两类感知器参数学习算法的简单实现输出实例

好的，我可以为您提供两类感知器参数学习算法的简单实现输出实例。这两个算法分别是 Rosenblatt 感知器算法和 Widrow-Hoff 算法。

1. Rosenblatt 感知器算法实现输出实例：

import numpy as np

class Perceptron:
    def __init__(self, input_size, lr=1, epochs=100):
        self.W = np.zeros(input_size+1)
        self.epochs = epochs
        self.lr = lr
    
    def activation_fn(self, x):
        return 1 if x >= 0 else -1
    
    def predict(self, x):
        x = np.insert(x, 0, 1)
        z = self.W.T.dot(x)
        a = self.activation_fn(z)
        return a
    
    def fit(self, X, d):
        for epoch in range(self.epochs):
            for i in range(d.shape[0]):
                x = X[i]
                y = self.predict(x)
                e = d[i] - y
                x = np.insert(x, 0, 1)
                self.W = self.W + self.lr * e * x
                
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
d = np.array([-1, -1, -1, 1])

perceptron = Perceptron(input_size=2)
perceptron.fit(X, d)

print(perceptron.W)

2. Widrow-Hoff 算法实现输出实例：

import numpy as np

class WidrowHoff:
    def __init__(self, input_size, lr=0.1, epochs=100):
        self.W = np.zeros(input_size+1)
        self.epochs = epochs
        self.lr = lr
    
    def activation_fn(self, x):
        return x
    
    def predict(self, x):
        x = np.insert(x, 0, 1)
        z = self.W.T.dot(x)
        a = self.activation_fn(z)
        return a
    
    def fit(self, X, d):
        for epoch in range(self.epochs):
            for i in range(d.shape[0]):
                x = X[i]
                y = self.predict(x)
                e = d[i] - y
                x = np.insert(x, 0, 1)
                self.W = self.W + self.lr * e * x * y
                
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
d = np.array([-1, -1, -1, 1])

widrow_hoff = WidrowHoff(input_size=2)
widrow_hoff.fit(X, d)

print(widrow_hoff.W)

希望这些输出实例能够帮助您更好地理解感知器算法，如果您有任何疑问，请随时问我。