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@jaredly
Created January 30, 2014 06:14
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Submission Perceptron Notebook
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{
"metadata": {
"name": "Perceptron"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": "import numpy as np\nfrom pandas import DataFrame, Series\nfrom scipy.io.arff import loadarff\nimport scipy",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": "data, meta = loadarff('ex_1.arff')",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": "df = DataFrame(data)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": "df[df.major == 'cs']",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>friends</th>\n <th>age</th>\n <th>major</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td> 8</td>\n <td> 20</td>\n <td> cs</td>\n </tr>\n <tr>\n <th>1</th>\n <td> 2</td>\n <td> 21</td>\n <td> cs</td>\n </tr>\n <tr>\n <th>2</th>\n <td> 0</td>\n <td> 25</td>\n <td> cs</td>\n </tr>\n <tr>\n <th>3</th>\n <td> 1</td>\n <td> 23</td>\n <td> cs</td>\n </tr>\n </tbody>\n</table>\n</div>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": " friends age major\n0 8 20 cs\n1 2 21 cs\n2 0 25 cs\n3 1 23 cs"
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": "dc = df.copy()",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 50
},
{
"cell_type": "code",
"collapsed": false,
"input": "dc.major = True",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 51
},
{
"cell_type": "code",
"collapsed": false,
"input": "dc",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>friends</th>\n <th>age</th>\n <th>major</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td> 8</td>\n <td> 20</td>\n <td> True</td>\n </tr>\n <tr>\n <th>1</th>\n <td> 2</td>\n <td> 21</td>\n <td> True</td>\n </tr>\n <tr>\n <th>2</th>\n <td> 0</td>\n <td> 25</td>\n <td> True</td>\n </tr>\n <tr>\n <th>3</th>\n <td> 1</td>\n <td> 23</td>\n <td> True</td>\n </tr>\n <tr>\n <th>4</th>\n <td> 8</td>\n <td> 25</td>\n <td> True</td>\n </tr>\n <tr>\n <th>5</th>\n <td> 20</td>\n <td> 20</td>\n <td> True</td>\n </tr>\n <tr>\n <th>6</th>\n <td> 10</td>\n <td> 23</td>\n <td> True</td>\n </tr>\n <tr>\n <th>7</th>\n <td> 7</td>\n <td> 25</td>\n <td> True</td>\n </tr>\n </tbody>\n</table>\n</div>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 52,
"text": " friends age major\n0 8 20 True\n1 2 21 True\n2 0 25 True\n3 1 23 True\n4 8 25 True\n5 20 20 True\n6 10 23 True\n7 7 25 True"
}
],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": "dc.friends /= 2",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 61
},
{
"cell_type": "code",
"collapsed": false,
"input": "dc.major = df.major == 'cs'",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 64
},
{
"cell_type": "code",
"collapsed": false,
"input": "dc.age[df.major == 'cs'] = 2",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 68
},
{
"cell_type": "code",
"collapsed": false,
"input": "df.major.unique()",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 72,
"text": "array(['cs', 'law'], dtype=object)"
}
],
"prompt_number": 72
},
{
"cell_type": "code",
"collapsed": false,
"input": "m = dc.major.copy().astype(int)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 77
},
{
"cell_type": "code",
"collapsed": false,
"input": "dm = df.copy()\ndm.major = numpy.arange(len(dm.major))",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 86
},
{
"cell_type": "code",
"collapsed": false,
"input": "df[(df.major == 'cs') + (df.friends >= 10)]",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>friends</th>\n <th>age</th>\n <th>major</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td> 8</td>\n <td> 20</td>\n <td> cs</td>\n </tr>\n <tr>\n <th>1</th>\n <td> 2</td>\n <td> 21</td>\n <td> cs</td>\n </tr>\n <tr>\n <th>2</th>\n <td> 0</td>\n <td> 25</td>\n <td> cs</td>\n </tr>\n <tr>\n <th>3</th>\n <td> 1</td>\n <td> 23</td>\n <td> cs</td>\n </tr>\n <tr>\n <th>5</th>\n <td> 20</td>\n <td> 20</td>\n <td> law</td>\n </tr>\n <tr>\n <th>6</th>\n <td> 10</td>\n <td> 23</td>\n <td> law</td>\n </tr>\n </tbody>\n</table>\n</div>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 99,
"text": " friends age major\n0 8 20 cs\n1 2 21 cs\n2 0 25 cs\n3 1 23 cs\n5 20 20 law\n6 10 23 law"
}
],
"prompt_number": 99
},
{
"cell_type": "code",
"collapsed": false,
"input": "nums = df[df.columns[:-1]]",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 108
},
{
"cell_type": "code",
"collapsed": false,
"input": "nums['man'] = Series(np.zeros(len(nums)), index=nums.index)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 127
},
{
"cell_type": "code",
"collapsed": false,
"input": "list(nums.columns)",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 147,
"text": "['friends', 'age', 'man']"
}
],
"prompt_number": 147
},
{
"cell_type": "code",
"collapsed": false,
"input": "data, meta = loadarff('iris.arff')\ndf = DataFrame(data)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": "from itertools import cycle\nfrom IPython.display import display\n\ndef plot_weights(weights, labels):\n styles = cycle(('-', '--', '-.'))\n n = len(weights[0])\n ncolors = 7\n i = -1\n for i in range(0, n/ncolors):\n plot(weights[:,i*ncolors:(i+1)*ncolors], ls=styles.next())\n plot(weights[:,(i+1)*ncolors:], ls=styles.next())\n legend(list(labels) + ['Bias'], loc='upper left', bbox_to_anchor=(1.01,1))\n\ndef test(fname):\n m = p.fromArff(fname, .1)\n a = m.trainUp()\n print 'Done Training'\n # Plot the accuracies over epochs\n ps = m.perceptrons.items()\n for i in range(len(a[0][1])):\n figure()\n plot_weights(array([x[1][i] for x in a]), m.data.columns[:-1])\n title(ps[i][0][0] + ' vs ' + ps[i][0][1])\n xlabel('Epoch')\n ylabel('Weight')\n\n if len(a[0][0]) > 1:\n figure()\n plot(array([x[0] for x in a]))\n legend(list(l[0] + ' vs ' + l[1] for l, p in ps), loc='upper left', bbox_to_anchor=(1.01,1))\n ylim(-0.1, 1.1)\n title('Individual Perceptron Accuracy')\n xlabel('Epoch')\n ylabel('% Accuracy')\n\n figure()\n plot(array([array(x[0]).mean() for x in a]))\n title('Mean Accuracy')\n xlabel('Epoch')\n ylabel('% Accuracy')\n return m, a\n\ndef show_resuls(m, data, a):\n # Plot the accuracies over epochs\n ps = m.perceptrons.items()\n print 'Weights vs Epochs for each perceptron:'\n for i in range(len(a[0][1])):\n f = figure()\n plot_weights(array([x[1][i] for x in a]), data.columns[:-1])\n title(ps[i][0][0] + ' vs ' + ps[i][0][1])\n xlabel('Epoch')\n ylabel('Weight')\n display(f)\n\n print 'Accuracies:'\n if len(a[0][0]) > 1:\n f = figure()\n plot(1 - array([x[0] for x in a]))\n legend(list(l[0] + ' vs ' + l[1] for l, p in ps), loc='upper left', bbox_to_anchor=(1.01,1))\n ylim(-0.1, 1.1)\n title('Individual Perceptron Misclassifications')\n xlabel('Epoch')\n ylabel('% Missed')\n display(f)\n f = figure()\n plot(1 - array([array(x[0]).mean() for x in a]))\n title('Misclassifications')\n xlabel('Epoch')\n ylabel('% Missed')\n display(f)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": "import perceive as p\nfrom pylab import *\n#a = m.train(d, split=.7)\n#a\n\ndef htmltitle(*args, **kwds):\n level = str(kwds.get('level', 2))\n message = ' '.join(map(str, args))\n display(HTML('<h' + level + '>' + message + '</h' + level + '>'))\n\ndef try_rates(meta, data, rates):\n allz = []\n for rate in rates:\n htmltitle('Learning Rate', rate)\n m = p.Main(meta, rate)\n results = m.train(data.copy(), split=None)\n print 'Best accuracy', m.best\n print 'Number of epochs:', len(results)\n show_resuls(m, data, results)\n allz.append(results)\n compare = DataFrame({\n \"Rate\": Series(rates),\n \"Epochs\": Series(tuple(len(results) for results in allz))\n }, columns=['Rate', 'Epochs'])\n htmltitle('Number of Epochs vs Learning Rate')\n display(compare)\n return allz\n \ndef trials(meta, data, num, split=.7, graphs=True):\n allz = []\n print 'Running', num, 'trials'\n accuracies = []\n epochss = []\n trains = []\n for i in range(num):\n htmltitle('Trial', i+1)\n m = p.Main(meta, .1)\n results, accuracy = m.train(data.copy(), split=split)\n print 'Best accuracy', m.best\n print 'Number of epochs', len(results)\n epochss.append(len(results))\n trains.append(m.best)\n htmltitle('Percent missed (of the test set):', accuracy[0], '; ', accuracy[1], ' instances', level=4)\n accuracies.append(accuracy[0])\n if graphs:\n show_resuls(m, data, results)\n allz.append(results)\n htmltitle('Final Results')\n print 'Mean accuracy over', num, 'runs:', 1 - sum(accuracies)/float(num)\n print 'Mean training accuracy', 1 - sum(trains) / float(num)\n print 'Mean epochs', sum(epochss) / float(num)\n return allz\n\ndef runthis(fname, split=.7, graphs=True):\n data, meta = loadarff(fname)\n print 'Running perceptron classification on', fname, 'with a testing split of', split\n \n #figsize(12, 6)\n rcParams['figure.figsize'] = (6, 3)\n return trials(meta, DataFrame(data), 5, split, graphs=graphs)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": "data, meta = loadarff('./ex_1.arff')\ntry_rates(meta, DataFrame(data), [1000, 100, 10, 1, .01, .001]);",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<h2>Learning Rate 1000</h2>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x2a72ec2c>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Fully trained\nBest accuracy 1.0\nNumber of epochs: 5\nWeights vs Epochs for each perceptron:\n"
},
{
"html": "<img 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CKz5q8xHj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"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x2abab1ac>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Accuracies:\n"
},
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"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x2ad0596c>"
},
{
"html": "<h2>Learning Rate 100</h2>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x2adfc52c>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Fully trained\nBest accuracy 1.0\nNumber of epochs: 3\nWeights vs Epochs for each perceptron:\n"
},
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Zru3r06ePXFxc1Lt3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"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x2ad2f42c>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Accuracies:\n"
},
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0GqEoCmRZhhCixFTi\nT88//zyef/55AGqbaty4saViEhFRJTp3Tp0+bN8u4fZtoEsXgWHDBB59lNMHW2axMuHq6gpPT08k\nJSWhe/fuSE1Nhbu7e4kykZ+fj9u3b6N27dq4du0a1qxZg0GDBlkqJhERmdGdO8CePer04fBhCY0b\nq9OHzz8XeOABrdORuVj0MEdYWBhmz56NVatWwcnJCREREQCA2NhY+Pj4wNvbG9evX8f7778PSZIg\nhECfPn3QuXNnS8YkIqIKOH9enT4kJkq4eVOdPgwaJNCmDacPVZUkhLDpa4omJCQgKChI6xhERNVW\nfj6QmqpOHw4dkvDww+r0oXt3wNVV63R0L4mJiQgODq7wfqxuASYREVm/ixfV6cO2bUBengRfX4HQ\nUIGpUzl9qI5YJoiIqEx/Th+2bJHw008SHnpIIDgYmDdPoG5dmx5wkxmwTBARUakyMv5c+wDk5kr4\n178EnnlGYMoUAdlilzwkW8AyQUREAICCAmDvXnX6kJYmoUEDgV69gLlzBerV4/SB7o5lgoioGrt0\nSS0P27YB165J8PER6NdPYNIkTh/IdCwTRETVSEEB8P33aoE4cEBC/frq9CEmRkCn4/SB7g/LBBFR\nFZeZqZaHrVuBnBwJ3t4CTz0lEBnJ6QOZB8sEEVEVU1gI7N+vLp48cECCm5t65kV0tIBez+kDmR/L\nBBFRFZCVBWzdKmHLFuDKFXX60Lu3wP/+J2Bnp3U6qupYJoiIbFBhIXDggDp92L9fgk4n0LMnMHOm\nQP36nD6QZbFMEBHZiOzsv6YP2dkSOnVSL1s9fjynD6QtlgkiIiulKMWnD3XrCgQFAdOmCbi7c/pA\n1oNlgojIihiNwLZtEhISgKwsdfoQHCzw3nsCNfgnNlkp/q9JRKQhRQHS0tTpw759EurUUdc+TJ0q\n0LAhpw9kG1gmiIgs7MoVdfqwZQtw+bKEDh3U6cM773D6QLaJ/9sSEVUyRQEOHlSnD6mpEh54QF37\nMGWKwIMPcvpAto9lgoioEly9CiQmqtOHjAwJ7dqp04e33hKoWVPrdETmxTJBRGQGQgCHDqnThz17\nJDg7q9OHyEiBhx/m9IGqNouXiYyMDERHRyM3NxfOzs4IDw+Hh4dHsW0KCwvx6aef4siRI7Czs8MD\nDzyAUaNGoWHDhpaOS0R0V7//DiQmqgXi4kUJjz8uEBIi8MYbArVqaZ2OyHIsXibi4uIQEhKCwMBA\n7NmzBzExMZg+fXqxbVJTU5Geno7Y2FjY2dnh66+/xpIlSzBu3DhLxyUiKiIEcOQIsHmzOn1wdAR6\n9FAvWe3hwekDVV8WLRM5OTlIT0/H5MmTAQC+vr6Ij4+HwWCAXq8v2k6n0yE/Px937tyBg4MD8vLy\nin2diMhSrl0Dtm9XC8T58+r0oVcvgYgITh+I/mTRMpGdnQ2dTgf5j3veSpIEvV5foky0bt0a7du3\nx5AhQ+Do6AidToepU6eWuk+j0YioqKgSz/v5+cHf379yfhAiqrKEAI4eVQ9dpKRIcHAAuncXeP99\ngcaNOX0g25ScnIyUlJQSz3t5eZll/1a5AHP37t04fvw4Fi9eDEdHRyxevBjz589HeHh4iW11Oh1C\nQ0M1SElEVUVuLrBjhzp9OHdOwmOPqdOH0aMF7O21TkdUcf7+/qX+gp2YmGiW/Vu0TLi5ucFoNEJR\nFMiyDCFEiakEABw5cgRPPPEEnJycAADdunXDpEmTLBmViKowIYDjx/+aPtSsCQQGCowbJ9C0KacP\nROVl0TLh6uoKT09PJCUloXv37khNTYW7u3uJMtGkSRPs27cPffv2hZ2dHX744Qc0btzYklGJqIq5\nfh1ISlKnD2fOSGjdWp0+hIUJODhonY7Itln8MEdYWBhmz56NVatWwcnJCREREQCA2NhY+Pj4wNvb\nGz179sRvv/2G0aNHQ5Zl6HQ6hIWFWToqEdkwIYATJ9Tpw65dEmrUUKcPY8cKeHpy+kBkTpIQwqY/\nVQkJCQgKCtI6BhFZgRs31OlDQoKEU6cktGqlTh/8/QFHR63TEVmfxMREBAcHV3g/VrkAk4jIFEIA\nv/6qHrrYtUuCLAPdugmEhws88ohN/55EZFNYJojIpuTlATt3qtOHX3+V0KKFetXJ//5X4I8120Rk\nYSwTRGT1Tp5Upw87d0oAgIAAgVGjBJo1E5AkjcMREcsEEVmfmzeBXbvU6cOJExKaNVOnDy+/LODs\nrHU6IvonlgkisgqnTqnlISlJgqIAXbsKvPaaQPPmnD4QWTuWCSLSxK1bQHKyevji558lPPKIeubF\n4sUCtWtrnY6IyoNlgogs5vRpdfqwY4eEggLA319dONmyJacPRLaMZYKIKo2i/HXPi2PHJHh6qtOH\nhQsFXFy0TkdE5sIyQUSV4tIlICxMRvv2Ai+9JNC6NacPRFUVywQRmd233wJz5siIjlbw6KNapyGi\nysYyQURmc/Mm8M476n0wNm5UeAMtompC1joAEVUNhw8DffrICAkRmDmTd+Ikqk44mSCiChECmDtX\nvTfGsmUK3N21TkRElsbJBBHdt6ws4Lnn1D9GVq1ikSCqrjiZIKL7smULMGOGjJkzFTz+uNZpiEhL\nLBNEVC63bgHvvy8hP19dZOnoqHUiItIaD3MQkcmOH1cXWQYECMTECBYJIgKgwWQiIyMD0dHRyM3N\nhbOzM8LDw+Hh4VFsmx07dmDDhg1Fjw0GA9q0aYN3333X0nGJCOoiy/h4CVu3Sli8WEHDhlonIiJr\nYvEyERcXh5CQEAQGBmLPnj2IiYnB9OnTi20TGBiIwMDAosevv/46AgICLJyUiAAgOxsYNUqCjw+w\nerUCmfNMIvoHi/6xkJOTg/T09KJi4Ovri8zMTBgMhrt+z6+//oqcnBx4e3tbKCUR/Wn7diA0VMa4\ncQIREYJFgohKZdHJRHZ2NnQ6HeQ//kSSJAl6vR4GgwF6vb7U79m2bRu6desGOzu7Ur9uNBoRFRVV\n4nk/Pz/4+/ubLzxRNXLnDjBhgoTcXGDDBgXOzlonIqKKSE5ORkpKSonnvby8zLJ/qz6b4/bt20hJ\nScGMGTPuuo1Op0NoaKgFUxFVbb/8AoweLeO11xQ8/bTWaYjIHPz9/Uv9BTsxMdEs+7domXBzc4PR\naISiKJBlGUKIe04l9uzZg0aNGuHhhx+2ZEyiakkI4IsvJGzcKGHBAgX82BGRqSx6BNTV1RWenp5I\nSkoCAKSmpsLd3f2ehziCgoIsGZGoWrpyBRg0SILRCKxdyyJBROVj8eVUYWFhSEhIwIgRI7BmzRqM\nHj0aABAbG4v9+/cXbXfp0iWcPn0afn5+lo5IVK3s2qVeEjs8XODttwXusjyJiOiuJCGE0DpERSQk\nJHB6QXQf8vOBDz6QYDAAM2cK1K6tdSIisrTExEQEBwdXeD880YuoGjp1Sr2SZdu2QHw8iwQRVYxV\nn81BROYlBLBkiYTVqyXExyto1EjrRERUFbBMEFUTOTnA6NESWrQA1q9XuDaCiMyGZYKoGkhJASIj\nZURFKXjiCa3TEFFVY/Kaifnz5yMvL68ysxCRmRUUAJGREhYtkrBuHYsEEVUOk8tEcnIymjRpgoiI\nCJw6daoyMxGRGZw5A/TtK6N5c2DBAoEHHtA6ERFVVSaXiS+//BKHDx+Gq6srunXrhpCQEHz77beV\nmY2I7tOXX0oYNUrG3LkKBg606bO/icgGlOvU0AYNGmDixIlYtmwZjh49isGDB6Nly5Zmu7Y3EVXM\ntWvASy9JOHVKvUFX06ZaJyKi6sDkBZg3b97El19+ibi4ODg4OGDGjBkYMGAA0tLS8Oyzz+LcuXOV\nmZOIyrB3LzBhgozISAVdumidhoiqE5PLRNOmTdGjRw/Ex8ejc+fORc97e3vzCpREGiosBKZOlXDy\nJLB6tQJXV60TEVF1Y3KZ+Omnn9CwYcNSv/b555+bLRARme7cOSAsTMbzzwu8/76AJGmdiIiqo3Kd\nGpqdnV30ODs7Gx988EGlhCKisq1cKWHECBmzZysYPJhFgoi0Y3KZWL9+Pdzc3Ioeu7m5Yd26dZUS\niojuLjcX+M9/JBw5AmzcqOCRR7RORETVXYVu9FVQUGCuHERkgv37gX79ZAwaJDB5skDNmlonIiIq\nR5lo1qwZZs6cCUVRUFhYiBkzZqBly5aVmY2I/lBYCHz8sYQ5cySsXKmga1etExER/cXkMjFnzhxs\n2rQJTk5OcHZ2RkJCAmJjYyszGxEBuHAB6N9fhl4PLFsmUK+e1omIiIoz+WyOhx56CElJSbh+/ToA\noHbt2pUWiohU69YB8fEy5sxR0KKF1mmIiEpXrruGZmZm4tixY7h161bRc7179zZ7KKLq7sYNYOxY\nCa6u6iLLWrW0TkREdHcml4lFixbhgw8+QHZ2Npo3b45Dhw7hiSeeKHeZyMjIQHR0NHJzc+Hs7Izw\n8HB4eHiU2O7s2bOIj4/H77//DkVRMHToUPzrX/8q12sR2aK0NODtt2W8+66C7t21TkNEVDaTy8TM\nmTPx448/onv37vjxxx+RnJyMhQsXlvsF4+LiEBISgsDAQOzZswcxMTGYPn16sW1u376NDz/8EBER\nEWjVqhUURSk6vEJUVSkKEB0t4YcfgK++UvC3M7GJiKyayQswa9asiXr16hWdDurv74+DBw+W68Vy\ncnKQnp6OgIAAAICvry8yMzNhMBiKbbdr1y60bNkSrVq1UkPKMh7g/ZOpCsvIAAYMkFG7NrBihWCR\nICKbYvJkwsHBAYqioFmzZoiNjUWjRo1w48aNcr1YdnY2dDodZFntMJIkQa/Xw2AwQK/XF213/vx5\n1KxZE5MmTYLRaESTJk3w8ssvl1oojEYjoqKiSjzv5+cHf3//cuUj0sI33wCxseqVLFu31joNEVVF\nycnJSElJKfG8l5eXWfZvcpmYMmUKrl27hmnTpmHEiBHIycnBJ598YpYQ/1RQUIC0tDTMnDkT9erV\nw9KlSzFv3jyMGzeuxLY6nQ6hoaGVkoOoMuXlAePGSbC3B775RoG9vdaJiKiq8vf3L/UX7MTERLPs\n36TDHIWFhTh8+DBcXV3xyCOPIDExEQcOHECPHj3K9WJubm4wGo1QFAUAIIQoMZUAgPr16+Pxxx9H\nvT9OqO/atStOnjxZrtcismaHDwN9+8r4978FZswQLBJEZNNMKhN2dnZYunRphV/M1dUVnp6eSEpK\nAgCkpqbC3d29RJno0qULTp48iby8PABAWloamjZtWuHXJ9KaEEBMjIQpU2QsX64gOFjrREREFWfy\nAswePXrgq6++qvALhoWFISEhASNGjMCaNWswevRoAEBsbCz2798PANDr9XjmmWfw1ltvYfTo0Th8\n+DBGjBhR4dcm0lJmJvDsszLs7ICvv1ZQv77WiYiIzEMSQghTNnRzc8OVK1fg4OAAZ2dn9ZslCVlZ\nWZUasCwJCQkICgrSNANRWTZvBqKjZcycqaBNG63TEBGpEhMTEWyGEanJCzAPHDhQ4Rcjqm5u3QLe\ne0+CogAbNihwdNQ6ERGR+ZlcJpo0aVKJMYiqnqNHgTfekDFmjIInn9Q6DRFR5TG5TJS2AFKSJJw+\nfdqsgYhsnRDAvHkStm+XsGSJggYNtE5ERFS5TC4T33zzTdG/37p1C0uXLkXdunUrJRSRrTIYgLAw\nGb6+AqtWKZBNXuJMRGS7TC4Tjz32WLHHnTp1whNPPIHIyEhzZyKySdu2AdOmyZg+XUG7dlqnISKy\nnHLdgvzvrly5ovmZHETW4PZtYPx4CTdvqossnZy0TkREZFkml4nOnTsX/XthYSHOnDmDsWPHVkoo\nIltx4gQwZoyMkSMV9O2rdRoiIm2YXCb+fpvwGjVqwNPTEw8++GClhCKydkIAn38u4dtvJXzxhYKH\nHtI6ERGRdkwuEz4+PrC3ty+646eiKLh16xYcHBwqLRyRNTIagddfl9ChA7B2LRdZEhGZ/MdgYGAg\ncnNzix5fu3at3Df6IrJ1SUnA88/LePNNgbFjBYsEERHKMZm4efMm6tSpU/TY1dUVN27cqJRQRNbm\nzh0gMlLClSvA+vUKatfWOhERkfUw+fcqRVFw/fr1ose5ubnIz8+vlFBE1uTXX4E+fWR07Cgwf75g\nkSAi+geTJxMvvvgievbsiZEjR0IIgXnz5mHIkCGVmY1IU0IAixZJWLtWwmefKfDw0DoREZF1MrlM\njBs3Dg3wK1SKAAAZj0lEQVQaNMDGjRsBACNHjsSgQYMqLRiRlq5eBUaPltC6tXpYw85O60RERNar\nXBetGjp0KIYOHVpZWYisQnIyMGmSjKgoBT4+WqchIrJ+Jq+ZCAkJgdFoLHpsMBjw73//u1JCEWkh\nPx+YOFHC0qUS1q1jkSAiMpXJZeLSpUvQ6XRFj/V6PS5evFgpoYgs7fRpoG9fGa1bA599JuDionUi\nIiLbYfJhDkVRkJ+fj5o1awIA7ty5gzt37lRaMCJLEAJYvlzCV19J+OQTBU2aaJ2IiMj2mFwmgoOD\n8cILLyA8PBxCCMyePRtPPvlkuV8wIyMD0dHRyM3NhbOzM8LDw+Hxj2XyR44cQWRkJB5++OGi56ZP\nn45atWqV+/WI7ub334ExYyR4eqqLLGvc923viIiqN5P/+IyKisLUqVPxxhtvAAD69OmDcePGlfsF\n4+LiEBISgsDAQOzZswcxMTHF7vvxp8aNG2PWrFnl3j+RKVJTgQkTZEyapOD//k/rNEREts3kNRO1\natXCxIkTsX//fuzfvx8jRoxAXFxcuV4sJycH6enpCAgIAAD4+voiMzMTBoOhXPshul8FBcCkSRI+\n/VTC2rUsEkRE5lDuwW5CQgIWLFiALVu2oEePHggPDzf5e7Ozs6HT6YpuFiZJEvR6PQwGA/R6fbFt\nL1y4gDFjxsDOzg5BQUEICQkpdZ9GoxFRUVElnvfz84O/v385fjKq6s6eBUaNkhEaKjBhgoAkaZ2I\niMgykpOTkZKSUuJ5Ly8vs+zfpDJx9uxZfPHFF1i0aBEaNmyI9PR0nDt3DnXr1jVLiH/y8vLCokWL\n4OTkBKPRiMjISLi4uKBLly4lttXpdAgNDa2UHFR1fP21hMWLJcydq8DTU+s0RESW5e/vX+ov2ImJ\niWbZf5mHOXr06AE/Pz/cvn0bW7Zswffffw8XF5f7KhJubm4wGo1QFAUAIIQodSrh5OQEJycnAGpZ\n6Nq1K44dO1bu1yPKzQVeeUXC8ePAhg0sEkRElaHMMpGeno7GjRujVatWaNSoUYVezNXVFZ6enkhK\nSgIApKamwt3dvUSZuHr1alHhyMvLw/79+802iqHqY/9+oF8/GUOGCHzwgcAfZzUTEZGZlXmY4/Tp\n09i+fTsWLFiAN998E71798atW7fu+wXDwsIwe/ZsrFq1Ck5OToiIiAAAxMbGwsfHB97e3khNTcV3\n330HOzs7FBYWokuXLujRo8d9vyZVL4WFwLRp6jRi1SoFlXQ0joiI/iAJIYSpG1+5cgXLly/HggUL\nkJ2djRdeeKHU0zotKSEhAUFBQZpmIOtx/jwQFiajf3+BoUO5yJKI6F4SExMRHBxc4f2YfGooANSr\nVw+vv/46Dh48iA0bNiAvL6/CAYjMZc0a4D//kTFzpoJhw1gkiIgs5b6v+dexY0d07NjRnFmI7sv1\n68DYsRLq1QM2blTAC6USEVlWuSYTRNbmxx+Bp5+W8fzzAh9+KFgkiIg0wLsRkE1SFGDmTAk//QR8\n/bWCv93QloiILIyTCbI5Fy8C/fvLcHUFli8XLBJERBord5lISkrCY489hqZNm2LhwoWVkYnorjZu\nBF56ScZHHyn4z3+4yJKIyBqUeZjj999/R506dYoex8XFYd++fZAkCZ07d8bw4cMrNSARAOTlAW+/\nLcHJSV1kaW+vdSIiIvpTmZOJPn36YPXq1UWPa9WqhbS0NPz444+oxdVuZAEHDwJ9+8ro21dg2jTB\nIkFEZGXKLBNbtmzBTz/9hGeeeQYZGRn48MMP8dlnn2HOnDn47LPPLJGRqilFAWbPlvDhhzK+/FIB\nr01GRGSdyjzM4eDggKioKBw+fBhDhw5Fv379sGTJEkg8WE2V6PJlYORIGT16CHz9tcK1EUREVqzM\nyURhYSG+/fZbZGVlISEhAXfu3EGvXr3w888/WyIfVUPffQcMGSJj0iQFI0dykSURkbUrczLx3HPP\nwdXVFXl5eVizZg3mzZuHfv36ITw8HO3atcPEiRMtkZOqgZs3gXfflSDL6iJLBwetExERkSnKLBPp\n6ek4ePAgAKBdu3YAgMaNG2PdunVYsWJF5aajauPoUeCNN2RERCgICdE6DRERlUeZZaJJkyZ4+eWX\nkZeXB29v72Jfe+GFFyotGFUPQgCffCIhKUnC0qUK3N21TkREROVVZplYtWoVtmzZglq1avFW32RW\nWVnAqFEyunQRWLlSgczrsRIR2aQyy0TNmjXx73//2xJZqBrZuhWYPl3GjBkK2rbVOg0REVUEb/RF\nFnX7NvD++xJu3wY2bFDg5KR1IiIiqiiLlomMjAxER0cjNzcXzs7OCA8Ph4eHR6nb3rlzBxEREbC3\nt8esWbMsGZMqyfHjQESEjFGjFDz1lNZpiIjIXCx6lDouLg4hISGYP38++vfvj5iYmLtuu3TpUrRo\n0cKC6aiyCAHEx0t4/30ZCxeySBARVTUWKxM5OTlIT09HQEAAAMDX1xeZmZkwGAwltj127BguXbqE\nbt26WSoeVZLsbGDgQAnXrwNr1ih48EGtExERkblZrExkZ2dDp9NB/mPJviRJ0Ov1JcrErVu38Pnn\nn2PkyJGWikaVZMcO4IUXZLz1lsCbbwqerUFEVEVZ3QLMhQsX4sknn0S9evVw8eLFMrc3Go2Iiooq\n8byfnx/8/f0rIyKV4c4d4H//k3DtGrB+vQJnZ60TERFVb8nJyUhJSSnxvJeXl1n2b7Ey4ebmBqPR\nCEVRIMsyhBAwGAzQ6/XFtjt+/Dh+/PFHfPXVV7hz5w6uX7+OUaNGYe7cuaXuV6fTITQ01BI/Apng\n11+B0aNlvPqqgn79tE5DREQA4O/vX+ov2ImJiWbZv8XKhKurKzw9PZGUlITu3bsjNTUV7u7uJcpE\nbGxs0b8fOXIECxcu5NkcNkAIYOFCCRs2SPj8cwUPP6x1IiIishSLHsUOCwtDQkICRowYgTVr1mD0\n6NEA1AKxf/9+S0YhM7p6FRg8WILBAKxdyyJBRFTdSEIIoXWIikhISOBlvjW0axcwebKMqVMVdO6s\ndRoiIiqPxMREBAcHV3g/VrcAk2xDfj4waZKEzExg3ToFLi5aJyIiIq3wZD0qt1OngD59ZLRpA3z6\nqWCRICKq5jiZIJMJASxdKmHVKgnz5yto3FjrREREZA1YJsgkOTnAmDESmjdXrx1hZ6d1IiIishYs\nE1Sm3buByEgZkycr+Ne/tE5DRETWhmWC7qqgAPjwQwlnz6qnfD7wgNaJiIjIGnEBJpXqzBmgb18Z\njzwCfPGFYJEgIqK74mSCSlixQsKyZRJiYxV4emqdhoiIrB3LBBW5dg0ID5fQqBGwYYOCGvy/g4iI\nTMDDHAQA2LcP6N9fxvDhApGRgkWCiIhMxr8yqrnCQuCjjyT88guwerUCV1etExERka3hZKIa++03\n4OmnZXh4AIsXCxYJIiK6L5xMVFOrVkn44gt1keUjj2idhoiIbBnLRDVz/Trw5psS9Hpg40YFNWtq\nnYiIiGwdD3NUIwcOqIc1Bg4UmDJFsEgQEZFZcDJRDSgKMGOGhEOHgJUrFdSrp3UiIiKqSjiZqOI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"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x2ad059ec>"
},
{
"html": "<h2>Learning Rate 10</h2>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x2afaeb8c>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Fully trained\nBest accuracy 1.0\nNumber of epochs: 3\nWeights vs Epochs for each perceptron:\n"
},
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jOlJwREfy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"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x2b04d16c>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Accuracies:\n"
},
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"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x2aa1902c>"
},
{
"html": "<h2>Learning Rate 1</h2>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x2b24ef0c>"
},
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"output_type": "stream",
"stream": "stdout",
"text": "Fully trained\nBest accuracy 1.0\nNumber of epochs: 7\nWeights vs Epochs for each perceptron:\n"
},
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lpsgB5OTA\n6NEG+/fDhAkmEZpA6fYK7AXnLoeRlsSe3/fg5eFF46qNiY9yTKaICY6xOuY1M02T3b/vZn3qepLS\nkthxdAc2w0aD8AbO0lYjpEaFKbFXyzRNNh/ezIIdC/gp5SeiA6NJjEukc+3OOi9T5DwqcuVHuSpy\nZ23cCC++aKNfP5P77zfR777y5UzBGX49/KtzBmZqZip+Xn40i2jmvEBtmH+Y1TEvyTRNUjJTnOX0\n18O/UmAWEBsa6yxtugBz6dh/Yj8Ldy5k+d7leHt406NOD3rW6UmVgCpWRxOxlIpc+VEuixxAfj5M\nmmTw008GEyfaqVnz+mWT6+903mk2pm9kfdp65y2jQnxDaBHZgvjoeFpEtrBspmPG6QySUpNYl7qO\nXw79Qk5+DjcE3+C8PMufqvxJl9a4Do7nHGfJ7iUs3rWYk7kn6VCzA73ielGnch2ro4lcdypy5Ue5\nLXJn7doFzz1no1Mnk4EDTTx0ykyFcTzneKFLcGSdyaJqQFVngWoa0bTU7/l5MvdkoZ+ZeSaTML8w\n5zl+zSKaaZa0C8jNz2XlbytZsGMByb8n0yq6FYlxicRHx2skVCoEFbnyo9wXOQC7Hd57z+Dzzw0m\nTLDTqFEZhxOXdSjrkOPOFGlJ/HLoF3Lzc6keUt15fbWGVRoW+3qB2XnZ/HLoF+ch3ozTGQT5BNEi\nsoVjBmlUC4J9gst4i+Ra2U0769PWs2DHApLSkqhdqTa96vWiQ40ObnGtPZGroSJXfpSoyI0ePZqh\nQ4decdn1cDV3dkhNhWefNWjQAIYMMfHRv9EVnmmaHDh5wHm+2tYjWykwC6hbua7zfLU6letQYC/g\nf0f+5yxtB04ewM/Tr9CN43XeVfmw+9huFuxcwMp9KwnyCeK2urfRo04P3aFEyhUVufKjREWuefPm\nbNy48YrLroervUWXacLnn8OMGTZeecXOjTeWQThxa3bTzq5ju5zXuNt9bDceNg8aVWnkvMjuDcE3\naAZpBXDk1BG+3v01X+/+mtz8XLrU7kJiXCLVQ6pbHU3kmqjIlR/FKnLLli3jv//9L7Nnz+ahhx7i\n7EtOnDhBUlISGzZsKPOgF7rWe60eOwYvvGAQFAQvv2wSGFiK4USk3Dmdd5oVe1ewYMcCUjJTuPGG\nG+ldrzdNqjZRqRe3oyJXfhTrZCBvb28CAgKw2WwEBAQ4i1x0dDQvvfRSmQYsK5Urw9tvmyxfDrff\nbmPIEDvX0AtFpJzz9/InMS6RxLhECuwF/HjwRz7a8hFDDg2hQXgDetXrRdtqbTUDWUSuqxIdWt20\naRPNmjVRP5t5AAAgAElEQVQryzzFdq0jcuc7dQpGjDA4fhzGjzcJc91LkImIizFNk20Z21iwcwFr\n9q+hSkAVbqt7G91iuxHoraF+cU0akSs/ijc97w+NGjVi7ty5JCcnU1BQgGmaGIbB8OHDyyrfdREQ\nAK+/brJ2LfTta6N/f5O77tKFhEXkygzDoGGVhjSs0hD+7LiH8KJdi3j4q4cxDIOutbuSGJeo26mJ\nSJkoUZG79957OXToEK1bt8ajHF6QrU0bWLTIzvjxBn37GkycaBLj/nd/EpHrKDoomgEtBjCgxQAy\nczP5JvkbXljxAhmnM0ionkCver2oH1Zf59WJSKkoUZH79ddf2bZtGzZb+b1gprc3DBtmsnUrDBhg\no3dvk/79TcrxJotIGQnyCaJPwz70adiHvII8Vu9fzcz1M9mesZ1mkc1IjEvkxpgb8bCVvw/GInJ9\nlKieVK9enby8vLLK4lIaNoQFC+zk5jomQ+zcaXUiEXFnXh5edKzVkcndJrPkviXc2+heVuxdQeLc\nRAYsGsCinYvIzsu2OqaIuJlijchNnz4dgLi4ODp16sSdd96Jzx9X0zUMgyeeeKLsElrIwwP+7/9M\nevUyGTzYRuvWJoMHm3hpUpqIXAPDMGgW2YxmkY7JY78d/42FuxZy3xf3YZomjao0ct5tRNcsFJHL\nKVaRW7dunfMfkjp16rBly5YyDeVqatSAefPszJljcNttNsaNs9OihdWpRKS8qFGpBoNaDWJQq0Hk\n2/MddxFJTWLsmrEcOHkAH08f511E4qPidRcREXGqEPdaLU2HD8M//mEQE+M4l85P9z8XkTKWk5/j\nuK/vH7eSyzidQaB3IM0jmzvu6xvZghDfEKtjihvR5UfKjxJNdpg+ffpFQ/whISG0bt2aunXrlmow\nV1W1KsyaZbJ4MfTqZWPoUDvt21udSkTKM19PX9rEtKFNTBvnspO5J9mYvpGk1CTe2fgOJ3NPEuYX\n5riNXHQ8zSKa4eelT5oi5V2JRuRuu+02Vq9eTadOnTBNk++++45WrVqxfft2Ro4cyaOPPnpNYZYt\nW8ZXX33FwYMHeeyxx+jZs+cl17VqRO58J0/CP/9pYLfD2LEmIfpALCIWyjidwfq09axLXccvh34h\nOy+bmKAYx/l20fH8qcqfdOcJATQiV56UaETOMAx+/fVXqlWrBsCBAwd4/PHH+fnnn+ncufM1F7k6\nderwwgsv8Nlnn13T+1wvwcEwbZrJqlXQp4+NJ5+0k5hodSoRqajC/cPpFtuNbrHnfkGnnEwhKS2J\nz7d9zqhVo8gryCM2NNZZ7uqF1cNm6PpKIu6qREVu7969zhIHUK1aNfbt20dUVBRepTCVs1atWgBu\nN0OrXTtYuNDO6NEGn30GEyaYRERYnUpEBGKCY4gJjqF3vd6A45Zie47vISk1ifc2vseOozswDIP6\nYfWd5a5mSE23+3dYpKIqUZGLiIhg7NixPPLII5imyaxZs4iIiKCgoKDC/6X39YXRo002bYKHHrLR\nr5/J/ffrNl8i4loMwyA2NJbY0FjuaXQPAAX2ArZlbCMpLYk3fnyDfSf24WXzoklEE8c5d1HxRAVF\nWZxcRIpSonPkUlJSePLJJ1m5ciUAHTp0YMqUKYSHh7Nr1y4aN2582dcPGTKEgwcPXhzCMJgyZQph\nf9ytfvLkydStW/ey58jNmTOH5OTki5YnJCTQrl274m5SmcjPh8mTDX780WDiRDs1a1oaR0SkxHLz\nc9lyeAtJaUkkpSaRnpWOv5e/c6Zsy6iWhPqFWh1TrmDVqlWsXr36ouWxsbH069fPgkRS2lzy8iPF\nKXKuMNnhSnbvhmeftdGpk8nAgSbl8Pa0IlKBnDpzyjFTNi2J9Wnr+T37dyr5VnLOlG0R2YIA7wCr\nY0oxaLJD+VGsQ6vff/89f/7zn1m8eHGRh1BvvfXWUg9WHtSpA19+aefddw0SE21MmGCnUSOrU4mI\nXJ0A7wDaVm9L2+ptnct+z/6dDekb+P7A97y57k1O550mIiDCWe6aVG2Cj6ePhalFyrdijcg99thj\n/Pvf/6ZDhw5FFrnvvvuuVMJ89913zJ49m6ysLDw9PfH19WX48OHOSRDnc4cRufOlpsKzzxo0aAAv\nvGDi7W11IhGRspGele68ePGWw1vIzc+lZqWaztuONQhvgIdNhyispBG58sMlD60Wh7sVOQDThM8/\nh5kzbYwaZefGG61OJCJS9kzT5LcTvznL3baMbdhNO/XC6hEf5ZgpGxsaW+EnzV1PKnLlR4mKXH5+\nPm+++Sa7d+/mzTffJDk5md9++42OHTuWZcYiuWORO+vYMXjhBYOgIHj5ZZPAQKsTiYhcX3bTzs6j\nO53lLvn3ZDwMD/5U9U/OchcTFKNyV0ZU5MqPEl1+5P/+7//Iy8tjzZo1AFSuXJl77rmHpKSkMglX\nXlWuDG+/bbJ8Odx+u40hQ+y4aScVEbkqNsNG/fD61A+vz/1N7gcgryCPX4/8SlJqEqNXjyYlMwVf\nT1+aRjR1lrtw/3CLk4u4lhIVuR9++IFNmzbRokULAEJDQ8nLyyuTYBVB585w0012Ro40+OQTGD/e\n5I8rsIiIVDheHl40j2xO88jmPMZjAGTnZbPp0CaSUpOY+7+5ZJzOINgnmBZRLWgV3Yrmkc0J9gm2\nOLmIdUpU5Hx9fQsNcxcUFGC320s9VEUSEOC4E8TPP0PfvjYee8ykTx9dSFhEBMDPy4+bbriJm264\nybnsZO5JNqRt4OeUn5mxfganzpzSbcZKaFDYIKsjSCkpUZFr0qQJH374IXa7nX379jFu3DjLL75b\nXrRuDYsW2Rk/3qBvX4OJE01iYqxOJSLieoJ9gulQswMdanawOorbWr58udURpJQU6yPMqFGjWLNm\nDRMnTmT16tWkp6fTunVrCgoKGD9+fFlnrDC8vWHYMJPhw00GDLDx9tsGGvAUERGRSylWkdu/fz8P\nP/wwUVFR7Nmzh6eeeoqvvvqKGTNmEKgpl6WuYUNYsMDOmTOOyRA7d1qdSERERFxRsYrcO++8w+7d\nu9m+fTsPPPAAycnJ9OvXj8qVK+uuDmXEwwMGDTKZNs3OSy/ZGD/eQPNKRERE5HwlOju0WrVq9O3b\nl0ceeYSHH36YyMhINm3aVFbZBKhRAz77zM4NN0Bioo2NG61OJCIiIq6i2PdaXblyJStXrmTfvn20\nbt2a9u3bs2jRIuLi4so6Y4VnGNCvn0mXLib/+IdBTIzjXDo/P6uTiYiIiJWKVeQSEhK48cYbGTZs\nGD169CjrTHIJVavCrFkmixdDr142hg6107691alERETEKsU6tPrtt9/StWtXXnvtNerWrcsDDzzg\nPG9Orr+ePWH+fDvz5hkMHGhw4oTViURERMQKxRqR69ChAx06dAAgJyeHtWvXsnLlSnr27ElWVhYp\nKSllmVGKEBwM06aZrF4NffrYePJJO4mJVqcSERGR66lEkx1SU1OZN28eH3zwAbNnzyYlJYUmTZqU\nVTYphoQEWLjQzs8/Gzz4oMGhQ1YnEhERkeulWEXuscceo27dutStW5dZs2ZRu3ZtPvzwQ44fP86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"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x2b2540ac>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Accuracies:\n"
},
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mTqfJH+PQsWNHt97j\ndYMjRURESqMjR/IHQk6fbtGkidNprp2Kg4iISAn78UcYMMBk9myLRo2cTnN9VBxERERK0MGDMGiQ\nybx5FtcwbYLX0SJXIiIiJeTAgfzSsGBB6SgNoOIgIiJSIr75BgYPNlm82KI0TUek4iAiIlLMdu2C\nYcNMli2zqF3b6TTFS8VBRESkGH35JYwebbJypUWtWk6nKX4qDiIiIsXks89g7FiTVassgoOdTlMy\ndFeFiIhIMUhNhZdfNlm92qJ6dafTlBwVBxERkev04YcQH59/pqFqVafTlCwVBxERkeuwYQMsWJBf\nGipXdjpNyVNxEBERuUbvvgsrVxosX25RsaLTaTxDxUFEROQarFljsH49LF1qU76802k8R8VBRETE\nTUuXGqSkwMKFNuXK2G/SMvbjioiIXJ/XXzfYsQNef93Gz8/pNJ6n4iAiIuKi2bMN9u+HxEQbs4zO\nhKTiICIi4oJp0wyOHYOZM20Mw+k0zlFxEBERuYq//MUgNxdeeqlslwbQlNMiIiKXZdswbpyBYcDE\niSoNoOIgIiJySbYNzz1nEBAAL7xgOx3Ha6g4iIiIXMSyYPhwg/r1YeRIlYbf8vgYh8OHDzNjxgyy\ns7OpUqUKsbGx1KlTp9A2eXl5vPbaa+zevRs/Pz+qV6/OH//4R2688UZPxxURkTImLw+GDDFo2RIG\nDVJpuJjHzzgkJibSuXNn5s6dS9euXYmPjy+yzdatW0lLSyMhIYGEhATuuusulixZ4umoIiJSxpw/\nD4MGGURGqjRcjkeLQ1ZWFmlpabRp0waAVq1acfToUdLT0wttFxQUxLlz58jNzcW2bXJycggJCfFk\nVBERKWNyc2HAAIMHHoA+fVQaLsejlyoyMjIICgrC/HXWDMMwCAkJIT09vVAxuP3227n77rvp168f\nlStXJigoiClTplxyn5mZmcTFxRV5PjIykqioqJL5QUREpFQ5exb69TPp3duiSxen0xS/1NRUNm/e\nXOT5hg0bur0vr5zHYcuWLXz99dcsXryYypUrs3jxYubOnUtsbGyRbYOCgujRo4cDKUVEpDTIyYG+\nfU1+/3uLTp2cTlMyoqKiLvllOjk52e19efRSRXBwMJmZmViWBYBt20XONgDs3r2be++9F39/fwzD\noG3btuzevduTUUVEpAw4eRJ69jSJiSm9paG4ebQ4BAQEEBYWRkpKCpA/CDI0NLRIcahfvz47d+4k\nLy8PgO3bt1OvXj1PRhURkVLuxAno0cNk1CiL6Gin0/gOj1+qiImJYebMmaxduxZ/f39GjBgBQEJC\nAhEREYSHh9OhQwe+//57hg0bhmmaBAUFERMT4+moIiJSSh0/Dr16mUyYYBER4XQa32LYtu3TQ0eT\nkpJo37690zFERMRHpKdDnz4mU6ZYNG/udBpnJScn07FjR7fe45WDI0VERErCkSP5AyGnT7do0sTp\nNL5JxUFERMqEH3+EAQNMZs+2aNTI6TS+S8VBRERKvYMHYdAgk3nzLK5h6gL5DS1yJSIipdqBA/ml\nYcEClYbioOIgIiKl1jffwODBJosXW+iu/uKh4iAiIqXSrl0wbJjJsmUWtWs7nab0UHEQEZFS58sv\nYfRok5UrLWrVcjpN6aLiICIipcpnn8HYsSarVlkEBzudpvTRXRUiIlJqpKbCyy+brF5tUb2602lK\nJxUHEREpFT78EBIS8ktDlSpOpym9VBxERLyEbcPGjfDaaya/LiIsbggIsFm50qJyZaeTlG4qDiIi\nDrMseO89mDfP5H/+x2bBAovAQKdTiVyaioOIiEPy8mDtWoM33jDo0MFmzRqLatWcTiVyZSoOIiIe\nlpsLy5cbrFhh8MgjNu++q9Pr4jtUHEREPOTMGVi40GDdOoOePW0++MCiQgWnU4m4R8VBRKSEnToF\nr71m8Le/GQwYYJOUZFFO//qKj9L/uiIiJeTECUhMNPjkE4M//MFi40YbU9PuiY9TcRARKWYZGZCQ\nYLB9u0FMjMWYMTaG4XQqkeKh4iAiUkyOHIEZMwz27TMYPtxiwgQVBil9VBxERK7T99/DK68YHDpk\nEBtr8dJLttORREqMioOIyDVKS4OpUw1++QVGjrS55x4VBin9PF4cDh8+zIwZM8jOzqZKlSrExsZS\np06dQtt89NFHvPvuuwWP09PTadKkCWPGjPF0XBGRIr7+Or8wWBaMHm1z551OJxLxHI8Xh8TERDp3\n7kx0dDSffvop8fHxTJ06tdA20dHRREdHFzweOnQobdq08XBSEZHC/vlPmDbNoEoVGDPG5tZbnU4k\n4nkevTEoKyuLtLS0ghLQqlUrjh49Snp6+mXf8+9//5usrCzCw8M9lFJEpLBt2+Dxx03eeMMgLs7m\ntddUGqTscrk4zJ07l5ycnOs6WEZGBkFBQZi/3shsGAYhISFXLA6bNm2ibdu2+Pn5XdexRUTcYdvw\nySfw6KMmb71lMGuWRUKCTf36TicTcZbLlypSU1P585//TO/evYmJieHmm28uyVwAnD17ls2bNzNt\n2rTLbpOZmUlcXFyR5yMjI4mKiirJeCJSCl1Y2nr2bJO777Z57TWLkBCnU4lcn9TUVDZv3lzk+YYN\nG7q9L5eLw4oVKzhy5Ajz5s2jbdu23Hnnnfzxj3/kwQcfdPlgwcHBZGZmYlkWpmli2zbp6emEXOZv\n5aeffkrdunW56aabLrvPoKAgevTo4XIGEZFLuXhp66VLtbS1lB5RUVGX/DKdnJzs9r7cGuNQq1Yt\nxo8fz7Jly9izZw99+/alUaNGLh84ICCAsLAwUlJSANi6dSuhoaGXLQ6bNm2iffv27kQUEXFLXh6s\nWmXQqZPJgQMGa9ZYjB1rqzSIXIbLxeH06dMsWLCA5s2bM2bMGKZNm8axY8dYsmQJAwcOdPmAMTEx\nJCUlMXjwYNatW8ewYcMASEhI4IsvvijY7qeffuLbb78lMjLSjR9HRMQ1ubn5K1V26mSSmQnvvmsx\napRNtWpOJxPxbi5fqmjQoAHt2rVj3rx5tGzZsuD58PBwt84K1K5du8jtl5B/y+Vv3Xjjjaxevdrl\n/YqIuEJLW4tcH5eLwz//+U9uvPHGS742f/78YgskIlIStLS1SPFw63bMjIyMgscZGRm8+OKLJRJK\nRKS4nDgBkycbdO1qUreuzcaNFr172yoNItfI5eLwzjvvEBwcXPA4ODiYt99+u0RCiYhcr4wMGD/e\noGdPk6ZN888wPPYYmB6d9k6k9Lmuzn3+/PniyiEiUiy0tLVIyXK5e99yyy288sorWJZFXl4e06ZN\no1GjRiWZTUTEZd9/D8OHG8TEmDz0kM2771pER6PSIFLMXD7jMGvWLPr06cMLL7wAQOvWrVm6dGmJ\nBRMRccWFpa1PnMhf2rpFCy1tLVKSXC4OtWvXJiUlhZMnTwJQtWrVEgslInI1v13aetQomyZNnE4k\nUja4Ncbh6NGj7N27lzNnzhQ898ADDxR7KBGRy9HS1iLOcrk4LFq0iBdffJGMjAxuvfVWdu7cyb33\n3qviICIesW0bTJ9uEhpqExenVSpFnOLy4MhXXnmFL7/8kptvvpkvv/ySjz76iFtuuaUks4lIGXdh\naetHHjFZty5/aevZs1UaRJzk8hmH8uXLc8MNNxTcghkVFcXw4cNLLJiIlF0XL239+uta2lrEW7hc\nHCpVqoRlWdxyyy0kJCRQt25dTp06VZLZRKSM0dLWIt7P5eLwl7/8hV9++YWXX36ZwYMHk5WVxauv\nvlqS2USkjMjLg7VrDd54w6BDB5s1ayytUinipVwqDnl5eezatYvo6GgCAgJITk4u6VwiUgbk5sLy\n5QYrVhg88kj+pE2VKzudSkSuxKXBkX5+fprsSUSKzZkzMGeOwQMPmFgWfPCBRUyMrdIg4gNcvqui\nXbt2rFq1qiSziEgpd+pU/joSDz1kUr06JCVZDBxoU6GC08lExFUuj3FYsGABP//8M0899RRVqlQB\nwDAMjh07VmLhRKR0OHECEhMNPvnE4A9/sNi40dYqlSI+yuXi8I9//KMkc4hIKZSbC5MnG2zfbjBk\niMWYMVqpUsTXuVwc6mvGFRFx01/+YhAWBuPHWyoMIqWEy8WhQYMGRZ4zDINvv/22WAOJSOmwbRv8\n+CNMnKjVKkVKE5eLw/r16wv+fObMGZYuXUqgZmYRkUs4dQrGjTN5803L6SgiUsxcLg533nlnocct\nWrTg3nvvZcKECW4d8PDhw8yYMYPs7GyqVKlCbGwsderUKbLdwYMHmTdvHidOnMCyLPr378//+3//\nz61jiYgzxowxGDPGIiDA6SQiUtzcWlb7t37++edruqMiMTGRzp07Ex0dzaeffkp8fDxTp04ttM3Z\ns2eZPHkyI0aMoHHjxliWxcmTJ681qoh40N//Dn5+cP/9TicRkZLgcnFo2bJlwZ/z8vL47rvvGDVq\nlFsHy8rKIi0tjUmTJgHQqlUr5s2bR3p6OiG/WcHmk08+oVGjRjRu3BgA0zSpXr26W8cSEc/7+Wd4\n+WWT997TJQqR0srl4vDbswLlypUjLCyM3/3ud24dLCMjg6CgIMxfb+A2DIOQkJAixeGHH36gfPny\nTJw4kczMTOrXr8/AgQMvWR4yMzOJi4sr8nxkZCRRUVFu5ROR6/PsswaTJ1v4+zudRER+KzU1lc2b\nNxd5vmHDhm7vy+XiEBERQcWKFQt+6VuWxZkzZ6hUqZLbB72a8+fPs2PHDl555RVuuOEGli5dypw5\nc3j++eeLbBsUFESPHj2KPYOIuGfNGoMGDSA83OkkInKxqKioS36Zvpa1p1yeuy06Oprs7OyCx7/8\n8gvt2rVz62DBwcFkZmZiWfmnMW3bLnK2AaBmzZrcdddd3HDDDQDcd9997N+/361jiYj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"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x2b01c74c>"
},
{
"html": "<h2>Learning Rate 0.01</h2>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x2b1aecec>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Fully trained\nBest accuracy 1.0\nNumber of epochs: 7\nWeights vs Epochs for each perceptron:\n"
},
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nzpwp3gsRERGRAuv/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jQ1NeHdu3cIDg5GREQEbGxsEB8fj4aGBmzZsgU3b95ETEwMWlpa0NDQgOzsbDQ2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\n'/>",
"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x2b3f330c>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Accuracies:\n"
},
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PPOAqUP2t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"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x2b24ef6c>"
},
{
"html": "<h2>Learning Rate 0.001</h2>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x2b4b198c>"
},
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"output_type": "stream",
"stream": "stdout",
"text": "Fully trained\nBest accuracy 1.0\nNumber of epochs: 7\nWeights vs Epochs for each perceptron:\n"
},
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X1LMXogSS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VRXV2OaJmFhYaSlpREXF9dirXl5eYwfP/4K3BWRi/vmG3jpJYOPPjJYvNjLd8+h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\n3uoLoZYFNTUXftCkstKgv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"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x2b58172c>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Accuracies:\n"
},
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vvUXYX1Yf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"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x2b10214c>"
},
{
"html": "<h2>Number of Epochs vs Learning Rate</h2>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x2b795e6c>"
},
{
"html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Rate</th>\n <th>Epochs</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td> 1000.000</td>\n <td> 5</td>\n </tr>\n <tr>\n <th>1</th>\n <td> 100.000</td>\n <td> 3</td>\n </tr>\n <tr>\n <th>2</th>\n <td> 10.000</td>\n <td> 3</td>\n </tr>\n <tr>\n <th>3</th>\n <td> 1.000</td>\n <td> 7</td>\n </tr>\n <tr>\n <th>4</th>\n <td> 0.010</td>\n <td> 7</td>\n </tr>\n <tr>\n <th>5</th>\n <td> 0.001</td>\n <td> 7</td>\n </tr>\n </tbody>\n</table>\n</div>",
"metadata": {},
"output_type": "display_data",
"text": " Rate Epochs\n0 1000.000 5\n1 100.000 3\n2 10.000 3\n3 1.000 7\n4 0.010 7\n5 0.001 7"
}
],
"prompt_number": 98
},
{
"cell_type": "code",
"collapsed": false,
"input": "data, meta = loadarff('./ex_2.arff')\ntry_rates(meta, DataFrame(data), [1000, 100, 10, 1, .01, .001]);",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<h2>Learning Rate 1000</h2>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x2b5f77ac>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Done classifying; no progress in past 20 epochs\nBest accuracy 0.75\nNumber of epochs: 46\nWeights vs Epochs for each perceptron:\n"
},
{
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qBXuCOnS6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TsGLmYdDnEdgoSbCWrHwrfNZzfD19YXoS6hQoeiMcHOwZy9nleXuECs1PACLrlZuuHdZ97F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\nvfVWgd/t1q1bvn3fHnnkER555JEil88aSpbUA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"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x2b19570c>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Accuracies:\n"
},
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WrFixAsuWLetwn6lT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"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x2aa1e9ec>"
},
{
"html": "<h2>Learning Rate 100</h2>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x2bd1230c>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Done classifying; no progress in past 20 epochs\nBest accuracy 0.875\nNumber of epochs: 37\nWeights vs Epochs for each perceptron:\n"
},
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Pbd++Hc7O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+PYOn+c6uuGXYW7rK4KTUjQjdeWnS1Z4u/qaqClhaB3b+7nMWOAAQOAtDSZr8nVsxmBNDQA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\n8PEBLl3S/s1u0SKG0aN59OkjQac0wBiDh6MHN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"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x2bf454cc>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Accuracies:\n"
},
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2pezbtw/9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"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x2b795eec>"
},
{
"html": "<h2>Learning Rate 10</h2>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x2c01edec>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Done classifying; no progress in past 20 epochs\nBest accuracy 0.875\nNumber of epochs: 44\nWeights vs Epochs for each perceptron:\n"
},
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mzZuHoKAg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\npYQ6n61ZY/Dss0rM8rJhw4Ycd5+aN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nmvldfKm4iIP9A1ZiLiM6ZpkpKSQlBQkN2hiIi4\nkrYyRcRntKIlIlI8WjETERERcQitmImIiIg4hBIzEREREYdQYiYiIiLiEErMRERERBxCiZmIiIiI\nQygxExEREXEIJWYiIiIiDqHETERERMQhlJiJiIiIOMT/B00HH94bd8sHAAAAAElFTkSuQmCC\n'/>",
"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x2bf456ac>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Accuracies:\n"
},
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zAQDZ2dmo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dSMBH/H3V1JCTBlij5fzHa79F5YJYZ+jMLSgUSgxYUGDODzrwMlGvHMWmtEjgA/7rY278et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\n/r2ZaxqEhfH2a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"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x2be5fe6c>"
},
{
"html": "<h2>Learning Rate 1</h2>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x2c327f2c>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Done classifying; no progress in past 20 epochs\nBest accuracy 0.75\nNumber of epochs: 21\nWeights vs Epochs for each perceptron:\n"
},
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Rjh49ipkz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\nnxRqFpctm205iKgonYMHzU6TNR08C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"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x2c32e20c>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Accuracies:\n"
},
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k6ps6VWD6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"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x2c32e1ec>"
},
{
"html": "<h2>Learning Rate 0.01</h2>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x2c49380c>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Done classifying; no progress in past 20 epochs\nBest accuracy 0.875\nNumber of epochs: 24\nWeights vs Epochs for each perceptron:\n"
},
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oNSRlUiyJ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\nPbf2ZGjDoUVO7j7/HGbMsLbYy+k8f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"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x2c3339ec>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Accuracies:\n"
},
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maDCJICIirXHZIh06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"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x2c283c4c>"
},
{
"html": "<h2>Learning Rate 0.001</h2>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x2c63638c>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Done classifying; no progress in past 20 epochs\nBest accuracy 0.875\nNumber of epochs: 37\nWeights vs Epochs for each perceptron:\n"
},
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w/nB0K1ja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\nqsHSpQrvvCNz5YrmFuG778osW6bg4GDp6CxLkjQzfz/9VKJp07KLW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"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x2c6fd38c>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Accuracies:\n"
},
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RMzEWod2m\nAB8wDBvG8P33/M8VFd5TMQtBPRLa6zy5sKaGE12S2k2P+7O7Yl3ecBwwfPj141xrDofngl2ezJvH\nsG1bx8c++ICGNXwJiECiu8k21CNBpPCVvKiz9ssGhSb68cZqpcJdcmtpEbd0c+ZMYOdOfh94m9Qt\nlB5Xbfh7jOqpiFdTE5+qXgibja950th4/bG8PA6Zmcq0zSwCJJDwPkaWmgocP66PA54Yj5geCQCY\nNAnYu5c/3oqL/V+xAejzwmN0V64AsbHC90lEBD/EUVUFnD0LDBzo//4MDwdqa/V1Lqqp4RAZKf51\n7Y9zo5k2jeHTT/l/V1by+1ds5tlAExCBRHdDG2Fh6BB9EiKG0KVlbsHBgN3OUFoqPZWyzaa/MXWj\nExsYAsCcOXxpcan7U29LC6X0sLQ/zo0mO/v6apzcXA5ZWTSs4UvABxIAH3E2N6vXHmIeQrLmdTZ/\nPsO2bRyKizkMHuz/e+txTN3o/Nmf7nF1sxXrOnsWGDDA/8/jPs61JKRyaWcpKcCpUxxcLuDDD/n9\nS7oXIIEE1+3yn+RkhlOn1GsPMY+KCvEXnlmzgB07+HHYsDD/37t3b6CqSme3sQbnz/6Mi+PTpB85\nIr1Yl55y20hZgQLwx/nHH2t7fFZVAVFR4v+g48cz7NvH79c+fRRomMkERCDha104Tbgk/vKnKzwy\nkr9gCM0/4Y3eusLNQOxQlduUKfyywcREae8fEaGfwl2FhdLm8LjnVlRXy9QgP/jz/QT41Ti/+pUF\nU6fqJKrTOVoZCz6BzIEDtMSHiFdVxXVbotib2bOZLMGrHHevFRXi7hyjoxnmzJH+vnrkcABDh4p/\nXXY2wz/+wfmsueKLewKtHib3FRUBP/qRtG3Mns2wYwfw4x/L0yax/BmqAoCxY4ELF0BFugQKiB4J\nXyfboUOpnDjxnz89Az/9KcPy5fo4SW3ezIla879qlcW0E5TFJC9qb8gQ4G9/c0l+fz3Nezl/nkO/\nftK2kZHBfFbVVJK/+9NiAT7+2IXUVPnbZEam75FobfVdmbFPH6CsjAOgjxM7Mb/wcP4/qUJD+VVH\nQtfJe1JcDPzqV0xwhcf9+4GLF4FBg/x/T73ytysckJYTxE1P2UqFVLX1JTkZOHlSu3Orv0NVgH89\nU4HK9D0SlZW+14W7JzjpZZITIULZbEzyhefcOXF3nvHxfLevGV25wiE6Wrv310vhrqoqIDJS+gkx\nOFjbFXH+Dm0QcVTvkSgtLcWaNWtQXV2NiIgILF++HP379+/yvNOnT+OVV15BVVUVXC4X7rnnHvzg\nBz8Q/X6qbujRAAAgAElEQVRC7zDsdr5gjt0u+i1IgGpokNYTIAd3V3hCgv/bEHvnmZjI90iYkZiS\n2Uqw2RiOHdM+kJCaE6O90FD+uyJlhZK/xGaeJf5RPZDYsGEDsrKyMH36dOzbtw/r1q3D6tWrOzyn\nsbERzz77LFasWIFhw4bB5XKhpqbGr/cTGpG6V25QIEGEElOwSylSu8KvXhV/55mQwHDkCA0FKkEv\nhbv4rKvybGvIEIYTJ4CRI+XZnhj+zpEg4qg6tFFZWYmSkhJMnToVAJCeno6ysjKUl5d3eN5nn32G\noUOHYtiwYXwjLRb07t3br/cUeiBR8S4ilpTxdLnws/z9P279ufNMSDBvj4TW9JL2XEo59M6GDNFu\neb2Ygl3Ef4IDiY0bN6JO4gJnh8MBq9UKy7V+VI7jYLfbuwQS586dQ3BwMH7/+9/jkUcewZo1a3D1\n6lU/31PYZBvKJUHE0kO3qc3GJF14iorEl4lOSDDnHInm5u7zzahBL4FEcTEny+RRgL9JKyzU5iat\npUV6vhbim+Cvze7du/Hb3/4WP/3pT5GTk4PBUnL7+tDS0oJDhw7hz3/+M2JjY7Fp0ya8/PLLeOKJ\nJ7o81+l0YtWqVV0enzx5MjIyMuBwCJtNnZIClJRQdy0Rjg9StW2D1Qp89ZX/ry8uBhYtEnfMR0YC\nV6+a77vCZ7XU9jOFhwP19dr/bevr5VlVBPA3aRs3yrMt4r/du3djz549XR5PSUmRvG3BgcRbb72F\nS5cu4ZVXXsG0adMwcuRIPPTQQ5g3b57gN7PZbHA6nXC5XLBYLGCMoby8HPZOExP69OmDG264AbHX\n8lpPmTIFv//97z1u02q1YvHixV7fs6JCWPdzSAhfbpYQoRwOICVF2xO+zSZtjkRRESe6C9usGTX1\nMFSlBy0t0pd9thcby6+G0To4CnQZGRnIyMjo8nheXp7kbYs6XPr27YuVK1di8+bNOHr0KO6++24M\nHTpUcEOio6ORnJyM/Px8AEBBQQHi4uK6BBKTJk3C8ePH24ZSDh06hKSkJDFNbSPm5BASQpVAiXDd\nladXi9QERlLuPM22XFoP+1MPTp8GkpLk37lqHy9SqpcScQT3SNTX1+Ott97Chg0bEBYWhueeew63\n3norDh06hNtuuw1nzpwRtJ2cnBysXbsW77zzDsLDw7FixQoAwPr16zFx4kRMmDABdrsdP/7xj/H4\n448jKCgINpsNOTk5fn3AigoOMTHCjuDUVIaSEmD4cL/eigQYob1dSurVC6iu9u9uT0iyNm+iohiq\nq/nCYWahh/0JXM9ro9VFUGqxLk8SEhguXpS2TFmsykoIPvcTaQQHEklJScjMzMQrr7yC8ePHtz0+\nYcIEzJw5U/AbJiYmdlnuCQDLli3r8PP06dMxffp0wdv1Rsy6cPfsYgokiBB66AqXUi1Syp2ne8Kl\nmQIJh4NDfLz2F55evRhqaq4XvVJbYSGHMWPk/Tu4J7OrGUjo4fsZKATfj3z99dfYvHlzhyDC7dVX\nX5W1UVrRcnYxMZ7qag69emndCv/xS/z8e60Zl4DqJQui1vU2pBwX3mhxbtXL/gwEopZ/Otod3Q6H\nA7/73e8UaZRWqHgXEUsPY7D+tkFKmeiEBIYLF3Tw4WWkl+RFWi8BvXiRQ3y8vNtMS1P/3KqX/RkI\nBAcS77//Pmzt9orNZsO///1vRRoll5YWcelupSb3IUQLYWEM9fXiXydlLDwhASgt9e+1eqWXrnCp\nK3HkIHeAPGgQcOqU2j0S/hfsIuJIWuTT0tIiVzsUceWK74JdnphtNjoxN38vPKWl/t95mjGQqKzk\nEBWldSu0LdxVUaHMBMWgIH5yr5poFY56BAcSqamp+POf/wyXy4XW1lY899xzGKrzOqv+3GHExzOU\nlSnTHmIedXVAz576iDiljKn7e+cZH893gZuJHGWz5SBHRVd/yVmsq7PwcKC2Vplte6KXVTiBQPDX\n5oUXXsC2bdsQHh6OiIgI5ObmYv369Uq2TTJ/JttQqmwihJ7GX2NjxfdISL3zDA2lBG5K8Wd/yqWo\nyP95M76kpTEcP67Ipj3Sy1BVIBAcSCQmJiI/Px8OhwMOhwN5eXmIl3tGjsz8OdnTyg0ihJ5OUv50\nhSt550mk0XLVhhIrNtz45fXqnVudTo6GNlQiqkRNWVkZjh07hoaGhrbH5s6dK3uj5OJwcIiLExdd\np6UB1xJvEuKVHgp2udlsDCdOiA0kOKSlSbvz5Dg+T4sehgOkamrST3Gn65O+1R86O3GCw+DByvVI\nfPSReoFEa6v2RdgCheBTwOuvv46bb74ZCxcuxFNPPYXs7Gw8++yzSrZNMn9O9klJwMmT1CNBuqeH\ngl1u/iwXlCN7od3O0Klwr2HxvZf6mPMSFqZdqv7GRv79lUDDxuYlOJD485//jIMHD2Lw4ME4ePAg\ndu3ahdTUVCXbJpk/Qxs9eqg/u5gYDz+0oY8Ljz+rNo4f5yC1gG9ionlWbuhpqEorSpdR793bXTWW\nmI3gQCI4OBixsbFtSz4zMjJw+PBhxRomB39PDmFh8GtdPgkcFRX6WVrmz5i6HHee8fHmCST0tD8B\nbRKdnTypfDVb93CY0lpbxeUQItIIDiTCwsLgcrmQmpqK9evXY8uWLahVcy2PH65c4RAdLf51Q4ao\nO7uYGI+e7mDDw4GaGuFXHrnuPBMTGUpLzXGHqaf96aZ2Phs1JuAOGMBw/ryy7wHwOYSoYJd6BAcS\nf/jDH3D16lX86U9/wpYtW/DMM8/gpZdeUrJtkvm7Llzt2cXEePR04RF79yrXnaeZeiT0lgWxd2+G\nq1fVfc/CQg5Dhij7N3AXRlSanr6fgUDQZba1tRXffvstoqOjMXjwYOTl5eGrr75CZmam0u2TxN+I\nPi2N0aQg0q3aWg7h4Vq34joxwYRcS/zMNkdCL6twAG2WgBYX8/WGlDR0qDrL6/W2P81OUCARFBSE\nTZs2Kd0W3aDZxUQIPRTs8occSz8BwG4HLl826B+hEz0lGAO0Kdx1+TIHu13Z91CreJfe9qfZCe74\nz8zMxN///ncl2yKr5mb/14VHRwNVVeY4QZLAEB7OUFcn7LlFRfLceVos5qlLo7eucLULdzHG/6d0\ncNy/P3D2rBo9EvoaqjI7wYHEX//6V9x5550IDw+H3W6H3W5Hnz59lGybJBUVgNUq7UAyy0mSyEuP\nx4WYrnA17jyNpqqKQ+/eWrfiOr7ehno3Mw4H0KeP8ge2xaLOqg0q2KUuwXO3v/rqKyXbITupdxj9\n+jFcuAD06ydfm4g51NUBERH6iibcXeEDBnT/PLnvPENC+KWkoaHybE9LehqqslqBs2fVe7/CQvVS\npkdGMlRXA5GRyr0HFexSl+BAYtCgQQo2Q35SJ9u4ZxdTIEE601s3OMAf60K6wuW+84yPZ7h4ETDY\n6UH3hO5Pucg1b0YI9xy0ceOUew89fkfNTHAgkZSU1OUxjuNw8uRJWRskF6m1EIYOZSgq4jBjhr7u\nPIn29HiS4sfUfddnKCyUtyiTewkoBRLyUnvVRnExMGmSOu/FBxIcxo1T7txaUcFRHgkVCQ4kPvj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afSO8bcKzbUu+BERfHr+I1CD/OphIiN7RhIFBRwSE/Xrj1KkKN4l9F6DM3EZyDR2tqKDz/8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\ndJ4ZeyDVdCdkWk0oFKKho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"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x2c3e4e6c>"
},
{
"html": "<h2>Number of Epochs vs Learning Rate</h2>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x2c907bec>"
},
{
"html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Rate</th>\n <th>Epochs</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td> 1000.000</td>\n <td> 46</td>\n </tr>\n <tr>\n <th>1</th>\n <td> 100.000</td>\n <td> 37</td>\n </tr>\n <tr>\n <th>2</th>\n <td> 10.000</td>\n <td> 44</td>\n </tr>\n <tr>\n <th>3</th>\n <td> 1.000</td>\n <td> 21</td>\n </tr>\n <tr>\n <th>4</th>\n <td> 0.010</td>\n <td> 24</td>\n </tr>\n <tr>\n <th>5</th>\n <td> 0.001</td>\n <td> 37</td>\n </tr>\n </tbody>\n</table>\n</div>",
"metadata": {},
"output_type": "display_data",
"text": " Rate Epochs\n0 1000.000 46\n1 100.000 37\n2 10.000 44\n3 1.000 21\n4 0.010 24\n5 0.001 37"
}
],
"prompt_number": 100
},
{
"cell_type": "code",
"collapsed": false,
"input": "data, meta = loadarff('./ex_1.arff')\ndata = DataFrame(data)\nm = p.Main(meta, .1)\nhistory = m.train(data)\n\nn = p.normalizers(data, meta)\ndata = p.normalize(n, data)\n\nf = figure()\n#data[data.columns[:2]].plot()\ncs = data[data.major == 'cs']\nscatter(cs.friends, cs.age, color='r')\n\nlaw = data[data.major == 'law']\nscatter(law.friends, law.age, color='b')\n\nwx, wy, bias = m.best_weights[0]\n\ny1 = min(data[data.columns[1]])\ny2 = max(data[data.columns[1]])\n\nx1 = (-bias - y1 * wy) / wx\nx2 = (-bias - y2 * wy) / wx\n\nlegend(('CS', 'Law'))\nplot([x1, x2], [y1, y2])\nxlabel('Friends (normalized)')\nylabel('Age (normalized)')\ntitle('Friends vs Age of CS and Law students')\ndisplay(f)\ndata, m.best_weights",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Fully trained\n"
},
{
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OpWWbOXMmfvzxR6Sm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\nOZ3CwsKgVqvx1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"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x2f1062cc>"
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 172,
"text": "( friends age major\n0 0.40 0.0 cs\n1 0.10 0.2 cs\n2 0.00 1.0 cs\n3 0.05 0.6 cs\n4 0.40 1.0 law\n5 1.00 0.0 law\n6 0.50 0.6 law\n7 0.35 1.0 law,\n [array([-0.215, -0.08 , 0.1 ])])"
}
],
"prompt_number": 172
},
{
"cell_type": "code",
"collapsed": false,
"input": "data, meta = loadarff('./ex_2.arff')\ndata = DataFrame(data)\nm = p.Main(meta, .1)\nhistory = m.train(data)\n\nn = p.normalizers(data, meta)\ndata = p.normalize(n, data)\n\nf = figure()\n#data[data.columns[:2]].plot()\ncs = data[data.hair_color == 'brown']\nscatter(cs.cats, cs.age, color='r')\n\nlaw = data[data.hair_color == 'blond']\nscatter(law.cats, law.age, color='b')\n\nwx, wy, bias = m.best_weights[0]\n\nx1 = min(data[data.columns[0]])\nx2 = max(data[data.columns[0]])\n\ny1 = (-bias - x1 * wx) / wy\ny2 = (-bias - x2 * wx) / wy\n\nlegend(('Brown', 'Blond'))\nplot([x1, x2], [y1, y2])\nxlabel('Number of Cats (normalized)')\nylabel('Age (normalized)')\ntitle('Hair Color of Cat Owners')\ndisplay(f)\ndata, m.best_weights",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Done classifying; no progress in past 20 epochs\n"
},
{
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g9GiDmjVz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\nOjRmQP9lrGyVB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"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x2dea49ec>"
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 168,
"text": "( cats age hair_color\n0 0.50 0.00 brown\n1 0.00 0.00 blond\n2 0.00 0.50 brown\n3 0.25 0.50 blond\n4 0.50 0.25 brown\n5 0.50 0.75 blond\n6 0.00 1.00 brown\n7 1.00 1.00 blond,\n [array([-0.125, -0.05 , 0.1 ])])"
}
],
"prompt_number": 168
},
{
"cell_type": "code",
"collapsed": false,
"input": "from base64 import encodestring\n\nimport matplotlib\n\nfrom IPython.display import display, HTML\nfrom IPython.core.pylabtools import print_figure\n\ndef png2x(fig):\n \"\"\"render figure to 2x PNG via HTML\"\"\"\n x,y = matplotlib.rcParams['figure.figsize']\n dpi = matplotlib.rcParams['savefig.dpi']\n x2x = int(x * dpi / 2)\n y2x = int(y * dpi / 2)\n png = print_figure(fig, 'png')\n png64 = encodestring(png).decode('ascii')\n return u\"<img src='data:image/png;base64,%s'/>\" % (png64)\n\nip = get_ipython()\nhtml_formatter = ip.display_formatter.formatters['text/html']\nhtml_formatter.for_type(matplotlib.figure.Figure, png2x)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": "import matplotlib",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": "plot",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": "<function matplotlib.pyplot.plot>"
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": "matplotlib.rcParams['lines.linewidth'] = .5\nmatplotlib.rcParams['axes.linewidth'] = .5\nmatplotlib.rcParams['patch.linewidth'] = .5\nmatplotlib.rcParams['font.size'] = 8\nmatplotlib.rcParams['figure.facecolor'] = 'white'\n\ngrey = [.6]*3\ndgrey = [.3]*3\nmatplotlib.rcParams['xtick.color'] = dgrey\nmatplotlib.rcParams['ytick.color'] = dgrey\nmatplotlib.rcParams['axes.edgecolor'] = grey\nmatplotlib.rcParams['axes.facecolor'] = [.99]*3\niris_res = runthis('iris.arff')",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Running perceptron classification on iris.arff with a testing split of 0.7\nRunning 5 trials\n"
},
{
"html": "<h2>Trial 1</h2>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x10be1d2c>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Using 105 for training, and 45 for testing\nDone classifying; no progress in past 20 epochs"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "\nBest accuracy"
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 0.981735159817\nNumber of epochs 28\n"
},
{
"html": "<h4>Percent missed (of the test set): 0.0666666666667 ; 3.0 instances</h4>",
"metadata": {},
"output_type": "display_data",
"text": "<IPython.core.display.HTML at 0x105edf2c>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Weights vs Epochs for each perceptron:\n"
},
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LljBp0iRm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\nMmnSp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"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x11c1562c>"
},
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3N+fOneP8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hn19fXlmWeewc/Pj5o1a/Lxxx/z448/lrj+iBEjOH78eJHzJpOJ6dOnlyaUEvniiy84efIk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\nUQAAABhDGAUAAIAx/wcOg1LnvnI24gAAAABJRU5ErkJggg==\n'/>",
"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x12a90d4c>"
},
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DRwMiB0WS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TO7Sxerh2Tduo7t7RgcDH36WM3Pj1VBfr412rh5s2LZMmsE1csL2rXTDByoueQS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\nV9etS7/g4H8Mt1pr3jhyhK+zspgdE0P9qtSUk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ggnkxFOIYQQQgjhVBI4hRDGor3vAAAA\nTElEQVRCCCGEU0ngFEIIIYQQTiWBUwghhBBCOJUETiGEEEII4VQSOIUQQgghhFNJ4BRCCCGEEE4l\ngVMIIYQQQjiVBE4hhBBCCOFU/w9BM+vAQgLz/QAAAABJRU5ErkJggg==\n'/>",
"metadata": {},
"output_type": "display_data",
"text": "<matplotlib.figure.Figure at 0x12c50cac>"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Accuracies:\n"
},
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blso65vK279ixA87O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\neXz++ecltk+bNg0NGjTA2bNnkZiYiGnTpikcIuTh4Y
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