改进的平均感知器参数学习算法的python实现

以下是一个改进的平均感知器参数学习算法的Python实现：

import numpy as np

class ImprovedPerceptron:
    def __init__(self, learning_rate=0.1, num_epochs=1000):
        self.learning_rate = learning_rate
        self.num_epochs = num_epochs
        self.weights = None
        self.bias = None
    
    def fit(self, X, y):
        n_samples, n_features = X.shape
        
        # Initialize weights and bias to zeros
        self.weights = np.zeros(n_features)
        self.bias = 0
        
        # Initialize counters for average weights and bias
        avg_weights = np.zeros(n_features)
        avg_bias = 0
        count = 0
        
        for epoch in range(self.num_epochs):
            # Iterate over all samples
            for i in range(n_samples):
                # Compute the predicted output
                y_pred = np.dot(self.weights, X[i]) + self.bias
                
                # Update the weights and bias if there is a misclassification
                if y[i]*y_pred <= 0:
                    self.weights += self.learning_rate*y[i]*X[i]
                    self.bias += self.learning_rate*y[i]
                    
                    # Update the average weights and bias
                    count += 1
                    avg_weights += self.weights
                    avg_bias += self.bias
            
        # Compute the average weights and bias
        self.weights = avg_weights/count
        self.bias = avg_bias/count
    
    def predict(self, X):
        # Compute the predicted output for X
        y_pred = np.dot(X, self.weights) + self.bias
        
        # Return the sign of the predicted output as the predicted class
        return np.sign(y_pred)

该算法在每次更新权重和偏差时，都会更新平均权重和偏差，最终的权重和偏差是所有平均权重和偏差的平均值。这有助于减少权重和偏差的波动，从而提高算法的稳定性和泛化能力。