Reimplemented gradient computation with more numpy builtin functions

sw/decoders/proximal.py

@@ -7,8 +7,6 @@ class ProximalDecoder:
     """
 
     # TODO: Is 'R' actually called 'decoding matrix'?
-    # TODO: How large should eta be?
-    # TODO: How large should step_size be?
     def __init__(self, H: np.array, R: np.array, K: int = 100, step_size: float = 0.1,
                  gamma: float = 0.05, eta: float = 1.5):
         """Construct a new ProximalDecoder Object.
@@ -48,24 +46,20 @@ class ProximalDecoder:
     # TODO: Is this correct?
     def _grad_h(self, x: np.array) -> np.array:
         """Gradient of the code-constraint polynomial. See 2.3, p. 2."""
-        # Calculate first term
+        # Pre-computations
 
-        result = 4 * (x**2 - 1) * x
+        k, _ = self._H.shape
 
-        # Calculate second term
+        A_prod_matrix = np.tile(x, (k, 1))
+        A_prods = np.prod(A_prod_matrix, axis=1, where=self._H > 0)
 
-        for k, x_k in enumerate(x):
-            sum_result = 0
+        # Calculate gradient
 
-            for i in self._B[k]:
-                prod = np.prod(x[self._A[i]])
-                sum_result += prod**2 - prod
+        sums = np.dot(A_prods**2 - A_prods, self._H)
 
-            term_2 = 2 / x_k * sum_result
+        result = 4 * (x**2 - 1) * x + (2 / x) * sums
 
-            result[k] += term_2
-
-        return np.array(result)
+        return result
 
     # TODO: Is the 'projection onto [-eta, eta]' actually just clipping?
     def _projection(self, x):