感知器的三种算法用python实现

以下是三种感知器算法的Python实现示例：

1. Rosenblatt算法：

import numpy as np

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

    def fit(self, X, y, epochs=100):
        n_samples, n_features = X.shape
        self.weights = np.zeros(n_features)
        self.bias = 0
        
        for _ in range(epochs):
            for idx, sample in enumerate(X):
                y_pred = np.dot(sample, self.weights) + self.bias
                if y[idx] * y_pred <= 0:
                    self.weights += self.learning_rate * y[idx] * sample
                    self.bias += self.learning_rate * y[idx]

    def predict(self, X):
        y_pred = np.dot(X, self.weights) + self.bias
        return np.sign(y_pred)

2. Widrow-Hoff算法：

import numpy as np

class Adaline:
    def __init__(self, learning_rate=0.1):
        self.learning_rate = learning_rate
        self.weights = None
        self.bias = None

    def fit(self, X, y, epochs=100):
        n_samples, n_features = X.shape
        self.weights = np.zeros(n_features)
        self.bias = 0
        
        for _ in range(epochs):
            for idx, sample in enumerate(X):
                y_pred = np.dot(sample, self.weights) + self.bias
                error = y[idx] - y_pred
                self.weights += self.learning_rate * error * sample
                self.bias += self.learning_rate * error

    def predict(self, X):
        y_pred = np.dot(X, self.weights) + self.bias
        return np.sign(y_pred)

3. Delta规则：

import numpy as np

class DeltaRule:
    def __init__(self, learning_rate=0.1):
        self.learning_rate = learning_rate
        self.weights = None
        self.bias = None

    def fit(self, X, y, epochs=100):
        n_samples, n_features = X.shape
        self.weights = np.zeros(n_features)
        self.bias = 0
        
        for _ in range(epochs):
            for idx, sample in enumerate(X):
                y_pred = np.dot(sample, self.weights) + self.bias
                error = y[idx] - y_pred
                self.weights += self.learning_rate * error * sample
                self.bias += self.learning_rate * error

    def predict(self, X):
        y_pred = np.dot(X, self.weights) + self.bias
        return np.sign(y_pred)