感知机学习算法 python实现

参考李航《统计学习方法》 一开始的感知机章节，看着不太复杂就实现一下。。。

 """
 感知机学习算法的原始形式
 例2.1
 """
 import numpy as np

 class Perceptron:
     def __init__(self,w,b,alpha):
         self.w = w
         self.b = b
         self.alpha = alpha

     def loss(self,x,y):
         return np.sum( y*(np.dot(x, self.w) + self.b) )

     def sgd(self,x,y):  # 随机梯度下降函数
         self.w += self.alpha * y * x
         self.b += self.alpha * y

     def train(self,X,Y):
         while(True):
             M = len(X)   # 错误分类数
             for i in range(len(X)):
                 if self.loss(X[i],Y[i])<=0:
                     self.sgd(X[i],Y[i])
                     print "w:",self.w," b:",self.b
                 else:
                     M -= 1
             if not M:
                 print "final optimal:","w:",self.w," b:",self.b
                 break

 class Perceptron_dual:
     def __init__(self,alpha,b,ita):
         self.alpha = alpha
         self.b = b
         self.ita = ita

     def gram(self,X):
         return np.dot(X,X.T)

     def train(self,X,Y):
         g = self.gram(X)

         M = len(X)   # 错误分类数
         while(True):
             M = len(X)   # 错误分类数
             for j in range(len(X)):
                 if Y[j] * (np.sum(self.alpha * Y * g[j]) + self.b) <= 0:
                     self.alpha[j] += self.ita
                     self.b += self.ita * Y[j]
                     print "a:",self.alpha," b:",self.b
                 else:
                     M -= 1
             if M == 0:
                 print "final optimal:","a:",self.alpha," b:",self.b
                 break

 if __name__ == "__main__":

     X = np.array([[3,3],[4,3],[1,1]])

     Y = np.array([1,1,-1])
     perc_d = Perceptron_dual(np.zeros(Y.shape),0,1)
     perc_d.train(X, Y)