Update app.py

app.py

@@ -117,17 +117,17 @@ def gradient_descent(intercept=4, slope=3, intercept_random=4, slope_random=3, g
   plt.contourf(b,w,Z, 50, alpha=0.5, cmap=plt.get_cmap('jet'))
 
   ### (5.5) Add optimal loss
-  #plt.plot(theta0_best, theta1_best, 'x', ms=12, markeredgewidth=3, color='orange')
-  #plt.text(theta0_best, theta1_best,'MSE:'+str(np.round(cal_mse(X,y,theta0_best, theta1_best),2)), color='red', fontsize=22)
+  plt.plot(theta0_best, theta1_best, 'x', ms=12, markeredgewidth=3, color='orange')
+  plt.text(theta0_best, theta1_best,'MSE:'+str(np.round(cal_mse(X,y,theta0_best, theta1_best),2)), color='red', fontsize=22)
   
   ### (5.6) Add loss of random lines
-  #if intercept_random != intercept or slope_random != slope: #avoid overlap
-    #plt.plot(intercept_random, slope_random, 'o', ms=5, markeredgewidth=3, color='orange')
-    #plt.text(intercept_random, slope_random,'MSE:'+str(np.round(cal_mse(X,y,intercept_random, slope_random),2)), fontsize=22)
+  if intercept_random != intercept or slope_random != slope: #avoid overlap
+    plt.plot(intercept_random, slope_random, 'o', ms=5, markeredgewidth=3, color='orange')
+    plt.text(intercept_random, slope_random,'MSE:'+str(np.round(cal_mse(X,y,intercept_random, slope_random),2)), fontsize=22)
     
     ### (5.7) draw gradient updates
-    #if gradient_descent:
-    #  plt.plot(b_history, w_history, 'o-', ms=3, lw=1.5, color='black')
+    if gradient_descent:
+      plt.plot(b_history, w_history, 'o-', ms=3, lw=1.5, color='black')