统计学习方法第二章

import numpy as np
training_set = [[[3,3],1],[[4,3],1],[[1,1],-1]]
eta = 1  #study rate
#原始形式
def perceptron1(train_data,eta):
    w = [0,0]
    b = 0
    for item in train_data:
        if item[1]*(np.dot(w,item[0]) + b) <= 0:
            step = item[0][0]*item[1]*eta
            w = [i + step for i in w]
            b = b + eta*item[1]
            print(w,b)
    return w,b
t1,t2 = perceptron1(training_set,eta)
#对偶形式
def perceptron2(train_data,eta):
    N = len(train_data)
    a = np.zeros(shape=N)
    b = 0
    X = np.array([i[0] for i in train_data])
    Y = np.array([i[1] for i in train_data])
   
    #计算Gram矩阵
    G = np.zeros(shape=(N,N))
    index = 0
    for i in X:
        G[index] = np.array([np.dot(i,j) for j in X])
        print(G[index])
        index = index + 1
    Gram = np.array(G)
   
    #根据每个样本进行迭代
    for iters in range(N):
        print(iters)
        print(Y[iters],np.sum(a*Y*Gram[iters])+b)
        if Y[iters]*(np.sum(a*Y*Gram[iters])+b) <= 0:
            a = a + 1
            b = b + train_data[iters][1]
            print(iters,a,b)
   
    w = np.dot(a*Y,X)
    return w,b
   
t3,t4 = perceptron2(training_set,eta)

不知道为什么两种方法得出的结果不一样，sad