y896fWr4oWoH6yqQlZmDzlN24ecQPTg0qY9rOsajZpBqnsfm5YuxbcBQn116Aua0Zxv85HO1FLXkJQUJkErb+shd3Tj6Eg1M5jPnDB616NVVbTGQyGW4du4/9i47h/ds4VGtUBW36NEeDDs6o07LGF90x/H+npDDVb1cHQxZ6oWHHuhr5f352LQBX992E3+lHKBBL0X1YZwxfMQDmNma813lw6UkcW3UWRmZGGLPGBy6D23NaY2JUEmZ1W4ro4Fj0Gd8dw5YPUOvcTMu3gVagvhHunXuM9WO3Iz0hHaJpveGzyIvzTTzkWRjWjvoLIc/C0dKtCSZuGsHbw+vZ9QBsmrQTUa9j0LhLfXQf1hlt3ZurLSQyqQzXD97BmQ0XEfo8AkQEob4QtVtUR4P2znBqUBk1fnDSxqRoiMyULAT6vcH74FhEB8ciJiQOEYFRyErL0agwvQ+Jw+VdN3DtwG0kx6TC1NIYnfq3xY8jXVCtYRXe8z38+yk2T9mN2HcJ6DqkA0avHsJZ4KLexGCW6xLkZuVh4anpaNSpHu/ra/k20QrUN0R2eg62Td+PSzuvo2LtCpi6dTTnek8yqQyn1l/EvgVHAQboN8sdop/doG/I3S1YWiDF2Y3/4Piac0iJTYN9FVuM+G0Q2nu20oh5ListG4F33+DlrVd4efsVQp+FQS5n/5ZaujWBx1Q3jdw8/7+hiFe6uP06bh71gzhPAgCwsDOHY00HONYsj07926Jx53pq/27TEtKxb+ExXNxxHQDQrHsjuPp0RMteTVXykkuITMLmKbtx7+xjVKxdARM2DOdeXkMmw9mN/2D3vMMwMDbAb5fnqSSOWr5dtAL1DfL48gv8OXYb4iOSeGeCiAtPwNZpe+F35jFsK1pj+PKBvGsxyeVyPL3ijx2zDiLsZSTqtqmFMWt8ULs5t0NqruRl5yEmJB73zz3Buc3/ID0pE9UbV0X3YZ3h3KomqtavpPW++gyZqVm4tv82Lu24joigaBgY66Nz/7bo6tMRlZ0deZ1JKkOcJ8bJtX/j6MozEOdJ0GuMK/rNdlfZ9b1AUoCTa//GwSUnAAAD54vgMbUnZ+/Pd/4RWDvqLwQ/fodmPRpjypaRajv6aPn20ArUN8pHmSDszDFu/TBe50v+t4KwddpehDwLR61m1TBmjQ/qteVXfVcmk+Hy7pvYM/8w0hIyULF2BTRoVwcNOrDBopoqcAiwN8DrB+/i5NrzRfFAegZCVP/BCXWaV0f1H5xgWc4c5jZmMLMxhbmNKa/d4fcIEUGcJ0F2WjYSo1MQH5aA2HcJiAtPQFxYAt48DEWBuAC1m1dHjxEu6OjdWuPnLpkpWbh//gn2LjiKpOgUtO7TDCNXDoJjzfIqzZefK8a1/bdxcu15vH8bhzY/NcPYtb6cgm4Bdpd/eMVpHFx6EqaWxhi7zhed+rXR7rr/R9EK1DdOyLMwrB29FSFPw9Ci5w8Y/+cwOFTldl4jl8tx/eAd7JpzCMkxqWjVuylGrx7Cu6R1blYe/t52DS/+DUDg3TfIzcwDAFStXwkD54nQtm9zteJlikNESIhMwpuHIXjzMASvH4Ui9FkYJPkF/+mrb6iHSs6OaOveAm37tkCl2hU0soavBREh7GUkbh69hzsnHyAxMumjzBkKbCpYwcGpXNFuk0vQKl/8bwZh9/zDCPILBgDUaOKE0auH8I5jUiCTynBh61XsXXAUWanZqN64KnwWeaOlWxPOcwQ/eYd1o7ci9Hk4ugxsh3HrfMsk1ZaWbwetQH0HyKQynNlwCXt+PQKSEwbNF8HjZzfO5pD8XDFOrfsbR347DalECtG0Xug/210lt2KZTIbwl2yy0gtbryA6OBYVajjAc1ovdB3SoUy886QFUsSExiMzOQuZKVnISM5CZnIm0pMy8ep+MF4/CAEAVKpTAW3dW8C5VU04NazyzefqK5AUIORZOILuvkGg3xsE+b1BRnIWBDoCNO5SH9UbVYGJpQlMLIxh62gFe6dysK9iW6Y7x5BnYdg19xCeXPaHraM1eo7uivrt6qBe29oq/y4D777Ghok7EeYfiUad62HIAi9eCYdzMnOxZ94RnNv8DyzszDFhw3C082ip0lq0fF9oBeo7IjE6GVum7sHdUw9R2dkRkzaPRIP2zpzHp8SlYcesA7i2/zZsKlhh5O+D1TKPyP6vvTuPj7K6Fz/++SaZ7HtC9oQskLCEVRYxgIBIEQVF0Iq1tbXWtj+3an+3t9ttvW297e1m7y1ee63WfaGCCKiIgCDKIvsWICzZ933PZJac+8czGaImLElIZsJ5v17zmsnkmWfO4Qnzneec83y/djs71+5l1e/XcXr/OcKiQ1j6yM3c/N35BIcP3Dfb6pIadr6zj0/f3kP2rtNY242zLV9/H+JGxpCYEUdMchTBEUEEhQcSFB5IcEQQgaEBzgt0vX29Mfma8PY1XfbZYEdHBzarHZvFhs1io63ZTGtTG21NbbQ2mWlraqO+qpGaklqqSmqoLqmlpqSWstwK55lh3IgYMmeOIjNrFNcunnJJCYX7Q2VhFcd35pC98xTZu3I4dzifoPBAVvzkdpb8vwW9CoadZ4I71+5l5zt7yT1awLDECL73p3uZtezSh6ltVhtbXv2EF//tDWrL6ln8/QXc9+SKQb2WTBtYOkC5oT3vHmDlw89TUVDF/K/P5v7f3XNZk9XZu3J4+pHnOXMwj+Sxicy7exZLH13U60SgSikObzvOP/+wjv2bjgAQHhtG/MgYUscNZ8mDCwds+K2loYUzB/OMJdanSyk6XUrx6TIq8quw2+yXtA8RwcPTyCDh6dmZSUJQHV2Lop0PTB32jkvar4eHEBYT6syxF5McxdjrMhiblTFgF5SaW9v54B8fcWjrMc4czKWqqAYAv0BfRl87kknzxrH4+wt6FQSa61v45x/Wse3NnZTnVSIiZM4cxcyl01n0wPxL/vtSSrF91S5e+PkblOVWkD4ljYdXfrvfF+lork8HKDfVmR16zVPvYvIx8Y0n7uTWhxZe8qo3u93O5pc+5sOXt3Nsx0mGJURw33/czby7Z/ZpWOzs4Tz2vn+IkrNllJwp4+zBPCxmK9ffOYMF984ZtIzjSinMLWaaaptprG2mqbaZ5roWzK3tWM1WLGYr7W0WLG0W7DY7dnsHHV1vHR14eHggAoiRScLDQ/Dy9sLLZGSRMB574hvgg3+QH35BfvgH+eIX5EdIZBBh0aGDlry2pbGVDf+ziTVPvUt9VSMJ6bGkTUwmM2s0Y7MySB0/vNdta65vYe1/vc+av7xLa2MbUxZOZObS6cxYcvlnggUni1n58PMc/ug4aROTnfNUehHE1UkHKDdXcraMpx99gX0bD5GcmcjDK++/rGE/MIrR/e2HL3HmQC6jpo3gvv+4m8yZo/ol4Wt9VQNr/vwu65/ZZGQcDwtgxq1Tmb3sWibNH68zTF8hSinK8yvJPVLAiV05vP/cVprrW5i6cCIrfnL7JV9fdyHN9S28/Zf3ePu/3qOloZWspdP4+i/uuOxrkZRSnD6Qy6YXtrHxuS34Bvhy35MrWPTA/H5bfKO5Jx2ghgClFLvW7eOZx16koqCKuSuyWP744ktOmQTGcNWWV3bwj5+9Tk1pHSYfEyMmJZMxZQQZ00aQddvUPuVqs7RbObj5KDtW72bXun20NLTiH+xH8thEwmJCCY8ONe5jQomMD2fivMwhv4y8P7Q0tnJo6zHqKxtprGmiprSWvGOF5B4toKWhFTCGFq+7bRorfrL0sv4mutPe1s6eDQfI3pXDhy9tp6WhlZm3T+eef1t+2YGpobqRjc9tZfMrH1N4sgSTj4kb7p7Jfb/92oDNwWmuTQeoIcTc2s6bv13LW39aj8Vs5YZ7ZnH/7+4hMi78kvfR1mJm73sHObX3LDn7z3Jmfy7m1nbCokNY9thibn5gfq/Kx3dltVg5tPU4u97ZS2luBXXl9dSW19NYc75AX1BYAPO/fj2LvjOf5LGuX4p9INltdnL2nWXzyx+z5dUdmFvanb/zD/YjOTOJtAnJpE0YTuqEZJIzE/EL8O3be9rtbH55By//chVVxTWYvL2Yfss1vQpMbS1mNj63lVf+/S2a61vInDmKG79+PbPvmNHnvy1taHGbACUi+UATYAdsF2v01RigOrU0trLqP99h9Z824Gny5Gs/X87tP7i5V8Npdrud7J05vPrr1Rzaegy/QF9u+vYNLH10ETHJUf3abqvFSl1FA4UnS9j04jY+XbMHm9XOmOsyWPCN6xl5TSqJo+L7/GHrjqqKa9i/6TD7Nh3m0JZjNNe3YPIxMXdFFgu/NY/YVGOVYn8v829rMXPgwyO89ItV5GcXkTE1jW/9ZgXjrx9z2UPAlYVVrHt6Exuf20JTXQuTbhjH95/65iWXmtGuPu4WoKYopaovZfurOUB1Kj1Xzt9++BK71+8nbkQMi+6fz4Q5Yxg5ObVXE+JnD+Wx+s8b2L5qF0opZi+/lqzbphGTEkVMShQhkcH9OpldX9XA5pd38P7fN1N8usz5fFRSJEmj40nMiCcsOpTAsAACQwMIDAsgKCwA3wBfTD5G6QtvX5OzJIanyXPQ5jSUUtistvPL0a12rO1WzC3ttDWbMbeYMTebaW0yU1tWR3VJLdWlxnL0qqJqyvOrAIiIC2PqVyYy5SsTmXzj+H5PZ1R6tpyTn50lZ+8ZTu07S/7xIjrsHSSkx/Kt36y4rGXiYAwdn9iVw9q/buTTtz8Dpci6fTq3P7KIsVmXfi2UdnXSAeoqsO+DQzz/09c5dzgfgLDoEL75q7v4yn1ze/WBXVVcwzv//T7vPrvZmVUCICE9lq/9fDlzV2T1ayBQSlF4qoTCE8UUniyh8JRxX5xTirm1/eI76CIxI470KWmkX5NG6oThxI2IISI2rF9X17U0tlJ4soSCE8UUniii4GQxOXvPOiv9XgpvXxOR8eHOku8jJ6cx5StXptpufnYRr/1mNTve2u1M3hsUFkDGtBFkTB3B6OkjuWbBhMtafamU4pM1e3jux69RlltBYGgAi+6/gSUPLrzktEaa5k4BKg+oAxTwv0qpZ7vZ5gHgAYCkpKRrCgoKBraRLq62vI5jO07yzsqNHP/0FKnjh3PPL+5gylcm9GrYrPMbd3l+FWXnKtj04jZyjxYQkxLFnDuvY/YdMxgxKeWKfktub2unub6V5rpm57251YLFbHFWurWYrcZ9m4W844Xk7Dv7uQq7IkJYdIgzGASGBeDja1TX9fX3wcffB08vD+y2DkcRQ6OgobXdSlNdC011zTTWNBlL2KubqC2vd+7b5GMiMSOOtEnJxKfFYvI5vyzd5OOFb4AvvgE++AWevw+LCSUoLPCKn13kHSvglV+v5pPVe5xDtxlT00ifOoL4ETG9fv9Te8/wtx++RPbOHFLHD2fZY7cwa3o8fls2QX09hIbCrbdCop5b1C7MnQJUnFKqVESigM3Aw0qpHT1tr8+geqaUYsfqPfz9R69QUVCFt6+JSTeMY8biKUy/5ZrLWlTRVUdHBzvX7uW9v2/h0NZjdNg7iEuLZvbyGWQtNYYCg8IDXWLpcHVpLQXZRVTkV1FdUktVcQ01pbVUl9TS2thmVNdtbae91dJt+fbOa6ACwwIJjjifnSIoLJD4ETEkjUlg+JgEYlKiXKK/LY2t5B8vIu9YIXnHCjh3JJ/snTn4B/lx28M3seyxW/qU166prpmC7CLe/d/NbH3tE+Ms/dcrKKsYzdf2PILv+2uh4/wFzcrDg3PjlzJi40qIiemPLmpDkNsEqM81QOQJoFkp9ceettEB6uJsVhtHd5xkz4b97N6wn/K8SgBSxw9n0g3j+Oq/3tbr5b4N1Y3sfGcfO1bvdgYrMD7YgyOCCBkWTMiwYFIyk1j8/QUMH+Oa36aVUlgtNuxWG55ennh6eRqZJVx47qTgZDHrn/6AghPFNNU201Dd+LkzRv8gP5IzE5k8fzxLH13Uq7RUSimjTMozm8g7VkhtmbF/k4+J5Y/fwl0/XsqqlQ3M/mkWaeSiTCZkyRLIyMB8NAfPd9dhwkZDZCohx3dBtC5YqX2ZWwQoEQkAPJRSTY7Hm4FfKaU+6Ok1OkBdHqUUBSeK2bNhP4e2HefwR8fx8fPmjh8uYdnjt/SpjENjTROHPjpOXXk99VUN1Fc20lDdSF1FPaf352JttzJxXia3PXQT1y6+xiXOONxNdWkt+zcdYcdbu9j3wWFMPiYypqY5cw/Gj4glZVwSKeOSiB4+rNcBtr2tnR1v7WH9M5s49dkZYlOjyZw1iuQxiQwfm0j6lDTnlxrz4uX4vruGA0zmX0au481PEwCYOxcaThSzyfdWxpoPwrJlsHp1v/1baEOHuwSoVGCt40cv4HWl1JMXeo0OUH1TlFPCCz9/g0/WfEZoVAhf+9kypi2aRHTysH4NIJ0Xa65/ZhNVRTVEDx/G9XfMIHF0AokZcSRmxOmSCl+glKK5voWzh/LY/8Fh9n94hNyjxnxrZHw4Nz9wI7d870ZCh/Xfxa752UW89+xmtryyg+b6FhLSY1n22GJu+va87heYFBZCSgrK05MbknPZdiaBYY61EVVVMGYMfPxqEZHTUo2hv/x8PSelfYlbBKje0AGqf5z87AzP/+Q1jmzPBsDk7UX8yFgSMuJISI8jfmQss5ZNJyDYv0/vY7fZ2bV+P+ufNhZw2KznE7sGRwQRlxZNYFgAfkF+BAT54R/sj3+wH96+3s4FBwEh/ky6YVyv59AGi81qI3tnDoWnSrBZbM6cgO2t7bS3WWhvs9BYYyy+qC2ro6683lknysvkSeas0UxZMJGpCyeSMi6pz8OPlYVV7N5wgOriGmrK6ig8WUzOvnOYvL2YuWw6N3/nRsZfP+bC77NyJTz8MCxbRuX/rCYz0whMAMOGwfHjEBUFLF8Oa9YY2z/4YJ/arQ09vQlQugb3VWT09JH8YesvOX0gl7yjBRTllFKUU0JBdhG71+/HbrPz7P9/iWWPLea2R27qdaDy9PJk1u3TmXX7dOw2O2V5lRTnlFKUU0pxTgnlBVU017dSWVhNa2ObcWtq63ZfSaPjGTdrDGNmpDNmRjrxI2Ndas7IYrZQcqaMU3vPsnfjIQ5uOfq55fqdTD4moxSInzfBEYGEx4aRkB5LeHSoI1t8LBPmjOlTCqquinJKWPvfG9n43BZsVjteJk/CY8OITIjggT98gwX3Xk9IZPCl7azesZIxI+PC26WnG/d1dRfeTtMukQ5QVxkRIWNKGhlTPp/HzWa1cfpALm/89m1e/MWbrHlqA7f/4BaWPnJTn2r2eHp5kjAyloSRsResuNrR0YHNYsNqMZaT15bVs3/TYQ59dIztq3by3rObAQgKD2Tk5BQiEyKIjAt3Xl8UERtGQIg/fkF+zmXefcnkbrfZaW+zGMvNa5ocN+NxRX4lhadKKDpVQnlepfN6o2EJEcy58zqmLZpMxtQ0vH2NgOTtaxqQYovl+ZVsX7WL7at2cu5wPp5eniy8bx53/ssSYlKiet+G0FAAzEdzmDvXOHvqOsQ3dy5s2wZRp08bT4YNTLkRbejTQ3zal5w+cI5Xf72a3ev3IyJEJw8jIT2WhPQ4EjPiiR8ZQ1B4IP7B/oREBvVrBoTudHR0UHiyhBO7T3Nydw55xwupKa2jtqzOGRy64xvgg8nHhJfJ07lqz9PkiYeHGK9Tynlvt3dgabM4S3VcqC6UycdEQnosSaPjSRqVQOKoeFLHJ5E0OuGKnt1ZzBZnnsOGKqMKcWN1E/WVDRz5ONtZjXj0tSOZ89UsZt8xo3+GSAsLUSkp2Do8SSGXkDEJbNtm/GruXDhxAuaNLGJLXiqi56C0Hug5KK1fnTmYy+71+41CgTmlFJ8u/VwyUzDOyLKWTmP544sZe91FhoD6md1up66igZqSWmrL651DheYuVXCtFht22/kLcu02oyiheIhxRiEYtaE8BB9f42zH21GV18fPm4DQAIIjjOuiQiKDCI4IIjgyaEBXJ1YUVLFu5Ubef26rM6t5Vx6eHqSMS2LOV7O4/s4ZxKb0/zLvs5OWM+LwGrJ9JxO96x0iJxkBqLISVsws4vdnbuMa9Co+rWc6QGlXlFKK6pJaSs+V09LQSmtjG3nHCp0JQ+NGxDB90WSmLZrM+Nmj+z3Z6dXEbrdzYtdp1v71fXa+/RmIMGvZdKYsmEhwZBAhkca1ZyGRRrn7Kz4vV15Ow7gsQqpzwcvLyB6Rng6nT6PWrUNsNkhNhV36OiitezpAaYOircXM1lc/Ydf6fRzZdhyL2Yqvvw+T5o9jzIwMImLDCI0OIdxRI2qgz0Bckd1up63JTEtDKy0NrVQV15B/vIj87ELyjxdReLIYi9lKUFgAi74znyUPLiQqMXJwG11eDg89BGs/n0kCDw9YuhSefloHJ61HOkBpg87c2s6R7dl89t5B9r5/kIqCqm63c65qcwyp+fh542nyxOTtRdqEZGYsmcqkG9yv0KHVYiV7Zw77PjhM9q5TmFvanYs/7FY7VovNOQTZnWEJESRnJpI8NpG0iSlcd9tU1ytJUlQE69cbq/XCwmDJEj3npF2UDlCay2lrbqO2vN5ZyLC2vJ7G6qbz1we1WbC0G49tVmPl3Kk9Z2htasPb10S6Y8Vh6oRk0iYmkzQ6vl/K1/cHpRQN1Y2U51Vy9lA++z44xKGtx2hrNuNl8iRj2giCI4LwMnni5W0klfXy8sIv0Bf/YD8CQvydt/DYMJLHJuoif9qQpa+D0lyOX6Af8SP8iB8Re8mvsVqsHNl+goObj3Dk4xNs+NuHWMxWwLiYNTYthvCYUEKjggmJDCY0KoTQYcH4BvoaGcX9vfEN8MXH3xuTtxcenh6fz73nIagOhVKOW4exms/abnXcbM7HrY1tNNe3GNnV61toqW+hpqyOstwKyvMqaWs2O9sdlRTJvLtnMe2mSUycl9mnlFKapukzKM0N2G12is+UkXukgNwj+RSfKTOWWVc2UF/ZQFNdy4C0w8PTg8DQAMJjQolJjSImOYrY1GhiUqJIGhXvchcRa5or0WdQ2pDk6eXJ8NEJDB+dwNy7sr70e6vFSmNNM+YWM+2tFsyt7Zhb2mlvbcdmtdPhXGLegd1u1IHy8BAQwcNDEBHEQ4xqvZ3Vex2P/YP9CQz1JyA0AF9/Hx2ANG0A6QCluT2Tt4mIWJ29QNOGmiuff0XTNE3TekEHKE3TNM0l6QClaZqmuSQdoDRN0zSXpAOUpmma5pJ0gNI0TdNckg5QmqZpmkvSAUrTNE1zSTpAaZqmaS5JByhN0zTNJekApWmaprkkHaA0TdM0lzQoAUpEFopIjoicFZEfD0YbNE3TNNc24AFKRDyBp4GbgDHAChEZM9Dt0DRN01zbYJxBTQPOKqVylVIW4E3g1kFoh6ZpmubCBqMeVDxQ1OXnYmD6FzcSkQeABxw/tovI8QFo20CKBKoHuxH9TPfJPQy1Pg21/sDQ7FPG5b5gMAJUdyVJv1R3Xin1LPAsgIjsv9xSwa5O98k96D65vqHWHxi6fbrc1wzGEF8xkNjl5wSgdBDaoWmaprmwwQhQ+4CRIpIiIt7AXcD6QWiHpmma5sIGfIhPKWUTkYeATYAn8A+lVPZFXvbslW/ZgNN9cg+6T65vqPUHdJ8AEKW+NP2jaZqmaYNOZ5LQNE3TXJIOUJqmaZpLcukANRRTIolIvogcE5HDvVl26SpE5B8iUtn1+jQRCReRzSJyxnEfNphtvBw99OcJESlxHKvDIrJoMNt4uUQkUUS2ichJEckWkUcdz7vzceqpT257rETEV0T2isgRR5/+3fF8ioh85jhOqxyLytzCBfr0oojkdTlOEy+4H1edg3KkRDoN3IixNH0fsEIpdWJQG9ZHIpIPTFFKYyuyZAAABiJJREFUufVFeCIyG2gGXlZKZTqe+z1Qq5T6neMLRZhS6l8Hs52Xqof+PAE0K6X+OJht6y0RiQVilVIHRSQIOADcBnwT9z1OPfXpTtz0WImIAAFKqWYRMQGfAo8CjwNvK6XeFJG/AUeUUs8MZlsv1QX69D3gXaXU6kvZjyufQemUSC5MKbUDqP3C07cCLzkev4TxweEWeuiPW1NKlSmlDjoeNwEnMTK5uPNx6qlPbksZmh0/mhw3BcwDOj/I3e049dSny+LKAaq7lEhu/YfooIAPReSAI53TUBKtlCoD44MEiBrk9vSHh0TkqGMI0G2Gwr5IRJKBScBnDJHj9IU+gRsfKxHxFJHDQCWwGTgH1CulbI5N3O7z74t9Ukp1HqcnHcfpKRHxudA+XDlAXVJKJDeUpZSajJHN/UHH0JLmmp4B0oCJQBnwp8FtTu+ISCCwBviBUqpxsNvTH7rpk1sfK6WUXSk1ESOzzjRgdHebDWyr+uaLfRKRTOAnwChgKhAOXHBo2ZUD1JBMiaSUKnXcVwJrMf4Yh4oKxxxB51xB5SC3p0+UUhWO/2QdwN9xw2PlGP9fA7ymlHrb8bRbH6fu+jQUjhWAUqoe2A5cC4SKSGcyBbf9/OvSp4WOIVqllGoHXuAix8mVA9SQS4kkIgGOiV1EJABYAAylLO3rgXsdj+8F1g1iW/qs80PcYSludqwcE9XPAyeVUn/u8iu3PU499cmdj5WIDBORUMdjP2A+xtzaNmC5YzN3O07d9elUly9GgjGndsHj5LKr+AAcS0X/wvmUSE8OcpP6RERSMc6awEgz9bq79klE3gDmYJQFqAB+CbwD/BNIAgqBO5RSbrHwoIf+zMEYMlJAPvDdzrkbdyAiM4FPgGNAh+Ppn2LM2bjrceqpTytw02MlIuMxFkF4Ypw0/FMp9SvH58WbGENhh4B7HGceLu8CffoIGIYxhXMY+F6XxRRf3o8rByhN0zTt6uXKQ3yapmnaVUwHKE3TNM0l6QClaZqmuSQdoDRN0zSXpAOUpmma5pJ0gNI0BxHpcblrN9vOEZHrrmR7LvL+PxCRb/TDft4UkZH90SZN6286QGla78wBBiVAObIL3Ae83g+7ewb4UT/sR9P6nQ5QmnYBIrLYUZPnkIhsEZFoR5LS7wGPOWrazHJcOb9GRPY5blmO1z/hSF66XURyReSRLvv+hiNp5hEReUVEghy1ckyO3weLUT/M9IVmzQMOdiYSdez7KRHZIUadpKki8rYYdYR+49gmQETec7zXcRH5qmNfnwDzu6TU0TSXof8oNe3CPgWuVUopEbkf+JFS6oeO+jzO+kMi8jrwlFLqUxFJAjZxPuHnKGAuEATkiMgzQDrwM4zkwdUiEq6UahKR7cDNGFk57gLWKKWsX2hTFkYdpK4sSqnZYhTwWwdcg1E+5JyIPIVxxleqlLrZ0d4QAKVUh4icBSZ0s09NG1Q6QGnahSUAqxw5xLyBvB62mw+MMVKMARDcmXcReM+RoqZdRCqBaBy1fjoLV3ZJNfQcxpDbO8C3gO90816xGLnauurMU3kMyO5M8yMiuRhJl48BfxSR/8QoGPdJl9dWAnHoAKW5GD3Ep2kX9ldgpVJqHPBdwLeH7TyAGUqpiY5bvKOgHkDX/Gl2jC+GQjflE5RSO4FkEbke8FRKdZdMs62bdnS+R8cX3q8D8FJKncY4qzoG/FZEftFlG1/HPjXNpegApWkXFgKUOB7f2+X5Jowhu04fAg91/iAiEy+y363AnSIS4dg+vMvvXgbewChH0J2TwIiLtrwLEYkDWpVSrwJ/BCZ3+XU6kH05+9O0gaADlKad5y8ixV1ujwNPAG+JyCdAdZdtNwBLOxdJAI8AUxyLHk5gLKLokVIqG3gS+FhEjgBdy2G8BoRhBKnubAQut9DlOGCvGBVOfwZ0Lp6IBtrcJfO3dnXR2cw1zcWIyHLgVqXU1y+wzVqMBRtn+vhejwGNSqnn+7IfTbsS9CIJTXMhIvJX4CZg0UU2/THGYok+BSigHnilj/vQtCtCn0FpmqZpLknPQWmapmkuSQcoTdM0zSXpAKVpmqa5JB2gNE3TNJekA5SmaZrmkv4Prcfje/owoMsAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "pval = utils.multivariateGaussian(Xval, mu, sigma2)\n",
     "\n",
@@ -385,9 +467,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise anomaly-detection-and-recommender-systems\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "               Estimate Gaussian Parameters |  15 /  15 | Nice work!\n",
+      "                           Select Threshold |  15 /  15 | Nice work!\n",
+      "               Collaborative Filtering Cost |   0 /  20 | \n",
+      "           Collaborative Filtering Gradient |   0 /  30 | \n",
+      "                           Regularized Cost |   0 /  10 | \n",
+      "                       Regularized Gradient |   0 /  10 | \n",
+      "                                  --------------------------------\n",
+      "                                            |  30 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[2] = selectThreshold\n",
     "grader.grade()"
@@ -404,9 +508,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best epsilon found using cross-validation: 1.38e-18\n",
+      "Best F1 on Cross Validation Set          : 0.615385\n",
+      "\n",
+      "  (you should see a value epsilon of about 1.38e-18)\n",
+      "   (you should see a Best F1 value of      0.615385)\n",
+      "\n",
+      "# Outliers found: 117\n"
+     ]
+    }
+   ],
    "source": [
     "#  Loads the second dataset. You should now have the\n",
     "#  variables X, Xval, yval in your environment\n",
@@ -452,9 +570,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average rating for movie 1 (Toy Story): 3.878319 / 5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Load data\n",
     "data = loadmat(os.path.join('Data', 'ex8_movies.mat'))\n",
@@ -531,7 +669,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -611,7 +749,105 @@
     "\n",
     "    # ====================== YOUR CODE HERE ======================\n",
     "\n",
+    "    J = (1/2)*np.sum(np.sum(R*(np.matmul(X,Theta.T) - Y)*(np.matmul(X,Theta.T) - Y)))\n",
+    "\n",
+    "    X_grad = np.matmul(R*(np.matmul(X,Theta.T) - Y), Theta)\n",
+    "    Theta_grad = np.matmul((R*(np.matmul(X,Theta.T) - Y)).T, X)\n",
+    "    # =============================================================\n",
+    "    \n",
+    "    grad = np.concatenate([X_grad.ravel(), Theta_grad.ravel()])\n",
+    "    return J, grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## WITH REGULARIZATION\n",
+    "\n",
+    "def cofiCostFunc(params, Y, R, num_users, num_movies,\n",
+    "                      num_features, lambda_):\n",
+    "    \"\"\"\n",
+    "    Collaborative filtering cost function.\n",
     "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    params : array_like\n",
+    "        The parameters which will be optimized. This is a one\n",
+    "        dimensional vector of shape (num_movies x num_users, 1). It is the \n",
+    "        concatenation of the feature vectors X and parameters Theta.\n",
+    "    \n",
+    "    Y : array_like\n",
+    "        A matrix of shape (num_movies x num_users) of user ratings of movies.\n",
+    "    \n",
+    "    R : array_like\n",
+    "        A (num_movies x num_users) matrix, where R[i, j] = 1 if the \n",
+    "        i-th movie was rated by the j-th user.\n",
+    "    \n",
+    "    num_users : int\n",
+    "        Total number of users.\n",
+    "    \n",
+    "    num_movies : int\n",
+    "        Total number of movies.\n",
+    "    \n",
+    "    num_features : int\n",
+    "        Number of features to learn.\n",
+    "    \n",
+    "    lambda_ : float, optional\n",
+    "        The regularization coefficient.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    J : float\n",
+    "        The value of the cost function at the given params.\n",
+    "    \n",
+    "    grad : array_like\n",
+    "        The gradient vector of the cost function at the given params.\n",
+    "        grad has a shape (num_movies x num_users, 1)\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the cost function and gradient for collaborative filtering.\n",
+    "    Concretely, you should first implement the cost function (without\n",
+    "    regularization) and make sure it is matches our costs. After that,\n",
+    "    you should implement thegradient and use the checkCostFunction routine \n",
+    "    to check that the gradient is correct. Finally, you should implement\n",
+    "    regularization.\n",
+    "    \n",
+    "    Notes\n",
+    "    -----\n",
+    "    - The input params will be unraveled into the two matrices:\n",
+    "        X : (num_movies  x num_features) matrix of movie features\n",
+    "        Theta : (num_users  x num_features) matrix of user features\n",
+    "\n",
+    "    - You should set the following variables correctly:\n",
+    "\n",
+    "        X_grad : (num_movies x num_features) matrix, containing the \n",
+    "                 partial derivatives w.r.t. to each element of X\n",
+    "        Theta_grad : (num_users x num_features) matrix, containing the \n",
+    "                     partial derivatives w.r.t. to each element of Theta\n",
+    "\n",
+    "    - The returned gradient will be the concatenation of the raveled \n",
+    "      gradients X_grad and Theta_grad.\n",
+    "    \"\"\"\n",
+    "    # Unfold the U and W matrices from params\n",
+    "    X = params[:num_movies*num_features].reshape(num_movies, num_features)\n",
+    "    Theta = params[num_movies*num_features:].reshape(num_users, num_features)\n",
+    "\n",
+    "    # You need to return the following values correctly\n",
+    "    J = 0\n",
+    "    X_grad = np.zeros(X.shape)\n",
+    "    Theta_grad = np.zeros(Theta.shape)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "    J = (1/2)*np.sum(np.sum(R*(np.matmul(X,Theta.T) - Y)*(np.matmul(X,Theta.T) - Y))) +(lambda_/2)*np.sum(Theta*Theta) + (lambda_/2)*np.sum(X*X)\n",
+    "\n",
+    "    X_grad = np.matmul(R*(np.matmul(X,Theta.T) - Y), Theta) + lambda_*X\n",
+    "    Theta_grad = np.matmul((R*(np.matmul(X,Theta.T) - Y)).T, X) + lambda_*Theta\n",
+    "\n",
     "    \n",
     "    # =============================================================\n",
     "    \n",
@@ -628,9 +864,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost at loaded parameters:  22.22 \n",
+      "(this value should be about 22.22)\n"
+     ]
+    }
+   ],
    "source": [
     "#  Load pre-trained weights (X, Theta, num_users, num_movies, num_features)\n",
     "data = loadmat(os.path.join('Data', 'ex8_movieParams.mat'))\n",
@@ -663,9 +908,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise anomaly-detection-and-recommender-systems\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "               Estimate Gaussian Parameters |  15 /  15 | Nice work!\n",
+      "                           Select Threshold |  15 /  15 | Nice work!\n",
+      "               Collaborative Filtering Cost |  20 /  20 | Nice work!\n",
+      "           Collaborative Filtering Gradient |   0 /  30 | \n",
+      "                           Regularized Cost |   0 /  10 | \n",
+      "                       Regularized Gradient |   0 /  10 | \n",
+      "                                  --------------------------------\n",
+      "                                            |  50 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[3] = cofiCostFunc\n",
     "grader.grade()"
@@ -724,9 +991,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-0.88461031 -0.88461031]\n",
+      " [ 1.78864507  1.78864507]\n",
+      " [ 0.99988838  0.99988838]\n",
+      " [ 1.50248467  1.50248467]\n",
+      " [-5.14295182 -5.14295182]\n",
+      " [ 2.34291712  2.34291712]\n",
+      " [ 0.432456    0.432456  ]\n",
+      " [-4.00036359 -4.00036359]\n",
+      " [ 2.32535975  2.32535975]\n",
+      " [-0.69661887 -0.69661887]\n",
+      " [ 4.8754096   4.8754096 ]\n",
+      " [-0.78400337 -0.78400337]\n",
+      " [ 1.44224907  1.44224907]\n",
+      " [-0.27586935 -0.27586935]\n",
+      " [ 1.09194879  1.09194879]\n",
+      " [ 1.05349628  1.05349628]\n",
+      " [-1.4008251  -1.4008251 ]\n",
+      " [ 0.382493    0.382493  ]\n",
+      " [ 0.41691754  0.41691754]\n",
+      " [-2.55151732 -2.55151732]\n",
+      " [ 2.29664715  2.29664715]\n",
+      " [-0.04867718 -0.04867718]\n",
+      " [ 1.79589923  1.79589923]\n",
+      " [ 0.64594658  0.64594658]\n",
+      " [ 2.35938822  2.35938822]\n",
+      " [ 8.25936422  8.25936422]\n",
+      " [ 1.72730399  1.72730399]]\n",
+      "\n",
+      "The above two columns you get should be very similar.(Left-Your Numerical Gradient, Right-Analytical Gradient)\n",
+      "If your cost function implementation is correct, then the relative difference will be small (less than 1e-9).\n",
+      "\n",
+      "Relative Difference: 1.12903e-12\n"
+     ]
+    }
+   ],
    "source": [
     "#  Check gradients by running checkcostFunction\n",
     "utils.checkCostFunction(cofiCostFunc)"
@@ -741,9 +1047,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise anomaly-detection-and-recommender-systems\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "               Estimate Gaussian Parameters |  15 /  15 | Nice work!\n",
+      "                           Select Threshold |  15 /  15 | Nice work!\n",
+      "               Collaborative Filtering Cost |  20 /  20 | Nice work!\n",
+      "           Collaborative Filtering Gradient |  30 /  30 | Nice work!\n",
+      "                           Regularized Cost |   0 /  10 | \n",
+      "                       Regularized Gradient |   0 /  10 | \n",
+      "                                  --------------------------------\n",
+      "                                            |  80 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[4] = cofiCostFunc\n",
     "grader.grade()"
@@ -768,9 +1096,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost at loaded parameters (lambda = 1.5): 31.34\n",
+      "              (this value should be about 31.34)\n"
+     ]
+    }
+   ],
    "source": [
     "#  Evaluate cost function\n",
     "J, _ = cofiCostFunc(np.concatenate([X.ravel(), Theta.ravel()]),\n",
@@ -789,9 +1126,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise anomaly-detection-and-recommender-systems\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "               Estimate Gaussian Parameters |  15 /  15 | Nice work!\n",
+      "                           Select Threshold |  15 /  15 | Nice work!\n",
+      "               Collaborative Filtering Cost |  20 /  20 | Nice work!\n",
+      "           Collaborative Filtering Gradient |  30 /  30 | Nice work!\n",
+      "                           Regularized Cost |  10 /  10 | Nice work!\n",
+      "                       Regularized Gradient |   0 /  10 | \n",
+      "                                  --------------------------------\n",
+      "                                            |  90 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[5] = cofiCostFunc\n",
     "grader.grade()"
@@ -821,9 +1180,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ -3.95586946  -3.95586946]\n",
+      " [ -1.62700992  -1.62700992]\n",
+      " [ -1.4556149   -1.4556149 ]\n",
+      " [  2.00515225   2.00515225]\n",
+      " [  0.73285583   0.73285583]\n",
+      " [  1.31233431   1.31233431]\n",
+      " [ -5.38810061  -5.38810061]\n",
+      " [  3.68619656   3.68619656]\n",
+      " [-10.57597535 -10.57597535]\n",
+      " [ -0.12571926  -0.12571926]\n",
+      " [  4.97104139   4.97104139]\n",
+      " [  6.47340274   6.47340274]\n",
+      " [  0.19478247   0.19478247]\n",
+      " [ -3.40362384  -3.40362384]\n",
+      " [ -7.33584526  -7.33584526]\n",
+      " [  1.53851051   1.53851051]\n",
+      " [ -1.608482    -1.608482  ]\n",
+      " [  4.36422262   4.36422262]\n",
+      " [ -1.14399801  -1.14399801]\n",
+      " [ -0.27786333  -0.27786333]\n",
+      " [ -0.91850302  -0.91850302]\n",
+      " [  0.86144566   0.86144566]\n",
+      " [ -5.56493585  -5.56493585]\n",
+      " [  7.47268389   7.47268389]\n",
+      " [  5.46686934   5.46686934]\n",
+      " [  1.40038456   1.40038456]\n",
+      " [ -2.79877168  -2.79877168]]\n",
+      "\n",
+      "The above two columns you get should be very similar.(Left-Your Numerical Gradient, Right-Analytical Gradient)\n",
+      "If your cost function implementation is correct, then the relative difference will be small (less than 1e-9).\n",
+      "\n",
+      "Relative Difference: 1.89391e-12\n"
+     ]
+    }
+   ],
    "source": [
     "#  Check gradients by running checkCostFunction\n",
     "utils.checkCostFunction(cofiCostFunc, 1.5)"
@@ -838,9 +1236,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Submitting Solutions | Programming Exercise anomaly-detection-and-recommender-systems\n",
+      "\n",
+      "Use token from last successful submission (waiyen.chan0819@gmail.com)? (Y/n): Y\n",
+      "                                  Part Name |     Score | Feedback\n",
+      "                                  --------- |     ----- | --------\n",
+      "               Estimate Gaussian Parameters |  15 /  15 | Nice work!\n",
+      "                           Select Threshold |  15 /  15 | Nice work!\n",
+      "               Collaborative Filtering Cost |  20 /  20 | Nice work!\n",
+      "           Collaborative Filtering Gradient |  30 /  30 | Nice work!\n",
+      "                           Regularized Cost |  10 /  10 | Nice work!\n",
+      "                       Regularized Gradient |  10 /  10 | Nice work!\n",
+      "                                  --------------------------------\n",
+      "                                            | 100 / 100 |  \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "grader[6] = cofiCostFunc\n",
     "grader.grade()"
@@ -857,9 +1277,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New user ratings:\n",
+      "-----------------\n",
+      "Rated 4 stars: Toy Story (1995)\n",
+      "Rated 3 stars: Twelve Monkeys (1995)\n",
+      "Rated 5 stars: Usual Suspects, The (1995)\n",
+      "Rated 4 stars: Outbreak (1995)\n",
+      "Rated 5 stars: Shawshank Redemption, The (1994)\n",
+      "Rated 3 stars: While You Were Sleeping (1995)\n",
+      "Rated 5 stars: Forrest Gump (1994)\n",
+      "Rated 2 stars: Silence of the Lambs, The (1991)\n",
+      "Rated 4 stars: Alien (1979)\n",
+      "Rated 5 stars: Die Hard 2 (1990)\n",
+      "Rated 5 stars: Sphere (1998)\n"
+     ]
+    }
+   ],
    "source": [
     "#  Before we will train the collaborative filtering model, we will first\n",
     "#  add ratings that correspond to a new user that we just observed. This\n",
@@ -915,9 +1355,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Recommender system learning completed.\n"
+     ]
+    }
+   ],
    "source": [
     "#  Now, you will train the collaborative filtering model on a movie rating \n",
     "#  dataset of 1682 movies and 943 users\n",
@@ -978,9 +1426,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top recommendations for you:\n",
+      "----------------------------\n",
+      "Predicting rating 5.0 for movie Aiqing wansui (1994)\n",
+      "Predicting rating 5.0 for movie Entertaining Angels: The Dorothy Day Story (1996)\n",
+      "Predicting rating 5.0 for movie Saint of Fort Washington, The (1993)\n",
+      "Predicting rating 5.0 for movie Prefontaine (1997)\n",
+      "Predicting rating 5.0 for movie Great Day in Harlem, A (1994)\n",
+      "Predicting rating 5.0 for movie Marlene Dietrich: Shadow and Light (1996)\n",
+      "Predicting rating 5.0 for movie Star Kid (1997)\n",
+      "Predicting rating 5.0 for movie They Made Me a Criminal (1939)\n",
+      "Predicting rating 5.0 for movie Someone Else's America (1995)\n",
+      "Predicting rating 5.0 for movie Santa with Muscles (1996)\n",
+      "\n",
+      "Original ratings provided:\n",
+      "--------------------------\n",
+      "Rated 4 for Toy Story (1995)\n",
+      "Rated 3 for Twelve Monkeys (1995)\n",
+      "Rated 5 for Usual Suspects, The (1995)\n",
+      "Rated 4 for Outbreak (1995)\n",
+      "Rated 5 for Shawshank Redemption, The (1994)\n",
+      "Rated 3 for While You Were Sleeping (1995)\n",
+      "Rated 5 for Forrest Gump (1994)\n",
+      "Rated 2 for Silence of the Lambs, The (1991)\n",
+      "Rated 4 for Alien (1979)\n",
+      "Rated 5 for Die Hard 2 (1990)\n",
+      "Rated 5 for Sphere (1998)\n"
+     ]
+    }
+   ],
    "source": [
     "p = np.dot(X, Theta.T)\n",
     "my_predictions = p[:, 0] + Ymean\n",
@@ -1001,6 +1482,20 @@
     "    if my_ratings[i] > 0:\n",
     "        print('Rated %d for %s' % (my_ratings[i], movieList[i]))"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -1019,7 +1514,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,