感知机算法python实现

1.python代码实现

包含算法的原始形式和对偶形式

# -*- coding: utf-8 -*-

import numpy as np

class Perceptron(object):

    def __init__(self, input_x, feature_num, input_y, learn_rate=1):
        self._input_x = np.array(input_x) # 输入数据集中的X
        self._input_y = np.array(input_y) # 输入数据集中的Y
        self._feature_num = feature_num   # 总共有多少个特征
        self._rate = learn_rate           # 学习速率
        self._final_w = 0                 # 最后学习到的w
        self._final_b = 0                 # 最后学习到的b

    def sgd_train(self): #算法原始形式
        total = len(self._input_y)
        feature_num = range(self._feature_num)
        data_num = range(total)
        w = np.zeros(self._feature_num)   #初始化向量w
        b = 0

        while True:
            separted = True
            for i in data_num:           # 遍历数据集，查找误分类点
                inner = np.inner(w, self._input_x[i])
                if self._input_y[i] * (inner+b) <= 0: # 误分类点
                    separted = False
                    w = w + self._rate * self._input_y[i] * self._input_x[i]
                    b = b + self._rate * self._input_y[i]
            if separted:
                break
            else:
                continue
        self._final_w = w
        self._final_b = b
        print(self._final_w, self._final_b)

    def pair_sgd_train(self): # 对偶形式
        total = len(self._input_y)
        feature_num = range(self._feature_num)
        data_num = range(total)
        gram_matrix = self._input_x.dot(self._input_x.T) # Gram 矩阵
        alpha = np.random.random(size=total) # 这里初始化alpha向量为随机值
        b = 0

        while True:
            separted = True
            for i in data_num:
                inner = np.sum(alpha * self._input_y * gram_matrix[i])
                if self._input_y[i] * (inner+b) <= 0:  # 误分类点
                    separted = False
                    alpha[i] = alpha[i] + self._rate   # 对偶形式只更新alpha向量中的一个分量
                    b = b + self._rate * self._input_y[i]
            if separted:
                break
            else:
                continue
        self._final_w = (alpha * self._input_y.T).dot(self._input_x)
        self._final_b = b
        print(self._final_w, self._final_b)

input_x = [[3,3], [4,3], [1,1], [2,3]]
input_y = [1,1,-1,-1]

pla = Perceptron(input_x, 2, input_y, 1)
pla.sgd_train()
pla.pair_sgd_train()

2. 图形显示

用matplotlib将结果用图形画出来，在类中添加如下函数

    def draw_result(self):
        total = len(self._input_y)
        self._positive_x = []
        self._nagtive_x = []

        for i in range(total):
            if self._input_y[i] >= 0:
                self._positive_x.append(self._input_x[i])
            else:
                self._nagtive_x.append(self._input_x[i])

        plt.figure(1)
        x1 = [x[0] for x in self._positive_x]
        x2 = [x[1] for x in self._positive_x]
        plt.scatter(x1, x2, label='positive', color='g', s=30, marker="o") # 正数据集
        x1 = [x[0] for x in self._nagtive_x]
        x2 = [x[1] for x in self._nagtive_x]
        plt.scatter(x1, x2, label='nagtive', color='r', s=30, marker="x") # 负数据集
        plt.xlabel('x1')
        plt.ylabel('x2')
        plt.axis([0, 5, 0, 5])
        def f(x):
            return -(self._final_b + self._final_w[0]*x)/self._final_w[1]
        x = np.array([0,1,2,3,4,5])
        plt.plot(x, f(x), 'b-', lw=2)  # 将最后的直线画出
        plt.title('Perceptron')
        plt.legend()
        plt.show()

pla = Perceptron(input_x, 2, input_y, 1)
pla.pair_sgd_train()
pla.draw_result()

测试

input_x = [[3,3], [4,3], [1,1], [2,3]]
input_y = [1,1,-1,-1]

pla = Perceptron(input_x, 2, input_y, 1) # 2个特征，学习速率为1
pla.pair_sgd_train()
pla.draw_result()

参考文献：

http://blog.csdn.net/wangxin1982314/article/details/73529499