Ë AÆVh)ã óÀ—dZddlZddlmZddlmZddlmZddlm Z ddl m Z ddlmZdd l mZdd l mZdd l mZdd lmZdd lmZddlmZddlmZm Z m!Z!ddl"m#Z#d%de#e!ejHfde#e!ejJfde#e!ejJffd„Z&ed«ejNe&««Z(de#e!ejHfde#e!ejJfde#e!ejJffd„Z)d%de#e!ejHfde#e!ejJfde#e!ejJfde#e!ejJfde#e!ejHff d„Z*ed«ejNe*««Z+de#e!ejHfde#e!ejJfde#e!ejJfde#e!ejJfde#e!ejHff d„Z,ejZdddg«Z.d%de#e!ejHfd e#e!ejHfd!e#e!ejJffd"„Z/ed#«ejNe/««Z0de#e!ejHfd e#e!ejHfd!e#e!ejJffd$„Z1y)&zUPython wrappers around TensorFlow ops. This file is MACHINE GENERATED! Do not edit. éN)Ú pywrap_tfe)Úcontext)Úcore)Úexecute)Údtypes)Úannotation_types)Úop_def_registry)Úops)Úop_def_library)Údeprecated_endpoints)Údispatch)Ú tf_export)ÚTypeVarÚListÚAny)Ú AnnotatedÚ distancesÚseedÚreturncób—tjxstj«}|j}|jr t j |d|||«}|Stjd|||¬«\}}}} | dd}tj «r&d} |j"} tj$d| | |«|\}|S#t j$r }tj||«Yd}~nd}~wt j$rYnwxYw t||||¬«S#t j$rYŒÒwxYw)aÒReturns the index of a data point that should be added to the seed set. Entries in distances are assumed to be squared distances of candidate points to the already sampled centers in the seed set. The op constructs one Markov chain of the k-MC^2 algorithm and returns the index of one candidate point to be added as an additional cluster center. Args: distances: A `Tensor` of type `float32`. Vector with squared distances to the closest previously sampled cluster center for each candidate point. seed: A `Tensor` of type `int64`. Scalar. Seed for initializing the random number generator. name: A name for the operation (optional). Returns: A `Tensor` of type `int64`. ÚKMC2ChainInitializationN©ÚnameÚctx)rrr©)Ú_contextrÚ_thread_local_dataÚis_eagerrÚTFE_Py_FastPathExecuteÚ_coreÚ_NotOkStatusExceptionÚ_opsÚraise_from_not_ok_statusÚ_FallbackExceptionÚ(kmc2_chain_initialization_eager_fallbackÚ_SymbolicExceptionÚ_op_def_libraryÚ_apply_op_helperÚ_executeÚmust_record_gradientÚinputsÚrecord_gradient) rrrÚ_ctxÚtldÚ_resultÚeÚ_Ú_opÚ_outputsÚ_attrsÚ _inputs_flats úX/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/ops/gen_clustering_ops.pyÚkmc2_chain_initializationr7s1€ô& × Ñ Ò 0œh×.Ñ.Ó0€$Ø ×Ñ€#؇\‚\ð Ü×1Ñ1Ø Ð'¨¨y¸$ó@€gà €nô(×8Ñ8Ø!¨Y¸TÈôNÑ€!€QˆˆXà ‘QˆK€'Ü ×"Ñ"Ô$Ø €FØ—:‘:€LÜ ×ÑØ! <°¸ôBà (€'Ø €.øô' × &Ñ &ò-Ü ×#Ñ# A t×,Ñ,ûÜ × #Ñ #ò Ù ð úð Ü 5Ø T ¨$ô0ð0øä × #Ñ #ò Ù ð ús0ÁB>Â>DÃC,Ã,DÄDÄ DÄD.Ä-D.zraw_ops.KMC2ChainInitializationcó8—tj|tj«}tj|tj«}||g}d}t j dd||||¬«}t j«rt jd|||«|\}|S)NsKMC2ChainInitializationé©r+Úattrsrrr© r"Úconvert_to_tensorÚ_dtypesÚfloat32Úint64r)rr*r,)rrrrr5r4r/s r6r%r%KsŽ€Ü×$Ñ$ Y´·±Ó@€)Ü × Ñ  ¤g§m¡mÓ 4€$ؘTÐ"€,Ø €&Ü × Ñ Ð7¸Ø$0¸ÀCØ"&ô (€'ô×"Ñ"Ô$Ü ×ÑØ! <°¸ôBà (€'Ø €.óÚpointsÚ num_to_sampleÚnum_retries_per_samplec ón—tjxstj«}|j}|jr t j |d|||||«}|Stjd|||||¬«\} } } } | dd}tj «r&d} | j"} tj$d| | |«|\}|S#t j$r }tj||«Yd}~nd}~wt j$rYnwxYw t||||||¬«S#t j$rYŒÖwxYw)aVSelects num_to_sample rows of input using the KMeans++ criterion. Rows of points are assumed to be input points. One row is selected at random. Subsequent rows are sampled with probability proportional to the squared L2 distance from the nearest row selected thus far till num_to_sample rows have been sampled. Args: points: A `Tensor` of type `float32`. Matrix of shape (n, d). Rows are assumed to be input points. num_to_sample: A `Tensor` of type `int64`. Scalar. The number of rows to sample. This value must not be larger than n. seed: A `Tensor` of type `int64`. Scalar. Seed for initializing the random number generator. num_retries_per_sample: A `Tensor` of type `int64`. Scalar. For each row that is sampled, this parameter specifies the number of additional points to draw from the current distribution before selecting the best. If a negative value is specified, a heuristic is used to sample O(log(num_to_sample)) additional points. name: A name for the operation (optional). Returns: A `Tensor` of type `float32`. ÚKmeansPlusPlusInitializationNr)rBrCrrDrr)rrrrrrr r!r"r#r$Ú.kmeans_plus_plus_initialization_eager_fallbackr&r'r(r)r*r+r,)rBrCrrDrr-r.r/r0r1r2r3r4r5s r6Úkmeans_plus_plus_initializationrHZsI€ô2 × Ñ Ò 0œh×.Ñ.Ó0€$Ø ×Ñ€#؇\‚\ð Ü×1Ñ1Ø Ð,¨d°F¸MØ Ð$ó&€gð€nô(×8Ñ8Ø&¨vØ6CØ-1Ø?UØ-1ô 3Ñ€!€QˆˆXð ‘QˆK€'Ü ×"Ñ"Ô$Ø €FØ—:‘:€LÜ ×ÑØ&¨ °f¸gôGà (€'Ø €.øô1 × &Ñ &ò-Ü ×#Ñ# A t×,Ñ,ûÜ × #Ñ #ò Ù ð úð Ü ;Ø - Ð'=ÀDØôðøô × #Ñ #ò Ù ð ús0ÁCÃD ÃC0Ã0D ÄD Ä DÄD4Ä3D4z$raw_ops.KmeansPlusPlusInitializationcóÌ—tj|tj«}tj|tj«}tj|tj«}tj|tj«}||||g}d}t j dd||||¬«}t j«rt jd|||«|\}|S)NsKmeansPlusPlusInitializationr9r:rFr<) rBrCrrDrrr5r4r/s r6rGrG˜sÁ€Ü × !Ñ ! &¬'¯/©/Ó :€&Ü×(Ñ(¨¼¿ ¹ ÓF€-Ü × Ñ  ¤g§m¡mÓ 4€$Ü×1Ñ1Ð2HÌ'Ï-É-ÓXÐؘ-¨Ð/EÐF€,Ø €&Ü × Ñ Ð<¸aØ$0¸ÀCØ"&ô (€'ô×"Ñ"Ô$Ü ×ÑØ&¨ °f¸gôGà (€'Ø €.rAÚNearestNeighborsÚnearest_center_indicesÚnearest_center_distancesÚcentersÚkcó´—tjxstj«}|j}|jr2 t j |d||||«}t j|«}|Stj d||||¬«\}}} } | dd}t#j$«r&d} | j&} t#j(d| | |«t j|«}|S#tj$r }tj||«Yd}~nd}~wtj$rYnwxYw t|||||¬«S#tj$rYŒåwxYw)atSelects the k nearest centers for each point. Rows of points are assumed to be input points. Rows of centers are assumed to be the list of candidate centers. For each point, the k centers that have least L2 distance to it are computed. Args: points: A `Tensor` of type `float32`. Matrix of shape (n, d). Rows are assumed to be input points. centers: A `Tensor` of type `float32`. Matrix of shape (m, d). Rows are assumed to be centers. k: A `Tensor` of type `int64`. Number of nearest centers to return for each point. If k is larger than m, then only m centers are returned. name: A name for the operation (optional). 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