Update app.py

app.py

@@ -78,7 +78,31 @@ def gradient_descent(intercept=4, slope=3, intercept_random=4, slope_random=3, g
           w_history.append(w)
           
           train_mse.append(cal_mse(X,y,b,w))
-  
+
+          if i == int(iteration/4):
+            X_tmp = np.array([[0], [2]])
+            X_tmp_b = np.c_[np.ones((2, 1)), X_tmp]  # add x0 = 1 to each instance
+            y_predict_tmp = X_tmp_b.dot(np.array([b, w]))
+            plt.plot(X_tmp, y_predict_tmp, "yellow", linewidth=2, label = "Fitted line in iteration "+str(i))
+              
+          if i == int(iteration/3):
+            X_tmp = np.array([[0], [2]])
+            X_tmp_b = np.c_[np.ones((2, 1)), X_tmp]  # add x0 = 1 to each instance
+            y_predict_tmp = X_tmp_b.dot(np.array([b, w]))
+            plt.plot(X_tmp, y_predict_tmp, "blue", linewidth=2, label = "Fitted line in iteration "+str(i))
+              
+          if i == int(iteration/2):
+            X_tmp = np.array([[0], [2]])
+            X_tmp_b = np.c_[np.ones((2, 1)), X_tmp]  # add x0 = 1 to each instance
+            y_predict_tmp = X_tmp_b.dot(np.array([b, w]))
+            plt.plot(X_tmp, y_predict_tmp, "gray", linewidth=2, label = "Fitted line in iteration "+str(i))
+              
+          if i == int(iteration-1):
+            X_tmp = np.array([[0], [2]])
+            X_tmp_b = np.c_[np.ones((2, 1)), X_tmp]  # add x0 = 1 to each instance
+            y_predict_tmp = X_tmp_b.dot(np.array([b, w]))
+            plt.plot(X_tmp, y_predict_tmp, "black", linewidth=2, label = "Fitted line in iteration "+str(i))
+              
   plt.xlabel("$x_1$", fontsize=22)
   plt.ylabel("$y$", rotation=0, fontsize=22)
   plt.xticks(fontsize=18)