error in mean calculation and the use eigh instead of eig

.gitignore

@@ -6,4 +6,6 @@ plot_style.txt
 plotting
 Figures
 src/__pycache__
-data
+data
+.venv/
+.idea/

main.py

@@ -60,10 +60,10 @@
     dt_string_for_save = now.strftime("%d_%m_%Y_%H_%M")
     # Operations commands
     commands = {
-        "SAVE_TO_FILE": True,  # Saving results to file or present them over CMD
-        "CREATE_DATA": False,  # Creating new dataset
-        "LOAD_DATA": True,  # Loading data from exist dataset
-        "LOAD_MODEL": True,  # Load specific model for training
+        "SAVE_TO_FILE": False,  # Saving results to file or present them over CMD
+        "CREATE_DATA": True,  # Creating new dataset
+        "LOAD_DATA": False,  # Loading data from exist dataset
+        "LOAD_MODEL": False,  # Load specific model for training
         "TRAIN_MODEL": True,  # Applying training operation
         "SAVE_MODEL": False,  # Saving tuned model
         "EVALUATE_MODE": True,  # Evaluating desired algorithms
@@ -78,13 +78,13 @@
     system_model_params = (
         SystemModelParams()
         .set_parameter("N", 8)
-        .set_parameter("M", 3)
-        .set_parameter("T", 200)
+        .set_parameter("M", 2)
+        .set_parameter("T", 100)
         .set_parameter("snr", 10)
         .set_parameter("signal_type", "NarrowBand")
         .set_parameter("signal_nature", "non-coherent")
         .set_parameter("eta", 0)
-        .set_parameter("bias", 0.05)
+        .set_parameter("bias", 0.0)
         .set_parameter("sv_noise_var", 0)
     )
     # Generate model configuration
@@ -96,8 +96,8 @@
         .set_model(system_model_params)
     )
     # Define samples size
-    samples_size = 100000  # Overall dateset size
-    train_test_ratio = 0.05  # training and testing datasets ratio
+    samples_size = 1024  # Overall dateset size
+    train_test_ratio = .1  # training and testing datasets ratio
     # Sets simulation filename
     simulation_filename = get_simulation_filename(
         system_model_params=system_model_params, model_config=model_config
@@ -160,10 +160,10 @@
         # Assign the training parameters object
         simulation_parameters = (
             TrainingParams()
-            .set_batch_size(2048)
-            .set_epochs(80)
+            .set_batch_size(16)
+            .set_epochs(20)
             .set_model(model=model_config)
-            .set_optimizer(optimizer="Adam", learning_rate=0.00001, weight_decay=1e-9)
+            .set_optimizer(optimizer="Adam", learning_rate=0.001, weight_decay=1e-9)
             .set_training_dataset(train_dataset)
             .set_schedular(step_size=80, gamma=0.2)
             .set_criterion()

src/data_handler.py

@@ -188,11 +188,11 @@ def autocorrelation_matrix(X: torch.Tensor, lag: int):
 
     """
     Rx_lag = torch.zeros(X.shape[0], X.shape[0], dtype=torch.complex128).to(device)
+    meu = torch.mean(X, dim=-1, keepdim=False).to(device).unsqueeze(-1)
     for t in range(X.shape[1] - lag):
-        # meu = torch.mean(X,1)
         x1 = torch.unsqueeze(X[:, t], 1).to(device)
         x2 = torch.t(torch.unsqueeze(torch.conj(X[:, t + lag]), 1)).to(device)
-        Rx_lag += torch.matmul(x1 - torch.mean(X), x2 - torch.mean(X)).to(device)
+        Rx_lag += torch.matmul(x1 - meu, x2 - meu.transpose(0,1)).to(device)
     Rx_lag = Rx_lag / (X.shape[-1] - lag)
     Rx_lag = torch.cat((torch.real(Rx_lag), torch.imag(Rx_lag)), 0)
     return Rx_lag

src/evaluation.py

@@ -365,14 +365,14 @@ def add_random_predictions(M: int, predictions: np.ndarray, algorithm: str):
 
 
 def evaluate(
-    model: nn.Module,
     model_type: str,
     model_test_dataset: list,
     generic_test_dataset: list,
     criterion: nn.Module,
     subspace_criterion,
     system_model,
     figures: dict,
+    model: nn.Module = None,
     plot_spec: bool = True,
     augmented_methods: list = None,
     subspace_methods: list = None,
@@ -402,16 +402,16 @@ def evaluate(
     if not isinstance(augmented_methods, list) and model_type.startswith("SubspaceNet"):
         augmented_methods = [
             # "mvdr",
-            "r-music",
-            "esprit",
+            # "r-music",
+            # "esprit",
             # "music",
         ]
     # Set default model-based subspace methods
     if not isinstance(subspace_methods, list):
         subspace_methods = [
             "esprit",
             # "music",
-            "r-music",
+            # "r-music",
             # "mvdr",
             # "sps-r-music",
             # "sps-esprit",

src/models.py

@@ -717,7 +717,7 @@ def root_music(Rz: torch.Tensor, M: int, batch_size: int):
     for iter in range(batch_size):
         R = Bs_Rz[iter]
         # Extract eigenvalues and eigenvectors using EVD
-        eigenvalues, eigenvectors = torch.linalg.eig(R)
+        eigenvalues, eigenvectors = torch.linalg.eigh(R)
         # Assign noise subspace as the eigenvectors associated with M greatest eigenvalues
         Un = eigenvectors[:, torch.argsort(torch.abs(eigenvalues)).flip(0)][:, M:]
         # Generate hermitian noise subspace matrix
@@ -774,7 +774,7 @@ def esprit(Rz: torch.Tensor, M: int, batch_size: int):
     for iter in range(batch_size):
         R = Bs_Rz[iter]
         # Extract eigenvalues and eigenvectors using EVD
-        eigenvalues, eigenvectors = torch.linalg.eig(R)
+        eigenvalues, eigenvectors = torch.linalg.eigh(R)
 
         # Get signal subspace
         Us = eigenvectors[:, torch.argsort(torch.abs(eigenvalues)).flip(0)][:, :M]

src/system_model.py

@@ -173,7 +173,7 @@ def steering_vec(
                 )
             # If calculation is applied through method (array mismatches are not known).
             else:
-                mis_distance, mis_geometry_noise = 0, 0
+                mis_distance, mis_geometry_noise, uniform_bias = 0, 0, 0
 
             return (
                 np.exp(