支持向量机SVM算法应用【Python实现】

支持向量机SVM算法应用【Python实现】

一. 代码实践：调用Python库sklearn实现

1.安装Python和机器学习库，和一些依赖包；

本人是直接安装了包含了众多包的Anaconda3 ，下载后再window7 64bit上双击安装即可；

Anaconda3较大，如果网速不好，可以从百度云下载地址：http://pan.baidu.com/s/1dFIfoYX

2.打开cmd 输入：pip list 可以查看到已经安装的包；

题目：

3. 在cmd中运行如下的Python程序：

from sklearn import svmx = [[2, 0], [1, 1], [2, 3]]y = [0, 0, 1]clf = svm.SVC(kernel = 'linear')clf.fit(x, y)print (clf)# get support vectorsprint (clf.support_vectors_)# get indices of support vectorsprint (clf.support_)# get number of support vectors for each classprint (clf.n_support_)

运行效果图：

2.运行一个更复杂的例子并且画图可视化下;

在cmd中运行如下的Python程序：

import numpy as npimport pylab as plfrom sklearn import svm# we create 40 separable pointsX = np.r_[np.random.randn(20, 2) - [2, 2], np.random.randn(20, 2) + [2, 2]]Y = [0]*20 +[1]*20#fit the modelclf = svm.SVC(kernel='linear')clf.fit(X, Y)# get the separating hyperplanew = clf.coef_[0]a = -w[0]/w[1]xx = np.linspace(-5, 5)yy = a*xx - (clf.intercept_[0])/w[1]# plot the parallels to the separating hyperplane that pass through the support vectorsb = clf.support_vectors_[0]yy_down = a*xx + (b[1] - a*b[0])b = clf.support_vectors_[-1]yy_up = a*xx + (b[1] - a*b[0])print ("w: ", w)print ("a: ", a)# print "xx: ", xx# print "yy: ", yyprint ("support_vectors_: ", clf.support_vectors_)print ("clf.coef_: ", clf.coef_)# switching to the generic n-dimensional parameterization of the hyperplan to the 2D-specific equation# of a line y=a.x +b: the generic w_0x + w_1y +w_3=0 can be rewritten y = -(w_0/w_1) x + (w_3/w_1)# plot the line, the points, and the nearest vectors to the planepl.plot(xx, yy, 'k-')pl.plot(xx, yy_down, 'k--')pl.plot(xx, yy_up, 'k--')pl.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],          s=80, facecolors='none')pl.scatter(X[:, 0], X[:, 1], c=Y, cmap=pl.cm.Paired)pl.axis('tight')pl.show()

运行效果图如下：

利用SVM处理人脸识别的demo：

在cmd中运行如下的Python程序：

from __future__ import print_functionfrom time import timeimport loggingimport matplotlib.pyplot as pltfrom sklearn.cross_validation import train_test_splitfrom sklearn.datasets import fetch_lfw_peoplefrom sklearn.grid_search import GridSearchCVfrom sklearn.metrics import classification_reportfrom sklearn.metrics import confusion_matrixfrom sklearn.decomposition import RandomizedPCAfrom sklearn.svm import SVCprint(__doc__)# Display progress logs on stdoutlogging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')################################################################################ Download the data, if not already on disk and load it as numpy arrayslfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)# introspect the images arrays to find the shapes (for plotting)n_samples, h, w = lfw_people.images.shape# for machine learning we use the 2 data directly (as relative pixel# positions info is ignored by this model)X = lfw_people.datan_features = X.shape[1]# the label to predict is the id of the persony = lfw_people.targettarget_names = lfw_people.target_namesn_classes = target_names.shape[0]print("Total dataset size:")print("n_samples: %d" % n_samples)print("n_features: %d" % n_features)print("n_classes: %d" % n_classes)################################################################################ Split into a training set and a test set using a stratified k fold# split into a training and testing setX_train, X_test, y_train, y_test = train_test_split(    X, y, test_size=0.25)################################################################################ Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled# dataset): unsupervised feature extraction / dimensionality reductionn_components = 150print("Extracting the top %d eigenfaces from %d faces"      % (n_components, X_train.shape[0]))t0 = time()pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)print("done in %0.3fs" % (time() - t0))eigenfaces = pca.components_.reshape((n_components, h, w))print("Projecting the input data on the eigenfaces orthonormal basis")t0 = time()X_train_pca = pca.transform(X_train)X_test_pca = pca.transform(X_test)print("done in %0.3fs" % (time() - t0))################################################################################ Train a SVM classification modelprint("Fitting the classifier to the training set")t0 = time()param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5],              'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }clf = GridSearchCV(SVC(kernel='rbf', class_weight='auto'), param_grid)clf = clf.fit(X_train_pca, y_train)print("done in %0.3fs" % (time() - t0))print("Best estimator found by grid search:")print(clf.best_estimator_)################################################################################ Quantitative evaluation of the model quality on the test setprint("Predicting people's names on the test set")t0 = time()y_pred = clf.predict(X_test_pca)print("done in %0.3fs" % (time() - t0))print(classification_report(y_test, y_pred, target_names=target_names))print(confusion_matrix(y_test, y_pred, labels=range(n_classes)))################################################################################ Qualitative evaluation of the predictions using matplotlibdef plot_gallery(images, titles, h, w, n_row=3, n_col=4):    """Helper function to plot a gallery of portraits"""    plt.figure(figsize=(1.8 * n_col, 2.4 * n_row))    plt.subplots_adjust(bottom=0, left=.01, right=.99, top=.90, hspace=.35)    for i in range(n_row * n_col):        plt.subplot(n_row, n_col, i + 1)        plt.imshow(images[i].reshape((h, w)), cmap=plt.cm.gray)        plt.title(titles[i], size=12)        plt.xticks(())        plt.yticks(())# plot the result of the prediction on a portion of the test setdef title(y_pred, y_test, target_names, i):    pred_name = target_names[y_pred[i]].rsplit(' ', 1)[-1]    true_name = target_names[y_test[i]].rsplit(' ', 1)[-1]    return 'predicted: %s\ntrue:      %s' % (pred_name, true_name)prediction_titles = [title(y_pred, y_test, target_names, i)                     for i in range(y_pred.shape[0])]plot_gallery(X_test, prediction_titles, h, w)# plot the gallery of the most significative eigenfaceseigenface_titles = ["eigenface %d" % i for i in range(eigenfaces.shape[0])]plot_gallery(eigenfaces, eigenface_titles, h, w)plt.show()

运行效果图：