import torch
[docs]class LogisticRegression(torch.nn.Module):
"""
Logistic regression implementation
Args:
n_features (int): Number of features
"""
def __init__(self, n_features):
super(LogisticRegression, self).__init__()
self.linear = torch.nn.Linear(n_features, 1, bias=False)
def forward(self, x):
y_pred = torch.sigmoid(self.linear(x))
return y_pred
[docs]class LinearRegression(torch.nn.Module):
"""
Ridge regression implementation
Args:
n_features (int): Number of features
"""
def __init__(self, n_features):
super(LinearRegression, self).__init__()
self.linear = torch.nn.Linear(n_features, 1, bias=False)
def forward(self, x):
return self.linear(x)