No idea what is going on ....

casangi · Feb 25, 2025 · 180e932 · 180e932
1 parent 2bda9f8
commit 180e932
Show file tree

Hide file tree

Showing 2 changed files with 6 additions and 11 deletions.
diff --git a/src/calviper/math/optimizer.py b/src/calviper/math/optimizer.py
@@ -47,16 +47,13 @@ def gradient(target: np.ndarray, model: np.ndarray, parameter: np.ndarray) -> np
             for antenna_j in range(n_antennas):
                 for p in [0, 1]:
                     for q in [0, 1]:
-                        #if antenna_i == antenna_j:
-                        #    continue
+                        if antenna_i == antenna_j:
+                            continue
 
                         numerator_[0, 0, p, antenna_i] += target[0, 0, p, q, antenna_i, antenna_j] * parameter[0, 0, q, antenna_j] * model[0, 0, p, q, antenna_i, antenna_j].conj()
                         denominator_[0, 0, p, antenna_i] += parameter[0, 0, q, antenna_j] * parameter[0, 0, q, antenna_j].conj() * model[0, 0, p, q, antenna_i, antenna_j].conj() * model[0, 0, p, q, antenna_i, antenna_j]
-                        #print(f"parameter[p={p}, j={antenna_j}]: {parameter[0, 0, q, antenna_j]}\tmodel_[p={p}, i={antenna_i}, j={antenna_j}]:{model[0, 0, p, q, antenna_i, antenna_j]}")
 
-        #print(f"\t --- denominator: {denominator_}")
-
-        gradient_ = (numerator_ / denominator_) - parameter
+        gradient_ = (numerator_ / denominator_).conj() - parameter
 
         return gradient_
 

diff --git a/src/calviper/math/solver/least_squares.py b/src/calviper/math/solver/least_squares.py
@@ -114,9 +114,7 @@ def solve(self, vis, iterations, optimizer=MeanSquaredError(), stopping=1e-3):
             eye = np.identity(n_antenna1, dtype=np.complex64)
             np.fill_diagonal(anti_eye, np.complex64(1., 0.))
 
-            #self.model_ = self.model_ #* eye
-            self.model_ = self.model_ * anti_eye
-            #self.model_ = self.model_ * np.random.uniform(low=0.0, high=1.0, size=self.model_.shape) * anti_eye
+            self.model_ = self.model_
 
         self.losses = []
 
@@ -142,8 +140,8 @@ def solve(self, vis, iterations, optimizer=MeanSquaredError(), stopping=1e-3):
 
             self.losses.append(optimizer.loss(y_pred, vis))
 
-            #if n % (iterations // 10) == 0:
-            #    logger.info(f"iteration: {n}\tloss: {np.abs(self.losses[-1])}")
+            if n % (iterations // 10) == 0:
+                logger.info(f"iteration: {n}\tloss: {np.abs(self.losses[-1])}")
 
             if self.losses[-1] < stopping:
                 logger.info(f"Iteration: ({n})\tStopping criterion reached: {self.losses[-1]}")