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May 29, 2013 00:24
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CSM 2013 Semester Project
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| Lamb's daily synoptic index - January 1st 1861 to December 31st 1997 | |
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| 27 16 20 20 17 17 18 16 16 16 16 16 16 0 0 0 0 0 0 0 5 20 20 18 0 0 0 -1 20 26 26 -9 | |
| 26 6 0 0 24 20 20 20 24 20 20 28 18 0 0 0 5 14 20 20 20 14 20 20 20 20 28 18 8 17 0 -9 | |
| 0 0 0 2 2 2 2 1 18 18 17 18 8 0 0 3 13 12 12 12 2 0 0 15 25 15 16 15 16 16 -9 -9 | |
| 16 17 6 16 16 17 16 0 7 0 15 -1 14 -1 20 20 15 15 15 15 6 4 14 14 24 20 20 20 18 6 26 -9 | |
| 16 16 26 20 20 26 27 26 16 20 20 11 18 0 0 -1 18 -1 20 28 27 27 28 20 20 1 -1 0 0 27 -9 -9 | |
| 7 16 17 20 20 -1 0 0 0 3 0 -1 0 16 -1 14 14 14 12 12 12 12 12 2 0 0 0 2 1 12 12 -9 | |
| 1 11 12 2 1 2 0 0 2 4 14 15 15 15 15 14 15 15 12 0 0 20 0 0 0 0 0 0 0 0 0 -9 | |
| 14 16 15 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 | |
| -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 | |
| -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 | |
| -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 | |
| -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 | |
| -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 | |
| -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 | |
| -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 | |
| -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 | |
| -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 | |
| -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 | |
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| { | |
| "metadata": { | |
| "name": "CSM_Project" | |
| }, | |
| "nbformat": 2, | |
| "worksheets": [ | |
| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Computational Stochastic Methods", | |
| "On Dataset 1: The Lamb Circulation Index Series for UK", | |
| "", | |
| "## Loading the Data" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": true, | |
| "input": [ | |
| "pylab.rcParams['figure.figsize'] = 12, 9" | |
| ], | |
| "language": "python", | |
| "outputs": [], | |
| "prompt_number": 1 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": true, | |
| "input": [ | |
| "f = open('lwtlamb.dat', 'r')", | |
| "lines = f.readlines()", | |
| "f.close()" | |
| ], | |
| "language": "python", | |
| "outputs": [], | |
| "prompt_number": 2 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "lines[0]" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 3, | |
| "text": [ | |
| "\"Lamb's daily synoptic index - January 1st 1861 to December 31st 1997\\r\\n\"" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 3 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": true, | |
| "input": [ | |
| "data = []", | |
| "month = []", | |
| "for i, l in enumerate(lines[1:]):", | |
| " d_str = [d for d in l.strip().split(' ')]", | |
| " d_int = [int(d) for d in d_str if d != '' and d != '-9']", | |
| " data += d_int", | |
| " month += [(i % 12) + 1 for d in d_int]", | |
| "data = np.array(data)", | |
| "month = np.array(month)", | |
| " " | |
| ], | |
| "language": "python", | |
| "outputs": [], | |
| "prompt_number": 4 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data.shape" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 5, | |
| "text": [ | |
| "(49701,)" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 5 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": true, | |
| "input": [ | |
| "classes, inverse = np.unique(data, return_inverse=True)" | |
| ], | |
| "language": "python", | |
| "outputs": [], | |
| "prompt_number": 6 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "print(classes)", | |
| "print(classes.shape)" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "[-1 0 1 2 3 4 5 6 7 8 11 12 13 14 15 16 17 18 20 21 22 23 24 25 26", | |
| " 27 28]", | |
| "(27,)" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 7 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Some statistics" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "np.bincount(inverse)" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 8, | |
| "text": [ | |
| "array([1934, 9078, 677, 1200, 475, 541, 443, 2277, 727, 995, 477,", | |
| " 1773, 866, 2126, 1448, 9078, 1864, 2296, 6508, 193, 541, 227,", | |
| " 620, 336, 1917, 439, 645])" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 8 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "bar(classes, np.bincount(inverse))", | |
| "xlabel('Lamb Class')", | |
| "ylabel('# Occurrences')" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 9, | |
| "text": [ | |
| "<matplotlib.text.Text at 0x329bed0>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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zBx10UL73ve9l1qxZueWWW/KLX/yix4MEAICh7HXNAf/Zz36W7373u3nf+96XJNm9e3ef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\nl5k3b15aW1vzxBN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| |
| } | |
| ], | |
| "prompt_number": 9 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Partition the data" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "inverse.shape[0] / 2" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 13, | |
| "text": [ | |
| "24850" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 13 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": true, | |
| "input": [ | |
| "train_data = inverse[:24850]", | |
| "test_data = inverse[24850:]", | |
| "train_months = inverse[:24850]", | |
| "test_months = inverse[24850:]" | |
| ], | |
| "language": "python", | |
| "outputs": [], | |
| "prompt_number": 14 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "fig, ax = subplots(1, 1)", | |
| "ax.bar(classes, np.bincount(train_data), width=0.4, color='b')", | |
| "ax.bar(classes+0.4, np.bincount(test_data), width=0.4, color='r')", | |
| "xlabel('Lamb Class')", | |
| "ylabel('# Occurrences')" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 104, | |
| "text": [ | |
| "<matplotlib.text.Text at 0xa398bd0>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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GZcKECVmxYkV1++rVqzNx4sSMGzcuc+fOHegy6cFLU2zt72ubKbaGrRGJD2AA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\nQmCC\n" | |
| } | |
| ], | |
| "prompt_number": 104 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Basic Estimators" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### Chance" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "print('Train Loss')", | |
| "print(train_data.shape[0] * np.log(1./27))", | |
| "print(\"%0.2f%%\"%(1./27*100))" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "Train Loss", | |
| "-81901.5461202", | |
| "3.70%" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 107 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "print('Test Loss')", | |
| "print(test_data.shape[0] * np.log(1./27))", | |
| "print(\"%0.2f%%\"%(1./27*100))", | |
| "chance_log_loss = test_data.shape[0] * np.log(1./27)" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "Test Loss", | |
| "-81904.8419571", | |
| "3.70%" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 110 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### Bernoulli Estimator" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": true, | |
| "input": [ | |
| "p = np.bincount(train_data) / 1.0 / train_data.shape[0]" | |
| ], | |
| "language": "python", | |
| "outputs": [], | |
| "prompt_number": 16 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": true, | |
| "input": [ | |
| "log_p = np.log(p)" | |
| ], | |
| "language": "python", | |
| "outputs": [], | |
| "prompt_number": 17 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "log_L = 0", | |
| "for d in train_data:", | |
| " log_L += log_p[d]", | |
| "print('TrainLoss')", | |
| "print(log_L)", | |
| "print(\"%0.2f%%\"%(np.exp(log_L/len(train_data))*100))" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "TrainLoss", | |
| "-67724.041737", | |
| "6.55%" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 45 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "log_L = 0", | |
| "for d in test_data:", | |
| " log_L += log_p[d]", | |
| "print('TestLoss')", | |
| "print(log_L)", | |
| "print(\"%0.2f%%\"%(np.exp(log_L/len(test_data))*100))", | |
| "bernoulli_log_loss = log_L" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "TestLoss", | |
| "-67779.4890571", | |
| "6.54%" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 111 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### Markov Chain Estimator" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "T = np.ones((classes.shape[0], classes.shape[0]))", | |
| "for d1, d2 in zip(train_data[:-1], train_data[1:]):", | |
| " T[d1, d2] += 1", | |
| "T = T / T.sum(0).reshape(-1, 1)" | |
| ], | |
| "language": "python", | |
| "outputs": [], | |
| "prompt_number": 47 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "imshow(T, interpolation=\"nearest\")", | |
| "colorbar()" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 48, | |
| "text": [ | |
| "<matplotlib.colorbar.Colorbar instance at 0x61e2ef0>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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JROcMAAB4hw8qFx+8RAAAkDJ8ULlwWhMAAMBFfFB/AgCAlOGDyoXOGQAAgIv4oP4EAAAp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\nSUVORK5CYII=\n" | |
| } | |
| ], | |
| "prompt_number": 48 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": true, | |
| "input": [ | |
| "lT = np.log(T).T" | |
| ], | |
| "language": "python", | |
| "outputs": [], | |
| "prompt_number": 24 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "log_L = 0", | |
| "for d1, d2 in zip(train_data[:-1], train_data[1:]):", | |
| " log_L += lT[d1, d2]", | |
| "print('TrainLoss')", | |
| "print(log_L)", | |
| "print(\"%0.2f%%\"%(np.exp(log_L/len(train_data))*100))" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "TrainLoss", | |
| "-55211.1629744", | |
| "10.84%" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 50 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "log_L = 0", | |
| "for d1, d2 in zip(test_data[:-1], test_data[1:]):", | |
| " log_L += lT[d1, d2]", | |
| "print('TestLoss')", | |
| "print(log_L)", | |
| "print(\"%0.2f%%\"%(np.exp(log_L/len(test_data))*100))", | |
| "markov_chain_log_loss = log_L" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "TestLoss", | |
| "-54834.1680786", | |
| "11.01%" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 112 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### Second Order Markov Chain" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": true, | |
| "input": [ | |
| "T_2 = np.ones((classes.shape[0], classes.shape[0], classes.shape[0]))", | |
| "for d1, d2, d3 in zip(train_data[:-2], train_data[1:-1], train_data[2:]):", | |
| " T_2[d1, d2, d3] += 1", | |
| "T_2 = T_2 / T_2.sum(0).reshape(classes.shape[0], classes.shape[0], 1)" | |
| ], | |
| "language": "python", | |
| "outputs": [], | |
| "prompt_number": 52 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "log_L = 0", | |
| "for d1, d2, d3 in zip(train_data[:-2], train_data[1:-1], train_data[2:]):", | |
| " log_L += np.log(T_2[d1, d2, d3])", | |
| "print('TrainLoss')", | |
| "print(log_L)", | |
| "print(\"%0.2f%%\"%(np.exp(log_L/len(train_data))*100))" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "TrainLoss", | |
| "-52245.1598814", | |
| "12.22%" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 53 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "log_L = 0", | |
| "for d1, d2, d3 in zip(test_data[:-2], test_data[1:-1], test_data[2:]):", | |
| " log_L += np.log(T_2[d1, d2, d3])", | |
| "print('TestLoss')", | |
| "print(log_L)", | |
| "print(\"%0.2f%%\"%(np.exp(log_L/len(test_data))*100))", | |
| "markov_chain2_log_loss = log_L" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "TestLoss", | |
| "-54803.710915", | |
| "11.02%" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 113 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Monthwise Estimators", | |
| "How about adding some external influence?", | |
| "The only thing I've got are months so lets train a bernoulli model for each month" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": true, | |
| "input": [ | |
| "d_m = []", | |
| "for m in range(1, 13):", | |
| " d_m.append(np.array([c for i, c in zip(month, train_data) if i==m]))" | |
| ], | |
| "language": "python", | |
| "outputs": [], | |
| "prompt_number": 30 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "p_m = np.array([np.bincount(x) / 1.0 / x.shape[0] for x in d_m])" | |
| ], | |
| "language": "python", | |
| "outputs": [], | |
| "prompt_number": 31 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "lines = plot(np.log(p_m))", | |
| "xlabel('Month')", | |
| "ylabel('log P')" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 109, | |
| "text": [ | |
| "<matplotlib.text.Text at 0xa268dd0>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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6GZGySHi7ekNr1KKuow5d+i6EeYeZtYqEe4WbBe0A94AR+/jeiur2avz19F/xaf6nmBc1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j6i1JJE/vGHEGsTJtz0g0paiTm/5tCnhw8jJkWwMvbm35tSmUNvb1fm5x+76Xv6QpIkLk5K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\nEREREZGRGKKJiIiIiIz0/2LGiUgB3OMtAAAAAElFTkSuQmCC\n" | |
| } | |
| ], | |
| "prompt_number": 109 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### Monthwise Bernoulli" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "log_L = 0", | |
| "for m, c in zip(month, train_data):", | |
| " log_L += np.log(p_m[m-1, c])", | |
| "print('TrainLoss')", | |
| "print(log_L)", | |
| "print(\"%0.2f%%\"%(np.exp(log_L/len(train_data))*100))" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "TrainLoss", | |
| "-66951.8167757", | |
| "6.76%" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 56 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "log_L = 0", | |
| "for m, c in zip(month, test_data):", | |
| " log_L += np.log(p_m[m-1, c])", | |
| "print('TestLoss')", | |
| "print(log_L)", | |
| "print(\"%0.2f%%\"%(np.exp(log_L/len(test_data))*100))", | |
| "monthwise_bernoulli_log_loss = log_L" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "TestLoss", | |
| "-67568.9783082", | |
| "6.59%" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 114 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### Monthwise Markov Chain" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "T = np.ones((12, classes.shape[0], classes.shape[0]))", | |
| "for m in range(12):", | |
| " for d1, d2 in zip(d_m[m][:-1], d_m[m][1:]):", | |
| " T[m, d1, d2] += 1", | |
| "", | |
| "T = T / T.sum(1).reshape(12, -1, 1)" | |
| ], | |
| "language": "python", | |
| "outputs": [], | |
| "prompt_number": 61 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "month_names = ['January', 'Febuary', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']", | |
| "", | |
| "fig, axes = subplots(3, 4, sharex=True, sharey=True)", | |
| "for i in range(12):", | |
| " axes[i//4, i%4].imshow(T[i], interpolation='nearest')", | |
| " axes[i//4, i%4].set_title(month_names[i])" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "png": 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8vu8H4GKnGkL1Qh6ALU43AsA/Pb6/qOaLyJf8mi//WB7sejIMo+ZPEJKMYHZN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\nQmCC\n" | |
| } | |
| ], | |
| "prompt_number": 62 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "log_L = 0", | |
| "for m, d1, d2 in zip(month, train_data[:-1], train_data[1:]):", | |
| " log_L += np.log(T[m-1, d1, d2])", | |
| "print('TrainLoss')", | |
| "print(log_L)", | |
| "print(\"%0.2f%%\"%(np.exp(log_L/len(train_data))*100))" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "TrainLoss", | |
| "-52815.1551562", | |
| "11.94%" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 63 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": true, | |
| "input": [ | |
| "test_months = month[24850:]" | |
| ], | |
| "language": "python", | |
| "outputs": [], | |
| "prompt_number": 64 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "log_L = 0", | |
| "for m, d1, d2 in zip(test_months, test_data[:-1], test_data[1:]):", | |
| " log_L += np.log(T[m-1, d1, d2])", | |
| "print('TestLoss')", | |
| "print(log_L)", | |
| "print(\"%0.2f%%\"%(np.exp(log_L/len(test_data))*100))", | |
| "monthwise_markov_chain_log_loss = log_L" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "TestLoss", | |
| "-55486.2485996", | |
| "10.72%" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 115 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "So now we are better on the training set but worse on the test set. That means we start to have some **overfitting**! Maybe we need a better way to cluster the data..." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Hidden Markov Model" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "from sklearn import hmm" | |
| ], | |
| "language": "python", | |
| "outputs": [], | |
| "prompt_number": 66 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "model=hmm.MultinomialHMM(4)", | |
| "model.n_symbols=classes.shape[0]", | |
| "model.fit([train_data])" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 73, | |
| "text": [ | |
| "MultinomialHMM(algorithm='viterbi', n_components=4,", | |
| " random_state=<mtrand.RandomState object at 0x7fba637d44b0>,", | |
| " startprob=None, startprob_prior=1.0, transmat=None,", | |
| " transmat_prior=1.0)" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 73 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "log_L, states = model.eval(train_data)", | |
| "print('TrainLoss')", | |
| "print(log_L)", | |
| "print(\"%0.2f%%\"%(np.exp(log_L/len(train_data))*100))" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "TrainLoss", | |
| "-65073.7717825", | |
| "7.29%" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 74 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "log_L, states = model.eval(test_data)", | |
| "print('TestLoss')", | |
| "print(log_L)", | |
| "print(\"%0.2f%%\"%(np.exp(log_L/len(test_data))*100))" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "TestLoss", | |
| "-65189.2435528", | |
| "7.26%" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 75 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Thats not good at all, so lets try other numbers of state and different initializations" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "all_models = []", | |
| "all_train_errors = []", | |
| "all_test_errors = []", | |
| "for nstates in range(2, 13):", | |
| " print('======================= %d States ======================' % nstates)", | |
| " models = []", | |
| " train_errors = []", | |
| " test_errors = []", | |
| " for trial in range(10):", | |
| " model=hmm.MultinomialHMM(nstates)", | |
| " model.n_symbols=classes.shape[0]", | |
| " model.fit([train_data])", | |
| " models.append(model)", | |
| " log_L, states = model.eval(train_data)", | |
| " train_errors.append(log_L)", | |
| " print('Trial %d' % trial)", | |
| " print(' Train Loss: %0.2f (%0.2f%%)' % (log_L, (np.exp(log_L/len(train_data))*100)))", | |
| " log_L, states = model.eval(test_data)", | |
| " test_errors.append(log_L)", | |
| " print(' Test Loss: %0.2f (%0.2f%%)' % (log_L, (np.exp(log_L/len(test_data))*100)))", | |
| " all_models.append(models)", | |
| " all_train_errors.append(train_errors)", | |
| " all_test_errors.append(test_errors)", | |
| " " | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "======================= 2 States ======================", | |
| "Trial 0" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -67077.16 (6.73%)", | |
| " Test Loss: -67067.90 (6.73%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 1" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -64118.14 (7.58%)", | |
| " Test Loss: -64143.48 (7.57%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 2" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -65005.50 (7.31%)", | |
| " Test Loss: -65000.06 (7.31%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 3" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -67398.72 (6.64%)", | |
| " Test Loss: -67389.08 (6.64%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 4" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -62947.11 (7.94%)", | |
| " Test Loss: -63026.96 (7.92%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 5" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -63232.70 (7.85%)", | |
| " Test Loss: -63456.12 (7.78%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 6" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -63552.05 (7.75%)", | |
| " Test Loss: -63774.38 (7.68%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 7" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -64702.72 (7.40%)", | |
| " Test Loss: -64631.86 (7.42%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 8" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -64368.12 (7.50%)", | |
| " Test Loss: -64470.05 (7.47%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 9" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -65524.74 (7.16%)", | |
| " Test Loss: -65549.70 (7.15%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "======================= 3 States ======================", | |
| "Trial 0" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -65963.56 (7.03%)", | |
| " Test Loss: -65887.09 (7.06%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 1" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -66059.07 (7.01%)", | |
| " Test Loss: -66053.68 (7.01%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 2" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -63894.34 (7.64%)", | |
| " Test Loss: -63935.10 (7.63%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 3" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -61907.83 (8.28%)", | |
| " Test Loss: -62092.78 (8.22%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 4" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -61802.83 (8.32%)", | |
| " Test Loss: -62060.02 (8.23%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 5" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -67043.18 (6.73%)", | |
| " Test Loss: -67042.39 (6.74%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 6" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -61890.42 (8.29%)", | |
| " Test Loss: -62106.08 (8.22%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 7" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -63116.69 (7.89%)", | |
| " Test Loss: -63222.16 (7.85%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 8" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -60573.14 (8.74%)", | |
| " Test Loss: -60731.06 (8.68%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 9" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -63703.09 (7.70%)", | |
| " Test Loss: -63894.18 (7.65%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "======================= 4 States ======================", | |
| "Trial 0" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -62750.97 (8.00%)", | |
| " Test Loss: -62954.05 (7.94%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 1" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -60641.00 (8.71%)", | |
| " Test Loss: -60665.08 (8.71%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 2" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -63623.74 (7.73%)", | |
| " Test Loss: -63525.34 (7.76%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 3" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -63248.21 (7.85%)", | |
| " Test Loss: -63394.12 (7.80%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 4" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -67372.91 (6.65%)", | |
| " Test Loss: -67385.05 (6.64%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 5" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -62711.16 (8.02%)", | |
| " Test Loss: -62815.20 (7.98%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 6" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -62629.87 (8.04%)", | |
| " Test Loss: -62832.26 (7.98%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 7" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -60940.76 (8.61%)", | |
| " Test Loss: -60982.85 (8.60%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 8" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -60550.79 (8.75%)", | |
| " Test Loss: -60726.47 (8.68%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 9" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -63764.28 (7.68%)", | |
| " Test Loss: -63925.84 (7.64%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "======================= 5 States ======================", | |
| "Trial 0" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -63657.98 (7.72%)", | |
| " Test Loss: -63602.38 (7.74%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 1" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -61246.52 (8.50%)", | |
| " Test Loss: -61528.04 (8.41%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 2" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -61320.93 (8.48%)", | |
| " Test Loss: -61351.78 (8.47%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 3" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -63385.93 (7.80%)", | |
| " Test Loss: -63595.83 (7.74%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 4" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -61439.30 (8.44%)", | |
| " Test Loss: -61569.52 (8.39%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 5" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -61495.77 (8.42%)", | |
| " Test Loss: -61592.15 (8.39%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 6" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -60846.18 (8.64%)", | |
| " Test Loss: -61012.18 (8.59%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 7" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -60341.90 (8.82%)", | |
| " Test Loss: -60415.94 (8.79%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 8" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -62609.31 (8.05%)", | |
| " Test Loss: -62603.30 (8.05%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 9" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -59883.34 (8.98%)", | |
| " Test Loss: -59955.25 (8.96%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "======================= 6 States ======================", | |
| "Trial 0" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -60121.50 (8.90%)", | |
| " Test Loss: -60067.80 (8.92%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 1" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -61322.16 (8.48%)", | |
| " Test Loss: -61526.74 (8.41%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 2" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -62083.48 (8.22%)", | |
| " Test Loss: -62029.11 (8.24%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 3" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -62461.51 (8.10%)", | |
| " Test Loss: -62610.01 (8.05%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 4" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -61377.31 (8.46%)", | |
| " Test Loss: -61532.17 (8.41%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 5" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -62496.10 (8.09%)", | |
| " Test Loss: -62638.90 (8.04%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 6" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -60120.92 (8.90%)", | |
| " Test Loss: -60289.48 (8.84%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 7" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -65070.58 (7.29%)", | |
| " Test Loss: -65138.38 (7.27%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 8" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -62789.59 (7.99%)", | |
| " Test Loss: -62876.46 (7.96%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 9" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -63151.63 (7.88%)", | |
| " Test Loss: -63141.80 (7.88%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "======================= 7 States ======================", | |
| "Trial 0" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -62728.97 (8.01%)", | |
| " Test Loss: -62795.71 (7.99%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 1" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -62280.37 (8.16%)", | |
| " Test Loss: -62369.80 (8.13%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 2" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -63376.92 (7.81%)", | |
| " Test Loss: -63398.06 (7.80%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 3" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -60919.43 (8.62%)", | |
| " Test Loss: -60902.72 (8.62%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 4" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -59425.24 (9.15%)", | |
| " Test Loss: -59448.61 (9.14%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 5" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -61576.10 (8.39%)", | |
| " Test Loss: -61706.40 (8.35%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 6" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -64202.54 (7.55%)", | |
| " Test Loss: -64449.80 (7.48%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 7" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -61521.26 (8.41%)", | |
| " Test Loss: -61749.01 (8.33%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 8" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -62964.37 (7.94%)", | |
| " Test Loss: -62971.12 (7.93%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 9" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -60301.36 (8.83%)", | |
| " Test Loss: -60428.73 (8.79%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "======================= 8 States ======================", | |
| "Trial 0" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -59648.34 (9.07%)", | |
| " Test Loss: -59880.00 (8.99%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 1" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -60320.99 (8.83%)", | |
| " Test Loss: -60359.33 (8.81%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 2" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -61175.59 (8.53%)", | |
| " Test Loss: -61267.02 (8.50%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 3" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -62490.34 (8.09%)", | |
| " Test Loss: -62614.66 (8.05%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 4" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -62409.04 (8.12%)", | |
| " Test Loss: -62718.20 (8.02%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 5" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -61996.29 (8.25%)", | |
| " Test Loss: -62014.41 (8.25%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 6" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -62516.60 (8.08%)", | |
| " Test Loss: -62790.02 (7.99%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 7" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -63578.90 (7.74%)", | |
| " Test Loss: -63591.01 (7.74%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 8" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -63720.76 (7.70%)", | |
| " Test Loss: -63873.41 (7.65%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 9" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -61007.33 (8.59%)", | |
| " Test Loss: -61055.37 (8.57%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "======================= 9 States ======================", | |
| "Trial 0" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -60677.21 (8.70%)", | |
| " Test Loss: -60618.23 (8.72%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 1" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -59918.84 (8.97%)", | |
| " Test Loss: -60023.36 (8.93%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 2" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -60649.36 (8.71%)", | |
| " Test Loss: -60720.16 (8.69%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 3" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -61315.17 (8.48%)", | |
| " Test Loss: -61414.27 (8.45%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 4" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -62258.66 (8.16%)", | |
| " Test Loss: -62329.43 (8.14%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 5" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -60890.72 (8.63%)", | |
| " Test Loss: -60978.08 (8.60%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 6" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -60066.33 (8.92%)", | |
| " Test Loss: -60312.86 (8.83%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 7" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -61017.72 (8.58%)", | |
| " Test Loss: -61206.48 (8.52%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 8" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -61548.47 (8.40%)", | |
| " Test Loss: -61638.74 (8.37%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 9" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -60238.82 (8.86%)", | |
| " Test Loss: -60447.89 (8.78%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "======================= 10 States ======================", | |
| "Trial 0" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -62650.23 (8.04%)", | |
| " Test Loss: -62761.63 (8.00%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 1" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -59895.08 (8.98%)", | |
| " Test Loss: -59868.09 (8.99%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 2" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -66997.93 (6.75%)", | |
| " Test Loss: -67011.13 (6.74%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 3" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -62338.34 (8.14%)", | |
| " Test Loss: -62536.06 (8.07%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 4" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -62908.53 (7.95%)", | |
| " Test Loss: -63066.81 (7.90%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 5" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -61055.90 (8.57%)", | |
| " Test Loss: -61175.43 (8.53%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 6" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -60073.37 (8.91%)", | |
| " Test Loss: -60314.61 (8.83%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 7" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -61470.13 (8.43%)", | |
| " Test Loss: -61737.46 (8.34%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 8" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -60778.57 (8.67%)", | |
| " Test Loss: -60879.72 (8.63%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 9" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -61221.50 (8.51%)", | |
| " Test Loss: -61331.04 (8.48%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "======================= 11 States ======================", | |
| "Trial 0" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -60835.96 (8.65%)", | |
| " Test Loss: -60840.25 (8.64%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 1" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -60945.75 (8.61%)", | |
| " Test Loss: -60880.18 (8.63%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 2" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -62279.66 (8.16%)", | |
| " Test Loss: -62289.73 (8.16%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 3" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -61375.76 (8.46%)", | |
| " Test Loss: -61416.35 (8.45%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 4" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -62080.66 (8.22%)", | |
| " Test Loss: -62149.25 (8.20%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 5" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -60040.95 (8.93%)", | |
| " Test Loss: -60108.07 (8.90%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 6" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -59091.10 (9.27%)", | |
| " Test Loss: -59224.13 (9.23%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 7" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -60931.58 (8.61%)", | |
| " Test Loss: -61113.46 (8.55%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 8" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -59946.83 (8.96%)", | |
| " Test Loss: -60062.07 (8.92%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 9" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -59816.84 (9.01%)", | |
| " Test Loss: -59909.14 (8.97%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "======================= 12 States ======================", | |
| "Trial 0" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -60969.99 (8.60%)", | |
| " Test Loss: -61058.58 (8.57%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 1" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -61638.98 (8.37%)", | |
| " Test Loss: -61823.81 (8.31%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 2" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -59714.55 (9.04%)", | |
| " Test Loss: -59808.09 (9.01%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 3" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -59162.88 (9.25%)", | |
| " Test Loss: -59073.59 (9.28%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 4" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -60419.95 (8.79%)", | |
| " Test Loss: -60652.95 (8.71%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 5" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -61639.17 (8.37%)", | |
| " Test Loss: -61688.44 (8.35%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 6" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -63309.42 (7.83%)", | |
| " Test Loss: -63585.17 (7.74%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 7" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -59792.67 (9.02%)", | |
| " Test Loss: -59955.51 (8.96%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 8" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -62055.53 (8.23%)", | |
| " Test Loss: -62086.20 (8.22%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| "Trial 9" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "", | |
| " Train Loss: -61782.30 (8.32%)", | |
| " Test Loss: -61793.04 (8.32%)" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 80 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "terr = np.array(all_test_errors)" | |
| ], | |
| "language": "python", | |
| "outputs": [], | |
| "prompt_number": 82 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "terr.max()" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 120, | |
| "text": [ | |
| "-59073.594202530207" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 120 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "terr[10, 3]" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 122, | |
| "text": [ | |
| "-59073.594202530207" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 122 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "fig, ax = subplots(1, 1)", | |
| "lines = ax.boxplot(terr.T)", | |
| "ticks = ax.set_xticklabels(range(2, 13))", | |
| "ax.set_xlabel('# hidden states')", | |
| "ax.set_ylabel('log Likelihood of test data')" | |
| ], | |
| "language": "python", | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 90, | |
| "text": [ | |
| "<matplotlib.text.Text at 0x4bfc410>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
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IESP01FNP6eDBgzp48GC/xyxdulRVVVXKzc1VUVGR6uvrVVhYqKqqKvl8PrW0tKi2tlal\npaWqqalRZ2enVq5cqaamJklSdna2iouL5fV64x4vAACIj/R0qatr8I/3DHKgyMiRUmfn4NcFjFTK\nKysrtXz5cqWkpEiSRo0aJUm64YYbdO+99+r666/vd/8TJ06ou7tbubm5kqRFixZpy5YtkqS6ujot\nXrxYkjRv3jxt375dkrR161YVFBTI6/XK6/UqPz9f9fX1cYkPAACY0dV1ob0g3pdo/hAAJEOV8paW\nFu3atUsrVqzQ8OHDtXr1ak2ZMiV0u+eyP1Pb29uVkZER+trv96u9vT1027hx4yRJycnJSktLU0dH\nh44fP97vMRkZGaHHXK78kiPx8vLylJeXF22IgLVsPLjVtphti1eyM2YA0WlsbFRjY+OA7x+zpDw/\nP18nT5684vpVq1apr69PXV1d2rNnj/bt26f58+fr8OHDsdpKWOX8pI0LG0eK2RhzRYV9yYttMdsW\nr2RnzACic3mht6Ki4pr3j1lS3tDQcNXbKisrNXfuXElSTk6OkpKS1NHRIZ/P95739/v9amtrC33d\n1tYWqoL7/X4dO3ZMY8eOVV9fn86cOSOfzye/39/vr5PW1lZNnz59CCLDYNn4C83GmAEAQOSM9JTP\nmTNHO3bskCQdOnRIvb29/RLyy8fFjBkzRqmpqdq7d6+CwaDWr1+v2bNnS5KKi4tVXV0tSdq0aZNm\nzJghSSooKNC2bdt0+vRpdXV1qaGhQTNnzoxHeAAAAEBEjMwpP3/+vEpKSnTgwAENGzZMzz77bKi8\nP378eHV3d6u3t1der1cNDQ26/fbb1dTUpCVLlqinp0dFRUV67rnnJF0Yibhw4ULt379fPp9PNTU1\nGj9+vCTpBz/4gZ5++mlJ0je+8Y3QAaGXYk45MLTcOuc3GrbFbFu8kntjduMsZ7euTcx2rM3Jg2KI\npBwYWm5NXqJhW8y2xSu5N2Y3Ji5uXZuY7Vg7IU8eBMCZ0tMv/NAZ7EUa/GPT083GPlhuPKA3mudZ\niu41Yup5NhWzW1/XAOKLSjmV8rixcaSYG2O2sfJhIxufZ9vWNbk2MduxNjFH+ljaV66JpDx+bEy4\n3BizjT9kbWTj82zbuibXJmY71ibmSB9L+woAAADgaCTlAAAAgGEk5QAAAIBhJOUAECW3HcwLAHAe\nknLEjRvHxkXLxphtVFFhegcAALdj+grTV4B+bDyaPlpu3LeNz7Nt65pcm5jtWJuYI30s01cAAAAA\nRyMpBwAAAAwjKQcAAAAMIykHgChxQC8AIFok5YgbG8fG2RizjXieAZjm8cT/MnKk6agTC9NXmL4S\nN26cUBEtN8Zs49H0NrLxebZt3YtrmzBypNTZaWZtG5/naLDv+K0bLudMHuSeAACAw0WTtLg1WYMd\ngvJIBv7oDF7y36FGUg4AAABX8ShorlIeo3+bnnIAAADAMJJyAIgSB3oCAKJFUo64cevYuPT0wR+Z\nLg3+senpZuPGwFVUmN4BAAyOW383JyKmrzB9BWG48QhvN65reu1ouHHfNj7Ptq0brfJyd34KxPNs\nBzc+z+FyTpJyknKE4cY3vhvXNb12NNy4bxufZ9vWtRXPsx3c+DyHyzlpXwEAAAAMIykHAAAADGNO\nOQDrpadLXV3R/RuDPXOiyTMfAokoEU8qAztQKUfcuPGAIRtd+IVm5hI08ZtUFxLyYNDMJdo/BgD0\n55GZN7PHpQk5v5udgwM9OdAzbtx6EIwbDyZx47om1yZmO9a2bd1oMX3FHetGi33Hb12mr4RBUh4/\nvPFZ16lrE7Mda9u2brTYtzvWjRb7jt+6TF8BAAAAHI6kHAAAADCMpBwAAAAwjKQccVNWZnoHAADg\nUvxudg4O9ORAT4ThxoNJ3LiuybWJ2Y61bVs3Wkxfcce6tnLj88z0lTBIyhGOG9/4blzX5NrEbMfa\ntq1rK55nO7jxeWb6CgAAAOBwJOUAAACAYSTlAAAAgGEk5YgbNx4wBABAIuN3s3NwoCcHesaNWw+C\ncePBJG5c1+TaxGzH2ratGy2mr7hj3Wix7/ity/SVMEjK44c3Pus6dW1itmNt29aNFvt2x7rRYt/x\nWzdczpk8yD0BAAAAxng88V9z5MjY/dsk5QAAAHCVaKrkTv10gAM9AQAAAMNIyhE3ZWWmdwAAAC7F\n72bn4EBPDvREGG48mMSN65pcm5jtWNu2daPF9BV3rIvImXuNMH3lmkjKEY5tP+BJ1uxYm5gTf11b\n8TwjHKcm5bSvAAAAwBpObdkxlpSvWbNGEyZM0MSJE1VaWipJamho0JQpUzRp0iRNmTJFO3fuDN2/\nqalJWVlZCgQCWrZsWej6c+fOacGCBQoEApo6daqOHj0auq26ulqZmZnKzMzUunXr4hccAAAAHMmp\nbVlGRiLu3LlTdXV1am5uVkpKik6dOiVJGjVqlH76059q9OjRev311zVz5ky1tbVJkpYuXaqqqirl\n5uaqqKhI9fX1KiwsVFVVlXw+n1paWlRbW6vS0lLV1NSos7NTK1euVFNTkyQpOztbxcXF8nq9JkIG\nAAAArspIpbyyslLLly9XSkqKpAvJuCTdddddGj16tCTpjjvuUE9Pj86fP68TJ06ou7tbubm5kqRF\nixZpy5YtkqS6ujotXrxYkjRv3jxt375dkrR161YVFBTI6/XK6/UqPz9f9fX1cY0T/Tn1L1MAAGzF\n72bnMFIpb2lp0a5du7RixQoNHz5cq1ev1pQpU/rdZ/PmzcrOzlZKSora29uVkZERus3v96u9vV2S\n1N7ernHjxkmSkpOTlZaWpo6ODh0/frzfYzIyMkKPuVz5Ja/IvLw85eXlDVGkuFRFBW9+AHALt05f\nQWT43Rw7jY2NamxsHPD9Y5aU5+fn6+TJk1dcv2rVKvX19amrq0t79uzRvn37NH/+fB0+fDh0n9df\nf11f+9rX1NDQEKvt9VPOqxEAgH5I1oDoXF7oraiouOb9Y5aUXyuhrqys1Ny5cyVJOTk5SkpKUkdH\nh3w+n9ra2jR37lytX79et956q6QLlfGLveWS1NbWFqqC+/1+HTt2TGPHjlVfX5/OnDkjn88nv9/f\n76+T1tZWTZ8+PQaRAgAAwC2c+imQkZ7yOXPmaMeOHZKkQ4cOqbe3Vz6fT6dPn9asWbP0j//4j/rY\nxz4Wuv+YMWOUmpqqvXv3KhgMav369Zo9e7Ykqbi4WNXV1ZKkTZs2acaMGZKkgoICbdu2TadPn1ZX\nV5caGho0c+bMOEcKAM4UlOfCsF4Dl6A8psMHYLEwBWtjjJw86Pz58yopKdGBAwc0bNgwPfvss8rL\ny9NTTz2lZ555RoFAIHTfhoYG3XTTTWpqatKSJUvU09OjoqIiPffcc5IujERcuHCh9u/fL5/Pp5qa\nGo0fP16S9IMf/EBPP/20JOkb3/hG6IDQS3HyoPhx64kVbDsRBSeVsWNtYk78daPFvt2xbrTcuu9o\nOPXkQZzRk6Q8IunpUldX/NcdOVLq7Iz/upJ9P+BJ1uxYm5gTf91osW93rBstp7ZyxJJTk3Ij01fg\nXl1d5n7YAQDix6lnPcTQsi0hdzIq5VTKI2JjBcK2mPle27E2MSf+urbieUY4Tq2UGznQEwAAADDB\nqZ8CUSmnUh4RGysQtsXM99qOtYk58de1Fc8znIpKOQAAAOBwJOUAAACW4kBP5yApBwAAVyBZs4NT\nT6RjI3rK6SmPiI29erbFzPfajrWJOfHXjRb7dse60XLrvt2InnIAAADg/3Pqp0BUyqmUR8TGCoRt\nMfO9tmNtYk78daPFvt2xbrTcuu9oOHVOOWf0BAAASFCeAZwS+1p3oXAZPyTlAAAACYqk2j1IygFY\nLyiPFL6YFKO1//JfxJap59nJz3G4Kmq4IisJHzB0SMoBWM+joNn+ajNLW8fU8+zk55ikGnAOknIA\nVxhAC2JMjBxpZl0AgD3Kykzv4L0NaPrKT3/6U73++ut6++23Qx91PfnkkzHfXDwwfSUyNh7VbmPM\n0XDjvnl92bG2bevaiucZThX1nPK/+7u/04YNG7RmzRpJ0oYNG3T06NGh2yEAAABgubCV8qysLL32\n2muaNGmSmpubdfbsWRUWFuqll16K1x5jikp5ZGysQNgYczTcuG9eX3asbdu6tuJ5hlNFXSkfMWKE\nJOmGG25Qe3u7kpOTdfLkyaHbIQAAAGC5sAd63n///erq6tJXv/pVZWdnS5L+9m//NuYbAwAAAGwR\ntn3l7bff1vDhw0P/f/Hri9e5He0rkbHxY0EbY45GefmFi5vw+rJjbdvWtRXPM8Ix9XsqXM4ZNim/\n++679eqrr4a9zq1IyiNj4w87G2O2Da8vO9a2bV1b8TwjHHOvkWvnnFdtXzlx4oSOHz+uP//5z3r1\n1VcVDAbl8Xj01ltv6c9//nNMNgsAAADY6KpJ+bZt2/TDH/5Q7e3t+spXvhK6/sYbb9TTTz8dl80B\nAAAANgjbvrJp0yZ9+tOfjtd+4o72lcjY+LGgjTHbhteXHWvbtq6teJ4RjlPbVwZ8Rs/f/OY3evvt\nt0PXcUZPO9n4w87GmG3D68uOtW1b11Y8zwjHqUn5gM/o+dxzzykYDHJGTwDX5LbJKwAAu5SVmd7B\ne+OMnlTKI2JjBcLGmKPhxn3z+rJjbdvWtRXPM5yKM3oCAAAADscZPQEAAADDBnSg50UXz+jp9Xpj\nuae4on0lMjZ+LGhjzNFw4755fdmxtm3r2ornGU416JMHbd68WR6P56oPnDt3bnQ7AwAAACDpGkn5\nT37yE3k8Hv3f//2fXn75ZU2fPl2StHPnTt1zzz0k5QDek1OPagcAQLowJcyJk8LCtq/k5+dr3bp1\nGjNmjCTpxIkTWrx4sbZt2xaXDcYa7SuRsfFjQRtjtg2vLzvWtm1dW13jQ/6YGjlS6uw0szYi49Q5\n5WEP9GxtbdXo0aNDX3/wgx/UsWPHhmZ3AAAAQyiaZIs/oGBS2KT8U5/6lGbOnKmHH35YwWBQtbW1\nys/Pj8feAAAAACsMaPrKCy+8oF/+8peSpE9+8pN68MEHY76xeKF9JTI2fvxrY8y24fVlx9q2rYvI\n8VzZwantKxGNRExEJOWRsfGXmo0x24bXlx1r02uMcPi5awenJuVhz+gJAJFw4hHtgHThl/BgL9E8\nnoQccBanTgmjUk6lPCJU9RJ/3Wi5cd+8vuxZe7DcuGdEzqmj8pAYoq6U/9u//duArgMAN/N4zFxG\njjQdOYCLSMhhUthK+eTJk7V///5+19111106cOBATDcWL1TKI0NVL/HXjZZb9x0NN8Zs43sqGm7c\nMwBnGfSc8h//+Mf60Y9+pCNHjuiBBx4IXd/d3S2fzze0uwQAAAAsdtWk/J577tGYMWN06tQp/f3f\n/30os09NTdWkSZPitkEAAExz6oFhABJH2PaVs2fPasSIEbruuuv0u9/9Tr/73e903333KSUlJV57\njCnaVyJjZSuHqTlqkis/L7fxQCk3tjbQvgLAVqZ+T0U9pzw7O1u//OUv1dXVpXvvvVc5OTkaNmyY\nnn/++SHfrAkk5ZGxMSm3MWZExo3PlY3vKSAcG4sKNnLtnPJ3331XN9xwg1544QU99thj2rhxow4e\nPDikmwQAN3NrawMTZ4D+KipM7wA2G9DJg371q1/p+eef16xZsyRdSNSjtWbNGk2YMEETJ05UaWmp\nJOmVV17R5MmTNXnyZE2aNEm1tbWh+zc1NSkrK0uBQEDLli0LXX/u3DktWLBAgUBAU6dO1dGjR0O3\nVVdXKzMzU5mZmVq3bl3UewaA9+LGypqpE+lwMh0AeG9XPdDzon/913/VN7/5TT344IP6yEc+oj/8\n4Q+aNm1aVIvu3LlTdXV1am5uVkpKik6dOiVJysrKUlNTk5KSknTy5ElNnDhRn/70p3Xddddp6dKl\nqqqqUm5uroqKilRfX6/CwkJVVVXJ5/OppaVFtbW1Ki0tVU1NjTo7O7Vy5Uo1NTVJutCGU1xcLK/X\nG9XeAQAAgKEWtlL+13/916qrq9PnP/95SdJtt92m5557LqpFKysrtXz58tDBoqNGjZIkjRgxQklJ\nF7bU09OjtLQ0XXfddTpx4oS6u7uVm5srSVq0aJG2bNkiSaqrq9PixYslSfPmzdP27dslSVu3blVB\nQYG8Xq+0cs1MAAAgAElEQVS8Xq/y8/NVX18f1b4BAHZy46chANwlbKX85Zdf1uc+9zl1d3ertbVV\nBw4c0He/+12tXbt20Iu2tLRo165dWrFihYYPH67Vq1drypQpki60sDzyyCM6cuSIfvzjH0uS2tvb\nlZGREXq83+9Xe3t76LZx48ZdCCY5WWlpaero6NDx48f7PSYjIyP0mMuVX/LTNi8vT3l5eYOODbAd\nB0ohEVVU8LoGEkW8jgNqbGxUY2PjgO8fNin/0pe+pPr6es2ePVvShbN5/uIXvwj7D+fn5+vkyZNX\nXL9q1Sr19fWpq6tLe/bs0b59+zR//nwdPnxYkpSbm6vXX39d//u//6vCwsK4JMjlMfhJ6xmCMXpM\nhYEbkbwAcCu3HrSNyMTrd9Tlhd6KMEcSh03KJenmm2/u/6Dk8A9raGi46m2VlZWaO3euJCknJ0dJ\nSUnq6Ojod6bQ22+/Xbfddpt+//vfKyMjQ21tbaHb2traQlVwv9+vY8eOaezYserr69OZM2fk8/nk\n9/v7/XXS2tqq6dOnDyTcIUFCDdjDtk8HSFyQqGx6H8N5wvaU33zzzdq9e7ckqbe3V6tXr9aECROi\nWnTOnDnasWOHJOnQoUM6f/68fD6f3njjDfX19UmSjh49qpaWFgUCAY0ePVqpqanau3evgsGg1q9f\nH6rcFxcXq7q6WpK0adMmzZgxQ5JUUFCgbdu26fTp0+rq6lJDQ4NmzpwZ1b4B4L3YNkaNxAUAhl7Y\nkndlZaWWLVum9vZ2+f1+FRQU6N///d+jWrSkpEQlJSXKysrSsGHDQkn1Sy+9pGeeeUYpKSlKSUnR\nd7/7XaWmpkqS1q5dqyVLlqinp0dFRUUqLCyUJD366KNauHChAoGAfD6fampqJEnp6el64oknlJOT\nI0kqKytj8goAABajtRROFvaMnrt379a9994b9jq34oyekbHx7JY2xhwNt+47GjbGbBvbWpQADL2o\nz+h5cRRiuOsQGX64I1HRb4xExM9sIHE49f181Ur5r371K7388sv6l3/5F335y18OZfbd3d168cUX\n9etf/zquG40VU5Vyt1bWbKwa2xgzIsNzBQDuYe73+iAr5b29veru7tY777yj7u5unT17VmfPnlVq\naqo2bdoUk80CgBN5PJ5rXqRwtycWp1aZAMDNwvaUv/HGGxo/fnycthN/VMojY2PV2MaYgWvhtQnA\nzZxaKQ+blCc6kvLI2Jig2hgzcC28NgG4mVOT8rAHegIAYDtadgDE2lWT8tLSUknShg0b4rYZmzCh\nAomK5AWJyLYTRAGJzKk52FXbVyZOnKjXXntNd999t/bv3x/vfcUNc8ojY2Mrh40xR8Ot+8bA2fgc\n2xgzgKEVLue86hk977vvPo0cOVJnz57VjTfeeMU/+tZbbw3dLgEAruHUKhMAuFnYAz2Li4tVV1cX\nr/3EHZXyyNhYNbYx5mi4dd/AtfC6BhCtIZm+8sc//lH79u2TJOXm5uoDH/jA0O3QMJLyyNiYoNoY\nczTcum/gWnhdA4hW1NNXNmzYoNzcXG3YsEG1tbXKzc3Vxo0bh3STAAA4GS07AGItbKV80qRJ+vnP\nfx6qjp86dUozZsxQc3NzXDYYa6Yq5eXl7pxSYWPV2MaYo+HW1zYAwA6mfk9F3b6SlZWl5ubm0Kmi\n3333Xd1555167bXXhnanhnDyoMjYmKDaGDMAAInKqScPuur0lYsKCws1c+ZMPfzwwwoGg6qtrdV9\n9903pJsEALgHn4YAwNAb0IGemzdv1u7duyVJn/jEJ/Tggw/GfGPxQqU8MjZWjW2MGbgWXpsA3Myp\nlfIBJeWJjKQ8MjYmqDbGDFwLr00AbubUpDzs9BUAAGxHuw6AWCMpN4TxWkhUJC9IRBUVpncAYKg4\nNQejfYWTB0XExlYOG2OOhlv3jYGz8Tm2MWYAQ2vQ01eysrKu+Y8mypxyAEBknFplAgA3u2pS/pOf\n/ESStHbtWknSwoULFQwG9fzzz8dnZ4CD/P8x/XE1cmT81wQGghYlABh6YdtX7rrrLh04cKDfdZMn\nT9b+/ftjurF4oX0lMrRyRMat+46GjTEj8fG6BhCtqKevBINBvfTSS6Gvd+/eTRILALAKLTsAYi1s\nUv79739fjz32mG655Rbdcssteuyxx/T9738/HntLaHz8i0RF8oJExM9sIHE49f084OkrZ86ckSSl\npaXFdEPxxsmDImSiufoiF37DXPs8AwCQoFx78qDTp0/r8ccf1/Tp0zV9+nR95StfCSXosI9HwQuv\n5DhfPCKzBZzCqVUmAJAuJL/XukjhbjcjbFJeUlKi1NRUbdy4URs2bNCNN96oRx55JB57A1wvEVs5\nwv2wC//DEG7HiXQAOFkwGIzqYkrY9pU777xTv/71r8Ne51a0r0SG6SsAeD8CQOSibl8ZMWKEfvnL\nX4a+fumll3TDDTcMze4AAHABWnYAxFrYSvmBAwe0aNGiUB/5yJEjVV1drTvvvDMuG4w1U5Xy8nJ3\n/pCnUg7AxvejjTEDGFrhcs4BT1956623JEmpqalDszOH4ORBkSEpB2Dj+9HGmAEMrSGbvjJt2jRN\nmzaN6SsAYLlEPIAZAExj+goQQ25sUQLC4XUNAEOP6Su0r0SE9pXIuHXfAPrjvQwgWkxfAQAgSrTs\nAIg1pq8wfSUiVMoj49Z9AwCAocX0lTA4eVBkSMoj49Z9AwCAoRUu50wO9w+8/fbb2rx5s9544w29\n8847CgaD8ng8evLJJ4d0owAAd3DrJ30A4GRhe8pnz56turo6paSk6H3ve5/e//73633ve1889ga4\nHn2oSEQVFaZ3AACJJ2z7ysSJE3Xw4MF47SfuaF+JDO0rAHg/AkDkop6+cs8996i5uXlINwUAgJvQ\nrgMg1q5aKc/KypIkvfPOO2ppadGtt96q66+//sKDPJ6ESdSjqZSnp0tdXUO8oQEYOVLq7Iz/uhKV\ncgB2vh9tjBnA0Br09JU33njjmv/w+PHjo9mXY0STlNuYoNoYM4D+EvX96PF4ono8J6IDcC2Dnr6S\nnp6u1NRUdZoqyQIAHClRD2AmqQZg0lUr5bNmzdJ//dd/afz48e9ZPThy5EjMNxcPVMrdsbZTK3NU\n1gAAwEAM2cmDEhVJuTvWdmpSDgAAMBCDnr7y6quvXvMSrTVr1mjChAmaOHGiSktL+9127Ngxvf/9\n79ezzz4buq6pqUlZWVkKBAJatmxZ6Ppz585pwYIFCgQCmjp1qo4ePRq6rbq6WpmZmcrMzNS6deui\n3jMAAAAQC1ftKf/yl798zY/md+7cOehFd+7cqbq6OjU3NyslJUWnTp26Yu1Zs2b1u27p0qWqqqpS\nbm6uioqKVF9fr8LCQlVVVcnn86mlpUW1tbUqLS1VTU2NOjs7tXLlSjU1NUmSsrOzVVxcLK/XO+h9\nAwAAALFw1aS8sbExZotWVlZq+fLlSklJkSSNGjUqdNuWLVv0oQ99qN9ZQ0+cOKHu7m7l5uZKkhYt\nWqQtW7aosLBQdXV1qvj/p5ebN2+ePv/5z0uStm7dqoKCglASnp+fr/r6en32s5+NWVwAAADAYFw1\nKb/oT3/6k771rW/p2LFj+t73vqeWlhb97ne/0/333z/oRVtaWrRr1y6tWLFCw4cP1+rVqzVlyhSd\nPXtW//RP/6Sf//zn+ud//ufQ/dvb25WRkRH62u/3q729PXTbuHHjLgSTnKy0tDR1dHTo+PHj/R6T\nkZEReszlyi85K0ReXp7y8vIGHRsAJLryck6mAwDhNDY2RlTkDpuUP/LII8rOztbLL78sSRo7dqw+\n/elPh03K8/PzdfLkySuuX7Vqlfr6+tTV1aU9e/Zo3759mj9/vg4fPqzy8nI9/vjjuuGGG+I6laKc\n3y4AMGAVFSTlABDO5YXei50dVxM2Kf/DH/6gDRs2qKamRpL6tZVcS0NDw1Vvq6ys1Ny5cyVJOTk5\nSkpK0ptvvqlXXnlFmzdv1j/8wz/o9OnTSkpK0ogRIzR37ly1tbWFHt/W1haqgvv9fh07dkxjx45V\nX1+fzpw5I5/PJ7/f3++vk9bWVk2fPn1AewcAAADi6arTVy66/vrr1dPTE/r6D3/4g66//vqoFp0z\nZ4527NghSTp06JB6e3t10003adeuXTpy5IiOHDmiL33pS/r617+uxx57TKNHj1Zqaqr27t2rYDCo\n9evXa/bs2ZKk4uJiVVdXS5I2bdqkGTNmSJIKCgq0bds2nT59Wl1dXWpoaNDMmTOj2jcAAAAQC2Er\n5eXl5SosLFRbW5sefvhh7d69Wz/84Q+jWrSkpEQlJSXKysrSsGHDBjSucO3atVqyZIl6enpUVFSk\nwsJCSdKjjz6qhQsXKhAIyOfzhSr66enpeuKJJ5STkyNJKisrY/IKAAAAHCnsyYM6OjoUDAa1Z88e\nSdJHP/pRnT17VrfeemtcNhhrnDzIHWtz8iDAOXg/AkDkBn3yoIseeOABpaSk6P7779f999+vU6dO\nRTV5BQDgbmVlpncAAIknbFL+9a9/XQ888IDOnj2rpqYmfeYzn9Hzzz8fj70BAByIySsAMPTC9pTP\nmjVLvb29ys/P19mzZ/XCCy/or/7qr+KxNwAAAMAKV+0p/8IXvtDv6x07dui2227TLbfcIo/Ho+ee\ney4uG4w1esrdsTY9rAAAwM3C5ZxXrZRnZ2f3e/ClX3s8nqHfKQAAAGCpsNNXEh2VcnesTaUcAAC4\n2aAr5Z/5zGe0ceNGZWVlvec/2tzcPDQ7BAC4Snk5B3sCwFC7aqX8+PHjGjt2rN54440rH+Tx6JZb\nbon13uKCSrk71qZSDjgH70cAiFy4nHNQ7Sv33nuvdu/eHdXGnIKk3B1rkwQAzsH7EQAiF/XJg97L\nsWPHBr0hAAAAAP0NKikHAAAAMHSueqDn5s2bryizX/y6p6cnLpsDAAAAbHDVpPwnP/nJVeeRP/DA\nAzHbEADA2crKTO8AABIPc8o50NMVa3NgGQAAcLOYHOgJAAAAYOiQlAMAAACGkZQDAAAAhl31QM+L\nLk5huVRaWpqysrL0gQ98IGYbAwAAAGwR9kDPWbNm6Ve/+pWmTZsmSWpsbNTdd9+tI0eO6Mknn9Si\nRYvistFY4UBPd6zNgZ6Ac5SXX7gAAAYuXM4ZtlJ+/vx5/fa3v9UHP/hBSdIf//hHLVy4UHv37tUn\nP/lJ1yflAIArXW0k7kUVFdd+vOWDvQAgYmGT8tbW1lBCLkkf+MAH1NraKp/Pp2HDhsV0cwAAM0iq\nASC+wibl06ZN06xZszR//nwFg0Ft3rxZeXl5+tOf/iSv1xuPPQIAAAAJLWxP+bvvvqsXXnhBu3fv\nliTde++9mjdvXtiPNt2CnnJ3rE1POQAAcLOoe8qTkpL08Y9/XNdff70k6aMf/WjCJOQAAACAE4Sd\nU75hwwZ99KMf1caNG7Vx40bl5uZq48aN8dib4wXluVDCjfMlKP4oAgAASCRh21cmTZqkn//856GZ\n5KdOndKMGTPU3Nwclw3GGu0r7lib9hUAAOBm4XLOsJXyYDCoUaNGhb72+XwclQ8AAAAMobA95YWF\nhZo5c6YefvhhBYNB1dbW6r777ovH3gAAAAArhG1fCQaDeuGFF/TSSy/J4/HoE5/4hB588MF47S/m\naF9xx9q0rwAAADcLl3OGTcoTHUm5O9YmKQcAAG426JGI73//+686+tDj8eitt96KfncAAAAAqJRT\nKXfH2lTKAQCAm0U9fQUAAABAbJGUAwAAAIaRlAMAAACGkZQDAAAAhpGUAwAAAIaRlAMAAACGkZQD\nAAAAhpGUAwAAAIaRlAMAAACGkZQDAAAAhpGUAwAAAIaRlAMAAACGkZQDAAAAhpGUAwAAAIYZS8rX\nrFmjCRMmaOLEiSotLZUkvfHGGxoxYoQmT56syZMn67HHHgvdv6mpSVlZWQoEAlq2bFno+nPnzmnB\nggUKBAKaOnWqjh49GrqturpamZmZyszM1Lp16+IXHAAAABCBZBOL7ty5U3V1dWpublZKSopOnToV\nuu3DH/6w9u/ff8Vjli5dqqqqKuXm5qqoqEj19fUqLCxUVVWVfD6fWlpaVFtbq9LSUtXU1Kizs1Mr\nV65UU1OTJCk7O1vFxcXyer1xixMAAAAYCCOV8srKSi1fvlwpKSmSpFGjRl3z/idOnFB3d7dyc3Ml\nSYsWLdKWLVskSXV1dVq8eLEkad68edq+fbskaevWrSooKJDX65XX61V+fr7q6+tjFRIAAAAwaEYq\n5S0tLdq1a5dWrFih4cOHa/Xq1ZoyZYok6ciRI5o8ebLS0tL01FNP6eMf/7ja29uVkZERerzf71d7\ne7skqb29XePGjZMkJScnKy0tTR0dHTp+/Hi/x2RkZIQec7ny8vLQ/+fl5SkvL2+IIwYAAIBNGhsb\n1djYOOD7xywpz8/P18mTJ6+4ftWqVerr61NXV5f27Nmjffv2af78+Tp8+LDGjh2r1tZWjRw5Uq++\n+qrmzJmj119/PVZbDLk0KQcAAACidXmht6Ki4pr3j1lS3tDQcNXbKisrNXfuXElSTk6OkpKS1NHR\nIZ/Pp2HDhkmS7r77bt12221qaWmR3+9XW1tb6PFtbW2hKrjf79exY8c0duxY9fX16cyZM/L5fPL7\n/f3+OmltbdX06dNjECkAAAAQHSM95XPmzNGOHTskSYcOHVJvb698Pp/efPNNvfPOO5Kkw4cPq6Wl\nRR/60Ic0ZswYpaamau/evQoGg1q/fr1mz54tSSouLlZ1dbUkadOmTZoxY4YkqaCgQNu2bdPp06fV\n1dWlhoYGzZw500C0AAAAwLUZ6SkvKSlRSUmJsrKyNGzYsNC4wl27dunJJ59USkqKkpKS9J3vfCc0\nLWXt2rVasmSJenp6VFRUpMLCQknSo48+qoULFyoQCMjn86mmpkaSlJ6erieeeEI5OTmSpLKyMiav\nAAAAwJE8wWAwaHoTJnk8Hg32W+DxSCa+e6bWNbm2yZiBcBobJY4PBwBcS7ickzN6AkCUIji4HgCA\n90RSDgAAABhmpKccANyusfEvFfJLp1zl5dHKAgCIHEk5AAzC5ck3pzsAAESD9hUAAADAMJJyAIgS\n7SoAgGgxEpGRiK5Ym5GIAADAzRiJCAAAADgcSTkAAABgGEk5AAAAYBhJOQAAAGAYSTkAAABgGEk5\nAAAAYBhJOQAAAGAYSTkAAABgGEk5AAAAYBhJOQAAAGBYsukNuJ3HE/81R46M/5qXsjFmAACAWCIp\nj0IwOPjHejzRPd4UG2MGAACINdpXAAAAAMNIygEAAADDSMoBAAAAw0jKAQAAAMNIyg0pKzO9g/iz\nMWYAAICB8ASDds/D8Hg8svxbAAAAgBgLl3NSKQcAAAAMIykHAAAADCMpBwAAAAwjKQcAAAAMIyk3\npLzc9A7iz8aYAQAABoLpK4amr3g8km3feRtjBgAAkJi+AgAAADgeSTkAAABgGEk5AAAAYBhJOQAA\nAGAYSbkhZWWmdxB/NsYMAAAwEExfMTR9BQAAAPZg+goAAADgcCTlAAAAgGEk5QAAAIBhJOUAAACA\nYSTlhpSXm95B/NkYMwAAwEAwfcXQ9BWPR7LtO29jzAAAABLTVwAAAADHIykHAAAADCMpBwAAAAwj\nKQcAAAAMM5aUr1mzRhMmTNDEiRNVWloaur65uVkf+9jHNHHiRE2aNEm9vb2SpKamJmVlZSkQCGjZ\nsmWh+587d04LFixQIBDQ1KlTdfTo0dBt1dXVyszMVGZmptatWxe/4AagrMz0DuLPxpgBAAAGwsj0\nlZ07d+rpp5/Wz372M6WkpOjUqVMaNWqU+vr6lJ2drf/4j/9QVlaWurq6lJaWpqSkJOXm5urb3/62\ncnNzVVRUpC9+8YsqLCzU2rVrdfDgQa1du1a1tbV68cUXVVNTo87OTuXk5KipqUmSlJ2draamJnm9\n3v7fAEPTVwAAAGAPR05fqays1PLly5WSkiJJGjVqlCRp27ZtmjRpkrKysiRJI0eOVFJSkk6cOKHu\n7m7l5uZKkhYtWqQtW7ZIkurq6rR48WJJ0rx587R9+3ZJ0tatW1VQUCCv1yuv16v8/HzV19fHNU4A\nAABgIJJNLNrS0qJdu3ZpxYoVGj58uFavXq0pU6aopaVFHo9HhYWFOnXqlD772c/qq1/9qtrb25WR\nkRF6vN/vV3t7uySpvb1d48aNuxBMcrLS0tLU0dGh48eP93tMRkZG6DGXK7/krDZ5eXnKy8sb+qAB\nAABgjcbGRjU2Ng74/jFLyvPz83Xy5Mkrrl+1apX6+vrU1dWlPXv2aN++fZo/f74OHz6s8+fP66WX\nXtL//M//aMSIEZoxY4ays7OVlpYWq21K6p+UAwAAANG6vNBbUVFxzfvHLClvaGi46m2VlZWaO3eu\nJCknJ0dJSUl68803NW7cOH3yk59Uenq6JKmoqEivvvqq/uZv/kZtbW2hx7e1tYWq4H6/X8eOHdPY\nsWPV19enM2fOyOfzye/39/vrpLW1VdOnT49BpAAAAEB0jPSUz5kzRzt27JAkHTp0SL29vbrppptU\nUFCg1157TT09Perr69MvfvELfeQjH9Ho0aOVmpqqvXv3KhgMav369Zo9e7Ykqbi4WNXV1ZKkTZs2\nacaMGZKkgoICbdu2TadPn1ZXV5caGho0c+ZME+G+JxuL8zbGDAAAMBBGpq+cP39eJSUlOnDggIYN\nG6Znn302VN5//vnn9c1vflMej0ezZs3SM888I+nCSMQlS5aop6dHRUVFeu655yRdGIm4cOFC7d+/\nXz6fTzU1NRo/frwk6Qc/+IGefvppSdI3vvGN0AGhlzI1fcXjkWwb+mJjzAAAAFL4nNNIUu4kJOXx\nY2PMAAAAkkNHIgIAAAD4C5JyAAAAwDCScgAAAMAwknJDyspM7yD+bIwZAABgIDjQ09CBngAAALAH\nB3oCAAAADkdSDgAAABhGUg4AAAAYRlIOAAAAGEZSbkh5uekdxJ+NMQMAAAwE01cMTV+x8ZTzNsYM\nAAAgMX0FAAAAcDyScgAAAMAwknIAAADAMJJyAAAAwDCSckPKykzvIP5sjBkAAGAgmL5iaPoKAAAA\n7MH0FQAAAMDhSMoBAAAAw0jKAQAAAMNIygEAAADDSMoNKS83vYP4szFmAACAgWD6iqHpKx6PZNt3\n3saYAQAAJKavAAAAAI5HUg4AAAAYRlIOAAAAGEZSDgAAABhGUm5IWZnpHcSfjTEDAAAMBNNXDE1f\nAQAAgD2YvgIAAAA4HEk5AAAAYBhJOQAAAGAYSTkAAABgGEm5IeXlpncQfzbGDAAAMBBMXzE0fcXj\nkWz7ztsYMwAAgMT0FQAAAMDxSMoBAAAAw0jKAQAAAMNIygEAAADDSMoNKSszvYP4szFmAACAgWD6\niqHpKwAAALAH01cAAAAAhyMpBwAAAAwjKQcAAAAMIykHAAAADCMpN6S83PQO4s/GmAEAAAaC6SuG\npq94PJJt33kbYwYAAJAcPH1lzZo1mjBhgiZOnKivfe1rkqTnn39ekydPDl2uu+46NTc3S5KampqU\nlZWlQCCgZcuWhf6dc+fOacGCBQoEApo6daqOHj0auq26ulqZmZnKzMzUunXr4hsgAAAAMEBGKuU7\nd+7U008/rZ/97GdKSUnRqVOnNGrUqH73OXjwoObMmaPf//73kqTc3Fx9+9vfVm5uroqKivTFL35R\nhYWFWrt2rQ4ePKi1a9eqtrZWL774ompqatTZ2amcnBw1NTVJkrKzs9XU1CSv19tvHSrl8WNjzAAA\n92hslPLyTO8CicqRlfLKykotX75cKSkpknRFQi5JP/rRj/TQQw9Jkk6cOKHu7m7l5uZKkhYtWqQt\nW7ZIkurq6rR48WJJ0rx587R9+3ZJ0tatW1VQUCCv1yuv16v8/HzV19fHPDYAAOBOjY2mdwCbJZtY\ntKWlRbt27dKKFSs0fPhwrV69WlOmTOl3nw0bNqiurk6S1N7eroyMjNBtfr9f7e3todvGjRsnSUpO\nTlZaWpo6Ojp0/Pjxfo/JyMgIPeZy5ZccgZiXl6c8/kwGAABAFBobG9UYwV96MUvK8/PzdfLkySuu\nX7Vqlfr6+tTV1aU9e/Zo3759mj9/vg4fPhy6z969e3XDDTfojjvuiNX2+ik3MBakrCzuSxpnY8wA\nAGdrbPxLhbyi4i/X5+XRyoLoXF7orbj0BfYeYpaUNzQ0XPW2yspKzZ07V5KUk5OjpKQkdXR0yOfz\nSZJqamr08MMPh+7v9/vV1tYW+rqtrS1UBff7/Tp27JjGjh2rvr4+nTlzRj6fT36/v99fJ62trZo+\nffpQhhiVRBwP6PF4wt4nzOvRSH8/AMBelyffifj7Ge5gpKd8zpw52rFjhyTp0KFD6u3tDSXk7777\nrjZu3KjPfvazofuPGTNGqamp2rt3r4LBoNavX6/Zs2dLkoqLi1VdXS1J2rRpk2bMmCFJKigo0LZt\n23T69Gl1dXWpoaFBM2fOjGeY1gkGg1FfAAAAbGSkp7ykpEQlJSXKysrSsGHD+o0r3LVrl26++WaN\nHz++32PWrl2rJUuWqKenR0VFRSosLJQkPfroo1q4cKECgYB8Pp9qamokSenp6XriiSeUk5MjSSor\nK7ti8goAAMBFtKvAJE4eZGgkIgAAAOzhyJGIAAAAAP6CpNwQDiQBAADARbSvcEZPAAAAxBjtKwAA\nAIDDkZQDAAAAhpGUAwAAAIaRlAMAAACGkZQbUlZmegcAAABwCqavcPIgAAAAxBjTVwAAAACHIykH\nAAAADCMpBwAAAAwjKQdiqLHR9A4AAIAbkJQbUl5uegeIB5JyAAAwECTlhlRUmN4BAAAAnCLZ9AaA\nRNPY+JcK+aV/fOXlXbgAAABcjqQ8RjwezwDuc+3bmZ/uTpcn37QqAQCAcEjKY4SEGgAAAANFTzkQ\nQ7SrAACAgfAELS/phjvlKQAAABCtcDknlXIAAADAMJJyAAAAwDCScgAAAMAwknIAAADAMJJyAAAA\nwPKe+48AABBBSURBVDCScgAAAMAwknIAAADAMJJyAAAAwDCScgAAAMAwknIAAADAMJJyAAAAwDCS\ncgAAAMAwknIAAADAMJJyAAAAwDCScgAAAMAwknIAAADAMJJyAAAAwDCScgAAAMAwknIAAADAMJJy\nAAAAwDCScgAAAMAwknIAAADAMJJyAAAAwDCScgAAAMAwknIAAADAMJJyAAAAwDCSckMaGxtNbyHu\niNkOxJz4bItXImZbELMdnBqzsaR8zZo1mjBhgiZOnKjS0lJJ0ttvv62HHnpIkyZN0h133KFnnnkm\ndP+mpiZlZWUpEAho2bJloevPnTunBQsWKBAIaOrUqTp69GjoturqamVmZiozM1Pr1q2LX3AD4NQX\nRCwRsx2IOfHZFq9EzLYgZjs4NeZkE4vu3LlTdXV1am5uVkpKik6dOiVJqqmpkSQ1Nzerp6dHd9xx\nhx5++GHdfPPNWrp0qaqqqpSbm6uioiLV19ersLBQVVVV8vl8amlpUW1trUpLS1VTU6POzk6tXLlS\nTU1NkqTs7GwVFxf/v/buNSaqa+0D+H+4lajFKSMXAYkNymWYYYAio6UKKlCrtgparxVh0FprxJqm\nbfzQaIu3VpsqjdrEe2tb46Va8Va1qGAJOgwjKKIkIipCUURQGOgw+JwPxjlSwPfkPe5ZHHh+yUpm\n9l6b/V8kkIc1i70gl8tFDJkxxhhjjLFOCZkp37RpE5YsWQJHR0cAgJubGwCgf//+aGxsRGtrKxob\nG+Hk5AQXFxdUVVXh0aNHiIyMBAAkJSXh4MGDAIBDhw5h9uzZAIBJkybhjz/+AAD8/vvviI+Ph1wu\nh1wuR1xcHI4fP27roTLGGGOMMfZ/IwFCQ0Np6dKlpNVqKTo6mvR6vfXczJkzyc3NjXr37k2bN28m\nIiK9Xk+xsbHWPtnZ2TR+/HgiIlKpVHTnzh3rOT8/P6qpqaG1a9fS8uXLrcfT09Np7dq17bIA4MaN\nGzdu3Lhx48ZN8vY8ki1fiYuLw19//dXu+IoVK2CxWPDgwQPk5eVBr9djypQpKCsrw65du9DU1ISq\nqirU1tZi+PDhGD16tFQRAQBP6nLGGGOMMcbEkawoP3nyZKfnNm3ahMTERADAkCFDYGdnh5qaGuTm\n5iIhIQH29vZwc3NDVFQUDAYD3njjDVRUVFivr6iogI+PDwDA29sbt27dgpeXFywWC+rr66FQKODt\n7d1mIf/t27cxatQoaQbLGGOMMcbYf0HImvKJEyciKysLAFBaWoqWlhb069cPgYGB1uONjY3Iy8tD\nYGAgPD094eLigvPnz4OI8OOPP2LChAkAgHfeeQc7d+4EAOzbt886sx4fH48TJ06grq4ODx48wMmT\nJ/Hmm28KGC1jjDHGGGPPJ+TpKzqdDjqdDmq1Gk5OTtaiet68eUhNTYVarcbjx4+h0+mgUqkAABs3\nbkRycjKampowduxYjBkzBgCQmpqKWbNmYfDgwVAoFNYnuLi6uuLzzz/HkCFDAABLly7lJ68wxhhj\njLEuSUa8qNqmbt++jaSkJNy9excymQzvv/8+0tLSRMeSVHNzM6Kjo/H333/DbDZjwoQJWLVqlehY\nNtHa2oqIiAj4+PggMzNTdBzJDRw4EC4uLrC3t4ejoyMuXLggOpLk6urqMGfOHBQXF0Mmk2Hbtm0Y\nOnSo6FiSuXbtGqZNm2Z9X1ZWhvT09G79e2zVqlXYtWsX7OzsoFarsX37drz00kuiY0lq/fr12LJl\nC4gIc+fObbM/SHeh0+lw5MgRuLu749KlSwCA2tpaTJ06FTdv3sTAgQOxZ8+ebjWh19GY9+7di2XL\nluHq1avQ6/UIDw8XnPLF6mjMn3zyCQ4fPgwnJyf4+flh+/bt6Nu3r+CkvKOnzTk6OuLbb79FcXEx\n8vLysGHDBpSUlIiOJSlnZ2ecPn0aFy9eRFFREU6fPo1z586JjmUT69evh1KphEwmEx3FJmQyGc6c\nOQOj0dgjCnIAWLRoEcaOHYuSkhIUFRUhKChIdCRJBQQEwGg0wmg0wmAwoFevXkhISBAdSzLl5eXY\nvHkzCgoKcOnSJbS2tlo/ke2uLl++jC1btkCv16OwsBCHDx/G9evXRcd64VJSUto9Knn16tWIi4tD\naWkpRo8e3WYTw+6gozGr1WocOHAAI0aMEJRKWh2NOT4+HsXFxSgsLIS/v3+XmSjkotzGPD09ERoa\nCgDo06cPgoKCUFlZKTiV9Hr16gUAMJvNaG1thaurq+BE0quoqMDRo0cxZ86cHvWUn5401vr6euTk\n5ECn0wEAHBwcusRsi62cOnUKfn5+GDBggOgoknFxcYGjoyNMJhMsFgtMJhO8vb1Fx5LU1atXodVq\n4ezsDHt7e0RHR+PXX38VHeuFGz58OF555ZU2x57d+2T27NnWPVG6i47GHBgYCH9/f0GJpNfRmOPi\n4mBn96QE1mq1bR4mIhIX5QKVl5fDaDRCq9WKjiK5x48fIzQ0FB4eHhg5ciSUSqXoSJJbvHgx1qxZ\nY/3B7wlkMhliY2MRERGBzZs3i44juRs3bsDNzQ0pKSkIDw/H3LlzYTKZRMeymd27d2PGjBmiY0jK\n1dUVH3/8MXx9feHl5QW5XI7Y2FjRsSSlUqmQk5OD2tpamEwmHDlypMsULVKrrq6Gh4cHAMDDwwPV\n1dWCEzGpbdu2DWPHjhUdAwAX5cI0NDRg8uTJWL9+Pfr06SM6juTs7Oxw8eJFVFRUIDs7u83jKruj\nw4cPw93dHWFhYT1q5vjPP/+E0WjEsWPHsGHDBuTk5IiOJCmLxYKCggJ8+OGHKCgoQO/evbvdx92d\nMZvNyMzMxLvvvis6iqSuX7+OdevWoby8HJWVlWhoaMBPP/0kOpakAgMD8dlnnyE+Ph5vvfUWwsLC\netTkwlMymazHLD3sqVasWAEnJ6cuM7nQ837KuoCWlhZMmjQJ7733HiZOnCg6jk317dsX48aNQ35+\nvugoksrNzcWhQ4fw6quvYvr06cjKykJSUpLoWJLr378/AMDNzQ0JCQndfl25j48PfHx8rE95mjx5\nMgoKCgSnso1jx47htddeg5ubm+goksrPz8frr78OhUIBBwcHJCYmIjc3V3Qsyel0OuTn5+Ps2bOQ\ny+UICAgQHckmPDw8rBsfVlVVwd3dXXAiJpUdO3bg6NGjXeqPbC7KbYyIkJqaCqVSiY8++kh0HJuo\nqalBXV0dAKCpqQknT55EWFiY4FTSWrlyJW7fvo0bN25g9+7dGDVqFH744QfRsSRlMpnw6NEjAE/2\nGThx4gTUarXgVNLy9PTEgAEDUFpaCuDJGuvg4GDBqWzjl19+wfTp00XHkFxgYCDy8vLQ1NQEIsKp\nU6d6xPK7u3fvAgBu3bqFAwcOdJmZRKk9u/fJzp07e9zEWU/5ZPf48eNYs2YNfvvtNzg7O4uO82/E\nbConJ4dkMhlpNBoKDQ2l0NBQOnbsmOhYkioqKqKwsDDSaDSkVqvp66+/Fh3Jps6cOUNvv/226BiS\nKysrI41GQxqNhoKDg2nlypWiI9nExYsXKSIigkJCQighIYHq6upER5JcQ0MDKRQKevjwoegoNvHV\nV1+RUqkklUpFSUlJZDabRUeS3PDhw0mpVJJGo6GsrCzRcSQxbdo06t+/Pzk6OpKPjw9t27aN7t+/\nT6NHj6bBgwdTXFwcPXjwQHTMF+qfY966dSsdOHCAfHx8yNnZmTw8PGjMmDGiY75QHY150KBB5Ovr\na63D5s+fLzomERHxc8oZY4wxxhgTjJevMMYYY4wxJhgX5YwxxhhjjAnGRTljjDHGGGOCcVHOGGOM\nMcaYYFyUM8ZYF7dkyRKcOXMGBw8e7HRzouTkZOzfv7/d8crKyk43+ImJiYHBYGh3fMeOHVi4cOF/\nF/o5du7ciaqqqhfWjzHGugMuyhljrIu7cOEChg4dirNnz2LEiBEd9uls50EvLy/s3bu302tE7Fi4\nY8cOVFZWvrB+jDHWHXBRzhhjXdSnn34KjUYDvV6PYcOGYevWrZg/fz6WL1/eYf/s7GxERUXBz8/P\nOmteXl5u3cSpqakJ06ZNg1KpRGJiIpqamqzXbt++HQEBAdBqtW12rLx37x4mT56MyMhIREZGWs8t\nW7YMOp0OI0eOhJ+fH7777rt2eVpbW5GcnAy1Wo2QkBCsW7cO+/fvR35+PmbOnInw8HA0Nzfjyy+/\nRGRkJNRqNebNmwcA2LdvX7t+BoMBMTExiIiIwJgxY6w7L2ZkZCA4OBgajaZHbGjEGOumRD8onTHG\nWOf0ej2lpaVRS0sLRUVFddpv9uzZNGXKFCIiunLlCg0aNIiIiG7cuEEqlYqIiL755htKTU0loieb\nejk4OJDBYKDKykry9fWlmpoaMpvNFBUVRQsXLiQiounTp9O5c+eIiOjmzZsUFBRERERLly6lqKgo\nMpvNVFNTQwqFgiwWS5tM+fn5FBcXZ31fX19PREQxMTFkMBisx2tra62vZ82aRZmZme36mc1mGjZs\nGNXU1BAR0e7du0mn0xERkZeXl3VDn6f3YIyx/zUOov8oYIwx1jmDwYCQkBCUlJQgKCio034ymcy6\nJXhQUBCqq6vb9cnJycGiRYsAwDp7TUQ4f/48YmJioFAoAABTp05FaWkpAODUqVMoKSmxfo1Hjx6h\nsbERMpkM48aNg6OjIxQKBdzd3VFdXQ0vLy9rXz8/P5SVlSEtLQ3jxo1DfHy89Rw9s29dVlYW1qxZ\nA5PJhNraWqhUKowfP75Nv2vXrqG4uBixsbEAnszCP71XSEgIZsyYgYkTJ/a4bdEZY90HF+WMMdYF\nFRYWIjk5GRUVFejXrx9MJhOICOHh4cjNzYWzs3O7a5ycnKyvqZPNmjs6/s915URkPfa0aH/2a3d0\nP3t7e1gsljbn5XI5ioqKcPz4cXz//ffYs2cPtm7d2uaezc3NWLBgAQwGA7y9vfHFF1+gubm5XTYi\nQnBwcJulNU8dOXIE2dnZyMzMxIoVK3Dp0iXY29t3OH7GGOuqeE05Y4x1QRqNBkajEf7+/igpKcGo\nUaNw4sQJFBQUdFiQ/ydGjBiBn3/+GQBw+fJlFBUVQSaTQavV4uzZs6itrUVLS0ubfwyNj49HRkaG\n9X1hYeF/fL/79+/DYrEgMTER6enpMBqNAICXX34ZDx8+BABrAa5QKNDQ0NDm3s/2CwgIwL1795CX\nlwcAaGlpwZUrV0BEuHXrFmJiYrB69WrU19ejsbHx//PtYYwxoXimnDHGuqh79+7B1dUVAHD16lUE\nBgY+t/+zM94dvZ4/fz5SUlKgVCoRFBSEiIgIAICnpyeWLVuGYcOGQS6XIywszHptRkYGFixYAI1G\nA4vFgujoaGzcuLHdPTpy584dpKSk4PHjxwBgfZxjcnIyPvjgA/Tq1Qu5ubmYO3cuVCoVPD09odVq\nrdf/s9++ffuQlpaG+vp6WCwWLF68GP7+/pg1axbq6+tBRFi0aBFcXFye/41ljLEuSEadfcbJGGOM\nMcYYswlevsIYY4wxxphgXJQzxhhjjDEmGBfljDHGGGOMCcZFOWOMMcYYY4JxUc4YY4wxxphgXJQz\nxhhjjDEm2L8A838Bg6PrTEQAAAAASUVORK5CYII=\n" | |
| } | |
| ], | |
| "prompt_number": 90 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Summary", | |
| "", | |
| "", | |
| "<table border=\"1\">", | |
| " <tr>", | |
| " <td> Chance </td><td>-81904.84</td>", | |
| " </tr>", | |
| " <tr>", | |
| " <td> Bernoulli </td><td>-67779.49</td>", | |
| " </tr>", | |
| " <tr>", | |
| " <td> Markov Chain </td><td>-54834.17</td>", | |
| " </tr>", | |
| " <tr>", | |
| " <td> 2nd Order Markov Chain </td><td>-54803.71</td>", | |
| " </tr>", | |
| " <tr>", | |
| " <td> Monthwise Bernoulli </td><td>-67568.98</td>", | |
| " </tr>", | |
| " <tr>", | |
| " <td> Monthwise Markov Chain </td><td>-55486.25</td>", | |
| " </tr>", | |
| " <tr>", | |
| " <td> Hidden Markov Model (12 states) </td><td>-59073.59</td>", | |
| " </tr>", | |
| " ", | |
| "</table>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "<br><br><br><br><br><br><br><br><br>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### Prediction" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "last_state_p = model.startprob_", | |
| "new_state_p = np.dot(last_state_p, model.transmat_)", | |
| "postdiction = np.sum(new_state_p.reshape(-1, 1) * model.emissionprob_, 0)", | |
| "log_L = np.log(postdiction[train_data[0]])", | |
| "", | |
| "for i in range(1, len(train_data[:1000])):", | |
| " ignore, states = model.eval(train_data[:i])", | |
| " last_state_p = states[-1]", | |
| " new_state_p = np.dot(last_state_p, model.transmat_)", | |
| " postdiction = np.sum(new_state_p.reshape(-1, 1) * model.emissionprob_, 0)", | |
| " log_L += np.log(postdiction[train_data[i]])", | |
| " if i % 100 == 0:", | |
| " print(i, log_L)", | |
| "print(log_L)", | |
| "", | |
| " ", | |
| " ", | |
| " ", | |
| " ", | |
| " " | |
| ], | |
| "language": "python", | |
| "outputs": [] | |
| } | |
| ] | |
| } | |
| ] | |
| } |
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