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metody_eksploracji_danych [2016/10/03 16:40]
pszwed [Zbiory danych]
— (current)
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-====== Metody eksploracji danych ====== 
  
-===== Laboratorium 1 ===== 
- 
- 
-==== Zbiory danych ==== 
- 
-[[http://home.agh.edu.pl/~pszwed/med/regression/xy-001.arff]] 
-[[http://home.agh.edu.pl/~pszwed/med/regression/xy-002.arff]] 
-[[http://home.agh.edu.pl/~pszwed/med/regression/xy-003.arff]] 
-[[http://home.agh.edu.pl/~pszwed/med/regression/xy-004.arff]] 
-[[http://home.agh.edu.pl/~pszwed/med/regression/xy-005.arff]] 
-[[http://home.agh.edu.pl/~pszwed/med/regression/xy-006.arff]] 
-[[http://home.agh.edu.pl/~pszwed/med/regression/xy-007.arff]] 
-[[http://home.agh.edu.pl/~pszwed/med/regression/xy-008.arff]] 
-[[http://home.agh.edu.pl/~pszwed/med/regression/xy-009.arff]] 
-[[http://home.agh.edu.pl/~pszwed/med/regression/xy-010.arff]] 
- 
-==== Zbiory danych ==== 
- 
-<code lang=Python> 
-# % matplotlib notebook 
- 
-import matplotlib.pyplot as plt 
-import numpy as np 
-from scipy import stats 
-from io import StringIO 
- 
-data = """ 
-0.246939,9.011391 
-0.895519,8.950505 
-0.971588,9.671047 
-1.188316,14.735488 
-1.741884,10.265625 
-2.196002,13.501097 
-2.637403,13.887849 
-2.788188,17.180626 
-3.50202,19.321529 
-3.531476,16.089503 
-3.995073,14.937624 
-4.66407,18.389229 
-4.88705,22.798099 
-5.644447,25.351644 
-6.537993,26.94125 
-6.654565,20.387372 
-6.981497,21.255345 
-7.099548,27.745566 
-7.453511,22.811026 
-8.145089,22.388721 
-8.865577,28.027686 
-8.983554,25.146826 
-9.939295,34.305519 
-10.132365,33.500249 
-10.804992,35.683783 
-10.956247,33.300984 
-11.135499,30.047647 
-12.080814,35.787975 
-12.647943,40.051468 
-12.741248,35.344707 
-13.37512,33.765994 
-13.699004,38.776812 
-13.795843,37.091575 
-14.474034,38.114638 
-15.033079,45.589492 
-15.592465,40.631264 
-16.192292,48.739644 
-16.65253,48.830867 
-16.832643,50.325774 
-17.577283,45.206373 
-17.853024,53.339617 
-18.763727,48.279457 
-19.045983,55.953631 
-19.26704,50.470961 
-19.537664,51.928816 
-19.987968,49.376176 
-20.603744,52.380207 
-21.373748,61.677885 
-22.242239,57.668626 
-22.710625,56.161207 
-23.706639,65.423664 
-23.991602,60.008664 
-24.26953,61.870482 
-24.899023,66.002296 
-25.110234,70.342272 
-25.960459,68.472507 
-26.712467,72.751912 
-26.730612,73.491421 
-26.832166,73.970975 
-27.234095,74.192411 
-27.263899,67.012532 
-28.186481,71.667746 
-28.609198,70.126676 
-29.00642,71.713675 
-29.344191,78.395062 
-29.652469,72.427284 
-30.009633,80.863626 
-30.176943,73.542808 
-30.616236,80.771572 
-30.89822,80.967313 
-31.684718,77.600474 
-32.564911,79.397491 
-33.03557,80.840347 
-33.132668,83.721291 
-33.820815,88.970661 
-34.109682,89.530881 
-34.661445,93.863877 
-35.162583,89.337648 
-35.432228,87.968034 
-36.122985,90.976234 
-36.532793,91.573681 
-37.008879,97.673479 
-37.712701,92.459677 
-38.486883,94.410451 
-38.99117,104.222796 
-39.663589,105.80185 
-40.241739,101.458148 
-40.519365,105.707817 
-40.910886,103.881927 
-40.998451,99.379055 
-41.420003,105.555433 
-42.057595,103.837871 
-42.374651,107.950421 
-43.164247,106.545838 
-44.086193,107.721106 
-44.137013,110.703987 
-44.41119,107.948371 
-45.148115,115.274231 
-45.845917,112.39734 
-46.47258,115.871904 
-""" 
-inp =  StringIO(data) 
-x, y = np.loadtxt(inp, delimiter=',', usecols=(0, 1), unpack=True,skiprows=6) 
- 
-plt.scatter(x,y,s=80, marker='+') 
- 
- 
-#plot function 
-fx=np.linspace(-10,60,100) 
-fy=2.3702*fx+6.1973 
-ftrue=2.37*fx+7 
-plt.plot(fx,fy,linewidth=2,color='r') 
-plt.plot(fx,ftrue,linewidth=1,linestyle='--',color='g') 
- 
-plt.xlim(-10,60) 
-plt.grid(True) 
-plt.xlabel('X') 
-plt.ylabel('Y') 
-r = stats.pearsonr(x, y)[0]  
-plt.title('Regression $f_{true} = 2.37x+7$ r=' + str(r)) 
-plt.show() 
- 
-</code> 
metody_eksploracji_danych.1475505637.txt.gz · Last modified: 2016/10/03 16:40 by pszwed
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