Files
coco 85d885e008 a
2026-07-03 16:29:47 +08:00

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{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import random\n",
"from base64 import b64decode\n",
"from json import loads\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"# set matplotlib to display all plots inline with the notebook\n",
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 62
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def parse(x):\n",
" \"\"\"\n",
" to parse the digits file into tuples of \n",
" (labelled digit, numpy array of vector representation of digit)\n",
" \"\"\"\n",
" digit = loads(x)\n",
" array = np.fromstring(b64decode(digit[\"data\"]),dtype=np.ubyte)\n",
" array = array.astype(np.float64)\n",
" return (digit[\"label\"], array)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 63
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# read in the digits file. Digits is a list of 60,000 tuples,\n",
"# each containing a labelled digit and its vector representation.\n",
"with open(\"digits.base64.json\",\"r\") as f:\n",
" digits = map(parse, f.readlines())"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 64
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# pick a ratio for splitting the digits list into a training and a validation set.\n",
"ratio = int(len(digits)*0.25)\n",
"validation = digits[:ratio]\n",
"training = digits[ratio:]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 65
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def display_digit(digit, labeled = True, title = \"\"):\n",
" \"\"\" \n",
" graphically displays a 784x1 vector, representing a digit\n",
" \"\"\"\n",
" if labeled:\n",
" digit = digit[1]\n",
" image = digit\n",
" plt.figure()\n",
" fig = plt.imshow(image.reshape(28,28))\n",
" fig.set_cmap('gray_r')\n",
" fig.axes.get_xaxis().set_visible(False)\n",
" fig.axes.get_yaxis().set_visible(False)\n",
" if title != \"\":\n",
" plt.title(\"Inferred label: \" + str(title))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 66
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# writing Lloyd's Algorithm for K-Means clustering.\n",
"# (This exists in various libraries, but it's good practice to write by hand.)\n",
"def init_centroids(labelled_data,k):\n",
" \"\"\"\n",
" randomly pick some k centers from the data as starting values for centroids.\n",
" Remove labels.\n",
" \"\"\"\n",
" return map(lambda x: x[1], random.sample(labelled_data,k))\n",
"\n",
"def sum_cluster(labelled_cluster):\n",
" \"\"\"\n",
" from http://stackoverflow.com/questions/20640396/quickly-summing-numpy-arrays-element-wise\n",
" element-wise sums a list of arrays. assumes all datapoints in labelled_cluster are labelled.\n",
" \"\"\"\n",
" # assumes len(cluster) > 0\n",
" sum_ = labelled_cluster[0][1].copy()\n",
" for (label,vector) in labelled_cluster[1:]:\n",
" sum_ += vector\n",
" return sum_\n",
"\n",
"def mean_cluster(labelled_cluster):\n",
" \"\"\"\n",
" computes the mean (i.e. the centroid at the middle) of a list of vectors (a cluster).\n",
" take the sum and then divide by the size of the cluster.\n",
" assumes all datapoints in labelled_cluster are labelled.\n",
" \"\"\"\n",
" sum_of_points = sum_cluster(labelled_cluster)\n",
" mean_of_points = sum_of_points * (1.0 / len(labelled_cluster))\n",
" return mean_of_points"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 73
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def form_clusters(labelled_data, unlabelled_centroids):\n",
" \"\"\"\n",
" given some data and centroids for the data, allocate each datapoint\n",
" to its closest centroid. This forms clusters.\n",
" \"\"\"\n",
" # enumerate because centroids are arrays which are unhashable,\n",
" centroids_indices = range(len(unlabelled_centroids))\n",
" \n",
" # initialize an empty list for each centroid. The list will contain\n",
" # all the datapoints that are closer to that centroid than to any other.\n",
" # That list is the cluster of that centroid.\n",
" clusters = {c: [] for c in centroids_indices}\n",
" \n",
" for (label,Xi) in labelled_data:\n",
" # for each datapoint, pick the closest centroid.\n",
" smallest_distance = float(\"inf\")\n",
" for cj_index in centroids_indices:\n",
" cj = unlabelled_centroids[cj_index]\n",
" distance = np.linalg.norm(Xi - cj)\n",
" if distance < smallest_distance:\n",
" closest_centroid_index = cj_index\n",
" smallest_distance = distance\n",
" # allocate that datapoint to the cluster of that centroid.\n",
" clusters[closest_centroid_index].append((label,Xi))\n",
" return clusters.values()\n",
"\n",
"def move_centroids(labelled_clusters):\n",
" \"\"\"\n",
" returns a list of centroids corresponding to the clusters.\n",
" \"\"\"\n",
" new_centroids = []\n",
" for cluster in labelled_clusters:\n",
" new_centroids.append(mean_cluster(cluster))\n",
" return new_centroids\n",
"\n",
"def repeat_until_convergence(labelled_data, labelled_clusters, unlabelled_centroids):\n",
" \"\"\"\n",
" form clusters around centroids, then keep moving the centroids\n",
" until the moves are no longer significant, i.e. we've found\n",
" the best-fitting centroids for the data.\n",
" \"\"\"\n",
" previous_max_difference = 0\n",
" while True:\n",
" unlabelled_old_centroids = unlabelled_centroids\n",
" unlabelled_centroids = move_centroids(labelled_clusters)\n",
" labelled_clusters = form_clusters(labelled_data, unlabelled_centroids)\n",
" # we keep old_clusters and clusters so we can get the maximum difference\n",
" # between centroid positions every time. we say the centroids have converged\n",
" # when the maximum difference between centroid positions is small. \n",
" differences = map(lambda a, b: np.linalg.norm(a-b),unlabelled_old_centroids,unlabelled_centroids)\n",
" max_difference = max(differences)\n",
" difference_change = abs((max_difference-previous_max_difference)/np.mean([previous_max_difference,max_difference])) * 100\n",
" previous_max_difference = max_difference\n",
" # difference change is nan once the list of differences is all zeroes.\n",
" if np.isnan(difference_change):\n",
" break\n",
" return labelled_clusters, unlabelled_centroids"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 68
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def cluster(labelled_data, k):\n",
" \"\"\"\n",
" runs k-means clustering on the data. It is assumed that the data is labelled.\n",
" \"\"\"\n",
" centroids = init_centroids(labelled_data, k)\n",
" clusters = form_clusters(labelled_data, centroids)\n",
" final_clusters, final_centroids = repeat_until_convergence(labelled_data, clusters, centroids)\n",
" return final_clusters, final_centroids"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 69
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def assign_labels_to_centroids(clusters, centroids):\n",
" \"\"\"\n",
" Assigns a digit label to each cluster.\n",
" Cluster is a list of clusters containing labelled datapoints.\n",
" NOTE: this function depends on clusters and centroids being in the same order.\n",
" \"\"\"\n",
" labelled_centroids = []\n",
" for i in range(len(clusters)):\n",
" labels = map(lambda x: x[0], clusters[i])\n",
" # pick the most common label\n",
" most_common = max(set(labels), key=labels.count)\n",
" centroid = (most_common, centroids[i])\n",
" labelled_centroids.append(centroid)\n",
" return labelled_centroids"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 70
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def classify_digit(digit, labelled_centroids):\n",
" \"\"\"\n",
" given an unlabelled digit represented by a vector and a list of\n",
" labelled centroids [(label,vector)], determine the closest centroid\n",
" and thus classify the digit.\n",
" \"\"\"\n",
" mindistance = float(\"inf\")\n",
" for (label, centroid) in labelled_centroids:\n",
" distance = np.linalg.norm(centroid - digit)\n",
" if distance < mindistance:\n",
" mindistance = distance\n",
" closest_centroid_label = label\n",
" return closest_centroid_label\n",
"\n",
"def get_error_rate(digits,labelled_centroids):\n",
" \"\"\"\n",
" classifies a list of labelled digits. returns the error rate.\n",
" \"\"\"\n",
" classified_incorrect = 0\n",
" for (label,digit) in digits:\n",
" classified_label = classify_digit(digit, labelled_centroids)\n",
" if classified_label != label:\n",
" classified_incorrect +=1\n",
" error_rate = classified_incorrect / float(len(digits))\n",
" return error_rate"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 71
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"error_rates = {x:None for x in range(5,25)+[100]}\n",
"for k in range(5,25):\n",
" trained_clusters, trained_centroids = cluster(training, k)\n",
" labelled_centroids = assign_labels_to_centroids(trained_clusters, trained_centroids)\n",
" error_rate = get_error_rate(validation, labelled_centroids)\n",
" error_rates[k] = error_rate\n",
"\n",
"# Show the error rates\n",
"x_axis = sorted(error_rates.keys())\n",
"y_axis = [error_rates[key] for key in x_axis]\n",
"plt.figure()\n",
"plt.title(\"Error Rate by Number of Clusters\")\n",
"plt.scatter(x_axis, y_axis)\n",
"plt.xlabel(\"Number of Clusters\")\n",
"plt.ylabel(\"Error Rate\")\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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XLVuGZcuWabIMIiJqIM6Q1gE+Pj7aLkGj9Ll/+tw3gP1ryzQ6Q7q5yGSyJh8/IyJqa5ry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\nREQkwXAgIiIJhgMREUn8Hx8l01FBRdURAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7f628caa0990>"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"k = 16\n",
"trained_clusters, trained_centroids = cluster(training, k)\n",
"labelled_centroids = assign_labels_to_centroids(trained_clusters, trained_centroids)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"-c:52: RuntimeWarning: invalid value encountered in double_scalars\n"
]
}
],
"prompt_number": 74
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for x in labelled_centroids:\n",
" display_digit(x, title=x[0])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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W2wf3j0BVF4stS0tLsra2tvGmjxdmj2pE8s/Kso7FHpO5xpHSZXUSdWSeKls4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\nfcDtswzJBglt1viZy4j7Zi5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"text": [
"<matplotlib.figure.Figure at 0x7f628ce5a390>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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cpmlWudm6gUfqAQHG657ri1xrD1hUp1ZIYU1RNzk1qs5AWwum9X0tBHDEyqJt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\nu7i4OBxIgWDlZYaWIZXLntW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"text": [
"<matplotlib.figure.Figure at 0x7f628d2d0f90>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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l3FhyXO6cljKqvIr+cRV1dlUObc3iEk/xciqwWgIqZW4JjfG8jFLqGBKIKRos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\n",
"text": [
"<matplotlib.figure.Figure at 0x7f628d4a74d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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EVmu5VBqsdNlr00YCUVFLy9+h5WXDRyrVdY8tYCX3OKq2wtsoaKlKrG0EZA1W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\nTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7f628d403c10>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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eRMYuqMBq4kjXsecCHZbBbLPKDNxS9njeYFX1GtppJiAsrHY6SkbhS+X2syJv\nvc9qQfFcdhJF0Hn7Zv2l+Fl7/Aoshq4lUI8fP76hCyh60EQB1n1hhpjN67t6De1WKQqHM3CqW6pb\n6MXubcd2cURu1/UYrFg4iW3S7GDrabevAON+B4OBrKysrHtVzyadtGzBtW1aPa/MYfHmME+Qoiq0\n/y/9onFeFla9oDDBZIXhsl7A+iACuyBLLlu6MFm702u3sieborG9kWD9WP213xbDX52HDtvmWWw8\nJgvuvDlvhVbi19dsOYKVhbIetLb/1V64JQfBOkfTnrwbgk57jxXawT74YCHBfdjoQqHFRxzRcb1H\nHSOXZy6bhbOPEFdoiaLkku2nZOEwtvXsdtgraehezAXt9uw2cfsZ2Qsdy1oP9iPp9rFED1AMk3V/\n9jx6jzIqtGy/eH48p/XAZd9Hn7Xw0LKw2C5r47ieM9r1Mby0FyWDNHJZb+wJAUMIRqPROqezNxn7\nHLHdHj4dhXXHG4z3ooDnsni+WJ298JhN23p582ddCw+tFToqW1aClW3LOrPXPlNoVV6Zgcqclwld\nCi98C4/N6qLDojtrHSKn1bqxsBjfsWXg6jkqJaMiWKPz4nUTzaIqtIFYWKxdDBZYGxazz9q2rAeM\nF16yOuH27fzoM7hfduPQX4BUWBVe5qb2cxZcFiUoDNhEwMyx58Reu5btj4HXpu066+AuNLTZ0Nhr\n12p4qOB6n0XIWSJFy5lssBeyl8SgRSdbWVkZg2of7kcobYLKhv52X3iMIrLOUbHLJ5uEihJRpXZu\n1lFnGdzeQuvBhvMj94qAtYkLBqsOFlb7eRZORxeaveDtdti0dyMpycLGwLU/C2NBtdvHUNW6sD1v\neGwKQeSkbbp6vC6ftpplOD31DtousNrpLLgicVeQBzGuq20++7STN9j9Yj1smcGUATdyWc1+R2G3\ntnsxgcTcWNe34Or5YHUoPfmUOW+LopmHNvpS2LKM07b9om1oxWDVsq6rg15wNgHFLmhs99n9Ynkz\nwmPPaS143udZWxSjCQQ9gja6iWD7P2rDLhLIMwdt9oRnnRWnGbhsnxZUBBbBtZ9hiSuva0LHDNoI\nWLavDLRZp8X9at1Go/898aWDBZfVwwKr4T9z16zLMnA9zTO8MwPtVsNqy20dF8G1jqIPq9t1LbAs\nLPbqY7dhx3a7tszmlSKTktOyz6h0XX02GF02AtfCm3XZLsmnRdBMQLsZwLaFOSNs0yK4uK6u473v\n2cbl7Tbjhe2xAAAT1ElEQVTZvC7hcQkUBq2tJwLLwmPMIOu41KbVMj4eyYbIcTfje591TR3azQa2\na5js1SMKU/UitwknbOdGDhvtrxQSe9PRPro4rX7OQru2trYO3JLT6n4stOypsIzTlm6CpfM7D5oq\ntJmT6118WJ4kTM7KwoFuitsttVmjfUwKrKcuTmsB0Ycv0GkRXPZ55rRZcD2H9c6z/S7mUVODttT2\nKs3PhJoZN+7yRTNQPHijbTPQMnBGYEfHoeBZSO00OrE+bKFjD1IvPGdwMRBZe5UB2ubczrOmHh5b\nlVzVTrcBuE0o7d3JmTKuqZ/3AGXTJTC9ZVG9tB7WTTWMV9lnpO04Snzh9rVccsRofkaZc9913qxr\nKtC2CWVxXgRrCc5syMxCra4QRxcFgy0LKluHyd40MExnADNgvaej2L48OLHNnwlzJ4l85llTd9ps\nSGuns2NvXrSfzN0fpzNOhyq5bKnszcM62vkKDsKK7hoBi/tFReCyed4N0QO2LZjzCPK2QztJCNsW\n2gy8bD3vrs8+q+5hx57ssqjcBlpcpnVlEGOiyQuPGbzZtitOq8vqMmy/6vpsOyV557rLTbNPmrrT\nokqgRWFUG4jZ/krb9WSBLbkuc8aSg0awRvuwdS+FxyLr//aDwZoFGMdZp43UpQ3LlvcZVtVMZI/b\nOKR3UWSXsf1n94VlC0IJQm8ZzsuEwCVobV1L4bFdD1/wx4HBWnLezRhQWfDmAVCmqTttW2AnmYfb\n9epSqpcVuzDagJYJeT1QPHjwRqLTEax2Pxl3bRsmR4kouy5T5MKlyGYeNXVombLOmSnjPNyHt++o\njCq1VdtCnPlcVMbjQXhLn9F12YBt0jYPQtjt4r5YfdiNYjPPTV811S6fLq7ZZRrLXn2ied6FxWQh\nYWVv/c24uKJoQcsMtuhldPzRNfx3d/z1RPZDbJGYm7OkWJf+467ndJZB31ZoIzjYheWVozt4CeJM\nXby6e21YlV1u14mAZdvwtl9SKbxnblmCl4HaFtjohpkBlo2j0N1uNzq33rmeZWBFZiQRZecxeDPg\ntgG5VI9SnRVOD1y2PlPWETIgl4DVsee0+PiiB27GdT1wvZuaB2AELL6w78HbVrMOrMg2QutBw+Zl\nwewKrrdPJnYXthdgBra22oxwOQIW4cXfGEZYPWeNnBZ/edJz2yyoUSbbu3m2BbkPwIpsE7SZtuQk\ncJZejI6AzTgtC7sQ2AzEur+tTJh4juu5rJ0utWdLTlsKka28tmgEcJthnrXl0JactQRNFtRJwMXp\nqI1Turt7btJVXVyX3QxtmcHrhcalUNkDlwHb1m09p50E2my7dpa1pdBGQEbOVwpxva6FDLjRvq2Y\nA7b5YrFtmw2jN+siypzDTJs224712rNRaKzHGcFqgfWmGazz7LpTzR5nHYEBm3kHs8s7mRFAkSPY\neSKy4aVy7yVz3JcCzsa4PjufpXMXhb9ra2vrhmPHjq0r66Dz7GdYeFwKi6176ju69l1d/Ckb1vXj\nJaTYuZ0XbRm03p3Vu9BweQlYD97IhXH7bCxSfvBBLxT7WCC2dfWleHZR6f680M4Dl51HPJ/Rjcv+\nXIwFVctvvvmmvPHGGxvGr7/+urzxxhvjeTogwPofu6x/lkFq/6eWDbrM+z2qTKhs99/HUJhpS6Bl\nwHquivNKjssSKCWAWX8h7sMqglZ/ZsbCagFUUHHM3No7dxmnZee5FI2go2JZYbTAskGBZc7rRTee\nu3rgIrB2/cht27RV+wrv1LPHbV0W4fUyod7Fi/vtAq2F144VLgQWt1EaIqdl5wvPWZQdxhDYDtZF\nI2DtYD+vf4yNddO6Z4C1097vULV12r7C6WnqT0TpfM8BS6Bm4fXcljmCjvFLZ6BasHTagqv7iR7K\nZy+lR04bhfjReVFnxbaqlj1QWYiMTqsujmJNi1JoHP3xdDZExu8Q69NnbXuXDwMF12XAlgDGznwP\n4mj7IvGD+zYsVkAtvAxcC6zdloVVt5t1Wg9adj7stA2HFTYLHrooc1yvTbu2tibD4TA8fxjSZtu1\nCCsLkSNYvTYuK/dBmw6t56bR+pH7ZcLiSX43tw20eoFYWEuQoux2LKzWlSOnZWG93bf3lxsILWaE\njx07RkH1wGVOq9DqecHz6WWLI1ijRBQC631n86Zt66fNwJxxVxy3hbcErUj8tI69aDIRAcpzIAtw\nBlx2s7NdOti9MxqNKKwWQJuE8tq16LTWbW3UIfLWz8qyPlbrtJmscQQugzdS30He9jZtmwucfS4D\nLINX/2qCgWu7JzJOWxrshTUajWg4Z0NtDI+xHjrO3NSip5kUWAUOx9p21cHr7kF31bEN47XO9pxY\nMFdXVzcMO3bsGJej8LhLQqrvoFrN1EvwGediF2oJXvaD3Gys8sDBNlkJWCxju465BNuvljNNB3zI\nH6G14GHZJp0QXh1YSGxveDbZhNngHTt2yI4dO2Tnzp20rIMFuZRJbgvoPMA7U9BatQHVa+eydp39\nPJaZU9hyF6eN2mEMXG/f9pzY+uJxRI8aetBap0V3RafFJ6Dwhoehr4KnTmoHBXbnzp0b4LWfa/OA\nBX5fpYRTHyGeCWhZUgWXtXFd9ppY1N6N3NYqA23ktBGsmLhh9cDjxDI+lsgeU0RQM9li67K4fWxa\noNPaUBihZeBiyFxKROENLwKYfZ991NShRUBt2Uu8ZEJjBnApURW1q0ViaAeDAYXUcwYv+2n3xc6V\n16a3GWIc7NNPCCmCi48x2rJ267BnjO35wcywBdZCWgqRo77bUntW6+OB2VdgRWbwX/M81/XacyVI\nbagYJaxs2yxSm/CYgcuAxTZtdL6im44Hqs5jzsoGbz18thhvdsxpMy6LZdamZX9iXQJWFUUvfdRM\n/NwM676IXJYlkjLweu1chBbhRSdhrlkC1obHEbiRoqjCJpvYc8U2c5wBlo3xMUX83kQ2tmkZuAgr\nc99MeOy1aRm4bLqvmnp4jPLAtcszwDJw1Sm8Ni/bJwI8qdNGbluSlxXXMXu9Dh+m8GC1mWFvGA6H\n6+rDoPDatJHLRsAyl40cF+vlAdxnTR3aUkjK7ux2WSlsZhd45MCR47MMMnPf6N3QyGlL58a7Kdn2\nbFtosevHezZZb3heu3EwGLiuunPnTjnhhBPGg86zA+vy8fpqo+6dUl5gHjR1aLX/MbOeHUfKJKxY\nXyb2e7IQMOqyKTmvlzlGaL3knNedhX2xbNA2LXsSSqdt+9f2wXrNA5zesWPHOjhPPPHEdcNJJ500\nLluAM8B6503rFSXy5g3eqUOLitolXYD14PVCZ68PFJMtLPNbgrf0Oa2/PRY79iIGzBx77Vp0Uvug\nhO3OKWWHbZvVRhUKLcLKBua6EbBR4mneoCxp5qDtqlLo7GWM7cXu9d/qIBI/l4wuzMqlIWrTl1xW\nnZZljxFiz41Zk0LF2qv2yScMg9FhdcyAzThtlC22mneIZxrarkkEvPDRPaN2bekhDC8S8ByYhcUe\n+Lb+DN4oicb6aL2flWHdQdoHW4oyEFr71JKC6AHLwuMTTjhhXXjMnoLK3vQWRTMBLbZrJ/kCon5d\nr21rL/QogWUvXqy/jr3Bdul44Eb1F9kILQIcPVjhPSVl51lAMWqxx2EfUbSJJwTWgotuq+Bap8WM\ncQlc71qad00NWpaAikKdNl8GyyZn2rSsSwjdN3NckROUEihREiwTJTBwsa2K7Vadl4kkvO4cDI0R\nXi8JhYmo0ju03jksXT/zpJlwWpGc25bCoEwCqhRSsovZgmv3xcoRnLicHVPpGNjNBI8lKnt9vNoH\nmwnpl5aWNgDrtWfRaRFsdNroHdrSeWTXyzxqZqBV2RPtXdiZhE6mrVMCA9+Y8fpxWZcVOw48Jrbc\nixKwPV5yXc9ZvaaC7YMV2fgjbEtLS7K6uroueWTbsDt37qRtV1s+8cQT163PHlv0+rnZ99klCpsH\nbSu0nptiW9ELIdkdv8uA7cwIcAQ6AqrNefDOTanLCh0yetgCk2q4P+uoGhp7P/OibVh0SXRXhZYl\nnlimmD3xhN8NXhPeOSyd43nRtjtt1Ja1CQ+dH4GKT+h4gHqPEEbtzShEjcoqrxwpA23XwUYELCQX\nkXHYiy+v6zQ+POG1Yb2kk80Ue107mZtpCd55BlZkC6C1UEZlldeOjaDaDLf17uioUvaZgRH1tWKZ\nLStBWyp7STg8h/acq8vaLhcc79ixg7ZLFdjSgxX2AQoLLssQe2HxogMrMgNtWgS4FA6ru1q3LUHZ\nNTwutXlZGFpyS7ttO2b7i+D1wvRSmxvPpy1rCIztTJ224bCFN1NWl828dse+G7xWFhVYkRmA1oqB\nypYhvAhu1oWzwEZdRfgYZAksu20t4/4i4LM3Be/86hjPhbose+9VwWWuimP2coCF1vu5VK2Ddw1U\nYN/SVKFloLB1Iudtmrd+ILwrrBG8Wi9MBrFuFQyVvbA1AtCbH4Fu53mhL863D0nY5JP3203osrat\nypJNNktst4OJLu+lAO84MtfRvGvLobVtWZ1WIaQlmCK3VXiz7VgPXqvIbb2HFzLJoK4D1s0731ET\nANez4LDX6bAP1gPWQsuA37lzp/uHWjY0tteI57R4vIumqXb52PkicZsWXRXLTdPI8vJyCMxoNJKV\nlRUZDofji1UhRMcZjf73e8V2mReGl9zZa39GIOM2dNqeL1SpHc+eF8ZEE4a1rFsH3dUmmlh4bbt3\n8Akn1heL10UFdr22BVoGK3NcXc9zVQapFYakzJ28tl/J1a0z4VjLmJTCsgdqts1rzxtzI9ZOtdNe\nZljLXmjL4GUDtoFLr9mVwuHSNbWo2hJoPUgRTrsM12NhMLouDuqG3gWv5Shp0wVavQBLjwmWHs7A\nerJy6eaCNxJb9n7ZHx/6x6HUVrVhMd4MWJdOFKkgjAzORQZWZAudNgqF0TUUUgurXa4XpK5rQ2MU\ncyvcl1dfD1aEwoMXXziwZRu6M3DtE0vecbF6YTn6D5zSX3EwaKN52HZlXTnRwxOlDH4FlmtLw2Pm\nqMxtLbA6LbLx/2Cty4q0c9KoXRQ5qwesFyZHzwUzYFlWOTqfLJmGITBmZi08CCmOo0RS1F61D/yz\n/UfvxeL3U9uwsbY9e2znodNiWdcVkbHT6jpsP2yZdxdnyzOhZwba6J1XBix7zJAdG9bD62vFRBNC\ni7B50Ea/lMgewFhZWaFZ4dqls7natkSUyMYw2HNdK3VZLWtojNvPQMtgKLksczPrGh607DW/TB9u\ndCFHobmOvUcQox8NLw1eexW3H2WGWRvWc1q8bqrWaypdPlFozKSwaqic2Y8tewkP5qQRxB40dr7C\nast28IC1bVrPhbwkU6ndagcW5nqhL0LOHj3Eaa/NrWP2/eC8qlhTfyIK3TcSA7atk+L8jHti2Lm2\ntiarq6vhz7l4jzlGIbKtG9ZTj99LMtmH/dngPaKIThqN2VNM2Gb1bnpRm7UC205Tf/aYua/O99bX\ni0EkfmOGXTy4DWyn2gsRf6kw+3tL3ovq7MkonI5uNFF70TptFMKW4GRdNth1g4M9j3je2fdXNZlm\n9jeioqQMyyB7IaUOtq+XgbqysiLHjx8fO+rx48fHrur9EBo6K/sJUq+flg1YZ5z22rG2HxYHDGkj\nJ2ZPSeHgNQ+ierPvN5pXFWvqTisSP2yBwvYt6/axoA6HQxda+zijAmvHx48flx07drhOysJgfP7Y\ne+dW6xxB67W7o0SU197MAM0+i+FwJtFkjwPL+L1XtddUf7nCS0hFXUAqBdYC4F3sCoy92BAyfSaZ\n/baSB2cEa/TCgNYXyxGwGM6zdjhrZ3r9phGcUdvZ1gPLXrTjXQdV3TR1p82Ai+syh2Jupd1FNtmj\n8+yDECykjcBkY+yHZWWsr8prh2MiZzAYbHC3qF3eZfCScghqm2RTTTxtrqYCrRcOt8kkWy0vL69z\nKztWSDGk9tyw9Oyw56jshQBWRuE8loG1YHjAIrheu9fLkuN2mKNGUYD9HrHMpqu6a+pOaxWFxsyR\nvRBTYUVQvaeQ0BUjOJmLMjeNkk3R8XsOi8BmAS6tMwmcUQhcId06zVT2GOfbL74ULnuhM863ALMu\nlxKcXh9rCdQSsHo8JWijNmVbMO0+2iSSqotOVzPxcEVmPmvvRmG0B3EJatsWZu3TKPQtJZq8euJ0\nyfFK8GVBzQIb1bdq+zX18DgCV2RjkqYNsLbMIGKDAmvDaw/OkrN60Eb1RnBK4LJpVi6FvZnwt4bB\ns6FBE8RtHlBbodJ+7PJSmYWlHrBsHhtsIikbCrcNjXXMwMXptlCXIPXctQI7HUXsTd1pVcxZveWs\nzNbHdXWcLbdxUQaoV8ZjwnnM4bIgs3necm8fWLcK7GxpZqBVldy9K7AMXm+cKUdjFYM2utijcLQE\nXLQsG/Zi3aLzWzU9zRy0IhsviqxLseUId9swm41ZnUrTmbqy6c0oR65ZctTMsqrt1UxCi8pcMJ7z\nsnkMqAx0GRCzsLYFJHuT6rq87XpV01MvoM2ozcXG2s/e57cqEZepbwWoiqn8MxBzrCw4mwlPdnsV\n2CpPc+O0XVVKfNn1tksV2KpIC+20qlmCZJbqUjWbWninVZX6ibdr/1VVJVVoQZnupq3YT1VVVhXa\ngipcVbOm2qatquqZKrRVVT1Thbaqqmeq0FZV9UwV2qqqnqlCW1XVM1Voq6p6pgptVVXPVKGtquqZ\nKrRVVT1Thbaqqmeq0FZV9UwV2qqqnqn4lk99y6WqarYUQjutF8Krqqp81fC4qqpnqtBWVfVMFdqq\nqp6pQltV1TNVaKuqeqb/A+D6aZOYZ6LYAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7f628d394250>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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yWXqd5QIjYL2hHCsAlYkQZ1zkEvdYW1ovWmwBnH2N8Vh9LFAK8TyA6/8VWwUq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\nm5oqU4O2qaky/R9Nhb6So/Bb4wAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7f628d2ff2d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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YaLG84ErnSudTMD2wemJZGs9yGWOujzEFwvM+HgtU7p08HlD7BkDf6tOmFgra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\nQBsKFaaANhQqTP8F2/Hfhex7SIwAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7f628cc42b50>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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o4WEzpggrWskSuCwplUlgeV1UFlqE1CujpfXGFtt1LPmUzRDbz7KDKDQssNen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\nAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7f628bb7f750>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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ZtziKEmf7YeV1o7R1fahOTKPmAlpZ2ViWC50BONumzQSh5LKU167V54/a6JZ1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\n",
"text": [
"<matplotlib.figure.Figure at 0x7f628a282e10>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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dHrnGpdCyFfWsrLVwW6A2knXJWNdZoI4sLbelZW0jV9k6/zKtcaee07axrrM0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\nPVOFtqqqZ6rQVlX1TBXaqqqe6f8AU+qYrm7HVVsAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7f628a200390>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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PmcfiAZNJAEYej7W0Cq89Lz02Wtfo/m+G9R0MtFYIcHQBRwW1T/IpipkQVmZx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\nSUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7f628a184d90>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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1rsnWrL5ePttmLzeUNAaeLGDZdiZ6XNxjXa7o8Q4D14oc42cjSIcKZWSDhbZP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\nXv/zqZIu0K6s+L+7i+ez3HZ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"text": [
"<matplotlib.figure.Figure at 0x7f628a12bd90>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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rmcZd1zYC3qmFNrKwdpsSuNGbPPg+LEs6eZZW5VXQbEwbVUysQBZaNmTdY+/a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\nrAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7f628cfc5190>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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i2pzRULnZ461U46D1FCWjWKtZN6bV7zIX1PaNoqXNtbC2WwczzNHYYjZEUaQe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\nVQotzr1ysqrjFntdOhaU3O6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"text": [
"<matplotlib.figure.Figure at 0x7f628cd34450>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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es+PkWMZpAOsBnIppUxDjMUqysiIdhjbHjYoSUFEiKmVlGbBeTMti3OiBAC8B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\nq6oKU4W2qqowVWirqgpThbaqqjD9HzUNGLnkpzC3AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7f628c62d0d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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LzCoqgZppWz1HnDwgvfvgaVm7fysbCXBvoI1gLQWXpXciQEs6sltN26Z4wkvr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\n61LT1ksr6PG9VI7+vjQPi9D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"text": [
"<matplotlib.figure.Figure at 0x7f628c898650>"
]
}
],
"prompt_number": 78
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}