Fixed usage of n and k

sw/decoders/maximum_likelihood.py

@@ -25,6 +25,7 @@ class MLDecoder:
         """Generate a list of all possible codewords.
 
         :return: Numpy array of the form [[codeword_1], [codeword_2], ...]
+            (Each generated codeword is an element of [0, 1]^n)
         """
         k, n = self._G.shape
 
@@ -44,7 +45,7 @@ class MLDecoder:
 
         :param y: Vector of received values. (y = x + w, where 'x' is element of [-1, 1]^n
             and 'w' is noise)
-        :return: Most probably sent dataword (element of [0, 1]^k)
+        :return: Most probably sent codeword (element of [0, 1]^k)
         """
         correlations = np.dot(self._codewords_bpsk, y)
 

sw/decoders/proximal.py

@@ -73,7 +73,7 @@ class ProximalDecoder:
 
         :param y: Vector of received values. (y = x + w, where 'x' is element of [-1, 1]^n
             and 'w' is noise)
-        :return: Most probably sent codeword (element of [0, 1]^k)
+        :return: Most probably sent codeword (element of [0, 1]^n)
         """
         s = np.zeros(self._n)
         x_hat = np.zeros(self._n)

sw/utility/codes.py

@@ -4,7 +4,6 @@ Code from https://github.com/gnuradio/gnuradio/blob/master/gr-fec/python/fec/LDP
 
 
 import numpy as np
-from enum import Enum
 
 
 #
@@ -233,15 +232,15 @@ def get_systematic_H(G: np.array) -> np.array:
     :param G: Generator matrix of the systematic code
     :return: Parity check matrix H
     """
-    n, k = G.shape
+    k, n = G.shape
 
-    I = G[:, :n]
-    assert np.array_equal(I, np.identity(n))
+    I = G[:, :k]
+    assert np.array_equal(I, np.identity(k))
 
-    P = G[:, n:]
+    P = G[:, k:]
 
-    H = np.zeros(shape=(k-n, n + k-n))
-    H[:, :n] = P.T
-    H[:, n:] = np.identity(k-n)
+    H = np.zeros(shape=(n-k, k + n-k))
+    H[:, :k] = P.T
+    H[:, k:] = np.identity(n-k)
 
     return H

sw/utility/simulations.py

@@ -18,7 +18,6 @@ def count_bit_errors(d: np.array, d_hat: np.array) -> int:
     return np.sum(d != d_hat)
 
 
-# TODO: Fix uses of n, k, a everywhere
 def test_decoder(x: np.array,
                  decoder: typing.Any,
                  SNRs: typing.Sequence[float] = np.linspace(1, 4, 7),