comments to code

KernelRegression.py

@@ -15,14 +15,6 @@ def gauss_exp(ker_x, xi, h):
     den = h*h
     return num/den
 
-def gauss_exp(ker_x, xi, h): 
-    """
-    Returns the gaussian function exponent term
-    """
-    num =  - 0.5*np.square((xi- ker_x))
-    den = h*h
-    return num/den
-
 def kernel_function(h, ker_x, xi): 
     """
     Returns the gaussian function value. Combines the gauss_const and
@@ -48,7 +40,7 @@ def single_y_pred_gauss(bw_manual, input_x, iks, igrek):
 
 def epanechnikov_one(h, ker_x, xi): 
     """
-    Returns the epanechnikov function value.
+    Returns the epanechnikov function value for one element.
     """
     value = 0.75*(1-np.square((xi-ker_x)/h))
     if (value < 0):
@@ -57,7 +49,7 @@ def epanechnikov_one(h, ker_x, xi):
 
 def epanechnikov_list(h, ker_x, xi): 
     """
-    Returns the epanechnikov function value.
+    Returns the epanechnikov function value for list of elements.
     """
     value = 0.75*(1-np.square((xi-ker_x)/h))
     value = [0 if i < 0 else i for i in value]
@@ -78,19 +70,19 @@ def single_y_pred_epanechnikov(bw_manual, input_x, x_values, y_values):
     return y_single
 
 def ker_reg(x_values, y_values, bw = 1,  ker_fun = 'gauss'):
-
-    ker_x = np.arange(0,40,0.1)
-    
+    """
+    x_values, y_values, bw = 1,  ker_fun = 'gauss'
+    ker_fun = 'gauss' or 'epanechnikov'
+    returns Y_pred
+    """
     if (ker_fun == 'gauss'):
         Y_pred = []
         for input_x in x_values: 
-            w = []
             Y_single = single_y_pred_epanechnikov(bw, input_x, x_values, y_values)
             Y_pred.append(Y_single)
     elif (ker_fun == 'epanechnikov'):
         Y_pred = []
         for input_x in x_values: 
-            w = []
             Y_single = single_y_pred_gauss(bw, input_x, x_values, y_values)
             Y_pred.append(Y_single)
     else: