用python代码实现逻辑回归模型的参数估计算法
时间: 2023-08-15 12:42:03 浏览: 49
好的,下面是用Python代码实现逻辑回归模型的参数估计算法的步骤:
1. 导入必要的库
```python
import numpy as np
from sklearn.datasets import load_iris
```
2. 加载数据集
```python
iris = load_iris()
X = iris.data[:, :2]
y = (iris.target != 0) * 1
```
3. 定义逻辑回归模型的参数估计算法
```python
def logistic_regression(X, y, num_steps, learning_rate):
# 初始化参数
intercept = np.ones((X.shape[0], 1))
X = np.concatenate((intercept, X), axis=1)
theta = np.zeros(X.shape[1])
# 梯度下降
for i in range(num_steps):
z = np.dot(X, theta)
h = 1 / (1 + np.exp(-z))
gradient = np.dot(X.T, (h - y)) / y.size
theta -= learning_rate * gradient
return theta
```
4. 调用逻辑回归模型的参数估计算法
```python
theta = logistic_regression(X, y, num_steps=300000, learning_rate=0.1)
```
5. 可视化结果
```python
import matplotlib.pyplot as plt
plt.scatter(X[y == 0][:, 0], X[y == 0][:, 1], color='b', label='0')
plt.scatter(X[y == 1][:, 0], X[y == 1][:, 1], color='r', label='1')
plt.legend()
x1_min, x1_max = X[:,1].min(), X[:,1].max(),
x2_min, x2_max = X[:,2].min(), X[:,2].max(),
xx1, xx2 = np.meshgrid(np.linspace(x1_min, x1_max), np.linspace(x2_min, x2_max))
grid = np.c_[xx1.ravel(), xx2.ravel()]
probs = np.dot(np.concatenate((np.ones((grid.shape[0], 1)), grid), axis=1), theta)
probs = np.reshape(probs, xx1.shape)
plt.contour(xx1, xx2, probs, [0.5], linewidths=1, colors='black')
plt.show()
```
以上是用 Python 实现逻辑回归模型的参数估计算法的步骤。