写一段基于yale人脸数据集进行pca+svm的代码

在Python中，你可以使用scikit-learn库来实现基于Yale人脸数据集的PCA（主成分分析）和SVM（支持向量机）模型。以下是示例代码：

# 导入所需的库
from sklearn.datasets import fetch_lfw_people
from sklearn.decomposition import PCA
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# 加载Yale人脸数据集
data = fetch_lfw_people(min_faces_per_person=70, resize=0.4)
X = data.images.reshape((len(data.images), -1))  # 将图像数据展平为一维数组
y = data.target

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 应用PCA进行特征提取
pca = PCA(n_components=150)  # 可视化效果好，选择150个主成分
X_train_pca = pca.fit_transform(X_train)
X_test_pca = pca.transform(X_test)

# 初始化SVM模型
svm = SVC(kernel='linear')  # 线性核，可根据需要尝试其他核函数

# 训练模型
svm.fit(X_train_pca, y_train)

# 预测
predictions = svm.predict(X_test_pca)

# 评估性能
accuracy = accuracy_score(y_test, predictions)
print(f"Accuracy: {accuracy * 100}%")

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