import numpy as np import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression,Ridge,Lasso from sklearn.neighbors import KNeighborsRegressor from sklearn.ensemble import ExtraTreesRegressor from sklearn.datasets import fetch_olivetti_faces
faces=fetch_olivetti_faces() data=faces['data'] target=faces['target'] #data.shape
#人脸补全 #人脸数据一分为二,上半部分作为数据,下半部分作为target face_up,face_down=data[:,:2048],data[:,2048:] from sklearn.model_selection import train_test_split x_train,x_test,y_train,y_test=train_test_split(face_up,face_down,test_size=0.1) #5个算法分别识别 estimators={'knn':KNeighborsRegressor(), 'LinearRe':LinearRegression(), 'Ridge':Ridge(alpha=0.1), 'Lasso':Lasso(alpha=0.5), 'ExtraTree':ExtraTreesRegressor()} #face_down[2048] result_ = {} for key,estimator in estimators.items(): estimator.fit(x_train,y_train) y_ = estimator.predict(x_test) result_[key] = y_
plt.figure(figsize=(2*6,10*2)) for i in range(10): if i : axes=plt.subplot(10,6,i*6+1) else: axes=plt.subplot(10,6,1,title='True Face') axes.axis('off') face_up=x_test[i] face_down=y_test[i] face_full=np.hstack((face_up,face_down)) face_image=face_full.reshape((64,64)) axes.imshow(face_image,cmap='gray') for j,key in enumerate(result_): if i : axes=plt.subplot(10,6,i*6+2+j) else: axes=plt.subplot(10,6,2+j,title=key) face_up=x_test[i] y_=result_[key] face_down_predict=y_[i] face_full_predict=np.hstack((face_up,face_down_predict)) face_image_predict=face_full_predict.reshape((64,64)) axes.imshow(face_image_predict,cmap='gray')
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