感知器算法实验代码

以下是一个简单的感知器算法实验代码，可以用于二分类问题：

import numpy as np

class Perceptron:
    def __init__(self, learning_rate=0.1, num_epochs=100):
        self.learning_rate = learning_rate
        self.num_epochs = num_epochs
        
    def fit(self, X, y):
        self.weights = np.zeros(X.shape[1])
        self.bias = 0
        
        for epoch in range(self.num_epochs):
            for i in range(X.shape[0]):
                if y[i] * (np.dot(X[i], self.weights) + self.bias) <= 0:
                    self.weights += self.learning_rate * y[i] * X[i]
                    self.bias += self.learning_rate * y[i]
                    
    def predict(self, X):
        y_pred = np.sign(np.dot(X, self.weights) + self.bias)
        return y_pred

其中，`learning_rate` 为学习率，`num_epochs` 为迭代次数。`fit` 方法用于训练模型，`X` 为特征矩阵，`y` 为标签向量。`predict` 方法用于预测新样本的标签，`X` 为特征矩阵。