机器学习与数据挖掘-实验五

用线性核与高斯核训练支持向量机

使用Sklearn，在西瓜集3.0a上分别使用线性核和高斯核训练一个SVM，并比较其支持向量的差别。

data_file_watermelon_3a = "watermelon_3a.csv"
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import svm

df = pd.read_csv(data_file_watermelon_3a, header=None, )
df.columns = ['id', 'density', 'sugar_content', 'label']
df.set_index(['id'])
X = df[['density', 'sugar_content']].values
y = df['label'].values

########## SVM training and comparison
# based on linear kernel as well as gaussian kernel
from sklearn import svm 
for fig_num, kernel in enumerate(('linear', 'rbf')): 
	 #补充构建SVM模型及训练代码
     # initial
    svc = svm.SVC(C=1000, kernel=kernel)  # classifier 1 based on linear kernel
   # train
    svc.fit(X, y)
    #给定新的样本X_test，预测其标签
    X_test = [[0.719,0.103]]
    #补充预测代码 
    y_train_pred = svc.predict( X_test)
    print('The predction result:',y_train_pred)
    # get support vectors
    sv = svc.support_vectors_
    ##### draw decision zone
    plt.figure(fig_num)
    plt.clf()
    
    # plot point and mark out support vectors
    plt.scatter( X[:,0],  X[:,1], edgecolors='k', c=y, cmap=plt.cm.Paired,  zorder=10)
    plt.scatter(sv[:,0], sv[:,1], edgecolors='k', facecolors='none', s=80, linewidths=2, zorder=10)
    # plot the decision boundary and decision zone into a color plot
    x_min, x_max = X[:, 0].min() - 0.2, X[:, 0].max() + 0.2
    y_min, y_max = X[:, 1].min() - 0.2, X[:, 1].max() + 0.2
    XX, YY = np.meshgrid(np.arange(x_min, x_max, 0.02), np.arange(y_min, y_max, 0.02))
    Z = svc.decision_function(np.c_[XX.ravel(), YY.ravel()]) 
    Z = Z.reshape(XX.shape)
    plt.pcolormesh(XX, YY, Z>0, cmap=plt.cm.Paired)
    plt.contour(XX, YY, Z, colors=['k', 'k', 'k'], linestyles=['--', '-', '--'], levels=[-.5, 0, .5])
    
    plt.title(kernel)
    plt.axis('tight')
    plt.show()