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()