├── .DS_Store ├── 1.Linear_model.py ├── 2.Decision_tree.py ├── 3.BP_neural_network.py ├── 4.Support_vector_machine.py ├── 5.Naive_bayes_classifier.py ├── 6.AdaBoost.py ├── 7.K-means.py ├── 8.PCA.py ├── 9.FastICA.py ├── A10.SFA.py ├── Epileptic Seizure Recognition Data Set.csv ├── Gradient Boost Decision Tree（GBDT）及其拓展模型.pptx ├── LICENSE ├── README.md ├── pic ├── .DS_Store ├── GBDT │ ├── .DS_Store │ └── 屏幕快照 2018-04-29 下午1.50.31.png ├── ICA │ ├── ICA.png │ ├── LDC2017S07.clean.wav │ ├── LDC2017S10.scaled.wav │ ├── LDC93S1.wav │ ├── Res.png │ ├── combine.png │ └── orig.png ├── SFA │ ├── .DS_Store │ ├── orl_faces_full │ │ ├── 10.pgm │ │ ├── 100.pgm │ │ ├── 101.pgm │ │ ├── 102.pgm │ │ ├── 103.pgm │ │ ├── 104.pgm │ │ ├── 105.pgm │ │ ├── 106.pgm │ │ ├── 107.pgm │ │ ├── 108.pgm │ │ ├── 109.pgm │ │ ├── 11.pgm │ │ ├── 110.pgm │ │ ├── 111.pgm │ │ ├── 112.pgm │ │ ├── 113.pgm │ │ ├── 114.pgm │ │ ├── 115.pgm │ │ ├── 116.pgm │ │ ├── 117.pgm │ │ ├── 118.pgm │ │ ├── 119.pgm │ │ ├── 12.pgm │ │ ├── 120.pgm │ │ ├── 121.pgm │ │ ├── 122.pgm │ │ ├── 123.pgm │ │ ├── 124.pgm │ │ ├── 125.pgm │ │ ├── 126.pgm │ │ ├── 127.pgm │ │ ├── 128.pgm │ │ ├── 129.pgm │ │ ├── 13.pgm │ │ ├── 130.pgm │ │ ├── 131.pgm │ │ ├── 132.pgm │ │ ├── 133.pgm │ │ ├── 134.pgm │ │ ├── 135.pgm │ │ ├── 136.pgm │ │ ├── 137.pgm │ │ ├── 138.pgm │ │ ├── 139.pgm │ │ ├── 14.pgm │ │ ├── 140.pgm │ │ ├── 141.pgm │ │ ├── 142.pgm │ │ ├── 143.pgm │ │ ├── 144.pgm │ │ ├── 145.pgm │ │ ├── 146.pgm │ │ ├── 147.pgm │ │ ├── 148.pgm │ │ ├── 149.pgm │ │ ├── 15.pgm │ │ ├── 150.pgm │ │ ├── 151.pgm │ │ ├── 152.pgm │ │ ├── 153.pgm │ │ ├── 154.pgm │ │ ├── 155.pgm │ │ ├── 156.pgm │ │ ├── 157.pgm │ │ ├── 158.pgm │ │ ├── 159.pgm │ │ ├── 16.pgm │ │ ├── 160.pgm │ │ ├── 161.pgm │ │ ├── 162.pgm │ │ ├── 163.pgm │ │ ├── 164.pgm │ │ ├── 165.pgm │ │ ├── 166.pgm │ │ ├── 167.pgm │ │ ├── 168.pgm │ │ ├── 169.pgm │ │ ├── 17.pgm │ │ ├── 170.pgm │ │ ├── 171.pgm │ │ ├── 172.pgm │ │ ├── 173.pgm │ │ ├── 174.pgm │ │ ├── 175.pgm │ │ ├── 176.pgm │ │ ├── 177.pgm │ │ ├── 178.pgm │ │ ├── 179.pgm │ │ ├── 18.pgm │ │ ├── 180.pgm │ │ ├── 181.pgm │ │ ├── 182.pgm │ │ ├── 183.pgm │ │ ├── 184.pgm │ │ ├── 185.pgm │ │ ├── 186.pgm │ │ ├── 187.pgm │ │ ├── 188.pgm │ │ ├── 189.pgm │ │ ├── 19.pgm │ │ ├── 190.pgm │ │ ├── 191.pgm │ │ ├── 192.pgm │ │ ├── 193.pgm │ │ ├── 194.pgm │ │ ├── 195.pgm │ │ ├── 196.pgm │ │ ├── 197.pgm │ │ ├── 198.pgm │ │ ├── 199.pgm │ │ ├── 20.pgm │ │ ├── 200.pgm │ │ ├── 201.pgm │ │ ├── 202.pgm │ │ ├── 203.pgm │ │ ├── 204.pgm │ │ ├── 205.pgm │ │ ├── 206.pgm │ │ ├── 207.pgm │ │ ├── 208.pgm │ │ ├── 209.pgm │ │ ├── 21.pgm │ │ ├── 210.pgm │ │ ├── 211.pgm │ │ ├── 212.pgm │ │ ├── 213.pgm │ │ ├── 214.pgm │ │ ├── 215.pgm │ │ ├── 216.pgm │ │ ├── 217.pgm │ │ ├── 218.pgm │ │ ├── 219.pgm │ │ ├── 22.pgm │ │ ├── 220.pgm │ │ ├── 221.pgm │ │ ├── 222.pgm │ │ ├── 223.pgm │ │ ├── 224.pgm │ │ ├── 225.pgm │ │ ├── 226.pgm │ │ ├── 227.pgm │ │ ├── 228.pgm │ │ ├── 229.pgm │ │ ├── 23.pgm │ │ ├── 230.pgm │ │ ├── 231.pgm │ │ ├── 232.pgm │ │ ├── 233.pgm │ │ ├── 234.pgm │ │ ├── 235.pgm │ │ ├── 236.pgm │ │ ├── 237.pgm │ │ ├── 238.pgm │ │ ├── 239.pgm │ │ ├── 24.pgm │ │ ├── 240.pgm │ │ ├── 241.pgm │ │ ├── 242.pgm │ │ ├── 243.pgm │ │ ├── 244.pgm │ │ ├── 245.pgm │ │ ├── 246.pgm │ │ ├── 247.pgm │ │ ├── 248.pgm │ │ ├── 249.pgm │ │ ├── 25.pgm │ │ ├── 250.pgm │ │ ├── 251.pgm │ │ ├── 252.pgm │ │ ├── 253.pgm │ │ ├── 254.pgm │ │ ├── 255.pgm │ │ ├── 256.pgm │ │ ├── 257.pgm │ │ ├── 258.pgm │ │ ├── 259.pgm │ │ ├── 26.pgm │ │ ├── 260.pgm │ │ ├── 261.pgm │ │ ├── 262.pgm │ │ ├── 263.pgm │ │ ├── 264.pgm │ │ ├── 265.pgm │ │ ├── 266.pgm │ │ ├── 267.pgm │ │ ├── 268.pgm │ │ ├── 269.pgm │ │ ├── 27.pgm │ │ ├── 270.pgm │ │ ├── 271.pgm │ │ ├── 272.pgm │ │ ├── 273.pgm │ │ ├── 274.pgm │ │ ├── 275.pgm │ │ ├── 276.pgm │ │ ├── 277.pgm │ │ ├── 278.pgm │ │ ├── 279.pgm │ │ ├── 28.pgm │ │ ├── 280.pgm │ │ ├── 281.pgm │ │ ├── 282.pgm │ │ ├── 283.pgm │ │ ├── 284.pgm │ │ ├── 285.pgm │ │ ├── 286.pgm │ │ ├── 287.pgm │ │ ├── 288.pgm │ │ ├── 289.pgm │ │ ├── 29.pgm │ │ ├── 290.pgm │ │ ├── 291.pgm │ │ ├── 292.pgm │ │ ├── 293.pgm │ │ ├── 294.pgm │ │ ├── 295.pgm │ │ ├── 296.pgm │ │ ├── 297.pgm │ │ ├── 298.pgm │ │ ├── 299.pgm │ │ ├── 30.pgm │ │ ├── 300.pgm │ │ ├── 301.pgm │ │ ├── 302.pgm │ │ ├── 303.pgm │ │ ├── 304.pgm │ │ ├── 305.pgm │ │ ├── 306.pgm │ │ ├── 307.pgm │ │ ├── 308.pgm │ │ ├── 309.pgm │ │ ├── 31.pgm │ │ ├── 310.pgm │ │ ├── 311.pgm │ │ ├── 312.pgm │ │ ├── 313.pgm │ │ ├── 314.pgm │ │ ├── 315.pgm │ │ ├── 316.pgm │ │ ├── 317.pgm │ │ ├── 318.pgm │ │ ├── 319.pgm │ │ ├── 32.pgm │ │ ├── 320.pgm │ │ ├── 321.pgm │ │ ├── 322.pgm │ │ ├── 323.pgm │ │ ├── 324.pgm │ │ ├── 325.pgm │ │ ├── 326.pgm │ │ ├── 327.pgm │ │ ├── 328.pgm │ │ ├── 329.pgm │ │ ├── 33.pgm │ │ ├── 330.pgm │ │ ├── 331.pgm │ │ ├── 332.pgm │ │ ├── 333.pgm │ │ ├── 334.pgm │ │ ├── 335.pgm │ │ ├── 336.pgm │ │ ├── 337.pgm │ │ ├── 338.pgm │ │ ├── 339.pgm │ │ ├── 34.pgm │ │ ├── 340.pgm │ │ ├── 341.pgm │ │ ├── 342.pgm │ │ ├── 343.pgm │ │ ├── 344.pgm │ │ ├── 345.pgm │ │ ├── 346.pgm │ │ ├── 347.pgm │ │ ├── 348.pgm │ │ ├── 349.pgm │ │ ├── 35.pgm │ │ ├── 350.pgm │ │ ├── 351.pgm │ │ ├── 352.pgm │ │ ├── 353.pgm │ │ ├── 354.pgm │ │ ├── 355.pgm │ │ ├── 356.pgm │ │ ├── 357.pgm │ │ ├── 358.pgm │ │ ├── 359.pgm │ │ ├── 36.pgm │ │ ├── 360.pgm │ │ ├── 361.pgm │ │ ├── 362.pgm │ │ ├── 363.pgm │ │ ├── 364.pgm │ │ ├── 365.pgm │ │ ├── 366.pgm │ │ ├── 367.pgm │ │ ├── 368.pgm │ │ ├── 369.pgm │ │ ├── 37.pgm │ │ ├── 370.pgm │ │ ├── 371.pgm │ │ ├── 372.pgm │ │ ├── 373.pgm │ │ ├── 374.pgm │ │ ├── 375.pgm │ │ ├── 376.pgm │ │ ├── 377.pgm │ │ ├── 378.pgm │ │ ├── 379.pgm │ │ ├── 38.pgm │ │ ├── 380.pgm │ │ ├── 381.pgm │ │ ├── 382.pgm │ │ ├── 383.pgm │ │ ├── 384.pgm │ │ ├── 385.pgm │ │ ├── 386.pgm │ │ ├── 387.pgm │ │ ├── 388.pgm │ │ ├── 389.pgm │ │ ├── 39.pgm │ │ ├── 390.pgm │ │ ├── 391.pgm │ │ ├── 392.pgm │ │ ├── 393.pgm │ │ ├── 394.pgm │ │ ├── 395.pgm │ │ ├── 396.pgm │ │ ├── 397.pgm │ │ ├── 398.pgm │ │ ├── 399.pgm │ │ ├── 40.pgm │ │ ├── 400.pgm │ │ ├── 401.pgm │ │ ├── 402.pgm │ │ ├── 403.pgm │ │ ├── 404.pgm │ │ ├── 405.pgm │ │ ├── 406.pgm │ │ ├── 407.pgm │ │ ├── 408.pgm │ │ ├── 409.pgm │ │ ├── 41.pgm │ │ ├── 42.pgm │ │ ├── 43.pgm │ │ ├── 44.pgm │ │ ├── 45.pgm │ │ ├── 46.pgm │ │ ├── 47.pgm │ │ ├── 48.pgm │ │ ├── 49.pgm │ │ ├── 50.pgm │ │ ├── 51.pgm │ │ ├── 52.pgm │ │ ├── 53.pgm │ │ ├── 54.pgm │ │ ├── 55.pgm │ │ ├── 56.pgm │ │ ├── 57.pgm │ │ ├── 58.pgm │ │ ├── 59.pgm │ │ ├── 60.pgm │ │ ├── 61.pgm │ │ ├── 62.pgm │ │ ├── 63.pgm │ │ ├── 64.pgm │ │ ├── 65.pgm │ │ ├── 66.pgm │ │ ├── 67.pgm │ │ ├── 68.pgm │ │ ├── 69.pgm │ │ ├── 70.pgm │ │ ├── 71.pgm │ │ ├── 72.pgm │ │ ├── 73.pgm │ │ ├── 74.pgm │ │ ├── 75.pgm │ │ ├── 76.pgm │ │ ├── 77.pgm │ │ ├── 78.pgm │ │ ├── 79.pgm │ │ ├── 80.pgm │ │ ├── 81.pgm │ │ ├── 82.pgm │ │ ├── 83.pgm │ │ ├── 84.pgm │ │ ├── 85.pgm │ │ ├── 86.pgm │ │ ├── 87.pgm │ │ ├── 88.pgm │ │ ├── 89.pgm │ │ ├── 90.pgm │ │ ├── 91.pgm │ │ ├── 92.pgm │ │ ├── 93.pgm │ │ ├── 94.pgm │ │ ├── 95.pgm │ │ ├── 96.pgm │ │ ├── 97.pgm │ │ ├── 98.pgm │ │ └── 99.pgm │ ├── resault.png │ └── yale_faces │ │ ├── 0Yale人脸库（美国）.docx │ │ ├── s1.bmp │ │ ├── s10.bmp │ │ ├── s100.bmp │ │ ├── s101.bmp │ │ ├── s102.bmp │ │ ├── s103.bmp │ │ ├── s104.bmp │ │ ├── s105.bmp │ │ ├── s106.bmp │ │ ├── s107.bmp │ │ ├── s108.bmp │ │ ├── s109.bmp │ │ ├── s11.bmp │ │ ├── s110.bmp │ │ ├── s111.bmp │ │ ├── s112.bmp │ │ ├── s113.bmp │ │ ├── s114.bmp │ │ ├── s115.bmp │ │ ├── s116.bmp │ │ ├── s117.bmp │ │ ├── s118.bmp │ │ ├── s119.bmp │ │ ├── s12.bmp │ │ ├── s120.bmp │ │ ├── s121.bmp │ │ ├── s122.bmp │ │ ├── s123.bmp │ │ ├── s124.bmp │ │ ├── s125.bmp │ │ ├── s126.bmp │ │ ├── s127.bmp │ │ ├── s128.bmp │ │ ├── s129.bmp │ │ ├── s13.bmp │ │ ├── s130.bmp │ │ ├── s131.bmp │ │ ├── s132.bmp │ │ ├── s133.bmp │ │ ├── s134.bmp │ │ ├── s135.bmp │ │ ├── s136.bmp │ │ ├── s137.bmp │ │ ├── s138.bmp │ │ ├── s139.bmp │ │ ├── s14.bmp │ │ ├── s140.bmp │ │ ├── s141.bmp │ │ ├── s142.bmp │ │ ├── s143.bmp │ │ ├── s144.bmp │ │ ├── s145.bmp │ │ ├── s146.bmp │ │ ├── s147.bmp │ │ ├── s148.bmp │ │ ├── s149.bmp │ │ ├── s15.bmp │ │ ├── s150.bmp │ │ ├── s151.bmp │ │ ├── s152.bmp │ │ ├── s153.bmp │ │ ├── s154.bmp │ │ ├── s155.bmp │ │ ├── s156.bmp │ │ ├── s157.bmp │ │ ├── s158.bmp │ │ ├── s159.bmp │ │ ├── s16.bmp │ │ ├── s160.bmp │ │ ├── s161.bmp │ │ ├── s162.bmp │ │ ├── s163.bmp │ │ ├── s164.bmp │ │ ├── s165.bmp │ │ ├── s17.bmp │ │ ├── s18.bmp │ │ ├── s19.bmp │ │ ├── s2.bmp │ │ ├── s20.bmp │ │ ├── s21.bmp │ │ ├── s22.bmp │ │ ├── s23.bmp │ │ ├── s24.bmp │ │ ├── s25.bmp │ │ ├── s26.bmp │ │ ├── s27.bmp │ │ ├── s28.bmp │ │ ├── s29.bmp │ │ ├── s3.bmp │ │ ├── s30.bmp │ │ ├── s31.bmp │ │ ├── s32.bmp │ │ ├── s33.bmp │ │ ├── s34.bmp │ │ ├── s35.bmp │ │ ├── s36.bmp │ │ ├── s37.bmp │ │ ├── s38.bmp │ │ ├── s39.bmp │ │ ├── s4.bmp │ │ ├── s40.bmp │ │ ├── s41.bmp │ │ ├── s42.bmp │ │ ├── s43.bmp │ │ ├── s44.bmp │ │ ├── s45.bmp │ │ ├── s46.bmp │ │ ├── s47.bmp │ │ ├── s48.bmp │ │ ├── s49.bmp │ │ ├── s5.bmp │ │ ├── s50.bmp │ │ ├── s51.bmp │ │ ├── s52.bmp │ │ ├── s53.bmp │ │ ├── s54.bmp │ │ ├── s55.bmp │ │ ├── s56.bmp │ │ ├── s57.bmp │ │ ├── s58.bmp │ │ ├── s59.bmp │ │ ├── s6.bmp │ │ ├── s60.bmp │ │ ├── s61.bmp │ │ ├── s62.bmp │ │ ├── s63.bmp │ │ ├── s64.bmp │ │ ├── s65.bmp │ │ ├── s66.bmp │ │ ├── s67.bmp │ │ ├── s68.bmp │ │ ├── s69.bmp │ │ ├── s7.bmp │ │ ├── s70.bmp │ │ ├── s71.bmp │ │ ├── s72.bmp │ │ ├── s73.bmp │ │ ├── s74.bmp │ │ ├── s75.bmp │ │ ├── s76.bmp │ │ ├── s77.bmp │ │ ├── s78.bmp │ │ ├── s79.bmp │ │ ├── s8.bmp │ │ ├── s80.bmp │ │ ├── s81.bmp │ │ ├── s82.bmp │ │ ├── s83.bmp │ │ ├── s84.bmp │ │ ├── s85.bmp │ │ ├── s86.bmp │ │ ├── s87.bmp │ │ ├── s88.bmp │ │ ├── s89.bmp │ │ ├── s9.bmp │ │ ├── s90.bmp │ │ ├── s91.bmp │ │ ├── s92.bmp │ │ ├── s93.bmp │ │ ├── s94.bmp │ │ ├── s95.bmp │ │ ├── s96.bmp │ │ ├── s97.bmp │ │ ├── s98.bmp │ │ └── s99.bmp ├── k_means.png ├── linear_model.png ├── pca.png └── support vector machine.png └── pima-indians-diabetes dataset /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LiangjunFeng/Machine-Learning/06a2e22db93434d27c4b94b2eb0d850a2b2a449c/.DS_Store -------------------------------------------------------------------------------- /1.Linear_model.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*-coding: utf-8-*- 3 | # Author : LiangjunFeng 4 | # Blog : http://my.csdn.net/Liangjun_Feng 5 | # GitHub : https://www.github.com/LiangjunFeng 6 | # File : linear model.py 7 | # Date : 2017/08/31 1:57 8 | # Version: 0.1 9 | # Description: code of Three different linear models 10 | 11 | 12 | ''' 13 | 1.using least square method figure out linear regression 14 | ''' 15 | import numpy as np 16 | import matplotlib.pyplot as plt 17 | 18 | def linear_regression(x,y): 19 | def preprocess(x): 20 | X = np.mat(x) 21 | b = np.mat([1]*len(x)) 22 | X = np.hstack((X,b.T)) 23 | return X 24 | 25 | def cal_w(x,y): 26 | X = preprocess(x) 27 | Y = np.mat(y).T 28 | return (X.T*X).I*X.T*Y 29 | 30 | return preprocess(x)*cal_w(x,y),cal_w(x,y).tolist() 31 | 32 | 33 | #visiable and output test 34 | x = [[0.697,0.460],[0.774,0.376],[0.634,0.264],[0.608,0.318],[0.556,0.215],[0.403,0.237],[0.481,0.149], 35 | [0.437,0.211],[0.666,0.091],[0.243,0.267],[0.245,0.057],[0.343,0.099],[0.639,0.161],[0.657,0.198], 36 | [0.360,0.370],[0.593,0.042],[0.719,0.103]] 37 | y = [1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0] 38 | yy,w = linear_regression(x,y) 39 | i,x1,x2 = 0,[],[] 40 | while i < len(x): 41 | x1.append(x[i][0]) 42 | x2.append(x[i][1]) 43 | i += 1 44 | 45 | i = 0 46 | plt.figure(1) 47 | plt.subplot(121) 48 | plt.title('linear regression') 49 | while i < len(x1): 50 | if y[i] == 0: 51 | plt.scatter(x1[i],x2[i],color = 'r') 52 | elif y[i] == 1: 53 | plt.scatter(x1[i],x2[i],color = 'g') 54 | i += 1 55 | a = -(w[2][0]/w[1][0]) 56 | b = -(w[0][0]+w[2][0])/w[1][0] 57 | plt.plot([0,1],[a,b]) 58 | 59 | 60 | 61 | 62 | ''' 63 | 2.Newton method figure out logistic regression 64 | ''' 65 | def logistic_regression(x,y,error,n): 66 | def preprocess(x,y): 67 | X = np.mat(x) 68 | b = np.mat([1]*len(x)) 69 | X = np.hstack((X,b.T)) 70 | w = [1]*(len(x[0])+1) 71 | W = np.mat(w).T 72 | Y = y 73 | return X,W,Y 74 | 75 | def func_p(X,W): 76 | a = (X*W).tolist() 77 | b = float(a[0][0]) 78 | temp = np.exp(b)/(1+np.exp(b)) 79 | return temp 80 | 81 | def dfunc(X,Y,W): 82 | i,num,sum1 = 0,len(X),0 83 | while i < num: 84 | temp = Y[i] - func_p(X[i],W) 85 | sum1 += X[i]*temp 86 | i += 1 87 | return sum1*(-1) 88 | 89 | def d2func(X,Y,W): 90 | i,num,sum1 = 0,len(X),0 91 | while i < num: 92 | temp = func_p(X[i],W)*(1 - func_p(X[i],W)) 93 | sum1 += X[i]*(X[i].T)*temp 94 | i += 1 95 | sum1 = sum1.tolist() 96 | return float(sum1[0][0]) 97 | 98 | def Newton(x,y,error,n): #Newton method 99 | X,W,Y = preprocess(x,y) 100 | i = 1 101 | while i < n: 102 | d1 = dfunc(X,Y,W) 103 | a = (d1*d1.T).tolist() 104 | a = float(a[0][0]) 105 | if a < error: 106 | return W 107 | break 108 | temp = dfunc(X,Y,W) 109 | W = W - temp.T*(d2func(X,Y,W)**(-1)) 110 | i += 1 111 | if i == n: 112 | return 'error' 113 | 114 | w = Newton(x,y,error,n) 115 | X,W,Y = preprocess(x,y) 116 | yy = (X*w).tolist() 117 | w = w.tolist() 118 | return w,yy 119 | 120 | 121 | #visiable and output test 122 | w,yy = logistic_regression(x,y,0.0001,1000) 123 | 124 | i,x1,x2,z = 0,[],[],[] 125 | while i < len(x): 126 | x1.append(x[i][0]) 127 | x2.append(x[i][1]) 128 | z.append(yy[i][0]) 129 | i += 1 130 | 131 | i = 0 132 | 133 | plt.subplot(122) 134 | plt.title('logistic regression') 135 | while i < len(x1): 136 | if y[i] == 0: 137 | plt.scatter(x1[i],x2[i],color = 'r') 138 | elif y[i] == 1: 139 | plt.scatter(x1[i],x2[i],color = 'g') 140 | i += 1 141 | a = -(w[2][0]/w[1][0]) 142 | b = -(w[0][0]+w[2][0])/w[1][0] 143 | plt.plot([0,1],[a,b]) 144 | 145 | 146 | 147 | ''' 148 | 3.Linear Discriminant Analysis for binary classification problem 149 | ''' 150 | def LDA(x,y): 151 | def preprocess(x,y): 152 | i = 0 153 | X0,X1 = [],[] 154 | while i < len(y): 155 | if y[i] == 0: 156 | X0.append(x[i]) 157 | elif y[i] == 1: 158 | X1.append(x[i]) 159 | i += 1 160 | return X0,X1 161 | 162 | def average(X): 163 | X = np.mat(X) 164 | i = 1 165 | while i < len(X): 166 | X[0] = X[0] + X[i] 167 | i += 1 168 | res = X[0]/i 169 | return res 170 | 171 | 172 | def Sw(X0,X1,u0,u1): # calculate the within-class scatter matrix 173 | X_0 = np.mat(X0) 174 | X_1 = np.mat(X1) 175 | Sw0,i = 0,0 176 | temp0 = (X_0 - u0).T*((X_0 - u0)) 177 | temp1 = (X_1 - u1).T*((X_1 - u1)) 178 | return temp0 + temp1 179 | 180 | X0,X1 = preprocess(x,y) 181 | u0,u1 = average(X0),average(X1) 182 | 183 | SW = Sw(X0,X1,u0,u1) 184 | return (SW**(-1)*numpy.mat(u0-u1).T).tolist() 185 | 186 | #visiable and output test 187 | W = LDA(x,y) 188 | 189 | i,x1,x2,z = 0,[],[],[] 190 | while i < len(x): 191 | x1.append(x[i][0]) 192 | x2.append(x[i][1]) 193 | i += 1 194 | i = 0 195 | plt.figure(2) 196 | plt.subplot(121) 197 | plt.title('LDA') 198 | while i < len(x1): 199 | if y[i] == 0: 200 | plt.scatter(x1[i],x2[i],color = 'r') 201 | elif y[i] == 1: 202 | plt.scatter(x1[i],x2[i],color = 'g') 203 | i += 1 204 | print(W) 205 | 206 | plt.plot([0,-W[0][0]],[0,-W[1][0]]) 207 | 208 | 209 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 220 | 221 | -------------------------------------------------------------------------------- /3.BP_neural_network.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*-coding: utf-8-*- 3 | # Author : LiangjunFeng 4 | # Blog : http://my.csdn.net/Liangjun_Feng 5 | # GitHub : https://www.github.com/LiangjunFeng 6 | # File : BP_neural_network.py 7 | # Date : 2017/09/17 11:12 8 | # Version: 0.1 9 | # Description: BP neural network 10 | 11 | import random 12 | import math 13 | import numpy 14 | 15 | random.seed(0) 16 | def rand(low,high): #random function 17 | return (high-low)*random.random()+low 18 | 19 | def sigmoid(x): #sigmoid function 20 | return 1.0/(1+math.exp(-x)) 21 | 22 | def vectorSigmoid(vect): #using sigmoid function processes all the elements in the vector 23 | length = numpy.shape(vect)[1] 24 | for i in range(length): 25 | vect[0,i] = sigmoid(vect[0,i]) 26 | return vect 27 | 28 | def makeMatrix(length,bordth,fill = 0.0): #creat a matrix 29 | mat = [] 30 | for i in range(length): 31 | mat.append([fill]*bordth) 32 | return numpy.mat(mat) 33 | 34 | def randfillMatrix(mat,low,high): #filling the matrix with random number 35 | length = numpy.shape(mat)[0] 36 | bordth = numpy.shape(mat)[1] 37 | for i in range(length): 38 | for j in range(bordth): 39 | mat[i,j] = rand(low,high) 40 | return mat 41 | 42 | def vectorMultiply(vect1,vect2): #vector x vector to get a matrix 43 | length = numpy.shape(vect1)[1] 44 | bordth = numpy.shape(vect2)[1] 45 | mat = makeMatrix(length,bordth) 46 | for i in range(length): 47 | for j in range(bordth): 48 | mat[i,j] = vect1[0,i]*vect2[0,j] 49 | return mat 50 | 51 | class BPNeuralNetwork: 52 | def __init__(self,inputNodes,hiddenNodes,outputNodes): #init the variables 53 | self._inputNodes = inputNodes #inputlayer's nodes number 54 | self._hiddenNodes = hiddenNodes #hiddenlayer's nodes number 55 | self._outputNodes = outputNodes #outputlayer's nodes number 56 | self._inputLayer = [] #inputlayer's data 57 | self._hiddenLayer = [] #hiddenlayer's data 58 | self._outputLayer = [] #outputlayer's data 59 | self._inputWeights = [] #the weights from inputlayer to hiddenlayer 60 | self._outputWeights = [] #the weighyts from hiddenlayer to outputlayer 61 | self._hiddenThreshold = [] #the hiddenlayer's Threshold 62 | self._outputThreshold = [] #the outputlayer's Threshold 63 | 64 | def setup(self): 65 | self._inputLayer = makeMatrix(1,self._inputNodes,1) 66 | self._hiddenLayer = makeMatrix(1,self._hiddenNodes,1) 67 | self._outputLayer = makeMatrix(1,self._outputNodes,1) #give inital data to three layers's structure 68 | 69 | self._hiddenThreshold = makeMatrix(1,self._hiddenNodes,1) 70 | self._outputThreshold = makeMatrix(1,self._outputNodes,1) #give inital data to two layer 71 | 72 | self._inputWeights = makeMatrix(self._inputNodes,self._hiddenNodes) 73 | self._outputWeights = makeMatrix(self._hiddenNodes,self._outputNodes) #creat weights' matrix 74 | 75 | self._inputWeights = randfillMatrix(self._inputWeights,-1,1) 76 | self._outputWeights = randfillMatrix(self._outputWeights,-1,1) #give inital random data to weights' matrix 77 | 78 | def getInput(self,_input): #load an example to inputlayer 79 | for i in range(self._inputNodes): 80 | self._inputLayer[0,i] = _input[i] 81 | 82 | def predict(self,_input): #predict the outputlayer according to the input data 83 | self.getInput(_input) 84 | self._hiddenLayer = vectorSigmoid(self._inputLayer*self._inputWeights - self._hiddenThreshold) 85 | self._outputLayer = vectorSigmoid(self._hiddenLayer*self._outputWeights - self._outputThreshold) 86 | 87 | def backPropagation(self,y,lRate): #using backPropagation to reset the weights and threshold 88 | G = numpy.multiply(numpy.multiply(self._outputLayer,1-self._outputLayer),y-self._outputLayer) 89 | self._outputWeights = self._outputWeights + lRate*vectorMultiply(self._hiddenLayer,G) 90 | self._outputThreshold = self._outputThreshold - lRate*G 91 | E = numpy.multiply(numpy.multiply(self._hiddenLayer,1-self._hiddenLayer),(self._outputWeights*G.T).T) 92 | self._inputWeights = self._inputWeights + lRate*vectorMultiply(self._inputLayer,E) 93 | self._hiddenThreshold = self._hiddenThreshold - lRate*E 94 | return float((y-self._outputLayer)*(y-self._outputLayer).T) 95 | 96 | def fit(self,input_,y_,limit = 10000,accuracy = 0.001,lRate = 0.2): #using data fit the model 97 | self.setup() 98 | num = len(input_) 99 | error,times = num,1 100 | while((error > accuracy) and (times < limit)): 101 | i,error = 0,0 102 | while(i < num): 103 | _input = input_[i] 104 | y = y_[i] 105 | self.predict(_input) 106 | error += self.backPropagation(y,lRate) 107 | i += 1 108 | times += 1 109 | 110 | if __name__ == '__main__': #taking exclusive_OR as an example 111 | nn = BPNeuralNetwork(2,7,1) 112 | cases = [[0,0],[0,1],[1,0],[1,1]] 113 | labels = [[0],[1],[1],[0]] 114 | nn.fit(cases,labels) 115 | for case in cases: 116 | nn.predict(case) 117 | print(case,' ',nn._outputLayer) 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | -------------------------------------------------------------------------------- /4.Support_vector_machine.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*-coding: utf-8-*- 3 | # Author : LiangjunFeng 4 | # Blog : http://my.csdn.net/Liangjun_Feng 5 | # GitHub : https://www.github.com/LiangjunFeng 6 | # File : support_vector_machine.py 7 | # Date : 2017/09/21 11:12 8 | # Version: 0.1 9 | # Description: support_vector_machine 10 | 11 | import numpy 12 | import cvxopt 13 | import matplotlib.pyplot as plt 14 | 15 | def linearKernel(x1,x2): #the linear kernel function 16 | return numpy.dot(x1,x2) 17 | 18 | def gaussianKernel(x1,x2,sigma = 3): #the guassian kernel function 19 | return numpy.exp(-numpy.linalg.norm(x1-x2)**2/(2*(sigma ** 2))) 20 | 21 | def makeKernelMatrix(input_,kernel,par = 3): #set up kernel matrix according to the kernel 22 | num = input_.shape[0] 23 | K = numpy.zeros((num,num)) 24 | for i in range(num): 25 | for j in range(num): 26 | if kernel == 'linearKernel': 27 | K[i,j] = linearKernel(input_[i],input_[j]) 28 | else: 29 | K[i,j] = gaussianKernel(input_[i],input_[j],par) 30 | return K 31 | 32 | def calculateA(input_,label,C,K): #using cvxopt bag figure out the convex quadratic programming 33 | num = input_.shape[0] 34 | P = cvxopt.matrix(numpy.outer(label,label)*K) 35 | q = cvxopt.matrix(numpy.ones(num)*-1) 36 | A = cvxopt.matrix(label,(1,num)) 37 | b = cvxopt.matrix(0.0) 38 | 39 | if C is None: 40 | G = cvxopt.matrix(numpy.diag(numpy.ones(num)*-1)) 41 | h = cvxopt.matrix(numpy.zeros(num)) 42 | else: 43 | temp1 = numpy.diag(numpy.ones(num)*-1) 44 | temp2 = bp.identity(num) 45 | G = cvxopt.matrix(numpy.vstack(temp1,temp2)) 46 | temp1 = numpy.zeros(num) 47 | temp2 = numpy.ones(num)*self.C 48 | h = cvxopt.matrix(numpy.hstack(temp1,temp2)) #P\q\A\b\G\h are parameters of cvxopt.solvers.qp() function 49 | 50 | solution = cvxopt.solvers.qp(P,q,G,h,A,b) #figure out the model 51 | 52 | a = numpy.ravel(solution['x']) #transfer the 'a' into a vector 53 | return a 54 | 55 | def calculateB(a,supportVectorLabel,supportVector,K,indexis): #calculate the parameter 'b' 56 | b = 0 57 | for i in range(len(a)): 58 | b += supportVectorLabel[i] 59 | b -= numpy.sum(a*supportVectorLabel*K[indexis[i],supportVector]) 60 | b /= len(a) #set the 'b' with the mean value 61 | return b 62 | 63 | def calculateWeight(kernel,features,a,supportVector,supportVectorLabel): #calculate the model's weights according to a 64 | if kernel == linearKernel: 65 | w = numpy.zeros(features) 66 | for i in range(len(a)): 67 | w += a[i]*supportVectorLabel[i]*supportVector[i] #linear kernel calculate as the linear model 68 | else: 69 | w = None #nonlinear nodel need not calculate ,we will process it later 70 | return w 71 | 72 | class SVM: #SVM class 73 | def __init__(self,kernel = linearKernel,C = None): #init 74 | self.kernel = kernel #the kernel's algrithm 75 | self.C = C #the soft margin's parameter 76 | self.a = None #the process parameter 77 | self.b = 0 #the model's risdual 78 | self.w = [] #the model's weights 79 | self.supportVector = [] #the support vector 80 | self.supportVectorLabel = [] #the support's label 81 | if self.C is not None: 82 | self.C = float(self.C) 83 | 84 | def fit(self,input_,label): #fit data 85 | samples,features = input_.shape 86 | 87 | K = makeKernelMatrix(input_,self.kernel) #calculate the kernel matrix 88 | a = calculateA(input_,label,self.C,K) #calculate the process parameter 'a' 89 | supportVector = a > 1e-5 #if a < 1e-5,then think a is 1 90 | indexis = numpy.arange(len(a))[supportVector] #the support vectors' indexis 91 | self.a = a[supportVector] #the support vectors' a 92 | self.supportVector = input_[supportVector] #the support vectors 93 | self.supportVectorLabel = label[supportVector] #the support vectorss' label 94 | 95 | print(len(self.a),' support vectors out of ',samples,' points') 96 | 97 | self.b = calculateB(self.a,self.supportVectorLabel,supportVector,K,indexis) #calculate the model's risdual 98 | self.w = calculateWeight(self.kernel,features,self.a,self.supportVector,self.supportVectorLabel) #calculate the model's weights 99 | 100 | def predict(self,input_): #predict function 101 | if self.w is not None: 102 | return numpy.dot(inpt_,self.w) + self.b 103 | else: 104 | predictLabel = numpy.zeros(len(input_)) 105 | for i in range(len(input_)): 106 | s = 0 107 | for a,sv_y,sv in zip(self.a,self.supportVectorLabel,self.supportVector): 108 | s += a * sv_y * self.kernel(input_[i],sv) 109 | predictLabel[i] = s 110 | return numpy.sign(predictLabel+self.b) 111 | 112 | def gen_non_lin_separable_data(): #creat the data 113 | mean1 = [-1, 2] 114 | mean2 = [1, -1] 115 | mean3 = [4, -4] 116 | mean4 = [-4, 4] 117 | cov = [[1.0,0.8], [0.8, 1.0]] 118 | X1 = np.random.multivariate_normal(mean1, cov, 60) 119 | X1 = np.vstack((X1, np.random.multivariate_normal(mean3, cov, 60))) 120 | y1 = np.ones(len(X1)) 121 | X2 = np.random.multivariate_normal(mean2, cov, 60) 122 | X2 = np.vstack((X2, np.random.multivariate_normal(mean4, cov, 60))) 123 | y2 = np.ones(len(X2)) * -1 124 | return X1, y1, X2, y2 125 | 126 | def split_train(X1, y1, X2, y2): #split the train data from the original data 127 | X1_train = X1[:90] 128 | y1_train = y1[:90] 129 | X2_train = X2[:90] 130 | y2_train = y2[:90] 131 | X_train = np.vstack((X1_train, X2_train)) 132 | y_train = np.hstack((y1_train, y2_train)) 133 | return X_train, y_train 134 | 135 | def split_test(X1, y1, X2, y2): #split the test data from the original data 136 | X1_test = X1[90:] 137 | y1_test = y1[90:] 138 | X2_test = X2[90:] 139 | y2_test = y2[90:] 140 | X_test = np.vstack((X1_test, X2_test)) 141 | y_test = np.hstack((y1_test, y2_test)) 142 | return X_test, y_test 143 | 144 | def test_non_linear(): #test 145 | X1, y1, X2, y2 = gen_non_lin_separable_data() 146 | X_train, y_train = split_train(X1, y1, X2, y2) 147 | print(y_train,'$$$') 148 | X_test, y_test = split_test(X1, y1, X2, y2) 149 | clf = SVM(gaussianKernel) 150 | 151 | X_train = numpy.array(X_train) 152 | y_train = numpy.array(y_train) 153 | print(y_train,'$$$') 154 | clf.fit(X_train, y_train) 155 | y_predict = clf.predict(X_test) 156 | plt.title('scatter diagram') 157 | for i in range(len(X_test)): 158 | if y_predict[i] == 1: 159 | plt.plot(X_test[i,0],X_test[i,1],'ro') 160 | else: 161 | plt.plot(X_test[i,0],X_test[i,1],'go') 162 | plt.show() 163 | 164 | 165 | 166 | if __name__ == "__main__": 167 | test_non_linear() 168 | 169 | -------------------------------------------------------------------------------- /7.K-means.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*-coding: utf-8-*- 3 | # Author : LiangjunFeng 4 | # Blog : http://my.csdn.net/Liangjun_Feng 5 | # GitHub : https://www.github.com/LiangjunFeng 6 | # File : K-means.py 7 | # Date : 2017/09/29 3:25 8 | # Version: 0.1 9 | # Description: K-means algorithm ,clustering 10 | 11 | import numpy 12 | import random 13 | import matplotlib.pyplot as plt 14 | 15 | def creatData(): 16 | data1 = numpy.mat(numpy.random.randint(0,12,size=(2,50))) 17 | data2 = numpy.mat(numpy.random.randint(8,20,size=(2,50))) #creat two different kinds of data 18 | data = numpy.hstack((data1,data2)).T #combine the matrix 19 | 20 | label1 = numpy.mat(numpy.zeros(50)).T 21 | label2 = numpy.mat(numpy.ones(50)).T #create the label 22 | label = numpy.vstack((label1,label2)) #combine the matrix 23 | return data,label 24 | 25 | def minkowskiDistance(vector1,vector2,p=2): #calculate the distance between two samples 26 | vector1 = numpy.ravel(vector1) 27 | vector2 = numpy.ravel(vector2) 28 | length,distance = len(vector1),0 29 | for i in range(length): 30 | distance += abs(vector1[i] - vector2[i])**p 31 | return distance**(1.0/p) 32 | 33 | def randomInt(minimum,maximum): #use to choose inital random averageVector 34 | return random.randrange(minimum,maximum,1) 35 | 36 | class K_means: #K-means class 37 | def __init__(self): 38 | self._classes = 0 #record the number of the class that want to cluster from the data 39 | self._averageVectorList = [] #the average Vector list 40 | self._classSetList = [] #the list of different kinds data 41 | 42 | def setUp(self,data,number): #setup the K-means class 43 | self._classes = number 44 | self._averageVectorList = [0]*self._classes 45 | for i in range(self._classes): 46 | self._classSetList.append([]) 47 | 48 | def randomVector(self,data): #init the beginning average Vector 49 | for i in range(self._classes): 50 | self._averageVectorList[i] = data[randomInt(0,data.shape[0]),:] 51 | 52 | def classifiy(self,data): #compare distance to classifiy the samples 53 | samples,attributes = data.shape 54 | for i in range(samples): 55 | k = 0 56 | for j in range(self._classes): 57 | if minkowskiDistance(data[i,:],self._averageVectorList[k]) > minkowskiDistance(data[i,:],self._averageVectorList[j]): 58 | k = j 59 | self._classSetList[k].append(i) 60 | 61 | def renewAverageVector(self,data): #renew the average vector 62 | for i in range(self._classes): 63 | sum = 0 64 | for j in range(len(self._classSetList[i])): 65 | sum += data[self._classSetList[i][j],:] 66 | self._averageVectorList[i] = numpy.ravel(sum/len(self._classSetList[i])) 67 | 68 | def compare(self,vector): #campare the new average vector with the old average vector,if they are equal,return True 69 | if vector == []: 70 | return False 71 | for i in range(len(vector)): 72 | if not (self._averageVectorList[i] == vector[i]).all(): 73 | return False 74 | return True 75 | 76 | def clustering(self,data,number,limits = 100): 77 | self.setUp(data,number) 78 | self.randomVector(data) 79 | oldAverageVector,times = [],0 80 | while (not self.compare(oldAverageVector)) and (times < limits): #set the limit times 81 | oldAverageVector = self._averageVectorList.copy() 82 | self._classSetList.clear() 83 | for i in range(self._classes): 84 | self._classSetList.append([]) 85 | self.classifiy(data) 86 | self.renewAverageVector(data) 87 | times += 1 88 | 89 | 90 | if __name__ == '__main__': 91 | data,label = creatData() 92 | 93 | C = K_means() 94 | C.clustering(data,2) 95 | 96 | for i in range(len(C._classSetList[0])): #visualable 97 | p1=plt.scatter(data[C._classSetList[0][i],0],data[C._classSetList[0][i],1],marker='x',color='g') 98 | for i in range(len(C._classSetList[1])): 99 | p2=plt.scatter(data[C._classSetList[1][i],0],data[C._classSetList[1][i],1],marker='+',color='r') 100 | plt.show() 101 | 102 | 103 | -------------------------------------------------------------------------------- /8.PCA.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*-coding: utf-8-*- 3 | # Author : LiangjunFeng 4 | # Blog : http://my.csdn.net/Liangjun_Feng 5 | # GitHub : https://www.github.com/LiangjunFeng 6 | # File : pca.py 7 | # Date : 2017/010/1 6:25 8 | # Version: 0.1 9 | # Description: pca algorithm ,use k-means algorithm for clustering 10 | 11 | from pandas import read_csv 12 | import numpy 13 | import random 14 | import matplotlib.pyplot as plt 15 | 16 | def loadData(): # read the CSV data file 17 | dataSet = read_csv('/Users/zhuxiaoxiansheng/Desktop/data_set/Epileptic Seizure Recognition Data Set/data.csv',header = None) 18 | return dataSet 19 | 20 | def dataPrepreocess(dataSet): #preprocess the data,separating the attributes and label 21 | data = dataSet.iloc[:,0:178] 22 | label = dataSet[178] 23 | 24 | data = numpy.matrix(data.as_matrix(columns=None)) #convert to matrix 25 | data = data[0:5000,:] 26 | label = numpy.matrix(label.as_matrix(columns=None)) #convert to matrix 27 | label = label[:,0:5000] 28 | 29 | return data,label.T 30 | 31 | 32 | def zeroMean(data): #minus the mean value from the data 33 | meanValue = numpy.mean(data,axis = 0) 34 | meanData = data - meanValue 35 | return meanData,meanValue 36 | 37 | def percentage(eigenValues,per): #remain the 'percentage' of the data 38 | sortArray = numpy.sort(eigenValues)[-1::-1] 39 | arraySum = sum(sortArray) 40 | tempSum,num = 0,0 41 | for i in sortArray: 42 | tempSum += i 43 | num += 1 44 | if tempSum >= arraySum * per: 45 | return num 46 | 47 | def pca(data,per = 0.95): #the pca function 48 | data,mean = zeroMean(data) 49 | covariance = numpy.cov(data,rowvar = 0) 50 | eigenValues,eigenVectors = numpy.linalg.eig(numpy.mat(covariance)) calculate the eigenvalue 51 | num = percentage(eigenValues,per) 52 | eigenVlueIndice = numpy.argsort(eigenValues) 53 | sel_eigenValInd = eigenVlueIndice[-1:-(num+1):-1] 54 | sel_eigenVectors = eigenVectors[:,sel_eigenValInd] 55 | lowerData = data*sel_eigenVectors #the lower dimension data 56 | recondata = (lowerData*sel_eigenVectors.T)+mean 57 | return lowerData,recondata 58 | 59 | def minkowskiDistance(vector1,vector2,p=2): #calculate the distance between two samples 60 | vector1 = numpy.ravel(vector1) 61 | vector2 = numpy.ravel(vector2) 62 | length,distance = len(vector1),0 63 | for i in range(length): 64 | distance += abs(vector1[i] - vector2[i])**p 65 | return distance**(1.0/p) 66 | 67 | def randomInt(minimum,maximum): #use to choose inital random averageVector 68 | return random.randrange(minimum,maximum,1) 69 | 70 | class K_means: #K-means class 71 | def __init__(self): 72 | self._classes = 0 #record the number of the class that want to cluster from the data 73 | self._averageVectorList = [] #the average Vector list 74 | self._classSetList = [] #the list of different kinds data 75 | 76 | def setUp(self,data,number): #setup the K-means class 77 | self._classes = number 78 | self._averageVectorList = [0]*self._classes 79 | for i in range(self._classes): 80 | self._classSetList.append([]) 81 | 82 | def randomVector(self,data): #init the beginning average Vector 83 | for i in range(self._classes): 84 | self._averageVectorList[i] = data[randomInt(0,data.shape[0]),:] 85 | 86 | def classifiy(self,data): #compare distance to classifiy the samples 87 | samples,attributes = data.shape 88 | for i in range(samples): 89 | k = 0 90 | for j in range(self._classes): 91 | if minkowskiDistance(data[i,:],self._averageVectorList[k]) > minkowskiDistance(data[i,:],self._averageVectorList[j]): 92 | k = j 93 | self._classSetList[k].append(i) 94 | 95 | def renewAverageVector(self,data): #renew the average vector 96 | for i in range(self._classes): 97 | sum = 0 98 | for j in range(len(self._classSetList[i])): 99 | sum += data[self._classSetList[i][j],:] 100 | self._averageVectorList[i] = numpy.ravel(sum/len(self._classSetList[i])) 101 | 102 | def compare(self,vector): #campare the new average vector with the old average vector,if they are equal,return True 103 | if vector == []: 104 | return False 105 | for i in range(len(vector)): 106 | if not (self._averageVectorList[i] == vector[i]).all(): 107 | return False 108 | return True 109 | 110 | def clustering(self,data,number,limits = 100): 111 | self.setUp(data,number) 112 | self.randomVector(data) 113 | oldAverageVector,times = [],0 114 | while (not self.compare(oldAverageVector)) and (times < limits): #set the limit times 115 | oldAverageVector = self._averageVectorList.copy() 116 | self._classSetList.clear() 117 | for i in range(self._classes): 118 | self._classSetList.append([]) 119 | self.classifiy(data) 120 | self.renewAverageVector(data) 121 | times += 1 122 | 123 | def assess(self,label): #using for assess the result,but it's just for reference, not accuracy 124 | count1 = [0]*int(max(label)) 125 | count2 = [0]*self._classes 126 | for i in range(label.shape[0]): 127 | count1[int(label[i]-1)] += 1 128 | for i in range(label.shape[0]): 129 | for j in range(self._classes): 130 | count2[j] = len(self._classSetList[j]) 131 | count1.sort() 132 | count2.sort() 133 | return count1,count2 134 | 135 | if __name__ == '__main__': 136 | dataSet = loadData() 137 | data,label = dataPrepreocess(dataSet) 138 | lowerData,recondata = pca(data,0.95) 139 | 140 | C = K_means() 141 | C.clustering(lowerData,2) 142 | count1,count2 = C.assess(label) 143 | print('Truth: ', count1) 144 | print('Clustering: ', count2) 145 | 146 | for i in range(len(C._classSetList[0])): #visualization 147 | p1=plt.scatter(data[C._classSetList[0][i],1],data[C._classSetList[0][i],6],marker='x',color='g') 148 | for i in range(len(C._classSetList[1])): 149 | p2=plt.scatter(data[C._classSetList[1][i],1],data[C._classSetList[1][i],6],marker='*',color='r') 150 | plt.show() 151 | 152 | 153 | -------------------------------------------------------------------------------- /A10.SFA.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*-coding: utf-8-*- 3 | # Author : LiangjunFeng 4 | # Blog : http://my.csdn.net/Liangjun_Feng 5 | # GitHub : https://www.github.com/LiangjunFeng 6 | # File : SFA.py 7 | # Date : 2017/011/28 10:00 8 | # Version: 0.1 9 | # Description: Slowness Feature Analysis,Apply it to reduce pictures' dimension 10 | 11 | import skimage.io as io 12 | import numpy as np 13 | import math 14 | import random 15 | from sklearn.naive_bayes import GaussianNB 16 | from sklearn.decomposition import PCA 17 | from sklearn.svm import SVC 18 | from sklearn.linear_model import LogisticRegression 19 | from sklearn import preprocessing 20 | 21 | def LoadData(number): #Load the picture data 22 | if number == 1: 23 | path = '/Users/zhuxiaoxiansheng/Desktop/yale_faces/*.bmp' #the data's path 24 | num =11 25 | elif number == 2: 26 | path = '/Users/zhuxiaoxiansheng/Desktop/orl_faces_full/*.pgm' #the data's path 27 | num =10 28 | pictures = io.ImageCollection(path) 29 | data = [] 30 | for i in range(len(pictures)): 31 | data.append(np.ravel(pictures[i].reshape((1,pictures[i].shape[0]*pictures[i].shape[1])))) 32 | label = [] 33 | for i in range(len(data)): 34 | label.append(int(i/num)) 35 | return np.matrix(data),np.matrix(label).T 36 | 37 | def SplitData(data,label,number,propotion): #split data to train data set and test data set 38 | if number == 1: 39 | classes = 15 40 | elif number == 2: 41 | classes = 40 42 | samples = data.shape[0] 43 | perClass = int(samples/classes) 44 | selected = int(perClass*propotion) 45 | 46 | trainData,testData = [],[] 47 | trainLabel,testLabel = [],[] 48 | count1 = [] 49 | for i in range(classes): 50 | count2,k = [],math.inf 51 | for j in range(selected): 52 | count2.append(k) 53 | k = random.randint(0,perClass-1) 54 | while k in count2: 55 | k = random.randint(0,perClass-1) 56 | trainData.append(np.ravel(data[perClass*i+k])) 57 | trainLabel.append(np.ravel(label[perClass*i+k])) 58 | count1.append(11*i+k) 59 | for i in range(samples): 60 | if i not in count1: 61 | testData.append(np.ravel(data[i])) 62 | testLabel.append(np.ravel(label[i])) 63 | return np.matrix(trainData),np.matrix(trainLabel),np.matrix(testData),np.matrix(testLabel) 64 | 65 | class SFA: #slow feature analysis class 66 | def __init__(self): 67 | self._Z = [] 68 | self._B = [] 69 | self._eigenVector = [] 70 | 71 | def getB(self,data): 72 | self._B = np.matrix(data.T.dot(data))/(data.shape[0]-1) 73 | 74 | def getZ(self,data): 75 | derivativeData = self.makeDiff(data) 76 | self._Z = np.matrix(derivativeData.T.dot(derivativeData))/(derivativeData.shape[0]-1) 77 | 78 | def makeDiff(self,data): 79 | diffData = np.mat(np.zeros((data.shape[0],data.shape[1]))) 80 | for i in range(data.shape[1]-1): 81 | diffData[:,i] = data[:,i] - data[:,i+1] 82 | diffData[:,-1] = data[:,-1] - data[:,0] 83 | return np.mat(diffData) 84 | 85 | def fit_transform(self,data,threshold = 1e-7,conponents = -1): 86 | if conponents == -1: 87 | conponents = data.shape[0] 88 | self.getB(data) 89 | U,s,V = np.linalg.svd(self._B) 90 | 91 | count = len(s) 92 | for i in range(len(s)): 93 | if s[i]**(0.5) < threshold: 94 | count = i 95 | break 96 | s = s[0:count] 97 | s = s**0.5 98 | S = (np.mat(np.diag(s))).I 99 | U = U[:,0:count] 100 | whiten = S*U.T 101 | Z = (whiten*data.T).T 102 | 103 | self.getZ(Z) 104 | PT,O,P = np.linalg.svd(self._Z) 105 | 106 | self._eigenVector = P*whiten 107 | self._eigenVector = self._eigenVector[-1*conponents:,:] 108 | 109 | return data.dot(self._eigenVector.T) 110 | 111 | def transfer(self,data): 112 | return data.dot(self._eigenVector.T) 113 | 114 | def show_accuracy(predictLabel,Label): #show the accuracy of the classifier 115 | Label = np.ravel(Label).tolist() 116 | predictLabel = predictLabel.tolist() 117 | count = 0 118 | for i in range(len(Label)): 119 | if Label[i] == predictLabel[i]: 120 | count += 1 121 | print(count/len(Label)) 122 | 123 | 124 | def Faceidentifier( trainDataSimplified,trainLabel,testDataSimplified,testLabel): #three different kinds of classifers 125 | print("=====================================") 126 | print("GaussianNB") 127 | clf1 = GaussianNB() 128 | clf1.fit(trainDataSimplified,np.ravel(trainLabel)) 129 | predictTestLabel1 = clf1.predict(testDataSimplified) 130 | show_accuracy(predictTestLabel1,testLabel) 131 | print() 132 | 133 | print("SVC") 134 | clf3 = SVC(C=8.0) 135 | clf3.fit(trainDataSimplified,np.ravel(trainLabel)) 136 | predictTestLabel3 = clf3.predict(testDataSimplified) 137 | show_accuracy(predictTestLabel3,testLabel) 138 | print() 139 | 140 | print("LogisticRegression") 141 | clf4 = LogisticRegression() 142 | clf4.fit(trainDataSimplified,np.ravel(trainLabel)) 143 | predictTestLabel4 = clf4.predict(testDataSimplified) 144 | show_accuracy(predictTestLabel4,testLabel) 145 | print() 146 | print("=====================================") 147 | 148 | if __name__ == "__main__": 149 | 150 | data,label = LoadData(2) 151 | pca = PCA(30,True,True) 152 | datapca = pca.fit_transform(data) 153 | trainData,trainLabel,testData,testLabel = SplitData(datapca,label,2,0.6) 154 | 155 | trainData = preprocessing.scale(trainData.T).T 156 | testData = preprocessing.scale(testData.T).T 157 | 158 | sfa = SFA() 159 | trainDataS = sfa.fit_transform(trainData,conponents = 30) 160 | testDataS = sfa.transfer(testData) 161 | 162 | Faceidentifier(trainDataS,trainLabel,testDataS,testLabel) 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | -------------------------------------------------------------------------------- /Gradient Boost Decision Tree（GBDT）及其拓展模型.pptx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LiangjunFeng/Machine-Learning/06a2e22db93434d27c4b94b2eb0d850a2b2a449c/Gradient Boost Decision Tree（GBDT）及其拓展模型.pptx -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2017 Mr. Little Zhu 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Machine-Learning 2 | 3 | it's a package of some basic machine learning algorithms, 4 | 5 | I made it step by step,welcome to give me a hand 6 | 7 | there are more information about the code on my [blog](http://write.blog.csdn.net/postlist) 8 | 9 | # INTRODUCTION 10 | 11 | ## · 1.linear_model.py 12 | 13 | It contains three different kinds of linear models 14 | 15 | They are `Linear Regresiion`、`Logistic Regression` and `Linear Discriminant Analysis` 16 | 17 | There are more details in : [detail document about the algorithms](http://blog.csdn.net/liangjun_feng/article/details/77758179) 18 | 19 | the result of the py file: 20 | 21 | ![](https://github.com/LiangjunFeng/Machine-Learning/blob/master/pic/linear_model.png) 22 | 23 | ## · 2.decision_Tree.py 24 | 25 | It's a `Decision Tree` algorithm's file,and actually, I am not so satisfied with the code 26 | 27 | It's not easy to give the visuable result of the algorithm,if you could help me with the 28 | 29 | code and visualable ,it's thankful 30 | 31 | There are more details in : [detail document about the algorithms](http://blog.csdn.net/baimafujinji/article/details/51724371) 32 | 33 | ## · 3.BP_neural_network.py 34 | 35 | The code of the `BP_neural_network`.py is pretty good ,and I test it using the 'xor' 36 | 37 | There are more details in : [detail document about the algorithms](http://blog.csdn.net/liangjun_feng/article/details/78016019) 38 | 39 | ## · 4.support_vector_machine.py 40 | 41 | when i study the `Support Vector Machine` ，I think it a little hard to figure out the 42 | 43 | model. so I use a package of the python to help me complete the code, And It's shoud be right 44 | 45 | There are more details in : [detail document about the algorithms](http://blog.csdn.net/liangjun_feng/article/details/78046526) 46 | 47 | the result of the py file: 48 | 49 | ![](https://github.com/LiangjunFeng/Machine-Learning/blob/master/pic/support%20vector%20machine.png) 50 | 51 | ## · 5.naive_bayes_classifier.py 52 | 53 | I use a famous data set in the `naive bayes classifier` called 'pima-indians-diabetes dataset' 54 | 55 | and the accurancy could be 70% when the average is 65% , I give the link to the [set](https://github.com/LiangjunFeng/Machine-Learning/blob/master/pima-indians-diabetes%20dataset),you could try by yourself 56 | 57 | There are more details in : [detail document about the algorithms](http://blog.csdn.net/liangjun_feng/article/details/78057753) 58 | 59 | ## · 6.AdaBoost.py 60 | 61 | I use the same data set with the bayes algorithm in the `AdaBoost`，It shows a greater function than the bayes 62 | 63 | and the accuracy could be 82% 64 | 65 | There are more details in : [detail document about the algorithms](http://blog.csdn.net/liangjun_feng/article/details/78123583) 66 | 67 | ## · 7.K-means.py 68 | 69 | The famous clustering algorithm ,`K-means` , It's easy to understand and easy to implement 70 | 71 | I test the code by some dots creating by myself 72 | 73 | There are more details in : [detail document about the algorithms](http://blog.csdn.net/Liangjun_Feng/article/details/78154549) 74 | 75 | the result of the py file: 76 | 77 | ![](https://github.com/LiangjunFeng/Machine-Learning/blob/master/pic/k_means.png) 78 | 79 | ## · 8.pca.py 80 | 81 | I combine the `pca` and K-means inthis file ,but it's even more diificult to test the fuction 82 | 83 | than the decision tree , I make some resluts just for reference, if you have better way, please 84 | 85 | help me ,thank you. and the data set I use is [here](https://github.com/LiangjunFeng/Machine-Learning/blob/master/Epileptic%20Seizure%20Recognition%20Data%20Set.csv) 86 | 87 | There are more details in : [detail document about the algorithms](http://blog.csdn.net/watkinsong/article/details/38536463) 88 | 89 | the result of the py file: 90 | 91 | ![](https://github.com/LiangjunFeng/Machine-Learning/blob/master/pic/pca.png) 92 | 93 | ## 9.FastICA.py 94 | I use `FastICA` to split the sound mixed with three different voices([baby](https://github.com/LiangjunFeng/Machine-Learning/blob/master/pic/ICA/LDC2017S10.scaled.wav)/[women](https://github.com/LiangjunFeng/Machine-Learning/blob/master/pic/ICA/LDC2017S07.clean.wav)/[women](https://github.com/LiangjunFeng/Machine-Learning/blob/master/pic/ICA/LDC93S1.wav)), the two others are noise,firstly I make them the same size, and mix them with each other to create three new sounds,and then,using ICA to get the three original sound . It has a pretty good effect 95 | 96 | you could fun much nore details from here : [detail document about the algorithms](http://blog.csdn.net/liangjun_feng/article/details/78293613) 97 | 98 | the mixing sound,and FastICA resault: 99 | 100 | ![](https://github.com/LiangjunFeng/Machine-Learning/blob/master/pic/ICA/Res.png) 101 | 102 | ## A10.SFA.py 103 | SFA,slowness feature analysis,a very useful algorithm in feature engineering.I combine it with different classifiers to identify the humen face of different dataset,the [yale_faces](https://github.com/LiangjunFeng/Machine-Learning/tree/master/pic/SFA/yale_faces) and [orl_faces](https://github.com/LiangjunFeng/Machine-Learning/tree/master/pic/SFA/orl_faces_full)，you could choose the different dataset by filling '1' or '2' 104 | 105 | you could fun much nore details from here : [detail document about the algorithms](http://blog.csdn.net/sichuantzh/article/details/77411464?locationNum=2&fps=1) 106 | 107 | the yale's resault,and orl's resault: 108 | 109 | ![](https://github.com/LiangjunFeng/Machine-Learning/blob/master/pic/SFA/resault.png) 110 | 111 | 112 | 113 | # CONTRIBUTOR 114 | 115 | ----------------------------------------------------------------------- 116 | CANTACT CARD 117 | Author: LiangjunFeng 118 | Blog: http://my.csdn.net/Liangjun_Feng 119 | E-mail: zhumavip@163.com 120 | School: zhejiang University 121 | Begin from: 2017/8 122 | ----------------------------------------------------------------------- 123 | -------------------------------------------------------------------------------- /pic/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LiangjunFeng/Machine-Learning/06a2e22db93434d27c4b94b2eb0d850a2b2a449c/pic/.DS_Store -------------------------------------------------------------------------------- /pic/GBDT/.DS_Store: -------------------------------------------------------------------------------- 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