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@SirEdvin
Created September 16, 2015 16:07
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{
"cells": [
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy\n",
"import re\n",
"from pylab import *\n",
"import matplotlib.patches as mpatches\n",
"import matplotlib.lines as mlines\n",
"\n",
"def parsefile(file):\n",
" lines = file.readlines()\n",
" data = {'real':[], 'prediction':[], 'delta':[], 'scale':[]}\n",
" for line in lines:\n",
" s = re.split('\\s{2,}',line)\n",
" if len(s)<2:\n",
" break\n",
" elif s[0]=='future':\n",
" data['future'] = float(s[1])\n",
" data['prediction'].append(float(s[1]))\n",
" elif (s[0]=='Sum of Delta' or s[0]=='Sum of (Delta ^ 2)' or s[0]=='MSE'):\n",
" data[s[0]] = float(s[1])\n",
" else:\n",
" data['scale'].append(float(s[0]))\n",
" data['real'].append(float(s[1]))\n",
" data['prediction'].append(float(s[2]))\n",
" data['delta'].append(float(s[3]))\n",
" if len(data['prediction']) > len(data['scale']):\n",
" scale = data['scale']\n",
" scale.append(scale[len(scale)-1]+1)\n",
" return data\n",
"\n",
"def drawresults(results, image='result.png', linestyle='k-', secondlinestyle='k--', xlabel = \"Point\", ylabel = \"IPC\"):\n",
" count = len(results)\n",
" fig, axy = subplots(count//2+count%2,2)\n",
" for result in results:\n",
" index = results.index(result)\n",
" plt = axy[index//2,index%2]\n",
" plt.plot(result['scale'],result['prediction'],linestyle)\n",
" plt.plot(result['scale'] if 'future' not in result else result['scale'][:-1], result['real'],secondlinestyle)\n",
" plt.set_xlabel(xlabel)\n",
" fig.tight_layout()\n",
" prediction_line = mlines.Line2D([], [], color='black', label='Prediction IPC')\n",
" real_line = mlines.Line2D([], [], color='black', label='Real IPC', linestyle='--')\n",
" lgd = fig.legend([prediction_line, real_line], ['Prediction IPC', 'Real IPC'],bbox_to_anchor=(0, 1, 1, 1), loc = 3,\n",
" ncol=2, mode=\"expand\", borderaxespad=0.)\n",
" fig.savefig(image, dpi=500,bbox_inches='tight', bbox_extra_artist=[lgd])\n",
" show()\n",
" \n",
"def drawpredictions(results, image='prediction.png', linestyle='k-', secondlinestyle='k--', label='Window number', legendlabel = 'IPC(+1)', legendcolor = \"black\"):\n",
" IPC1data = list(map(lambda x:x['prediction'][len(x['prediction'])-1], results))\n",
" RIPC1data = list(map(lambda x:x['real'][len(x['real'])-1],results))\n",
" RIPC1data.pop()#Тому що останнє вікно в якому є лише\n",
" #future значення не враховується\n",
" ipc1line = plot(range(1,len(IPC1data)+1), IPC1data, linestyle, label='Prediction IPC(+1)')\n",
" ripc1line = plot(range(1,len(RIPC1data)+1),RIPC1data, secondlinestyle,label='Real IPC(+1)')\n",
" xlabel(label)\n",
" ipc1_line = mlines.Line2D([],[],color='black',label='Predicted IPC(+1)', linestyle= '-')\n",
" ripc1_line = mlines.Line2D([],[],color='black',label='Real IPC(+1)', linestyle='--')\n",
" legend(handles=[ipc1_line,ripc1_line])\n",
" savefig(image, dpi = 500)\n",
" show()\n",
" \n",
"def drawmse(results, image='mse.png', linestyle='k-', label='Window number', legendlabel='MSE', legendcolor='black'):\n",
" MSEdata = list(map(lambda x: x['MSE'], results))\n",
" plot(range(1,len(MSEdata)+1), MSEdata, linestyle)\n",
" xlabel(label)\n",
" mse_line = mlines.Line2D([],[],color='black',label='MSE',linestyle='-')\n",
" legend(handles=[mse_line])\n",
" savefig(image, dpi = 500)\n",
" show()\n",
" \n",
"def comparemse(results, v1results, image='compare.png', linestyle='k-', secondlinestyle = 'k--', label='Window number', firstlabel='MSE', secondlabel='Full MSE', legendcolor= 'black'):\n",
" MSEdata = list(map(lambda x: x['MSE'], results))\n",
" V1MSEdata = list(map(lambda x: x['MSE'], v1results))\n",
" plot(range(1,len(MSEdata)+1), MSEdata, linestyle, range(1,len(V1MSEdata)+1), V1MSEdata, secondlinestyle)\n",
" xlabel(label)\n",
" mse_line = mlines.Line2D([],[],color=legendcolor, linestyle='-', label=firstlabel)\n",
" v1mse_line = mlines.Line2D([],[],color='black',linestyle='--', label=secondlabel)\n",
" legend(handles=[mse_line, v1mse_line])\n",
" savefig(image, dpi = 500)\n",
" show()"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Аналіз результатів було виконано\n",
"MSE: [0.35, 0.44, 0.25, 0.3, 0.16, 0.17]\n"
]
},
{
"data": {
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npydZWlrWmMEy1O3Ro0dkYGBArVu3rtRA/XK5nObPn0+NGjUiKysrWrdu3Vs/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\nawcWk0seYWFhxMXFVXrJfmZmJrNmzeKbb75h+/btHDhwgJiYmLJzvmf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"text/plain": [
"<matplotlib.figure.Figure at 0x7f0328fff160>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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kSZM4cuQIAPv37+ebb74B8nqaAQEBBAYGcvz4cY/28vMFBgYyffp0HnjgARYvXsypU6fI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\nbx+1a9d2OhwRqab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"text/plain": [
"<matplotlib.figure.Figure at 0x7f0328fff438>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f0326fe7860>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Порівняння з першим варіантом\n",
"Аналіз результатів першого варіанту було виконано\n",
"MSE: [0.25, 0.59, 0.28, 0.25, 0.21, 0.12]\n"
]
},
{
"data": {
"image/png": 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1DB8+HPv27cO5c+eyL7w+/fTT6N+/v2lxmS09PR2vvfYaDhw4wJUavRATOrmd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\nO1X1Z1XtU4T2PeWGiiLHaV9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"text/plain": [
"<matplotlib.figure.Figure at 0x7f03271abc50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"files = [\"Lab1.txt\",\"Lab1_2.txt\",\"Lab1_3.txt\",\"Lab1_4.txt\",\"Lab1_5.txt\",\"Lab1_6.txt\"]\n",
"results = []\n",
"for file in files:\n",
" a = open(file)\n",
" results.append(parsefile(a))\n",
" a.close()\n",
"print('Аналіз результатів було виконано')\n",
"print('MSE:', list(map(lambda x: x['MSE'], results)))\n",
"drawresults(results)\n",
"drawpredictions(results)\n",
"drawmse(results)\n",
"print('Порівняння з першим варіантом')\n",
"v1files = [\"./V1_Lab1/Lab1_1.txt\",\"./V1_Lab1/Lab1_2.txt\",\"./V1_Lab1/Lab1_3.txt\",\"./V1_Lab1/Lab1_4.txt\",\"./V1_Lab1/Lab1_5.txt\",\"./V1_Lab1/Lab1_6.txt\"]\n",
"v1results = []\n",
"for v1file in v1files:\n",
" a = open(v1file)\n",
" v1results.append(parsefile(a))\n",
" a.close()\n",
"print('Аналіз результатів першого варіанту було виконано')\n",
"print('MSE:', list(map(lambda x: x['MSE'], v1results)))\n",
"comparemse(results, v1results)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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