# 3j0rSSKrSSKJrJr \"SSSS9S5r\"SSSSS9S 5r"S S \5rS r g) N)njitprangezNi8(i8[:], i8[:], i8[:], i8[:], i8[:], f4[:, :, :], f4[:], f4[:, :], i8[:], i8)T)cachenogilc BURSn [R"S[R5n Sn Sn SU -nSX'U S- n URSX'U S- n XU 'U S- n U S- n U S:Ga-U S-n XnU S-n XnU S-n XnX_S4nX_S4n[ U 5HnUUU4UU'UUU4UU'M [ US-U5HCn[ U 5H1n[ UUUUU45UU'[ UUUUU45UU'M3 ME UU- U::aUX'UX'SX/'SX?'SXO'SXo'GO^UU- n[R"U5nUUSUU--nUnUS- nUU:aUU:a#UUU4U:aUS- nUU:a UUU4U:aMUU:a#UUU4U:aUS-nUU:a UUU4U:aMUU:a>[ U 5HnUUU4nUUU4UUU4'UUUU4'M UUnUUUU'UUU'UU:aMUUU4U:aUS- nUn[ UU -[ UU - U55nU nU S- n U nU S- n UX'U S- n UX'U S- n UX'U S- n UX'U S- n UX'U S- n UX'U S- n UX'UX'UX/'UX?'UXO'UXo'U S:aGM-U $)Nrg?)shapenpemptyint64rangeminmaxargmax)i0_indsi1_inds less_inds more_inds split_dimsbounds split_valuespointsindices min_leaf_size dimensionstack stack_sizen_nodes max_leaf_sizei_nodei1i0lohidilengths split_dim split_valuejtemp temp_i_nodei_splitlessmores O/home/wildlama/miniconda3/lib/python3.13/site-packages/pymatting/util/kdtree.py _make_treer5s QI HHT288 $EJG %ME!OJ QE!OJ* qLG!OJ q.a "a  a   AI  AI y!A2q5MBqE2q5MBqE" rAvr"A9%BqE6!Q$<01BqE6!Q$<01&# 7m # GO GO "I  "I !#J #&L 2gG '*IY-# 0B*BBKAQAa%!eq)| 4{ BFA!eq)| 4{ B!eq)| 4 CFA!eq)| 4 Cq5"9-%ad|'-ad|q!t '+q!t . #*!*K!(GAJ!,GAJa%"al#k1QG"},c"}2Dg.NOGD qLGD qLG!#E  !OJ 'E  !OJ $E  !OJ!(E  !OJ "E  !OJ $E  !OJ GO GO $I  $I !*J #.L } q.@ Nzgvoid(i8[:], i8[:], i8[:], i8[:], i8[:], f4[:, :, :], f4[:], f4[:, :], f4[:, :], i8[:, :], f4[:, :], i8))rrparallelc URSn [URS5GHn XnXnXn[R"S[R5nSnSUS'SnUS:Ga{US-nUUnUU :aTSn[ U 5H5nUUnU[ UUSU4[UUSU4U55- nUUU-- nM7 UUUS- :aMkUUS:Xa[ UUUU5HnSn[ U 5HnUUUUU4- nUUU-- nM Un[ US- SS5HnUUU:dMUnM UU :dMZ[ [UU S- 5US5HnUUS- UU'UUS- UU'M UUU'UUU'[US-U 5nM ODUUnUUnUUn UU UU:aUUU'US- nUUU'US- nOUUU'US- nUUU'US- nUS:aGM{US- nGM g)Nrrr r r )r rrrrrrr)!rrrrrrrr query_points out_indices out_distanceskri_query query_point distancesrr n_neighborsr!r$distr)pdpr*distancedd insert_posr.r2r3r,s! r4 _find_knnrGs QI,,,Q/0"+ !* &rxx( a 1n !OJ:&Fay)A#AAS1 !5s6&!Q,;OQR7STTBBGOD*)K!O44&!R'wv@A"H"9-(^fQTl: BG+. "-J";?B;$Q<(2)*J< "A~!&s;A'> B!OA)0QGAJ+4QU+;IaL"P /0 +08 *-&)+/1&= /A4!( (&v. y)L,@@(,E*%!OJ(,E*%!OJ)-E*%!OJ(,E*%!OJ}1nB a _1r6c(\rSrSrSrSSjrSrSrg)KDTreezKDTree implementationc LUR[R:XdeURup4SX2-S- U--n[R"U[R 5Ul[R"U[R 5Ul[R"U[R 5Ul[R"U[R 5Ul [R"U[R 5Ul [R"USU4[R5Ul [R"U[R5Ul UR5Ul[R"U5R![R 5Ul[%UR URURURURURURURUR"U5 Ulg)a3Constructs a KDTree for given data points. The implementation currently only supports data type `np.float32`. Parameters ---------- data_points: numpy.ndarray (of type `np.float32`) Dataset with shape :math:`n \times d`, where :math:`n` is the number of data points in the data set and :math:`d` is the dimension of each data point min_leaf_size: int Minimum number of nodes in a leaf, defaults to 8 Example ------- >>> from pymatting import * >>> import numpy as np >>> data_set = np.random.randn(100, 2) >>> tree = KDTree(data_set.astype(np.float32)) r rN)dtyperfloat32r rrrrrrrrrcopyshuffled_data_pointsarangeastypeshuffled_indicesr5r")self data_pointsrn_datar max_nodess r4__init__KDTree.__init__sh"  BJJ...'--&014FG xx 2884 xx 2884 )RXX6)RXX6((9bhh7hh 1i8"**E HHY ;$/$4$4$6! " & 1 8 8 B! LL LL NN NN OO KK     % %  ! !    r6cUR[R:XdeURRSU:a)[ SURRSSUS35eURSn[R "X24[R5n[R "X24[R5n[URURURURURURURURUUUU5 UR Un[R""U5nXe4$)aQuery the tree Parameters ---------- query_points: numpy.ndarray (of type `np.float32`) Data points for which the next neighbours should be calculated k: int Number of neighbors to find Returns ------- distances: numpy.ndarray Distances to the neighbors indices: numpy.ndarray Indices of the k nearest neighbors in original data array Example ------- >>> from pymatting import * >>> import numpy as np >>> data_set = np.random.randn(100, 2) >>> tree = KDTree(data_set.astype(np.float32)) >>> tree.query(np.array([[0.5,0.5]], dtype=np.float32), k=3) (array([[0.14234178, 0.15879704, 0.26760164]], dtype=float32), array([[29, 21, 20]])) rzNumber of data points (z4) is less than the number of neighbors requested (k=z<). Please provide a larger dataset or reduce the value of k.)rLrrMrOr ValueErrorrrrGrrrrrrrrRsqrt)rSr9r<n_querysquared_distancesrr?s r4query KDTree.querys64!!RZZ///  $ $ * *1 - 1)$*C*C*I*I!*L)MNBCDBEEMM  $$Q'HHg\2::>((G<2 LL LL NN NN OO KK     % %     ''0GG-. !!r6) rrrrrr"rOrRrrN))__name__ __module__ __qualname____firstlineno____doc__rWr^__static_attributes__r6r4rIrIs, \9"r6rIc:[U5nURX5$)aFind k nearest neighbors in a data set. The implementation currently only supports data type `np.float32`. Parameters ---------- data_points: numpy.ndarray (of type `np.float32`) Dataset with shape :math:`n \times d`, where :math:`n` is the number of data points in the data set and :math:`d` is the dimension of each data point query_points: numpy.ndarray (of type `np.float32`) Data points for which the next neighbours should be calculated k: int Number of neighbors to find Returns ------- distances: numpy.ndarray Distances to the neighbors indices: numpy.ndarray Indices of the k nearest neighbors in original data array Example ------- >>> from pymatting import * >>> import numpy as np >>> data_set = np.random.randn(100, 2) >>> knn(data_set.astype(np.float32), np.array([[0.5,0.5]], dtype=np.float32), k=2) (array([[0.16233477, 0.25393516]], dtype=float32), array([[25, 17]])) )rIr^)rTr9r<trees r4knnrjYs6 + D ::l &&r6) numpyrnumbarrr5rGobjectrIrjrgr6r4rnsu V^bjno~p~Bow{DHSWX`X`Dj"Vj"Z'r6