统计学习方法2-python实现感知机

1.感知机

题目：

1. 使用python 实现其详细代码过程

import numpy as np
import time
X = np.array([[3,3],[4,3],[1,1]])
Y = np.array([1,1,-1])
w = np.array([0,0], dtype = np.float32)
i,b,eta = 0,0.0,1
while True:
    neg_id = -1
    for cur_id,(x,y) in enumerate(zip(X,Y)):
        if (np.dot(w,x) + b)*y <= 0:
            neg_id = cur_id
            w +=  eta*x*y
            b += y
            break
    if neg_id == -1:
        break
    i +=1
    print('Step{}: neg_id = {}, w = {}, b = {}'.format(i,neg_id,w,b))

2. 调库实现

import numpy as np
import time
from sklearn.linear_model import Perceptron

X =np.array([[3,3],[3,4],[1,1]],dtype= np.float32)
Y = np.array([1,1,-1])
module = Perceptron(penalty=None,
                    alpha=0.0001,
                    fit_intercept=True,
                    max_iter=5,
                    tol=None,
                    shuffle=False,
                    eta0=.01)
start_time = time.time()
module.fit(X,Y)

print(time.time() - start_time)
print(module.score(X,Y))
print(module.coef_,module.intercept_,module.n_iter_)