python svm pca实践二
继上一片的内容，这片来·讲一下sklearn来进行简单的人脸识别，这里用的方法是pca和svm 先导入必要的包和数据集 import numpy as np import matplotlib.pyplot as plt from scipy import stats from sklearn.decomposition import PCA from sklearn.svm import SVC from sklearn import datasets lfw_people = datasets.fetch_lfw_people(min_faces_per_person=70, \ resize=0.4) sklearn的人脸数据集包含5千多个不同人的人脸，但有些人的人脸只包含一张， n_samples, h, w = lfw_people.images.shape print('height and width of images:', h, w) # The images in X have been collapsed into a 1D array # just like for the handwritten digits X = lfw_people.data # X.shape[0] tells you the number of images (faces); # this is the same as n_samples ahove # X.shape[1] gives the number of pixels for each image # or, "features" print('X.shape', X.shape) n_features = X.shape[1] # the label/target to predict is the id of the person -- y is an integer y = lfw_people.target # target_names are actually names target_names = lfw_people.target_names print('target_names.shape', target_names.shape) print('target_names', target_names) # n_classes gives the number of people # Different from the number of faces (n_samples)!! n_classes = target_names.shape[0] print("Total dataset size:") print("n_samples (number of faces): {0}".format(n_samples)) # n_features = 1850, which is 50x37, the dimension of the images. print("n_features (number of pixels): {0}".format(n_features)) print("n_classes (number of people): {0}".format(n_classes)) 通过打印可以看到数据集人脸的尺寸为50x37，为7类共1288张人脸 pca = PCA(n_components=4,whiten = True) X_proj = pca.fit_transform(X[:500]) print("eigen vector",pca.components_) print("...") print('eigen value', pca.explained_variance_[:2]) print(np.var(X_proj[:,0])) print(np.var(X_proj[:,1])) 取500组数据将其降维为4个维度，并进行归一化处理 explained_variance_，它代表降维后的各主成分的方差值。方差值越大，则说明越是重要的主成分 from sklearn import svm def plot_faces(n_features): # nside = 1 X = lfw_people.data # fig, axes = plt.subplots(nside, nside, figsize=(8, 8)) plt.imshow(X[5].reshape(50,37)) plot_faces(n_features= 16) plt.show() 试着打一下其中的一幅图片 Xtrain = lfw_people.data[:1000] Xtest = lfw_people.data[1000:,] ytrain = lfw_people.target[:1000] ytest = lfw_people.target[1000:,] # Xtest = X[select_idx].reshape(1, -1) # test_img = X[select_idx] # ytest = y[select_idx] # n_comp = 50 pca = PCA(n_comp, whiten = True) pca.fit(Xtrain) # pca.fit(Xtest) Xtrain_proj = pca.transform(Xtrain) # projecting test data onto pca axes Xtest_proj = pca.transform(Xtest) print(Xtrain_proj.shape) print(Xtest_proj.shape) # ************************************* The SVM Section ******************************** # instantiating an SVM classifier clf = svm.SVC(gamma=0.001, C=100.) # apply SVM to training data and draw boundaries. clf.fit(Xtrain_proj, ytrain) # Use SVM-determined boundaries to make # a prediction for the test data point. ypred = clf.predict(Xtest_proj) correct = np.sum(ytest == ypred) print(correct/288*100) 接下来之前载入的数据用pca和svm进行训练识别，在1288个数据中取前1000组为训练集，后288个为测试集，pca将维为50维，并用训练集训练的模型对测试集进行预测，最后的测试精度为：81.25%,相对于现状流行的深度学习来说精度还是差了一点。
