Cluster#

Clustering algorithm implementations using PyTorch.

class anomalib.models.components.cluster.GaussianMixture(n_components, n_iter=100, tol=0.001)#

Bases: DynamicBufferMixin

Gaussian Mixture Model.

Parameters:

n_components (int) – Number of components.
n_iter (int) – Maximum number of iterations to perform. Defaults to 100.
tol (float) – Convergence threshold. Defaults to 1e-3.

Example

The following examples shows how to fit a Gaussian Mixture Model to some data and get the cluster means and predicted labels and log-likelihood scores of the data.

>>> import torch
>>> from anomalib.models.components.cluster import GaussianMixture
>>> model = GaussianMixture(n_components=2)
>>> data = torch.tensor(
...     [
...             [2, 1], [2, 2], [2, 3],
...             [7, 5], [8, 5], [9, 5],
...     ]
... ).float()
>>> model.fit(data)
>>> model.means  # get the means of the gaussians
tensor([[8., 5.],
        [2., 2.]])
>>> model.predict(data)  # get the predicted cluster label of each sample
tensor([1, 1, 1, 0, 0, 0])
>>> model.score_samples(data)  # get the log-likelihood score of each sample
tensor([3.8295, 4.5795, 3.8295, 3.8295, 4.5795, 3.8295])

fit(data)#

Fit the model to the data.

Parameters:: data (Tensor) – Data to fit the model to. Tensor of shape (n_samples, n_features).
Return type:: None

predict(data)#

Predict the cluster labels of the data.

Parameters:: data (Tensor) – Samples to assign to clusters. Tensor of shape (n_samples, n_features).
Returns:: Tensor of shape (n_samples,) containing the predicted cluster label of each sample.
Return type:: Tensor

score_samples(data)#

Assign a likelihood score to each sample in the data.

Parameters:: data (Tensor) – Samples to assign scores to. Tensor of shape (n_samples, n_features).
Returns:: Tensor of shape (n_samples,) containing the log-likelihood score of each sample.
Return type:: Tensor

class anomalib.models.components.cluster.KMeans(n_clusters, max_iter=10)#

Bases: object

Initialize the KMeans object.

Parameters:

n_clusters (int) – The number of clusters to create.
max_iter (int, optional)) – The maximum number of iterations to run the algorithm. Defaults to 10.

fit(inputs)#

Fit the K-means algorithm to the input data.

Parameters:: inputs (torch.Tensor) – Input data of shape (batch_size, n_features).
Returns:: A tuple containing the labels of the input data with respect to the identified clusters and the cluster centers themselves. The labels have a shape of (batch_size,) and the cluster centers have a shape of (n_clusters, n_features).
Return type:: tuple
Raises:: ValueError – If the number of clusters is less than or equal to 0.

predict(inputs)#

Predict the labels of input data based on the fitted model.

Parameters:: inputs (torch.Tensor) – Input data of shape (batch_size, n_features).
Returns:: The predicted labels of the input data with respect to the identified clusters.
Return type:: torch.Tensor
Raises:: AttributeError – If the KMeans object has not been fitted to input data.

Cluster

Contents

Cluster#