implementation update

KernelRegression.py

@@ -1,4 +1,5 @@
 import numpy as np
+from numpy.core.fromnumeric import squeeze
 
 def gauss_const(h):
     """
@@ -14,6 +15,14 @@ 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
@@ -31,7 +40,59 @@ def weights(bw_manual, input_x, all_input_values ):
         w_row.append(ki/ki_sum)
     return w_row
 
-def single_y_pred(bw_manual, input_x, iks, igrek): 
+
+def single_y_pred_gauss(bw_manual, input_x, iks, igrek): 
     w = weights(bw_manual, input_x, iks)
     y_single = np.sum(np.dot(igrek,w))
-    return y_single
+    return y_single
+
+def epanechnikov_one(h, ker_x, xi): 
+    """
+    Returns the epanechnikov function value.
+    """
+    value = 0.75*(1-np.square((xi-ker_x)/h))
+    if (value < 0):
+        value = 0
+    return value
+
+def epanechnikov_list(h, ker_x, xi): 
+    """
+    Returns the epanechnikov function value.
+    """
+    value = 0.75*(1-np.square((xi-ker_x)/h))
+    value = [0 if i < 0 else i for i in value]
+    return value
+
+
+def weights_epanechnikov(bw_manual, input_x, all_input_values ): 
+    w_row = []
+    for x_i in all_input_values: 
+        ki = epanechnikov_one(bw_manual, x_i, input_x)
+        ki_sum = np.sum(epanechnikov_list(bw_manual, all_input_values, input_x))
+        w_row.append(ki/ki_sum)
+    return w_row
+
+def single_y_pred_epanechnikov(bw_manual, input_x, x_values, y_values): 
+    w = weights_epanechnikov(bw_manual, input_x, x_values)
+    y_single = np.sum(np.dot(y_values,w))
+    return y_single
+
+def ker_reg(x_values, y_values, bw = 1,  ker_fun = 'gauss'):
+
+    ker_x = np.arange(0,40,0.1)
+    
+    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:
+        return 0
+    return Y_pred

data6.tsv

@@ -0,0 +1,20 @@
+21.252	-555.640
+179.842	3840.141
+118.162	2274.989
+114.269	1146.575
+121.444	1840.589
+87.624	1663.894
+170.039	3504.537
+192.651	3708.239
+12.390	-358.240
+144.264	2444.162
+169.900	3348.941
+63.254	271.623
+72.439	900.423
+71.108	77.543
+179.476	3313.424
+169.084	2525.653
+99.073	734.413
+195.528	4067.410
+131.023	2182.147
+12.424	490.714