感知机python代码

以下是一个基本的感知机Python代码示例：

import numpy as np

class Perceptron:
    def __init__(self, learning_rate=0.01, max_iterations=100):
        self.learning_rate = learning_rate
        self.max_iterations = max_iterations
    
    def fit(self, X, y):
        self.weights = np.zeros(1 + X.shape[1])
        self.errors = []
        
        for _ in range(self.max_iterations):
            errors = 0
            for xi, target in zip(X, y):
                update = self.learning_rate * (target - self.predict(xi))
                self.weights[1:] += update * xi
                self.weights[0] += update
                errors += int(update != 0.0)
            self.errors.append(errors)
        return self
    
    def net_input(self, X):
        return np.dot(X, self.weights[1:]) + self.weights[0]
    
    def predict(self, X):
        return np.where(self.net_input(X) >= 0.0, 1, -1)

这是一个简单的二元分类感知机实现。其中，`fit`方法用于训练感知机模型；`net_input`方法用于计算输入的加权和；`predict`方法用于预测输出结果。