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Renamed naive soft decision to maximum likelihood; Removed reference to R matrix

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This commit is contained in:
Andreas Tsouchlos 2022-11-10 08:40:25 +01:00
parent 5865a8683f
commit f29c2e71de
4 changed files with 10 additions and 31 deletions
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2
sw/decoders/naive_soft_decision.py → sw/decoders/maximum_likelihood.py
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@ -4,7 +4,7 @@ import itertools
# TODO: Unify the interface regarding [0, 1]^n and [-1, 1]^n # TODO: Unify the interface regarding [0, 1]^n and [-1, 1]^n
class SoftDecisionDecoder: class MLDecoder:
"""This class naively implements a soft decision decoder. The decoder calculates """This class naively implements a soft decision decoder. The decoder calculates
the correlation between the received signal and each codeword and then chooses the the correlation between the received signal and each codeword and then chooses the
one with the largest correlation. one with the largest correlation.

14
sw/main.py
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@ -8,8 +8,8 @@ import os
from itertools import chain from itertools import chain
from timeit import default_timer from timeit import default_timer
from decoders import proximal, naive_soft_decision from decoders import proximal, maximum_likelihood
from utility import simulations, encoders, codes from utility import simulations, codes
def test_decoders(G, decoders: typing.List) -> pd.DataFrame: def test_decoders(G, decoders: typing.List) -> pd.DataFrame:
@ -27,7 +27,7 @@ def test_decoders(G, decoders: typing.List) -> pd.DataFrame:
decoder=decoder, decoder=decoder,
SNRs=SNRs, SNRs=SNRs,
target_bit_errors=100, target_bit_errors=100,
N_max=30000) N_max=50000)
data[f"BER_{decoder_name}"] = BERs_sd data[f"BER_{decoder_name}"] = BERs_sd
@ -91,10 +91,10 @@ def main():
H = codes.get_systematic_H(G) H = codes.get_systematic_H(G)
decoders = { decoders = {
"naive_soft_decision": naive_soft_decision.SoftDecisionDecoder(G, H), "naive_soft_decision": maximum_likelihood.MLDecoder(G, H),
"proximal_0_01": proximal.ProximalDecoder(H, K=100, gamma=0.01), "proximal_0_01": proximal.ProximalDecoder(H, gamma=0.01),
"proximal_0_05": proximal.ProximalDecoder(H, K=100, gamma=0.05), "proximal_0_05": proximal.ProximalDecoder(H, gamma=0.05),
"proximal_0_15": proximal.ProximalDecoder(H, K=100, gamma=0.15), "proximal_0_15": proximal.ProximalDecoder(H, gamma=0.15),
} }
data = test_decoders(G, decoders) data = test_decoders(G, decoders)

8
sw/test/test_soft_decision.py
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@ -1,6 +1,6 @@
import unittest import unittest
import numpy as np import numpy as np
from decoders import naive_soft_decision from decoders import maximum_likelihood
class CodewordGenerationTestCase(unittest.TestCase): class CodewordGenerationTestCase(unittest.TestCase):
@ -14,12 +14,8 @@ class CodewordGenerationTestCase(unittest.TestCase):
H = np.array([[1, 0, 1, 0, 1, 0, 1], H = np.array([[1, 0, 1, 0, 1, 0, 1],
[0, 1, 1, 0, 0, 1, 1], [0, 1, 1, 0, 0, 1, 1],
[0, 0, 0, 1, 1, 1, 1]]) [0, 0, 0, 1, 1, 1, 1]])
R = np.array([[0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 1]])
decoder = naive_soft_decision.SoftDecisionDecoder(G, H, R) decoder = maximum_likelihood.MLDecoder(G, H)
expected_datawords = np.array([[0, 0, 0, 0], expected_datawords = np.array([[0, 0, 0, 0],
[0, 0, 0, 1], [0, 0, 0, 1],

17
sw/test/test_utility.py
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@ -58,23 +58,6 @@ class CodesTestCase(unittest.TestCase):
self.assertEqual(np.array_equal(expected_H, H), True) self.assertEqual(np.array_equal(expected_H, H), True)
def test_get_systematic_R(self):
# Hamming(7,4) code
G = np.array([[1, 0, 0, 0, 0, 1, 1],
[0, 1, 0, 0, 1, 0, 1],
[0, 0, 1, 0, 1, 1, 0],
[0, 0, 0, 1, 1, 1, 1]])
expected_R = G = np.array([[1, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0]])
R = codes.get_systematic_R(G)
self.assertEqual(np.array_equal(expected_R, R), True)
if __name__ == '__main__': if __name__ == '__main__':
unittest.main() unittest.main()