class LogisticRegression(): def __init__(self, num_feature: int, learning_rate: float) -> None: ''' Constructor Parameters: num_features is the number of features. learning_rate is the learning rate. Return: there is no return value. ''' self.num_feature = num_feature self.w = np.random.randn(num_feature + 1) self.learning_rate = learning_rate这段是什么意思

这是一个 Logistic 回归模型的 Python 类的初始化方法。在初始化方法中：

- `num_feature` 表示特征的数量；
- `learning_rate` 表示学习率。

在初始化方法中，首先将特征的数量 `num_feature` 和学习率 `learning_rate` 存储到类的属性中。然后，使用 `np.random.randn` 方法初始化权重向量 `self.w`，其中 `num_feature + 1` 表示权重向量的长度，因为还需要添加一个偏置项。最后，将学习率也存储到类的属性中。

相关问题

代码为：class LogisticRegression(nn.Module): def __inti__(self,n_input_features): super(LogisticRegression,self).__inti() self.linea=nn.Linear(n_input_features,1) def forward(self,x): y_predicted=torch.sigmoid(self.linea(x)) return y_predicted model=LogisticRegression(n_feature)

这段代码定义了一个 LogisticRegression 类，继承了 nn.Module。构造函数 __init__() 中传入了输入特征的数量 n_input_features，然后调用了父类的构造函数，并定义了一个线性层 self.linea，其输入特征数量为 n_input_features，输出特征数量为 1。

forward() 方法定义了模型的前向传播过程。输入 x 经过线性层 self.linea 后，再经过 sigmoid 函数得到 y_predicted，即预测值。

最后，利用该类构造一个模型对象 model，传入特征数量 n_feature。

class LogisticRegression(object): def __init__(self, input_size, output_size, eta, max_epoch, eps):

def __init__(self, input_size, output_size, eta=0.01, max_epoch=1000, eps=1e-7):
"""
Constructor for LogisticRegression class.

:param input_size: number of features in the input
:param output_size: number of classes in the output
:param eta: learning rate for gradient descent
:param max_epoch: maximum number of epochs for training
:param eps: small value to prevent division by zero
"""
self.input_size = input_size
self.output_size = output_size
self.eta = eta
self.max_epoch = max_epoch
self.eps = eps
self.weights = None
self.bias = None

def fit(self, X, y):
"""
Train the logistic regression model on the given training data.

:param X: input training data of shape (n_samples, n_features)
:param y: output training data of shape (n_samples, n_classes)
"""
n_samples, n_features = X.shape
_, n_classes = y.shape
self.weights = np.zeros((n_features, n_classes))
self.bias = np.zeros((1, n_classes))

for epoch in range(self.max_epoch):
# Forward pass
z = np.dot(X, self.weights) + self.bias
y_pred = self.softmax(z)

# Backward pass
error = y_pred - y
grad_weights = np.dot(X.T, error)
grad_bias = np.sum(error, axis=0, keepdims=True)

# Update weights and bias
self.weights -= self.eta * grad_weights
self.bias -= self.eta * grad_bias

# Check for convergence
if np.abs(grad_weights).max() < self.eps:
break

def predict(self, X):
"""
Predict the output for the given input data.

:param X: input data of shape (n_samples, n_features)
:return: predicted output of shape (n_samples, n_classes)
"""
z = np.dot(X, self.weights) + self.bias
y_pred = self.softmax(z)
return y_pred

def softmax(self, z):
"""
Apply the softmax function to the given input.

:param z: input data of shape (n_samples, n_classes)
:return: output data of shape (n_samples, n_classes)
"""
exp_z = np.exp(z - np.max(z, axis=1, keepdims=True))
return exp_z / np.sum(exp_z, axis=1, keepdims=True)