Update pages/2_LinearRegression.py

pages/2_LinearRegression.py

@@ -1 +1,82 @@
-# Linear
+import streamlit as st
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import matplotlib.pyplot as plt
+
+# Define the dataset
+def generate_data(n_samples):
+    torch.manual_seed(42)
+    X = torch.randn(n_samples, 1) * 10
+    y = 2 * X + 3 + torch.randn(n_samples, 1) * 3
+    return X, y
+
+# Define the linear regression model
+class LinearRegressionModel(nn.Module):
+    def __init__(self):
+        super(LinearRegressionModel, self).__init__()
+        self.linear = nn.Linear(1, 1)
+        
+    def forward(self, x):
+        return self.linear(x)
+
+# Train the model
+def train_model(X, y, lr, epochs):
+    model = LinearRegressionModel()
+    criterion = nn.MSELoss()
+    optimizer = optim.SGD(model.parameters(), lr=lr)
+    
+    for epoch in range(epochs):
+        model.train()
+        optimizer.zero_grad()
+        outputs = model(X)
+        loss = criterion(outputs, y)
+        loss.backward()
+        optimizer.step()
+    
+    return model
+
+# Plot the results
+def plot_results(X, y, model):
+    plt.scatter(X.numpy(), y.numpy(), label='Original data')
+    plt.plot(X.numpy(), model(X).detach().numpy(), label='Fitted line', color='r')
+    plt.legend()
+    plt.xlabel('X')
+    plt.ylabel('y')
+    st.pyplot(plt.gcf())
+
+# Streamlit interface
+st.title('Simple Linear Regression with PyTorch')
+n_samples = st.slider('Number of samples', 20, 100, 50)
+learning_rate = st.slider('Learning rate', 0.001, 0.1, 0.01)
+epochs = st.slider('Number of epochs', 100, 1000, 500)
+
+X, y = generate_data(n_samples)
+model = train_model(X, y, learning_rate, epochs)
+
+st.subheader('Training Data')
+plot_results(X, y, model)
+
+st.subheader('Model Parameters')
+st.write(f'Weight: {model.linear.weight.item()}')
+st.write(f'Bias: {model.linear.bias.item()}')
+
+st.subheader('Loss Curve')
+losses = []
+model = LinearRegressionModel()
+criterion = nn.MSELoss()
+optimizer = optim.SGD(model.parameters(), lr=learning_rate)
+for epoch in range(epochs):
+    model.train()
+    optimizer.zero_grad()
+    outputs = model(X)
+    loss = criterion(outputs, y)
+    loss.backward()
+    optimizer.step()
+    losses.append(loss.item())
+
+plt.figure()
+plt.plot(range(epochs), losses)
+plt.xlabel('Epoch')
+plt.ylabel('Loss')
+st.pyplot(plt.gcf())