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\n", - "At the end of each section in this notebook, we have a cell which contains code for submitting the solutions thus far to the grader. Execute the cell to see your score up to the current section. For all your work to be submitted properly, you must execute those cells at least once. They must also be re-executed everytime the submitted function is updated.\n", - "
\n", - "\n", - "\n", - "## Debugging\n", - "\n", - "Here are some things to keep in mind throughout this exercise:\n", - "\n", - "- Python array indices start from zero, not one (contrary to OCTAVE/MATLAB). \n", - "\n", - "- There is an important distinction between python arrays (called `list` or `tuple`) and `numpy` arrays. You should use `numpy` arrays in all your computations. Vector/matrix operations work only with `numpy` arrays. Python lists do not support vector operations (you need to use for loops).\n", - "\n", - "- If you are seeing many errors at runtime, inspect your matrix operations to make sure that you are adding and multiplying matrices of compatible dimensions. Printing the dimensions of `numpy` arrays using the `shape` property will help you debug.\n", - "\n", - "- By default, `numpy` interprets math operators to be element-wise operators. If you want to do matrix multiplication, you need to use the `dot` function in `numpy`. For, example if `A` and `B` are two `numpy` matrices, then the matrix operation AB is `np.dot(A, B)`. Note that for 2-dimensional matrices or vectors (1-dimensional), this is also equivalent to `A@B` (requires python >= 3.5)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## 1 Simple python and `numpy` function\n", - "\n", - "The first part of this assignment gives you practice with python and `numpy` syntax and the homework submission process. In the next cell, you will find the outline of a `python` function. Modify it to return a 5 x 5 identity matrix by filling in the following code:\n", - "\n", - "```python\n", - "A = np.eye(5)\n", - "```\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def warmUpExercise():\n", - " \"\"\"\n", - " Example function in Python which computes the identity matrix.\n", - " \n", - " Returns\n", - " -------\n", - " A : array_like\n", - " The 5x5 identity matrix.\n", - " \n", - " Instructions\n", - " ------------\n", - " Return the 5x5 identity matrix.\n", - " \"\"\" \n", - " # ======== YOUR CODE HERE ======\n", - " A = [] # modify this line\n", - " \n", - " # ==============================\n", - " return A" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The previous cell only defines the function `warmUpExercise`. We can now run it by executing the following cell to see its output. You should see output similar to the following:\n", - "\n", - "```python\n", - "array([[ 1., 0., 0., 0., 0.],\n", - " [ 0., 1., 0., 0., 0.],\n", - " [ 0., 0., 1., 0., 0.],\n", - " [ 0., 0., 0., 1., 0.],\n", - " [ 0., 0., 0., 0., 1.]])\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "warmUpExercise()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.1 Submitting solutions\n", - "\n", - "After completing a part of the exercise, you can submit your solutions for grading by first adding the function you modified to the grader object, and then sending your function to Coursera for grading. \n", - "\n", - "The grader will prompt you for your login e-mail and submission token. You can obtain a submission token from the web page for the assignment. You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n", - "\n", - "Execute the next cell to grade your solution to the first part of this exercise.\n", - "\n", - "*You should now submit your solutions.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# appends the implemented function in part 1 to the grader object\n", - "grader[1] = warmUpExercise\n", - "\n", - "# send the added functions to coursera grader for getting a grade on this part\n", - "grader.grade()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2 Linear regression with one variable\n", - "\n", - "Now you will implement linear regression with one variable to predict profits for a food truck. Suppose you are the CEO of a restaurant franchise and are considering different cities for opening a new outlet. The chain already has trucks in various cities and you have data for profits and populations from the cities. You would like to use this data to help you select which city to expand to next. \n", - "\n", - "The file `Data/ex1data1.txt` contains the dataset for our linear regression problem. The first column is the population of a city (in 10,000s) and the second column is the profit of a food truck in that city (in $10,000s). A negative value for profit indicates a loss. \n", - "\n", - "We provide you with the code needed to load this data. The dataset is loaded from the data file into the variables `x` and `y`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Read comma separated data\n", - "data = np.loadtxt(os.path.join('Data', 'ex1data1.txt'), delimiter=',')\n", - "X, y = data[:, 0], data[:, 1]\n", - "\n", - "m = y.size # number of training examples" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.1 Plotting the Data\n", - "\n", - "Before starting on any task, it is often useful to understand the data by visualizing it. For this dataset, you can use a scatter plot to visualize the data, since it has only two properties to plot (profit and population). Many other problems that you will encounter in real life are multi-dimensional and cannot be plotted on a 2-d plot. There are many plotting libraries in python (see this [blog post](https://blog.modeanalytics.com/python-data-visualization-libraries/) for a good summary of the most popular ones). \n", - "\n", - "In this course, we will be exclusively using `matplotlib` to do all our plotting. `matplotlib` is one of the most popular scientific plotting libraries in python and has extensive tools and functions to make beautiful plots. `pyplot` is a module within `matplotlib` which provides a simplified interface to `matplotlib`'s most common plotting tasks, mimicking MATLAB's plotting interface.\n", - "\n", - "
\n", - "You might have noticed that we have imported the `pyplot` module at the beginning of this exercise using the command `from matplotlib import pyplot`. This is rather uncommon, and if you look at python code elsewhere or in the `matplotlib` tutorials, you will see that the module is named `plt`. This is used by module renaming by using the import command `import matplotlib.pyplot as plt`. We will not using the short name of `pyplot` module in this class exercises, but you should be aware of this deviation from norm.\n", - "
\n", - "\n", - "\n", - "In the following part, your first job is to complete the `plotData` function below. Modify the function and fill in the following code:\n", - "\n", - "```python\n", - " pyplot.plot(x, y, 'ro', ms=10, mec='k')\n", - " pyplot.ylabel('Profit in $10,000')\n", - " pyplot.xlabel('Population of City in 10,000s')\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def plotData(x, y):\n", - " \"\"\"\n", - " Plots the data points x and y into a new figure. Plots the data \n", - " points and gives the figure axes labels of population and profit.\n", - " \n", - " Parameters\n", - " ----------\n", - " x : array_like\n", - " Data point values for x-axis.\n", - "\n", - " y : array_like\n", - " Data point values for y-axis. Note x and y should have the same size.\n", - " \n", - " Instructions\n", - " ------------\n", - " Plot the training data into a figure using the \"figure\" and \"plot\"\n", - " functions. Set the axes labels using the \"xlabel\" and \"ylabel\" functions.\n", - " Assume the population and revenue data have been passed in as the x\n", - " and y arguments of this function. \n", - " \n", - " Hint\n", - " ----\n", - " You can use the 'ro' option with plot to have the markers\n", - " appear as red circles. Furthermore, you can make the markers larger by\n", - " using plot(..., 'ro', ms=10), where `ms` refers to marker size. You \n", - " can also set the marker edge color using the `mec` property.\n", - " \"\"\"\n", - " fig = pyplot.figure() # open a new figure\n", - " \n", - " # ====================== YOUR CODE HERE ======================= \n", - " \n", - "\n", - " # =============================================================\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now run the defined function with the loaded data to visualize the data. The end result should look like the following figure:\n", - "\n", - "![](Figures/dataset1.png)\n", - "\n", - "Execute the next cell to visualize the data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plotData(X, y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To quickly learn more about the `matplotlib` plot function and what arguments you can provide to it, you can type `?pyplot.plot` in a cell within the jupyter notebook. This opens a separate page showing the documentation for the requested function. You can also search online for plotting documentation. \n", - "\n", - "To set the markers to red circles, we used the option `'or'` within the `plot` function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "?pyplot.plot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### 2.2 Gradient Descent\n", - "\n", - "In this part, you will fit the linear regression parameters $\\theta$ to our dataset using gradient descent.\n", - "\n", - "#### 2.2.1 Update Equations\n", - "\n", - "The objective of linear regression is to minimize the cost function\n", - "\n", - "$$ J(\\theta) = \\frac{1}{2m} \\sum_{i=1}^m \\left( h_{\\theta}(x^{(i)}) - y^{(i)}\\right)^2$$\n", - "\n", - "where the hypothesis $h_\\theta(x)$ is given by the linear model\n", - "$$ h_\\theta(x) = \\theta^Tx = \\theta_0 + \\theta_1 x_1$$\n", - "\n", - "Recall that the parameters of your model are the $\\theta_j$ values. These are\n", - "the values you will adjust to minimize cost $J(\\theta)$. One way to do this is to\n", - "use the batch gradient descent algorithm. In batch gradient descent, each\n", - "iteration performs the update\n", - "\n", - "$$ \\theta_j = \\theta_j - \\alpha \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta(x^{(i)}) - y^{(i)}\\right)x_j^{(i)} \\qquad \\text{simultaneously update } \\theta_j \\text{ for all } j$$\n", - "\n", - "With each step of gradient descent, your parameters $\\theta_j$ come closer to the optimal values that will achieve the lowest cost J($\\theta$).\n", - "\n", - "
\n", - "**Implementation Note:** We store each example as a row in the the $X$ matrix in Python `numpy`. To take into account the intercept term ($\\theta_0$), we add an additional first column to $X$ and set it to all ones. This allows us to treat $\\theta_0$ as simply another 'feature'.\n", - "
\n", - "\n", - "\n", - "#### 2.2.2 Implementation\n", - "\n", - "We have already set up the data for linear regression. In the following cell, we add another dimension to our data to accommodate the $\\theta_0$ intercept term. Do NOT execute this cell more than once." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Add a column of ones to X. The numpy function stack joins arrays along a given axis. \n", - "# The first axis (axis=0) refers to rows (training examples) \n", - "# and second axis (axis=1) refers to columns (features).\n", - "X = np.stack([np.ones(m), X], axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "#### 2.2.3 Computing the cost $J(\\theta)$\n", - "\n", - "As you perform gradient descent to learn minimize the cost function $J(\\theta)$, it is helpful to monitor the convergence by computing the cost. In this section, you will implement a function to calculate $J(\\theta)$ so you can check the convergence of your gradient descent implementation. \n", - "\n", - "Your next task is to complete the code for the function `computeCost` which computes $J(\\theta)$. As you are doing this, remember that the variables $X$ and $y$ are not scalar values. $X$ is a matrix whose rows represent the examples from the training set and $y$ is a vector whose each elemennt represent the value at a given row of $X$.\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def computeCost(X, y, theta):\n", - " \"\"\"\n", - " Compute cost for linear regression. Computes the cost of using theta as the\n", - " parameter for linear regression to fit the data points in X and y.\n", - " \n", - " Parameters\n", - " ----------\n", - " X : array_like\n", - " The input dataset of shape (m x n+1), where m is the number of examples,\n", - " and n is the number of features. We assume a vector of one's already \n", - " appended to the features so we have n+1 columns.\n", - " \n", - " y : array_like\n", - " The values of the function at each data point. This is a vector of\n", - " shape (m, ).\n", - " \n", - " theta : array_like\n", - " The parameters for the regression function. This is a vector of \n", - " shape (n+1, ).\n", - " \n", - " Returns\n", - " -------\n", - " J : float\n", - " The value of the regression cost function.\n", - " \n", - " Instructions\n", - " ------------\n", - " Compute the cost of a particular choice of theta. \n", - " You should set J to the cost.\n", - " \"\"\"\n", - " \n", - " # initialize some useful values\n", - " m = y.size # number of training examples\n", - " \n", - " # You need to return the following variables correctly\n", - " J = 0\n", - " \n", - " # ====================== YOUR CODE HERE =====================\n", - "\n", - " \n", - " # ===========================================================\n", - " return J" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once you have completed the function, the next step will run `computeCost` two times using two different initializations of $\\theta$. You will see the cost printed to the screen." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "J = computeCost(X, y, theta=np.array([0.0, 0.0]))\n", - "print('With theta = [0, 0] \\nCost computed = %.2f' % J)\n", - "print('Expected cost value (approximately) 32.07\\n')\n", - "\n", - "# further testing of the cost function\n", - "J = computeCost(X, y, theta=np.array([-1, 2]))\n", - "print('With theta = [-1, 2]\\nCost computed = %.2f' % J)\n", - "print('Expected cost value (approximately) 54.24')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*You should now submit your solutions by executing the following cell.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "grader[2] = computeCost\n", - "grader.grade()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "#### 2.2.4 Gradient descent\n", - "\n", - "Next, you will complete a function which implements gradient descent.\n", - "The loop structure has been written for you, and you only need to supply the updates to $\\theta$ within each iteration. \n", - "\n", - "As you program, make sure you understand what you are trying to optimize and what is being updated. Keep in mind that the cost $J(\\theta)$ is parameterized by the vector $\\theta$, not $X$ and $y$. That is, we minimize the value of $J(\\theta)$ by changing the values of the vector $\\theta$, not by changing $X$ or $y$. [Refer to the equations in this notebook](#section2) and to the video lectures if you are uncertain. A good way to verify that gradient descent is working correctly is to look at the value of $J(\\theta)$ and check that it is decreasing with each step. \n", - "\n", - "The starter code for the function `gradientDescent` calls `computeCost` on every iteration and saves the cost to a `python` list. Assuming you have implemented gradient descent and `computeCost` correctly, your value of $J(\\theta)$ should never increase, and should converge to a steady value by the end of the algorithm.\n", - "\n", - "
\n", - "**Vectors and matrices in `numpy`** - Important implementation notes\n", - "\n", - "A vector in `numpy` is a one dimensional array, for example `np.array([1, 2, 3])` is a vector. A matrix in `numpy` is a two dimensional array, for example `np.array([[1, 2, 3], [4, 5, 6]])`. However, the following is still considered a matrix `np.array([[1, 2, 3]])` since it has two dimensions, even if it has a shape of 1x3 (which looks like a vector).\n", - "\n", - "Given the above, the function `np.dot` which we will use for all matrix/vector multiplication has the following properties:\n", - "- It always performs inner products on vectors. If `x=np.array([1, 2, 3])`, then `np.dot(x, x)` is a scalar.\n", - "- For matrix-vector multiplication, so if $X$ is a $m\\times n$ matrix and $y$ is a vector of length $m$, then the operation `np.dot(y, X)` considers $y$ as a $1 \\times m$ vector. On the other hand, if $y$ is a vector of length $n$, then the operation `np.dot(X, y)` considers $y$ as a $n \\times 1$ vector.\n", - "- A vector can be promoted to a matrix using `y[None]` or `[y[np.newaxis]`. That is, if `y = np.array([1, 2, 3])` is a vector of size 3, then `y[None, :]` is a matrix of shape $1 \\times 3$. We can use `y[:, None]` to obtain a shape of $3 \\times 1$.\n", - "
\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def gradientDescent(X, y, theta, alpha, num_iters):\n", - " \"\"\"\n", - " Performs gradient descent to learn `theta`. Updates theta by taking `num_iters`\n", - " gradient steps with learning rate `alpha`.\n", - " \n", - " Parameters\n", - " ----------\n", - " X : array_like\n", - " The input dataset of shape (m x n+1).\n", - " \n", - " y : arra_like\n", - " Value at given features. A vector of shape (m, ).\n", - " \n", - " theta : array_like\n", - " Initial values for the linear regression parameters. \n", - " A vector of shape (n+1, ).\n", - " \n", - " alpha : float\n", - " The learning rate.\n", - " \n", - " num_iters : int\n", - " The number of iterations for gradient descent. \n", - " \n", - " Returns\n", - " -------\n", - " theta : array_like\n", - " The learned linear regression parameters. A vector of shape (n+1, ).\n", - " \n", - " J_history : list\n", - " A python list for the values of the cost function after each iteration.\n", - " \n", - " Instructions\n", - " ------------\n", - " Peform a single gradient step on the parameter vector theta.\n", - "\n", - " While debugging, it can be useful to print out the values of \n", - " the cost function (computeCost) and gradient here.\n", - " \"\"\"\n", - " # Initialize some useful values\n", - " m = y.shape[0] # number of training examples\n", - " \n", - " # make a copy of theta, to avoid changing the original array, since numpy arrays\n", - " # are passed by reference to functions\n", - " theta = theta.copy()\n", - " \n", - " J_history = [] # Use a python list to save cost in every iteration\n", - " \n", - " for i in range(num_iters):\n", - " # ==================== YOUR CODE HERE =================================\n", - " \n", - "\n", - " # =====================================================================\n", - " \n", - " # save the cost J in every iteration\n", - " J_history.append(computeCost(X, y, theta))\n", - " \n", - " return theta, J_history" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After you are finished call the implemented `gradientDescent` function and print the computed $\\theta$. We initialize the $\\theta$ parameters to 0 and the learning rate $\\alpha$ to 0.01. Execute the following cell to check your code." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# initialize fitting parameters\n", - "theta = np.zeros(2)\n", - "\n", - "# some gradient descent settings\n", - "iterations = 1500\n", - "alpha = 0.01\n", - "\n", - "theta, J_history = gradientDescent(X ,y, theta, alpha, iterations)\n", - "print('Theta found by gradient descent: {:.4f}, {:.4f}'.format(*theta))\n", - "print('Expected theta values (approximately): [-3.6303, 1.1664]')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will use your final parameters to plot the linear fit. The results should look like the following figure.\n", - "\n", - "![](Figures/regression_result.png)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# plot the linear fit\n", - "plotData(X[:, 1], y)\n", - "pyplot.plot(X[:, 1], np.dot(X, theta), '-')\n", - "pyplot.legend(['Training data', 'Linear regression']);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Your final values for $\\theta$ will also be used to make predictions on profits in areas of 35,000 and 70,000 people.\n", - "\n", - "
\n", - "Note the way that the following lines use matrix multiplication, rather than explicit summation or looping, to calculate the predictions. This is an example of code vectorization in `numpy`.\n", - "
\n", - "\n", - "
\n", - "Note that the first argument to the `numpy` function `dot` is a python list. `numpy` can internally converts **valid** python lists to numpy arrays when explicitly provided as arguments to `numpy` functions.\n", - "
\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Predict values for population sizes of 35,000 and 70,000\n", - "predict1 = np.dot([1, 3.5], theta)\n", - "print('For population = 35,000, we predict a profit of {:.2f}\\n'.format(predict1*10000))\n", - "\n", - "predict2 = np.dot([1, 7], theta)\n", - "print('For population = 70,000, we predict a profit of {:.2f}\\n'.format(predict2*10000))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*You should now submit your solutions by executing the next cell.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "grader[3] = gradientDescent\n", - "grader.grade()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.4 Visualizing $J(\\theta)$\n", - "\n", - "To understand the cost function $J(\\theta)$ better, you will now plot the cost over a 2-dimensional grid of $\\theta_0$ and $\\theta_1$ values. You will not need to code anything new for this part, but you should understand how the code you have written already is creating these images.\n", - "\n", - "In the next cell, the code is set up to calculate $J(\\theta)$ over a grid of values using the `computeCost` function that you wrote. After executing the following cell, you will have a 2-D array of $J(\\theta)$ values. Then, those values are used to produce surface and contour plots of $J(\\theta)$ using the matplotlib `plot_surface` and `contourf` functions. The plots should look something like the following:\n", - "\n", - "![](Figures/cost_function.png)\n", - "\n", - "The purpose of these graphs is to show you how $J(\\theta)$ varies with changes in $\\theta_0$ and $\\theta_1$. The cost function $J(\\theta)$ is bowl-shaped and has a global minimum. (This is easier to see in the contour plot than in the 3D surface plot). This minimum is the optimal point for $\\theta_0$ and $\\theta_1$, and each step of gradient descent moves closer to this point." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# grid over which we will calculate J\n", - "theta0_vals = np.linspace(-10, 10, 100)\n", - "theta1_vals = np.linspace(-1, 4, 100)\n", - "\n", - "# initialize J_vals to a matrix of 0's\n", - "J_vals = np.zeros((theta0_vals.shape[0], theta1_vals.shape[0]))\n", - "\n", - "# Fill out J_vals\n", - "for i, theta0 in enumerate(theta0_vals):\n", - " for j, theta1 in enumerate(theta1_vals):\n", - " J_vals[i, j] = computeCost(X, y, [theta0, theta1])\n", - " \n", - "# Because of the way meshgrids work in the surf command, we need to\n", - "# transpose J_vals before calling surf, or else the axes will be flipped\n", - "J_vals = J_vals.T\n", - "\n", - "# surface plot\n", - "fig = pyplot.figure(figsize=(12, 5))\n", - "ax = fig.add_subplot(121, projection='3d')\n", - "ax.plot_surface(theta0_vals, theta1_vals, J_vals, cmap='viridis')\n", - "pyplot.xlabel('theta0')\n", - "pyplot.ylabel('theta1')\n", - "pyplot.title('Surface')\n", - "\n", - "# contour plot\n", - "# Plot J_vals as 15 contours spaced logarithmically between 0.01 and 100\n", - "ax = pyplot.subplot(122)\n", - "pyplot.contour(theta0_vals, theta1_vals, J_vals, linewidths=2, cmap='viridis', levels=np.logspace(-2, 3, 20))\n", - "pyplot.xlabel('theta0')\n", - "pyplot.ylabel('theta1')\n", - "pyplot.plot(theta[0], theta[1], 'ro', ms=10, lw=2)\n", - "pyplot.title('Contour, showing minimum')\n", - "pass" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optional Exercises\n", - "\n", - "If you have successfully completed the material above, congratulations! You now understand linear regression and should able to start using it on your own datasets.\n", - "\n", - "For the rest of this programming exercise, we have included the following optional exercises. These exercises will help you gain a deeper understanding of the material, and if you are able to do so, we encourage you to complete them as well. You can still submit your solutions to these exercises to check if your answers are correct.\n", - "\n", - "## 3 Linear regression with multiple variables\n", - "\n", - "In this part, you will implement linear regression with multiple variables to predict the prices of houses. Suppose you are selling your house and you want to know what a good market price would be. One way to do this is to first collect information on recent houses sold and make a model of housing prices.\n", - "\n", - "The file `Data/ex1data2.txt` contains a training set of housing prices in Portland, Oregon. The first column is the size of the house (in square feet), the second column is the number of bedrooms, and the third column is the price\n", - "of the house. \n", - "\n", - "\n", - "### 3.1 Feature Normalization\n", - "\n", - "We start by loading and displaying some values from this dataset. By looking at the values, note that house sizes are about 1000 times the number of bedrooms. When features differ by orders of magnitude, first performing feature scaling can make gradient descent converge much more quickly." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Load data\n", - "data = np.loadtxt(os.path.join('Data', 'ex1data2.txt'), delimiter=',')\n", - "X = data[:, :2]\n", - "y = data[:, 2]\n", - "m = y.size\n", - "\n", - "# print out some data points\n", - "print('{:>8s}{:>8s}{:>10s}'.format('X[:,0]', 'X[:, 1]', 'y'))\n", - "print('-'*26)\n", - "for i in range(10):\n", - " print('{:8.0f}{:8.0f}{:10.0f}'.format(X[i, 0], X[i, 1], y[i]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Your task here is to complete the code in `featureNormalize` function:\n", - "- Subtract the mean value of each feature from the dataset.\n", - "- After subtracting the mean, additionally scale (divide) the feature values by their respective “standard deviations.”\n", - "\n", - "The standard deviation is a way of measuring how much variation there is in the range of values of a particular feature (most data points will lie within ±2 standard deviations of the mean); this is an alternative to taking the range of values (max-min). In `numpy`, you can use the `std` function to compute the standard deviation. \n", - "\n", - "For example, the quantity `X[:, 0]` contains all the values of $x_1$ (house sizes) in the training set, so `np.std(X[:, 0])` computes the standard deviation of the house sizes.\n", - "At the time that the function `featureNormalize` is called, the extra column of 1’s corresponding to $x_0 = 1$ has not yet been added to $X$. \n", - "\n", - "You will do this for all the features and your code should work with datasets of all sizes (any number of features / examples). Note that each column of the matrix $X$ corresponds to one feature.\n", - "\n", - "
\n", - "**Implementation Note:** When normalizing the features, it is important\n", - "to store the values used for normalization - the mean value and the standard deviation used for the computations. After learning the parameters\n", - "from the model, we often want to predict the prices of houses we have not\n", - "seen before. Given a new x value (living room area and number of bedrooms), we must first normalize x using the mean and standard deviation that we had previously computed from the training set.\n", - "
\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def featureNormalize(X):\n", - " \"\"\"\n", - " Normalizes the features in X. returns a normalized version of X where\n", - " the mean value of each feature is 0 and the standard deviation\n", - " is 1. This is often a good preprocessing step to do when working with\n", - " learning algorithms.\n", - " \n", - " Parameters\n", - " ----------\n", - " X : array_like\n", - " The dataset of shape (m x n).\n", - " \n", - " Returns\n", - " -------\n", - " X_norm : array_like\n", - " The normalized dataset of shape (m x n).\n", - " \n", - " Instructions\n", - " ------------\n", - " First, for each feature dimension, compute the mean of the feature\n", - " and subtract it from the dataset, storing the mean value in mu. \n", - " Next, compute the standard deviation of each feature and divide\n", - " each feature by it's standard deviation, storing the standard deviation \n", - " in sigma. \n", - " \n", - " Note that X is a matrix where each column is a feature and each row is\n", - " an example. You needto perform the normalization separately for each feature. \n", - " \n", - " Hint\n", - " ----\n", - " You might find the 'np.mean' and 'np.std' functions useful.\n", - " \"\"\"\n", - " # You need to set these values correctly\n", - " X_norm = X.copy()\n", - " mu = np.zeros(X.shape[1])\n", - " sigma = np.zeros(X.shape[1])\n", - "\n", - " # =========================== YOUR CODE HERE =====================\n", - "\n", - " \n", - " # ================================================================\n", - " return X_norm, mu, sigma" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Execute the next cell to run the implemented `featureNormalize` function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# call featureNormalize on the loaded data\n", - "X_norm, mu, sigma = featureNormalize(X)\n", - "\n", - "print('Computed mean:', mu)\n", - "print('Computed standard deviation:', sigma)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*You should now submit your solutions.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "grader[4] = featureNormalize\n", - "grader.grade()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After the `featureNormalize` function is tested, we now add the intercept term to `X_norm`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Add intercept term to X\n", - "X = np.concatenate([np.ones((m, 1)), X_norm], axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### 3.2 Gradient Descent\n", - "\n", - "Previously, you implemented gradient descent on a univariate regression problem. The only difference now is that there is one more feature in the matrix $X$. The hypothesis function and the batch gradient descent update\n", - "rule remain unchanged. \n", - "\n", - "You should complete the code for the functions `computeCostMulti` and `gradientDescentMulti` to implement the cost function and gradient descent for linear regression with multiple variables. If your code in the previous part (single variable) already supports multiple variables, you can use it here too.\n", - "Make sure your code supports any number of features and is well-vectorized.\n", - "You can use the `shape` property of `numpy` arrays to find out how many features are present in the dataset.\n", - "\n", - "
\n", - "**Implementation Note:** In the multivariate case, the cost function can\n", - "also be written in the following vectorized form:\n", - "\n", - "$$ J(\\theta) = \\frac{1}{2m}(X\\theta - \\vec{y})^T(X\\theta - \\vec{y}) $$\n", - "\n", - "where \n", - "\n", - "$$ X = \\begin{pmatrix}\n", - " - (x^{(1)})^T - \\\\\n", - " - (x^{(2)})^T - \\\\\n", - " \\vdots \\\\\n", - " - (x^{(m)})^T - \\\\ \\\\\n", - " \\end{pmatrix} \\qquad \\mathbf{y} = \\begin{bmatrix} y^{(1)} \\\\ y^{(2)} \\\\ \\vdots \\\\ y^{(m)} \\\\\\end{bmatrix}$$\n", - "\n", - "the vectorized version is efficient when you are working with numerical computing tools like `numpy`. If you are an expert with matrix operations, you can prove to yourself that the two forms are equivalent.\n", - "
\n", - "\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def computeCostMulti(X, y, theta):\n", - " \"\"\"\n", - " Compute cost for linear regression with multiple variables.\n", - " Computes the cost of using theta as the parameter for linear regression to fit the data points in X and y.\n", - " \n", - " Parameters\n", - " ----------\n", - " X : array_like\n", - " The dataset of shape (m x n+1).\n", - " \n", - " y : array_like\n", - " A vector of shape (m, ) for the values at a given data point.\n", - " \n", - " theta : array_like\n", - " The linear regression parameters. A vector of shape (n+1, )\n", - " \n", - " Returns\n", - " -------\n", - " J : float\n", - " The value of the cost function. \n", - " \n", - " Instructions\n", - " ------------\n", - " Compute the cost of a particular choice of theta. You should set J to the cost.\n", - " \"\"\"\n", - " # Initialize some useful values\n", - " m = y.shape[0] # number of training examples\n", - " \n", - " # You need to return the following variable correctly\n", - " J = 0\n", - " \n", - " # ======================= YOUR CODE HERE ===========================\n", - "\n", - " \n", - " # ==================================================================\n", - " return J\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*You should now submit your solutions.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "grader[5] = computeCostMulti\n", - "grader.grade()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def gradientDescentMulti(X, y, theta, alpha, num_iters):\n", - " \"\"\"\n", - " Performs gradient descent to learn theta.\n", - " Updates theta by taking num_iters gradient steps with learning rate alpha.\n", - " \n", - " Parameters\n", - " ----------\n", - " X : array_like\n", - " The dataset of shape (m x n+1).\n", - " \n", - " y : array_like\n", - " A vector of shape (m, ) for the values at a given data point.\n", - " \n", - " theta : array_like\n", - " The linear regression parameters. A vector of shape (n+1, )\n", - " \n", - " alpha : float\n", - " The learning rate for gradient descent. \n", - " \n", - " num_iters : int\n", - " The number of iterations to run gradient descent. \n", - " \n", - " Returns\n", - " -------\n", - " theta : array_like\n", - " The learned linear regression parameters. A vector of shape (n+1, ).\n", - " \n", - " J_history : list\n", - " A python list for the values of the cost function after each iteration.\n", - " \n", - " Instructions\n", - " ------------\n", - " Peform a single gradient step on the parameter vector theta.\n", - "\n", - " While debugging, it can be useful to print out the values of \n", - " the cost function (computeCost) and gradient here.\n", - " \"\"\"\n", - " # Initialize some useful values\n", - " m = y.shape[0] # number of training examples\n", - " \n", - " # make a copy of theta, which will be updated by gradient descent\n", - " theta = theta.copy()\n", - " \n", - " J_history = []\n", - " \n", - " for i in range(num_iters):\n", - " # ======================= YOUR CODE HERE ==========================\n", - "\n", - " \n", - " # =================================================================\n", - " \n", - " # save the cost J in every iteration\n", - " J_history.append(computeCostMulti(X, y, theta))\n", - " \n", - " return theta, J_history" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*You should now submit your solutions.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "grader[6] = gradientDescentMulti\n", - "grader.grade()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 3.2.1 Optional (ungraded) exercise: Selecting learning rates\n", - "\n", - "In this part of the exercise, you will get to try out different learning rates for the dataset and find a learning rate that converges quickly. You can change the learning rate by modifying the following code and changing the part of the code that sets the learning rate.\n", - "\n", - "Use your implementation of `gradientDescentMulti` function and run gradient descent for about 50 iterations at the chosen learning rate. The function should also return the history of $J(\\theta)$ values in a vector $J$.\n", - "\n", - "After the last iteration, plot the J values against the number of the iterations.\n", - "\n", - "If you picked a learning rate within a good range, your plot look similar as the following Figure. \n", - "\n", - "![](Figures/learning_rate.png)\n", - "\n", - "If your graph looks very different, especially if your value of $J(\\theta)$ increases or even blows up, adjust your learning rate and try again. We recommend trying values of the learning rate $\\alpha$ on a log-scale, at multiplicative steps of about 3 times the previous value (i.e., 0.3, 0.1, 0.03, 0.01 and so on). You may also want to adjust the number of iterations you are running if that will help you see the overall trend in the curve.\n", - "\n", - "
\n", - "**Implementation Note:** If your learning rate is too large, $J(\\theta)$ can diverge and ‘blow up’, resulting in values which are too large for computer calculations. In these situations, `numpy` will tend to return\n", - "NaNs. NaN stands for ‘not a number’ and is often caused by undefined operations that involve −∞ and +∞.\n", - "
\n", - "\n", - "
\n", - "**MATPLOTLIB tip:** To compare how different learning learning rates affect convergence, it is helpful to plot $J$ for several learning rates on the same figure. This can be done by making `alpha` a python list, and looping across the values within this list, and calling the plot function in every iteration of the loop. It is also useful to have a legend to distinguish the different lines within the plot. Search online for `pyplot.legend` for help on showing legends in `matplotlib`.\n", - "
\n", - "\n", - "Notice the changes in the convergence curves as the learning rate changes. With a small learning rate, you should find that gradient descent takes a very long time to converge to the optimal value. Conversely, with a large learning rate, gradient descent might not converge or might even diverge!\n", - "Using the best learning rate that you found, run the script\n", - "to run gradient descent until convergence to find the final values of $\\theta$. Next,\n", - "use this value of $\\theta$ to predict the price of a house with 1650 square feet and\n", - "3 bedrooms. You will use value later to check your implementation of the normal equations. Don’t forget to normalize your features when you make this prediction!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "Instructions\n", - "------------\n", - "We have provided you with the following starter code that runs\n", - "gradient descent with a particular learning rate (alpha). \n", - "\n", - "Your task is to first make sure that your functions - `computeCost`\n", - "and `gradientDescent` already work with this starter code and\n", - "support multiple variables.\n", - "\n", - "After that, try running gradient descent with different values of\n", - "alpha and see which one gives you the best result.\n", - "\n", - "Finally, you should complete the code at the end to predict the price\n", - "of a 1650 sq-ft, 3 br house.\n", - "\n", - "Hint\n", - "----\n", - "At prediction, make sure you do the same feature normalization.\n", - "\"\"\"\n", - "# Choose some alpha value - change this\n", - "alpha = 0.1\n", - "num_iters = 400\n", - "\n", - "# init theta and run gradient descent\n", - "theta = np.zeros(3)\n", - "theta, J_history = gradientDescentMulti(X, y, theta, alpha, num_iters)\n", - "\n", - "# Plot the convergence graph\n", - "pyplot.plot(np.arange(len(J_history)), J_history, lw=2)\n", - "pyplot.xlabel('Number of iterations')\n", - "pyplot.ylabel('Cost J')\n", - "\n", - "# Display the gradient descent's result\n", - "print('theta computed from gradient descent: {:s}'.format(str(theta)))\n", - "\n", - "# Estimate the price of a 1650 sq-ft, 3 br house\n", - "# ======================= YOUR CODE HERE ===========================\n", - "# Recall that the first column of X is all-ones. \n", - "# Thus, it does not need to be normalized.\n", - "\n", - "price = 0 # You should change this\n", - "\n", - "# ===================================================================\n", - "\n", - "print('Predicted price of a 1650 sq-ft, 3 br house (using gradient descent): ${:.0f}'.format(price))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*You do not need to submit any solutions for this optional (ungraded) part.*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### 3.3 Normal Equations\n", - "\n", - "In the lecture videos, you learned that the closed-form solution to linear regression is\n", - "\n", - "$$ \\theta = \\left( X^T X\\right)^{-1} X^T\\vec{y}$$\n", - "\n", - "Using this formula does not require any feature scaling, and you will get an exact solution in one calculation: there is no “loop until convergence” like in gradient descent. \n", - "\n", - "First, we will reload the data to ensure that the variables have not been modified. Remember that while you do not need to scale your features, we still need to add a column of 1’s to the $X$ matrix to have an intercept term ($\\theta_0$). The code in the next cell will add the column of 1’s to X for you." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Load data\n", - "data = np.loadtxt(os.path.join('Data', 'ex1data2.txt'), delimiter=',')\n", - "X = data[:, :2]\n", - "y = data[:, 2]\n", - "m = y.size\n", - "X = np.concatenate([np.ones((m, 1)), X], axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Complete the code for the function `normalEqn` below to use the formula above to calculate $\\theta$. \n", - "\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def normalEqn(X, y):\n", - " \"\"\"\n", - " Computes the closed-form solution to linear regression using the normal equations.\n", - " \n", - " Parameters\n", - " ----------\n", - " X : array_like\n", - " The dataset of shape (m x n+1).\n", - " \n", - " y : array_like\n", - " The value at each data point. A vector of shape (m, ).\n", - " \n", - " Returns\n", - " -------\n", - " theta : array_like\n", - " Estimated linear regression parameters. A vector of shape (n+1, ).\n", - " \n", - " Instructions\n", - " ------------\n", - " Complete the code to compute the closed form solution to linear\n", - " regression and put the result in theta.\n", - " \n", - " Hint\n", - " ----\n", - " Look up the function `np.linalg.pinv` for computing matrix inverse.\n", - " \"\"\"\n", - " theta = np.zeros(X.shape[1])\n", - " \n", - " # ===================== YOUR CODE HERE ============================\n", - "\n", - " \n", - " # =================================================================\n", - " return theta" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*You should now submit your solutions.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "grader[7] = normalEqn\n", - "grader.grade()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Optional (ungraded) exercise: Now, once you have found $\\theta$ using this\n", - "method, use it to make a price prediction for a 1650-square-foot house with\n", - "3 bedrooms. You should find that gives the same predicted price as the value\n", - "you obtained using the model fit with gradient descent (in Section 3.2.1)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate the parameters from the normal equation\n", - "theta = normalEqn(X, y);\n", - "\n", - "# Display normal equation's result\n", - "print('Theta computed from the normal equations: {:s}'.format(str(theta)));\n", - "\n", - "# Estimate the price of a 1650 sq-ft, 3 br house\n", - "# ====================== YOUR CODE HERE ======================\n", - "\n", - "price = 0 # You should change this\n", - "\n", - "# ============================================================\n", - "\n", - "print('Predicted price of a 1650 sq-ft, 3 br house (using normal equations): ${:.0f}'.format(price))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Exercise1/utils.py b/Exercise1/utils.py deleted file mode 100755 index d0c909d5..00000000 --- a/Exercise1/utils.py +++ /dev/null @@ -1,48 +0,0 @@ -import numpy as np -import sys -sys.path.append('..') - -from submission import SubmissionBase - - -class Grader(SubmissionBase): - X1 = np.column_stack((np.ones(20), np.exp(1) + np.exp(2) * np.linspace(0.1, 2, 20))) - Y1 = X1[:, 1] + np.sin(X1[:, 0]) + np.cos(X1[:, 1]) - X2 = np.column_stack((X1, X1[:, 1]**0.5, X1[:, 1]**0.25)) - Y2 = np.power(Y1, 0.5) + Y1 - - def __init__(self): - part_names = ['Warm up exercise', - 'Computing Cost (for one variable)', - 'Gradient Descent (for one variable)', - 'Feature Normalization', - 'Computing Cost (for multiple variables)', - 'Gradient Descent (for multiple variables)', - 'Normal Equations'] - super().__init__('linear-regression', part_names) - - def __iter__(self): - for part_id in range(1, 8): - try: - func = self.functions[part_id] - - # Each part has different expected arguments/different function - if part_id == 1: - res = func() - elif part_id == 2: - res = func(self.X1, self.Y1, np.array([0.5, -0.5])) - elif part_id == 3: - res = func(self.X1, self.Y1, np.array([0.5, -0.5]), 0.01, 10) - elif part_id == 4: - res = func(self.X2[:, 1:4]) - elif part_id == 5: - res = func(self.X2, self.Y2, np.array([0.1, 0.2, 0.3, 0.4])) - elif part_id == 6: - res = func(self.X2, self.Y2, np.array([-0.1, -0.2, -0.3, -0.4]), 0.01, 10) - elif part_id == 7: - res = func(self.X2, self.Y2) - else: - raise KeyError - yield part_id, res - except KeyError: - yield part_id, 0 diff --git a/Exercise2/Figures/decision_boundary1.png b/Exercise2/Figures/decision_boundary1.png deleted file mode 100755 index f1399c5c..00000000 Binary files a/Exercise2/Figures/decision_boundary1.png and /dev/null differ diff --git a/Exercise2/Figures/decision_boundary2.png b/Exercise2/Figures/decision_boundary2.png deleted file mode 100755 index 52c9f755..00000000 Binary files a/Exercise2/Figures/decision_boundary2.png and /dev/null differ diff --git a/Exercise2/Figures/decision_boundary3.png b/Exercise2/Figures/decision_boundary3.png deleted file mode 100755 index 82b6ea1b..00000000 Binary files a/Exercise2/Figures/decision_boundary3.png and /dev/null differ diff --git a/Exercise2/Figures/decision_boundary4.png b/Exercise2/Figures/decision_boundary4.png deleted file mode 100755 index b6c1370b..00000000 Binary files a/Exercise2/Figures/decision_boundary4.png and /dev/null differ diff --git a/Exercise2/exercise2.ipynb b/Exercise2/exercise2.ipynb deleted file mode 100755 index 39983d90..00000000 --- a/Exercise2/exercise2.ipynb +++ /dev/null @@ -1,965 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Programming Exercise 2: Logistic Regression\n", - "\n", - "## Introduction\n", - "\n", - "In this exercise, you will implement logistic regression and apply it to two different datasets. Before starting on the programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics.\n", - "\n", - "All the information you need for solving this assignment is in this notebook, and all the code you will be implementing will take place within this notebook. The assignment can be promptly submitted to the coursera grader directly from this notebook (code and instructions are included below).\n", - "\n", - "Before we begin with the exercises, we need to import all libraries required for this programming exercise. Throughout the course, we will be using [`numpy`](http://www.numpy.org/) for all arrays and matrix operations, and [`matplotlib`](https://matplotlib.org/) for plotting. In this assignment, we will also use [`scipy`](https://docs.scipy.org/doc/scipy/reference/), which contains scientific and numerical computation functions and tools. \n", - "\n", - "You can find instructions on how to install required libraries in the README file in the [github repository](https://github.com/dibgerge/ml-coursera-python-assignments)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# used for manipulating directory paths\n", - "import os\n", - "\n", - "# Scientific and vector computation for python\n", - "import numpy as np\n", - "\n", - "# Plotting library\n", - "from matplotlib import pyplot\n", - "\n", - "# Optimization module in scipy\n", - "from scipy import optimize\n", - "\n", - "# library written for this exercise providing additional functions for assignment submission, and others\n", - "import utils\n", - "\n", - "# define the submission/grader object for this exercise\n", - "grader = utils.Grader()\n", - "\n", - "# tells matplotlib to embed plots within the notebook\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Submission and Grading\n", - "\n", - "\n", - "After completing each part of the assignment, be sure to submit your solutions to the grader. The following is a breakdown of how each part of this exercise is scored.\n", - "\n", - "\n", - "| Section | Part | Submission function | Points \n", - "| :- |:- | :- | :-:\n", - "| 1 | [Sigmoid Function](#section1) | [`sigmoid`](#sigmoid) | 5 \n", - "| 2 | [Compute cost for logistic regression](#section2) | [`costFunction`](#costFunction) | 30 \n", - "| 3 | [Gradient for logistic regression](#section2) | [`costFunction`](#costFunction) | 30 \n", - "| 4 | [Predict Function](#section4) | [`predict`](#predict) | 5 \n", - "| 5 | [Compute cost for regularized LR](#section5) | [`costFunctionReg`](#costFunctionReg) | 15 \n", - "| 6 | [Gradient for regularized LR](#section5) | [`costFunctionReg`](#costFunctionReg) | 15 \n", - "| | Total Points | | 100 \n", - "\n", - "\n", - "\n", - "You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n", - "\n", - "
\n", - "At the end of each section in this notebook, we have a cell which contains code for submitting the solutions thus far to the grader. Execute the cell to see your score up to the current section. For all your work to be submitted properly, you must execute those cells at least once. They must also be re-executed everytime the submitted function is updated.\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1 Logistic Regression\n", - "\n", - "In this part of the exercise, you will build a logistic regression model to predict whether a student gets admitted into a university. Suppose that you are the administrator of a university department and\n", - "you want to determine each applicant’s chance of admission based on their results on two exams. You have historical data from previous applicants that you can use as a training set for logistic regression. For each training example, you have the applicant’s scores on two exams and the admissions\n", - "decision. Your task is to build a classification model that estimates an applicant’s probability of admission based the scores from those two exams. \n", - "\n", - "The following cell will load the data and corresponding labels:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Load data\n", - "# The first two columns contains the exam scores and the third column\n", - "# contains the label.\n", - "data = np.loadtxt(os.path.join('Data', 'ex2data1.txt'), delimiter=',')\n", - "X, y = data[:, 0:2], data[:, 2]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.1 Visualizing the data\n", - "\n", - "Before starting to implement any learning algorithm, it is always good to visualize the data if possible. We display the data on a 2-dimensional plot by calling the function `plotData`. You will now complete the code in `plotData` so that it displays a figure where the axes are the two exam scores, and the positive and negative examples are shown with different markers.\n", - "\n", - "To help you get more familiar with plotting, we have left `plotData` empty so you can try to implement it yourself. However, this is an optional (ungraded) exercise. We also provide our implementation below so you can\n", - "copy it or refer to it. If you choose to copy our example, make sure you learn\n", - "what each of its commands is doing by consulting the `matplotlib` and `numpy` documentation.\n", - "\n", - "```python\n", - "# Find Indices of Positive and Negative Examples\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", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def plotData(X, y):\n", - " \"\"\"\n", - " Plots the data points X and y into a new figure. Plots the data \n", - " points with * for the positive examples and o for the negative examples.\n", - " \n", - " Parameters\n", - " ----------\n", - " X : array_like\n", - " An Mx2 matrix representing the dataset. \n", - " \n", - " y : array_like\n", - " Label values for the dataset. A vector of size (M, ).\n", - " \n", - " Instructions\n", - " ------------\n", - " Plot the positive and negative examples on a 2D plot, using the\n", - " option 'k*' for the positive examples and 'ko' for the negative examples. \n", - " \"\"\"\n", - " # Create New Figure\n", - " fig = pyplot.figure()\n", - "\n", - " # ====================== YOUR CODE HERE ======================\n", - "\n", - " \n", - " # ============================================================" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we call the implemented function to display the loaded data:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plotData(X, y)\n", - "# add axes labels\n", - "pyplot.xlabel('Exam 1 score')\n", - "pyplot.ylabel('Exam 2 score')\n", - "pyplot.legend(['Admitted', 'Not admitted'])\n", - "pass" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### 1.2 Implementation\n", - "\n", - "#### 1.2.1 Warmup exercise: sigmoid function\n", - "\n", - "Before you start with the actual cost function, recall that the logistic regression hypothesis is defined as:\n", - "\n", - "$$ h_\\theta(x) = g(\\theta^T x)$$\n", - "\n", - "where function $g$ is the sigmoid function. The sigmoid function is defined as: \n", - "\n", - "$$g(z) = \\frac{1}{1+e^{-z}}$$.\n", - "\n", - "Your first step is to implement this function `sigmoid` so it can be\n", - "called by the rest of your program. When you are finished, try testing a few\n", - "values by calling `sigmoid(x)` in a new cell. For large positive values of `x`, the sigmoid should be close to 1, while for large negative values, the sigmoid should be close to 0. Evaluating `sigmoid(0)` should give you exactly 0.5. Your code should also work with vectors and matrices. **For a matrix, your function should perform the sigmoid function on every element.**\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def sigmoid(z):\n", - " \"\"\"\n", - " Compute sigmoid function given the input z.\n", - " \n", - " Parameters\n", - " ----------\n", - " z : array_like\n", - " The input to the sigmoid function. This can be a 1-D vector \n", - " or a 2-D matrix. \n", - " \n", - " Returns\n", - " -------\n", - " g : array_like\n", - " The computed sigmoid function. g has the same shape as z, since\n", - " the sigmoid is computed element-wise on z.\n", - " \n", - " Instructions\n", - " ------------\n", - " Compute the sigmoid of each value of z (z can be a matrix, vector or scalar).\n", - " \"\"\"\n", - " # convert input to a numpy array\n", - " z = np.array(z)\n", - " \n", - " # You need to return the following variables correctly \n", - " g = np.zeros(z.shape)\n", - "\n", - " # ====================== YOUR CODE HERE ======================\n", - "\n", - " \n", - "\n", - " # =============================================================\n", - " return g" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following cell evaluates the sigmoid function at `z=0`. You should get a value of 0.5. You can also try different values for `z` to experiment with the sigmoid function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Test the implementation of sigmoid function here\n", - "z = 0\n", - "g = sigmoid(z)\n", - "\n", - "print('g(', z, ') = ', g)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After completing a part of the exercise, you can submit your solutions for grading by first adding the function you modified to the submission object, and then sending your function to Coursera for grading. \n", - "\n", - "The submission script will prompt you for your login e-mail and submission token. You can obtain a submission token from the web page for the assignment. You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n", - "\n", - "Execute the following cell to grade your solution to the first part of this exercise.\n", - "\n", - "*You should now submit your solutions.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# appends the implemented function in part 1 to the grader object\n", - "grader[1] = sigmoid\n", - "\n", - "# send the added functions to coursera grader for getting a grade on this part\n", - "grader.grade()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "#### 1.2.2 Cost function and gradient\n", - "\n", - "Now you will implement the cost function and gradient for logistic regression. Before proceeding we add the intercept term to X. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Setup the data matrix appropriately, and add ones for the intercept term\n", - "m, n = X.shape\n", - "\n", - "# Add intercept term to X\n", - "X = np.concatenate([np.ones((m, 1)), X], axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, complete the code for the function `costFunction` to return the cost and gradient. Recall that the cost function in logistic regression is\n", - "\n", - "$$ J(\\theta) = \\frac{1}{m} \\sum_{i=1}^{m} \\left[ -y^{(i)} \\log\\left(h_\\theta\\left( x^{(i)} \\right) \\right) - \\left( 1 - y^{(i)}\\right) \\log \\left( 1 - h_\\theta\\left( x^{(i)} \\right) \\right) \\right]$$\n", - "\n", - "and the gradient of the cost is a vector of the same length as $\\theta$ where the $j^{th}$\n", - "element (for $j = 0, 1, \\cdots , n$) is defined as follows:\n", - "\n", - "$$ \\frac{\\partial J(\\theta)}{\\partial \\theta_j} = \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta \\left( x^{(i)} \\right) - y^{(i)} \\right) x_j^{(i)} $$\n", - "\n", - "Note that while this gradient looks identical to the linear regression gradient, the formula is actually different because linear and logistic regression have different definitions of $h_\\theta(x)$.\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def costFunction(theta, X, y):\n", - " \"\"\"\n", - " Compute cost and gradient for logistic regression. \n", - " \n", - " Parameters\n", - " ----------\n", - " theta : array_like\n", - " The parameters for logistic regression. This a vector\n", - " of shape (n+1, ).\n", - " \n", - " X : array_like\n", - " The input dataset of shape (m x n+1) where m is the total number\n", - " of data points and n is the number of features. We assume the \n", - " intercept has already been added to the input.\n", - " \n", - " y : arra_like\n", - " Labels for the input. This is a vector of shape (m, ).\n", - " \n", - " Returns\n", - " -------\n", - " J : float\n", - " The computed value for the cost function. \n", - " \n", - " grad : array_like\n", - " A vector of shape (n+1, ) which is the gradient of the cost\n", - " function with respect to theta, at the current values of theta.\n", - " \n", - " Instructions\n", - " ------------\n", - " Compute the cost of a particular choice of theta. You should set J to \n", - " the cost. Compute the partial derivatives and set grad to the partial\n", - " derivatives of the cost w.r.t. each parameter in theta.\n", - " \"\"\"\n", - " # Initialize some useful values\n", - " m = y.size # number of training examples\n", - "\n", - " # You need to return the following variables correctly \n", - " J = 0\n", - " grad = np.zeros(theta.shape)\n", - "\n", - " # ====================== YOUR CODE HERE ======================\n", - "\n", - " \n", - " \n", - " # =============================================================\n", - " return J, grad" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once you are done call your `costFunction` using two test cases for $\\theta$ by executing the next cell." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize fitting parameters\n", - "initial_theta = np.zeros(n+1)\n", - "\n", - "cost, grad = costFunction(initial_theta, X, y)\n", - "\n", - "print('Cost at initial theta (zeros): {:.3f}'.format(cost))\n", - "print('Expected cost (approx): 0.693\\n')\n", - "\n", - "print('Gradient at initial theta (zeros):')\n", - "print('\\t[{:.4f}, {:.4f}, {:.4f}]'.format(*grad))\n", - "print('Expected gradients (approx):\\n\\t[-0.1000, -12.0092, -11.2628]\\n')\n", - "\n", - "# Compute and display cost and gradient with non-zero theta\n", - "test_theta = np.array([-24, 0.2, 0.2])\n", - "cost, grad = costFunction(test_theta, X, y)\n", - "\n", - "print('Cost at test theta: {:.3f}'.format(cost))\n", - "print('Expected cost (approx): 0.218\\n')\n", - "\n", - "print('Gradient at test theta:')\n", - "print('\\t[{:.3f}, {:.3f}, {:.3f}]'.format(*grad))\n", - "print('Expected gradients (approx):\\n\\t[0.043, 2.566, 2.647]')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*You should now submit your solutions.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "grader[2] = costFunction\n", - "grader[3] = costFunction\n", - "grader.grade()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 1.2.3 Learning parameters using `scipy.optimize`\n", - "\n", - "In the previous assignment, you found the optimal parameters of a linear regression model by implementing gradient descent. You wrote a cost function and calculated its gradient, then took a gradient descent step accordingly. This time, instead of taking gradient descent steps, you will use the [`scipy.optimize` module](https://docs.scipy.org/doc/scipy/reference/optimize.html). SciPy is a numerical computing library for `python`. It provides an optimization module for root finding and minimization. As of `scipy 1.0`, the function `scipy.optimize.minimize` is the method to use for optimization problems(both constrained and unconstrained).\n", - "\n", - "For logistic regression, you want to optimize the cost function $J(\\theta)$ with parameters $\\theta$.\n", - "Concretely, you are going to use `optimize.minimize` to find the best parameters $\\theta$ for the logistic regression cost function, given a fixed dataset (of X and y values). You will pass to `optimize.minimize` the following inputs:\n", - "- `costFunction`: A cost function that, when given the training set and a particular $\\theta$, computes the logistic regression cost and gradient with respect to $\\theta$ for the dataset (X, y). It is important to note that we only pass the name of the function without the parenthesis. This indicates that we are only providing a reference to this function, and not evaluating the result from this function.\n", - "- `initial_theta`: The initial values of the parameters we are trying to optimize.\n", - "- `(X, y)`: These are additional arguments to the cost function.\n", - "- `jac`: Indication if the cost function returns the Jacobian (gradient) along with cost value. (True)\n", - "- `method`: Optimization method/algorithm to use\n", - "- `options`: Additional options which might be specific to the specific optimization method. In the following, we only tell the algorithm the maximum number of iterations before it terminates.\n", - "\n", - "If you have completed the `costFunction` correctly, `optimize.minimize` will converge on the right optimization parameters and return the final values of the cost and $\\theta$ in a class object. Notice that by using `optimize.minimize`, you did not have to write any loops yourself, or set a learning rate like you did for gradient descent. This is all done by `optimize.minimize`: you only needed to provide a function calculating the cost and the gradient.\n", - "\n", - "In the following, we already have code written to call `optimize.minimize` with the correct arguments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# set options for optimize.minimize\n", - "options= {'maxiter': 400}\n", - "\n", - "# see documention for scipy's optimize.minimize for description about\n", - "# the different parameters\n", - "# The function returns an object `OptimizeResult`\n", - "# We use truncated Newton algorithm for optimization which is \n", - "# equivalent to MATLAB's fminunc\n", - "# See https://stackoverflow.com/questions/18801002/fminunc-alternate-in-numpy\n", - "res = optimize.minimize(costFunction,\n", - " initial_theta,\n", - " (X, y),\n", - " jac=True,\n", - " method='TNC',\n", - " options=options)\n", - "\n", - "# the fun property of `OptimizeResult` object returns\n", - "# the value of costFunction at optimized theta\n", - "cost = res.fun\n", - "\n", - "# the optimized theta is in the x property\n", - "theta = res.x\n", - "\n", - "# Print theta to screen\n", - "print('Cost at theta found by optimize.minimize: {:.3f}'.format(cost))\n", - "print('Expected cost (approx): 0.203\\n');\n", - "\n", - "print('theta:')\n", - "print('\\t[{:.3f}, {:.3f}, {:.3f}]'.format(*theta))\n", - "print('Expected theta (approx):\\n\\t[-25.161, 0.206, 0.201]')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once `optimize.minimize` completes, we want to use the final value for $\\theta$ to visualize the decision boundary on the training data as shown in the figure below. \n", - "\n", - "![](Figures/decision_boundary1.png)\n", - "\n", - "To do so, we have written a function `plotDecisionBoundary` for plotting the decision boundary on top of training data. You do not need to write any code for plotting the decision boundary, but we also encourage you to look at the code in `plotDecisionBoundary` to see how to plot such a boundary using the $\\theta$ values. You can find this function in the `utils.py` file which comes with this assignment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot Boundary\n", - "utils.plotDecisionBoundary(plotData, theta, X, y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "#### 1.2.4 Evaluating logistic regression\n", - "\n", - "After learning the parameters, you can use the model to predict whether a particular student will be admitted. For a student with an Exam 1 score of 45 and an Exam 2 score of 85, you should expect to see an admission\n", - "probability of 0.776. Another way to evaluate the quality of the parameters we have found is to see how well the learned model predicts on our training set. In this part, your task is to complete the code in function `predict`. The predict function will produce “1” or “0” predictions given a dataset and a learned parameter vector $\\theta$. \n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def predict(theta, X):\n", - " \"\"\"\n", - " Predict whether the label is 0 or 1 using learned logistic regression.\n", - " Computes the predictions for X using a threshold at 0.5 \n", - " (i.e., if sigmoid(theta.T*x) >= 0.5, predict 1)\n", - " \n", - " Parameters\n", - " ----------\n", - " theta : array_like\n", - " Parameters for logistic regression. A vecotor of shape (n+1, ).\n", - " \n", - " X : array_like\n", - " The data to use for computing predictions. The rows is the number \n", - " of points to compute predictions, and columns is the number of\n", - " features.\n", - "\n", - " Returns\n", - " -------\n", - " p : array_like\n", - " Predictions and 0 or 1 for each row in X. \n", - " \n", - " Instructions\n", - " ------------\n", - " Complete the following code to make predictions using your learned \n", - " logistic regression parameters.You should set p to a vector of 0's and 1's \n", - " \"\"\"\n", - " m = X.shape[0] # Number of training examples\n", - "\n", - " # You need to return the following variables correctly\n", - " p = np.zeros(m)\n", - "\n", - " # ====================== YOUR CODE HERE ======================\n", - "\n", - " \n", - " \n", - " # ============================================================\n", - " return p" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After you have completed the code in `predict`, we proceed to report the training accuracy of your classifier by computing the percentage of examples it got correct." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Predict probability for a student with score 45 on exam 1 \n", - "# and score 85 on exam 2 \n", - "prob = sigmoid(np.dot([1, 45, 85], theta))\n", - "print('For a student with scores 45 and 85,'\n", - " 'we predict an admission probability of {:.3f}'.format(prob))\n", - "print('Expected value: 0.775 +/- 0.002\\n')\n", - "\n", - "# Compute accuracy on our training set\n", - "p = predict(theta, X)\n", - "print('Train Accuracy: {:.2f} %'.format(np.mean(p == y) * 100))\n", - "print('Expected accuracy (approx): 89.00 %')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*You should now submit your solutions.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "grader[4] = predict\n", - "grader.grade()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2 Regularized logistic regression\n", - "\n", - "In this part of the exercise, you will implement regularized logistic regression to predict whether microchips from a fabrication plant passes quality assurance (QA). During QA, each microchip goes through various tests to ensure it is functioning correctly.\n", - "Suppose you are the product manager of the factory and you have the test results for some microchips on two different tests. From these two tests, you would like to determine whether the microchips should be accepted or rejected. To help you make the decision, you have a dataset of test results on past microchips, from which you can build a logistic regression model.\n", - "\n", - "First, we load the data from a CSV file:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Load Data\n", - "# The first two columns contains the X values and the third column\n", - "# contains the label (y).\n", - "data = np.loadtxt(os.path.join('Data', 'ex2data2.txt'), delimiter=',')\n", - "X = data[:, :2]\n", - "y = data[:, 2]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.1 Visualize the data\n", - "\n", - "Similar to the previous parts of this exercise, `plotData` is used to generate a figure, where the axes are the two test scores, and the positive (y = 1, accepted) and negative (y = 0, rejected) examples are shown with\n", - "different markers." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plotData(X, y)\n", - "# Labels and Legend\n", - "pyplot.xlabel('Microchip Test 1')\n", - "pyplot.ylabel('Microchip Test 2')\n", - "\n", - "# Specified in plot order\n", - "pyplot.legend(['y = 1', 'y = 0'], loc='upper right')\n", - "pass" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above figure shows that our dataset cannot be separated into positive and negative examples by a straight-line through the plot. Therefore, a straight-forward application of logistic regression will not perform well on this dataset since logistic regression will only be able to find a linear decision boundary.\n", - "\n", - "### 2.2 Feature mapping\n", - "\n", - "One way to fit the data better is to create more features from each data point. In the function `mapFeature` defined in the file `utils.py`, we will map the features into all polynomial terms of $x_1$ and $x_2$ up to the sixth power.\n", - "\n", - "$$ \\text{mapFeature}(x) = \\begin{bmatrix} 1 & x_1 & x_2 & x_1^2 & x_1 x_2 & x_2^2 & x_1^3 & \\dots & x_1 x_2^5 & x_2^6 \\end{bmatrix}^T $$\n", - "\n", - "As a result of this mapping, our vector of two features (the scores on two QA tests) has been transformed into a 28-dimensional vector. A logistic regression classifier trained on this higher-dimension feature vector will have a more complex decision boundary and will appear nonlinear when drawn in our 2-dimensional plot.\n", - "While the feature mapping allows us to build a more expressive classifier, it also more susceptible to overfitting. In the next parts of the exercise, you will implement regularized logistic regression to fit the data and also see for yourself how regularization can help combat the overfitting problem.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Note that mapFeature also adds a column of ones for us, so the intercept\n", - "# term is handled\n", - "X = utils.mapFeature(X[:, 0], X[:, 1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### 2.3 Cost function and gradient\n", - "\n", - "Now you will implement code to compute the cost function and gradient for regularized logistic regression. Complete the code for the function `costFunctionReg` below to return the cost and gradient.\n", - "\n", - "Recall that the regularized cost function in logistic regression is\n", - "\n", - "$$ J(\\theta) = \\frac{1}{m} \\sum_{i=1}^m \\left[ -y^{(i)}\\log \\left( h_\\theta \\left(x^{(i)} \\right) \\right) - \\left( 1 - y^{(i)} \\right) \\log \\left( 1 - h_\\theta \\left( x^{(i)} \\right) \\right) \\right] + \\frac{\\lambda}{2m} \\sum_{j=1}^n \\theta_j^2 $$\n", - "\n", - "Note that you should not regularize the parameters $\\theta_0$. The gradient of the cost function is a vector where the $j^{th}$ element is defined as follows:\n", - "\n", - "$$ \\frac{\\partial J(\\theta)}{\\partial \\theta_0} = \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta \\left(x^{(i)}\\right) - y^{(i)} \\right) x_j^{(i)} \\qquad \\text{for } j =0 $$\n", - "\n", - "$$ \\frac{\\partial J(\\theta)}{\\partial \\theta_j} = \\left( \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta \\left(x^{(i)}\\right) - y^{(i)} \\right) x_j^{(i)} \\right) + \\frac{\\lambda}{m}\\theta_j \\qquad \\text{for } j \\ge 1 $$\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def costFunctionReg(theta, X, y, lambda_):\n", - " \"\"\"\n", - " Compute cost and gradient for logistic regression with regularization.\n", - " \n", - " Parameters\n", - " ----------\n", - " theta : array_like\n", - " Logistic regression parameters. A vector with shape (n, ). n is \n", - " the number of features including any intercept. If we have mapped\n", - " our initial features into polynomial features, then n is the total \n", - " number of polynomial features. \n", - " \n", - " X : array_like\n", - " The data set with shape (m x n). m is the number of examples, and\n", - " n is the number of features (after feature mapping).\n", - " \n", - " y : array_like\n", - " The data labels. A vector with shape (m, ).\n", - " \n", - " lambda_ : float\n", - " The regularization parameter. \n", - " \n", - " Returns\n", - " -------\n", - " J : float\n", - " The computed value for the regularized cost function. \n", - " \n", - " grad : array_like\n", - " A vector of shape (n, ) which is the gradient of the cost\n", - " function with respect to theta, at the current values of theta.\n", - " \n", - " Instructions\n", - " ------------\n", - " Compute the cost `J` of a particular choice of theta.\n", - " Compute the partial derivatives and set `grad` to the partial\n", - " derivatives of the cost w.r.t. each parameter in theta.\n", - " \"\"\"\n", - " # Initialize some useful values\n", - " m = y.size # number of training examples\n", - "\n", - " # You need to return the following variables correctly \n", - " J = 0\n", - " grad = np.zeros(theta.shape)\n", - "\n", - " # ===================== YOUR CODE HERE ======================\n", - "\n", - " \n", - " \n", - " # =============================================================\n", - " return J, grad" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once you are done with the `costFunctionReg`, we call it below using the initial value of $\\theta$ (initialized to all zeros), and also another test case where $\\theta$ is all ones." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize fitting parameters\n", - "initial_theta = np.zeros(X.shape[1])\n", - "\n", - "# Set regularization parameter lambda to 1\n", - "# DO NOT use `lambda` as a variable name in python\n", - "# because it is a python keyword\n", - "lambda_ = 1\n", - "\n", - "# Compute and display initial cost and gradient for regularized logistic\n", - "# regression\n", - "cost, grad = costFunctionReg(initial_theta, X, y, lambda_)\n", - "\n", - "print('Cost at initial theta (zeros): {:.3f}'.format(cost))\n", - "print('Expected cost (approx) : 0.693\\n')\n", - "\n", - "print('Gradient at initial theta (zeros) - first five values only:')\n", - "print('\\t[{:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}]'.format(*grad[:5]))\n", - "print('Expected gradients (approx) - first five values only:')\n", - "print('\\t[0.0085, 0.0188, 0.0001, 0.0503, 0.0115]\\n')\n", - "\n", - "\n", - "# Compute and display cost and gradient\n", - "# with all-ones theta and lambda = 10\n", - "test_theta = np.ones(X.shape[1])\n", - "cost, grad = costFunctionReg(test_theta, X, y, 10)\n", - "\n", - "print('------------------------------\\n')\n", - "print('Cost at test theta : {:.2f}'.format(cost))\n", - "print('Expected cost (approx): 3.16\\n')\n", - "\n", - "print('Gradient at initial theta (zeros) - first five values only:')\n", - "print('\\t[{:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}]'.format(*grad[:5]))\n", - "print('Expected gradients (approx) - first five values only:')\n", - "print('\\t[0.3460, 0.1614, 0.1948, 0.2269, 0.0922]')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*You should now submit your solutions.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "grader[5] = costFunctionReg\n", - "grader[6] = costFunctionReg\n", - "grader.grade()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.3.1 Learning parameters using `scipy.optimize.minimize`\n", - "\n", - "Similar to the previous parts, you will use `optimize.minimize` to learn the optimal parameters $\\theta$. If you have completed the cost and gradient for regularized logistic regression (`costFunctionReg`) correctly, you should be able to step through the next part of to learn the parameters $\\theta$ using `optimize.minimize`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.4 Plotting the decision boundary\n", - "\n", - "To help you visualize the model learned by this classifier, we have provided the function `plotDecisionBoundary` which plots the (non-linear) decision boundary that separates the positive and negative examples. In `plotDecisionBoundary`, we plot the non-linear decision boundary by computing the classifier’s predictions on an evenly spaced grid and then and draw a contour plot where the predictions change from y = 0 to y = 1. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.5 Optional (ungraded) exercises\n", - "\n", - "In this part of the exercise, you will get to try out different regularization parameters for the dataset to understand how regularization prevents overfitting.\n", - "\n", - "Notice the changes in the decision boundary as you vary $\\lambda$. With a small\n", - "$\\lambda$, you should find that the classifier gets almost every training example correct, but draws a very complicated boundary, thus overfitting the data. See the following figures for the decision boundaries you should get for different values of $\\lambda$. \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " No regularization (overfitting)\n", - " \n", - " Decision boundary with regularization\n", - " \n", - " \n", - " Decision boundary with too much regularization\n", - " \n", - "
\n", - "\n", - "This is not a good decision boundary: for example, it predicts that a point at $x = (−0.25, 1.5)$ is accepted $(y = 1)$, which seems to be an incorrect decision given the training set.\n", - "With a larger $\\lambda$, you should see a plot that shows an simpler decision boundary which still separates the positives and negatives fairly well. However, if $\\lambda$ is set to too high a value, you will not get a good fit and the decision boundary will not follow the data so well, thus underfitting the data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize fitting parameters\n", - "initial_theta = np.zeros(X.shape[1])\n", - "\n", - "# Set regularization parameter lambda to 1 (you should vary this)\n", - "lambda_ = 1\n", - "\n", - "# set options for optimize.minimize\n", - "options= {'maxiter': 100}\n", - "\n", - "res = optimize.minimize(costFunctionReg,\n", - " initial_theta,\n", - " (X, y, lambda_),\n", - " jac=True,\n", - " method='TNC',\n", - " options=options)\n", - "\n", - "# the fun property of OptimizeResult object returns\n", - "# the value of costFunction at optimized theta\n", - "cost = res.fun\n", - "\n", - "# the optimized theta is in the x property of the result\n", - "theta = res.x\n", - "\n", - "utils.plotDecisionBoundary(plotData, theta, X, y)\n", - "pyplot.xlabel('Microchip Test 1')\n", - "pyplot.ylabel('Microchip Test 2')\n", - "pyplot.legend(['y = 1', 'y = 0'])\n", - "pyplot.grid(False)\n", - "pyplot.title('lambda = %0.2f' % lambda_)\n", - "\n", - "# Compute accuracy on our training set\n", - "p = predict(theta, X)\n", - "\n", - "print('Train Accuracy: %.1f %%' % (np.mean(p == y) * 100))\n", - "print('Expected accuracy (with lambda = 1): 83.1 % (approx)\\n')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*You do not need to submit any solutions for these optional (ungraded) exercises.*" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Exercise2/utils.py b/Exercise2/utils.py deleted file mode 100755 index 7c52dbe4..00000000 --- a/Exercise2/utils.py +++ /dev/null @@ -1,147 +0,0 @@ -import sys -import numpy as np -from matplotlib import pyplot - -sys.path.append('..') -from submission import SubmissionBase - - -def mapFeature(X1, X2, degree=6): - """ - Maps the two input features to quadratic features used in the regularization exercise. - - Returns a new feature array with more features, comprising of - X1, X2, X1.^2, X2.^2, X1*X2, X1*X2.^2, etc.. - - Parameters - ---------- - X1 : array_like - A vector of shape (m, 1), containing one feature for all examples. - - X2 : array_like - A vector of shape (m, 1), containing a second feature for all examples. - Inputs X1, X2 must be the same size. - - degree: int, optional - The polynomial degree. - - Returns - ------- - : array_like - A matrix of of m rows, and columns depend on the degree of polynomial. - """ - if X1.ndim > 0: - out = [np.ones(X1.shape[0])] - else: - out = [np.ones(1)] - - for i in range(1, degree + 1): - for j in range(i + 1): - out.append((X1 ** (i - j)) * (X2 ** j)) - - if X1.ndim > 0: - return np.stack(out, axis=1) - else: - return np.array(out) - - -def plotDecisionBoundary(plotData, theta, X, y): - """ - Plots the data points X and y into a new figure with the decision boundary defined by theta. - Plots the data points with * for the positive examples and o for the negative examples. - - Parameters - ---------- - plotData : func - A function reference for plotting the X, y data. - - theta : array_like - Parameters for logistic regression. A vector of shape (n+1, ). - - X : array_like - The input dataset. X is assumed to be a either: - 1) Mx3 matrix, where the first column is an all ones column for the intercept. - 2) MxN, N>3 matrix, where the first column is all ones. - - y : array_like - Vector of data labels of shape (m, ). - """ - # make sure theta is a numpy array - theta = np.array(theta) - - # Plot Data (remember first column in X is the intercept) - plotData(X[:, 1:3], y) - - if X.shape[1] <= 3: - # Only need 2 points to define a line, so choose two endpoints - plot_x = np.array([np.min(X[:, 1]) - 2, np.max(X[:, 1]) + 2]) - - # Calculate the decision boundary line - plot_y = (-1. / theta[2]) * (theta[1] * plot_x + theta[0]) - - # Plot, and adjust axes for better viewing - pyplot.plot(plot_x, plot_y) - - # Legend, specific for the exercise - pyplot.legend(['Admitted', 'Not admitted', 'Decision Boundary']) - pyplot.xlim([30, 100]) - pyplot.ylim([30, 100]) - else: - # Here is the grid range - u = np.linspace(-1, 1.5, 50) - v = np.linspace(-1, 1.5, 50) - - z = np.zeros((u.size, v.size)) - # Evaluate z = theta*x over the grid - for i, ui in enumerate(u): - for j, vj in enumerate(v): - z[i, j] = np.dot(mapFeature(ui, vj), theta) - - z = z.T # important to transpose z before calling contour - # print(z) - - # Plot z = 0 - pyplot.contour(u, v, z, levels=[0], linewidths=2, colors='g') - pyplot.contourf(u, v, z, levels=[np.min(z), 0, np.max(z)], cmap='Greens', alpha=0.4) - - -class Grader(SubmissionBase): - X = np.stack([np.ones(20), - np.exp(1) * np.sin(np.arange(1, 21)), - np.exp(0.5) * np.cos(np.arange(1, 21))], axis=1) - - y = (np.sin(X[:, 0] + X[:, 1]) > 0).astype(float) - - def __init__(self): - part_names = ['Sigmoid Function', - 'Logistic Regression Cost', - 'Logistic Regression Gradient', - 'Predict', - 'Regularized Logistic Regression Cost', - 'Regularized Logistic Regression Gradient'] - super().__init__('logistic-regression', part_names) - - def __iter__(self): - for part_id in range(1, 7): - try: - func = self.functions[part_id] - - # Each part has different expected arguments/different function - if part_id == 1: - res = func(self.X) - elif part_id == 2: - res = func(np.array([0.25, 0.5, -0.5]), self.X, self.y) - elif part_id == 3: - J, grad = func(np.array([0.25, 0.5, -0.5]), self.X, self.y) - res = grad - elif part_id == 4: - res = func(np.array([0.25, 0.5, -0.5]), self.X) - elif part_id == 5: - res = func(np.array([0.25, 0.5, -0.5]), self.X, self.y, 0.1) - elif part_id == 6: - res = func(np.array([0.25, 0.5, -0.5]), self.X, self.y, 0.1)[1] - else: - raise KeyError - yield part_id, res - except KeyError: - yield part_id, 0 diff --git a/Exercise3/Figures/neuralnetwork.png b/Exercise3/Figures/neuralnetwork.png deleted file mode 100755 index 140fdb01..00000000 Binary files a/Exercise3/Figures/neuralnetwork.png and /dev/null differ diff --git a/Exercise3/exercise3.ipynb b/Exercise3/exercise3.ipynb deleted file mode 100755 index e37be91f..00000000 --- a/Exercise3/exercise3.ipynb +++ /dev/null @@ -1,923 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Programming Exercise 3\n", - "# Multi-class Classification and Neural Networks\n", - "\n", - "## Introduction\n", - "\n", - "\n", - "In this exercise, you will implement one-vs-all logistic regression and neural networks to recognize handwritten digits. Before starting the programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics. \n", - "\n", - "All the information you need for solving this assignment is in this notebook, and all the code you will be implementing will take place within this notebook. The assignment can be promptly submitted to the coursera grader directly from this notebook (code and instructions are included below).\n", - "\n", - "Before we begin with the exercises, we need to import all libraries required for this programming exercise. Throughout the course, we will be using [`numpy`](http://www.numpy.org/) for all arrays and matrix operations, [`matplotlib`](https://matplotlib.org/) for plotting, and [`scipy`](https://docs.scipy.org/doc/scipy/reference/) for scientific and numerical computation functions and tools. You can find instructions on how to install required libraries in the README file in the [github repository](https://github.com/dibgerge/ml-coursera-python-assignments)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# used for manipulating directory paths\n", - "import os\n", - "\n", - "# Scientific and vector computation for python\n", - "import numpy as np\n", - "\n", - "# Plotting library\n", - "from matplotlib import pyplot\n", - "\n", - "# Optimization module in scipy\n", - "from scipy import optimize\n", - "\n", - "# will be used to load MATLAB mat datafile format\n", - "from scipy.io import loadmat\n", - "\n", - "# library written for this exercise providing additional functions for assignment submission, and others\n", - "import utils\n", - "\n", - "# define the submission/grader object for this exercise\n", - "grader = utils.Grader()\n", - "\n", - "# tells matplotlib to embed plots within the notebook\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Submission and Grading\n", - "\n", - "\n", - "After completing each part of the assignment, be sure to submit your solutions to the grader. The following is a breakdown of how each part of this exercise is scored.\n", - "\n", - "\n", - "| Section | Part | Submission function | Points \n", - "| :- |:- | :- | :-: \n", - "| 1 | [Regularized Logistic Regression](#section1) | [`lrCostFunction`](#lrCostFunction) | 30 \n", - "| 2 | [One-vs-all classifier training](#section2) | [`oneVsAll`](#oneVsAll) | 20 \n", - "| 3 | [One-vs-all classifier prediction](#section3) | [`predictOneVsAll`](#predictOneVsAll) | 20 \n", - "| 4 | [Neural Network Prediction Function](#section4) | [`predict`](#predict) | 30\n", - "| | Total Points | | 100 \n", - "\n", - "\n", - "You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n", - "\n", - "
\n", - "At the end of each section in this notebook, we have a cell which contains code for submitting the solutions thus far to the grader. Execute the cell to see your score up to the current section. For all your work to be submitted properly, you must execute those cells at least once. They must also be re-executed everytime the submitted function is updated.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1 Multi-class Classification\n", - "\n", - "For this exercise, you will use logistic regression and neural networks to recognize handwritten digits (from 0 to 9). Automated handwritten digit recognition is widely used today - from recognizing zip codes (postal codes)\n", - "on mail envelopes to recognizing amounts written on bank checks. This exercise will show you how the methods you have learned can be used for this classification task.\n", - "\n", - "In the first part of the exercise, you will extend your previous implementation of logistic regression and apply it to one-vs-all classification.\n", - "\n", - "### 1.1 Dataset\n", - "\n", - "You are given a data set in `ex3data1.mat` that contains 5000 training examples of handwritten digits (This is a subset of the [MNIST](http://yann.lecun.com/exdb/mnist) handwritten digit dataset). The `.mat` format means that that the data has been saved in a native Octave/MATLAB matrix format, instead of a text (ASCII) format like a csv-file. We use the `.mat` format here because this is the dataset provided in the MATLAB version of this assignment. Fortunately, python provides mechanisms to load MATLAB native format using the `loadmat` function within the `scipy.io` module. This function returns a python dictionary with keys containing the variable names within the `.mat` file. \n", - "\n", - "There are 5000 training examples in `ex3data1.mat`, where each training example is a 20 pixel by 20 pixel grayscale image of the digit. Each pixel is represented by a floating point number indicating the grayscale intensity at that location. The 20 by 20 grid of pixels is “unrolled” into a 400-dimensional vector. Each of these training examples becomes a single row in our data matrix `X`. This gives us a 5000 by 400 matrix `X` where every row is a training example for a handwritten digit image.\n", - "\n", - "$$ X = \\begin{bmatrix} - \\: (x^{(1)})^T \\: - \\\\ -\\: (x^{(2)})^T \\:- \\\\ \\vdots \\\\ - \\: (x^{(m)})^T \\:- \\end{bmatrix} $$\n", - "\n", - "The second part of the training set is a 5000-dimensional vector `y` that contains labels for the training set. \n", - "We start the exercise by first loading the dataset. Execute the cell below, you do not need to write any code here." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# 20x20 Input Images of Digits\n", - "input_layer_size = 400\n", - "\n", - "# 10 labels, from 1 to 10 (note that we have mapped \"0\" to label 10)\n", - "num_labels = 10\n", - "\n", - "# training data stored in arrays X, y\n", - "data = loadmat(os.path.join('Data', 'ex3data1.mat'))\n", - "X, y = data['X'], data['y'].ravel()\n", - "\n", - "# set the zero digit to 0, rather than its mapped 10 in this dataset\n", - "# This is an artifact due to the fact that this dataset was used in \n", - "# MATLAB where there is no index 0\n", - "y[y == 10] = 0\n", - "\n", - "m = y.size" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.2 Visualizing the data\n", - "\n", - "You will begin by visualizing a subset of the training set. In the following cell, the code randomly selects selects 100 rows from `X` and passes those rows to the `displayData` function. This function maps each row to a 20 pixel by 20 pixel grayscale image and displays the images together. We have provided the `displayData` function in the file `utils.py`. You are encouraged to examine the code to see how it works. Run the following cell to visualize the data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Randomly select 100 data points to display\n", - "rand_indices = np.random.choice(m, 100, replace=False)\n", - "sel = X[rand_indices, :]\n", - "\n", - "utils.displayData(sel)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "### 1.3 Vectorizing Logistic Regression\n", - "\n", - "You will be using multiple one-vs-all logistic regression models to build a multi-class classifier. Since there are 10 classes, you will need to train 10 separate logistic regression classifiers. To make this training efficient, it is important to ensure that your code is well vectorized. In this section, you will implement a vectorized version of logistic regression that does not employ any `for` loops. You can use your code in the previous exercise as a starting point for this exercise. \n", - "\n", - "To test your vectorized logistic regression, we will use custom data as defined in the following cell." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# test values for the parameters theta\n", - "theta_t = np.array([-2, -1, 1, 2], dtype=float)\n", - "\n", - "# test values for the inputs\n", - "X_t = np.concatenate([np.ones((5, 1)), np.arange(1, 16).reshape(5, 3, order='F')/10.0], axis=1)\n", - "\n", - "# test values for the labels\n", - "y_t = np.array([1, 0, 1, 0, 1])\n", - "\n", - "# test value for the regularization parameter\n", - "lambda_t = 3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "#### 1.3.1 Vectorizing the cost function \n", - "\n", - "We will begin by writing a vectorized version of the cost function. Recall that in (unregularized) logistic regression, the cost function is\n", - "\n", - "$$ J(\\theta) = \\frac{1}{m} \\sum_{i=1}^m \\left[ -y^{(i)} \\log \\left( h_\\theta\\left( x^{(i)} \\right) \\right) - \\left(1 - y^{(i)} \\right) \\log \\left(1 - h_\\theta \\left( x^{(i)} \\right) \\right) \\right] $$\n", - "\n", - "To compute each element in the summation, we have to compute $h_\\theta(x^{(i)})$ for every example $i$, where $h_\\theta(x^{(i)}) = g(\\theta^T x^{(i)})$ and $g(z) = \\frac{1}{1+e^{-z}}$ is the sigmoid function. It turns out that we can compute this quickly for all our examples by using matrix multiplication. Let us define $X$ and $\\theta$ as\n", - "\n", - "$$ X = \\begin{bmatrix} - \\left( x^{(1)} \\right)^T - \\\\ - \\left( x^{(2)} \\right)^T - \\\\ \\vdots \\\\ - \\left( x^{(m)} \\right)^T - \\end{bmatrix} \\qquad \\text{and} \\qquad \\theta = \\begin{bmatrix} \\theta_0 \\\\ \\theta_1 \\\\ \\vdots \\\\ \\theta_n \\end{bmatrix} $$\n", - "\n", - "Then, by computing the matrix product $X\\theta$, we have: \n", - "\n", - "$$ X\\theta = \\begin{bmatrix} - \\left( x^{(1)} \\right)^T\\theta - \\\\ - \\left( x^{(2)} \\right)^T\\theta - \\\\ \\vdots \\\\ - \\left( x^{(m)} \\right)^T\\theta - \\end{bmatrix} = \\begin{bmatrix} - \\theta^T x^{(1)} - \\\\ - \\theta^T x^{(2)} - \\\\ \\vdots \\\\ - \\theta^T x^{(m)} - \\end{bmatrix} $$\n", - "\n", - "In the last equality, we used the fact that $a^Tb = b^Ta$ if $a$ and $b$ are vectors. This allows us to compute the products $\\theta^T x^{(i)}$ for all our examples $i$ in one line of code.\n", - "\n", - "#### 1.3.2 Vectorizing the gradient\n", - "\n", - "Recall that the gradient of the (unregularized) logistic regression cost is a vector where the $j^{th}$ element is defined as\n", - "\n", - "$$ \\frac{\\partial J }{\\partial \\theta_j} = \\frac{1}{m} \\sum_{i=1}^m \\left( \\left( h_\\theta\\left(x^{(i)}\\right) - y^{(i)} \\right)x_j^{(i)} \\right) $$\n", - "\n", - "To vectorize this operation over the dataset, we start by writing out all the partial derivatives explicitly for all $\\theta_j$,\n", - "\n", - "$$\n", - "\\begin{align*}\n", - "\\begin{bmatrix} \n", - "\\frac{\\partial J}{\\partial \\theta_0} \\\\\n", - "\\frac{\\partial J}{\\partial \\theta_1} \\\\\n", - "\\frac{\\partial J}{\\partial \\theta_2} \\\\\n", - "\\vdots \\\\\n", - "\\frac{\\partial J}{\\partial \\theta_n}\n", - "\\end{bmatrix} = &\n", - "\\frac{1}{m} \\begin{bmatrix}\n", - "\\sum_{i=1}^m \\left( \\left(h_\\theta\\left(x^{(i)}\\right) - y^{(i)} \\right)x_0^{(i)}\\right) \\\\\n", - "\\sum_{i=1}^m \\left( \\left(h_\\theta\\left(x^{(i)}\\right) - y^{(i)} \\right)x_1^{(i)}\\right) \\\\\n", - "\\sum_{i=1}^m \\left( \\left(h_\\theta\\left(x^{(i)}\\right) - y^{(i)} \\right)x_2^{(i)}\\right) \\\\\n", - "\\vdots \\\\\n", - "\\sum_{i=1}^m \\left( \\left(h_\\theta\\left(x^{(i)}\\right) - y^{(i)} \\right)x_n^{(i)}\\right) \\\\\n", - "\\end{bmatrix} \\\\\n", - "= & \\frac{1}{m} \\sum_{i=1}^m \\left( \\left(h_\\theta\\left(x^{(i)}\\right) - y^{(i)} \\right)x^{(i)}\\right) \\\\\n", - "= & \\frac{1}{m} X^T \\left( h_\\theta(x) - y\\right)\n", - "\\end{align*}\n", - "$$\n", - "\n", - "where\n", - "\n", - "$$ h_\\theta(x) - y = \n", - "\\begin{bmatrix}\n", - "h_\\theta\\left(x^{(1)}\\right) - y^{(1)} \\\\\n", - "h_\\theta\\left(x^{(2)}\\right) - y^{(2)} \\\\\n", - "\\vdots \\\\\n", - "h_\\theta\\left(x^{(m)}\\right) - y^{(m)} \n", - "\\end{bmatrix} $$\n", - "\n", - "Note that $x^{(i)}$ is a vector, while $h_\\theta\\left(x^{(i)}\\right) - y^{(i)}$ is a scalar (single number).\n", - "To understand the last step of the derivation, let $\\beta_i = (h_\\theta\\left(x^{(m)}\\right) - y^{(m)})$ and\n", - "observe that:\n", - "\n", - "$$ \\sum_i \\beta_ix^{(i)} = \\begin{bmatrix} \n", - "| & | & & | \\\\\n", - "x^{(1)} & x^{(2)} & \\cdots & x^{(m)} \\\\\n", - "| & | & & | \n", - "\\end{bmatrix}\n", - "\\begin{bmatrix}\n", - "\\beta_1 \\\\\n", - "\\beta_2 \\\\\n", - "\\vdots \\\\\n", - "\\beta_m\n", - "\\end{bmatrix} = x^T \\beta\n", - "$$\n", - "\n", - "where the values $\\beta_i = \\left( h_\\theta(x^{(i)} - y^{(i)} \\right)$.\n", - "\n", - "The expression above allows us to compute all the partial derivatives\n", - "without any loops. If you are comfortable with linear algebra, we encourage you to work through the matrix multiplications above to convince yourself that the vectorized version does the same computations. \n", - "\n", - "Your job is to write the unregularized cost function `lrCostFunction` which returns both the cost function $J(\\theta)$ and its gradient $\\frac{\\partial J}{\\partial \\theta}$. Your implementation should use the strategy we presented above to calculate $\\theta^T x^{(i)}$. You should also use a vectorized approach for the rest of the cost function. A fully vectorized version of `lrCostFunction` should not contain any loops.\n", - "\n", - "
\n", - "**Debugging Tip:** Vectorizing code can sometimes be tricky. One common strategy for debugging is to print out the sizes of the matrices you are working with using the `shape` property of `numpy` arrays. For example, given a data matrix $X$ of size $100 \\times 20$ (100 examples, 20 features) and $\\theta$, a vector with size $20$, you can observe that `np.dot(X, theta)` is a valid multiplication operation, while `np.dot(theta, X)` is not. Furthermore, if you have a non-vectorized version of your code, you can compare the output of your vectorized code and non-vectorized code to make sure that they produce the same outputs.\n", - "
\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def lrCostFunction(theta, X, y, lambda_):\n", - " \"\"\"\n", - " Computes the cost of using theta as the parameter for regularized\n", - " logistic regression and the gradient of the cost w.r.t. to the parameters.\n", - " \n", - " Parameters\n", - " ----------\n", - " theta : array_like\n", - " Logistic regression parameters. A vector with shape (n, ). n is \n", - " the number of features including any intercept. \n", - " \n", - " X : array_like\n", - " The data set with shape (m x n). m is the number of examples, and\n", - " n is the number of features (including intercept).\n", - " \n", - " y : array_like\n", - " The data labels. A vector with shape (m, ).\n", - " \n", - " lambda_ : float\n", - " The regularization parameter. \n", - " \n", - " Returns\n", - " -------\n", - " J : float\n", - " The computed value for the regularized cost function. \n", - " \n", - " grad : array_like\n", - " A vector of shape (n, ) which is the gradient of the cost\n", - " function with respect to theta, at the current values of theta.\n", - " \n", - " Instructions\n", - " ------------\n", - " Compute the cost of a particular choice of theta. You should set J to the cost.\n", - " Compute the partial derivatives and set grad to the partial\n", - " derivatives of the cost w.r.t. each parameter in theta\n", - " \n", - " Hint 1\n", - " ------\n", - " The computation of the cost function and gradients can be efficiently\n", - " vectorized. For example, consider the computation\n", - " \n", - " sigmoid(X * theta)\n", - " \n", - " Each row of the resulting matrix will contain the value of the prediction\n", - " for that example. You can make use of this to vectorize the cost function\n", - " and gradient computations. \n", - " \n", - " Hint 2\n", - " ------\n", - " When computing the gradient of the regularized cost function, there are\n", - " many possible vectorized solutions, but one solution looks like:\n", - " \n", - " grad = (unregularized gradient for logistic regression)\n", - " temp = theta \n", - " temp[0] = 0 # because we don't add anything for j = 0\n", - " grad = grad + YOUR_CODE_HERE (using the temp variable)\n", - " \n", - " Hint 3\n", - " ------\n", - " We have provided the implementatation of the sigmoid function within \n", - " the file `utils.py`. At the start of the notebook, we imported this file\n", - " as a module. Thus to access the sigmoid function within that file, you can\n", - " do the following: `utils.sigmoid(z)`.\n", - " \n", - " \"\"\"\n", - " #Initialize some useful values\n", - " m = y.size\n", - " \n", - " # convert labels to ints if their type is bool\n", - " if y.dtype == bool:\n", - " y = y.astype(int)\n", - " \n", - " # You need to return the following variables correctly\n", - " J = 0\n", - " grad = np.zeros(theta.shape)\n", - " \n", - " # ====================== YOUR CODE HERE ======================\n", - "\n", - "\n", - " \n", - " # =============================================================\n", - " return J, grad" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 1.3.3 Vectorizing regularized logistic regression\n", - "\n", - "After you have implemented vectorization for logistic regression, you will now\n", - "add regularization to the cost function. Recall that for regularized logistic\n", - "regression, the cost function is defined as\n", - "\n", - "$$ J(\\theta) = \\frac{1}{m} \\sum_{i=1}^m \\left[ -y^{(i)} \\log \\left(h_\\theta\\left(x^{(i)} \\right)\\right) - \\left( 1 - y^{(i)} \\right) \\log\\left(1 - h_\\theta \\left(x^{(i)} \\right) \\right) \\right] + \\frac{\\lambda}{2m} \\sum_{j=1}^n \\theta_j^2 $$\n", - "\n", - "Note that you should not be regularizing $\\theta_0$ which is used for the bias term.\n", - "Correspondingly, the partial derivative of regularized logistic regression cost for $\\theta_j$ is defined as\n", - "\n", - "$$\n", - "\\begin{align*}\n", - "& \\frac{\\partial J(\\theta)}{\\partial \\theta_0} = \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta\\left( x^{(i)} \\right) - y^{(i)} \\right) x_j^{(i)} & \\text{for } j = 0 \\\\\n", - "& \\frac{\\partial J(\\theta)}{\\partial \\theta_0} = \\left( \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta\\left( x^{(i)} \\right) - y^{(i)} \\right) x_j^{(i)} \\right) + \\frac{\\lambda}{m} \\theta_j & \\text{for } j \\ge 1\n", - "\\end{align*}\n", - "$$\n", - "\n", - "Now modify your code in lrCostFunction in the [**previous cell**](#lrCostFunction) to account for regularization. Once again, you should not put any loops into your code.\n", - "\n", - "
\n", - "**python/numpy Tip:** When implementing the vectorization for regularized logistic regression, you might often want to only sum and update certain elements of $\\theta$. In `numpy`, you can index into the matrices to access and update only certain elements. For example, A[:, 3:5]\n", - "= B[:, 1:3] will replaces the columns with index 3 to 5 of A with the columns with index 1 to 3 from B. To select columns (or rows) until the end of the matrix, you can leave the right hand side of the colon blank. For example, A[:, 2:] will only return elements from the $3^{rd}$ to last columns of $A$. If you leave the left hand size of the colon blank, you will select elements from the beginning of the matrix. For example, A[:, :2] selects the first two columns, and is equivalent to A[:, 0:2]. In addition, you can use negative indices to index arrays from the end. Thus, A[:, :-1] selects all columns of A except the last column, and A[:, -5:] selects the $5^{th}$ column from the end to the last column. Thus, you could use this together with the sum and power ($^{**}$) operations to compute the sum of only the elements you are interested in (e.g., `np.sum(z[1:]**2)`). In the starter code, `lrCostFunction`, we have also provided hints on yet another possible method computing the regularized gradient.\n", - "
\n", - "\n", - "Once you finished your implementation, you can call the function `lrCostFunction` to test your solution using the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "J, grad = lrCostFunction(theta_t, X_t, y_t, lambda_t)\n", - "\n", - "print('Cost : {:.6f}'.format(J))\n", - "print('Expected cost: 2.534819')\n", - "print('-----------------------')\n", - "print('Gradients:')\n", - "print(' [{:.6f}, {:.6f}, {:.6f}, {:.6f}]'.format(*grad))\n", - "print('Expected gradients:')\n", - "print(' [0.146561, -0.548558, 0.724722, 1.398003]');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After completing a part of the exercise, you can submit your solutions for grading by first adding the function you modified to the submission object, and then sending your function to Coursera for grading. \n", - "\n", - "The submission script will prompt you for your login e-mail and submission token. You can obtain a submission token from the web page for the assignment. You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n", - "\n", - "*Execute the following cell to grade your solution to the first part of this exercise.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# appends the implemented function in part 1 to the grader object\n", - "grader[1] = lrCostFunction\n", - "\n", - "# send the added functions to coursera grader for getting a grade on this part\n", - "grader.grade()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### 1.4 One-vs-all Classification\n", - "\n", - "In this part of the exercise, you will implement one-vs-all classification by training multiple regularized logistic regression classifiers, one for each of the $K$ classes in our dataset. In the handwritten digits dataset, $K = 10$, but your code should work for any value of $K$. \n", - "\n", - "You should now complete the code for the function `oneVsAll` below, to train one classifier for each class. In particular, your code should return all the classifier parameters in a matrix $\\theta \\in \\mathbb{R}^{K \\times (N +1)}$, where each row of $\\theta$ corresponds to the learned logistic regression parameters for one class. You can do this with a “for”-loop from $0$ to $K-1$, training each classifier independently.\n", - "\n", - "Note that the `y` argument to this function is a vector of labels from 0 to 9. When training the classifier for class $k \\in \\{0, ..., K-1\\}$, you will want a K-dimensional vector of labels $y$, where $y_j \\in 0, 1$ indicates whether the $j^{th}$ training instance belongs to class $k$ $(y_j = 1)$, or if it belongs to a different\n", - "class $(y_j = 0)$. You may find logical arrays helpful for this task. \n", - "\n", - "Furthermore, you will be using scipy's `optimize.minimize` for this exercise. \n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def oneVsAll(X, y, num_labels, lambda_):\n", - " \"\"\"\n", - " Trains num_labels logistic regression classifiers and returns\n", - " each of these classifiers in a matrix all_theta, where the i-th\n", - " row of all_theta corresponds to the classifier for label i.\n", - " \n", - " Parameters\n", - " ----------\n", - " X : array_like\n", - " The input dataset of shape (m x n). m is the number of \n", - " data points, and n is the number of features. Note that we \n", - " do not assume that the intercept term (or bias) is in X, however\n", - " we provide the code below to add the bias term to X. \n", - " \n", - " y : array_like\n", - " The data labels. A vector of shape (m, ).\n", - " \n", - " num_labels : int\n", - " Number of possible labels.\n", - " \n", - " lambda_ : float\n", - " The logistic regularization parameter.\n", - " \n", - " Returns\n", - " -------\n", - " all_theta : array_like\n", - " The trained parameters for logistic regression for each class.\n", - " This is a matrix of shape (K x n+1) where K is number of classes\n", - " (ie. `numlabels`) and n is number of features without the bias.\n", - " \n", - " Instructions\n", - " ------------\n", - " You should complete the following code to train `num_labels`\n", - " logistic regression classifiers with regularization parameter `lambda_`. \n", - " \n", - " Hint\n", - " ----\n", - " You can use y == c to obtain a vector of 1's and 0's that tell you\n", - " whether the ground truth is true/false for this class.\n", - " \n", - " Note\n", - " ----\n", - " For this assignment, we recommend using `scipy.optimize.minimize(method='CG')`\n", - " to optimize the cost function. It is okay to use a for-loop \n", - " (`for c in range(num_labels):`) to loop over the different classes.\n", - " \n", - " Example Code\n", - " ------------\n", - " \n", - " # Set Initial theta\n", - " initial_theta = np.zeros(n + 1)\n", - " \n", - " # Set options for minimize\n", - " options = {'maxiter': 50}\n", - " \n", - " # Run minimize to obtain the optimal theta. This function will \n", - " # return a class object where theta is in `res.x` and cost in `res.fun`\n", - " res = optimize.minimize(lrCostFunction, \n", - " initial_theta, \n", - " (X, (y == c), lambda_), \n", - " jac=True, \n", - " method='TNC',\n", - " options=options) \n", - " \"\"\"\n", - " # Some useful variables\n", - " m, n = X.shape\n", - " \n", - " # You need to return the following variables correctly \n", - " all_theta = np.zeros((num_labels, n + 1))\n", - "\n", - " # Add ones to the X data matrix\n", - " X = np.concatenate([np.ones((m, 1)), X], axis=1)\n", - "\n", - " # ====================== YOUR CODE HERE ======================\n", - " \n", - "\n", - "\n", - " # ============================================================\n", - " return all_theta" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After you have completed the code for `oneVsAll`, the following cell will use your implementation to train a multi-class classifier. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "lambda_ = 0.1\n", - "all_theta = oneVsAll(X, y, num_labels, lambda_)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*You should now submit your solutions.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "grader[2] = oneVsAll\n", - "grader.grade()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "#### 1.4.1 One-vs-all Prediction\n", - "\n", - "After training your one-vs-all classifier, you can now use it to predict the digit contained in a given image. For each input, you should compute the “probability” that it belongs to each class using the trained logistic regression classifiers. Your one-vs-all prediction function will pick the class for which the corresponding logistic regression classifier outputs the highest probability and return the class label (0, 1, ..., K-1) as the prediction for the input example. You should now complete the code in the function `predictOneVsAll` to use the one-vs-all classifier for making predictions. \n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def predictOneVsAll(all_theta, X):\n", - " \"\"\"\n", - " Return a vector of predictions for each example in the matrix X. \n", - " Note that X contains the examples in rows. all_theta is a matrix where\n", - " the i-th row is a trained logistic regression theta vector for the \n", - " i-th class. You should set p to a vector of values from 0..K-1 \n", - " (e.g., p = [0, 2, 0, 1] predicts classes 0, 2, 0, 1 for 4 examples) .\n", - " \n", - " Parameters\n", - " ----------\n", - " all_theta : array_like\n", - " The trained parameters for logistic regression for each class.\n", - " This is a matrix of shape (K x n+1) where K is number of classes\n", - " and n is number of features without the bias.\n", - " \n", - " X : array_like\n", - " Data points to predict their labels. This is a matrix of shape \n", - " (m x n) where m is number of data points to predict, and n is number \n", - " of features without the bias term. Note we add the bias term for X in \n", - " this function. \n", - " \n", - " Returns\n", - " -------\n", - " p : array_like\n", - " The predictions for each data point in X. This is a vector of shape (m, ).\n", - " \n", - " Instructions\n", - " ------------\n", - " Complete the following code to make predictions using your learned logistic\n", - " regression parameters (one-vs-all). You should set p to a vector of predictions\n", - " (from 0 to num_labels-1).\n", - " \n", - " Hint\n", - " ----\n", - " This code can be done all vectorized using the numpy argmax function.\n", - " In particular, the argmax function returns the index of the max element,\n", - " for more information see '?np.argmax' or search online. If your examples\n", - " are in rows, then, you can use np.argmax(A, axis=1) to obtain the index \n", - " of the max for each row.\n", - " \"\"\"\n", - " m = X.shape[0];\n", - " num_labels = all_theta.shape[0]\n", - "\n", - " # You need to return the following variables correctly \n", - " p = np.zeros(m)\n", - "\n", - " # Add ones to the X data matrix\n", - " X = np.concatenate([np.ones((m, 1)), X], axis=1)\n", - "\n", - " # ====================== YOUR CODE HERE ======================\n", - "\n", - "\n", - " \n", - " # ============================================================\n", - " return p" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once you are done, call your `predictOneVsAll` function using the learned value of $\\theta$. You should see that the training set accuracy is about 95.1% (i.e., it classifies 95.1% of the examples in the training set correctly)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pred = predictOneVsAll(all_theta, X)\n", - "print('Training Set Accuracy: {:.2f}%'.format(np.mean(pred == y) * 100))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*You should now submit your solutions.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "grader[3] = predictOneVsAll\n", - "grader.grade()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2 Neural Networks\n", - "\n", - "In the previous part of this exercise, you implemented multi-class logistic regression to recognize handwritten digits. However, logistic regression cannot form more complex hypotheses as it is only a linear classifier (You could add more features - such as polynomial features - to logistic regression, but that can be very expensive to train).\n", - "\n", - "In this part of the exercise, you will implement a neural network to recognize handwritten digits using the same training set as before. The neural network will be able to represent complex models that form non-linear hypotheses. For this week, you will be using parameters from a neural network that we have already trained. Your goal is to implement the feedforward propagation algorithm to use our weights for prediction. In next week’s exercise, you will write the backpropagation algorithm for learning the neural network parameters. \n", - "\n", - "We start by first reloading and visualizing the dataset which contains the MNIST handwritten digits (this is the same as we did in the first part of this exercise, we reload it here to ensure the variables have not been modified). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# training data stored in arrays X, y\n", - "data = loadmat(os.path.join('Data', 'ex3data1.mat'))\n", - "X, y = data['X'], data['y'].ravel()\n", - "\n", - "# set the zero digit to 0, rather than its mapped 10 in this dataset\n", - "# This is an artifact due to the fact that this dataset was used in \n", - "# MATLAB where there is no index 0\n", - "y[y == 10] = 0\n", - "\n", - "# get number of examples in dataset\n", - "m = y.size\n", - "\n", - "# randomly permute examples, to be used for visualizing one \n", - "# picture at a time\n", - "indices = np.random.permutation(m)\n", - "\n", - "# Randomly select 100 data points to display\n", - "rand_indices = np.random.choice(m, 100, replace=False)\n", - "sel = X[rand_indices, :]\n", - "\n", - "utils.displayData(sel)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### 2.1 Model representation \n", - "\n", - "Our neural network is shown in the following figure.\n", - "\n", - "![Neural network](Figures/neuralnetwork.png)\n", - "\n", - "It has 3 layers: an input layer, a hidden layer and an output layer. Recall that our inputs are pixel values of digit images. Since the images are of size 20×20, this gives us 400 input layer units (excluding the extra bias unit which always outputs +1). As before, the training data will be loaded into the variables X and y. \n", - "\n", - "You have been provided with a set of network parameters ($\\Theta^{(1)}$, $\\Theta^{(2)}$) already trained by us. These are stored in `ex3weights.mat`. The following cell loads those parameters into `Theta1` and `Theta2`. The parameters have dimensions that are sized for a neural network with 25 units in the second layer and 10 output units (corresponding to the 10 digit classes)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Setup the parameters you will use for this exercise\n", - "input_layer_size = 400 # 20x20 Input Images of Digits\n", - "hidden_layer_size = 25 # 25 hidden units\n", - "num_labels = 10 # 10 labels, from 0 to 9\n", - "\n", - "# Load the .mat file, which returns a dictionary \n", - "weights = loadmat(os.path.join('Data', 'ex3weights.mat'))\n", - "\n", - "# get the model weights from the dictionary\n", - "# Theta1 has size 25 x 401\n", - "# Theta2 has size 10 x 26\n", - "Theta1, Theta2 = weights['Theta1'], weights['Theta2']\n", - "\n", - "# swap first and last columns of Theta2, due to legacy from MATLAB indexing, \n", - "# since the weight file ex3weights.mat was saved based on MATLAB indexing\n", - "Theta2 = np.roll(Theta2, 1, axis=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### 2.2 Feedforward Propagation and Prediction\n", - "\n", - "Now you will implement feedforward propagation for the neural network. You will need to complete the code in the function `predict` to return the neural network’s prediction. You should implement the feedforward computation that computes $h_\\theta(x^{(i)})$ for every example $i$ and returns the associated predictions. Similar to the one-vs-all classification strategy, the prediction from the neural network will be the label that has the largest output $\\left( h_\\theta(x) \\right)_k$.\n", - "\n", - "
\n", - "**Implementation Note:** The matrix $X$ contains the examples in rows. When you complete the code in the function `predict`, you will need to add the column of 1’s to the matrix. The matrices `Theta1` and `Theta2` contain the parameters for each unit in rows. Specifically, the first row of `Theta1` corresponds to the first hidden unit in the second layer. In `numpy`, when you compute $z^{(2)} = \\theta^{(1)}a^{(1)}$, be sure that you index (and if necessary, transpose) $X$ correctly so that you get $a^{(l)}$ as a 1-D vector.\n", - "
\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def predict(Theta1, Theta2, X):\n", - " \"\"\"\n", - " Predict the label of an input given a trained neural network.\n", - " \n", - " Parameters\n", - " ----------\n", - " Theta1 : array_like\n", - " Weights for the first layer in the neural network.\n", - " It has shape (2nd hidden layer size x input size)\n", - " \n", - " Theta2: array_like\n", - " Weights for the second layer in the neural network. \n", - " It has shape (output layer size x 2nd hidden layer size)\n", - " \n", - " X : array_like\n", - " The image inputs having shape (number of examples x image dimensions).\n", - " \n", - " Return \n", - " ------\n", - " p : array_like\n", - " Predictions vector containing the predicted label for each example.\n", - " It has a length equal to the number of examples.\n", - " \n", - " Instructions\n", - " ------------\n", - " Complete the following code to make predictions using your learned neural\n", - " network. You should set p to a vector containing labels \n", - " between 0 to (num_labels-1).\n", - " \n", - " Hint\n", - " ----\n", - " This code can be done all vectorized using the numpy argmax function.\n", - " In particular, the argmax function returns the index of the max element,\n", - " for more information see '?np.argmax' or search online. If your examples\n", - " are in rows, then, you can use np.argmax(A, axis=1) to obtain the index\n", - " of the max for each row.\n", - " \n", - " Note\n", - " ----\n", - " Remember, we have supplied the `sigmoid` function in the `utils.py` file. \n", - " You can use this function by calling `utils.sigmoid(z)`, where you can \n", - " replace `z` by the required input variable to sigmoid.\n", - " \"\"\"\n", - " # Make sure the input has two dimensions\n", - " if X.ndim == 1:\n", - " X = X[None] # promote to 2-dimensions\n", - " \n", - " # useful variables\n", - " m = X.shape[0]\n", - " num_labels = Theta2.shape[0]\n", - "\n", - " # You need to return the following variables correctly \n", - " p = np.zeros(X.shape[0])\n", - "\n", - " # ====================== YOUR CODE HERE ======================\n", - "\n", - "\n", - "\n", - " # =============================================================\n", - " return p" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once you are done, call your predict function using the loaded set of parameters for `Theta1` and `Theta2`. You should see that the accuracy is about 97.5%." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pred = predict(Theta1, Theta2, X)\n", - "print('Training Set Accuracy: {:.1f}%'.format(np.mean(pred == y) * 100))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After that, we will display images from the training set one at a time, while at the same time printing out the predicted label for the displayed image. \n", - "\n", - "Run the following cell to display a single image the the neural network's prediction. You can run the cell multiple time to see predictions for different images." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if indices.size > 0:\n", - " i, indices = indices[0], indices[1:]\n", - " utils.displayData(X[i, :], figsize=(4, 4))\n", - " pred = predict(Theta1, Theta2, X[i, :])\n", - " print('Neural Network Prediction: {}'.format(*pred))\n", - "else:\n", - " print('No more images to display!')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*You should now submit your solutions.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "grader[4] = predict\n", - "grader.grade()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Exercise3/utils.py b/Exercise3/utils.py deleted file mode 100755 index 633a5636..00000000 --- a/Exercise3/utils.py +++ /dev/null @@ -1,104 +0,0 @@ -import sys -import numpy as np -from matplotlib import pyplot - -sys.path.append('..') -from submission import SubmissionBase - - -def displayData(X, example_width=None, figsize=(10, 10)): - """ - Displays 2D data stored in X in a nice grid. - """ - # Compute rows, cols - if X.ndim == 2: - m, n = X.shape - elif X.ndim == 1: - n = X.size - m = 1 - X = X[None] # Promote to a 2 dimensional array - else: - raise IndexError('Input X should be 1 or 2 dimensional.') - - example_width = example_width or int(np.round(np.sqrt(n))) - example_height = n / example_width - - # Compute number of items to display - display_rows = int(np.floor(np.sqrt(m))) - display_cols = int(np.ceil(m / display_rows)) - - fig, ax_array = pyplot.subplots(display_rows, display_cols, figsize=figsize) - fig.subplots_adjust(wspace=0.025, hspace=0.025) - - ax_array = [ax_array] if m == 1 else ax_array.ravel() - - for i, ax in enumerate(ax_array): - ax.imshow(X[i].reshape(example_width, example_width, order='F'), - cmap='Greys', extent=[0, 1, 0, 1]) - ax.axis('off') - - -def sigmoid(z): - """ - Computes the sigmoid of z. - """ - return 1.0 / (1.0 + np.exp(-z)) - - -class Grader(SubmissionBase): - # Random Test Cases - X = np.stack([np.ones(20), - np.exp(1) * np.sin(np.arange(1, 21)), - np.exp(0.5) * np.cos(np.arange(1, 21))], axis=1) - - y = (np.sin(X[:, 0] + X[:, 1]) > 0).astype(float) - - Xm = np.array([[-1, -1], - [-1, -2], - [-2, -1], - [-2, -2], - [1, 1], - [1, 2], - [2, 1], - [2, 2], - [-1, 1], - [-1, 2], - [-2, 1], - [-2, 2], - [1, -1], - [1, -2], - [-2, -1], - [-2, -2]]) - ym = np.array([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3]) - - t1 = np.sin(np.reshape(np.arange(1, 25, 2), (4, 3), order='F')) - t2 = np.cos(np.reshape(np.arange(1, 41, 2), (4, 5), order='F')) - - def __init__(self): - part_names = ['Regularized Logistic Regression', - 'One-vs-All Classifier Training', - 'One-vs-All Classifier Prediction', - 'Neural Network Prediction Function'] - - super().__init__('multi-class-classification-and-neural-networks', part_names) - - def __iter__(self): - for part_id in range(1, 5): - try: - func = self.functions[part_id] - - # Each part has different expected arguments/different function - if part_id == 1: - res = func(np.array([0.25, 0.5, -0.5]), self.X, self.y, 0.1) - res = np.hstack(res).tolist() - elif part_id == 2: - res = func(self.Xm, self.ym, 4, 0.1) - elif part_id == 3: - res = func(self.t1, self.Xm) + 1 - elif part_id == 4: - res = func(self.t1, self.t2, self.Xm) + 1 - else: - raise KeyError - yield part_id, res - except KeyError: - yield part_id, 0 diff --git a/Exercise4/Figures/ex4-backpropagation.png b/Exercise4/Figures/ex4-backpropagation.png deleted file mode 100755 index 62e1861f..00000000 Binary files a/Exercise4/Figures/ex4-backpropagation.png and /dev/null differ diff --git a/Exercise4/Figures/neural_network.png b/Exercise4/Figures/neural_network.png deleted file mode 100755 index 140fdb01..00000000 Binary files a/Exercise4/Figures/neural_network.png and /dev/null differ diff --git a/Exercise4/exercise4.ipynb b/Exercise4/exercise4.ipynb deleted file mode 100755 index ab2e6145..00000000 --- a/Exercise4/exercise4.ipynb +++ /dev/null @@ -1,928 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Programming Exercise 4: Neural Networks Learning\n", - "\n", - "## Introduction\n", - "\n", - "In this exercise, you will implement the backpropagation algorithm for neural networks and apply it to the task of hand-written digit recognition. Before starting on the programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics.\n", - "\n", - "\n", - "All the information you need for solving this assignment is in this notebook, and all the code you will be implementing will take place within this notebook. The assignment can be promptly submitted to the coursera grader directly from this notebook (code and instructions are included below).\n", - "\n", - "Before we begin with the exercises, we need to import all libraries required for this programming exercise. Throughout the course, we will be using [`numpy`](http://www.numpy.org/) for all arrays and matrix operations, [`matplotlib`](https://matplotlib.org/) for plotting, and [`scipy`](https://docs.scipy.org/doc/scipy/reference/) for scientific and numerical computation functions and tools. You can find instructions on how to install required libraries in the README file in the [github repository](https://github.com/dibgerge/ml-coursera-python-assignments)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# used for manipulating directory paths\n", - "import os\n", - "\n", - "# Scientific and vector computation for python\n", - "import numpy as np\n", - "\n", - "# Plotting library\n", - "from matplotlib import pyplot\n", - "\n", - "# Optimization module in scipy\n", - "from scipy import optimize\n", - "\n", - "# will be used to load MATLAB mat datafile format\n", - "from scipy.io import loadmat\n", - "\n", - "# library written for this exercise providing additional functions for assignment submission, and others\n", - "import utils\n", - "\n", - "# define the submission/grader object for this exercise\n", - "grader = utils.Grader()\n", - "\n", - "# tells matplotlib to embed plots within the notebook\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Submission and Grading\n", - "\n", - "\n", - "After completing each part of the assignment, be sure to submit your solutions to the grader. The following is a breakdown of how each part of this exercise is scored.\n", - "\n", - "\n", - "| Section | Part | Submission function | Points \n", - "| :- |:- | :- | :-: \n", - "| 1 | [Feedforward and Cost Function](#section1) | [`nnCostFunction`](#nnCostFunction) | 30 \n", - "| 2 | [Regularized Cost Function](#section2) | [`nnCostFunction`](#nnCostFunction) | 15 \n", - "| 3 | [Sigmoid Gradient](#section3) | [`sigmoidGradient`](#sigmoidGradient) | 5 \n", - "| 4 | [Neural Net Gradient Function (Backpropagation)](#section4) | [`nnCostFunction`](#nnCostFunction) | 40 \n", - "| 5 | [Regularized Gradient](#section5) | [`nnCostFunction`](#nnCostFunction) |10 \n", - "| | Total Points | | 100 \n", - "\n", - "\n", - "You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n", - "\n", - "
\n", - "At the end of each section in this notebook, we have a cell which contains code for submitting the solutions thus far to the grader. Execute the cell to see your score up to the current section. For all your work to be submitted properly, you must execute those cells at least once.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Neural Networks\n", - "\n", - "In the previous exercise, you implemented feedforward propagation for neural networks and used it to predict handwritten digits with the weights we provided. In this exercise, you will implement the backpropagation algorithm to learn the parameters for the neural network.\n", - "\n", - "We start the exercise by first loading the dataset. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# training data stored in arrays X, y\n", - "data = loadmat(os.path.join('Data', 'ex4data1.mat'))\n", - "X, y = data['X'], data['y'].ravel()\n", - "\n", - "# set the zero digit to 0, rather than its mapped 10 in this dataset\n", - "# This is an artifact due to the fact that this dataset was used in \n", - "# MATLAB where there is no index 0\n", - "y[y == 10] = 0\n", - "\n", - "# Number of training examples\n", - "m = y.size" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.1 Visualizing the data\n", - "\n", - "You will begin by visualizing a subset of the training set, using the function `displayData`, which is the same function we used in Exercise 3. It is provided in the `utils.py` file for this assignment as well. The dataset is also the same one you used in the previous exercise.\n", - "\n", - "There are 5000 training examples in `ex4data1.mat`, where each training example is a 20 pixel by 20 pixel grayscale image of the digit. Each pixel is represented by a floating point number indicating the grayscale intensity at that location. The 20 by 20 grid of pixels is “unrolled” into a 400-dimensional vector. Each\n", - "of these training examples becomes a single row in our data matrix $X$. This gives us a 5000 by 400 matrix $X$ where every row is a training example for a handwritten digit image.\n", - "\n", - "$$ X = \\begin{bmatrix} - \\left(x^{(1)} \\right)^T - \\\\\n", - "- \\left(x^{(2)} \\right)^T - \\\\\n", - "\\vdots \\\\\n", - "- \\left(x^{(m)} \\right)^T - \\\\\n", - "\\end{bmatrix}\n", - "$$\n", - "\n", - "The second part of the training set is a 5000-dimensional vector `y` that contains labels for the training set. \n", - "The following cell randomly selects 100 images from the dataset and plots them." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Randomly select 100 data points to display\n", - "rand_indices = np.random.choice(m, 100, replace=False)\n", - "sel = X[rand_indices, :]\n", - "\n", - "utils.displayData(sel)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.2 Model representation\n", - "\n", - "Our neural network is shown in the following figure.\n", - "\n", - "![](Figures/neural_network.png)\n", - "\n", - "It has 3 layers - an input layer, a hidden layer and an output layer. Recall that our inputs are pixel values\n", - "of digit images. Since the images are of size $20 \\times 20$, this gives us 400 input layer units (not counting the extra bias unit which always outputs +1). The training data was loaded into the variables `X` and `y` above.\n", - "\n", - "You have been provided with a set of network parameters ($\\Theta^{(1)}, \\Theta^{(2)}$) already trained by us. These are stored in `ex4weights.mat` and will be loaded in the next cell of this notebook into `Theta1` and `Theta2`. The parameters have dimensions that are sized for a neural network with 25 units in the second layer and 10 output units (corresponding to the 10 digit classes)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Setup the parameters you will use for this exercise\n", - "input_layer_size = 400 # 20x20 Input Images of Digits\n", - "hidden_layer_size = 25 # 25 hidden units\n", - "num_labels = 10 # 10 labels, from 0 to 9\n", - "\n", - "# Load the weights into variables Theta1 and Theta2\n", - "weights = loadmat(os.path.join('Data', 'ex4weights.mat'))\n", - "\n", - "# Theta1 has size 25 x 401\n", - "# Theta2 has size 10 x 26\n", - "Theta1, Theta2 = weights['Theta1'], weights['Theta2']\n", - "\n", - "# swap first and last columns of Theta2, due to legacy from MATLAB indexing, \n", - "# since the weight file ex3weights.mat was saved based on MATLAB indexing\n", - "Theta2 = np.roll(Theta2, 1, axis=0)\n", - "\n", - "# Unroll parameters \n", - "nn_params = np.concatenate([Theta1.ravel(), Theta2.ravel()])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### 1.3 Feedforward and cost function\n", - "\n", - "Now you will implement the cost function and gradient for the neural network. First, complete the code for the function `nnCostFunction` in the next cell to return the cost.\n", - "\n", - "Recall that the cost function for the neural network (without regularization) is:\n", - "\n", - "$$ J(\\theta) = \\frac{1}{m} \\sum_{i=1}^{m}\\sum_{k=1}^{K} \\left[ - y_k^{(i)} \\log \\left( \\left( h_\\theta \\left( x^{(i)} \\right) \\right)_k \\right) - \\left( 1 - y_k^{(i)} \\right) \\log \\left( 1 - \\left( h_\\theta \\left( x^{(i)} \\right) \\right)_k \\right) \\right]$$\n", - "\n", - "where $h_\\theta \\left( x^{(i)} \\right)$ is computed as shown in the neural network figure above, and K = 10 is the total number of possible labels. Note that $h_\\theta(x^{(i)})_k = a_k^{(3)}$ is the activation (output\n", - "value) of the $k^{th}$ output unit. Also, recall that whereas the original labels (in the variable y) were 0, 1, ..., 9, for the purpose of training a neural network, we need to encode the labels as vectors containing only values 0 or 1, so that\n", - "\n", - "$$ y = \n", - "\\begin{bmatrix} 1 \\\\ 0 \\\\ 0 \\\\\\vdots \\\\ 0 \\end{bmatrix}, \\quad\n", - "\\begin{bmatrix} 0 \\\\ 1 \\\\ 0 \\\\ \\vdots \\\\ 0 \\end{bmatrix}, \\quad \\cdots \\quad \\text{or} \\qquad\n", - "\\begin{bmatrix} 0 \\\\ 0 \\\\ 0 \\\\ \\vdots \\\\ 1 \\end{bmatrix}.\n", - "$$\n", - "\n", - "For example, if $x^{(i)}$ is an image of the digit 5, then the corresponding $y^{(i)}$ (that you should use with the cost function) should be a 10-dimensional vector with $y_5 = 1$, and the other elements equal to 0.\n", - "\n", - "You should implement the feedforward computation that computes $h_\\theta(x^{(i)})$ for every example $i$ and sum the cost over all examples. **Your code should also work for a dataset of any size, with any number of labels** (you can assume that there are always at least $K \\ge 3$ labels).\n", - "\n", - "
\n", - "**Implementation Note:** The matrix $X$ contains the examples in rows (i.e., X[i,:] is the i-th training example $x^{(i)}$, expressed as a $n \\times 1$ vector.) When you complete the code in `nnCostFunction`, you will need to add the column of 1’s to the X matrix. The parameters for each unit in the neural network is represented in Theta1 and Theta2 as one row. Specifically, the first row of Theta1 corresponds to the first hidden unit in the second layer. You can use a for-loop over the examples to compute the cost.\n", - "
\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def nnCostFunction(nn_params,\n", - " input_layer_size,\n", - " hidden_layer_size,\n", - " num_labels,\n", - " X, y, lambda_=0.0):\n", - " \"\"\"\n", - " Implements the neural network cost function and gradient for a two layer neural \n", - " network which performs classification. \n", - " \n", - " Parameters\n", - " ----------\n", - " nn_params : array_like\n", - " The parameters for the neural network which are \"unrolled\" into \n", - " a vector. This needs to be converted back into the weight matrices Theta1\n", - " and Theta2.\n", - " \n", - " input_layer_size : int\n", - " Number of features for the input layer. \n", - " \n", - " hidden_layer_size : int\n", - " Number of hidden units in the second layer.\n", - " \n", - " num_labels : int\n", - " Total number of labels, or equivalently number of units in output layer. \n", - " \n", - " X : array_like\n", - " Input dataset. A matrix of shape (m x input_layer_size).\n", - " \n", - " y : array_like\n", - " Dataset labels. A vector of shape (m,).\n", - " \n", - " lambda_ : float, optional\n", - " Regularization parameter.\n", - " \n", - " Returns\n", - " -------\n", - " J : float\n", - " The computed value for the cost function at the current weight values.\n", - " \n", - " grad : array_like\n", - " An \"unrolled\" vector of the partial derivatives of the concatenatation of\n", - " neural network weights Theta1 and Theta2.\n", - " \n", - " Instructions\n", - " ------------\n", - " You should complete the code by working through the following parts.\n", - " \n", - " - Part 1: Feedforward the neural network and return the cost in the \n", - " variable J. After implementing Part 1, you can verify that your\n", - " cost function computation is correct by verifying the cost\n", - " computed in the following cell.\n", - " \n", - " - Part 2: Implement the backpropagation algorithm to compute the gradients\n", - " Theta1_grad and Theta2_grad. You should return the partial derivatives of\n", - " the cost function with respect to Theta1 and Theta2 in Theta1_grad and\n", - " Theta2_grad, respectively. After implementing Part 2, you can check\n", - " that your implementation is correct by running checkNNGradients provided\n", - " in the utils.py module.\n", - " \n", - " Note: The vector y passed into the function is a vector of labels\n", - " containing values from 0..K-1. You need to map this vector into a \n", - " binary vector of 1's and 0's to be used with the neural network\n", - " cost function.\n", - " \n", - " Hint: We recommend implementing backpropagation using a for-loop\n", - " over the training examples if you are implementing it for the \n", - " first time.\n", - " \n", - " - Part 3: Implement regularization with the cost function and gradients.\n", - " \n", - " Hint: You can implement this around the code for\n", - " backpropagation. That is, you can compute the gradients for\n", - " the regularization separately and then add them to Theta1_grad\n", - " and Theta2_grad from Part 2.\n", - " \n", - " Note \n", - " ----\n", - " We have provided an implementation for the sigmoid function in the file \n", - " `utils.py` accompanying this assignment.\n", - " \"\"\"\n", - " # Reshape nn_params back into the parameters Theta1 and Theta2, the weight matrices\n", - " # for our 2 layer neural network\n", - " Theta1 = np.reshape(nn_params[:hidden_layer_size * (input_layer_size + 1)],\n", - " (hidden_layer_size, (input_layer_size + 1)))\n", - "\n", - " Theta2 = np.reshape(nn_params[(hidden_layer_size * (input_layer_size + 1)):],\n", - " (num_labels, (hidden_layer_size + 1)))\n", - "\n", - " # Setup some useful variables\n", - " m = y.size\n", - " \n", - " # You need to return the following variables correctly \n", - " J = 0\n", - " Theta1_grad = np.zeros(Theta1.shape)\n", - " Theta2_grad = np.zeros(Theta2.shape)\n", - "\n", - " # ====================== YOUR CODE HERE ======================\n", - "\n", - " \n", - " \n", - " # ================================================================\n", - " # Unroll gradients\n", - " # grad = np.concatenate([Theta1_grad.ravel(order=order), Theta2_grad.ravel(order=order)])\n", - " grad = np.concatenate([Theta1_grad.ravel(), Theta2_grad.ravel()])\n", - "\n", - " return J, grad" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Use the following links to go back to the different parts of this exercise that require to modify the function `nnCostFunction`.
\n", - "\n", - "Back to:\n", - "- [Feedforward and cost function](#section1)\n", - "- [Regularized cost](#section2)\n", - "- [Neural Network Gradient (Backpropagation)](#section4)\n", - "- [Regularized Gradient](#section5)\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once you are done, call your `nnCostFunction` using the loaded set of parameters for `Theta1` and `Theta2`. You should see that the cost is about 0.287629." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "lambda_ = 0\n", - "J, _ = nnCostFunction(nn_params, input_layer_size, hidden_layer_size,\n", - " num_labels, X, y, lambda_)\n", - "print('Cost at parameters (loaded from ex4weights): %.6f ' % J)\n", - "print('The cost should be about : 0.287629.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*You should now submit your solutions.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "grader = utils.Grader()\n", - "grader[1] = nnCostFunction\n", - "grader.grade()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### 1.4 Regularized cost function\n", - "\n", - "The cost function for neural networks with regularization is given by:\n", - "\n", - "\n", - "$$ J(\\theta) = \\frac{1}{m} \\sum_{i=1}^{m}\\sum_{k=1}^{K} \\left[ - y_k^{(i)} \\log \\left( \\left( h_\\theta \\left( x^{(i)} \\right) \\right)_k \\right) - \\left( 1 - y_k^{(i)} \\right) \\log \\left( 1 - \\left( h_\\theta \\left( x^{(i)} \\right) \\right)_k \\right) \\right] + \\frac{\\lambda}{2 m} \\left[ \\sum_{j=1}^{25} \\sum_{k=1}^{400} \\left( \\Theta_{j,k}^{(1)} \\right)^2 + \\sum_{j=1}^{10} \\sum_{k=1}^{25} \\left( \\Theta_{j,k}^{(2)} \\right)^2 \\right] $$\n", - "\n", - "You can assume that the neural network will only have 3 layers - an input layer, a hidden layer and an output layer. However, your code should work for any number of input units, hidden units and outputs units. While we\n", - "have explicitly listed the indices above for $\\Theta^{(1)}$ and $\\Theta^{(2)}$ for clarity, do note that your code should in general work with $\\Theta^{(1)}$ and $\\Theta^{(2)}$ of any size. Note that you should not be regularizing the terms that correspond to the bias. For the matrices `Theta1` and `Theta2`, this corresponds to the first column of each matrix. You should now add regularization to your cost function. Notice that you can first compute the unregularized cost function $J$ using your existing `nnCostFunction` and then later add the cost for the regularization terms.\n", - "\n", - "[Click here to go back to `nnCostFunction` for editing.](#nnCostFunction)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once you are done, the next cell will call your `nnCostFunction` using the loaded set of parameters for `Theta1` and `Theta2`, and $\\lambda = 1$. You should see that the cost is about 0.383770." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Weight regularization parameter (we set this to 1 here).\n", - "lambda_ = 1\n", - "J, _ = nnCostFunction(nn_params, input_layer_size, hidden_layer_size,\n", - " num_labels, X, y, lambda_)\n", - "\n", - "print('Cost at parameters (loaded from ex4weights): %.6f' % J)\n", - "print('This value should be about : 0.383770.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*You should now submit your solutions.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "grader[2] = nnCostFunction\n", - "grader.grade()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2 Backpropagation\n", - "\n", - "In this part of the exercise, you will implement the backpropagation algorithm to compute the gradient for the neural network cost function. You will need to update the function `nnCostFunction` so that it returns an appropriate value for `grad`. Once you have computed the gradient, you will be able to train the neural network by minimizing the cost function $J(\\theta)$ using an advanced optimizer such as `scipy`'s `optimize.minimize`.\n", - "You will first implement the backpropagation algorithm to compute the gradients for the parameters for the (unregularized) neural network. After you have verified that your gradient computation for the unregularized case is correct, you will implement the gradient for the regularized neural network." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### 2.1 Sigmoid Gradient\n", - "\n", - "To help you get started with this part of the exercise, you will first implement\n", - "the sigmoid gradient function. The gradient for the sigmoid function can be\n", - "computed as\n", - "\n", - "$$ g'(z) = \\frac{d}{dz} g(z) = g(z)\\left(1-g(z)\\right) $$\n", - "\n", - "where\n", - "\n", - "$$ \\text{sigmoid}(z) = g(z) = \\frac{1}{1 + e^{-z}} $$\n", - "\n", - "Now complete the implementation of `sigmoidGradient` in the next cell.\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def sigmoidGradient(z):\n", - " \"\"\"\n", - " Computes the gradient of the sigmoid function evaluated at z. \n", - " This should work regardless if z is a matrix or a vector. \n", - " In particular, if z is a vector or matrix, you should return\n", - " the gradient for each element.\n", - " \n", - " Parameters\n", - " ----------\n", - " z : array_like\n", - " A vector or matrix as input to the sigmoid function. \n", - " \n", - " Returns\n", - " --------\n", - " g : array_like\n", - " Gradient of the sigmoid function. Has the same shape as z. \n", - " \n", - " Instructions\n", - " ------------\n", - " Compute the gradient of the sigmoid function evaluated at\n", - " each value of z (z can be a matrix, vector or scalar).\n", - " \n", - " Note\n", - " ----\n", - " We have provided an implementation of the sigmoid function \n", - " in `utils.py` file accompanying this assignment.\n", - " \"\"\"\n", - "\n", - " g = np.zeros(z.shape)\n", - "\n", - " # ====================== YOUR CODE HERE ======================\n", - "\n", - "\n", - "\n", - " # =============================================================\n", - " return g" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When you are done, the following cell call `sigmoidGradient` on a given vector `z`. Try testing a few values by calling `sigmoidGradient(z)`. For large values (both positive and negative) of z, the gradient should be close to 0. When $z = 0$, the gradient should be exactly 0.25. Your code should also work with vectors and matrices. For a matrix, your function should perform the sigmoid gradient function on every element." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "z = np.array([-1, -0.5, 0, 0.5, 1])\n", - "g = sigmoidGradient(z)\n", - "print('Sigmoid gradient evaluated at [-1 -0.5 0 0.5 1]:\\n ')\n", - "print(g)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*You should now submit your solutions.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "grader[3] = sigmoidGradient\n", - "grader.grade()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.2 Random Initialization\n", - "\n", - "When training neural networks, it is important to randomly initialize the parameters for symmetry breaking. One effective strategy for random initialization is to randomly select values for $\\Theta^{(l)}$ uniformly in the range $[-\\epsilon_{init}, \\epsilon_{init}]$. You should use $\\epsilon_{init} = 0.12$. This range of values ensures that the parameters are kept small and makes the learning more efficient.\n", - "\n", - "
\n", - "One effective strategy for choosing $\\epsilon_{init}$ is to base it on the number of units in the network. A good choice of $\\epsilon_{init}$ is $\\epsilon_{init} = \\frac{\\sqrt{6}}{\\sqrt{L_{in} + L_{out}}}$ where $L_{in} = s_l$ and $L_{out} = s_{l+1}$ are the number of units in the layers adjacent to $\\Theta^{l}$.\n", - "
\n", - "\n", - "Your job is to complete the function `randInitializeWeights` to initialize the weights for $\\Theta$. Modify the function by filling in the following code:\n", - "\n", - "```python\n", - "# Randomly initialize the weights to small values\n", - "W = np.random.rand(L_out, 1 + L_in) * 2 * epsilon_init - epsilon_init\n", - "```\n", - "Note that we give the function an argument for $\\epsilon$ with default value `epsilon_init = 0.12`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def randInitializeWeights(L_in, L_out, epsilon_init=0.12):\n", - " \"\"\"\n", - " Randomly initialize the weights of a layer in a neural network.\n", - " \n", - " Parameters\n", - " ----------\n", - " L_in : int\n", - " Number of incomming connections.\n", - " \n", - " L_out : int\n", - " Number of outgoing connections. \n", - " \n", - " epsilon_init : float, optional\n", - " Range of values which the weight can take from a uniform \n", - " distribution.\n", - " \n", - " Returns\n", - " -------\n", - " W : array_like\n", - " The weight initialiatized to random values. Note that W should\n", - " be set to a matrix of size(L_out, 1 + L_in) as\n", - " the first column of W handles the \"bias\" terms.\n", - " \n", - " Instructions\n", - " ------------\n", - " Initialize W randomly so that we break the symmetry while training\n", - " the neural network. Note that the first column of W corresponds \n", - " to the parameters for the bias unit.\n", - " \"\"\"\n", - "\n", - " # You need to return the following variables correctly \n", - " W = np.zeros((L_out, 1 + L_in))\n", - "\n", - " # ====================== YOUR CODE HERE ======================\n", - "\n", - "\n", - "\n", - " # ============================================================\n", - " return W" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*You do not need to submit any code for this part of the exercise.*\n", - "\n", - "Execute the following cell to initialize the weights for the 2 layers in the neural network using the `randInitializeWeights` function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('Initializing Neural Network Parameters ...')\n", - "\n", - "initial_Theta1 = randInitializeWeights(input_layer_size, hidden_layer_size)\n", - "initial_Theta2 = randInitializeWeights(hidden_layer_size, num_labels)\n", - "\n", - "# Unroll parameters\n", - "initial_nn_params = np.concatenate([initial_Theta1.ravel(), initial_Theta2.ravel()], axis=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### 2.4 Backpropagation\n", - "\n", - "![](Figures/ex4-backpropagation.png)\n", - "\n", - "Now, you will implement the backpropagation algorithm. Recall that the intuition behind the backpropagation algorithm is as follows. Given a training example $(x^{(t)}, y^{(t)})$, we will first run a “forward pass” to compute all the activations throughout the network, including the output value of the hypothesis $h_\\theta(x)$. Then, for each node $j$ in layer $l$, we would like to compute an “error term” $\\delta_j^{(l)}$ that measures how much that node was “responsible” for any errors in our output.\n", - "\n", - "For an output node, we can directly measure the difference between the network’s activation and the true target value, and use that to define $\\delta_j^{(3)}$ (since layer 3 is the output layer). For the hidden units, you will compute $\\delta_j^{(l)}$ based on a weighted average of the error terms of the nodes in layer $(l+1)$. In detail, here is the backpropagation algorithm (also depicted in the figure above). You should implement steps 1 to 4 in a loop that processes one example at a time. Concretely, you should implement a for-loop `for t in range(m)` and place steps 1-4 below inside the for-loop, with the $t^{th}$ iteration performing the calculation on the $t^{th}$ training example $(x^{(t)}, y^{(t)})$. Step 5 will divide the accumulated gradients by $m$ to obtain the gradients for the neural network cost function.\n", - "\n", - "1. Set the input layer’s values $(a^{(1)})$ to the $t^{th }$training example $x^{(t)}$. Perform a feedforward pass, computing the activations $(z^{(2)}, a^{(2)}, z^{(3)}, a^{(3)})$ for layers 2 and 3. Note that you need to add a `+1` term to ensure that the vectors of activations for layers $a^{(1)}$ and $a^{(2)}$ also include the bias unit. In `numpy`, if a 1 is a column matrix, adding one corresponds to `a_1 = np.concatenate([np.ones((m, 1)), a_1], axis=1)`.\n", - "\n", - "1. For each output unit $k$ in layer 3 (the output layer), set \n", - "$$\\delta_k^{(3)} = \\left(a_k^{(3)} - y_k \\right)$$\n", - "where $y_k \\in \\{0, 1\\}$ indicates whether the current training example belongs to class $k$ $(y_k = 1)$, or if it belongs to a different class $(y_k = 0)$. You may find logical arrays helpful for this task (explained in the previous programming exercise).\n", - "\n", - "1. For the hidden layer $l = 2$, set \n", - "$$ \\delta^{(2)} = \\left( \\Theta^{(2)} \\right)^T \\delta^{(3)} * g'\\left(z^{(2)} \\right)$$\n", - "Note that the symbol $*$ performs element wise multiplication in `numpy`.\n", - "\n", - "1. Accumulate the gradient from this example using the following formula. Note that you should skip or remove $\\delta_0^{(2)}$. In `numpy`, removing $\\delta_0^{(2)}$ corresponds to `delta_2 = delta_2[1:]`.\n", - "$$ \\Delta^{(l)} = \\Delta^{(l)} + \\delta^{(l+1)} (a^{(l)})^{(T)} $$\n", - "\n", - "1. Obtain the (unregularized) gradient for the neural network cost function by dividing the accumulated gradients by $\\frac{1}{m}$:\n", - "$$ \\frac{\\partial}{\\partial \\Theta_{ij}^{(l)}} J(\\Theta) = D_{ij}^{(l)} = \\frac{1}{m} \\Delta_{ij}^{(l)}$$\n", - "\n", - "
\n", - "**Python/Numpy tip**: You should implement the backpropagation algorithm only after you have successfully completed the feedforward and cost functions. While implementing the backpropagation alogrithm, it is often useful to use the `shape` function to print out the shapes of the variables you are working with if you run into dimension mismatch errors.\n", - "
\n", - "\n", - "[Click here to go back and update the function `nnCostFunction` with the backpropagation algorithm](#nnCostFunction).\n", - "\n", - "\n", - "**Note:** If the iterative solution provided above is proving to be difficult to implement, try implementing the vectorized approach which is easier to implement in the opinion of the moderators of this course. You can find the tutorial for the vectorized approach [here](https://www.coursera.org/learn/machine-learning/discussions/all/threads/a8Kce_WxEeS16yIACyoj1Q)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After you have implemented the backpropagation algorithm, we will proceed to run gradient checking on your implementation. The gradient check will allow you to increase your confidence that your code is\n", - "computing the gradients correctly.\n", - "\n", - "### 2.4 Gradient checking \n", - "\n", - "In your neural network, you are minimizing the cost function $J(\\Theta)$. To perform gradient checking on your parameters, you can imagine “unrolling” the parameters $\\Theta^{(1)}$, $\\Theta^{(2)}$ into a long vector $\\theta$. By doing so, you can think of the cost function being $J(\\Theta)$ instead and use the following gradient checking procedure.\n", - "\n", - "Suppose you have a function $f_i(\\theta)$ that purportedly computes $\\frac{\\partial}{\\partial \\theta_i} J(\\theta)$; you’d like to check if $f_i$ is outputting correct derivative values.\n", - "\n", - "$$\n", - "\\text{Let } \\theta^{(i+)} = \\theta + \\begin{bmatrix} 0 \\\\ 0 \\\\ \\vdots \\\\ \\epsilon \\\\ \\vdots \\\\ 0 \\end{bmatrix}\n", - "\\quad \\text{and} \\quad \\theta^{(i-)} = \\theta - \\begin{bmatrix} 0 \\\\ 0 \\\\ \\vdots \\\\ \\epsilon \\\\ \\vdots \\\\ 0 \\end{bmatrix}\n", - "$$\n", - "\n", - "So, $\\theta^{(i+)}$ is the same as $\\theta$, except its $i^{th}$ element has been incremented by $\\epsilon$. Similarly, $\\theta^{(i−)}$ is the corresponding vector with the $i^{th}$ element decreased by $\\epsilon$. You can now numerically verify $f_i(\\theta)$’s correctness by checking, for each $i$, that:\n", - "\n", - "$$ f_i\\left( \\theta \\right) \\approx \\frac{J\\left( \\theta^{(i+)}\\right) - J\\left( \\theta^{(i-)} \\right)}{2\\epsilon} $$\n", - "\n", - "The degree to which these two values should approximate each other will depend on the details of $J$. But assuming $\\epsilon = 10^{-4}$, you’ll usually find that the left- and right-hand sides of the above will agree to at least 4 significant digits (and often many more).\n", - "\n", - "We have implemented the function to compute the numerical gradient for you in `computeNumericalGradient` (within the file `utils.py`). While you are not required to modify the file, we highly encourage you to take a look at the code to understand how it works.\n", - "\n", - "In the next cell we will run the provided function `checkNNGradients` which will create a small neural network and dataset that will be used for checking your gradients. If your backpropagation implementation is correct,\n", - "you should see a relative difference that is less than 1e-9.\n", - "\n", - "
\n", - "**Practical Tip**: When performing gradient checking, it is much more efficient to use a small neural network with a relatively small number of input units and hidden units, thus having a relatively small number\n", - "of parameters. Each dimension of $\\theta$ requires two evaluations of the cost function and this can be expensive. In the function `checkNNGradients`, our code creates a small random model and dataset which is used with `computeNumericalGradient` for gradient checking. Furthermore, after you are confident that your gradient computations are correct, you should turn off gradient checking before running your learning algorithm.\n", - "
\n", - "\n", - "
\n", - "**Practical Tip:** Gradient checking works for any function where you are computing the cost and the gradient. Concretely, you can use the same `computeNumericalGradient` function to check if your gradient implementations for the other exercises are correct too (e.g., logistic regression’s cost function).\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "utils.checkNNGradients(nnCostFunction)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Once your cost function passes the gradient check for the (unregularized) neural network cost function, you should submit the neural network gradient function (backpropagation).*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "grader[4] = nnCostFunction\n", - "grader.grade()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### 2.5 Regularized Neural Network\n", - "\n", - "After you have successfully implemented the backpropagation algorithm, you will add regularization to the gradient. To account for regularization, it turns out that you can add this as an additional term *after* computing the gradients using backpropagation.\n", - "\n", - "Specifically, after you have computed $\\Delta_{ij}^{(l)}$ using backpropagation, you should add regularization using\n", - "\n", - "$$ \\begin{align} \n", - "& \\frac{\\partial}{\\partial \\Theta_{ij}^{(l)}} J(\\Theta) = D_{ij}^{(l)} = \\frac{1}{m} \\Delta_{ij}^{(l)} & \\qquad \\text{for } j = 0 \\\\\n", - "& \\frac{\\partial}{\\partial \\Theta_{ij}^{(l)}} J(\\Theta) = D_{ij}^{(l)} = \\frac{1}{m} \\Delta_{ij}^{(l)} + \\frac{\\lambda}{m} \\Theta_{ij}^{(l)} & \\qquad \\text{for } j \\ge 1\n", - "\\end{align}\n", - "$$\n", - "\n", - "Note that you should *not* be regularizing the first column of $\\Theta^{(l)}$ which is used for the bias term. Furthermore, in the parameters $\\Theta_{ij}^{(l)}$, $i$ is indexed starting from 1, and $j$ is indexed starting from 0. Thus, \n", - "\n", - "$$\n", - "\\Theta^{(l)} = \\begin{bmatrix}\n", - "\\Theta_{1,0}^{(i)} & \\Theta_{1,1}^{(l)} & \\cdots \\\\\n", - "\\Theta_{2,0}^{(i)} & \\Theta_{2,1}^{(l)} & \\cdots \\\\\n", - "\\vdots & ~ & \\ddots\n", - "\\end{bmatrix}\n", - "$$\n", - "\n", - "[Now modify your code that computes grad in `nnCostFunction` to account for regularization.](#nnCostFunction)\n", - "\n", - "After you are done, the following cell runs gradient checking on your implementation. If your code is correct, you should expect to see a relative difference that is less than 1e-9." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check gradients by running checkNNGradients\n", - "lambda_ = 3\n", - "utils.checkNNGradients(nnCostFunction, lambda_)\n", - "\n", - "# Also output the costFunction debugging values\n", - "debug_J, _ = nnCostFunction(nn_params, input_layer_size,\n", - " hidden_layer_size, num_labels, X, y, lambda_)\n", - "\n", - "print('\\n\\nCost at (fixed) debugging parameters (w/ lambda = %f): %f ' % (lambda_, debug_J))\n", - "print('(for lambda = 3, this value should be about 0.576051)')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "grader[5] = nnCostFunction\n", - "grader.grade()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.6 Learning parameters using `scipy.optimize.minimize`\n", - "\n", - "After you have successfully implemented the neural network cost function\n", - "and gradient computation, the next step we will use `scipy`'s minimization to learn a good set parameters." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "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", - "\n", - "# You should also try different values of lambda\n", - "lambda_ = 1\n", - "\n", - "# Create \"short hand\" for the cost function to be minimized\n", - "costFunction = lambda p: nnCostFunction(p, input_layer_size,\n", - " hidden_layer_size,\n", - " num_labels, X, y, lambda_)\n", - "\n", - "# Now, costFunction is a function that takes in only one argument\n", - "# (the neural network parameters)\n", - "res = optimize.minimize(costFunction,\n", - " initial_nn_params,\n", - " jac=True,\n", - " method='TNC',\n", - " options=options)\n", - "\n", - "# get the solution of the optimization\n", - "nn_params = res.x\n", - " \n", - "# Obtain Theta1 and Theta2 back from nn_params\n", - "Theta1 = np.reshape(nn_params[:hidden_layer_size * (input_layer_size + 1)],\n", - " (hidden_layer_size, (input_layer_size + 1)))\n", - "\n", - "Theta2 = np.reshape(nn_params[(hidden_layer_size * (input_layer_size + 1)):],\n", - " (num_labels, (hidden_layer_size + 1)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After the training completes, we will proceed to report the training accuracy of your classifier by computing the percentage of examples it got correct. If your implementation is correct, you should see a reported\n", - "training accuracy of about 95.3% (this may vary by about 1% due to the random initialization). It is possible to get higher training accuracies by training the neural network for more iterations. We encourage you to try\n", - "training the neural network for more iterations (e.g., set `maxiter` to 400) and also vary the regularization parameter $\\lambda$. With the right learning settings, it is possible to get the neural network to perfectly fit the training set." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pred = utils.predict(Theta1, Theta2, X)\n", - "print('Training Set Accuracy: %f' % (np.mean(pred == y) * 100))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3 Visualizing the Hidden Layer\n", - "\n", - "One way to understand what your neural network is learning is to visualize what the representations captured by the hidden units. Informally, given a particular hidden unit, one way to visualize what it computes is to find an input $x$ that will cause it to activate (that is, to have an activation value \n", - "($a_i^{(l)}$) close to 1). For the neural network you trained, notice that the $i^{th}$ row of $\\Theta^{(1)}$ is a 401-dimensional vector that represents the parameter for the $i^{th}$ hidden unit. If we discard the bias term, we get a 400 dimensional vector that represents the weights from each input pixel to the hidden unit.\n", - "\n", - "Thus, one way to visualize the “representation” captured by the hidden unit is to reshape this 400 dimensional vector into a 20 × 20 image and display it (It turns out that this is equivalent to finding the input that gives the highest activation for the hidden unit, given a “norm” constraint on the input (i.e., $||x||_2 \\le 1$)). \n", - "\n", - "The next cell does this by using the `displayData` function and it will show you an image with 25 units,\n", - "each corresponding to one hidden unit in the network. In your trained network, you should find that the hidden units corresponds roughly to detectors that look for strokes and other patterns in the input." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "utils.displayData(Theta1[:, 1:])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.1 Optional (ungraded) exercise\n", - "\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." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Exercise4/utils.py b/Exercise4/utils.py deleted file mode 100755 index 6b7c3bdc..00000000 --- a/Exercise4/utils.py +++ /dev/null @@ -1,226 +0,0 @@ -import sys -import numpy as np -from matplotlib import pyplot - -sys.path.append('..') -from submission import SubmissionBase - - -def displayData(X, example_width=None, figsize=(10, 10)): - """ - Displays 2D data stored in X in a nice grid. - """ - # Compute rows, cols - if X.ndim == 2: - m, n = X.shape - elif X.ndim == 1: - n = X.size - m = 1 - X = X[None] # Promote to a 2 dimensional array - else: - raise IndexError('Input X should be 1 or 2 dimensional.') - - example_width = example_width or int(np.round(np.sqrt(n))) - example_height = n / example_width - - # Compute number of items to display - display_rows = int(np.floor(np.sqrt(m))) - display_cols = int(np.ceil(m / display_rows)) - - fig, ax_array = pyplot.subplots(display_rows, display_cols, figsize=figsize) - fig.subplots_adjust(wspace=0.025, hspace=0.025) - - ax_array = [ax_array] if m == 1 else ax_array.ravel() - - for i, ax in enumerate(ax_array): - # Display Image - h = ax.imshow(X[i].reshape(example_width, example_width, order='F'), - cmap='Greys', extent=[0, 1, 0, 1]) - ax.axis('off') - - -def predict(Theta1, Theta2, X): - """ - Predict the label of an input given a trained neural network - Outputs the predicted label of X given the trained weights of a neural - network(Theta1, Theta2) - """ - # Useful values - m = X.shape[0] - num_labels = Theta2.shape[0] - - # You need to return the following variables correctly - p = np.zeros(m) - h1 = sigmoid(np.dot(np.concatenate([np.ones((m, 1)), X], axis=1), Theta1.T)) - h2 = sigmoid(np.dot(np.concatenate([np.ones((m, 1)), h1], axis=1), Theta2.T)) - p = np.argmax(h2, axis=1) - return p - - -def debugInitializeWeights(fan_out, fan_in): - """ - Initialize the weights of a layer with fan_in incoming connections and fan_out outgoings - connections using a fixed strategy. This will help you later in debugging. - - Note that W should be set a matrix of size (1+fan_in, fan_out) as the first row of W handles - the "bias" terms. - - Parameters - ---------- - fan_out : int - The number of outgoing connections. - - fan_in : int - The number of incoming connections. - - Returns - ------- - W : array_like (1+fan_in, fan_out) - The initialized weights array given the dimensions. - """ - # Initialize W using "sin". This ensures that W is always of the same values and will be - # useful for debugging - W = np.sin(np.arange(1, 1 + (1+fan_in)*fan_out))/10.0 - W = W.reshape(fan_out, 1+fan_in, order='F') - return W - - -def computeNumericalGradient(J, theta, e=1e-4): - """ - Computes the gradient using "finite differences" and gives us a numerical estimate of the - gradient. - - Parameters - ---------- - J : func - The cost function which will be used to estimate its numerical gradient. - - theta : array_like - The one dimensional unrolled network parameters. The numerical gradient is computed at - those given parameters. - - e : float (optional) - The value to use for epsilon for computing the finite difference. - - Notes - ----- - The following code implements numerical gradient checking, and - returns the numerical gradient. It sets `numgrad[i]` to (a numerical - approximation of) the partial derivative of J with respect to the - i-th input argument, evaluated at theta. (i.e., `numgrad[i]` should - be the (approximately) the partial derivative of J with respect - to theta[i].) - """ - numgrad = np.zeros(theta.shape) - perturb = np.diag(e * np.ones(theta.shape)) - for i in range(theta.size): - loss1, _ = J(theta - perturb[:, i]) - loss2, _ = J(theta + perturb[:, i]) - numgrad[i] = (loss2 - loss1)/(2*e) - return numgrad - - -def checkNNGradients(nnCostFunction, lambda_=0): - """ - Creates a small neural network to check the backpropagation gradients. It will output the - analytical gradients produced by your backprop code and the numerical gradients - (computed using computeNumericalGradient). These two gradient computations should result in - very similar values. - - Parameters - ---------- - nnCostFunction : func - A reference to the cost function implemented by the student. - - lambda_ : float (optional) - The regularization parameter value. - """ - input_layer_size = 3 - hidden_layer_size = 5 - num_labels = 3 - m = 5 - - # We generate some 'random' test data - Theta1 = debugInitializeWeights(hidden_layer_size, input_layer_size) - Theta2 = debugInitializeWeights(num_labels, hidden_layer_size) - - # Reusing debugInitializeWeights to generate X - X = debugInitializeWeights(m, input_layer_size - 1) - y = np.arange(1, 1+m) % num_labels - # print(y) - # Unroll parameters - nn_params = np.concatenate([Theta1.ravel(), Theta2.ravel()]) - - # short hand for cost function - costFunc = lambda p: nnCostFunction(p, input_layer_size, hidden_layer_size, - num_labels, X, y, lambda_) - cost, grad = costFunc(nn_params) - numgrad = computeNumericalGradient(costFunc, nn_params) - - # Visually examine the two gradient computations.The two columns you get should be very similar. - print(np.stack([numgrad, grad], axis=1)) - print('The above two columns you get should be very similar.') - print('(Left-Your Numerical Gradient, Right-Analytical Gradient)\n') - - # Evaluate the norm of the difference between two the solutions. If you have a correct - # implementation, and assuming you used e = 0.0001 in computeNumericalGradient, then diff - # should be less than 1e-9. - diff = np.linalg.norm(numgrad - grad)/np.linalg.norm(numgrad + grad) - - print('If your backpropagation implementation is correct, then \n' - 'the relative difference will be small (less than 1e-9). \n' - 'Relative Difference: %g' % diff) - - -def sigmoid(z): - """ - Computes the sigmoid of z. - """ - return 1.0 / (1.0 + np.exp(-z)) - - -class Grader(SubmissionBase): - X = np.reshape(3 * np.sin(np.arange(1, 31)), (3, 10), order='F') - Xm = np.reshape(np.sin(np.arange(1, 33)), (16, 2), order='F') / 5 - ym = np.arange(1, 17) % 4 - t1 = np.sin(np.reshape(np.arange(1, 25, 2), (4, 3), order='F')) - t2 = np.cos(np.reshape(np.arange(1, 41, 2), (4, 5), order='F')) - t = np.concatenate([t1.ravel(), t2.ravel()], axis=0) - - def __init__(self): - part_names = ['Feedforward and Cost Function', - 'Regularized Cost Function', - 'Sigmoid Gradient', - 'Neural Network Gradient (Backpropagation)', - 'Regularized Gradient'] - super().__init__('neural-network-learning', part_names) - - def __iter__(self): - for part_id in range(1, 6): - try: - func = self.functions[part_id] - - # Each part has different expected arguments/different function - if part_id == 1: - res = func(self.t, 2, 4, 4, self.Xm, self.ym, 0)[0] - elif part_id == 2: - res = func(self.t, 2, 4, 4, self.Xm, self.ym, 1.5) - elif part_id == 3: - res = func(self.X, ) - elif part_id == 4: - J, grad = func(self.t, 2, 4, 4, self.Xm, self.ym, 0) - grad1 = np.reshape(grad[:12], (4, 3)) - grad2 = np.reshape(grad[12:], (4, 5)) - grad = np.concatenate([grad1.ravel('F'), grad2.ravel('F')]) - res = np.hstack([J, grad]).tolist() - elif part_id == 5: - J, grad = func(self.t, 2, 4, 4, self.Xm, self.ym, 1.5) - grad1 = np.reshape(grad[:12], (4, 3)) - grad2 = np.reshape(grad[12:], (4, 5)) - grad = np.concatenate([grad1.ravel('F'), grad2.ravel('F')]) - res = np.hstack([J, grad]).tolist() - else: - raise KeyError - yield part_id, res - except KeyError: - yield part_id, 0 diff --git a/Exercise5/Figures/cross_validation.png b/Exercise5/Figures/cross_validation.png deleted file mode 100644 index e6a8f28f..00000000 Binary files a/Exercise5/Figures/cross_validation.png and /dev/null differ diff --git a/Exercise5/Figures/learning_curve.png b/Exercise5/Figures/learning_curve.png deleted file mode 100755 index c4d3e1fb..00000000 Binary files a/Exercise5/Figures/learning_curve.png and /dev/null differ diff --git a/Exercise5/Figures/learning_curve_random.png b/Exercise5/Figures/learning_curve_random.png deleted file mode 100644 index ee965256..00000000 Binary files a/Exercise5/Figures/learning_curve_random.png and /dev/null differ diff --git a/Exercise5/Figures/linear_fit.png b/Exercise5/Figures/linear_fit.png deleted file mode 100755 index 826912f6..00000000 Binary files a/Exercise5/Figures/linear_fit.png and /dev/null differ diff --git a/Exercise5/Figures/polynomial_learning_curve.png b/Exercise5/Figures/polynomial_learning_curve.png deleted file mode 100644 index 39e4af46..00000000 Binary files a/Exercise5/Figures/polynomial_learning_curve.png and /dev/null differ diff --git a/Exercise5/Figures/polynomial_learning_curve_reg_1.png b/Exercise5/Figures/polynomial_learning_curve_reg_1.png deleted file mode 100644 index 01b52b04..00000000 Binary files a/Exercise5/Figures/polynomial_learning_curve_reg_1.png and /dev/null differ diff --git a/Exercise5/Figures/polynomial_regression.png b/Exercise5/Figures/polynomial_regression.png deleted file mode 100644 index 530ae53e..00000000 Binary files a/Exercise5/Figures/polynomial_regression.png and /dev/null differ diff --git a/Exercise5/Figures/polynomial_regression_reg_1.png b/Exercise5/Figures/polynomial_regression_reg_1.png deleted file mode 100644 index e27bb13d..00000000 Binary files a/Exercise5/Figures/polynomial_regression_reg_1.png and /dev/null differ diff --git a/Exercise5/Figures/polynomial_regression_reg_100.png b/Exercise5/Figures/polynomial_regression_reg_100.png deleted file mode 100644 index cb060bc9..00000000 Binary files a/Exercise5/Figures/polynomial_regression_reg_100.png and /dev/null differ diff --git a/Exercise5/exercise5.ipynb b/Exercise5/exercise5.ipynb deleted file mode 100755 index c5e5c679..00000000 --- a/Exercise5/exercise5.ipynb +++ /dev/null @@ -1,927 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Programming Exercise 5:\n", - "# Regularized Linear Regression and Bias vs Variance\n", - "\n", - "## Introduction\n", - "\n", - "In this exercise, you will implement regularized linear regression and use it to study models with different bias-variance properties. Before starting on the programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics.\n", - "\n", - "All the information you need for solving this assignment is in this notebook, and all the code you will be implementing will take place within this notebook. The assignment can be promptly submitted to the coursera grader directly from this notebook (code and instructions are included below).\n", - "\n", - "Before we begin with the exercises, we need to import all libraries required for this programming exercise. Throughout the course, we will be using [`numpy`](http://www.numpy.org/) for all arrays and matrix operations, [`matplotlib`](https://matplotlib.org/) for plotting, and [`scipy`](https://docs.scipy.org/doc/scipy/reference/) for scientific and numerical computation functions and tools. You can find instructions on how to install required libraries in the README file in the [github repository](https://github.com/dibgerge/ml-coursera-python-assignments)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# used for manipulating directory paths\n", - "import os\n", - "\n", - "# Scientific and vector computation for python\n", - "import numpy as np\n", - "\n", - "# Plotting library\n", - "from matplotlib import pyplot\n", - "\n", - "# Optimization module in scipy\n", - "from scipy import optimize\n", - "\n", - "# will be used to load MATLAB mat datafile format\n", - "from scipy.io import loadmat\n", - "\n", - "# library written for this exercise providing additional functions for assignment submission, and others\n", - "import utils\n", - "\n", - "# define the submission/grader object for this exercise\n", - "grader = utils.Grader()\n", - "\n", - "# tells matplotlib to embed plots within the notebook\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Submission and Grading\n", - "\n", - "\n", - "After completing each part of the assignment, be sure to submit your solutions to the grader. The following is a breakdown of how each part of this exercise is scored.\n", - "\n", - "\n", - "| Section | Part | Submitted Function | Points |\n", - "| :- |:- |:- | :-: |\n", - "| 1 | [Regularized Linear Regression Cost Function](#section1) | [`linearRegCostFunction`](#linearRegCostFunction) | 25 |\n", - "| 2 | [Regularized Linear Regression Gradient](#section2) | [`linearRegCostFunction`](#linearRegCostFunction) |25 |\n", - "| 3 | [Learning Curve](#section3) | [`learningCurve`](#func2) | 20 |\n", - "| 4 | [Polynomial Feature Mapping](#section4) | [`polyFeatures`](#polyFeatures) | 10 |\n", - "| 5 | [Cross Validation Curve](#section5) | [`validationCurve`](#validationCurve) | 20 |\n", - "| | Total Points | |100 |\n", - "\n", - "\n", - "You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n", - "\n", - "
\n", - "At the end of each section in this notebook, we have a cell which contains code for submitting the solutions thus far to the grader. Execute the cell to see your score up to the current section. For all your work to be submitted properly, you must execute those cells at least once.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## 1 Regularized Linear Regression\n", - "\n", - "In the first half of the exercise, you will implement regularized linear regression to predict the amount of water flowing out of a dam using the change of water level in a reservoir. In the next half, you will go through some diagnostics of debugging learning algorithms and examine the effects of bias v.s.\n", - "variance. \n", - "\n", - "### 1.1 Visualizing the dataset\n", - "\n", - "We will begin by visualizing the dataset containing historical records on the change in the water level, $x$, and the amount of water flowing out of the dam, $y$. This dataset is divided into three parts:\n", - "\n", - "- A **training** set that your model will learn on: `X`, `y`\n", - "- A **cross validation** set for determining the regularization parameter: `Xval`, `yval`\n", - "- A **test** set for evaluating performance. These are “unseen” examples which your model did not see during training: `Xtest`, `ytest`\n", - "\n", - "Run the next cell to plot the training data. In the following parts, you will implement linear regression and use that to fit a straight line to the data and plot learning curves. Following that, you will implement polynomial regression to find a better fit to the data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Load from ex5data1.mat, where all variables will be store in a dictionary\n", - "data = loadmat(os.path.join('Data', 'ex5data1.mat'))\n", - "\n", - "# Extract train, test, validation data from dictionary\n", - "# and also convert y's form 2-D matrix (MATLAB format) to a numpy vector\n", - "X, y = data['X'], data['y'][:, 0]\n", - "Xtest, ytest = data['Xtest'], data['ytest'][:, 0]\n", - "Xval, yval = data['Xval'], data['yval'][:, 0]\n", - "\n", - "# m = Number of examples\n", - "m = y.size\n", - "\n", - "# Plot training data\n", - "pyplot.plot(X, y, 'ro', ms=10, mec='k', mew=1)\n", - "pyplot.xlabel('Change in water level (x)')\n", - "pyplot.ylabel('Water flowing out of the dam (y)');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.2 Regularized linear regression cost function\n", - "\n", - "Recall that regularized linear regression has the following cost function:\n", - "\n", - "$$ J(\\theta) = \\frac{1}{2m} \\left( \\sum_{i=1}^m \\left( h_\\theta\\left( x^{(i)} \\right) - y^{(i)} \\right)^2 \\right) + \\frac{\\lambda}{2m} \\left( \\sum_{j=1}^n \\theta_j^2 \\right)$$\n", - "\n", - "where $\\lambda$ is a regularization parameter which controls the degree of regularization (thus, help preventing overfitting). The regularization term puts a penalty on the overall cost J. As the magnitudes of the model parameters $\\theta_j$ increase, the penalty increases as well. Note that you should not regularize\n", - "the $\\theta_0$ term.\n", - "\n", - "You should now complete the code in the function `linearRegCostFunction` in the next cell. Your task is to calculate the regularized linear regression cost function. If possible, try to vectorize your code and avoid writing loops.\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def linearRegCostFunction(X, y, theta, lambda_=0.0):\n", - " \"\"\"\n", - " Compute cost and gradient for regularized linear regression \n", - " with multiple variables. Computes the cost of using theta as\n", - " the parameter for linear regression to fit the data points in X and y. \n", - " \n", - " Parameters\n", - " ----------\n", - " X : array_like\n", - " The dataset. Matrix with shape (m x n + 1) where m is the \n", - " total number of examples, and n is the number of features \n", - " before adding the bias term.\n", - " \n", - " y : array_like\n", - " The functions values at each datapoint. A vector of\n", - " shape (m, ).\n", - " \n", - " theta : array_like\n", - " The parameters for linear regression. A vector of shape (n+1,).\n", - " \n", - " lambda_ : float, optional\n", - " The regularization parameter.\n", - " \n", - " Returns\n", - " -------\n", - " J : float\n", - " The computed cost function. \n", - " \n", - " grad : array_like\n", - " The value of the cost function gradient w.r.t theta. \n", - " A vector of shape (n+1, ).\n", - " \n", - " Instructions\n", - " ------------\n", - " Compute the cost and gradient of regularized linear regression for\n", - " a particular choice of theta.\n", - " You should set J to the cost and grad to the gradient.\n", - " \"\"\"\n", - " # Initialize some useful values\n", - " m = y.size # number of training examples\n", - "\n", - " # You need to return the following variables correctly \n", - " J = 0\n", - " grad = np.zeros(theta.shape)\n", - "\n", - " # ====================== YOUR CODE HERE ======================\n", - "\n", - "\n", - "\n", - " # ============================================================\n", - " return J, grad" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When you are finished, the next cell will run your cost function using `theta` initialized at `[1, 1]`. You should expect to see an output of 303.993." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "theta = np.array([1, 1])\n", - "J, _ = linearRegCostFunction(np.concatenate([np.ones((m, 1)), X], axis=1), y, theta, 1)\n", - "\n", - "print('Cost at theta = [1, 1]:\\t %f ' % J)\n", - "print('This value should be about 303.993192)\\n' % J)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After completing a part of the exercise, you can submit your solutions for grading by first adding the function you modified to the submission object, and then sending your function to Coursera for grading. \n", - "\n", - "The submission script will prompt you for your login e-mail and submission token. You can obtain a submission token from the web page for the assignment. You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n", - "\n", - "*Execute the following cell to grade your solution to the first part of this exercise.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "grader[1] = linearRegCostFunction\n", - "grader.grade()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### 1.3 Regularized linear regression gradient\n", - "\n", - "Correspondingly, the partial derivative of the cost function for regularized linear regression is defined as:\n", - "\n", - "$$\n", - "\\begin{align}\n", - "& \\frac{\\partial J(\\theta)}{\\partial \\theta_0} = \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta \\left(x^{(i)} \\right) - y^{(i)} \\right) x_j^{(i)} & \\qquad \\text{for } j = 0 \\\\\n", - "& \\frac{\\partial J(\\theta)}{\\partial \\theta_j} = \\left( \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta \\left( x^{(i)} \\right) - y^{(i)} \\right) x_j^{(i)} \\right) + \\frac{\\lambda}{m} \\theta_j & \\qquad \\text{for } j \\ge 1\n", - "\\end{align}\n", - "$$\n", - "\n", - "In the function [`linearRegCostFunction`](#linearRegCostFunction) above, add code to calculate the gradient, returning it in the variable `grad`. Do not forget to re-execute the cell containing this function to update the function's definition.\n", - "\n", - "\n", - "When you are finished, use the next cell to run your gradient function using theta initialized at `[1, 1]`. You should expect to see a gradient of `[-15.30, 598.250]`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "theta = np.array([1, 1])\n", - "J, grad = linearRegCostFunction(np.concatenate([np.ones((m, 1)), X], axis=1), y, theta, 1)\n", - "\n", - "print('Gradient at theta = [1, 1]: [{:.6f}, {:.6f}] '.format(*grad))\n", - "print(' (this value should be about [-15.303016, 598.250744])\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*You should now submit your solutions.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "grader[2] = linearRegCostFunction\n", - "grader.grade()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fitting linear regression\n", - "\n", - "Once your cost function and gradient are working correctly, the next cell will run the code in `trainLinearReg` (found in the module `utils.py`) to compute the optimal values of $\\theta$. This training function uses `scipy`'s optimization module to minimize the cost function.\n", - "\n", - "In this part, we set regularization parameter $\\lambda$ to zero. Because our current implementation of linear regression is trying to fit a 2-dimensional $\\theta$, regularization will not be incredibly helpful for a $\\theta$ of such low dimension. In the later parts of the exercise, you will be using polynomial regression with regularization.\n", - "\n", - "Finally, the code in the next cell should also plot the best fit line, which should look like the figure below. \n", - "\n", - "![](Figures/linear_fit.png)\n", - "\n", - "The best fit line tells us that the model is not a good fit to the data because the data has a non-linear pattern. While visualizing the best fit as shown is one possible way to debug your learning algorithm, it is not always easy to visualize the data and model. In the next section, you will implement a function to generate learning curves that can help you debug your learning algorithm even if it is not easy to visualize the\n", - "data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# add a columns of ones for the y-intercept\n", - "X_aug = np.concatenate([np.ones((m, 1)), X], axis=1)\n", - "theta = utils.trainLinearReg(linearRegCostFunction, X_aug, y, lambda_=0)\n", - "\n", - "# Plot fit over the data\n", - "pyplot.plot(X, y, 'ro', ms=10, mec='k', mew=1.5)\n", - "pyplot.xlabel('Change in water level (x)')\n", - "pyplot.ylabel('Water flowing out of the dam (y)')\n", - "pyplot.plot(X, np.dot(X_aug, theta), '--', lw=2);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## 2 Bias-variance\n", - "\n", - "An important concept in machine learning is the bias-variance tradeoff. Models with high bias are not complex enough for the data and tend to underfit, while models with high variance overfit to the training data.\n", - "\n", - "In this part of the exercise, you will plot training and test errors on a learning curve to diagnose bias-variance problems.\n", - "\n", - "### 2.1 Learning Curves\n", - "\n", - "You will now implement code to generate the learning curves that will be useful in debugging learning algorithms. Recall that a learning curve plots training and cross validation error as a function of training set size. Your job is to fill in the function `learningCurve` in the next cell, so that it returns a vector of errors for the training set and cross validation set.\n", - "\n", - "To plot the learning curve, we need a training and cross validation set error for different training set sizes. To obtain different training set sizes, you should use different subsets of the original training set `X`. Specifically, for a training set size of $i$, you should use the first $i$ examples (i.e., `X[:i, :]`\n", - "and `y[:i]`).\n", - "\n", - "You can use the `trainLinearReg` function (by calling `utils.trainLinearReg(...)`) to find the $\\theta$ parameters. Note that the `lambda_` is passed as a parameter to the `learningCurve` function.\n", - "After learning the $\\theta$ parameters, you should compute the error on the training and cross validation sets. Recall that the training error for a dataset is defined as\n", - "\n", - "$$ J_{\\text{train}} = \\frac{1}{2m} \\left[ \\sum_{i=1}^m \\left(h_\\theta \\left( x^{(i)} \\right) - y^{(i)} \\right)^2 \\right] $$\n", - "\n", - "In particular, note that the training error does not include the regularization term. One way to compute the training error is to use your existing cost function and set $\\lambda$ to 0 only when using it to compute the training error and cross validation error. When you are computing the training set error, make sure you compute it on the training subset (i.e., `X[:n,:]` and `y[:n]`) instead of the entire training set. However, for the cross validation error, you should compute it over the entire cross validation set. You should store\n", - "the computed errors in the vectors error train and error val.\n", - "\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def learningCurve(X, y, Xval, yval, lambda_=0):\n", - " \"\"\"\n", - " Generates the train and cross validation set errors needed to plot a learning curve\n", - " returns the train and cross validation set errors for a learning curve. \n", - " \n", - " In this function, you will compute the train and test errors for\n", - " dataset sizes from 1 up to m. In practice, when working with larger\n", - " datasets, you might want to do this in larger intervals.\n", - " \n", - " Parameters\n", - " ----------\n", - " X : array_like\n", - " The training dataset. Matrix with shape (m x n + 1) where m is the \n", - " total number of examples, and n is the number of features \n", - " before adding the bias term.\n", - " \n", - " y : array_like\n", - " The functions values at each training datapoint. A vector of\n", - " shape (m, ).\n", - " \n", - " Xval : array_like\n", - " The validation dataset. Matrix with shape (m_val x n + 1) where m is the \n", - " total number of examples, and n is the number of features \n", - " before adding the bias term.\n", - " \n", - " yval : array_like\n", - " The functions values at each validation datapoint. A vector of\n", - " shape (m_val, ).\n", - " \n", - " lambda_ : float, optional\n", - " The regularization parameter.\n", - " \n", - " Returns\n", - " -------\n", - " error_train : array_like\n", - " A vector of shape m. error_train[i] contains the training error for\n", - " i examples.\n", - " error_val : array_like\n", - " A vecotr of shape m. error_val[i] contains the validation error for\n", - " i training examples.\n", - " \n", - " Instructions\n", - " ------------\n", - " Fill in this function to return training errors in error_train and the\n", - " cross validation errors in error_val. i.e., error_train[i] and \n", - " error_val[i] should give you the errors obtained after training on i examples.\n", - " \n", - " Notes\n", - " -----\n", - " - You should evaluate the training error on the first i training\n", - " examples (i.e., X[:i, :] and y[:i]).\n", - " \n", - " For the cross-validation error, you should instead evaluate on\n", - " the _entire_ cross validation set (Xval and yval).\n", - " \n", - " - If you are using your cost function (linearRegCostFunction) to compute\n", - " the training and cross validation error, you should call the function with\n", - " the lambda argument set to 0. Do note that you will still need to use\n", - " lambda when running the training to obtain the theta parameters.\n", - " \n", - " Hint\n", - " ----\n", - " You can loop over the examples with the following:\n", - " \n", - " for i in range(1, m+1):\n", - " # Compute train/cross validation errors using training examples \n", - " # X[:i, :] and y[:i], storing the result in \n", - " # error_train[i-1] and error_val[i-1]\n", - " .... \n", - " \"\"\"\n", - " # Number of training examples\n", - " m = y.size\n", - "\n", - " # You need to return these values correctly\n", - " error_train = np.zeros(m)\n", - " error_val = np.zeros(m)\n", - "\n", - " # ====================== YOUR CODE HERE ======================\n", - " \n", - "\n", - " \n", - " # =============================================================\n", - " return error_train, error_val" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When you are finished implementing the function `learningCurve`, executing the next cell prints the learning curves and produce a plot similar to the figure below. \n", - "\n", - "![](Figures/learning_curve.png)\n", - "\n", - "In the learning curve figure, you can observe that both the train error and cross validation error are high when the number of training examples is increased. This reflects a high bias problem in the model - the linear regression model is too simple and is unable to fit our dataset well. In the next section, you will implement polynomial regression to fit a better model for this dataset." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "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", - "error_train, error_val = learningCurve(X_aug, y, Xval_aug, yval, lambda_=0)\n", - "\n", - "pyplot.plot(np.arange(1, m+1), error_train, np.arange(1, m+1), error_val, lw=2)\n", - "pyplot.title('Learning curve for linear regression')\n", - "pyplot.legend(['Train', 'Cross Validation'])\n", - "pyplot.xlabel('Number of training examples')\n", - "pyplot.ylabel('Error')\n", - "pyplot.axis([0, 13, 0, 150])\n", - "\n", - "print('# Training Examples\\tTrain Error\\tCross Validation Error')\n", - "for i in range(m):\n", - " print(' \\t%d\\t\\t%f\\t%f' % (i+1, error_train[i], error_val[i]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*You should now submit your solutions.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "grader[3] = learningCurve\n", - "grader.grade()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "## 3 Polynomial regression\n", - "\n", - "The problem with our linear model was that it was too simple for the data\n", - "and resulted in underfitting (high bias). In this part of the exercise, you will address this problem by adding more features. For polynomial regression, our hypothesis has the form:\n", - "\n", - "$$\n", - "\\begin{align}\n", - "h_\\theta(x) &= \\theta_0 + \\theta_1 \\times (\\text{waterLevel}) + \\theta_2 \\times (\\text{waterLevel})^2 + \\cdots + \\theta_p \\times (\\text{waterLevel})^p \\\\\n", - "& = \\theta_0 + \\theta_1 x_1 + \\theta_2 x_2 + \\cdots + \\theta_p x_p\n", - "\\end{align}\n", - "$$\n", - "\n", - "Notice that by defining $x_1 = (\\text{waterLevel})$, $x_2 = (\\text{waterLevel})^2$ , $\\cdots$, $x_p =\n", - "(\\text{waterLevel})^p$, we obtain a linear regression model where the features are the various powers of the original value (waterLevel).\n", - "\n", - "Now, you will add more features using the higher powers of the existing feature $x$ in the dataset. Your task in this part is to complete the code in the function `polyFeatures` in the next cell. The function should map the original training set $X$ of size $m \\times 1$ into its higher powers. Specifically, when a training set $X$ of size $m \\times 1$ is passed into the function, the function should return a $m \\times p$ matrix `X_poly`, where column 1 holds the original values of X, column 2 holds the values of $X^2$, column 3 holds the values of $X^3$, and so on. Note that you don’t have to account for the zero-eth power in this function.\n", - "\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def polyFeatures(X, p):\n", - " \"\"\"\n", - " Maps X (1D vector) into the p-th power.\n", - " \n", - " Parameters\n", - " ----------\n", - " X : array_like\n", - " A data vector of size m, where m is the number of examples.\n", - " \n", - " p : int\n", - " The polynomial power to map the features. \n", - " \n", - " Returns \n", - " -------\n", - " X_poly : array_like\n", - " A matrix of shape (m x p) where p is the polynomial \n", - " power and m is the number of examples. That is:\n", - " \n", - " X_poly[i, :] = [X[i], X[i]**2, X[i]**3 ... X[i]**p]\n", - " \n", - " Instructions\n", - " ------------\n", - " Given a vector X, return a matrix X_poly where the p-th column of\n", - " X contains the values of X to the p-th power.\n", - " \"\"\"\n", - " # You need to return the following variables correctly.\n", - " X_poly = np.zeros((X.shape[0], p))\n", - "\n", - " # ====================== YOUR CODE HERE ======================\n", - "\n", - "\n", - "\n", - " # ============================================================\n", - " return X_poly" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now you have a function that will map features to a higher dimension. The next cell will apply it to the training set, the test set, and the cross validation set." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p = 8\n", - "\n", - "# Map X onto Polynomial Features and Normalize\n", - "X_poly = polyFeatures(X, p)\n", - "X_poly, mu, sigma = utils.featureNormalize(X_poly)\n", - "X_poly = np.concatenate([np.ones((m, 1)), X_poly], axis=1)\n", - "\n", - "# Map X_poly_test and normalize (using mu and sigma)\n", - "X_poly_test = polyFeatures(Xtest, p)\n", - "X_poly_test -= mu\n", - "X_poly_test /= sigma\n", - "X_poly_test = np.concatenate([np.ones((ytest.size, 1)), X_poly_test], axis=1)\n", - "\n", - "# Map X_poly_val and normalize (using mu and sigma)\n", - "X_poly_val = polyFeatures(Xval, p)\n", - "X_poly_val -= mu\n", - "X_poly_val /= sigma\n", - "X_poly_val = np.concatenate([np.ones((yval.size, 1)), X_poly_val], axis=1)\n", - "\n", - "print('Normalized Training Example 1:')\n", - "X_poly[0, :]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*You should now submit your solutions.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "grader[4] = polyFeatures\n", - "grader.grade()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.1 Learning Polynomial Regression\n", - "\n", - "After you have completed the function `polyFeatures`, we will proceed to train polynomial regression using your linear regression cost function.\n", - "\n", - "Keep in mind that even though we have polynomial terms in our feature vector, we are still solving a linear regression optimization problem. The polynomial terms have simply turned into features that we can use for linear regression. We are using the same cost function and gradient that you wrote for the earlier part of this exercise.\n", - "\n", - "For this part of the exercise, you will be using a polynomial of degree 8. It turns out that if we run the training directly on the projected data, will not work well as the features would be badly scaled (e.g., an example with $x = 40$ will now have a feature $x_8 = 40^8 = 6.5 \\times 10^{12}$). Therefore, you will\n", - "need to use feature normalization.\n", - "\n", - "Before learning the parameters $\\theta$ for the polynomial regression, we first call `featureNormalize` and normalize the features of the training set, storing the mu, sigma parameters separately. We have already implemented this function for you (in `utils.py` module) and it is the same function from the first exercise.\n", - "\n", - "After learning the parameters $\\theta$, you should see two plots generated for polynomial regression with $\\lambda = 0$, which should be similar to the ones here:\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - "\n", - "You should see that the polynomial fit is able to follow the datapoints very well, thus, obtaining a low training error. The figure on the right shows that the training error essentially stays zero for all numbers of training samples. However, the polynomial fit is very complex and even drops off at the extremes. This is an indicator that the polynomial regression model is overfitting the training data and will not generalize well.\n", - "\n", - "To better understand the problems with the unregularized ($\\lambda = 0$) model, you can see that the learning curve shows the same effect where the training error is low, but the cross validation error is high. There is a gap between the training and cross validation errors, indicating a high variance problem." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "lambda_ = 0\n", - "theta = utils.trainLinearReg(linearRegCostFunction, X_poly, y,\n", - " lambda_=lambda_, maxiter=55)\n", - "\n", - "# Plot training data and fit\n", - "pyplot.plot(X, y, 'ro', ms=10, mew=1.5, mec='k')\n", - "\n", - "utils.plotFit(polyFeatures, np.min(X), np.max(X), mu, sigma, theta, p)\n", - "\n", - "pyplot.xlabel('Change in water level (x)')\n", - "pyplot.ylabel('Water flowing out of the dam (y)')\n", - "pyplot.title('Polynomial Regression Fit (lambda = %f)' % lambda_)\n", - "pyplot.ylim([-20, 50])\n", - "\n", - "pyplot.figure()\n", - "error_train, error_val = learningCurve(X_poly, y, X_poly_val, yval, lambda_)\n", - "pyplot.plot(np.arange(1, 1+m), error_train, np.arange(1, 1+m), error_val)\n", - "\n", - "pyplot.title('Polynomial Regression Learning Curve (lambda = %f)' % lambda_)\n", - "pyplot.xlabel('Number of training examples')\n", - "pyplot.ylabel('Error')\n", - "pyplot.axis([0, 13, 0, 100])\n", - "pyplot.legend(['Train', 'Cross Validation'])\n", - "\n", - "print('Polynomial Regression (lambda = %f)\\n' % lambda_)\n", - "print('# Training Examples\\tTrain Error\\tCross Validation Error')\n", - "for i in range(m):\n", - " print(' \\t%d\\t\\t%f\\t%f' % (i+1, error_train[i], error_val[i]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One way to combat the overfitting (high-variance) problem is to add regularization to the model. In the next section, you will get to try different $\\lambda$ parameters to see how regularization can lead to a better model.\n", - "\n", - "### 3.2 Optional (ungraded) exercise: Adjusting the regularization parameter\n", - "\n", - "In this section, you will get to observe how the regularization parameter affects the bias-variance of regularized polynomial regression. You should now modify the the lambda parameter and try $\\lambda = 1, 100$. For each of these values, the script should generate a polynomial fit to the data and also a learning curve.\n", - "\n", - "For $\\lambda = 1$, the generated plots should look like the the figure below. You should see a polynomial fit that follows the data trend well (left) and a learning curve (right) showing that both the cross validation and training error converge to a relatively low value. This shows the $\\lambda = 1$ regularized polynomial regression model does not have the high-bias or high-variance problems. In effect, it achieves a good trade-off between bias and variance.\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - "\n", - "For $\\lambda = 100$, you should see a polynomial fit (figure below) that does not follow the data well. In this case, there is too much regularization and the model is unable to fit the training data.\n", - "\n", - "![](Figures/polynomial_regression_reg_100.png)\n", - "\n", - "*You do not need to submit any solutions for this optional (ungraded) exercise.*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### 3.3 Selecting $\\lambda$ using a cross validation set\n", - "\n", - "From the previous parts of the exercise, you observed that the value of $\\lambda$ can significantly affect the results of regularized polynomial regression on the training and cross validation set. In particular, a model without regularization ($\\lambda = 0$) fits the training set well, but does not generalize. Conversely, a model with too much regularization ($\\lambda = 100$) does not fit the training set and testing set well. A good choice of $\\lambda$ (e.g., $\\lambda = 1$) can provide a good fit to the data.\n", - "\n", - "In this section, you will implement an automated method to select the $\\lambda$ parameter. Concretely, you will use a cross validation set to evaluate how good each $\\lambda$ value is. After selecting the best $\\lambda$ value using the cross validation set, we can then evaluate the model on the test set to estimate\n", - "how well the model will perform on actual unseen data. \n", - "\n", - "Your task is to complete the code in the function `validationCurve`. Specifically, you should should use the `utils.trainLinearReg` function to train the model using different values of $\\lambda$ and compute the training error and cross validation error. You should try $\\lambda$ in the following range: {0, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1, 3, 10}.\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def validationCurve(X, y, Xval, yval):\n", - " \"\"\"\n", - " Generate the train and validation errors needed to plot a validation\n", - " curve that we can use to select lambda_.\n", - " \n", - " Parameters\n", - " ----------\n", - " X : array_like\n", - " The training dataset. Matrix with shape (m x n) where m is the \n", - " total number of training examples, and n is the number of features \n", - " including any polynomial features.\n", - " \n", - " y : array_like\n", - " The functions values at each training datapoint. A vector of\n", - " shape (m, ).\n", - " \n", - " Xval : array_like\n", - " The validation dataset. Matrix with shape (m_val x n) where m is the \n", - " total number of validation examples, and n is the number of features \n", - " including any polynomial features.\n", - " \n", - " yval : array_like\n", - " The functions values at each validation datapoint. A vector of\n", - " shape (m_val, ).\n", - " \n", - " Returns\n", - " -------\n", - " lambda_vec : list\n", - " The values of the regularization parameters which were used in \n", - " cross validation.\n", - " \n", - " error_train : list\n", - " The training error computed at each value for the regularization\n", - " parameter.\n", - " \n", - " error_val : list\n", - " The validation error computed at each value for the regularization\n", - " parameter.\n", - " \n", - " Instructions\n", - " ------------\n", - " Fill in this function to return training errors in `error_train` and\n", - " the validation errors in `error_val`. The vector `lambda_vec` contains\n", - " the different lambda parameters to use for each calculation of the\n", - " errors, i.e, `error_train[i]`, and `error_val[i]` should give you the\n", - " errors obtained after training with `lambda_ = lambda_vec[i]`.\n", - "\n", - " Note\n", - " ----\n", - " You can loop over lambda_vec with the following:\n", - " \n", - " for i in range(len(lambda_vec))\n", - " lambda = lambda_vec[i]\n", - " # Compute train / val errors when training linear \n", - " # regression with regularization parameter lambda_\n", - " # You should store the result in error_train[i]\n", - " # and error_val[i]\n", - " ....\n", - " \"\"\"\n", - " # Selected values of lambda (you should not change this)\n", - " lambda_vec = [0, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1, 3, 10]\n", - "\n", - " # You need to return these variables correctly.\n", - " error_train = np.zeros(len(lambda_vec))\n", - " error_val = np.zeros(len(lambda_vec))\n", - "\n", - " # ====================== YOUR CODE HERE ======================\n", - "\n", - "\n", - "\n", - " # ============================================================\n", - " return lambda_vec, error_train, error_val" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After you have completed the code, the next cell will run your function and plot a cross validation curve of error v.s. $\\lambda$ that allows you select which $\\lambda$ parameter to use. You should see a plot similar to the figure below. \n", - "\n", - "![](Figures/cross_validation.png)\n", - "\n", - "In this figure, we can see that the best value of $\\lambda$ is around 3. Due to randomness\n", - "in the training and validation splits of the dataset, the cross validation error can sometimes be lower than the training error." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "lambda_vec, error_train, error_val = validationCurve(X_poly, y, X_poly_val, yval)\n", - "\n", - "pyplot.plot(lambda_vec, error_train, '-o', lambda_vec, error_val, '-o', lw=2)\n", - "pyplot.legend(['Train', 'Cross Validation'])\n", - "pyplot.xlabel('lambda')\n", - "pyplot.ylabel('Error')\n", - "\n", - "print('lambda\\t\\tTrain Error\\tValidation Error')\n", - "for i in range(len(lambda_vec)):\n", - " print(' %f\\t%f\\t%f' % (lambda_vec[i], error_train[i], error_val[i]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*You should now submit your solutions.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "grader[5] = validationCurve\n", - "grader.grade()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.4 Optional (ungraded) exercise: Computing test set error\n", - "\n", - "In the previous part of the exercise, you implemented code to compute the cross validation error for various values of the regularization parameter $\\lambda$. However, to get a better indication of the model’s performance in the real world, it is important to evaluate the “final” model on a test set that was not used in any part of training (that is, it was neither used to select the $\\lambda$ parameters, nor to learn the model parameters $\\theta$). For this optional (ungraded) exercise, you should compute the test error using the best value of $\\lambda$ you found. In our cross validation, we obtained a test error of 3.8599 for $\\lambda = 3$.\n", - "\n", - "*You do not need to submit any solutions for this optional (ungraded) exercise.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.5 Optional (ungraded) exercise: Plotting learning curves with randomly selected examples\n", - "\n", - "In practice, especially for small training sets, when you plot learning curves to debug your algorithms, it is often helpful to average across multiple sets of randomly selected examples to determine the training error and cross validation error.\n", - "\n", - "Concretely, to determine the training error and cross validation error for $i$ examples, you should first randomly select $i$ examples from the training set and $i$ examples from the cross validation set. You will then learn the parameters $\\theta$ using the randomly chosen training set and evaluate the parameters $\\theta$ on the randomly chosen training set and cross validation set. The above steps should then be repeated multiple times (say 50) and the averaged error should be used to determine the training error and cross validation error for $i$ examples.\n", - "\n", - "For this optional (ungraded) exercise, you should implement the above strategy for computing the learning curves. For reference, the figure below shows the learning curve we obtained for polynomial regression with $\\lambda = 0.01$. Your figure may differ slightly due to the random selection of examples.\n", - "\n", - "![](Figures/learning_curve_random.png)\n", - "\n", - "*You do not need to submit any solutions for this optional (ungraded) exercise.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Exercise5/utils.py b/Exercise5/utils.py deleted file mode 100755 index b2340ad7..00000000 --- a/Exercise5/utils.py +++ /dev/null @@ -1,164 +0,0 @@ -import sys -import numpy as np -from scipy import optimize -from matplotlib import pyplot - -sys.path.append('..') -from submission import SubmissionBase - - -def trainLinearReg(linearRegCostFunction, X, y, lambda_=0.0, maxiter=200): - """ - Trains linear regression using scipy's optimize.minimize. - - Parameters - ---------- - X : array_like - The dataset with shape (m x n+1). The bias term is assumed to be concatenated. - - y : array_like - Function values at each datapoint. A vector of shape (m,). - - lambda_ : float, optional - The regularization parameter. - - maxiter : int, optional - Maximum number of iteration for the optimization algorithm. - - Returns - ------- - theta : array_like - The parameters for linear regression. This is a vector of shape (n+1,). - """ - # Initialize Theta - initial_theta = np.zeros(X.shape[1]) - - # Create "short hand" for the cost function to be minimized - costFunction = lambda t: linearRegCostFunction(X, y, t, lambda_) - - # Now, costFunction is a function that takes in only one argument - options = {'maxiter': maxiter} - - # Minimize using scipy - res = optimize.minimize(costFunction, initial_theta, jac=True, method='TNC', options=options) - return res.x - - -def featureNormalize(X): - """ - Normalizes the features in X returns a normalized version of X where the mean value of each - feature is 0 and the standard deviation is 1. This is often a good preprocessing step to do when - working with learning algorithms. - - Parameters - ---------- - X : array_like - An dataset which is a (m x n) matrix, where m is the number of examples, - and n is the number of dimensions for each example. - - Returns - ------- - X_norm : array_like - The normalized input dataset. - - mu : array_like - A vector of size n corresponding to the mean for each dimension across all examples. - - sigma : array_like - A vector of size n corresponding to the standard deviations for each dimension across - all examples. - """ - mu = np.mean(X, axis=0) - X_norm = X - mu - - sigma = np.std(X_norm, axis=0, ddof=1) - X_norm /= sigma - return X_norm, mu, sigma - - -def plotFit(polyFeatures, min_x, max_x, mu, sigma, theta, p): - """ - Plots a learned polynomial regression fit over an existing figure. - Also works with linear regression. - Plots the learned polynomial fit with power p and feature normalization (mu, sigma). - - Parameters - ---------- - polyFeatures : func - A function which generators polynomial features from a single feature. - - min_x : float - The minimum value for the feature. - - max_x : float - The maximum value for the feature. - - mu : float - The mean feature value over the training dataset. - - sigma : float - The feature standard deviation of the training dataset. - - theta : array_like - The parameters for the trained polynomial linear regression. - - p : int - The polynomial order. - """ - # We plot a range slightly bigger than the min and max values to get - # an idea of how the fit will vary outside the range of the data points - x = np.arange(min_x - 15, max_x + 25, 0.05).reshape(-1, 1) - - # Map the X values - X_poly = polyFeatures(x, p) - X_poly -= mu - X_poly /= sigma - - # Add ones - X_poly = np.concatenate([np.ones((x.shape[0], 1)), X_poly], axis=1) - - # Plot - pyplot.plot(x, np.dot(X_poly, theta), '--', lw=2) - - -class Grader(SubmissionBase): - # Random test cases - X = np.vstack([np.ones(10), - np.sin(np.arange(1, 15, 1.5)), - np.cos(np.arange(1, 15, 1.5))]).T - y = np.sin(np.arange(1, 31, 3)) - Xval = np.vstack([np.ones(10), - np.sin(np.arange(0, 14, 1.5)), - np.cos(np.arange(0, 14, 1.5))]).T - yval = np.sin(np.arange(1, 11)) - - def __init__(self): - part_names = ['Regularized Linear Regression Cost Function', - 'Regularized Linear Regression Gradient', - 'Learning Curve', - 'Polynomial Feature Mapping', - 'Validation Curve'] - super().__init__('regularized-linear-regression-and-bias-variance', part_names) - - def __iter__(self): - for part_id in range(1, 6): - try: - func = self.functions[part_id] - # Each part has different expected arguments/different function - if part_id == 1: - res = func(self.X, self.y, np.array([0.1, 0.2, 0.3]), 0.5) - elif part_id == 2: - theta = np.array([0.1, 0.2, 0.3]) - res = func(self.X, self.y, theta, 0.5)[1] - elif part_id == 3: - res = np.hstack(func(self.X, self.y, self.Xval, self.yval, 1)).tolist() - elif part_id == 4: - res = func(self.X[1, :].reshape(-1, 1), 8) - elif part_id == 5: - res = np.hstack(func(self.X, self.y, self.Xval, self.yval)).tolist() - else: - raise KeyError - except KeyError: - yield part_id, 0 - yield part_id, res - diff --git a/Exercise6/ex6.pdf b/Exercise6/ex6.pdf new file mode 100644 index 00000000..2da2af7c Binary files /dev/null and b/Exercise6/ex6.pdf differ diff --git a/Exercise6/exercise6.ipynb b/Exercise6/exercise6.ipynb index cdc43975..1868d109 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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\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", @@ -147,9 +160,22 @@ }, { "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": [ "# You should try to change the C value below and see how the decision\n", "# boundary varies (e.g., try C = 1000)\n", @@ -180,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -213,8 +239,8 @@ " sim = 0\n", " # ====================== YOUR CODE HERE ======================\n", "\n", - "\n", - "\n", + " sim = np.exp(-np.sum((x1 - x2)**2) / (2*sigma**2))\n", + " \n", " # =============================================================\n", " return sim" ] @@ -228,9 +254,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gaussian Kernel between x1 = [1, 2, 1], x2 = [0, 4, -1], sigma = 2.00:\n", + "\t0.324652\n", + "(for sigma = 2, this value should be about 0.324652)\n", + "\n" + ] + } + ], "source": [ "x1 = np.array([1, 2, 1])\n", "x2 = np.array([0, 4, -1])\n", @@ -251,9 +288,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise support-vector-machines\n", + "\n", + "Use token from last successful submission (ajasmineflower@gmail.com)? (Y/n): Y\n", + " Part Name | Score | Feedback\n", + " --------- | ----- | --------\n", + " Gaussian Kernel | 25 / 25 | Nice work!\n", + " Parameters (C, sigma) for Dataset 3 | 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 +329,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "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 +368,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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kSmwpy0knP9If9x7DuHE1ity8ElTdhqG5dJT3PpzNwwd3OXJoMxIzGfL2L4rC29CH7HB/cacgCBIcvD8lcv9CbKvsSk6FrBU9Ux7unMPi+TNFYV2VFJ292Oi/nsyMNNEoahj7qhqvrIPBDVDvwmeCz++cg71cSbH6Lk6j9Fh5yvaZ5J0PBJ2WnNO7cPSZibKrD4eD9pOXl8PhQ7vo4jGUlIcJte4i8iP96dnvdfFd+m/eiEWrXsicm5JzKgC7gR+RE74RwUyBqv0L5EQH4OQ7G0WTTjxaN57nnx9M/OULWJvJUVeaL+IiWQOcZtlNn9qh8u6ga8/RXLlwqNb+5kVsRGlhVe0CUnVuP2vf5bPU1pOoTkN/hlbnC0Wp5OVkoTW3JGPffOq/u9bo+sxDy5gxZRZDh/tUy6O8KJXL8eex9p5JVTJoVgMHv2wUFLPg6+NoVS6oT+7AftCH1X7EJQ+vi0ZRqPhAQ1Zh0cWLBhPXI0iklOdnEh76C8UlJVi07k3M2TBjjLSZBwkXDzFryi769+vJylWriIrczYR3P+C3vXtYufInzpw5ya5d2/Dy/g+HDm7jzdffIugw2PnOJTvM3wTmKdfkoL2faIT31hu/Wux30d14so+vRd6wvSj05Z2GkBa8pkbN28rdiwendxoZRa06DSYrZC2WrTzRXI9+rPEGr+W75WuQKlzEHVZlfN56yGTSD/jhOOqzx4FTXYaRFfYTlJeibNOHjEPf0eDDTaTGHuTggW0o2/Qh/mIwhw6F893Sb4iqZnE27CLqN+4svn+D22LuzVScX/5aD5Mh4DjwPRSNOxlFfFq6exMeHoCsfhtKHl7DafTn4rnKi2Tld63o7CXCafJOg7l75vHuAGDm5Df1yb5q6O+YUa9wPDycNs0bcPHwUiOBD3qFo6a0AvDsa811Gnqdhm7024CfGzRlh2o0ZavuI1i3YQ15hTp+272Zrj1Hi+cMQTBOlbToymTZzYvkn4NZt3oBFq16i14NjZt7kHZ6P8o2fci/dBTL9i9QnHyFjEPLUHUfgbWHr5FRVOXuQ1bIWrx9XuPipWiykvQQS27oehAkWPX6D/lXwrkvEUSMVOn5MvlXwkkDEf+XqVrz2pvNxPD+GZ9OAgSGDv8PO3dsQNGyJ0uWLcS8uR4XdvSZSfp+PyP83a7/u6hP/0pp5r0a8V4v79eIOhkpaoZ55w+YaN4ZgUux9hyFzKkp6oifUXYcSGF8MGb2rkis65MVsoamzbtQVpxOlqBDZ+1CVvBafEe8gVThYrTDWr3Kz0gLdX7HGCvPDF6DICD6t6dsnUbKpk8o16iNjg0f0hdBKsXOd47Ju6zJi6hnv7dIvh5C3G9f643WI6o3kKrcfciLC6b4URLOVTyOHH1mGsNpIWt57Y0PCQzc/TiKNGQdfQeMNfp+a0v2Je88jJ2/BiCr15ro09FGC4iBLLpWn1bAwL+637Wd+yfKgKc9VxvVaejPyOq8zG86gnNLEfao/JEZIiEdvGeQmRjJhtULkTZoS+iRtbwx+nkyMtK4fCEYQW6JxNJevEd9ZBnlmOH0ytdo87Mpyc0Qt/4Gr4b42DAjQZIRuBzdvUv06zOAiPAAChIjsfbwQVKoxsO9O2dC1iJ3bc+1axfZvGkHe/cEsH37FuwdnMgxdyDn9K9YtOhBacZd0nKzxAXKokUPih9d50TEYaZOmWLilZOydSrakiJ2bFuD02g9PJKy5RaFN2NI3ToNq27eCAUZdOjgTnzYBhQ2+nTQEk02OnTYv/QeVcmymzfXrp3j86+WcyzwZ6IPL61WYDWo58KjqG3oECoWtiC8hnqRlaPhdHQo8oZulJXlGT3vguVr6Obuafwu3Zri6PQDQQc2EV2NFppxaBmCRIKysvulz0zS9n5rtGPQL5prcK6k2VemmryIkq7FcTj+POXmliZadubRlVh188La3RtBItXvECp2E4ZrMgKXouoxEudX55N/MYjsyJ8ByEy7TXFhLuZtupBzMgCFnQv2qsfpiZ+U7Evl7kPB1ShKHl03WUAeX+ON+ubpagO1xPGt5ndt5/5pMqBOQ3+K654VHk+6btw701i8YDoWLYw/xPTApejKSlC26knW0VVYuXujDt9EecoNLJp354MP36G4MB9Bao5F066kbp+J7QvjUR9fh0RqhrxlF9J2zAZzCywqwQFWgyZx4oCf0Ueocvch+/halApLTkQew3HkPErSbpN9fANOjq7EXjhv5KXw4+rV9B84gkEjHJHrHrFj21ps+r5O/pVwEAQkFf7tNr30Gjo6aO/WlbiEOyZeOY4+s0jfvwin0Y93GCoPX9QntlKckUxp2EYc7O1p3mEo496dRkRoIEcO+YMg4DBiXo0L4MOkaH5csYR7dy6hU9phjTH+r+zQn0cJEXqf8ArsuzQzmaNHD6MTJOLzpm+fxZdfzuPGjWv0enEcUoWLaB+o7ENdmy+4qvsIcqK2U5h0mpT0u6JHkeuEqv7tazBzaGzq3unhIwrkql5ElfF70Wi9fRZWnYeQFbIOm97/oTDpFIVJp7DqPJjywjyKH1zl0ZYpWLv7khWyBpW7N7lnfkNzNQqVuxdoy7Ef9BFRsQew851jlBO9ctvVJfuquoCoug0nO3RjFc+oH7By9zXyjKpsk/gzv7f/63XPSltPojoN/Q/kUV261+pCzGvi0br5Dv77ybukbJmGyt2brOPrEQQB54qEVynbZpJ17EckMgVOFcInZes0ygvycB5T8f+X6WQF/4jMXIH9CEPwzKfoch5hl59cKxyQG/4TZhIpmnItFi09UTTphEXTLihb9kD9BG+bZX7+mLu2M9LQS9JuYdPLWENPTLhIZ7emoldO8tZpOHjrBVtlyKTobjxZx9cD4PLyV0Z+9F07etG14xROnwqjwK6ZkZBQH1lGaXExFhULoGWnQdw6thqJTI5Fg3ZkBq3A5XU/rLv7Yt3dF522nPtXo4yMq45eM/ReNS9OeGxcbdSB09GHULbtW2ELGMuli+eMfKgHvPgSUce31Ah7WXv4or11lleGDmLHtk2k7fkW1ypeNumBy1A07442P0sUyNlhP2E3YCL5ccfQXItC1XWYiRdRVfzeps/rZB9eSnbkZuSubdFci6LeuOX6hSw6AOeR88gIWoGZlSPZoeuxfW4s1t1HYOk2gNSdc8kKWY/9wA+w6tAfqw79jd5LVW8pw7tM2Tkb8w6DyTq+DptexgtIduhGBLmlCJlpTmzmlZfHExMTRsru08g6DEQTuZmFi5aLNomn+fZqO/csypE/W0OvC/3/g+hC7FnmzplGgW0zvv5mLlqtttoQ89ooMzOdkqJizBu0IedUAFIrOywqAmsEiRRHr2mY2dQTPRAEiRSVuw+CVPb4Gu9PUTo1FhNlCRIpyi7DsbSyZusve2jfqAHqwMWmbR/+jrKSYlQvvY9tv7fQJJ3i0U/v8dD/IwCc3lohCinDR/355/PF+998/S1KHiSKYfiCVIa58+MIVMOxvn2eB8DZxZX167fSo01T0vfON+lPRtAKJAornEd/ZhRqHx4aKF4z8YNZ1C/PQL1rLvmXQ8nevwBdaYk+RH3oJHTl5WQd+xFBaobTyLk4DP0Enbachz+OJTdmHzptOYJEikWb3miSTpGydTqlWQ8qPDBWis+bG7OPvAuBOI35Avshn5BdAn4LP2funGn6dAhDP+FBThHr1q3Eoorx+P6PY8mp1JaswyB2bPOnsLgIh8EfmTy3dY+RFN25gO2gD1G26av3Iho5F6sOA7Dp/TrlmcmUnd7OwkXLad22s3jfooXLsS14KI5FzuElzJg2iwZODhQ/vIZd/3f1KQy6++L6/kYUTTqhcvdGV5xPw8k7se6u17a1BdmYo8WjazcKYw+Y9K+6tA6Gdzl2hDdlZwPw6NqNst9P4vLaIpSt++iTffV6BV1RLt1aNILY3Szy+x5be0eKCgvx6teLsjMBLFy03AjGqqP/O9VBLn8Aj+oCgj58/y2uJyWK7nLp22fx2WezuXHjWrVVeSrzMAiS0qwHIt5r8OJwrRJ+nh22EamVfZXIvpVG1xiCVvbuP8SFC2dwqMa/2MrDl7zzB8k5/RtajRpl615okk5j0bKnqNUafKczA5dgqVCQoS4V3SeD9nwHMgsklvYIEikOwyabhOZbe/gSHLKZl4aNZc/eg3x5LpACTR721Rj+rN19yL9ynKzQjTj5zqI8L5P8SH9cG7kxoL8n730wi/Q8BR9P/oZfA9Zz+vg6VCobyht0qKRpTyPrwCJsBrxn5GWSE76RnFMBFFyPRtGoA5rECJxGfU5BQhhpe+fjOsHYu0gdvQNlm761Bm0pOg2lMHsLZdkPSdk6DVU3bxHGKEw6jeZqpB4nDl6DFmr0srH28EGTGEF6wBwaTd5p5J2SfeQ7zOWWfDJtvmiMrRxK37PfW5QV3CA8NICe/V4nTwPp6ek14tbW7t5orkXxYO14XF5dgMzeleygH3j+uYFERQXXkELgsUG2sjsq9KNtx+dp0iKB2At647xhATF3bqYP/mrViwtx5/FbtpUbNxLElBRR0dEM9J0qwlj/NBikDnLh3wW5VBcQdHPPt0Z+28ouQzkdshZl274mYdtVecBjHNKyyxDKMpKrD/g5uhK7AROwbP9CRWTfQhpUctsDvUb16mvvUV6cxbr1q4ww6spk7eGL5uoJStKTcXlFD3GUZj3A3KU5hTmpRvlVystKKavfzajikEaTj0Wr3qRun4nLG4sxd2hk1F9D7hMrS0vKi1KN8ptUbyTzIv9KKIJURmbQCiSaLPr27kNYRBjKNn3Y5L+chUt+RleSwYXz0Shb98Gu6AGy8nSj4CqXd4xhnNzwnxB0WhxHzKU0/S7q6O0o2/YFoPDm+Wr98K26Dqfg4hHUeSlYDZpUrQ911vH1OI2ah6JRB9RR28g6vk6EMXTacjIOfU92iN6uoWxZxRh5aBmq7iOx9vARi0tkBRsvKkV34ykvLcG8lafRuBtcQg1zqmtHL6ZOmUJcwp1q51TG4eV6LL4i1YKq6zCyQzeKi7a8eXfCwg7XmG/HYJB9dOsEgTEnMGvenbiYfcyaMpZfd20VM1RW3s1VDv5K2TqN9Ss/I/nevVpTUjztt1dTBsuq0GdtPP5NkIv0q6++eqoL/2hasuz7r1q060VqupqoM4nI5TKT37Wde9rr/goeA158kWsxIWTHHUfm2h6ZvSsqdy/MbFyAxx+t7XNjsX1+LGmxwdy5cQfXRi1NeKSeO6IXYkdXYdtvHHnnAyl5lITj8KkiP5F0OgoSwzGzcSH3zG4ch00xuUZbriMhYi+nT5/AonUfVB4+CIJA0d14UnbMRIeAvH4rBIkUwUxGycPr2L/0PoIgQZCYkXduL9bdR5Abswdzp6akH/DDeeRcVO7ePIo5Qvy5MwQeDMBx1Geo3L3RXIsm/1KQuIU3UOqueQhSGS2atWD37m1oLWywaNYNlbv34/4EzEan0yKv31rfH5k5RTfPIzG3oKyshFtJCTiP+RKVuzd5iSc4HRZIUNABbHxmoXL3IjMuFM+uniiEcu6cDsSq6zCjPmTunofCzAxpMw9UHj4oXNshtXIg9+xeNFdP4DxqXrVCTNG4I4W/n8FBpiU78SSWXYYanX/0yzTQ6Sh5kIhFs25YtntOX2A5Zi+Kxp0oTb9L4blf6dN/LB3btyXxzFE0108hSGVkHFpG21bteHAxDE3SaQQzGVnBa7Dt+yZy13aAHvLJDF6N/aAPsOn1Hx7FHOHk8SMEBf2G9YD3sOn1Sq1zKv3CMUqzH5Ed/hOq7iPIi9mL5no0ZeoUcqJ3YDfgPUpTbqIrKURzMwZF405G8yRz9zy0Wi3mFfOkHAlJEbtR9XmNwtuX0BSXcPnCOQ4G/oayTV+je9P3zden4a1YrBSNO/Hg3DFsh03RQ2mChHKdhGthu+jY9cX/27cXFcm61QspdWlP9LE9lEsbkHz7Kkv8PqO8vhvRx/bSrJUHaRk5z4Qc+SPaSk1XExS449FXX321oTq5+rcJ9PXrN3w1ceJEHt67zvYt3zPSZxitWzZFLpfR2a0p9Zxtqedsa/S/pt+G/+VFqSxf9iUDXnyR3p6dqOdsWyv/p23rSf3o7dmJEcTTSxEAACAASURBVL6jSU6KJ+H4LlTdvIyeNWX7p1h1GYJt71cRBAlaJGRfPsbUyZNNeBTlpBEb+DMKG0dkDdpT+PuZGgWNef1W5F88TN6FQzW6uGlLC1HHh2A/6EOKbl8k/1KQKEgEMznF9y5TeOMsSKT60HDvTzGzdanYIazCuvtI1FFbkaBDc+MciuYVQlgixbxRB+6fO4LNkEnixylIZRQlx5sIdHQ6SjLukPHwDvKWPbEb+CEFl49TcDkEBCkZR35AV1pEeV4GBfEhCGYyssP8cfSajmWHF8mNPYiyTW+9zaBCMKgTIrGrKIRhGNebJ3bz6NF9bIZMqnYBVOTdJ/deEoVXjpNz7gAlN05hqbSkFCnm9VqTFbQCwUxO+r4FoNOJQqxUnUJm0jnsqiysRXfjKUiMRNnSk5LMZDRJpzF3bEz6AT/kDTtQcOU4eXHH+HTaLGxtbfhtlz+y5t0h7QYFN87i6z2C87ExyJp1p/jBFQpvnsemzxvYeI6i6G48adtnoLl9AWWb3hTduYRVp4EgNed+zBGUbfqIx7SCtMY5td1/JZr7iSjb9NUrByPmkHvuICUPr6Js04fiu3FYdhpEXsxenEd/Ru65fRRcPo4gNSc/ZDVTJ0/j2snD5FwOpxyB7OC1mNk3pPDmORSNOqAtLiA1IxVLt/4UXAmnMOl0xRz7jl6ez3PncjSapFMoGnesVtnJD1nNhPc/pX8/T5PvtZ6zLXdvJ7J541K8hw3BycmJzm5NeXjvOssWf4Gd72xU7l7kXA5DVpLG0SO/Vizw3uRcDsPVXsGA/v3+T3Lkf5EBf+R1Tzr3s/+aZ0+gL1n6/VflgpIlfp9RrKrPvh0bqNegLRcT7iOXy4iKiuTrr2aSX6zE2tr2iavWnr0H+dl/GSVO7YiNCKRN+56cPHnCZLU+efbqn7I6nzwZxd49v2A3rBpNWpBQdPsClh0HUJx8mdzgVXj0eRl7BxcjHmkZuTRs0halbTvkWjW3Th3AopWnsRa7bQY6eKzFmplTdCcO+0Efitdk7JyDtkIYZRxYjKJxR2x6v4pVx5coy00nNzoA2+fexNHnU0oy7lGSepOSB4k4ek1H0US/KKTumodtv3HknPkVSWkRXbsPp6wwk9wHN9AknUbRqAMye1eUnYcYfZyZQStw8p1pMgbm9VuRHx9Cp3btKVenkH3tNHZDJiFIZeTG7EFiboFFc3ecRn9O8YOr5J3bjyBXYt3NC5m9K1Ire/JiD1F44yzyhm4mgiE3Zh/ZkZsoLyvDboTexa7objzp+xeiaNwJqYU1snqtSI89hk4qozQvE4tm3ZAUqCks1GDu0oL8S0dQNO5E7rn9esNwYgT5ccEU3YmjIDEc+8EfoWzVU3zW1F+/ID8+BOdR81C5e1OQGImZyoG82IM4jdALmoLLocjsXblyNoKTJ4Kx852tx66TztC1fQdORkdg4zMLaw9vym6fx0IKJbnp6AQp6qDv0ZaX4zT6c/2u5OIR1JFbKbp9AefR+h1Rfnww6hPbKL19ju59/2Myp06ejCLm7AmRR0F8CGVZDynLfiD2Oz8+mILLIZhb2SKxdqL091N07+JO8tkjTHj/U5q06Ey5pAFNnC25FbUHCTpK8rMr7veiICEcmVMzNFdP4DRiNhKlDerInxG0ZTxMeYi8hSdlDxIouHkea3djZSdz9zzGjB5HTrENd28nmn6vJ0+YaOEKuTmLFsylrL6bqFyYNWjPndOBRspFuU5CXNA22nfuV6eh/9n01VdffnX+fDQWnmPQXDuJonl34k8e4I033kJbnMayxV9QVt+NvHuxfDBxAvVd7GpctR7eu87P/suw9an4iK5GUpydzP69201Wa3d3jz98dS4vSmXZ4i+w8ZlVoyZdcDlUv6U9tY2pk6bToyJ4orq2ku9c5eiRX7HuP4Gi2xcpuHIcBAmZR1ehbNqFwoRQCq6dRDAzJz/CHxdnZzIvhaKTmJF7bCXTpn4qalTmrXpREHeMstvnkDXsgGW757Dp9TJy17YUJ18h9/ROnHxnYT9gIma2lYRwBZyjdOuPgzaXDz76lI8+eJ/MtEdcu3SW0tuxWFXsRAyCM/9KKLYvjseydS9xq45Oh6xeKxFCyUmMIiBgP5fPnebB+SDshk7B2sMHi2bdKIg/Tm7MXkrTbqFs1QtdVjL5SWcwd2xC5tGVKJp3p+RREoW/n9H7SVdQ0d14Mo/9iLJ1b8rys7Ht/y7FyVdI378QeUM3cs/uxarLECRSMwSZuR5eGfOF3u/6SjhmDg0pSb2JcwVsVHgzBpltA+wHfURx6k0Kr58UtWHLjgPIO7efzGM/YtG0K+UV7ZXcS6TwZgy64gIcK3YNxclXKLgaQXluBoWa/IrF2adid+PG/XNHsB78yeMdhiBFknqV/s+9RPLp/VhaqdA17CIKLUXjThTdOi/yN8BiRbdicXJyYuJ7k+nSoZnRnPJbNI/Seu1FHvJGbuRfPIzD0ElGPCSp13j7zXHEBW1jwYJlvDn2HTp2fZH+/Typ52yLQmHOCJ8hvPb6Wxw+fACtaycRRpE3bC/ytGjSuQLKsqfw9nmcRn+OuXMT8q9G4eQzwxQS1JajTjpFJ7e2bNqw1Oh7Lcq6y/6924208CbOlozwGUKvnr2JDQ8kPfaYCHNadR1movnPX7CULp3d6jT0P5s+++Krr6x6vyq6tancvcm8dJwrsWfZv69CEHfzIuNCCBmPUlCq6tW4ai1aMJfyBh3ESStzbU9S5D6jj8WwWsusWv3hq/P2Ld8/1hYqa9KCIGrSSCTkxuzBsqs3104eFjWNmnBBO9/ZKFv1xLJDf3QlReTG7MHRezoqD19KbsbQ0sWBtLhwJrz/Kd4jxpF8K5nsy8cY/56xRnX7zBF69BmDnaKc5LOHRfz3cRKmObUuQjLHxmgyHnLnxh00Gg27dm5C0bInZYW5WHUZQnHyZRFe0GrUFN44i8TChryQVXTq8hLF986THReKVpCQHbwWuUJJgaaMkOAD2AyZRJk6lfT9C1G26YO5S3O9xluhfRZcjcLM2lHUeM2dmlCQEIFjJcFgeA6Dtll4NYLc6AAKEiNwMmjOV0LJPfMbEgsVWcH63C42vV7RC8kmnSi4EmYi4NTR2zF3aUHuqZ2ioM+/eISChEgKrkaIWH5BQjg5p3aiuXoCRePO6MpLsOn7un5c9i9E0aQL2lINll2GoIkLpuz2eVEAVd3d5Aav4u13p1OodWLcuPG0bdeF2PBA8hLDMDfsSroNN9kROfrORn0rjnu37+LaqJXRnGrZqoMpjy5Dje07R37AQqHg9xu/0/2512jT1k3c9a5dvQBbh8ZcuHJfnKOt23YiJngP6suhKBp3qp7noWWYu7ZH7tqOjINLa4ENW5MWG0zsqeOYNXM30rir+4YNWHthMXT3fIG4mFNkXQ41sWuk75zDy2PeomGzjs+MLe5fraEvXLvlq9K0O8gbdTDSQLIuhxptm3QSKcmn9zP5k09qXLV69ezNyaA95FwOw6xBO2T2rlhWmWCG1bpp06Z/+Oo80mcYseGBZF4MoVwnkHHoO6y6DKnQrkNBIhHxYGXbvkaaRlV+RhqVIFCcfAXNqW1YdfPGok1fBIkUrURKftIp5i/eRP9+PanvYo9ro5ZMnTyZ9u3amGhUmRmpeo1/8CfimKTvX/h47CsWodRd89Dpyo0WobyYvVh2H0F67EHOx5zAboTeIKpJjKT4fiI5p3eJ8ILm2kmE8lJ0d2Px8/uBLt168/6Ed5Fpi7l0+GfKtVqERl24GLEfh5FzEBCMsObCm+dRNOn8eD5UErboIOPgEhPBYPQcEinyxp0ovnUe+8parNQcbXIsmhvnMFfZo9VqKUgIR96wfbXCKPPQdzSo50LqhWAsWvQwMugVXA7G0ftTkXdZTirF9xMqFiE9xCKOy8g5FQtKGKW/n+a9j+ZiJS3mRtR+lJ2HGH0PmbvnMW3ydHx8fMX50KJZY9q4eRKyfzP5N8+j6jbc6J6UgDnYvfA2lq17g9SclPMHmTF9utGcatGsMYLUgrPhByi8E4eqiqE4ddc8LN1eJC85AVw7kXfvAh9MnMCj+0n87L+M8gYdSL4cyehRr4jaf07WI44c2YeZvSsFV8JQVRGmqbvmIZjJ0RbmokmMMIENq84zrSBBdz+e+lZmZF4MqfUbNmDt9ZxteXT/d73yV629BLKTonn3nXdq3d3Xaeh/EM3/btVXjj4zKYg/Tv7Fw+JKX/UlZgetYPzEGZRq5TWuWoXFUC5tgKwkzUgLNVDl1frPWJ2dnJzo7vkCQomGuCObkDdsj8OwyVh1GEBJ6i1yz/yKZft+yBxcyTiwGPNWvbgasY/AQ/tR52hYu2YxTi7NuXDhIhfOn0KXl4Xm2gm98TRoBS+PeZuHceGkXwgGqRl54Rt5+91pJN7I4u7tRBNbQ+U+Vtb4KwtCReNOFMQfF+GcjEPfoWzpSf7FwxTfjEEnMSMrZB1WHV6iMOY3BKkcadNuRtvsvNhAHIdNFoUbghTN7QtYyBUMGPI6UWcSsVDIycpWE3M2EjvfCgF3LZqyrIfkntsnLgZ5F49gXr81ZZn3yas0H1RdhopavLxxB6w9RyMIArkx+0jd/QUWzbpRlnmPgiuhooCuuvXOOvIDlkoVU2Ys5NHd62So1Zg7Nyf/4mETYZQSMAeZhSXP9xmITlCS/nsMmoQwci8ewbLd89j2eR0zGxcRQy+6G/fYs6MK/FBZ49c9uorMwpWTJwKNFlYDabVarp08bGLn2bvvELdvXMTRe7qp0AIKEsORWjvpjbmCQGf3l0zm6DK/2ZSWl+PoZWrfKcm6T0F8CM5j9Bh7xoUQ4s+dZvt2fz2E2c3LxIPm6y+nobVrTEnqTRyGVm98Lkm/g7ZYg8yxESX3Eym8eU6EDW37jUMduYWi38+A1Iz8CH969H2FcW+/z6NbCTV6KBmwdsO8XuL3WY35Y8zrtyItNpj0h49q3d3Xaeh/EM3/btVX8gZt0dyMAZ2WwquRqLp5GRmycg8v4eUxbzP2zddqXLUe3rvO8mVf0qJpAyLCDmM9+BNRABiMYZVXa4XC/E9Zneu72NOrZ09cG7tx71qMXltHSsnFg8yYMZfLUUFkXDiKonFHCuKOgbaMUtuGXIoOwqxJV66dPcrJyGPoGnbCRijiZW8vEo7vZPzEGYx98zUaN2rEsYM7Kbx9iXouzsyb9yXJd67iv36Jia2hOgxV5tiEjP2LEMzk5BxciCC3QtG6NwWJkWhunkNuJkFWmMXCBctoUs+JuKBtDB44hAeXo/nmmyV0cX+BK1EHSInYhrlLc+QN2qDqOkwca8FMTnboBqS6MhYs/I6unTsY9aHEuZ2R5p0XG4ijl7EWnXv2N1S9XkZzJZSi2xcez4cDfli07EHhzXNorkVTlpNK7tnfULbuTWHSKRRWNmiRUBAfbIStA2TunouupBBJU3eunz3Mnbu3sPEcRX58SLXCSIeO4tRbpN5LYuacRfR/oS/Bh/cib9yJgiuhomE7/YAfisadKMm4C+VlaK5G1qjxZwd9z4R33+fg/p9rFUBV7TwG21Dlep5V7ym4HErexSOYoeX9j+fSv19Pk+8jOPgQjjXwUJ/4RR816lELtl/FK2tXwBby0+/V7n0VH0J5QTbagmycRs5FamEtwobKVr3QCRKKfj+NIusW33yzBLeO7miL0wnYsblGD6Xs69F0cmvLz/5LuRJ/jrL6+p1ZcfJlUn/9goKYPQiCINpstILkibv7f5uG/lSh/4IgDBEE4bogCDcEQTCJvBAEoYkgCKGCIMQLghAhCELDJ/HUFheSfsAPc+dmlOWkYfvS++LHK3NqRmbQChSdhxN54qhJyPyF2LMs+HoSx4IO6kPrLVzZsW0dKq8ZoMOIh06nxcrdi5T8Un7d/eQK7f+/ZAiDftN3OGVnA1jk9z3OzvXIzcnGefTnFUKkHmYN2lGScgOn0Z9jP3QSaZoyzFr3xX7IJ6hLBQSJhL37gmnVphMXYs/y2bwZOI76nIafbCOrsBxvrxdYv2aBPvR8yCek5xYwauQgsZq7gRYtXI4i43fS9y1A5tSU7OPrmPThx1jcDCPjgB+Kxh2RaMtYsPB7vvXzx92jJ/95dRzf+vkz7dPP2bsvmG7unqizM8jJUWPRsgcZgcvQ6bRG7ys7dAMW5jLe//gzk/DtRQuXY5t3l5QtU8XQeteJ64yCT7LD/TG3cUIT4Y+ZTK53s6wUlOIwdBIyh0aUZSaTdyEQ5zFf4jBsMma29bAsy6Mk9Sb21YTSK7oMR7Ctj0W753jw8CE2fd+oNqOlgazdvZGYyXiub7+K1A1TcRj1GQ7DJoMOMo+sfNynYZOROTRC6fYCZrYNSN/vZ8Iv48j3KMzlBB4+aBLkk75xIvnnDxilBdi1Sz9HL8SeZeasySb33F89jpxz+8V7rLoMRpBIcWnQkJatO5q0//0PSzCvKDVn4JGyfrzYrpPvLIofJRm9G4exP5ikeRj3zuTHY2qhNMmPn7J+PLkxe8V+WXf3FdMEWzTt8jjlQAVfm+4jkNrWp03rtnRz9xTTZFSOvq1Mlt28uJ+hZv2ahWQpG6JQWFCvLJ20zZ+Qvm8B8vptUFlYYP8oBvXueeRfCUUTuZnPPvvWhNe/mZ4YKSoIghRYDQwE7gPnBEE4qNPpKsejLgO26nS6LYIg9AcWAWNr41umfojLqwsqIhLvU3AlQiwvJm/oRuoOvetdRqHWqIamIURecGqB3+JvcBr9OfKGbpRk3NX7wN489zgqbdun5J47gE2PkdVmcvszw37bdnyeQSMckcidWfD1JKNIUkffWaQfWGwkVFTuvuSc0td7lHcczPbtW2jb8XmiziRy4tg6MZtdcfIVinIysWjRHdJvi8dKcjOQtvQU85wbckonXYtDnZ2Jc8U4pWXeIywsQgwJlzd0IyP7PlHRZ41qOVZ+rqqVeFK2zyTzyEqj95WyLRlZaS4p2dJqUzqUlJajLSkyqgdqoMyjK7Hr/y6lGff0PvUVEbaPfp5cJWnWdNL2zsehUiEOlbsPaSE1F6yw9vClMOk0WUGr9Klxr4RXm1rWqPamhy8RkQGUhoZi1rwHOq2WlK3TsO33FurILfpoUh2kbJ2GZdvnUJ/cgUQqw2nUPJP2Ve7emCeFM/atKaxZvYSsgNnIOw4mJ+wnXn3tPSJPHBVzyudH+DPxg9liFSmZa0eKbsSQsWMmFp30ZeVkzi3IPfPr46RXYf44+nyK+lSAURUhw9hXztEu7ziY/Eh/OrkPJeXO41z2kkI1zZu1Irma4hTqoyt4uaLS1a97DlSqy7qJ9G2fouwyDHXoBl57/X2OBe/jUWIE1h6+ZAWvQe7a3jQitsdIrN0fR8SeCVlnkn3TcH3OsZUou3pj5e5F8b0EitTpOFUkqkvfOYd2DR25fet3MSV0+vZZ9HXzwE2QcOzoL7z7/sxq0wn8L995bef+6OuedK42eprQ/x7ADZ1OdwtAEISdgC9QuZX2wLSK3+HA/icxlSisHn+ow6eZ5P2w6jyYnFMB2PR5TczqVznvclboRiwq55X2/pS0vfONhWTX4XoYQGltksmtuvDgpGtxzP/yA8zMzBn/3uNsctVdW11Ybk3ZFiWCDqucO6grhaRXFmoGDdVpxGyxFNfixSvo7NaUpGtxSCXgoHlA6uZPKMnLwqb3KxUpaiV6wXrjjDjR1bvnGuWUrpqFz95rerX1Hqur5Wj4refhWev7Mox1TSHcE8ZPFBfgqqTq5k3uuf2UZT/CslLxCZWHL9nH15OSkYyj1/SKVLOPQ+INqWblDd1qrbBj1WUI6sitlGU9BEGg+OH1Smlj1yG1siMv7ij5CWFYVxRwWLbsRxYs+Irsm+fQXD+Fsk1vck7tpN5b3+tdIg/4YdGiO+roHQiCoA/7r2FBUd86S3bGbb785geSEqLZtWsbH/53HmNG+TB+/Nv89ut2tm/fgp+fPn965bxAmQGzKc24S1bIWmyfG6uP0N32KaXpd8iO2Iy152h9UY52z3H38lE6uy0ymoed3frg6PS43cWLVyCRO9OhbSOx3QnvfsDmzT9VW+lK2XV4RaWrMWxYvYgyqTlHDu3g1dfGE7DtR4pP/YK1yoahQ17i2NHdlKmzUUdtw6bPa+Se3StmDjUUJMmPO0Zh0mmsOg8mK3gNNtbWOFgLRjVxZW4D0ZzYzNSpM9mzdzcPd0ZTlJdllP7Zeshkrh5ealQNStllKCdP7GbvvmD6D3z6uqR/RDj+X9lWbSTodLraLxCEMcAQnU43oeL/WMBTp9P9t9I1O4CzOp1uhSAIo4A9gKNOp8uswus94D0AqZnMXVGvJTZDpyCzdzVqs+huPGl7v0XVbTiaS0f54OO5tG7bmc/mvI+2flvsh06iLPsRGYFLQYeYV7oqD3WgHx7dn+PChbO8O3Eardt2JupMIi62pWKlHLvCR0yfuZgbSZdZs/IbdIIEZeuemKff4ctvVnAj6bLJtdEx13iuZ3tAv3o+17O9uHOofN2+/Yf0mlazHpil36RV8+Yk3L5jogU9WD8Bmz6vYdVhAKkbJtCxYz/Gjh0nasaKlj2x1Twk9dF9pC4tKEm9Kaao1WnLsR/wOM1r/uVQys4GMGjEdJ7r2Z601AesXuVHqblCTJtb3Ti99+EcUrLNTJ4LIC31AYvmT0dqWw9H7xnV8kg/4IdV50GUJkaw+Lst1Y5NTfixTlvOoy1TaWJnRVFpGZnFWpSdh5If6U/HroO4FHMYVI4mSbPu/ziOTm3dSM/ONLrn5VcmcDhoP6UyBfJOg8kOWYfM0g6XCWvJOr6B/MvHsWjWldL0O9gP/i+l6XfJjT2IRXMPim7FYmYuZ8iLg3iQkkvsmQNiwY3UHXMQzBUU33+cdO3eqjdQtvTEYdhkBEFiolkKEin5V0IpO/P4nVQd38r/F3w9iQK7ZtgPnYQgSCjNekD2QT+s+080eseFJ7cgkcopKFDrbQm/n6bvi2/Sya1prfO1atuG3UBt7yb1l+mUpCcjyMyxaOlJ4e9nENBh0aoXmt/1CdyU2TfJUWcha9adotux2PR7h7yIjcjM5BQWa8QEYTptOXmxh1Cf3AblZVi264dd4UM8+71FX892RIQGcuzoPia8N51WbTqh1ZazadNGbl0/hYVSRb4gx27Y1GrnYOb+hXz433m0atOpxvGt7ff/eu6vbAtg0sThsTqdzsPkZfHHJeeaAfwoCMLbwAngAVBe9SKdTrcB2ADQqHFTXbb6ITmHFuM4zngLnhu8ki4dO3H50hFeff0DXh6tzzjn4/MKu3dtJHPbdGyGz6De2O/IDFpVbeIqQ93FIUN9jWoUVq2Uo949l22b/LhwMRadIMF5tH779sj/Iz6b9TalJSXY+s5B3tCN7J1zqq2oci0+gnXrf8Ru0IdYtutXiecF8Vjaz5O4dOks9iNMMx2qunmTH3cMS7cXsezmTcqdaMoKU/Bfv0SEOTIDZlFWWkjZg6uiMEndMQfL9i+Y4J2L/L5HInfW99Gt9io6tdVyFH+7NUWrXcqq5XNJ3zufBlUEa8ahZShb96L0ynGjHN0GHtUmiTq0DFWPUWIBBOvuvtwMXsux4FOsWbuWqMjdLF68gojIk8TqtDgP+tB03HqM5Or5vRw8GGp0T9duPfDo+QJJCdFs27YZqUTAdsh/ESRSSh5ew7Ldc1i2fwF1xGbK87MojjuErYcPUsfGlDy8hqLdc0RFHsFMpjDKsugwbDJpVZKu2XiOIef0LkozklF18yI/0p9p02azZ+/uJ+b5rk4jM9QGrbybc37bOG99fsRGJkz4kI3+60TYLFP9gKLcW/iv3200t59UAWjB18erSeD1nb78YAU0YuXuTVbwWpxGzkWnLUdzPVos/FGSdR8EyMhIewzh7ZhJ0cmfeW/iR2z4aY1RtkfDu9bpyilICMduyMfk/PqZUZ77qtr18317os5IYsH8paxatYLEauaxIQndmFHV19ut+v/fqqE/jUB/ADSq9L9hxTGRdDrdQ2AUgCAIVsBonU6nrpXp/XsgU+DsNcHknLzzMC6dDEDZuheHj+zHw/NFbiRdrqg16YnufjyZ+xdhO+A9EcetSoY0n1VrFFatlCPrMJjY4DUoW/emKDke84btKb6XQLkmF12L7mgrcGpBkGBegW1XrvK+6aflFBXmY9GmN/mXjiK1tKcgO53YhzfFOp1SS/uK8m/Vb8tV7l5orkeTdz4QlbsX6Vej+PLLOUb9tBk6lZL9fti/NNEEljKkWM0+8j2vvDweidzZCHerrYpOTbUcq+J20acvUlJahv1QU8Fq3X0kead38f5Hs0lVy0ww9HHvTOPHFV+JMEd2uD8ShRV55w5QkBCuhznC/JFa2TNt6se0cx/Fl/NHEBa8lwP7tz0x1WzlewDiEu4QHXMdF1sbiouLsB/5eFvu6DOT9H0L0VyLRtmmN1kha+nSuTv3rwaTnp6Ksk0fck/v5sOP5xJz/gqxZw9Sknkfx+FTq01fnHMqAJu+bwAC2aEbaNq8M/Ubd+ajSR0qtM3HWO7TYqaG2qBXqsG0s4N+4JX/TOS3vXuM7DI2Q6cSf8APm0pGRZlbzRWADKlvneu3JSE+nJLM+6i66HF6M0Eg5+QONNeiUXUdSlbwWqQqJ+SNOpCyZapYk7Uy/FZZaFt0HkZR9C9s9F8n5uU3fXe+FCadIf/CEWRuxtWXqrPfKFr2ZNr0/5KdlVlt+mdFZy8OB+3Ho+eLSCSSvxTX/qdh6OeAVoIgNEMvyF8FXq98gSAIjkCWTqfTAnOATU9iqgNcakidau3hS/6lYxTdu4JOqSLogD8nTkSg6q0vQIxUgdTGRfQ2qC3NZ/jR7Zw5d07EtSdN+YLNPy3mwao3UXmOJvfsHlHjfbRlKg9WvwVaLU4j54hacN75QMydm5F1fB0+XiN4rmd7yotSq6u5BgAAIABJREFU2bB6AaVanXh/ytZppP32DYLUzEiLzghcJuJ/UL0hzqrLYHJO6oWzsutQys/swKk8g0eVKgw1GG+sqRlwdwMp3b2JiQnj3XffARBtBbVtqWur5VjZNlArDw8fym+dQVeWx3M9u5rycGuK34JcJLZW+oINI2YjtXIg7bdvkDdoS050AE6+synLTSfx+Dre+/gzOrs1Zfrk3aKGbHjm6lLNJoY8vqcyVbczUB9ZRllxMc5jPhfLzSVeOY8glYnvLFP9AF1ZHsm3zmHRqhdAtXnSMw4tQ+XujU2PUQBILazJPBsg9qM6bfNpNLIb189xOP4c1t6zTMZa3rw7v+7eyPxvl7LixxWod81F1mEQeecP4Og7W4Qiqt2tVVDltLvXEqJQtOyJRKYgK2Qtclc3StNu4Og1g7KMe+REB6Dq5kXepSBSd8zGtt9b5ETvNMq/XzVNsubEZlTWKjQOXYzfXdAKrN19jOa8+sQvmOvKjCogGcbiQuxZcZeq05aTvvek0e6oMlm5e5H1ezRJCSf5z6vjah3fp30PfwaPv11D1+l0ZYIg/Bc4BkiBTTqdLkEQhG+A8zqd7iDwArBIEAQdesjl4yfxlSgsTWomWnv4oqqwaGs1aixaelKaeY8TsfEoeow2KmVWlByPZRXXqcyjK7HqOkzM9Uy9dhw8tB/LNn1E74+o6FgyMtJRtPAg59SuxxWBBAlOvrNI37cA+4EfmGjBAJZtnyciMhx1oTVnTgSgVdqidG1nVPA3vRotOitkLdxPED0Nso+vQ2ppS17cUQoqVVI3t3Em/0ooOaE/8cHHc2nZugPfL/uGtIN+Rttu+H/snWdAVNe693/TGHqvil0RC6Co2KImMYlibzFNTWI0iTkHS0w0tlS7JjFqTKyx16hgAQWxI6Ki0iyoKCpNOgxlYMr7YTPDDDOgOed9b95771lfGGZml9lr7bWf9Tz/UosMMRzc9l2Hk7n3Mr+sXq1HrCxdtshkUqub430eAujf3UfqnQTs7BwoKchA4uSFxNYFdWk+YgtL7LsOw6UGolgQtY7hI8frt5v88Sw2rF9hZBjRvGUAGVf+1BtGFESuY/iIcWYjnAkffs7WLavI3TVLn1+3tLRG2rSzkd1cYdhS7PvX9pmlfzC7dm2j5ysTuBazj/x8IZ1Qt9kHjaY8NQatVoPycTKKc5vp0e9d/Qql7nV8XkQmElVSlLOADb8/wGGo6aqkMj2RsjsXsGrZlUVLvqf/4H+ScvUAdyPXYd32JSMjksKIVWZXa3URS892zYLH1ylTKo2CEFXeEyNzCrcRcyhLOUvh6c14fbi63lSn7rjNW/kaXfui6PWILawpT42hPDVGj9CRSCSMrkHR1F3ZLV22SL9KNVwZ6K6FcL8P1j/c5f7G6LC61/dF++Gvfvb/U4T+3KLo/6smEku08kZtBc9Egwq4uqIUjaJAj9rI2T0HqVMjI4hc1rYZyFybUp2bjkgsFsyNa/ZRfPlPxFILLJt31kPgdPnEnh3acPbc6Vqn+Z2zUJcXI7Vz1kcbhs0QB63MeUBpzB5WrFjDsuWLKXNqgV33MRRErAa0uAwyX9zNDVuKbbs+2BelMXTQEPYf2M28ed9z8NAhLsee4eV+r3AtPp6vv17I+bOnOB5xlLff/YTJkz7ienwcX3013WxkXHI1DEXyKUSVCqy7DNU/xBTJ0XBtP98s3EBAh+Y8fZLO7DlfUKIWI+vwunGOV6GqN8drmE//d/ahrsxh7pzPkbToimNZBtYWEh5kZqOtrsKqVZAAX313KRnrPuCtESP55LPPjY4dn5DKn7vXcjnuElM+nUpbv760a9OIbxZ8weW4S3z6yT9p49OOpcsW8dOPteiiAwfDOBK6nYU/rOBQ6BEunAtnwYKFuLl5MHvOTAqUGhwHfW62z0qOLWfJ0p+5/zCLTeuX4TDUvOiaVqMmZ/ccQYI24RjTPgvBq2mAUTRl+Fvqew2Q9nAvzRst5/2PoMCmN87B0/VF1tKoFUib9kBxJ8bIKKKlq4ORK5buXOy7Daf0ahiuOVdZv34HSbcf6481fvxoCqy9jYquJcdXYNNvolHRtfjSHhp/sklfsJfauenvBR3Xo+7qWBeYNXF3ISRkJr/8sgJXVy8SEq4g1mpxrsmxl8Yfo/T6UVwGTkWV+xCX7Ks15hwtja7N0yfpfPvdXDJKKrHt9S7FMXtBBLb+Ayg8sxlb/zcovRGOzLUpdp2DKY7ewIoVawjs0t3k+r5oP/wrn/1XHgugX68O9RZF/z753GUrvq1SVlCVeVvPHrPp+CpF57dj7dOzYSq11AJFwkkkVQo6+viSfvkoUlsn5N7tqb53CW/PluTeuWCkn60T+zHS7hZLUT5JxsKjFaXxR83qXDj2m4B1m57IvXxQ3r+CDAlujQMpeRxPSWocjgNCqM5/Wi+N3Onl97HvPpq8G1F4uHjw4eQvuf/gAWGHdwoogWdP6DvgU1RVCvbv+wN5qx48SIhFLLFi+bIFDTILFQkn8WvdBlX2XfJuRKEVS1Cc3cz7EwVZAEs5ONqvZczw61ho80iJvsa386t4qdd5Bg3IRarOI+XUNb5dUEXngHDcHPeCZgNa9XrhtfY0WlE3HBw90cq8cbGWknbhIF17v0lQj750DRLkDu6c3sdHH3+Bm2crI5qyTtJYJ7SWe/0kuU8fgFZbo3MyRK9Cadm4PVnJl0yo7zFX7jJ48DAGDh6Lq0czLly+hbW1JR0DemLj0hE7G4sGJVcvRR6mqc+rvP/+R4ikNnqZCLkqz6xMhI5i7t3Cj59//AZRk4B/WXTNHIW7pDiP3Tu+Ydcfy9nw2zpCD64jN+d3Oview95eTbcuaq6fyyT/5iXUWgtKo1YybYqCixFPkbfsYaQr8/RKOK7DanVlEAnnYt91mBH1/WmuRn8OOrGusttn6xUJyz+xRi81oNWqKT63DcXt87iPmtvgZJ4bthSrFp0peHyXyJNHqbRtzNPUG1i16Ylao8Kx3/uIxBK0qiqUT5Kx9X8dyxaBPIuPJDn+Grt3bTISAtMJcJ04uIXKzDt4TvjRSKiu+NI+HF9+H5ljI4pidiMRwcCh7/+X0/H/Q/0HFnz99bfuo+cLsq01g0kkEqOpqqT89nnK7pxvUMnNSiZj6bJVfPDhx4wbN5GC3AIex4byww/L6dU3mPfefov4M8caFPvJP7EGq9bdKb99rsaAuQ4NXKOi9OoRvfSqViLlcWwokz8O4ZPJH/E4NZHbUXtQ5j40TyPXaihPOVtjSiBs26dX0HOE+QX535T4s4iaGjv6mJWjvX2B3btDyc/K5nFsKN9/v5zX+vfHyeE2vs1nYG15G6lUTYcOGsaOUePlpQHUiMU1771Z+55IpEYAJ+le54JmL82betHI62V69uzDO+++j7OLEIXr5A4EmdUeJjTl335dZKSCKW/SkYr0RFwHTzeZhFyGzPzLEsfpD2+xef3yF5ZcNZEnNqOpoqOYf/Thh7h7tiI98dy/LLrm6e5ISXEe8ZdC2bJ+EfGxx7kSc4TAThlMnw5TpkDfvpCWpmXVKmjRAtq3h0ED1UjVChJPXqVLpyrCw6FcoUJU9JCym8eRuLVC7uWDXZehde6LH7H17Y1li0Aj6vuECRP110wn+KUsfGxWJMwwiAFBd7/8bgwSG0cc+4wjL3QJ8iYdhECpZlxm7ZhJ2a2zeier8tRY1OXFaCqK9SqY5bfOoq2qRJlxm/yTa2uEzk5h6/86yoJMcm6dgyYBJkJgWU/vcSr6JM6DZiBz9ELe2Bf7rsP0v7v81lmsffugvneJj6d8pRfu+q+k4/+3o/7/v2gSR0+zkadjn/cQ27qgra4m78hyk8/zT6xGbOVAy1a+dA4MEvYlkfDq6yOYP/8Hfl61nGc5GXg3acb69dvx8XQw7yp/7EcsvNpSlnK63kKLfdfhiC0sydw0BUVyNAVRv9O2jQ+LvpvKqcjjnDkdRXVFSf008q7D0KqU5B37iZLTG5FIxKxYUZsXFIkl2L4xleS0h3rKs44pKkKElzqPgj1foUiKpuTYcsaMeBeX7Kt6arPi7Gbmz/8BEXf46P3L/LnXjk4dl6NRDqRl4zmA4l/ombpNw8OH36KtGoZG8+gvbTn509l4qfMo2jdXTytvNHGNMeU/egPOA/5pQn2vr6XeSWD8+NE8fZLOgX2bTa5lzM1ko2sp6/AGZ6KP6re/Hh/Hxt+XNkgx18lEGMo4FEWvR+7dHse+E/B4Z4kgdxD1O7a+fZA36aA/f8NjXY69wKpl/8BC+ifz55fhYA8rV8Inn0DjxiCRCH8nT4ZFi2DJEsjOdqJbtzMMHbIPsVaKt7eUNWsgMhI2b4YRwSWUhC2g4sE1o/POO7qCQQOs8VQ+fi71/X5qMufPn8Wm30STz2wDB1MUvZFiAxq/XddhqAsyyNv1Jda+fahIjSV315cokqJ5dmghIgtLrHWkH7FECI4cvfT3hUgswTZgIMWx+ym6uAv3MV/jEjwVrUopgA5qUqPOA0N4mlfEgq8+4umTdK7HxzF3zufY9BhL4enNVBdkCA+QrdOoLsgQdHu0kB+2BGsbGxwcXRoekP8L2t8WoS/6+bdv7ToN1MtpGtp9qYufUZl+s14fzarcR+Q8uU/noAH6ZYg5x6J9e7Zx5swJXIeaCusjQlDoa9xOcOBp5k/1s0dk7/mKqoIMii7sxKp5JyR2LlQ8uEZV5h1sO77Gg2unUTl6c+7EQbBxwrJ5ZzPSoBoDn04LFNdC0Wo00CQQWWURkrJ88m9ENqgw+dHHXzJg0Fs8uv+IwqSTfDh5JsVKB0aPehNRVTlpFw7S7aWRvPHKRSxly8jLfcaOHXksXZrHhg0lhIVBXp4wYdjb/2t9lJEBO3bA0qWwYYOCsEO7yc05RX6pK2KJ9XOXh9eTnzJ69JtkpaWQfvkoNp2MU1q5e7/C3saasoy7Jukis0vWOh6Sfl2Hk5caY3QtzWmMt27biz+2rKaouJyff1qIrIkf9t1H6VUbnx34Bq2yAnnTjnrPTJ12vpWlHFePZmgkHiiyb1GYcAqNSErl9VDefucTslOvGaW7uvYag7OrB8kpt1m+KIQli5UMHKjh0CFo1w4GDjR3pcHdHUpLRRQUvI2fXxeCg0fy/fcVDByowd4exGKhH7t2hc6d4ejvscjb9EViZScMZxFUPoln7aosZFp/4o9HMXGyaRqsPvVNXZN7+VB5L5byu5cE1c0aNcR2HV+mOOcBmuy7TPpkForiSvITwpFKpWiklmgqFSiSTumdrOzqKF4WRqxCo6rCpt1LRjaCZYmRuA77Qm8IUnLzBNJmgcScPMTZs6dR2ntRfucicu+OlMQdpPRmhCCrXBPdqwozUWbeRdS0M9fPHKNthx4mHqL/m1Iuf1tRVCyz1Dq/8Zm+mFlyNRREYqyad9aLL9UVb3IdNgtVyTNBD8TdA4lMxpLFPxFz8YwRsadg7xx8GzkRfz2+3uhbq1GTveWfVJc8w9qnN8rse6hLniG1skNVocDapxfK7HuoinMRW9oglspQKQpw6jseRfIZJNYOqEryEFvIQSwVMLzRG3B6dRJlSVFoNWrsOg+iIPI3xGgQ2bnhNmo+BQe/RakoQiyzAnU1nhN+MirMZax7n7GjJ/DJxwI+v77iiEZ9FbXyMySScuLihOhu8GAYNAg8PSE7G8LD4fhx2LBhHiNGfFRzBCUgN7gShv/Xvj5+/AgTJ37OoEEas/ucM38uPXu/1+A5GhZFzUXEimthuGRdpV2Hrlw8H8H8+T+YFLR0r3XRmv3QWfoit29jLxITroJ7K9RlRSYaMbmbJjMieBAHDx9A5N6aqgzBQ7Mq7SpyF29EXu0ovX4Um7YvoUq/jsTBE8uAgfUWeA3p8t99t4TOgUGo1WpWr1nDhXPhzJ//g36bNat+wEK6n8mTBWG5UaNgzRrhAVtfy8iAGTPseeedMeTn72DSpOp6v/vbeglRaQOxf0XgBWg1aor2zeC9IQ8Z+6YWaAmyjYjFnkbXs25RVCeVIA8YpJ9oFcnRFEZvwt7WBolEwltj32XzlvXIWgbRSJNnVGhVq9V8NftzUh4+BJcWVOc+NEG+5G6azLTPQpDIXQV6f5lKT5rSNZ3KqlsN+qZo/1wCmnsRExuj12vK2jYDeeN2OL/2MdnbP0cks6Qq5z7uY76p0XL5kg/HjOSttyf8ry2K/t9iiv7lZmttTVH0BiQ2jqAFVUkeVi27UHr9mBH2uOTKYYou7sK6bW9yw5aiKslD7tmGzIxb2LR9iRmf/4PcnEysfQUSj037l7EfOI3rO2Zi7WsMa8w9uhL77gI7UfkkBZWiQD8YsrbNwMbagUplWS0uffvnWDYPQPkkCXmzAMi8i13X4YitHCjQkZGyUkGRJ1Cx0aK4EY5t4CAKo9ZTELkOqXMTVAVPsW7kS+7BhahKC5G5NEVV8MQEagZg23U4oYd3E9TzVROCRETkeQ7vX82Nq6coKVHj4ADdu8OlS7B4MXToUHt9dUv5Xr1gypSf6NJlJK1atQDKAWuDnjD8X3j94MFDJk2aw8KFmnr3OW/eYn5ddwXv5j/w4NFT/XcMz/d5tHKbwCFk7o1BlXQLqcyS/GKVXkhJR3zp3GM0YAxhE4nE2L4xlcSwpVh1H6NXT6zb5P6D2HtgD/a93qY4Zi8SW2fsuo+mKP8JjuJqnlwLQ+rgjl2PMZQWZ2InllB+eU+DRCBD0TUdzE5m56MnNV24fAuppIBTkftYu7Y2WCouFh6KDTUPDygoKGX37v2sWlX/ZA4wbIia0InHUeWmUZ37CGVZJRZWMnbsFNG6lZbY2DSio/tTXAzWNpZ06/EqL708ykSsy1AkLC81BquAYAqjN+I6fDalF3bh19ybzVvW65FhmXvnGMFiU+8kcP36Zex6vVVvP1gGDGbn7p107/c+/5j2Pbu3rTYhTRWe2YxV6+5G/Rt/dDluBoQl+661Anauw2bVsHZrAz/rToP/A1v8uyJ033YdtR9PmcFXs6aisbDGdcjngr50/hNyD/6AyMLKKFrXTboSG0dBdlYP35qBRaPap7Zli0Cc+r1PdUEG+eG/6GFOBVHrsA0cSlXGLdBqUZcVIvfuoNfgqC7IIPfgDzgP+MwIvlUQ9ZsRLEzq1EgQw9IpD27/nODeQXz+xXwep6fxz39OprS0BK1Wi2Ofd/WDXHf+IomE6rzH+t9kCDWDmpXD9s+Z9NYYo0jjcuwFFn0XwpAh1UYR86JF4OcnFNjqa+vWQUSEjIkTP2Dq1E9o1aqdwaeVgKXR62nTppOfv7XBCHHjRqiqgqlT7VGLViKW9AaMo4mxY4cZ6ZLookGrTkOwCazVOSmI/A0b35dwLH2IWCKlb7/BHNy/BUmLrljl38PKypJpIV/w+4ZfySqt0hOtDGGl9a7Cds1CVZiFVq3C2qcnqqIsnAeGUHj4B6rKSrBq3QNVUSY2fm+gvrKXI0ej9dv/1UhLq9WSkfELjdw207+/hshIIU8Ofy1Cz88vJTJSq9/WXLt0Cb7/HkaMgKFDa8fDxo0QFwcjRworttqVlYTjx2XM/XoVcttGerGud8f/kzGjhqFWqxk18nVKFOW4Dp+NVfNO9UIaiRdgsbrVl44j0lA/FO2fi1+L5txLTaS4uNjkIV9dkMGzP79DIrfGZah5baZnB79HYu+G+6j5Zj8vCFvMihVr6BwY9L82Qv/bcujfL1z8bXjEUZxGzMX5tU/0+TaJtQN2nQehrVZSEnfQyA3Gspk/iqRoXAcZQA8lFpQlRWLfbYRgkhCzB4eebyKxstf7cRae2YKNtQ1UVeA4IASRRIby6S00ynJjYwIDF3kdakDeuD0OvcYaQygNXHpEEgvSYo7SPqAflVUipNatSEu9jEWrICoe3sCyqYHFXjN/FMmncRv6hRHKo/DsFkRSub6GIJJaGLmVK0rTWbEohMWL1QwciFFOdfNmmDGj4Tx5o0Zw4oQGb+8kZs/ehr9/e9q0aQqoEKJykdHrCRM+IiSkssF9enrCzz/DgQNKNvx+lNCD68nNzSCvRIRYYkVObhEKpTUlj+OFvHMd9yVd3rkgch2OfcZh2bwT2VfDUXm258aZUJyGz0bm1oy8+AjUnh1JiIkiZMYPJF6N1XtImrPRy971ZQ2sULiWqsIslBm39Y48ZYlRVD5OQpn7WG8bp0iMpDz5FGKpJR5erVGUa/5yLtTWpgBb+USc7M8CWsLCBASL7hrm5UFaGnTpUv813b9fRvfu73L/fiovvVRV7/XPyIC5c2H5ciHFphsPpaWwZQssWwbBwcbjpEsXLX5+Kr775hSO7m3xD+jBy68O5db9fORyGc/ySujg15XEhBsoM28/1/f01v18vZeu0Tg3qCUZ1cUQk3p2H2VlZYhsnLDrFCyYz9Tst+DkWlyGzabs+jEq7l8xsdvL3z8Pv/b+5Dy+R3V6AjZ1IMZ5e+cQEPgGnQJ7/Qe2+He0+fPnfWvRuqfRICj8cz4qlQp5I18svduDWIwiIZKKuzG1EMbOptBD16EzURXlkHfsRxz7vIe8sS8gwCDljX0RS+V4iBX4+bTk3vlQnAdNx77bCGwDBqB8epvSK4dMnG7y9s4BlRKtWNqgG03RiV+wtbVh5PCh+LRuzuNHt7kaexplQSYimQXaSoWRf6V9YN2Hxkrsugyh8uF1FDfChSLq2c0sXLiCzgEd8XA5z8Xob2nXTmu2oLZhgxCdixvAK1lbCxP/L79o6NhRRUhIOGPGvI2zsxvCZG6JkH0TXs+Z890L7fOPP4SIUIDfaUl7cIejBy/Ss0dvugYGYG/vyCeTBU/RlKg9evel4cNHk5+VTdKJP7Bo3B5rn156I2G7LkMpf3AFdWkhJZcP6N8rTjoN5bkk3Lysd7QRSeWUxh+l4n4ciCTkH/+Jpl4e5CRfojLtGoglFMfswdq3tz4/LG/SgdIbEbgNq32oisRSKtKuIW/ZjcdJ5/6SD6WHmwNNPP+keaNFSCUlteOnzgTeuDGsWiWsptzdTa9nSgps3GjJpk2bKS8vJTExmcBAjekXEQrVbdsKk3bd959feFVRqRAzbJipC9jzII2Ff85nxlTB9zT94S3u3r6BraacSpWGquJnlCWdQiSxIP/4j8gsbajMeSC8J7WgIPI3kMoRAVbNO1OWfAqJvRvZO76g7NZZLJt0RBF/FHWlwiyEGK2WqowUKirKcQyeZupKBhSnnuNqXDRDBw3Ezc1N/7syn9xl17afGTlsED6tmyOXy1BX5vDTym/o/8or9Oru/z8Gtvi3Tei//bbpW2utUh+9lUSuYcyo97l78RjFSdEglVF8fgeoVTTzcCfrRhR2gcaTriFmNnvnF9h2Gohjr7dNjmXh1YbsaxGkJl1FI5Vj1SIQiZU9ysfJFMXsNuvXqNaoqSrKxuuDVSif3jFLHHq2ezbqqgpoEkjMyYOoNDJ+W/MDWrEU6zbdURVkgESGhWsz88SjnV9i22kgTv0+wKbjqwK++WY4PV9+l06dWmAt+xRXx+MsXQrTppmPwutGguZaVhacPg1vvaW7qSE5uYKBA1/BXIS+du2vDUaIhvucPNkwCgQ/PzXffB2Fe2N/ric90aNE6kaDT3M1jBg+gtSbF8m5Fl5rJCyWCDd3HTKZWism4cROvR2bjvpt2bIb1fmPqXychEQqozA/FyufXlTnP0H5JBmnlydS+fAmipsnsGwqIDDsTVZiK3HsMx7HvuP/sg9lWVk0TTxWm1yfVq2a89NPlXTsqMLdXbg+LVrAd98J19/TU3goZmUJkfnGjXJ27txKUFAAPj7ezJ69S79t3bZokflVWUPjRNc8PeHXtWmMfTMTxEFcunqPqmqNPvo7dPhY/b6nai13Lh5HpZGxft0S1F5+yKsVNPJqS0nOPaoUhVQ+SsDZ2Y1X+g0gI+02nq5NyLl+HImzN9pKhf4BrUiIpORqKCKRCPdR85C5NUNxM8JI5MuwyTzbkHM1AolbCxSJUQJCrYlgLm7j1x9NdaV+NRdz8pCe4HXhwjmWLp6L2qsD547to1XbIA4dPsqWjcupcvPl2ulQfDv24lle8X8i9H+nbdy06dvNW/Yi0yhJiNjJokUrGTxkKL4de9LE1Zrrx/7A0sKCQUPe4uaNOBwHTTf71C67dQYbv/4gElP56Ibe87Hwz/loNbUknKqCLJRZ97Bq0UUfHeSGLq7XF1Hu5UNZ0imqc9OpSI0R7M7qDnCtBlVpPm6jF1CcfIYbMSeoVmv1g7Ys5QwyRw8q02+aJx6JRFQ+vI6NX39EYgnWbXujSY+na/tiBryyFplUkJNvKAp/kaX8vn3QrBkECbB9PDw0rFz5gC+//ApzEXpGRgaJiUn1Rojm9qlr7u6gUMCzbGv69O3fYNTRq7s/I4aP5vq1q2Tdu0HF/Sv1roTyj/+I3Ls9Dj3HCp6eoYtxGzkH+y5Dqbgbg4VHa6qePcR99IKa9y5h6/cadp2Dkdi5URIfRkVaPPZ1VmLZu77UBwI6/8y/4kPpZJ8GmlOAIcxTzM6dRchkMqKioKxMjKenhjZtwNcXIiJEbN0K27aJOHvWlurqlhQWFvPHH3tZu3Yd5eVVjB//IXPnnqG0VOgvw8k/Pl7DZ5+ZjocXXa1t2gTvT7iDVHQAn9YD8fLw00eyWzevbJCdXHAzisunwxDZOuP0+qco7l/Fy9GSnJxMnEfMxfn1Tym7c4GXunZi0eKfaNHan/fefouwA9uQtwqqTZ826UilAcksL1TwaLXrOsyYlUstK1cks6DkahhaVXXNPTYERcJJlBm3KLn8p9Fqrpm7DU0bO7F8yTycRgiEp7wbJ6kufsqFs2E417xXnHSKpq7WvNb/5f9E6P9OW77y52/btO+Fq0czZLZtaN68OTm5RVyMu0PXrt147Y1ReDdpye6dv9WLmdWZ5GqrKrDvOhTFjXAqn9xCcXmfPldbmBBKV8NmAAAgAElEQVSNMj9DX1zV5UxL44/WSgzoGG87vwCoHUBSC0ou7683apB7+QgMuGolNt1GUpEWj/NgA+NjqYVAmqj3odEGxY1wKh5ex7ptb32u8cGFI4wdo9J/r6Eo/EWW8r//DjNnCpHhjh1Cca6wUMmSJYv5/vvl/PzzKjIzn+Dj442zsxM+Pq2YPXtbvRGi4T7NnZOnp5Zff31I0zY9jaI/c1HHxYsXiAj/E5dhs1GX5ptdyeTvn0fwwJEUZ6eTdyMSxe1zAv5fLw/RgdL4I7gOngFayAtbirVPL4ov7UEslVNwZgtoVLgNM8dHEOsfqsrHSfoccbVG/txIKzcvCzurhUilxcTFwZw5Qspj2jQtU6ZAv34qystFRERAZKQlmzeriY+3Y8SIcWzbtpoBAwZw6NBhevbMY+pUJVOmwEsvVZGYmMS6dadYsWIppaWurFyZxsaN1Zw/b0f37mNJTb1Pnz7VJtf+r67WNBolWvUR7j30JSNbzJJFc2uZvfVMqhqxhPIH17BqEUhZcjQOA0JIjz1mJKmh1oqNakBubm4EBHTn4vGdKG5dwLKpnwlWXSSVU3otlKq0K2hFEvKOrdSzchU3IhBJZRRErkNi7YhVq65Gq7nSq6G4Gtx3aq2YO6f3EXPpPFrvgNpx0tSPW9H7je5RJBZcCduMf2D//0To/05bv37Dt5MnT27waaRzrG+o2IJYTEncQaEoKrWg9Goor/YfzPXrMSxeuBwXWytij+/Cyqe3UXRQcS8WTUUZ5bfP1Vi5rUJTVYlakS+8JxZTeGoDaMF5wD+Mi25oDbQ8JBRf3o9T3/HYdBpkWlStiSrrO3+R1IKymxFo0uNQI6Xs3Dq+W6DEy6v2WimVXbh7N89sxKxbyi9YIBBTvLxql/L79gkT75w5UFIi/PX1henT4bPPoH9/sLCAu3erEYsTWbhwN/7+XQgK6oG/f3tCQsJNIsSdO4W8uW7yMteEnH01ixd9gJdHm3qjDnVljl4GQYSI4th9ZlcyGo2aivTrbNy4g4LsHDIf3cVBXElh4hl98c6u8yAhrxq2FLl3BxQJJ3h77Ls8vBxOuaK4Xk11w6Cg9Pw2Pp/+BcOGDX9upOXnew93x0+RSgvIyBCux6JFmBSte/bUEhCgJTxchUoFVlZy/Pz88PT05p13JpglDwUGCrWOuXPPs2nT7yxZsoyvv57LqFGjiIqK4ubNJA4c0JqQx15ktbZ3r/HKSiQCV5cBeHp0oWePXlyMOEhx0ukaqQODSfVm7aTq2Gccjn0noLgRjra6EtcRc5A6eAjj+8DXqO7Hsmjxj3QK6KC/bsWF2URFhSN2akRZ8mmTh3bugW+YOf1L2rduScyBdVg0bo/LoGnY+vVHVZJLyaU9OL70Ho59xlOWeApF4slaElOdupQi6lcmffKl4KebcZ+SuINYeLRE3qgtdoGD9fl3kVQumJtbypkxfcb/iAj9b6P+v0hbsvgnHMsyKdo3F0VSNLmHfkAqkVCacIKs7TP0kDcLS0v9a6lLE05HH6PA2psfFi7gzbHjeOe9T6m4F0v29pm1rvOfbMbrg1VY+/Sm6MIOtFotHmO/xev9mvfO76ihPovI3jlLOH7YUqxadkMRu19Pry46tR6ZTGZy7vnHf0RmbY+qJJfsbTPqPf+yc5tZ+K2E9wanwrV1LP5eSefOwj4sLdvSvXsC8+ZtJSJCRkqK+etkawtSqRxLy7eYMkXGgAEQEiLACn/9Fby9BeLRokXw8cemtPPFi+HGDTUhIRWMGzeOBw/SCA5+jStX4nB1/ZAZM+wZOFDEjBn2RETI+PprAf9eX8vJqYkSq99Eq4mv93s/rxI0ubUaTYPwQ7suw8hWVHPo4B5efX0EYUdPs337Qdo3aUTJ8RWAsTKmS/BU5C7eFBTkU1BYaCK7+nTteIqvHtZT2207DRAIRl2Gc/DQfjSa+lNNAO5Ou0E1HagCIDRUgAgaYvYNW4cOAoxw1ChYtaqU/PytDB8+Bn9/ZYPbBAdXs2aNoMEeEXGSLl26Ul7+Jxs3aoiKElZaFhbwj38IUMURIwTSV33jJCUFDh8WAgDDplVHodVq8W7SjC9mL2Xc8MEUn16P3LuDIHXw7hIsm3em8NR6HPuMw77bSEESoMswSm+Eo9Vq9Ndf7tUWJxdXAjrVouoMKfxV2fdxevUj6jbbbiP4adUyevbsw5wFq2hipaFo31xURdk49XufJtP3Y99tJDLnxjj0fpeqnAcUHllqsp/SqDVMnzGLNm39+fCDyaCuxqpVN/KOrjQ6T5lbCwpObUCkUfH9DyvMXzCDdj0+jkXfTeXpk3Sj98aPH82znIwGtvyvbX9ryqVVu54NLi90ynjN3G1Iu3CQoJfG4uliQ/q9JOSerSm/fRELa3tcbS3JuXEKm46vUJVxG7dR87ALHMKz+EgSrlwiKjIMlxFzUJcVUHo1VF9c1aFgSq4cwunVj7Bu07P2vfijOL0yEVu/AZSlnKbySRIOQaNRJp/E168fhZl3qE6/iaq6CuehX5pElSJEaLJSsUBFF78AHseF8/Gns/FwdeNp2i0auTWh/N5JvltQQrduShwdQVmhZutWIRd69KgckWgQ7du3p1Wrlvj7+xMScpzSUpFJTnX9egv69etNdPQZSkqUyGRCBPbOO8Kk/WLoBwGv7OurK5j2prCwmLi4OJKSkqmoqMbKSkbLls0pLCykZ8/6+QuG+fXsXFvSHnub7ef+r7zCnStRQlG0dZBx/nTXl2ipTX8Z0vHTH95i/rxpPEq7h2OwUNvI2b9AYOcGDkVq44i8iR+p5w5h12c8lQ9vUpZ8CkRio6jTnMDWixRFbSzXYmtdi2h50WLkunXCAzQwUENAgIadO7UNpkiEWsc9Ro0awoABg1m0SGkEU6wtRAvF1q5dBQLTH39AWRlmV2sTJsDWrdCnj+Fx74E6Aq24J5fjM/Fp60fnLr25EXNKUGX07ohNuz449HgTeWNhWaZDmEltXajKuk9x7D7cRnxVk6uOJC8rB2t7Ly5cvsWubT9TaSdQ+Ot7aMu9fCi7e4lTx//E3r0TY0aP5e6NCzy5EmFa99g+HQDnwaZoGF3hNiunggP7fsdlZA1y6t5llE/vGJznECruXUZcreT1QeMaLIpeuHDORNHz0OGjJlIjzyus/lekXP42pqi1ldwILF/fa+H/IcyYPp2ElEesXDoT23Z9jE10j6/AfczXFJ7ejFXrWlsu+4HTSDkmOJ6ghYoH18wy2eyCRuo9PXWMTbvAwYITfUke1q27oyrKpDLhGKNHvcnBQweQteiG8l4srvW4Ltl2GUrVvUuMHznUhIo8fdoHKMuGYyETJgVD6v6aNToyiJKIiJ0EBe1l585dBAcP4cqVC6xZs4Fp03ZTWKhAJgONphqRqJr7988wZ46WgABhYj5yRBCB+uwzOHUK1q5tuD8GDRKi+jVrqpkx4wADBrzBuHEfEhxczapV1TXnpCAi4j4HD6pp3BjGjjXdT0qKECX++qvwv5e7E40a1d/Pr/brweLFCzl7NpyCPbOR+w1EcW4z/Xr14fzF/VTdE9iLOjr+g0fZbPxtCdIW3RAj2ANWpieiqSjFqlU3cnbPxuPdpVi4NMHtfQF9YtOuHwWnNlAQtU4fdaLV1rz3O3YdXtULbMn9B3Dh3H5mTJ9u9nwBFMVWRr/5RVmgxcW1/3foIPR3aKgQYde3TUFBGatXr2fgwIaj+TfegHnzhAfpokUQGyv0Z3ExSKUwbJjQJ40bQ3GxmNBQTZ3jPoTqYYwYuByxpPlzvWh1BivyRr7kHVluNFHbBg7jVOQ2Pp8xA4ARb6zhw4lvY9Wqmxn3qRF6LX+7LkMpPLUBleI+qcl5eq13w1aZnohWq23QuajowSWuXz5kZNGns8ozPE+7zoMpOLWeu0nnefvdD/T7MOxzdWWOiQdxRNhmvaWj3LsDBTVewzqnpLr7aHhu+9c+q6/9fxWhX7hwjp9//AY3j5Z6csfBQ0f47ddFep1kXVSXdyMSC+8ORuQHy6b+lF47KuTX9GJNwfrcaoPRQU0eVYdhl3v5oEg4iVXLrrgM+IyypGg0EhmJV2Nqn/qplxFbO2DZuJ1e3laj0ejz4xqRcXFIv/Ioj8bF4SRAg/lXXS41JOQIY8YMpVUrD7RaGYcOHWbkSBEzZ2r49FMhF15aKqAXWrUSovFu3SAgABYuFKI1c6gIw6ZDP3z6KWzYoCQ8/LhRfjcrS5h8IiM1lJVBQgKcPy/C2xvc3HRRoEifs9fl17PzfOqN0HUQxmfFFkz66BPE1ZV6vfVRY8bx+oCRpD9IpzDxJB9M+pzCIgW//7pYj1Aov3UW5dPbgq2dTrr17kUUNyKw7zZC/9tEIjGl0esZNGAUxTlCYRWxjMrroXQOGoIq7y6FCdFoRGK9QJhKa1lvlCQTHzaK0P9qMVLXPD2FSdbwvbrbnD9vS1JSMlOnNgwjbdRIUGQsLxdqJEFBwn5fe014oP/4Y+35eXpq+f13G8aPb4RabfCUQUtFRSa373d7LoRRq1FRlngK+x6jTYS48sNXIZNI6Nj5VaOi6JnQLZSnxiKSyMg/sQb7biMoiTtI+b04RBIJhac3YxswkNsXj3DlSqzZSVvAnvsZ1dVy9whoLUMSk/LhTary0ym7e6m2CGtGPtuu80DiI/+styiqI0/pirA6XwWrzoMpuXIYy2YBYOVIQsROpHJHvvt2FgqlNfb2jv+7i6K6ApnI219P7sh6msrWzStRN+qo10nu3SOAEcNHk3Q1lqdXw43IDxIrexSxe1AV51GZnqg3rKiXUQhGRgWFZ7eARGoEkypLFFiocu/2lN2+YFRNF0lkFJ/bhsTGGUXUr8yY9jl3Lh6nOOkMakRGBCHDwoaj3V3QnAVeNB0iIjlZQ5s2vgQHjzBbSDNceuuW0+7uUFkpIy1Nwssva15owunbFyIjZQwZomHgQCGXbIzgqC2olpYKaYRt2+D8eXt8fVsSEpJnVCy1te2Ml+fg5xaLOnVsSc8ePUz01hs3ac2MadPo0N7XuEhuhG6ZbtQnlY8TjSZ0ALRaytPj9YVVQTt/BZ0Ce/HJpI/08NmFC1fwWv/+DZ6vVLQdqaRUv+u8PHj4UEJg4IuloXRNDyF83/w2O3fC7dsaiosrX+iBvHkzKJXG+6v/uCoWLlxFXl6o0X5kMnu8vKY8F8IoBDwn0Kqq9UEQQPaer5Coq1iy7Bejcd+qZTO8vH1JvnqawlsxOPX/GPvAwdh3GyGwwq8IwIaqm8extrFD0jzQBAyh1Wqw7zGGsqRoFIknEYml5IevwlIqwkNSxrP4SDQiMeXn/mDSJ1/yKO0OJbkZAlw10BSuat2mO2XJpxssio4cNoj4M0eNfBUkTo0pOLUBuXdHSq8fpSr1Ih9+MIktm1aj8upA6ZN4Pp08qUFy2v9I2KIuQjdytulSm/vetWszjsO+wi5wCDnXTpD+IJ3GTVqzd/dWTp85jt1L47BwN67wFF0+BIDrkFrpXUGm8xSKBGEQ5IWvQibSoHx6m7K7MfrqvVQiRZmTRsW9WBCLBaVEuTVWLQLNslRLItfQtLk/ZakX+HDyTJq1CkAtrs35d+01hg5+XYyessrKA9hZLUMkEs73RfKvQi41lfLyYho3TtRPtHWbLheekmKMOT9+XIxEInkhXPnDhzLu3IEZM6opLYXffhNyrsuWma4gdFKusbFWxMWdo2/fYjIyrhpI7kJYaAK5OWdo7N2J5LtZehjjvxKdmHPbMYkOI37Bbfgsk4hS5ik4+ORlZSOz82HChImIpDZcuHxLT34S4LMt6j0PK8tq5OIvsLG6a7Tvxo1h9WoLOnZU/yWYZ1aWED2/bcqFq2GOwrffarh4EV555fkrgOhosLSsjfgbOu7583aEhAzl2bPD+vczMmDH9gKWLFxP2OFQJBIVYqkMsWMTqp89JH//PLQaY4SWziFJ17RaDbYVufR9dYyJjO2NlAzGjXuf/GeZZN66jE2nYH3Nyr7rMIqO/8iY0RNw9gqg5HE8BTej0Igk5B37EftuI6i4f5mK+1ewCXidsuTTVD5OAnU1H/9jXq3UdM1qLqdIhk8rb5KTrpqVz9aipfRGBKhVTJ4yB5XW0myfu7m50a37y2SlpfAo9ihSZ2/9at+uyxDKEiOxk1txOe6C3pkr73oUeVnZJnUYw2yDolzDwUNH+OXnBezft4vyajvs7R25cOFcg1H+f4sI/bdfF6G0b0T53UtYNe+EvHUPnl4Nx7LTIP2yRmTtRGHSSRq5O7Bz5yas2/am8tFNgZRTMztWpidSfvu8Ce5bp+uiqaqg6PxWxBo1K35cx2vB71CUmcr984f5bMpUXgt+h4z0O5QU5lP24BpStPi2bE56TChlKWeMaMZ5e77E1saGiZOmM3/efNq3a4unuyOWlhaMGDZQiDRdPfRPVhvrUjq0mo+D7TH9ZA4vTgbZuLGa+/cfvJDGiuEy3toatmzR8PSpJR07VpudcM6eFQppaWkQH68BNGRlCdGjVitMJnVp5rpmuIKwte3Oxx8fwddXeEjVOvLkseqn/QS/EUDXLn3+cnSio2m/PfZNuvV8A2XhY1LPHTKxj8vePRvHlz/AxqenXkrCkFxmzsHnRc/D1ekaPs1mIrd4YnINWrd+m1dfXcDUqcdMYJ6G0NG6MM+9eyXcvi2mvFxa7zbdukFhIdy7JzxA62v79gmErqAg4QHT0HH37ZNSXd2SxYv3sH59FWFhkJQkjAEdjv6zz6D/q1pUufe5+WcEZbcuMnPGLG6fDyPn2glEUgt9MVnqWDtZyr18KE05h7eLFf1f7Uf6w1v8sWmFno6vUeayZ/cfDbpFvfPuB3z68SSePb5HwomdiG1dUD66gdvI+YgkMkqvHMIhaDSV6QkMHvoWkz+aaLKaS394i13b1+I8Ym69K4yKuzH06BLE1JAQkz43lAsoK3nGnt1/YNV5MAVRvyNvWqOnX6PpXphyFqdBtSt3rVhiQk7TrXh02YbAgA5sXr+YSjVUVlVRUZpJ967+LFs8F4WyitKCR3z26ScmUf5/iwg9/UkuT2/HYNnED8XNE9j3GI3Y0XhZo0g5g6ODO+fPReE+5muBiZkUTVXOA0qj16PRaCiOO2iSX9PhvuWNfLD0bo/E1hnV4wSuxcdTXm3HyNHvMHDwWAoKi9mwfiUTJ83Ewc6OR2l3GTT4TWIvnUGjViNv4qenGSsfJ1GSfAZRkwAjUX3z0aUUVdVWWjT6GpEoz+Q6HDkio0+f56dDzp61oaSkjFOnhMitPgOLust4XR52x44thIQcp7hYi6enVj95/PSTcPMPGwaff16bTsnMhPv3hYj/eeJfHh4ali27Q1jYMX74odokkteng749j1vjQErL1S8coRsal8ScPEh2biUXzobp0S2GTavVUBKzG1V5CYVnNhH8xkjy78aSdyOKqoJMSmL2MGrMB6RlVJqNmnRelnWjpNz8J7RpMrXGlq+2SSQOdOoUSpMm42jTxodu3bqxe/c1Nm3KZ+tWoTjs7Azz55tOqikpsGmTJYcP7yc5uZolS+6wdauW06eFldLMmbXbNG4Mv/wC/v71k8fWrYPcXLh1C44da/i4v/yioXPnQmbOrGTKFEEXZs8eYVVVt++6dYXOnTScjoaXXn2LK5fPUYmMyrRruA6ZCUDu3jlotRosdIikmtqRVO5oZEiS9ayCbZtXYtV9jBCoGQRIlemJFMUdpKyyivSHTygvL2f/vj+wbNeXqqcpWPv0oiwlGucB/8DCrQUF0RuwbtOdO3FRdOz0Crn5JUbjRvCD9TexcKy7wngcF45f51eMxl76w1t6VMvpsF0cO3oI6+5vUhx7AKvWQVSmJ1B+Lw7LmhpeXRx8YcQvTJz8hZ6cptufLtuQGXOYiCP7QCLDqlU3VIUZlJaWEHniKBqxBKtW3SjJvE9BQakeKfTfJkLPfHKXXTvW6guNioQTKDNTjeBFisRILNxaUK0sw8mQ5SUSU3J5Pw62NlgUP0UtkqAsykGRFCWIBIX/guPL71N26wyKxChEEgn5kevQajXgHUDevYvExZ6iVTMv1q1djsjbnyfJ51m4cBmW1i7s3bkONeKagptgaKx8eovi2H16/8SS5GiautnQ/9V+JlGdrU0+HVrOpqTojHEaIgwKCmwIDj6MSmX1XJr93r0SUlJUDB+uZcYMw8gXvRelt7fw3brFN0HB7z1CQqYxZswYUlIqWb78Hhs3VnH0qLDM/vFHTOBw3boJE0ho6IsVVLdurWb4cNFz00HPsm0YNnTYC0XGhnlcuy5DyL96lIe3Y/V6LnWb3MsHZWoM5fevYOP7Esq8NDZu2MHdxBukXTmJddvelDy9xehRY9Eon7H4hy+4f/cqmpqoyb9DW35f+x2XYqJRN+qoz4V6uGlBs73O0aT06ZOFlVVLQERExDneeec9evbMZ+ZMgZrv6ipcP41GKFrWhZr26fMyK1b8SFxcAmKxlq1bBVhjUFAtq3fpUoEMJJXCiRMCQcxwX3v3CisypRLmz/+K06cFvPq6dUcAUZ3IX8ovv2j48EOYNKm2BnPokICUaaiOoygTkZttzfTpc7gZd55KLBA7eqGI+pU+vV/h7vnDlN+/ikgioSDqdzp26MDJiFAjb9esB9fRuDTXOxCVXj9KxbVDVOVnUBSzG8smHQWRr/zHpKenUiqyRpl+s4bhPdTo/tNBJMvvxlCUeZf33n3HaBy5ebQ08oMtiVzD59Nmkn4tyijX/tHHX+j9cD3dTX1q829GokFM5aObtce8cwF1WREVD65i19l4lVj453zeHPMB48fVip/9sWkFVe7t9LWfossH0apVeqmCinuXUVcUIxKLa4v7qbE8unWVmZ/P/O8VoRvRjWuWMHWFmUQSCxQJJ/D6ZCMyRwEfpmNiikUgatoFbWkhr/R5hbSUq1SVFqJ8nIjEzgXnAZ8htXMXBs+Da4hAiPBrXOhLy8q5cOZkDXJCyN/fuhlP6KEdqMUSrGtEo5RPUii/dwlVcY5eu11YXkn1SBZj9EYObZp+wLWrBUYFRUNj4HnzDjJ+/LusW3e6QZr96tVaZs3SMmZM/RhkHx/hxvz5Z2EyCAuD1FQ4d07G5s0/4ezshLOzHQMH9mbWrFl8881s8vLyaNEiqcFJ+MCBF8vfHjkCM2c2vNLw9IQ1q9No3/mNF4rQ646N4vhjyJsFGOPV93xVY/knRIdaiQxl5l083lnMs/hIEq/Gcu1qDK6j52PXZajeYf7ggS0oyipwHSlwFrKvRhAbHUp5pRKXkXONcqEOzo64OhoXD8ViOc2bzwBUPHhwy6xlXJs20K+foF2+di1s3y7i/Hk7PD378PDhI/z8HjBtWhWffSYUlnWpt7pF6ClThNdnztQWP7dsESLx+/eFIvg778Dq1fGMGTOUoKBOjBkzkORkLStX3tfLBlRXN6dz5yImTTLu7xep43h5almzOo0e/d6kW/eXjawRw8MPI2nWCctm/pReOYTTyxPJuh2P/YAQIzp+2b04VCXPBJ5IjQ5LY0d7nt2/rn+vLDEKT9emjHt/CudO7Me6bW8DiYf2lFw5hOuQGUZzw/0Lh+nczXhM6ewPdXaNXXu/SVCPvnQNetkk12449owie7EEeVN/yu9crAOGsKD87kWzqpAatZbU2Ag9Lt2Qb6FD5lXci8WymYEkQZMOKNMTjGW5xVLUT5Pp2PmVvxSh/60GFxu27Ofpk3Smz/gHxWoxrsNmmRWuzw1bisTGCVv/N/RGENm/T0RTqcB51Hw9FrS3nw9Rp07gNrrWkEJsbU/lo5u4j/maguhNWLi3MDa1CFuGc/9JRgL+hVG/Yd22N9UPr1GtAbG1PZqyIqxq8Og6h6G6ovpgaHaQy9OHL/OPfwiQRHMY4pQU+Ppra1asWMKXX84hOLia4OBqPDwEtmVEhIywMC1du2r55hu16Q5q2vr1woQ6fLixqcGRI3DypJw9e7YTHDyEBw/SWL36Z3bvPkBBgQKZTMvmzQ2bLixbJtzoDRlobNokY/fuaqKiaNCUQaWCAQNFnLmQ/ELi/k+fpDN7zheUqMXYvhECYGRaUnh6IxKtBo9G3hSppMj9B1AQtR63kXPrNWgoPLuV0uvHkNi5IG/cDhcDPkNu6FKcX5tsYuZw8NA2tFWvGf0WsdiKvn2FFNq0aSHPtYzbtEmGq+sEQkKmExTUne+/LzcaEzoDDMDsmPn1V4EVOnlyw/3g6vohq1b9jDnjEjc3T1atKjXp7/79MTLjMNcM+w6M+yzqdEy91nJQAyA4thxLa1uqPXyNOCTmDDRUcXs4cjSauNiLzJv/BSJ7D1yH1Wd68R2fTZnG2Lff/79iOvEiv+XZwe9rGLN1kFQIZh4Fe+cwoQ7/RGfVd+vRI+wHf0l++Cq0ahWuQ78we4z80MWsXGnerKMhg4u/nfrv3aQZIpEITVUFuWHLTD7XERjsu4+i9Hqto7pV4BA0FjZYeLdHJJZgP3AaMTeTcRv9tYHT+ABBgbHGis5t+Gyq89LJ3jaj1oX+w9VG3qUFUb/h0GcczsFTkbt607pZE9Qlz3AbOQeX4BDQQuk14TyKT/7CW29/rJ/MDZtWc/mFKOHBwdUkJiZy4MCf3L7dho8+EkgiH30Et2+3QSyWMmlS/ZM5wJAhIJOZ0vqnTIFFi5SMG/cBW7ZsIyioO/n5O1i1SnDEqa5+PiHmnXeEaL8hOnlEhBQnJxuysxveV04OODjYNPwlg6ajovfs0JqS4yuQOTfG490lNbTt9YjFYr6YNZ/t2/8koFULik6tx93VlcrLe/X96zJ+lVH/Km4cx8a3N26jFqAqyCRn1+zasTBxjfF3z21mwYKFzz3P3bv3ExzcsGVccHA1u3fvZ/Xq1QQHV5uMif79Bb/W+sZMdLSQFnv+MfbW+3lBgcJsfzs48G/1nbtHY9av307PDq0pOpifBFYAACAASURBVLrc5HMdHf8fU7/GS51H0b659fZP+fk/mPDhNAC693yJ8IjzeFiJyT30g8l+846tpH//oYx92xT3mXongfHjR/9lqv7zfkv+sZVYeLTErgbZU5meSM6GSSiuhemlJOT+A9i3b6fRdgk3r5GQcAWbfhNr5AveoyovndxQU/mCvPCfsbe3N5JPeNH2t6dchKJoHpkPE3AdPMMsgaE0/hjldy/hNuQLfUVd7uVDxe2zKK4cxrJlF7PuKnnHfuS114eTc/8WZbfPIm/dA/vuo1FmpprXJ9/1JTLXJoIYVw2JIOfmGZwNUiyIxHqollaD0fIqJ7eIy9eu07rJMuTS7S8MSVy69A4HDuynZ888ff61f38oKioiKamKzp2hSZP696Fbhhvij3VSrps3Q2GhisjI4yxZUm2UEngRQoxCARERwnK/ro73nj0iNm2yYufO9Vhb2zZoygBCvtfW8XWc3Nu8cFG0LrlF+TiZ4pjdWLfpgbq8hLR7d9FgSdjhnVi16YmNVklzL0/SY49iW8fVJn//PIIHjOTpnZtUPIrHcUAI1flPzY6FvH1z8O/8Op0Ce1FQmG2SchGJZDRrNhVQ8dVXwqS/bFltjaRuwboWqXTXLFJJp5r54IFADKr7uSEaqlam1/h4bdrA3r1VvPfeSL777jsmTPiIOXO+Y+3aX8nIeExqaqpZlcYXEfXas0eMneNrOLm3MVv8f/LoDocO7tCbjxg2HR3f0qEtY0aPJSsthcdxx01QSvn75zFm9ASKlQ76fV+6dJGzZ8JxMZPeEInEZCZdMkpv6IrahsXYhqj65sZeQ79FJBJRdf8y5amxIJZSErmG5s39yU6MourBFTRiCcXRG3n9jRH8/PMSFEprsjPT9UVRnZZ/7uFFiCVSXAebyoJrgZK0m/9SUfRvi9B11H91ZQ6J8RFGZrCGzb7rcEQSGRYeLSk+uRrFtTAqHt0ge/vnWPn2QVxdrhdoMmylUWv46quvmT//G775fhW9/XwoOb4C5ZMUKh5cMSsQ5BA0ClFxFoV75+gjCPcPjaO2wjObcQmeCgg042qZJakpFwno0Bz/dhomjl2Eve1V4MUp4UVFFXz/fQWTJlUbRdiTJlXz44/CjZvRgP6PED0JrzMyBHTDpEnCzb9mjbCcHznSNOrTRYUNtfBwoVj266+C2FdISO0K4uhRCUVFlUyY8BklJWUcOyZpMJIPD4fJk/9JQIfm9OnRnoAOzU1eG/6vrswxMpk2FuAKQWJtT86zbDb8tgSXkXNxDg6hsErDzeuxOPQ3zU3YBA7lzt0bLPj2J3r7+VAY+kO9Y8G2y3CyM5Lwa9cU39am4mtCsyQi4hxyOcjlwrWOjDQVzdL1kbOzLQUFZWbHROPGQt5c99Cs23RRdFycsF8LC9PjhYSAlZWMoKC+5OfvZdUqBZGRWlatUpCfv4OqqkqOHze95V9E1CsiQsOkSSPN9pmHY7WeHl8fHb9YJUKluI+2Ko+kxGvYvWJ6zW0Ch3Llyml6B/nq+3/zekG6o779Gt5/fXq0N9rGOTiEYpWohqovjCPn4BCz26xcOhMXe9EL/JahWLp609rdGeL3M+mjT3n0IB6Llt2wUpXCtf0MGfoWJyMOUubUgpQbxzgSuk0vQwCQe/wnANzq+V32XYYicfAk6sSfZu+PhtrfHqGb02AW4EW1FHqRzALFzRN0CnyDktTz5F0/gWVTP8punkSrVeMQbPqU00UFLdp05XDocS6eP4plp0EUnNpQrwSAhVcblPcv4yRVk590ziTCy94zB4lWjcSpkVl6v0Z1AHubWnVBwwi4vqjKwgJiYoRI3lxzdxceDLdvm5pJ6NqePdC8uYAZnzVLuPFXrKhFrixbZn6lUJ+Wuu5cFy2C+Hh4+lRAUfTqBcX/h7v3jo+i+v7/n7ubTe+VEhCI9N5CFQSEkFBCR3ijooCoCAgqTUQFhCCoEaQHFQiETgiE0HsNvReJ0gJJCOl9y/z+uNnN9kTf789XH7/zTyazs3fvzJ25c+45r/N6ZStISZEYOFDB5MkaPYf3vXv3uX9fxsGDCgoK5EboiqgoAbUE2BSzke1bf+Hu3fs4ufqZQRitJkVlMlK3zjJOJgU2pCDpAt5hZZWiWpmCwuQ7eL8x1uw66YqLbl2+xLUrp1GrSvDrP92qQk765YMUZu6jeZOfjWoHAJRKP0pKuhMa2pvJk9WkpIiXXlQUeuhh376waJFIWiYkKGnTZggPHjywqgYVGCjYEDt3Nh+r9HRBt7Bhg2WaiJYtBSopIUHDp5+qGDDAnJLX0VEiOlqieXPj8dZRMH/1lcC8G6JoDPHsnj6+/PmkipmHXhGIoAY5tw6s5/DhfXj0nWrkJOmk5BxrttAnrX/79Wd2x+3A/tU2ZhXeMpnMKr2GtVJ9Q752LWXfMYXFJp47hax6YyFZaONc0q4d5e13JxK1OrIUhdWH3DunqFujFseP7xfontLEevOGTcl7nkT6lQNIMgWFv5/BuU5721oMCjs0T2783yRFZTJZT+AnQAFESZIUYfJ5dWAt4Fl6zDRJkmz6foZJUV3iS9mwO3nH1zB58jTWR/9GWoEGt1Z9yTyyBnsXT5rUqcP16xfw6DMVh8CGpKydhH3V+vj0MM/Y6ZTGX2/RmP0H9uLZdxqZR9ag9K+pT4TpZMxcm4fpCYLybh4m88By/CzwSGQn7sDxwVHslI7kaBQoG3Un79gaIiIiadGyDZJ6FZLmJ/3xS5eCg4OMxo0lPflWWJihEruY2OvXFxOwNUtOFl5ZbKz5Z7duwWefCaz4smXiwa5UyTh5ZivppSMGCwsT/Xv0SLwAwsJEbN4wwRoXByBn0SKt1STvzJmOhIf3Iz5+NxkZ+Tg7ixdN377GCdv4eAV79woVeif3auUmRZUNu5N3LAoXN0/yZQ7WleF3zMVvwBfIkPHywFLstCU4t+iHa8veFD+5RXr8D2gKc7Bz9zNKiurvhRa9cS+dDPJuHoaLy9i2qdjodxwcatKsWTzTpv3I9eu/cOWKxuLYxseLSlq5XCBdhgwZhFyuQKXaYTWBunSpgCiONXkfJSeLfX37ilyJNVuxAjQay4RfS5dCWpp4Mej6q0vA790rxtfHBzIylBQUiNBMt27Cg69aFWSKScjsRhuNERgnEpUNu1Nw4lc+mTSF7Tu28CynBIcmIRQc/xVHZxdUAfX1SdGcxJ1kndqAc90OqDOfE/CfCLKOryP38h6c67Sn6NE17FCj9KqKQ+MQMg4ux96/FrKc59h7V0HZqIfR83ft1kN83GV8/c0MnueW4N5zosV7JGt3BAsW/IQkSUyb9okR8VYNbw9u3b6Kwqc6bs1CyTi0gi6du/L0WbLRuYwaO4W42HVkOAeWm+Tl0ha2bktg8ZIlnDy+l54hocRs3oidVyBuLXqRcXAZlfwrkZr+Quxr2YuMA8vp27sfkz//8i8lRcud0GUymQK4D3QHngIXgGGSJN02OGYVcEWSpOUymawBsFeSpBqW2tNZ9Rq1pc9nisnvxJmbqPMfcPTwbpq16cfA/n2Y+/V4srRKtIXZ+PScgMLNh4xdEXh0M0YhZJ+JoerYKIoeXSd7/2Kcm/fBtWVv/QOZcWA5TjVb4NtvGurM56Rt+waZvRPuLfuSeXQNXl1HkX1uG3I7e9xa9dUT+Lu37m/WZ10Gu1blWtSqUVn0NzicgQNEgsTPazOVfMrwysnJ8OGHCkDD/PnWkS7Tp4sSe2toE7VahDmGDTN/COPjBQlXaqqYzBMSxBLcsC0dgsJa+8nJwvM7fFgIHnz/vfW+Tp0qUDXW2oqKssPX9x3mzfuAnTtbl4vymT7DgY4hnxDao5N+/8lzt3mtbQPA+N54+92JPH8p59HdA1x/8IBK7y01ai9l5SiclQoKUVKSk47Tq21wTP8dDXYUabX6fUWPr+M3dDaZCT8jSVrx4B5ehVfX0eTfOChoeJuHkX/cmJ8eoFatuVSr9jYymQve3tXQaAqYN8/6+c2YAUVFYoX07Jkde/aIt+q331pmTzx2TLxQFy0ybzM8XLy0baGSkpNF6GXHDvPPDJE0sbFivLOzRThHN3EDTJrkxokTfUlJ2WD0/efpI0nPGgwYj9HJc7fpEFyXrTErOXvmKP9560Nat+2KVqth8Y8RPPrzKsP+8wHpOXbcvLyHLBXIqzTQq4g5BDYkZd1kZEpHSlIf4D/oKySthvRd36FQOuDq6ExWVhoujbpSnHwXO5mMoMr+PHnyp9Hzp+uTVqvhx0WzeZGbid87PxmdQ+qq0TRu3Jm33nqbb7+ZQL5XTaMJOWNXBO5dRqF68Yjcy7txrtsR2YNTfDNvFb/8EsWjB4m8NXICqVlK6tb04LdfInlZrMUrbJJVtMqHH39B7bpN9P379psJ5HnWQOlfg7zL8Xh0GE7+hZ0Wf3dOxBqjaw0wYUyv/2pCbwd8LUlSSOn/0wEkSZpvcMxK4A9JkhaUHv+9JEntbbWr89DBMozo6ZNH5b5pX+yYg6NXAE4t++s9e0Ov4OW+n5HbO6FAi6PfK8gq1yfnQixyeye06mIcPSvh0mYg+cd+oUePUOL3xqGsUg//wV/rvbbcg0twatYblxZlLwn1OQGrMu27pF6GpDGeZL79NhBv76c2YX/LlgmvbvZsy58nJ8PHHwvmPGsP4ahRIgH69tvm3nhFIW937gRRv36STfjdqlWgUlmnfE1OhkmT3ElLe85773WkuPiKzd9dvdqO5y9C+PqbMkSBLVjZ1u27WLPyO9z7TDFbQeVd3IXTH8dJeZ6MT/8v9F5X3SqVuHr1PN79Zgg46/rJUFyAS9shZB9ZhZOTC56eHmRpHLBv3J3cw6txcrHj65k5RpN5lSofUqfOInQwQHt7FwYPtn1dV62CbdvEmICY5KdNc8DOTkavXhojmGpcnFixyeX2KBRa+vaFsDC1/vO33qJi0NAQcZ+YWkXhiT17yrh5821SUtYafSZTjEdm9wFgPkaaolRmTJ+MokYrqkjprFy5nqtXLjBt2icoawVTRZvORxPm0KTBK2zbuoFVq37GsXZbvEMNIMTb5+Ad8hFICDGZoNYU/H4OALfmoeRdO4BTUDDFz+/hrtCwM/agxXvl8qXzes/b0j3i/PA069Zu5lnyEyNYrDW45fyIH83gg5bgiKYUwy+ixjCg7zDGjBll9J2KzG26VcRfhS1WhA+9KmBIYPEUMNWr+Ro4IJPJxgMuwBtYMJlM9j7wPoCXtx/Xbj0ExJtVZ4bb4ybOFm/a3d+ZvWkzEyJ5c/hYCgryOXo4hradh1O5elM+mtCIxT9G8Me+n5HZ2eMU1JqiPy7iISvh6cVdyO0dRfnuHxfxdHaj8FwMoz+YSp16TSlRVOfm5T1kxEzDoXEIecfXMHjIaI6f2MeLuydxbhZKzpHVKJWOHDxyGv+Aqpw8d5v7d6+xfctSFnybbIZGuXw5lcXmovBGFh4uJuTkZMveV3y8mMzHjbM8karVImFZqVJZ8sywnX79xPfat7fuSSYk2AGPmTzZNvyuVy/hAdrm8M4Fiti9+3cije9xkpONvUM3NzUlJQkcOtofP3+hu2ftfrh/9xorls6zmiRzadGb5zcOI3Pz1/NgKxuFcOnwKrzeeN+Irzv/xK9oEjfTsds7DOzfB61WIzyw85sZ+/FMOra7RYDPBpNf0CAm8wJAhFLKgxL26mXsLTdsCOHhWuzseuPp6c2kSVvIyMjD29uF4cOHcuvWhwQFBZCUlMqSJcuZNGkzGRn5eHu74uJSREqKyqaHbpggNzVL94al73t7u+r5kQxN0ixFktVDrmhL0sOn+v3bd8Tpk44OgQ15tmk6U6dM4tq1RKN9v/yymtGjx1KvcSemzQxi6ZIIMmKm6Se1KmNWGCW+HQIbUpL2J5KqiNxL8fgP+lIfam3QsAnXbj00u1fu373G6hUROLcZLLhmDGpbih5dJ+fGYfKAnxYvpmv3frTt/A6P7x3kppU5Zsjg95A7+Fv8LRD35OXL54Tmgok5Nu1NfEIsrdp2QS6XV3huy96/mCYtQy3+bnn2v0K5DAN+kyQpEAgD1st0ShEGJknSKkmSWkmS1Mrf399ixtxwWypJJ+XZfYsZcdeW4Vy8cIyJEyYQt/swA/v3oWnDGjRvHERm+kNkCiX+A74QGpWelUl5kYpc6YBf/xn4hE5A4VGJrJdPidt9mMEDw9EUpXLl3Hbmf7uAt/v3QX0+htGjPuDEib3Mm7uAkA6voTq7AZkkIa/ejC0xy2hcvzqVvfKIWjmHXM+6zI1wwFC9TKFwJytLXSGki0plPUYeH1/miVuy1FThgS9aJCb2t94Sy+ulS8teEtOnCwGE1avFPrVa/I2KUjJrljPR0RvIyir8y0INlvri7e0GOJohOiwhNJYuhT59tCz+7mMKc57YvB/iYtfhWLutkUjCi6gxRhhg99bhqLOe83LjVPJuHiHzSBROr7Yl7+o+vQRZ3vE1RET8aHbfjB49Vn8/VPL3tHB2CkSxjjPgqH+Jlne9VCbvyNBQFXv3HiIycjFpaSmo1XmcPXsMSZLTtm0XFIrqtG3bBUmSc/bsMdTqPNLSUnjvvZHs3m35d3S2e7fwxC1Zt26iutSWJSQoGT58GIGB41Ao3Ew+1YJ6HJ4uX9A/rL5+XO7eOFQmJlFaE3L74UO9hyyTK3BoHMKjB4n673Tv2kGPPsvaXVZ/knnUQKRGrsAvfCoypSP+g77Ut+XWKpw7t69avFfiYtch83+VrFMxqPMySNs+h7TVo8k4sJy0HXPR5GeiLingxLF4UU7vrbGKunFtGU5i4hEa169u8bf+DgqnInObS4s+eoTVX0W5VMRDTwYM/c7A0n2GNgroCSBJ0lmZTOYI+AJp1hotKCy26aHr3rS2oFDPNp3Wv2kN21BpJJzrtNN7aT69JpEe9x0+XUcZqJWEkXf8N67deqj/LWXN1syYOZVPpyygWXAWUWtWoKwZzIyZU6lZpyPFxUV4lS7ldV7I9Wun8OgrKlNTNj9k2/Y/GTJYwt9/MHXrzsPbuzkpKXnlekXu7oKro18/MQlcvy4QE3/8UQYXNExQGVp8vAK5XIOnp4hvGybmxo0Tk3mbNmLy/OUXsRpQq2V4e7syfPgAEhPHExRUE29vlwr11ZoHCMLTHz58MFBk1F5ycpmuqeEqQVcA1alTEdNnTOSTqUu5+0em2f0A8Pa7k/VenUPjELKPrObNYe9z4MBOnt88gntpAt3B0x9yXpBxYBn+g8qqhnMv7qbwym6r3o/htr9XFgE+pmdn7KGLYqr8Co2toYlVTF5pW5CQcIgRI0YSGqo2UIfKJSHhN4KDo4mO/o06dYLIysokNlagZqyttHbtEiGgpUvNw3OVK4sXeseOtldqiYnv4+JSk/btL3H37jRevDAOyGdk7KOo5E9+fyzgd83bDtSvbHXetmH4QfcSbdt5uP6ZB9gZG69X/tGZb98pvNz7EynrP8O396f64j/DtjIPreDDj7+0OH6dOoexYV3p6rxmCwqTLlCpSiB/Xj+AXOmg31evaUe2bt9lc8Vna445ee42J/avQFGzlZGDkb1/MU7Ne5dRBzQJYcOGtdRr3KnCc5tLi968uHPS4u+WZxWJodshkqLdEBP5BWC4JEm3DI5JADZLkvSbTCarDxwGqko2Gi8vhv7WWwONMsi6i+XSoo9RPJuLW9ix84CFrPtSnmYV4tXrU4sxqsxd8xg77gtqvRLAjOmTce8zRR9zbdewNseOHzHKfks5L5ACm1Qgo72M29d/w9+/L1DExInTSElZbTOGvnq1gAXu3Ak+Pu68fJmDUinCLBoNnDsnSJmcSpXPPvqobKmvQ7lMnQqvv27e9q1bwjOfNUu0c/CgIxs3biQ0NASApKQ7LF68ko0bN/HyZS7OzoIq19KLA0S8/+VL+PJLy781a5YTiYmJBAXVMiqJr0gcf/VqO1Sawbz2xgirMfQrN5K4f+s0mzdHM/ytjxk0oC8jRgwgtRCjBHra9jn4hHxklkD3aNlHH0OVy+VW4/WW8iGmMfSJEz8hPf0XxoyxTcugVhuHqESewY20tBSSkv6wSAVgeE0NY+5VqqhYtaoMlWSaIA8OhpMnxfgZopT27BErwEGD3mTv3l2EhqrNaCYSEpRER/9KaKhODEKcZ2rqDu7cecukZ77IHY7rr1ujetVYMH8WJy/fMAshvIgaw8SPxlO5elP99bUV55a0Gl4m/ETx89+pOnq50WfJK0djp1Dw3n9G6EvrNUWp/Bj5HW+/O5lVy+bwIv0l/gMFJUjqhqlosp6h1Wrx6y9yKCnRU7DLS8XXv9LfnmNMETUOjUP06J7ojdFkq+VmKLj/xdwG/2XpvyRJauBjYD9wB9giSdItmUw2WyaT6ZjtPwXGyGSya0AMMNLWZF4Rmz/vBypr0smImUbejcPk7PmOQf2G45NygawtX5B38zAFx39l5kzzkmD/gKp8MHY8JS+sl9Z6e3sTVLuRXnlet8RTNgrh4JFDuHYZrV/iUak++Xk5+BY9s1m2nH9iGTOnFePsXEP/WxMmTCAuznbRRny8iG/7+Lhz9uxpPD2deO89OHVK0KD+/LMIT6xcKWBrS5YIVEpUlJJp0+xo21ZhcTKHMr3JGTNAperJxYun9JN5QsJ+goNf4+XL34iMzOXgQfEbSqVxUYxhX/fvt+fcOQXLlxuHblavtisN3fxGUFCt0nP/iIQEJbduVax0PSxMzaGDtmMKcrmCoW++zY6dB6hdV1z7iPk/4u+kwNnDF4WbD0rvqlQds8JiQZhLi95klsDWLdFWf0OSSpA0CbY7ixjbffscyinIMQ+XibDGEJKS/mDIkCEUFxcwYYJxmExnnp4gScXMnVvE6NEqwsLEMSqVWLWFhIgVV2ameGknJorQ29ixxjQQY8eKl35s7FY0GtiyRcWoUeL7Eye64uv7LomJ5wkNNU9/OTvXNNtnateuXuTEiWMWQwhOzXqzfccWtAbxyB8jvzMqtil6dJ1ny0eSnbiDosfXKUy6aBGO7NaiD2rkbNq0HhDe7ozpk8lwDmTtr5HY2dmXrc7lCnx6TULu5qcv4pHJFbg1D0OhsGP+vB/wzH9GRsxU8m4cJmt3RIXnGBDUFCtXruONdh3g0hbmR/xIz9BwPp0SwYjwXnBxC2M+mEaLlsYpR0tzW8c2Hck7t4XMzdPJu3mYvGNrGDpkeLlUBab2j5FzGcIWTSFQum1j2NOHFMsqlcGjTh/G1c2dCZPn6pOTAZ4qtm5eQ/VarbmaGKenvjXDk1+IJffcVho06kTTRrXZHLMapYc/Ti3DRcw1KFhPwpV7YRdZp6JxrtsRr4JnVA6owo2kPwh4d4lRmy+i3uKtQZm8fAlHjzqTmVlYmugawq1b9zh37jTh4ZZhh9Onw40bgrxJkiQePlzL8eMam3C/zz6DoUP7Exe3nyVLCmxCEqOjRem+SiXT9yk8vC+DBw9j9uxCm5C7JUvEpJCQICB3Go1E164aVCoN586JJb3AmitYvPgH3ntvACLGDFBAQsIZRowYSWZmYQURGjIGvvudxfvB1r1y4sxNzh+N4unzZ1R++wejRFjajjnYB9Qm4M05RkglU0jYyXO3CXldTY3Kc1AoCsz65+g4m5iYh2zcuKU0UelCnTq1uXjxCv37Q58+ZWO7Z4+YzHXhLsPrOmuWEwsXzubzz2fRvXshffqY49cNw2Q6nh5rtnq1CMuB9WN19Qahocaee0KCXaln/lvpZF5gNH7gTG7uVS5dMg7MqzVe3PlTvBQNk6KWQgh6uG+VVxk9WgDs01KTWbokApW9Iw6NQ8g6vAqZpEFy9kSbn62PmVtqK2XtJFo3qE/zlh30IROHwIa82DCV2gHe3Lx9FTuvKvhaq1XYPgdfP3+++OpnduyM4/TRaCSZHT7e3nTu+REd29Tn2OHd7N+3k1FjJgOwdfMamrcdSFhIZ/05371xiDEfTOXen9nl3qP3715j3dplTPhkFv4BVY3mti5de3HsaAIOQW1Qpt5CkuCVoFbcu3UCZc1glOl/8NU3PyEv5bC2BVusSAz9/8R0pf86s7R9+dJ5kp/cxKlOexLPH+ajCXOQSl5w4dwxJJkcTZVGbIlZxsqV67l/9xprVgpv+/L5OLQS+A+0XlpbcOck924c5cHtkzi82gbto0tmMdeXexdTcO+UHiubvm4iqaXwN1NTBLZi3bqD9O8PP/1UUPrA5JGQsJ5LlxRIkh0pKWq9Ersurrl0KWRlCa8tMfET2rZtT6tWmnJJvfr2BU9Pb7KzrScydQ9xr14C0lipkqTvU3j4Olq1kmz+Rs+egkLA09OdXr1CgN1ERFjGT9+6peHzz6fTuXMHgoLKVBVCQ3uTmJhI06aNK4Sw8PB00SeBdGaaCLL02ebon3ma/Bjnuh15mfATAcMjKH58kxe7InCu056C+2d4GTMNx6Y99ctg3Xd1fwO81+LvvcWsXzKZI8+fj2fcuAhCQ1UGce484uKucP26qKbVja2rq3g5dekiqj/VauOwxsKFi/j880+ZPbsQT09zTHjLlkLce8UKsX/JErMuGVlYmIC1qlRihWVqtvIXo0eraddOzYgRwkMPCqpCGUsjpdsOZm3aKTJp0qASMpkj335zyMzbNoX7OjQJ4dG5GJo2LEU7N6yBr18k92+dJjr6V+QyCe8BghHVMbChUVumxX9urcO5e3ELT54k6ZPkMpkcr16T+CN+Ie7th5J1cgMvYiOo8p7xxUvf+yMKVy+K7FxI2LWG8yeP4DvgS31oVZ33gOaNe9O88Sd07d6vDI5ZsxW3ruxh6idvcfXKBc6diEFZK5gtMcv4aMIcm/eoLnmqqNFKP19dvXKB5Cc3UVapx5HDe/Ab+KXVkG/Gpuncv3WKoW++bftG4F9Q+m9NQ/LkyeNGKh9plw5w/tg+duzciFaS4V/Kb/305DZi1q3iTo5CzAAAIABJREFU/JmjePWbjluL3uQkxuJcu60xb3b0Z0ai0DKFHfm/n8e7lA874+xWnOu0M+JezrsSr+dBLn58k5yr+/C1IK6gynxO/onlLPpOY1aS3aKFlsaN1Rw7Zsfz53b06iVnyhQhMlCtmnjIV692IDr6N4KDmzJ9+lxevCif1KtKFVi06HecnOwtlpInJwsvz1KZeIsWWpo21RIdLdkk56pSBY4fdyMt7SFHjx6iWjXb3OkZGSpWrjzGG290LkW6FAAyvL3duH59OUlJJbYJoDYpcPN8g/Rcl7+kN7r4x7mcOXPEphBC4f0zKAteIj27bVHrNfVFFpV9vkVpZwxJ8fbugZfXagYMmGBRnLt1a1HYtWmTCI19/LEoAHv9dUG0FRkppN1OnHCjTZshREWtIC5uF1WrXsHLS2uRK//5c7h7V7wkHjyomERhVJSY0C2JkVRMiBxu3iykZ88OgAxQ68dPkop5+nSF+Rc165FktcktqcvzO2fJvHYILXJyDixh0IB3eHbtKOlXDiLJFeQdW0Or9oPw9g3Qj+Wp83dp2bIVZ88cQVtVUAc4vdKU3Eu7yb9+QIjU7FuCZ+e3yb20h/wbh5DZ2ZN3bA3vvDeZNu26mWnMyj2rCC1gG8RXRY+uYh/Ulod3rxlRAmgkOXePbNYrGBlpHZeW8V+/cNZI6zjt0gEePnhI1Wqv2lTcsqaXXHDvDI7Vm+jnnfKoCv71fOhgnIzaun0XcbHrUJWUkOtZC+/QCRQ/vkn63ki0hTmiJPjxDap8sJqSJ7d5ETsfp6DWlLz4k8ojf0Imk5N9bht5iduw966KU9MwUTIcUAtJkkCrxa1FGBkHluP52n/0FaHZ57aRc34bSt/q+ISag/2TV4/FoUo9PZd6mRcSijbnOT2CDvDBWOvJsagoJUrlQDw83Nm4cRMZGbl4e7sxfPibjB//vt6r9fOrREZGbrkFII8fiwSjvb2S/HwVnp7GKJiKJCHLKxLSFZmo1Xn4+QUQGWkbAZOcDB98APb2zkRHRxMa2hmdt7dtWz1Gj35iM4z0xReOLF+9k/Rs7V/itX799VY412lvxHNvKSmacXAZx45ftpoILSloi50i16hfrVtfZMaMlbx8+ZvNgitd2MP0WiYnixh1enoqhrzk06fnMnu27Srazz4TY1iR6tBJk9yRJK3FMSqvUtiwjbS0PzHlUQeBbLl582202nzzL8u6opV/z/Ztm9iwYS3ffDOf5i2CuXjhLF9/PQOFXMZXX89H4RigT2DOn/cDL3MkixQPucei6NihEydOHsPeww+nVv31xX/HTxzj66/n6ePSV24kkbDrF05fvYnPW5Ekr3ofTX4W/gNnWg/ZRE9BnZ1CtfFldQa6IqLRH0xlUGnl6ZAhfc0qSW3xt+vM8J4qrw1VRjJpO+Yit7O3qgdhWGQEtpOi/5iHPnvuvK+37dhqpOUYs+EX1q/7GZVXLRw1hTip80k7H0vulX1IGhX+g74U8ky3j5F9aiMFd07i13966b7jSCWFSOoSsg+vZOxH0/H39OCPk9tp3KIn2vxUSrRgX70xeVfi8es7BZcGrwOlSbMjq/ENn0pJyu/kXUnArWVvo/5mn96EqyaPvDsnS72QxYwbI/HkyiXS793j889sq/UEBGhZsuQRhw/vYcCAcCSpkBs37nD8+Dk2bNhIcvJj6tSpSUFBATduXLGpEnT+vIhv9+mDnm7XVJJu7VrLNKyGZioobWo6dfgBA3rx/fc/V0jP9Ndf4bvvVIwfH8egQT3x9vYC1OTkRFGtWhbffGNOw7txo5wVK+0ZPnIGLh6VLXrhppqPOs3PlGePuHvnGnnPfqfw/lkcqzdGk/uS4sfXcW3cHYWTeymV8iI6dOiGb6U6Vtv3dNts5qFXrfoOo0ZN/cvi3IbXZNWqEoKDm1K7diVAxvTpc3FxgQYNbHvNOTmQkQH5+WIlYM2io6Fq1c507NiK69fvmFEYV1SIfNWqEr766mNMPXRQ4+TkT2DgeIqKHpOfbwqj+5Okx/Xx9GmB0rU2NWrU4OTJ4yxc8CXy6s3xsNPQvlO4EYXt4dh1HNwfh1+lWsiVbmgUVXAmmwcnttG8TR+GDhtN954DUGjV3D2ymdFjP6d1ux6l7dfU3w+m9Mp5VxPKCNxKV+cvNk1Dq9Uarc6Ln9w0EqiwRN2bV+xMzuNLZF47pF8BmFJ05xxYQqsOg/H2CbDooZfXhirtIcW3DuHl4kHO3ZMWKYWbNHuDZi3aV4ic6x+Loae/SMGlYRd9DOrw3nXE7YnFuW5H1JnPyVMoqevnybOnf6Bw88Whan19rMy37xQzD8y1aQjZZ2KwV8h4c9j7DBkkBmvSJ5/oYVVTPvuQy1cTqPTW9yZJs7kond0oeniV4qe38R80y6y/Hu0G4ZNykTe69mDT5tVEzM2lWTMI7Qndu1eswCQjI4+EhOOMGDHCLBabkLCe4OBNLFy4CJlsLfHxGovJLV081JQ/pGpV4Y23b2+bhtW0T7aKhBISlLRv34FWrTrSv7+I21vDuEMZRl0Id6hZsuQ3IiN1GGKZPskXG1sab86R4eHhQrOWnVkZ9TFVA6vrf9taDN0wV3LpzGaO7cvCrkYr5LlnUWWnkbLpC1CX4BQUrI+lv9i9kDat2/DtvIU221eZ50EBB6uUtxW5ljocuohRnyAoqD7e3q4cPpzL0qXmxxtaeLigA9i3z55OnUqsevL79oFcfoIdOzYxePB22rUzFtCoaIWoUimRlJRqlAMxjKcrFI40aLCW7OzzFBc/Mfp+7Vo+yOQ1gLKYsSHsV1DYiriwpNXwYudpXOq0N8qBxW89jVOd9qQ8vU7j+tWRy+X6WLa1HJtpMtav/xe83BtJyvrPcWsuVuddXu/G/aRzvLh3Cufmvcg4tAr//l8Y9V9H3WsaD58ycQQL5s/idPxCs9L+3INLmDx5mhEc07SPhm2ctFAVmntwCYMGDmH7jq1GeHzDfqU8PK2/HuXZP+ahz1286mv/IbNLY+P7uXzlnFEM1C6wIU9+v4Vf389xaxZK/vVD5F3fj2O1Rii9q+Le0lhp++W+Jfj2+RS5q7+Z6MTJc7c5eyKeo0f3CfXwm4dwadyN4sc3RNKsdhtUaX9QlHyXgMFfWaHWrcOLyweo4ifn+4ir+ge8okIRz5/DsWMu7Nix3WIstkULLY0aqZgx4whjxoxk7dpLbN0qYqOGHnFsLNSrJ9AKlszfX0DYHj6smBbooUPw5pvmn926BStWKHn06BFz5xaZiUgb6pm+9prYt3GjoPENDhYrkkWLfufzz8cDapKTf0GtzsDdXXw+dCi887YHQ0esJbOwGo5Objbj5KkvsvTCBV7h01D6Vif9UoJeXLzgwXk0eenIkEpXbULUWyopxKFqPR5fPkrDpp3N1OFPnruNo6OEQvoGF6f7ZtchMHAUK1fGWKW8NbyWhuLcOtu8WVyT2rXVnDnzgn79wkhOTubkySsVEt9et05Gjx5dWbXqD/LzjVc2OmrbGTPA01PGkydKpkz5nPHj48nNRU9h/PixYNFsZUMAZ/NmKCmRkZ5eTM+e3TD10A23k5OjUKuzjBvQHkWSv8bZi89ZsTRCT2Fb/OQWuffO8Dj5GZ5hkwRPy855KFy98er+AS9vn+Va4hl2x8UYUc6mP0/B2a2SzfvBEvV21u4FODfshtK3OrkXduDa5A2e3zpP/SZv8PD2KYqe3sGv71QcXzF+xnXUyobxcEOxC90KwOiUSym6NYoqODrYW71/bQlmFKcnc+VonFXhcx2Ns+H1+FcKXMiVTvoy4ZeF+fgP+spIOq7g7kk9llhINg2nJO1PXuycZ9aWTqbOsXoTs3Lbpg1roMq5y67YDfgP+kqIU0jwcu9iA7GECcjdA7D3e8VmWblDkxAOHTjO0qUiLtmtm/gbEKBgyxbbi52EBCWvvPIKoaFqm8iSJk1KWLHiFwYMULBsmbGAwUcfCc+4Vy/b1zY8XLAmlideER+vQK1WEBWltEAH4ESXLq/Tq5fGZn979RIvGV2Vog5zLVYk+QgPz5GaNb/G/HbLBtUA+vU4SZMGgeVSQRiW/mcd+w3neh31eGPf3p+i9KpqhDd2bRpC7uXduLfqC66+bNu42KzNkNdVNAoagbeHOZuVj09vnJwaMHz4MBISrIlcCNu717zk3pC2oXdviZiYnSQkHGfChEnY21uWfUtORn9/hYSAnZ3E0aPHmDChrGI4JET8LSkRx7Zpo5Of26FHFfn6vsOkSe707CkjMdGlXBnB+HgYNUpi48Yd+jHTURyYbteq9TVicje0rNKxPMP3iyKprEknfe0EXsTOx75SHSRHNxyqNeLl/p8BcKhSj4x9S3ALGS+IrQzGzblZKCeP7y33fvjh+yV45ovaEB2OfNLHE/BLu4T6wVk8OgxDdfMQX389j3s3j+BUpwNVP/wFx1ea6J/v3AvGz7chPcFfEe6wJNBSkTaKku/gVKe92bxj2C/n5sbXw5b9YyEXdcZTfYFO1TFlGXSdrqdp2CN9zyJkMhne3c2FC1ybh5F9ZgvO9TshVyiNym0BdsdtwrluhzIqgLCJvNg+x0jowr1VOBkHl5P263jUqmKkghyG/ecDjp/YR9r1A6X7MnGy1+i5SMrCDxp27BAe9JAh5ueqK6mWpIc2ya+Sk+HyZQ0REdbDKePHVyyUUlhYVrBkCRrXvDlcumRHXNxa4uIOGpBEuTJ8+BASE0fStm0okZG2ybrCwkTRysGDotpVt6wXnC4u6Mrb/f174uFxgRs3RpGXd9W4EW00qSlFpGa8CwiM77fflGF8QYRaCgoKkGfdIyNmGp6d3yH79CZSN07TJ7ENIWpFj66TcXgVfv2ml/J/9OXcwRVG5eL2yqfUqW4OQZXJnGjY8Bd8fTsCJUyYMJbg4GizUIbOdC+z2bPLIIqGePKqVcV+lQpGjPgPiYknCQ3tSlzcEaMKYkOYqeH9FRenYtky0ZZtUjRBJxAUVIXx40ciSZIeM6/ViophSzzoun42aWJMSaCjODDd9vfvjbv7Ba5fH0lBwU2DYyTQrsXOroQePQexYuk8/EpF3HUwYG1hrlEVZ9Efl81oArKPrObDj78ol7wPoG3nd0rplY0J+n75JYpH52IYNXYKCscAI3oCQ+K9+IRYMn4/jUOTEPKOraFtp2H63123dpnF0n5Dim5lox7s37eert37WexjeW34hU8lbcdcUtZOwq1lX4v9yj68mg/GzTCiTbBm/xjKxcPDS9K6+prFpZ78/Baujbvh1Xmkfl/Ro+uk7fwW/wGWlyWSVkPKusloCrLx7PQWGQeWMW3qV/QMCwfg++8XsWfPZuw8q5iphxc9us7LA0tR57ygQ/tuJF44iSKgNuqU3/n1102kpaUwbdon2AU2QnpynkWLLGO3daiEHj3sGDJEbbGkunfvNzlwQDJCrxiyD2ZlUW7pfe/etvnIdW2OHy8qA2fPFt66qcBEXBzs3+9ATMw6s1Jv3bZC4WvWX1PT0bUuXCiw0zpmQZ3KfVkMvaz933+fSnLyz8YNybsjV0bqS8INKVelkhd6LLBn/jMa1K3NmWu38P7P97xMWEJJWhJVTAq9ni0bCaoCZJ5VcGvZh4wDy/nwg/EMHTZSj0KQtOeQVMaVjQqFF+3b30ehcDa6HgkJexgx4l1CQ1VmJfPbt6to1EjAFE2pjXXjpBuTsDAlvr7v0qRJQ8aNm6jnPdeJmNhGAQmP3NLYG9IJJCTsZ8SI/+jL+ytVgoEDRSXp2bOWKZh1fDu6NizdD8b4dPH//fuTefbMBPwuD+PN4bfI83rVCHmUHvedfiUNZXQMPj0nkHlUMCNm7fiaAX2H0bJFIzM0DFQM9WTtuEb1qrFt6wYjJI4hlcTMmXNQOAZYFe6wRNGtE7sYPDDcYp8qIv6Rf+wXWrbuyN07V/nyy7lm/Ro2YpxR+/8tfe7/ieXm5eLfa4rZfvc2Ayi8fxZJ0qIjbMw8ukZfzguly5LdC3EP7l9WbFBKzJR9KgaFqw+ro5bRo2cftm5ex57dm3B4pRna5Bu82D6HKqUrAh1Vp1OtVjij4sLFU7i2G0r22a04BQUz7uNRlBQV4d1vBiUPTtK9he1CnAED7Lh3rzaTJiWXwhJdGT58WCn5VRW8vV1JScnVP5DWPDJLCUedvfaaYNT74APr1zY+XjyogYFC/cZSAlVgnosZMWKknnvF1Ez7a8l0idBLl8pCDmJFoiQx0TKBjbNzkMX9ly+dZ8b0yfpkWsqWGWxc+xM3rl/Uc+2kb5zC4cP78R04S68P69fPPJnk1ro/PimJKOUy7hxYzrA3RzB02EjrJ1Jq9va+pZO5sYWGvkFi4nmWLFnCpEkxpasZATsdMiQDjWYXCxdaX83oQjKhoSomTNgIaHj33TKvOSODcovJdOEtS176li12VKtWFR8ffzIz83FzEysmEPDX7t3FGFmjYBZ9tGP4cAtLTBtmaSyvXDpKVqYCbYncaAVVeWRZQlCHLPNoP7SU+1wksV2a9mL//u1s27oGu5qt+Wb2DD6aYLn0/q+aQiFoI+o17qSfcHVUErqiHUMv2D+gKitXrmPb1g1s3hzN+x9Op2doX7r36F2qPrSFefN/QOEYYOHXzNvYsGGtnlvdsI35ET8avUhM+1URz1xn/9iErvCsZKWKsy+F986ScXAlRX9ewrV5L3z6fEZGwhJSoj/HrVkYGYdW4vXG++RfP0Dh/bO4NutJ5uHV+PWbgeMrTQTm+PAqPhz7Dvfu38K5bkdKUh6gVqnxD/8IKJvMdbzLKWsnoXWwI/vs1rJ96yZjX6cVjtWbkL1nDn2t6H7qLCxMzcGDT0hLe1S6x7iMevjwwSQkCLIqW9V7uvCKJY9s2DBR2m2LcS8uTnjxsbHCo7c1SYSGqliyJJLIyO8wXWIb9tea7d0rwjfx8cIDjIqy069IgoICKFu+664HgHl72Tn5RCz4toxXRybHtccEru+KwMNAzMKpaRjFJ9frRRCs6cO6tuxN8sYTFKc/xqVuB06eOUNwx3AjXmoXp1Rqmb2sJKyFHIKCqhAZOZ/IyC8pG1dISrpNcHC8zZBMfLwYy4AAyMvLY/BgO4YMgQ4dxDgdPSqqeW1ZWJg5F31yslgd3b2rJjz8LpMnW3YMKsKJv2OHmqVL61k9f2MT/7u61sfUIn/Wogxqh1fIeDL2LbVSsRmJa9OeRs9b6sbpFKf9QcGLVH3FtiGPOpiHXKyFY/6Xx9Vr3ImvStkSdZOr0q0OX83t95fa6NHPV8/y+XfaqIj9YxO6XCmoA4seXSd990LcggfgXlot5doshIwDy/Hu8SF5V/dTcOc4ri16kbl/OUWn1lKlcmWybxzApUkPsk9Gk3UyWj+Zixj8CpQBNbl3/7Zezqrg3hn8DXRCjXiXZXJ8w6eaxdXdWvYl+0wMAMV5FeMKL0sE6qxse8KESQQHb6JdOxXHjv09j0yhEHwdM2aI0Iwhf4hO8UajEZPH1atl4szWLDRUzaRJWw1CI5b7ayt2rFKJ0EtEhDvDhw8mMfGTUo/fdJmua988wejh+pjvF63gm9kzeW5Aw2rImVP06DoZh1biN+CL0vFrbaFEvJf+PnJqFkbxifV4h04ga8sMo/Lppg1roFUfE4y4RibD2vhZ2w4KakB09AYGDBhA//6WWRB1sfTkZBECCw1VA2LfuHEiVFVRaGRystg+cEBUp8rl5rKBpo7BrFnw6qvw6afQv7940Zv28d134fPPZ9G5c0+DFZu18xf/e3p2pXnzg1y/PhSNJgOAb78pZva80ySvv0NRVg5+A77A1Nxa9SXnzGacagcb5bbS474zek4dGofw6LwBZQAVo4L4vzju3/JbtuwfTYrm3ThM5sHlKANqUfj7WQp/P4tr0xAyDizD8ZVmuDR4HZcGr5O6ZRaZB1fgUK0h7to8Pp22kAVzp5B2cIXArWc9w6G6eLBf7luMR/uhFNw9qb8xnv82Eee6xplkKT+T4qeFxkk1k+Rs5tE1+uW80l5GSopUAbWXskSgJQ8vOvpXRowYSXFxoUXeDUOz5JHt3SuKUfr1E5P9Bx+IwhMXF+G1r14tHvC4OLh8WUDWbPU5IABevszBzy9ATzg1fPgQJkx4t3Si+rWUq1tFaKgajQZiYgRFa0EBODkpGD68DzNnziQoqCZlq5Iy3nDDawDg4OBnoSdPqRowiuXLf2L8+J94ZAGzm75nEcjlZJ/agHO918g+uwVV2kPcWvYl4+AKXBp0Ivv0RvJvHxPc6KWrNplcgbJhD32i/MyFawT6L8TL/ZhZLxwcKlO+h2p+XqGhnRkyZBAXL25n3z7JjK9HNwYJCUpKSlRmk3dFseIuLkomTXLk5ctc7O3FhF2pkm3HoHlzMaFXry5CL2o1FjmFqlaFnBwVc+bMxsPDzYiEbPjw/kyYMLF0jI2vgYdHPTp0uMODBzN49mwNgYEwdlQx07/MwG+AZRiweyk8ueDeGbQZT/EKm2wxLGPKo/5PeOj/pt8qz/6xCd3HyxPtpS1Mn/41a6PXka2WoXHxI6M03nnx8iVStnwBlepR8uyuniArdf2nbPx1IWlpj8r2lYoXuLcOx7VFbwrvn6HSOz/qY/BupQgWbeZTnJuGkXcsSrixTu6oMp7xInY+Vd4zTtIZQiEB7CvXY/fuOzZj14IWdSi2vDodrKx27cZ/uVjFcOletaqY1A8eFPF30wdaxwsyY4bt0vHUVJGIjYzMMylyiiE6eoO+v0uWRPLRRxspLi6kXz9DIQ0NCQnxBAcfMCv3Nz9/8b+PTz9q117A779PBwyrGlO5fulN/vzDBY8+5l6dZ5uB5J7bgpSXQe6VePz6z0D14hHZp2NwadSVwgfnkbQSDlXqkX06xmjVVnDiV+ZH/EjjesU0qDnXIpuit3dXGjTYYKP/ts9r1qyvCA6OZ86cQn2iU19EVUrapdVqcXd3IiWl0GhMunUTL2tbVA3x8Qree08kmvv3H4C9/X79+FszgZwSwtNffim8dd2qwJJVqaJmyZKtDByoNCl820Rw8PbSMdZx6T9j8eLFbNwYo5/4X39d3JeLlzng8GoHM3SHIee3e3A/VGeiade0Pid3L8DvHWOtRmuFO/9Gr/m/OU6StLg5n6dGtQv6/V7u6QRWzjfbRuaELfvHJnRHJ1d09Lll0KPdtO4+krad+hDcMZxjh3cTFyuoa/XLsj6fc3X7HD1uHcqqRN1bh+Pesg+F987oJ3iA7NMbcarVGvv0+xScWo+zsxuqyg1wqt+J9F0L8H7DvCTTrUUf8q7tx6VhF2QyOW5vTGRX9Ie89pp1lItQexlJeR5eUFAVfHxcK6wOlJxsvnQHMVlYC9skJwsFeY1GSNKZcr3obM8e4fHr9gkGPhXt2qn0ELugoJqMHz+S9es3WkywGh9/gKCgBhbOv+x/mQyqVh2On184168PIy/vCgBXrsCMWQ549LGMZnJp0ZuCOyfwdYCXhWqcajTDqUYz/Tg/e3AehYu7GY+2oTZkVuZ8PN2M+yWTOdGo0Vp8fNoB9vwdDx0oXYGtZMSIsTRpUsLlyxp69zZOeMfHS8TGFrFqlSjK0llFYtw7d2qIj+9OUtIdEhL2s2aNEKC25RgY5lGys20fm5wsaAIE8qYsz2Hpnrh//zYjRow1UVnK08ftx4wpZlf8aVI3P0TZMJz8E2vo1GUAd26fJuN+GRyvd5+hJOy1XCXp2LQ30RujadO5DGb1b/Sa/5vj7JWPqFllFjWqpBuF/wL90f9vuF2e/Yvoc3vry/R1+5s3/oSTx/eR9eiaUWjEEm7duWEXPRDftVkI2adj9A+6W4s+FNw7jcbZm5CuHRg8sD9TpoznWexp/Ada5l12a9mb/FtHSI+dj2/4NOx9AlE2Duezz2IZMMAcy7tvnyCkEpNZ+V7d8OFDyk04xsWJEn6d/JxpgtQataohesaWJJ1OgMFSCXpZ+f4qIiN/ZPHiX8stijIv9zc9f+P/7e29ad78ICdP+gIQ+bMDdjVte3XOzcNIPrAcP5M6haJH19HK5MgU9nqElC5s5lT/dRITjzBq1Lui6NEEqVu79gJ8fHphPeZf3nbZ/6Ghvdi6dTvh4X0t1hO8/76WDh0ExPXYsTKVKUPdVx1nuWmMu21bBXFxB5GkwzZFwQ3N8B4p79jYWHFflzfGc+cuIi5ulxmXvmncfsmSYs6e+5Mt25cxf04xzZpnopWvY/u2rWzeHE3vPkPZn7DdZuFOVtKZ0sKdMm6lf7OHLkklONg/om6QeK4d7J0tboOWyr7H8fUsR+T1L9o/NqGXpymqs9avDePGpd2kpj3jxa4IM7zxi90LkdQqCm4dRZX2J27NQ8k8sga/8LI3vlvL3mJCd/Vn/76ddO3eD7VGwtmkQit9zyLcWvczgkJmHV5FxqbpODQJoeT2Ed4a+SZZ6ZuMYpA9eriRmHiUoKCaJCXdZvHiX03ij2UxaZ1NmPAuwcExFhOOyclC+/P0aeFhay0z1lr0uCqCnpkxQ6ArTp0qW4JbstBQFZMmxRAZOZ+NG7eUW2Qkjt9cipgBa56s4bZMVqLfo0um6by6ghO/0KlLfyOvLuPAMjxfG2E0Aejhp0GtKfzjIjkX4nAIqKWHwxU9uUmyTGL1qjGMefecWb+FwL31mL9uOynpTxYvXlbO2Bawa9c2wsNlNifGfv0EOuX338ucg8BAkQeJixOTeF6ecYwbNEyatBlJElQLKSnis7g4rEocGt4j5YV1KsK9Hhqq4sMPt9O3r20Ib69eol/jxkkMGVyKn5R2IdccZvDgHxj6Zhz9+/cvV5PTtHDn3+yhuzmfpXqlhdSpXoxU+qjUqY7FbQBfSzrk/6X9izx0y9sA3V+rw9QpE/DuZn4nugf3J/t0DI6vtsWhUi2yT8fg0XYw6fHf49YqXD85uzYLIfPAcj6aMIumDWsw6t0xRHw3h5ToZNxASdx8AAAgAElEQVSahZJ5dA2er79Lzrlt5N8+jnurvuQf/4XvFvxEUtLvbNiwloiISJo3VyCpNhnFIB0dfQkKqm9U0GFOvCVi0nXq1NbHHTMzC/nsM+jXT0bv3pIeubBsmaEohXVsuiWPy1YYBsT+kBDxsCltV7MbVB86VpigyhbKx/x/R+RyR6pVm8iTJz8RGAjLlxSzfYfw6ubNUdOs1StITGbb1g2sWrUEh8CGuLUSK6+iR9fJ2ruIkuJi/Ad8odeLzD4llHQMxUok9wD27z/LmHeNe+PkVBtf3/4G/bLcXyHk/K4VUjVdvkHEljdu3Fruy693b0GqpSvnz84WmrGBgSLsYeklq1aL6ytJ4oWwd6/4O2qUyJdYGnNX17J7pLywTlZWxZA2eXkqq1xCOgsLg08+ceLjj7VIUrHBJ3mgfh9vz3AW/7SAb2YvIHXzDH3RzaRSTc6spHMoG3XXF+78m2PocnkuDYO+ASmRv29KPD07GRBwaSmjyijbVig8ga1WW/nXe+jbd8Rx5tgGfAZY5jd2bxVO/u3jFN4/jebFH3i0H0bmkdV4dhlF9vHfKNYxrB1YTni//5CapWTr9l2sXhGBb7/SpNqZGD1c0bVRV3IuxpF5aCXt2nVB6VyZeo0r06OfLwrHAB78eZOgQPPzSUq6w4gR/7G4DNXFHwcNGoRarUWlEh5W//5Qty5s3Sqxc6fwxCsCQdOFXrp1E0txQ1bGinhZ4eFiMoiIsF19KFA7rkAR3t4uFYr5W0f5mP5vmFP4DH///ly7NghIZ8hgA69O/Q0yNjF4yE906NCWSZ9+ri/fzj6yGhcXV+yqNy+Dn/aebJGJM+PgMuYuMJxYoGbNr6le/T1kMtuonKSkPxkxYqTNsS3LNwSQkZFXoYkxJ8e40EdXTQpiTEwrOtu1E9dXo5Ho0CGf2bPFPeHrK6qCLUkcajRl94gurDN9usibhIeXHRsfj55fpnxWxopN/JmZRXTseJc7dyaRnr7X6PP09F1UDdjF8sWwfYecLdt/Z96cVjQP7kKe2osnSZc5eliU7qdmKf8fo1y0+HltokGtzWiLy4LXjYIktMUys+2GtSSzMN5fMS+vrjRsuAQ7O8OLqkOLmW7Dv3JCr6iH/tUXe7APamMcGon/AbdWffXet4CoRaHOTSfzxDojHdGiU2tRn4vhhx+W6ZW3F0V8irJWMI6vNMGpRjPs/WuSe2AJqqa9cW3ZG4/g/iic3bl7cYtZvyRtutGySWeLF68sN8bcp4+WnByBBdZ53T/9JLDlAwYIlkRvb9vedVgY7NwpXgYlJXbs2aOmQ4ey75SX+IIyrpfyqg8FamcY4FihmH/5KB/T/8u23dxa06HDHyQlzeLp00iT79wDVRivtZtHzMYNbN+2g82bo/nw4y9o27opX38znafRn+HV61OLOZbMg8v4cEwxzZuLfU5O9WnaNA5HxyqYx83N+1uRsS3LN8z/SxW2hqZDNY0bZ7l6eNYseOON9jx58oQLF+7oY+4tW4rwWUqKORxx9mz4+mv090hgIEgSvHxpfmyXLuYOgqklJChLJ35VBV7ubigUvjRqtJXMzJPcuDEYrdZYQEShgCGDtaUv8NPINSEMCFuEXPEJkz75BDAv4/+/9NDtlY+p+8oYwJw5TYTmJLNtc3NEqXS18lmZKRQuvPrqj/j6hvDX8jfW7V+nWGQ6eAP69ySroBCFm68IjZR631nHfsPOwx+3lr3JPCzEKbJPxeBctwPurcP1CiTzI35E7uBv1L6Pu4yvv5nB89wSHBqH6PkVojdGk62Wo2zUXa87qVNGKeP/uIikesfoXBwda9KnTzqRkbYfYp0HpuM7SU4Wsc/583XUARVTlhk1SizVXVyUtGvXjsTERHr31hAaquLjj0WxSUX7YdonnQkxY+dSnclaJCXdITi4E7NnF1hFYYjjTxjwaVvm/7C9DQUFt7h6dRAlJY8t9N4ZEA+LSq1GaWeHRiMRsSCHc7f88HnLWCrtRdRbTBiTSUgIgII6dRZTufJQZHr4V/l8JX5+lSyOrSEPT3a28FzHjn2PnJx8VKodf1nhSDe2pis0nd26BTNnOiJJMiSpkHnzyojX9uxBH4oxLDbbvVuMrb298MgzMoTTYCmOXhE+mWnT7OjRIwQnpwM2z09w+bxLZOR8/TXVarO5d28qqanrrX6vzLzQFaDpxtl029Znf+84LZKUXjpZ/z0LDJxIrVozkMt1E3r599df/Uwmc/n3cblU1D4c/yWbNiwlKek+mUei8O03HadXmuJQpR6pm2aQdWK9Hm+syc3QwxdzDy7hk0lTaN4i2IwLIbDaK6xcuc6IS6F5i2ACAhvpCXHGfDBNP5lXxCq6zDbElcfGiofvr3rXKpXAn6ekqEhIOItMpiAvrwuTJp2joCDHZpIMjKleTasPywjF7Fi4cFFpvH8TGRl5uLs7Mm2agq5dZVYIyKINCk/+vjk7B9Gu3W0ePfqehw+/Mvm0AF0oRFl6916/DqfOOuDR5yOztpya9WdH3AYGDGhGs2ZbUSp9MKYjKN8sja01Hp6EhPXs2aMA5LRrZ31i1NUTGFpcnFCbsp1s1LBxo4pvvy3jgenXTzBeHjki+PNjY8XLwt5ecLFHRIixLY9iQBeSmTFD5FlMQzLx8dCqlcThw4fLPT/B5TPeaL9c7kD9+iuoWvV9bt4cQknJc8sdASBTv6U0mKWUJjOWtc/+7nF/dzJ3dKxF48Y7cHGpzV+9v/6X9q+f0P0DqrJq1XqiVi9hy5YYCk5HY+fmi71vNap9XPamN63sdGrWm+07ttAjpI/+mMuXzhOx4Ft++H4JgdVeoWv3fnR+rR0//LiA+fN++NuEOPDXiKx0Zhrvrmi1oIeHWKoaxnBnzTpJYqJIyjRr1pxOnSyHCEwnk7LqQycjndOFCxvy+eefmSQBC0lIsGPXLjh2zIn8/KLS403L/f97k8lk1KjxGZUq9efataEUFt6xeFwZdt1yRaJLi3BSt57lxMn+tG7t87f6Yjq2tpBEuvGYNs2BmTMd6dGjiL59yybGPXsEVNSwngDKaBTmmdP9638zNhYOHlShVIqqTyP1p9Kk6iuvwI8/Wr6Hxo2D7dttOw1t2sDMmaII6cABAZvVhWREgZqGW7c0TJtmz8yZjvTqpTFjnyx7uVu+H9zdW9C+/QMMPc/MzEPcuDHCLCTzz5mSevUWU6nS2wb7KrbC/CftHwu5VK9RW9IVFp08d5vX2jYw2zb9/8SZmzy+d5Cbf/xpVhaevHI0Hh2G4dpIuJ6SVkPGpum80a4DSrc6BHiqWL0iAmXN1ngVPufTKQvYGbuHcydiUNYMxqvwGW06v0Ondo1s9sPZ8SZBgVONftvRsSYxMV14+dJ2jNl0md2tG0Zi0BURdrYmRrxqlQJ//5FERn7HL79EM27cRMLDjZffhsVJOqRMVJQdvr7vGJBzOZOU9CfBwa+ZJQF1JsIrTvqiI+sJHNNkzt87TpKcSE7+jQcPpmJaYfHOKAcyXDrgHfqJgXj3Qpya9celhcix5N08jP31bTx7fNVi++X1Y+LEz3n5ch2jRwv+lYqMU1SUEju7fmzcuBVX17JKUbVajPvQocbe744d4rP9+83FwQ1XA2Fh4lgHB/NYd0VCJpMnC0isLadhwQIxiduqio6KssPOrj+enu5s3Li5FMbpyvDhAxg/frwJRUDFxlmrVXDv3qekpsZY/+H/B+bl1ZkGDX5FqVTyv7h///pxtj+TyXz+fSEXa0nR+3evsSjiU+bP+4HAaq8A6JXCO3UO01Opmppri15kHl6NOj8Lj9b99AokJ49vYdiImjY1DnX7KlLAYC0paovIyhBXrlIJz7xbN2NIGVSsWtDSUh3EUnzixM1ERi7mvfdGAzB+/GRiYzWoVJZ5RcqWxp9gCNv7a0nAH/XfK7O/nhS1dZxM5khg4IdUqvQWhYW3ENWcACXEbkvl3dHf8mjrdBT1e1B06hd++O5jVkTF82j7BeT136Do1G9s3fn3S/rF2MbQrp24JhXFa48fvwe53DgvYkoH4OEBbduKFwSYr9AsrQYGDBD3iWEyHGyjWPbuFfF0mcw2bh0ET095PEOC1G0/aWnPS50BW95qxcZZLnekfv0oatX6kpKSDINjSjAc87JtW5/9veOUSlccHWv/jXP5Xx9X3meW7V/lod+/e40VS+fh+GpbvAqfGXnRMr9XKU6+bcTEZmiSVsPztZNQ57xA6RGAW6s+5B1bw5gPprFu7TK0levhHTpBT7aftfs73LoYk+0XnFrPqDGT2bp5Dc3bDiQspLO+/fI89LZtL5ZilUfqhQVMceWmIsu7dgk1968MwsSm3pihF7d3r2WOdCgTmtBoXpbuKSApKZURI8Zw5coVi8myXbtg+vRPmTVrhv474Iyf3ytERtqGKJaJITzkn/ZcNBoNkT/9zPeRv7Jx3c+8/npHNJpcIn9az/eRq9iwdhFdurzxX/Vj9ux5zJ//PeHhsHWr8crKkqnV0KOHQI74+tqeQFevFtzlO3cKz93Q87a2GrB2n+zdKybs5s3h5s2yUEyLFnDxInTtKiZsUwoHnd26JV42Bw/aPr/Hj0Wf3N1dbRbQ/XOe7P9ff8u2h/6vQbloilKZMX2yXsgga8uM/4+78w6Pqtre/+dMSQ9JSELoxQiK9GJoIogKBEJCV7iAShU1CSBKU5AiTdFApApSDF1KIBC6FFE6AoLCNUoLQiAhvWfO74+dMzNnWibce7/X+9vPkydz2j5tn73XXut930WbBnU5ekxY0WmHV+JSqY4xA0r+rcukJi7Cq3k3I3wx+5fDpP+wAa0koZcLmTXrc5q3aKXKGuLVOVKVsQhKGWoJ8+jarR/7936Htk5L/HLvsW7tZiPQvyyUS+vWIhVXUtKvxMauYMOGTaSmZuLigjErjWVRshxNmGCigIO5v1RglV1cxIf4j3/YnyonJ8Pw4ZCXl6PcFUlJ9wgJaUVkZC5Xr1rjmhs0gNhYd7MEF8Ii0Wq9nMpU1LWrxLvvDjcTZ/Ji4MB+REWNNSJjFi1abgyqio9+AFFRUQQHV+XfG/3/z9WRlPQHISEhREbmcfWqGAxXrSobSfT22yKJ8/DhjtuAIm87fbpwp5nv6wj5pLSTQ4fEe7Wl15OcDO+9J/DoCppq3TqhmGnpklMGea3WcVas06fFgNC1q9pIUYLp5iSrv9u7/N8/l2OUy9/GQj++fxk5fnXsWtFFacmkbJ+FpNNToUUEaQeX4ffqKDJPbxPrWkaQdmApgb0/oiQrlfwf1uBVoaIxL2W7kGfYsHYRl39PUmlsA9xdPIRalStz++5t/HtNxrV6Ax6un0CXF9qrKMdlWeiimEbT6OgxpKZusvKrW6ad0+shOFhi+HCZxo1NwaX4eJmWLQ2cPGmgXz/HPtsVK4RQU2FhailFfSGrV28kNLTYoXVoy4furIU+bBj07as3pjkTH7VIcBEdHc2XX8bQtWsBYWGycXtCgsS+fa5s2LCC0NDuVs/Mevm/b2k9iQ996VIRUPzwQxFk/Pln28QfJaZx+bLIKFSligj0Kpb3kCHOzQa6dBHtyda2zp2hXz8xS1D87GPGYHeQ//xz0VHbwqM746d3Lr5iufy/bDX/j1nokiR1BRYCWmClLMtzLbZ/CbxUuugBVJJl2aFSQY2atWWNi7sxZ6B/BYkPP4zkr5QUAnp8gHud5qr9829d5sF3n+BepzlFKTdBo0EfWIv8W5fxfK4j+X+cB40Gt1pNKfjtKBqNFn1wK6u8lBXMst8oJePsTrJObcE//EPcazUFhAuG81vYvuMA4LyFbj6aBgYGqTrG5GTxoZ8/Lz4Yc+smIUFi506ZwkLw9/dm4MABrFu3ntjYHN4pReM5miZPngw6nRdxcesYNGgQoaGFJCQU22WBKkW4TiqQkvKX8drFQLTGYYB3yRJBTvn4Y+ttR4+KwJpjq9SV8+fP2UmE8feytCxx6M50au+/L1wpNWqYrPWQENFZW+bzTE8XVrosi+cKpgE/J6fsIKY9LoGybdgw0XZ++kkgbLp3d+wC+uILAYGcN8/6/hYvFgaII/KRCYP+JX+3d/m/f65/0UKXJEkL3ABeBe4CZ4EBsixfs7N/JNBMluWhjurVaLWy53OdjOiSyn4lfL1sLro6LclPOke1yDijnjkIK1rnW5mggXONFrwlxTv7ymHSDi5Fo9EQUJptPG3TJCp5+pJ85xe7qm6yoYQHGyapSEmpO2cz+r0p1H1G7H/i1DWCfIvYtmUJ82ffpXop/f/iRVi42IuDiUeoWzcY89HUPMny6dMiibIs2/5QwNy6ERK0yvHLlsGjR2rLzdLKa9ZMQ+3afdi1K8GITrFE0dgqiuukuPiR8drLQrkcPQrz5wsr1RzWpkz1580T0gaOOo0lS8DTsx+rVy/j/9ZKKn8dtpJl2/NhKzOU4mKZJUvybeaPNd8/Pl64Ot56S7g5LN/Xv4J8UrZt2SISoHTvrtbTt1eUAUirFda9eXB11CjnkpSPHq3n4sWfStMQlv89JCU9cCiEZlsozTwJx3+/3fxnzuXYQtfYWmlRQoDfZVn+Q5blQmATEOFg/wFAmbgjrW8VKoZGklEscfu3g0YUin9oNFq/KmSd263a37dVH+THyTzeNImitGQjxdtI8b91mcdHVqLx8CGgVPdF0mhFCqs/f0b/VIg6yfTKEWSe2aGS3M26IM6ZdTCW1weMpG/vcJo0qE2TBrUJ8i1i1fL5ZPk+w8w5rhgMJgx0qkcIrw+KxGBwQTx4N8CtVP/EhFQICREfhzMStOJ4L+7fFx3lxYtimqyIOXXpIv4XFor1ly+7YjBoCA01oWwUXLujolC0xTWLaw8Ork9c3BqmTHHj3XclevY0ddhDhoh7iYgQHcOBA8LH6+IiOpTTp0XQLTzc8XkjImDbtp2q85r+POz8tr0tKeke0dHTCAysjVYbQGBgbaKjp5GUdK+c9dveT3kP5qVVK3H/5u9j2DAICHiDM2fO8Oabg0lM1Dvcf9Qo4WqJjIT160Xw0vI8PXuKTvjqVdvPUfHp9+xpe1tCgtAH+vRTMShkZTlHXsvNFX79XbuED15pb7m5zh2fk1NESMiLJCb+aPU8y3r2iYk/EhLyIqmp3xITk82BAzIxMdmkpn5LSEhnZsz40s72TaXnPIa6bUwkMLD+v9g2yt9unny/srbZL8506NWAO2bLd0vXWRVJkmoBdYAjdraPlCTpnCRJ5zR6d9GRdo7i8u9JRleIpNHi3bSbsXNVimfzMLQ+lXErzCRlm3UW8EeJC/F7eTjV316l6uSzj62ieetwfHPukbZxItlXDpO+ey5tn29HxsmN3F8/gexfDvP4yCr8u0YB4NYkjD2JO7l45Q8uXb3J1m3xLFs8mwo9PqRi1yjuZ1dj3nzJSGip2DWaPx7m8GVMDGI0zQfyGTiwF4mJeqMC4sWLwjpzVEJDi1i2bBVarRf5+fnMni2oa5MmQUyM6DhjYwVeOTZWWFELFuiIi1vO7t27SU0tpndv0QEXFooPOTnZ/vkSE3UMHNgPkzhVfulfAZIEjRqZOu7Fi4VAlKsrNG0qrDSF4DRihDjXnDnCTeCsap/1eS2XHW9LTEwgJCSE1NR1xMRklX7cWaSmriMkJITExIRy1G97P5Es2xrha54PtH9/0Gg0ZGSk0rp1G776aiXffVfEzJkiY9DixWq3SMeOAkL48ceiTXTsqKVCBV9277Y+h6LZ8vXX4l0WF5vcd++/D/n5sHu3pNq2ZAl88IEgIPXqRbkHeR8f4RosLBSxmcOHxbX7+jp//IwZuQwa9CZJSb8igvTXiI6OJDAwCK3Wi8DAWkRHR5Zuzy3d59dSIbRchg8vUrWx4cOLiIzMY86cWXa3i3P+g6Ska2ZtY41Zx/+kbaP87ebJ9ytrm/3iTIdenvI68J0syzbza8iyvEKW5ZayLLcsfnzXaGkHvRWr6oTTDi7F49n2PFw5gqyz8UYr2qNZNx6lpuDf5R2rur2bdyf70n5k2SQerqSwGjxoMOvWbmJIrx4Un97I8GFvc/KHQwT0mozns+1FurKIibjVKlXnaxFGkd6NG1d/oEmD2uzauQ63uq2Fop9Gi1eXCfx4paqRnShptGjrv8qCmK8xH02joqJJTNRz4ID4aMtD7ReuliIaNpSNrgtLC+/ddyEhQUt8/C7AldzcXCpWFB39gQNiatyokXB9nD5tfS4Fhx4ZqeDQTVbNoEGjmDUrn7ffllUfjaI9M2eO9UChiH25ujr30Qvs9ZNbLgKW+RYzZuQxfHixxcddzIwZeQwa9BZJSQ+s6ijbcjOdKypqLPHxjq3kXbtAkgzk58cbO49Vq8S7FPEN03uJjRVQRkmCu3fF8T/84EJc3AYOHvSwOo9i3d+/L2YBnTsLl0hWlujk586FS5dkhg0zWdLZ2QIZdeOGQLIoRdFEd1T27hWwxz17wNtb/S6dPV4JsCp8hcTEY7Rs+SopKatVVnVKympatnzBaMmXxYG4elXMRsqa5c6cucisbVh2/I7bxv/vFnoyUMNsuXrpOlvldZxwtwC46bU8tGFpP9z9GfpKT5FzcQ9Ro9/F89ZJ0rdMIfuXwzaTGyilQssIkFG5anQ1m/PFF3N58NddtFpB6x/8ZhQrVi5FqvocbrUaU+H5CPy7RpF1MFY1eLg27sLmzUJXe87sL/DNuUf65snGQch/8HLVIJR/cg0b1qnZGMHBdYiLizNOc8tjHd2/b4IuZmcLrPqKFeLjjI2FAQP0aLXubN26lVq1ajBo0JssWCAsZVsdsGIpmltwU6d6EBe3BhCInMDAWmi1XjRr1pzOnfPLTGCwc6f1NmUGsmeP4/sUcq1lCLKXURYtWqJyMdm6ztDQImJjl6rWJyYeomXL50lJWamy3ETn8jyJiftV+wcHP4VGo2PyZGsr+euvYeJE8bznz4dRo0wDIAiXyoIFwr1i771MmeJGXNwaOnXqQFxcHFOnurNypV51nr17dZw+rcVgEO9/926BoKlRQ2DMly4V5/H2Fts//FBAYouK1EaEMy6c+Hg4c0bMDKpWFYNVeY7fs8fkAgoNLSIubgMDBgxg1qx81fOpVk08r1mz8hkwYAhJSX+wYcMmQkPtB+MPHxZa8o5KaGgR27fvfKK28b9enOnQzwJ1JUmqI0mSC6LT3mW5kyRJzyIk0n5y5sS5ublUtGFpV2jVGwlw8a/Obzdu0erFN+jUqg3ph5ZbJTe4+9VgMs5st+kHz791mayr31OElgXzJ3Pxyh9s3RbPkkUzKJF0FN25TErcB0YXTO/wAcjXDpO2aRLZvxwm4/DXDBj0Lpeu3iQ1U6Z1hzd4tlplMnbPtbrmrIOfs2TRLDp2DMFyqhQa2gE/P09jdhlnrJuGDYX1rbhXDh40JbsYORLeecedgIAhnDlzgNDQDixa9GWZjTcsTPhEFd/tvn16zpw5DhRYTUs1miLCwhwHy7t1sw2TCwoSs4hdu8q2aHv37sm/MhXdsGGLw48fxEe7YcNm4zFJSb8yYMDgMjqXASo3AOSTnV3AV1+ZZkidO4vnGB8vSEGhodZWozPJRnr0kOjVqwehoW2BfEJDO3DmzAECAoYwdqw3XbtKREa6Ex8vU6VKCX37liXepR5oLV0k5i6cFSuwctOMHy/cacuWCSbzrVuoZieOXEBffy3Wm+vUCF30bLp0KXB43Z07FzBr1owyRe6cneUWFBQZ20ZyspjhKK7I3r3FcvPm6rZRnrandh8FEBgYZOU++lu6XGRZLgbeA/YDvwJbZFm+KknSDEmSzENfrwObZCeB7Tq/qrYt7RaiSsmnKieO7eXFtg0ZO2YM69Z9R4CUT/rmycZO2MfTjaxzu3mwUXTCjw9/jdZQSNqBpQKzrtHgXqc5hSVF7N25khWLPwWtDvc6zZE1OoID/eD8FubNW8iIEcOYNj2GIb16wLktvP3uZPr1iTAGRStXLOHK5XN4vWQNN3Bv2pOFi9dZBUWV34MGDSQxUe+UdZOQIHztShDL0qpbsAB0OonIyDHG/KXr1gm8uaPSo4eY9nt7w8sv6xg69C3AlUGDRllNS50NnJkrRypFcaXIso5Jk2x/9JMmiY5RljWlLo4nm4qWP4uSG7NmfUaXLoU25RkWLxY+7czMApo0aa1ywVSs6IlGIwbaCRMEiiciQri1PD1tB4EPHy47ZhIWJrNt207V1D84+DliYhaRknKf69fPIkkS8+aVkJoqOmxHxXKgVZKgmBfFhVNUBO+8YxqcsrPF/YweLQyL8eNFQFSvRzU7adFCGAfnz6Ny8xQWinrNmcxKQgxng+S2AtDmxdlZrjIzOX1abRyZB/FnzIDUVJGRqzxtLzHxGCEhne0EbR0Fgv8eLhdkWd4ry3I9WZaDZVn+tHTdVFmWd5nt84ksyxOdqQ9Aoxd61Pm3LpO8fDiZZu4Or6ZdyPv9FAMHv8eJU9dUVnKnVm0oPr2R1h0G8k7UdCoHBCBnp/H46Bq0GOjX501yfhEtOrDXZPxDo9D7VeXw0SMUIxnXaSoE8duvl9Dp3XiUXsSlqzc5eeY6Gr0POr0bv99K59LVm1ZBUXuKfraCosrvqKhRJCbqSE+3b90sWSI+mkaNROMve6oozpWU9CsZGXlOZ8gZOBB++EFPZORIu5Z9eVxDliUhQSIsLJTOnTtRWCgYicOGiY5m9GgxWMmyQOaUlGwvDU7twWT5/Frq/rEVODM908TEBPR62UkUj5JFKZ/vvttu1blYfvQHD8Ly5cWkpKymYcNGaDSe5OcXMHu2xIULJm0VZcDNzLQ9ADprTWZnF9GiRbvSIJ3aIlu0aKHxHTlbn/lAq6SqszQilIDu7Nng7q6lX79+HDkCb76pnnl8/bXY98UX1fGbWbNEPZ07mwKm775rDWdMTNRRUuL8c6hZsxpbttjH2b78sjB6bBVlUB41Siz37hMvkOsAACAASURBVC1cWmPGWBtHShDfxQWztlW21WyemcxeULZ379d44403yrDW/zeCok6X4sd3yb5ymJRtM/BpN4DcGyeNlnbagaV06vAS8TvW8kwdH5WVfOH8CZYs+YZG9WuybvUXzPl0PsOHvIG3mysjR77H8eN78fH1x6NeG2MQ07/7WLQVKhkzGUkaLd7NumGQZXL86rBl4xIa1a9phCbm+NXh6sUEGtWvaRUUBUphj4PJPmdy92ifsw6KWsIAp0714MoVPVOnio9FgYMNGwY3b4pO/do155AwGzZsBTxYtGg5Hh7OdcAeHrB5sztxcesJDq7Phg1bbVr25Ql8mRcRZHXh0KHv8fT8nlWrTO6iXr2E1VSxopjOd+umfAB5DBo0iqSke6WWz4ukpm6yY/kIOJoI2r5Fu3ZlX6c6i5IbOTlFqs7FXPzK8qMfNUrm88+FNT59ughQK6gU80HQ3gDo7MDo4QEGQwH9+w+xmrFs2LDD6Dp4koG2WjXhpnv/fdG+zI2I5cuFFR4bG8vq1WsoLFTPPA4eFMlS6tUT8gIdO4qOW+nAP/4Yjh93PONMTNQjSc5dt14P9ev/zoEDJWzZItZbukv27xeaN0ePqo9XBmW93nTtS5aImWlMjG1QgEjWLREbuwJnrWZnhOt69YJz57Y9MWzzX7HQ/2vUfy9vXzm3oADf9oOo8HxPZEMJWecTyLqwG7c6zci9dgz3p9vgkvon06Yv5PcbV4zCXR6Pk0h9lIJ73TZWIl76OiFo7l+ngrcXj/KLqRg23qZ2S8q26fi2H4x3ix6kbZrEczWqcuHCKZvU/5QHySyOnUuRixuujbqQc3wJY94rYPsuV+7nVEPfMILcY8tZuDCGbqEvUK1KUOmZrMkSsbFLS8kQ2aXaJ/15/DiVkpI9DB9eVE4y0G0CA+vTsmW23Sw0SlmyBP74oz6bN68wiifZIsyAc0zICRPElNVcqmDPHi3FxQbmzrV2aSjH2cph+uWXWn79tSp37txxyC5VSFeLFq0mNfVbQkOLnKShHzDes6urv4p5WV7iTkSEogtu2m6vjvLU3bGjeKZ9+/ZjzZovsEVOe1KS0ZIlohNu1EgEaTMyxCAiy1oWLZrL0KFDSUr6k4YNW9p9/lu2wOrVorPq3t1ENFqxQnSWvXqJOI1JTE7L/v0uxMUt57XXhhIWVuyQXbpihYirJCSYNI6aNNHw668GwsLEIKqwqvfs0bBjh4HWrbUMH15CSYmYOThiUtvLnasWmYOyyD6BgfWdksWIjBSzA/syCGWf6z9G/f9PFI1WJ1d6baZd5ub99R/i8cwLFP/+E+0a1eP48aNG4a7768biUrU+FV8ZaSXipbBDX2rRkL174tH4VqXqUAvtlq+GoPWtTOV/mFinmXs+w7PDUBXr1Jz6f/FKkjGb0ZhoH15oc42SEti2XWLLNhemTCigWTMPvHy+omWz10rPlI9pRDX/rV42T+/28cdC++Onn6x1NhQmpomu/ydabQBr1shERpZNRb969YpRFCsxcT+9evW2EplSdGb27RMWdXi4deaahAQRbEtJgaIiCX9/LwYOHMDjxxmUlDhOvbZkiajfy0vcU506wgoEcR5lumyrCEr5ENav32Kk4ttjYCYkiPO4uLiTnZ1fOni+zqpVqwkPN3Uuzqb9UzDktgZcewOgMwOjeUezYgUkJOjJzLyP0jbM5SPKW5+y7qOP3IiI6MnevXtLk5iI9yV0y0V7iI4eQ0rKSkaNst8ffP65aJMGg2gb7u4m99k//2lqr97eUFysZdeu3XTq1Io33niHbds2O2RIT5gA7duL/wBffKHl0CEDn30mO0yH5+LiSnZ2Dr16OdZvt8emNRlH2aVrHH+z9owgyzoVbR37Mghln8vetr+lOJfWo4JcPXK9UTkxY/8iPJr1wKtFmFE5MePkRir1m25T6jbjx41UG7XSrhRu6v6v0OhdVQmjlZJxdicZJzeg961KQPgHNi14W9R/8yQcoS/lULPKHLQadSZ5WYa2ba/g6lqV8ozAiYk/MmjQm3h65pGWZhJyMpfbVYScrlwRHVtMzCdGi+HuXftyqsLn6E5Gxl2UmUJISHuaNhW+d8Xis+wcDQbhAz9+XDAEXVzEOm9vDwYPHkBk5GiV5eGsqFdkpOhEN28WU2hXV4HJtrR8bR07dqwXqak5qo/KMrenkkiiY0cRMzBXA9y500BxcYkxb6ezMyLlA7U3ANgbWFauhFOnxNTfUp/cMtmIoruSn3/H2DYsBd4cSQjs3WsKcpqnEoyLW24mhIaxDZhT5/V62SkFyffeE0Sj+/eFRZ2YKAYRS0lnZfCNifmEpKQHNG3aFkkqpEcP6/a5e7dQg1yxwnR+55Js6AkIGMj69ducbneWejf/SQtdydlrW2a67HP9T1nobu4esmtQMPoGr5J9bBXjxk1k2/YtJGcU4NakK2kHl1Hx1dF4NeykOi7/1mUebp+Ji08lKvacbNudsnUaaHVU6vOx/RnAt+MpTPkDfcVqVB22RLX94coR9A4fwIgRw4zrbCWyluU85ELr59qy5U94eTXG0govS0r2yJFjhIf3YN68ErtWyaRJwv/u6enJ4MGvkZ6eY7SKLTs2xbIvLNRRp85Qo5UQHT2R1NQ1KpeFr2/Z1t/48dC//2tMnfqBWSJojPfprOyu0jkuXizqbdYMNm1y3tXk5+dlNyG3M1bs+PHiPBERzmubKB+oI7fHhQuiU7p5U53Ts3dv+OwzYbnamnGZ31/nzmAwpGJr9qbcj63k1LVq1aJhw+c4ceKkXStcKYmJCQwa9BahoUVGpczOnZ9M1dGeO8N8FqnMCAcMGEC1aoXcuyeTmSn89VWrCm31jz5SDwq9ejmX7HzsWG9SU7Odbnc+Po6+DSjLala+HWczk6lnAP95C/2/FhStVKmaCrFSpWYT3omaQXC1uhSf2sgzdeuTdz7e6riM/Yt4feAognwDSd893+Z2V3dvPOq1VQUx7y4dSsbZncYgpneLMCStnoqvWpsAltT/S1dvGtE25r8vX3tAbp6X1fEiC4oakRES0r4U651lFuxbo0J5xMd/R8+eGocBl65dhSZMbGwOqanr2LUrnvh4iatX1VR0JXDVsaMJ1aJc04YNGwkNLVJhij/91DrYZ3luEezZQkhIezNEhuk+Fe0aR8U8aHf4MNy7J87rvO6MZykV3zYpyRnsd69eWmRZw88/SxQXq4kztop5ALhnT5Of17ycPi1iCs2bowoGt2ghZh5arbDsHSFCHjwALy895qiG4OAg4uJWq8hGQUHiOrp2FYNEVBQ8//w9jh49ytq1X1Nc/IiUlGvExMwp7czN8dO2qfVPimyyRzITcNEs47lDQztw/vwhXn11KHl5Yh9JgueeE0FySwtfQQ7Zw5Arz0Exjpy5dnd3NXRRr4cDB4pp3LgBlm3ZHvJEQayVBT1WiFWizXrZre//G5SLh4crY8eMYdfuw/Tp1YMmDWrTrFEww4eP4pNPZnPr1h94vzTM6ji3pt04cjiev5Kv29zu2bwHvn5+VCl5xKP1gjiUsn0WPi8MJO/GjzzYMNGMdfoPmxa8V4swCnUuRup/Sf4Dju9fhn8FiSYNatO+9XNG5I1er7NqdLVrhxIdPZGkpAdGRIZ9mJMJ5SFQJ46JMhERGDvv4cOLmTUrH61WYsoUVyt24cqVeiMbVFjUImJujt9WMMm3b5eNce7eHf76SzajTasFjgYOHGCzozV/PoMHm/DK6ekmzLszyBoFsRIVNZbERL3Nj8oZ7Hf37iVoNBpCQl7D1dWzTFq/OfOxWjVo0kQwMRXo6e3bIiD36acCUWILHqfVwoYNjq8rPh769OmDJcIhNDSMM2fOoNf3UeG+i4rEc1Qjht6yge03/baH0nhSZBPYJpkJ9I7OJr5+1KgRDBigtzuwgeh89++3jyF/912xrMxE7A3wSklIEOQv83czcqSIC3zwwSQ7Yl3WyBOBWFvPlCmuVqihFStEDKC4WEg6gNJmB9it77+CQ/+/LDd+u2RXtxwExT+1QMLlmRftYMLDyMaVV17uzJt9emI4sxF//0CKrx6iYpdIPJ55gbSD1qzTByuGkXnWpL7o1iSUzZvjuHD+NJMnjSPHtw7TZ0zGYDCoznfmTJFVo1u4MLfU+m7PuHEfOEFBLiY2NrZMlhyYcMaWRJji4hJ+/bUuUVFedO0qMXasNwEBb3HmzGlCQ19R1WFJ3qhWDfLynMc4m7Dw6mBzVFQUiYk6jh41deCdOgm/sAjOKRhv8bxcXTFCLp2jpMuEh/cgOPgpuxT59HRn1QSL2bVrF3Fx37Bjx3YmTtSxbFnZzMerVwUtXskuFBkpMhJ17ep4VhARITTGHd3fgQOufPTRFJvbg4OfokIFb/r21du18u29F/Nij1pfXkq/ebFFMtu9G4qKiqhbtyV+fgFER48hKelPQGkntgdk5VyyrGHJEttwUmWQXLIEmjZt4lR9iYm2r92ZZ2ZZ6tWrC4h8AObKp0VFom3Pny/iHEePChx+ZGSk03X/q+Vvk7FICTh+NGmUKv9n/q3LpCfG4N48TJVqLuOkCIrm37pM6r5FeDVTp6IrPrWRmXNXAXD8x1+4ff0gv/zxJ4FvLKQoLZnUvQtBAq/GXcg48jUdO3blyJG96AJq4t0slLQDS3mm3nP8efN3FXrmlTbt0HvXo33r53iY8hcL541g9mz7kfjx40Xja97certSFF+gLMtOBVzeeUdMV5XAmHUKsDWlVHJbQZVcoqOnkZr6rcoP6CzaQwm6KenxBMLFpEOt5N7s2VPA2GwFdZXp9dWrQhGwZ09hLdkL+O3eLf5CQrRcvuxSen+vkJR0jdjYNSoYaF5ePsuWFTkNK/voIzfCw8PYtOk7Xn5ZBDAVv3RwsBiMFGjmnj0CA+3i4qbSOnf22Q0bBq6uLnTrJqQVTIgcJYvT2tLB13ZArHy5Xq/ZrENBaShaQebxlueegytXxPM3hyaWlc/WMuBoDmtt0kSdyUp5d7by75oHclu0aI6f38kyNfX37NFw+fIZbtxIslnfrl0CsTV5su1rVz+zm3afvfmyZQYrW2XZMiGct3Xr6n9zZq6/aVDUMqeoEnA0z/+pb/AqmUeWY0BC1rqg96uCV5MupO77Cknngk/rfmSe2Y5fp2FknPoOSedChZbhZB9dxdy5MTRvId7g1m3xrFo+X2X1K7j3jNNbqRroj6TRkeZRHX2l2mRf2INPu4EUXNxlE8o4bdYKmjSoTWzMTPTarYwcaVNcEhCNLivLBMeyVczzc5YVcFmxQkzN5893hL324MyZ42aBS3WARck1ah5ocwbjPH06nD1rjcARH6Gezz77nA8+eN9uYgxbAbT58+HYMdP9WAb8vL2F5TNzphgUTfd32mZeUmeyLZkHrZYsgatXJX77TVYFBW0FmDt1Eh36e+8NVQ2IL78Ma9aIzsMe1FR5x9evXyY2NrY0D2s2FSt6M3Dg60RGjrT7vpTf5cn1KhKWWNcRGFiZt97KYsUKa4NASTBdv77o2IuKJDw8dNSoUczkybLdgWTFCvE8e/USbXPfPtvIF/W7e0qVf1cEck3PonXrl+wGvpWiJOIYNmwkMTFf2qwvPT2TVauEiFnZz8wycGk7UGmZwcretUVHe/Ho0a0y6wNr0ISPjzt16tTi5s3bpKfnGmG3UVFRPP10o79fh27PQj9x6hrtQp7h6OHdHNz3HfkFhbjXbU1B0lmKtXokQzEYSnB/uhV5v58moOdEJDQ82DYTCQMuLm60fvF1+vQO58Zvl1i/9iuysjPxtwFfBNGxp22aROuGDTl/4RxFLm5U6BptEz2TvnsuI0dP4v5jHe1bP8dH4/uyZHGeU5bZ9OllWwg//fS9w0xBiuXTqROMG2f/nAqcKybmCwCSkq6xaNFqVXaXtm3bcPz4Cbp3LyY0tLhMcoYzaeUmTtQSHg4jRtgf4CzxwMnJgm7u6iow7+aWob3OwRwSZ2nFlJVtyXJQUaxLcM7KFu9pb2mihbxSgS2RRMKSAGM+K6leXUAuU1Juqa7XVOxZZP9eC/2NN0axbdt3ZWLCFYKTAnG1fJ7KgHfggDBY9HqTS+STT+w/R9O7m+/wnrXamk4jV/z91da1klN3w4YdZGZmO5XCrzwWurNYdIX8V1Z9lrOV27eFJHK3bupZrjJ7SUvL+6csy/Vsnfe/FxR1dzUGFs2DjO1bP0ezRsF0aN8Gg0EmoPcUKoZGofEJhMI85KICk0aLf02KUm7xcPdnaDQaPJ55gRKDgV49wyjJf8Cq5fPJyMvD9elWVtmKsswQL66Nu3DxwkkGDByKnJ5MRoJt9My4cRPp2zvceL3ZWflO65vb0g9XihI4UQIulr7hCxeEq+P99wUe/PhxU6TfVhHSANtxJCTk7X0UWYacnE6MHVuBoUMlwL3UlyxZ+ZI//1ydKMGyNGgAslxCt272O3OwDqAFBYHBING37+vs3q1j6FC1WNTSpdYDobn0gXXgyJUXXujA+PHWVHd7aoAZGeUJyg4gOPi50vfkwZdf6jAYxEBoz9c7Zw5s2aJTSRA8iYa2M8E/UxDOdh0ajbZMFFC3biAyT3rYfJ4//ijao04n1h08KPKe9ughyGZKQNBWCQ0t4ptv1pVml6pZqkM/0UKX3KNMkS4woW7S0rKNx1nKR4SFlS3lbD9wafsdOXttIhOY4/osQRMgDKfZs60ll5XAtyQRbO+8/7UOPTevwCEkcO68T9HWaWnSY+nxIVovPyr1nWrUY/Fq0kUEMosLCew9Bf/QKFwqVuXLz6czceIYKvT4kKABs8n/4xwPS6VyU3fOpu3z7Ug/uYH7344n+5fDZB9dRfsXu7Lsq1nkFhTh1dEaPePeLIy4DXFcvPKH8Rq9vN2cbnShobb1w0XARmeEFVrKp3bpIvx/jRub4HCKe0RJ+WZZFDhXWUJCn36az/HjJ/jppyMUFz8iI+MGP/98Cnf3vlYqejpd2SiY8gRWQXQOCxaAXi/z7bebAQlZhrVrBSrhww9tW1aWkDgF2qVkqPH2PsynnwoSlLlezp07ttUAfXycCwqa3lNu6Xs6zt27TxMWVpaYGhw+LBMZ+Sblh6mZfjsDmTO/RhNU0ST1umHDZqeUD/fs2UNi4h6r5zl6tPCN2+pwRo8W6x0ZL0p6OnV2qTUWUNhcBg7sZ5W9ybLs3QutW5tggUlJ10rbuwmS2bu3bXEy28/MOSihvQxW5mXLFh01alQ15hgQ8rpjrITALAXynIHd+voi2Tvv39JCb9KgNl8siKVKySPSNk40yyGqTirx+PtVSDoXlRCXb7dxPMx6bEwI7eJfA58ObyJlPYDzWwgLf11kK+o5GY/6L/L40AoaNWzE3t2bSslIH9l0zXi36EFGscSNqz8Yr7Fzlwj27HEw78IE9QoLE+4DW7DCzz6by6JFy405MVu3DkWWNWzatBlfX3cWLBC+QnvWn+XHo2BfnRESUjLKmMOyVq9ew44dm/H19aBbNyH764ykboUKzmOZFTElX18xUCkZmnr1Ep2wrYFKfX9q60edvaiI5s1FPTt2iBnBggVCx8SyKO/Hkc63eE8mUTNz0bXbt5PL7CDDwsDFxcUod/ykFrq5yJttiKr1NVrO0IqLnRt0U1OzVNLKyvPs0gX69CmfJrt5Ud6/bfiuCXIZFTWWPXscD1579oBerzNa14sWrbZq7+V7r85Z6I5gsyB0bw4cKKZ+/d8d5DwV9ZlDlZOTBRqnLNitow79b+VDv/HbJdatXULUmKlUCqqGwVDCnBkfkJqXQ7URy1TH3/1qCK41G+HT7nXSEmOR5RICuo8rk8ZviaJRpAMKC/NxrVYffzN0jS05guJTG+nc830jyuWL2aOZN882LNHcXxsUZPL3mZiir9G4cWM++GCi0X9m7i+Lj5dp2dLAtGkG68pLiy2NivJSou35D83FxDIyssr0Rc6bJzr1spAJ2dli2l4eTRLLOqpWHabyoVtS5G0Vy2dl6zyWwVAXFxg1ahiRkW8aBb5soUb+Xf5Ux7/Fsum9bC6NiQiRN8trtOX/dhaRM3q0nvBw2QrJ4ezxQ4cKnXhLRqw9TRWwjo18800c774bbURMWcomDBwo1EMV8StHMQblvR46JN5rQIC3jWdm+bztv6PExD0MGjTKClWzZYuOAweKnRCZO0FwcJAxVnDunDDOMjPFLNxRexo5Em7ckG126n8blEtJ/gMmTxqHtnZLqsqPWL78W7ZuXsfSZYuo1HeatR7LmR1knv4OnV9VvJp0Ju3AUvQ+lag6XN3x3186BDe9C0uWfEP1GrU4eOQkq7+ex/2Uh/j3GI97HYEnVKCMsmzAu2komd9/zfjxk9m2fQv3s4vRN3yV3GOrmT3nC7RuQUZUzsZN37FmxTQrFTpLmJ4SfBPJFsAe2sS8XL0q3C2ONE5sQcYUlEu9es+Xo7NxHOFv0aIF9er95lA8a8EC+P57HAbc3n9fPI+qVR0LcS1fLq7N8sNXoKC//HJFhXIxF7GyV8x1ZHbvlkhIEHK49oLV9oWV1KgRZxAPY8d6c+/eZZt1WC87SwkvRKcLsFuHLZq6M2imlSv1xMdjE/5ZHu2bdevU34GXl+OB2lIuAOCbb9YSFRWJJJWQmyus+9athWUu8rCuJjRU5KQrHwrIlhiX5bLjbUlJ96wQSzVqVKd+/RsOIY2mdjWHwMDaTJqUxYwZwsD5+OOyB8yhQ+GPP2x36I4dQf/BovjQAbZt38Wp4xuNeO97myYxetQbXL9x1WZnDlChZTi5144iF+aTfmwdGp0LFTu/o9on/9ZlCgsK0NVoyoRJ4xk/YR4nTp7n0aOHuAW35NHuz6keGYckaUSy6oFzuLd4CHk/rKXtS4NK5QgacvTwbvbv+5Zhoz5E6xbEiVPXjOdIzfGgaYsO/PzzMfbtU0PWzBvu3r3QpIlE/UYh7Nq+mrp1qziVOk6ZvtqyaEBNNDLh0FcTHBxUSol23MlZU5ONb0i1359/3ub33+GFF+x31sePi2DZlCn2RcJKSoQ7ZdUq+9cEwiIbNUpYd+Z1xMfDCy+8wKJFX6pQO6mpzpGy0tMhMtKdgoICZFnGy5ZyAybf6pkz5r5V62cj/KmOMcl790KHDln88EMdxxf4BEWSquHlswydPpiAiqikmzds2EhMjHrG0rOnaEtt29p/j4mJOnJzbQf8FYmAstqU4lYZMUKca8IEwZ346CP7x6pjI6IMHfoaHTo0UvENzp0TM5IvvhhNcHAQCh3e+fbuaTzGsp2bL9tChw0c2J+oqHcIDg4iOLgqMTFziIn5GHMk0rhxjrOHhYYWMXbsRmJiPmbgwH6sWrWa7t1lo1jcrl2OZ7m2MoUp5W/hQ//tyiH0T4UY/eAVukZzI+kGHs++oEKn3LHIIerdMhxD5gPkonwrVcX8W5d5GD+XSr2n4N8tmrSCEhLjVxkHDv/QaHR+VVRJpSWNFp82/aleoya9IrpTkv+AL+Z/SL8+vZi3YC39+kSoZACU3/37D+HePTdmzrSt1XH1qiDG/HRGT7KmCq8PisRgcHOK6t+9uwkVYkvXYsECYS2NHi2sqvT0fIYMGU509DS6deteDlSE2meYlPSA6OiJRr9+enouEyfazyU5YYLAiXfrJq7RPLuNEljt2FHHu++OpKhIcqrzzctT13H/PkiSC2fPnrNC7Tib5KNiRS8kCebPNzBtmv3sUR995GYlmWDLt1qWP9URw/LfUWQ5maz0Hjx+NIeHqcWqa7SVos9Zn7I9JMeTSAQoyJkWLezPhkC8H51OtkC/3FOl5BM6NfeJiVmkihUIFFB/J9t72WijJ00z53xaxGyUWEFSkmz0m/fsSZlSFI469L+Fy0UhEyWnplNUYiCw1xQK7l3n8ZGV6Pyq4N20G2kHl9C0aSuu/HIBTYUgvFuGk3tsNd7e3uQG1KNiaLTK911cYsC1VmOjT9ye5rkiw6sU2VBC+pbJNKpTmwvnTqKt05Kqhke8EzUTufCh0S3km3ObRykP0NV5nqryIzq078i3axbSo4eskkmNj4eEPVBs0FGx1wxcqzcg87spfPTea3z4wVSnsbYKesCSDLJihaCi9+6tpVu3EpUPXiSckJk713aCXpM/7wzBwU+hTCkTE/czaNA/VH79Pn1ERw22FR3btBFYe0dJBpRztW7d1ik3xXvvwdatziXQWLxYYKEdJVFYuVLPr7/WpX79fxrdELYIRFWqSDz//GusWbMYZ9wginph164FdOtW4lAi17JcvAgxX7ny6fQCqle3v865okOjEdel0eiIiMhl0aJCJ33KFVQEJ3sErSfRZFeOGzXKfvo4EOzKzEzhljMnrJm7Vcoi51gqU1pem2PSnVh2xhVq67uB8rjgTK4lS1eRJa/Bsj15ecHdu38zH7plULSST4ExI1HhgyRKch7j9lRLcq+fRKNzwU2nobC4BNfgVugfXKWgsJhhI8bh6xfAmm9iSC0w4NEklOxjq2jcIpQ7f57jYeojtL6V7QZLH8bPxad1f/J+TlAFPx8fXUPWhQQq9Z2KbCjh0a75VK5Ui9RHt/ENnyjWxc/DrU5LAsLGkrImiqLMh7jUbEzx7fNoNAYKCwzo9TIa32oUZqQT0HMS7rWaAmIg0f28hZLCDKf8vm+/LazwJ0ug4ApAWJiB5s2LOHlSdF6ZmcKXGhraic8/n2/UNbdHJHGOSarl3DmJiAjJJp1b0eV2hjq9fLnEnj06cnOLzTI7ZVBSstPmcc48i6lT3ZFlidjY3H+JnGMZLCsqSmbv3sHExV2yGugiIqBqNR1bt5awao0rs6YX0rKlBtkgs3BRCQmJbrhWe4Ya7tdZElvEpUsyH03To6vTjirFP7J4YSFanWkiLRtkJI34liWKkeziHZz1l+sICHijlOhjujdHBK3Tp8Vz7tq1bI13pSjSwLGx5RsI1EHEsrP+KHkFCtBjYgAAIABJREFUhDRwsVkbNEkP1KtX1aErxZY0huPnZroGZ9q2ZfDXMpi7eLGQyHZ1tc08njEDrl+33aH/LVwuQb5FrFw2j4DeH+HZ8CWKM1II7DmRgG7RuFSqjfvTz5NXUIB/r8n4d4vC4OlPl6696Ncnglc7tWPd2k10adcezm9h3ryFDB48hA0bdjBi6Eikx3d4HD/X6vyPEhbgUbcN+We/Y+x7UQQ8OEvaJpHTNPviHjyfbYdsMPBo12e4B4fwIOU2Pj0+BBmx7ulW5P95jvzbVyjMSiOg90cE9pyExj+YIskbtK749Z5NpSFL0PvXoqiUIZh/6zJZRxfzbN1H5OfnM3iwWhLUsuzZo6WkRGCZLT8EZzCrYWEGevUKJzv7ZaZMEXjyxYtN8q6enidU+TrtQR2dwWlfvuxKfPx3BAS8RXS0lxH/vWVLEbKsYd++oyQl3XPKTXHggDsXL15QTbH37t1vMwcqmFwJkycLMpJt1cn1TifUVqbEjqCEsuzKvXubOHmyGT4+l6yki9+NfI30/J1s2zGW5StdcQluy7SZPqA7z7SZnUlIdMXjmbYUptwkObM2n3/Rg4+nVcCnxzQqdo3mQW4wS1YOQ+d2yfh37c9dZr83g9TG7n04h63XExk5xurezCGSlgSty5dFIpf794UrrHNnMZtSVDRtzUYePABfX3ebkMtly6wJX0qxBa11BPdUlCkDAt5g7NgKpUJ1FQgIeIMzZ84A+jJdKc64QkNDi0vJbeV3wZmeuTjOkjDWs6eIR1nmcFXcuIr8sK3yt7DQJ45/C6lGI/y7RXN/7Vj0leqoXCWPds3Hr5M6I1HuD98yb8FaY32W8gEKDPLrZXONmHTzknl2B5knNzH6vcnUe7YpBkMJ33yzklu/n6FHxEB27thITk46lfp8jGv1BjzYMAmdX1Xyks4Q2HNiaSq8cRRnp+H+VAvV9T7cOYeKr4y06dq5v+R19HI24eH2aeLm4lVTp7pjMMBXX1lLDDiCkClTakVEy8UF2rUTEXLL/c1zb7ZuHWp31qCIZ3Xrpkb0qIWX2lpZSSY3kGk/KLAJ+1ILjL2iCkylpmbj4wOvvGKdHEIpZ88KpACYkkw0aFCPzz6bT6dO7f8t9HnIJT8/g0uX+pGX96vV8UXFfvyZPJOCojqs/HopVy4epFLfacY2U0HOI/Vxqmqd5O6NNj/LKvOW0s5v/HaJrZtX0ax1H7p16QAIMMFvVw4xZmxXnm+6Cq3GWivbnuCZ5axJ0OWXWFitvYiI6ENERF90umIyM20n53DW3RUQMJDIyEhVXl2dTqZTJ/jHPxwjucqf9cc23LMsWYipU915/DivTOigWjNHfS57kEZLgTJHsyFH723DhiKDwSDbvLq/hQ+9d6+upOfmofOphE/b18k4uQkk8A+1ramSmTCf4W9PoG9vE6PDMqOQAoO0J8OraLgM6dWD114fYlVH//49yPatg3+3aIcDS9qhZbgE1nF4vQ/j5xLYcyJa70DS4kbz+XyTBWzuw01PFx1Qq1bg62uCZYWFvW7T124PQmbZGBwNGkpZuVKPTteTDRu24umJ3Y83OVmQdRISoLhYKhVU6kdk5Bij4JKzfkxwZdasT9m2bRvZ2UXo9eDioqd37wimTp3GjRv/tPLlO7qP06eFDz80VPghLcXD4uLi2LcvsUzxLnNYmaXfXJZlbt/+nD///MTGkRIP0vpSufLHpdR5eLlTK1yfbqVqRw+3z6Ji59FW7ajm2K3Gmszb+VO1gkTspk5L/HLvsW7tZn6+eJaJE8egfyqEqoZHLFu2huu/X6R+PeF0v3NnN9WDlgLWmax8fXUMGvQGUVFjCA6uSmLiMQYNGqTKYGT+3F58sT2enkfsPjPFP+4IrmrtuxbPtHw4/vJk/bHez5lsQ44gm+bF5Af/y+a5bEEazb8Ty2u0zCIVFCRmQqtWSSQlyaW5e4V42cKFK36RZbmRrev6W1joe/cf48r53TxI+QvJw5cqby4kNTGWood/UuXNharjHqwYTt+eAynQVDFa5GBtoR/fv4wcvzoqGd6M/YtwbxaGd4seNmV2zev49ttvOX96Fzr/mgR0H2s71d226fi2H4x3ix52rzd5+XB82g3Aq+HLZH6/hM5P7eftUULvxF7Hu2sX7N2rY+HCBQwd2ttuHkNbFvqTBq327hV1RUSoO0N7uS9t5WFMSvqTiIi+/PHHTUpKhIaNh4dI/jtokOl8irXWpUs3u5Z8QoL4wj/91H5A11Jkyxkf+tatG+nXb0CZVppC/DC3+HJzH3DpUh8KCm5ZHVdQWJk/783kyA/pqna59bsdnP5hK3gHEtDDdv5apR1VeN4EhVHa+Z37OSpI78P1E2hUuzqXLp2xK+sMoi13aFuTmpXn4O3xs/WN4oGXz2IK8qsQHtarDDEzVyRJYtasfAfCbC7odBqj2Ju92Zul1exsjs5/h4Xu7LkEqYon8qGX95rsEfkUOWhb5Kf/Cfncoty/mDhxDBV7TgYZo1VraV1nn4vH//5Z3omaSbNGTxnXW1ro/hUkPpk+2SjDm3t8NWPGfkjchjgyijXoG75qJbOr1HHh/GkmThyDT9iH5Fw9arOjvrt4CLqKNag84FOVFW6dkHoHWT9upkKb18k7u56Vy/LK0QEJq2bRouU2LQtbQS9nAmG2FA/Lupb33xdBms6dbedhTEw8Rv/+A5DlAqNqovkAZZ5MODkZoqI8AdmuJT9zJgQEOMbjmt+H89P+t+jS5WUra8jkhhCWfGhoF0xWeQl//DGNO3fE/apRKBr+evQGD/5qz5cL5zPkrXG82qmd8ZyXrt6kft2qvPGPCB7my1QdvlR1TXe+GoxL5acJ6jtNtV5p5xkZmeT4PaViNmckzMero3qmaDizkelzVlnlvQX448/N1K46H1vpy5YsqYaLy4Myg3jZ2S9x4sQPxmdWUiISiJ84QSnhx53w8HA0GgN79uw3k8M1t0qf3Gq2TfCC8ljo5ZkN+Pi4PxHKxdlrKinJJTPzJLKshDELARezOsyXTb81Gjf8/Drb7dD/VsSisjpzEBmJ7m06yTfffM3w4YJqaCkZcOLUNYJ8i8jIyCSkcUtO/vAtw9+eQJWajWn1opbinN/5/vBGWr84AK1bkPE6FMLQ3Hmfon8qBMDoM7cs3s/3IvOnzeTd/JlH8XOtcPBKqdAinILffiD//E4KckwBOWcCmkomlaioSEJC4mjTRk1CskUSOXxYWNqOSrduIpildOg7d5adT7R3b+ES0ushIaGYxYsboHQQSUnXGDBgAJJUwNy56noU0aYXXzSxXoOCIDs7h3799HbPefGi4/tIToa0NMFM3b5dXNdLL4n19iwwc0LHmTPHiY1dytix5vT53pw5E1mKphBkouzsX/j5534UF6cYr2vyVFd0ddox/dNzjBm/kK3bTnHq+Bj0dUJY/NVcAgIXotGID/XEqWscObCdv1IeUMmi0waoENKbzNPfYTAUo9GYPkelnVfzr40+6x6PNnyIb6iYKQYMMRkX+bcuk3ZoOQEBgRz/6RfjenPy24lT3rzYZj3VK32Br/dJ1fkPHUous72I5/aj8Zm9884GCgry6NlTMHrFwJ1HYuJ2EhJ0Zj5iMFmhluQssS0qapTNtq0UxwQvLJbtE+Mgt1xEu7VrVzpMwBEXt7yUqVz+a3r4cA/Xro1AlgvsX8gTlr8FysWcWPT4+1W4P/28ilCUvHw4GWd2qORub/1+xpjvc9Xy+RgqP8uWjUtoVL8mQb5FrFo+n9yKT3H79g3mfLaavr3DadKgNi+2bWjKZVq6zpYwmNujf/Jwx6d2B5YKLcPR+QTxaLdAvKgTUr+lIkB5NOuGi16Hp6eLkazhTO5LRQbXnqwuQLNmWsaPhxUrtCQnCx+pPRSHQkx67z3ROSvomgMHnMsneuqU7TyMixatplq1Qnr0KFt1cOdO8WFIEg6RBI7uQxH2qlhREfYS/ytWtK9ACWr0SnBw/VKyyi2Ki7NLkTRfGIlEBoOW69encu5cB6vOXEGhpOTVYPf2eKNLpGJoJEV6N2Mu2iYNalOU+RvxO9c7ZDzrvAN4sHEKD1eOIPtcvKqd3797jbHR4yh48CcPd9pAa+2Nwe/lERTovSjO/t2u4F3j556lYsAK0Ax2+jmrn1sOwcH1iYwcg04n2RSMsxbZKlvsyl7bdpQTt7yiZspyeeSHBVrmBAEBb5mhZZS0jmdKsxA5JieBGyUlMkVFORQV5ZCf/5iLF3ty9eqQ/0hnDn8TC71Z6z78ciGBtI0T8Xi2PZmntvAw7S4eTbqRdnApns91IPPHjeRf/wHPZt3IPrqK1i8OYOu2eCOKRZEMmPDhWC5cOIV/r8nGdebWvNpyuaa6JvPlwsISPJ9pq+qohQ++O94two1M1bQDS/F4/AdpGyfi2qgL6YeX46LX43Ljex7e+BGPpqHG69UU32HPnv2MHFlSjg9JBIIUWd3Y2DWMHbtFJfD18cc92LVrB2PHbkeny7JJzTb313/1ldpHXlAgEh07slzMZW9Ns4cYYmLms2HDFoqKZCZPdnw/PXqImYGrq57CwiKH92+PYp6cLO7D0j1kTjO3pxcioHMeOGPVXbnSj8ePj6iOj/nKFV0dwV6WJA1enaM4sXu+CkXl2qgL69ev5dlGLwKwe9cmPJ5pp2pHjxI+x/v5XlRoad6OltB/0LscOLCD+78cwbs081brFwcwbdpEDJKWgFes/WgVWkaQc+Ugno06s39fHJ1eFX54e+3cz9uP6kGm452l8it0eWfkKszbRllWM2CnbQv/8Zkzanr/v2Khl3c2EBwcVErtn2NWp7k/3P65iouz+fXXt0lNTbQ+0b9QyvKQO2WhS5LUVZKk65Ik/S5JkrX/QezTX5Kka5IkXZUkqYz85moLvVuXDqxbu4khvXog/7KPzz+LpXNIMzKOrKBZs9a43v+Fz+YvokW9p8k4vIL3x02iT+9wdu1cZyUZcO3mTWN2IkmjxbWRyZpXrJWS/Ad8Pvd9nqnjY1xvTulv0qA2UWOnUqUklfTNkwUKIX42HnoJ3zunTHj171fi61ORLz5fxJBePeD8Ft6J/Jh9B35k06Z4gY0/t4W5c2Po0zucESMi2btXYFSVD8lRMemsiNFeTYHOJiXlFjExi+jU6VViYr4gJeU+b789wsoKMe8EbSVhWLBAZEixp2GtXIuPj2nZlGRC0J2dkddVBoXERD1+fp4O7//ll20nJnDGVWVPvnX3bsjPz7dL2zb/nZPzm9Xxn87sTZWSVJWkc+AbC1WSztnHVjF9+hxjG3vt9RGU3DrP/XXjyL5ymJRtMwjt1IncM99x/9v3eXx0DWkHltK8WWtaNG9IZnoahqJC0g4uZdzYifh5FZOTk2VX1lkfWJOiR3fIPLSM4SPft9mWza31GtVfRjZDvDmf2EPQ5Z3DaBfZxGhbWrLm8hL16r3I+vVbGDDgda5fP2uX3v+vWOj/V7OBhw+/5+TJemV25iUl7sDTwNPkF9Q0/rZcNv8taVo4rLPMDl0S+KvFQCjwHDBAkqTnLPapC0wC2smy3AAYU1a9lkWr1fLa60OYOXcVkiRx4OA+3Oq24WFqCt9t24dGo+Hc2RO41W3Dd9s3YzAYmDP7C6qUPCJ982TjB+Y/OEb1geUeX82Qt6KN57nx2yUmTxpHmkd11q6OwWAwsHnjGt5//12y3IOYPmMyBoOB9MePyM/PI6xDG4pOrQfZQGFQA3R6HS+3bkvRT+uRDQaKKj/HzFkf06//ILbvOEDdZxob76fTqz3ZvuOAMeharXpNJk+NYcoUNypXdjaTSv9yPUdbGdCd89fb17AGa30Ok5CSsN68vZ0boPR6iIuLY9Cg1xxOf3v2FMFUS4KGM64qy6xIIOrZtw/Gjy9h0KA3SUr6w+7xhYWPKCp6bLW+Rq1wli9fx3M1qpK55zNAtLG/1kQLtc49C+jTbxg3rl/j/fff4c69exw5spv4+MO0rFuLxweX4uvjw4B/DGXu52uoG+hN1oXdeD7TjjvJt5k0cSzurfpiyMvAo25bFi9dSPzO9VaaRsnLh5N5Np68mxdLSW4hyFotwXUbcuH8aSZPGkeObx1jWzYvkuZpfru5CtFBlId8JKLTaWlqETRb+kLbt0NqaqajV0Ri4n5CQtqTmrrGTrKL/Q6Pf9ISGtrFhiulQqkr5bSZ79+58vjxcWJj61Er2J8NG/w5fjyAq1eHcOFCAW8Mc7WfvUnzFtf+3IzGNR6Nazz/vLPU+NtyWbXNZZ3D6ykT5SJJUhvgE1mWu5QuTwKQZXmO2T7zgRuyLK+0XYt1sZdT1FJ50R5M66mqTzN8+CgMhhI2rF3E5d+TCHpLHd2xhDje+O0SyxbPNrpjHq6fQC0/D67fuIrHMy9Q/PgvdJJM06ef4vSpY7g93RqPx0mkP36EX8Qk47n93Ty4f++GFWys06s9bRKcLH8/TPmLuHVL+OvWBYe6yVOmaNm9Zyft27UtXescPMoyR+F77wk3S1lwrXfftZ9VyZZuuAIli44ew6FDcTRtKttF1yQnw6xZcOeOjry8Enx93SksLOD990vo2NH2OceOFZoWEREi36iiKe+sfOv+/bYp6fYgZ7Kcw717W/jnPycA6lR6BlnD9ZuruXY12diGlAC+e3AIxen3cHu6FbmnvqO4pBCPeu3Iv30ZubiQHmH9qVHzKaO0hV/ePerUe4HTxzcZ29D9deMwFOZhyM9WEdd0/jUoyUgxyjo/PvI1fi+PIPP0doozUqjU92PjvpUr+PLg3j8dwhlBaYv1qVhhL9UqLXFAYrGGHJoTs+zBbvfsEVyF7ds3W0AVy5YVUN69fbr/vwciWP791MvFxQ/59dexHDqUaAySVy05ydLYAi5dQrVu8cISNBoJgyxTXFyVW/enUFhU3W7/YLlsuS1qRPcnhy1KktQX6CrL8vDS5cFAK1mW3zPbZydwA2gHaBEDwD5H9VrCFk2EnnAVftyeqFbx6Y3s2n3YaJHYIhA9PrCUghsnGD/hMzq/8gL9+3UjQ1+RktwMAiMmUHDvOmkHllCp71QjGxQkilKSCFTWfTsOWdJRefBnSJKG7F+O8PjwCvxeGYlXg07iPEfXkHspkbVrt5KaKduEjpn/VpbzMu8we8YYQrsVEdbdnqiTJ02abMPPrz3l0W42z4D+6FGmU8y3zp1h4EC9Kqpvj8RjDiVLSvqVFi1ewGDIZ84c6wFK0f4IDRUds8l/r2X79hJat9YyfHiJStBs714hxduokQkel5MjIJmrVpU9OA0bJnDw9shRlqQQg6GQixdfJSvrnI0aK3Hj1ifkZrkZ25o5GsvEJK5M7vUfzdrTRIqz0tAVZaHRulChx4e4Vm9A+pbJyJkPkas3VrXzR9tn4WdBOMr4cSMVO7/Hwx2z0Lh5ERA2Dreajbm3OgqXSmri28NtM6nY5R3V8ZzfwrRZK6zaXpMGtZFlGbmwofGZKOSjzEzbhLFFi5azevUaQkOLCA93HnZrKYRlT/jLvDijRW+9bPt3UtIfpXLLW81iTwOIiopSaeo7rs+0nJ39CxcuvMT587nGILlr9Qakbx5L6/p/cvyki3Fd2qYJDOkVwWuvD7HZBzjqH+xt69C2wX+8Q08AioD+QHXgONBIluV0i7pGAiMB/CoGtpg+bw2gHoH27j/GLxcSSC+CCl3tMC+3zaBFm560er4xXy+bi0erfuRcPUpA+IfG/RVsuPtTLZHvXuWtYZEs+2oWBhk86rWlKPUOsqEEl6CnVLT9lG0z8bf4KNIPr8AloAYeTbvx+MhKo0UWNHAuWWfjSf8hDs9nXsAv7y9adXiDF9s0tLoveyPww5S/2LL5G+7f+pmsrDx8fGSrDghAp3+FWrXnUqOauaa2c9aEM3T3Cxdg2jQdIJGTI1iber2JFATWjNYBA/ry0UcTCQ4OIjHxR/r3H4wsFxpx6ArbbepUxyzCiRN1uLi4kpGRi04nO5QomDhRS0SE5BA3vWKF6Mztacjbom1nZl7kwgXr6faD1NdJefwPTpz6TUVWExIVtfEPdcwkTv8hDhc3Lyq8PEK1PvvYGgKDqjhu56UDxuMjq9AH1jZ23vm3LvNw93w0Lh5oPX3tMpTTd89l5OhJ3H+ss2P9yTxbuxd6nWXH6oJnhflUrdKZalWCVDO+5s2LmDFDSOFWrlx2ooyAgIHExHxRukY86/LJL9zEvC2XpVFu+T2Yrt2SvKaWPjCVsr+pGzcmcu/e17wxzJU0z3ZUDB1jZnzOwLPDu1bG58y5q8plhf8nLXRnXC7LgNOyLK8uXT4MTJRl+ay9eu1Z6Jeu3qThszWYN2cqJ3/+Bf/BMarj7n41GJ1fFSq5AnIJD2VPCu//jntwCIUpSeiK89DVakHObyfMpq5jKUlLRtbqCSx1t9xfNw63Os0puHO1TJmBT2cvYMXyxVy/cU1lzev8qpJ7/QfjOkV2986dJObM/sJorV84f5q58z7liwWxVK9Ry+Y9K79lOZf0tCh8vH6y8dRcadx4BxUrdlCuEGcsl+joSIfqcadPCyJPjx4SYWGyihC0Z49Q1Tt0yKThYk2pF/KmSUl/MHPmDLZv30lhYRGFheDuLhEeDqNG2W9nijUmy3KZltuXX+o4cgTmzrWdK9XZLE+WFnpGxhkuXnzJYs9ANK5HATVZ7a+sQnRPhZB1agtugTXxCbXNJH64cw6BvSZZ6fQrlP5eEd3ttnNzhnFRWjIp301HcnGnQotwHh/5Gt+XhvH4+9VIkozWy5+qQ79SHf9gxXDGvhdF19AIh9bf9d/3UbfGdMDa563VhRBU6SvatOmoItmcPg3Tpjk3UxKdsgiuKFb+smVfU1xse/akFFt0f1uyzo5kdssvgwvOfFPXr0fy11/fcPcuzJjtyoPcanh1nmB3UJ03byHNmof8n1jozqBczgJ1JUmqI0mSC/A6sMtin51ARwBJkgKAeoD9qBMm2OKlqzc5ceqa6vf2nQkcPXYEzw5DrY7zbtVbTHdzS/Dz8acw+VcCe07EPzQSCYn6Tz9L7q/HcA9+3oh+CQifgMYnyEj+kTRavFuEk/vb/+PuvcOiuN64788Weu8qFuwdFRQ1JjHGREUBa0xiElONafYYaxJNojHGJPbee1dERcUGSlRUVARUFBUV6X1Zyrb3j2GHXXYXSX7vez15n3NdXs62YWbOmTP3ue9vuYDPqF+RuzYg98hCk79VdHIpw9/6hJgLf3MvJRn7Nr3EfXoMnIAq5xHeI34Q90m9tvwde4Z8u4ZMn/ktMZcS2XcgnOnTJ1Lq6sf0md9y4/ZDs+es305IzmbH4WGkPluAVmtd44gquHdvDOZdyQ23jV+PH/+xRbf49HRh2fzbb8KkW9PF/bffBN2WiRMFDLop7ljJyJGjcHf3olUrf44di+Tjjz8gKekaWu1T7O0dCAmpPWgQUBG72Llz1wsRFCNHqpFKBSOGmqqKhkYbtU00kZFyRo16S7xOhYXnuXVrsMn3yiokRn2UV6zj6wk/0axBC3SJJ/ji61k0crEn+8DPJr/NPfYnErm1KXO4akxlFshrH+cBoShunUSn02Ll7kv9T5ajzk+n4Ox6PIfMwMn/Tdz6fIJOrcL9DVN6rF2XELbv3M6N2w+NxlfN8RYVrSXxwVbyCoNN9qFRx7Fw4SQTmGL37sIK6J/AbiMjj4pF0I0bhTrIsmXCSs8cd6Cmk1Zq6h3ef/890bTa3DgUit13xN/8E4hlXe4pnU7Bw4c/kpGxEYCGDWHVsgp6tHlE0dH5JvsvOrkU/8BgpDbeRtd934FwRo4M4/jJaLEfDhw8wsiRYUSdjWXfgXBmzxhL1NlY8Xf630SdjTX5O4bthTh0nU6nlkgk3wAnEfLjG3U6XZJEIvkJuKbT6Y5UfdZPIpEkI1STpup0urza9quHLeqbfjvl7i02rFloViERBOZl2d1YrJv48/DJDbyGfy9+z6nrYNKu72X6zEWsXTWfzC2TxDRMg09XivsoT0ug4NwGvIZMp+JpkkU2qENAKCdP7icnOwuPAV+juHGCrJ3TxWjeUA6gOO6QqKGuj9af3I3iWMI1sUhVuHcmKUkXRTEwc+evby2b+6HTNkGnetfofa1WhXHUYGm7+nXz5u3Yvn2HkQCTPl89f76E4GBdrYN+yBAhmjFXvBRgghqKi5VVxgQKIiO3ERS0m+3bN/0jBxedrm6TRGlpOffuJdC2bUeiooz1on/6SfiXlFSbxZoVcXET0Wh03Lkzjtxcc/AeWzJyJhj1i6Y8i78W6+n9v7Jn12bu3r2N3MVbRFnpm1PXMEqTzlH2+CaF5zeJ49AhIJTz58IpVShRlpVaHOdOgSGUJp0l59B8QZZZbo1Lr3covryfoos70JTkUXh+oxhQmP4+lMLUS6QkXeSVHq+ajC/D1/7t/YBFaCsLQWe8Mjx8+BpLlpg+ZOuOX3ckNfU577//sUkRtDbugLGTFhZlnfWtfXt4440yRo4czd69e2jevAE7d+4zseCr2YKD1UyatI/Fi5cavGt6T5WW3uHGjRDUamMoV0ICxMTa4BJqapDrEBBK5uNYOrZtLDKH9URIWdOuJN04yrSJH3DzxlUux+zCqlkQm9f/Rm5uLnK/buzdtZI1a7aJc6KsaVf27lpp8ncMW52IRTqd7jhwvMZ7Pxhs64DJVf/q1AyJRYbEh61bViJrGlgrocexSzBFscZOQ3oM8OdfziAlrYRZc5azc8tSEiMW4vWhsQ5L3omluL3+KZWZqRRe3GHxpnAICCHj9hmsvZogc3BHqypHYm1L9oGf8R2zWnwweIZ9R2HsThz0EXwV6SQhfAEuBsVaq/b9RNJJXQhOttY5tGxs7uqZI1nUTrgQyBsxLFu2jEmTDorkjbKycmbOrH3Q6wlBlnLS+s8NI6aePVW8//5HuLrak5lZWqcbX6fT1Zma3bx5A1xcHM026it3AAAgAElEQVTmYvUWa7VJxjZv3oAHD+aYncyLFD14lvUt0Zce8Ur5Y6AafaWn9xfkf8KqlYuRWtlg06AteZFL8Bm1AIlEuHGduw5GeS+WnEPzsG/VS/xc5tmY9AvPkVjZYtfUmBGdd3QRjt2G4Nx1sBFxLXPHNJy6BFN85SCeYdMpvnqQ/NNrsG/V0+j3ORG/49J9mHifWHUQxlu/IZ5G52dp7DWuJ8WlhsdqQUGF2YesHr9eWw5dmJSH/WPvXHN0f3P+qDVb9+4QEZFM+/YdUamoc4AgwG/N31NarYoHD2bz/LkpgC8+XsLMH+xxDZtlcf7IuXOBJUuX8vqbQ8wi+KZ9N0lE8Om0GtIPxYqS3ZaIkoCPyR+rav/HmKKWIvSwsJHs2rGazLx0nDoHk396Ne3btOVO7C5K78bi1CWY/FOrcOjQ12h/RSeXMnnydAYEh7HvQDjz535NUVERrmGmkbdjl4EUXd6PujgHhxosvpyI33HuPgznqpvCudtg8k6tIufgPOxb90SZcgmvYbOqi67Ng8iLXIJTl0FUJp8mf9d0schlCKPUY+J/XfCXeK61Reid2vuh05ahMzuG/1mErt8W6O5/snhx9VNeJnOsMyHon3yuNya4c6clkZH3a82LHz0qoaysnNJSFWPHCmgYS3rnhpGb4CFpWhvo3l2I9g4fFujpSiV4eAgWa4JWi4BsqKzMNf0D0qG4efyCmwdodXZoyrNYsGAu+fm5uA+ZhU3D9mRv+oZVy+ehk8rw1tdktn/H0yXv4vLS29UTcpeBFMZswyN4HJnbp5J3fDFlqdfwHv49Ugc3sndOJ3fHVOw6DUQRvYFvJ37Hhk1ryUyOFidzl16jkEhlFMXuwmvwdCqzUql4loR7/+oVo6N/fwrObcDWrzNFsbvRpF7GqkM/lNGbmP/rn8hsfWqN0PXbWpU9GMPWcXOzITOzwqQv6mY2bUVc3Dh69Ojzwsl44EBBksLGRv/Q3WEktfuilZ4eQhkaWo2kGj68rqsIJ8zdR0VF10lIeBuNxpSTUFrWhiUrNVg1a4JOqyVj8wScug6m7PIubDuFIPdqTOH5zdi3fYUL0ceZNHEi8+YaeCdLpLgNmkTysd/FVVrG5gnYt3pJ/Nx5wASSj/0uEiVBYCIrnt75703oliL0I0f2YtuyJ1befgJk680veXg9HI8hM1HlpFEUuwungBCU9y4a7U+fMywu1bFq2c/oJFIjdp1hNO3cdTClydHYNGhN2YM4MrZMwjkwjPyolchtHVEmx1CWcgnHTv3Jj1qDVCYXi6mq/HRKE88bGV1k7ZyB1N4FmUt9rMpLKDSzKiiIXMzItz4xyqfpm+UI/blJhK5S5XDtxl6srLubuLzXuMJ12K67U7ohS7SunwcHq4iMTOPxY3mtdOuICB0//aSiU6dqHPPXX5tCJWtGbuPHf0xQ0C6z+/b1FVJEZ8/acfPmKSP5UVBSVvaM/Pwa7CMgI9uD3MLHQHVULvfrira4FOuG7ah4mkSlohC7lj2pzHkk3nxOXQaiiN6ILvE0WXdicAwMJT9qDV5DZwp1nJAp5Bz8xUgbyKX3RyhjNqO+sosevUfRwK8Ls35czsaN60k6vQabhu1wDhqCRCLF2rspuYfno9WoRF0Yh7a9Kbl+lKK/d4ljMWPLJOzVSooubOPTsd8hs/WpVeLCOEJXmkTogwd3ITLyuslD09BsesAAYSI1XQltonlzHxMykrmmDwo8PUcTF6eXi62Ommsbp5bkIN58sy6rCMN6ivC3NJpy7t2bSHb2PpPva7VWPM2eyLHT3oz+2IXVK+aRc/Ai9q17UXB6Ne++O5bw8N0UlBZi3+olii7uwtXdk6izsaLESfamcWh0OryGzjIqhjt1HUzB6dXk7kwXhdgMP9dnIUD3yNL5yObMmWP5bP8/bGvWrJ0zZswY6nm7YmNjRaf2ftTzdsXLpxlPEmJQZD3FLWwadk064dA5GCvXetj4tsHay4/CmC14hk5B7lL9oLKu35K8qxFcjT2FxMENu6YBgu65REJx3CHyTi7HtrE/pbdP4+j/JpqibMpSLmHfqifqwkyUqXHY21jT+5VXSc/MwrZNb4rjDiC1scfWr4uooW7bqAOKG8fwCB4vFEMlUpBIKbl6EJumXSl+dBP3QZOMjg0AHRSkxBLQqT1//vEjffv04aXu/tTzdiXtUTKb1v9O6MABtGrhJ14PD3cfVBX7TJxoKivCKS9LRstr1POuj/BcliBEFnIzry1ty0lPf0JCQiIBATVCM4O2cyf4+UFQkPnP9+yBJk1MP7e3hw0b1Ozfv4dx4yIoKQEfHy329pCRAdu3C2p9s2dDt24CicjZWYDEdewIc+YILksKBezda8W6dbZs376ZoKBegBx3dzf8/QPN7rv6+zsICuomnrNOJyUt7S+Skt5Dqy017iKdFGfX76jn04rnT++xecMiXMOm4xQYSmnSeYpid6K8cwGvoTNwCgxFmRyNrrIMnbqS3KN/MGhgGB9/Po1GnvZcO7IBjUaFNv8J1g3bY+Xui1NAiDguytMSKD29kgW/LWbylJnk5mSyaf3vhA0Kpm37QN57521iTx3i+bltoANl9Aa0Wg12rV7CqWsYEomEiieJFJzbgFPXMBzavIJEKqP8yW0Kn91jzNipjHxrKPW8XYmNjmD50rm8/upLBHTpaHS/GW1bnQGdMZYhMPADfv75Kh06qPD2Nu7fhg2FSHjzZhmnTzuwfr2KmBgnunf/gPXr1xMU1B2QsHz5Sl5+uRJnZ4tDjIwMuHDBmTNnzuDu7kbNsZyenkVCwm2z43TbNmjbVniwGDZfX1i8WBhLNY8dhABh3Tob1q/fgLu7FyAnPz+a69dfR6Ew1ZAvKQ3AzukAbq7dsbGxwlZeRuTxcDyGzsIpMBT1w2t4Osp4+DAFj6GzsPJqgvLuBWRNAnhyO4Z3R31E967+nDp6AJvG/pRcO0LJrZPYNvFHZudM0ZH5DAsbQkrCVcoeXsehszElumD/bCaNn8LFmDOP58yZs9bcdfw/prZoqXn7+LJmzVZ6tm8h0qsNmz7/jQ4TdbrKinKsm3fHa9j3qPOfk7VjGgXnN1N4cTveI37AI3g8OnUFWTtnUBIfIb4ntXVErqnkzf7DiIk5j+Nrn+LcbTC+Y9fjPfwH1PnPydz2rVj4qv+RsX5H/unVWNdvJRZFzeXTHANDeJZbyLRpE4xkB+KvX2Hd6gXk2zc0oWpLJA7cS9sAkv6mF0p3jsLcvqhU+f/T9R4//qsXeiAePmx+WZ2eLsAdDxwQ6N7DhgmomN9+E7b79we5XMeJE5Hs27cfT8/RIt36yy+tSEqSsGqVeQ/K9u2FG/Szz6iVmi1Qua8Y7Vv4/uiq7xtfu5s3B/L4sSkiAfy4l7YeiVRIv/21eKGRTpDX4GnI7F1EGKJEKsOxU39K4iPIO7EUh7a9uRgbg1Qq491RH3HiZCynz8TRoXFDs+O46ORSJk76ji4BQWbHQHZ2Jnl5udg3D0IRd4B58//gtwWLUT26RuYWQRcmJ3wBrr0/pCzlElm7ZpATvpCy1CvYt36J9esWoVar2bNrM+GHt2PdrBuzvv8Otdoyft9c8/PzZPv27Rb1T5Yts2ffvp3k5mZV6Qs9ZvHivwxggDBq1Dt1VDl8x+Ln5iQt9M2SHIThKmLtWks+s5vFY336dBkJCcNNHvRgB/LVPM74GYnESXz3r8ULkTetRtM5B08k9maikOat8h72HvGDwFtQqNi5ZQkzZ0zCw8A72dq7KXmRS9DptMgbB7Bnzw6UFSqcXzddVth1DuHAwb21Xsf/hGNRTRC9j6vKohdoUdxBFAlR6BT5vDtqLNExJyioBBv//hSfXYurmzcKiQ2uAyZQ/iiewtgd2LfqaUQAMceoK43ZhFqtNspX6ZtOqyEvcgmVGfdNDAoyVn9Ch9ZtuXXrKnateuIeXE3+KIhcjGPgYBwDQ6h4miTI8Q6b9Y+dZ17p0Q5720SaN5xmch07dz6Kq2tP/heqsyWn9GPHhCXrG28IOigDB1a7vJ86JWC9Bw2qzlmePGn6niUSR93JJY5kZxs6BP1vFO7z572omSjOzP2AnMK3uXD5jnjds7PSWbFsASpr2xcSfyqyUimO3cVX38wiq9Ba3MfZUwcJD9+Be7+vRFaxPvVn2zQAl5wkwga/x/o1vxmNAQ9be54/TUZn74bXsNmUnFzGGz170bBRU9avWURFWQnI5HhXIbx0Wg3Zh+ZTkXbzhd6lrep70zZwmFnSSuN683FxNIbFtWixgIYNx5CamsyyZZsN3HQEpc9x4740S+gx7Ie6enmap/pX71NPEnrjjTJRCiIrCz74gFqZ0Ka2ieaPPT5+IMXFxvhJZXlzTpz5gL27t5n4uSbdjEQmt6JYa4XbQGMuQsbmCSbEs7zD83F9Y6wFlnEDlCl/I5FKLXor6G0zFU+Tn+l0ukbmzvU/41ikL8zsOxDOhjULa/UCzdw6maB2bZg3byEajYb9+3awY8cW5syZT6fOXZk+bbKguvjBYuFCHl/yQvKQk7MTSo/WRpZ1JVHLsOscgsyzMblHfjerjV5yNRzPrKt8P/sXfvp5Nhklldh07I8yZhPD3/qEuLizZCrUKItysWnY3oiqbU7SoCZV2/DalJX0xcbaGDbVocMBPD0H8L84uQjKdw/55Zef2LVrn1nKfHo6bNwIsbECBtnaGiMdmrpawOlJHDKZ4z/0kvx352W4rdGUcuGC6dpbYn0JicTZhMBx43YqkeEbuRCfgNeHS41+Y0j80Wk15O76jo+GDaFNRwEiuGfXZlatXoZ9616oCzPwGbWAiieJ1dovBc+xloG8rABVvfZGEgB5++dSqchHYm2HzNEdp4BQ1Je3o1QoUGs1SK1sTG76p0vewa4O3qUFp1ezeMV+s+NLq5oI2iij82zR4g8aNvzC5PrqSUI7d+42kLt9h/Hjx5r4hpojBZm6RBmTgiz1ZWrqHUaOHE1KSnKVU5LgWrVmTV1ITuY8QKu34+P7Ulx82eh3NxO+ZOasXZb9XDW5uLh4cCctzSjfrcpPJ2vbFKw9fHEbOPmFk31O+G/o1BXY+LYVmev6Oci2UwiOgSGibWbescUqnc6EpAL8H8yhL1z015zmbXuSlVPIhcvJ2NhYkZVTyF9//Iikkb+Y/y5PSyBv7yx0Wh3W9VsikcqQyK15cvk4Hbv0ITu3GE+fJlg5tsTPrykXL14g4sguXAaMR+7ig8zOGYcOr1Px7A4l18JxCjB2csjbO4sRw0fTt/9wrp+LoPTOebRIKYhcwlsjPuLh5aPkxp/Ae5h5aJJ1/ZZkXz9FhULJyHe/4PGDxxTcPsnHY6ZQVOHC8GFvIalUknr3No66MkqSorHybYeVuy/2nQYY5VSLTy3jo0+nkPwgT7wehtdGJonE0d6Y0ZedvZfnWflIpO1wdrID1FX/lAj58tq2q1+7uztx7txpmja9w5IlWt5+W8iJ6/Oezs7QuzeUl1shl7fk1VeLGTCgOtK1lMfUN29vKCmBxMRyBgzow/LlK+qUV42JcWTq1HH/+rz027m5J7l+vU/V6+qm1clIShlEZk6J0bXOyink4KGjXIyJEMeSYdNp1ZRcPYJj5wFIZXJ0Ujm3Irdj5diStEfJrFohLLedAkMpvX0G5b2LFF/Zj/ewWTgFhqC4cRydvSso8nCVa8m6fIiSG8eRu/iguHsRiUyGXfNuVGY9oCzlb9RqNRqdBonMyuxYtPZpRvGVAyjvXMS2cUezOfvco4sYMvQDnuVozY4vW6uz2Fo/Ndqvu/vrODt3MrqekZEnCA4ejK9vPOPGlfHll/Dyy5UkJNxm2rQt+Pu3o2XLxkb90LKlHyNGDCAxUceiRQ9Yt07F+fMOqFR+FBQUsWnTbpYvX0J6+hNatWpYlUc37Ut3dxveeKMfGzduY+FCNePGQUEBPHwo1F4stb175XTvPooBA/pYHDcZGTuoqKjWkL5xA2bMvotL2HScAkLIjY8i4eoltm5di/uQmTgFhJB5LZKn929j1SyQvKN/Yu3TDCu3+sjsnEEqo+L+ZSrTbuDQOZjytASy9n6PTqOiMvMBynsXsW3sL/RVl2DsmgZQmnAaRcIpJFIZhSeW8Nbwj0i5FEnpnWi0EimK8xvQqCpS58yZs9zcef4nHIsM9ZrHT/xBlMRV3D5DYcQCJn0zHs+sq6IOuTJ6E5+OmWziNqQH7ddMm+jJQ+5vmgf/x8Wdpe9rPfnxp8WirvmX38xizJhPsbO1NTG6yFr7mYmzzIXo43Tp2JzPPhvLkYgzjBgWxis92tGlY3MmTZzI74u3s2dPOL06tjKbUy2JWsbkydNp1sTHRMtar61998E4oKXJbyvLVvPg/puUl2fzb3Si9ds7dx6qk9Z1UtI9k+/VzYFJLepkC5DD2kFWhjrc//a8tFoJt259TGLie+h0NT01HXn07Ff827c0GYea8iwRM2zeaWgwUmtbnq//EkXiGfKjVvPaq6/xSo92HDm8VZS81bOKNUXZRqxip8AwKtISmDt3AZMmTEZTkoe1TwsKzqxFp9PhNXQmHsHjsXKtj9TaFp1UjtzRwwR7rpfStW3SCd8vN6EuyTPPXD26iEHBoUycONHEzUi/7eJsb/I7sDK6noYkIUPGJkBFhQq1uoxBg97Gw6MxEyZMJzU1S+wTQz3/iIgDqNVqysvvodUqAR0qlYLTpzcRGPgGkZHRFvu5Wtfcnr/+kpOdLdRwXiwBPLHWcVMzW7F4uQ1WzXqI/eg2aBIJDx7gMXSW2I92nQaC3JrSxLPYtQgi9+gfaLVC0GDl7YdKo8apz2eCGfjBX9CUFmLToC1SOxcozDDqKyt3X3xG/YquKIOyi1v4/fdljBnzKT/OrZqXqrwVgBLzZ/ofjNDjE58xfLgQ1T68cICuvd4iqMerdA16TYh+E07y0WeTySq0MoqmLlxOZseWv1DXb28U3Wfv+JaSpPN4W8hLWdUTIuyc5xmk50JgYFdeez1UjJJbtOrA3bjT5N44hU4io/jUMvw7v0HF02sU3DojPjU//GQyap2t0bnU3H76+C4HD2zDuf84k4hPq9FxM2ovx44dRlO/PbEnD9K0ZVcOHopg84ZFVHq15eq5KNr4/4Va446T/VUkEsM9KFCpcvHy6s+/jWRnzJjHl18KaBNLzd4eNm8WlBANv7d2LXX67bp1lfzww2QyMp6xaNFJunR5EQLhD4vRWl3OKycnXDR3Nmz5RW+Q+mwBZ2PzzfbXr/NmomnQwWgsZe6ajk6nxaZ+K3GlWJZ6jcrnd3Hs8AZpN2KwcmxJ3z59uBsXRcGt0+JqzCnQOFrOO7EMayd3cjMz2b59vYiUUN6LRebgiusr7wvBQqP2lD++icfACTh1GUjp7SgUNyKRyKzIO7EM196jKU0+R2niGcrTElDlPsYrbKrpigK4e/Eo7Tv1Jjbu7r+O0OfO/Qlf3xtGq7MrV4TiY9u2gkTEV1/BK6+ozETswj5SU+/Tt28wUEnv3sJvvvxSWAGWlMC9exr27TvE228PrsKIm/Zzy5Z+eHl5s2rVKQICtPTrB7//DsXFQu3GGO1kw/btawgKCjQ7bjSacu7cGUtBwWmjc+8WqOHyaQ35N2PEfnTsMtBk1aNTq/B5a47Qf3cvoLgRidytvlAUHTYLdJB98BckUmnVCi2UkvijaCqUJn0loObkyAozeK3vcLJzi7h45a44L0nljkRG7MywhHL5z8EWbWys6NyhGTZWEm7cvE5Y2DBe6u5PfR93SktLSXt8j49Gf4iXl5f4G/3vhoYN5Pq5CHKun0SLFEXUChwdHJA06ixCvYQUjqBlbVVPSOFoJVKeXDrM6NGfmMC5mjdtzJDBw8nLyOTJpcP8/PPvdA54ibGffYqVtoJbkdv55ZffeaNvX7Pnot9Oe5TMhjULcQmbZvbBolWVkRt/Qryxi26fpTw/jYsxEVXQuRBK70TT0MOOXr0+Q4IcdMYFHHv7Vnh7j+SfwBYNXy9fvqTWNEh6upCrfPIEtmwRZG5zc4Xc5dmzghH0i1MozgwbNpKRI0fx/vtqlixBdDvS34S7dsHSpbB48VL69u1j8Xjrcl4lJQnk5kYYH4ikHQ7OG/Hx9rDYXz17vMTFyAMU3T6LRich79ifWNs5Up71iNLbp5HIrck/tRJbF0+ceoyk4upBRo/+mBNHt/H+u+8wevQnPElJICX6EA6djXVSsvbMwrX3aJyChvHkylFcgyeIEFiJzJrS5POUpVyqTp10DhbTh5qyEkrvXqAy4x5eYVOxb9kThw6vo3wQR/mj6xa9S23qt0Jx5wKFz+8xaFBYnWGL7u7BODv3EK/n6NGfMG5cmdjP6enCZD5vnpBuc3auhp8GBGjp0EHNuHHHGTHiHdzd3QFbJk2aRnJyAgsWmP4mMBD8/SEqSkNubjFDhgw328+pqU8YOfId5s2rYOBAaNlSUAZNThZIZevXC0XQTp1Gsm3btqrJ3HSs5OfHcO1aH0pLE0yumU7ag5Fvb+Hp/SQeXDiMfSfjfGLm7pkgkYopMMP+K79/Bbvm3XDqGkbu4V/RaVTYtQjCKTCUiqdJlCaetehCZV2/pXiv9329t1Ef1fN2ZfOGlf+9Cd1ShH7hcjJpj5JZuGA2lV5tuX4+gtbtenDxYgyrV8xH5dOO2JMH0MgaYGtjbRShe3l54erqzvW/T6NLT+DTz6fycu9g/j62k/xLewDIP7WCkW99wuO44+TcOIVEbk1B1Go+/mwKd1KFaO3ChWj++uNHvHyaoVBqyc4tJv5WCvm5T2jboSfxic+ws7Uxyt0bHoe56OdFtYHc8N+wbdxRePBIZcgbtCMl+hAuA6rx7lqk3IrcTrtOvVGr7+DkYKzd7eDQCm/vEP5thJ6enkZCwh2zWF99FNaxI0ydKkRUr74q5C4XLxYKoTk5dctjXrlyCV/fG3zwgZZXXhGicf1NePasgHn385NTWenNgAG9LB5vXc5LoUggN9fYGkpZUY87D7rW2l9lFaCRNaCJtwMPLxwg6OWRdGzbkmcPk3GxdyHn1ilsfNthW1FI+aOb9B8wlL17t4qrK63OhoMHtlvMv5cmnMa5x3AcOhtHfHmRS4SzUORRnpaAU5fqPFZ5WgL5J1fg89Yc3N8YK/5OIpFSELUKewOMenlaApk7pqIDoxXFg5hDOHh0+NcR+owZvxitxOpeOykT+/LDDz8hNFRLsKkemPgbhQLCw+8xcuRg5s6dy+jRnzJjxlyWL19BevoToqJO0axZstFKwdlZqPu8/TZ8+KFQ73F378aoUcPMjhW1upCrV19CUP6ubhqtLY+f/8ChE/48TbtvcVWNDrTKAlxeHiVe89yji+jY+U06tGtLavx5yu5fxq51L8of30RdmEnZgyso71/CtkmnGqu/Geh0GrGvtJLqe71mbae2CP0/R/03FOfSw7giwzcQE3Ne1DMo3DuzyuE8xGifmvIsNq5dhNyvKw10uQwbEsK+PVspLy/BvnUviq/sx9rFm7zsRxQX5mPT6mWKLu7C1s0HnbqYV3q8Wi2e49dVFMcxFM/Zu2slX43/+YW0fcPt+OtXsJJJcCp/Tv6emVi1f5Pic+twcnLC5enf5NyPxb7tK5Rc2kv+ruc4D5go+lXqm54l9ttvSwRZAI07OhNIsYy6yQKYlwgYP34CQUEHTJiX8fEC3rymprmhuNLMmXDrlikVXG+ccOoUlJSocXPbjUpVyZw5anEfX39tqhOTnq4XTVpo8Xjrdl6m+Gd7W1uL/WfalyFMmjhRRIP0fqUn06dPNJZMblWfk5EHxDGbu3UCa1bOw2Oo+QjMuetglMnRlFyLwLlbtcpj7vG/kGgq0SFFamWDew1T6IJzG0wMp/VILI/QqeQd+5PMLZNxCgwlP2olg4JDOX3uIMo70cJ7p1bx5RfjaOPfrs7U/+ocunA93d0dycwsERElZ84Iqonmmr7vo6JUFBdvYMeOPYwa9S4VFWoGDTL/G30bNAgOHFARFPQqwcEqFi/Wy+UK4m/796vYsKH2fQQHq2oIbxmPFa22CCEZZdzktrG0aGaLz43wWoUCnQJDUN6LFftRX6foF/oxndr7MXPmbJYuW8aF6OP88ccKZkybQGXOY6T2LgJPxkC2QafRoLh5AuW9WIF1fH4DCxYsNisTUlv7z1H/BXGurkZ6BhcijGGMVu37cfLENtHhHEyt6/TCNteuXhRxuFk7Z2DTqD0X4uONUh+K22fYsWMLnYMKze7DECv+fPcMNm5cx2efjSXl7i22blnJ+Ik/4O3jKx7HvLmnGfPFNO49KiLl7i3WrV6AVdNu2Cmf0yeoB6dPbUOjVlPp3R6VMp1mDZqTdvsEY7+aybUr50kIX2Bip1dTitPDJZ8GXjWvqoYXC3dZlgho3tyH7ds3GWHS09KE5XRY2IuNmR88EORr9Xj1tDThITBwoIBPF27IUo4cERQRa1L7DVu1aJL5401NfcTSpUvYufOQGaMDAcusVivIyNhssu/C4lKeZj0GLNPgLX224Ld5Rnoc5kTYKpQKbFv0MJp4CyMXYxcQYiS8VRS7y2hCdwoMpeTSHrQaLT5maj6eYd+Rd3yxOGkXnV3HO+9+LnAxUsCl92gKT6+h4PQq/AP60S/0Y14Pfo/fF3xPVtRqBg95jzb+r/0j6r8QveqlZGHUqLeM9HOKiswLYBna061YgcFkvPmF0rvp6UKRE6CgQMnx41BRUQ2h/ewzFTt3/lPhLeN7QKfTkZ5uGuRqtXKSkgVosOFcBFUcguN/YR8YKvajY+f+Yj86dRvKqTP7sfGoXqZaObXix1+GkHL3FpWVFSCV4hk8HpuG7Y1kGyoyUym6tBdtaT7lF7fR49V3kdn6mMiEvKj9p1EulpzVlTGbRIdz/b+7t08bMfucB0wg+fFjvAyQBY6d+qO8ewGn1z6l4OwGVPnp4iHM0OEAACAASURBVP7ef280sed2IPPtYLIP/RNaIpVh07E/aQ/ixEheW68Ne3etpGPbxiIyotStKXt3rcTbpUJ8wrsHj6dYI6W8NBetRo3XiB9wDx5HsUZKsyb1ORJxhpbNGnA74RoufU1ZYg4BoWSm30alzGDRgilmonOojtD/HcoF7AkODiEuLg5Pzw8ZP96Rn34CuRxCQkz/mmEbNAhu34YRI97CweEd8bfz58PYscY66mFhQmrmxx+rjYVXrBBuZH2rFk0yPd7IyGiCgl4lL283ixcrqgyGFeTlbSMo6FUiI6NF5/WiIlP9aEXZa2bHnn5bjyqy0QnX2sNZgo+rinlzvqJCWUL5g8vkbZ+CKj8dTUkeWokUmaMHIMgoa8qKqcx6RNbO6ShunyHv0C9oyxWUJp0ja5eA1Mo/vRaPAeONjsu562DkLvWw9mxkNIlkrvmE4riDyF3rCYqOtg7kR63i3ffGMmbMp2zZvJu+Xf0pPruBGdPn8scfK8lMT8bDWUJgp1bMmP0bf/yxgvj4WDycJf8TymX8+ElGjE29jK5hM9RWGTPGVEO/NkPxK1eE1ZqNjcB7iIoyr5vu6lo3U/Jq4a3qcaRUZnLpUiBpaQvMjI1OFhF3eYfnUanIp+jSXjK2TkaReIaCsxtw6jKIjM0TsGveFRw9uXfjiAn6bvXSn0AqFWsceuE/37HrsW3sj3PXMKxcvGnm15zjkdEMHxZmto9eFKn//476XxK1jImTvqNla+PoZcwX00weBB4fLDam6EetwqlLCDnhC7DyEii3xaeWMnTocDZuXItti+5oM+5QsHuGxX0oYzbxet8Q0VvSPXgcmQoVC+Z/z8wZk4XJe4Dw3paNi6tXG1IZjv3GE3szUVxtSKQyrNr349yZiFq9UUGQ4swr1zD9u/Hk2zfkp18i0VqWX/mfWvPmzVi8eCHvv/8uI0ZYoVDULRpSqSRs3ryaTZs2iL815y/69dfC/jZssGx0YEgFT019yIQJE/HyqodU6sGwYcP46SelRaODUaPe5tSp0eh0FTWO0gnkWygotrze1/dDvp0vO3esJs/Ol2nTxrFm5XwUrs0pUpRg3bQbsopi8g7MIfvgL6gV+eQcWYhOp6Xo7104tn2Feh/+iX2rXhRe3I7c0RX3YbOp/+Fi7Fv1ouD8ZiRygRdSU77CsWsYmvx0cndMRXH7DPnh85k8biJeWfHk7vyOwphtVD6/i0PrXpw/H4lWq+XWzWucijqBXcuX2Lp9EzOmT6LUtakoI5By95ZwTmbkJf5pa968mZEUQPfuApvYsB0+LDzgLa3o+vUTCpY1m+GDoKaZypgxwvu//ip8r29fgclcWzMnJ1BUdIW4uO5UVqbV+LYUZFNJy5grvqOfi94fPAiu70WjqkQit8G+RXd0leUURG9Fp1ZR+PdurLz8yD+xDMfAUJKTjXVg/lq8EI1Egn3rl40e1M9WjKbo6iGx7526hpH6KPV/6p//ZFH0RfC+uxePmRRF9XDHjIdJPL4UgaNBMQkgc/u32DYNoDTxDF5DqlAjt8+glVmRcDVWIAoEhlLx4DJucg15t6NN9qEnIZ04GV5d4DQoYDr3HycWMDU6KRWPbuJurRPha5qSPEofxuPY8U1kds5VS/G/QGbN5csX0fp2NC2UoMOmilClk8ope3YHn3fmkRN/Frkm3+im+V+LojW3R4/+gnHjyv8BgsWJqVM/BySMHv0p48aVG/2mNkSEXpBr7lxhst+xw4b161dw5crfRiQWgA4dqLWgVlKiJSnJWCxMoWxPStpysnKdLBZCDxw8wuYNi3AJmyZA0B7EIbNzIv9RMp7DZuMUGEpZahy2TTqhzE2nsiALqVwuEoCQyLHybEzFvQuoHl7FMWgYbq9+gGNAKHIXHyQSKTp1JaVJZ3Hq1J/Si1sYMexDnt86R+6NKHRSGYrzG+jZexQdWjbh4YUDdHt5pADb7f4a+c8fkRp3Eu8RP+IUGCoSXXbs2FB1zCFkXY3Eqmkg7v2/Ivv6KW7F/U3EkV24DRbIMdnXT/H4wWN8G7X418QiQ5JQZGQKt26pjOCnCxbAhAmWx4teNMvf3xiyWtcCa1ISNG0qIK1eDHtdYQR9zMraTWFhtNF3NVpb7j3eSGZOWy5cvmM0NvTghwYNGnP92t94DRfGQWlyNOrC58jkcryqxobi1ikUCafoGPAmvo1aivvo26cPSbevUZyeQtm9v0XIqVufj1Emn6c08QxIpeSfWonc3pX8vEKL5K8XFUX/cykXH1dVrYUIx8AQitSSqqKoMbFIV5lrMWXh1H0Y5Y9vYmtgTecxcAI6jVp0PRJSKgNIf5Ym7qM8LYGMzRNQ5aeLJKTQ0LdQPbxKwe7ptaaFxnwxla1bdtOrYyvy9s81WhnodFoKjy9CIpEibdgRRwcH6mtyxOVd9sFfcHl5FGUpfwtL98QzFJxZh+egyUikMqw7DmLvgZrs3/895WK4rdeg1psZ1Gzp6UKqZNgwQU+jqKgEZ+emfPjhV2b1q18UubVvL0RvCxbAq6++SlpaNu+/P9aIxHLuHC8sqA0cKBTrjPrf+UuzBCLD7ZppO89Bk1HlpBkTgjoPRHHjWFUOVSoSgOQu9Sj6exdlD68jd/bE11FmcYXZp/frSB5c5Lfflogpk/69XhGJIyOGD2bSxIkciThDx7aNWbRgCl6ucp4+TcXBgLDkNmiSSUrQqetgyh9dF1OGN+7dE4l2hinDf5tyqUkSysvL5tChPfzwg70o3mUpr65vvr5CrWXKFKG2ohfNiop6MTlt4EBhVbd2LXz8sSC8tW6dsfDWmjXw7bfw++9/VMkQGB6/adlQJg+hfZuAWsfGkcNbsasidQlyyJOx8WiI57DZ1de+y0Bs7ezJen7HiBj45uu92LfvGNYyGZW5T8g/vRqvIdNx7NAXl5dGoS7KJv/UKpwCQnF+eRQXoo9bPI7/bMrFkqeouUJE1trPKLlavSy16tCPkycOGfkkHjh4hOnTJ2IbNFzMj+tbeVoCpYnnkDl5Uv74JplbJokTcYOPl9ZIy6zE5ZX3sW3sLwow6Sdh+y4DeZJdwO6da5D7dUVT8Jz8cOM8XHlaAjmHfuHNfkNF38jTUZFUKvJF71N0kHd8KZUVFbgNmYl78DhyKyS0a9eV17v3pDRmCx4eXlTePoV7/3HYt35ZMDgYMhPbJv5ViJe1zJ5eM6WgL4rW5o1ouF39OjX1TlVawweZzBEvrybY28vJzBSKUceOGTPx9KkTa2shZRIVJeQ8Q0LUHDiwB6lUZ5LjrAubdPBgsLMDB4ezDB4chr9/hdED4EWTBZg33Lj/yLKHrX67S4/huJY+J2/nNIvKmgXnNuDUJYT8qFW4Vo0TiVSGbeOOoNVg7d2M8tJiUh7cN+sVatNpIDEXo1FrILdQxa2kxxw8fJRTJw4hkcrIK1IbeU+uXjFf9Kj94MNJSNLvGI1f07TiSuRWNtXj+7NVRp8rojfQufsQk/PfdyCcDz68yrNn1cd64wb0eeMv7t9PojbP2uDgl4iLi6lSvHRELn9xfrtJE2HslJQI6ZV+/apJQS/q27Iy4aE+cqQQUFRWCo5Z/fsL/6vV0K+fjISE62aO17T4lFdQYvZ6GPp6jv54ctW1nyxe23qfrDBJ6aoqK4z8g/X7O3j4KGqNBoc2LyN3rYdNow4CzDFiIbZNOiNzdKf0/mVKzq3n3fe/tjhG9UASS+0/F6H/W+r/3dunkXi3oOjSPqy8mpITvoDsdZ9RcGoVOeELsPZuhk6jQluuoJWPC7mHfjE5ptyji7D2aY5T17Aq1/b5BpOwjvwTK6gszsVz2GwcOrxGWUWF0WpAdDFq2ZNTUYfxci5j7Yp5aCUSEW5mZDA9bJZR5HTxwgkmTZzIwr+2sXdvBL06tqLo2KLq4kkTYfCURC1j8sTX6dy55hn8uwjdUpGxcWM1R44Yy5CuWyfAGC0VvT7/XEC2WFsLOuqGra6TcXGxkA9fsEBNfLzWqFhqrghXsxkbbsh4lv0NrVoMqLUQ2qm9HwP792brlt287N+6Vunm4utHkNo5Y9e8GyAUQouvhSNz8sCmcQd0ZSW1eH2GIXHyRmXnzN5dK1EpM1i7Yh5l5UrKPFqwd9dKegW1MZKxcA8eR5FawrXLkVSUFyO1dyEn/Dezx2fj5IG7tY7M3bPI2DwBReJZcYVZeHwRjo5ONKpnLxZ7DSUzCp0C+PlXG7RaYTKf+YMNWdateef9cWi1hmPGPB1foPWn8cUXY14ol3v8OHTuDBcvCpO7Pj0zfLhpgbxm30ql1YGBHvZ68KAQMBw8KLweOVIjyky8KEL3cHMyKyFiCHjwcJFTUV6MtlJpXloh4nesvJsida2P24BvKFJLqvxchf2tW/UrnsNm4zFwAlIrO/KOLxUVFz2CxyGzdwFVOQP6D+Kt4YP/dYT+n8uh/1vqf9rTHJ7dicWrSvyo9NZJfF2dyX4QL76nSIjC1d6JzKx03AaaMaGQQOWDK1Q8uEJJcrRgbFFF9BFgRkfwHDRJkL88NF9QwuscLObDsw/+gszBBfc3v0D56AZx54+hQYJ7vy8pf3QDxY1IE/YfGAtzqbQ2RnUEc8QUrUbHg8sXCB1UbET//zc59NTUZIKDh/LTT2UMGKA1ymt37lyd6+zSBZEEtG6dkMO2FG17e0NpKURGGuc4w8Prlos/e1Ygh+hzpsnJ1fnw3NwXCzHpDTfad2zDg6dLOHleaiIT8W/kGdDpKE0+h337PlQ+T6H8SQKOHd8ga8/3SGRy7Py6oIg/hl3LHjgbEHyy9sxCp60mjUisrClPvU6JWsKJQzvRSuUiJTwj7jhPHj7hZOSBahkLgzqNXZdBKO9exHOg6bhAp6M86yFFORlIJCB380VxUxhzJfERVCqK0Lk34cbFSDT1BAJURlYZWzZW1Q0CQsm98TdpKYVs3GqDS+iPOAaEknntBNbaEl7q2ZW61F5atWrDtGlb6NBBbTG/vWoVPH0qBAJ6+v9XXwnberJa06aCiYZh271b4DvUlJ5ITxdy8AsWCOmYM2dAoahg1KhhRjn0oqK/KSyMMdpnXmEjHjxubaaOEiLWIXbs2IBDj5GUP76JZ4jp3KGTSFDnPQOpHFQVyBu0FcXaREkScS5pR8n1CDwHVjOEkcgoe3yDjPRnJmSif5JD/89S/3v26MG7oz7E3UPwQ6zv445voxZMmjCB9u3amNBh16/9AxpWFyptm/iTnxyDa/DEalqu3JqCpHOiuE7NZl2/FepHV/H3q09edgZuskqKEqORN2hbpYgmsPqy9n6PTlWOnV8XShNPI3P2IufgzyCRVL13Bo9Bk1Dei0Vq54hL0HCcuw2hNCma0oRTOAUaYwBzd89g8sRvCQsbXCeZAOv6LSlIiEaurlkUbfePqf9z587H1/emEeNO35ydoVkz+P57Ibpu3Vq44fbvh8mTa5+Y69UTJvQTJwTGno+PlvJy4Wa8caP6xjOUD3B2NnU/qldPiNjeflt4XRcXmtWrYcoUKV71z+PtVc9krNSUZNC7RRXkPX/hdS+9fQYrz8ZoFXloFAWosh+hLkjHu6pYprx/GVXuU8pSYkEiJe/EMhza96E47iDKlL9RF2ZRFLsLtz6f4BQYRllaAs5dQymOO4Rtk05IHdwpuH2SpUvXiDIWeh0RmZsv+afXmpVx1h+fIiEKbVkxUjtn1IUZ4nEVXz2MRKtBXZKL9/DvRXmJ56nXkPkFipT0kntxpGfZ4jxAuAYVTxIpSjzH9bjrTP12CnWRYHB3r4+/fyezTlK7dws57hYtID8fi/R/fYH8lVeqx1lSkiAJYW0NffpUv2+oJTNhQjWLWSqFGTO24+8fSMuWLQFbioouU1h43ui62dt3oX49wd1p1Yp51Ro+UhlWvu14dvU4tp0HUnRpn8Vrb1O/FaW3zyB38UF5NwZtWjy/zPsdPz8/hoYN5HLUQTKvHsdGH9B1GYi6MIucw/ORyG0oOLMWuU7DRx99xuZNy0RHs+dP77Fjy18MDRtIqxZ+euq/wpLa4n8uQq/5NKotmjL8nqLCnuIn140EkWrK0+Yd/QObhu1w6TnSojSvBilZN84yYNhU3nl7FBkPk3hy5ZioxyHk48+IEZXi1ikU18KNqt2lCVGoi3NRZT/CtklnShNPoy0vRXk3Gk8zwklanYDcadqyK9m5RRZlArRarcFxykk6E8/IERpxP/8mQh89eowJGsWwNWwIbdoIk2R4OGzZIqGiwjRCqtkE+zmQSKBTp+EsX/6U+PhK0tIElIrhjaePyKRSYck8ZUr1zWpvL0gCfPih8NrZWYjc5s411YDZs0c4zhkzoHVrG26n9K91TF24EG0kJxF3+SKSxh2FtIhhdG1AyUYqpSTuIM5BQylPu4WusgzPgQZBg1ROxdNEHDu+QXHcATxDp6C8so/gfoN5kHiV8ud3sG/1EmWpV3HuMRxrj0bkn16LTcMOlMRHUH43hqBX3savWSu6dX+NW3F/k3/7DA6dg8k5PB+bRh1MxoXOYFyoi7KozLyPXbOuaBQFuL7+KZVPkwU1wObd0GlUuPb+UBhDBkis3LhDFN86jW3jTmhUGlxeHkXFk9vkHJ6PbZPOeNup+PrLD5BINHUaXwISZiiJiWUsWvSQdetUREQIE/mUKYLAW1hY7Wil4mJhEvf1re7b778XSEZPnwq2hbUhp7p2hQ4dVIwbd4QRIwbg7u72wgjd0jySH7Xa5Npn7ZkFuuq5A6mUkquHQafB3s6Bjl16E5/4DC8vL7r37Mvta5fJuXUSxy6DxPSsTcMOlFw7jBQYFPYOe/duFSVPpDJ7Fv72PZr67TkbvoN9+3ZRVFxGwo3LbnPm/PiTuev2nzO4qGkyYOkzc9/r0KYRv/36Axfib5uYNOesH8Pot0dx9EQkxRopVu3fRBG9gcmTp3Pg4F4yFWqsOrxp5JSuKc8ywYanrxuLTYM2RkYC+eG/4tL3c/E7Bec3i3Z0Ng3bk7FpPOqiTIvCSTqthsK9M3l/cAhvvzOaqLOx7Nm5kqxSNVbt30QZs4mJk75j+87tFKmlVce5jnk/KejSpXo/Xl5Dad9+e9WruhlB1NVoon9/ITr64Qd7QMLSpaUvNBT45hvhd1FR9vz++yKmTp1Sq2vNt98KRS3DVI7eOOPwYdP96w031GoJzs46I0MOsENqI2jdmBsrhn2rp/D7ubuQlHwTmUcjnDoPJD9qFQ5te1N6NwYrN1+cuoZWoRFCUNw6ideQGWJdA6iuu9RgeSquhWOXepac7BxcB08X3YOsvJoamY1nbp3My/5tGDLyK1EyYvr0iSKKxdCsxdG/PwWnV/Pd1Nls37mFbKUGW78uVdaK1Q5F6pI80GrwGjpDZEvbt+6FtXdTio8u5PMvv6GRz3amz8rAY1g1o1ru1sDo2Ir3z2L2N+8zZfI4M+OobkYjXl4+oktV//5C/71oDH38sRCRAyiVwsMbZEgkMhYsqOT8eeHz2oyg16+3wtNzNIsXLyUtbRGPHv1o/AXpCKRWc8WxYW4e0V97nboCp8Aw8qNW4d7vSxS3TgIIhvKnVmHXPIiyR9exb9kdX10+X43/mS4dmxn1ZU3HosztU5ErstHqdLiETsOmYXuyN49Ho8jDfcjMqr6chLayHG1pAVJHd1R5zySYaf/XROgvzjtruRd7HIVCScvGjUi7dITOQSF4efkQf/0SAR068fBiOJ9+/i1e9Zob5dKMUAIJUWgUeSiTo8V8eE1JzYJz67Bv00uI9KQySm4cq87HW5Bi1RgIbxnWEe6e3cNnY6fSpHknNNJqsajxE3rRq2eK0Tn+mwi9rkYTZ88Kk21JCSiV9SkoKKRrV8u/0adOvvpKiJK+/fYEISFas6kdECIypRLy8ozx47t3y7hzR4pSKTdaukdGWnHxog179mzjhx+8GTjwqpEhh0YjI7GWCN1IarkqR/3s6gncgicgs3el+MoBrDybUPE4nndHfcG9hCuUpFxCJ5FR+fwucnsn3Pp+Lk7ito39yTn8K26vfYR9y55G52ZVryXp57Zh0zxIzKPaNvY3MRuXyKy5d/4A9m5tefLoDgsXzDaC7+rNWnSV5RRfOYDMzgl1RSX3U5KwbhqI4nYU9q2rx51tY3/KUuPwHDTJyNC8OO4AqtQrjBj+AYMHreS7GUWoXNuhTLmEnV9n7Fv2QHHjGE4Bg8RUkNbGhbgjW5gy6bN/NL4sib9t2vTiVV5iIsTECJH8lCnC9/v0AblcxoMHUqKiZNy/r3lh+s/HR8uiRfeZMOEdHjyYgVpt7MNrGKFbmkf0177sUTwl8UdxeXkUzl0GVfdH3AHsWvWkLOUSXsNm4hwYJtZDSktLjfrSaKVVZTxfknIFN4OVXlHcIWwMani2jf1R3r+C56BJVDxN5Ptvx881d67/yRy6pXxnbd+rS945N/4kKq2OjCf3sW/dC2X6bS5dPI2mQQe0hU+Z8f0S+r7W0zSXJpFQHHeIvJPL8R7+Pa6vfUxR7C6UD67gHGCcD8/cNQPnoKFUpt+jNPE0Ng3b4dD2VUpvn6Y08bSQV41cglRVjru2iKLEaDRIUEZv4udfFtKlUwejOkLHLn14vXd36nm7YmtrzZCwAbw76kOaN80C7QWjv/1vcujp6U9ISEg0q7Cob4Z57cxMLcePF5KaCp06vSiPLdxo3t6wc6eOSZO0L8y7G+bLk5Jg/XpbDh06zNOndixadJ916yqJiXGme/cPWLJkIjCJoqJzJvsqLn2Jpn7vWxxThlLLhlrXVq71sPFtg7WXH+U3j7Jo0XJCQkKxtnPlelwMEitb0KrxCJuKpiib7IO/oNOoKHsUj0On/iiTzyNz9qJw//fotDpRollVkIHy7gWUKX9Xu9TUKIznnViGlaMbzX3dOX8ugkrvtkZL/IL9s9FqBVEo525DkFjbkXrhADYteuDe/2scWvei5FoEpQmnqv9GwCCTv+EZOgW5szcF92Jp0vAJEUdBVZyPbeOOKG6ewLnHcKzcGxqlgiruxXB4/zb8/Jr/o/Fl+LpVq+ZMm7aVDh1UREcb58FrtvR0QfBt4UJhxVZTlrdjRw3nz1tRUqKuU/pv3ToVffqsQK3OA4Q6zvTZNnQN0FCvwVTq+3Ti+dN7bFoznyuXz5rlwUgkUuxb96Is9SrWXk2x8W2DRCLFxrcNzl3DsGsWSFlqHDI7V2wbtkUnlVNw+yRpj+8Z9aVEboPiWjjKlMvYNupg1l2qPOk0njZasq+fNMi7C+NFceuExQn9P0f9/7dt354NJvj1Z8s/MKbWdhuMpiQHr+Hf4z5gHIUqCfJWL4tU/fNnjoj7M5QSUNw+Q1HsThzaCNDDyvS7oNXg0e9Lk+Nw6hpG2YM4nF96B3X2Y3IOzxedSOxb9aLo711YWVvzzntfsXXrft4fPAj15V3M//VPAgItKFX9f9jGj//Kops6CJPqsWNCKiM9XShk/vmnkMs0R+pYt054f8YM4+V0WVnd8eM1Xdlff703ixf/RXZ2Bmp1LtnZGfz4Yxj5+e+iVtfEMMpBPpenWaaG2oYtOzuTivIymnm6ihBFQxJZSdQy3hr5GV0Cgoi/foW1K+ejk8qxaxqAxMoGxe1zommBXdMANCW5oNOh02goODyfCV+NwyOzGmpbdicGrGzRVZZZhBy6vf4pjt1HcO5MBL/O/5P6mlzydwl6MMVHFzLhq3E4pMVSuHcWisQzKM5v4Lups8VxWpF+F4qzUOc9IefQfIt/w7axPw4BIWQqKpjzkxyNzgbvET/gMXAC6LQmkDokUmQSeO21V2vvwBe05s2bVskG2FO/voQjRyx/9/BhYSKvXRBOg6OjvE4wVmdnHXplRT0kM9+hF7N/agmSXqLkQ1FZGfKm3YzmkZryDE5dBgr1DpG+f9jsZ4rz6xn98QSTvlRGb+TbKdORK7LIOWgKny6JWsbb74xl69YDBLX2M/sdS+3/mpSLYTFDi5S8o39g5dkYdd4zA2rtKlxfeQ+Hdq+JqJfS26dw7jYEjU7K3bN76Nilj1n4ZMfAAahzU8m5Ek5JQhReQ2dYqHa3RHHrFKXxR5BIpXgMmizSvvVPch1SUi5F0rZDT7zq+dVZU91wW6269f+KHrq7uw3+/oGMG3eM/Hw1DRqYLzK2bSvAwtq0EQpZDRtWwxgXLoStW4W0TJMmQmTe1sAnOD1dQLvs3y9M+DWRLfqWkSFofJw/70j37u+xfv1KgoLaYm4Jn5m5naKiv43OX61x4u7jTWTmNDWhcBuOFX06rdK7Lel3r+EyYByaomyxSFWaeBq7dn25fymS1u168NMPk1GWl4uF8LL7l6nMuItEIsFrqCAZoUy5RPnDq7i8NBLd8zsMeesLI6jtiJGf8OBuIpXKQjxDJpv3KL1+lIp7Fwl6eSR+zVrTrftrRh61NdNuXV8aQVCPV+nW/TUxPWdlbUWlWmv5bxj4oGqQobh/HTu9jnotqaCyh9eYPfObOhdFLcEbW7ZsyYgRQ3n0qICDB5NNqPt6+GFEhCD2ZmmsgJBKOXZMhkwmq/MKUz+Zu4T+iFNAKDnxZ4zlEwJCKIrZSkXKJXRSOcWnllXLM8SfQieTkx+1GscOb1AcvREqy6l4fo+y1KsglVJwdgOeIVPIj1iInZ099m6tadioCd26v0bi9WtkxR/j7VGfI5U7cOVyNB5m+kmr0ZFyKRK5lQMREXtNINa1Rej/1xVF9+/bwY4dW/jm64ns3b+bTIWK/4e98w6L4uz6/2eXJh0RsGBiwZJo7Ioak5ioUUAQWxJFozFqjBpRbIgx+mgsWBIR7C1RERQjdrFhx4K9YMeOUpfet/z+mN1hl12Kxrzv8zzv71yXl8vMvbMz99xzz7nP+Z7vV1KrCXn3zlDNzVdMYmmyzI69hSRF1v5FjPjRn/59e5V7/F5eX6Cq3VJMigrL2GCsWnmIlJraSdGykqCybQEMQNZ7mgAAIABJREFU6ePFNwOGvNU1qxRbUcl1vbC3SYpq/o6Pf8zXX3/Fgwf3RDV13SSjUOIfEqKfyCprO5TQqLq5gZeXhkZVKCw5cECXQldIXg0jKGhBhef75Mmv+mx50qFITaaW22/aySmVUkHqnkWYvdeMwpe3cexdss2yRQ9Ur+4wpI8Xu3f/RaZNXZ1EePJfs0EiwanfTEzsnQUirei1SFRKpvnPpIe77jgqneAsbSqlgsTNE3H7uB3dPL594+cBYMfOPaxbHVjub7zePBFlXiZ2n31L7qmV+I0rZHOYGSkFzjj08tdRpwetYjlbe2ZP+fFvJUVL74uK2o+Pz1Dc3Arx9FTp0C17epY/VkBYFbq5SbC1NWfOnLwyk+0//yyE8pydYehwwTO3d58g3susA4ux7Py92GfZN49ScHYTJqZVmD17Aa1au6JQKERu808+/pSTp08yeNAQNm5ci3XPyRSnPCP76j6sW/Uk+3oUirwMLBp2oGpeIn7jJ7Jw4WwyMjIwdWmPXfYTUlNTsfUyHB5WKRU6SdHSbV5vmkDh64cGk6L/dnzoleGkLq/dB80+o3tvB6q/14Sx45sQtimYm48u4zxqvU577eVn0toR9P/qexLTjQ2ek+bvB/duUFhYgPJFHC+DB2HTvh9ZsZFU7TKc9FNbyDofgU2H/uRcOyCGZ0CdKI0KwqqNN1ZtPEVh6a1bN/FBs8/e6prfNR+6i0stIiLW4erancBAw0iUjAzDYRMN10tppIE2e15Zwhiahy0jA6KijImN/aFS52uohDslLYvEtKdA2f2m4TNXKZWk7l2MeQNX8h9dRGJugzwrlfTj6zFv4ErO9SikVazYvHkDEybPZ0XIApK3TMLec7JAmVuQg7mLK2lRy7Dt5EP60RWAFPPGndgStgWn2h8hlUoN8qiDMCYyDwdj0cpLHBPWbXtx6nQ4ZvZtDJ57edcFAn936d+QHfwdyza9RGfDpm0vck+thMsrmT+nkFatIHQbKIsKSNmzkFrDgnWOqXlOAH4LWsGkiZqb/OZ8+6X3ubt/zOXLZwkJWYWvbzi5uXn89lvFY0XjOAj0uJb89NNPTJ4ciLe34DRUry7s27dP8PAHDbLA2Vn47XmzC5kzP4bX4a9FIZlq3waJv1fw7Ca5p//gh9EBJKYbi/oDUMJtDpBRYMv6DavFl6d53ZaYOtUjWR0eceo3Q0AMbZnElEljUEmNxW0vlw/GooFrufODvLgQM5f2Om3SDgVj3doLynHCKzWhSyQSN2AZQm35epVKFVhq/3fAYkBTsLtcpVLpzqClrCzFotKfy9tXUburVy4KZF1eU/V+37q1Fzk3DmPZ9AuRdKs8JSKNkpJVxwFknt+BeQNXsi7+hUPvaUiQgqIIcxdXss5HMHXKDHbu2sHr8GmYNetB3uk/+Pqr74mNPU5ixHlMPvqyQkWSiq75n1AscnFpQmjoVgYPHqwWuCgWH46oKBNMTYtJTNT3xHv3FqCFpdWKKkPG5eEB8+dLSEw0JzQ0VE2mVNrL0z9fpVLfQXGsZkP1GnXFvw312++/hTB+wmhSdsXg1O8XAVa6yQ9VcQGyI6tw6i9sK0p+gkRqjJ05dP28I7K04axZ/itJEbNQFeWJkLOksABSds1DglRHxehB3Fm+GTBE/O3ffwvBP2AyGWrFKh3IrHpMaENm3+Z58J0wU4C7bg/ApGl38k6vZ/J4L7ZFHCbt8UlMPvIm91TJRA7g4NCTX2d5MHzUZKr1/UWvT61aeZB54S9Mi7PZsTusnPtS8fgytE9DGaBSqUhL20LTprpycBrTiKjs3l2ibhUVZULPnu4sWxaMv7/gjY8bJ+RhNCtMf38ICsrjiy+EcVu7NqwKKWTxbzWIObBYZzIHQUhm4sRpuLn30lsBQUl/z5stUI3Iotfj6O1P/qNYMs5uRVLFCpQKpJb2As2H1xRSdgdi322k6GnbuPYjJ/YvZOXMD5LCbKrmPCdp80SsWnuRfmKDQDtxaTdy2SuDfQSVmNAlEokRsAL4EngJXJJIJHtVKlVpGY3tKpXqp4qOp7F/ykPXfNYoBVVGPsq6jScvw07jP/FbpgQswqm6s3iMHds3MPJHfzZvWonKsb5YLabB+Wac2ow8I1Fr2zPuPXzK2PFz2LhxPc8uhjPiR38S040Z4zuHk9H7OBEdXq4iyf+Gh6757O7emdjY04SEhODnF4lMloO9vRU+Pn0ZODCXqKg9oloNlMiMKRRC7LxXL4Fgq3p1gbBrxQq9rtexnj1h/35jrl07rVYa0leX0TaVKpv4+Nm8fKnrSQIkpRaSLHsKlD9WiooUWDT6WFQdcvT2J2XPQpz6/1Ly0LX1JjMmnAwTe2bMmMb5mGgwMkFqZESVBoKHn7h5Inadh5J+fAP2X44CFSRunojFB58aXH116DwUee4j4f539qHm+y0Y4/sRJ6P3cfjQFoaPmopRlepv9TwA3H+SyRT/r7hyfg5/7XrE/F8LadkyjK5fwM5ICRE7SyZzqdSaZs1CuX69gLHjf8Sh7y8GnxONXN6Qr3vxxRft+bvjq6x9YWERBAUZnsw15uEhTNhjxwqTd1SUMZ6eguPx+efw+ef6UoYADx/qvghu3oSTpy9j1ytAr615K09Cw0KpXrsZMbH3dPbpqDvVb8uVC3sxb9iR5Mi5KLJSMKv9IUWvH2Hu0o6kMH+q+wRiWu09an1foj5W8Owm+ZcjGTU6gKiDB3gWvYaBg0ZTKKnBGN857Ahfw4Vja2jd3otBgwaxf3cox4+twdTWEVQgyUlFJS/UxV1qWYUxdIlE0hH4l0ql6qH+OwBApVIt0GrzHdD2TSb0fyKGrvl7x849rFm1ADOX9ti7+4qx7tT9S7B27YuNGv+ZczuazLPhOP+4Xk1Pu54GLi6sWbOFnbv2Cdqi9dpSS5nKV9+MZvHCKZjUa6cTS02JnIt999E6cnZciSBy15G3uq7KXvO7jqFXpl18/CtcXduL8UptmTEPD1AqITwczpwRuFxAmNQrKlpyc5Mgl+dUeE45OXe5ft0duTxF7zh5BQ2wtNmERGIHlN+/69ZvIHzraqR2NXHsNdVg3Dh55xzsu48BlYqM6NUoVRKc+s3AyKoaKbvmI89MxqLxx8gzXlPdJ5DC57eFWLNLOwoenmfx4hDatO3wVuP3bdqpVHlkyMZja6WbKDZk1av70LjxcqRSMz5s5kqCtBZV3caJz0le9HLMWvTEopWn+JyY3vyLV8+1J7i/H0PX/vtNCtwGDDDm0CFTQkNDGTJkiFisVJYlJAgvgshI3aRoRUV+HzT7zGDfa+dDNI5d4xp2xN29g6N61Ze4ZTKK3HRqj/lT5/hJa4fj99N4nJxqMG3aBEzqu1JLmcoY319RFaUwPWAiRvXaUjXvFZs3bUcqlXLtVjwP4mLYvj2UX36Zy4Sfhl1RqVQGq0AqE3JxBrRZ718ChvB1/SQSyWfAA8BPpVK9KN1AIpH8APwAUNXe8R/x0I8fiWTPnq2Y1fyQoseXkIVPQ1qrCdlX92Ns70zWue3kxZ3Aum0vZEdWYWrrWMI17u3Pq3Ph+E/14+rVC6Io9attAew7cIAp/otYETKHxE1+OKgnglojVonnUPDsJpnH1zH6p5/1PO838bT+Nz308tq5uNQSNUc/+aSYkyflzJ+vG1KZOlX4FxcneOyGQjTapomDlpx32edx65a33mSuUkl5mezLvqPOfNohA8gAyu43zfio+uWP5N44SvLOX3EeuVrnmClq5rzMCzuQ5mdgaWlNkY0zsuj1AiIqJ00MzSSFBZB2MFinqjJF9pLTZy9gbF7jrVeYb9LO2uIi79dYiK1VaTplXTMyqkaLFjuwsWmIAOErYG/kKr4ZNJGnf/2M9INuFMT8wcrgeSxb8SdPdp5H+uGXFJz9kx27VlOZe/S2Hrq9vSWJieVPzElJYGICe/aoCA5egLt7Z4O8+6VNm045aLkZxvU6lZvLMPmoO1u3bqJ7bwed42jnQzTQRolEikOvqcTvWyTqKgBYt/Yk48wWvXOxaNWLDRvXkZGeSlXvAHF+WbpkNomvHogviZSt/iwLDqbLl72Jib3Ppx0+Y1azimGj7yopug8IV6lUhRKJZBSwCehSupFKpVoLrAXBQ3/XMfR7N0+yZ/dWLBp3Qp7+GlP7WlSV5PPy6j4sGn+CPP01KmsHGla3JfFyBGPGzWDr5hDSo9fj4D0N87otMbZx4s6BxaIoACCIAlwMp3u3BXT94hDT/CcKbUrF37KPhjBg4A/079uLq1cucvrwanp3D6H2e3UAUBQksTRoEQvm/y5SYr7tNf8TMfTKtBM0R8/w9ddDcHe/U258vF492LtX4GwpywSZsG8qdU4KRa7e96Wmm6jzfms+7VB2vFPz+eqVi+zdsxWLRh+TeSYcZX4GTv1LlYEDNu37kn//PEiN+aTTp3To8CmBC2dj3vBj0qPXloikSKRU8xhP6t5FOqRNFi09OHMqAr8JEwyeh6HPb9NOKs2hqcscUF3UuwaJxAJra40HqqRq1S+pU2cqUqkx2p5xw4ZNuXT+OEHLVvJb0Er+2r2Jzz/vxqBB3xK0bBm/Ba1jx66t6nDL24+bivb5+HytIz5tyA4eFJKen3+uYMqUADp37lLpF4GGTlmTFE3anohJ0x5vlMsAoe+HDxtB4KJfSdz8HAevyaLAjcYKnt0k/fg6HPtM1zsXqzaeJN05Dra1xDFk4zaelH26oj4WLd3LHEPlWWUm9ATgPa2/a1OS/ARApVKlaf25HlhU0UHfdQx9Z+RezhzbJCamksICUJjb8fLJNZHbIiksAFOHOrxMuMevgRs4c+EOP034lT83BpFxLgxjG0eDWe+cUxvo0NmHG3FPeXDvBlevXsD64294/ed40VMHMHq/NWGhK9m7O5SCwnxM67sybfpkenn78Mf6IOTyQkzrt2fa9Mm07zz0b13z/4aHrjEXl+o8f/6ciRNL/76ujRwpIBM++8xwYlQTB42N/Y6KvL/k5Ejk8gy9Y9x/lEthccV5CE1OxbHfTFRKBXn3Y8rk1rFp04v8++cxdajDyZPHiTl3Fsd+wrhK3CSwaCamv8ahp58ogqGxgmc3kR1bzccfdyl3lfam91wiKaB29d9pUv8iSrUj3qSeXFMro2PVqw+icePZSKVVtfrQAl1sOOI+IyMLJk0cqUaw5AEFGBnBpInfMWniGK1joPO9ij9Xfp+v7zBcXcPp2LG4zLFy4EAJysXdvZiQkCB8fPoQFbWtwhdB167CZ01SdPWGxhw9UvlcBghzzKyf95OTnYFD7+nkxh0nJfJXao3QXeGl7l9ClXqtRZGctEPBWLX2FEO9Vm16kXFsLSlbp1LVw8/gC6GslX5FVpkJ/RLQUCKR1EOYyAcAPtoNJBJJTZVK9Vr9Zy/gbkUHfdcol3mzj2GhkefS8py0seBWLXogO7qK+UtX6aBLunTuIJDx7FukR+qVfTSEiROnUfP9FiLxvfXH3whIFzVsTRNDzYk7gUqlIjs3R4QopYX7szrkV5QSI3FbRsR0tYReCW3Af4qHrrHKLHWbNxeY8fz9wctLgqenSgc1ExVloka2NCnzt4qKUrl1qy/Z2Zf0jl9YVJPGDdsjkQjsTeX14ZLASSKkL3GTnzhWQBcSZq1edlu1FMiWpObW2Km5UACs23qTfnw9UlNzErdMosa3v4kv9IJnN0neNRfz+m24d/e6QQTTm47zlOTXnD4SSPSxMyJ6Q4PFvnhRF9HRr58dHh77sbFpRWWQQm++75/z0LURVh065KFUwoULAuOiubkQP9ee6N3di/Hz28H588dxdd1ZqReBxoyM4Kexgxg3bq5OHqJVswl0+bK3wftw9cpFLpwOx7heW5T5t1ChIv9RrEEv3LpdHzJjwsi+eYz04+uo2mUEubeOknfvDNatPMg79QeLFi1j+/ZtFa70S59HRVZh6b9KpZIDPwGHESbqCJVKFSeRSOZIJBLNL/pKJJI4iURyA/AFvqvUr79DKynVDyhTPkx2dBVdu3rRqrWrzndvXL/M6dMnsf5iuN5xzVt6sjMyAqVSydKgRTpIl2ru41AVF5K0ZbJYBm5s44BF449FdSJbdz+kdrVw6leiPWjStDsnovf9j/TLP2X29laVKrm2s4M5c+DAAWP8/Kzp0QNGjzZhzx7IyMhnyJChjB8/hfj4x3rfLyx8zfnzLQ1O5kgH8eD5anEyr8g05dcZ26dj13koctlrQav1VjTJO+dg22kgeQ9iSAoXSvU1rIq1R/+hJ+FWxcSYwoQ7mDdoL+rDagpwLBp2JP/xFaZN0w/lvKldOHeY4EUjKSw4Q/v2QqVkRoagq/r0KcyYIehrhoSAmZmUsWML+euvm2opwTpqKcEajB8/hePHT6m318DIyEG9fQLx8U/+9nm+K3N378HixUuIjhaudfly4frWrBGK1+7fFxSxLl4U4uIyWbYOnYBG01RDQ6Ghj5gxw67ckExFpqEFsOs1jWru45Eam5AaOU+PVVNjNm17YWxXk5xT63Go5oD8TjSWzb9Ekfac4vNbmb/gd+If3efy5RjMWpdM2hrqCdNGn3Lq9CGUyrKrX8uySsXQVSrVQeBgqW0ztT4HAPoYoHLsXYdc7j/JZOz4OawO/hePDCS60g4FY9GgPVeuX+Harcdi0UdF8EbL1p682hbDxo3rGDJsIgvnTcLcxVUnIZISORcjq6qYOX+ITfv+yKKCSdrqTzUPoXChNGxJO4Tzttf8PxFyiY9/QnDwSsLCIpDJcrG3t8TH52t8fYfh4/NVpWKeXbsKnnpenpzt21cwePAo3N3luLsXqysBs4mK2oyrazihoX/i7t5NPI/c3OuoVPl6x33wbCWFxXXU/WGs1zel/z5z4Q6fdmjC2PFzhEKzk39QfWgQsmNrRW1Qq4+6Ytnkc7Kv7CczJhyLpl9QmHAHlUopsBQCGYeW0bRJS65dv4jdp4PJuX0CJBK9pGhxylM2bfoDMyvntwq5RB05zY4tk7gUe49hwwQpv549hQlOu3pyzpyS6skRI5TY2OQzduwY+vUzJihILvbv+vV/snbtRvr2NSIoSKHV73/i6hqq1e/ljwdde7chF814mzRposHioh9+gE6dhOudO1fgw7e3t6JsqK0lPj7fEBs7msRED4pLDdMH8a8oKDKv1LMXuHCeyBMlkUjByFiUlAThmU49uBTrNl4lBVztvJFfCKdzjzFqqOp2PukyhH59e3H8SCS7IzcjrWJF1qU9WDb5nMLntwWn0MSM3MdXKJAai0nRdx1y+UfsnygsunfzJPcf3MGp/0xKm3VrL/Lux4CxmU7Rh/ZSHISbk300hCotPHWqOp9dCOfLLgtoVD8M3/Gj9JAuGr7ktINBVHMfT9bFnaTuXaQTXwXIPLxMDOH8OydFo6KOMXjwMNzdiwkK0ky+OURFbcHVNZzFi39jypRtlVrqCkkpSwYPHqXHh+7sDCNGyOnYUc7gwcOIjb2Ii0st9XkY8L4l9figUedy+6asfdqFZoUv4si7d0YnJKd5EG3aeaNSKkgKCyD78j5s2nkDYNGqJ9djtmFa60Mx5Fac+ozChLs6SVHrNr24c2x1pUMuSsU5nJ0OYW93losXkjh35CK1aqno1k2YzCtTaQtC2yVLoGlT3YFx7ZpSvV2hc4wRI4rp2LFYq9/rq/f+8yGX+PjHBAcvJSxshzgBv//+e3TqVFBuot3LC65fhw0bJPj4DESjFSoUKP1OUNBKdeuSkFNion4RWiOXWkikdYGKx83vv4Xwr9nTeR0+DRu38Tj2+Zm0g8tICpsm8NIfX4dKISc37gR592Kwbu1B7qmNLFiwlPiniew9fYaVKzeSlqVCUZDEnsjNSE3MMK/XmvzHl0mKmEXhi9s624zrtOTMqYNvnBT9ryHn2hm5l8iItWXyp2jkwyS21Yk7Hy3q9nX94gvuxR4VSb0yopZSxdQMZfIT8u6fQSmRkhW9BqQmVK/VECNTW4zM62OulOkoGWn4kjNObKDg0XmKUp5RzV2fl12lhHtnD6AwqkUVM9O3vuZ3Rc5laF98/EPc3b0N6oy2bq3ko4/kTJ9+nMWLA5k8+RhZWUpq1iyb1CsiwgS5vC4dO6aWy4eenQ23b+fj5tYJkJCfH09SUoROu6Jia+IeflLpcXPmzCmW/jaL3NwigpfNx7aXvyAwsHs+5g3b69DTJm6dggqVjjpRVuxObNr2Uo+hRuQ/PE9h4kO1jJsnuXEn1N698JLR1Dt49xmMhXXNcu+luXkh5sYTMDVah7nZExJe3icg4CXz5gll67VqCRTFbm6GnyFNn8XFwb17Ql+Xbrtli+Ht2sdISytm/PhVzJkzn6VLl/Pq1QsaNaqHvb3ZG42byraLijqEu3tPnJ2vMW5cAaNHwyefFJGamkp0tCB7WFpLVGM1agjXlJoKFy5cZfnyFSQkPC/3fF+8WI5SqYuQevj0ExISpZV69vILEdWjki/tJS/+EtU8/JAYmZAVuxOlvAgjCxsce/+MxMiYzFN/4tq+M2YW9jqKWAqjWqxbOZtihVKX6C35CVIjYx2it8Lntxj+w1Tkqip651iepuh/jYeunRQF9YO1bzG27ftipSb81yS65v2+Ui8p+teOrWxcvwKFQomiZlMKH1+iv3sP9uzagEKhxOy9ZixeMIX167dSw17BgZuXsSlFKVD4Ig6lSgUFuWXG16zaeJIRf+7fOikaHLwGd3d5uZ6Su7ucmzfj2LNnL716eXHokILs7JIEnQaNICBZTIAXTJyoz7+ibZpEV1DQIspSaDc1Na70uNEksY3qtmXH9g0Y12un5nARCLny7p+jOPUF1q08SD+xAYtGHcmM2Ub+wwtYtehBevQ6kEjJvLRbC6Hghfz0Fp1Ee+a5cNGLT92/hIHfDGbUGL9yz9HOKpr3akwHSmIB2lQJmZlCUnC5QeXIEtNUT4IQSy9t0dGGt2ubt7dQAObhAfv353Hz5iZcXbcRGvoH7u7afP9/30OPj3/F4MHDDK7UfvxRYPAszdmibdWrQ0GBwO5Zo4ZKa9VY3vn+PQ8dhDj6qxd3UCDFvPZHyA6HCJWgTvVI3jUPM+cmpB0KpsaghRhZ2HDr3BauXo4Ra1kyIqbz/N5R8vJydaqUq/X0I3XvIpFXCsC6lQe5p//gq37eBs+pPPuvIedq1aEft6/uF/kRZEdX0bWbF/cfnCflfgwWLd2RHVlFs1bdDJbcv3qdSmFxcYl815ZJ7Nq5ncLiIp1to38cSn5BAQ59dYWmNUkxY+tqmDl/qIeg0IYtmXzUncOHttDly95vfc3/ZAw9LCy8wjJsYfINJyhoATt2/MHgwaMYOFCuw/+yfr2xGsnyB56eAypVACKTZQN5JCbu4N49/cLj7Bx4+vopUDGM9cLpcLFQIy3cH9WzK6Q8OCdyuCSHTkGRnoDs2BokJmbk3z2Dvb0D2Tky0o9vRCkvxMa6KtkXdpB/Pwarlm6kH9+Ao/c0QKNOtUFg7FSbjWtfzpw/h+unj3XIuTTnaGwko07N2bxX45HetWlPvra2wqT+JkUzhtpW9hhZWdphHAUTJuQxePB3xMaeUVMyvJsYenDwUtzdi7GzEybt6GhdpE7v3vqcLdqmwZRrJnvd0FFZ56uP7XyTGPqDezdYs2IuCqQ4qido7YIyp74/ixWjqft/p+jxZaxtbFHWbilO3Fbdfbm5J5Cq3UeTcy2KxM2TRAx7adhr5vF1dOoyuMwcW3n2X+OhA0wdP1ikz126dFUp2ssIfv99pUHiI6HgJAyLxiWwRwevKSRHzsXJe7RW9ZcXsmNrMKn2nl7MvahYgXkDVzEpmhg6FeuW7qRHr8XOAGxp+Kipfy+GLrdBpaCUvRsPvbLVdzJZDkKxUU9iY2PVSalwdUzUGh+fr4iNnYCLS301Kia7UlWjV670Jjv7ioEW9rxOLbvftD/PCthHMUYiSZKtux+yyNkYmVhiZFUNidQIy1Y9KTj7JxQXY1q3FZYZT5g6eTqz5wRQXJyP1QefUUOZhrWlBVdvXCfj9BYcvaeJFMzajJ0as27jRUb8eT1yLpVKhb3tHpwd/QH9sJOjY38yM/8S+71rV4iKqlylraZoxlBbW9s3O4aGBCsuTliFhYSsJShoqbrl3/fQw8J2MGxYMWPHCr8TEqKb6B07VkiArl9veELXxpRrm2bVaPh8/56HviRwEgojM8zrt9GBRafsmq+XO0k/tpox436ho2tLnbi7ib0z1YcJb2sjC3uSd84mZXegDmACIPXgUhyr2dPHu2e580NZ9l8TQz9z4Q7mVcxwqF5HLRhRl6SUDJJTs3iZomTIkO959Ciepb/NwrF6fXLylJy5cIdnT+6wYN50TGo2pvDFbfLux4haoTalZKHSDoVg3dKNgsdXkD+5jBIp6VHL+Kr/dyS+ekJWwiMKXtzGqkUP8uKikT+/RVV7ewpTX2De/Evy447Dq7t8/8NkkjJM3vqas7NPY2c1D4lE1/OwsmqKo6M7fzfGWVmd0dOnrZky5Sfi4+8QHLyKsLBtWmiYr/D1HaZOtMlJSEjg5s1b5QoRRESY0LChM02b6sonXbsGUwKqYes4n0s3cnRi49r3UuynM6c4F3OMKi5tyTy9mSoNXFHmyMi+fZwqdVqSe/sYRjaOZB4KoliupFofIZ6Zemk/Rw5GYlSnDSqVAsd+v/AqJpKXzx5h0bgToML2Ex8kEvUEoVKRff0g2Rd2ABJR/V1bH/bMhTtYWcqwMvsee5uTlPYWTUxq06bNUZydv2L58nVivzs7C2IfUim0aUOZphFvqF8f4uP126amwuPH5R8jLAzq1i3RctVIAfr6Klmy5AFTpvzEu4qhT5sWyO3bQqLXzU1XWq5NG2jWDBYvFrz2oUPRsdLShqVN0A7VP9+/G0Nv0PAj7t25SlbCI/IenNeSjdOV98s4tIwx434hKcMER0dhnjK2AAAgAElEQVRHMe4uuxUt5toKnt0kZdc8pMYmOPScoJ9jA9IfXeVlQirO7zUwOD/8n4ihV7RPO54aEb6SNWu2iJS4VRp2QP70CqbW1SjKTCI5ci7OWhwtIHhjVs27Uxx3jNE//YxKnsX27aGM/uln+vftxffff0fE9i38+cda5BfD+f33VUjNnHREN5YsWSHKzJUmXarMtUgkBXzUYCmojlHaJBJzatf241146D4+A4mK+rNcSKJQsj+QqKhTDB48CHd3uUE0TGjoVtzde+Dr66euBDQcm9fE2jdvrgXEi9sFMiVLjOu1IiJ8NWN8fzV4LzX9dPXKRTasWSTGLpNDJ5O8dSoqJDqUt+n7F2NjW5VCJyE8Vvj8NvLcDBz7zhDbyA6toDhbplNprI16EZKi0Xz0QVNyki7plY43b/I+jlW3UqNaOIaW/XXqzKRu3SlqWGSBTr87OwuiyCEhAlyvPCTRzJmCWtS+ffpty6I21j7Gnj2CR6wxTRhHexWmGRu69ubjy8rKBA8Pw8goKKFU3rVLwJJrwnd79giY9NLShtpW9vn+zRh607p06byLGT/7c+HCCWS75lJ9uDA/aMJu5GcwefJ0Hdrdq1cuivwsGks7LCRFysSwt/Ei7+4ZHsadRlHQh6VBixgybKJOLB8kZfTef1EM/U3iqa+2BegRcMm2TYOsNArlRYa1Qlt7kXkunIEDRpKcaSqS5eyM3EvEtl6M/NGfJi0+x2uAE9XtilkQOJdWHfoBnUXRDU3s/m2vxc7SH1T6sVcHh558+OHvGBk58C5i6L6+o3B1DS0TknjyJOzZo8TIaAurV681iBsuiWsOYseOcPbs2YtSqWLcOAEN8+mnMHCgULUXFVUSa3d23oBMTQ5awoz3c5kkRq+2BbAsOBgT60aAPmbY3nOyHh+1VYsepJ/YQGZWJhbye8jCp1GYn4NpzYYiv3U1j/Gk7A7EUStXYtWiB7JjqwEl1upEu3Vbb+6e2cKCxRt1SsfNrQspzP2CGtVSgRKaYU3MWFjFvMTX974Y8y3d7x4eQj/4+wufNbTE2uINhYUCLluoFIXJk6FPHyGcUV3t/LVoIXi1ffoISkCaY2iUgAoLdSdJTQhGCIFZoU9prDtuhBXaHzr1Cp6ebiiVcPDgIa1VWx8UChU9e+qPKW0TYugSRo82JienGFM1enX+fGjduuzvlX2+fy+GDkIc/eLF05g3/JiilCeoVEothk1XChPusmXrFpF2t6z6FomRMRaNOpYSt1iKResSDLt1aw+yjm8Q2BjrubJieSAOjst49OAW61YHIq1iVfqNKdr/CQ993uxjItZcQ4ajT8DlhuzoyjL5PazbeJJ79xSHD+1gxuxV4hv4wulwTOq76nn9RvXaEndtP/4TvkWqliR/kxWFoX352TmUtsaNV1Oz5rfol3rD23roLi4fEhr6p4hD1010GnHhgoK+fUEmy8fevnwBi+bNi/D27o+3t4Tly4vFeOm+fUKs1MzMnGHDfMRY+82bG8XvazPjlUViZNasB2dORTBrbm8dzPDL0MlU7TnJYGFXevQ6VColZg07IE2+T4ePGnLq/HkKX97FolFHUvctocaQ3/S+Jzu6ioEDBnPm3Dky4i+K3vjwUVNp1cxFXTruRbNGEaAMFb+rTTNcEjPOFdEZixcv4caNy4SF7SA9PZ/Jk6F3b4EqoXt3of2GDRL27lVRVCRU33btKnjVpb3V7Gy4elXQcNUkGzt0EKiN5XJ9AYiZM4UXgrZp4tQHDxqrsd5le+hRUYcNrtD27v2LAweEVUaPHppV2zYUCjnPn5cf069eHYqKIC9Pw91TwPjx07h69U9at6541fiuPfSrVy6ybtUCHNTJT0MMm0lh00hJz+BB3Fk+7fCZwfqWzMPBWH7wGXnPbpK4ZQrWrTyQHV3FtGkz2bI1lNQHMZi39CDr+DpQKbHrNUPtbAYQtWcDp0+fxK7XNNKPbyizDyos/f93tQf3bvDtt/14+eKZuO3qlYt8+20/kpN0uMO0aAGmi7QA1b4NKkULsBIz56Y6NyBhzQiyLmmpfbfuiSw9nZPRe3XKge3dxpGYU0zg/F9YtzoQG6+p2LuNI70IdkSE8k+auXn9ihu9hbm7dyM29iIODsPw87PBzU2Cr68Vly9LWLIERo5UcPFiiRdpyBIS4OpVBYGBcjGMYGRUAlH77TcwNpYwbtxoraKWEps3u5Ca8hgytk8V75vjUF06h7zTf/DLLyUzUu336vDjqJ/IT3xMyq55esdM3f8bSnkhTv1mUM1jPHlSM6KPHaK4IA+n/r9QzWM8qJRkX95X6ntLGDhgMKNG+zFpaiCDvXvC5QjmL/idRh+0ENu51J6sM5lry/CNHIlOH4wYUcycOXmMHTuGp083ExSUzdGjQtubN2H4cOjRQ0JgoA1ffjkSCwtLtmwReL3HjjU8KX7zDSQnC22io4X/J04UJvOxY3W3jx0LV67oJhk1YZymTSEyUk7z5i3LvL/Hj5/i66+/Qi7PJzy8mHHjhBg3CAybCxfC2rUlSdkRI4r57TcIDBT6pSwTPG1rnW2+vr5ERZkQF2f4Oxqit3EaDOdbmqF5ZcGCWRRLjEtUiDzGU5QULyZEJVIjrFq4UVxUwPbtwr3XUE3IwgV6iaz9i+jf2wenrAeYGRthXLUmsqOrGDPaFzd3byb7B/Jdv95wOYKqVati1rCEPsTGbTwx129j4zVVGPsS/ReUxv4jk6JnzpzSAezXa9iWyF37BCV3xw+5cnIfjZt0IDk1k6SUDK7efkmzpo25fPoAeY+vYNVKdxZK3DoFY7saFKe9IO9+DBIjE9IOhWDZ9AuyL+8m7+FFJEbGpB/fgFULNx5cOMSly7HIazYVClOkRhjXasKDU7uwdStRS1eWSo5VJvFZ3j7LKn9RxUy3FL5GjW+oUqUm77LwQ/PZ3t4eN7duTJkygZkzf+LVqxTef/+mWBy0dq3w4ErLcAsqU9RSUkzUFZCTlPQX+flCDN3GBjzcFNy96ciTc6fFxJLGUrYF8FX/odSu10xnbCycHwBGJjj09DOQdFJRnPIMuy7DkUqNqfJ+c/Kf3sCh5wQdlXvtgiIAJBISb5+jXsO2xMTep02btnzexQupsZXOParlsEInWb1lCzRpIsXNzbCQjJOTIAYikSjp2lXoy5o1BW++VSs4f96cixdP4ePTn+nT55Xb3yCEs9av100ovn4tTNKDB+u2jYuDVatgwgTIyYFt24QJuXVrITw0ZAgsXXqc/v29ShXtyImKOkC/fgPw9FTg5yeMg88+ExKwQUECdXKrViWFT5qEq5OTsEK4e7dkW2nbvt2YDh18cHP7Au2x2Lx5c8aNO0B2toTq1ZViIVtEhAlr1pjy6acfM2vWHAICZusUHOXkbK1UUrT0vKIwqsXzJ3eJOXsMs3ptyDi9CfMGrphWq411Kw9dwMTBIIxUcr4bMZk7j2RiUvTpo6ek3zrMsJGTyCy0pV/fr5AW5/P81jnaf/oNn37eg6SUDM5evCeOqQ+atOLKiX3k3j2JiXMTTOydsWjhJv5eVuwuZvpPnG2o7/7jQi6ahJg2YD9qzwYunD4uxlVl2wJ0YGMP7t1g7YoFFCtVOHX7Qe9cbNr1Jv/yLiEpmp2K7NgarFt5kHPtACZWVSnOSkZ2VNhWHHeM4aOmGoQllabAzDm1gYULlxksAX+Tay4JuRgiojLlXSatymsXFrZDB59eESSuMkUtAp49kqCglUJcsjBJZ//Nm3DlygtsvAxxS3sTG3uc4cOHAUI/zZ31I3IlOPUrizjJm9w7p0hYPYIaA+YZDsmUwpYXPLtJzu3jFBlLxSW1IW57lSoDVZEuikfog/JJljw9S6TVtK00FK+y0E8NBFFj+/YJ27STjAcPGrNrlwKlUsWwYYJwhFIpMBs6OJQU9mRlaX5/AZrxIHjmw5BKFUREwOHDJRjy0pQE2rJxGvPyEv42BEuMixNWBitWtKH0WNRw8YeErMXPbxsyWTb29lZ06tQJleok1tZnDCTmtxEQYErbUvo+pUMupZPpmkKgAzcvi0IUrzdPJCk8gPd+0l11p+5fgrmpCXPn/07rNu11AA8jRoyiRVNB3E2zvVWzCfhNmKDT7sG9GywJnMSC+b/TomknHByDCN24kKulGD0B5JmJZQ6oCiXo/il7v25D1ZQZwgSoIU8q/dnQvtOHV5NbtZ4oLVcsSyBj3yKsvyjBA+fcikZ+MZxfAzfw4N4NVgbPAXVpbVmyUylbp2BnYkpudgrNW7TlypWLfD/Cj4SXTzl+bC8OTi6kpTzm2+98Scow4dMOTVAqFSxdMoeU7HQ92t2ktSNo1qwz3347pFLXVZl971f/FltrXTnBli33Y2fXkRLOa41p/13W54rbaRN0paXlYGsL3boJD+/u3WBqKjzEhqxrVwGZUBkJuszMs1y/3p/i4tfivsrIhcm2BdCtYyfSs6Xcu3WMoqICipw+EGUCC57dJDVqGTZteonUuDm3o8mKXouxrRNO3+m+cRLWjMC200CUeVlkxIRTxaUthU+vC4mv1/exUBXRqn1vIXdSz5Wq+a9o33kovbq/xtkpBKlEtzigsn3Qo4cw+Ze2hATw87MmOfkp48dPIS1tMyNGyMV92olWW1sh3l6njpBIBWGCnDwZ3N3dOXv2rA5x1ebN4YSE5FUo3yb8/h3AgqioY3z99SA8PeV4eupiyA8cKCELW7dOiIOPGqV/bXI5dO8uhIcMJWl9fGD7dvNSRUKGx298/B1cXbvrVZ5qLC7OcOXpw+fBFBS5iM/XvNm+evOKbE8gtl1H6swrGWe26MnKZcZGwu3DDPp2NH9F/EGrDv3w6CFQQeyM3Mu9W8cY+aM/959kGnzOH9y7weoV86nSoANV818xaepCgpfO40n8NUGYR0vmMP3EBuQ5MhQ5MoNxl/84D71395A38oyXBE4CU3OxKEDTJvVgEDZtvcWH3KJVT/IuhLNv/3FAF1ZY+m1aHixJY5atvUh8GkOzD99/h0nR/1kPXYAkDi5F0KVbALJ2rWFIXEICVKkiIC+0KQF699ZHVNjZmXDpUie9KxOSop/oJ5Zae2HZuoQ47fjRLRQWFWFS3xWH/ASMFam8UKuly46twcjSjrwHMeQ9iBFK9Y+tQSqRYNNF/01k1cqDzLPhyHNlWDb+hLyH5wWky3vNSNzsR6O67+sgpjIiArA1+p33qifpHcvY2ImqVXNITCx/0jTkVWtMG4qnDf3MyTGUaBUUog4dEu7R8+cCEiYgYBozZ/6idVQhgR4SsvENCsgsxLL9wEB5hWRhGs+8d2/9a0tKEkJDhpK0+isDQ0VNJZ+Dg/+okKbCUOVpaQ+9NAGXdiEQlK9CZNPWm8Q7p1i7Yj5mjT4WwRDXr13i3MmtmLq4snXTMsZPDhTnjaVBi/isswdzZwWTmZkprgzSwv2Z6vs1xUqlFlx2GrKja8i7d0YghJOVnYD4j4uhlwXY11haxM80b9mNlq0/JiklgwYNP+La5QvkJz8h/+FFkBiRdiiEqp8PJffOCXJuHkViZETW8XW06/QV9g7V9eL1s/81lZxCC2xs7EQisGVLf+HAgV1U9Tbs9ZvUaEjq1aOkvk7EwrrGf1wMPT7+Du7ufQwSdGkXgPzwg/B/drYwqVhYCBPKrFmC9zVxouH4qoZ8KSLCmPfeK9aLp+blN8Kplj+Xjx8h9+5JlEjJOhJC85bdKHxxmfQb0SJxmlyhoKp3ANatPUm7EY2tmQlJzx9S9Po+Dr2mUPT6IeaNOmLqUIeMM1tQFBfiUAaJm1nNRuTcPo5Fg/ZU6zGW/IcXkFrYUqV2E6q835wXsYd18iQKlZR7J8/wdX9dz7x2bV9atNjGnTt3CA+/x8aNwstvzx6h2MfZuaQ4RlMcZCimrF3AZW9vTfPmjRkz5iAnTypYsEC/OKddO4Gu+NdfQaFwYcuWP/juO2+D9/zNCsh+YPbsBTg7X6uQYC0uDjp3FmL5Zmb617Z1q5An8PMTvPShQ4X/XV1L+qSsIqHS43fIkJGMG1dQ7jVoCqW++aZkW+kYujYBl6F5JTFsKnafD8OyUUcKnt0kafvPoFKJxWQSY1PyX96l+oB5pF49ys1L59myZR32vQXCrcRLB3jx5Bm5uXksCpxBkeMHXD2xm7xiJWb12mLdVoDBmtRuSs6jWBx7TRHHGBIjsi5sx6nvDKzbeJJ14S9m/TLjvyOG/saecdO6ODiu4ODuDZw8d47Mc+Fidtqyyee8XjmEwpgtLFoUrEcLIBawaEEQr1+7pKNcYlpbWEIZot21aFW+LuC/s4deWc/nyRMB+rZ2LezcKWCazcwwiE0v7cVlZAhQs+BgfdIuS9uddPlcStVqDUXF84ULl+kVa9lXcyTXvqEOtPHxzl+p/vVsccJWZKeReS4c51HrybkdjblTvXLVimzaeZMZE65GNnmSHr0WCxdXg6vBvNPrmP+rrjizre0nNGiwgKiow+zde4ju3aFXL/0S94AAsLLSV9TRttJQPHf3nvTp05uCggiaNjUcLm3aFPr3N8HBoStdunxJWZDWNykgAwu9HIoh0/bMra31r01TyFRUJJCPGVq1QUVFTSWf31QkWmOGYIvlzSvWbb3JOLERVJBxciNVuwwn58ZhcuOOC4LzR9fg2Gc6EqkRVXv6cXvfQux7lzh7Vq17ce/keh7GnRVXd0WylwJJ3K2jJG+ZhL2nwO3iPHKN+LsauKzdp9+Kx5Kal/32+o8rLKqMIEXK3TMiOTzArt0HuHD6BFVLg/ylRli3/xrLZzFITB3ZGbmXebOFeNeZmCtcOB2ORfuvyLl9gmTAf6ofN27EijckactEXq0chsTEDElhNt8MGMWBqN0k3zqCvLgQVV4Wo8fNMKgL+DaFRe9XL8JMb04voqLCj7cRIAgLi6jUw/vjj0KM2MMDfvlFgMOZmZWPTffwgPnzJSQmVmHTpjVYWQ3Ra3fzzlNAqqd4rukPTbFW43q2/LkxSEefsZaWuInmgbD6qCsqpQJHb3+SI+eSuMkP6za9kB1dham1Pbl3ToohGQ0BlyZ816hBY57uCdRZggNkHQ7Cd2w+LUsh+yQSKfHxdxk8eBBz5xaU+WLz94fiYgGeaCgkU6K5+gPaBWP79x8gKKj83Jc2eVpZ97yiAjLd389DJsup9OS5b5/wch83TtiWkCBM5AcPgq+vBpsuhIiGDxeSsm5uJZN72UVCutfypiLRGitdWFTRvGLT1pu8u2fIOPWHjkOYujuQ9KOrqVbNkTwtXWLHocHid4VwzXqMqlhi4+5bwv3S0oPMc+E4DQwkeetUPW4XjaShbacB2LQT5rKs2F0oslPKvNb/OA+9MoIU2p6xpvinrBulobM9cWgrF04fFIuEMjKykDg10BEyiHvyROc4Vq2FCcGibiuq5r1k+PBh2Fa1Z+2qQMzqu2KZ8YR+fbzeXQw953/OQ6+M56NQCBOStjd+4kQJssVQ0q5rV+jYEfbvN+batVjq1q3JmTP6x27epK6oFFRR33Tp3IFp/hMN6jOm7ltMxw6fkypLIDHiZ0w++hJpfgZuPXoScy6CMeN+4dG9S5y9dgvTRp3IjAkXCbiS1o6gX5+v2LV7J7alqJIBLFp7smvfVrp3V2hBCU2pW/cXZs2qmILY01MIQ4WFgUwmoD80CcK9e4UJ0MxMRXDwGnx9fUWs/puSpwmmf8/LKyATNF+NCQ3diovLh0BBpVE21tawf78R7u5u/PnnaX7/PQdjYxWdOglyctpMiZpw3PTpwgtAs3K5dausIiHdzz4+X1daOUvbSnvoZRUCWbTyxEoNTbZu40lmTLiOIIpD72nItgXQ37snTx4/JMbQGIxaRtWuI7D6qOQkNI6GffcxKHPTUUkk2HcbqbNfI2mYFbsL63a9KXpxh4yzW5GaWZV5rf9xHvqQYRNFj8yihTs5pzbw1dcjOBC1G9nDGMya9yAzeh0/jp3OjbinBC6cV8aN8hJfANT4kL37d+PUf6ZYTm5jbEFRwl0c1VSrSWEBmDbuBCp4/ed4rNt6Izu6ErtPv8XEsQ6v9i5i/LhR3Ll7U0xwpGz1L1NG6k09dGvLGOrWTDTQk8W8Sw9dg2oxMRGqFMtKZgKEhwvbtSctDV2r4erIEum03NxiXFxqoVDoS8xBiYdemb45fiSSS5fPYt99jLhPAz+0bNaVuw+vMmDAd2z5M5iic6F07OxDN49evF+/ORvXB1FclIt9b6HEX8PTAmDesifbdoTjUAY6yrK1N4nbT/LXzid8/ZUKe/uuNGkSgrFxdcLC+lS4wvHyEjzYCRNgyRIjDh0yISurAEtLgRph3TqQSgtKScV9XGmvtDJl++7uH6vl21bh5xehRsFY4ePzNbGx36kFvIVjVEZ2cN8+kMuN2LEjTJQTHD9+FmlpZX9P83IrKhKKqgICQCqVcuWKZmViePzGxz8hI0PGzp3FdOxYPt9N6ZCWxkPXrMr79f+OvXvDxXkl/egqmrbswr0LEeTcOYV1G0/Sj2/AtsNXJK0dgXkrT7EGxax5D7ZsXo9coTAcrmndk5wbh7Fs+oXopKTuW4yxbXUyYsJRFebqsDZqJnNNFWri5okkhU5FnpGIU/+Z5VaK/q/BFj/48CPV2o0RAAYRJBoztK+EEvcgv/wyl1atXbl2K16MtQ4cPFYkh3/54hn+AZPJUkgxafolOac2MHHiNHZGRvAqqwiz5j1IP7oai8adKoRCpp/6E5QKzF1cyX94HmOH97H7ZBCpexdh7tKOgseXqNZrKuZ1Worf4UoEkbuOVOq6DO27ffcWTVwWg0qfTtbEpCbt21/F2NgG/Tip9t9lfdb9Oypqv463VhYkTWOenroeFwhivjNmCJN2aek0jWmgdLdv31J76A56bSSmt5BIpBX2272bJ1m1OgSLxp104F0ajg15+iskSjkK2UvMGn2MXfYTiuUqhg8byW+/zce4XjuKUp5Q87tl4sOmMZVSQeKWyVg0+Qzbdn3E1aB5S3csW3uLEEgur+Tapdm89944sT+NjKw4ckRVKbiinZ0FixcvYMqUAObMySuzz2bOtCA29jTBwWtISys/9r1+vQkODsPUKJHKjo3y9hUQH/8KV9f25Z7jtGnG7Nmzly5dOovfc3SsS1BQ+Z59QoLwcouMhJUrobjYjV27dpZ5TgLlgIDAqlWrmLVrhVCehsOmZJVhosahZ+j8nsTkL65dyxL4Uuq7UkuZysqVfzJr1s+cj4nGtHYTHKUFzJ2zgEmTxiJLT8eqpTvFcceY4DeV0LBQMuVSTD76kqzoNUilRth5B5QJr00KC8CicSfRYciMjSQzZpuoRVxNPfcUPLtJcuSvWDT6WITeFssSSN75K9V6jKHK+815vWkCha8fGoQt/keW/hsZGdHly95E7jpCq9ZC+lwqNeKbAUOI3HVEpxS79nt1mOyvLtW+EsEPowNwc/dm9erNdOvYCS5HMM1/plimW1aJuezYGlAU49h7GtXcx2Fs74wi+TEpkXPV23wxrlqb4uRn4ndyTm3QKUt/U1Opsmhc53uDk3nNmiPo2FEzmf99i49/zODB3zFnTp5emf7IkcLkvGBBScl2XBzk5emLJ2g4RjTKO9qWkCB4Sr/8IiyvW7ZszZgxg8otAy/PHty7weo1ITj1n0k1d19QQfKOf5G8c454nwCkVWuhNDXHoklnEl69IseuLgsX/YptL4GES2pShezL+wTPaP1Ici5r0T208SQrZptYvj1+zDiqJvyFbOtPpG6bTPaxpciSC2nQYBo2NnYMHTqK+PjH6vBE+eeflASWlibExl7kxo0buLuXz0Lo7l5MSMiq/7Ey+NLm4lKf0NBQZs60YN06YxIShJdSQoJQcTptmjHLlv2mNZkL9iaxdxBIyGJizpXZVhirg8Wx6uEhjKviYuGl0KOHJi7fj9jYi3TsaKZ3jGtXb+tRdyxe+C8uXzqDY/+ZOPWfRXoRXLx4jogdB/lx1E9IHp1lQeBS3Ny9degfrGyqYuLSXicKkLR2OJmxkeI4smrZg+yrJXQSNm29MXF4D4tGnZDLXpEUJlAEJO+cg3HVWuQ/vkzipokUyxJQZKchNTHDyKoaAMpCw6ta+A8sLHrTdpU9RnlFQglrRgBg9t5H4pu0WJZASuRc7LuP1vHiNWiKd1FYVMU0nobv+5bqOSlt257Ayuoj/k7BUOl2pYtWDNnatQIyxd5eYEdUKmH58nwdzyshQXiYNmzQ9dy1QzAeHmUXpADkF1Tn0csNgKTcfpsRMAplzcbYuxv2ZEC4JwJPTxORf8OsdlMSt07F8oPPsGnnLa6+pIpiBgz8gVOnD5FeBGbNe5BzcgNt233K1asXGfHDJBo2bs7LR0NYHpKGp6cueuXAASH2LZGY0q3b51hanqiEFz2EoKBFODrWISio/DCKUORjRXLyM7WI93e4u8vLiH2vwd1dQ2tY2bFR3r6Szxs3huLrOxGJREFeXgkBmImJMWfPmqhDQ93E7zk6flipa9N46JpiM7k81eB5VBTCAUEty8FhKEFBi4iJ+ZDi4mSd/QOHvEeOXeNKFyhC2c/swcOnuH11PxnFAllc5vF1WJhbkCtXYWzrKMoZSo2MsWn/lRjqzbkdTWZMOPbdfyJ1/2KQSLDrPIzcG4dRKIpR5WejUspBXiSsNjNeUd0nkOeLe6NSKv47Covelg+9omOUC1lq7UX29SgKHl4gMfUFDp4TBTSFFmd66ZLxd1FYpFLmoyo1Zk1MHLCy0q5j/vsl/VBFDUkrmcwNJTTbtxd4t0ePHkps7ASCg4P1YG/OzkIsVNsj0yaoqgjK6Fzbg/iEEbRoWk9sp11mX92umCWBkxjsMxSJSk7evRgKXj1AIpHg2OdnnPUQLisxrfkBiuxUXXUZNcLA1KkeWSfWYVWlCkOHTxW57TWwyMDAIJ1y7pcv4pmxKo1Fi/33/n8AAA8CSURBVPSvpWdPYVKNiSliz54jmJoKOO7vv9fPP2j432NjJ/BmKlG5lFcG7+MzkNjYcbi41Krwnr/5virExz9mypQAFi5UGFhNyOneXc7gwcOIjb0oJnErA4/UTlyWkHMZPo/KwCfd3eVqfdpgDLEtLlk0ldm/bngj6g4o+5nVqKVpNBI6tGvBrH8F8Ox1Iukn/8QIJd98/T0nT+4nUQN1PLIK69aepO5bVAKDvHkYyxZfIju0AiQSpCZmerJ35dl/XFL0TdtV5hgVQZas23iSczuaRg2b8PTpA1Ii5+pM5qAvR1YaPvk251vF9BUN39c7HSrmPC9vn+F22svi8hKaEgn06NEZF5daZcLe7Ox0OV60xY8NmQbKuCn0Y7z6jeXMhTuoVMIyWcNlb1LPFb+JY0lLTcakZmMCF87BvGFHKLiGPCsVy0YdSYtaRnWfQDEWnnYoGMsPP6fg+Q2cR5UoOGi/fNMPBvHNgB9w/bgrZy7cEcdkaQ77MxfuYG52l6jd0/DwEM5Z+6WXkSHQINSvL/B2t2hRAssbNUqgke3evbQX/Yd64i14g0SnJZr77+JSnaCgBWpoouZeanvU5d/zN9snfNZoglYcGgpSi33r87yXttKJy6goY3x8vqKscV7ZEI6gT1uAIT70qzfuk5WVhY2RBRn7FuE4dJk4Lhx6TSXzcDDN27gjNXOqcJ7aGbmXeery/llz13Lmwh3Ox14nNTkZRW4mGJlgZ+/I88QcEl8nYPJeM2RHVmFWpwV598/qwCCzr+wn49RmJEbGGGnpE2tk71L3LsK4as0yr/v/e+jlQJa0S8xt2nlz78gqTM3MsDcggGHVuicZ0etQ5mVj3dbrnRQWGfLQBXu3pFtQRYSkQcXe9ODBo4iNjVXD3raKfNiapX/79oJ82qhRwncrQ9LVsyeM873JjJl1xevXhpwK2X4/TGo1pijxkYg+er3JD4tGH2Lf7Qc9RSGr1j3JPBuGY9+fdX5L++VriOBL2wTSLRW1HJZTzS5KhGVWhOLRhJA0sLzJkwV4Z7VqNvj4DCA29gc1HFCwysDvhCKfb3hX9/xtjlE577hYyztGZ5y4uRXj4SHX42/RKBGVXrkYOo/K69NqvHxdD/3aNVi1fDNGdduR9/A8Dn2mi8gScxdX0qKWYdnKk8Rn53RW2GC4yNGQJsLaFfMEMkA1ci4jYjqXzu7ArEEHipKfYPHBJzqJ0IQ1I8Titty445g41sWmw1eiPrFDTz9RUPr1pgllXvf/aQ+9IshS0r1jJN4uqQQDsPXyL0M6qheF985ScGU3xY/O6cEn/909dAGStpnCQnmF3rS2B+bu3pnY2COEhPwpwt5sbS0oKirkk08EHHal1eszcnX6ShtyKpFIcejlT8qehTqhE5u23mSeC1dzUvcg81y4OKHbtOlF3t3TFCc/E5FHIITQNDAyqzaevNoWo6N6pDHNeRgbyfiwXhQgXItS+QYhJGehTb9+Jjg4+BAU9LtWvxeI3/X1HabmaamoyOc7DN//0n//Mx76m3vHwve0x8m4cWFkZOSL8MzgYCEBv359Sfxfs3IxdB6VgU/qevklHnoJ4VtJtWbu7ZOlxCoCUKpUpOYrdQoUyytyLK2EhoUdFlretVV3X1J3/oo86TFIpRSnPCUpbBpWzQX1rKpdhpN1aTe5ccex+PBTss5FoMpIwLJ5D9KPrES2ZwHVhy0vv+P5D4Utvkm7svZdvXKxQsiSSiknuUCCMj8TpEZ68KK0Q8FYteqJjZqHIed2NPIL4QwaNFQPPvk256tS3kVV3F+n30xMnOjU6Yn6r7eDnxlqFx//GFdXV+TyfFauLF9RRkjO2ZCc/FrnGNqftZVs9u8v1mO7M3TMcb5W7N5/USteXQI5teo+TodCFHTxuqggZfd8MDLBtn1/HWbFzJhwnZCLSqkgMXQKFh98iq1rHzX0MIJZc9cavg+q16iKhCRf376C521vXzbLJJSwDWoIoUpYCzXQF/37oE2Gpp/oNCE0NBR39856fa19jDeFqr7pMRwda1QKglgyPgwfLz7+MSEhQYSF7dCJ/48bZyj+/+bwyZkzzdWryPrExNQTk6JDh5shs+yEvfsEMRmauneRTrhUA26w7TQQLguwY9B/ZpcETkJmUVsnsZp1YDGWnb/HyKoaaQeXoVIpcOg5URy7KqWCtKgQChMfYd38S7Kv7qOauno0ed0IOrZpw42b1/nGZzQosgndsoH8ggIdGoHyYIv/Jz10Q2/W6QGTuXr1Ao7qJVLKVn86tWgJ92+TYWyPRccBZMZs03mrWjXvTlZMOEUPYzBv4U7OyQ10+GwgHzQTStW1Y7L/7h66i0stQkPX0LPnkDf0wAwfX9sjUyo3s3dvMaP1I1WiHTgALdt01lvNdOg8lOf3j3JbHefUNu3QyctVw1Ap5Nh3HUnOjcNiGb/syGrM6jTn5Yoh/6+984+J8rwD+Od7x297ggTKUqmy0narc40FZ9c0qWUsi9biNMuMNmzpatzqVrPZpcnWpa7arNous5eSrU5xPzJTqrbdZqxUtkWGMh1DBRLZL6a2UqqAtRQ6Bxz33R/vAXfcTyjcHcfzSd7kfd977rnvfd/n/b7P832f7/fB8ZnVIw9fR9EKrv1xN/aM2fTV7mXDo98Leh2Sk7r4ZIF1rrQUqqut+fehGJsHfDRyM3jv2tJZHRUVFWze/LpPqtuGho1eqWQJWkf4/Yl+5j2Si8Q1NNw7DlxfYeFNOJ1Pe/zsw58F8v/7y2G11V96ZvkMsny5K+Asn4KCbFpbv+Yzw+VHW/vZ9mw9HS93kLl81I0xjDVFeReOohU+I2wIHeQ4nHbCO0o0896H6HptK9d+v4MbPWkj+i+dGxkN+AeylfHv8/U8tW0X9Q3/IC/LRv/AgI8xD8eM9KF7+8yDrTGasWg5Z08f4OCr1Ty3fQvH635N3ld20nv68EiCr96aCtau+zpZmans37+PHTucfgm+Poq80fShg5X4ac6cWVy+/OE4/JPB6y8sXIDT+SKbNn2HJUvu5r77gveojhyx8dKex5ibbz3BvBP/v9HSyOwAofferhNHcRl9TUf578kqZi1agVuhp74KR9EKes8cpuT+UupOHMDV9hdS71zGh3/+BWUPlFFbV+U3k8Ubq4eeig5Yx6tWWUnIxpsQajRyM/R1KCy8A6dzJ07nzzznxrNWbKjPJqcOK4XvK2FcQ2N94JMvrzXLp4GKCiebNx/0m+WTnd1Gff0ncLt91+HNz4ftzxazZ3dOwDD9nqMvUrL0czS3nOTRbz05MsIeZjxpJ3re3Ik9OZXZpdZQzntEGSoNSah1SXv/UMHQQPCH6bQMLPqoeK/3F2yN0eGgoOamRurqanGUrB95OTr3G5WkzbuT9EUPcvxEDV9eU87rv62hqPjuML88XtJwDTkYfakjpKUVTPJv+FJe/hDV1ckhy1g9sLUR1zkakJJOZWWyT0DKnj2Wv/n7T5aMGPNhzpz+68garcFmH6FYL0IXfxFbSir3FBeTc+U0rraTZN67jr4zh1m1upwfbn2empp6vrp6Ja5TVWzf/gKPP/EUz+zYG8F1S2LQlQVYbiOHg4iChrwTQlk6WxOJuuKaUNeysjKZLVvS2bdvX8A1YqdCFqfzeTo738Xl6qOz8yJO5wsUFt5Cb2+TnzGHdEh6idoTD1J3vI5ZSx/xqzPjrjLaO97h1dfe9AlQDEZzUyPNzQ1+df3vrRYG+/t9OonXju0l/VZfA3151yM+gWzJC78Qcl3Sb39zE0N9V8cOYUaImQ9dRLqAt8IWnFoKsCfPScmd7/NgG+i84MY99DYwAHJrUvZNNltKuv+3VRm8esmtroEOwHuFgxyg2/8L04JUERbk52NLD/CXr1+H9nbcqrQC/f4lQvIxEVKAbFXsIgwB76lyJXBd8ilb2g1pSZl5IFaEnOuDK9gzsrBnZIIIQ9c/YKjvPVJyC6z9nq5B0BYgDyQP9ALQO045QyLCvKwscnJzA0xw9tDdDaqQmxuRzqZje0kVIY+Ir+WEmAq9OCZ4T4+rrsHuS0hyCkmzvdru+++6sdltYrNjS5/NUG+35VQX6Rd7SpptVqZtqKfLDdqGb5sd25bnq2puIIFiZtATGRFpVNXF4UvOLIxeAmP0Ehijl/EzI10uBoPBkIgYg24wGAwJgjHoU8PuWAsQpxi9BMboJTBGL+PE+NANBoMhQTA9dIPBYEgQjEE3GAyGBMEY9AkiIstE5J8i0iYifknUReRxEWkVkRYR+ZOIzI+FnNEmnF68yn1JRFREZsy0tEh0IyJrPO3mnIi8HG0ZY0EE99I8ETkmImc999MDsZBzWqCqZhvnBtiB/wC3AClAM7BgTJkSIMOzvxHYH2u540EvnnIOoA44BSyOtdzxohvgNuAsMMdzfGOs5Y4TvewGNnr2FwAXYy13vG6mhz4xlgBtqnpeVQeAVwCfpA+qekxVh0N0TwH5UZYxFoTVi4dngOfwzh+b+ESimw3AT1X1GoCqdpL4RKIXBYYXz80EOqIo37TCGPSJMRe45HXc7jkXjPVA9ZRKFB+E1YuIFAE3q+ob0RQsDoikzdwO3C4i9SJySkSWRU262BGJXp4GykWkHTgCTO7q1wlEzLItzhREpBxYDCwNVzbREWttuJ3AwzEWJV5JwnK73I81oqsTkU+r6vsxlSr2rAN+pao/EZF7gN+IyEJVdcdasHjD9NAnxjvAzV7H+Z5zPojI54EfACtVdbISFsUz4fTiABYCtSJyEfgscGiGvBiNpM20A4dUdVBVLwD/wjLwiUwkelkPHABQ1ZNYuXRzoiLdNMMY9InxN+A2Efm4iKQAa4FD3gVE5C7g51jGfCb4QiGMXlS1R1VzVLVAVQuw3i2sVNXG2IgbVcK2GeB3WL1zRCQHywVzPppCxoBI9PI2UAogIndgGfSuqEo5TTAGfQKoqgt4DDgK/B04oKrnRGSbiKz0FPsxcANwUESaRGRsI004ItTLjCRC3RwFropIK3AMeEJVr8ZG4ugQoV6+C2wQkWagCnhYPVNeDL6Y0H+DwWBIEEwP3WAwGBIEY9ANBoMhQTAG3WAwGBIEY9ANBoMhQTAG3WAwGBIEY9ANBoMhQTAG3WAwGBKE/wOpDOOOilcceAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# SVM Parameters\n", "C = 1\n", @@ -326,9 +409,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "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 +452,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -406,12 +502,20 @@ " np.mean(predictions != yval)\n", " \"\"\"\n", " # You need to return the following variables correctly.\n", - " C = 1\n", - " sigma = 0.3\n", "\n", " # ====================== YOUR CODE HERE ======================\n", - "\n", - " \n", + " c_sigma = [0.1, 0.3, 1, 3, 10, 30, 100, 300]\n", + " error = float('inf')\n", + " for i in c_sigma:\n", + " for j in c_sigma:\n", + " model = utils.svmTrain(X, y, i, gaussianKernel, args=(j,))\n", + " pred = utils.svmPredict(model, Xval)\n", + " error_temp = np.mean(pred != yval)\n", + " if(error_temp < error):\n", + " error = error_temp\n", + " C = i\n", + " sigma = j\n", + " print(i, j, C, sigma)\n", " \n", " # ============================================================\n", " return C, sigma" @@ -426,9 +530,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.1 0.1 0.1 0.1\n", + "0.3 0.1 0.3 0.1\n", + "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 +577,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise support-vector-machines\n", + "\n", + "Use token from last successful submission (ajasmineflower@gmail.com)? (Y/n): Y\n", + " Part Name | Score | Feedback\n", + " --------- | ----- | --------\n", + " Gaussian Kernel | 25 / 25 | Nice work!\n", + " Parameters (C, sigma) for Dataset 3 | 25 / 25 | Nice work!\n", + " Email Processing | 0 / 25 | \n", + " Email Feature Extraction | 0 / 25 | \n", + " --------------------------------\n", + " | 50 / 100 | \n", + "\n" + ] + } + ], "source": [ "grader[2] = lambda : (C, sigma)\n", "grader.grade()" @@ -555,7 +701,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -643,6 +789,11 @@ " # Stem the email contents word by word\n", " stemmer = utils.PorterStemmer()\n", " processed_email = []\n", + " \n", + " vocab_to_index = {}\n", + " for i in range(len(vocabList)):\n", + " vocab_to_index[vocabList[i]] = i\n", + " \n", " for word in email_contents:\n", " # Remove any remaining non alphanumeric characters in word\n", " word = re.compile('[^a-zA-Z0-9]').sub('', word).strip()\n", @@ -655,7 +806,8 @@ " # Look up the word in the dictionary and add to word_indices if found\n", " # ====================== YOUR CODE HERE ======================\n", "\n", - " \n", + " if word in vocabList:\n", + " word_indices.append(vocab_to_index[word])\n", "\n", " # =============================================================\n", "\n", @@ -676,9 +828,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "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 +875,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise support-vector-machines\n", + "\n", + "Use token from last successful submission (ajasmineflower@gmail.com)? (Y/n): Y\n", + " Part Name | Score | Feedback\n", + " --------- | ----- | --------\n", + " Gaussian Kernel | 25 / 25 | Nice work!\n", + " Parameters (C, sigma) for Dataset 3 | 25 / 25 | Nice work!\n", + " Email Processing | 25 / 25 | Nice work!\n", + " Email Feature Extraction | 0 / 25 | \n", + " --------------------------------\n", + " | 75 / 100 | \n", + "\n" + ] + } + ], "source": [ "grader[3] = processEmail\n", "grader.grade()" @@ -738,7 +925,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -799,7 +986,8 @@ "\n", " # ===================== YOUR CODE HERE ======================\n", "\n", - " \n", + " for word in word_indices:\n", + " x[word] = 1\n", " \n", " # ===========================================================\n", " \n", @@ -815,9 +1003,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "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 +1042,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise support-vector-machines\n", + "\n", + "Use token from last successful submission (ajasmineflower@gmail.com)? (Y/n): Y\n", + " Part Name | Score | Feedback\n", + " --------- | ----- | --------\n", + " Gaussian Kernel | 25 / 25 | Nice work!\n", + " Parameters (C, sigma) for Dataset 3 | 25 / 25 | Nice work!\n", + " Email Processing | 25 / 25 | Nice work!\n", + " Email Feature Extraction | 25 / 25 | Nice work!\n", + " --------------------------------\n", + " | 100 / 100 | \n", + "\n" + ] + } + ], "source": [ "grader[4] = emailFeatures\n", "grader.grade()" @@ -862,9 +1084,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "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", @@ -1021,7 +1253,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/Exercise7/exercise7.ipynb b/Exercise7/exercise7.ipynb index 2dbde786..1bfe12ff 100755 --- a/Exercise7/exercise7.ipynb +++ b/Exercise7/exercise7.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -142,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -185,8 +185,12 @@ " idx = np.zeros(X.shape[0], dtype=int)\n", "\n", " # ====================== YOUR CODE HERE ======================\n", - "\n", " \n", + " for i in range(len(X)):\n", + " x = X[i]\n", + " minimum = np.linalg.norm(centroids - x, axis=1)\n", + " ind = np.where(minimum == minimum.min())[0][0]\n", + " idx[i] = ind\n", " \n", " # =============================================================\n", " return idx" @@ -201,9 +205,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Closest centroids for the first 3 examples:\n", + "[0 2 1]\n", + "(the closest centroids should be 0, 2, 1 respectively)\n" + ] + } + ], "source": [ "# Load an example dataset that we will be using\n", "data = loadmat(os.path.join('Data', 'ex7data2.mat'))\n", @@ -230,9 +244,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise k-means-clustering-and-pca\n", + "\n", + "Use token from last successful submission (ajasmineflower@gmail.com)? (Y/n): Y\n", + " Part Name | Score | Feedback\n", + " --------- | ----- | --------\n", + " Find Closest Centroids (k-Means) | 30 / 30 | Nice work!\n", + " Compute Centroid Means (k-Means) | 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 +292,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -304,8 +339,9 @@ "\n", "\n", " # ====================== YOUR CODE HERE ======================\n", - "\n", " \n", + " for i in range(K):\n", + " centroids[i] = np.mean(X[idx == i], axis = 0)\n", " \n", " # =============================================================\n", " return centroids" @@ -320,9 +356,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Centroids computed after initial finding of closest centroids:\n", + "[[2.42830111 3.15792418]\n", + " [5.81350331 2.63365645]\n", + " [7.11938687 3.6166844 ]]\n", + "\n", + "The centroids should be\n", + " [ 2.428301 3.157924 ]\n", + " [ 5.813503 2.633656 ]\n", + " [ 7.119387 3.616684 ]\n" + ] + } + ], "source": [ "# Compute means based on the closest centroids found in the previous part.\n", "centroids = computeCentroids(X, idx, K)\n", @@ -344,9 +396,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise k-means-clustering-and-pca\n", + "\n", + "Use token from last successful submission (ajasmineflower@gmail.com)? (Y/n): Y\n", + " Part Name | Score | Feedback\n", + " --------- | ----- | --------\n", + " Find Closest Centroids (k-Means) | 30 / 30 | Nice work!\n", + " Compute Centroid Means (k-Means) | 30 / 30 | Nice work!\n", + " PCA | 0 / 20 | \n", + " Project Data (PCA) | 0 / 10 | \n", + " Recover Data (PCA) | 0 / 10 | \n", + " --------------------------------\n", + " | 60 / 100 | \n", + "\n" + ] + } + ], "source": [ "grader[2] = computeCentroids\n", "grader.grade()" @@ -367,11 +440,4496 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "scrolled": false }, - "outputs": [], + "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 +4980,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -453,8 +5011,9 @@ " centroids = np.zeros((K, n))\n", "\n", " # ====================== YOUR CODE HERE ======================\n", - "\n", - "\n", + " cluster_index = np.random.choice(m, K, replace=False)\n", + " for i in range(len(cluster_index)):\n", + " centroids[i] = X[cluster_index[i]]\n", " \n", " # =============================================================\n", " return centroids" @@ -503,9 +5062,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "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", @@ -586,9 +5158,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "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 +5212,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -674,7 +5259,8 @@ "\n", " # ====================== YOUR CODE HERE ======================\n", "\n", - " \n", + " sigma = np.dot(X.T,X)/m\n", + " U, S, _ = np.linalg.svd(sigma, full_matrices=True)\n", " \n", " # ============================================================\n", " return U, S" @@ -699,9 +5285,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "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 +5342,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise k-means-clustering-and-pca\n", + "\n", + "Use token from last successful submission (ajasmineflower@gmail.com)? (Y/n): Y\n", + " Part Name | Score | Feedback\n", + " --------- | ----- | --------\n", + " Find Closest Centroids (k-Means) | 30 / 30 | Nice work!\n", + " Compute Centroid Means (k-Means) | 30 / 30 | Nice work!\n", + " PCA | 20 / 20 | Nice work!\n", + " Project Data (PCA) | 0 / 10 | \n", + " Recover Data (PCA) | 0 / 10 | \n", + " --------------------------------\n", + " | 80 / 100 | \n", + "\n" + ] + } + ], "source": [ "grader[3] = pca\n", "grader.grade()" @@ -763,7 +5391,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -808,7 +5436,7 @@ "\n", " # ====================== YOUR CODE HERE ======================\n", "\n", - "\n", + " Z = np.dot(X,U[:,:K])\n", " \n", " # =============================================================\n", " return Z" @@ -823,9 +5451,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "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 +5480,30 @@ }, { "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", + "Use token from last successful submission (ajasmineflower@gmail.com)? (Y/n): Y\n", + " Part Name | Score | Feedback\n", + " --------- | ----- | --------\n", + " Find Closest Centroids (k-Means) | 30 / 30 | Nice work!\n", + " Compute Centroid Means (k-Means) | 30 / 30 | Nice work!\n", + " PCA | 20 / 20 | Nice work!\n", + " Project Data (PCA) | 10 / 10 | Nice work!\n", + " Recover Data (PCA) | 0 / 10 | \n", + " --------------------------------\n", + " | 90 / 100 | \n", + "\n" + ] + } + ], "source": [ "grader[4] = projectData\n", "grader.grade()" @@ -864,7 +5522,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -913,7 +5571,7 @@ "\n", " # ====================== YOUR CODE HERE ======================\n", "\n", - " \n", + " X_rec = np.dot(U[:,:K], Z.T).T\n", "\n", " # =============================================================\n", " return X_rec" @@ -932,9 +5590,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "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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\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 +5641,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise k-means-clustering-and-pca\n", + "\n", + "Use token from last successful submission (ajasmineflower@gmail.com)? (Y/n): Y\n", + " Part Name | Score | Feedback\n", + " --------- | ----- | --------\n", + " Find Closest Centroids (k-Means) | 30 / 30 | Nice work!\n", + " Compute Centroid Means (k-Means) | 30 / 30 | Nice work!\n", + " PCA | 20 / 20 | Nice work!\n", + " Project Data (PCA) | 10 / 10 | Nice work!\n", + " Recover Data (PCA) | 10 / 10 | Nice work!\n", + " --------------------------------\n", + " | 100 / 100 | \n", + "\n" + ] + } + ], "source": [ "grader[5] = recoverData\n", "grader.grade()" @@ -985,9 +5685,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Load Face dataset\n", "data = loadmat(os.path.join('Data', 'ex7faces.mat'))\n", @@ -1010,9 +5723,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# normalize X by subtracting the mean value from each feature\n", "X_norm, mu, sigma = utils.featureNormalize(X)\n", @@ -1037,9 +5763,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The projected data Z has a shape of: (5000, 100)\n" + ] + } + ], "source": [ "# Project images to the eigen space using the top k eigenvectors \n", "# If you are applying a machine learning algorithm \n", @@ -1068,9 +5802,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Project images to the eigen space using the top K eigen vectors and \n", "# visualize only using those K dimensions\n", @@ -1099,9 +5858,799 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support. ' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
');\n", + " this._root_extra_style(this.root)\n", + " this.root.attr('style', 'display: inline-block');\n", + "\n", + " $(parent_element).append(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", + " fig.send_message(\"send_image_mode\", {});\n", + " if (mpl.ratio != 1) {\n", + " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", + " }\n", + " fig.send_message(\"refresh\", {});\n", + " }\n", + "\n", + " this.imageObj.onload = function() {\n", + " if (fig.image_mode == 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function() {\n", + " fig.ws.close();\n", + " }\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "}\n", + "\n", + "mpl.figure.prototype._init_header = function() {\n", + " var titlebar = $(\n", + " '
');\n", + " var titletext = $(\n", + " '
');\n", + " titlebar.append(titletext)\n", + " this.root.append(titlebar);\n", + " this.header = titletext[0];\n", + "}\n", + "\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._init_canvas = function() {\n", + " var fig = this;\n", + "\n", + " var canvas_div = $('
');\n", + "\n", + " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", + "\n", + " function canvas_keyboard_event(event) {\n", + " return fig.key_event(event, event['data']);\n", + " }\n", + "\n", + " canvas_div.keydown('key_press', canvas_keyboard_event);\n", + " canvas_div.keyup('key_release', canvas_keyboard_event);\n", + " this.canvas_div = canvas_div\n", + " this._canvas_extra_style(canvas_div)\n", + " this.root.append(canvas_div);\n", + "\n", + " var canvas = $('');\n", + " canvas.addClass('mpl-canvas');\n", + " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", + "\n", + " this.canvas = canvas[0];\n", + " this.context = canvas[0].getContext(\"2d\");\n", + "\n", + " var backingStore = this.context.backingStorePixelRatio ||\n", + "\tthis.context.webkitBackingStorePixelRatio ||\n", + "\tthis.context.mozBackingStorePixelRatio ||\n", + "\tthis.context.msBackingStorePixelRatio ||\n", + "\tthis.context.oBackingStorePixelRatio ||\n", + "\tthis.context.backingStorePixelRatio || 1;\n", + "\n", + " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband = $('');\n", + " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", + "\n", + " var pass_mouse_events = true;\n", + "\n", + " canvas_div.resizable({\n", + " start: function(event, ui) {\n", + " pass_mouse_events = false;\n", + " },\n", + " resize: function(event, ui) {\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " stop: function(event, ui) {\n", + " pass_mouse_events = true;\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " });\n", + "\n", + " function mouse_event_fn(event) {\n", + " if (pass_mouse_events)\n", + " return fig.mouse_event(event, event['data']);\n", + " }\n", + "\n", + " rubberband.mousedown('button_press', mouse_event_fn);\n", + " rubberband.mouseup('button_release', mouse_event_fn);\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband.mousemove('motion_notify', mouse_event_fn);\n", + "\n", + " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", + " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", + "\n", + " canvas_div.on(\"wheel\", function (event) {\n", + " event = event.originalEvent;\n", + " event['data'] = 'scroll'\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " mouse_event_fn(event);\n", + " });\n", + "\n", + " canvas_div.append(canvas);\n", + " canvas_div.append(rubberband);\n", + "\n", + " this.rubberband = rubberband;\n", + " this.rubberband_canvas = rubberband[0];\n", + " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", + " this.rubberband_context.strokeStyle = \"#000000\";\n", + "\n", + " this._resize_canvas = function(width, height) {\n", + " // Keep the size of the canvas, canvas container, and rubber band\n", + " // canvas in synch.\n", + " canvas_div.css('width', width)\n", + " canvas_div.css('height', height)\n", + "\n", + " canvas.attr('width', width * mpl.ratio);\n", + " canvas.attr('height', height * mpl.ratio);\n", + " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", + "\n", + " rubberband.attr('width', width);\n", + " rubberband.attr('height', height);\n", + " }\n", + "\n", + " // Set the figure to an initial 600x600px, this will subsequently be updated\n", + " // upon first draw.\n", + " this._resize_canvas(600, 600);\n", + "\n", + " // Disable right mouse context menu.\n", + " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", + " return false;\n", + " });\n", + "\n", + " function set_focus () {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "}\n", + "\n", + "mpl.figure.prototype._init_toolbar = function() {\n", + " var fig = this;\n", + "\n", + " var nav_element = $('
');\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('