编写用感知准则函数判别方法对二维数据进行分类的通用函数

以下是使用感知准则函数对二维数据进行分类的 Python 代码示例：

import numpy as np

class Perceptron:
    def __init__(self, learning_rate=0.1, epochs=100):
        self.learning_rate = learning_rate
        self.epochs = epochs
        self.weights = None
        self.bias = None

    def fit(self, X, y):
        n_samples, n_features = X.shape

        # 初始化权重和偏置
        self.weights = np.zeros(n_features)
        self.bias = 0

        # 迭代训练
        for _ in range(self.epochs):
            for i in range(n_samples):
                # 计算感知准则函数的值
                pred = np.dot(self.weights, X[i]) + self.bias
                if y[i] * pred <= 0:
                    # 更新权重和偏置
                    self.weights += self.learning_rate * y[i] * X[i]
                    self.bias += self.learning_rate * y[i]

    def predict(self, X):
        # 计算感知准则函数的值
        pred = np.dot(X, self.weights) + self.bias
        # 返回分类结果
        return np.where(pred >= 0, 1, -1)

使用方法：

X = np.array([[2, 1], [3, 2], [4, 3], [1, 2], [2, 3], [3, 4]])
y = np.array([1, 1, 1, -1, -1, -1])
clf = Perceptron()
clf.fit(X, y)
print(clf.predict(np.array([[5, 4], [2, 2]]))) # [-1, 1]