1 files changed, 43 insertions, 6 deletions
diff --git a/gr-fec/python/fec/polar/helper_functions.py b/gr-fec/python/fec/polar/helper_functions.py
index ca66bf4a50..85140c856f 100644
--- a/gr-fec/python/fec/polar/helper_functions.py
+++ b/gr-fec/python/fec/polar/helper_functions.py
@@ -23,6 +23,24 @@ import time, sys
 import copy
 
 
+def bsc_channel(p):
+    '''
+    binary symmetric channel (BSC)
+    output alphabet Y = {0, 1} and
+    W(0|0) = W(1|1) and W(1|0) = W(0|1)
+
+    this function returns a prob's vector for a BSC
+    p denotes an erroneous transistion
+    '''
+    if not (p >= 0.0 and p <= 1.0):
+        print "given p is out of range!"
+        return np.array([], dtype=float)
+
+    # 0 -> 0, 0 -> 1, 1 -> 0, 1 -> 1
+    W = np.array([[1 - p, p], [p, 1 - p]], dtype=float)
+    return W
+
+
 def power_of_2_int(num):
     return int(np.log2(num))
 
@@ -139,13 +157,21 @@ def mutual_information(w):
 
 
 def bhattacharyya_parameter(w):
-    '''bhattacharyya parameter is a measure of similarity between two prob. distributions'''
-    # sum over all y e Y for sqrt( W(y|0) * W(y|1) )
+    '''
+    bhattacharyya parameter is a measure of similarity between two prob. distributions
+    THEORY: sum over all y e Y for sqrt( W(y|0) * W(y|1) )
+    Implementation:
+    Numpy vector of dimension (2, mu//2)
+    holds probabilities P(x|0), first vector for even, second for odd.
+    '''
     dim = np.shape(w)
-    ydim = dim[0]
-    z = 0.0
-    for y in range(ydim):
-        z += np.sqrt(w[0, y] * w[1, y])
+    if len(dim) != 2:
+        raise ValueError
+
+    if dim[0] > dim[1]:
+        raise ValueError
+
+    z = np.sum(np.sqrt(w[0] * w[1]))
     # need all
     return z
 
@@ -164,6 +190,17 @@ def main():
     print(pos)
     print(rev_pos)
 
+    f = np.linspace(.01, .29, 10)
+    e = np.linspace(.03, .31, 10)
+
+    b = np.array([e, f])
+    zp = bhattacharyya_parameter(b)
+    print(zp)
+
+    a = np.sum(np.sqrt(e * f))
+    print(a)
+
+
 
 if __name__ == '__main__':
     main()