feat: add LiteralLinearTransformInitializer

prototorch/core/initializers.py

@@ -218,7 +218,7 @@ class AbstractStratifiedCompInitializer(AbstractClassAwareCompInitializer):
         for k, v in distribution.items():
             stratified_data = self.data[self.targets == k]
             if len(stratified_data) == 0:
-              raise ValueError(f"No data available for class {k}.")
+                raise ValueError(f"No data available for class {k}.")
             initializer = self.subinit_type(
                 stratified_data,
                 noise=self.noise,
@@ -460,11 +460,19 @@ class AbstractDataAwareLTInitializer(AbstractLinearTransformInitializer):
 
 class PCALinearTransformInitializer(AbstractDataAwareLTInitializer):
     """Initialize a matrix with Eigenvectors from the data."""
+
     def generate(self, in_dim: int, out_dim: int):
         _, _, weights = torch.pca_lowrank(self.data, q=out_dim)
         return self.generate_end_hook(weights)
 
 
+class LiteralLinearTransformInitializer(AbstractDataAwareLTInitializer):
+    """'Generate' the provided weights."""
+
+    def generate(self, in_dim: int, out_dim: int):
+        return self.generate_end_hook(self.data)
+
+
 # Aliases - Components
 CACI = ClassAwareCompInitializer
 DACI = DataAwareCompInitializer
@@ -497,3 +505,4 @@ Eye = EyeTransformInitializer
 OLTI = OnesLinearTransformInitializer
 ZLTI = ZerosLinearTransformInitializer
 PCALTI = PCALinearTransformInitializer
+LLTI = LiteralLinearTransformInitializer

setup.py

@@ -23,6 +23,7 @@ INSTALL_REQUIRES = [
     "torchvision>=0.7.1",
     "numpy>=1.9.1",
     "sklearn",
+    "matplotlib",
 ]
 DATASETS = [
     "requests",
@@ -40,7 +41,6 @@ DOCS = [
     "sphinx-autodoc-typehints",
 ]
 EXAMPLES = [
-    "matplotlib",
     "torchinfo",
 ]
 TESTS = ["codecov", "pytest"]