├── .vscode └── settings.json ├── Final └── zhuwy │ ├── CNN-thre-F1.png │ ├── CNN-thre-Precision.png │ ├── CNN-thre-Recall.png │ ├── CNN-thre-mse.png │ ├── PCA-k-F1.png │ ├── PCA-k-Precision.png │ ├── PCA-k-Recall.png │ ├── PCA-k-mse.png │ ├── PCA-thre-F1.png │ ├── PCA-thre-Precision.png │ ├── PCA-thre-Recall.png │ ├── PCA-thre-mse.png │ ├── README.md │ ├── closeset-cnn.py │ ├── cnn.png │ ├── cnn.py │ ├── crop.py │ ├── log.npy │ ├── openset-cnn.py │ ├── openset_pca_knn.py │ └── pca_knn.py ├── HW1 ├── zhuwy │ ├── README.md │ ├── binary-repair.bmp │ ├── chars │ │ ├── train0.bmp │ │ ├── train1.bmp │ │ ├── train2.bmp │ │ ├── train3.bmp │ │ ├── train4.bmp │ │ ├── train6.bmp │ │ ├── train8.bmp │ │ └── train9.bmp │ ├── denoise.bmp │ ├── denosie.py │ ├── down.png │ ├── feature.py │ ├── mask.bmp │ ├── myresize.py │ ├── remove-line.py │ ├── repair.bmp │ ├── test.py │ ├── test │ │ ├── 1.bmp │ │ ├── 2.bmp │ │ ├── 3.bmp │ │ ├── 4.bmp │ │ ├── 5.bmp │ │ ├── 6.bmp │ │ ├── cap.bmp │ │ ├── extra1.bmp │ │ ├── extra2.bmp │ │ ├── noise.bmp │ │ ├── 划痕.bmp │ │ ├── 噪声.bmp │ │ ├── 补充1.bmp │ │ └── 补充2.bmp │ ├── tmp.tar │ ├── train.py │ ├── train │ │ ├── 0.bmp │ │ ├── 1.bmp │ │ ├── 2.bmp │ │ ├── 3.bmp │ │ ├── 4.bmp │ │ ├── 6.bmp │ │ ├── 8.bmp │ │ └── 9.bmp │ └── up.png ├── zx │ ├── README │ ├── hw_1_2.m │ ├── hw_f1.m │ └── hw_hog.m └── zyq │ ├── 1.png │ ├── 2.png │ ├── README.md │ ├── huahen_mask.bmp │ ├── preprocess.py │ ├── question1.py │ ├── question2.py │ ├── test │ ├── 1.bmp │ ├── 2.bmp │ ├── 3.bmp │ ├── 4.bmp │ ├── 5.bmp │ ├── 6.bmp │ ├── 7.bmp │ ├── 8.bmp │ ├── huahen.bmp │ ├── huahen_mask.bmp │ ├── zaosheng.bmp │ ├── 噪声.bmp │ ├── 补充1.bmp │ └── 补充2.bmp │ ├── test_projection │ ├── 0.bmp │ ├── 1.bmp │ ├── 10.bmp │ ├── 11.bmp │ ├── 12.bmp │ ├── 13.bmp │ ├── 2.bmp │ ├── 3.bmp │ ├── 4.bmp │ ├── 5.bmp │ ├── 6.bmp │ ├── 7.bmp │ ├── 8.bmp │ └── 9.bmp │ └── train │ ├── 0.bmp │ ├── 1.bmp │ ├── 2.bmp │ ├── 3.bmp │ ├── 4.bmp │ ├── 6.bmp │ ├── 8.bmp │ └── 9.bmp ├── HW2 ├── zhuwy │ ├── dist.png │ ├── face.jpg │ ├── face00208.jpg │ ├── k-err.png │ ├── min_dist_classifier.py │ ├── nonface.jpg │ └── pca.py ├── zx │ ├── PCA.m │ ├── README │ └── hw_4.m └── zyq │ ├── README.md │ ├── q11.png │ ├── q12.png │ ├── q2.png │ ├── question1.m │ ├── question2.m │ ├── test │ ├── face.jpg │ └── nonface.jpg │ └── train │ ├── face00002.jpg │ ├── face00006.jpg │ ├── face00009.jpg │ ├── face00013.jpg │ ├── face00014.jpg │ ├── face00016.jpg │ ├── face00018.jpg │ ├── face00020.jpg │ ├── face00023.jpg │ ├── face00025.jpg │ ├── face00026.jpg │ ├── face00028.jpg │ ├── face00033.jpg │ ├── face00035.jpg │ ├── face00040.jpg │ ├── face00046.jpg │ ├── face00047.jpg │ ├── face00055.jpg │ ├── face00058.jpg │ ├── face00063.jpg │ ├── face00067.jpg │ ├── face00068.jpg │ ├── face00071.jpg │ ├── face00072.jpg │ ├── face00075.jpg │ ├── face00076.jpg │ ├── face00077.jpg │ ├── face00079.jpg │ ├── face00081.jpg │ ├── face00086.jpg │ ├── face00088.jpg │ ├── face00090.jpg │ ├── face00091.jpg │ ├── face00093.jpg │ ├── face00094.jpg │ ├── face00095.jpg │ ├── face00100.jpg │ ├── face00102.jpg │ ├── face00106.jpg │ ├── face00108.jpg │ ├── face00112.jpg │ ├── face00114.jpg │ ├── face00118.jpg │ ├── face00125.jpg │ ├── face00126.jpg │ ├── face00127.jpg │ ├── face00128.jpg │ ├── face00131.jpg │ ├── face00132.jpg │ ├── face00137.jpg │ ├── face00139.jpg │ ├── face00142.jpg │ ├── face00146.jpg │ ├── face00149.jpg │ ├── face00150.jpg │ ├── face00151.jpg │ ├── face00159.jpg │ ├── face00160.jpg │ ├── face00161.jpg │ ├── face00162.jpg │ ├── face00166.jpg │ ├── face00167.jpg │ ├── face00168.jpg │ ├── face00169.jpg │ ├── face00172.jpg │ ├── face00175.jpg │ ├── face00176.jpg │ ├── face00181.jpg │ ├── face00182.jpg │ ├── face00183.jpg │ ├── face00188.jpg │ ├── face00193.jpg │ ├── face00195.jpg │ ├── face00198.jpg │ ├── face00199.jpg │ ├── face00200.jpg │ ├── face00202.jpg │ ├── face00206.jpg │ ├── face00207.jpg │ ├── face00208.jpg │ ├── face00209.jpg │ ├── face00210.jpg │ ├── face00213.jpg │ ├── face00216.jpg │ ├── face00218.jpg │ ├── face00220.jpg │ ├── face00229.jpg │ ├── face00230.jpg │ ├── face00233.jpg │ ├── face00239.jpg │ ├── face00243.jpg │ ├── face00244.jpg │ ├── face00245.jpg │ ├── face00249.jpg │ ├── face00251.jpg │ ├── face00254.jpg │ ├── face00261.jpg │ ├── face00262.jpg │ ├── face00265.jpg │ ├── face00266.jpg │ ├── face00267.jpg │ ├── face00268.jpg │ ├── face00269.jpg │ ├── face00271.jpg │ ├── face00272.jpg │ ├── face00273.jpg │ ├── face00276.jpg │ ├── face00278.jpg │ ├── face00279.jpg │ ├── face00281.jpg │ ├── face00282.jpg │ ├── face00287.jpg │ ├── face00289.jpg │ ├── face00291.jpg │ ├── face00292.jpg │ ├── face00294.jpg │ ├── face00295.jpg │ ├── face00296.jpg │ ├── face00299.jpg │ ├── face00300.jpg │ ├── face00305.jpg │ ├── face00307.jpg │ ├── face00308.jpg │ ├── face00309.jpg │ ├── face00312.jpg │ ├── face00318.jpg │ ├── face00323.jpg │ ├── face00326.jpg │ ├── face00328.jpg │ ├── face00331.jpg │ ├── face00333.jpg │ ├── face00336.jpg │ ├── face00337.jpg │ ├── face00340.jpg │ ├── face00341.jpg │ ├── face00342.jpg │ ├── face00344.jpg │ ├── face00345.jpg │ ├── face00346.jpg │ ├── face00349.jpg │ ├── face00350.jpg │ ├── face00352.jpg │ ├── face00354.jpg │ ├── face00356.jpg │ ├── face00358.jpg │ ├── face00359.jpg │ ├── face00360.jpg │ ├── face00363.jpg │ ├── face00364.jpg │ ├── face00367.jpg │ ├── face00369.jpg │ ├── face00371.jpg │ ├── face00376.jpg │ ├── face00378.jpg │ ├── face00379.jpg │ ├── face00382.jpg │ ├── face00386.jpg │ ├── face00390.jpg │ ├── face00391.jpg │ ├── face00392.jpg │ ├── face00400.jpg │ ├── face00405.jpg │ ├── face00408.jpg │ ├── face00411.jpg │ ├── face00412.jpg │ ├── face00414.jpg │ ├── face00415.jpg │ ├── face00416.jpg │ ├── face00422.jpg │ ├── face00423.jpg │ ├── face00424.jpg │ ├── face00425.jpg │ ├── face00432.jpg │ ├── face00435.jpg │ ├── face00436.jpg │ ├── face00438.jpg │ ├── face00439.jpg │ ├── face00440.jpg │ ├── face00443.jpg │ ├── face00447.jpg │ ├── face00448.jpg │ ├── face00449.jpg │ ├── face00457.jpg │ ├── face00459.jpg │ ├── face00460.jpg │ ├── face00461.jpg │ ├── face00462.jpg │ ├── face00463.jpg │ ├── face00464.jpg │ ├── face00472.jpg │ ├── face00476.jpg │ ├── face00477.jpg │ ├── face00484.jpg │ ├── face00486.jpg │ ├── face00490.jpg │ ├── face00492.jpg │ ├── face00494.jpg │ ├── face00497.jpg │ ├── face00498.jpg │ ├── face00501.jpg │ ├── face00503.jpg │ ├── face00505.jpg │ ├── face00506.jpg │ ├── face00507.jpg │ ├── face00511.jpg │ ├── face00514.jpg │ ├── face00516.jpg │ ├── face00517.jpg │ ├── face00520.jpg │ ├── face00521.jpg │ ├── face00522.jpg │ ├── face00525.jpg │ ├── face00527.jpg │ ├── face00529.jpg │ ├── face00530.jpg │ ├── face00532.jpg │ ├── face00535.jpg │ ├── face00538.jpg │ ├── face00539.jpg │ ├── face00540.jpg │ ├── face00542.jpg │ ├── face00543.jpg │ ├── face00545.jpg │ ├── face00549.jpg │ ├── face00550.jpg │ ├── face00558.jpg │ ├── face00560.jpg │ ├── face00561.jpg │ ├── face00562.jpg │ ├── face00563.jpg │ ├── face00566.jpg │ ├── face00567.jpg │ ├── face00568.jpg │ ├── face00569.jpg │ ├── face00570.jpg │ ├── face00571.jpg │ ├── face00572.jpg │ ├── face00573.jpg │ ├── face00574.jpg │ ├── face00577.jpg │ ├── face00581.jpg │ ├── face00582.jpg │ ├── face00585.jpg │ ├── face00586.jpg │ ├── face00587.jpg │ ├── face00592.jpg │ ├── face00594.jpg │ ├── face00600.jpg │ ├── face00603.jpg │ ├── face00604.jpg │ ├── face00605.jpg │ ├── face00606.jpg │ ├── face00611.jpg │ ├── face00616.jpg │ ├── face00618.jpg │ ├── face00619.jpg │ ├── face00620.jpg │ ├── face00621.jpg │ ├── face00622.jpg │ ├── face00623.jpg │ ├── face00624.jpg │ ├── face00626.jpg │ ├── face00627.jpg │ ├── face00628.jpg │ ├── face00630.jpg │ ├── face00632.jpg │ ├── face00633.jpg │ ├── face00635.jpg │ ├── face00637.jpg │ ├── face00645.jpg │ ├── face00646.jpg │ ├── face00654.jpg │ ├── face00656.jpg │ ├── face00660.jpg │ ├── face00665.jpg │ ├── face00667.jpg │ ├── face00668.jpg │ ├── face00670.jpg │ ├── face00672.jpg │ ├── face00674.jpg │ ├── face00676.jpg │ ├── face00678.jpg │ ├── face00685.jpg │ ├── face00686.jpg │ ├── face00687.jpg │ ├── face00688.jpg │ ├── face00690.jpg │ ├── face00691.jpg │ ├── face00697.jpg │ ├── face00698.jpg │ ├── face00700.jpg │ ├── face00702.jpg │ ├── face00705.jpg │ ├── face00706.jpg │ ├── face00708.jpg │ ├── face00711.jpg │ ├── face00715.jpg │ ├── face00717.jpg │ ├── face00718.jpg │ ├── face00720.jpg │ ├── face00721.jpg │ ├── face00723.jpg │ ├── face00729.jpg │ ├── face00732.jpg │ ├── face00733.jpg │ ├── face00734.jpg │ ├── face00735.jpg │ ├── face00736.jpg │ ├── face00737.jpg │ ├── face00741.jpg │ ├── face00744.jpg │ ├── face00746.jpg │ ├── face00750.jpg │ ├── face00757.jpg │ ├── face00759.jpg │ ├── face00760.jpg │ ├── face00761.jpg │ ├── face00764.jpg │ ├── face00768.jpg │ ├── face00769.jpg │ ├── face00775.jpg │ ├── face00777.jpg │ ├── face00778.jpg │ ├── face00780.jpg │ ├── face00784.jpg │ ├── face00787.jpg │ ├── face00788.jpg │ ├── face00792.jpg │ ├── face00794.jpg │ ├── face00795.jpg │ ├── face00799.jpg │ ├── face00802.jpg │ ├── face00806.jpg │ ├── face00807.jpg │ ├── face00808.jpg │ ├── face00811.jpg │ ├── face00818.jpg │ ├── face00822.jpg │ ├── face00824.jpg │ ├── face00826.jpg │ ├── face00827.jpg │ ├── face00829.jpg │ ├── face00830.jpg │ ├── face00832.jpg │ ├── face00841.jpg │ ├── face00846.jpg │ ├── face00849.jpg │ ├── face00850.jpg │ ├── face00854.jpg │ ├── face00856.jpg │ ├── face00861.jpg │ ├── face00864.jpg │ ├── face00865.jpg │ ├── face00866.jpg │ ├── face00870.jpg │ ├── face00871.jpg │ ├── face00875.jpg │ ├── face00878.jpg │ ├── face00879.jpg │ ├── face00881.jpg │ ├── face00882.jpg │ ├── face00883.jpg │ ├── face00884.jpg │ ├── face00885.jpg │ ├── face00886.jpg │ ├── face00889.jpg │ ├── face00891.jpg │ ├── face00900.jpg │ ├── face00908.jpg │ ├── face00912.jpg │ ├── face00914.jpg │ ├── face00916.jpg │ ├── face00917.jpg │ ├── face00921.jpg │ ├── face00923.jpg │ ├── face00925.jpg │ ├── face00929.jpg │ ├── face00931.jpg │ ├── face00932.jpg │ ├── face00940.jpg │ ├── face00942.jpg │ ├── face00943.jpg │ ├── face00945.jpg │ ├── face00946.jpg │ ├── face00947.jpg │ ├── face00948.jpg │ ├── face00949.jpg │ ├── face00950.jpg │ ├── face00962.jpg │ ├── face00965.jpg │ ├── face00966.jpg │ ├── face00969.jpg │ ├── face00970.jpg │ ├── face00972.jpg │ ├── face00973.jpg │ ├── face00975.jpg │ ├── face00976.jpg │ ├── face00980.jpg │ ├── face00983.jpg │ ├── face00985.jpg │ ├── face00987.jpg │ ├── face00989.jpg │ ├── face00990.jpg │ ├── face00991.jpg │ ├── face00996.jpg │ ├── face01000.jpg │ ├── face01001.jpg │ ├── face01004.jpg │ ├── face01009.jpg │ ├── face01010.jpg │ ├── face01011.jpg │ ├── face01012.jpg │ ├── face01013.jpg │ ├── face01016.jpg │ ├── face01020.jpg │ ├── face01021.jpg │ ├── face01022.jpg │ ├── face01026.jpg │ ├── face01027.jpg │ ├── face01031.jpg │ ├── face01033.jpg │ ├── face01037.jpg │ ├── face01039.jpg │ ├── face01042.jpg │ ├── face01043.jpg │ ├── face01044.jpg │ ├── face01051.jpg │ ├── face01054.jpg │ ├── face01055.jpg │ ├── face01058.jpg │ ├── face01062.jpg │ ├── face01063.jpg │ ├── face01064.jpg │ ├── face01065.jpg │ ├── face01068.jpg │ ├── face01069.jpg │ ├── face01071.jpg │ ├── face01072.jpg │ ├── face01073.jpg │ ├── face01075.jpg │ ├── face01076.jpg │ ├── face01080.jpg │ ├── face01083.jpg │ ├── face01085.jpg │ ├── face01086.jpg │ ├── face01087.jpg │ ├── face01088.jpg │ ├── face01090.jpg │ ├── face01091.jpg │ ├── face01093.jpg │ ├── face01098.jpg │ ├── face01099.jpg │ ├── face01102.jpg │ ├── face01105.jpg │ ├── face01108.jpg │ ├── face01109.jpg │ ├── face01111.jpg │ ├── face01114.jpg │ ├── face01118.jpg │ ├── face01123.jpg │ ├── face01127.jpg │ ├── face01131.jpg │ ├── face01132.jpg │ ├── face01135.jpg │ ├── face01137.jpg │ ├── face01138.jpg │ ├── face01141.jpg │ ├── face01142.jpg │ ├── face01143.jpg │ ├── face01145.jpg │ ├── face01146.jpg │ ├── face01147.jpg │ ├── face01153.jpg │ ├── face01154.jpg │ ├── face01158.jpg │ ├── face01166.jpg │ ├── face01167.jpg │ ├── face01168.jpg │ ├── face01170.jpg │ ├── face01171.jpg │ ├── face01172.jpg │ ├── face01174.jpg │ ├── face01176.jpg │ ├── face01177.jpg │ ├── face01179.jpg │ ├── face01181.jpg │ ├── face01182.jpg │ ├── face01183.jpg │ ├── face01188.jpg │ ├── face01191.jpg │ ├── face01193.jpg │ ├── face01198.jpg │ ├── face01199.jpg │ ├── face01200.jpg │ ├── face01201.jpg │ ├── face01204.jpg │ ├── face01207.jpg │ ├── face01211.jpg │ ├── face01212.jpg │ └── face01213.jpg ├── HW3 ├── zhuwy │ ├── 2d.png │ ├── 3d.png │ ├── non-linear-classify.py │ ├── svm.png │ ├── svm.py │ └── tsne.png └── zyq │ ├── README.md │ ├── p1.png │ └── question1.m ├── HW4 ├── zhuwy │ ├── gd.png │ ├── gd.py │ ├── images │ │ ├── knn-1.png │ │ ├── knn-2.png │ │ ├── knn-3.png │ │ ├── knn-4.png │ │ ├── knn-5.png │ │ ├── knn-6.png │ │ ├── knn-7.png │ │ ├── knn-8.png │ │ └── knn-9.png │ ├── kNN.py │ ├── lda.png │ ├── lda.py │ └── trainData.txt └── zyq │ ├── README.md │ ├── q1_1.png │ ├── q2.png │ ├── q3_1.png │ ├── q3_2.png │ ├── question1.m │ ├── question2.asv │ ├── question2.m │ ├── question3.asv │ └── question3.m ├── HW5 ├── zhuwy │ ├── NaiveBayes.py │ └── nb.png └── zyq │ ├── README.md │ ├── q1.png │ └── question1.m ├── HW6 └── zhuwy │ ├── DIANA.py │ ├── hierarchy.png │ ├── kmeans.png │ ├── kmeans.py │ └── testSet.txt └── README.md /.vscode/settings.json: -------------------------------------------------------------------------------- 1 | { 2 | "python.pythonPath": "/usr/bin/python3" 3 | } -------------------------------------------------------------------------------- /Final/zhuwy/CNN-thre-F1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Learner0x5a/Pattern-Recognition/5ec3728b3d0c0abf0384c026c2684659585ec4bb/Final/zhuwy/CNN-thre-F1.png -------------------------------------------------------------------------------- /Final/zhuwy/CNN-thre-Precision.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Learner0x5a/Pattern-Recognition/5ec3728b3d0c0abf0384c026c2684659585ec4bb/Final/zhuwy/CNN-thre-Precision.png 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-------------------------------------------------------------------------------- /Final/zhuwy/closeset-cnn.py: -------------------------------------------------------------------------------- 1 | 2 | import numpy as np 3 | import pickle 4 | from sklearn.model_selection import train_test_split 5 | from keras.models import Sequential 6 | from keras.layers import Dense, Dropout, Flatten, BatchNormalization 7 | from keras.layers import Conv2D, MaxPooling2D 8 | from keras.optimizers import Adam 9 | from keras.utils import to_categorical,plot_model 10 | from keras import backend as K 11 | np.random.seed(1) 12 | import matplotlib 13 | matplotlib.use('AGG')#或者PDF, SVG或PS 14 | import matplotlib.pyplot as plt 15 | 16 | f = open('RESIZE_X.pkl','rb') 17 | X = pickle.load(f) 18 | f.close() 19 | GLASS,MIRROR,OTHERS = X 20 | print(GLASS.shape, MIRROR.shape, OTHERS.shape) 21 | 22 | GM_X = np.concatenate((GLASS,MIRROR),axis=0) 23 | GM_Y = np.concatenate((np.zeros(GLASS.shape[0]),np.ones(MIRROR.shape[0])),axis=0) 24 | print(GM_X.shape, GM_Y.shape) # (N,144,144,3) (N,) 25 | # GM_Y = to_categorical(GM_Y) 26 | 27 | O_X = OTHERS # 负样本 28 | O_Y = np.full(O_X.shape[0],2) # 标签 29 | full_X = np.concatenate((GM_X,O_X),axis=0) 30 | full_Y = np.concatenate((GM_Y,O_Y),axis=0) 31 | full_Y = to_categorical(full_Y) 32 | X_train, X_test, Y_train, Y_test = train_test_split(full_X,full_Y,test_size=0.3) 33 | 34 | 35 | def precision(y_true, y_pred): 36 | # Calculates the precision 37 | true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) 38 | predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1))) 39 | precision = true_positives / (predicted_positives + K.epsilon()) 40 | return precision 41 | 42 | def recall(y_true, y_pred): 43 | # Calculates the recall 44 | true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) 45 | possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) 46 | recall = true_positives / (possible_positives + K.epsilon()) 47 | return recall 48 | 49 | def fbeta_score(y_true, y_pred, beta=1): 50 | # Calculates the F score, the weighted harmonic mean of precision and recall. 51 | if beta < 0: 52 | raise ValueError('The lowest choosable beta is zero (only precision).') 53 | 54 | # If there are no true positives, fix the F score at 0 like sklearn. 55 | if K.sum(K.round(K.clip(y_true, 0, 1))) == 0: 56 | return 0 57 | 58 | p = precision(y_true, y_pred) 59 | r = recall(y_true, y_pred) 60 | bb = beta ** 2 61 | fbeta_score = (1 + bb) * (p * r) / (bb * p + r + K.epsilon()) 62 | return fbeta_score 63 | 64 | def fmeasure(y_true, y_pred): 65 | # Calculates the f-measure, the harmonic mean of precision and recall. 66 | return fbeta_score(y_true, y_pred, beta=1) 67 | 68 | model = Sequential() 69 | model.add(BatchNormalization(input_shape=X_train.shape[1:])) 70 | model.add(Conv2D(32, (3, 3), activation='relu')) 71 | model.add(Conv2D(32, (3, 3), activation='relu')) 72 | model.add(MaxPooling2D(pool_size=(2, 2))) 73 | model.add(Dropout(0.25)) 74 | model.add(BatchNormalization()) 75 | model.add(Conv2D(64, (3, 3), activation='relu')) 76 | model.add(Conv2D(64, (3, 3), activation='relu')) 77 | model.add(MaxPooling2D(pool_size=(2, 2))) 78 | model.add(Dropout(0.25)) 79 | model.add(Flatten()) 80 | model.add(BatchNormalization()) 81 | model.add(Dense(256, activation='relu')) 82 | model.add(Dropout(0.5)) 83 | model.add(Dense(3,activation='softmax')) 84 | 85 | adam = Adam() 86 | model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=["accuracy",precision, recall, fmeasure]) 87 | 88 | model.fit(X_train, Y_train, batch_size=32, epochs=30,validation_data=(X_test,Y_test)) 89 | 90 | ''' 91 | epoch 30 92 | loss: 0.0682 - accuracy: 0.9704 - precision: 0.9710 - recall: 0.9710 - fmeasure: 0.9710 93 | - val_loss: 4.6051 - val_accuracy: 0.5968 - val_precision: 0.5969 - val_recall: 0.5969 - val_fmeasure: 0.5969 94 | ''' -------------------------------------------------------------------------------- /Final/zhuwy/cnn.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Learner0x5a/Pattern-Recognition/5ec3728b3d0c0abf0384c026c2684659585ec4bb/Final/zhuwy/cnn.png -------------------------------------------------------------------------------- /Final/zhuwy/cnn.py: -------------------------------------------------------------------------------- 1 | 2 | import numpy as np 3 | import pickle 4 | from sklearn.model_selection import train_test_split 5 | from keras.models import Sequential 6 | from keras.layers import Dense, Dropout, Flatten, BatchNormalization 7 | from keras.layers import Conv2D, MaxPooling2D 8 | from keras.optimizers import Adam 9 | from keras.utils import to_categorical,plot_model 10 | np.random.seed(1) 11 | import matplotlib 12 | matplotlib.use('AGG')#或者PDF, SVG或PS 13 | import matplotlib.pyplot as plt 14 | 15 | f = open('RESIZE_X.pkl','rb') 16 | X = pickle.load(f) 17 | f.close() 18 | GLASS,MIRROR,OTHERS = X 19 | print(GLASS.shape, MIRROR.shape, OTHERS.shape) 20 | 21 | GM_X = np.concatenate((GLASS,MIRROR),axis=0) 22 | GM_Y = np.concatenate((np.zeros(GLASS.shape[0]),np.ones(MIRROR.shape[0])),axis=0) 23 | print(GM_X.shape, GM_Y.shape) # (N,144,144,3) (N,) 24 | # GM_Y = to_categorical(GM_Y) 25 | 26 | X_train, X_test, Y_train, Y_test = train_test_split(GM_X,GM_Y,test_size=0.3) 27 | 28 | 29 | model = Sequential() 30 | model.add(BatchNormalization(input_shape=X_train.shape[1:])) 31 | model.add(Conv2D(32, (3, 3), activation='relu')) 32 | model.add(Conv2D(32, (3, 3), activation='relu')) 33 | model.add(MaxPooling2D(pool_size=(2, 2))) 34 | model.add(Dropout(0.25)) 35 | model.add(BatchNormalization()) 36 | model.add(Conv2D(64, (3, 3), activation='relu')) 37 | model.add(Conv2D(64, (3, 3), activation='relu')) 38 | model.add(MaxPooling2D(pool_size=(2, 2))) 39 | model.add(Dropout(0.25)) 40 | model.add(Flatten()) 41 | model.add(BatchNormalization()) 42 | model.add(Dense(256, activation='relu')) 43 | model.add(Dropout(0.5)) 44 | model.add(Dense(1, activation='sigmoid')) 45 | 46 | adam = Adam() 47 | model.compile(loss='binary_crossentropy', optimizer=adam, metrics=["accuracy"]) 48 | plot_model(model,'cnn.png',show_shapes=True,rankdir='LR') 49 | model.fit(X_train, Y_train, batch_size=32, epochs=30) 50 | test_loss,test_acc = model.evaluate(X_test, Y_test, batch_size=32) 51 | pred = model.predict(X_test) 52 | zeros = np.where(pred<0.5)[0] 53 | ones = np.where(pred>=0.5)[0] 54 | pred[zeros] = 0 55 | pred[ones] = 1 56 | from sklearn.metrics import mean_squared_error,accuracy_score,precision_score,recall_score,f1_score 57 | mse = mean_squared_error(Y_test,pred) 58 | acc = accuracy_score(Y_test,pred) 59 | recall = recall_score(Y_test,pred) 60 | precision = precision_score(Y_test,pred) 61 | f1 = f1_score(Y_test,pred) 62 | print('mse:',mse,'precision:',precision,'recall:',recall,'f1:',f1) 63 | 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14 | f = open('RESIZE_X.pkl','rb') 15 | X = pickle.load(f) 16 | f.close() 17 | GLASS,MIRROR,OTHERS = X 18 | print(GLASS.shape, MIRROR.shape, OTHERS.shape) 19 | 20 | GM_X = np.concatenate((GLASS,MIRROR),axis=0) 21 | GM_Y = np.concatenate((np.zeros(GLASS.shape[0]),np.ones(MIRROR.shape[0])),axis=0) 22 | print(GM_X.shape, GM_Y.shape) 23 | 24 | N,x,y,z = GM_X.shape 25 | GM_X = np.reshape(GM_X,(N,x*y*z)) 26 | 27 | log = [] 28 | for k in range(1,12): 29 | pca = PCA(n_components=k) 30 | newGM_X = pca.fit_transform(GM_X) 31 | scaler = StandardScaler().fit(newGM_X) 32 | newGM_X = scaler.transform(newGM_X) 33 | # recover_GM_X = pca.inverse_transform(newGM_X) 34 | # err = mean_squared_error(GM_X,recover_GM_X) 35 | # print(pca.explained_variance_,err,newGM_X.shape,recover_GM_X.shape) 36 | 37 | X_train, X_test, y_train, y_test = train_test_split(newGM_X,GM_Y,test_size=0.3) 38 | model = KNeighborsClassifier(n_neighbors=1) 39 | model.fit(X_train,y_train) 40 | pred = model.predict(X_test) 41 | mse = mean_squared_error(y_test,pred) 42 | acc = accuracy_score(y_test,pred) 43 | recall = recall_score(y_test,pred) 44 | precision = precision_score(y_test,pred) 45 | f1 = f1_score(y_test,pred) 46 | conf_mat = confusion_matrix(y_test,pred) 47 | print(k,'mse:',mse,'precision:',precision,'recall:',recall,'f1:',f1) 48 | log.append((k,mse,precision,recall,f1,conf_mat)) 49 | 50 | log = np.asarray(log) 51 | np.save('log.npy',log) 52 | 53 | 54 | log = np.load('log.npy',allow_pickle=True) 55 | 56 | plt.plot(log[:,0],log[:,1],c='r') 57 | plt.xlabel('k') 58 | plt.ylabel('mse') 59 | plt.savefig('PCA-k-mse.png') 60 | plt.clf() 61 | 62 | plt.plot(log[:,0],log[:,2],c='r') 63 | plt.xlabel('k') 64 | plt.ylabel('Precision') 65 | plt.savefig('PCA-k-Precision.png') 66 | plt.clf() 67 | 68 | plt.plot(log[:,0],log[:,3],c='r') 69 | plt.xlabel('k') 70 | plt.ylabel('Recall') 71 | plt.savefig('PCA-k-Recall.png') 72 | plt.clf() 73 | 74 | plt.plot(log[:,0],log[:,4],c='r') 75 | plt.xlabel('k') 76 | plt.ylabel('F1') 77 | plt.savefig('PCA-k-F1.png') 78 | plt.clf() -------------------------------------------------------------------------------- /HW1/zhuwy/README.md: -------------------------------------------------------------------------------- 1 | # Pattern-Recognition 2 | 2019-2020春季学期模式识别 3 | 4 | opencv + Python 5 | 6 | 按需预处理(例如去噪) --> 二值化 --> 图像分割 --> 模板匹配 7 | 8 | * 干扰线采用灰度分层解决，不具有普适性 9 | * 图像分割不鲁棒，有时候会失败 10 | * 统计学特征不靠谱 11 | -------------------------------------------------------------------------------- /HW1/zhuwy/binary-repair.bmp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Learner0x5a/Pattern-Recognition/5ec3728b3d0c0abf0384c026c2684659585ec4bb/HW1/zhuwy/binary-repair.bmp -------------------------------------------------------------------------------- /HW1/zhuwy/chars/train0.bmp: -------------------------------------------------------------------------------- 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img = cv2.imread('test/noise.bmp',cv2.IMREAD_GRAYSCALE) 4 | img = cv2.GaussianBlur(img,(5,5),0) 5 | img = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 145, 8) 6 | img = cv2.medianBlur(img,5) 7 | img = cv2.medianBlur(img,3) 8 | 9 | cv2.imwrite('denoise.bmp',img) -------------------------------------------------------------------------------- /HW1/zhuwy/down.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Learner0x5a/Pattern-Recognition/5ec3728b3d0c0abf0384c026c2684659585ec4bb/HW1/zhuwy/down.png -------------------------------------------------------------------------------- /HW1/zhuwy/mask.bmp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Learner0x5a/Pattern-Recognition/5ec3728b3d0c0abf0384c026c2684659585ec4bb/HW1/zhuwy/mask.bmp -------------------------------------------------------------------------------- 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/HW1/zyq/1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Learner0x5a/Pattern-Recognition/5ec3728b3d0c0abf0384c026c2684659585ec4bb/HW1/zyq/1.png -------------------------------------------------------------------------------- /HW1/zyq/2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Learner0x5a/Pattern-Recognition/5ec3728b3d0c0abf0384c026c2684659585ec4bb/HW1/zyq/2.png -------------------------------------------------------------------------------- /HW1/zyq/README.md: -------------------------------------------------------------------------------- 1 | ### Yunqi's Solution for HW1 2 | 思路 3 | - 模板匹配解决第一问 4 | - 用最低亮度的条纹做mask去除划痕 5 | - 硬核resize直接算放大缩小的倍数 6 | - 用ppt上的做法建立新特征发现并没有什么用 7 | 8 | 效果图1： 9 | 10 | 11 | 12 | 13 | 效果图2: 14 | 15 | 16 | 17 | 18 | -------------------------------------------------------------------------------- /HW1/zyq/huahen_mask.bmp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Learner0x5a/Pattern-Recognition/5ec3728b3d0c0abf0384c026c2684659585ec4bb/HW1/zyq/huahen_mask.bmp -------------------------------------------------------------------------------- /HW1/zyq/preprocess.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import cv2 as cv 3 | 4 | img = cv.imread('./test/huahen.bmp') 5 | mask = cv.imread('./test/huahen_mask.bmp', 0) 6 | 7 | dst = cv.inpaint(img, mask, 3, cv.INPAINT_TELEA) 8 | cv.imshow('img', img) 9 | cv.imshow('mask', mask) 10 | 11 | cv.imshow('dst', dst) 12 | cv.waitKey(0) 13 | cv.destroyAllWindows() 14 | cv.imwrite('./test/7.bmp', dst) 15 | 16 | img = cv.imread('./test/zaosheng.bmp') 17 | dst = cv.medianBlur(img, 5) 18 | # guass = cv.GaussianBlur(img, (3,3), 0) 19 | 20 | cv.imshow('img', img) 21 | cv.imshow('dst', dst) 22 | # cv.imshow('guass', guass) 23 | cv.waitKey(0) 24 | cv.destroyAllWindows() 25 | cv.imwrite('./test/8.bmp', dst) -------------------------------------------------------------------------------- /HW1/zyq/question1.py: -------------------------------------------------------------------------------- 1 | # encoding:utf-8 2 | # Adapted from this blog[https://www.cnblogs.com/FHC1994/p/9123393.html] 3 | # classify several numbers per time 4 | import cv2 as cv 5 | import numpy as np 6 | import os 7 | 8 | def local_threshold(image): 9 | gray = cv.cvtColor(image, cv.COLOR_RGB2GRAY) 10 | binary = cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY, 25, 10) 11 | return binary 12 | 13 | def template_demo(tp_path, target_path): 14 | tp0 = cv.imread(tp_path) 15 | target = cv.imread(target_path) 16 | # tp0 = local_threshold(tp0) 17 | # target = local_threshold(target) 18 | # cv.namedWindow('template image', cv.WINDOW_NORMAL) 19 | # cv.imshow('template image', tp0) 20 | # cv.namedWindow('target image', cv.WINDOW_NORMAL) 21 | # cv.imshow('target image', target) 22 | methods = [cv.TM_SQDIFF_NORMED] 23 | # methods = [cv.TM_SQDIFF_NORMED] 24 | th, tw = tp0.shape[:2] 25 | for md in methods: 26 | print(md) 27 | result = cv.matchTemplate(target, tp0, md) 28 | min_val, max_val, min_loc, max_loc = cv.minMaxLoc(result) 29 | # print('-=-=-=-=-=-') 30 | # print(max_val) 31 | # print(min_val) 32 | # print(result.shape) 33 | # print('=-=-=-=-=-=') 34 | threshold = 0.013 35 | loc = np.where(result <= threshold) 36 | # print(loc) 37 | values = [(0, 0, 1)] 38 | for t1 in zip(*loc[::-1]): 39 | # print(t1) 40 | va = result[t1[1], t1[0]] 41 | flag = 1 42 | for num in range(len(values)): 43 | item = values[num] 44 | if (t1[0]-item[0])**2 + (t1[1]-item[1])**2 < 100: 45 | flag = 0 46 | if va < item[2]: 47 | values[num] = (t1[0], t1[1], va) 48 | break 49 | if flag == 1: 50 | values.append(((t1[0], t1[1], va))) 51 | for t1 in values: 52 | if t1[2] == 1: 53 | continue 54 | 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-------------------------------------------------------------------------------- 1 | import numpy as np 2 | import matplotlib 3 | matplotlib.use('AGG')#或者PDF, SVG或PS 4 | import matplotlib.pyplot as plt 5 | 6 | W1 = np.array([(2,3), (2,2), (2,4), (3,3), (3,4), (2.5,3), (1.5,2), (3.5,2.5), (4,4), (0.5,0.5)]) 7 | W2 = np.array([(0,2.5), (-2,2), (-1,-1), (1,-2), (3,0), (-2,-2), (-3,-4), (-5,-2), (4,-1)]) 8 | M1 = np.mean(W1,axis=0) 9 | M2 = np.mean(W2,axis=0) 10 | x1 = M1[0] 11 | x2 = M2[0] 12 | y1 = M1[1] 13 | y2 = M2[1] 14 | 15 | line_x = np.linspace(-4,4,20) 16 | line_y = (y1+y2)/2.+-1/((y2-y1)/(x2-x1))*(line_x-(x1+x2)/2) 17 | print('y = (y1+y2)/2+-1/((y2-y1)/(x2-x1))*(x-(x1+x2)/2) = '+str((y1+y2)/2.)+'+'+str(-1/((y2-y1)/(x2-x1)))+'*(x-'+str((x1+x2)/2)+')') 18 | 19 | 20 | 21 | W = np.vstack((W1,W2)) 22 | print(M1,M2) 23 | x1 = W1[:,0] 24 | y1 = W1[:,1] 25 | x2 = W2[:,0] 26 | y2 = W2[:,1] 27 | # print(W,x,y) 28 | 29 | 30 | 31 | plt.xlabel(u'x',FontSize=16) 32 | plt.ylabel(u'y',FontSize=16) 33 | # plt.title(img,fontsize='large',fontweight='bold') 34 | plt.scatter(x1,y1,c='black',marker='x',label='W1') 35 | plt.scatter([M1[0]],[M1[1]],c='blue',marker='x',label='M1') 36 | plt.scatter(x2,y2,c='red',marker='s',label='W2') 37 | plt.scatter([M2[0]],[M2[1]],c='green',marker='s',label='M2') 38 | plt.plot(line_x,line_y,color='orange',linestyle='-.',label='separating plane') 39 | 40 | plt.legend() 41 | plt.savefig("dist.png") 42 | # plt.clf() 43 | -------------------------------------------------------------------------------- /HW2/zhuwy/nonface.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Learner0x5a/Pattern-Recognition/5ec3728b3d0c0abf0384c026c2684659585ec4bb/HW2/zhuwy/nonface.jpg -------------------------------------------------------------------------------- /HW2/zhuwy/pca.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import numpy as np 3 | from sklearn.decomposition import PCA 4 | from sklearn.metrics import mean_squared_error 5 | 6 | import matplotlib 7 | matplotlib.use('AGG')#或者PDF, SVG或PS 8 | import matplotlib.pyplot as plt 9 | 10 | thre = 1e2 11 | 12 | def myPCA(img,color): 13 | X = cv2.imread(img,cv2.IMREAD_GRAYSCALE) 14 | X = np.array(X) 15 | # print(X.shape) 16 | max_k = X.shape[1] 17 | thre_k = 1 18 | plt_x = [] 19 | plt_y = [] 20 | for k in range(1,max_k+1): 21 | pca = PCA(n_components=k) 22 | newX = pca.fit_transform(X) 23 | recover_X = pca.inverse_transform(newX) 24 | err = mean_squared_error(X,recover_X) 25 | if err > thre: 26 | thre_k = k 27 | 28 | plt_x.append(k) 29 | plt_y.append(err) 30 | 31 | print(img,'-> thre_k = ',str(thre_k),' Final Error:',err,' Feature weights:',pca.explained_variance_ratio_[0:3]) 32 | 33 | 34 | # plt.scatter(plt_x,plt_y,c='black',marker='s',s=8,alpha=0.5) 35 | plt.xlabel(u'k',FontSize=16) 36 | plt.ylabel(u'MSE',FontSize=16) 37 | # plt.title(img,fontsize='large',fontweight='bold') 38 | plt.plot(plt_x,plt_y,color=color,label=img) 39 | plt.legend() 40 | 41 | plt.savefig("k-err.png") 42 | # plt.clf() 43 | 44 | myPCA('face00208.jpg','r') 45 | myPCA('face.jpg','g') 46 | myPCA('nonface.jpg','b') -------------------------------------------------------------------------------- /HW2/zx/PCA.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Learner0x5a/Pattern-Recognition/5ec3728b3d0c0abf0384c026c2684659585ec4bb/HW2/zx/PCA.m -------------------------------------------------------------------------------- /HW2/zx/README: -------------------------------------------------------------------------------- 1 | :) 2 | -------------------------------------------------------------------------------- /HW2/zx/hw_4.m: -------------------------------------------------------------------------------- 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[3,2,4,3,4,3,2,2.5,4,0.5]; 5 | 6 | figure; 7 | scatter(w1_x,w1_y,'r'); 8 | hold on; 9 | 10 | w2_x = [0,-2,-1,1,3,-2,-3,-5,4]; 11 | w2_y = [2.5,2,-1,-2,0,-2,-4,-2,-1]; 12 | scatter(w2_x,w2_y,'b'); 13 | hold on; 14 | legend('W1','W2'); 15 | p1_x = mean(w1_x); 16 | p1_y = mean(w1_y); 17 | 18 | p2_x = mean(w2_x); 19 | p2_y = mean(w2_y); 20 | scatter(p1_x,p1_y,'r','fill'); 21 | scatter(p2_x,p2_y,'b','fill'); 22 | hold on; 23 | 24 | k_1 = (p2_y-p1_y)/(p2_x-p1_x); 25 | k = -1/k_1; 26 | mid = [(p2_x+p1_x)/2,(p2_y+p1_y)/2]; 27 | scatter(mid(1),mid(2),'k'); 28 | legend('W1_M','W2_M','M','fill'); 29 | b = mid(2)-k*mid(1); 30 | refline(k,b) 31 | 32 | legend('W1','W2','W1\_M','W2\_M','M','sperate line'); 33 | 34 | 35 | 36 | -------------------------------------------------------------------------------- /HW2/zyq/test/face.jpg: -------------------------------------------------------------------------------- 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import numpy as np 8 | from sklearn.decomposition import PCA, KernelPCA 9 | from sklearn.metrics import mean_squared_error 10 | 11 | import matplotlib 12 | matplotlib.use('AGG') 13 | import matplotlib.pyplot as plt 14 | fig = plt.figure() 15 | 16 | W1 = np.array([(1,1),(2,2),(3,3),(1,3),(3,1)]) 17 | W2 = np.array([(1,2),(2,1),(2,3),(3,2)]) 18 | W = np.vstack((W1,W2)) 19 | x = W[:,0] 20 | y = W[:,1] 21 | # print(W,x,y) 22 | Z = np.power(np.power(x-2,2) - np.power(y-2,2),2)[:,np.newaxis] 23 | print(W,Z) 24 | 25 | W = np.concatenate((W,Z),axis=1) 26 | from mpl_toolkits.mplot3d import Axes3D 27 | fig = plt.figure() 28 | ax = Axes3D(fig) 29 | X = np.linspace(0,4,150) 30 | Y = np.linspace(0,4,150) 31 | X, Y = np.meshgrid(X, Y) 32 | Z = np.full(X.shape,1/2) 33 | ax.plot_surface(X,Y,Z,color='green',alpha=0.2) 34 | ax.set_zlabel('z') 35 | ax.set_ylabel('y') 36 | ax.set_xlabel('x') 37 | ax.scatter(W[0:5,0], W[0:5,1], W[0:5,2],c='b',label='W1') 38 | ax.scatter(W[5:,0], W[5:,1], W[5:,2],c='r',label='W2') 39 | plt.legend() 40 | plt.savefig("3d.png") 41 | plt.clf() 42 | 43 | plt.scatter(W1[:,0],W1[:,1],marker='x',label='W1',c='b') 44 | plt.scatter(W2[:,0],W2[:,1],marker='o',label='W2',c='r') 45 | plt.xlim((0, 4)) 46 | plt.ylim((0, 4)) 47 | plt.xlabel('x') 48 | plt.ylabel('y') 49 | 50 | half = np.abs(X-2) - np.abs(Y-2) 51 | X1 = X[np.where(half<=0)] 52 | Y1 = 2 + np.sqrt(np.power(X1-2,2) + 1/2) 53 | Y2 = 2 - np.sqrt(np.power(X1-2,2) + 1/2) 54 | X2 = X[np.where(half<=0)] 55 | Y3 = 2 + np.sqrt(np.power(X2-2,2) - 1/2) 56 | Y4 = 2 - np.sqrt(np.power(X2-2,2) - 1/2) 57 | plt.scatter(X1,Y1,s=1,c='g',label='hyperplane') 58 | plt.scatter(X1,Y2,s=1,c='g') 59 | plt.scatter(X2,Y3,s=1,c='g') 60 | plt.scatter(X2,Y4,s=1,c='g') 61 | plt.legend() 62 | plt.savefig('2d.png') 63 | plt.clf() 64 | 65 | 66 | 67 | from sklearn.manifold import TSNE 68 | def plot_with_labels(low_dim_embs, labels, filename): # 绘制词向量图 69 | plt.figure() 70 | for i, label in enumerate(labels): 71 | x, y = low_dim_embs[i, :] 72 | if label == 'W1': 73 | color = 'b' 74 | else: 75 | color = 'r' 76 | plt.scatter(x, y,c=color) # 画点，对应low_dim_embs中每个词向量 77 | plt.xticks(()) # 不显示刻度 78 | plt.yticks(()) # 不显示刻度 79 | plt.xlabel('x') 80 | plt.ylabel('y') 81 | plt.annotate(label, # 显示每个点对应哪个单词 82 | xy=(x, y), 83 | xytext=(5, 2), 84 | textcoords='offset points', 85 | ha='right', 86 | va='bottom') 87 | plt.savefig(filename) 88 | 89 | tsne = TSNE(n_components=2) 90 | low_dim_embs = tsne.fit_transform(W) 91 | labels = ['W1','W1','W1','W1','W1','W2','W2','W2','W2'] 92 | plot_with_labels(low_dim_embs, labels, 'tsne.png') 93 | -------------------------------------------------------------------------------- /HW3/zhuwy/svm.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Learner0x5a/Pattern-Recognition/5ec3728b3d0c0abf0384c026c2684659585ec4bb/HW3/zhuwy/svm.png -------------------------------------------------------------------------------- /HW3/zhuwy/svm.py: -------------------------------------------------------------------------------- 1 | ''' 2 | SVM 3 | ''' 4 | import numpy as np 5 | from sklearn.metrics import mean_squared_error 6 | import matplotlib 7 | matplotlib.use('AGG') 8 | import matplotlib.pyplot as plt 9 | 10 | W1 = np.array([(1,1),(2,2),(3,3),(1,3),(3,1)]) 11 | W2 = np.array([(1,2),(2,1),(2,3),(3,2)]) 12 | W = np.vstack((W1,W2)) 13 | 14 | labels = np.array([0,0,0,0,0,1,1,1,1]) 15 | def plot_hyperplane(model,X,y,h=0.01,draw_sv=False): 16 | # create a mesh to plot in 17 | x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 18 | y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 19 | xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) 20 | 21 | plt.xlim(xx.min(), xx.max()) 22 | plt.ylim(yy.min(), yy.max()) 23 | plt.xlabel('x') 24 | plt.ylabel('y') 25 | 26 | Z = model.predict(np.c_[xx.ravel(), yy.ravel()]) # SVM的分割超平面 27 | Z = Z.reshape(xx.shape) 28 | plt.contourf(xx, yy, Z, cmap='tab10', alpha=0.5) 29 | 30 | markers = ['o', 's', '^'] 31 | colors = ['b', 'r', 'c'] 32 | labels = np.unique(y) 33 | for label in labels: 34 | plt.scatter(X[y==label][:, 0], X[y==label][:, 1], c=colors[label], marker=markers[label]) 35 | # 支持向量 36 | if draw_sv: 37 | sv = model.support_vectors_ 38 | plt.scatter(sv[:, 0], sv[:, 1], c='y', marker='x') 39 | plt.savefig("svm.png") 40 | 41 | from sklearn import svm 42 | W = np.vstack((W1,W2)) 43 | model = svm.SVC(C=100.0) 44 | model.fit(W,labels) 45 | preds = model.predict(W) 46 | print(mean_squared_error(preds,labels)) 47 | plot_hyperplane(model, W, labels) 48 | -------------------------------------------------------------------------------- /HW3/zhuwy/tsne.png: -------------------------------------------------------------------------------- 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np.concatenate((W2,np.ones(W2.shape[0])[:,np.newaxis]),axis=1) 14 | X = np.concatenate((Z1,-Z2),axis=0) 15 | 16 | # Y = np.concatenate((np.zeros(5),np.ones(5)),axis=0) 17 | print(X) 18 | plt.scatter(W1[:,0],W1[:,1],label='W1') 19 | plt.scatter(W2[:,0],W2[:,1],label='W2') 20 | plt.xlabel('x') 21 | plt.ylabel('y') 22 | 23 | 24 | def linear_neuron(w,x): 25 | y = np.sum(np.multiply(w,x)) 26 | return y 27 | 28 | w = np.random.randn(3) 29 | lr = 1e-2 30 | epochs = 1000 31 | for i in range(epochs): 32 | for x in X: 33 | if linear_neuron(w,x) > 0: 34 | continue 35 | else: 36 | w = w + lr*x 37 | print('{:.2f}*x + {:.2f}*y + {:.2f} = 0'.format(w[0],w[1],w[2])) 38 | X = X = np.concatenate((W1,W2),axis=0) 39 | line_x = np.linspace(-1,2) 40 | ''' 41 | w0*x + w1*y + w2 = 0 <-> y = -1/w1 * (w0*x + w2) 42 | ''' 43 | line_y = -1/w[1] * (w[0]*line_x + w[2]) 44 | plt.plot(line_x,line_y,c='r',label='{:+.2f}*x {:+.2f}*y {:+.2f} = 0'.format(w[0],w[1],w[2])) 45 | plt.legend() 46 | plt.savefig('gd.png') 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-------------------------------------------------------------------------------- /HW4/zhuwy/kNN.py: -------------------------------------------------------------------------------- 1 | from sklearn.neighbors import KNeighborsClassifier 2 | from sklearn.metrics import mean_squared_error 3 | import numpy as np 4 | import matplotlib 5 | matplotlib.use('AGG') 6 | import matplotlib.pyplot as plt 7 | fig = plt.figure() 8 | 9 | x = np.arange(-6,6,0.1) 10 | y = np.arange(-6,6,0.1) 11 | Zx,Zy = np.meshgrid(x,y) 12 | Zx = np.reshape(Zx,(Zx.shape[0]*Zx.shape[1],)) 13 | Zy = np.reshape(Zy,(Zy.shape[0]*Zy.shape[1],)) 14 | X_test = np.array(list(zip(Zx,Zy))) 15 | print(X_test.shape) 16 | 17 | 18 | 19 | f = open('trainData.txt','r') 20 | plt_x1 = [] 21 | plt_y1 = [] 22 | plt_x2 = [] 23 | plt_y2 = [] 24 | label_train = [] 25 | for line in f.readlines(): 26 | data = line.split() 27 | if data[2] == '1': 28 | plt_x1.append(float(data[0])) 29 | plt_y1.append(float(data[1])) 30 | else: 31 | plt_x2.append(float(data[0])) 32 | plt_y2.append(float(data[1])) 33 | label_train.append(float(data[2])-1) 34 | f.close() 35 | 36 | x_train = plt_x1 + plt_x2 37 | y_train = plt_y1 + plt_y2 38 | x_train = np.array(x_train)[:,np.newaxis] 39 | y_train = np.array(y_train)[:,np.newaxis] 40 | 41 | X = np.concatenate((x_train,y_train),axis=1) 42 | label_train = np.array(label_train) 43 | 44 | print(X.shape,label_train.shape) 45 | 46 | 47 | for k in range(1,10): 48 | model = KNeighborsClassifier(n_neighbors=k) 49 | model.fit(X,label_train) 50 | 51 | label_test = model.predict(X_test) 52 | 53 | plt.scatter(X_test[:,0],X_test[:,1],c=label_test) 54 | plt.scatter(plt_x1,plt_y1,c='r') 55 | plt.scatter(plt_x2,plt_y2,c='g') 56 | plt.xlabel('x') 57 | plt.ylabel('y') 58 | plt.title('k='+str(k)) 59 | plt.savefig('images/knn-'+str(k)+'.png') 60 | plt.clf() 61 | 62 | -------------------------------------------------------------------------------- /HW4/zhuwy/lda.png: 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19 | plt.ylabel('y') 20 | 21 | from sklearn.discriminant_analysis import LinearDiscriminantAnalysis 22 | from sklearn.metrics import mean_squared_error 23 | model = LinearDiscriminantAnalysis() 24 | model.fit(X,Y) 25 | mse = mean_squared_error(Y,model.predict(X)) 26 | print('mse:',mse) 27 | print(model.intercept_,model.coef_) 28 | w = model.coef_[0] 29 | line_x = np.linspace(-2,3) 30 | line_y = -1/w[1] * (w[0]*line_x + model.intercept_) 31 | plt.plot(line_x,line_y,c='r',label='{:+.2f}*x {:+.2f}*y {:+.2f} = 0; mse={:.2f}'.format(w[0],w[1],model.intercept_[0],mse)) 32 | plt.legend() 33 | plt.savefig('fisher.png') 34 | 35 | main(W1,W2) 36 | # main(W3,W4) -------------------------------------------------------------------------------- /HW4/zyq/README.md: -------------------------------------------------------------------------------- 1 | ### Yunqi's Solution for HW4 2 | 思路 3 | - 第一题如果用算平均的话要取个合适的c。不过感觉也快不了多少啊 4 | - 第三题归一化后的w 有 w'*x 就是Fisher判别线上的长度 5 | - matlab画图我要死了 6 | 7 | 效果图1： 8 | 9 | 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-------------------------------------------------------------------------------- /HW5/zhuwy/NaiveBayes.py: -------------------------------------------------------------------------------- 1 | from math import log # natual log 2 | import numpy as np 3 | import matplotlib 4 | matplotlib.use('AGG') 5 | import matplotlib.pyplot as plt 6 | fig = plt.figure() 7 | 8 | priors = [0.5,0.5] 9 | X1 = np.array([(3,4),(3,8),(2,6),(4,6)]) 10 | X2 = np.array([(3,0),(3,-4),(1,-2),(5,-2)]) 11 | X = np.concatenate((X1,X2),axis=0) 12 | Y = np.concatenate((np.zeros(X1.shape[0]),np.ones(X2.shape[0])),axis=0) 13 | plt.scatter(X1[:,0],X1[:,1],label='W1') 14 | plt.scatter(X2[:,0],X2[:,1],label='W2') 15 | plt.xlabel('x') 16 | plt.ylabel('y') 17 | plt.legend() 18 | # print(X1,X2) 19 | 20 | m1 = np.mean(X1,axis=0) 21 | m2 = np.mean(X2,axis=0) 22 | m1_col = m1[:,np.newaxis] 23 | m1_row = m1[np.newaxis,:] 24 | m2_col = m2[:,np.newaxis] 25 | m2_row = m2[np.newaxis,:] 26 | # print(m1,m2,m1_col.shape,m1_row.shape) 27 | 28 | 29 | 30 | X1 = np.matrix(X1).T 31 | X2 = np.matrix(X2).T 32 | c1 = np.matrix(np.cov(X1)) 33 | c2 = np.matrix(np.cov(X2)) 34 | c1_inv = np.linalg.inv(c1) 35 | c2_inv = np.linalg.inv(c2) 36 | c1_det = np.linalg.det(c1) 37 | c2_det = np.linalg.det(c2) 38 | W1 = -1/2*c1_inv 39 | W2 = -1/2*c2_inv 40 | w1 = c1_inv*m1_col 41 | w2 = c2_inv*m2_col 42 | w10 = -1/2*np.dot(np.dot(m1_row,c1_inv),m1_col) - 1/2*log(c1_det) + log(priors[0]) 43 | w20 = -1/2*np.dot(np.dot(m2_row,c2_inv),m2_col) - 1/2*log(c2_det) + log(priors[1]) 44 | 45 | # print(W1.shape,w1.shape,w10.shape) 46 | W12 = W1 - W2 47 | w12 = w1 - w2 48 | 49 | print(W1,w1,w10,W2,w2,w20) 50 | 51 | 52 | print("%f x1^2 + %f x2^2 + %f x1*x2 + %f x1 + %f x2 + %f = 0" % 53 | (W12[0, 0], W12[1, 1], W12[0, 1] + W12[1, 0],w12[0, 0], w12[1, 0], w10-w20)) 54 | 55 | 56 | 57 | 58 | x = np.arange(0,6,0.01) 59 | y = np.arange(-5,10,0.01) 60 | x,y = np.meshgrid(x,y) 61 | # -0.562500 x1^2 + 0.000000 x2^2 + 0.000000 x1*x2 + 3.375000 x1 + 3.000000 x2 + -10.369353 = 0 62 | z = -0.562500*np.power(x,2) + 3.375000*x + 3*y - 10.369353 63 | plt.contour(x,y,z,0) 64 | plt.savefig('nb.png') -------------------------------------------------------------------------------- /HW5/zhuwy/nb.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Learner0x5a/Pattern-Recognition/5ec3728b3d0c0abf0384c026c2684659585ec4bb/HW5/zhuwy/nb.png -------------------------------------------------------------------------------- /HW5/zyq/README.md: -------------------------------------------------------------------------------- 1 | ### Yunqi's Solution for HW5 2 | 思路 3 | - 又是听不懂老师在讲什么心里想着不如去睡觉于是就去睡觉了的一下午 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /HW5/zyq/q1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Learner0x5a/Pattern-Recognition/5ec3728b3d0c0abf0384c026c2684659585ec4bb/HW5/zyq/q1.png -------------------------------------------------------------------------------- /HW5/zyq/question1.m: -------------------------------------------------------------------------------- 1 | clc;clear;close all; 2 | point1 = [[3,4];[3,8];[2,6];[4,6]]; 3 | point2 = [[3,0];[3,-4];[1,-2];[5,-2]]; 4 | 5 | figure; 6 | scatter(point1(:,1),point1(:,2),'b'); 7 | hold on; 8 | scatter(point2(:,1),point2(:,2),'r'); 9 | hold on; 10 | 11 | len1 = size(point1,1); 12 | len2 = size(point2,1); 13 | M1 = sum(point1)'/len1; 14 | M2 = sum(point2)'/len2; 15 | 16 | sigma1 = cov(point1); 17 | sigma2 = cov(point2); 18 | 19 | W1 = -0.5*inv(sigma1); 20 | W2 = -0.5*inv(sigma2); 21 | 22 | 23 | w1 = inv(sigma1)*M1; 24 | w2 = inv(sigma2)*M2; 25 | 26 | w10 = -0.5*w1'*M1-0.5*log(det(sigma1)); 27 | w20 = -0.5*w2'*M2-0.5*log(det(sigma2)); 28 | 29 | digits(4) 30 | W_minus =vpa( W1-W2); 31 | w_minus = vpa(w1-w2); 32 | w0_minus = vpa(w10-w20); 33 | syms x1 34 | syms x2 35 | 36 | ezplot(W_minus(1,1)*x1^2+(W_minus(1,2)+W_minus(2,1))*x1*x2+W_minus(2,2)*x2^2+w_minus(1,1)*x1+w_minus(2,1)*x2+w0_minus==0); 37 | 38 | axis([0,6,-5,9]); 39 | 40 | -------------------------------------------------------------------------------- /HW6/zhuwy/hierarchy.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Learner0x5a/Pattern-Recognition/5ec3728b3d0c0abf0384c026c2684659585ec4bb/HW6/zhuwy/hierarchy.png -------------------------------------------------------------------------------- /HW6/zhuwy/kmeans.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Learner0x5a/Pattern-Recognition/5ec3728b3d0c0abf0384c026c2684659585ec4bb/HW6/zhuwy/kmeans.png -------------------------------------------------------------------------------- /HW6/zhuwy/kmeans.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from sklearn.cluster import KMeans 3 | from sklearn.metrics import mean_squared_error 4 | import matplotlib 5 | matplotlib.use('AGG') 6 | import matplotlib.pyplot as plt 7 | fig = plt.figure() 8 | 9 | f = open('testSet.txt','r') 10 | lines = f.readlines() 11 | f.close() 12 | X = [] 13 | for line in lines: 14 | x = float(line.split()[0]) 15 | y = float(line.split()[1]) 16 | X.append((x,y)) 17 | X = np.asarray(X) 18 | 19 | 20 | # # init_points = np.array([(-4.822,4.607),(-0.7188,-2.493),(4.377,4.864)]) 21 | # # init_points = np.array([(-3.594,2.857),(-0.6595,3.111),(3.998,2.519)]) 22 | # # init_points = np.array([(-0.7188,-2.493),(0.8458,-3.59),(1.149,3.345)]) 23 | # init_points = np.array([(-3.276,1.577),(3.275,2.958),(4.377,4.864)]) 24 | # model = KMeans(n_clusters=3,init=init_points,max_iter=300,verbose=1) 25 | 26 | # init_points = np.array([(-0.00675,3.227),(-0.46,-2.77)]) 27 | # model = KMeans(n_clusters=2,init=init_points,max_iter=300,verbose=1) 28 | 29 | init_points = np.array([(0.355,-3.36),(2.934,3.128),(-1.126,-2.302),(-2.947,3.236)]) 30 | model = KMeans(n_clusters=4,init=init_points,max_iter=300,verbose=1) 31 | 32 | model.fit(X) 33 | # print(model.cluster_centers_) 34 | # init_points = model.cluster_centers_ 35 | pred = model.predict(X) 36 | # print(pred) 37 | plt.scatter(X[:,0],X[:,1],c=pred,alpha=0.5) 38 | plt.scatter(init_points[:,0],init_points[:,1],c='r',alpha=0.5,label='initial points') 39 | plt.xlabel('x') 40 | plt.ylabel('y') 41 | plt.legend() 42 | plt.savefig('kmeans.png') -------------------------------------------------------------------------------- /HW6/zhuwy/testSet.txt: -------------------------------------------------------------------------------- 1 | 3.275154 2.957587 2 | -3.344465 2.603513 3 | 0.355083 -3.376585 4 | 1.852435 3.547351 5 | -2.078973 2.552013 6 | -0.993756 -0.884433 7 | 2.682252 4.007573 8 | -3.087776 2.878713 9 | -1.565978 -1.256985 10 | 2.441611 0.444826 11 | -0.659487 3.111284 12 | -0.459601 -2.618005 13 | 2.177680 2.387793 14 | -2.920969 2.917485 15 | -0.028814 -4.168078 16 | 3.625746 2.119041 17 | -3.912363 1.325108 18 | -0.551694 -2.814223 19 | 2.855808 3.483301 20 | -3.594448 2.856651 21 | 0.421993 -2.372646 22 | 1.650821 3.407572 23 | -2.082902 3.384412 24 | -0.718809 -2.492514 25 | 4.513623 3.841029 26 | -4.822011 4.607049 27 | -0.656297 -1.449872 28 | 1.919901 4.439368 29 | -3.287749 3.918836 30 | -1.576936 -2.977622 31 | 3.598143 1.975970 32 | -3.977329 4.900932 33 | -1.791080 -2.184517 34 | 3.914654 3.559303 35 | -1.910108 4.166946 36 | -1.226597 -3.317889 37 | 1.148946 3.345138 38 | -2.113864 3.548172 39 | 0.845762 -3.589788 40 | 2.629062 3.535831 41 | -1.640717 2.990517 42 | -1.881012 -2.485405 43 | 4.606999 3.510312 44 | -4.366462 4.023316 45 | 0.765015 -3.001270 46 | 3.121904 2.173988 47 | -4.025139 4.652310 48 | -0.559558 -3.840539 49 | 4.376754 4.863579 50 | -1.874308 4.032237 51 | -0.089337 -3.026809 52 | 3.997787 2.518662 53 | -3.082978 2.884822 54 | 0.845235 -3.454465 55 | 1.327224 3.358778 56 | -2.889949 3.596178 57 | -0.966018 -2.839827 58 | 2.960769 3.079555 59 | -3.275518 1.577068 60 | 0.639276 -3.412840 61 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Pattern-Recognition 2 | 2019-2020春季学期 清华大学电子工程系 研究生课程 模式识别 3 | 4 | HW1: 模板匹配数字识别 + 基于特征的数字识别 5 | 6 | HW2: PCA, 最小距离分类 7 | 8 | HW3: 非线性分类 9 | 10 | HW4: 梯度下降, KNN, LDA(Fisher) 11 | 12 | HW5: 朴素贝叶斯分类 13 | 14 | HW6: K-means, 朴素贝叶斯, 层次聚类(DIANA) `暴力画图` 15 | 16 | Final: PCA+KNN / CNN 17 | --------------------------------------------------------------------------------