Python机器学习之SVM支持向量机

# -*- coding: utf-8 -*- 
import numpy as np 
import scipy as sp 
from sklearn import svm 
from sklearn.cross_validation import train_test_split 
import matplotlib.pyplot as plt 
 
data  = [] 
labels = [] 
with open("data\\1.txt") as ifile: 
    for line in ifile: 
      tokens = line.strip().split(' ') 
      data.append([float(tk) for tk in tokens[:-1]]) 
      labels.append(tokens[-1]) 
x = np.array(data) 
labels = np.array(labels) 
y = np.zeros(labels.shape) 
y[labels=='fat']=1 
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.0) 
 
h = .02  
# create a mesh to plot in 
x_min, x_max = x_train[:, 0].min() - 0.1, x_train[:, 0].max() + 0.1 
y_min, y_max = x_train[:, 1].min() - 1, x_train[:, 1].max() + 1 
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), 
           np.arange(y_min, y_max, h)) 
 
''''' SVM ''' 
# title for the plots 
titles = ['LinearSVC (linear kernel)', 
     'SVC with polynomial (degree 3) kernel', 
     'SVC with RBF kernel', 
     'SVC with Sigmoid kernel'] 
clf_linear = svm.SVC(kernel='linear').fit(x, y) 
#clf_linear = svm.LinearSVC().fit(x, y) 
clf_poly  = svm.SVC(kernel='poly', degree=3).fit(x, y) 
clf_rbf   = svm.SVC().fit(x, y) 
clf_sigmoid = svm.SVC(kernel='sigmoid').fit(x, y) 
 
for i, clf in enumerate((clf_linear, clf_poly, clf_rbf, clf_sigmoid)): 
  answer = clf.predict(np.c_[xx.ravel(), yy.ravel()]) 
  print(clf) 
  print(np.mean( answer == y_train)) 
  print(answer) 
  print(y_train) 
 
  plt.subplot(2, 2, i + 1) 
  plt.subplots_adjust(wspace=0.4, hspace=0.4) 
   
  # Put the result into a color plot 
  z = answer.reshape(xx.shape) 
  plt.contourf(xx, yy, z, cmap=plt.cm.Paired, alpha=0.8) 
   
  # Plot also the training points 
  plt.scatter(x_train[:, 0], x_train[:, 1], c=y_train, cmap=plt.cm.Paired) 
  plt.xlabel(u'身高') 
  plt.ylabel(u'体重') 
  plt.xlim(xx.min(), xx.max()) 
  plt.ylim(yy.min(), yy.max()) 
  plt.xticks(()) 
  plt.yticks(()) 
  plt.title(titles[i]) 
   
plt.show() 

''''' 数据生成 ''' 
h = 0.1 
x_min, x_max = -1, 1 
y_min, y_max = -1, 1 
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), 
           np.arange(y_min, y_max, h)) 
n = xx.shape[0]*xx.shape[1] 
x = np.array([xx.T.reshape(n).T, xx.reshape(n)]).T 
y = (x[:,0]*x[:,0] + x[:,1]*x[:,1] < 0.8) 
y.reshape(xx.shape) 
 
x_train, x_test, y_train, y_test\ 
  = train_test_split(x, y, test_size = 0.2)