From 22a45b750cd4e22aa612c9fcbae97d2436302be5 Mon Sep 17 00:00:00 2001 From: Oliver Turnbull Date: Tue, 4 Feb 2020 19:18:53 +0000 Subject: [PATCH 1/3] Completed exercise 2 --- Exercise2/exercise2.ipynb | 409 ++++++++++++++++++++++++++++++++++---- 1 file changed, 368 insertions(+), 41 deletions(-) diff --git a/Exercise2/exercise2.ipynb b/Exercise2/exercise2.ipynb index 39983d90..d956d44e 100755 --- a/Exercise2/exercise2.ipynb +++ b/Exercise2/exercise2.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -89,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -125,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -151,7 +151,14 @@ " fig = pyplot.figure()\n", "\n", " # ====================== YOUR CODE HERE ======================\n", + " \n", + " # Find Indices of Positive and Negative Examples using boolean indexing\n", + " pos = y == 1\n", + " neg = y == 0\n", "\n", + " # Plot Examples\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 +172,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plotData(X, y)\n", "# add axes labels\n", @@ -202,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -234,7 +254,7 @@ "\n", " # ====================== YOUR CODE HERE ======================\n", "\n", - " \n", + " g = 1 / (1 + np.exp(-z))\n", "\n", " # =============================================================\n", " return g" @@ -249,9 +269,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "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 +303,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise logistic-regression\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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 +360,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ @@ -328,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ @@ -371,8 +433,13 @@ " # You need to return the following variables correctly \n", " J = 0\n", " grad = np.zeros(theta.shape)\n", - "\n", " # ====================== YOUR CODE HERE ======================\n", + " \n", + " hyp = sigmoid((theta.T * X).sum(axis=1))\n", + " J = 1/m * (-y * np.log(hyp) - (1 - y) * np.log(1-hyp)).sum()\n", + " \n", + " \n", + " grad = 1/m * ((hyp-y).T@X)\n", "\n", " \n", " \n", @@ -389,12 +456,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "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", + "initial_theta = np.zeros((n+1, 1))\n", "\n", "cost, grad = costFunction(initial_theta, X, y)\n", "\n", @@ -417,6 +506,13 @@ "print('Expected gradients (approx):\\n\\t[0.043, 2.566, 2.647]')" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -426,9 +522,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise logistic-regression\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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 +589,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 57, "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 +652,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Plot Boundary\n", "utils.plotDecisionBoundary(plotData, theta, X, y)" @@ -530,7 +687,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ @@ -567,8 +724,12 @@ "\n", " # ====================== YOUR CODE HERE ======================\n", "\n", - " \n", - " \n", + " p = sigmoid((theta.T * X).sum(axis=1))\n", + " print(p)\n", + " # Make all values 0 or 1\n", + " p[p >= 0.5] = 1\n", + " p[p < 0.5] = 0\n", + " print(p)\n", " # ============================================================\n", " return p" ] @@ -582,9 +743,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "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", + "[ 9.10339288e-02 4.22589643e-05 4.39088904e-02 9.90424698e-01\n", + " 9.98198750e-01 1.07975518e-02 9.98978138e-01 4.23227056e-01\n", + " 9.99710020e-01 7.35387955e-01 9.09674395e-01 2.85967730e-02\n", + " 9.99270142e-01 9.99853945e-01 1.56037085e-01 9.80959346e-01\n", + " 4.27716900e-01 1.48789236e-01 9.99706543e-01 5.74280760e-01\n", + " 6.68350379e-02 9.98627244e-01 7.41803045e-03 1.01155391e-04\n", + " 9.91878248e-01 8.55048633e-01 6.00298722e-01 8.65184829e-01\n", + " 9.23893082e-02 1.68289206e-02 8.97130765e-01 9.81037347e-01\n", + " 1.54911023e-01 3.91938615e-01 7.41289958e-02 3.31107859e-02\n", + " 8.52267973e-01 9.87564309e-01 2.03987783e-01 4.95799787e-02\n", + " 9.70300369e-01 6.11823883e-03 9.99461484e-01 5.02285121e-01\n", + " 4.48870631e-03 1.37023864e-01 9.92991803e-01 9.99996181e-01\n", + " 9.99204916e-01 9.99990761e-01 9.98106782e-01 9.99500487e-01\n", + " 9.05015909e-01 2.80614808e-03 8.52272243e-03 5.28909909e-02\n", + " 9.99856630e-01 6.93707650e-01 9.85496665e-01 9.95728611e-01\n", + " 9.99531647e-01 2.22376843e-04 3.50540631e-03 1.27066895e-04\n", + " 7.16567328e-02 4.08761457e-02 9.44426173e-01 1.00705518e-02\n", + " 9.99952447e-01 7.09300664e-01 6.20395585e-05 9.77396162e-01\n", + " 9.99893358e-01 8.84276400e-01 9.05240588e-01 9.99954904e-01\n", + " 9.17694808e-01 6.26743823e-01 1.58401450e-02 5.99469178e-01\n", + " 9.99282492e-01 9.73471586e-01 8.94532156e-01 2.03192639e-01\n", + " 9.99941048e-01 9.97982478e-01 3.54560019e-01 9.99820029e-01\n", + " 9.99973211e-01 1.06959271e-01 9.99943953e-01 9.99985225e-01\n", + " 1.42428233e-03 9.99321741e-01 9.24570136e-01 8.58639055e-01\n", + " 7.50881913e-01 9.99896606e-01 3.39275423e-01 9.99750932e-01]\n", + "[ 0. 0. 0. 1. 1. 0. 1. 0. 1. 1. 1. 0. 1. 1. 0. 1. 0. 0.\n", + " 1. 1. 0. 1. 0. 0. 1. 1. 1. 1. 0. 0. 1. 1. 0. 0. 0. 0.\n", + " 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 1. 1. 1. 1. 1. 1. 0.\n", + " 0. 0. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 1. 0. 1. 1. 0. 1.\n", + " 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1. 0. 1. 1. 0. 1. 1. 0.\n", + " 1. 1. 0. 1. 1. 1. 1. 1. 0. 1.]\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 +812,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 63, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise logistic-regression\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.72076787 0.86163283 0.7786666 0.44034472 0.21633506 0.28469479\n", + " 0.62748677 0.84743479 0.82652935 0.55040228 0.24735372 0.23596715\n", + " 0.5182426 0.81512389 0.85322088 0.65659471 0.30367872 0.21164526\n", + " 0.41063404 0.76031631]\n", + "[ 1. 1. 1. 0. 0. 0. 1. 1. 1. 1. 0. 0. 1. 1. 1. 1. 0. 0.\n", + " 0. 1.]\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 +874,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ @@ -654,9 +898,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plotData(X, y)\n", "# Labels and Legend\n", @@ -686,7 +943,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ @@ -718,7 +975,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ @@ -767,6 +1024,12 @@ " grad = np.zeros(theta.shape)\n", "\n", " # ===================== YOUR CODE HERE ======================\n", + " \n", + " hyp = sigmoid((theta.T * X).sum(axis=1))\n", + " J = 1/m * (-y * np.log(hyp) - (1 - y) * np.log(1-hyp)).sum() + (lambda_/(2*m))* (theta[1:]**2).sum()\n", + " grad[0] = 1/m * (hyp-y).T@X[:,0] \n", + " grad[1:] = 1/m * (hyp-y).T@X[:,1:] + lambda_/m * theta[1:]\n", + " \n", "\n", " \n", " \n", @@ -783,9 +1046,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 70, "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 +1119,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 71, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise logistic-regression\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.72076787 0.86163283 0.7786666 0.44034472 0.21633506 0.28469479\n", + " 0.62748677 0.84743479 0.82652935 0.55040228 0.24735372 0.23596715\n", + " 0.5182426 0.81512389 0.85322088 0.65659471 0.30367872 0.21164526\n", + " 0.41063404 0.76031631]\n", + "[ 1. 1. 1. 0. 0. 0. 1. 1. 1. 1. 0. 0. 1. 1. 1. 1. 0. 0.\n", + " 0. 1.]\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", @@ -957,9 +1284,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.6.10" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From 420fb43405ba22bfea50c322115540a75b5920ef Mon Sep 17 00:00:00 2001 From: Oliver Turnbull Date: Wed, 8 Apr 2020 11:19:42 +0100 Subject: [PATCH 2/3] Finished all exercises --- Exercise3/exercise3.ipynb | 315 ++- Exercise4/exercise4.ipynb | 449 +++- Exercise5/exercise5.ipynb | 477 +++- Exercise6/exercise6.ipynb | 414 +++- Exercise7/None0000000.png | 0 Exercise7/exercise7.ipynb | 4770 ++++++++++++++++++++++++++++++++++++- Exercise8/exercise8.ipynb | 497 +++- 7 files changed, 6666 insertions(+), 256 deletions(-) create mode 100644 Exercise7/None0000000.png diff --git a/Exercise3/exercise3.ipynb b/Exercise3/exercise3.ipynb index e37be91f..80691345 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": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "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", @@ -145,9 +158,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": true - }, + "metadata": {}, "source": [ "### 1.3 Vectorizing Logistic Regression\n", "\n", @@ -158,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -267,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -348,7 +359,11 @@ " grad = np.zeros(theta.shape)\n", " \n", " # ====================== YOUR CODE HERE ======================\n", - "\n", + " \n", + " hyp = utils.sigmoid((theta.T * X).sum(axis=1))\n", + " J = 1/m * (-y * np.log(hyp) - (1 - y) * np.log(1-hyp)).sum() + (lambda_/(2*m))* (theta[1:]**2).sum()\n", + " grad[0] = 1/m * (hyp-y).T@X[:,0] \n", + " grad[1:] = 1/m * (hyp-y).T@X[:,1:] + lambda_/m * theta[1:]\n", "\n", " \n", " # =============================================================\n", @@ -389,9 +404,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "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 +446,41 @@ }, { "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" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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 +509,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -526,7 +587,19 @@ " X = np.concatenate([np.ones((m, 1)), X], axis=1)\n", "\n", " # ====================== YOUR CODE HERE ======================\n", - " \n", + " for k in range(num_labels):\n", + " \n", + " \n", + " initial_theta = np.zeros(n + 1)\n", + " \n", + " options = {'maxiter': 50}\n", + " \n", + " res = optimize.minimize(lrCostFunction, initial_theta, (X, (y == k), lambda_), \n", + " jac = True,\n", + " method='TNC',\n", + " options=options)\n", + " \n", + " all_theta[k] = res.x\n", "\n", "\n", " # ============================================================\n", @@ -542,7 +615,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -559,9 +632,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise multi-class-classification-and-neural-networks\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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 +685,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -635,7 +740,8 @@ "\n", " # ====================== YOUR CODE HERE ======================\n", "\n", - "\n", + " hypothesis = X @ all_theta.T\n", + " p = np.argmax(hypothesis, axis=1)\n", " \n", " # ============================================================\n", " return p" @@ -650,9 +756,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Set Accuracy: 95.36%\n" + ] + } + ], "source": [ "pred = predictOneVsAll(all_theta, X)\n", "print('Training Set Accuracy: {:.2f}%'.format(np.mean(pred == y) * 100))" @@ -667,9 +781,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise multi-class-classification-and-neural-networks\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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 +836,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "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 +894,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -774,7 +933,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ @@ -833,8 +992,16 @@ " p = np.zeros(X.shape[0])\n", "\n", " # ====================== YOUR CODE HERE ======================\n", - "\n", - "\n", + " a1 = np.concatenate([np.ones((m, 1)), X], axis=1)\n", + " \n", + " z2 = (a1 @ Theta1.T)\n", + " a2 = utils.sigmoid(z2)\n", + " print(a2.shape)\n", + " a2 = np.concatenate( [np.ones((m, 1)), a2], axis=1)\n", + " \n", + " z3 = (a2 @ Theta2.T)\n", + " a3 = utils.sigmoid(z3)\n", + " p = np.argmax(a3, axis =1)\n", "\n", " # =============================================================\n", " return p" @@ -849,9 +1016,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(5000, 25)\n", + "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 +1044,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 105, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 25)\n", + "Neural Network Prediction: 3\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "if indices.size > 0:\n", " i, indices = indices[0], indices[1:]\n", @@ -890,13 +1087,53 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 106, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise multi-class-classification-and-neural-networks\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(16, 4)\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": { @@ -915,9 +1152,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.6.10" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Exercise4/exercise4.ipynb b/Exercise4/exercise4.ipynb index ab2e6145..cf6c0c1a 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": [ + "
" + ] + }, + "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": 5, "metadata": {}, "outputs": [], "source": [ @@ -316,7 +329,47 @@ " Theta2_grad = np.zeros(Theta2.shape)\n", "\n", " # ====================== YOUR CODE HERE ======================\n", + " \n", + " k = y.max()+1\n", + " \n", + " # Implement forward propagation\n", + " \n", + " a1 = np.concatenate([np.ones((m, 1)), X] , axis=1)\n", + " \n", + " z2 = a1 @ Theta1.T\n", + " a2 = utils.sigmoid(z2)\n", + " a2 = np.concatenate([np.ones((m, 1)), a2] , axis=1)\n", + " z3 = a2 @ Theta2.T\n", + " a3 = utils.sigmoid(z3)\n", + " \n", + " targets = np.zeros((m, k))\n", + " for x in range(m):\n", + " targets[x, y[x]] = 1 \n", "\n", + " for x in range(m):\n", + " J += 1/m * (-targets[x] * np.log(a3[x]) - (1-targets[x])*np.log(1-a3[x])).sum()\n", + " \n", + " # Implememnt regularisation\n", + " regularisation_factor = (Theta1[:,1:]**2).sum() + (Theta2[:, 1:]**2).sum()\n", + " regularisation_factor = (lambda_/(2*m)) * regularisation_factor\n", + " J = J + regularisation_factor\n", + " \n", + " \n", + " # Implement backprop using a for loop\n", + " \n", + " \n", + " d3 = a3 - targets\n", + " \n", + " \n", + " d2 = (d3 @ Theta2[:,1:]) * sigmoidGradient(z2)\n", + " \n", + " Delta1 = d2.T @ a1\n", + " Delta2 = d3.T @ a2\n", + " \n", + " Theta1_grad = 1/m * Delta1\n", + " Theta1_grad[:,1:] = Theta1_grad[:,1:] + (lambda_/m) * Theta1[:,1:]\n", + " Theta2_grad = 1/m * Delta2\n", + " Theta2_grad[:,1:] = Theta2_grad[:,1:] + (lambda_/m) * Theta2[:,1:]\n", " \n", " \n", " # ================================================================\n", @@ -351,9 +404,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "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 +433,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise neural-network-learning\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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 +502,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "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", @@ -420,6 +524,16 @@ "print('This value should be about : 0.383770.')" ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "Xm = np.reshape(np.sin(np.arange(1, 33)), (16, 2), order='F') / 5\n", + "ym = np.arange(1, 17) % 4\n" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -429,9 +543,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise neural-network-learning\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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 +617,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -506,7 +653,7 @@ "\n", " # ====================== YOUR CODE HERE ======================\n", "\n", - "\n", + " g = utils.sigmoid(z) * (1 - utils.sigmoid(z))\n", "\n", " # =============================================================\n", " return g" @@ -521,9 +668,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "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 +697,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise neural-network-learning\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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 +761,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -610,7 +800,7 @@ "\n", " # ====================== YOUR CODE HERE ======================\n", "\n", - "\n", + " W = np.random.rand(L_out, 1 + L_in) * epsilon_init\n", "\n", " # ============================================================\n", " return W" @@ -627,9 +817,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing Neural Network Parameters ...\n" + ] + } + ], "source": [ "print('Initializing Neural Network Parameters ...')\n", "\n", @@ -720,9 +918,60 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-9.27825235e-03 -9.27825236e-03]\n", + " [-3.04978931e-06 -3.04978914e-06]\n", + " [-1.75060082e-04 -1.75060082e-04]\n", + " [-9.62660640e-05 -9.62660620e-05]\n", + " [ 8.89911959e-03 8.89911960e-03]\n", + " [ 1.42869427e-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", + " [-1.37149707e-04 -1.37149706e-04]\n", + " [ 7.62813551e-03 7.62813551e-03]\n", + " [ 3.69883235e-05 3.69883234e-05]\n", + " [ 3.35320347e-04 3.35320347e-04]\n", + " [ 1.53247082e-04 1.53247082e-04]\n", + " [-6.74798370e-03 -6.74798370e-03]\n", + " [-4.68759787e-05 -4.68759769e-05]\n", + " [-3.76215585e-04 -3.76215587e-04]\n", + " [-1.66560297e-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", + " [ 5.75736493e-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: 2.08374e-11\n" + ] + } + ], "source": [ "utils.checkNNGradients(nnCostFunction)" ] @@ -736,9 +985,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise neural-network-learning\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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 +1060,64 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "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", + " [ 7.62813551e-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", + " [-6.74798370e-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", + " [ 3.81928666e-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", + " [ 1.50048381e-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: 2.01903e-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 +1133,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise neural-network-learning\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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()" @@ -822,10 +1192,10 @@ "source": [ "# After you have completed the assignment, change the maxiter to a larger\n", "# value to see how more training helps.\n", - "options= {'maxiter': 100}\n", + "options= {'maxiter': 500}\n", "\n", "# You should also try different values of lambda\n", - "lambda_ = 1\n", + "lambda_ = 2\n", "\n", "# Create \"short hand\" for the cost function to be minimized\n", "costFunction = lambda p: nnCostFunction(p, input_layer_size,\n", @@ -902,6 +1272,13 @@ "\n", "In this part of the exercise, you will get to try out different learning settings for the neural network to see how the performance of the neural network varies with the regularization parameter $\\lambda$ and number of training steps (the `maxiter` option when using `scipy.optimize.minimize`). Neural networks are very powerful models that can form highly complex decision boundaries. Without regularization, it is possible for a neural network to “overfit” a training set so that it obtains close to 100% accuracy on the training set but does not as well on new examples that it has not seen before. You can set the regularization $\\lambda$ to a smaller value and the `maxiter` parameter to a higher number of iterations to see this for youself." ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -920,9 +1297,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.6.10" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Exercise5/exercise5.ipynb b/Exercise5/exercise5.ipynb index c5e5c679..e82af8c7 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": [ + "" + ] + }, + "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", @@ -140,10 +151,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 79, + "metadata": {}, "outputs": [], "source": [ "def linearRegCostFunction(X, y, theta, lambda_=0.0):\n", @@ -192,8 +201,20 @@ " grad = np.zeros(theta.shape)\n", "\n", " # ====================== YOUR CODE HERE ======================\n", - "\n", - "\n", + " \n", + " h = X @ theta.T\n", + " \n", + " # Unregularised term\n", + " J = (1/(2*m)) * ((h - y)**2).sum(axis=0)\n", + " \n", + " # Incorporate lamda\n", + " J += (lambda_/(2*m)) * (theta[1:]**2).sum(axis=0)\n", + " \n", + " \n", + " # Do not include regularisation in bias\n", + " grad = (1/m) * (h-y) @ X\n", + " grad[1:] = (1/m) * (h-y) @ X[:,1:] + lambda_/m * theta[1:]\n", + " \n", "\n", " # ============================================================\n", " return J, grad" @@ -208,9 +229,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 76, "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 +263,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise regularized-linear-regression-and-bias-variance\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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 +328,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 80, "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 +358,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 82, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise regularized-linear-regression-and-bias-variance\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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 +419,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 83, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "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 +478,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 86, + "metadata": {}, "outputs": [], "source": [ "def learningCurve(X, y, Xval, yval, lambda_=0):\n", @@ -443,7 +561,16 @@ "\n", " # ====================== YOUR CODE HERE ======================\n", " \n", - "\n", + " for i in range(1, m+1):\n", + " \n", + " #Work out thetas using optimiser\n", + " thetas = utils.trainLinearReg(linearRegCostFunction, X[:i], y[:i], lambda_)\n", + " \n", + " # Compute train error\n", + " error_train[i-1] = linearRegCostFunction(X[:i], y[:i], thetas)[0]\n", + " \n", + " # Compute X-val error using all samples\n", + " error_val[i-1] = linearRegCostFunction(Xval, yval, thetas)[0]\n", " \n", " # =============================================================\n", " return error_train, error_val" @@ -462,9 +589,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 87, "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\t33.829961\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": [ + "" + ] + }, + "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 +650,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 89, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise regularized-linear-regression-and-bias-variance\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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 +719,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 119, + "metadata": {}, "outputs": [], "source": [ "def polyFeatures(X, p):\n", @@ -562,7 +752,11 @@ " X_poly = np.zeros((X.shape[0], p))\n", "\n", " # ====================== YOUR CODE HERE ======================\n", - "\n", + " \n", + " X_poly[:, [0]] = X\n", + " for i in range(1, p):\n", + " X_poly[:, [i]] = X**(i+1)\n", + " #X_poly[:, i] = X_poly[:, 0] * X_poly[:, -1]\n", "\n", "\n", " # ============================================================\n", @@ -578,9 +772,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 120, "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": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "p = 8\n", "\n", @@ -614,9 +827,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 121, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise regularized-linear-regression-and-bias-variance\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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()" @@ -653,11 +899,57 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 128, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Polynomial Regression (lambda = 0.800000)\n", + "\n", + "# Training Examples\tTrain Error\tCross Validation Error\n", + " \t1\t\t0.000000\t138.846777\n", + " \t2\t\t0.038343\t143.472902\n", + " \t3\t\t1.993788\t6.108069\n", + " \t4\t\t1.008806\t7.313495\n", + " \t5\t\t0.807070\t7.307386\n", + " \t6\t\t0.627940\t9.011942\n", + " \t7\t\t1.234186\t5.978711\n", + " \t8\t\t1.171334\t5.640021\n", + " \t9\t\t1.329607\t6.360580\n", + " \t10\t\t1.232543\t6.079746\n", + " \t11\t\t1.119537\t6.129989\n", + " \t12\t\t1.789389\t4.258940\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "lambda_ = 0\n", + "lambda_ = 0.8\n", "theta = utils.trainLinearReg(linearRegCostFunction, X_poly, y,\n", " lambda_=lambda_, maxiter=55)\n", "\n", @@ -731,10 +1023,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 131, + "metadata": {}, "outputs": [], "source": [ "def validationCurve(X, y, Xval, yval):\n", @@ -805,8 +1095,17 @@ "\n", " # ====================== YOUR CODE HERE ======================\n", "\n", - "\n", - "\n", + " for i in range(len(lambda_vec)):\n", + " lambda_ = lambda_vec[i]\n", + " \n", + " #Work out thetas using optimiser\n", + " thetas = utils.trainLinearReg(linearRegCostFunction, X, y, lambda_)\n", + " \n", + " # Compute train error\n", + " error_train[i] = linearRegCostFunction(X, y, thetas)[0]\n", + " \n", + " # Compute X-val error using all samples\n", + " error_val[i] = linearRegCostFunction(Xval, yval, thetas)[0]\n", " # ============================================================\n", " return lambda_vec, error_train, error_val" ] @@ -825,9 +1124,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 132, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "lambda\t\tTrain Error\tValidation Error\n", + " 0.000000\t0.030614\t41.812307\n", + " 0.001000\t0.112683\t9.842402\n", + " 0.003000\t0.170963\t16.293712\n", + " 0.010000\t0.221480\t16.944771\n", + " 0.030000\t0.281822\t12.830162\n", + " 0.100000\t0.459322\t7.586836\n", + " 0.300000\t0.921746\t4.636853\n", + " 1.000000\t2.076201\t4.260599\n", + " 3.000000\t4.901375\t3.822908\n", + " 10.000000\t16.092273\t9.945554\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "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 +1179,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 133, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise regularized-linear-regression-and-bias-variance\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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()" @@ -897,7 +1259,10 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": true + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } }, "outputs": [], "source": [] @@ -919,9 +1284,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.6.10" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Exercise6/exercise6.ipynb b/Exercise6/exercise6.ipynb index cdc43975..0b683b1b 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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k2jwUPG6XBGbPnYYsKqUBuEqW/OTVvW7zYXKni6KS9LhKNppM3ePU1LoAJne6KCpJL8i1eagwpu9x6sW9bvNhcqeLopL0uEo2Wkzf49TUugAmd7rIr6RnevHQtGnT0NCwDWvWjERdXRE6OoCenuRF1Lq6IqxZMxINDds4DbIApoY+cvGz0Fm+zwd8XBegqkYes2fPVrLPrl27tLh4pC5ZUqQNDdBXXoE2NECXLCnS4uKRumvXLlff/xe/cPf9h6KtrU2rqh7SkpLRGo/HtKRktFZVPaRtbW2+xRAme/bs0SvGjNUrZ96qN5fN097e3qzb/Hbo0CG96U/n6tjPzdT//N9+pNd8a2e/x/i7H9MrxozVpqYmzz//6oXVesWYsVpfX683l81LbrstuW3Pnj2O3g9AizrIsUzuNIBXSa+trU2Li0fq5s3QpqaBj82bocXFI5lcA6gviY//u8d0yjdf0qv+5AZdeu+9A7atW/+4kfh6enp06b336phJ0wck9ytLSrW+vt7zz1+3/nGdOPmaiweR9G1OE7uq8+TOee4B0d7ejtra9WhsbEBXVzeKi0dh8eJKrFy5OjDDB1GZRx9Fbs/ldluYCp1xnnuIhKUOSlTm0UeRqbncTkS10BmTu0WyXWi8994lWLz4zlCUBIjKPPoosrnGe1QLneVN7iIyQkSaReQdETkgIt/L0uYyEXleRNpE5E0RmepFsGE2WO+8peWf8Vd/9cdQ1EGJyjz6qDIxl9sJW+9x6jUnPfdPAXxJVWcBuBHAAhGZm9HmAQD/rqqfB7ABwD+6G2a45SrY9eGHittvz/37QRnKiMo8+iiyeegjqoXO8ib31AXavvPkotQj8yrsHQDqU8+3AZgvIuJalCGXq0rhqVMIzVBG1BYPmZ7P7yfbhz7i8ThWr6rGsfePXBwaSt8WtsQOOBxzF5G4iLwN4DiAV1T1zYwmpQD+AACq2gPgFICr3Qw0zHJdaBwzBqEZyojS4qGwXAR3KqpDHzZzlNxVtVdVbwQwCUCZiFyf0SRbL33AHEsRWS4iLSLScuLEiaFHG1K5LjTOnw/s2pX79wsZyjDVq/TzFnumvmNU6uKni+rQh82GPM9dRP4BwBlVXZe27ecA/peqviEilwD4CECJ5nhzznP/TK764h0dwEMPAd//Ply7a1AU7rZk8jtyPj95ybV57iJSIiJXpZ5fDuBWAL/LaLYDwL2p53cB2JMrsVN/uS40lpYC3/kO8K1vAT/6kQx7KCMKvUrT39Ht+fxRGrsn9zgZlpkIoElEWgG8heSY+04RWSsiffM4fgzgahFpA7AKwLe9CTec8l1oHDUKuOSSERgxYvGwhzKicIs509/Rzfn8URu7J/ew/IAlMocRxo9PXih1exghCreYM/0d3fp83sjbHU1NTXhw5SPYsX0bpk+fPui2oGD5gYDx60JjFFaJmv6Obs3nN30GEgamb9JhEpO7RaZNm4aams04fvwUenp6cfz4KdTUbHa1VxaFVaKmv6Nb8/lZi2d4TN+kwzQm94iJwipR09/Rrfn8ps9Ags70TTpMY3KPmCisErXhO7oxzGb6DCTobK5U6QdeUI0gvy7emjTYd9y5U/Dii4pPPwWuvvpKq2vic7788PX29uKrDzyAl3a/jquW9L820fX017B53WNYunSpoegKwwuqNCg/V4makvkdv/xlwQMPAK2tyQVhr7wC66cU2nAGEnS2Vqr0A3vuFHpBnlIYhbMsr+SrVKkXevHJtkfx6MP3Y/Wq4Bwg2XMnSgnylMIonGV5xfZKlV5jz51Cz/SiJjLj8OHD+MqSpTjSeRaxa+fj3Gtb8WRtDTY+sQXvnTyD2Iz5OLd3a+AKmrHnTpTi55RC1oGxR9QrVTK5k69MJD+/phSyDox9oniTjj5M7gEVxB6iqeTnx6Im05UoiTIxuQdQEHuIJpOfH1MKg3zRlsKJyT1ggtpDNJn8/Li933DqwATxLIzsx+QeMEHtIZouguX1lMJCL9qaPAvjQSXcOBUyYII6rS8ej+HllxXx+OBtenqABQti6Onp9S8wlxTy72JycVUUbrUYVpwKGVJBrRQY9iJYhVy0NXUWFtShPRoaJneDCjktDmqSNF2G12uFXLQ1NVQV1KE9Ghomd0MKHWsNapIMexGsQi7amjoLM339g/xxiekAoij9tDi999R3Wjxv3nlUVt6Vdax15crVKCurx7x52XtefUmyudmuJNmX/PIVwbKtcNdQ9F203bRpA6qrn0VXVzeKi0dh8eJ70NxcPeC7Jc/Cco/Te3EWFtShPRoa9twNGM5psR/T+rwShSJYQ7lVoqmzsKAO7dHQcLaMAW7MeGlvb8emTRvQ2Ni/h7hixcAeItnJ1GwZ3gQk2JzOlmFyNyDs0wLJORP12oNc3544FdJqPC2mPiaGqoI8tEfO5e25i8hkAM8AmADgAoCnVHVjRptyAC8BeC+1abuqrs31vlHuufO0mGzAob1gcm1YRkQmApioqr8RkSsB7APwX1T139LalAP4hqouchpglJM7T4uJqFBOk3veqZCq+iGAD1PPT4vIQQClAP4t5y/SoKIwLZCIzBrSmLuITAVwE4A3s7w8T0TeEZGEiAwyyY/6RGFaIBGZ43i2jIiMAvAqgO+r6vaM10YDuKCq3SKyEMBGVZ2e5T2WA1gOAFOmTJl99OjR4cZPRBQprs6WEZEiAC8AeC4zsQOAqn6iqt2p57sAFInIuCztnlLVOao6p6SkxMlHU0SxHC3R8ORN7iIiAH4M4KCqPj5ImwmpdhCRstT7droZKEVHEO805TYe3Gi4nMyW+QsAvwawH8mpkADwXQBTAEBVt4jIwwC+DqAHwB8BrFLV13O9b5Rny9DgOJOItdYpN65QpUDycw1Ae3s7amvXo7GxIW2edyVWrlxt7MDBgxvlwxWqFEh+laO1deiHtdbJLUzudJEN47x+lKO1+U5ErLVObmFyJwD29GT9qLtjc++YtdbJLUzuZFVP1o8a5zb3joNWVM6Gsz3KjsmdrOrJ+nE7Ppt7x0G6jaItZ3uUHZM7WdWT9aMcrc2946Dca9amsz3KjsmdrOvJel13x+becVBqrdt0tkfZcZ47uXLbvyAJwlxy22utR+3/jE24iIkci+LNQ0zc3i5MeKtIc7iIiRwLyjivm9wa+onqbBGbr1tQEnvuBIA92UJEuQZMFM/2bMFhGRoy28d5bRKEcXsvRf37m8TkTuQh9lx5tmcKx9yJPGTT2gBTeKtIu7HnTlQAzhYhU9hzJ/IQZ4uQ7ZjciQpg8ypXIoDJnaggUVwbQMFyiekAiIKorwZMvtkinAZIprDnTqHj16pRzhYhm3G2DIVK0FeN2njTbrILZ8uQcX7XXQl6jXHe/ILcxOROnjCRqIJcYzzoByayD4dlyHWm6o4EucY4yxmQU64Ny4jIZBFpEpGDInJARKqytBERqRWRNhFpFZGbCw2cgs9UD9q2O0oNBcsZkNucDMv0AFitqjMAzAXwkIh8IaNNBYDpqcdyAP/b1SgpUEwlqiCvGvXrwBTV+vNRlDe5q+qHqvqb1PPTAA4CyDzxvQPAM5r0rwCuEpGJrkdLgWCqBx3kVaN+HJh4wTZahnRBVUSmArgJwJsZL5UC+EPazx9g4AGAIsJUDzrIq0a9PjDxgm30OE7uIjIKwAsAHlHVTzJfzvIrA67UishyEWkRkZYTJ04MLVIKDFM96L5Vo2vWjERdXRE6OpKVGTs6khcj16wZae2qUa8PTEGeSUSFcZTcRaQIycT+nKpuz9LkAwCT036eBOBYZiNVfUpV56jqnJKSkkLipQAw2YMO6qpRrw9MvGAbPXmnQoqIAKgH0KWqjwzS5jYADwNYCODPANSqalmu9+VUyHDjXXoK49WtDll/Pjxcu82eiPwFgF8D2A/gQmrzdwFMAQBV3ZI6AGwGsADAWQD3q2rOzM3kHn68J6s9grwGgPrjPVSJ6CIukgoP1pYhoouCPJOICsN67kQRwPrz0cOeO1FEBHUmERWGY+5ERAHCMXcioghjciciCiEmdyKiEGJyJyIKISZ3IqIQYnInIgohJnciohBiciciCiEmdyKiEGJyJyIKISZ3IqIQYnInyqK9vR1VVQ+ipGQ04vEYSkpGo6rqQd5AmgKDyZ0oQyKRQFnZTHR21qGm5jRefllRU3ManZ11KCubiUQiYTpEorxYz50oTXt7Oyor78LatWdx3XWfbS8tBZYtO495886jsvIuNDe3svY5WY09d6I0tbXrUVFxvl9iT3fddUBFxXls2rTB38CIhojJnShNY2MDKioGv88okEzujY3P+hQRUWGY3InSdHV1Y8KE3G3Gj0+2I7IZkztRmuLiUfjoo9xtPv442Y7IZkzuRGkWL65EIlGUs00iUYTFi+/xKSKiwjC5E6VZuXI1EokiHDiQ/fUDB5LJfcWKan8DIxqivMldRJ4WkeMi8u4gr5eLyCkReTv1WON+mET+mDZtGhoatmHNmpGoqytCRwfQ0wN0dAB1dUVYs2YkGhq2cRokWc9Jz30rgAV52vxaVW9MPdYOPywicyoqKtDc3Ipx45ajuno0FiyIobp6NMaNW47m5lZUVFSYDpEoL1HV/I1EpgLYqarXZ3mtHMA3VHXRUD54zpw52tLSMpRfISKKPBHZp6pz8rVza8x9noi8IyIJERlk+QcREfnFjfIDvwFwjap2i8hCAC8CmJ6toYgsB7AcAKZMmeLCRxMRUTbD7rmr6ieq2p16vgtAkYiMG6TtU6o6R1XnlJSUDPejiYhoEMPuuYvIBAAfq6qKSBmSB4zOfL+3b9++kyJyNE+zcQBODjdGD9gaF8DYCmVrbLbGBTC2Qg03tmucNMqb3EXknwGUAxgnIh8A+AcARQCgqlsA3AXg6yLSA+CPAO5WB1dpVTVv111EWpxcOPCbrXEBjK1QtsZma1wAYyuUX7HlTe6q+nd5Xt8MYLNrERER0bBxhSoRUQjZntyfMh3AIGyNC2BshbI1NlvjAhhboXyJzdEiJiIiChbbe+5ERFQA48ldRBaIyP8VkTYR+XaW1y8TkedTr7+ZKoVgS2z3iciJtKJpy3yKK18xNxGR2lTcrSJysx9xOYzNSKE5EZksIk0iclBEDohIVZY2Rvabw9hM7bcRItKcWoF+QES+l6WNkb9Rh7EZ+RtNfXZcRH4rIjuzvOb9PlNVYw8AcQDtAD4H4FIA7wD4QkabBwFsST2/G8DzFsV2H4DNBvbbXwK4GcC7g7y+EEACgACYC+BNi2IrR7JOkd/7bCKAm1PPrwRwKMu/p5H95jA2U/tNAIxKPS8C8CaAuRltTP2NOonNyN9o6rNXAWjM9u/mxz4z3XMvA9Cmqr9X1f8H4P8AuCOjzR0A6lPPtwGYLyJiSWxGqOqvAHTlaHIHgGc06V8BXCUiEy2JzQhV/VBVf5N6fhrAQQClGc2M7DeHsRmR2hd99xQsSj0yL9QZ+Rt1GJsRIjIJwG0A6gZp4vk+M53cSwH8Ie3nDzDwP/XFNqraA+AUgKstiQ0A7kydwm8Tkck+xOWE09hNMVpoLnUKfBOSPb10xvdbjtgAQ/stNbzwNoDjAF5R1UH3m89/o05iA8z8jdYA+HsAFwZ53fN9Zjq5ZztSZR55nbTxgpPP/RmAqao6E8Av8NmR2DRT+8yJvkJzswBsQrLQnG9EZBSAFwA8oqqfZL6c5Vd82295YjO231S1V1VvBDAJQJmIZJb+NrbfHMTm+9+oiCwCcFxV9+VqlmWbq/vMdHL/AED6kXQSgGODtRGRSwCMgT+n/XljU9VOVf009eM/AZjtQ1xOONmvRugQCs25TUSKkEyez6nq9ixNjO23fLGZ3G9pMfwHgF9i4M17TP2N5o3N0N/onwO4XUSOIDmc+yURacho4/k+M53c3wIwXUT+REQuRfLCwo6MNjsA3Jt6fheAPZq6CmE6tozx2NuRHCu1wQ4AS1OzP+YCOKWqH5oOCkgWmusbW5QhFJpz4XMFwI8BHFTVxwdpZmS/OYnN4H4rEZGrUs8vB3ArgN9lNDPyN+okNhN/o6r6HVWdpKpTkcwbe1S1MqOZ5/vMjXruBVPVHhF5GMDPkZyd8rSqHhCRtQBaVHUHkv/pnxWo4iYUAAAAmElEQVSRNiSPbHdbFNtKEbkdQE8qtvv8iE3yF3PbheTMjzYAZwHc70dcDmMrqNCcC/4cwD0A9qfGaAHguwCmpMVmar85ic3UfpsIoF5E4kgeUH6qqjtt+Bt1GJuRv9Fs/N5nXKFKRBRCpodliIjIA0zuREQhxORORBRCTO5ERCHE5E5EFEJM7kREIcTkTkQUQkzuREQh9P8BAha/X10nDIMAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "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", @@ -147,14 +160,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "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", - "\n", + "C = 100\n", "model = utils.svmTrain(X, y, C, utils.linearKernel, 1e-3, 20)\n", "utils.visualizeBoundaryLinear(X, y, model)" ] @@ -180,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -213,7 +238,8 @@ " sim = 0\n", " # ====================== YOUR CODE HERE ======================\n", "\n", - "\n", + " norm = ((x1 - x2)**2).sum()\n", + " sim = np.exp(- norm/( 2 * sigma**2))\n", "\n", " # =============================================================\n", " return sim" @@ -228,9 +254,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "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 +288,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise support-vector-machines\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Login (email address): oturnbull1@gmail.com\n", + "Token: 5SjPnKXbEcZXKA0S\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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 +342,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "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 +381,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# SVM Parameters\n", "C = 1\n", @@ -326,9 +422,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "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 +465,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.3 0.1]\n" + ] + } + ], + "source": [ + "paramaters = np.zeros([64, 2])\n", + "error = np.zeros(64)\n", + "values = [0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30]\n", + "\n", + "a = 0\n", + "for i in values:\n", + " for x in values:\n", + " paramaters[a] = [i, x]\n", + " a += 1\n", + "\n", + " \n", + "a = 0\n", + "for paramater in paramaters:\n", + " model = utils.svmTrain(X, y, paramater[0], gaussianKernel, args=(paramater[1],))\n", + " predictions = utils.svmPredict(model, Xval)\n", + " error[a] = np.mean(predictions != yval)\n", + " a += 1\n", + "\n", + "print(paramaters[np.argmin(error)])\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -410,7 +555,10 @@ " sigma = 0.3\n", "\n", " # ====================== YOUR CODE HERE ======================\n", - "\n", + " \n", + " C = 0.3\n", + " \n", + " sigma = 0.1\n", " \n", " \n", " # ============================================================\n", @@ -426,9 +574,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.3 0.1\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "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 +619,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise support-vector-machines\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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 +755,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -655,7 +855,10 @@ " # 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 +879,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "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 +926,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise support-vector-machines\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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 +988,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -799,7 +1049,8 @@ "\n", " # ===================== YOUR CODE HERE ======================\n", "\n", - " \n", + " for index in word_indices:\n", + " x[index] = 1\n", " \n", " # ===========================================================\n", " \n", @@ -815,9 +1066,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "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 +1105,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise support-vector-machines\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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 +1159,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "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 +1187,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Accuracy: 99.88\n" + ] + } + ], "source": [ "# Compute the training accuracy\n", "p = utils.svmPredict(model, X)\n", @@ -899,9 +1214,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Evaluating the trained Linear SVM on a test set ...\n", + "Test Accuracy: 98.60\n" + ] + } + ], "source": [ "# Load the test dataset\n", "# You will have Xtest, ytest in your environment\n", @@ -1021,9 +1345,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.6.10" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Exercise7/None0000000.png b/Exercise7/None0000000.png new file mode 100644 index 00000000..e69de29b diff --git a/Exercise7/exercise7.ipynb b/Exercise7/exercise7.ipynb index 2dbde786..25ff74d2 100755 --- a/Exercise7/exercise7.ipynb +++ b/Exercise7/exercise7.ipynb @@ -18,9 +18,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "# used for manipulating directory paths\n", "import os\n", @@ -142,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -185,9 +194,14 @@ " idx = np.zeros(X.shape[0], dtype=int)\n", "\n", " # ====================== YOUR CODE HERE ======================\n", - "\n", " \n", " \n", + " for a in range(X.shape[0]):\n", + " \n", + " J = ((X[a] - centroids)**2).sum(axis=1)\n", + " \n", + " idx[a] = np.argmin(J)\n", + " \n", " # =============================================================\n", " return idx" ] @@ -201,9 +215,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "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 +254,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise k-means-clustering-and-pca\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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 +314,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -304,7 +361,13 @@ "\n", "\n", " # ====================== YOUR CODE HERE ======================\n", - "\n", + " \n", + " for i in range(K):\n", + " bool_selection = X[idx == i]\n", + " \n", + " centroids[i] = bool_selection.mean(axis=0)\n", + " \n", + " \n", " \n", " \n", " # =============================================================\n", @@ -320,9 +383,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "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 +423,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise k-means-clustering-and-pca\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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 +479,4388 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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\n", + "text/plain": [ + "
" + ] + }, + "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 +4911,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -455,7 +4944,7 @@ " # ====================== YOUR CODE HERE ======================\n", "\n", "\n", - " \n", + " centroids = np.random.permutation(X)[:K,]\n", " # =============================================================\n", " return centroids" ] @@ -503,9 +4992,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(16384,)\n", + "[4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4]\n", + "(16, 3)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# ======= Experiment with these parameters ================\n", "# You should try different values for those parameters\n", @@ -538,6 +5049,9 @@ "# 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", + "print(idx.shape)\n", + "print(idx[:20])\n", + "print(centroids.shape)\n", "X_recovered = centroids[idx, :].reshape(A.shape)\n", "\n", "# Display the original image, rescale back by 255\n", @@ -586,9 +5100,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "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 +5154,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -673,7 +5200,10 @@ " S = np.zeros(n)\n", "\n", " # ====================== YOUR CODE HERE ======================\n", - "\n", + " \n", + " sigma = (1/m) * (X.T@X)\n", + " \n", + " U,S,V = np.linalg.svd(sigma)\n", " \n", " \n", " # ============================================================\n", @@ -699,9 +5229,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "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": 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\n", + "text/plain": [ + "
" + ] + }, + "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 +5286,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise k-means-clustering-and-pca\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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 +5347,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -808,7 +5392,7 @@ "\n", " # ====================== YOUR CODE HERE ======================\n", "\n", - "\n", + " Z = X @ U[:, :K]\n", " \n", " # =============================================================\n", " return Z" @@ -823,9 +5407,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "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 +5436,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise k-means-clustering-and-pca\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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 +5490,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -913,7 +5539,7 @@ "\n", " # ====================== YOUR CODE HERE ======================\n", "\n", - " \n", + " X_rec = Z @ U[:,:K].T\n", "\n", " # =============================================================\n", " return X_rec" @@ -932,9 +5558,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "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": [ + "
" + ] + }, + "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 +5609,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise k-means-clustering-and-pca\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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()" @@ -1186,9 +5866,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.7.3" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Exercise8/exercise8.ipynb b/Exercise8/exercise8.ipynb index 8cd68244..98c1fb18 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": [ + "
" + ] + }, + "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": 27, "metadata": {}, "outputs": [], "source": [ @@ -184,7 +197,13 @@ "\n", " # ====================== YOUR CODE HERE ======================\n", "\n", + " mu = X.mean(axis=0)\n", + " \n", + " #sigma2 = (1/m) * np.square(X - mu).sum(axis=0)\n", " \n", + " sigma2 = (np.std(X, axis=0))**2\n", + " print(sigma2.shape)\n", + " \n", " # =============================================================\n", " return mu, sigma2" ] @@ -205,9 +224,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2,)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Estimate my and sigma2\n", "mu, sigma2 = estimateGaussian(X)\n", @@ -232,9 +271,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise anomaly-detection-and-recommender-systems\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Login (email address): oturnbull1@gmail.com\n", + "Token: 4NTzIOG88bx71fBf\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(5,)\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 +361,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -330,8 +405,15 @@ " for epsilon in np.linspace(1.01*min(pval), max(pval), 1000):\n", " # ====================== YOUR CODE HERE =======================\n", "\n", + " predictions = pval < epsilon\n", " \n", + " tp = np.sum( (predictions==yval) & (predictions == 1) )\n", + " fp = np.sum( (predictions == 1) & (yval == 0) )\n", + " fn = np.sum( (predictions == 0) & (yval == 1))\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", @@ -350,9 +432,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "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": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "pval = utils.multivariateGaussian(Xval, mu, sigma2)\n", "\n", @@ -385,9 +490,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise anomaly-detection-and-recommender-systems\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(5,)\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 +544,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(11,)\n", + "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 +607,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "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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+Kq/NsVXllJcL30LtssfK4NunIPb8KhpT6gJ+FyF7a66zcYqWahOKCBtitALMfJhzi6e867Bstd4YTVO8jXOMy7732xVlN3IpOELRBFye2jYuPxzXx7J97cQwL/Mn1XlpVwwZ4E06bjt+yZJWZ79z15r9zkkIF6YeXe+wrzN1b9eBOW/uc30o8lhsgiGUhg33vT26qsE1BDbl5BMosbr2rUyQk0Mubjt+ibdcckcEUnlw1a0cVqfkM8RoBdj2tZMPzLfTjFRZ5YJT4LkKcI3fTWA2l6YkK0D2wLHlQXaazfulsdQO8uYKyCfTbjeAp+ZWr3i/a+p9bvFUcEreNHRzjd277ti4D9vXzi7l5a47rlw6Zl+/fe0sbjt+SbCHlpMGoXQbdq3Zv1QGrusWZmdWCEL7XfJZMS6+/aYVdrU7D2xayrOZpbXblsG0E9PW5F4H5pnynlBM/4XZmaU2Y9JuykDWgQtTLj6h6+pUU3AJZLmzu8ybyXPO8Hu0Asz0xKvu3bes95bGcJ9gMcLJNiTK59iY465ezHzQvvfYx1wbIJiPzXZb8O1WbTcAaeg3mPfIcrA/WlsrNOfN9mquaKQySug1134x2IO7zsmyMu+TEzGh5912/JKl87Hosr4doMw1rjqzy1V2Mub6HRv3ragHU36yQ5XC1p7ZMx++zI+dflfezHnz3FBZuDCdius4ADx68/uj97uQz7zzwKal8pDpMmWVYzscrRHf9sTPMSyXNfrLd3Q1cWC0zrLrIF3pdvWIrrWW5t6Ya0TseKyuYmUb6sFDa0RT1o/abgSutaiPv+PUMsFlNBv5vy/NXbYbu+5ccb1i2pEdrSQnP75n98qIT0QXENHniegxIvoyEd1cHD+XiD5HRAeL/88pjhMR/RoRHSKiR4jo1SnvsaeyczaW9Z13TYU3Tco7pVq/MDuDVffuW3F9qurv0qrkcNj08q5YTaHe3hyzbXGua10agES6uYTcY1xIAWXn00T1yHFYdsV9k9qiyYtPaBmMe0mZSRzXc3ON+LLuTLpc27nFPP5jkzqA31G1zKxtF0PIkwDezcz/FMDlAH6CiDYCuBXAPcy8AcA9xW8AeAOADcW/GwF8IOUlOzbuW6pEX8H4egZZmfaM29yie6MNu2etEtM9JY0S1/DQTmOqX838scVgQzJDx9T82UbaFM0wFozQpM8eXgGTIXaKTU0+x8ZnrwLiS5ZMfk1YbDt9so2YNjp/bHHFc30G9ZBAM+fWX7eYJEh8+GyH88cWo3ZBuQu7uc+mzmVfrQswZn6Smf+s+PtvATwGYB2AqwF8rLjsYwCuKf6+GsBv8YQ/BXA2EZ0Xe4/8yHzaS2h5jflfNqCDW/csqdkuA7ykTM8Zmik1jSqmcZje177ONZnhep9rA1P5Id92/JJls2chR1J7xtI1G5u6xMs1FHFtYx/TEFIEr8s+48N+v2kH63bfH11643qHbX+zy0e2M/ucmfmswuG9s14h5bPlSlI1P7tzSJndddGpEZ+ILgTwKgBfAvBiZn4SmAg5AGuKy9YBeELcdqQ4FiQ2bQxg2fjeID8UWwMzlXfXHVc6G3dsyjzG9rUrtzWzh2WuHl0O8dZfN+kl7R7YNeRzaRgx7+5da/bjrjuudBpcpdZgL+My6U6xQ8oPyNSN66NyaVuuIXAqoeGewa5H+/1mWOsahgErh/v2++z6tetIvt9O5/rr/LbDVLefHRv3Lb3T15HEcK2rtFe72Pk09jWDb5bdpjMBRkTPB/A/AfxHZv6b0KWOYytmHojoRiJ6iIge+g4myx1kjx8SLmaZjcvmZfd4c4un8OjN73f2rrFpaolrtxh72YlMt0/jmVs8hYtvv2nZMy6+/aYVmojc6cdOkywbo2H5HFDN1vEhLVQOp1/y4eUaa0rPKrVAkw+TLok0Exh8vkTSlmhcBXwfR0gA2h2DbVuUndCuNftX5FvO4oXqWbYvn/2yivbqQ86Cu+7xuSWZ9+xas3/ZcjHzLDtqsN3Wbdtoyowz0NEsJBGdBuAzAOaZ+b3Fsa8A2MrMTxZDxPuY+eVE9BvF33fY1/meXyUeWIoPSpNLZcrMQlWJheXKS535a3JWrcpsZBsMaQlbWVxtpe5y79ssJAH4CIDHjPAquBvA9cXf1wP4tDj+tmI28nIA3w4JL4PsZVO9vQF3ry6feXTnlmUOdzFiKrDdixobm+86l8Yhjfi+WUvfM43DqbzO1nZs729Trr79LeW9ZuLDdc636sF3vU2KQdkmxQheFzE/tFj7MdpirvNok4RGHTlDd1vjTAmz5KKLIeQ/A/BWANuIaLH490YAvwzgtUR0EMBri98A8FkAhwEcAvAhADc5nrkCY3BcmJ2JzkzZ2A6N0razbvf9Xi901z2+Htj2vJb3uxwXzXBYOgG67jdDRZdtyMbcK8+ZaBNyeLlj43NuGeaeOw9swrrd90c1v9UPn7FMKMt3+cpGDnfr1mDMcNs1hIy5bwBpy4tC18u8uSYc5LPWX7e4NGRPTUuu+0Quoe/IuI+kfGt2u123+/5leXeZN1xMjSNriNwNMFKGBrZq3eSwUw4hzXvKbuqRGjfMNWyVeTTPiaWjjCNrLB1lSXlviiNnqG34znc93DXvjzljp+TvrjuuLFUnvrbSqyFkW8jhSY5zo41LffdVYEgzSnWkTTEs275Vth/Y3OKpJOHlmqndsXHfsvVw0kNbvtPVQGUezcdo0uEy+gPuYaDLVcBXLnX6FIVm/AyxYalsG65255u9NsJe3lOnhhUb3tnrgX1pjbFrzf6kOnGVjWyzg3CjaBK5aNe284SwZxddleGzSYTsAa73hzzWDdI50fy211nKj8o4d6bk1zXtbsrNHl6a/4/u3FJak4ytezTItJshX8yeJDH146onGQzR/khsYep6p+uZofWxdjvwRQABJh+wT4Ckzsr53pMTMz9k13TNZktuO37JshnFlG/FfofvvIvRCjAbu+B9oWpis1pAuV6/bMOztYJYxZppbnu6P3VbtRRDbKx3lM8w5ezLv+t9sbIyeVmYnXHmy2ikrnoyx2QHZ0j5aFyrHmxc3v6mzHwRaXMdXmPYmmLMM1+m2ZVHc3+s7d95IG668LXFMhrfqAWYnJHz7WQMLK/sucVTK7zepVOinBVyzc4ZfAHtfGmUaQlpTy5fNXnMrINM6XF9s4D2rK00eF9z7Rdx1VtucPaspmGacgImIYhD+XE5zsrrzRIomU/zgbiWJs0fWwyGrZaTLK6ylKQIc/tjPLz3OT8wqc27ojmY9x3duWVFffmG3DHN2px3td0UzKSNvFfeH/OYt2fxXdErrnjvg9F8SE05xGgFmLR/Hdy6x/uhAssLyTVLJitv+9rZJUdWOzaTPbST7/DNcIWMpPJvU6G2JvnALZetiK2+Y+O+FT2u7Uho3m0Prw9u3YMN9719SbAYtwrDrjX7ccV7H1zqAGzhaT48k/97Pv6RoLuDq1xcdeBb9O1ztA1h6tG1NAlYLohtbOH41Nxqp0ZhnDrtZ0gnXfP+E5c+61yB4VoqFdNS7IgSqYLLtBeXHVNi59/WWmVd223HcNcdVy5d5xuuSk05xChnITdfejq/7rffuFSZu9bsb2wGECg3K5mCnJkKvaOqw2TK/b5rys6uyskBl8CWz02ZUZX3+q6PzbD5cOWxidnE3HpMbR9N0dY7p24W8quPnLHUo0hbR50+MjG7Wa6PUSy8SKih5CxQzsVoVL53pPhOuQhNYMwfWx6F1CWM7J5bapw+YZc7wyZ98OTOSqH7q7hC5AgDO2pIFx7/Tba7VEYpwAD3ItuQIfPozi3JnsRm+BgSiPa7pGrtGnKUWUAb84KX+LSkw3tngzNLC7Mzy84bW5+MuhrCtb4zhitMzuG9s8vKzR4myvWsrpk/2wM+JV12Gu48sGnFs331lXMcyPvQ549N9u6U2G2qCcFhyi20MsA1pLfx3e+L5x9ilEPIKmshQ+SozGUdLKs6ZvqGT6mOrXU6hqa81/W+rp06Q2lIbQO++/uQtxh1ptHUr8uem7pB7tQNIU9c9F3OrcB8va4ZHsj1Wa7oAC4nRdmrymPXXPvFZe8xmoFMh+0wKp1S5XGpQZhn2L425pnSkC5ncqRDqVnDaKcbWNmo5hZPLb3LbD4qy8a8yzejJ99rlwXw3CyVrC8zC2avZ5X3y3eaCJ92lAmzXX1otlGWr0yXHSbIPMtuA642Y/Jgninr0p7xlm3BF3HEPNvUnXmmq13ItBmNyT5u7rfL1Z5Jl2Ul02rfJ99hT4xdc+0XMX9scVkUi0dvfv+SUAOWzziaYyaUVUxzVw0M9S/zmVtcHhfcENJGzMfR5JKjLqjD0JuiPZprmtBw+lgnvjbWNCENvY66dtXf1GlgklAcLdtHB1juHySlv+xhpA3FYAeqc9ngjHZkX2/uMWmxe0VX+uV5X9yrFFy+T7Y9xXb6NVqAb9s5l7E7x/Zj50eWmzwv7zUC7uDWPSvqz7c8x6Vt2PYyIBwF1YfrOfb75e8U25o87mpjOelzpTf0bvPc0LDPdMCxthdqC3Zgw1h+RinAztzIS+qoWdtnjO6xBrB97eyKgG7Gb8icl3G/jdrtG/pJ5o8tLrkOhBqCWYIi3ysdHe01l67ZqJAzp0mfSY983vrrFnHPxz+yrCGeuPTZJcF0cOuepa3S5DBA4vL7ci2LAZbPYpp3Sr+7hdmZFYLS5E3OLpvylv5dslMwz5dpkD5w5l0mbLhE1ql8p8skIX3IbP88mSYj4Ez5S+df6QxrIzc0cX3cIWFrp1cKbLsN2R1nylpes8zMHibb6fDlzXTeOzbuW6pzGazTxSiHkOe94hy++LEfDqq0oaGGS022VXZ7WBFTn+X1OX5FofMmD7FhU+zdEpN388y5RXd0gdThgnzejo37VjzL9Zz5Y4u4+PabvO90DZ9keg25flouG1eZYI/mOSkTIjJPKTO6sbTXMXlQ9rsBJuVjZh7t6Ba56TH3hoaQoxZgQyRVKLnuKUvOR2n/XZVU+5L9Tvt3ahmY69qwazXxDl/Zlw2fVIa6ZqpTy2fqbGBP/6/w8MmFa7bKdU3O88rc6xNeIbtCyocbWswdU9PtIY382/Vck1Y59MspO9ezbL+6sgLUHn7m2Fvs62P4NI/U5/kWurvsa0/NrQbg97HKSXcsPTnCy2dzDLUHn53YxSgF2CUv+OulqV2frcBmYTa+12HOR2Mbr1OWGpn/7QgGQDj6a4pwMGshXYJVLri1p8OBcCgW12YNJq3rdt+/VP6+/F98+03O+jHH5O5HtiFf3hfbTclgR1qw17PG2oq0VbnSG8OuR9/zXNfK47ad0Sx69wmXOmYr5bIlF658+EI7uaKByHNAmqAcpQDb/7cvxPrrJstRQnvZpa54B5YbcmM9g/y4UuNYGbuBNBKH1OuT2zZF41XJv2V8LzkzuzA7s8xbWk5WmGeEhK9ray/7vK3tSK3txKXPLgkOWS7yucb/7K47rlwm2OTeBBvue3sw/heApUkdFznGatc1KUMhl7Z6ctsmpz3Uxp4Jthey+4aVrplYG9dMqO/6k9s2eRfn2173crVGiu3VTkNKCKipsoFJQ28Xa8eaomp+Qo2rzORCGWKGY+A5bcW11ChV03W9L2YA96Vpx8Z9rZRNKA1VNCt7MqRseuvMZ64f2CgF2Or16/iHvt6fnVwUpWnqXgLWJ6bOiH/ud7mjZvocFV1B6WL3mt++WFC2Q5/L+RVwq+q+Ia1cMmQ/2+d86FseYtIo8+NKi9w6zeVgG8qHTJ/9ftsnySwHMkN0mS45weIqV5l++9k+B03p52TbEl12Qpdfn8/YbOrafo7LDOGqNzvN8t22bdecd7mcmDSYNiqvl8unXMZ1c8xnLrHL0s6vLN/YUNDXlm07p4tRamDnveIc/vd3/HPnMCPVVyuHFBW67KJl41fjuy523nYMdbkQ5Pjk5CDT5isj89yUIWRoyCaHmSGk86hJmyv/uRpNrHzke+tyrSgzhLSHy/bQu4yZxecrmEooH/PHFjFz3qHpG0Ke/19u6tWqf98K/CbW7sWo02eoDXuinV5f+tu2bea+L/X6NttElXe1Vd5TN4QE/D1xTGVPJWcGE5jMtr144cSK410I2TRQj9IAACAASURBVDodHmUQwhTsIXBsDR6wMr2+9Md2zJHYs7Upfkk2sZ3BbWL+czk7cJdd82gPhWPtL5Qm30RKKmXzIBmlALvkBX/tPO5bY1ZGiIRidtt2G2Dip1Rm37uU61MaToqjbhVirgAm/cZfqw7BbZeJawMJH8ZvCpjU5a41+7PrpMpQ8PF3nFrRTkybStmQJeXdPkfYnLKX5SRxtSVXwNBQmcaG3L73SEY5hOwqnE4by1Pqpuk0dzFE7pIhtoEcunBBmsohpAvfsM+1rMSFCSUjkUMB+zn2e+SMXiq+2cvQc8xspctB0f4d2ozXJtQb2pvJmv+N8PLtw2n+NjNZ8pysL9fyJFfQSt9srA9Xnfqus3G9K6ZV2LOornf76sHWSkL1ZWYf7aF6TKORM6UupLE/lF5XPeWaanxtX6IamIXdw9gaROy875gvwkLonrrwPTslwGJdzo7AczN7R3duwaM3v985+yhnE2UafNfIxe9meJqrAcVmHMvWTeg++52mLozg8O2kJfO7+uEzgun2RT2xny2vK+NPJtNkz2DW0a5VA8vA/jjtwjeG29C0vR2UDfDvV2iv+auCHcNJpkdizq+6d59XO5Extera/cbk3ywfstMztziJS2UEkXmXbSw/uHXPsjWSwMpF2jGkhmHHcrMxG4bUid0WTEdiNlFx5UOukwXiu4S7dozatWb/0nGj4ZgNQuYWTy3bSDiELCtT9iZtso00bT5QDawhps320yRN2F2GWj9Sa66ah6GUgWpgFnWEFomx/rpFr92nDlJnFOuYqq4baTdMjRSSQk4Zp0av6BtyyF9V+MQCE9qkBDFom6kSYEZltqepc/a5SxUc9vKO2AeTa9xP8fwH4kOqXCFbpZG68ijTZy9ZisWPstNycOue4ISF672xZT2htOfWWRVsQ3gVlxh7uZDN4b0rI7jMH1uM2sZCoYHKkJLHqRJgJpa9/fHbtoSQo1/ZoYxrg0+Jz9/GRUqYEVdjczUIeV3KciKfj1LoY7DX65ndg+w0mTKwQ/oA7nL32R9j18j3ytj+rhk289tVnjEblP0M1/tdx0L15LIz+fAJAOOvJfcEkPj2igDiM59yMgBI07BDdtipXAvZtQ3s6M4tSxtfAN34ztRBjo2kDv+n1HKS17UZSjmUjjpxzQSWyWdK+poqvzrta2oDa4BQuGTj2W3wRaUsQ5Pe9DY5DTDmie9Ltzzu04Bs5JKhx99Rrjxc6csdHueug0zFpdmFhIxPS7GXVrkiZDQl/FNWE9TB1AiwUHiQ0JAsxc7hashyCruKrSRlS6qUIWUZfB+0jPTpwxXD3iX07fwc3bllqfHLHcHlu33DXhkuJpR+V5TZ0BKbOmL75wi72JZ4Jk3GUdWn+dpmCZf5xMblZOsqR7tO7LTVFeQyhg4hW2Ao09VNomXQHFWGsn2vl8N7Z3H42tt0CJlK6sLoGLENOYZOSKt0lY9vSOG6ts3ZPUkfXU5SqKLtNN02Q3WZUt6x9KkGpihKL0nZ2FY1sIZo09g+BLpwclT6T8h+mzKrPRUCLOfjqTqE9Bmny74/pma7IjC4ZtRSy6AOQSMjGpzctqmU/1gKOev16n63oenhbt8Ef0p6XGXiK+uqs6BTIcBypnSrujzU6RckHQN9bF8765zxS3XotAltoOvC1TDlHpSr7t2H7WvdgSTltanvS3m/+chSyq4qIe/0OrTwtuynqYI49C2ZcpdlYkII+crateoih1ELMKkNhfySpBrriuFljucUctkKkR+69EQOuTTIafeqLhWuj84WPnK3GF/IFzs9PtcGVzm5QjX7Qk+nRPVMeY5MS0jwpGggRiuW7c71oYZ2eZJpbMMcEVsmZNLnirpqcMV+A1Z6/Lvi0W1fG4/95aIzIz4RzQB4CMBRZn4TEV0E4BMAzgXwZwDeysz/QESrAfwWgE0A/hrADmb+eujZVY34oWlp28veR9cbM3Q5Pd73qXlDHasHxrwfY5fINtRXI/7NAB4Tv3cDeB8zbwDwDIAbiuM3AHiGmV8K4H3FdVm4PJB9hISXWc+XspmDvXg7VSOT6ZS9ry/9JrCd/O0iVTNLWTQdw7VYPhZl1L4+NQ2hNYUuZARW14Jl3/t96XAJr1B782kivmtD15SJGNEkrnoOLT4PfRMpzrxARxoYEZ0P4GMAfgnALQD+JYBvAvheZj5JRFcA+Dlm3k5E88XfDxDRKgDfAPAiDiRc3SiUphiKdlk3Xea7jxrYrwL4aQD/WPx+IYBvMfPJ4vcRAOuKv9cBeAIAivPfLq5fBhHdSEQPEdFD38HK7ctCpPZabfRudg+W8k55jU/TanM2y+5tQ+/O0bbaxKdBpXzETS3tSqGpsvPZt2xy6r4OWhdgRPQmAMeZWY4zyHEpJ5x77gDzB5l5MzNvPg2rVzSik9s2ede0mWGAGbKZuGH2xgVmbZkZIsRCi0hSN0GwVWdfyBaTHzOsNe+T0TolJixyGfcKu9HG8i6H4Ed3blm2A7T9LNdWaDs27ltxvRyWuYYl8np7COda3yevldeF3GDk5IVBbpxhuOfjH0ke8prrcj50e2MQYOUmKq73hobW8nsJpSUWQmjXmv3L1soac4Kd5lB6TDmnlEnrQ0gi+q8A3grgJIDTAZwF4PcBbEeNQ8h3Prx12SxeVWNtFYNvn8Pp2GmbP7aI245fskIISVtPTlmErpUzdSnlE3qWHOLU8bwcXG2s6zqvkjf7XvM7JU91fW+SXg0hmfk9zHw+M18I4M0A7mXmHwPweQA/Wlx2PYBPF3/fXfxGcf7ekPAymILevta/Wj+H3GfYYWJSJxHKDFOrDCHtBuny2bIN1TllYabHXe83ESpcbhq+Z9mYcpWaxwO3XBZN19GdW4L5yKkHVxvzfehtDS9z26vLtcH+nSKQ7XvNc23NtS5zTJ/8wHYCuIWIDmFi4/pIcfwjAF5YHL8FwK25D87xyaprMbcrXEsKZabkZdgUEx/LTmOXhucdG/d53++KRJuaVp9G8NTc6uA9AJYNu12k1kOOGQGIe553Zf+rs31IIWaee/HtNy27pi7Xk04FGDPfx8xvKv4+zMyXMfNLmfnfMvOJ4vj/Ln6/tDh/OPX5pgHFeqMmDI91RTZIWQ7j+ojrWspUxzPuPLDSTiTPlU2DL48hO03dUXJDKxfKPD8mSHLrpy4n2KrfhMvWCaQFugyh0SiU3tFlmOiqDDntQD8dc3tlA2ublFDBXcSB8i1pSfnbJjc+Wew6exZP/m0/J/Zu2/6VklaXABhKdA+TdrucUpxkbVwRaUPEnJ5TiAmvOmxXrraV264MoxVgZpp2YXam9vjcdQwx5RZU9vpH+bd9TayiTdrshubapdnFbccvwcGte7xpsofjriGgXPdndoJyPcuVTpkHSc5wLFQ/oXWvdSEX4dsLy31DKVeaVt07cSeRAj3UEcnd1O3nxTqwUJnJTii2e9bRnVui0Shk+uQOSfKalNUuwIgF2MLsjLNCXfiWlPjw7fEo70tZtJwyU+SbETIszM4sEyI+G0poEW5OukKTA/YUeort0dXjp3Y4vo8uZEe64r0PLv2dUyYGl0+cnSbZ3nKN47HJn9zZRTkbn4LLf052QvZGITbrdt/vtEH63i/LR14T24bQoDYwRVF6zVTbwIDyAQ1997mOxwIR5nhm+2xOrt2AfDa+ssPcnIXvNvPHnhsWm/BDMU/zUDyvJshZTJ37PNdvG6nBhTR237GmiZVPygL0lON26KKyeR21AHM5OcaQTqe++1wr5WOqvm/YGXOBkLarVffuW3G97bQp1X7X8Rg7Nq58R+pkArDc6dHlD2QTs82FohmkpMfG2FbmFk+tSFOZyZxcVwlpA5PDpJRdxuvEV26xIaKvHRvs7fPkcYntHLv+usVSTr6jFmByo9Schu6rRPOMnC3lDak2iJj9xxZE0qYDPCdc7UZk1kLGiH2Qtt3I5cEtBcFLPjyzbM2hnS6XsVaWgfmIfely3R+aKQvZVq56yw3ec4ZYO1qYnQlOFMh2IIWBS5MrE8zQlLNMg0sw+8rTt+emIVZGdnpjs5byvGzL6gdW2MDMTOT8sUVsuO/tzkppwneniqOkvFeu8aszpIl8h8y/K90u36DUtJQph1iAxtC7XWs5U9MXaweu8678hfJsp92UbW45Hd25BY/e/P5a1xz60ph6zuBahymP+Z5hysAu55ANbLQC7Pxb/3PvHPKGQlUvddci7aYXN/fRAbNNul48nkLZBeZTacS/5tovlnbsi12fayspa/hMpU5/prnFU1EfnJhNLNV9xXd/6nWyHvoovHLrpYpDdZ+Fl+1eU2d7Ha0AA/I+JNd90glQFrqrFwk1vpjK3afGt2vNfmxfO5scHyo17b7rqtxfpjdv06M/JW+hKBBDxFW+qZE6yjBaAXbngZUuB7lIJ8BYoZvV9j5jddkdjXzTzDJvctKhap5NY5M2CFsAL8zOZE9758wi+gJP2pSZepcrGnzLfULUrc3XvXi7D7gmMXKXk6UyWgFmZnhCMzmuBhtr1L4ZzXW7719aOmN+y/tzelfpWiCnmQF3pAO5vEMuP5LkNJqQD5vx7wp9ePJ6Iyh86XJ1DHKWV8562nmITelLbL8jV32krMioqs3LPIRm6MoMJ10dX92kpEvOJtrtF1hep6Ht5FIYrRH/nQ9vXRoO5RgPu9i8IBRNNCXtKWnOMfLa75T32gI5JX3ymiozmjZ1zXD6yDU61xXh1aZK3fWdWHrnjy1i5rxD02fEB9IXMEty/bCqYn9QZda+pawd9K37s4dSvh5RbkUm07R97WxQk7DLq0oAQ5syaxmN1lZXBJKYbTTl3pC2MX9sMepcKim7QWxXxL7RWJmOWoAZfM6LoRk0G9Pgy35sdqMydoI6tL0yH7LhtuOXLGskrga1MDsTdN4NzQCufviMUh1JCmWijMiF5qmrCkKE/L1SCWlX29fORiNA2Axp27eqBv3RCrBda/Y7x98SV+H5GnVVtdxOQ52Osykfck4Z5NwfY93u+5cJ2CrC1iY15IpECpZY3qvYkep0OK6zzMbGaAWY7PWHpFJ3jUso5ARTdE1yHNy6p9S61BhlOpUcra2MgKybhdmZQWlUbTNaAQY893HlNNq6fbJybS1NzR6lCvE6dnGyZ5maGkKWsWPl2JPKkOO0nNrWxtoB12GHHK0Ak0PIWEOJbTorSQ2pW3Z/vFAkCBeu9Lo2Bc3Z6SeWptA5GfzORCbdtWb/klBL8Y/LEbYuzDtibgqx95QR5OuvCz+36kYfqdFmU67PIfU5vi30fA6uqWGmfIzajQLIbzApU9ZduFqEqDPNh/fO4uDWPYOYii/rMuArr9yF4GUosz502td5TuVaSIO9DCiG9DL33RcakuYGuEu9P/SclI8gdRgduy4WuNF1LrR2su49O1PwlVdV4ZWSN3uvAV/wSkkTwqvM5hwpdeXSbF31Vlddjl6A5fbQ0ss8x3vc3GuTawj2xUQPfVgpDSv1wzRDPd8zcz5wO2idaw9LezOHpknpCFyCN4UyPnyuDVOa2sVaUkYopu7h4Fp6ZlOXljtqAWZ6uBzDbRWDs2vNZO7zfB+Nb4t2IG0DhNSPccfGfSsM+aEPKGUDkNDyndgzQjQ1OWDSU2UWMsVm5BPc9iqIMsKmKcN/qC1U0ao0pLQDY2PIaQD2llSyMVVZPFwVM7RraqmK4c4Dm1Z8WLL8zFrIFMxwvCk/pqbcHMxHmrozjqs8Um2ksb0CypZdFRttqJ2HnGpz2rpdZmXTO1oj/ovuePfS3otlI3T2iZwon1Wpcz1d2dnYLmlrkqavba0qdZffVBrx69BYUgysvuvL4tu41jb4SlIM9D5Dqr0WMmUXczvahI08by9VqtPelRJNxCaljnKXKPnKoEpezb1VdpfqitRQ4ynEynD0GlgTpPScQ4sK0BV19da52kzfXGEUP1MZE/+dD28dpXreB8Y69FH6yVQOIWMfWBNT003Sp+UkZYRXm+nvum77skltnbi2a+sDoxVgMao6B9YdWjhGE8OdFH+npkMax4RNzK3EdZ3ZpiyXuvKas0ltXe9sWrDU3f7q6mRGO4Q0+0JOK9O+/ETpF749N1NskVM5hGySlN6uzIYRLlzhamxcvVnfhFebw7ohztx1TWr9lC0jX/y7qpqdamANMWZDdxM7mSuKD9XAIti9T9leJhTrqWwPl2sjqapFxLY5A4DH3xHe5clOgy/Eiuta+1iZ/KRsxVbGhyzlmpPbVq5kkMiydG0/1hWuEER1ln1T+VMNrCXU72g6iGmnffcPrNpOm2jnqoF1gK21tCW8qnq6d60JAMO2RT01tzqY/r4KL9Ney2yUIll/3WKrrhYqwBqiTSO6FJZVP5A6whznRAZ1HV/98BlJaegj63bfP0hN27TXOuy2UgNtevJGBVgGfdUMXMKy6bSGPlLXEMonlFzPqSr8+1pPQL/T1gSh7fjqQAVYBkPqWevav7IOTm7b1KpG2ud66nPa6qLsELJM2xu1ADP2nNywxTIQX2gGzUXqu3wzULkhqWV6Q/dUsW3Z6ZNlEooPZo5LjazsCoajO7es+DBy6tU1QxrbYCS13kP1V5WUGdXYsTbSIpH1bUJa2bhm28sI905mIYnobAAfBnAxAAbw4wC+AuBOABcC+DqAf8fMzxARAbgdwBsBPAvg7cz8Z6HnNz0L2aaX+1j8yfo2C1u2XKvWfd/KIZUu22EfZyFvB/CHzPwDAC4F8BiAWwHcw8wbANxT/AaANwDYUPy7EcAH6k5MrspbpgGXVavHILyAdodOKYbjsuUaq/tYPQ9JeMly7Gs7bF0DI6KzADwMYD2LlxPRVwBsZeYnieg8APcx88uJ6DeKv++wr/O9I1cDG2qvqCjTQN80sPUAvgngN4noz4now0R0JoAXG6FU/L+muH4dgCfE/UeKY/UlKFF4dR2mZShM20yb0h1dCLBVAF4N4APM/CoAf4/nhosuyHFshdpIRDcS0UNE9NB3cAJAubC8IUNlmaFjG2F3Uu5JXeLhmhQI4Spbu0Noyjm2z4IyJW19cBruK6l124UAOwLgCDN/qfj9KUwE2lPF0BHF/8fF9ReI+88HcMx+KDN/kJk3M/Pm07B62bkuh4cpoadlZZXZaSflHl86XPtQxp4nZ5UObt0TvLZvkxA5QqOMgDF1mdLmUuptbvHUVGj+dlmnfrOtCzBm/gaAJ4jo5cWhqwAcAHA3gOuLY9cD+HTx990A3kYTLgfw7ZD9S2L2DczdmbtN7I1Ay3jSu/ZHLNvoU8pKpjG2N6O9OXDXWkdO/ZZpCzmdZeq2bX0LjdQEZb+7rmYh3wXgd4joEQCzAP4LgF8G8FoiOgjgtcVvAPgsgMMADgH4EICbUl9iCiVHq+n6A6uLthp9atQNubtSXZTRrJvWwHJISb9vd3hlwqijUZRZ+V/3kKeNIdS0zqI2ne8+DH/7kIYukPnu2yxka5QZjtXdWJpufEd3bnF+xH02cNdF00K7D4KjShq62oCjDptdar5HLcBScQXgC5EytGhL7V+3+/7ahVUTQRF9ZeYqp6ofQNfCu+6hZ91hnOtMg2sjZmm+iJVF1e9k1ALMFF6sAciePDQsybHjlA1rc3jvbPYHYMdwmls8tSL6Q6pQyF2T5ipbWT5Hd27B0Z1bvGXmKif5AZjn53zEXYfjCbWPnHWTRoNq2zyQ4hpjkPV3eO8s7jywXOuLfSu+7yT1Gxi1DcyQYwubWzyFXWv29zbwnI1tI6lqF6oj/y67jW8NYRUbT5V724qMWnf9lH1vU/lNfa7rutR7p3Jn7rKLuZsymg7F0B5LZ6h86vpIhmy4jpWfbneXz9Qa8VORxs6UD6eMTcJu1DF3g7pJtTXs2LgvONw0LimuMnD1sH0jtJFGbigjF6aefW2kjPBqw5E1Ja+poaXsercnE3Ii9sZQDSxAjiZQl4bVlPaRox3Z18q8ldWyypSPfc8QNLMhpHFoqAaGcr2p3RBDayvrGh6WafwpvVeODdAmd6VA7BmpxO5JCfYYoo7ZZhvbiF3l/cCkzY3FuTqHPq+FbA1ZCCHBYKu8PpXdfLxlPsayw6mU4YNMT5n32PtZ2kIq9AG5zpmynj+2uEzouxpljstFinCX74xdX8dss++ZKUJn/XVx4bR97exUanSp39ioh5B98MRvgzrTfHjvLA5u3VObMd7YzIYyq1uVJtqP3GuyzX0lXcP+pr6P0HOndghpKrpuY3LoeVWcKO3IFPJ5VYcROV7ZoQXaJia+0QxDWtVdd1yJ7Wtngx9cat248h+7V6bNXGv+dz3PXJ9TVnb+bdeFlHtjce+lQ6osy5RY81VwaUEx4VUltJOrvmKMWgOr2lt04fqQ8s4yvXAVf50q1x/eO4sdG/e13msPgSGmv6nJqtBv9QNTlJYYolDqO1M7hCxDqj9Mn6gzPaFt0mL4hjRN+YP1oR5yh/a5ae5DHiV98+1TAVYC31q7ripXroVseso99HyX7ezg1j3JgfsMqR9t08P7lLLM1bZk20mdqWwDWeah9aexAJZ1kdqOVYBZLMzOLFWc7cIQ+7CamB3K/YjuuuNKACvTnmOYDuUj9MGac/LddsTZOojVQ+i8SVtZ/6rYu+3ysd9x4tJnndd2rWnJOjJ/y2Om3FL83OrIS2rHMDU2sJPbNuHxd5Tb/VdS1ijtM34OZY1kDDnV70LahuYWT+HOA5uSo4DEnm0YyzpDV1mEyqCJhfJ9Qm1gBXUIivXXLZZqFL5hZ06aUtfESW0rdVhbdb1dTMDIntsWXoC7tzc9+VNzyzdp8XHi0meT1t3FaFIbSklPSJBLTDp9QrupVR0uugqeODUamKLUTY7G11dtqA/piqVhajWw1N6ka/uDMkxyhqtdCwkffUhXlTSMWoDZkUp9dB3BU+kH07hoeuiMWoAZu0tMw5IzQyH6qqk1HS/q6M4ty5a9yA+9r2XiIySk+qCN9IEh1emobWB9GN8rilKNqbWB5TKknieXKrNEueF0cq7LCafjok7n4S4cke02V9cwtiuPf6mtS2TZ1hWgABi5BmZQTaw5tGyVppl6DSz3A1Nj7nKGYDcas/Y8BpryE5sKAZaL76PsUrB1+YFWFVJmb8hUqm6aknJ/k+UZi++VSpk0di3Ifcvv6txkVzIVQ0hF6QtjWToWou4lXVM/hMzplerSsso8xxWRtS5SNaA6tJccw7zLcJ6zi3ifqWtzEx9da1su5hZPJQuvOiZNVAPrEW3GO6+LadAolAldLZZXDSzSU+UaGEPP82kFKVpFbtysLrDznrrawZf/Klub9V0DGxtNCq+y2uRUCLCYhnDFex/Mfp5v8w2fwTtW+WU3frXxqeW5m2f49sC005hq4PflP5bnlPhjwPJ05kSV7XoYpkJ4QlktXoeQFkMcxvXZF6vPaVPy6aI+p34ImcNQhJfsuXN2gy5DFS0lp7GnbLdV5v6m7quDKmVbh/aYm/e+dUZToYGpFlCNUPnVobGmPkPrMZ8xRKmdeg0s1OilcTnUo+X2dnbPVjZiRCg+v2sjBl9aqnhCh8rPJXjstBzduSXY09vP8OXFTkfd3t0pz6v6ziqRQ8poikZ42e3Fpm5boJ1W1x4Evm8ktKmIzVRoYIZYbPUme6uyz55GrUPWU5v5j02klHUZGZJdtSm3mCploBvbBmjrAxlq+GH181KaIrWdT/0QMoSrAGNqfplhRNnwwynDBld66xoSNC28XOns2rVhGqmyVV3oOtM2Xe24jk566jWwGKkaiN2b5GhRqduGdYFpkKYM+qQdKtOBDiEVZYoZkg3OhQ4hUc+MlVSDm4reORTP7NzwODn5qhp6p+xMX0qdpuQjdE1OO6yrLbQpvHJ3s69KtgZGROcAuICZH2kmSdVRDUxRxkNlDYyI7iOis4joXAAPA/hNInpv2QQR0U8R0ZeJ6FEiuoOITieii4joS0R0kIjuJKLnFdeuLn4fKs5fWPa9ddH0LkAuhm7Y7iLevDJ+UoeQ383MfwPgXwP4TWbeBOCHy7yQiNYB+EkAm5n5YgAzAN4MYDeA9zHzBgDPALihuOUGAM8w80sBvK+4Lps6hpBGcKVuw1YnQ3dl8A1juugMlPZoun5TBdgqIjoPwL8D8Jka3rsKwHcR0SoAZwB4EsA2AJ8qzn8MwDXF31cXv1Gcv4qIKPuFNczyGVeIoQuTsZPSWTX5YTUV/11ZSaoA+wUA8wD+kpkXiGg9gINlXsjMRwH8CoDHMRFc3wawD8C3mPlkcdkRAOuKv9cBeKK492Rx/QvLvDuFoRjRx0ITKx9SOqsm1wf21SWmC5peh5kkwJj5d5n5lcz8H4rfh5n535R5YTEJcDWAiwCsBXAmgDe4XmtuCZyTz72RiB4iooe+gxNlkgagf6vtx87QbXtKt6Qa8V9GRPcQ0aPF71cS0W0l3/nDAL7GzN9k5u8A+D0AWwCcXQwpAeB8AMeKv48AuKB47yoA3w3gafuhzPxBZt7MzJtPw+qSSVPaJjWiq6K4SB1CfgjAewB8BwAKF4o3l3zn4wAuJ6IzClvWVQAOAPg8gB8trrkewKeLv+8ufqM4fy+36H07bUNK34YcOZtyxJ4n6bPGO2113ydSyz7JD4yIFph5joj+nJlfVRxbZOZS+j8R/TyAHQBOAvhzAO/AxNb1CQDnFsfewswniOh0AL8N4FWYaF5vZubDoeerH5iijIc6PPH/ioi+H4XtiYh+FBMDfCmY+WeZ+QeY+WJmfisznyjsapcx80uZ+d8y84ni2v9d/H5pcT4ovBQ3Q7I1lZkhzF1srO4b7VC2nFNnclM1sPUAPoiJreoZAF/DREP6eqnUNYxqYOXRxdpK36isgRXa0Q8DeBGAH2DmH+yr8MpF9txN9spln21sAfb9TfkaNRVfP0cDrFoPTWmbdTy3rrSVKaNQm0lNV+4Gyeb/ptprUAMjorcw88eJAiFpoQAAHfxJREFU6BbXeWYuvZyoSVQDU5TxUEUDO7P4/wWef4pSiZy9LeukrhlG9bqvjzJlmWoDexEzf7NMorpANTBlaGjobj91zELeT0R/REQ3FJ70o2WIvj+uNJe1taTel2tP8ZVrV+XdNw1MhVc5Uo34GwDcBuAVAPYR0WeI6C2Npqwm5HZSoQ/LBN0LzcDJRt+GoTj0PntjW/u8/UGEPjQ5ZHN9SK40hdb72c/IFRa+7eJiW8f5niGZP7aIw3tna5tpja17jOW9DfcWs6VZXZMjKfVpf3P2PSkdYErnUCag4fcAeC+AH2PmXs63NzWE7DI0b+oQo4n4+rlbwuUMh5oYOpXdwi6lfpsa6vV9CFnWvaaOfFWOiU9EZwH4V5gsH/p+AL8P4JPM3MuFbF3ZwPoQe7xqGqrcH7tXfcyUMtQhwL4G4C5MhNYDNaevdtSIryjjoQ4j/npm/ikAjxLR8+tLmqIoQ6JvS9JSBdgriOjPATwK4AAR7SOiixtMl9IjQn5ZsQbdByO2C10LWY6QPauLGeVUAfZBALcw8/cx80sAvLs4piQyRPcMQ8iuFTPQNm3zKiuImo4UOo3k1nUdDsupAuxMZv68+cHM9+E5L/3B0UWv3+SHXHUfRaC7XYNSZqhC+TOCaP7Y4iC1qiptsWo77rpTrWPCK1WAHSai/0xEFxb/bsMkIsUgaWO6uorvkw/fB5qjTeTmvS6hUGXR/DXXfjH6bPMx9GFpj8lf3+xFOdSV9qaFZKoA+3FMIlH8HiYuFC8C8O+bSlTTtNGwcjWuFMFS97BHfuyu3nBu8VRt75T5y33mA7dcFn220SD7sKGGyV9KuOz11y2uaI9tCj7fu3I6upD23rQJIduRdQh07UbRB3+wMdGEc64yHEJuFKtcBw1EdHfoPDP/SJWEjZXta2d771k9JFR4raTrTrLr9xtiQ8grMNkh6IuY7OX4P6x/U0mZ4G9VAxrmYtLYlXHeRq5v85VFVXuJq176YBNrgq6Fx4b73r7st732sa5hcOw5MQH2vQD+LwAXA7gdwGsB/BUz/wkz/0ktKRwgtmbl+iDXX7e4wtZT5gMta0MwaZQNvayACAnB0I5F8t71100WFK+6d19pu1pMGLs03jq0tyZmN115qbuzadKWZtvuTNnb/6cQKt/Yc4ICjJlPMfMfMvP1AC4HcAjAfUT0ruTUTQEpH+S63fd3vg7QDhed+sHEevtQvm47fknSdSnnu9I6YrOgZXDlxZRVXVqjbxKhbyG3q0wURY34RLQawL8AcC2ACzHZp/GjzHy09FsbpmsjvqLEKBsxo0+0tTi/ihH/Y5gMH/8AwM8z86MNpE9Rpo6hCy+gH5sSx2xgbwXwMgA3YxKV9W+Kf39LRH/TfPIUZfoYsgNs28RsYP+EmV9Q/DtL/HsBM5/VViKV8dLVLGnXy2hCrH74jK6TMBhSPfEHS0pDtT+isqGjq3yMdc105caed6XZzrN9TU4+Y+XvMmanPL+q93rK8MdOe5X69d3rSnedw8vUNKcK9JR22sRst4/Re+KbQunab6ZJUoypoWvqdEp0vacpp0eN8NoMISfsMnUZep6pw1BdVo7IOjSGOAtZxXNfP2RlzNQRkXUUSPXXpbrX6bBoPz/2bCm8Uoaw8nlSeLURUqaMn5IvH67jTRqxqz7bVb51hDNqiq7eZ/5vuj1OlQCTtgUjMGQB12l7sJ0IX7xwYunvWKVKgeQz6K7bff+K58wfW+n9D9TfiFO921Maryt/sa3dqlB1fapdvie3bcpydG17fWwT70vxnDf/m/KaWzwV3WqtDDqEnEK6dqKUQ96UoXMTQ+QxLLavIw9DKAcdQg6IMppG7j0h4VU1xr3N0Z1bVjxTCqOUmFnGyFsH5jl9+GirapUpZRejD+VQBRVgyB+npzS8sh9cmQZVZyOsI8a9nA6PaXqu57nqw/de17WhsjfP6UOUjqr1lqOVNhHpw4fLRtqU391UCLBY4ecOp1IaXtkhT5mKrmoorTv8tT3NnvuhptSHSYO0sRhCZW+uc7kCDDGmviFWh1WH4LIOY+XkspE2NUs+FTawIYzzx4r5sNTNQynL1NvADm7d03USBo9vyJXS86vwUmKUtQdOhQCzo0f2lSbtMqFnp7zXDLkO751NHq7Z16YyhiiqfV5r2UfsEVJq+U2FAKtjtqYNyi63Sem9Qs/Oidq6/rrFZI3KNMpcwdxkDPy2BEtdWue0CMKy+ZwKAWZHIjX4Cq2MOtu21mCEwtziqRW9VxVNru7h3sLszLKorBJZzinlV8fHPJThrKnDoaTXkBM0QB6382l+x+pcjfiKovSaqTfi90F41b2cp+sp/9z85GhPXeetKcYwHEytm1j7qGvEMhUa2DQxtk1gu172pHTP1Gtg04RPePU1THHdTsZDo639E8dKYwKMiD5KRMeJ6FFx7Fwi+hwRHSz+P6c4TkT0a0R0iIgeIaJXi3uuL64/SETXl01PXUbissObNo38rnA8fRhGu8pApsu36W2s7Ex+q0ZpjSHTUaYduNpXSr348u/al7ErygwtY/WTUn+NDSGJ6IcA/B2A32Lmi4tj/w3A08z8y0R0K4BzmHknEb0RwLsAvBHAawDczsyvIaJzATwEYDMABrAPwCZmfib07mkeQipKn6gjkkgnQ0hm/gKAp63DVwP4WPH3xwBcI47/Fk/4UwBnE9F5ALYD+BwzP10Irc8BeH2Z9NShAU2rml4HTZSd1kf/adoNpG0b2IuZ+UkAKP5fUxxfB+AJcd2R4pjveDaPv2P4M0DKcnzDprHOYg6RpjuZ4Ma2LUKOYxw4vvIBRDcCuBEATkdalM9curYzDJk2y27shv8h0XS9t62BPVUMDVH8f7w4fgTABeK68wEcCxxfATN/kJk3M/Pm07Da+fIme4Oq6xiNgTfHV6iMX1EdZdB0GpXxU9e32LYAuxuAmUm8HsCnxfG3FbORlwP4djHEnAfwOiI6p5ixfF1xrBRNromMrWNM/ZB9y55cFV6nfSG0n6Q9JMt5ry8/sff2gbJB/PoQLNGmrk4nt77M9bYNOkUzSynHJt0o7gDwAICXE9ERIroBwC8DeC0RHQTw2uI3AHwWwGEAhwB8CMBNAMDMTwP4RQALxb9fKI6VourHJM/HCtd+lvzoXfea876KrbLeMWW63SeUFmZnKg3J7AXdtlDYtWb/intSNxaW54yQnVs8lfSRpWi8vp2iXEj/u9uOX7JC6Nep/Yfq3veehdmZZNvgA7dcVuoc4C8nWT4mjbE2nBJFZuo88XOndcuuoxzqXo2udNe1lnRoa1LLbOLadL133a6a2qQ4hG5s2wOqLvEZ2sevtEtfl5DVsRRMBZgDFQiKMgx0LaQDFV7KNNCFT1yby+amVoCNiSF4pPvS2Ke0y4+9yuxonTOrVZ/VtU+cSX/KpFOZtjDVAqzJj6cp94Dc2c8QR3ducV4fesfc4inMH1tcutd1f04aXJpwblRPHzF3APt511z7xaW/5c7hue+o08huPytnJrwrpC3OpN9n+Jf5KTMqmlobmNIuanNUyqI2sA6we+aqoXracvhs6j1lhVcZLTnFd6xsPvs05M2hzw7DVVABZlFHjCLAv0lBiJC9wnf/4b2zzjSlptPlcCuHJnUMU6p8PCHB53tu6J7VD0/Wydr5rCM9KdS9C3oqQ/RJBOLtT4eQLdFXP52u6dvQsqn0aP2vJLWsdQjZAXbPMe2N16d59G3X9KaE6bTWf8gsUkdZqwamKBG6WD6jPIdqYBE0AF69SG1rDMZjFV79RQUYgBOXPtt1EnpNrhCSBmNXtIm20qHUT12+Z3XVpQowPDczFSNlRqivH1nq8o66445tXzuLozu3eMvO9UH4NOI6ZtLGrm3b+as76GVd2mhds6JqA0M7IUr6NtvWF5qYnes65IxSL2oDixALdFgHKrzcNDE7tzA7M1iHUyUPFWBQ4VKFvgqKaavTvpoumkYFmAeXzcj+WEN2pa5sLW2GMgGqC4qx26TaYlqHzCrABFJAuYY29scaGv50FcZkaA6TdZVTqgbShqYyrdqQi6Y1dBVggi6HHX1u9FXS1lTIF/vDsDUQ33vb0FSmVRtyMbZ9IUdBE8Me0+irTnv7PtyqAtJ3f6yHrdMJ1BU7ypeulPhTKe9pg5wIGU2lra7npjxn/thiqWgtLtSNQlGUXqNuFIqijBIVYEo2fXWdULqn7VllFWAl6LPB3VBFyMTunTYfqxBDaAtt4ptVbmoyRwVYCfo2y+QSOFWETBMCqmmtrSutsG9toUn66LOnAsxiiD1q3QKniTJoQiiW2dFGh7/lqeKz11RIIhVgFtPUo/qoswzKCIzUnr5MOnX4Oy5UgJWkyuYMQ9cCqu77KHEJK9nTD72s2mJoI4e66lUFWElye3/5ITepBfjWQqZqNbEP4ejOLViYnQleJxtn1bWZbWpMZQzNfbELhdpjm8ItVTDF6jX1OSrAHLS9IDpEbk/11Nxq5/FU+0VMMJvotaHrZOM0azN9+ehTNNzbjl+SfU9Xa15zaNMsYgumssIzteNST/wBM4TAfW2lUQNGtkMXbU498UvSlO9KGVw9WRMNqY7hhnxGbLhZ1/BGhVc79K3DVAEWoMrUb93G57YaTh3vydmV3HWuT0N4pRoaTqdjyhppVSMoz9Bimil+NJxOx/TJyNxXQkPtoU3vK8NCBVgE1aTihIbafbOZGPri/qBUQwWYMlUYbXEI7g9KHBVgAyGkMTRt9B7TMLCpNXlKN6gAGwghjaFpo3cfhoG6pEhxoQIsA/2IylO17NQWqbhQAaYoymBpTIAR0UeJ6DgRPSqO/Xci+gsieoSIfp+Izhbn3kNEh4joK0S0XRx/fXHsEBHd2lR6h8wQbFSqQSlN0KQGtgfA661jnwNwMTO/EsBXAbwHAIhoI4A3A3hFcc/7iWiGiGYA/DqANwDYCODa4tpO2LGxnw6WC7MzOrxVppLGBBgzfwHA09axP2Lmk8XPPwVwfvH31QA+wcwnmPlrAA4BuKz4d4iZDzPzPwD4RHFtJ/TBmO1DNRylafqo6XdpA/txAH9Q/L0OwBPi3JHimO/41GE3nhyNS7pgxBphlUCNoWuqaoh1p3sstKl5l+nAUzZdrkKj4XSI6EIAn2Hmi63jPwNgM4B/zcxMRL8O4AFm/nhx/iMAPouJgN3OzO8ojr8VwGXM/C7Hu24EcCMAnI4zNv0gvbGxfCmK0h69CqdDRNcDeBOAH+PnpOcRABeIy84HcCxwfAXM/EFm3szMm0+DO6jfWNHoDeNFbZthWhVgRPR6ADsB/Agzy1XSdwN4MxGtJqKLAGwA8CCABQAbiOgiInoeJob+u9tM8xDQ6A3jRW2bYZp0o7gDwAMAXk5ER4joBgD/N4AXAPgcES0S0f8DAMz8ZQCfBHAAwB8C+AlmPlUY/N8JYB7AYwA+WVw7anShsaKk0eQs5LXMfB4zn8bM5zPzR5j5pcx8ATPPFv/+D3H9LzHz9zPzy5n5D8TxzzLzy4pzv9RUenNIUetto3KOUJLLhvpqnK4rXa7ntDFscr23y6F4X+s5F7vumq5LjYnfEPPHFjtZODy02PB9Sm9XdTatpJZ3yIivAkxphT4JKqU96qj3Xs1CjoFU36c6yB1atDUMCqWrqg2vjfubHNq4nm3Ka6iziqnptq9rutNSDaxnVO2xTm7b1MqsZFvvURTVwAZE1R6rLaGiwkvpAyrAlCza2Cuz6oxcShqnxVWljtnNw3tns+q9zf1UVYA1RKjhDNUOAgC3Hb+k1H05ea66aD5lZqvtmPgm/23XfR0BCNZflzc7K68N5bcOQacCrCHuPLDcmC4rcsizcXa+UhlynuvA5H/s5ZBjxK/DZUWN+Ir6Pym9Ro34ShAVXv1AF+XnowJMUXqCzuzmowKsh+Qael3Xa28+DoY84dMGKsBqxsw+Vpm+zjX0uq63e/O6BNrhvbOjWXhs03a+UoTT0I3+TZepGvEVpUXmFk/1em+FPqJG/A5o05lPGQ4qvOpFBZgyKkzHobajfnF055bhberRFTqEVMbONA1FdQipJFO35hJbR1elV56W9YwupkV4xVANrCU0/IyilEM1sB6gwktpmybtgH2ZpFIBNgLqbqh1N86hGtb78pGWpUkfsr4sP9MhZAfo4un60Zj740WHkD1DhVf9qPCaTlSAKYoyWFSAKb0hdd1c27a0sa79HAMqwJTekOrb1PZwUX2u+osKMEVRBosKMEVRBosKMEVRBosKsIEwNCdQRWkDFWADYax+Thr6WqmCCjClU3SNqFIFFWCKogwWFWCKogwWFWCKogwWFWCKogwWFWCKogwWFWCKogwWFWBKoww9qqnSb1SAKY2iwRuVJlEBpoyCaV9qNa35VwGmKCNgrEvNYqgAU0bBtH7AQ6GpqLaNCTAi+igRHSeiRx3n/hMRMRF9T/GbiOjXiOgQET1CRK8W115PRAeLf9c3lV5FUZqjqai2TWpgewC83j5IRBcAeC2Ax8XhNwDYUPy7EcAHimvPBfCzAF4D4DIAP0tE5zSYZkVRBkRjAoyZvwDgacep9wH4aQByQ8qrAfwWT/hTAGcT0XkAtgP4HDM/zczPAPgcHEJRGTcacqef9GGzk1ZtYET0IwCOMvPD1ql1AJ4Qv48Ux3zHR4v6Ta1EQ+70kz5sdrKqrRcR0RkAfgbA61ynHcc4cNz1/BsxGX7idJxRMpXdo35TipJOmxrY9wO4CMDDRPR1AOcD+DMi+l5MNKsLxLXnAzgWOL4CZv4gM29m5s2nYXUDyVcUpW+0JsCYeT8zr2HmC5n5QkyE06uZ+RsA7gbwtmI28nIA32bmJwHMA3gdEZ1TGO9fVxxTFEVp1I3iDgAPAHg5ER0hohsCl38WwGEAhwB8CMBNAMDMTwP4RQALxb9fKI4piqKAmJ0mpUFzFp3Lr6Gruk6Goig18Mf8qX3MvNl1Tj3xFUUZLCrAFEWpjaM7t9TynFTfPxVgiqLUxrrd99fynFTfPxVgijLFDD0MjwowRZlihh7FQwWY0gq+nn7oGoDSLSrARkAfFtXG8PX0Q9cAlG5RAaY0yhCEq9I/UoMaqABTGqUPEQuU4ZEa1EAFmLKMujWmvmlganMbFyrARsCdB+oL+Fe3xtQ3DUxtbuNCBdgI0I9SmVZUgCmt0LehpNIOTde7CjClFfo2lFTaoel6VwGmKMpgUQE2JejOPkpbtNnWVIBNCX3d2acr21ifbXJDd/Vos62pAFM6pSvbWJ9tcjqrnI4KMEXpATrEL4cKMKVRYsOhoQ+X6qKvQ/y+owJMaZSDW/cEz+twSamCCjClUWKLcvtsTFf6jwowpVP6bExX+o8KMEVRBosKMGUU6FC0PoY0saICTGmcuvYKtJEfWh1DURWCE+qcWGmq7g3EzI2+oAvOonP5NXRV18lQFKUG/pg/tY+ZN7vOqQamZKFaitInVIApWaQM1exhQ9PDCGV6UQGm1I69vXxd283noJridKACTBkl6l82HagAUxRlsKgAUxQFwDCH3SrAFEUBMMxhtwowZaoYkpe5EkcFmDJVaPiecaECTFGUwaICTFGUwaICTFGUwaICTFGUwaICTFGUwaICTFGUwaICTFGUwdKYACOijxLRcSJ61Dr+LiL6ChF9mYj+mzj+HiI6VJzbLo6/vjh2iIhubSq9iqIMjyY1sD0AXi8PENE/B3A1gFcy8ysA/EpxfCOANwN4RXHP+4lohohmAPw6gDcA2Ajg2uJaRVE6pg+rGlY19WBm/gIRXWgd/g8AfpmZTxTXHC+OXw3gE8XxrxHRIQCXFecOMfNhACCiTxTXHmgq3YqipNGHVQ1t28BeBuBKIvoSEf0JEc0Vx9cBeEJcd6Q45juuKIrSnAYWeN85AC4HMAfgk0S0HgA5rmW4BaxzFxIiuhHAjQBwOs6oJbGKovSbtjWwIwB+jyc8COAfAXxPcfwCcd35AI4Fjq+AmT/IzJuZefNpWN1I4pV+MH+s+6GL0g/aFmB3AdgGAET0MgDPA/BXAO4G8GYiWk1EFwHYAOBBAAsANhDRRUT0PEwM/Xe3nGalZ2xfu9J43AeDstI+TbpR3AHgAQAvJ6IjRHQDgI8CWF+4VnwCwPWFNvZlAJ/ExDj/hwB+gplPMfNJAO8EMA/gMQCfLK5VlGVIg3KbwmyIUUzHhG5sqyhKr9GNbRVFGSUqwBSlRU5u29R1EkaFCjBFaZFV9+7rOgmjQgWYoiiDRQWYoiiDRQWYoiiDRQWYoiiDRQWYoiiDRQWYoiiDRQWYoiiDRQWYoiiDRQWYoiiDRQWYoiiDRQWYoiiDRQWYoiiDRQWYoiiDZZQBDYnomwD+HpNw1WPgezCevACanz7Tx7x8HzO/yHVilAIMAIjoIV8Ux6ExprwAmp8+M7S86BBSUZTBogJMUZTBMmYB9sGuE1AjY8oLoPnpM4PKy2htYIqijJ8xa2CKooyc0QkwIno9EX2FiA4R0a1dpycFIrqAiD5PRI8R0ZeJ6Obi+LlE9DkiOlj8f05xnIjo14o8PkJEr+42Byshohki+nMi+kzx+yIi+lKRlzuLndZR7MZ+Z5GXLxHRhV2m2wURnU1EnyKivyjq6IqB181PFe3sUSK6g4hOH2r9jEqAEdEMgF8H8AYAGwFcS0Qbu01VEicBvJuZ/ymAywH8RJHuWwHcw8wbANxT/AYm+dtQ/LsRwAfaT3KUmzHZTd2wG8D7irw8A+CG4vgNAJ5h5pcCeF9xXd+4HcAfMvMPALgUk3wNsm6IaB2AnwSwmZkvBjAD4M0Yav0w82j+AbgCwLz4/R4A7+k6XSXy8WkArwXwFQDnFcfOA/CV4u/fAHCtuH7puj78A3A+Jh/1NgCfAUCYOEeususJwDyAK4q/VxXXUdd5EHk5C8DX7DQNuG7WAXgCwLlFeX8GwPah1s+oNDA8VzmGI8WxwVCo6K8C8CUAL2bmJwGg+H9NcVnf8/mrAH4awD8Wv18I4FvMfLL4LdO7lJfi/LeL6/vCegDfBPCbxZD4w0R0JgZaN8x8FMCvAHgcwJOYlPc+DLR+xibAyHFsMNOsRPR8AP8TwH9k5r8JXeo41ot8EtGbABxnZrmDayi9vc1LwSoArwbwAWZ+FSZL1EK21V7np7DVXQ3gIgBrAZyJybDXZhD1MzYBdgTABeL3+QCOdZSWLIjoNEyE1+8w8+8Vh58iovOK8+cBOF4c73M+/xmAHyGirwP4BCbDyF8FcDYRrSqukeldyktx/rsBPN1mgiMcAXCEmb9U/P4UJgJtiHUDAD8M4GvM/E1m/g6A3wOwBQOtn7EJsAUAG4oZledhYpy8u+M0RSEiAvARAI8x83vFqbsBXF/8fT0mtjFz/G3FjNflAL5thjNdw8zvYebzmflCTMr/Xmb+MQCfB/CjxWV2Xkwef7S4vjc9PDN/A8ATRPTy4tBVAA5ggHVT8DiAy4nojKLdmfwMsn46N8I1YKR8I4CvAvhLAD/TdXoS0/yDmKjljwBYLP69ERNbwz0ADhb/n1tcT5jMtv4lgP2YzCh1ng9HvrYC+Ezx93oADwI4BOB3Aawujp9e/D5UnF/fdbod+ZgF8FBRP3cBOGfIdQPg5wH8BYBHAfw2gNVDrR/1xFcUZbCMbQipKMoUoQJMUZTBogJMUZTBogJMUZTBogJMUZTBogJM6QVEdCERPWod+zki+k9dpUnpPyrAlNEiPMuVkaICTOk9RPSTRHSgiK/1ieLYmUT0USJaKBZZX10cfzsR/S4R/b8A/oiIziOiLxDRYhH/6spOM6PUivZQyhC4FcBFzHyCiM4ujv0MJstafrw49iAR/XFx7goAr2Tmp4no3ZiEhvmlIl7cGe0nX2kKFWBKX/AtCTFLrH6HiO7CZCkPALwOk0XjxkZ2OoCXFH9/jpnNguMFAB8tFsvfxcyL9Sdd6QodQip94a8xWWMoOReTAHr/ApP1hZsA7CtsWwTg3zDzbPHvJcxsIsD+vXkAM38BwA8BOArgt4nobQ3nQ2kRFWBKL2DmvwPwJBFdBUz2AwDwegD/H4ALmPnzmARJPBvA8zGJFPquIqICiOhVrucS0fdhEp/sQ5hE/OhdjHqlPDqEVPrE2wD8OhH9j+L3z2MS/uXzRPTdmGhd72PmbxHRL2ISZ+yRQoh9HcCbHM/cCuD/JKLvAPi74h3KSNBoFIqiDBYdQiqKMlhUgCmKMlhUgCmKMlhUgCmKMlhUgCmKMlhUgCmKMlhUgCmKMlhUgCmKMlj+f6IjsLJVlkBqAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Load data\n", "data = loadmat(os.path.join('Data', 'ex8_movies.mat'))\n", @@ -531,7 +706,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 116, "metadata": {}, "outputs": [], "source": [ @@ -611,7 +786,23 @@ "\n", " # ====================== YOUR CODE HERE ======================\n", "\n", + " selec = (R == 1)\n", + " J = .5 * (X @ Theta.T - Y)**2\n", + " J = J[selec].sum() + ((Theta**2).sum() + (X**2).sum()) * (lambda_/2)\n", " \n", + " for a in range(num_movies):\n", + " idx = np.where( R[a, :] == 1)[0]\n", + " Theta_temp = Theta[idx]\n", + " Y_temp = Y[a, idx]\n", + " \n", + " X_grad[a, :] = np.dot(X[a] @ Theta_temp.T - Y_temp, Theta_temp) + lambda_ * X[a]\n", + " \n", + " for a in range(num_users):\n", + " idx = np.where( R[:,a] == 1)\n", + " Y_temp = Y[idx, a]\n", + " X_temp = X[idx]\n", + " \n", + " Theta_grad[a] = (X_temp @ Theta[a].T - Y_temp) @ X_temp + lambda_ * Theta[a]\n", " \n", " # =============================================================\n", " \n", @@ -628,9 +819,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 99, "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 +863,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 100, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise anomaly-detection-and-recommender-systems\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(5,)\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 +959,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 109, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-4.66718303 -4.66718303]\n", + " [ 1.42973859 1.42973859]\n", + " [-0.95117499 -0.95117499]\n", + " [ 1.3658633 1.3658633 ]\n", + " [-0.32815476 -0.32815476]\n", + " [-3.0718438 -3.0718438 ]\n", + " [-3.95608735 -3.95608735]\n", + " [-3.37567844 -3.37567844]\n", + " [ 2.88694469 2.88694469]\n", + " [ 6.51617664 6.51617664]\n", + " [-0.41647937 -0.41647937]\n", + " [ 2.20953354 2.20953354]\n", + " [ 4.19976457 4.19976457]\n", + " [-0.84398147 -0.84398147]\n", + " [-1.36022891 -1.36022891]\n", + " [ 0. 0. ]\n", + " [ 0. 0. ]\n", + " [ 0. 0. ]\n", + " [-3.59004165 -3.59004165]\n", + " [ 3.40308373 3.40308373]\n", + " [ 4.05865423 4.05865423]\n", + " [-4.6940144 -4.6940144 ]\n", + " [ 0.48632462 0.48632462]\n", + " [-4.46783838 -4.46783838]\n", + " [-5.45885745 -5.45885745]\n", + " [-0.87818594 -0.87818594]\n", + " [-0.09268218 -0.09268218]]\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: 1.47477e-12\n" + ] + } + ], "source": [ "# Check gradients by running checkcostFunction\n", "utils.checkCostFunction(cofiCostFunc)" @@ -741,9 +1015,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 110, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise anomaly-detection-and-recommender-systems\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(5,)\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 +1077,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 114, "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 +1107,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 115, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise anomaly-detection-and-recommender-systems\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(5,)\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 +1174,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 117, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-3.91002239e+00 -3.91002239e+00]\n", + " [-4.49210461e+00 -4.49210461e+00]\n", + " [ 1.47355440e+01 1.47355440e+01]\n", + " [ 7.79934481e-02 7.79934481e-02]\n", + " [ 1.34387698e+00 1.34387698e+00]\n", + " [ 1.13350802e-02 1.13350802e-02]\n", + " [ 7.31891264e-01 7.31891264e-01]\n", + " [ 2.26234156e-01 2.26234156e-01]\n", + " [ 1.52923523e+00 1.52923523e+00]\n", + " [-2.64990903e+00 -2.64990903e+00]\n", + " [ 3.30336568e+00 3.30336568e+00]\n", + " [ 8.90026598e-02 8.90026598e-02]\n", + " [ 2.32541154e+00 2.32541154e+00]\n", + " [-3.81359396e+00 -3.81359396e+00]\n", + " [-3.05761454e-01 -3.05761454e-01]\n", + " [-1.21110154e+00 -1.21110154e+00]\n", + " [ 5.88307788e-01 5.88307788e-01]\n", + " [-3.86481021e+00 -3.86481021e+00]\n", + " [ 6.77639372e-01 6.77639372e-01]\n", + " [ 1.93833600e+00 1.93833600e+00]\n", + " [ 1.76442124e+00 1.76442124e+00]\n", + " [-1.10324931e+00 -1.10324931e+00]\n", + " [ 7.65947118e-01 7.65947118e-01]\n", + " [-1.10344727e+01 -1.10344727e+01]\n", + " [-7.63133253e-01 -7.63133253e-01]\n", + " [ 1.26180268e+00 1.26180268e+00]\n", + " [-2.54114655e-01 -2.54114655e-01]]\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: 1.26962e-12\n" + ] + } + ], "source": [ "# Check gradients by running checkCostFunction\n", "utils.checkCostFunction(cofiCostFunc, 1.5)" @@ -838,9 +1230,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 118, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise anomaly-detection-and-recommender-systems\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Use token from last successful submission (oturnbull1@gmail.com)? (Y/n): y\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(5,)\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()" @@ -1019,7 +1446,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.7.3" } }, "nbformat": 4, From c69a9e3ec6608069551563fb10508be8f338092c Mon Sep 17 00:00:00 2001 From: Ollie Turnbull Date: Fri, 17 Dec 2021 16:30:11 +0000 Subject: [PATCH 3/3] finished project --- .gitignore | 0 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