anomalib.models.components.dimensionality_reduction.pca

Principle Component Analysis (PCA) with PyTorch.

Module Contents

Classes

PCA

Principle Component Analysis (PCA).
class anomalib.models.components.dimensionality_reduction.pca.PCA(n_components: Union[float, int])[source]

Bases: anomalib.models.components.base.DynamicBufferModule

Principle Component Analysis (PCA).

Parameters

n_components (float) – Number of components. Can be either integer number of components or a ratio between 0-1.

fit(self, dataset: torch.Tensor) → None[source]

Fits the PCA model to the dataset.

Parameters

dataset (Tensor) – Input dataset to fit the model.

fit_transform(self, dataset: torch.Tensor) → torch.Tensor[source]

Fit and transform PCA to dataset.

Parameters

dataset (Tensor) – Dataset to which the PCA if fit and transformed

Returns

Transformed dataset

transform(self, features: torch.Tensor) → torch.Tensor[source]

Transforms the features based on singular vectors calculated earlier.

Parameters

features (Tensor) – Input features

Returns

Transformed features

inverse_transform(self, features: torch.Tensor) → torch.Tensor[source]

Inverses the transformed features.

Parameters

features (Tensor) – Transformed features

Returns: Inverse features

forward(self, features: torch.Tensor) → torch.Tensor[source]

Transforms the features.

Parameters

features (Tensor) – Input features

Returns

Transformed features