Add sweep_parameter_space.py

python/examples/repetition_code.py

@@ -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}")
+    homotopy = homotopy_generator.HomotopyGenerator(H, received)
-    print(f"F: {homotopy.F}")
+
-    print(f"H: {homotopy.H}")
+    # print(f"G: {homotopy.G}")
-    print(f"DH: {homotopy.DH}")
+    # 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)

python/examples/simulate_error_rate.py

@@ -29,17 +29,14 @@ class SimulationArgs:
 
 
 def decode(tracker, y, H, args: SimulationArgs) -> np.ndarray:
-    x_hat = np.mod(np.round(y), 2).astype('int32')
+    x_hat = np.where(y >= 0, 0, 1)
 
     s = np.concatenate([y, np.array([0])])
     for i in range(args.homotopy_iter):
-        x_hat = np.mod(np.round(s[:-1]), 2).astype('int32')
+        x_hat = np.where(s[:-1] >= 0, 0, 1)
         if not np.any(np.mod(H @ x_hat, 2)):
             return x_hat
 
-        # if s[-1] > 1.5:
-        #     return x_hat
-
         try:
             s = tracker.step(s)
         except:
@@ -54,35 +51,41 @@ 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)
 
-        x_hat = decode(tracker, y, H, args)
+        u = np.random.randint(2, size=k)
+        # u = np.zeros(shape=k).astype(np.int32)
+        c = np.array(GF(u) @ G)
+        x = 1 - 2*c
 
-        bit_errors += np.sum(x_hat != np.zeros(n))
+        y = x + np.sqrt(N0) * np.random.normal(size=n)
-        frame_errors += np.any(x_hat != np.zeros(n))
 
-        if np.any(np.mod(H @ x_hat, 2)):
+        homotopy.update_received(y)
+
+        c_hat = decode(tracker, y, H, args)
+
+        if np.any(np.mod(H @ c_hat, 2)):
+            tracker.set_sigma(-1 * args.sigma)
+            c_hat = decode(tracker, y, H, args)
+            tracker.set_sigma(args.sigma)
+
+            if np.any(np.mod(H @ c_hat, 2)):
                 decoding_failures += 1
 
+        bit_errors += np.sum(c_hat != c)
+        frame_errors += np.any(c_hat != c)
         num_frames += 1
 
         if frame_errors >= args.target_frame_errors:
@@ -95,7 +98,7 @@ def simulate_error_rates_for_SNR(H, Eb_N0, args: SimulationArgs) -> typing.Tuple
     return FER, BER, DFR, frame_errors
 
 
-def simulate_error_rates(H, Eb_N0_list, args: SimulationArgs) -> typing.Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+def simulate_error_rates(H, Eb_N0_list, args: SimulationArgs) -> pd.DataFrame:
     FERs = []
     BERs = []
     DFRs = []
@@ -106,8 +109,11 @@ def simulate_error_rates(H, Eb_N0_list, args: SimulationArgs) -> typing.Tuple[np
         BERs.append(BER)
         DFRs.append(DFR)
         frame_errors_list.append(num_frames)
+        print(pd.DataFrame({"SNR": Eb_N0_list[:len(FERs)], "FER": FERs, "BER": BERs,
+                            "DFR": DFRs, "frame_errors": frame_errors_list}))
 
-    return np.array(FERs), np.array(BERs), np.array(DFRs), np.array(frame_errors_list)
+    return pd.DataFrame({"SNR": Eb_N0_list, "FER": FERs, "BER": BERs,
+                         "DFR": DFRs, "frame_errors": frame_errors_list})
 
 
 def main():
@@ -157,11 +163,10 @@ def main():
         target_frame_errors=args.target_frame_errors)
 
     SNRs = np.arange(args.snr[0], args.snr[1], args.snr[2])
-    FERs, BERs, DFRs, frame_errors_list = simulate_error_rates(
+    df = simulate_error_rates(H, SNRs, simulation_args)
-        H, SNRs, simulation_args)
 
-    df = pd.DataFrame({"SNR": SNRs, "FER": FERs, "BER": BERs, "DFR": DFRs, "frame_errors": frame_errors_list})
     print(df)
 
+
 if __name__ == "__main__":
     main()

python/examples/sweep_parameter_space.py

@@ -0,0 +1,171 @@
+from pathlib import Path
+from ray import tune
+import ray
+import typing
+import numpy as np
+import galois
+import argparse
+from dataclasses import dataclass
+import pandas as pd
+
+# autopep8: off
+import sys
+import os
+sys.path.append(f"{os.path.dirname(os.path.abspath(__file__))}/../")
+
+from hccd import utility, homotopy_generator, path_tracker
+# autopep8: on
+
+
+@dataclass
+class SimulationArgs:
+    euler_step_size: float
+    euler_max_tries: int
+    newton_max_iter: int
+    newton_convergence_threshold: float
+    sigma: int
+    homotopy_iter: int
+
+    max_frames: int
+    target_frame_errors: int
+
+
+def decode(tracker, y, H, args: SimulationArgs) -> np.ndarray:
+    x_hat = np.where(y >= 0, 0, 1)
+
+    s = np.concatenate([y, np.array([0])])
+    for i in range(args.homotopy_iter):
+        x_hat = np.where(s[:-1] >= 0, 0, 1)
+        if not np.any(np.mod(H @ x_hat, 2)):
+            return x_hat
+
+        try:
+            s = tracker.step(s)
+        except:
+            return x_hat
+
+    return x_hat
+
+
+def simulate_error_rates_for_SNR(H, Eb_N0, args: SimulationArgs) -> typing.Tuple[float, float, float, int]:
+    GF = galois.GF(2)
+    H_GF = GF(H)
+    G = H_GF.null_space()
+    k, n = G.shape
+
+    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 range(args.max_frames):
+        Eb_N0_lin = 10**(Eb_N0 / 10)
+        N0 = 1 / (2 * k / n * Eb_N0_lin)
+
+        u = np.random.randint(2, size=k)
+        # u = np.zeros(shape=k).astype(np.int32)
+        c = np.array(GF(u) @ G)
+        x = 1 - 2*c
+
+        y = x + np.sqrt(N0) * np.random.normal(size=n)
+
+        homotopy.update_received(y)
+
+        c_hat = decode(tracker, y, H, args)
+
+        if np.any(np.mod(H @ c_hat, 2)):
+            tracker.set_sigma(-1 * args.sigma)
+            c_hat = decode(tracker, y, H, args)
+            tracker.set_sigma(args.sigma)
+
+            if np.any(np.mod(H @ c_hat, 2)):
+                decoding_failures += 1
+
+        bit_errors += np.sum(c_hat != c)
+        frame_errors += np.any(c_hat != c)
+        num_frames += 1
+
+        if frame_errors >= args.target_frame_errors:
+            break
+
+    BER = bit_errors / (num_frames * n)
+    FER = frame_errors / num_frames
+    DFR = decoding_failures / num_frames
+
+    return FER, BER, DFR, frame_errors
+
+
+def simulation(config):
+    simulation_args = SimulationArgs(
+        euler_step_size=config["euler_step_size"],
+        euler_max_tries=20,
+        newton_max_iter=20,
+        newton_convergence_threshold=config["newton_convergence_threshold"],
+        sigma=config["sigma"],
+        homotopy_iter=config["homotopy_iter"],
+        max_frames=100000,
+        target_frame_errors=200)
+
+    FER, BER, DFR, frame_errors = simulate_error_rates_for_SNR(
+        config["H"], config["Eb_N0"], simulation_args)
+
+    return {"FER": FER, "BER": BER, "DFR": DFR, "frame_errors": frame_errors}
+
+
+def main():
+    # Parse command line arguments
+
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("-c", "--code", type=str, required=True,
+                        help="Path to the alist file containing the parity check matrix of the code")
+
+    args = parser.parse_args()
+
+    H = utility.read_alist_file(args.code)
+
+    # Run parameter sweep
+
+    ray.init()
+
+    search_space = {
+        "Eb_N0": tune.grid_search([6]),
+        "newton_convergence_threshold": tune.grid_search(np.array([0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1])),
+        "euler_step_size": tune.grid_search(np.array([0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1])),
+        "homotopy_iter": tune.grid_search(np.array([500])),
+        "sigma": tune.grid_search(np.array([1, -1])),
+        "H": tune.grid_search([H])
+    }
+
+    tuner = tune.Tuner(
+        simulation,
+        param_space=search_space,
+        tune_config=tune.TuneConfig(
+            max_concurrent_trials=12,
+        ),
+        run_config=tune.RunConfig(
+            name="param_sweep",
+            storage_path=Path.cwd() / "sim_results"
+        )
+    )
+
+    results = tuner.fit()
+
+    df = results.get_dataframe()
+
+    keep_columns = ["FER", "BER", "DFR", "frame_errors"]
+    config_columns = [col for col in df.columns if col.startswith("config/")]
+    columns_to_keep = keep_columns + config_columns
+    df = df[columns_to_keep].drop(columns=["config/H"])
+
+    df.to_csv("sweep_results.csv", index=False)
+    print(df.head())
+
+
+if __name__ == "__main__":
+    main()

python/examples/toy_homotopy.py

@@ -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]

python/hccd/homotopy_generator.py

@@ -20,20 +20,17 @@ class HomotopyGenerator:
         self.x_vars = [sp.symbols(f'x{i+1}') for i in range(self.num_vars)]
         self.t = sp.symbols('t')
 
-        self.G = self._create_G()
+        self._F = self._create_F()
-        self.F = self._create_F()
+        self._F_lambda = self._create_F_lambda(self._F)
-        self.H = self._create_H()
-        self.DH = self._create_DH(self.H)
 
-        self._H_lambda = self._create_H_lambda()
+    def update_received(self, received: np.ndarray):
-        self._DH_lambda = self._create_DH_lambda()
+        """Update the received vector and recompute G."""
-
+        self._G = self._create_G(received)
-    def _create_G(self) -> List[sp.Expr]:
+        self._G_lambda = self._create_G_lambda(self._G)
-        G = []
+        self._H = self._create_H()
-        for var in self.x_vars:
+        self._H_lambda = self._create_H_lambda(self._H)
-            G.append(var)
+        self._DH = self._create_DH()
-
+        self._DH_lambda = self._create_DH_lambda(self._DH)
-        return G
 
     def _create_F(self) -> sp.MutableMatrix:
         F = []
@@ -52,51 +49,63 @@ class HomotopyGenerator:
         groebner_basis = sp.groebner(F)
         return sp.MutableMatrix(groebner_basis)
 
-    def _create_H(self) -> List[sp.Expr]:
+    def _create_G(self, received) -> sp.MutableMatrix:
+        G = []
+        F_y = self._F_lambda(*received)
+
+        for f, f_y_i in zip(self._F, F_y):
+            G.append(f - f_y_i)
+
+        return sp.MutableMatrix(G)
+
+    def _create_H(self) -> sp.MutableMatrix:
         H = []
 
-        for g, f in zip(self.G, self.F):
+        for f, g in zip(self._F, self._G):
             H.append((1 - self.t) * g + self.t * f)
 
-        return H
+        return sp.MutableMatrix(H)
 
-    def _create_DH(self, H: List[sp.Expr]) -> sp.MutableMatrix:
+    def _create_DH(self) -> sp.MutableMatrix:
         all_vars = self.x_vars + [self.t]
         DH = sp.Matrix([[sp.diff(expr, var)
-                         for var in all_vars] for expr in self.H])
+                         for var in all_vars] for expr in self._H])
 
         return DH
 
-    def _create_H_lambda(self) -> Callable:
+    def _create_F_lambda(self, expr) -> Callable:
+        all_vars = self.x_vars
+        return sp.lambdify(all_vars, expr, 'numpy')
+
+    def _create_G_lambda(self, expr) -> Callable:
+        all_vars = self.x_vars
+        return sp.lambdify(all_vars, expr, 'numpy')
+
+    def _create_H_lambda(self, expr) -> Callable:
         all_vars = self.x_vars + [self.t]
-        return sp.lambdify(all_vars, self.H, 'numpy')
+        return sp.lambdify(all_vars, expr, 'numpy')
 
-    def _create_DH_lambda(self) -> Callable:
+    def _create_DH_lambda(self, expr) -> Callable:
         all_vars = self.x_vars + [self.t]
-        return sp.lambdify(all_vars, self.DH, 'numpy')
+        return sp.lambdify(all_vars, expr, 'numpy')
 
-    def evaluate_H(self, y: np.ndarray) -> np.ndarray:
+    def H(self, y):
-        """
-        Evaluate H at point y.
-
-        Args:
-            y: Array of form [x1, x2, ..., xn, t] where xi are the variables
-               and t is the homotopy parameter.
-
-        Returns:
-            Array containing H evaluated at y.
-        """
         return np.array(self._H_lambda(*y))
 
-    def evaluate_DH(self, y: np.ndarray) -> np.ndarray:
+    def DH(self, y):
-        """
+        return np.array(self._DH_lambda(*y))
-        Evaluate the Jacobian of H at point y.
 
-        Args:
-            y: Array of form [x1, x2, ..., xn, t] where xi are the variables
-               and t is the homotopy parameter.
 
-        Returns:
+def main():
-            Matrix containing the Jacobian of H evaluated at y.
+    import utility
-        """
+    H = utility.read_alist_file(
-        return np.array(self._DH_lambda(*y), dtype=float)
+        "/home/andreas/workspace/work/hiwi/ba-sw/codes/BCH_7_4.alist")
+    a = HomotopyGenerator(H)
+
+    y = np.array([0.1, 0.2, 0.9, 0.1, -0.8, -0.5, -1.0, 0])
+
+    print(a.DH(y))
+
+
+if __name__ == "__main__":
+    main()

python/hccd/path_tracker.py

@@ -3,10 +3,6 @@ import typing
 import scipy
 
 
-def _sign(val):
-    return -1 * (val < 0) + 1 * (val >= 0)
-
-
 class PathTracker:
     """
     Path trakcer for the homotopy continuation method. Uses a
@@ -18,9 +14,9 @@ class PathTracker:
           Information and Systems, vol. 15, no. 2, pp. 119-307, 2015
     """
 
-    def __init__(self, Homotopy, euler_step_size=0.05, euler_max_tries=10, newton_max_iter=5,
+    def __init__(self, homotopy, euler_step_size=0.05, euler_max_tries=10, newton_max_iter=5,
                  newton_convergence_threshold=0.001, sigma=1):
-        self.Homotopy = Homotopy
+        self._homotopy = homotopy
         self._euler_step_size = euler_step_size
         self._euler_max_tries = euler_max_tries
         self._newton_max_iter = newton_max_iter
@@ -43,21 +39,14 @@ class PathTracker:
 
         raise RuntimeError("Newton corrector did not converge")
 
-    # TODO: Make sure the implementation with null_space instead of QR is correct
     def _perform_euler_predictor_step(self, y, step_size) -> typing.Tuple[np.ndarray, np.ndarray]:
         # Obtain y_prime
 
-        DH = self.Homotopy.evaluate_DH(y)
+        DH = self._homotopy.DH(y)
         ns = scipy.linalg.null_space(DH)
 
         y_prime = ns[:, 0] * self._sigma
 
-        # Q, R = np.linalg.qr(np.transpose(DH), mode="complete")
-        # y_prime = Q[:, 2]
-        #
-        # if _sign(np.linalg.det(Q)*np.linalg.det(R[:2, :])) != _sign(self._sigma):
-        #     y_prime = -y_prime
-
         # Perform prediction
 
         y_hat = y + step_size*y_prime
@@ -70,10 +59,10 @@ class PathTracker:
         for _ in range(self._newton_max_iter):
             # Perform correction
 
-            DH = self.Homotopy.evaluate_DH(y)
+            DH = self._homotopy.DH(y)
             DH_pinv = np.linalg.pinv(DH)
 
-            y = y - DH_pinv @ self.Homotopy.evaluate_H(y)
+            y = y - np.transpose(DH_pinv @ self._homotopy.H(y))[0]
 
             # Check stopping criterion
 
@@ -83,3 +72,6 @@ class PathTracker:
             prev_y = y
 
         raise RuntimeError("Newton corrector did not converge")
+
+    def set_sigma(self, sigma):
+        self._sigma = sigma