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sklearn for learning
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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"'1.11.3'" | |
] | |
}, | |
"execution_count": 1, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"#numpy 배우기\n", | |
"import numpy\n", | |
"numpy.version.full_version" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"array([0, 1, 2, 3, 4, 5])" | |
] | |
}, | |
"execution_count": 2, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"#namespace 가 섞이는 게 싫다면 아래와 같이 한다\n", | |
"import numpy as np\n", | |
"a = np.array([0,1,2,3,4,5])\n", | |
"a" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"(6,)" | |
] | |
}, | |
"execution_count": 3, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"a.shape" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"(3, 2)" | |
] | |
}, | |
"execution_count": 4, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"# 이제는 이 배열을 이차원 행렬로 변형해보자\n", | |
"b = a.reshape((3,2))\n", | |
"b.shape" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"# 첫 번째 기계학습 애플리케이션\n", | |
"# 질문. 시간당 100,000 요청이 있으면 현재 장비가 언제 최대가 될까? 앞으로 최대로 가용하며 모든 요청을 완벽하게 처리하기 위해 추가장비를 설치해야 하는 시점을 알고 싶다." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 6, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"# 데이터 읽기\n", | |
"import scipy as sp\n", | |
"data = sp.genfromtxt('/home/partrita/Documents/learn/ch01/data/web_traffic.tsv',delimiter = '\\t')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 7, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"[[ 1.00000000e+00 2.27200000e+03]\n", | |
" [ 2.00000000e+00 nan]\n", | |
" [ 3.00000000e+00 1.38600000e+03]\n", | |
" ..., \n", | |
" [ 7.41000000e+02 5.39200000e+03]\n", | |
" [ 7.42000000e+02 5.90600000e+03]\n", | |
" [ 7.43000000e+02 4.88100000e+03]]\n" | |
] | |
} | |
], | |
"source": [ | |
"print data" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 8, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"8" | |
] | |
}, | |
"execution_count": 8, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"# 데이터 정리와 전처리\n", | |
"# 첫번째 벡터 x는 시간이며 두 번째 y는 특정 시간의 요청수 이다. \n", | |
"x = data[:,0]\n", | |
"y = data[:,1]\n", | |
"# y의 일부값에 Nan 값이 있는지 확인해 본다\n", | |
"sp.sum(sp.isnan(y))" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 9, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"# Nan값을 제거 하기 위해 \n", | |
"x = x[~sp.isnan(y)]\n", | |
"y = y[~sp.isnan(y)]\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 10, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
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/aFL6BNyei3H+5LK9jkeZWzP8LuQx83U6RtlGW3YfI2kFb5Qk1LjaTSeaDddt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\nRkXcZFOcYVMx2vdPKVjn1TLKptSy+VwmAoVS2C7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| |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7fc2236a60d0>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# 데이터의 형태를 파악하기 위해 산점도를 그려보자\n", | |
"import matplotlib.pyplot as plt\n", | |
"%matplotlib inline\n", | |
"# 크기가 10인 점으로 그린다\n", | |
"plt.scatter(x,y,s=10)\n", | |
"# 약간 불투명한 격자를 그린다.\n", | |
"plt.grid(True,linestyle='-',color='0.7')\n", | |
"plt.show()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 11, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"# 적절한 모델과 학습 알고리즘 선택\n", | |
"# 단순한 직선으로 시작하기\n", | |
"fp1, residuals, rank,sv, rcond = sp.polyfit(x,y,1,full=True)\n", | |
"f1 = sp.poly1d(fp1)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 12, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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XN9fx8pZ627ZdvUE6r/W5ClC31Nc1gWY+OdJiy4G0cXuDZSNQz2ZPMDRi1ZlG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\nBIIgCBqYSAJBEAQNzHfavEqeQ2fTagAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7fc21f191050>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"plt.scatter(x,y,s=10)\n", | |
"plt.grid(True,linestyle='-',color='0.7')\n", | |
"#도표를 위한 x 값을 생성한다\n", | |
"fx = sp.linspace(0,x[-1],1000)\n", | |
"plt.plot(fx,f1(fx),linewidth=4,color='red')\n", | |
"plt.show()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 13, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"[ 1.05322215e-02 -5.26545650e+00 1.97476082e+03]\n" | |
] | |
} | |
], | |
"source": [ | |
"# 좀 더 복잡한 모델\n", | |
"# 이차 다항식 모델\n", | |
"f2p = sp.polyfit(x,y,2)\n", | |
"print f2p\n", | |
"f2 = sp.poly1d(f2p)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 14, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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7JK+s2RMyXF6jK/WHkTx2pI7Vm20Dp5uuOBJeSeGqUrXnpq2piflfvctdz/0N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\nYyUjEIxG4Fbg7/h4ZYgay3R9Bng1ZI31lnZ0B30IOJV4f9q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"<matplotlib.figure.Figure at 0x7fc21e9b9f50>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"plt.scatter(x,y,s=10)\n", | |
"plt.grid(True,linestyle='-',color='0.7')\n", | |
"#도표를 위한 x 값을 생성한다\n", | |
"fx = sp.linspace(0,x[-1],1000)\n", | |
"plt.plot(fx,f2(fx),linewidth=4,color='red')\n", | |
"plt.show()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 22, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"100,000 hits/hours exprected at week 19.708090\n" | |
] | |
} | |
], | |
"source": [ | |
"# 2차 다항식을 가지고 100,000이 되는 값을 찾으면 된다.\n", | |
"from scipy.optimize import fsolve\n", | |
"reached_max = fsolve(f2-100000,x0=700)/(7*24)\n", | |
"print '100,000 hits/hours exprected at week %f' % reached_max[0]" | |
] | |
} | |
], | |
"metadata": { | |
"anaconda-cloud": {}, | |
"kernelspec": { | |
"display_name": "Python [conda root]", | |
"language": "python", | |
"name": "conda-root-py" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 2 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython2", | |
"version": "2.7.12" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 2 | |
} |
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