app.py

app.py

@@ -1,12 +1,62 @@
-import numpy as np
-import gradio as gr
-#function to predict the input hours
-def predict_score(hours):
-    #hours = np.array(hours)
-    pred_score = -0.5738734424645411 + 2.1659122905141825*hours
-    return   pred_score     #np.round(pred_score[0], 2)
-input = gr.inputs.Number(label='Number of Hours studied')
-output = gr.outputs.Textbox(label='Predicted Score')
-gr.Interface( fn=predict_score,
-              inputs=input,
-              outputs=output).launch();
+import torch
+
+# Input data
+x1 = torch.tensor([50, 60, 70, 80, 90])
+x2 = torch.tensor([20, 21, 22, 23, 24])
+y_actual = torch.tensor([30, 35, 40, 45, 50])
+
+# Learning rate and maximum number of iterations
+alpha = 0.01
+max_iters = 1000
+
+# Initial values for Theta0, Theta1, and Theta2
+Theta0 = torch.tensor(0.0, requires_grad=True)
+Theta1 = torch.tensor(0.0, requires_grad=True)
+Theta2 = torch.tensor(0.0, requires_grad=True)
+
+# Start the iteration counter
+iter_count = 0
+
+# Loop until convergence or maximum number of iterations
+while iter_count < max_iters:
+    # Compute the predicted output
+    y_pred = Theta0 + Theta1 * x1 + Theta2 * x2
+
+    # Compute the errors
+    errors = y_pred - y_actual
+
+    # Compute the cost function
+    cost = torch.sum(errors ** 2) / (2 * len(x1))
+
+    # Print the cost function every 100 iterations
+    if iter_count % 100 == 0:
+        print("Iteration {}: Cost = {}, Theta0 = {}, Theta1 = {}, Theta2 = {}".format(iter_count, cost, Theta0.item(), Theta1.item(),
+                                                                                      Theta2.item()))
+
+    # Check for convergence (if the cost is decreasing by less than 0.0001)
+    if iter_count > 0 and torch.abs(cost - prev_cost) < 0.0001:
+        print("Converged after {} iterations".format(iter_count))
+        break
+
+    # Perform automatic differentiation to compute gradients
+    cost.backward()
+
+    # Update Theta0, Theta1, and Theta2 using gradient descent
+    with torch.no_grad():
+        Theta0 -= alpha * Theta0.grad
+        Theta1 -= alpha * Theta1.grad
+        Theta2 -= alpha * Theta2.grad
+
+        # Reset gradients for the next iteration
+        Theta0.grad.zero_()
+        Theta1.grad.zero_()
+        Theta2.grad.zero_()
+
+    # Update the iteration counter and previous cost
+    iter_count += 1
+    prev_cost = cost
+
+# Print the final values of Theta0, Theta1, and Theta2
+print("Final values: Theta0 = {}, Theta1 = {}, Theta2 = {}".format(Theta0.item(), Theta1.item(), Theta2.item()))
+print("Final Cost: Cost = {}".format(cost.item()))
+print("Final values: y_pred = {}, y_actual = {}".format(y_pred, y_actual))