多分类感知器算法的python实现

多分类感知器算法的Python实现可以通过构建多个二分类感知器来实现。以下是一个简单的多分类感知器算法的Python实现示例：

import numpy as np

class MultiPerceptron:
    def __init__(self, n_features, n_classes, learning_rate=0.01, n_epochs=100):
        self.n_features = n_features
        self.n_classes = n_classes
        self.learning_rate = learning_rate
        self.n_epochs = n_epochs
        self.weights = np.zeros((n_classes, n_features))
        self.biases = np.zeros(n_classes)

    def predict(self, X):
        scores = np.dot(X, self.weights.T) + self.biases
        return np.argmax(scores, axis=1)

    def fit(self, X, y):
        for epoch in range(self.n_epochs):
            for i in range(X.shape[0]):
                scores = np.dot(X[i], self.weights.T) + self.biases
                y_pred = np.argmax(scores)
                if y_pred != y[i]:
                    self.weights[y[i]] += self.learning_rate * X[i]
                    self.biases[y[i]] += self.learning_rate
                    self.weights[y_pred] -= self.learning_rate * X[i]
                    self.biases[y_pred] -= self.learning_rate

其中，`n_features`为特征数，`n_classes`为类别数，`learning_rate`为学习率，`n_epochs`为迭代次数。`weights`为权重矩阵，`biases`为偏置向量，可通过`fit`方法进行训练，通过`predict`方法进行预测。