Feature/mlmlvq

prototorch/core/distances.py

@@ -73,6 +73,19 @@ def omega_distance(x, y, omega):
     return distances
 
 
+def ML_omega_distance(x, y, omegas, masks):
+    """Multi-Layer Omega distance."""
+    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
+    omegas = [torch.mul(_omega, _mask) for _omega, _mask in zip(omegas, masks)]
+    omega = omegas[0] @ omegas[1]
+    for _omega in omegas[2:]:
+        omega = omega @ _omega
+    projected_x = x @ omega
+    projected_y = y @ omega
+    distances = squared_euclidean_distance(projected_x, projected_y)
+    return distances
+
+
 def lomega_distance(x, y, omegas):
     r"""Localized Omega distance.
 

prototorch/core/initializers.py

@@ -17,6 +17,7 @@
 # Components
 class AbstractComponentsInitializer(ABC):
     """Abstract class for all components initializers."""
+
     ...
 
 
@@ -34,9 +35,9 @@ def generate(self, num_components: int = 0):
         """Ignore `num_components` and simply return `self.components`."""
         provided_num_components = len(self.components)
         if provided_num_components != num_components:
-            wmsg = f"The number of components ({provided_num_components}) " \
-                f"provided to {self.__class__.__name__} " \
-                f"does not match the expected number ({num_components})."
+            wmsg = (f"The number of components ({provided_num_components}) "
+                    f"provided to {self.__class__.__name__} "
+                    f"does not match the expected number ({num_components}).")
             warnings.warn(wmsg)
         if not isinstance(self.components, torch.Tensor):
             wmsg = f"Converting components to {torch.Tensor}..."
@@ -127,10 +128,12 @@ class AbstractDataAwareCompInitializer(AbstractComponentsInitializer):
 
     """
 
-    def __init__(self,
-                 data: torch.Tensor,
-                 noise: float = 0.0,
-                 transform: Callable = torch.nn.Identity()):
+    def __init__(
+            self,
+            data: torch.Tensor,
+            noise: float = 0.0,
+            transform: Callable = torch.nn.Identity(),
+    ):
         self.data = data
         self.noise = noise
         self.transform = transform
@@ -429,6 +432,7 @@ def generate(self, distribution: Union[dict, list, tuple]):
 # Transforms
 class AbstractTransformInitializer(ABC):
     """Abstract class for all transform initializers."""
+
     ...
 
 
@@ -486,11 +490,13 @@ def generate(self, in_dim: int, out_dim: int):
 class AbstractDataAwareLTInitializer(AbstractLinearTransformInitializer):
     """Abstract class for all data-aware linear transform initializers."""
 
-    def __init__(self,
-                 data: torch.Tensor,
-                 noise: float = 0.0,
-                 transform: Callable = torch.nn.Identity(),
-                 out_dim_first: bool = False):
+    def __init__(
+        self,
+        data: torch.Tensor,
+        noise: float = 0.0,
+        transform: Callable = torch.nn.Identity(),
+        out_dim_first: bool = False,
+    ):
         super().__init__(out_dim_first)
         self.data = data
         self.noise = noise