import numpy as np
from scipy import io as spio
from matplotlib import pyplot as plt
from sklearn import svm

def SVM():
 '''data1——线性分类'''
 data1 = spio.loadmat('data1.mat')
 X = data1['X']
 y = data1['y']
 y = np.ravel(y)
 plot_data(X,y)
 
 model = svm.SVC(C=1.0,kernel='linear').fit(X,y) # 指定核函数为线性核函数
 plot_decisionBoundary(X, y, model) # 画决策边界
 '''data2——非线性分类'''
 data2 = spio.loadmat('data2.mat')
 X = data2['X']
 y = data2['y']
 y = np.ravel(y)
 plt = plot_data(X,y)
 plt.show()
 
 model = svm.SVC(gamma=100).fit(X,y) # gamma为核函数的系数,值越大拟合的越好
 plot_decisionBoundary(X, y, model,class_='notLinear') # 画决策边界
 
 
 
# 作图
def plot_data(X,y):
 plt.figure(figsize=(10,8))
 pos = np.where(y==1) # 找到y=1的位置
 neg = np.where(y==0) # 找到y=0的位置
 p1, = plt.plot(np.ravel(X[pos,0]),np.ravel(X[pos,1]),'ro',markersize=8)
 p2, = plt.plot(np.ravel(X[neg,0]),np.ravel(X[neg,1]),'g^',markersize=8)
 plt.xlabel("X1")
 plt.ylabel("X2")
 plt.legend([p1,p2],["y==1","y==0"])
 return plt
 
# 画决策边界
def plot_decisionBoundary(X,y,model,class_='linear'):
 plt = plot_data(X, y)
 
 # 线性边界 
 if class_=='linear':
 w = model.coef_
 b = model.intercept_
 xp = np.linspace(np.min(X[:,0]),np.max(X[:,0]),100)
 yp = -(w[0,0]*xp+b)/w[0,1]
 plt.plot(xp,yp,'b-',linewidth=2.0)
 plt.show()
 else: # 非线性边界
 x_1 = np.transpose(np.linspace(np.min(X[:,0]),np.max(X[:,0]),100).reshape(1,-1))
 x_2 = np.transpose(np.linspace(np.min(X[:,1]),np.max(X[:,1]),100).reshape(1,-1))
 X1,X2 = np.meshgrid(x_1,x_2)
 vals = np.zeros(X1.shape)
 for i in range(X1.shape[1]):
 this_X = np.hstack((X1[:,i].reshape(-1,1),X2[:,i].reshape(-1,1)))
 vals[:,i] = model.predict(this_X)
 
 plt.contour(X1,X2,vals,[0,1],color='blue')
 plt.show()
 


if __name__ == "__main__":
 SVM()