Update app.py

app.py

@@ -27,8 +27,7 @@ def plot_figure(N, u1, std1, u2, std2, show_dist, classifier=None):
   import numpy as np
   import matplotlib.pyplot as pp
   pp.style.use('default')
-  
-  val = 0. # this is the value where you want the data to appear on the y-axis.
+  #val = 0. # this is the value where you want the data to appear on the y-axis.
 
   points_class1 = [stats.norm.rvs(loc=u1, scale = std1) for x in range(N)]
   points_class2 = [stats.norm.rvs(loc=u2, scale = std2) for x in range(N)]
@@ -94,16 +93,29 @@ def plot_figure(N, u1, std1, u2, std2, show_dist, classifier=None):
     from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
     model_sk = LinearDiscriminantAnalysis()
     model_sk.fit(X_data,y_labels)
-    Z = model_sk.predict(np.c_[xx.ravel()])
-    Z = Z.reshape(xx.shape)
+    zz = model_sk.predict(np.c_[xx.ravel()])
+
+    #Predictions for each point on meshgrid
+    #zz = np.array(  [model_sk.predict( [[xx,yy]])[0] for xx, yy in zip(np.ravel(X), np.ravel(Y)) ] )
+
+    #Reshaping the predicted class into the meshgrid shape
+    Z = zz.reshape(X.shape)
 
     pp.pcolormesh(xx, yy, Z, cmap=cmap_light, alpha=0.2)
   elif classifier == 'QDA':
     from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
     model_sk = QuadraticDiscriminantAnalysis()
     model_sk.fit(X_data,y_labels)
-    Z = model_sk.predict(np.c_[xx.ravel()])
-    Z = Z.reshape(xx.shape)
+    
+    model_sk.fit(X_data,y_labels)
+    zz = model_sk.predict(np.c_[xx.ravel()])
+
+    #Predictions for each point on meshgrid
+    #zz = np.array(  [model_sk.predict( [[xx,yy]])[0] for xx, yy in zip(np.ravel(X), np.ravel(Y)) ] )
+
+    #Reshaping the predicted class into the meshgrid shape
+    Z = zz.reshape(X.shape)
+    
     print("Z: ",Z)
     pp.pcolormesh(xx, yy, Z, cmap=cmap_light, alpha=0.2)
   elif classifier == 'NaiveBayes':
@@ -119,9 +131,9 @@ def plot_figure(N, u1, std1, u2, std2, show_dist, classifier=None):
 
   pp.xlim([x_min, x_max])
   pp.ylim([y_min, y_max])
-  pp.xlabel("Feature 1 (X)", size=20)
+  pp.xlabel("Feature 1 (X1)", size=20)
   pp.xticks(fontsize=20)
-  pp.xlabel("Feature 1 (X)")
+  pp.ylabel("Feature 2 (X2)")
   pp.legend(loc='upper right', borderpad=0, handletextpad=0, fontsize = 20)
   pp.savefig('plot.png')
 
@@ -129,8 +141,6 @@ def plot_figure(N, u1, std1, u2, std2, show_dist, classifier=None):
 
 
 
-# 1. define mean and standard deviation for class 1
-
 set_mean_class1 = gr.inputs.Slider(-20, 20, step=0.5, default=1, label = 'Mean (Class 1)')
 set_std_class1 = gr.inputs.Slider(0, 10, step=0.5, default=1.5, label = 'Standard Deviation (Class 1)')
 
@@ -155,6 +165,8 @@ set_out_plot_table = gr.outputs.Dataframe(type='pandas', label ='Simulated Datas
 
 
 
+
+
 ### configure gradio, detailed can be found at https://www.gradio.app/docs/#i_slider
 interface = gr.Interface(fn=plot_figure, 
                          inputs=[set_number_points,set_mean_class1,set_std_class1,set_mean_class2,set_std_class2, set_show_dist, set_classifier],