3jHSSKJr SSKrSSKJrJr SSKJr "SS5rg)) annotationsN) KORNIA_CHECKKORNIA_CHECK_SHAPEeuclidean_distancec\rSrSrSrS S Sjjr\SSj5r\SSj5rSSjr SSjr SS jr SS jr S r g)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: torch.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))) Nc[US:gS5 Ub[USS/5 XlX lX0lX@lSUlSUlUb[R"U5 gg)Nrznum_clusters can't be 0CD) rr num_clusters_cluster_centers tolerancemax_iterations_final_cluster_assignments_final_cluster_centerstorch manual_seed)selfrcluster_centersrrseeds O/home/wildlama/miniconda3/lib/python3.13/site-packages/kornia/contrib/kmeans.py__init__KMeans.__init__-sj \Q&(AB  & c ;( /",?C';?#     d # c[UR[R5(a UR$[UR[R5(a UR$[ S5e)aReturn the current cluster centers. Returns: A tensor with shape :math:`(C, D)`: - ``C`` is the number of clusters. - ``D`` is the feature dimension of each sample. If :meth:`fit` has already been called, this returns the learned final centers. Otherwise, it returns the initialization provided during construction. Raises: TypeError: If no initial centers were provided and ``fit`` has not been run. #Model has not been fit to a dataset) isinstancerrTensorr TypeErrorrs rrKMeans.cluster_centersFsV d115<< @ @.. . d++U\\ : :(( (AB Brc[UR[R5(a UR$[ S5e)a9Return cluster labels assigned during the most recent ``fit`` call. Returns: A 1D tensor with shape :math:`(N,)`, where ``N`` is the number of samples given to :meth:`fit`. Each value is the cluster index assigned to the corresponding sample. Raises: TypeError: If ``fit`` has not been run yet. r)rrrr r!r"s rcluster_assignmentsKMeans.cluster_assignments]s3 d55u|| D D22 2AB Brcj[U5n[R"X1RS9nUSUnXnU$)zChooses num_cluster points from X as the initial cluster centers. Args: X: 2D input torch.Tensor to be clustered num_clusters: number of desired cluster centers Returns: 2D torch.Tensor with num_cluster rows deviceN)lenrrandpermr))rXr num_samplespermidx initial_states r_initialise_cluster_centers"KMeans._initialise_cluster_centersns8q6 ~~k((;=L! rc:USS2SS4nUSn[X45nU$)zCompute pairwise squared distance between 2 sets of vectors. Args: data1: 2D torch.Tensor of shape N, D data2: 2D torch.Tensor of shape C, D Returns: 2D torch.Tensor of shape N, C N.)N.r)rdata1data2ABdistances r_pairwise_euclidean_distance#KMeans._pairwise_euclidean_distances- !T3,  ) %a+rc >[USS/5 URc!URXR5UlO][ UR SURR S:HSUR SSURR S35 URnSnSnUR X5nURS5nUR5n[UR5Hn[R"Xg:H5R5n[R"USU5nUR SS:Xa,U[R"[U5S UR S 9nUR#SS 9X''M [R$"[R&"[R$"X#- S -SS 955n US-nUR(bU S -UR(:aO#UR*S:waX@R*:aOGMgX`lX lg) zFit iterative KMeans clustering till a threshold for shift in cluster centers or a maximum no of iterations have reached. Args: X: 2D input torch.Tensor to be clustered Nr NzPDimensions at position 1 of X and cluster_centers do not match.  != r)r=r()dim)rrr1rrshaper9argminclonerangernonzerosqueeze index_selectrandintr*r)meansumsqrtrrrr) rr,current_centersprevious_centers iterationr8cluster_assignmentindexselected center_shifts rfit KMeans.fits 1sCj)  ($($D$DQHYHY$ZD !  d3399!<< D!6!6!>!$) s1vtAHH!MNH)11)=&2!99UZZ ?;]bc:cij0k%lmL!A I~~)lAo.N""a'I9L9L,L58+='&5#rc[URSURRS:HSURSSURRS35 URXR5nUR S5nU$)zFind the cluster center closest to each point in x. Args: x: 2D torch.Tensor Returns: 1D torch.Tensor containing cluster id assigned to each data point in x r=zPDimensions at position 1 of x and cluster_centers do not match. r>r?)rrBrr9rC)rxr8rPs rpredictKMeans.predicts  GGAJ$..44Q7 7 D!5!5!;!;A!> ? A 44Q8L8LM%__R0!!r)rrrrrr)gMbP?rN) rintrztorch.Tensor | NonerfloatrrZrz int | NonereturnNone)r\ torch.Tensor)r,r^rrZr\r^)r4r^r5r^r\r^)r,r^r\r])rWr^r\r^)__name__ __module__ __qualname____firstlineno____doc__rpropertyrr%r1r9rTrX__static_attributes__rrr r s(! $$-$ $  $  $ $2CC,CC "$86t"rr ) __future__rrkornia.core.checkrrkornia.geometry.linalgrr rfrrrjs(# >5B"B"r