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Make Homotopy generator use Newton Homotopy

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This commit is contained in:
Andreas Tsouchlos
2025-05-08 06:07:28 +02:00
parent fffb15595b
commit 5529cd92cc
5 changed files with 92 additions and 70 deletions
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12
python/examples/repetition_code.py
View File

@@ -18,12 +18,14 @@ def track_path(args):
[0, 1, 1, 0],
[0, 0, 1, 1]])
homotopy = homotopy_generator.HomotopyGenerator(H)
received = np.array([0, 0, 0, 0])
print(f"G: {homotopy.G}")
print(f"F: {homotopy.F}")
print(f"H: {homotopy.H}")
print(f"DH: {homotopy.DH}")
homotopy = homotopy_generator.HomotopyGenerator(H, received)
# print(f"G: {homotopy.G}")
# print(f"F: {homotopy.F}")
# print(f"H: {homotopy.H}")
# print(f"DH: {homotopy.DH}")
tracker = path_tracker.PathTracker(homotopy, args.euler_step_size, args.euler_max_tries,
args.newton_max_iter, args.newton_convergence_threshold, args.sigma)

32
python/examples/simulate_error_rate.py
View File

@@ -54,35 +54,35 @@ def simulate_error_rates_for_SNR(H, Eb_N0, args: SimulationArgs) -> typing.Tuple
G = H_GF.null_space()
k, n = G.shape
homotopy = homotopy_generator.HomotopyGenerator(H)
# print(f"G: {homotopy.G}")
# print(f"F: {homotopy.F}")
# print(f"H: {homotopy.H}")
# print(f"DH: {homotopy.DH}")
tracker = path_tracker.PathTracker(homotopy, args.euler_step_size, args.euler_max_tries,
args.newton_max_iter, args.newton_convergence_threshold, args.sigma)
num_frames = 0
bit_errors = 0
frame_errors = 0
decoding_failures = 0
homotopy = homotopy_generator.HomotopyGenerator(H)
tracker = path_tracker.PathTracker(homotopy, args.euler_step_size, args.euler_max_tries,
args.newton_max_iter, args.newton_convergence_threshold, args.sigma)
for _ in tqdm(range(args.max_frames)):
Eb_N0_lin = 10**(Eb_N0 / 10)
N0 = 1 / (2 * k / n * Eb_N0_lin)
y = np.zeros(n) + np.sqrt(N0) * np.random.normal(size=n)
homotopy.update_received(y)
x_hat = decode(tracker, y, H, args)
if np.any(np.mod(H @ x_hat, 2)):
tracker.set_sigma(-1 * args.sigma)
x_hat = decode(tracker, y, H, args)
tracker.set_sigma(args.sigma)
if np.any(np.mod(H @ x_hat, 2)):
decoding_failures += 1
bit_errors += np.sum(x_hat != np.zeros(n))
frame_errors += np.any(x_hat != np.zeros(n))
if np.any(np.mod(H @ x_hat, 2)):
decoding_failures += 1
num_frames += 1
if frame_errors >= args.target_frame_errors:
@@ -160,8 +160,10 @@ def main():
FERs, BERs, DFRs, frame_errors_list = simulate_error_rates(
H, SNRs, simulation_args)
df = pd.DataFrame({"SNR": SNRs, "FER": FERs, "BER": BERs, "DFR": DFRs, "frame_errors": frame_errors_list})
df = pd.DataFrame({"SNR": SNRs, "FER": FERs, "BER": BERs,
"DFR": DFRs, "frame_errors": frame_errors_list})
print(df)
if __name__ == "__main__":
main()

4
python/examples/toy_homotopy.py
View File

@@ -30,7 +30,7 @@ class ToyHomotopy:
[t]]
"""
@staticmethod
def evaluate_H(y: np.ndarray) -> np.ndarray:
def H(y: np.ndarray) -> np.ndarray:
"""Evaluate H at y."""
x1 = y[0]
x2 = y[1]
@@ -43,7 +43,7 @@ class ToyHomotopy:
return result
@staticmethod
def evaluate_DH(y: np.ndarray) -> np.ndarray:
def DH(y: np.ndarray) -> np.ndarray:
"""Evaluate Jacobian of H at y."""
x1 = y[0]
x2 = y[1]