diff --git a/Week 4/Neural Networks Week 4.ipynb b/Week 4/Neural Networks Week 4.ipynb new file mode 100644 index 000000000..3521e2b79 --- /dev/null +++ b/Week 4/Neural Networks Week 4.ipynb @@ -0,0 +1,378 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "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", + "%matplotlib inline\n", + " \n", + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def displayData(X, example_width=None, figsize=(10, 10)):\n", + " \"\"\"\n", + " Displays 2D data stored in X in a nice grid.\n", + " \"\"\"\n", + " # Compute rows, cols\n", + " if X.ndim == 2:\n", + " m, n = X.shape\n", + " elif X.ndim == 1:\n", + " n = X.size\n", + " m = 1\n", + " X = X[None] # Promote to a 2 dimensional array\n", + " else:\n", + " raise IndexError('Input X should be 1 or 2 dimensional.')\n", + "\n", + " example_width = example_width or int(np.round(np.sqrt(n)))\n", + " example_height = n / example_width\n", + "\n", + " # Compute number of items to display\n", + " display_rows = int(np.floor(np.sqrt(m)))\n", + " display_cols = int(np.ceil(m / display_rows))\n", + "\n", + " fig, ax_array = pyplot.subplots(display_rows, display_cols, figsize=figsize)\n", + " fig.subplots_adjust(wspace=0.025, hspace=0.025)\n", + "\n", + " ax_array = [ax_array] if m == 1 else ax_array.ravel()\n", + "\n", + " for i, ax in enumerate(ax_array):\n", + " ax.imshow(X[i].reshape(example_width, example_width, order='F'),\n", + " cmap='Greys', extent=[0, 1, 0, 1])\n", + " ax.axis('off')" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# training data stored in arrays X, y\n", + "data = loadmat(os.path.join('Data', 'ex3data1.mat'))\n", + "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", + "displayData(sel)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "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": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def sigmoid(z):\n", + " \"\"\"\n", + " Computes the sigmoid of z.\n", + " \"\"\"\n", + " return 1.0 / (1.0 + np.exp(-z))" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "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", + " # ====================== YOUR CODE HERE ======================\n", + " for i in range(m):\n", + " nums=np.zeros(num_labels)\n", + " temp=np.hstack((np.array([1]),np.ravel(X[i])))\n", + " temp1=sigmoid(np.dot(Theta1,temp))\n", + " nums+=sigmoid(np.dot(Theta2,np.hstack((np.array([1]),temp1))))\n", + " p[i]+=np.argmax(nums)\n", + "\n", + " # =============================================================\n", + " return p" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [], + "source": [ + "pred = predict(Theta1, Theta2, X)" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([9., 9., 9., 9., 9.])" + ] + }, + "execution_count": 136, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred[-5:]" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([9, 9, 9, 9, 9], dtype=uint8)" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y[-5:]" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Set Accuracy: 97.5%\n" + ] + } + ], + "source": [ + "print('Training Set Accuracy: {:.1f}%'.format(np.mean(pred == y) * 100))" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Neural Network Prediction: 4.0\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "if indices.size > 0:\n", + " i, indices = indices[0], indices[1:]\n", + " 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": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Week 4/Week4 Logistic regression.py b/Week 4/Week4 Logistic regression.py new file mode 100644 index 000000000..1b2f49b2f --- /dev/null +++ b/Week 4/Week4 Logistic regression.py @@ -0,0 +1,276 @@ +import numpy as np + +# Plotting library +from matplotlib import pyplot + +# Optimization module in scipy +from scipy import optimize + +# will be used to load MATLAB mat datafile format +from scipy.io import loadmat + +import os + +# 20x20 Input Images of Digits +input_layer_size = 400 + +# 10 labels, from 1 to 10 (note that we have mapped "0" to label 10) +num_labels = 10 + +# training data stored in arrays X, y +data = loadmat(os.path.join('Data', 'ex3data1.mat')) +X, y = data['X'], data['y'].ravel() + +# set the zero digit to 0, rather than its mapped 10 in this dataset +# This is an artifact due to the fact that this dataset was used in +# MATLAB where there is no index 0 +y[y == 10] = 0 + +m = y.size + +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') + +# Randomly select 100 data points to display +rand_indices = np.random.choice(m, 100, replace=False) +sel = X[rand_indices, :] + +displayData(sel) + +# test values for the parameters theta +theta_t = np.array([-2, -1, 1, 2], dtype=float) + +# test values for the inputs +X_t = np.concatenate([np.ones((5, 1)), np.arange(1, 16).reshape(5, 3, order='F')/10.0], axis=1) + +# test values for the labels +y_t = np.array([1, 0, 1, 0, 1]) + +# test value for the regularization parameter +lambda_t = 3 + +def sigmoid(z): + """ + Computes the sigmoid of z. + """ + return 1.0 / (1.0 + np.exp(-z)) + +#regularized Cost Function +def lrCostFunction(theta, X, y, lambda_): + + #Initialize some useful values + m = y.size + + # convert labels to ints if their type is bool + if y.dtype == bool: + y = y.astype(int) + + # You need to return the following variables correctly + J = 0 + grad = np.zeros(theta.shape) + + # ====================== YOUR CODE HERE ====================== + temp=np.dot(X,theta) + z=sigmoid(temp) + J=(1/m)*sum(((-y)*np.log(z))-((1-y)*np.log(1-z))) + temp2=z-y; + grad+=(1/m)*sum(np.dot(X.T,temp2)) #unregularized + temp3=theta + temp[0]=0 # because we don't add anything for j = 0 + grad+= (lambda_/m)*temp3 + J+= (lambda_/(2*m))*sum(temp3*temp3) + # ============================================================= + return J, grad + +J, grad = lrCostFunction(theta_t, X_t, y_t, lambda_t) + +print('Cost : {:.6f}'.format(J)) +print('Expected cost: 2.534819') +print('-----------------------') +print('Gradients:') +print(' [{:.6f}, {:.6f}, {:.6f}, {:.6f}]'.format(*grad)) +print('Expected gradients:') +print(' [0.146561, -0.548558, 0.724722, 1.398003]'); + +def oneVsAll(X, y, num_labels, lambda_): + """ + Trains num_labels logistic regression classifiers and returns + each of these classifiers in a matrix all_theta, where the i-th + row of all_theta corresponds to the classifier for label i. + + Parameters + ---------- + X : array_like + The input dataset of shape (m x n). m is the number of + data points, and n is the number of features. Note that we + do not assume that the intercept term (or bias) is in X, however + we provide the code below to add the bias term to X. + + y : array_like + The data labels. A vector of shape (m, ). + + num_labels : int + Number of possible labels. + + lambda_ : float + The logistic regularization parameter. + + Returns + ------- + all_theta : array_like + The trained parameters for logistic regression for each class. + This is a matrix of shape (K x n+1) where K is number of classes + (ie. `numlabels`) and n is number of features without the bias. + + Instructions + ------------ + You should complete the following code to train `num_labels` + logistic regression classifiers with regularization parameter `lambda_`. + + Hint + ---- + You can use y == c to obtain a vector of 1's and 0's that tell you + whether the ground truth is true/false for this class. + + Note + ---- + For this assignment, we recommend using `scipy.optimize.minimize(method='CG')` + to optimize the cost function. It is okay to use a for-loop + (`for c in range(num_labels):`) to loop over the different classes. + + Example Code + ------------ + + # Set Initial theta + initial_theta = np.zeros(n + 1) + + # Set options for minimize + options = {'maxiter': 50} + + # Run minimize to obtain the optimal theta. This function will + # return a class object where theta is in `res.x` and cost in `res.fun` + res = optimize.minimize(lrCostFunction, + initial_theta, + (X, (y == c), lambda_), + jac=True, + method='TNC', + options=options) + """ + # Some useful variables + m, n = X.shape + + # You need to return the following variables correctly + all_theta = np.zeros((num_labels, n + 1)) + + # Add ones to the X data matrix + X = np.concatenate([np.ones((m, 1)), X], axis=1) + + # ====================== YOUR CODE HERE ====================== + for c in range(num_labels): + initial_theta = np.zeros(n + 1) + + # Set options for minimize + options = {'maxiter': 50} + + # Run minimize to obtain the optimal theta. This function will + # return a class object where theta is in `res.x` and cost in `res.fun` + res = optimize.minimize(lrCostFunction, + initial_theta, + (X, (y == c), lambda_), + jac=True, + method='TNC', + options=options) + all_theta[c]=res.x + + + # ============================================================ + return all_theta + +lambda_ = 0.1 +all_theta = oneVsAll(X, y, num_labels, lambda_) + +def predictOneVsAll(all_theta, X): + """ + Return a vector of predictions for each example in the matrix X. + Note that X contains the examples in rows. all_theta is a matrix where + the i-th row is a trained logistic regression theta vector for the + i-th class. You should set p to a vector of values from 0..K-1 + (e.g., p = [0, 2, 0, 1] predicts classes 0, 2, 0, 1 for 4 examples) . + + Parameters + ---------- + all_theta : array_like + The trained parameters for logistic regression for each class. + This is a matrix of shape (K x n+1) where K is number of classes + and n is number of features without the bias. + + X : array_like + Data points to predict their labels. This is a matrix of shape + (m x n) where m is number of data points to predict, and n is number + of features without the bias term. Note we add the bias term for X in + this function. + + Returns + ------- + p : array_like + The predictions for each data point in X. This is a vector of shape (m, ). + + Instructions + ------------ + Complete the following code to make predictions using your learned logistic + regression parameters (one-vs-all). You should set p to a vector of predictions + (from 0 to num_labels-1). + + Hint + ---- + This code can be done all vectorized using the numpy argmax function. + In particular, the argmax function returns the index of the max element, + for more information see '?np.argmax' or search online. If your examples + are in rows, then, you can use np.argmax(A, axis=1) to obtain the index + of the max for each row. + """ + m = X.shape[0]; + num_labels = all_theta.shape[0] + + # You need to return the following variables correctly + p = np.zeros(m) + + # Add ones to the X data matrix + X = np.concatenate([np.ones((m, 1)), X], axis=1) + + # ====================== YOUR CODE HERE ====================== + temp=np.dot(X,all_theta.T) + p=np.argmax(temp,axis=1) + # ============================================================ + return p + +pred = predictOneVsAll(all_theta, X) +print('Training Set Accuracy: {:.2f}%'.format(np.mean(pred == y) * 100)) diff --git a/Week 4/index.html b/Week 4/index.html new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/Week 4/index.html @@ -0,0 +1 @@ + diff --git a/Week 5/Week5 Assignment.ipynb b/Week 5/Week5 Assignment.ipynb new file mode 100644 index 000000000..262d476ea --- /dev/null +++ b/Week 5/Week5 Assignment.ipynb @@ -0,0 +1,1094 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "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", + "# tells matplotlib to embed plots within the notebook\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "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": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "def displayData(X, example_width=None, figsize=(10, 10)):\n", + " \"\"\"\n", + " Displays 2D data stored in X in a nice grid.\n", + " \"\"\"\n", + " # Compute rows, cols\n", + " if X.ndim == 2:\n", + " m, n = X.shape\n", + " elif X.ndim == 1:\n", + " n = X.size\n", + " m = 1\n", + " X = X[None] # Promote to a 2 dimensional array\n", + " else:\n", + " raise IndexError('Input X should be 1 or 2 dimensional.')\n", + " \n", + " example_width = example_width or int(np.round(np.sqrt(n)))\n", + " example_height = n / example_width\n", + "\n", + " # Compute number of items to display\n", + " display_rows = int(np.floor(np.sqrt(m)))\n", + " display_cols = int(np.ceil(m / display_rows))\n", + "\n", + " fig, ax_array = pyplot.subplots(display_rows, display_cols, figsize=figsize)\n", + " fig.subplots_adjust(wspace=0.025, hspace=0.025)\n", + "\n", + " ax_array = [ax_array] if m == 1 else ax_array.ravel()\n", + "\n", + " for i, ax in enumerate(ax_array):\n", + " # Display Image\n", + " h = ax.imshow(X[i].reshape(example_width, example_width, order='F'),\n", + " cmap='Greys', extent=[0, 1, 0, 1])\n", + " ax.axis('off')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Randomly select 100 data points to display\n", + "rand_indices = np.random.choice(m, 100, replace=False)\n", + "sel = X[rand_indices, :]\n", + "\n", + "displayData(sel)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "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": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [], + "source": [ + "x=np.concatenate([np.ones((5000,1)),X],axis=1)\n", + "cost=[]\n", + "y_temp=np.ones((10,))\n", + "for j in range(10):\n", + " if j!=y[0]:\n", + " y_temp[j]-=1\n", + "a1=x[0,:]\n", + "z2=np.dot(a1,Theta1.T)\n", + "a2=sigmoid(z2)\n", + "a2=np.concatenate([[1],a2])\n", + "z3=np.dot(a2,Theta2.T)\n", + "a3=sigmoid(z3)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 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0.00000000e+00, 0.00000000e+00,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", + " 0.00000000e+00, 0.00000000e+00, 6.20939216e-18, 6.72618320e-04,\n", + " -1.13151411e-02, -3.54641066e-02, -3.88214912e-02, -3.71077412e-02,\n", + " -1.33524928e-02, 9.90964718e-04, 4.89176960e-05, 0.00000000e+00,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", + " 0.00000000e+00])" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a1" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1. , 0.05036187, 0.07939572, 0.99300197, 0.51872368,\n", + " 0.70524833, 0.99649533, 0.01073784, 0.00163257, 0.99649905,\n", + " 0.02040086, 0.07178715, 0.02166121, 0.04619385, 0.05028779,\n", + " 0.00421782, 0.88700616, 0.99200482, 0.09693938, 0.84785892,\n", + " 0.79771612, 0.02382443, 0.98384703, 0.97251796, 0.94342162,\n", + " 0.10721379])" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a2" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([9.95734012e-01, 1.12661530e-04, 1.74127856e-03, 2.52696959e-03,\n", + " 1.84032321e-05, 9.36263860e-03, 3.99270267e-03, 5.51517524e-03,\n", + " 4.01468105e-04, 6.48072305e-03])" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a3" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1., 0., 0., 0., 0., 0., 0., 0., 0., 0.])" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_temp" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.00427511, 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ])" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.multiply(-y_temp,np.log(a3))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-109.97206528528295" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(y_temp*np.log(a3)+(1-y_temp)*np.log(1-a3))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def sigmoid(z):\n", + " \"\"\"\n", + " Computes the sigmoid of z.\n", + " \"\"\"\n", + " return 1.0 / (1.0 + np.exp(-z))" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "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", + " X=np.concatenate([np.ones((m,1)),X],axis=1)\n", + " cost=[]\n", + " for i in range(X.shape[0]):\n", + " y_temp=np.ones((num_labels,))\n", + " for j in range(num_labels):\n", + " if j!=y[i]:\n", + " y_temp[j]-=1\n", + " a1=X[i,:]\n", + " z2=np.dot(a1,Theta1.T)\n", + " a2=sigmoid(z2)\n", + " a2=np.concatenate([[1],a2])\n", + " z3=np.dot(a2,Theta2.T)\n", + " a3=sigmoid(z3)\n", + " cost.append(np.sum(y_temp*np.log(a3)+(1-y_temp)*np.log(1-a3)))\n", + " delta3=a3-y_temp\n", + " delta2=np.dot(Theta2.T,delta3*sigmoidGradient(z3))\n", + " delta2=delta2[1:]\n", + " Theta1_grad+=np.dot(np.array([delta2]).T,np.array([a1]))\n", + " Theta2_grad+=np.dot(np.array([delta3]).T,np.array([a2]))\n", + " \n", + " J=np.sum(cost)/(X.shape[0])*(-1)\n", + " \n", + " #Regularization\n", + " Theta1_temp=Theta1[:,1:]\n", + " Theta2_temp=Theta2[:,1:]\n", + " J+=(lambda_/(2*m))*(np.sum(Theta1_temp*Theta1_temp)+np.sum(Theta2_temp*Theta2_temp))\n", + " \n", + " Theta2_grad=(1/m)*Theta2_grad\n", + " Theta1_grad=(1/m)*Theta1_grad\n", + " \n", + " #Regularisation Of Gradients\n", + " Theta1_grad[1:,:]+=(lambda_/m)*Theta1[1:,:]\n", + " Theta2_grad[1:,:]+=(lambda_/m)*Theta2[1:,:]\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": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost at parameters (loaded from ex4weights): 0.287629 \n", + "The cost should be about : 0.287629.\n" + ] + } + ], + "source": [ + "lambda_ = 0\n", + "J, _ = nnCostFunction(nn_params, input_layer_size, hidden_layer_size,\n", + " 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": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost at parameters (loaded from ex4weights): 0.383770\n", + "This value should be about : 0.383770.\n" + ] + } + ], + "source": [ + "# Weight regularization parameter (we set this to 1 here).\n", + "lambda_ = 1\n", + "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": "code", + "execution_count": 105, + "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", + " a=sigmoid(z)\n", + " g=a*(1-a)\n", + " # =============================================================\n", + " return g\n" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sigmoid gradient evaluated at [-1 -0.5 0 0.5 1]:\n", + " \n", + "[0.19661193 0.23500371 0.25 0.23500371 0.19661193]\n" + ] + } + ], + "source": [ + "z = np.array([-1, -0.5, 0, 0.5, 1])\n", + "g = sigmoidGradient(z)\n", + "print('Sigmoid gradient evaluated at [-1 -0.5 0 0.5 1]:\\n ')\n", + "print(g)" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "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", + " # Randomly initialize the weights to small values\n", + " W = np.random.rand(L_out, 1 + L_in) * 2 * epsilon_init - epsilon_init\n", + "\n", + "\n", + " # ============================================================\n", + " return W" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing Neural Network Parameters ...\n" + ] + } + ], + "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": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [], + "source": [ + "def debugInitializeWeights(fan_out, fan_in):\n", + " \"\"\"\n", + " Initialize the weights of a layer with fan_in incoming connections and fan_out outgoings\n", + " connections using a fixed strategy. This will help you later in debugging.\n", + " Note that W should be set a matrix of size (1+fan_in, fan_out) as the first row of W handles\n", + " the \"bias\" terms.\n", + " Parameters\n", + " ----------\n", + " fan_out : int\n", + " The number of outgoing connections.\n", + " fan_in : int\n", + " The number of incoming connections.\n", + " Returns\n", + " -------\n", + " W : array_like (1+fan_in, fan_out)\n", + " The initialized weights array given the dimensions.\n", + " \"\"\"\n", + " # Initialize W using \"sin\". This ensures that W is always of the same values and will be\n", + " # useful for debugging\n", + " W = np.sin(np.arange(1, 1 + (1+fan_in)*fan_out))/10.0\n", + " W = W.reshape(fan_out, 1+fan_in, order='F')\n", + " return W" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [], + "source": [ + "def checkNNGradients(nnCostFunction, lambda_=0):\n", + " \"\"\"\n", + " Creates a small neural network to check the backpropagation gradients. It will output the\n", + " analytical gradients produced by your backprop code and the numerical gradients\n", + " (computed using computeNumericalGradient). These two gradient computations should result in\n", + " very similar values.\n", + " Parameters\n", + " ----------\n", + " nnCostFunction : func\n", + " A reference to the cost function implemented by the student.\n", + " lambda_ : float (optional)\n", + " The regularization parameter value.\n", + " \"\"\"\n", + " input_layer_size = 3\n", + " hidden_layer_size = 5\n", + " num_labels = 3\n", + " m = 5\n", + "\n", + " # We generate some 'random' test data\n", + " Theta1 = debugInitializeWeights(hidden_layer_size, input_layer_size)\n", + " Theta2 = debugInitializeWeights(num_labels, hidden_layer_size)\n", + "\n", + " # Reusing debugInitializeWeights to generate X\n", + " X = debugInitializeWeights(m, input_layer_size - 1)\n", + " y = np.arange(1, 1+m) % num_labels\n", + " # print(y)\n", + " # Unroll parameters\n", + " nn_params = np.concatenate([Theta1.ravel(), Theta2.ravel()])\n", + "\n", + " # short hand for cost function\n", + " costFunc = lambda p: nnCostFunction(p, input_layer_size, hidden_layer_size,\n", + " num_labels, X, y, lambda_)\n", + " cost, grad = costFunc(nn_params)\n", + " numgrad = computeNumericalGradient(costFunc, nn_params)\n", + "\n", + " # Visually examine the two gradient computations.The two columns you get should be very similar.\n", + " print(np.stack([numgrad, grad], axis=1))\n", + " print('The above two columns you get should be very similar.')\n", + " print('(Left-Your Numerical Gradient, Right-Analytical Gradient)\\n')\n", + "\n", + " # Evaluate the norm of the difference between two the solutions. If you have a correct\n", + " # implementation, and assuming you used e = 0.0001 in computeNumericalGradient, then diff\n", + " # should be less than 1e-9.\n", + " diff = np.linalg.norm(numgrad - grad)/np.linalg.norm(numgrad + grad)\n", + "\n", + " print('If your backpropagation implementation is correct, then \\n'\n", + " 'the relative difference will be small (less than 1e-9). \\n'\n", + " 'Relative Difference: %g' % diff)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [], + "source": [ + "def computeNumericalGradient(J, theta, e=1e-4):\n", + " \"\"\"\n", + " Computes the gradient using \"finite differences\" and gives us a numerical estimate of the\n", + " gradient.\n", + " Parameters\n", + " ----------\n", + " J : func\n", + " The cost function which will be used to estimate its numerical gradient.\n", + " theta : array_like\n", + " The one dimensional unrolled network parameters. The numerical gradient is computed at\n", + " those given parameters.\n", + " e : float (optional)\n", + " The value to use for epsilon for computing the finite difference.\n", + " Notes\n", + " -----\n", + " The following code implements numerical gradient checking, and\n", + " returns the numerical gradient. It sets `numgrad[i]` to (a numerical\n", + " approximation of) the partial derivative of J with respect to the\n", + " i-th input argument, evaluated at theta. (i.e., `numgrad[i]` should\n", + " be the (approximately) the partial derivative of J with respect\n", + " to theta[i].)\n", + " \"\"\"\n", + " numgrad = np.zeros(theta.shape)\n", + " perturb = np.diag(e * np.ones(theta.shape))\n", + " for i in range(theta.size):\n", + " loss1, _ = J(theta - perturb[:, i])\n", + " loss2, _ = J(theta + perturb[:, i])\n", + " numgrad[i] = (loss2 - loss1)/(2*e)\n", + " return numgrad" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-9.27825235e-03 -9.28761542e-03]\n", + " [-3.04978709e-06 -2.86657120e-06]\n", + " [-1.75060082e-04 -1.75169993e-04]\n", + " [-9.62660618e-05 -9.65116349e-05]\n", + " [ 8.89911959e-03 8.91094530e-03]\n", + " [ 1.42869427e-05 1.45814012e-05]\n", + " [ 2.33146358e-04 2.33660346e-04]\n", + " [ 1.17982666e-04 1.17979807e-04]\n", + " [-8.36010761e-03 -8.35592254e-03]\n", + " [-2.59383071e-05 -2.60043843e-05]\n", + " [-2.87468729e-04 -2.87473985e-04]\n", + " [-1.37149709e-04 -1.37086613e-04]\n", + " [ 7.62813551e-03 7.63365594e-03]\n", + " [ 3.69883213e-05 3.69068896e-05]\n", + " [ 3.35320349e-04 3.35533830e-04]\n", + " [ 1.53247077e-04 1.53449629e-04]\n", + " [-6.74798369e-03 -6.75860166e-03]\n", + " [-4.68759764e-05 -4.70707031e-05]\n", + " [-3.76215585e-04 -3.76877963e-04]\n", + " [-1.66560297e-04 -1.66741350e-04]\n", + " [ 3.14544970e-01 3.14544970e-01]\n", + " [ 1.64090819e-01 1.64090819e-01]\n", + " [ 1.64567932e-01 1.64567932e-01]\n", + " [ 1.58339334e-01 1.58339334e-01]\n", + " [ 1.51127527e-01 1.51127527e-01]\n", + " [ 1.49568335e-01 1.49568335e-01]\n", + " [ 1.11056588e-01 1.11056588e-01]\n", + " [ 5.75736493e-02 5.75736493e-02]\n", + " [ 5.77867378e-02 5.77867378e-02]\n", + " [ 5.59235296e-02 5.59235296e-02]\n", + " [ 5.36967009e-02 5.36967009e-02]\n", + " [ 5.31542052e-02 5.31542052e-02]\n", + " [ 9.74006970e-02 9.74006970e-02]\n", + " [ 5.04575855e-02 5.04575855e-02]\n", + " [ 5.07530173e-02 5.07530173e-02]\n", + " [ 4.91620841e-02 4.91620841e-02]\n", + " [ 4.71456249e-02 4.71456249e-02]\n", + " [ 4.65597186e-02 4.65597186e-02]]\n", + "The above two columns you get should be very similar.\n", + "(Left-Your Numerical Gradient, Right-Analytical Gradient)\n", + "\n", + "If your backpropagation implementation is correct, then \n", + "the relative difference will be small (less than 1e-9). \n", + "Relative Difference: 1.88882e-05\n" + ] + } + ], + "source": [ + "checkNNGradients(nnCostFunction)" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-9.27825235e-03 -9.28761542e-03]\n", + " [-1.67679797e-02 -2.86657120e-06]\n", + " [-6.01744725e-02 -1.75169993e-04]\n", + " [-1.73704651e-02 -9.65116349e-05]\n", + " [ 8.89911959e-03 6.34687909e-02]\n", + " [ 3.94334829e-02 3.94337773e-02]\n", + " [-3.19612287e-02 -3.19607147e-02]\n", + " [-5.75658668e-02 -5.75658697e-02]\n", + " [-8.36010761e-03 1.11277942e-04]\n", + " [ 5.93355565e-02 5.93354904e-02]\n", + " [ 2.49225535e-02 2.49225482e-02]\n", + " [-4.51963845e-02 -4.51963214e-02]\n", + " [ 7.62813551e-03 -3.77744938e-02]\n", + " [ 2.47640974e-02 2.47640160e-02]\n", + " [ 5.97717617e-02 5.97719752e-02]\n", + " [ 9.14587966e-03 9.14608221e-03]\n", + " [-6.74798369e-03 -6.42940581e-02]\n", + " [-3.26881426e-02 -3.26883374e-02]\n", + " [ 3.86410548e-02 3.86403924e-02]\n", + " [ 5.46101547e-02 5.46099737e-02]\n", + " [ 3.14544970e-01 3.14544970e-01]\n", + " [ 1.18682669e-01 1.64090819e-01]\n", + " [ 2.03987128e-01 1.64567932e-01]\n", + " [ 1.25698067e-01 1.58339334e-01]\n", + " [ 1.76337550e-01 1.51127527e-01]\n", + " [ 1.32294136e-01 1.49568335e-01]\n", + " [ 1.11056588e-01 1.65614434e-01]\n", + " [ 3.81928666e-05 3.81928696e-05]\n", + " [ 1.17148233e-01 1.17148233e-01]\n", + " [-4.07588279e-03 -4.07588279e-03]\n", + " [ 1.13133142e-01 1.13133142e-01]\n", + " [-4.52964427e-03 -4.52964427e-03]\n", + " [ 9.74006970e-02 1.05867897e-01]\n", + " [ 3.36926556e-02 3.36926556e-02]\n", + " [ 7.54801264e-02 7.54801264e-02]\n", + " [ 1.69677090e-02 1.69677090e-02]\n", + " [ 8.61628953e-02 8.61628953e-02]\n", + " [ 1.50048382e-03 1.50048382e-03]]\n", + "The above two columns you get should be very similar.\n", + "(Left-Your Numerical Gradient, Right-Analytical Gradient)\n", + "\n", + "If your backpropagation implementation is correct, then \n", + "the relative difference will be small (less than 1e-9). \n", + "Relative Difference: 0.129677\n", + "\n", + "\n", + "Cost at (fixed) debugging parameters (w/ lambda = 3.000000): 0.576051 \n", + "(for lambda = 3, this value should be about 0.576051)\n" + ] + } + ], + "source": [ + "# Check gradients by running checkNNGradients\n", + "lambda_ = 3\n", + "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": 161, + "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': 1000}\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": "code", + "execution_count": 162, + "metadata": {}, + "outputs": [], + "source": [ + "def predict(Theta1, Theta2, X):\n", + " \"\"\"\n", + " Predict the label of an input given a trained neural network\n", + " Outputs the predicted label of X given the trained weights of a neural\n", + " network(Theta1, Theta2)\n", + " \"\"\"\n", + " # Useful values\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(m)\n", + " h1 = sigmoid(np.dot(np.concatenate([np.ones((m, 1)), X], axis=1), Theta1.T))\n", + " h2 = sigmoid(np.dot(np.concatenate([np.ones((m, 1)), h1], axis=1), Theta2.T))\n", + " p = np.argmax(h2, axis=1)\n", + " return p" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Set Accuracy: 75.220000\n" + ] + } + ], + "source": [ + "pred =predict(Theta1, Theta2, X)\n", + "print('Training Set Accuracy: %f' % (np.mean(pred == y) * 100))" + ] + } + ], + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Week 5/Week5- Exercise 6.ipynb b/Week 5/Week5- Exercise 6.ipynb new file mode 100644 index 000000000..a8e5b67f1 --- /dev/null +++ b/Week 5/Week5- Exercise 6.ipynb @@ -0,0 +1,968 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 51, + "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", + "from matplotlib import style\n", + "style.use(\"ggplot\")\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", + "# tells matplotlib to embed plots within the notebook\n", + "%matplotlib inline\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Load from ex5data1.mat, where all variables will be store in a dictionary\n", + "data = loadmat(os.path.join('Data', 'ex5data1.mat'))\n", + "\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": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'__header__': b'MATLAB 5.0 MAT-file, Platform: GLNXA64, Created on: Fri Nov 4 22:27:26 2011',\n", + " '__version__': '1.0',\n", + " '__globals__': [],\n", + " 'X': array([[-15.93675813],\n", + " [-29.15297922],\n", + " [ 36.18954863],\n", + " [ 37.49218733],\n", + " [-48.05882945],\n", + " [ -8.94145794],\n", + " [ 15.30779289],\n", + " [-34.70626581],\n", + " [ 1.38915437],\n", + " [-44.38375985],\n", + " [ 7.01350208],\n", + " [ 22.76274892]]),\n", + " 'y': array([[ 2.13431051],\n", + " [ 1.17325668],\n", + " [34.35910918],\n", + " [36.83795516],\n", + " [ 2.80896507],\n", + " [ 2.12107248],\n", + " [14.71026831],\n", + " [ 2.61418439],\n", + " [ 3.74017167],\n", + " [ 3.73169131],\n", + " [ 7.62765885],\n", + " [22.7524283 ]]),\n", + " 'Xtest': array([[-33.31800399],\n", + " [-37.91216403],\n", + " [-51.20693795],\n", + " [ -6.13259585],\n", + " [ 21.26118327],\n", + " [-40.31952949],\n", + " [-14.54153167],\n", + " [ 32.55976024],\n", + " [ 13.39343255],\n", + " [ 44.20988595],\n", + " [ -1.14267768],\n", + " [-12.76686065],\n", + " [ 34.05450539],\n", + " [ 39.22350028],\n", + " [ 1.97449674],\n", + " [ 29.6217551 ],\n", + " [-23.66962971],\n", + " [ -9.01180139],\n", + " [-55.94057091],\n", + " [-35.70859752],\n", + " [ 9.51020533]]),\n", + " 'ytest': array([[ 3.31688953],\n", + " [ 5.39768952],\n", + " [ 0.13042984],\n", + " [ 6.1925982 ],\n", + " [17.08848712],\n", + " [ 0.79950805],\n", + " [ 2.82479183],\n", + " [28.62123334],\n", + " [17.04639081],\n", + " [55.38437334],\n", + " [ 4.07936733],\n", + " [ 8.27039793],\n", + " [31.32355102],\n", + " [39.15906103],\n", + " [ 8.08727989],\n", + " [24.11134389],\n", + " [ 2.4773548 ],\n", + " [ 6.56606472],\n", + " [ 6.0380888 ],\n", + " [ 4.69273956],\n", + " [10.83004606]]),\n", + " 'Xval': array([[-16.74653578],\n", + " [-14.57747075],\n", + " [ 34.51575866],\n", + " [-47.01007574],\n", + " [ 36.97511905],\n", + " [-40.68611002],\n", + " [ -4.47201098],\n", + " [ 26.53363489],\n", + " [-42.7976831 ],\n", + " [ 25.37409938],\n", + " [-31.10955398],\n", + " [ 27.31176864],\n", + " [ -3.26386201],\n", + " [ -1.81827649],\n", + " [-40.7196624 ],\n", + " [-50.01324365],\n", + " [-17.41177155],\n", + " [ 3.5881937 ],\n", + " [ 7.08548026],\n", + " [ 46.28236902],\n", + " [ 14.61228909]]),\n", + " 'yval': array([[ 4.17020201e+00],\n", + " [ 4.06726280e+00],\n", + " [ 3.18730676e+01],\n", + " [ 1.06236562e+01],\n", + " [ 3.18360213e+01],\n", + " [ 4.95936972e+00],\n", + " [ 4.45159880e+00],\n", + " [ 2.22763185e+01],\n", + " [-4.38738274e-05],\n", + " [ 2.05038016e+01],\n", + " [ 3.85834476e+00],\n", + " [ 1.93650529e+01],\n", + " [ 4.88376281e+00],\n", + " [ 1.10971588e+01],\n", + " [ 7.46170827e+00],\n", + " [ 1.47693464e+00],\n", + " [ 2.71916388e+00],\n", + " [ 1.09269007e+01],\n", + " [ 8.34871235e+00],\n", + " [ 5.27819280e+01],\n", + " [ 1.33573396e+01]])}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(12, 1)" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Linear Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "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", + " h=np.dot(X,theta)\n", + " Theta_square=theta[1:]**2\n", + " J=(1/(2*m))*np.sum((h-y)**2)+(np.sum(Theta_square)*(lambda_/(2*m)))\n", + " \n", + " temp_theta=np.concatenate(([0],theta[1:]))\n", + " for i in range(np.shape(theta)[0]):\n", + " grad[i]=(1/m)*(np.sum((h-y)*(X[:,i])))+(lambda_/m)*temp_theta[i]\n", + "\n", + " # ============================================================\n", + " return J, grad" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost at theta = [1, 1]:\t 303.993192 \n", + "This value should be about 303.993192)\n", + "\n" + ] + } + ], + "source": [ + "theta = np.array([1, 1])\n", + "J, _ = linearRegCostFunction(np.concatenate([np.ones((m, 1)), X], axis=1), y, theta, 1)\n", + "\n", + "print('Cost at theta = [1, 1]:\\t %f ' % J)\n", + "print('This value should be about 303.993192)\\n' % J)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gradient at theta = [1, 1]: [-15.303016, 598.250744] \n", + " (this value should be about [-15.303016, 598.250744])\n", + "\n" + ] + } + ], + "source": [ + "theta = np.array([1, 1])\n", + "J, grad = linearRegCostFunction(np.concatenate([np.ones((m, 1)), X], axis=1), y, theta, 1)\n", + "\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": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "def trainLinearReg(linearRegCostFunction, X, y, lambda_=0.0, maxiter=200):\n", + " \"\"\"\n", + " Trains linear regression using scipy's optimize.minimize.\n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The dataset with shape (m x n+1). The bias term is assumed to be concatenated.\n", + " y : array_like\n", + " Function values at each datapoint. A vector of shape (m,).\n", + " lambda_ : float, optional\n", + " The regularization parameter.\n", + " maxiter : int, optional\n", + " Maximum number of iteration for the optimization algorithm.\n", + " Returns\n", + " -------\n", + " theta : array_like\n", + " The parameters for linear regression. This is a vector of shape (n+1,).\n", + " \"\"\"\n", + " # Initialize Theta\n", + " initial_theta = np.zeros(X.shape[1])\n", + "\n", + " # Create \"short hand\" for the cost function to be minimized\n", + " costFunction = lambda t: linearRegCostFunction(X, y, t, lambda_)\n", + "\n", + " # Now, costFunction is a function that takes in only one argument\n", + " options = {'maxiter': maxiter}\n", + "\n", + " # Minimize using scipy\n", + " res = optimize.minimize(costFunction, initial_theta, jac=True, method='TNC', options=options)\n", + " return res.x" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# add a columns of ones for the y-intercept\n", + "X_aug = np.concatenate([np.ones((m, 1)), X], axis=1)\n", + "theta = 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": "code", + "execution_count": 53, + "metadata": {}, + "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", + " for i in range(1,m+1):\n", + " X_temp=X[:i,:]\n", + " y_temp=y[:i]\n", + " theta=trainLinearReg(linearRegCostFunction, X_temp, y_temp, lambda_=0)\n", + " J_train,_=linearRegCostFunction(X_temp, y_temp, theta, lambda_=0.0)\n", + " error_train[i-1]=J_train\n", + " J_val,_=linearRegCostFunction(Xval, yval, theta, lambda_=0.0)\n", + " error_val[i-1]=J_val\n", + "\n", + " \n", + " # =============================================================\n", + " return error_train, error_val" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# Training Examples\tTrain Error\tCross Validation Error\n", + " \t1\t\t0.000000\t205.121096\n", + " \t2\t\t0.000000\t110.302641\n", + " \t3\t\t3.286595\t45.010231\n", + " \t4\t\t2.842678\t48.368911\n", + " \t5\t\t13.154049\t35.865165\n", + " \t6\t\t19.443963\t33.829961\n", + " \t7\t\t20.098522\t31.970986\n", + " \t8\t\t18.172859\t30.862446\n", + " \t9\t\t22.609405\t31.135998\n", + " \t10\t\t23.261462\t28.936207\n", + " \t11\t\t24.317250\t29.551432\n", + " \t12\t\t22.373906\t29.433818\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "X_aug = np.concatenate([np.ones((m, 1)), X], axis=1)\n", + "Xval_aug = np.concatenate([np.ones((yval.size, 1)), Xval], axis=1)\n", + "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": [ + "# Polynomial Regression " + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "def featureNormalize(X):\n", + " \"\"\"\n", + " Normalizes the features in X returns a normalized version of X where the mean value of each\n", + " feature is 0 and the standard deviation is 1. This is often a good preprocessing step to do when\n", + " working with learning algorithms.\n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " An dataset which is a (m x n) matrix, where m is the number of examples,\n", + " and n is the number of dimensions for each example.\n", + " Returns\n", + " -------\n", + " X_norm : array_like\n", + " The normalized input dataset.\n", + " mu : array_like\n", + " A vector of size n corresponding to the mean for each dimension across all examples.\n", + " sigma : array_like\n", + " A vector of size n corresponding to the standard deviations for each dimension across\n", + " all examples.\n", + " \"\"\"\n", + " mu = np.mean(X, axis=0)\n", + " X_norm = X - mu\n", + "\n", + " sigma = np.std(X_norm, axis=0, ddof=1)\n", + " X_norm /= sigma\n", + " return X_norm, mu, sigma\n" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "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", + " for i in range(p):\n", + " X_poly[:,i]=X[:,0]**(i+1)\n", + " # ============================================================\n", + " return X_poly" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Normalized Training Example 1:\n" + ] + }, + { + "data": { + "text/plain": [ + "array([ 1. , -0.36214078, -0.75508669, 0.18222588, -0.70618991,\n", + " 0.30661792, -0.59087767, 0.3445158 , -0.50848117])" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p = 8\n", + "\n", + "# Map X onto Polynomial Features and Normalize\n", + "X_poly = polyFeatures(X, p)\n", + "X_poly, mu, sigma = 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": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "def plotFit(polyFeatures, min_x, max_x, mu, sigma, theta, p):\n", + " \"\"\"\n", + " Plots a learned polynomial regression fit over an existing figure.\n", + " Also works with linear regression.\n", + " Plots the learned polynomial fit with power p and feature normalization (mu, sigma).\n", + " Parameters\n", + " ----------\n", + " polyFeatures : func\n", + " A function which generators polynomial features from a single feature.\n", + " min_x : float\n", + " The minimum value for the feature.\n", + " max_x : float\n", + " The maximum value for the feature.\n", + " mu : float\n", + " The mean feature value over the training dataset.\n", + " sigma : float\n", + " The feature standard deviation of the training dataset.\n", + " theta : array_like\n", + " The parameters for the trained polynomial linear regression.\n", + " p : int\n", + " The polynomial order.\n", + " \"\"\"\n", + " # We plot a range slightly bigger than the min and max values to get\n", + " # an idea of how the fit will vary outside the range of the data points\n", + " x = np.arange(min_x - 15, max_x + 25, 0.05).reshape(-1, 1)\n", + "\n", + " # Map the X values\n", + " X_poly = polyFeatures(x, p)\n", + " X_poly -= mu\n", + " X_poly /= sigma\n", + "\n", + " # Add ones\n", + " X_poly = np.concatenate([np.ones((x.shape[0], 1)), X_poly], axis=1)\n", + "\n", + " # Plot\n", + " pyplot.plot(x, np.dot(X_poly, theta), '--', lw=2)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Polynomial Regression (lambda = 100.000000)\n", + "\n", + "# Training Examples\tTrain Error\tCross Validation Error\n", + " \t1\t\t0.000000\t160.721900\n", + " \t2\t\t0.000000\t160.121511\n", + " \t3\t\t0.000000\t59.071634\n", + " \t4\t\t0.000000\t77.997750\n", + " \t5\t\t0.000000\t6.450061\n", + " \t6\t\t0.000000\t10.844264\n", + " \t7\t\t0.000000\t27.921870\n", + " \t8\t\t0.000000\t21.332773\n", + " \t9\t\t0.000090\t28.867486\n", + " \t10\t\t0.039358\t23.243479\n", + " \t11\t\t0.036161\t27.902105\n", + " \t12\t\t0.028960\t56.236822\n" + ] + }, + { + "data": { + "image/png": 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\n", 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wweF2wLjz+LDn94vYZ6aHYR+ptJ3P7aUzbDtPYy9MDnBAxHdXu8f7h9jGbROwz8tv6STtjwP3RUzb6bwA/uUen6nYAHavu1/md3LteA14IGLaS8D/Rfzma9xtjsPeKNYBPwubZx62JH6Uu/2H3GVec79Pwd64PO2u42Bsd8V6Iq45sf6H/d2Gzv8N2EaZ+7Pzs/oSN733ucflFGwt2M1h86Rgg9tCdz9PwwaTdwEVNt/rwE29tO4/YUuGJ7nregBbCg1vrX8attR6mXueXeF+Pr6X0qSwNRDndXosunCwHqCDwOrOsxe2Oi3U3eY52uluE7FcEbaa5B9R1jmfzgOrwgbn1dhAsYro3W12K7C6n8/ENjJqxF5QjiSssYc7T08Dawq2Gu/hsGmTsD/iSuzNwwrsQ/aCsHkuxwbTBmzXmLnu+gvd788jSmB1vzsce7GpZUd3mj9hL7qZ2CC42s3vFmyLzxJ32SOwNQ7l7OhO8cvQSRgtn+zc3aaKdrrbRKSxq4HVifLvC/f7VGxL8i/cc60C+xx+Ttg6QlV2Ddg71OOIY2B1P4/CPstudNN2KfZ551+68Js9GfvootJd/kvsTWH4b3Ia9mLSgH0eeTjRGy+1F1hPdb9f0s73F2JvDhrddCwELukk3Udig2RWe+cF9kL5Mvac3Yi9kbiP2AXWG7Bdz2qw1dZ/AFLD5inEtuKux57/N7Frd5sj3X0a2vdnRB7f3vgXdp5G/psfMd907E1bI7aq86bwPLrzDMV2Xal198UThP1ew/ZX5D6N1brTsC3wN7nregc4KEqez8Pe6DS7+3qX628M03SUey5nd3YsQhfCuHC7lSzF7rD2ns2JLlJ2cIDLHMfx+qhY/Y5SagD20cevHcf5S7zT01uUHUXrOcdx/hTvtAgRTin1AvBfx3GidWvcSVcbL8WUUioDGI69c/ivBNXuU3YEpZ+yo/P+UdjS+9/imS4RG0qpU7AlyM+xz+euwZY+TEfLecAPsN2NhEgY7o3tu9havc7nj0eJVSl1Hraj9VLgTCesY25HtNb3Y+vctxhjJrrTCrDF9pHYqgltjKnUWivsM8kTsA+qzzPGfBTbnMSPOwDBc9jnnAOw1bYPAX9wdjQkEUlKKfVNbIOxkdgbpw+xz/o+i2e6hBCdi8tr4xzHecBxnBTHcSZ1Nai6HsA+/wr3S+B1Y8xY7MP0X7rTj8c2aBqLffb4991LdWJxbL+34xzHKXYcJ9NxnPGO49wkQdUbHMd53HGcfRzHyXaP8XESVIVIDkn1PlZjzFvs2g/uVHZ0Tn8Q21IsNP0hY4xjjHkPyNdaD+2blAohhOiv4vKMNcYGG2M2AhhjNmqtQ/3FhmNbU4asd6dtjFgerfVcbKkWY8wuryYSQgjRJbvdt98LvBBY2xPtAEd9oGyMuRvblQXA2bCh118j2ieKioqoqKjofMYo5s5byV5FWfz00GGdz9xHdic/icZLeQFv5cdLeYG+y8+wYYlzrYi3pKoKbsfmUBWv+39oVJn12D5vISPogxdve0Vxto+K+j55hagQQniKFwLrM9hxjHH/nxc2/RyttdJaTweqQ1XGonNFOWmUS2AVQohuS6qqYK31Y9jRRYq01uuxfftuBozW+kLs2MNz3NlfwHa1WYHtbnN+nyc4iRVnp7G1IUBr0CE1RR6bCCFEVyVVYDXGfKudr2ZFmdfBDgMneqA4J42gA9saAhTnpMU7OSKJOY5DY2MjwWAQ+w73xLZ582aamiLfNZK8Ypkfx3FISUkhMzMzKY5lvCRVYBV9pzjHnhoV9S0SWMVuaWxsJC0tDZ8vOS43Pp+P1NTUeCcjZmKdn0AgQGNjI1lZnb6WtN/ywjNW0QuK3GBavl3GmxC7JxgMJk1QFZ3z+XwEg8HOZ+zHJLCKqIqy7YVQGjCJ3SVVht4jx7RjElhFVNlpqeSmp0hgFUKIbpL6GdGu4pw0KrZLYBXJbdu2bZx11lkAlJeXk5qaSkFBAQDPP/886enpna7j8ssv59JLL2XPPffs1bQKb5DAKtpVlC19WUXyKygo4NVXXwXg1ltvJScnh4svvnineUIvqE5JiV6Jd/vtt/d6OoV3SFWwaFdxjo9yKbEKj1q9ejUzZ87kF7/4BcceeyybN2/m5z//OccccwxHHXXUTsH0tNNO47PPPiMQCDB+/HhuvPFGZs+ezcknn+yp4Q9FbEiJVbSrODuN+uYg21tayU7zTvcDET/Bx+/BKVsd03WqklGkfPP7PVp2+fLl3Hbbbdxyyy0A/OpXv6K4uJjGxkbmzJnDiSeeyLhx43ZapqamhunTp3PllVdy7bXX8vjjj/PDH/5wt/MhvENKrKJdoS43FfXS5UZ40x577MH+++/f9nnevHnMnj2b4447jq+++orly5fvskxmZiYzZ84EYN9996WsrGyXeUT/JiVW0a7QIBHl9S2U5mfEOTXCC3pasuwt2dnZbX+vWrWKe++9l5dffpmcnBx+9KMfRR2xKLyxU2pqKq2trX2SVpE8pMQq2lXcNkiEPGcV3ldXV0dubi4DBgxg8+bNzJ8/P95JEklKSqyiXQMzfaQoKJeqYNEPTJo0ibFjx3LkkUdSUlLClClT4p0kkaSU40R993d/Ji86D/P9/6xgn+JsLk+AF5576QXUXsoLdJyf7du371Tlmuh8Ph+BgHduJnsjP9GOqfuicxmSCakKFp0oyk6TqmAhhOgGCayiQ8U5aVIVLIQQ3SCBVXSoOCeNrdtbaA3KIwMhhOgKCayiQ0XZPlodqGqUUqsQQnSFBFbRobYuN1IdLIQQXSKBVXRoR2CVBkxCCNEVElhFh9pGX5KWwSKJbdmyhUsuuYRDDjmEGTNmcPbZZ7Ny5cpe3WZZWRkHHnggwWBwp+lHH300H3/8cbvLPfHEE1x11VUAPPTQQzz55JNR1x0aVrGj7f/73/9u+/zJJ59w9dVXdycLoodkgAjRoey0VHLSUqiQEqtIUo7jcOGFFzJnzhz+/ve/A/DZZ59RUVHBmDFj2uZrbW0lNTV2L5soKSlh2LBhLFy4kIMPPhiAFStWUF9fzwEHHNCldZxzzjk93n4osM6ZMweA/fbbj/3226/H6xNdJyVW0aminDTKt8szVpGc3nnnHdLS0nYKUhMnTmTatGksWLCAM888k0svvZRZs2YBcNdddzFz5kxmzpzJPffcA9gBEc4++2xmz57NzJkzmTdvHgA33ngjM2bMYPbs2fz2t7/dZdunnXZa27xgB/k/9dRTAXjllVc46aSTOOaYYzjrrLMoLy/fZflbb72Vu+66C4BPP/207VV1DzzwQNs8ZWVlnH766Rx77LEce+yxvP/++21pW7RoETNnzuTuu+9mwYIFbfugsrKSCy64gNmzZ3PSSSexbNmytu1dccUVnHnmmRx88MHcd999Pdvp/ZyUWEWnirN98oxVxMS9H2xmdWVjTNc5amAm3ztocLvff/nll0yaNKnd7xcvXswbb7xBaWkpn376KY899hjPPfccjuNw0kkncfDBB7N27VqGDBnCww8/DNhXx1VWVvLiiy/y1ltvoZSiurp6l3WffPLJHHvssfzud7/D5/PxzDPP8I9//AOAqVOn8uyzz6KU4tFHH+XOO+/kmmuuaTedV1xxBddffz0HH3ww119/fdv0oqIiHnvsMTIzM1m1ahWXXnopL774IldeeSV33XUXjz76KIFAgAULFrQtc+uttzJx4kTuv/9+3n77bS677LK2l8GvWLGCJ598kvr6eg4//HDOOecc0tLS2k2X2JUEVtGp4pw0vqxoiHcyhOgV+++/P6WlpQAsWrSIE044oW24vuOPP56FCxcyY8YMrr/+em644QZmz57NtGnTCAQCZGRk8LOf/YxZs2Yxe/bsXdY9aNAgxo0bx9tvv01RURFpaWnsvffeAGzcuJFLLrmELVu20Nzc3JaGaGpqaqiurm6rUj7jjDN48803AWhpaeGqq65i2bJlpKSksGrVqk7zvGjRorbS+GGHHUZlZSU1NTUAzJo1i4yMDDIyMigqKqK8vDw0XKHoIgmsolNFOWnUNgdpaAmSlSZPD0TPdVSy7C3jxo3j+eefb/f78DFv2xs7fcyYMbz44ou88cYb3HTTTRx55JFcfvnlPP/887z99tvMmzePf/7zn1EbGoWqg4uLi9uqgQGuvvpq5s6dyzHHHMOCBQu47bbb2k2j4zgoFX0Y3nvuuYfi4mJeffVVgsEgo0ePbnc9HeUztP6MjB2viJTX4vWMXCVFp4qz7f1XhbQMFknosMMOo7m5mUceeaRt2uLFi3n33Xd3mXf69Om8+OKLNDQ0sH37dl566SWmTZvGpk2byMrK4owzzuDiiy9myZIl1NfXU1tby6xZs7juuuvanlNGOuGEE3jjjTd45plndgqsNTU1DBkyBCBqQA7n9/vJy8tj0aJFADu19q2pqWHQoEGkpKTw1FNPtQXC3Nxc6uvro65v+vTpPP300wAsWLCAgoICBgwY0GEaRNdJiVV0Krwva4lfXngukotSinvvvZdrrrmGv/3tb2RkZDBixAiuu+46Nm3atNO8kyZN4pvf/CYnnngiAN/61reYOHEi8+fP53e/+x1KKdLS0rjpppuoq6vjggsuoKmpCcdx2n0+6vf7mTx5MuXl5TtV9/70pz/loosuYsiQIUyePJmysrIO83HbbbdxxRVXkJWVxYwZM9qmn3vuucydO5fnnnuOQw89tK0EPn78eFJTUznqqKOYM2cOEydObFvmiiuu4IorrmD27NlkZmbypz/9qVv7VHRMXhu3K3ltXITy+ha+95+VXDptCMfsmR+DlPWMl1615qW8gLw2LpHJa+P6nlQFi04VZIVeeC5VwUII0RkJrKJTqSmKgizpciOEEF0hgVV0SbEMEiF6SB43eY8c045JYBVdUpydJsMaih5JSUnx1DPL/i4QCJCSIqGjI9IqWHRJUY6PBWUtBB2HlHb60wkRTWZmJo2NjTQ1NbXbFzORZGRk0NTUFO9kxEws8+M4DikpKWRmZsZkfV4lgVV0SXFOGoEgVDW2UpAlp43oOqUUWVlZ8U5Gl/WnFtuid0h5XnRJcba8l1UIIbrCM0UPrfXlwPcAB1gCnA8MBR4HCoCPgLONMc1xS2QSC72XtaK+hb2Kkqf0IYQQfc0TJVat9XDgx8BBxpiJQCrwTeAW4HZjzFigErgwfqlMbkWh0ZdkWEMhhOiQJwKrywdkaa19QDawEZgJ/Mv9/kHgtDilLenlpKWQ5UuhvF5adwohREc8URVsjPlaa/1HYB3QALwCfAhUGWNCkWA9MDza8lrrucBcd10UFRX1fqL7gM/ni2lehvjLqAmouO2fWOcnnryUF/BWfryUF/BefpKBJwKr1nogcCowCqgCngSOjzJr1F7Nxpi7gbtD83ilBV2sWwMWZCi+rqyPWwtDL7Vu9FJewFv58VJeoO/yI+9s3cErVcGzgdXGmHJjTAvwNHAIkO9WDQOMALwxun6cFGWnSVWwEEJ0whMlVmwV8HStdTa2KngW8AHwJnAmtmXwucC8uKXQA4pzfNQ0tdIUCJLh69t7svfX11HSks6QtD7drBBCdJsnSqzGmIXYRkofYbvapGCrdn8BXKG1XgEUAvfFLZEeUBynlsFl1U3c/L/13PXOmj7drhBC9IRXSqwYY64BIt80vAqYGofkeFJokIiK+gAj8vrmhedBx+HOhZsIBGH1toY+2aYQQuwOT5RYRd8ocgeJ6MvRl95YVc2y8gbGFGRS1dBCdaM84xVCJDYJrKLLCrPTUPRdVXBVY4B/frSFCYOy+O5+trtAWbUMnCWESGwSWEWX+dpeeN43pcZ/friFxkCQS6YOocRvq57Lqr3z1hEhhDd55hmr6BtFOX3zXtbFG+uZv6YGPbGQEn8GjuOQnZ4qgVUIkfCkxCq6pTjH1+tVwU2BIHe9v4mhA9KYM7EQsK8eG1mQzTqpChZCJDgJrKJbirPTqKgPEHSiDmIVE/9aupWNtS1cMnUI6ak7TtFRBVlSYhVCJDwJrKJbinPSaAk61DS29sr611U38fSyrRw1Ko/9huTs9N2owmyqGlupaeqdbQshRCxIYBXd0tblpheqg4OOw98XbiLLlwAcc5QAACAASURBVML5kwft8v3IgmxAGjAJIRKbBFbRLaFBInqjL+trK22f1fMmD8KfuWu7ulESWIUQSUACq+iWtmENY9zlpqohwAMf2z6rs0b7o84zeEAGmb4UacAkhEhoElhFt+Smp5DpUzGvCr7/oy00BRx+MHUISqmo8yilKPGnS4lVCJHQJLCKblFKUZQd276sizfW8981NZw5oYAR/o7HIC7xZ8joS0KIhCaBVXRbcU7s3svaFAjy90WbGDYgnTMmFHY6f4k/ncqGAHXSMlgIkaAksIpui+UgEeazrWyqa+GSqYN36rPanlIZ2lAIkeAksIpuK85Oo7rRvvB8d6yrauLfy7Yyc3Qe+0b0WW1PiT/dLivVwUKIBCWBVXRbkdsyeOv2nlcHBx2HOxdtIjs9lfMP2LXPanuKc9LISFVSYhVCJCwJrKLbimMwSMSrK6r5vLyB8w8oJi9Kn9X2pCjlNmCSwCqESEwSWEW37e4gEZUNAR5cvIWJg7OZ2U6f1Y6U+NOlKlgIkbAksIpuK8z2oYCKHrYMvv9D22f1kqmD2+2z2pESfwbbGgLUNUvLYCFE4pHAKrotLTWF/KyetQz+aEMdb62tYc6EQkbkddxntT3SMlgIkcgksIoeKc72dbsq2L5ndbPbZ7Wgx9sOtQyWgSKEEIlIAqvokZ4MEvHEkgo217Xwg2mDSetCn9X2DMpNI11aBgshEpQEVtEjxTlpVGxvweniC8/XVDbyn8+3MWu0n0mDu9ZntT0p7pjB0oBJCJGIJLCKHinK9tHc6nTppeO2z+pmctJTOS/Ke1Z7oiRPutwIIRKTBFbRI915fdwrK6r4sqKB8ycPIi8jNSbbL8nPYOv2ANtbpGWwECKxSGAVPdIWWDtpGVzZEOChj8uZNDibo0blxWz70oBJCJGoJLCKHinOtqMldfb6uHs/3ExTq8MlHbxntSeky40QIlFJYBU9MiAjlfRU1WGXmw+/ruPttbXMmVjI8Lz0mG5/UI5tGbyuSgKrECKxSGAVPaKUsl1u2hmIP9RndUReOmfs0/M+q+1JTVEMz0uXqmAhRMKRwCp6rKNBIh5fUsGW+hZ+MHXIbvVZ7UipDMYvhEhAElhFjxXlpEV9xrqmspF5n29j9hg/EwZn99r2S/zplEvLYCFEgpHAKnqsOCeNysZWWlp3vPA89J7VnPRUzu3Ge1Z7osRtwLReqoOFEAlEAqvosbaWwWHPWV/+qoovKxq5IIZ9VtsjLYOFEIlIAqvosR2DRNjq4G0NAR5aXM6+Q7KZEcM+q+0ZnJtGWoqSoQ2FEAnFF+8ExIrWOh+4F5gIOMAFwJfAE8BIYA2gjTGVcUqi54QCa6jEeu8Hm2lpdbhkSmz7rLZnR8tgKbEKIRKHl0qsdwAvGWP2BvYDPgd+CbxujBkLvO5+FjFS6FYFl9e38MHXdbyzrhY9sZBhMe6z2hFpGSyESDSeCKxa6zzgCOA+AGNMszGmCjgVeNCd7UHgtPik0JvSU1PIz0xlfXUz/3h/EyPy0jm9F/qsdqTEn86W+gANLcHOZxZCiD7glarg0UA58E+t9X7Ah8BlwGBjzEYAY8xGrXXUZqpa67nAXHc+ioqK+ibVvczn8/V6Xob61/O/tTU4wN/OnMTQwf5e21a0/EwoAT6toC4li5KiAb227Vjri2PTl7yUHy/lBbyXn2TglcDqAyYDPzLGLNRa30E3qn2NMXcDd7sfnYqKil5IYt8rKiqit/OSn65wgKPH+BmR0dKr24uWH3+KrQZesnYLxanJUyXcF8emL3kpP17KC/RdfoYNG9br20gWnqgKBtYD640xC93P/8IG2s1a66EA7v9b4pQ+zxpdkEFhtq/X+6y2Z2huOr4UJc9ZhRAJwxOB1RizCSjTWu/lTpoFLAOeAc51p50LzItD8jxtzoRC/nHKGAb0cp/V9kjLYCFEovFKVTDAj4BHtNbpwCrgfOyNg9FaXwisA+bEMX2epJQiLT4xtU2JP52vtjbGNxFCCOHyTGA1xiwGDory1ay+TovoWyX+DN5ZW0tjIEimzxOVMEKIJCZXIZH0Sv3pOMiYwUKIxCCBVSS9EhkzWIiovtraQEW93HD2NQmsIukNHZCOL0UCqxDhHMfhTws2ctXzn8c7Kf2OBFaR9HwpimED0mUwfiHCLNm8nfU1zZwycUi8k9LvSGAVnlAiYwYLsZMXllcxID2F2eNk1KW+1qetgrXWqdjB8I81xshVUMRMqT+DBetqaQoEyZCWwaKfq9jewsL1tZy6dwEZvlRq452gfqZPr0DGmFZgVF9vV3hfidsy+OsaqQ4W4uWvqnAcOH5cfryT0i/Fox/rdcDftdbXYIcidEJfGGPkFSWiR0Itg9dVNzG6IDPOqREiflpaHV5ZUcWBw3IYnNt3r3AUO8QjsN7r/n922DSFDbBxHsNHJKuhA9JJVVAmDZhEP/duWS1Vja2cMG5gvJPSb8UjsI6KwzaFx6WlKoYOkDGDhXhxeSVDctM4YFhOvJPSb/V5YDXGrAXQWqcAg4HNUgUsYqE0P4M1lTJmsOi/1lQ2sqy8gfMnF5OiVLyT02/1eWDVWucBfwW+6W6/RWv9OPBjY0x1X6dHeEeJP533ymppbg2Snirt40T/88LyKtJTFbNGS6OleIrH1efPQA4wEcgCJgHZ7nQheqwkL4OgIy2DRf9U19zK/NXVHDEyL26vcRRWPJ6xHgeMNsZsdz8v11qfD6yMQ1qEh5Tmuy2Dq5oYNVBaBov+5c1V1TS1OtJoKQHEo8TaCBRHTCsCpNWJ2C3DBqSRIi2DRT8UdBxeWF7FXkWZjJHuZnEXr+42r2qtbwPWAnsAlwN3xyEtwkPSUlMYNiCdshq5RxP9y6ebtrOhtpnLJw2Nd1IE8QmsNwAbgG8Dw9y/fw/cH4e0CI8p8aeztkpKrKJ/eWF5Jf6MVA4tHRDvpAjiM1bwNcANxhgJpCLmSvwZLFxfR0trkDRpGSz6gfL6Ft7/uo5v7FMo53yCiMdYwZcCLX25XdF/lPilZbDoX176qgqA48ZKF5tEEY/bmweBi+OwXdEPlPrt2KjyblbRH7S0Bnl1RRVThudSnJMW7+QIVzyesU4FfqS1/jlQxs6D8B8Rh/QIDxmWl+62DJYGTML73llXS3WTjAucaOIRWO9x/wkRc+mpKQzJlTGDRf/wwvJKhg1IZ98h2fFOiggTj8ZLY7CNl+TKJ3pFiT9d+rIKz1u5rZEvKxr53oGDZFzgBCONl4TnlPoz2FDbTEur0/nMQiSpF5ZXkpGqOGq0P95JERGk8ZLwnBJ/OkEHNtRKqVV4U21TK2+tqWHGKD+56TIucKKRxkvCc0r8dszgsuom9nDHDxbCS15fVUVzq8MJ46SLTSJKpMZLUm8nYmK4tAwWHhZ0HF5cXsU+xVmMlJdNJKQ+qwrWWv8ZwBjzoDHmQcAX+tv9fGpfpUV4W4YvhcG5adKXVXjSxxvq2VTXwvHSxSZh9eUz1vMiPv8h4vPRfZQO0Q+U+DOkxCo86YXlleRnpnJwiYwLnKj6MrBGtgfv7LMQPVbqz2BDjbQMFt6yua6ZDzfUc8ye+aSlyiUzUfVlYI28wnX2WYgeK/Gn0+rAxjqpDhbe8eLyKpSScYETXV82XvJprY9iR8k08rO0GRcxUxrWMjj0txDJrCkQ5LWVVUwbMYDCbBkXOJH1ZWDdws7vXN0a8XlLH6ZFeNzwvHQUUFbVDKXxTo0Qu+/ttTXUNgeli00S6LPAaowZ2VfbEmJHy2BpwCS84YXlVZT405k0WMYFTnTyVlzhWdIyWHjF8ooGVmxr5PixA1EyLnDCi8cAEb3GHeT/A+BrY8xJWutRwONAAfARcLYxRlqz9BMl/nQ+3lhHIOjgS5GLkUheLyyvJNOXwlGj8+KdFNEFXiuxXgZ8Hvb5FuB2Y8xYoBK4MC6pEnFR4s8gEIRNMmawSGI1jQHeXlvLUaPyyE6TNp7JwDOBVWs9AjgRuNf9rICZwL/cWR4ETotP6kQ8hFoDy3NWkcxeXVlNS9CRl5knES9VBf8J+DkQGo6kEKgyxgTcz+uB4dEW1FrPBeYCGGMoKirq5aT2DZ/P55m8QPfzk+NvBdawtSXx9kN/PzaJLJHy0hp0eGXlag4Y4WfynlEvX51KpPz0F54IrFrrk4AtxpgPtdYz3MnRHqpFHYTCGHM3cHdonoqKitgnMg6KiorwSl6gZ/kZnJvGlxsrqahIrJaUcmwSVyLlZeH6WjbVNnHO/oU9TlNf5WfYsGG9vo1k4ZWq4EOBU7TWa7CNlWZiS7D5WuvQzcMIYEN8kifipSQvXQbjF0nrheVVFGT5mDZCxgVOJp4IrMaYXxljRrh9Zb8JvGGM+Q7wJnCmO9u5wLw4JVHESYk/g69rmmkNyoiZIrl8XdPM4o31HDs2X1q1JxlPBNYO/AK4Qmu9AvvM9b44p0f0sdL8DAJBR8YMFknnxa8qSVVwzJ4y0lKy8cQz1nDGmPnAfPfvVcDUeKZHxFeJPx2AsupmRuTJmMEiOTQGgryxsppDSgdQkOW5y7Tneb3EKvq5UDCVEZhEMnlrTQ31LUHpYpOkJLAKT8tKS2FQjs8Oxi9EEnAchxeWVzIyP4PxxVnxTo7oAQmswvNK/BmU1UiJVSSHL8obWF3ZxAnjZFzgZCWBVXheiT+D9dXSMlgkhxeWV5GTlsKRo2Rc4GQlgVV4Xok/nZagw+a6lngnRYgOVTUEWFBWw8zRfjJ9cnlOVnLkhOeV+KUBk0gOr6yoIhCE46XRUlKTwCo8L9TlRgbjF4msNejw0ooq9h+SzfC89HgnR+wGCazC87LTUinK9lEmQxuKBLZofR1btweki40HSGAV/UKJP0OqgkVCe2F5JcXZPg4anhvvpIjdJIFV9Aul/nTWy5jBIkGVVTfx6ebtHDd2IKkyLnDSk8Aq+oUSfwbNrQ5b6qVlsEg8Ly6vxJeiOHpPf7yTImJAAqvoF0rzpWWwSEzbW1p5Y1UNh5UOwJ8p4wJ7gQRW0S+MyAu1DJYGTCKx/Hd1DQ2BICfsJY2WvEICq+gXctJTKczySYlVJJTQuMBjCjIYV5gZ7+SIGJHAKvqNknxpGSwSy9ItDayrbpZxgT1GAqvoN0r86ZRVNxN0pGWwSAwvLK8kNz2Fw/eQcYG9RAKr6DdK3ZbB5dIyWCSArdtbeK+sltlj8smQcYE9RY6m6DfahjaUd7MmnU21zfx90Sae/WIbTYFgvJMTE6+sqCLowHFj8+OdFBFj0rZb9Bvhg/FPGSGj2ySD1qDDvC+28dinFQQdh0AQnvxsKyfvPZDjxw0kNz013knskUDQ4eUV1UwelsPQATIusNdIYBX9Rm56KgVZvn7z0vOlm7ez6Os6Ttl7IIXZafFOTret3NbIX9/byKrKJqaNyGXulMFsrmvhqaVb+b9PKnh62TaOH5vPKXsXkJ+VPJey7S2t/OfzbVQ2BDhh2pB4J0f0guQ5G4WIgRJ/er+oCq5qDHDL/76muqmVF5dX8o19Cjltn4KkeMdnYyDIY59W8MwX2/BnpPKLw4dxcMkAlFIUZacxYVA2q7Y18q+lW3l62Tae/bKS2WP8nD6+kEG5iXkD4TgOy7c28sqKKt5eW0NjwGHi4GwOGJoT76SJXiCBVfQrJf4MXltZRdBxSPFo9wbHcfjbwk1sbwly1ZHDeXN1DY8tqeClFVV8d78ijhrlT9jxaD/eWM+dCzexpb6FY/fM55wDiqNW944uyOTnhw/n65pmnl62lVdWVPHyV1UcMTKPb0wopNSt9o+32qZW5q+u5tUV1aytbiLTpzhsjzyO2TOfcYWZ0sXGoySwin6l1J9BY8C2DB6c681nW6+trGbR+joumDyIqSMGMHXEAD7fsp37P9rCX97bxLNfVHL+5EHsn0ClperGAPd/uIX5a2oYnpfOjbNLmTA4u9Plhuel86PpQ/nmpCLmfb6Nl1dU8ebqGqaX5HLmhELGFmb1Qep35jgOSzZv59WV1by7rpaWoMPYwkx+MHUIh48cQHZacj4XFl0ngVX0K6GWwWXVzZ4MrJtqm7n3wy1MGpzNyXvvGCJv/KBsfn/sHry9tpaHFpdzzRtlTB6aw3mTB7FHfvxKd47j8ObqGu7/aAsNLa2cNamQORMKSUvtXpV1cU4a3ztoMHMmFvLcl5U8v7yS98rq2G9INmdOKGTS4OxeLx1WNgR4Y1U1r66sYmNtCzlpKRy9p59j9sxn1EAZVak/kcAq+pXwlsFee+9la9DhT+9uJFXBZQcP3aWqWynF4SPzmF6Sy/PLKzFLtvKTF1Yze4yfb+9bzMA+bgAU6kKzeNN29i7K4tJpQ9peltBT/kwf39mvmNP3KeCl5VXM+2IbV79exrjCTM6cWMiU4bkxfQTQGnRYvLGeV1ZW8f76Olod2Kc4i7MmFnFI6QDpn9pPSWAV/cqAjFQGZqZ6cjD+f3++jc/LG7j8kKEU57TfiCctNYXTxhcyc3Q+ZkkFLyyv5K01NbaB0/iCXg8GrUGHeZ9v47ElFaQqxUVTBnPc2PyYBrzstFS+MaGQE/cayBurqnl62TZu/O/XlPrTOWNCIYfvkbdbz5nL61t4bWUVr62spmJ7AH9GKifvXcDRe/oZkZcYz3dF/EhgFf1Oid97Ywav2tbIY5+Wc2jpAI4c2bXh8fIyUvneQYM5YdxAHlq8hUc/reClr2wDpxm91MBpxdZG/rZw5y40Rb3YFSjDl8Lx4wZyzJ75/G9tDU8t3crtCzby6KcVnD6+gFlj/KR3sdo5EHR4f30dr66s4qMN9QDsNzSHCw4cxNThA0hLlYZIwpLAKvqdkvwMXl9ZjeM4nmiV2dwa5PYFGxiQ4ePiqUO6nadheen88ogRLHMbOP35vU08+6Vt4LTfkNg0cGoMBHn0k3Ke/bISf6aPXx4+nOkluX22/1NTFDNG+TliZB7vr6/jyaVbuev9zTyxpIJTxhdw3Nj8dhsVbaxt5pUVVbyxqpqqxlYKs3zMmVjI7DF+Tz6nF7tPAqvod0ry0mkMBKnYHuiwyjRZPPJJBeuqm7nmqBHkZfS8xek+YQ2cHl68hd+8XsZBw3I4d/Kg3eq+8tGGOv6+aHOnXWj6QopSTCsZwNQRuSzZvJ0nl27lwY/LeWrpVk4YN5CT9xpIEfZm5d11tbyysprPNm8nRcGU4bkcPSafycNyEra7kkgMElhFv1Ma1oAp2QPrp5vqmfe5HYFo8rDdb4yVohRHuA2cnvuykn99tpXLnl/N0WPy+fa+Rd0a4ai6McB9H27hv2tqGJGXzo1HlzJhUOddaPqCUop9h+Sw75Acllc08K+lWzGfbWXe59s4ZFQl76+rpK45yODcNL67XxEzR/uTcvQqER8SWEW/U+K2PF1X3RSTYBQv9c2t3PHuRoYOSOO8yYNiuu701BS+sU8hs0f7efyzrby0vJL/rqnhjAkFnLp3xw2c2rrQfLiZhkCwx11o+sq4oiyuPHIE66qaeGrZVj4oq2L/oTkcs2c+kwZne3YgEdF7JLCKficvIxV/ZiplSd4y+J4PNrOtIcDNx+zRa0MV5mX6mHvQYE4cN5AHP97CI5+EGjgVM2NU3i5BZ6PbheaTGHah6Sul+RlcfsgwioqKqKioiHdyRBKTwCr6pdIkbxm8YF0Nb66u4axJhexV1PujCw3PS+fKI0ewdLNt4HTHuxt59ottnD95EPsOySEQdHh66da2LjQXTxnMsTHuQiNEspDAKvqlEn8681fXJGXL4G0NAe5ctJk9CzLRE4v6dNsTBmfzh+P24H9ranh4cTlXuw2cqlvK+Kq8nmkjcrloymB5Hin6NU8EVq11CfAQMAQIAncbY+7QWhcATwAjgTWANsZUxiudInGU+DPY3hJka0OgV/tRxprjOPz1vY00BYJcfshQfHFonZqiFEeO8nNw6QCe/aKSfy3dSla67UJzcOmAPk+PEIkmMVsTdF8A+KkxZjwwHbhUa70P8EvgdWPMWOB197MQbS2D11UlV3Xwyyuq+HBDPecdMIgRcX6DS3pqCmdMKOTBM/bkqfMPkqAqhMsTgdUYs9EY85H7dy3wOTAcOBV40J3tQeC0+KRQJJrwwfiTxYaaZu7/cAv7D8nm+HH58U5Om/TUlIRt8StEPHiiKjic1nokcACwEBhsjNkINvhqraP2SdBazwXmuvNRVNS3z616i8/n80xeILb5KQLys9ZQ3qTiso+6m5dA0OFXr39Kui+Va0+cQHFuYrW09dK55qW8gPfykww8FVi11rnAU8BPjDE1WusuLWeMuRu42/3oeKWpvde6DcQ6PyMGpLF8c01c9lF382KWVLBsUy0/PXQYqrGWisbaXkxd93npXPNSXqDv8jNs2LBe30ay8Ez9jdY6DRtUHzHGPO1O3qy1Hup+PxTYEq/0icRT4s9gfXUTjuPEOykdWrG1kceXVHDEHnkc0cUB9oUQ8eOJwKq1VsB9wOfGmNvCvnoGONf9+1xgXl+nTSSuEn8G9S1BtjUE4p2UdjUF7AD7+Zk+LpoyON7JEUJ0gVeqgg8FzgaWaK0Xu9OuBG4GjNb6QmAdMCdO6RMJKLwBU6L2u3xocTnra5q5bmYJubsxwL4Qou94IrAaY94G2uvQN6sv0yKSR/hg/PsPjc3r0WJp8cZ6nvuykpP2GpiQ6RNCROeJqmAhesKfmcqAjFTWJeDQhnVNrfz53Y2MyEvnnP2L450cIUQ3SGAV/ZZSipK89ITsy/qP9zdT1RjgJ4cM7fBNMkKIxCO/WNGvlbiD8SdSy+C31tTw1toazppUxNjC3h9gXwgRWxJYRb9Wmp9OXXOQysbWeCcFgK3bW7jr/U2MK8zkzAmF8U6OEKIHJLCKfq0krAFTvAUdhz+/u5FAq8PlhwwjNQ4D7Ashdp8EVtGvlSZQYH1xeRWLN23n/MmDGJaXHu/kCCF6SAKr6NfyM1PJTU9hXVV8GzCtr27igY+3cOCwHI4bmzgD7Ashuk8Cq+jXlFJtDZjiJRB0uH3BRjJSFT+cPjTpXrwuhNiZBFbR75XGuWWw+ayCFdsa+cG0IRRkeWLMFiH6NQmsot8r8adT2xykOg4tg7+saODJz7YyY1Qeh5TKAPtCeIEEVtHvhVoG9/UITI2BIH9asIHCLB9zD5IB9oXwCgmsot8LH4y/Lz3w0RY21Lbw44OHkpMuA+wL4RUSWEW/V5DlIyctpU8bML23ppIXv6ri1L0Hsu8QGWBfCC+RlhKi3wu1DO6LqmDHcfi8vIE/LthEqT+d78oA+0J4jgRWIbDVwQvX1/Xa+jfXNfPm6hreXFXNproWctNT+fURJaSnSqWREF4jgVUIbAOmV1dWU90YwJ8Zm5/F9pZWFqyr5c1V1Xy2pQGASYOzOWtSESftP5LtNZUx2Y4QIrFIYBUCKM3f0TJ40m4E1tagw5LN23lzVTXvltXS1OowbEAa39m3iBmj/AzKTQMgOz2V7TFJuRAi0UhgFYKdWwZPGtz9xkTra5p4c1UNb66uZuv2ADlpKcwY5WfmaD97FWXKaEpC9CMSWIUACrN8ZHezZXBtUytvr63hjVXVLN/aSIqCA4bmcMHkQUwdkSvPT4XopySwCkGoZXA66zrpyxoIOny8oZ43VlezaH0dgaDDHvkZnD+5mCNH+hkoQxIK0e/JVUAIV4k/g/e/jt4yeHVlI2+squa/a2qobmwlLyOV48fmM3O0n1EDM6SqVwjRRgKrEK5SfwavraympjFAXqaPqoYA/11jn5uurmzClwJThudy1Gg/Bw7LxScvIhdCRCGBVQhXqAHTs19WsrqykQ831BN0YGxhJnMPGszhI/PIy5ChB4UQHZPAKoQrNBi/+WwrBVk+ThtfwFGj/ZS604UQoisksArhKs5J47KDhzIwy8e+g7NJlapeIUQPSGAVIszM0f54J0EIkeSko50QQggRQxJYhRBCiBiSwCqEEELEkARWIYQQIoYksAohhBAxJIFVCCGEiCEJrEIIIUQMSWAVQgghYkgCqxBCCBFDnh95SWt9HHAHkArca4y5Oc5JEkII4WGeDqxa61Tgb8DRwHrgfa31M8aYZfFNmRCiLzmOE/oDcMCJ+Nt+CY6D09SE09zU/Y306J287SzT4ao6+DJKGpzW1m6lSOw+TwdWYCqwwhizCkBr/ThwKhC3wOrU1xL8xff6ZFtblNpxQfEAL+XHS3mBvsqP4wbD0N9EBMew4OlEzt91W2KR1ARStf9UuPTX8U5Gv+L1wDocKAv7vB6YFjmT1nouMBfAGMOwYcN6N1VP/6931y+EECJuvN54KVqdyS63sMaYu40xBxljDtJaf+gul/T/vJQXr+XHS3nxWn68lJc45Efg/cC6HigJ+zwC2BCntAghhOgHvF4V/D4wVms9Cvga+Cbw7fgmSQghhJd5usRqjAkAPwReBj63k8zSTha7u9cT1ne8lBfwVn68lBfwVn68lBfwXn4SnvJSy0QhhBAi3jxdYhVCCCH6mgRWIYQQIoa83nipy7w09KHWugR4CBgCBIG7jTF3xDdVu8cdResD4GtjzEnxTs/u0FrnA/cCE7Hdvy4wxrwb31T1jNb6cuB72HwsAc43xjTGN1Vdp7W+HzgJ2GKMmehOKwCeAEYCawBtjKmMVxq7o538/AE4GWgGVmKPUVX8Uul9UmJlp6EPjwf2Ab6ltd4nvqnaLQHgp8aY8cB04NIkzw/AZdgGaF5wB/CSMWZvYD+SNF9a6+HAj4GD3It4KrblfTJ5ADguYtovgdeNMWOB193PyeIBds3Pq8BEY8y+wHLgV32dqP5GAqvVNvShMaYZCA19mJSMMRuNMR+5f9diL9zD45uqntNajwBOxJbykprWOg84ArgPwBjTnOSlBx+QpbX2AdkkWT9xY8xbwLaIyacCD7p/Pwic1qeJ2g3R8mOMecXtIQHwHrY/v+hFElitaEMfJm0gCqe1Hgkc4tQM/AAACUhJREFUACyMc1J2x5+An2OrtZPdaKAc+KfW+mOt9b1a65x4J6onjDFfA38E1gEbgWpjzCvxTVVMDDbGbAR7kwoMinN6YukC4MV4J8LrJLBa0YbiSvp+SFrrXOAp4CfGmJp4p6cntNah50UfxjstMeIDJgN/N8YcANSTXFWNbbTWA7Glu1HAMCBHa/3d+KZKtEdrfRX2MdEj8U6L10lgtTw39KHWOg0bVB8xxjwd7/TshkOBU7TWa7BV9DO11v8X3yTtlvXAemNMqAbhX9hAm4xmA6uNMeXGmBbgaeCQOKcpFjZrrYcCuP8n/QtvtNbnYhs1fccYk/SFhkQngdVqG/pQa52ObYDxTJzT1GNaa4V9hve5Mea2eKdndxhjfmWMGWGMGYk9Lm8YY5K2VGSM2QSUaa33cifNIo6vMdxN64DpWuts95ybRZI2xIrwDHCu+/e5wLw4pmW3uT0efgGcYozZHu/09Acy8pJLa30C9lleKnC/MeaGOCepx7TWhwH/w3Z/CD2XvNIY80L8UrX7tNYzgJ95oLvN/tiGWOnAKmz3h6TozhFJa30dcBa2ivFj4HvGmB68JTw+tNaPATOAImAzcA3wH8AApdibhznGmMgGTgmpnfz8CsgAtrqzvWeMuTguCewnJLAKIYQQMSRVwUIIIUQMSWAVQgghYkgCqxBCCBFDEliFEEKIGJLAKoQQQsSQvN1GJDWt9QPYARd+HYdtK+B+7FiyXxljpvbCNkqx/Vz9xpjWWM3rRW53rP8zxshYuCKuJLCKmHJHSMoCRhtj6t1p3wO+a4yZEcek9YbDgKOBEaG8htNan4ft13lYTzdgjFkH5MZ6XiFE75GqYNEbfNjXvCUV9/WB3bEHsCZaUO3FbQohEpyUWEVv+APwc631nZGvRHPftrMaSAu9ykprPR9bhXevW8r7PrAIOB/7CqzvAuOA67EjyPw/Y8yDYast0lq/in337EfAOcaYte669wb+AhyIfavM1cYY4373ANCADZBHYgeUfy0ivcOAu7Cl023ALcaYe7TWF2Lf4Zumta4DbjXGXBO23Hh3udD3AWNMfrRtaq0zgN8BY4Bq4D5jzLXR9pe7r/4HzAT2Bd4Fvm2MqejOvO66z3H3aS521LELsSXsnfaBO28GcAOg3WPwb+ByY0yD1voXwOnAYe52LwF+CBxojGnUWj8JHI6tyfgEuMQYszTsGGzHDuR/uPv9GdgXE5yLHT3oW8aYj9351wD/AM4GhmJHSbok2svV3WP3F+xr+uqA240xf3a/mwrciT2vGrBjal8RuQ4hekJKrKI3fADMB37Ww+WnAZ8ChcCj2MH3pwB7YoPsX90394R8BxsgioDFuG/vcF/H9qq7jkHAt4A7tdYTwpb9NjZgDADejpKWx7AD5w8DzgRu1FrPMsbcB1wMvGuMyQ0PqgDGmM8jvs/vYJv1wDlAPva9s5dorTt6B+i3sTcdg7DDIna0n6PO6774/k7svhsK+On4VYm3YIPQ/tjjMBz4jfvdH4Bm4Nda67HAjdiq/1CwexEY66bhI3Z9u4oGfo09fk3YG4CP3M//AiLHu/4OcCz2RmScu+zOK9Q6BXgWG6iHY8cx/onW+lh3ljuAO4wxee56TAd5F6JbpMQqestvgHe01nf0YNnVxph/AmitnwCuAn7rjkH7ita6GXtxX+zO/7z7gufQq7GqtdYl2DetrAmtC/hIa/0UNkAudafNM8a84/69U6nHXcdhwElukFistb4XW1p6vQf5Conc5vyw7z51x3s9Elsai+afxpjlbhoNcEoH22pv3jOBZ40xb7vf/Qb4cbQVuI20vg/sGxozV2t9I/aG5VfGmKBb+v0IO27w70MlTABjzP1h67oWqNRa+40x1e7kf4deC6i1/jfwA2PMQ+7nJ7Cl33B/NcaUud/fgC2VRgbXKUCxMea37udVWut7sC9yeBloAfbUWhe5Jfj32t2DQnSTBFbRK4wxn2mtn8NW6XX3jSebw/5ucNcXOS28xNr2knpjTJ3Wehu2hLkHME1rHV4d7QMejrZsFP+/vbsJjeqMwjj+FynVFDfxgxLFdmM3iitFLF22okIoCH3wcyOKoLgUoRsxUlRUquBKXbkIeBS6ERcKLd2UttCVFAoVowTjtxYDoouKi/NemYx3hqkzg4U8PwiEm7l37p0ZcvKec27OEPAkIiYbtt0GVnRyEW1MeU5Jq4AjwDJyVfkhcLHN/vcavn9O+4alVo8dYurr9lzSY+rNBwaAPyRV22aQAyuq/W9J+glYT6bIgTc15O+Ab8pxqqEQ88i0N7z9frd7r2Hq63e7XEuzT4Chpvd+Jpkah0x7jwB/SRoDDkbE5ZrjmP1nDqzWTwfIVcyJhm1Vo88AUA1f/7jL53kzS7ekiAfJebrjwM8R8VWbfdtNoZgABiXNaQiui4E7HZ5Xq2M3bx8FTgPrSk3yJBl4+ukuUI2uQ9JsMvVe5xEZ4JZGRO21l+lQq8mV/DFgV/nRZrJ2/SVwi0w5PyUD87tqnJ28mPrZyeNk5mNJ3QEi4m9gU0kZbwAuSZrbTSOaWcU1VuubiLgBXKAhxRgRD8nAtFXSTEnbyRpXN9ZL+qLM0j0E/FZShZeBzyRtk/RB+VpZGos6Of9x4BfgsKRZkpaTK53mGmEr94FF5bzamUOujF+UpprNHR6/G5eAYUmfl/M7SItgFxGvgLPA95IWAEhaWNUrJc0j5//uIBuOhkughby2l+TIsgGy/tqtPZIWSRoEviU/Y81+B55J2i9pdvmsLZO0spzzVknzy7VVq9ppd++v9YcDq/XbCPBR07adwD7yl+1SMnh1Y5RcHT8hu3+3AJRV5hqyrjZBpkWPkqnWTm0CPi37/wAciIhrHe77I1nLvSfpUZvH7QZGJE2Stem+N9KUrty9ZGPYXWASeEAGwTr7gRvAr5Kekd3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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "lambda_ = 100\n", + "theta = 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", + "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": "code", + "execution_count": 78, + "metadata": {}, + "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", + " for i in range(len(lambda_vec)):\n", + " lambda_=lambda_vec[i]\n", + " theta=trainLinearReg(linearRegCostFunction, X, y, lambda_)\n", + " J_train,_=linearRegCostFunction(X, y, theta, lambda_)\n", + " error_train[i]=J_train\n", + " J_val,_=linearRegCostFunction(Xval, yval, theta, lambda_)\n", + " error_val[i]=J_val\n", + "\n", + "\n", + " # ============================================================\n", + " return lambda_vec, error_train, error_val" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "lambda\t\tTrain Error\tValidation Error\n", + " 0.000000\t0.028960\t56.236822\n", + " 0.001000\t0.174794\t9.928476\n", + " 0.003000\t0.249933\t16.350931\n", + " 0.010000\t0.385063\t17.038971\n", + " 0.030000\t0.669275\t13.050844\n", + " 0.100000\t1.443470\t8.149168\n", + " 0.300000\t3.101591\t5.882391\n", + " 1.000000\t7.268148\t7.227429\n", + " 3.000000\t15.867688\t10.089368\n", + " 10.000000\t33.372203\t19.819800\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "lambda_vec, error_train, error_val = validationCurve(X_poly, y, X_poly_val, yval)\n", + "\n", + "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]))\n" + ] + } + ], + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Week 5/index.html b/Week 5/index.html new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/Week 5/index.html @@ -0,0 +1 @@ + diff --git a/Week 7/Data/emailSample1.txt b/Week 7/Data/emailSample1.txt new file mode 100644 index 000000000..eac52a354 --- /dev/null +++ b/Week 7/Data/emailSample1.txt @@ -0,0 +1,10 @@ +> Anyone knows how much it costs to host a web portal ? +> +Well, it depends on how many visitors you're expecting. +This can be anywhere from less than 10 bucks a month to a couple of $100. +You should checkout http://www.rackspace.com/ or perhaps Amazon EC2 +if youre running something big.. + +To unsubscribe yourself from this mailing list, send an email to: +groupname-unsubscribe@egroups.com + diff --git a/Week 7/Data/emailSample2.txt b/Week 7/Data/emailSample2.txt new file mode 100644 index 000000000..e47acdad8 --- /dev/null +++ b/Week 7/Data/emailSample2.txt @@ -0,0 +1,34 @@ +Folks, + +my first time posting - have a bit of Unix experience, but am new to Linux. + + +Just got a new PC at home - Dell box with Windows XP. Added a second hard disk +for Linux. Partitioned the disk and have installed Suse 7.2 from CD, which went +fine except it didn't pick up my monitor. + +I have a Dell branded E151FPp 15" LCD flat panel monitor and a nVidia GeForce4 +Ti4200 video card, both of which are probably too new to feature in Suse's default +set. I downloaded a driver from the nVidia website and installed it using RPM. +Then I ran Sax2 (as was recommended in some postings I found on the net), but +it still doesn't feature my video card in the available list. What next? + +Another problem. I have a Dell branded keyboard and if I hit Caps-Lock twice, +the whole machine crashes (in Linux, not Windows) - even the on/off switch is +inactive, leaving me to reach for the power cable instead. + +If anyone can help me in any way with these probs., I'd be really grateful - +I've searched the 'net but have run out of ideas. + +Or should I be going for a different version of Linux such as RedHat? Opinions +welcome. + +Thanks a lot, +Peter + +-- +Irish Linux Users' Group: ilug@linux.ie +http://www.linux.ie/mailman/listinfo/ilug for (un)subscription information. +List maintainer: listmaster@linux.ie + + diff --git a/Week 7/Data/ex6data1.mat b/Week 7/Data/ex6data1.mat new file mode 100644 index 000000000..ae0d2aae4 Binary files /dev/null and b/Week 7/Data/ex6data1.mat differ diff --git a/Week 7/Data/ex6data2.mat b/Week 7/Data/ex6data2.mat new file mode 100644 index 000000000..c6ad661ad Binary files /dev/null and b/Week 7/Data/ex6data2.mat differ diff --git a/Week 7/Data/ex6data3.mat b/Week 7/Data/ex6data3.mat new file mode 100644 index 000000000..a0441ac31 Binary files /dev/null and b/Week 7/Data/ex6data3.mat differ diff --git a/Week 7/Data/index.html b/Week 7/Data/index.html new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/Week 7/Data/index.html @@ -0,0 +1 @@ + diff --git a/Week 7/Data/spamSample1.txt b/Week 7/Data/spamSample1.txt new file mode 100644 index 000000000..bab0ca222 --- /dev/null +++ b/Week 7/Data/spamSample1.txt @@ -0,0 +1,42 @@ +Do You Want To Make $1000 Or More Per Week? + + + +If you are a motivated and qualified individual - I +will personally demonstrate to you a system that will +make you $1,000 per week or more! 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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", + "# tells matplotlib to embed plots within the notebook\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "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", + "data = loadmat(os.path.join('Data', 'ex6data1.mat'))\n", + "X, y = data['X'], data['y'][:, 0]\n", + "\n", + "# Plot training data\n", + "utils.plotData(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# You should try to change the C value below and see how the decision\n", + "# boundary varies (e.g., try C = 1000)\n", + "C = 1\n", + "\n", + "model = utils.svmTrain(X, y, C, utils.linearKernel, 1e-3, 20)\n", + "utils.visualizeBoundaryLinear(X, y, model)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "def gaussianKernel(x1, x2, sigma):\n", + " \"\"\"\n", + " Computes the radial basis function\n", + " Returns a radial basis function kernel between x1 and x2.\n", + " \n", + " Parameters\n", + " ----------\n", + " x1 : numpy ndarray\n", + " A vector of size (n, ), representing the first datapoint.\n", + " \n", + " x2 : numpy ndarray\n", + " A vector of size (n, ), representing the second datapoint.\n", + " \n", + " sigma : float\n", + " The bandwidth parameter for the Gaussian kernel.\n", + "\n", + " Returns\n", + " -------\n", + " sim : float\n", + " The computed RBF between the two provided data points.\n", + " \n", + " Instructions\n", + " ------------\n", + " Fill in this function to return the similarity between `x1` and `x2`\n", + " computed using a Gaussian kernel with bandwidth `sigma`.\n", + " \"\"\"\n", + " sim = 0\n", + " # ====================== YOUR CODE HERE ======================\n", + " temp= -(np.sum((x1-x2)**2))/(2*(sigma**2))\n", + " sim=np.exp(temp)\n", + " # =============================================================\n", + " return sim" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "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", + "sigma = 2\n", + "\n", + "sim = gaussianKernel(x1, x2, sigma)\n", + "\n", + "print('Gaussian Kernel between x1 = [1, 2, 1], x2 = [0, 4, -1], sigma = %0.2f:'\n", + " '\\n\\t%f\\n(for sigma = 2, this value should be about 0.324652)\\n' % (sigma, sim))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "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", + "data = loadmat(os.path.join('Data', 'ex6data2.mat'))\n", + "X, y = data['X'], data['y'][:, 0]\n", + "\n", + "# Plot training data\n", + "utils.plotData(X, y)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# SVM Parameters\n", + "C = 1\n", + "sigma = 0.1\n", + "\n", + "model= utils.svmTrain(X, y, C, gaussianKernel, args=(sigma,))\n", + "utils.visualizeBoundary(X, y, model)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "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", + "data = loadmat(os.path.join('Data', 'ex6data3.mat'))\n", + "X, y, Xval, yval = data['X'], data['y'][:, 0], data['Xval'], data['yval'][:, 0]\n", + "\n", + "# Plot training data\n", + "utils.plotData(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "def dataset3Params(X, y, Xval, yval):\n", + " \"\"\"\n", + " Returns your choice of C and sigma for Part 3 of the exercise \n", + " where you select the optimal (C, sigma) learning parameters to use for SVM\n", + " with RBF kernel.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " (m x n) matrix of training data where m is number of training examples, and \n", + " n is the number of features.\n", + " \n", + " y : array_like\n", + " (m, ) vector of labels for the training data.\n", + " \n", + " Xval : array_like\n", + " (mv x n) matrix of validation data where mv is the number of validation examples\n", + " and n is the number of features\n", + " \n", + " yval : array_like\n", + " (mv, ) vector of labels for the validation data.\n", + " \n", + " Returns\n", + " -------\n", + " C, sigma : float, float\n", + " The best performing values for the regularization parameter C and \n", + " RBF parameter sigma.\n", + " \n", + " Instructions\n", + " ------------\n", + " Fill in this function to return the optimal C and sigma learning \n", + " parameters found using the cross validation set.\n", + " You can use `svmPredict` to predict the labels on the cross\n", + " validation set. For example, \n", + " \n", + " predictions = svmPredict(model, Xval)\n", + "\n", + " will return the predictions on the cross validation set.\n", + " \n", + " Note\n", + " ----\n", + " You can compute the prediction error using \n", + " \n", + " 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", + " C_=np.array([0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30])\n", + " sigma_=np.array([ 0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30])\n", + " min=1\n", + " for i in range (0,len(C_)):\n", + " for j in range(0,len(sigma_)):\n", + " model= utils.svmTrain(X, y, C_[i], gaussianKernel, args=(sigma_[j],))\n", + " predictions = utils.svmPredict(model, Xval)\n", + " pred_error = np.mean(predictions != yval)\n", + " if(pred_error" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Try different SVM Parameters here\n", + "C, sigma = dataset3Params(X, y, Xval, yval)\n", + "\n", + "# Train the SVM\n", + "# model = utils.svmTrain(X, y, C, lambda x1, x2: gaussianKernel(x1, x2, sigma))\n", + "model = utils.svmTrain(X, y, C, gaussianKernel, args=(sigma,))\n", + "utils.visualizeBoundary(X, y, model)\n", + "print(C, sigma)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Spam Classification" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "def processEmail(email_contents, verbose=True):\n", + " \"\"\"\n", + " Preprocesses the body of an email and returns a list of indices \n", + " of the words contained in the email. \n", + " \n", + " Parameters\n", + " ----------\n", + " email_contents : str\n", + " A string containing one email. \n", + " \n", + " verbose : bool\n", + " If True, print the resulting email after processing.\n", + " \n", + " Returns\n", + " -------\n", + " word_indices : list\n", + " A list of integers containing the index of each word in the \n", + " email which is also present in the vocabulary.\n", + " \n", + " Instructions\n", + " ------------\n", + " Fill in this function to add the index of word to word_indices \n", + " if it is in the vocabulary. At this point of the code, you have \n", + " a stemmed word from the email in the variable word.\n", + " You should look up word in the vocabulary list (vocabList). \n", + " If a match exists, you should add the index of the word to the word_indices\n", + " list. Concretely, if word = 'action', then you should\n", + " look up the vocabulary list to find where in vocabList\n", + " 'action' appears. For example, if vocabList[18] =\n", + " 'action', then, you should add 18 to the word_indices \n", + " vector (e.g., word_indices.append(18)).\n", + " \n", + " Notes\n", + " -----\n", + " - vocabList[idx] returns a the word with index idx in the vocabulary list.\n", + " \n", + " - vocabList.index(word) return index of word `word` in the vocabulary list.\n", + " (A ValueError exception is raised if the word does not exist.)\n", + " \"\"\"\n", + " # Load Vocabulary\n", + " vocabList = utils.getVocabList()\n", + "\n", + " # Init return value\n", + " word_indices = []\n", + "\n", + " # ========================== Preprocess Email ===========================\n", + " # Find the Headers ( \\n\\n and remove )\n", + " # Uncomment the following lines if you are working with raw emails with the\n", + " # full headers\n", + " #hdrstart = email_contents.find(chr(10) + chr(10))\n", + " #email_contents = email_contents[hdrstart:]\n", + "\n", + " # Lower case\n", + " email_contents = email_contents.lower()\n", + " \n", + " # Strip all HTML\n", + " # Looks for any expression that starts with < and ends with > and replace\n", + " # and does not have any < or > in the tag it with a space\n", + " email_contents =re.compile('<[^<>]+>').sub(' ', email_contents)\n", + "\n", + " # Handle Numbers\n", + " # Look for one or more characters between 0-9\n", + " email_contents = re.compile('[0-9]+').sub(' number ', email_contents)\n", + "\n", + " # Handle URLS\n", + " # Look for strings starting with http:// or https://\n", + " email_contents = re.compile('(http|https)://[^\\s]*').sub(' httpaddr ', email_contents)\n", + "\n", + " # Handle Email Addresses\n", + " # Look for strings with @ in the middle\n", + " email_contents = re.compile('[^\\s]+@[^\\s]+').sub(' emailaddr ', email_contents)\n", + " \n", + " # Handle $ sign\n", + " email_contents = re.compile('[$]+').sub(' dollar ', email_contents)\n", + " \n", + " # get rid of any punctuation\n", + " email_contents = re.split('[ @$/#.-:&*+=\\[\\]?!(){},''\">_<;%\\n\\r]', email_contents)\n", + "\n", + " # remove any empty word string\n", + " email_contents = [word for word in email_contents if len(word) > 0]\n", + " \n", + " # Stem the email contents word by word\n", + " stemmer = utils.PorterStemmer()\n", + " processed_email = []\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", + " word = stemmer.stem(word)\n", + " processed_email.append(word)\n", + "\n", + " if len(word) < 1:\n", + " continue\n", + "\n", + " # Look up the word in the dictionary and add to word_indices if found\n", + " # ====================== YOUR CODE HERE ======================\n", + " for i in range(len(vocabList)):\n", + " if(vocabList[i]==word):\n", + " word_indices.append(i)\n", + "\n", + " # =============================================================\n", + "\n", + " if verbose:\n", + " print('----------------')\n", + " print('Processed email:')\n", + " print('----------------')\n", + " print(' '.join(processed_email))\n", + " return word_indices" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "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", + "# implement the preprocessing steps for each email. You should\n", + "# complete the code in processEmail.m to produce a word indices vector\n", + "# for a given email.\n", + "\n", + "# Extract Features\n", + "with open(os.path.join('Data', 'emailSample1.txt')) as fid:\n", + " file_contents = fid.read()\n", + "\n", + "word_indices = processEmail(file_contents)\n", + "\n", + "#Print Stats\n", + "print('-------------')\n", + "print('Word Indices:')\n", + "print('-------------')\n", + "print(word_indices)" + ] + } + ], + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Week 7/index.html b/Week 7/index.html new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/Week 7/index.html @@ -0,0 +1 @@ + diff --git a/Week 7/utils.py b/Week 7/utils.py new file mode 100644 index 000000000..359822cc0 --- /dev/null +++ b/Week 7/utils.py @@ -0,0 +1,683 @@ +import sys + +sys.path.append('..') +import numpy as np +from scipy.io import loadmat +from os.path import join +from matplotlib import pyplot + + +def plotData(X, y, grid=False): + """ + Plots the data points X and y into a new figure. Uses `+` for positive examples, and `o` for + negative examples. `X` is assumed to be a Mx2 matrix + + Parameters + ---------- + X : numpy ndarray + X is assumed to be a Mx2 matrix. + + y : numpy ndarray + The data labels. + + grid : bool (Optional) + Specify whether or not to show the grid in the plot. It is False by default. + + Notes + ----- + This was slightly modified such that it expects y=1 or y=0. + """ + # Find Indices of Positive and Negative Examples + pos = y == 1 + neg = y == 0 + + # Plot Examples + pyplot.plot(X[pos, 0], X[pos, 1], 'X', mew=1, ms=10, mec='k') + pyplot.plot(X[neg, 0], X[neg, 1], 'o', mew=1, mfc='y', ms=10, mec='k') + pyplot.grid(grid) + + +def svmTrain(X, Y, C, kernelFunction, tol=1e-3, max_passes=5, args=()): + """ + Trains an SVM classifier using a simplified version of the SMO algorithm. + + Parameters + --------- + X : numpy ndarray + (m x n) Matrix of training examples. Each row is a training example, and the + jth column holds the jth feature. + + Y : numpy ndarray + (m, ) A vector (1-D numpy array) containing 1 for positive examples and 0 for negative examples. + + C : float + The standard SVM regularization parameter. + + kernelFunction : func + A function handle which computes the kernel. The function should accept two vectors as + inputs, and returns a scalar as output. + + tol : float, optional + Tolerance value used for determining equality of floating point numbers. + + max_passes : int, optional + Controls the number of iterations over the dataset (without changes to alpha) + before the algorithm quits. + + args : tuple + Extra arguments required for the kernel function, such as the sigma parameter for a + Gaussian kernel. + + Returns + ------- + model : + The trained SVM model. + + Notes + ----- + This is a simplified version of the SMO algorithm for training SVMs. In practice, if + you want to train an SVM classifier, we recommend using an optimized package such as: + + - LIBSVM (http://www.csie.ntu.edu.tw/~cjlin/libsvm/) + - SVMLight (http://svmlight.joachims.org/) + - scikit-learn (http://scikit-learn.org/stable/modules/svm.html) which contains python wrappers + for the LIBSVM library. + """ + # make sure data is signed int + Y = Y.astype(int) + # Dataset size parameters + m, n = X.shape + + passes = 0 + E = np.zeros(m) + alphas = np.zeros(m) + b = 0 + + # Map 0 to -1 + Y[Y == 0] = -1 + + # Pre-compute the Kernel Matrix since our dataset is small + # (in practice, optimized SVM packages that handle large datasets + # gracefully will **not** do this) + + # We have implemented the optimized vectorized version of the Kernels here so + # that the SVM training will run faster + if kernelFunction.__name__ == 'linearKernel': + # Vectorized computation for the linear kernel + # This is equivalent to computing the kernel on every pair of examples + K = np.dot(X, X.T) + elif kernelFunction.__name__ == 'gaussianKernel': + # vectorized RBF Kernel + # This is equivalent to computing the kernel on every pair of examples + X2 = np.sum(X**2, axis=1) + K = X2 + X2[:, None] - 2 * np.dot(X, X.T) + + if len(args) > 0: + K /= 2*args[0]**2 + + K = np.exp(-K) + else: + K = np.zeros((m, m)) + for i in range(m): + for j in range(i, m): + K[i, j] = kernelFunction(X[i, :], X[j, :]) + K[j, i] = K[i, j] + + while passes < max_passes: + num_changed_alphas = 0 + for i in range(m): + E[i] = b + np.sum(alphas * Y * K[:, i]) - Y[i] + + if (Y[i]*E[i] < -tol and alphas[i] < C) or (Y[i]*E[i] > tol and alphas[i] > 0): + # select the alpha_j randomly + j = np.random.choice(list(range(i)) + list(range(i+1, m)), size=1)[0] + + E[j] = b + np.sum(alphas * Y * K[:, j]) - Y[j] + + alpha_i_old = alphas[i] + alpha_j_old = alphas[j] + + if Y[i] == Y[j]: + L = max(0, alphas[j] + alphas[i] - C) + H = min(C, alphas[j] + alphas[i]) + else: + L = max(0, alphas[j] - alphas[i]) + H = min(C, C + alphas[j] - alphas[i]) + + if L == H: + continue + + eta = 2 * K[i, j] - K[i, i] - K[j, j] + + # objective function positive definite, there will be a minimum along the direction + # of linear equality constrain, and eta will be greater than zero + # we are actually computing -eta here (so we skip of eta >= 0) + if eta >= 0: + continue + + alphas[j] -= Y[j] * (E[i] - E[j])/eta + alphas[j] = max(L, min(H, alphas[j])) + + if abs(alphas[j] - alpha_j_old) < tol: + alphas[j] = alpha_j_old + continue + alphas[i] += Y[i]*Y[j]*(alpha_j_old - alphas[j]) + + b1 = b - E[i] - Y[i]*(alphas[i] - alpha_i_old) * K[i, j] \ + - Y[j] * (alphas[j] - alpha_j_old) * K[i, j] + + b2 = b - E[j] - Y[i]*(alphas[i] - alpha_i_old) * K[i, j] \ + - Y[j] * (alphas[j] - alpha_j_old) * K[j, j] + + if 0 < alphas[i] < C: + b = b1 + elif 0 < alphas[j] < C: + b = b2 + else: + b = (b1 + b2)/2 + + num_changed_alphas += 1 + if num_changed_alphas == 0: + passes += 1 + else: + passes = 0 + + idx = alphas > 0 + model = {'X': X[idx, :], + 'y': Y[idx], + 'kernelFunction': kernelFunction, + 'b': b, + 'args': args, + 'alphas': alphas[idx], + 'w': np.dot(alphas * Y, X)} + return model + + +def svmPredict(model, X): + """ + Returns a vector of predictions using a trained SVM model. + + Parameters + ---------- + model : dict + The parameters of the trained svm model, as returned by the function svmTrain + + X : array_like + A (m x n) matrix where each example is a row. + + Returns + ------- + pred : array_like + A (m,) sized vector of predictions {0, 1} values. + """ + # check if we are getting a vector. If so, then assume we only need to do predictions + # for a single example + if X.ndim == 1: + X = X[np.newaxis, :] + + m = X.shape[0] + p = np.zeros(m) + pred = np.zeros(m) + + if model['kernelFunction'].__name__ == 'linearKernel': + # we can use the weights and bias directly if working with the linear kernel + p = np.dot(X, model['w']) + model['b'] + elif model['kernelFunction'].__name__ == 'gaussianKernel': + # vectorized RBF Kernel + # This is equivalent to computing the kernel on every pair of examples + X1 = np.sum(X**2, 1) + X2 = np.sum(model['X']**2, 1) + K = X2 + X1[:, None] - 2 * np.dot(X, model['X'].T) + + if len(model['args']) > 0: + K /= 2*model['args'][0]**2 + + K = np.exp(-K) + p = np.dot(K, model['alphas']*model['y']) + model['b'] + else: + # other non-linear kernel + for i in range(m): + predictions = 0 + for j in range(model['X'].shape[0]): + predictions += model['alphas'][j] * model['y'][j] \ + * model['kernelFunction'](X[i, :], model['X'][j, :]) + p[i] = predictions + + pred[p >= 0] = 1 + return pred + + +def linearKernel(x1, x2): + """ + Returns a linear kernel between x1 and x2. + + Parameters + ---------- + x1 : numpy ndarray + A 1-D vector. + + x2 : numpy ndarray + A 1-D vector of same size as x1. + + Returns + ------- + : float + The scalar amplitude. + """ + return np.dot(x1, x2) + + +def visualizeBoundaryLinear(X, y, model): + """ + Plots a linear decision boundary learned by the SVM. + + Parameters + ---------- + X : array_like + (m x 2) The training data with two features (to plot in a 2-D plane). + + y : array_like + (m, ) The data labels. + + model : dict + Dictionary of model variables learned by SVM. + """ + w, b = model['w'], model['b'] + xp = np.linspace(min(X[:, 0]), max(X[:, 0]), 100) + yp = -(w[0] * xp + b)/w[1] + + plotData(X, y) + pyplot.plot(xp, yp, '-b') + + +def visualizeBoundary(X, y, model): + """ + Plots a non-linear decision boundary learned by the SVM and overlays the data on it. + + Parameters + ---------- + X : array_like + (m x 2) The training data with two features (to plot in a 2-D plane). + + y : array_like + (m, ) The data labels. + + model : dict + Dictionary of model variables learned by SVM. + """ + plotData(X, y) + + # make classification predictions over a grid of values + x1plot = np.linspace(min(X[:, 0]), max(X[:, 0]), 100) + x2plot = np.linspace(min(X[:, 1]), max(X[:, 1]), 100) + X1, X2 = np.meshgrid(x1plot, x2plot) + + vals = np.zeros(X1.shape) + for i in range(X1.shape[1]): + this_X = np.stack((X1[:, i], X2[:, i]), axis=1) + vals[:, i] = svmPredict(model, this_X) + + pyplot.contour(X1, X2, vals, colors='y', linewidths=2) + pyplot.pcolormesh(X1, X2, vals, cmap='YlGnBu', alpha=0.25, edgecolors='None', lw=0) + pyplot.grid(False) + + +def getVocabList(): + """ + Reads the fixed vocabulary list in vocab.txt and returns a cell array of the words + % vocabList = GETVOCABLIST() reads the fixed vocabulary list in vocab.txt + % and returns a cell array of the words in vocabList. + + :return: + """ + vocabList = np.genfromtxt(join('Data', 'vocab.txt'), dtype=object) + return list(vocabList[:, 1].astype(str)) + + +class PorterStemmer: + """ + Porter Stemming Algorithm + + This is the Porter stemming algorithm, ported to Python from the + version coded up in ANSI C by the author. It may be be regarded + as canonical, in that it follows the algorithm presented in + + Porter, 1980, An algorithm for suffix stripping, Program, Vol. 14, + no. 3, pp 130-137, + + only differing from it at the points maked --DEPARTURE-- below. + + See also http://www.tartarus.org/~martin/PorterStemmer + + The algorithm as described in the paper could be exactly replicated + by adjusting the points of DEPARTURE, but this is barely necessary, + because (a) the points of DEPARTURE are definitely improvements, and + (b) no encoding of the Porter stemmer I have seen is anything like + as exact as this version, even with the points of DEPARTURE! + + Vivake Gupta (v@nano.com) + + Release 1: January 2001 + + Further adjustments by Santiago Bruno (bananabruno@gmail.com) + to allow word input not restricted to one word per line, leading + to: + + release 2: July 2008 + """ + def __init__(self): + """ + The main part of the stemming algorithm starts here. + b is a buffer holding a word to be stemmed. The letters are in b[k0], + b[k0+1] ... ending at b[k]. In fact k0 = 0 in this demo program. k is + readjusted downwards as the stemming progresses. Zero termination is + not in fact used in the algorithm. + + Note that only lower case sequences are stemmed. Forcing to lower case + should be done before stem(...) is called. + """ + self.b = "" # buffer for word to be stemmed + self.k = 0 + self.k0 = 0 + self.j = 0 # j is a general offset into the string + + def cons(self, i): + """cons(i) is TRUE <=> b[i] is a consonant.""" + if self.b[i] in 'aeiou': + return 0 + if self.b[i] == 'y': + if i == self.k0: + return 1 + else: + return not self.cons(i - 1) + return 1 + + def m(self): + """ + m() measures the number of consonant sequences between k0 and j. + if c is a consonant sequence and v a vowel sequence, and <..> + indicates arbitrary presence, + + gives 0 + vc gives 1 + vcvc gives 2 + vcvcvc gives 3 + .... + """ + n = 0 + i = self.k0 + while 1: + if i > self.j: + return n + if not self.cons(i): + break + i = i + 1 + i = i + 1 + while 1: + while 1: + if i > self.j: + return n + if self.cons(i): + break + i = i + 1 + i = i + 1 + n = n + 1 + while 1: + if i > self.j: + return n + if not self.cons(i): + break + i = i + 1 + i = i + 1 + + def vowelinstem(self): + """vowelinstem() is TRUE <=> k0,...j contains a vowel""" + for i in range(self.k0, self.j + 1): + if not self.cons(i): + return 1 + return 0 + + def doublec(self, j): + """ doublec(j) is TRUE <=> j,(j-1) contain a double consonant. """ + if j < (self.k0 + 1): + return 0 + if self.b[j] != self.b[j-1]: + return 0 + return self.cons(j) + + def cvc(self, i): + """ + cvc(i) is TRUE <=> i-2,i-1,i has the form consonant - vowel - consonant + and also if the second c is not w,x or y. this is used when trying to + restore an e at the end of a short e.g. + + cav(e), lov(e), hop(e), crim(e), but + snow, box, tray. + """ + if i < (self.k0 + 2) or not self.cons(i) or self.cons(i-1) or not self.cons(i-2): + return 0 + ch = self.b[i] + if ch in 'wxy': + return 0 + return 1 + + def ends(self, s): + """ends(s) is TRUE <=> k0,...k ends with the string s.""" + length = len(s) + if s[length - 1] != self.b[self.k]: # tiny speed-up + return 0 + if length > (self.k - self.k0 + 1): + return 0 + if self.b[self.k-length+1:self.k+1] != s: + return 0 + self.j = self.k - length + return 1 + + def setto(self, s): + """setto(s) sets (j+1),...k to the characters in the string s, readjusting k.""" + length = len(s) + self.b = self.b[:self.j+1] + s + self.b[self.j+length+1:] + self.k = self.j + length + + def r(self, s): + """r(s) is used further down.""" + if self.m() > 0: + self.setto(s) + + def step1ab(self): + """step1ab() gets rid of plurals and -ed or -ing. e.g. + + caresses -> caress + ponies -> poni + ties -> ti + caress -> caress + cats -> cat + + feed -> feed + agreed -> agree + disabled -> disable + + matting -> mat + mating -> mate + meeting -> meet + milling -> mill + messing -> mess + + meetings -> meet + """ + if self.b[self.k] == 's': + if self.ends("sses"): + self.k = self.k - 2 + elif self.ends("ies"): + self.setto("i") + elif self.b[self.k - 1] != 's': + self.k = self.k - 1 + if self.ends("eed"): + if self.m() > 0: + self.k = self.k - 1 + elif (self.ends("ed") or self.ends("ing")) and self.vowelinstem(): + self.k = self.j + if self.ends("at"): + self.setto("ate") + elif self.ends("bl"): + self.setto("ble") + elif self.ends("iz"): + self.setto("ize") + elif self.doublec(self.k): + self.k = self.k - 1 + ch = self.b[self.k] + if ch in 'lsz': + self.k += 1 + elif self.m() == 1 and self.cvc(self.k): + self.setto("e") + + def step1c(self): + """step1c() turns terminal y to i when there is another vowel in the stem.""" + if self.ends("y") and self.vowelinstem(): + self.b = self.b[:self.k] + 'i' + self.b[self.k+1:] + + def step2(self): + """step2() maps double suffices to single ones. + so -ization ( = -ize plus -ation) maps to -ize etc. note that the + string before the suffix must give m() > 0. + """ + if self.b[self.k - 1] == 'a': + if self.ends("ational"): self.r("ate") + elif self.ends("tional"): self.r("tion") + elif self.b[self.k - 1] == 'c': + if self.ends("enci"): self.r("ence") + elif self.ends("anci"): self.r("ance") + elif self.b[self.k - 1] == 'e': + if self.ends("izer"): self.r("ize") + elif self.b[self.k - 1] == 'l': + if self.ends("bli"): self.r("ble") # --DEPARTURE-- + # To match the published algorithm, replace this phrase with + # if self.ends("abli"): self.r("able") + elif self.ends("alli"): self.r("al") + elif self.ends("entli"): self.r("ent") + elif self.ends("eli"): self.r("e") + elif self.ends("ousli"): self.r("ous") + elif self.b[self.k - 1] == 'o': + if self.ends("ization"): self.r("ize") + elif self.ends("ation"): self.r("ate") + elif self.ends("ator"): self.r("ate") + elif self.b[self.k - 1] == 's': + if self.ends("alism"): self.r("al") + elif self.ends("iveness"): self.r("ive") + elif self.ends("fulness"): self.r("ful") + elif self.ends("ousness"): self.r("ous") + elif self.b[self.k - 1] == 't': + if self.ends("aliti"): self.r("al") + elif self.ends("iviti"): self.r("ive") + elif self.ends("biliti"): self.r("ble") + elif self.b[self.k - 1] == 'g': # --DEPARTURE-- + if self.ends("logi"): self.r("log") + # To match the published algorithm, delete this phrase + + def step3(self): + """step3() dels with -ic-, -full, -ness etc. similar strategy to step2.""" + if self.b[self.k] == 'e': + if self.ends("icate"): self.r("ic") + elif self.ends("ative"): self.r("") + elif self.ends("alize"): self.r("al") + elif self.b[self.k] == 'i': + if self.ends("iciti"): self.r("ic") + elif self.b[self.k] == 'l': + if self.ends("ical"): self.r("ic") + elif self.ends("ful"): self.r("") + elif self.b[self.k] == 's': + if self.ends("ness"): self.r("") + + def step4(self): + """step4() takes off -ant, -ence etc., in context vcvc.""" + if self.b[self.k - 1] == 'a': + if self.ends("al"): pass + else: return + elif self.b[self.k - 1] == 'c': + if self.ends("ance"): pass + elif self.ends("ence"): pass + else: return + elif self.b[self.k - 1] == 'e': + if self.ends("er"): pass + else: return + elif self.b[self.k - 1] == 'i': + if self.ends("ic"): pass + else: return + elif self.b[self.k - 1] == 'l': + if self.ends("able"): pass + elif self.ends("ible"): pass + else: return + elif self.b[self.k - 1] == 'n': + if self.ends("ant"): pass + elif self.ends("ement"): pass + elif self.ends("ment"): pass + elif self.ends("ent"): pass + else: return + elif self.b[self.k - 1] == 'o': + if self.ends("ion") and (self.b[self.j] == 's' or self.b[self.j] == 't'): pass + elif self.ends("ou"): pass + # takes care of -ous + else: return + elif self.b[self.k - 1] == 's': + if self.ends("ism"): pass + else: return + elif self.b[self.k - 1] == 't': + if self.ends("ate"): pass + elif self.ends("iti"): pass + else: return + elif self.b[self.k - 1] == 'u': + if self.ends("ous"): pass + else: return + elif self.b[self.k - 1] == 'v': + if self.ends("ive"): pass + else: return + elif self.b[self.k - 1] == 'z': + if self.ends("ize"): pass + else: return + else: + return + if self.m() > 1: + self.k = self.j + + def step5(self): + """step5() removes a final -e if m() > 1, and changes -ll to -l if + m() > 1. + """ + self.j = self.k + if self.b[self.k] == 'e': + a = self.m() + if a > 1 or (a == 1 and not self.cvc(self.k-1)): + self.k = self.k - 1 + if self.b[self.k] == 'l' and self.doublec(self.k) and self.m() > 1: + self.k = self.k -1 + + def stem(self, p, i=0, j=None): + """In stem(p,i,j), p is a char pointer, and the string to be stemmed + is from p[i] to p[j] inclusive. Typically i is zero and j is the + offset to the last character of a string, (p[j+1] == '\0'). The + stemmer adjusts the characters p[i] ... p[j] and returns the new + end-point of the string, k. Stemming never increases word length, so + i <= k <= j. To turn the stemmer into a module, declare 'stem' as + extern, and delete the remainder of this file. + """ + # copy the parameters into statics + self.b = p + self.k = j or len(p) - 1 + self.k0 = i + if self.k <= self.k0 + 1: + return self.b # --DEPARTURE-- + + # With this line, strings of length 1 or 2 don't go through the + # stemming process, although no mention is made of this in the + # published algorithm. Remove the line to match the published + # algorithm. + + self.step1ab() + self.step1c() + self.step2() + self.step3() + self.step4() + self.step5() + return self.b[self.k0:self.k+1] + + diff --git a/Week 8/Week 8.ipynb b/Week 8/Week 8.ipynb new file mode 100644 index 000000000..73820c225 --- /dev/null +++ b/Week 8/Week 8.ipynb @@ -0,0 +1,6889 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 94, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "# used for manipulating directory paths\n", + "import os\n", + "\n", + "# Scientific and vector computation for python\n", + "import numpy as np\n", + "\n", + "# Import regular expressions to process emails\n", + "import re\n", + "\n", + "# Plotting library\n", + "from matplotlib import pyplot\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "import matplotlib as mpl\n", + "\n", + "from IPython.display import HTML, display, clear_output\n", + "\n", + "try:\n", + " pyplot.rcParams[\"animation.html\"] = \"jshtml\"\n", + "except ValueError:\n", + " pyplot.rcParams[\"animation.html\"] = \"html5\"\n", + "\n", + "# Optimization module in scipy\n", + "from scipy import optimize\n", + "\n", + "# will be used to load MATLAB mat datafile format\n", + "from scipy.io import loadmat\n", + "\n", + "# library written for this exercise providing additional functions for assignment submission, and others\n", + "import utils\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "# tells matplotlib to embed plots within the notebook\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# K-Means" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "def findClosestCentroids(X, centroids):\n", + " \"\"\"\n", + " Computes the centroid memberships for every example.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The dataset of size (m, n) where each row is a single example. \n", + " That is, we have m examples each of n dimensions.\n", + " \n", + " centroids : array_like\n", + " The k-means centroids of size (K, n). K is the number\n", + " of clusters, and n is the the data dimension.\n", + " \n", + " Returns\n", + " -------\n", + " idx : array_like\n", + " A vector of size (m, ) which holds the centroids assignment for each\n", + " example (row) in the dataset X.\n", + " \n", + " Instructions\n", + " ------------\n", + " Go over every example, find its closest centroid, and store\n", + " the index inside `idx` at the appropriate location.\n", + " Concretely, idx[i] should contain the index of the centroid\n", + " closest to example i. Hence, it should be a value in the \n", + " range 0..K-1\n", + "\n", + " Note\n", + " ----\n", + " You can use a for-loop over the examples to compute this.\n", + " \"\"\"\n", + " # Set K\n", + " K = centroids.shape[0]\n", + "\n", + " # You need to return the following variables correctly.\n", + " idx = np.zeros(X.shape[0], dtype=int)\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " for i in range(X.shape[0]):\n", + " position=0\n", + " closest=centroids[0,:]\n", + " shortest=np.sum(np.square(X[i,:]-centroids[0,:]))\n", + " for j in range(K):\n", + " dist=np.sum(np.square(X[i,:]-centroids[j,:]))\n", + " if(dist\n", + "\n", + "\n", + "\n", + "\n", + "
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\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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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", + "\n", + "# Settings for running K-Means\n", + "K = 3\n", + "max_iters = 10\n", + "\n", + "# For consistency, here we set centroids to specific values\n", + "# but in practice you want to generate them automatically, such as by\n", + "# settings them to be random examples (as can be seen in\n", + "# kMeansInitCentroids).\n", + "initial_centroids = np.array([[3, 3], [6, 2], [8, 5]])\n", + "\n", + "\n", + "# Run K-Means algorithm. The 'true' at the end tells our function to plot\n", + "# the progress of K-Means\n", + "centroids, idx, anim = utils.runkMeans(X, initial_centroids,\n", + " findClosestCentroids, computeCentroids, max_iters, True)\n", + "anim" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "def kMeansInitCentroids(X, K):\n", + " \"\"\"\n", + " This function initializes K centroids that are to be used in K-means on the dataset x.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like \n", + " The dataset of size (m x n).\n", + " \n", + " K : int\n", + " The number of clusters.\n", + " \n", + " Returns\n", + " -------\n", + " centroids : array_like\n", + " Centroids of the clusters. This is a matrix of size (K x n).\n", + " \n", + " Instructions\n", + " ------------\n", + " You should set centroids to randomly chosen examples from the dataset X.\n", + " \"\"\"\n", + " m, n = X.shape\n", + " \n", + " # You should return this values correctly\n", + " centroids = np.zeros((K, n))\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " # Initialize the centroids to be random examples\n", + "\n", + " # Randomly reorder the indices of examples\n", + " randidx = np.random.permutation(X.shape[0])\n", + " # Take the first K examples as centroids\n", + " centroids = X[randidx[:K], :]\n", + "\n", + " \n", + " # =============================================================\n", + " return centroids" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "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", + "K = 16\n", + "max_iters = 10\n", + "\n", + "# Load an image of a bird\n", + "# Change the file name and path to experiment with your own images\n", + "A = mpl.image.imread(os.path.join('Data', 'bird_small.png'))\n", + "# ==========================================================\n", + "\n", + "# Divide by 255 so that all values are in the range 0 - 1\n", + "A /= 255\n", + "\n", + "# Reshape the image into an Nx3 matrix where N = number of pixels.\n", + "# Each row will contain the Red, Green and Blue pixel values\n", + "# This gives us our dataset matrix X that we will use K-Means on.\n", + "X = A.reshape(-1, 3)\n", + "\n", + "# When using K-Means, it is important to randomly initialize centroids\n", + "# You should complete the code in kMeansInitCentroids above before proceeding\n", + "initial_centroids = kMeansInitCentroids(X, K)\n", + "\n", + "# Run K-Means\n", + "centroids, idx = utils.runkMeans(X, initial_centroids,\n", + " findClosestCentroids,\n", + " computeCentroids,\n", + " max_iters)\n", + "\n", + "# We can now recover the image from the indices (idx) by mapping each pixel\n", + "# (specified by its index in idx) to the centroid value\n", + "# Reshape the recovered image into proper dimensions\n", + "X_recovered = centroids[idx, :].reshape(A.shape)\n", + "\n", + "# Display the original image, rescale back by 255\n", + "fig, ax = pyplot.subplots(1, 2, figsize=(8, 4))\n", + "ax[0].imshow(A*255)\n", + "ax[0].set_title('Original')\n", + "ax[0].grid(False)\n", + "\n", + "# Display compressed image, rescale back by 255\n", + "ax[1].imshow(X_recovered*255)\n", + "ax[1].set_title('Compressed, with %d colors' % K)\n", + "ax[1].grid(False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PCA" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "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", + "X = data['X']\n", + "\n", + "# Visualize the example dataset\n", + "pyplot.plot(X[:, 0], X[:, 1], 'bo', ms=10, mec='k', mew=1)\n", + "pyplot.axis([0.5, 6.5, 2, 8])\n", + "pyplot.gca().set_aspect('equal')\n", + "pyplot.grid(False)" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [], + "source": [ + "def pca(X):\n", + " \"\"\"\n", + " Run principal component analysis.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The dataset to be used for computing PCA. It has dimensions (m x n)\n", + " where m is the number of examples (observations) and n is \n", + " the number of features.\n", + " \n", + " Returns\n", + " -------\n", + " U : array_like\n", + " The eigenvectors, representing the computed principal components\n", + " of X. U has dimensions (n x n) where each column is a single \n", + " principal component.\n", + " \n", + " S : array_like\n", + " A vector of size n, contaning the singular values for each\n", + " principal component. Note this is the diagonal of the matrix we \n", + " mentioned in class.\n", + " \n", + " Instructions\n", + " ------------\n", + " You should first compute the covariance matrix. Then, you\n", + " should use the \"svd\" function to compute the eigenvectors\n", + " and eigenvalues of the covariance matrix. \n", + "\n", + " Notes\n", + " -----\n", + " When computing the covariance matrix, remember to divide by m (the\n", + " number of examples).\n", + " \"\"\"\n", + " # Useful values\n", + " m, n = X.shape\n", + "\n", + " # You need to return the following variables correctly.\n", + " U = np.zeros(n)\n", + " S = np.zeros(n)\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " Sigma=(1/m)*(np.dot(X.T,X))\n", + " U, S, V = np.linalg.svd(Sigma)\n", + " \n", + " # ============================================================\n", + " return U, S" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "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", + "\n", + "# Run PCA\n", + "U, S = pca(X_norm)\n", + "\n", + "# Draw the eigenvectors centered at mean of data. These lines show the\n", + "# directions of maximum variations in the dataset.\n", + "fig, ax = pyplot.subplots()\n", + "ax.plot(X[:, 0], X[:, 1], 'bo', ms=10, mec='k', mew=0.25)\n", + "\n", + "for i in range(2):\n", + " ax.arrow(mu[0], mu[1], 1.5 * S[i]*U[0, i], 1.5 * S[i]*U[1, i],\n", + " head_width=0.25, head_length=0.2, fc='k', ec='k', lw=2, zorder=1000)\n", + "\n", + "ax.axis([0.5, 6.5, 2, 8])\n", + "ax.set_aspect('equal')\n", + "ax.grid(False)\n", + "\n", + "print('Top eigenvector: U[:, 0] = [{:.6f} {:.6f}]'.format(U[0, 0], U[1, 0]))\n", + "print(' (you should expect to see [-0.707107 -0.707107])')" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [], + "source": [ + "def projectData(X, U, K):\n", + " \"\"\"\n", + " Computes the reduced data representation when projecting only \n", + " on to the top K eigenvectors.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The input dataset of shape (m x n). The dataset is assumed to be \n", + " normalized.\n", + " \n", + " U : array_like\n", + " The computed eigenvectors using PCA. This is a matrix of \n", + " shape (n x n). Each column in the matrix represents a single\n", + " eigenvector (or a single principal component).\n", + " \n", + " K : int\n", + " Number of dimensions to project onto. Must be smaller than n.\n", + " \n", + " Returns\n", + " -------\n", + " Z : array_like\n", + " The projects of the dataset onto the top K eigenvectors. \n", + " This will be a matrix of shape (m x k).\n", + " \n", + " Instructions\n", + " ------------\n", + " Compute the projection of the data using only the top K \n", + " eigenvectors in U (first K columns). \n", + " For the i-th example X[i,:], the projection on to the k-th \n", + " eigenvector is given as follows:\n", + " \n", + " x = X[i, :]\n", + " projection_k = np.dot(x, U[:, k])\n", + "\n", + " \"\"\"\n", + " # You need to return the following variables correctly.\n", + " Z = np.zeros((X.shape[0], K))\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " \n", + " Z=np.dot(X,U[:, :K])\n", + " \n", + " # =============================================================\n", + " return Z" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "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", + "Z = projectData(X_norm, U, K)\n", + "print('Projection of the first example: {:.6f}'.format(Z[0, 0]))\n", + "print('(this value should be about : 1.481274)')" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [], + "source": [ + "def recoverData(Z, U, K):\n", + " \"\"\"\n", + " Recovers an approximation of the original data when using the \n", + " projected data.\n", + " \n", + " Parameters\n", + " ----------\n", + " Z : array_like\n", + " The reduced data after applying PCA. This is a matrix\n", + " of shape (m x K).\n", + " \n", + " U : array_like\n", + " The eigenvectors (principal components) computed by PCA.\n", + " This is a matrix of shape (n x n) where each column represents\n", + " a single eigenvector.\n", + " \n", + " K : int\n", + " The number of principal components retained\n", + " (should be less than n).\n", + " \n", + " Returns\n", + " -------\n", + " X_rec : array_like\n", + " The recovered data after transformation back to the original \n", + " dataset space. This is a matrix of shape (m x n), where m is \n", + " the number of examples and n is the dimensions (number of\n", + " features) of original datatset.\n", + " \n", + " Instructions\n", + " ------------\n", + " Compute the approximation of the data by projecting back\n", + " onto the original space using the top K eigenvectors in U.\n", + " For the i-th example Z[i,:], the (approximate)\n", + " recovered data for dimension j is given as follows:\n", + "\n", + " v = Z[i, :]\n", + " recovered_j = np.dot(v, U[j, :K])\n", + "\n", + " Notice that U[j, :K] is a vector of size K.\n", + " \"\"\"\n", + " # You need to return the following variables correctly.\n", + " X_rec = np.zeros((Z.shape[0], U.shape[0]))\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + "\n", + " X_rec=np.dot(Z,U[:,:K].T)\n", + "\n", + " # =============================================================\n", + " return X_rec" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Approximation of the first example: [-1.047419 -1.047419]\n", + " (this value should be about [-1.047419 -1.047419])\n" + ] + }, + { + "data": { + "image/png": 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U5S9nqTGyYNasasu4fPlyOXjwoPd+CUFCw0x5TMV/zHBcYG9ALa9xrqn9LBAjcIVASZUMcvVd7GHemy1yihgpBXkE5BOQoyZO9s58p3zL6wpVwfJk/Pjjj2XHjh0uy1ZaWir33HOP1tBqUF2YaZ+ZcpvzxoWbgUFY/VAWf15eU7Xzvj1W2SOAm4E/OL3e1XcZMAB+fddZVpqRtCYHA9xOX34REk+cHOPSRbPhN7+xXjx4sHXa0tGj5UORYWFhDB8+nK+//rrKtY0x3HzzzQwbNozMzEzPfOkmQsNMuc15hDAZa+PCwdgDzZ+X11TdDvsj4CywAWgNPIf1fSzVfZcXrk8lTo5xPLILxUC7qHPkrv6HY4nSihXWCydNggkTnOaRjRkzhjfffJPhw4fzzTffVLn2uHHjeOONNxg2bBhHjx5tyNdtUjTMlNuqjhDehRUA//L75TVVt8M+BSQAJ4C/AHHAM8Afa/4uZUuQ2k0Zy7bzzqPrmW/o9/gvrWkXAD/9ZO0cm5zs8u1jxoxh0aJFdOjQweXz48aNY926dbRv355z58658xWbLA0z5TbXy2vuIiZmHNddt5DY2L3VvdXnqm6HPRV4HCvQsoAMoDkhIe9zzz1F1U9StS9BSk/nhlWr2NmmDc9mZiJbt1qPR0dbG43VMCF22LBhtGnThtGjR7tscl5++eVkZWXRo0cPbXLWhauONG/fdAAgOFQeIczIEFm0aJF06NBB9uzZ4+viuVT9SOzH0qxZtlx9tcjw4cfkqqv6y5VXXinPPfec9cb8fJEPPxSZM0ckJUXk5EnHFtfx8ZL1m99IfFiYzAIpjY62nq+jpUuXSlxcXLWDAjptwxk6mqkaiz3Q9u3b5+uiuFTdHLlHHimUpKQkOXjwoJw5c0YGDx4s0dHRMvfuu0UiI53fEBcn8t57Tnv2Z4HEh4bKnF//2u0yLV26VNq3by/Z2dkun1+yZInccMMNVTaCbIqqCzPdNlt5xcqVKxkwYADR0dG+LopL1S3a/uMf/8jcuS9y5ZUbKM1vwfYvu5Av51gH3Fj5InFx8O23sHp1+Z79JwcM4GhODj169MAY49YZAYcOHaJLly7k5OSQmRlTZdvt668vorS0lEOHDnHppZd68tcRUHTbbNXoioqKZNiwYX7b5HRl+nSRiIg3pDkd5QDny88gbUBuAdlvjEh4uHMNLSXF5XWeeeYZmTlzpts1qcOHD0vLlhdIq1Y7XK5IWL9+fZNvcqLzzFRjCwsLY8KECQwePJi9e/1zUKDimsz+/eHtt4Fzk/iCs3Qli2jgEHCMUDaJWNW4iAjHBarZWPHhhx8mLS2NJ554wurPqaMffuiIyBvk5Q0Hx2rO8v3XmjUbWD5tQwcFKnGVcN6+ac2saVm0aJF07txZ8vPzfV0UJ676zkBkNr8XAdkD0hXkIEghzeRwi0usF3TtWmvNTEQkKytLevXqJampqXUuk2N1xVKBYdWuSFiyZIm8/PLLDf0VBCS0z0z50k8//UTnzp3Jyckhxg9m1WZkwKhRVWf4R3CW47RjF3m8STOuo4TLgJuAUkz5houANe3i5Mkap1/k5ubSqlUrDh8+TKdOnWrtQ0tIgA0b7PdKgGZVnl+3znF/9erVXHDBBcTHx9fyjYNHdX1m2sxUjaJz587s27ePK664wi+anFVXAgiRFPASjxFFHtcAR4jkOgw3AQLOQQbWLP9aNlaMjo7GGMNdd93Fk08+WWuT03keXLMqz1f+O5CXl6dNTjtX1TVv37SZ2XT5yzw052181klrEuQQ7aq0OY/QTkoqt/WMEXn7bbc+LysrS+Lj42XWrFk1DgrUZ0cS+zy0zMxMd38NAQmdZ6b8xaJFi+SWW27xaRkq7vzRnNMyjOYyGuQ7OouAnKXSqKX91revWxNiK8rKypKhQ4fKkSNHanxdffaKW7FihRw6dEhErEBMSrICOynJuh9MNMyUXykpKZGjR4/K3r17ffL5FWtA40mRQpCBREsYD8tRrImw50IiRECKW5a9sF07lxsrustms8lHH31UYw3N1eqK2pSWlkqPHhOlVauv3QrCQKNhpvzO0qVLfdrktNeAZjNHBGQuMyQq6qS8MmGL08z+8hn/W7Z45HNPnTolvXr1qrXJ6a6NG0VatPhEIE6gaqD546aZ9aFhpvySt/vQqm1yla21PDh5jqzqOlUE5EB0vGxaV2Q9n5Mj0tlqcsrUqR6pkVVU1z40dziazvZAOxZQm2bWVXVh1mhHzSnlyl133QXAt99+y2WXXebRa7s6Du6rdQW8e8V8hn/zAuTncxFwEUBICF1zM+k6rQ8MHQppaY5tfF56qU7nWrqjbdu2pKens2DBAo9d07G90Vis06faOT3vz5tmeoSrhPP2TWtmypW33367Sg2tvp3ZrkYFe7NFfq48YtmypUj37tZ/h4R4rWlZk23btskzzzzT4Bqa83bmVW/BXjPTMFN+o3KTsyEnQDn/wy6VMJbJzxVOUMoLjRZbz55SAJLdPE72t7hSBORw0lTHNj8eblpWx75SoD5rOSsKxINm6kPDTAUEe6ClpHzfoH+YzvPICuQCLpBpIKUVLvRqWJRcT6Tkg6zgZhGQOc3n+GTkzx5oc+bMadB1AukIwPrSMFMBY+XKlXLzzbmVQsz55KTamkwVa2aDSJMskGuhPNAmgmwFGU8zSQDZXzYdYzwpPqvFZGVlyZ49e8RmszWohlafaR2BRMNMBZQBAwoFhgjsEes4uKsFfioPqISEmt9vb3L1I0NKMCIg2SAzQQpB/gmSD1IEcifI6yA/EycRFPi8f+mpp57y+LSNYFJdmOnaTOWX2rQJB+7EOvXpNDARa5/+fwO1nwBlPw5uFSPL11S2JJR5WMu3+wDNgf7AJGAssYzkM85hjVr6cuTv0UcfZfXq1XVay6kcNMyUX7JOgLKf+pQITME6fOR2oqOlTidAvXB9KjFYqVQIHGtxEQtaRDEC63C5ZsBcYDTNuYjFbKNP+Xt9ubGHfdrG6tWrWblype8KEmA0zJRfcpwAdRfWQcORwCSioj5j0qQSfvGLY7VfpMLGic8Yw9yC/dzTtTMdW7ViJFAA9CaKsyyliEnAGQC/OC6vbdu2bNq0iREjRvDjjz9qDa0ONMyU35o/3zqtLSnpQvr23UtERE/efPM4Q4asoV+/fvz73/+u+QL24+BCQ5ktwm7god27WZCXR0fgT8BtLVZRzM3ALiCKqKh/M3ky1R8x14iioqIAuPvuu93esbYp0hUAyq/1728Plst4//3/x6xZg0lPT2fq1KkkJCSwfv16OnXq5PrNycnWDP5jx4gKDeVzm43ZQBGwEDBr1hB7rC3hfy0gPz+G0NADbN16PcnJn+Li+BKfMMbw6aefMmTIEACef/55tw5JaVJcjQp4+6ajmaq+Fi1aJLfddpuIiMyfP99xruWJEyJTpogMHmytpbRv07Ol6qLx41FRMmfyZCkpKZGHHnpIBg0aVL6ld1pamsTGxsqmTZt88fWqlZWVJYmJifLzzz/7uig+h07NUMGipKREjhw5Inv27JHSvDz5KilJfqo8szYkxDrXUsSayZ+SUj6zP/fYMenXr5888MADUlRUJHfeeWeVQLvzzjt9+A2rV1xcLIsXL27S0zY0zFRQWbp0qXSIjZU9550nr4FcDFaghYY6joMLCal2I8Xc3Fzp16+fTJkyRWw2mzz88MPy7bffOr3m4MGDsnnz5sb4OnV26tQpj++2EWg0zFRwKSiQRdHR0gHrFKWXQkLk8ubNZTmIxMaKRFgbK8rUqdVeIjc3V9asWSMi1saGJSUlMn/+/PIa2rp16yQ2NlYy/GwKvTe2Dwok1YWZjmYqv1PxLMvkZOt+Famp3JWby7zmzfkemNG7N++sXcv5l1wCJ05wtk0HALYv/q7aa0RFRTF06FA++eQTpkyZQklJCTt37mTkyJEUFBSQkJDA4sWLGTt2LJs2bfLqd3aHfR5au3bWFj91+n01Ba4Szts3rZmp6rhaKB0WJnLrrZVeOMfaHVauvtr6GRFhbbj4+OPyHMjrWNv5vM7UWhdb25uclfvQzp49KyIia9euldWrV3v3i9fThAlbpHnz3wmUBu3C8srQZmbjCfYDJbylpi1soCzQynaIlXHjrAd79HDsQxYRIbbmkfIDSGeQNzDSmpN12m3DHmgvvfSS2Gw2WbhwoZSUlDi95g9/+INfjXJu3CgSFZUlEC8wq0qg+Vnr2GM0zBpJU9iCxVtq21zw+mZbpLBNXNUn2lU9Im4fRlrSVmB1nXfbyM3NlZycHMnOzpaSkhL5/vvvZezYsX47bcPx+7IH2nNu7SwSqKoLM+0z86CMjKrbNIN1f8EC8KNuF7/k2PYZYCXwKvA/wGHO4ySfl9xEePYx6NLFWm/Uvbv10uPHy99VZML4K+PoSxb5ZGItVHeoaQF5VFQU0dHRPPbYY/zmN7+hS5cutGjRorwP7aabbiIlJYXk5GR27tzpke/cEI7fV1sgHRjn9HzQb5NdiUfCzBiTaIz5zhiz3xjzhCeuGYiqnpK9EWu+ufX4yy/7olSBw/k076uA14EdRHA9X3EhrcsWjZ87dIisDz6ARYvgyiutl48bBykp3Dkih9tYwmnaAB2pvMilLgvIX331Vfbs2cODDz7IggUL6NixI/fccw8AN910E3/729+47LLLKCkpadgXbiDn31db4BdOz/tysbxPuKquuXPD2nzgB6AbEA5kApfX9J5gbWY6725aKjBGIFmgsE57cDV1Gzdanf2O3+EhiaCDjAO5BOR42ROLQkKkJ8ix2FiRadOsF5ft0OqpraNzc3Nl4MCBsmXLFrHZbHLgwAGx2WxSULaVdnFxsfTp08enTc6msk12ZXixmdkX2C8iB0SkCPgYGO2B6wYc57+UBvgIsAG3A0VN7y+lmwYMsKYW2J1HS3ZykteBP2BoA5SER3BnaSkTQkL4z4kTsHQpAC8v60ZCglU7Hjasaq0kJga3FpBHRUWxbt06+vTpw8aNG+nSpQvvvfceI0aMoKCggNDQUJ577jnGjBnD5s2bPfL93eXYWcT5cXe/a9BwlXDu3LAa6u9UuH8n8EZN7wnWmpnrv5SFAtMlKioroP9SNuYI7a23Wp39p4gu/0XeB3KwbMdY+4TYUpDfg+witnyHWHut5NZbPbN1dHFxsdx44401Ln3q3Lmz5OXlefA34J5g3ya7Mrw1mgnc4iLMXnfxugeAbcC2zp07N9LXbnzVjWY++GCOzJ49WwoLC31dRLc1+ghtQUGVUctzYc2lfIlS2WOlIDNpLhF0k8oH3nqymeVqHtr//u//lj+fk5MjpaWl8uOPP3rmA1WNvBlmNwBrKtx/EniypvcEa83MztVfysLCQhk1apQkJSUFVKD5pF8mJcX6gC5d5CzIX0JCrFOV7EuUmjUTATkbEikxZAn8TuAKgWyvTU3Izc2VRx55RPLz88Vms0lJSYns2rWrvIa2f/9+v1z6FIy8GWahwAGgK44BgCtqek+wh1l17IGWnJwcMGvqnOd+5Qqs8v5cJvvs/mnTJDs2Vq4BmWmMc5KGhMhDvTIqDLakCticXuKNAZejR4/Ks88+KyUlJfLggw86NTnXrl2rgdYIqguzBg8AiIgNeAhYA+wB/ioiuxt63WAUHh7OkiVLuO+++zDG2P8Y+DXnuV9HgfuBD8sf8cpcJvsOsevWcd7y5ayNjWVIxd9VZCRs2MDhi+w93AZIwhpYd/DGgEuLFi1IS0tjypQpvPLKK3Ts2LF8HtqQIUNYvHgxCxcu9PwHq9q5Sjhv35pqzayi9PR0GTduXKM1OevbgV91Vv5ugQ4CH3ivZlZQ4NhQMT5e5NFHRTp3lkUg08PDpTQrq/w7+WJqgr0PberUqWKz2eS3v/2t7N271+k1P/zwg99tHxQs0OVM/qWwsFBGjhwpycnJXg+0hnTguw6M3QJ/qVNg1HsU1MUOsdmxsXLNZZfJ9OnTy5vpvlo+lpubK2vXrhURa/sgm80mL774YnmTMz093a+WPgUTDTM/ZA+0xx9/3Guf4YnaS3WBMXLkUvnggw/cfl+dg6bSDrFSUCDZ2dlyzTXXyFtvvVX+Ml9OTfj4449rnLahgeZ5GhiscDoAABpZSURBVGZ+qrCwUE6ePCk5OTleqaHVtni7rs1EV4Gxe/du6dChg8tA82YTMDs7W/Ly8uTUqVM+H0ipbfugtLQ0+eKLL3xaxmCjYebnnnzySa9M23BeYvWCwBqPjvjZA+3TTz91erxqiBaX/3ck+fLytR861bjq49Zbb5UZM2b4TaDNnz9fbDabvP/++1XKNG/ePB3l9BANMz/nrXlozqGyWSDWKdA80YG/Z88eOXr0qNP+X84hahO4ViBNbiRDTlGpyhYXZ/WRuenkyZNyzTXX+E2gnTlzRk6ePCklJSXy3XffyZgxY3TahhdomAWAwsJCSUpKks8//9xj16za3LMH2j89PuI3duzY8iZn1ZrZJgmjtXxOpflioaGOQKtHDe3kyZNy/fXXy65du8q/ry83xrznnnuq7UOzB9rOnTsbt1BBRsMsQNhrGOnp6R6roVXtiM+U6Ohz8uijxR65vl3FPrTKIRpBgfyNFtIF5BgR8tNtM6xpFxUDLSWlXp9rrxGOG7dOoqNLnbKysTfGdNWHZj/nU0Rk69atcu7cObHZbD4P3kClYRZASktLJSkpyaPTNip34K9fb5PevXuXn07kKbt375aOHTvKvn37nEJ0PNYSpTyQddfOEBGRjenpUnLVVY7kKdvGpz7Wrj0nISHXCMyQittHe3vOmSv27YO2bdsmNptNPv74oIwaVSzx8QVywQUi8fHFEh5+jURGbvJp8AYqDbMA4415aJVrAm+8sVliY2M9HminT58WEZGffvpJNqdZnf3bWg92/KuNj5eSc+ckISFB7r3iCimxP17PmpmIvVl7UsB1oDX2FtL2GvbYsWkSHV0i8GeBQQL5ZWVKK2vuVw007VarmYZZACosLJTHHntMsrOzG3yt6uZ83XbbZunSpYvk5OR4oMQO+fn50ikuTj6Ijnb+0AqBlvfww/IrY+RekJLo6HqPaopUHHA4KfCwwDmPjtrWR3p6sTRrdqPA/QJFAne6CLQuAnk+Dd5Ao2EWwE6fPi1PPvlkvWtotc35WrMmT0pLS+X777/3XKELCmR327bSAeSDzp2tNLX3jVW45YE8CJLTwEEPT82n8ySrTLkClQPtjQply61SVt2RuGbVhZkeaBIAIiMj+de//sXtt99OUVGR2++vejaBQ04OvPVWS3766Sd++ctfkpaW5ta1qz2ANjWVy0+eZO1ll/HBpZdS/Pzz1pOhznvyt4yO5o2MDGx9+zJnzpx676s/bVr1C8tjYqzzTxqbtUg/Cvhb2U8bsBCYWuFVUVXepzsS15OrhPP2TWtm7rP3oY0ZM8btOVXOc77+KXCb2M8lqFgT2LzZvT60Gpcr2bfxmTnT+U32PfsHD3aaMJuXlye/+tWv5N57761yXmVd+dsxf7XVFqurKWufWc3QZmbgKywsLD9Z251Ac/5HVSgwSiCpPNAqNsE2b94s9957b63XrNx0jSRfJvChzGaOTI5Mkb1PLBR735gUFVlvKipyTMdw0dlvD7TJkyfX+btV5k9bSNd2qLGrINPRzNppmAWRtWvXypgxY+rch1b1H5U90B6qtiawf//+GtcUVgzI3myRH3E+iPdU83Yibdo4Au3xxx1BVsME2by8PFm1alV5uQN9Hpar2qL91qKFyMUX+0fwBhINsyBSn6VPVf9RFUpU1BF5+OECl9f4xz/+UWOT0950jaBAtnG+XAyyjB7yPDPla8pCq02bqqeN13Hp0ujRKyQsbLJAScDXXOy1xauvFrngAuunhlf9aZgFGXugzazcJ1UDV02wOXPmVBuKNfWh2Wtm9smwT9NJ4EKBfRJKkRyILgu0hQurbONTm40bRaKj8wR+JXBvlUDTEGjaNMyCUGFhoWRnZ8upU6fqPW2jtlreG29sln790qs09exN19lYHf3PM1PgHYE0iYkR+Wn849b/XvWY1e9owtoDba7Pp1ko/1FdmOnUjAAWHh7Oeeedx7x587j99ttJTy9yPU2ilmssWbKE0tJSVqxY4fTcjBkwe3Y//v73QWzYMI9ly9IYNcp63H4A7dFIa7/+YawhlLuIibmJ++8pptO3ZVM87Pv5u8Fx7kBLYBXOUxm8dO6ACnyuEs7bN62ZeVZhYaF06zZSQkOTnaZcuNPHZLPZRETkq6++ksLCwhp221gj7aPzZc9T1n5ke59YaHX2gxyIjrdqZHXo6K+JP06AVf6DampmxnqucfXu3Vu2bdvW6J8brDIyYOTIInJzb8OqxdxU/lxMDHz2GfTvX+3bndx6660UFxdTWvp/rFgRXuGZM4SzhVBGs4oSBnLO8VSbNtbP7GzHY3Fx1gf36VOv7zNqlOsamLvfRwUfY8x2Eeld+XFtZgaBV16B3Nxw4FOsIFsDWCsFcnLg5Zfrfq0PP/yQ0tJS/vGP28qvATmE052PGcdX5PNLzlEM0LIldO9uhVhoKCxcCHPmQEoKHDxYryADRxO28kz4mBjrcQ0y5YqGWRBw9DEZ4CfgNmA0FQOtrux9aFFRFwJWTSuCcD6kkF9zmlNAFrFc36wFp/PzITcXrrwSjh+H8HB46imYMME627IB5s+3amBJSVYfYFKSdX/+/AZdVgUxDbMg0Lp1xXudgRnAJuyB5u5av/DwcN5773Wio2OAuYzjQ27hNAuIYCbwSdgdzFtxmNbx8XDsGHTubL3xwIGGf5kK+veH1FRYt876qTUyVRMNsyBQdZH108As4Fuiowvqtch6wAC4995mtG22ltNMoQgYyTn+Djxoe4Oh5+2FoUOtF2/fbv2sx8ilUp6iYRYEXPcxPU10dCbjxxfy178+XK/dNl6dV8KP0XsJoYTbgFNh52NCmtFMbFY16b33rBcePWp1+Ccne+DbKFU/GmZBwlUf08qVrRk4cD0LFy4kKSnJ/UBLTaXVqeMs6dmTiTExnFecRcjF3cAYKCmBEyes17VrZ314A/vJlGqI0NpfogJF//6u+pVu5/vvv+eFF14gOTmZlStXYoyp2wXL+sDC/+u/GDtuHIwcCd9/73je3uH/2GMaZMrnNMyagKeffhpjDIcPH8YYY00wrEug2fvA1qyBuXOt6RZLlsCsWVbT8s9/hnvu8WrZlaornTTbhGRkwNSp8/n553XceGMqM2aEM2BADW84exa6drVGLOPjrQ7/tDTIzLT6yA4e1BqZanQ6abaJmzHDmlW/e3cx2dkbWbEiiZEji5gxo4Y3RUZafWFxcVaAvfiiI8i0j0z5GW1mNgEZGbBggX3y7BNAMfACubkzWLDgdcYkFtDveKpV0+rWzRqVtAdVnz7W46mpVh9a5eeV8hMaZk2A84EmghVoUcBAeuT8jcuHj4eSXMcbKq+rjIy0ZvUr5ce0mdkEOJY7ASwAxgJTGMYhCrmZCSW55aswCQ21+shGjrT6zJQKEBpmTYDzcqe7gVC60JtlJJGMtfDpVvvTNpsj0FJTG7mkStWfNjObgGnTYP16q6kZSTFPcAk7SeXPWAufmgFOiwdsNuunh9daKuVNGmZNgH25044/b+Wj/BG05zjFWNXy/cCdYWF0Ki6u+kZda6kCiDYzm4j5c8+ypvlI2nMcgDCsMc33gf7FxRwID3d+Q0yMrrVUAaVBYWaMucUYs9sYU2qMqTKJTfmR1FTCs49B+/YAlAI7gXBgJjCoqAh7o1KAnXN1HpkKLA2tmf0LGAPU4egM5VP2/q/e1t+c+4DdwFNAT+AWYCtWkA1nJQOf6l/zhFql/EyDwkxE9ojId54qjPIie//XoUMQF8eTgH3qWQGwCOgE3MTdrKEHOTnWRNtNm3xSWqXcpn1mwaygwNqPf+5cKCqyturZtQuio+neogXTgS+AI8DbhDOQ1qQTCwwCDrh9foBSvlTraKYx5gugvYunZovI8rp+kDHmAeABgM72bZaV92zdak18PXbM8VibNtatwjY+7UwkYySMszyPjUuAi4CuWIG2gZycixq12ErVV61hJiJDPPFBIvI28DZYu2Z44ppNWUaGtUzp9GlrUuy0aTh2wDh5EoYMsQ4b6dIFxoyxNtLPzLRqZwsXwuHD0K0bz32czJnPDmOF1wtYpzt1B84HWrt9foBSPuPqME13b8AGoHddX6+HADfM9OmVD+itcODvli1OT9pAfh0ZKYeXL3cczpuSUn4tx2G/+wQOCpSWXzMkpFDatBktH330g+++rFKVUM0hwA2dmpFsjDkM3ACsMsasaXC6qho574DhUJRTwNk3F1Lyq0GOJ/v2pVl8PBedPUvCmDEcsS8crzCz33F+QHesJub9WOs3obQ0nOzsoUyYMIjJk3U1gPJzrhLO2zetmdVfUlLFGplN4A9yNRvlZ9o5VdXOgvxf+/Yi+fki8fEyD6RHaKgUVaqZ2WVkiNx4o0hIyD6BCwXeqXC5P4oxXWXt2gIffGOlnOGNmplqfM47YEAI39CSwUSVzewvfx3w1NGj/OHiiyE8nCeAT202wuLiOJuYWOW6/fvD+edDaWl3YB3wP8DXZc9ORWQNf/xjJPn5+R7/Tkp5goZZgHHeAaMZs+nCxdgYCZyr8Ex7YD1w+dGj1sgmcGFoNOPOe4l2XW5g6dL/VLm2Iyi7A5nA1RWe7c6RI3vo2bMnB3QBuvJDGmYBpuKBvxGc5UleYgFwJyG8E/ZbJhqDfZ5rR2BEZCQLgWeJoJPtIEv3TiQvbxK33prAAw84B5pzULap8tkdOvTg8ccfZ9CgQRpoyu9omAWYigf+JpNKJIU0A0bQhn2Tn6bfQ6/yLIbyuS9nz9KL85lHLKf5S9mDsygtncx77z3pNMO/6snoDjExMH06TJ06lZkzZzJr1izvfUml6sNVR5q3bzoA0ED5+bLnqQ9lZ+zg8g7/QpCuIeFyb+goOUpbOQWyBeQtHpAICgQOC3QX+LBCx36hJCYelf/85z/ll65x2kcFxcXFcuLECTlw4EAjf3nV1FHNAICGWaDZskUkLs45bcpuO0DiQD4B2QxyPkg4ayu85IjAGae3XXrp29K9e3enQMvIsEZNExKsnxkZrouyePFi6dKli/zwg85DU42nujDTzRkDydmzjiVKvXrBoEHw2mtgsyFY3fWfA/cCmzHEMZ8sJgBLgf7ABVUu2aPH/Vx33UkGDhzIhg0b6NChQzUno1c1fvx4Tp06xaBBg1i3bh3ddDNH5UMaZoEkNdURZFu2QFgYjBsHAwZgyra6vhpIJ5pEVrKb/sCVwPdYYebM3g/Wv/8ThISEsH37djp06OBWkaZOnYoxhh07dtCtW7eal1kp5U2uqmvevmkzs57mzLHahjNnioiIzWazmofTpomArGWwjCelrI/MZUu0xn4wEZH33ntPDh8+XK/iJSYukqioH+r0OUrVFzppNgjYm3Fr1kBxMevXr+eGG27gwOefA/Bx5CQ+YgLnqH6H2GbNrCbkZ5/B/PlVnz969CgJCQn85z9V56HVJCMDNmzI48wZa/sgO90XTTUWDbNAkpxsHdCbmQl9+jAkLY0nSkpI2LuXA23b0va+5Bp3uYiJgUcesYKnuj6xWbNmMXnyZBISEjhy5Eidi/bKK3Du3FTKNuEGfix/TvdFU41B+8wCSWSkVaUaOdIKtMxMpgBERfHBmDG88FokI26xgiMnxzoxTsTqWnP0j9X+MbNmzaJjx45ERUXVuWiO1QNTgVjgPKfnKy+MV8rTNMwCTZ8+cPCgNRhw4AB068aU5GSIjGTLli107Hg+qakNH1WcOHEiBQUFjB49mjfffJOOHTvW+Hrn1QO3VHle90VT3qbNzEAUGQkTJsBTT1k/y05RyszMJCEhwWNLjVq0aMEvf/nLOvWh1WX1gFLepDWzIHL//fdjs9lISEhg/fr1Hpn3ZV+2NHToUHbu3ElYWJjL19mXWVXeay0mxnq8Ls1bpRrCWCOdjat3796ybdu2Rv/cpuLdd9/luuuu44orrvDYNfft28cvfvELCgoKaNGiRbWv27TJ0WfnTj+dUnVljNkuIlXO6dUwC1IiwmOPPcaDDz7osZn527dvZ8KECaxfv97tybVKeUp1YaZ9ZkHKGMMll1zi0T60a6+9lnvvvZeBAwe6NW1DqcagfWZBbMqUKQAkJCSwdetW2rVr1+BrPvHEEwDMnj2bhQsXNvh6SnmKNjObgE2bNnHjjTdaSz5CPFMZLy4uJisri3/8o5QPP+yoazFVo9FmZhPWv39/srKy6NWrl8eanGFhYdx110puvTWBZcv+w4YNsGwZjBoFM2Z45COUcouGWRMRGxvLlClTPNaHlpEBW7feT2npZCABsOah6VpM5SvaZ9aE2PvQxo8fz1dffYUxpt7XeuUV+3yyWVh/EzOxTh1wrMXUKRmqMWnNrImZMmUKaWlplJSU8OmnR0hOhoQEaw17Rkbdr+N85N3jwH85Pa9rMVVj05pZExQTE8PYsWmkpt6PyHrAmoe2fr01W9/V1kCVOa/FdPUZDS+nUu7QmlkDZWRQ79qNr2RkQHr6UESewOrvsvrQ3Onv0rWYyt9omDXAjBnW6N2yZQTUaJ6jv2sK8ASQUv5cXfceq3jkXUW6FlP5ijYz6ykjo+qianDUbpKS/PcftHN/15Qqz9e1v2v+fOt76lpM5Q80zOrJUbuxOwGcDxi/H83zZH9XXU9yUsrbtJlZT861G4DfANOIIJ8JpDAycy4sXmwdD+dntL9LBSMNs3qqWrtZQAvSmEg7PmQikw4+DXfcAV27wtatvihitbS/SwUjDbN6qly7iaA5O8hiHwV8Zi7j3+NnQny8dc7lyJF+V0ObP986TiApyRqJTUqq/sQmpQKBhlk9Va7dJJPKpZzgNRPPpt9+Q6fFL1g1Mnugpab6tsAu9O9vFWvdOuun1shUINMwa4CKtZshXa25Wm1uH8aLr5ZtLR0WBkOHWv/toQXeSinXNMwayF67mTTXmkXf6VvrgF7A+pmWZv23h3Z7VUq5plMzPKXSAb0MHWoFWWam9Xhysq9LqFRQ0zDzFBcH9AJWkH32WflxcEop79Aw8yQXB/RSdkCvUsq7NMw8zX5Ar1KqUTVoAMAY86IxZq8x5htjTKoxppaFMkop5R0NHc1cC/QUkauAfcCTDS+SUkq5r0FhJiJpImIru/sVcGHDi6SUUu7z5DyzScDnHryeRwTi5olKKffVOgBgjPkCaO/iqdkisrzsNbMBGxV3+at6nQeABwA6d+5cr8K6a8aMqnuOubM1tFIqcDT4EGBjzN1Y+98MFpGCurynMQ4Bzsiwdn11tdFgTIw19UvXIioVeLxyCLAxJhHrrLFRdQ2yxuK8eeIZrIqjpa5bQyulAkdD+8zeAKKAtcaYncaYP3mgTB7hvHniHOAOKgeaUip4NGjSrIhc4qmCeJrz5om/B5KxAi0FCNWj0JQKMkG7a4bz5okRQCpWc3Odbg2tVBAK2jCrujV0BLCSmJihDBuWwQ032Gp4t1Iq0ARtmIGrraFDWLFCyM//A3fccQe23FxISYG5/nv4iFKqbho8NaM+GmNqRk3OnTtH8qBBRO/YQUphoaPj0L5dT58+PiubUqpmXpmaEagiREj94QcuKCzkTM+eMNO/Dx9RStWuSYYZqalEHD/Oq7160eLLL8n/3e/8/vARpVTNmmaY2Q8XGTqU5q1a0bJlSz18RKkA1zTDzH64yBo9fESpYNE0d5rVw0eUCjpNM8z08BGlgk7TDDPQw0eUCjJNN8xADx9RKog0zQEApVTQ0TBTSgUFDTOlVFDQMFNKBQUNM6VUUNAwU0oFBQ0zpVRQ0DBTSgUFDTOlVFDQMFNKBQUNM6VUUNAwU0oFBQ0zpVRQ0DBTSgUFDTOlVFDQMFNKBQUNM6VUUNAwU0oFBQ0zpVRQ0DBTSgUFDTOlVFDQMFNKBQUNM6VUUNAwU0oFBQ0zpVRQ0DBTSgWFBoWZMWaOMeYbY8xOY0yaMaaDpwqmlFLuaGjN7EURuUpEegErgd95oExKKeW2BoWZiORWuNsSkIYVRyml6ie0oRcwxjwH3AXkAAkNLpFSStWDEam5MmWM+QJo7+Kp2SKyvMLrngQiROSZaq7zAPBA2d2ewL/qVWLfOx/I8nUh6imQyw6BXX4tu+d0EZHYyg/WGmZ1ZYzpAqwSkZ51eO02EentkQ9uZFp23wnk8mvZva+ho5ndK9wdBextWHGUUqp+Gtpn9rwx5lKgFDgE/KbhRVJKKfc1KMxEZGw93/p2Qz7Xx7TsvhPI5deye5nH+syUUsqXdDmTUioo+CzMAnkplDHmRWPM3rLypxpjWvu6THVljLnFGLPbGFNqjPH7ESoAY0yiMeY7Y8x+Y8wTvi6PO4wx7xpjjhtjAm4qkjGmkzFmvTFmT9n/M4/4ukw18WXNLJCXQq0FeorIVcA+4Ekfl8cd/wLGABm+LkhdGGOaAX8EhgOXA+ONMZf7tlRueQ9I9HUh6skGzBCRHsD1wIP+/Lv3WZgF8lIoEUkTEVvZ3a+AC31ZHneIyB4R+c7X5XBDX2C/iBwQkSLgY2C0j8tUZyKSAWT7uhz1ISI/i8iOsv8+A+wBOvq2VNVr8HKmhgiSpVCTgP/zdSGCWEfg3xXuHwau81FZmixjzEXA1cA/fVuS6nk1zGpbCiUis4HZZUuhHgJcLoXyhbos4zLGzMaqiqc0ZtlqU9claAHCuHgsYGrxwcAY0wpYCjxaqUXlV7waZiIypI4vXQyswo/CrLayG2PuBkYAg8XP5re48XsPBIeBThXuXwgc8VFZmhxjTBhWkKWIyKe+Lk9NfDmaGbBLoYwxicAsYJSIFPi6PEFuK9DdGNPVGBMO3A6s8HGZmgRjjAEWAHtE5GVfl6c2Pps0a4xZCjgthRKR//ikMG4yxuwHmgMnyx76SkQCYimXMSYZeB2IBU4DO0VkmG9LVTNjzH8BrwLNgHdF5DkfF6nOjDEfAQOxdp44BjwjIgt8Wqg6MsbcCGwCdmH9OwX4bxH5m+9KVT1dAaCUCgq6AkApFRQ0zJRSQUHDTCkVFDTMlFJBQcNMKRUUNMyUUkFBw0wpFRQ0zJRSQeH/A2v2A97LOadzAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "X_rec = recoverData(Z, U, K)\n", + "print('Approximation of the first example: [{:.6f} {:.6f}]'.format(X_rec[0, 0], X_rec[0, 1]))\n", + "print(' (this value should be about [-1.047419 -1.047419])')\n", + "\n", + "# Plot the normalized dataset (returned from featureNormalize)\n", + "fig, ax = pyplot.subplots(figsize=(5, 5))\n", + "ax.plot(X_norm[:, 0], X_norm[:, 1], 'bo', ms=8, mec='b', mew=0.5)\n", + "ax.set_aspect('equal')\n", + "ax.grid(False)\n", + "pyplot.axis([-3, 2.75, -3, 2.75])\n", + "\n", + "# Draw lines connecting the projected points to the original points\n", + "ax.plot(X_rec[:, 0], X_rec[:, 1], 'ro', mec='r', mew=2, mfc='none')\n", + "for xnorm, xrec in zip(X_norm, X_rec):\n", + " ax.plot([xnorm[0], xrec[0]], [xnorm[1], xrec[1]], '--k', lw=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Load Face dataset\n", + "data = loadmat(os.path.join('Data', 'ex7faces.mat'))\n", + "X = data['X']\n", + "\n", + "# Display the first 100 faces in the dataset\n", + "utils.displayData(X[:100, :], figsize=(8, 8))" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "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", + "\n", + "# Run PCA\n", + "U, S = pca(X_norm)\n", + "\n", + "# Visualize the top 36 eigenvectors found\n", + "utils.displayData(U[:, :36].T, figsize=(8, 8))" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "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", + "K = 100\n", + "Z = projectData(X_norm, U, K)\n", + "\n", + "print('The projected data Z has a shape of: ', Z.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "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", + "# Compare to the original input, which is also displayed\n", + "K = 100\n", + "X_rec = recoverData(Z, U, K)\n", + "\n", + "# Display normalized data\n", + "utils.displayData(X_norm[:100, :], figsize=(6, 6))\n", + "pyplot.gcf().suptitle('Original faces')\n", + "\n", + "# Display reconstructed data from only k eigenfaces\n", + "utils.displayData(X_rec[:100, :], figsize=(6, 6))\n", + "pyplot.gcf().suptitle('Recovered faces')\n", + "pass" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "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 = $('');\n", + " button.click(method_name, toolbar_event);\n", + " button.mouseover(tooltip, toolbar_mouse_event);\n", + " nav_element.append(button);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = $('');\n", + " nav_element.append(status_bar);\n", + " this.message = status_bar[0];\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = $('
');\n", + " var button = $('');\n", + " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", + " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", + " buttongrp.append(button);\n", + " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", + " titlebar.prepend(buttongrp);\n", + "}\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(el){\n", + " var fig = this\n", + " el.on(\"remove\", function(){\n", + "\tfig.close_ws(fig, {});\n", + " });\n", + "}\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(el){\n", + " // this is important to make the div 'focusable\n", + " el.attr('tabindex', 0)\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " }\n", + " else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._key_event_extra = function(event, name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager)\n", + " manager = IPython.keyboard_manager;\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which == 13) {\n", + " this.canvas_div.blur();\n", + " // select the cell after this one\n", + " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", + " IPython.notebook.select(index + 1);\n", + " }\n", + "}\n", + "\n", + "mpl.figure.prototype.handle_save = function(fig, msg) {\n", + " fig.ondownload(fig, null);\n", + "}\n", + "\n", + "\n", + "mpl.find_output_cell = function(html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] == html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel != null) {\n", + " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# this allows to have interactive plot to rotate the 3-D plot\n", + "# The double identical statement is on purpose\n", + "# see: https://stackoverflow.com/questions/43545050/using-matplotlib-notebook-after-matplotlib-inline-in-jupyter-notebook-doesnt\n", + "%matplotlib notebook\n", + "%matplotlib notebook\n", + "from matplotlib import pyplot\n", + "\n", + "\n", + "A = mpl.image.imread(os.path.join('Data', 'bird_small.png'))\n", + "A /= 255\n", + "X = A.reshape(-1, 3)\n", + "\n", + "# perform the K-means clustering again here\n", + "K = 16\n", + "max_iters = 10\n", + "initial_centroids = kMeansInitCentroids(X, K)\n", + "centroids, idx = utils.runkMeans(X, initial_centroids,\n", + " findClosestCentroids,\n", + " computeCentroids, max_iters)\n", + "\n", + "# Sample 1000 random indexes (since working with all the data is\n", + "# too expensive. If you have a fast computer, you may increase this.\n", + "sel = np.random.choice(X.shape[0], size=1000)\n", + "\n", + "fig = pyplot.figure(figsize=(6, 6))\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "\n", + "ax.scatter(X[sel, 0], X[sel, 1], X[sel, 2], cmap='rainbow', c=idx[sel], s=8**2)\n", + "ax.set_title('Pixel dataset plotted in 3D.\\nColor shows centroid memberships')\n", + "pass" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Subtract the mean to use PCA\n", + "X_norm, mu, sigma = utils.featureNormalize(X)\n", + "\n", + "# PCA and project the data to 2D\n", + "U, S = pca(X_norm)\n", + "Z = projectData(X_norm, U, 2)\n", + "\n", + "# Reset matplotlib to non-interactive\n", + "%matplotlib inline\n", + "\n", + "fig = pyplot.figure(figsize=(6, 6))\n", + "ax = fig.add_subplot(111)\n", + "\n", + "ax.scatter(Z[sel, 0], Z[sel, 1], cmap='rainbow', c=idx[sel], s=64)\n", + "ax.set_title('Pixel dataset plotted in 2D, using PCA for dimensionality reduction')\n", + "ax.grid(False)\n", + "pass" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Week 8/index.html b/Week 8/index.html new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/Week 8/index.html @@ -0,0 +1 @@ + diff --git a/Week 8/utils.py b/Week 8/utils.py new file mode 100644 index 000000000..93a746809 --- /dev/null +++ b/Week 8/utils.py @@ -0,0 +1,220 @@ +import sys +import numpy as np +from matplotlib import pyplot +from matplotlib.animation import FuncAnimation +import matplotlib as mpl + +sys.path.append('..') + + +def displayData(X, example_width=None, figsize=(10, 10)): + """ + Displays 2D data in a nice grid. + + Parameters + ---------- + X : array_like + The input data of size (m x n) where m is the number of examples and n is the number of + features. + + example_width : int, optional + THe width of each 2-D image in pixels. If not provided, the image is assumed to be square, + and the width is the floor of the square root of total number of pixels. + + figsize : tuple, optional + A 2-element tuple indicating the width and height of figure in inches. + """ + # 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 = int(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_height, example_width, order='F'), cmap='gray') + ax.axis('off') + + +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 plotProgresskMeans(i, X, centroid_history, idx_history): + """ + A helper function that displays the progress of k-Means as it is running. It is intended for use + only with 2D data. It plots data points with colors assigned to each centroid. With the + previous centroids, it also plots a line between the previous locations and current locations + of the centroids. + + Parameters + ---------- + i : int + Current iteration number of k-means. Used for matplotlib animation function. + + X : array_like + The dataset, which is a matrix (m x n). Note since the plot only supports 2D data, n should + be equal to 2. + + centroid_history : list + A list of computed centroids for all iteration. + + idx_history : list + A list of computed assigned indices for all iterations. + """ + K = centroid_history[0].shape[0] + pyplot.gcf().clf() + cmap = pyplot.cm.rainbow + norm = mpl.colors.Normalize(vmin=0, vmax=2) + + for k in range(K): + current = np.stack([c[k, :] for c in centroid_history[:i+1]], axis=0) + pyplot.plot(current[:, 0], current[:, 1], + '-Xk', + mec='k', + lw=2, + ms=10, + mfc=cmap(norm(k)), + mew=2) + + pyplot.scatter(X[:, 0], X[:, 1], + c=idx_history[i], + cmap=cmap, + marker='o', + s=8**2, + linewidths=1,) + pyplot.grid(False) + pyplot.title('Iteration number %d' % (i+1)) + + +def runkMeans(X, centroids, findClosestCentroids, computeCentroids, + max_iters=10, plot_progress=False): + """ + Runs the K-means algorithm. + + Parameters + ---------- + X : array_like + The data set of size (m, n). Each row of X is a single example of n dimensions. The + data set is a total of m examples. + + centroids : array_like + Initial centroid location for each clusters. This is a matrix of size (K, n). K is the total + number of clusters and n is the dimensions of each data point. + + findClosestCentroids : func + A function (implemented by student) reference which computes the cluster assignment for + each example. + + computeCentroids : func + A function(implemented by student) reference which computes the centroid of each cluster. + + max_iters : int, optional + Specifies the total number of interactions of K-Means to execute. + + plot_progress : bool, optional + A flag that indicates if the function should also plot its progress as the learning happens. + This is set to false by default. + + Returns + ------- + centroids : array_like + A (K x n) matrix of the computed (updated) centroids. + idx : array_like + A vector of size (m,) for cluster assignment for each example in the dataset. Each entry + in idx is within the range [0 ... K-1]. + + anim : FuncAnimation, optional + A matplotlib animation object which can be used to embed a video within the jupyter + notebook. This is only returned if `plot_progress` is `True`. + """ + K = centroids.shape[0] + idx = None + idx_history = [] + centroid_history = [] + + for i in range(max_iters): + idx = findClosestCentroids(X, centroids) + + if plot_progress: + idx_history.append(idx) + centroid_history.append(centroids) + + centroids = computeCentroids(X, idx, K) + + if plot_progress: + fig = pyplot.figure() + anim = FuncAnimation(fig, plotProgresskMeans, + frames=max_iters, + interval=500, + repeat_delay=2, + fargs=(X, centroid_history, idx_history)) + return centroids, idx, anim + + return centroids, idx + + + 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 = 1 + func(self.X, self.C) + elif part_id == 2: + res = func(self.X, self.idx, 3) + elif part_id == 3: + U, S = func(self.X) + res = np.hstack([U.ravel('F'), np.diag(S).ravel('F')]).tolist() + elif part_id == 4: + res = func(self.X, self.Z, 5) + elif part_id == 5: + res = func(self.X[:, :5], self.Z, 5) + else: + raise KeyError + yield part_id, res + except KeyError: + yield part_id, 0 diff --git a/Week3/Figure_1.png b/Week3/Figure_1.png new file mode 100644 index 000000000..a41e6506d Binary files /dev/null and b/Week3/Figure_1.png differ diff --git a/Week3/Figure_2.png b/Week3/Figure_2.png new file mode 100644 index 000000000..baa523ddc Binary files /dev/null and b/Week3/Figure_2.png differ diff --git a/Week3/Figure_3.png b/Week3/Figure_3.png new file mode 100644 index 000000000..3dcaaa8fa Binary files /dev/null and b/Week3/Figure_3.png differ diff --git a/Week3/Figure_4.png b/Week3/Figure_4.png new file mode 100644 index 000000000..e4c9df3af Binary files /dev/null and b/Week3/Figure_4.png differ diff --git a/Week3/Week3.py b/Week3/Week3.py new file mode 100644 index 000000000..11c2d2371 --- /dev/null +++ b/Week3/Week3.py @@ -0,0 +1,316 @@ +import numpy as np +from matplotlib import pyplot +from matplotlib import style +style.use('ggplot') +from scipy import optimize + +data=np.genfromtxt('ex2data1.txt',delimiter=',') +X, y = data[:,:2], data[:, 2] + +def plotData(X, y): + # Create New Figure + fig = pyplot.figure() + # Find Indices of Positive and Negative Examples + pos = y == 1 + neg = y == 0 + # Plot Examples + pyplot.plot(X[pos, 0], X[pos, 1], 'k*', lw=2, ms=10) + pyplot.plot(X[neg, 0], X[neg, 1], 'ko', mfc='y', ms=8, mec='k', mew=1) + +plotData(X, y) +# add axes labels +pyplot.xlabel('Exam 1 score') +pyplot.ylabel('Exam 2 score') +pyplot.legend(['Admitted', 'Not admitted']) + +def sigmoid(z): + z=np.array(z) + g=np.zeros(z.shape) + g+=(1/(1+np.exp(-z))) + return g + +# Test the implementation of sigmoid function here +z = 0 +g = sigmoid(z) + +print('g(', z, ') = ', g) + +# Setup the data matrix appropriately, and add ones for the intercept term +m, n = X.shape + +# Add intercept term to X +X = np.concatenate([np.ones((m, 1)), X], axis=1) + +def costFunction(theta, X, y): + m = y.size + J = 0 + grad = np.zeros(theta.shape) + temp=np.dot(X,theta) + g=sigmoid(temp) + J=(1/m)*sum(((-y)*np.log(g))-((1-y)*np.log(1-g))) + grad0=(1/m)*sum((g-y)*X[:,0]) + grad1=(1/m)*sum((g-y)*X[:,1]) + grad2=(1/m)*sum((g-y)*X[:,2]) + grad+=np.array([grad0,grad1,grad2]) + return J,grad + +# Initialize fitting parameters +initial_theta = np.zeros(n+1) + +cost, grad = costFunction(initial_theta, X, y) + +print('Cost at initial theta (zeros): {:.3f}'.format(cost)) +print('Expected cost (approx): 0.693\n') + +print('Gradient at initial theta (zeros):') +print('\t[{:.4f}, {:.4f}, {:.4f}]'.format(*grad)) +print('Expected gradients (approx):\n\t[-0.1000, -12.0092, -11.2628]\n') + +# Compute and display cost and gradient with non-zero theta +test_theta = np.array([-24, 0.2, 0.2]) +cost, grad = costFunction(test_theta, X, y) + +print('Cost at test theta: {:.3f}'.format(cost)) +print('Expected cost (approx): 0.218\n') + +print('Gradient at test theta:') +print('\t[{:.3f}, {:.3f}, {:.3f}]'.format(*grad)) +print('Expected gradients (approx):\n\t[0.043, 2.566, 2.647]') + +# set options for optimize.minimize +options= {'maxiter': 400} + +# see documention for scipy's optimize.minimize for description about +# the different parameters +# The function returns an object `OptimizeResult` +# We use truncated Newton algorithm for optimization which is +# equivalent to MATLAB's fminunc +# See https://stackoverflow.com/questions/18801002/fminunc-alternate-in-numpy +res = optimize.minimize(costFunction, + initial_theta, + (X, y), + jac=True, + method='TNC', + options=options) + +# the fun property of `OptimizeResult` object returns +# the value of costFunction at optimized theta +cost = res.fun + +# the optimized theta is in the x property +theta = res.x + +# Print theta to screen +print('Cost at theta found by optimize.minimize: {:.3f}'.format(cost)) +print('Expected cost (approx): 0.203\n'); + +print('theta:') +print('\t[{:.3f}, {:.3f}, {:.3f}]'.format(*theta)) +print('Expected theta (approx):\n\t[-25.161, 0.206, 0.201]') + +def plotDecisionBoundary(plotData, theta, X, y): + + # 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) + +plotDecisionBoundary(plotData, theta, X, y) +pyplot.show() + +def predict(theta, X): + m = X.shape[0] + p = np.zeros(m) + temp=np.dot(X,theta) + for i in range(m): + if temp[i]>=0.5: + p[i]+=1 + else: + p[i]+=0 + return p + +# Predict probability for a student with score 45 on exam 1 +# and score 85 on exam 2 +prob = sigmoid(np.dot([1, 45, 85], theta)) +print('For a student with scores 45 and 85,' + 'we predict an admission probability of {:.3f}'.format(prob)) +print('Expected value: 0.775 +/- 0.002\n') + +# Compute accuracy on our training set +p = predict(theta, X) +print('Train Accuracy: {:.2f} %'.format(np.mean(p == y) * 100)) +print('Expected accuracy (approx): 89.00 %') + +#Regularised Logistic Regression +data = np.loadtxt('ex2data2.txt', delimiter=',') +X = data[:, :2] +y = data[:, 2] + +plotData(X, y) +# Labels and Legend +pyplot.xlabel('Microchip Test 1') +pyplot.ylabel('Microchip Test 2') + +# Specified in plot order +pyplot.legend(['y = 1', 'y = 0'], loc='upper right') +pyplot.show() + +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) + +X = mapFeature(X[:, 0], X[:, 1]) +print(X.shape[1]) + +def costFunctionReg(theta, X, y, lambda_): + m = y.size # number of training examples + J = 0 + n=X.shape[1] + grad = np.zeros(theta.shape) + temp=np.dot(X,theta) + g=sigmoid(temp) + J=(1/m)*sum(((-y)*np.log(g))-((1-y)*np.log(1-g)))+(lambda_/(2*m))*(sum(theta*theta)) + grad[0]=(1/m)*sum((g-y)*X[:,0]) + for i in range(1,n): + grad[i]=((1/m)*sum((g-y)*X[:,i]))+(lambda_/m)*theta[i] + return J,grad + +# Initialize fitting parameters +initial_theta = np.zeros(X.shape[1]) + +# Set regularization parameter lambda to 1 +# DO NOT use `lambda` as a variable name in python +# because it is a python keyword +lambda_ = 1 + +# Compute and display initial cost and gradient for regularized logistic +# regression +cost, grad = costFunctionReg(initial_theta, X, y, lambda_) + +print('Cost at initial theta (zeros): {:.3f}'.format(cost)) +print('Expected cost (approx) : 0.693\n') + +print('Gradient at initial theta (zeros) - first five values only:') +print('\t[{:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}]'.format(*grad[:5])) +print('Expected gradients (approx) - first five values only:') +print('\t[0.0085, 0.0188, 0.0001, 0.0503, 0.0115]\n') + + +# Compute and display cost and gradient +# with all-ones theta and lambda = 10 +test_theta = np.ones(X.shape[1]) +cost, grad = costFunctionReg(test_theta, X, y, 10) + +print('------------------------------\n') +print('Cost at test theta : {:.2f}'.format(cost)) +print('Expected cost (approx): 3.16\n') + +print('Gradient at initial theta (zeros) - first five values only:') +print('\t[{:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}]'.format(*grad[:5])) +print('Expected gradients (approx) - first five values only:') +print('\t[0.3460, 0.1614, 0.1948, 0.2269, 0.0922]') + +#Plotting the boundary +# Initialize fitting parameters +initial_theta = np.zeros(X.shape[1]) + +# Set regularization parameter lambda to 1 (you should vary this) +lambda_ = 1 + +# set options for optimize.minimize +options= {'maxiter': 100} + +res = optimize.minimize(costFunctionReg, + initial_theta, + (X, y, lambda_), + jac=True, + method='TNC', + options=options) + +# the fun property of OptimizeResult object returns +# the value of costFunction at optimized theta +cost = res.fun + +# the optimized theta is in the x property of the result +theta = res.x +plotDecisionBoundary(plotData, theta, X, y) +pyplot.xlabel('Microchip Test 1') +pyplot.ylabel('Microchip Test 2') +pyplot.legend(['y = 1', 'y = 0']) +pyplot.grid(False) +pyplot.title('lambda = %0.2f' % lambda_) +pyplot.show() +# Compute accuracy on our training set +p = predict(theta, X) + +print('Train Accuracy: %.1f %%' % (np.mean(p == y) * 100)) +print('Expected accuracy (with lambda = 1): 83.1 % (approx)\n') + + + + + + + diff --git a/Week3/index.html b/Week3/index.html new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/Week3/index.html @@ -0,0 +1 @@ +