逻辑回归梯度下降的的Python代码
时间: 2023-09-28 11:07:33 浏览: 39
以下是逻辑回归梯度下降的Python代码示例:
```python
import numpy as np
class LogisticRegression:
def __init__(self, learning_rate=0.01, num_iterations=100000, fit_intercept=True, verbose=False):
self.learning_rate = learning_rate
self.num_iterations = num_iterations
self.fit_intercept = fit_intercept
self.verbose = verbose
def __add_intercept(self, X):
intercept = np.ones((X.shape[0], 1))
return np.concatenate((intercept, X), axis=1)
def __sigmoid(self, z):
return 1 / (1 + np.exp(-z))
def __loss(self, h, y):
return (-y * np.log(h) - (1 - y) * np.log(1 - h)).mean()
def fit(self, X, y):
if self.fit_intercept:
X = self.__add_intercept(X)
# 初始化权重
self.theta = np.zeros(X.shape[1])
for i in range(self.num_iterations):
z = np.dot(X, self.theta)
h = self.__sigmoid(z)
gradient = np.dot(X.T, (h - y)) / y.size
self.theta -= self.learning_rate * gradient
if(self.verbose == True and i % 10000 == 0):
z = np.dot(X, self.theta)
h = self.__sigmoid(z)
print(f'loss: {self.__loss(h, y)} \t')
def predict_prob(self, X):
if self.fit_intercept:
X = self.__add_intercept(X)
return self.__sigmoid(np.dot(X, self.theta))
def predict(self, X, threshold):
return self.predict_prob(X) >= threshold
```
其中,`fit`函数用于拟合模型,`predict_prob`函数用于预测概率,`predict`函数用于根据设定的阈值进行预测。在使用时,需要先初始化一个`LogisticRegression`对象,然后调用其`fit`函数拟合模型,并根据需要调用`predict_prob`或`predict`函数进行预测。