Viewing File: /home/ubuntu/.local/lib/python3.10/site-packages/sklearn/neighbors/_kd_tree.pyx.tp
{{py:
# Generated file: _kd_tree.pyx
implementation_specific_values = [
# The values are arranged as follows:
#
# name_suffix, INPUT_DTYPE_t, INPUT_DTYPE
#
('64', 'float64_t', 'np.float64'),
('32', 'float32_t', 'np.float32')
]
# By Jake Vanderplas (2013) <jakevdp@cs.washington.edu>
# written for the scikit-learn project
# License: BSD
}}
__all__ = ['KDTree', 'KDTree64', 'KDTree32']
{{for name_suffix, INPUT_DTYPE_t, INPUT_DTYPE in implementation_specific_values}}
DOC_DICT{{name_suffix}} = {
'BinaryTree': 'KDTree{{name_suffix}}',
'binary_tree': 'kd_tree{{name_suffix}}',
}
VALID_METRICS{{name_suffix}} = [
'EuclideanDistance{{name_suffix}}',
'ManhattanDistance{{name_suffix}}',
'ChebyshevDistance{{name_suffix}}',
'MinkowskiDistance{{name_suffix}}'
]
{{endfor}}
include "_binary_tree.pxi"
{{for name_suffix, INPUT_DTYPE_t, INPUT_DTYPE in implementation_specific_values}}
# Inherit KDTree{{name_suffix}} from BinaryTree{{name_suffix}}
cdef class KDTree{{name_suffix}}(BinaryTree{{name_suffix}}):
__doc__ = CLASS_DOC.format(**DOC_DICT{{name_suffix}})
pass
{{endfor}}
# ----------------------------------------------------------------------
# The functions below specialized the Binary Tree as a KD 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.
{{for name_suffix, INPUT_DTYPE_t, INPUT_DTYPE in implementation_specific_values}}
cdef int allocate_data{{name_suffix}}(
BinaryTree{{name_suffix}} tree,
intp_t n_nodes,
intp_t n_features,
) except -1:
"""Allocate arrays needed for the KD Tree"""
tree.node_bounds = np.zeros((2, n_nodes, n_features), dtype={{INPUT_DTYPE}})
return 0
cdef int init_node{{name_suffix}}(
BinaryTree{{name_suffix}} tree,
NodeData_t[::1] node_data,
intp_t i_node,
intp_t idx_start,
intp_t idx_end,
) except -1:
"""Initialize the node for the dataset stored in tree.data"""
cdef intp_t n_features = tree.data.shape[1]
cdef intp_t i, j
cdef float64_t rad = 0
cdef {{INPUT_DTYPE_t}}* lower_bounds = &tree.node_bounds[0, i_node, 0]
cdef {{INPUT_DTYPE_t}}* upper_bounds = &tree.node_bounds[1, i_node, 0]
cdef const {{INPUT_DTYPE_t}}* data = &tree.data[0, 0]
cdef const intp_t* idx_array = &tree.idx_array[0]
cdef const {{INPUT_DTYPE_t}}* data_row
# determine Node bounds
for j in range(n_features):
lower_bounds[j] = INF
upper_bounds[j] = -INF
# Compute the actual data range. At build time, this is slightly
# slower than using the previously-computed bounds of the parent node,
# but leads to more compact trees and thus faster queries.
for i in range(idx_start, idx_end):
data_row = data + idx_array[i] * n_features
for j in range(n_features):
lower_bounds[j] = fmin(lower_bounds[j], data_row[j])
upper_bounds[j] = fmax(upper_bounds[j], data_row[j])
for j in range(n_features):
if tree.dist_metric.p == INF:
rad = fmax(rad, 0.5 * (upper_bounds[j] - lower_bounds[j]))
else:
rad += pow(0.5 * abs(upper_bounds[j] - lower_bounds[j]),
tree.dist_metric.p)
node_data[i_node].idx_start = idx_start
node_data[i_node].idx_end = idx_end
# The radius will hold the size of the circumscribed hypersphere measured
# with the specified metric: in querying, this is used as a measure of the
# size of each node when deciding which nodes to split.
node_data[i_node].radius = pow(rad, 1. / tree.dist_metric.p)
return 0
cdef float64_t min_rdist{{name_suffix}}(
BinaryTree{{name_suffix}} tree,
intp_t i_node,
const {{INPUT_DTYPE_t}}* pt,
) except -1 nogil:
"""Compute the minimum reduced-distance between a point and a node"""
cdef intp_t n_features = tree.data.shape[1]
cdef float64_t d, d_lo, d_hi, rdist=0.0
cdef intp_t j
if tree.dist_metric.p == INF:
for j in range(n_features):
d_lo = tree.node_bounds[0, i_node, j] - pt[j]
d_hi = pt[j] - tree.node_bounds[1, i_node, j]
d = (d_lo + fabs(d_lo)) + (d_hi + fabs(d_hi))
rdist = fmax(rdist, 0.5 * d)
else:
# here we'll use the fact that x + abs(x) = 2 * max(x, 0)
for j in range(n_features):
d_lo = tree.node_bounds[0, i_node, j] - pt[j]
d_hi = pt[j] - tree.node_bounds[1, i_node, j]
d = (d_lo + fabs(d_lo)) + (d_hi + fabs(d_hi))
rdist += pow(0.5 * d, tree.dist_metric.p)
return rdist
cdef float64_t min_dist{{name_suffix}}(
BinaryTree{{name_suffix}} tree,
intp_t i_node,
const {{INPUT_DTYPE_t}}* pt,
) except -1:
"""Compute the minimum distance between a point and a node"""
if tree.dist_metric.p == INF:
return min_rdist{{name_suffix}}(tree, i_node, pt)
else:
return pow(
min_rdist{{name_suffix}}(tree, i_node, pt),
1. / tree.dist_metric.p
)
cdef float64_t max_rdist{{name_suffix}}(
BinaryTree{{name_suffix}} tree,
intp_t i_node,
const {{INPUT_DTYPE_t}}* pt,
) except -1:
"""Compute the maximum reduced-distance between a point and a node"""
cdef intp_t n_features = tree.data.shape[1]
cdef float64_t d_lo, d_hi, rdist=0.0
cdef intp_t j
if tree.dist_metric.p == INF:
for j in range(n_features):
rdist = fmax(rdist, fabs(pt[j] - tree.node_bounds[0, i_node, j]))
rdist = fmax(rdist, fabs(pt[j] - tree.node_bounds[1, i_node, j]))
else:
for j in range(n_features):
d_lo = fabs(pt[j] - tree.node_bounds[0, i_node, j])
d_hi = fabs(pt[j] - tree.node_bounds[1, i_node, j])
rdist += pow(fmax(d_lo, d_hi), tree.dist_metric.p)
return rdist
cdef float64_t max_dist{{name_suffix}}(
BinaryTree{{name_suffix}} tree,
intp_t i_node,
const {{INPUT_DTYPE_t}}* pt,
) except -1:
"""Compute the maximum distance between a point and a node"""
if tree.dist_metric.p == INF:
return max_rdist{{name_suffix}}(tree, i_node, pt)
else:
return pow(
max_rdist{{name_suffix}}(tree, i_node, pt),
1. / tree.dist_metric.p
)
cdef inline int min_max_dist{{name_suffix}}(
BinaryTree{{name_suffix}} tree,
intp_t i_node,
const {{INPUT_DTYPE_t}}* pt,
float64_t* min_dist,
float64_t* max_dist,
) except -1 nogil:
"""Compute the minimum and maximum distance between a point and a node"""
cdef intp_t n_features = tree.data.shape[1]
cdef float64_t d, d_lo, d_hi
cdef intp_t j
min_dist[0] = 0.0
max_dist[0] = 0.0
if tree.dist_metric.p == INF:
for j in range(n_features):
d_lo = tree.node_bounds[0, i_node, j] - pt[j]
d_hi = pt[j] - tree.node_bounds[1, i_node, j]
d = (d_lo + fabs(d_lo)) + (d_hi + fabs(d_hi))
min_dist[0] = fmax(min_dist[0], 0.5 * d)
max_dist[0] = fmax(max_dist[0], fabs(d_lo))
max_dist[0] = fmax(max_dist[0], fabs(d_hi))
else:
# as above, use the fact that x + abs(x) = 2 * max(x, 0)
for j in range(n_features):
d_lo = tree.node_bounds[0, i_node, j] - pt[j]
d_hi = pt[j] - tree.node_bounds[1, i_node, j]
d = (d_lo + fabs(d_lo)) + (d_hi + fabs(d_hi))
min_dist[0] += pow(0.5 * d, tree.dist_metric.p)
max_dist[0] += pow(fmax(fabs(d_lo), fabs(d_hi)),
tree.dist_metric.p)
min_dist[0] = pow(min_dist[0], 1. / tree.dist_metric.p)
max_dist[0] = pow(max_dist[0], 1. / tree.dist_metric.p)
return 0
cdef inline float64_t min_rdist_dual{{name_suffix}}(
BinaryTree{{name_suffix}} tree1,
intp_t i_node1,
BinaryTree{{name_suffix}} tree2,
intp_t i_node2,
) except -1:
"""Compute the minimum reduced distance between two nodes"""
cdef intp_t n_features = tree1.data.shape[1]
cdef float64_t d, d1, d2, rdist=0.0
cdef intp_t j
if tree1.dist_metric.p == INF:
for j in range(n_features):
d1 = (tree1.node_bounds[0, i_node1, j]
- tree2.node_bounds[1, i_node2, j])
d2 = (tree2.node_bounds[0, i_node2, j]
- tree1.node_bounds[1, i_node1, j])
d = (d1 + fabs(d1)) + (d2 + fabs(d2))
rdist = fmax(rdist, 0.5 * d)
else:
# here we'll use the fact that x + abs(x) = 2 * max(x, 0)
for j in range(n_features):
d1 = (tree1.node_bounds[0, i_node1, j]
- tree2.node_bounds[1, i_node2, j])
d2 = (tree2.node_bounds[0, i_node2, j]
- tree1.node_bounds[1, i_node1, j])
d = (d1 + fabs(d1)) + (d2 + fabs(d2))
rdist += pow(0.5 * d, tree1.dist_metric.p)
return rdist
cdef inline float64_t min_dist_dual{{name_suffix}}(
BinaryTree{{name_suffix}} tree1,
intp_t i_node1,
BinaryTree{{name_suffix}} tree2,
intp_t i_node2,
) except -1:
"""Compute the minimum distance between two nodes"""
return tree1.dist_metric._rdist_to_dist(
min_rdist_dual{{name_suffix}}(tree1, i_node1, tree2, i_node2)
)
cdef inline float64_t max_rdist_dual{{name_suffix}}(
BinaryTree{{name_suffix}} tree1,
intp_t i_node1,
BinaryTree{{name_suffix}} tree2,
intp_t i_node2,
) except -1:
"""Compute the maximum reduced distance between two nodes"""
cdef intp_t n_features = tree1.data.shape[1]
cdef float64_t d1, d2, rdist=0.0
cdef intp_t j
if tree1.dist_metric.p == INF:
for j in range(n_features):
rdist = fmax(rdist, fabs(tree1.node_bounds[0, i_node1, j]
- tree2.node_bounds[1, i_node2, j]))
rdist = fmax(rdist, fabs(tree1.node_bounds[1, i_node1, j]
- tree2.node_bounds[0, i_node2, j]))
else:
for j in range(n_features):
d1 = fabs(tree1.node_bounds[0, i_node1, j]
- tree2.node_bounds[1, i_node2, j])
d2 = fabs(tree1.node_bounds[1, i_node1, j]
- tree2.node_bounds[0, i_node2, j])
rdist += pow(fmax(d1, d2), tree1.dist_metric.p)
return rdist
cdef inline float64_t max_dist_dual{{name_suffix}}(
BinaryTree{{name_suffix}} tree1,
intp_t i_node1,
BinaryTree{{name_suffix}} tree2,
intp_t i_node2,
) except -1:
"""Compute the maximum distance between two nodes"""
return tree1.dist_metric._rdist_to_dist(
max_rdist_dual{{name_suffix}}(tree1, i_node1, tree2, i_node2)
)
{{endfor}}
class KDTree(KDTree64):
__doc__ = CLASS_DOC.format(BinaryTree="KDTree")
pass
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