支持向量机SVM应用

简单示例

# coding=utf-8 from sklearn import svm # 定义三个点 X = [[2, 0], [1, 1], [2,3]] # class label 定义点的分类,分别对应上边三个点,且线性可分 y = [0, 0, 1] # 建立分类器,选用SVC方程,kernel表示核函数(线性) clf = svm.SVC(kernel = 'linear') # 进行分类,建立模型 clf.fit(X, y) print clf # 打印支持向量 print clf.support_vectors_ # 打印之前传入点属于支持向量的index print clf.support_ # 打印 每个类有多少点属于支持向量 print clf.n_support_ # 预测 print clf.predict([2, .0])

运行结果

SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, DeprecationWarning) decision_function_shape=None, degree=3, gamma='auto', kernel='linear', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False) [[ 1. 1.] [ 2. 3.]] [1 2] [1 1] [0]

一个稍微复杂一点的例子

# coding=utf-8 print(__doc__) import numpy as np import pylab as pl from sklearn import svm # we create 40 separable points # seed设为0每次运行结果相同 np.random.seed(0) # 以正太分布生成点 X = np.r_[np.random.randn(20, 2) - [2, 2], np.random.randn(20, 2) + [2, 2]] # 前二十个是0类,后二十个是1类 Y = [0] * 20 + [1] * 20 # fit the model,构建分类器 clf = svm.SVC(kernel='linear') # 构建模型 clf.fit(X, Y) # In scikit-learn coef_ attribute holds the vectors of the separating hyperplanes for linear models. It has shape (n_classes, n_features) if n_classes > 1 (multi-class one-vs-all) and (1, n_features) for binary classification. # # In this toy binary classification example, n_features == 2, hence w = coef_[0] is the vector orthogonal to the hyperplane (the hyperplane is fully defined by it + the intercept). # # To plot this hyperplane in the 2D case (any hyperplane of a 2D plane is a 1D line), we want to find a f as in y = f(x) = a.x + b. In this case a is the slope of the line and can be computed by a = -w[0] / w[1]. # 得到超平面截距方程 w = 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 vectors b = 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: ", yy print "support_vectors_: ", clf.support_vectors_ print "clf.coef_: ", clf.coef_ # plot the line, the points, and the nearest vectors to the plane pl.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()