接下来,我们初始化模型的参数。为了简化,我们将权重初始化为零,偏置项也设为零:
```python
ef initialize_ith_zero(im/> = np.zero((im, 1/> b = 0
return , b
```
然后,我们编写一个函数来计算损失函数,这将帮助我们评估模型的表现:
```python
ef pute_cot(X, y, , b/> m = len(y> A = igmoi(np.ot(X,
cot = -(1 / m.um(y * np.log(A - y.log(1 - A/> return cot
```python
ef pute_graient(X, y, , b/> m = len(y> A = igmoi(np.ot(X,
z = A - y
= (1 / m.ot(X.T, z> b = (1 / m.um(z> return , b
```
接下来,我们编写一个函数来更新权重和偏置项:
```python
ef upate_parameter(, b, , b, learning_rate/> = - learning_rate *
b = b - learning_rate * b
return , b
```*****
现在,我们将所有这些步骤整合到一个训练函数中,并设置迭代次数和学习率:
```python
ef train_logitic_regreion(X, y, num_iteration=2000, learning_rate