KNN，线性回归，岭回归，lasso四种算法实现人脸自动补全对比

代码全部在jupyter中实现

一、 导包和导入相关数据

import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.linear_model import LinearRegression,Ridge,Lasso
from sklearn.neighbors import KNeighborsRegressor
import sklearn.datasets as datasets
#人脸数据
faces = datasets.fetch_olivetti_faces()

查看数据：

faces

data = faces['images']
data.shape

随意查看一张人脸

index = np.random.randint(400,size  =1)[0]
plt.imshow(data[index],cmap=plt.cm.gray)

二、进行人脸的图片分割

#上半张人脸
X = data[:,:32].reshape(400,-1)
#下半张人脸
y = data[:,32:].reshape(400,-1)

#导包，进行测试数据和训练数据的分割
from sklearn.model_selection import train_test_split

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 10)
#10个位测试数据，test_size可以为int类型

进行简单的测试：

index = np.random.randint(390,size = 1)[0]
face_up = X_train[index].reshape(32,64)
face_down = y_train[index].reshape(32,64)
ax = plt.subplot(1,3,1)
ax.imshow(face_up,cmap = plt.cm.gray)
ax = plt.subplot(1,3,2)
ax.imshow(face_down,cmap = plt.cm.gray)
ax = plt.subplot(1,3,3)
ax.imshow(np.concatenate([face_up,face_down],axis = 0),cmap = 'gray')

三、训练和测试

四种算法 KNN，线性回归，岭回归，lasso

estimator = {}

estimator['KNN'] = KNeighborsRegressor(n_neighbors=5)

estimator['Lr'] = LinearRegression()

estimator['Ridge'] = Ridge(alpha=1)

estimator['Lasso'] = Lasso(alpha=1)

进行训练：

predict_ = {}
for key,model in estimator.items():
    model.fit(X_train,y_train)
    
    #预测：根据上半张脸预测下半张连
    y_ = model.predict(X_test)
    predict_[key] = y_

可视化，进行比较

#可视化

#10行，6列
plt.figure(figsize = (6*2,10*2))

for i in range(10):
    #第一列
    ax = plt.subplot(10,6,1 + i * 6)
    face_up = X_test[i].reshape(32,64)
    face_down = y_test[i].reshape(32,64)
    ax.imshow(np.concatenate([face_up,face_down],axis = 0),cmap = 'gray')
    ax.axis('off') #去除刻度
    if i == 0:
        ax.set_title('True') #添加标题
    #第二列
    ax = plt.subplot(10,6,2 + i * 6)
    ax.imshow(face_up,cmap = 'gray')  
    if i == 0:
        ax.set_title('Face_up')
    #第三、四、五、六列
    #预测人脸predict_
    for j ,key in enumerate(predict_):
        ax = plt.subplot(10,6,3 + j + i*6 )

        y_ = predict_[key]
        face_down_ = y_[i].reshape(32,64)
        ax.imshow(np.concatenate([face_up,face_down_],axis = 0),cmap = 'gray')
        ax.axis('off')
        if i == 0:
            ax.set_title(key)

如图为测试结果图：

希望提出建议进行改进。