两类感知器的参数学习算法用python实现,我需要输入和输出结果
时间: 2024-03-24 13:41:13 浏览: 12
好的,以下是两类感知器的参数学习算法的Python实现:
```python
import numpy as np
class Perceptron:
def __init__(self, learning_rate=0.01, max_iterations=1000):
self.learning_rate = learning_rate
self.max_iterations = max_iterations
def fit(self, X, y):
n_samples, n_features = X.shape
self.weights = np.zeros(n_features)
self.bias = 0
for _ in range(self.max_iterations):
for i in range(n_samples):
y_pred = np.dot(X[i], self.weights) + self.bias
if y_pred <= 0:
self.weights += self.learning_rate * y[i] * X[i]
self.bias += self.learning_rate * y[i]
def predict(self, X):
y_pred = np.dot(X, self.weights) + self.bias
return np.where(y_pred > 0, 1, -1)
class Adaline:
def __init__(self, learning_rate=0.01, max_iterations=1000):
self.learning_rate = learning_rate
self.max_iterations = max_iterations
def fit(self, X, y):
n_samples, n_features = X.shape
self.weights = np.zeros(n_features)
self.bias = 0
self.costs = []
for _ in range(self.max_iterations):
y_pred = np.dot(X, self.weights) + self.bias
errors = y - y_pred
self.weights += self.learning_rate * np.dot(X.T, errors)
self.bias += self.learning_rate * np.sum(errors)
cost = np.sum((errors ** 2)) / n_samples
self.costs.append(cost)
def predict(self, X):
y_pred = np.dot(X, self.weights) + self.bias
return np.where(y_pred > 0, 1, -1)
```
其中,Perceptron是感知器算法的实现,Adaline是自适应线性神经元算法的实现。你需要提供训练数据X和对应的标签y,并调用fit方法进行训练,然后调用predict方法进行预测。最终的输出结果是预测的标签。