o :ήc/@sbddlZddlmZmZeddddddZeddddd d d ZGd d d eZddZ dS)N)njitprangezNi8(i8[:], i8[:], i8[:], i8[:], i8[:], f4[:, :, :], f4[:], f4[:, :], i8[:], i8)T)cachenogilc Cs|jd} tdtj} d} d} d| }d| | <| d7} |jd| | <| d7} | | | <| d7} | d7} | dkr| d8} | | }| d8} | | }| d8} | | }||df}||df}t| D]}|||f||<|||f||<q`t|d|D]#}t| D]}t|||||f||<t|||||f||<qqz|||kr|||<|||<d||<d||<d||<d||<n||}t|}||d||}|}|d}||krL||kr|||f|kr|d7}||kr|||f|ks||kr|||f|kr|d8}||kr|||f|ks||krHt| D]}|||f}|||f|||f<||||f<q ||}||||<|||<||ks|||f|krY|d7}|}t|| t|| |}| }| d7} | }| d7} || | <| d7} || | <| d7} || | <| d7} || | <| d7} || | <| d7} || | <| d7} |||<|||<|||<|||<|||<|||<| dks8| S)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_splitlessmorer2>> from pymatting import * >>> import numpy as np >>> data_set = np.random.randn(100, 2) >>> tree = KDTree(data_set.astype(np.float32)) rrN)dtyper float32r r rrrrrrrrcopyshuffled_data_pointsarangeastypeshuffled_indicesr4r )self data_pointsrn_datar max_nodesr2r2r3__init__s0   zKDTree.__init__c Cs|jtjksJ|jd}t||ftj}t||ftj}t|j|j|j |j |j |j |j |j|||| |j|}t|}||fS)a6Query 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]])) r)rGr rHr r rrDrrrrrrrrJrMsqrt)rNr6r9n_querysquared_distancesrr<r2r2r3querys*   z KDTree.queryN)rF)__name__ __module__ __qualname____doc__rRrVr2r2r2r3rEs  .rEcCst|}|||S)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]])) )rErV)rOr6r9treer2r2r3knnSs r\) numpyr numbarrr4rDobjectrEr\r2r2r2r3s   b g