Update app.py

app.py

@@ -10,6 +10,18 @@ from sklearn.naive_bayes import GaussianNB
 
 
 
+def gaussian(x, n, u, s):
+    #u = x.mean()
+    #s = x.std()
+
+    # divide [x.min(), x.max()] by n
+    x = np.linspace(x.min(), x.max(), n)
+
+    a = ((x - u) ** 2) / (2 * (s ** 2))
+    y = 1 / (s * np.sqrt(2 * np.pi)) * np.exp(-a)
+
+    return x, y, u, s
+    
 def plot_figure(N, u1, std1, u2, std2, show_dist, classifier=None):
   #N = 100
   import numpy as np
@@ -116,6 +128,7 @@ def plot_figure(N, u1, std1, u2, std2, show_dist, classifier=None):
   return 'plot.png', pd_all
 
 
+
 # 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)')