更新sklearn-svm-demo代码

src/python/6.SVM/sklearn-svm-demo.py

@@ -0,0 +1,80 @@
+#!/usr/bin/python
+# coding:utf8
+
+"""
+Created on 2017-06-28
+Updated on 2017-06-28
+SVM：最大边距分离超平面
+@author: 片刻
+《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import svm
+print(__doc__)
+
+
+# 创建40个分离点
+np.random.seed(0)
+# X = np.r_[np.random.randn(20, 2) - [2, 2], np.random.randn(20, 2) + [2, 2]]
+# Y = [0] * 20 + [1] * 20
+
+
+def loadDataSet(fileName):
+    """
+    对文件进行逐行解析，从而得到第行的类标签和整个数据矩阵
+    Args:
+        fileName 文件名
+    Returns:
+        dataMat  数据矩阵
+        labelMat 类标签
+    """
+    dataMat = []
+    labelMat = []
+    fr = open(fileName)
+    for line in fr.readlines():
+        lineArr = line.strip().split('\t')
+        dataMat.append([float(lineArr[0]), float(lineArr[1])])
+        labelMat.append(float(lineArr[2]))
+    return dataMat, labelMat
+
+
+X, Y = loadDataSet('input/6.SVM/testSet.txt')
+X = np.mat(X)
+
+print("X=", X)
+print("Y=", Y)
+
+# 拟合一个SVM模型
+clf = svm.SVC(kernel='linear')
+clf.fit(X, Y)
+
+# 获取分割超平面
+w = clf.coef_[0]
+# 斜率
+a = -w[0] / w[1]
+# 从-5到5，顺序间隔采样50个样本，默认是num=50
+# xx = np.linspace(-5, 5)  # , num=50)
+xx = np.linspace(-2, 10)  # , num=50)
+# 二维的直线方程
+yy = a * xx - (clf.intercept_[0]) / w[1]
+print("yy=", yy)
+
+# plot the parallels to the separating hyperplane that pass through the support vectors
+# 通过支持向量绘制分割超平面
+print("support_vectors_=", clf.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])
+
+# plot the line, the points, and the nearest vectors to the plane
+plt.plot(xx, yy, 'k-')
+plt.plot(xx, yy_down, 'k--')
+plt.plot(xx, yy_up, 'k--')
+
+plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=80, facecolors='none')
+plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)
+
+plt.axis('tight')
+plt.show()

src/python/6.SVM/svm-complete.py

@@ -497,26 +497,26 @@ def plotfig_SVM(xArr, yArr, ws, b, alphas):
 
 if __name__ == "__main__":
 
-    # # 无核函数的测试
-    # # 获取特征和目标变量
-    # dataArr, labelArr = loadDataSet('input/6.SVM/testSet.txt')
-    # # print labelArr
+    # 无核函数的测试
+    # 获取特征和目标变量
+    dataArr, labelArr = loadDataSet('input/6.SVM/testSet.txt')
+    # print labelArr
 
-    # # b是常量值， alphas是拉格朗日乘子
-    # b, alphas = smoP(dataArr, labelArr, 0.6, 0.001, 40)
-    # print '/n/n/n'
-    # print 'b=', b
-    # print 'alphas[alphas>0]=', alphas[alphas > 0]
-    # print 'shape(alphas[alphas > 0])=', shape(alphas[alphas > 0])
-    # for i in range(100):
-    #     if alphas[i] > 0:
-    #         print dataArr[i], labelArr[i]
-    # # 画图
-    # ws = calcWs(alphas, dataArr, labelArr)
-    # plotfig_SVM(dataArr, labelArr, ws, b, alphas)
+    # b是常量值， alphas是拉格朗日乘子
+    b, alphas = smoP(dataArr, labelArr, 0.6, 0.001, 40)
+    print '/n/n/n'
+    print 'b=', b
+    print 'alphas[alphas>0]=', alphas[alphas > 0]
+    print 'shape(alphas[alphas > 0])=', shape(alphas[alphas > 0])
+    for i in range(100):
+        if alphas[i] > 0:
+            print dataArr[i], labelArr[i]
+    # 画图
+    ws = calcWs(alphas, dataArr, labelArr)
+    plotfig_SVM(dataArr, labelArr, ws, b, alphas)
 
     # # 有核函数的测试
-    testRbf(0.8)
+    # testRbf(0.8)
 
     # 项目实战
     # 示例：手写识别问题回顾