Added PCA initializer and component for OmegaMatrix or LinearMappings (#6)

* Added PCA initializer and component for OmegaMatrix or LinearMappings

* [QA] Add default configuration for pre commit hooks

* [QA] Add more pre commit checks

* [QA] Add more pre commit checks

* test(githooks): Add gitlint to check commit messages on commit

* docs(githooks): Add usage guide for pre-commit  to readme

* fix(githooks): mypy only checks source now

reverts changes on docs conf.py

* docs(githooks): Fix typo

Co-authored-by: staps@hs-mittweida.de <staps@hs-mittweida.de>
Co-authored-by: Alexander Engelsberger <alexanderengelsberger@gmail.com>

prototorch/components/components.py

@@ -44,6 +44,45 @@ def _precheck_initializer(initializer):
         raise TypeError(emsg)
 
 
+class LinearMapping(torch.nn.Module):
+    """LinearMapping is a learnable Mapping Matrix."""
+    def __init__(self,
+                 mapping_shape=None,
+                 initializer=None,
+                 *,
+                 initialized_linearmapping=None):
+        super().__init__()
+
+        # Ignore all initialization settings if initialized_components is given.
+        if initialized_linearmapping is not None:
+            self._register_mapping(initialized_linearmapping)
+            if num_components is not None or initializer is not None:
+                wmsg = "Arguments ignored while initializing Components"
+                warnings.warn(wmsg)
+        else:
+            self._initialize_mapping(mapping_shape, initializer)
+
+    @property
+    def mapping_shape(self):
+        return self._omega.shape
+
+    def _register_mapping(self, components):
+        self.register_parameter("_omega", Parameter(components))
+
+    def _initialize_mapping(self, mapping_shape, initializer):
+        _precheck_initializer(initializer)
+        _mapping = initializer.generate(mapping_shape)
+        self._register_mapping(_mapping)
+
+    @property
+    def mapping(self):
+        """Tensor containing the component tensors."""
+        return self._omega.detach()
+
+    def forward(self):
+        return self._omega
+
+
 class Components(torch.nn.Module):
     """Components is a set of learnable Tensors."""
     def __init__(self,

prototorch/components/initializers.py

@@ -167,6 +167,14 @@ class StratifiedSelectionInitializer(ClassAwareInitializer):
         return samples
 
 
+# Omega matrix
+class PcaInitializer(DataAwareInitializer):
+    def generate(self, shape):
+        (input_dim, latent_dim) = shape
+        (_, eigVal, eigVec) = torch.pca_lowrank(self.data, q=latent_dim)
+        return eigVec
+
+
 # Labels
 class LabelsInitializer:
     def generate(self):
@@ -222,3 +230,4 @@ SMI = StratifiedMeanInitializer
 Random = RandomInitializer = UniformInitializer
 Zeros = ZerosInitializer
 Ones = OnesInitializer
+PCA = PcaInitializer