o oi@sNddlmZddlZddlmZddlmZmZddlm Z GdddZ dS)) annotationsN)Tensor) KORNIA_CHECKKORNIA_CHECK_SHAPEeuclidean_distancec@sfeZdZdZ   d#d$ddZed%ddZed%ddZd&ddZd'ddZ d(ddZ d)d!d"Z dS)*KMeansaImplements the kmeans clustering algorithm with euclidean distance as similarity measure. Args: num_clusters: number of clusters the data has to be assigned to cluster_centers: tensor of starting cluster centres can be passed instead of num_clusters tolerance: float value. the algorithm terminates if the shift in centers is less than tolerance max_iterations: number of iterations to run the algorithm for seed: number to set torch manual seed for reproducibility Example: >>> kmeans = kornia.contrib.KMeans(3, None, 10e-4, 100, 0) >>> kmeans.fit(torch.rand((1000, 5))) >>> predictions = kmeans.predict(torch.rand((10, 5))) MbP?rN num_clustersintcluster_centers Tensor | None tolerancefloatmax_iterationsseed int | NonereturnNonecCsbt|dkd|durt|ddg||_||_||_||_d|_d|_|dur/t |dSdS)Nrznum_clusters can't be 0CD) rrr _cluster_centersrr_final_cluster_assignments_final_cluster_centerstorch manual_seed)selfr r rrrrI/home/ubuntu/.local/lib/python3.10/site-packages/kornia/contrib/kmeans.py__init__.szKMeans.__init__rcCs,t|jtr |jSt|jtr|jStdNz#Model has not been fit to a dataset) isinstancerrr TypeErrorrrrrr Gs  zKMeans.cluster_centerscCst|jtr |jStdr )r!rrr"r#rrrcluster_assignmentsPs zKMeans.cluster_assignmentsXcCs0t|}tj||jd}|d|}||}|S)aChooses num_cluster points from X as the initial cluster centers. Args: X: 2D input tensor to be clustered num_clusters: number of desired cluster centers Returns: 2D Tensor with num_cluster rows deviceN)lenrrandpermr')rr%r num_samplespermidx initial_staterrr_initialise_cluster_centersWs   z"KMeans._initialise_cluster_centersdata1data2cCs(|ddddf}|d}t||}|S)zCompute pairwise squared distance between 2 sets of vectors. Args: data1: 2D tensor of shape N, D data2: 2D tensor of shape C, D Returns: 2D tensor of shape N, C N.)N.r)rr/r0ABdistancerrr_pairwise_euclidean_distancehs  z#KMeans._pairwise_euclidean_distancec Csbt|ddg|jdur|||j|_nt|jd|jjdkd|jdd|jjd|j}d}d} |||}|d }|}t |jD].}t ||k }t |d|}|jddkrr|t jt|d |jd }|jdd ||<qLt t t j||d dd } |d}|jdur| d |jkrn |jdkr||jkrnq8||_||_dS)zFit iterative KMeans clustering till a threshold for shift in cluster centers or a maximum no of iterations have reached. Args: X: 2D input tensor to be clustered NrNzPDimensions at position 1 of X and cluster_centers do not match.  != rT)r6r&)dim)rrr.r rshaper4argminclonerangernonzerosqueeze index_selectrandintr(r'meansumsqrtrrrr) rr%current_centersprevious_centers iterationr3cluster_assignmentindexselected center_shiftrrrfitzsB    " z KMeans.fitxcCsRt|jd|jjdkd|jdd|jjd|||j}|d}|S)zFind the cluster center closest to each point in x. Args: x: 2D tensor Returns: 1D tensor containing cluster id assigned to each data point in x r6zPDimensions at position 1 of x and cluster_centers do not match. r7r8)rr;r r4r<)rrNr3rIrrrpredicts   zKMeans.predict)r rN) r r r r rrrr rrrr)rr)r%rr r rr)r/rr0rrr)r%rrr)rNrrr) __name__ __module__ __qualname____doc__rpropertyr r$r.r4rMrOrrrrrs      :r) __future__rr kornia.corerkornia.core.checkrrkornia.geometry.linalgrrrrrrs