adding KMeans PyTorch Implementation to cfa model (#998)

* adding KMeans PyTorch Implementation to cfa model

* Required modifications and implementation comparison

* Update torch_model.py

* Update torch_model.py

* Update kmeans.py

* required changes and Google docstring format

* remove test notebook

* Update torch_model.py

* 'unindent does not match any outer indentation level (<unknown>, line 34)' (syntax-error)

* Update torch_model.py

* Fix pre-commit issues

* Revert torch model to sklearn

---------

Co-authored-by: Samet Akcay <samet.akcay@intel.com>
Co-authored-by: Ashwin Vaidya <ashwin.vaidya@intel.com>

src/anomalib/models/components/cluster/kmeans.py

@@ -0,0 +1,72 @@
+"""KMeans clustering algorithm implementation using PyTorch."""
+
+# Copyright (C) 2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import torch
+
+
+class KMeans:
+    def __init__(self, n_clusters: int, max_iter: int = 10):
+        """
+        Initializes the KMeans object.
+
+        Args:
+            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.
+        """
+        self.n_clusters = n_clusters
+        self.max_iter = max_iter
+
+    def fit(self, inputs):
+        """
+        Fits the K-means algorithm to the input data.
+
+        Args:
+            inputs (torch.Tensor): Input data of shape (batch_size, n_features).
+
+        Returns:
+            tuple: 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).
+
+        Raises:
+            ValueError: If the number of clusters is less than or equal to 0.
+        """
+        batch_size, _ = inputs.shape
+
+        # Initialize centroids randomly from the data points
+        centroid_indices = torch.randint(0, batch_size, (self.n_clusters,))
+        self.cluster_centers_ = inputs[centroid_indices]
+
+        # Run the k-means algorithm for max_iter iterations
+        for _ in range(self.max_iter):
+            # Compute the distance between each data point and each centroid
+            distances = torch.cdist(inputs, self.cluster_centers_)
+
+            # Assign each data point to the closest centroid
+            self.labels_ = torch.argmin(distances, dim=1)
+
+            # Update the centroids to be the mean of the data points assigned to them
+            for j in range(self.n_clusters):
+                mask = self.labels_ == j
+                if mask.any():
+                    self.cluster_centers_[j] = inputs[mask].mean(dim=0)
+        # this line returns labels and centoids of the results
+        return self.labels_, self.cluster_centers_
+
+    def predict(self, inputs):
+        """
+        Predicts the labels of input data based on the fitted model.
+
+        Args:
+            inputs (torch.Tensor): Input data of shape (batch_size, n_features).
+
+        Returns:
+            torch.Tensor: The predicted labels of the input data with respect to the identified clusters.
+
+        Raises:
+            AttributeError: If the KMeans object has not been fitted to input data.
+        """
+        distances = torch.cdist(inputs, self.cluster_centers_)
+        return torch.argmin(distances, dim=1)