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#!python
#cython: boundscheck=False
#cython: wraparound=False
#cython: cdivision=True
# Author: Jake Vanderplas <vanderplas@astro.washington.edu>
# License: BSD 3 clause
__all__ = ['BallTree']
DOC_DICT = {'BinaryTree': 'BallTree', 'binary_tree': 'ball_tree'}
VALID_METRICS = ['EuclideanDistance', 'SEuclideanDistance',
'ManhattanDistance', 'ChebyshevDistance',
'MinkowskiDistance', 'WMinkowskiDistance',
'MahalanobisDistance', 'HammingDistance',
'CanberraDistance', 'BrayCurtisDistance',
'JaccardDistance', 'MatchingDistance',
'DiceDistance', 'KulsinskiDistance',
'RogersTanimotoDistance', 'RussellRaoDistance',
'SokalMichenerDistance', 'SokalSneathDistance',
'PyFuncDistance', 'HaversineDistance']
include "binary_tree.pxi"
# Inherit BallTree from BinaryTree
cdef class BallTree(BinaryTree):
__doc__ = CLASS_DOC.format(**DOC_DICT)
pass
#----------------------------------------------------------------------
# The functions below specialized the Binary Tree as a Ball Tree
#
# Note that these functions use the concept of "reduced distance".
# The reduced distance, defined for some metrics, is a quantity which
# is more efficient to compute than the distance, but preserves the
# relative rankings of the true distance. For example, the reduced
# distance for the Euclidean metric is the squared-euclidean distance.
# For some metrics, the reduced distance is simply the distance.
cdef int allocate_data(BinaryTree tree, ITYPE_t n_nodes,
ITYPE_t n_features) except -1:
"""Allocate arrays needed for the KD Tree"""
tree.node_bounds_arr = np.zeros((1, n_nodes, n_features), dtype=DTYPE)
tree.node_bounds = get_memview_DTYPE_3D(tree.node_bounds_arr)
return 0
cdef int init_node(BinaryTree tree, ITYPE_t i_node,
ITYPE_t idx_start, ITYPE_t idx_end) except -1:
"""Initialize the node for the dataset stored in tree.data"""
cdef ITYPE_t n_features = tree.data.shape[1]
cdef ITYPE_t n_points = idx_end - idx_start
cdef ITYPE_t i, j
cdef DTYPE_t radius
cdef DTYPE_t *this_pt
cdef ITYPE_t* idx_array = &tree.idx_array[0]
cdef DTYPE_t* data = &tree.data[0, 0]
cdef DTYPE_t* centroid = &tree.node_bounds[0, i_node, 0]
cdef bint with_sample_weight = tree.sample_weight is not None
cdef DTYPE_t* sample_weight
cdef DTYPE_t sum_weight_node
if with_sample_weight:
sample_weight = &tree.sample_weight[0]
# determine Node centroid
for j in range(n_features):
centroid[j] = 0
if with_sample_weight:
sum_weight_node = 0
for i in range(idx_start, idx_end):
sum_weight_node += sample_weight[idx_array[i]]
this_pt = data + n_features * idx_array[i]
for j from 0 <= j < n_features:
centroid[j] += this_pt[j] * sample_weight[idx_array[i]]
for j in range(n_features):
centroid[j] /= sum_weight_node
else:
for i in range(idx_start, idx_end):
this_pt = data + n_features * idx_array[i]
for j from 0 <= j < n_features:
centroid[j] += this_pt[j]
for j in range(n_features):
centroid[j] /= n_points
# determine Node radius
radius = 0
for i in range(idx_start, idx_end):
radius = fmax(radius,
tree.rdist(centroid,
data + n_features * idx_array[i],
n_features))
tree.node_data[i_node].radius = tree.dist_metric._rdist_to_dist(radius)
tree.node_data[i_node].idx_start = idx_start
tree.node_data[i_node].idx_end = idx_end
return 0
cdef inline DTYPE_t min_dist(BinaryTree tree, ITYPE_t i_node,
DTYPE_t* pt) nogil except -1:
"""Compute the minimum distance between a point and a node"""
cdef DTYPE_t dist_pt = tree.dist(pt, &tree.node_bounds[0, i_node, 0],
tree.data.shape[1])
return fmax(0, dist_pt - tree.node_data[i_node].radius)
cdef inline DTYPE_t max_dist(BinaryTree tree, ITYPE_t i_node,
DTYPE_t* pt) except -1:
"""Compute the maximum distance between a point and a node"""
cdef DTYPE_t dist_pt = tree.dist(pt, &tree.node_bounds[0, i_node, 0],
tree.data.shape[1])
return dist_pt + tree.node_data[i_node].radius
cdef inline int min_max_dist(BinaryTree tree, ITYPE_t i_node, DTYPE_t* pt,
DTYPE_t* min_dist, DTYPE_t* max_dist) nogil except -1:
"""Compute the minimum and maximum distance between a point and a node"""
cdef DTYPE_t dist_pt = tree.dist(pt, &tree.node_bounds[0, i_node, 0],
tree.data.shape[1])
cdef DTYPE_t rad = tree.node_data[i_node].radius
min_dist[0] = fmax(0, dist_pt - rad)
max_dist[0] = dist_pt + rad
return 0
cdef inline DTYPE_t min_rdist(BinaryTree tree, ITYPE_t i_node,
DTYPE_t* pt) nogil except -1:
"""Compute the minimum reduced-distance between a point and a node"""
if tree.euclidean:
return euclidean_dist_to_rdist(min_dist(tree, i_node, pt))
else:
return tree.dist_metric._dist_to_rdist(min_dist(tree, i_node, pt))
cdef inline DTYPE_t max_rdist(BinaryTree tree, ITYPE_t i_node,
DTYPE_t* pt) except -1:
"""Compute the maximum reduced-distance between a point and a node"""
if tree.euclidean:
return euclidean_dist_to_rdist(max_dist(tree, i_node, pt))
else:
return tree.dist_metric._dist_to_rdist(max_dist(tree, i_node, pt))
cdef inline DTYPE_t min_dist_dual(BinaryTree tree1, ITYPE_t i_node1,
BinaryTree tree2, ITYPE_t i_node2) except -1:
"""compute the minimum distance between two nodes"""
cdef DTYPE_t dist_pt = tree1.dist(&tree2.node_bounds[0, i_node2, 0],
&tree1.node_bounds[0, i_node1, 0],
tree1.data.shape[1])
return fmax(0, (dist_pt - tree1.node_data[i_node1].radius
- tree2.node_data[i_node2].radius))
cdef inline DTYPE_t max_dist_dual(BinaryTree tree1, ITYPE_t i_node1,
BinaryTree tree2, ITYPE_t i_node2) except -1:
"""compute the maximum distance between two nodes"""
cdef DTYPE_t dist_pt = tree1.dist(&tree2.node_bounds[0, i_node2, 0],
&tree1.node_bounds[0, i_node1, 0],
tree1.data.shape[1])
return (dist_pt + tree1.node_data[i_node1].radius
+ tree2.node_data[i_node2].radius)
cdef inline DTYPE_t min_rdist_dual(BinaryTree tree1, ITYPE_t i_node1,
BinaryTree tree2, ITYPE_t i_node2) except -1:
"""compute the minimum reduced distance between two nodes"""
if tree1.euclidean:
return euclidean_dist_to_rdist(min_dist_dual(tree1, i_node1,
tree2, i_node2))
else:
return tree1.dist_metric._dist_to_rdist(min_dist_dual(tree1, i_node1,
tree2, i_node2))
cdef inline DTYPE_t max_rdist_dual(BinaryTree tree1, ITYPE_t i_node1,
BinaryTree tree2, ITYPE_t i_node2) except -1:
"""compute the maximum reduced distance between two nodes"""
if tree1.euclidean:
return euclidean_dist_to_rdist(max_dist_dual(tree1, i_node1,
tree2, i_node2))
else:
return tree1.dist_metric._dist_to_rdist(max_dist_dual(tree1, i_node1,
tree2, i_node2))
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