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from sklearn.decomposition import pca

import numpy as np

from sklearn.svm import svc

import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split

from sklearn.decomposition import pca

from sklearn.metrics import accuracy_score

from sklearn.linear_model import logisticregression

from sklearn import datasets

from sklearn.model_selection import gridsearchcv

# 加载人脸数据 lfw->labled faces wild:野外标记的人脸

data = datasets.fetch_lfw_people(resize = 1, min_faces_per_person = 70)

data

# 进行数据的降维

pca = pca(n_components=0.95)

x_pca = pca.fit_transform(x)

display(x.shape,x_pca.shape)

svc = svc()

# c为惩罚系数(防止过拟合)，kernel为核函数类型，tol为停止训练的误差值、精度

params = 跟单网gendan5.com{'c':np.logspace(-10,10,50),'kernel':['linear', 'poly', 'rbf', 'sigmoid'],'tol':[0.01,0.001,0.0001]}

gc = gridsearchcv(estimator = svc,param_grid = params,cv = 5)

gc.fit(x_pca,y)

gc.best_params_

svc = svc(c = 1.8420699693267165e-07,kernel='linear',tol = 0.001)

# 随机划分的

x_pca_train,x_pca_test,y_train,y_test, faces_train,faces_test = train_test_split(x_pca,y,faces)

svc.fit(x_pca_train,y_train)

print('训练数据得分：',svc.score(x_pca_train,y_train))

print('测试数据的得分：',svc.score(x_pca_test,y_test))

plt.figure(figsize=(5 * 2, 10 * 3))

for i in range(50):

    plt.subplot(10,5,i 1) # 子视图

    plt.imshow(faces_test[i],cmap = 'gray')

    plt.axis('off') # 刻度关闭

    # 贴上标签，并且对比实际数据和预测数据

    true_name = target_names[y_test[i]].split(' ')[-1]

    predict_name = target_names[y_pred[i]].split(' ')[-1]

    plt.title(f'true:{true_name}\npred:{predict_name}')