两类感知器的参数学习方法Python代码
时间: 2023-12-14 07:24:45 浏览: 22
以下是两类感知器的参数学习方法的 Python 代码示例:
感知器学习算法:
```python
import numpy as np
class Perceptron(object):
def __init__(self, learning_rate=0.1, epochs=50):
self.learning_rate = learning_rate
self.epochs = epochs
def fit(self, X, y):
self.weights = np.zeros(X.shape[1])
self.bias = 0
for _ in range(self.epochs):
for i in range(X.shape[0]):
y_pred = np.dot(X[i], self.weights) + self.bias
if y_pred * y[i] <= 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
y_pred[y_pred > 0] = 1
y_pred[y_pred <= 0] = -1
return y_pred
```
最小二乘法:
```python
import numpy as np
class LeastSquares(object):
def fit(self, X, y):
X = np.hstack((np.ones((X.shape[0], 1)), X))
self.weights = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(y)
def predict(self, X):
X = np.hstack((np.ones((X.shape[0], 1)), X))
y_pred = X.dot(self.weights)
y_pred[y_pred > 0] = 1
y_pred[y_pred <= 0] = -1
return y_pred
```
以上是示例代码,具体使用需要根据实际情况进行调整。