+ çj,Rt^RItRtRRltRRltR#)zt KNN algorithm for structural and image similarity computation. Uses numpy for fast Euclidean distance computation. Nc /pVFp\4pVFupVPV4pV'gK\V4P4'gK?VP \V4P4P 44Kw V'gK\ V4W#&K .pVF0pW29gK W#,FpVPV RV 24K K2 V'gA\P!\V4^34R.\\\V4443#\V4p\V4p \P!W3\PR7p \V4U U u/uFwrWbK p p p \V4FwrVFpW29gK VPV4pV'gK&\V4P4'gKGV R\V4P4P 4 2pW9gK{RWW,3&K K W\\V443#uup p i)a Build one-hot binary feature matrix from list of dicts. Args: records: list of dicts, each with keys from cat_cols cat_cols: list of column names to encode Returns: (matrix, feature_names, record_indices) - matrix: np.ndarray of shape (n_records, n_features) - feature_names: list of feature name strings - record_indices: list mapping matrix row -> original record index : no_features)dtype?)setgetstrstripaddlowersortedappendnpzeroslenlistrangefloat32 enumerate)recordscat_colsvocabcolvaluesrecval feature_namesnmmatrixinamefeat_idxrow_idxkeys&& L/Users/jokubas/Desktop/KTU/big_data/engineering/processing/algorithms/knn.pybuild_onehot_matrixr's EC''#,Css3x~~'' 3s8>>+1134 6EJM <zz$$uAcU^4" xxWq)*]OT%G BU=VVV G A MA XXqfBJJ /F(1'?@'?GA'?H@!'* C|ggcl33s8>>++ E3s8>>#3#9#9#;"<=C9< 56 + $uQx. 00As3 H:cW,pVeD\P!V4^,pWUV8g,p\V4^8Xd.#W,pM:\P!VP^,4pWwV8g,pW,pWd, p\P !\P !V^,^R74p \V\V44p \P!W^, 4RV p V \P!W,4,p .p V F7p \W],4p\W,4pV PW34K9 V #)a Find k nearest neighbors for query_idx in matrix using Euclidean distance. Args: query_idx: index of the query record in matrix matrix: np.ndarray of shape (n, d) k: number of neighbors to return city_mask: optional boolean array of shape (n,); if provided, only consider records where city_mask is True Returns: list of (neighbor_idx, distance) sorted by distance ASC (excluding query itself) N)axis) rwhererarangeshapesqrtsummin argpartitionargsortintfloatr) query_idxr k city_mask query_veccandidate_indicescandidate_matrix all_indicesdiff distancesk_actual top_k_localresults local_idxorig_idxdists&&&& r& knn_searchrC?s)!IHHY/2-9.LM  !Q &I!4ii Q0 'y(@A!4  'Dtqyq12I1c+,-H//)\:9HEKbjj)?@AKG (34Y)*'(! NcJV^8:dR#\RRW, , 4pV#)z9Convert Euclidean distance to similarity score in [0, 1].rg)max)distance max_distancescores&& r&distance_to_scorerJns&q S822 3E LrD) N)g$@)__doc__numpyrr'rCrJrDr&rOs 41n,^rD