Update pages/2_LinearRegression.py

pages/2_LinearRegression.py

@@ -1,82 +1,49 @@
 import streamlit as st
+import numpy as np
+import matplotlib.pyplot as plt
 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
+# Set a seed for reproducibility
+torch.manual_seed(59)
 
-# Define the linear regression model
-class LinearRegressionModel(nn.Module):
-    def __init__(self):
-        super(LinearRegressionModel, self).__init__()
-        self.linear = nn.Linear(1, 1)
-        
+# Define the Linear Model
+class LinearModel(nn.Module):
+    def __init__(self, in_features, out_features):
+        super(LinearModel, self).__init__()
+        self.linear = nn.Linear(in_features, out_features)
+    
     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())
+# Instantiate the model
+model = LinearModel(1, 1)
+
+# Print model weight and bias
+print(f'Model weight: {model.linear.weight.item()}')
+print(f'Model bias: {model.linear.bias.item()}')
+
+# Streamlit app title
+st.title('Interactive Scatter Plot with Noise and Number of Data Points')
+
+# Sidebar sliders for noise and number of data points
+noise_level = st.sidebar.slider('Noise Level', 0.0, 1.0, 0.1, step=0.01)
+num_points = st.sidebar.slider('Number of Data Points', 10, 100, 50, step=5)
+
+# Generate data
+np.random.seed(0)
+x = np.linspace(0, 10, num_points).reshape(-1, 1).astype(np.float32)
+with torch.no_grad():
+    x_tensor = torch.tensor(x)
+    y_tensor = model(x_tensor)
+    y = y_tensor.numpy().flatten() + noise_level * np.random.randn(num_points)
+
+# Create scatter plot
+fig, ax = plt.subplots()
+ax.scatter(x, y, alpha=0.6)
+ax.set_title('Scatter Plot with Noise and Number of Data Points')
+ax.set_xlabel('X-axis')
+ax.set_ylabel('Y-axis')
+
+# Display plot in Streamlit
+st.pyplot(fig)