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alkaline-ml / scikit-learn python

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scikit-learn / neighbors / _kd_tree.pypy36-pp73-x86_64-linux-gnu.so
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{BinaryTree}(X, leaf_size=40, metric='minkowski', **kwargs)

{BinaryTree} for fast generalized N-point problems

Parameters
----------
X : array-like of shape (n_samples, n_features)
    n_samples is the number of points in the data set, and
    n_features is the dimension of the parameter space.
    Note: if X is a C-contiguous array of doubles then data will
    not be copied. Otherwise, an internal copy will be made.

leaf_size : positive int, default=40
    Number of points at which to switch to brute-force. Changing
    leaf_size will not affect the results of a query, but can
    significantly impact the speed of a query and the memory required
    to store the constructed tree.  The amount of memory needed to
    store the tree scales as approximately n_samples / leaf_size.
    For a specified ``leaf_size``, a leaf node is guaranteed to
    satisfy ``leaf_size <= n_points <= 2 * leaf_size``, except in
    the case that ``n_samples < leaf_size``.

metric : str or DistanceMetric object
    the distance metric to use for the tree.  Default='minkowski'
    with p=2 (that is, a euclidean metric). See the documentation
    of the DistanceMetric class for a list of available metrics.
    {binary_tree}.valid_metrics gives a list of the metrics which
    are valid for {BinaryTree}.

Additional keywords are passed to the distance metric class.
Note: Callable functions in the metric parameter are NOT supported for KDTree
and Ball Tree. Function call overhead will result in very poor performance.

Attributes
----------
data : memory view
    The training data

Examples
--------
Query for k-nearest neighbors

    >>> import numpy as np
    >>> rng = np.random.RandomState(0)
    >>> X = rng.random_sample((10, 3))  # 10 points in 3 dimensions
    >>> tree = {BinaryTree}(X, leaf_size=2)              # doctest: +SKIP
    >>> dist, ind = tree.query(X[:1], k=3)                # doctest: +SKIP
    >>> print(ind)  # indices of 3 closest neighbors
    [0 3 1]
    >>> print(dist)  # distances to 3 closest neighbors
    [ 0.          0.19662693  0.29473397]

Pickle and Unpickle a tree.  Note that the state of the tree is saved in the
pickle operation: the tree needs not be rebuilt upon unpickling.

    >>> import numpy as np
    >>> import pickle
    >>> rng = np.random.RandomState(0)
    >>> X = rng.random_sample((10, 3))  # 10 points in 3 dimensions
    >>> tree = {BinaryTree}(X, leaf_size=2)        # doctest: +SKIP
    >>> s = pickle.dumps(tree)                     # doctest: +SKIP
    >>> tree_copy = pickle.loads(s)                # doctest: +SKIP
    >>> dist, ind = tree_copy.query(X[:1], k=3)     # doctest: +SKIP
    >>> print(ind)  # indices of 3 closest neighbors
    [0 3 1]
    >>> print(dist)  # distances to 3 closest neighbors
    [ 0.          0.19662693  0.29473397]

Query for neighbors within a given radius

    >>> import numpy as np
    >>> rng = np.random.RandomState(0)
    >>> X = rng.random_sample((10, 3))  # 10 points in 3 dimensions
    >>> tree = {BinaryTree}(X, leaf_size=2)     # doctest: +SKIP
    >>> print(tree.query_radius(X[:1], r=0.3, count_only=True))
    3
    >>> ind = tree.query_radius(X[:1], r=0.3)  # doctest: +SKIP
    >>> print(ind)  # indices of neighbors within distance 0.3
    [3 0 1]


Compute a gaussian kernel density estimate:

    >>> import numpy as np
    >>> rng = np.random.RandomState(42)
    >>> X = rng.random_sample((100, 3))
    >>> tree = {BinaryTree}(X)                # doctest: +SKIP
    >>> tree.kernel_density(X[:3], h=0.1, kernel='gaussian')
    array([ 6.94114649,  7.83281226,  7.2071716 ])

Compute a two-point auto-correlation function

    >>> import numpy as np
    >>> rng = np.random.RandomState(0)
    >>> X = rng.random_sample((30, 3))
    >>> r = np.linspace(0, 1, 5)
    >>> tree = {BinaryTree}(X)                # doctest: +SKIP
    >>> tree.two_point_correlation(X, r)
    array([ 30,  62, 278, 580, 820])

<strided and direct or indirect>unable to allocate array data.itemsize <= 0 for cython.arraysklearn.neighbors._kd_treeKernel code not recognizedInvalid shape in axis %d: %d.cannot pop on empty heapCannot index with type '%s'kernel = '%s' not recognized<contiguous and indirect><MemoryView of %r at 0x%x><MemoryView of %r object>kernel not recognized<contiguous and direct>_update_sample_weight<strided and indirect>get_valid_metric_idsX is an empty array<strided and direct>cline_in_tracebacksimultaneous_sort__pyx_unpickle_EnumMinkowskiDistanceManhattanDistanceEuclideanDistanceChebyshevDistanceVALID_METRIC_IDS_update_memviews__setstate_cython__return_distance__pyx_PickleErrordtype_is_objectallocate_bufferView.MemoryViewvalid_metricssample_weight__reduce_cython__rdist_to_dist__pyx_getbuffernodeheap_sortbreadth_firstVALID_METRICSNeighborsHeapstringsourcesort_results__pyx_checksumepanechnikov_dist_metricsRuntimeErrorNodeHeapDatavals_sortedkernel_normexponentialcheck_arraybinary_treePickleErrorMemoryErrorImportErrorzeros_likesize_guessreturn_log__pyx_vtable____pyx_resultget_metricget_arrayscount_onlyatleast_1dValueErrorIndexErrorBinaryTree__reduce_ex____pyx_statenode_datan_samplesminkowskiload_heapleaf_sizeidx_startenumeratedistancesTypeErrorCLASS_DOCwarnings_typedefs__setstate____pyx_typeitemsize__getstate__gaussiandualtreeNodeHeapNodeDataKeyErrorEllipsisDOC_DICTreshapememviewkd_treeis_leafindicesidx_endfortranasarrayargsortupdateunpacktophatstructresult__reduce__radiuspickleobjectnewObjnamen_nbrsmetriclinearkernel__import__formatencodecosinearangeKDTreezerosutilsstartshaperangeordernumpyn_ptsitemsi_valflagserroremptydtype__class__ITYPEDTYPEASCIIwarnvals__test__stopstepsortsizertolpackndim__name__mode__main__log2joinheapfull__dict__databaseatolvalsumrowobj__new__loginfexp__doc____all__,}:^pinpidi2i1(%s)rkjihdcXT{OC%s() got multiple values for keyword argument '%U'%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'Buffer dtype mismatch, expected %s%s%s but got %sBuffer dtype mismatch, expected '%s' but got %s in '%s.%s'memviewslice is already initialized!sklearn/neighbors/_binary_tree.pxiUnable to initialize pickling for %s%.200s.%.200s is not a type object%.200s.%.200s size changed, may indicate binary incompatibility. 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Expected %zd from C header, got %zd from PyObjectDoes not understand character buffer dtype format string ('%c')Cannot convert %.200s to %.200sView.MemoryView.memoryview.__str__View.MemoryView.memoryview.size.__get__View.MemoryView.memoryview.nbytes.__get__View.MemoryView.memoryview.itemsize.__get__View.MemoryView.memoryview.ndim.__get__View.MemoryView.memoryview.suboffsets.__get__View.MemoryView.memoryview.shape.__get__View.MemoryView.memoryview.assign_item_from_objectView.MemoryView._memoryviewslice.assign_item_from_objectView.MemoryView.memoryview.setitem_indexed%.200s() takes %.8s %zd positional argument%.1s (%zd given)View.MemoryView.array.memview.__get__View.MemoryView.array.__getattr__View.MemoryView.array.__getitem__sklearn/neighbors/_kd_tree.pyxsklearn.neighbors._kd_tree.min_rdist_dualsklearn.neighbors._kd_tree.min_rdistsklearn.neighbors._kd_tree.BinaryTree.get_arrayssklearn.neighbors._kd_tree.BinaryTree.get_tree_statssklearn.neighbors._kd_tree.NodeHeap.pushsklearn.neighbors._kd_tree.NeighborsHeap.get_arrayssklearn.neighbors._kd_tree.NeighborsHeap._pushBufferFormatFromTypeInfo.format_from_typeinfosklearn.neighbors._kd_tree._simultaneous_sortView.MemoryView.array.__setitem__Subscript deletion not supported by %.200ssklearn.neighbors._kd_tree.BinaryTree.sum_weight.__set__sklearn.neighbors._kd_tree.BinaryTree.sum_weight.__get__sklearn.neighbors._kd_tree.min_max_distsklearn.neighbors._kd_tree.BinaryTree.get_n_callssklearn.neighbors._kd_tree.BinaryTree.rdistsklearn.neighbors._kd_tree.BinaryTree.distsklearn.neighbors._kd_tree.NeighborsHeap.largestsklearn.neighbors._kd_tree.max_rdistsklearn.neighbors._kd_tree.NodeHeap.peekUnexpected format string character: '%c'Expected a dimension of size %zu, got %zuExpected %d dimensions, got %dPython does not define a standard format string size for long double ('g')..Buffer dtype mismatch; next field is at offset %zd but %zd expectedBig-endian buffer not supported on little-endian compilerBuffer acquisition: Expected '{' after 'T'Cannot handle repeated arrays in format stringExpected a dimension of size %zu, got %dExpected a comma in format string, got '%c'Expected %d dimension(s), got %dUnexpected end of format string, expected ')'Buffer has wrong number of dimensions (expected %d, got %d)Item size of buffer (%zd byte%s) does not match size of '%s' (%zd byte%s)__int__ returned non-int (type %.200s).  The ability to return an instance of a strict subclass of int is deprecated, and may be removed in a future version of Python.__%.4s__ returned non-%.4s (type %.200s)calling %R should have returned an instance of BaseException, not %Rraise: exception class must be a subclass of BaseExceptionsklearn.neighbors._kd_tree.NeighborsHeap.__reduce_cython__sklearn.neighbors._kd_tree.NeighborsHeap.__setstate_cython__sklearn.neighbors._kd_tree.NodeHeap.__reduce_cython__sklearn.neighbors._kd_tree.NodeHeap.__setstate_cython__View.MemoryView.array.__reduce_cython__View.MemoryView.array.__setstate_cython__View.MemoryView.memoryview.__reduce_cython__View.MemoryView.memoryview.__setstate_cython__View.MemoryView.memoryview.strides.__get__View.MemoryView._memoryviewslice.__reduce_cython__View.MemoryView._memoryviewslice.__setstate_cython__sklearn.neighbors._kd_tree._log_kernel_normPyObject_GetBuffer: view==NULL argument is obsoleteView.MemoryView.array.__getbuffer__View.MemoryView.memoryview.__getbuffer__sklearn.neighbors._kd_tree.NodeHeap.popAcquisition count is %d (line %d)View.MemoryView.array.get_memviewView.MemoryView.memoryview_cwrapperItem size of buffer (%zu byte%s) does not match size of '%s' (%zu byte%s)Buffer is not indirectly contiguous in dimension %d.Buffer and memoryview are not contiguous in the same dimension.C-contiguous buffer is not contiguous in dimension %dC-contiguous buffer is not indirect in dimension %dBuffer exposes suboffsets but no stridesBuffer not compatible with direct access in dimension %d.Buffer is not indirectly accessible in dimension %d.View.MemoryView.memoryview.is_slicetoo many values to unpack (expected %zd)View.MemoryView.memoryview_fromsliceView.MemoryView.memoryview_copy_from_slicesklearn.neighbors._kd_tree.BinaryTree.node_bounds.__get__sklearn.neighbors._kd_tree.BinaryTree.node_data.__get__sklearn.neighbors._kd_tree.BinaryTree.idx_array.__get__sklearn.neighbors._kd_tree.BinaryTree.sample_weight.__get__sklearn.neighbors._kd_tree.BinaryTree.data.__get__c-string too long to convert to PythonView.MemoryView.array_cwrappersklearn.neighbors._kd_tree.BinaryTree.__reduce__sklearn.neighbors._kd_tree.newObjView.MemoryView.memoryview.__repr__View.MemoryView.Enum.__reduce_cython__value too large to convert to intsklearn.neighbors._kd_tree.BinaryTree.__getstate__sklearn.neighbors._kd_tree.NeighborsHeap.pushsklearn.neighbors._kd_tree.NeighborsHeap._sortsklearn.neighbors._kd_tree.load_heapsklearn.neighbors._kd_tree.simultaneous_sortsklearn.neighbors._kd_tree.BinaryTree.idx_array.__set__sklearn.neighbors._kd_tree.BinaryTree.node_data.__set__sklearn.neighbors._kd_tree.BinaryTree.node_bounds.__set__Cannot create cython.array from NULL pointersklearn.neighbors._kd_tree.get_memview_DTYPE_1Dsklearn.neighbors._kd_tree.get_memview_NodeData_1Dsklearn.neighbors._kd_tree.get_memview_ITYPE_1Dsklearn.neighbors._kd_tree.get_memview_NodeHeapData_1Dsklearn.neighbors._kd_tree.get_memview_DTYPE_2Dsklearn.neighbors._kd_tree.get_memview_ITYPE_2Dsklearn.neighbors._kd_tree.get_memview_DTYPE_3Dsklearn.neighbors._kd_tree.BinaryTree._update_memviews'NoneType' object is not subscriptablehasattr(): attribute name must be stringView.MemoryView.__pyx_unpickle_Enum__set_stateView.MemoryView.Enum.__setstate_cython__FromPyStructUtility.__pyx_convert__from_py_struct____pyx_t_7sklearn_9neighbors_8_kd_tree_NodeData_tsklearn.neighbors._kd_tree.BinaryTree._two_point_singleCannot transpose memoryview with indirect dimensionsView.MemoryView.transpose_memsliceView.MemoryView.memoryview_copyView.MemoryView.memoryview.T.__get__View.MemoryView.copy_data_to_tempView.MemoryView.memoryview_copy_contentsCannot copy memoryview slice with indirect dimensions (axis %d)View.MemoryView.memoryview.copy_fortranView.MemoryView.memoryview.copyView.MemoryView.__pyx_unpickle_Enumsklearn.neighbors._kd_tree.BinaryTree._query_radius_singlesklearn.neighbors._kd_tree.NodeHeap.__cinit__integer division or modulo by zerovalue too large to perform divisionView.MemoryView.pybuffer_indexView.MemoryView.memoryview.get_item_pointerView.MemoryView.get_slice_from_memviewView.MemoryView.memoryview.is_f_contigView.MemoryView.memoryview.is_c_contigView.MemoryView.assert_direct_dimensionsView.MemoryView.memoryview.setitem_slice_assign_scalarView.MemoryView.memoryview.convert_item_to_objectView.MemoryView._memoryviewslice.convert_item_to_objectlocal variable '%s' referenced before assignmentneed more than %zd value%.1s to unpacksklearn.neighbors._kd_tree.BinaryTree.querysklearn.neighbors._kd_tree.BinaryTree._query_single_breadthfirstsklearn.neighbors._kd_tree.min_dist_dualsklearn.neighbors._kd_tree.max_rdist_dualsklearn.neighbors._kd_tree.max_dist_dualsklearn.neighbors._kd_tree.BinaryTree._two_point_dualsklearn.neighbors._kd_tree.BinaryTree._query_single_depthfirstsklearn.neighbors._kd_tree.kernel_normView.MemoryView.array.__cinit__Argument '%.200s' must not be Noneobject of type 'NoneType' has no len()expected bytes, NoneType foundArgument '%.200s' has incorrect type (expected %.200s, got %.200s)sklearn.neighbors._kd_tree.BinaryTree.query_radiusargument after ** must be a mapping, not NoneType'NoneType' object is not iterablesklearn.neighbors._kd_tree.allocate_datasklearn.neighbors._kd_tree.BinaryTree.__init__View.MemoryView.memoryview.__setitem__sklearn.neighbors._kd_tree.NodeHeap.__init__sklearn.neighbors._kd_tree.min_distsklearn.neighbors._kd_tree.max_distsklearn.neighbors._kd_tree.BinaryTree._kde_single_breadthfirstsklearn.neighbors._kd_tree.NeighborsHeap.__init__sklearn.neighbors._kd_tree.init_nodesklearn.neighbors._kd_tree.BinaryTree._recursive_buildView.MemoryView.memoryview.__cinit__sklearn.neighbors._kd_tree.NodeHeap.resizesklearn.neighbors._kd_tree.BinaryTree._kde_single_depthfirstsklearn.neighbors._kd_tree.BinaryTree.__cinit__sklearn.neighbors._kd_tree.BinaryTree._query_dual_depthfirstsklearn.neighbors._kd_tree.BinaryTree._query_dual_breadthfirstsklearn.neighbors._kd_tree.BinaryTree.__setstate__sklearn.neighbors._kd_tree.BinaryTree.kernel_densityAll dimensions preceding dimension %d must be indexed and not slicedView.MemoryView.slice_memviewsliceStep may not be zero (axis %d)View.MemoryView.memoryview.__getitem__View.MemoryView.memoryview.setitem_slice_assignmentsklearn.neighbors._kd_tree.NeighborsHeap.__cinit__sklearn.neighbors._kd_tree.nodeheap_sortsklearn.neighbors._kd_tree.BinaryTree._update_sample_weightsklearn.neighbors._kd_tree.BinaryTree.two_point_correlationcompiletime version %s of module '%.100s' does not match runtime version %ssklearn.neighbors._dist_metricssklearn/neighbors/_dist_metrics.pxdinvalid vtable found for imported type_ARRAY_API is not PyCapsule objectmodule compiled against ABI version 0x%x but this version of numpy is 0x%xmodule compiled against API version 0x%x but this version of numpy is 0x%xFATAL: module compiled as unknown endianFATAL: module compiled as little endian, but detected different endianness at runtimeinit sklearn.neighbors._kd_treesklearn.neighbors._kd_tree._memoryviewsliceInternal class for passing memoryview slices to Pythonsklearn.neighbors._kd_tree.memoryviewsklearn.neighbors._kd_tree.Enumsklearn.neighbors._kd_tree.arraysklearn.neighbors._kd_tree.KDTreesklearn.neighbors._kd_tree.BinaryTreesklearn.neighbors._kd_tree.NodeHeapNodeHeap

    This is a min-heap implementation for keeping track of nodes
    during a breadth-first search.  Unlike the NeighborsHeap above,
    the NodeHeap does not have a fixed size and must be able to grow
    as elements are added.

    Internally, the data is stored in a simple binary heap which meets
    the min heap condition:

        heap[i].val < min(heap[2 * i + 1].val, heap[2 * i + 2].val)
    sklearn.neighbors._kd_tree.NeighborsHeapA max-heap structure to keep track of distances/indices of neighbors

    This implements an efficient pre-allocated set of fixed-size heaps
    for chasing neighbors, holding both an index and a distance.
    When any row of the heap is full, adding an additional point will push
    the furthest point off the heap.

    Parameters
    ----------
    n_pts : int
        the number of heaps to use
    n_nbrs : int
        the size of each heap.
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    test fully loading the heap
        two_point_correlation(X, r, dualtree=False)

        Compute the two-point correlation function

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            An array of points to query.  Last dimension should match dimension
            of training data.
        r : array-like
            A one-dimensional array of distances
        dualtree : bool, default=False
            If True, use a dualtree algorithm.  Otherwise, use a single-tree
            algorithm.  Dual tree algorithms can have better scaling for
            large N.

        Returns
        -------
        counts : ndarray
            counts[i] contains the number of pairs of points with distance
            less than or equal to r[i]
        
        kernel_density(self, X, h, kernel='gaussian', atol=0, rtol=1E-8,
                       breadth_first=True, return_log=False)

        Compute the kernel density estimate at points X with the given kernel,
        using the distance metric specified at tree creation.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            An array of points to query.  Last dimension should match dimension
            of training data.
        h : float
            the bandwidth of the kernel
        kernel : str, default="gaussian"
            specify the kernel to use.  Options are
            - 'gaussian'
            - 'tophat'
            - 'epanechnikov'
            - 'exponential'
            - 'linear'
            - 'cosine'
            Default is kernel = 'gaussian'
        atol, rtol : float, default=0, 1e-8
            Specify the desired relative and absolute tolerance of the result.
            If the true result is K_true, then the returned result K_ret
            satisfies ``abs(K_true - K_ret) < atol + rtol * K_ret``
            The default is zero (i.e. machine precision) for both.
        breadth_first : bool, default=False
            If True, use a breadth-first search.  If False (default) use a
            depth-first search.  Breadth-first is generally faster for
            compact kernels and/or high tolerances.
        return_log : bool, default=False
            Return the logarithm of the result.  This can be more accurate
            than returning the result itself for narrow kernels.

        Returns
        -------
        density : ndarray of shape X.shape[:-1]
            The array of (log)-density evaluations
        
        query_radius(X, r, return_distance=False,
        count_only=False, sort_results=False)

        query the tree for neighbors within a radius r

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            An array of points to query
        r : distance within which neighbors are returned
            r can be a single value, or an array of values of shape
            x.shape[:-1] if different radii are desired for each point.
        return_distance : bool, default=False
            if True,  return distances to neighbors of each point
            if False, return only neighbors
            Note that unlike the query() method, setting return_distance=True
            here adds to the computation time.  Not all distances need to be
            calculated explicitly for return_distance=False.  Results are
            not sorted by default: see ``sort_results`` keyword.
        count_only : bool, default=False
            if True,  return only the count of points within distance r
            if False, return the indices of all points within distance r
            If return_distance==True, setting count_only=True will
            result in an error.
        sort_results : bool, default=False
            if True, the distances and indices will be sorted before being
            returned.  If False, the results will not be sorted.  If
            return_distance == False, setting sort_results = True will
            result in an error.

        Returns
        -------
        count       : if count_only == True
        ind         : if count_only == False and return_distance == False
        (ind, dist) : if count_only == False and return_distance == True

        count : ndarray of shape X.shape[:-1], dtype=int
            Each entry gives the number of neighbors within a distance r of the
            corresponding point.

        ind : ndarray of shape X.shape[:-1], dtype=object
            Each element is a numpy integer array listing the indices of
            neighbors of the corresponding point.  Note that unlike
            the results of a k-neighbors query, the returned neighbors
            are not sorted by distance by default.

        dist : ndarray of shape X.shape[:-1], dtype=object
            Each element is a numpy double array listing the distances
            corresponding to indices in i.
        
        query(X, k=1, return_distance=True,
              dualtree=False, breadth_first=False)

        query the tree for the k nearest neighbors

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            An array of points to query
        k : int, default=1
            The number of nearest neighbors to return
        return_distance : bool, default=True
            if True, return a tuple (d, i) of distances and indices
            if False, return array i
        dualtree : bool, default=False
            if True, use the dual tree formalism for the query: a tree is
            built for the query points, and the pair of trees is used to
            efficiently search this space.  This can lead to better
            performance as the number of points grows large.
        breadth_first : bool, default=False
            if True, then query the nodes in a breadth-first manner.
            Otherwise, query the nodes in a depth-first manner.
        sort_results : bool, default=True
            if True, then distances and indices of each point are sorted
            on return, so that the first column contains the closest points.
            Otherwise, neighbors are returned in an arbitrary order.

        Returns
        -------
        i    : if return_distance == False
        (d,i) : if return_distance == True

        d : ndarray of shape X.shape[:-1] + k, dtype=double
            Each entry gives the list of distances to the neighbors of the
            corresponding point.

        i : ndarray of shape X.shape[:-1] + k, dtype=int
            Each entry gives the list of indices of neighbors of the
            corresponding point.
        
        get_arrays(self)

        Get data and node arrays.

        Returns
        -------
        arrays: tuple of array
            Arrays for storing tree data, index, node data and node bounds.
        
        get_n_calls(self)

        Get number of calls.

        Returns
        -------
        n_calls: int
            number of distance computation calls
        
        reset_n_calls(self)

        Reset number of calls to 0.
        
        get_tree_stats(self)

        Get tree status.

        Returns
        -------
        tree_stats: tuple of int
            (number of trims, number of leaves, number of splits)
        
        set state for pickling
        
        get state for pickling
        
        reduce method used for pickling
        Get the arrays of distances and indices within the heap.

        If sort=True, then simultaneously sort the indices and distances,
        so the closer points are listed first.
        Given a string specification of a kernel, compute the normalization.

    Parameters
    ----------
    h : float
        The bandwidth of the kernel.
    d : int
        The dimension of the space in which the kernel norm is computed.
    kernel : str
        The kernel identifier.  Must be one of
        ['gaussian'|'tophat'|'epanechnikov'|
         'exponential'|'linear'|'cosine']
    return_log : bool, default=False
        If True, return the log of the kernel norm.  Otherwise, return the
        kernel norm.
    Returns
    -------
    knorm or log_knorm : float
        the kernel norm or logarithm of the kernel norm.
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