Viewing File: /home/ubuntu/.local/lib/python3.10/site-packages/sklearn/neighbors/_ball_tree.pyx.tp
{{py:
# Generated file: _ball_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')
]
# Author: Jake Vanderplas <vanderplas@astro.washington.edu>
# License: BSD 3 clause
}}
__all__ = ['BallTree', 'BallTree64', 'BallTree32']
{{for name_suffix, INPUT_DTYPE_t, INPUT_DTYPE in implementation_specific_values}}
DOC_DICT{{name_suffix}} = {
'BinaryTree': 'BallTree{{name_suffix}}',
'binary_tree': 'ball_tree{{name_suffix}}',
}
VALID_METRICS{{name_suffix}} = [
'BrayCurtisDistance{{name_suffix}}',
'CanberraDistance{{name_suffix}}',
'ChebyshevDistance{{name_suffix}}',
'DiceDistance{{name_suffix}}',
'EuclideanDistance{{name_suffix}}',
'HammingDistance{{name_suffix}}',
'HaversineDistance{{name_suffix}}',
'JaccardDistance{{name_suffix}}',
'MahalanobisDistance{{name_suffix}}',
'ManhattanDistance{{name_suffix}}',
'MinkowskiDistance{{name_suffix}}',
'PyFuncDistance{{name_suffix}}',
'RogersTanimotoDistance{{name_suffix}}',
'RussellRaoDistance{{name_suffix}}',
'SEuclideanDistance{{name_suffix}}',
'SokalMichenerDistance{{name_suffix}}',
'SokalSneathDistance{{name_suffix}}',
'WMinkowskiDistance{{name_suffix}}',
]
{{endfor}}
include "_binary_tree.pxi"
{{for name_suffix, INPUT_DTYPE_t, INPUT_DTYPE in implementation_specific_values}}
# Inherit BallTree{{name_suffix}} from BinaryTree{{name_suffix}}
cdef class BallTree{{name_suffix}}(BinaryTree{{name_suffix}}):
__doc__ = CLASS_DOC.format(**DOC_DICT{{name_suffix}})
pass
{{endfor}}
#----------------------------------------------------------------------
# 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.
{{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((1, 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 n_points = idx_end - idx_start
cdef intp_t i, j
cdef float64_t radius
cdef const {{INPUT_DTYPE_t}} *this_pt
cdef intp_t* idx_array = &tree.idx_array[0]
cdef const {{INPUT_DTYPE_t}}* data = &tree.data[0, 0]
cdef {{INPUT_DTYPE_t}}* centroid = &tree.node_bounds[0, i_node, 0]
cdef bint with_sample_weight = tree.sample_weight is not None
cdef const {{INPUT_DTYPE_t}}* sample_weight
cdef float64_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))
node_data[i_node].radius = tree.dist_metric._rdist_to_dist(radius)
node_data[i_node].idx_start = idx_start
node_data[i_node].idx_end = idx_end
return 0
cdef inline float64_t min_dist{{name_suffix}}(
BinaryTree{{name_suffix}} tree,
intp_t i_node,
const {{INPUT_DTYPE_t}}* pt,
) except -1 nogil:
"""Compute the minimum distance between a point and a node"""
cdef float64_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 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"""
cdef float64_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{{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 float64_t dist_pt = tree.dist(pt, &tree.node_bounds[0, i_node, 0],
tree.data.shape[1])
cdef float64_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 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"""
if tree.euclidean:
return euclidean_dist_to_rdist{{name_suffix}}(
min_dist{{name_suffix}}(tree, i_node, pt)
)
else:
return tree.dist_metric._dist_to_rdist(
min_dist{{name_suffix}}(tree, i_node, pt)
)
cdef inline 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"""
if tree.euclidean:
return euclidean_dist_to_rdist{{name_suffix}}(
max_dist{{name_suffix}}(tree, i_node, pt)
)
else:
return tree.dist_metric._dist_to_rdist(
max_dist{{name_suffix}}(tree, i_node, pt)
)
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"""
cdef float64_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 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"""
cdef float64_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 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"""
if tree1.euclidean:
return euclidean_dist_to_rdist{{name_suffix}}(
min_dist_dual{{name_suffix}}(tree1, i_node1, tree2, i_node2)
)
else:
return tree1.dist_metric._dist_to_rdist(
min_dist_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"""
if tree1.euclidean:
return euclidean_dist_to_rdist{{name_suffix}}(
max_dist_dual{{name_suffix}}(tree1, i_node1, tree2, i_node2)
)
else:
return tree1.dist_metric._dist_to_rdist(
max_dist_dual{{name_suffix}}(tree1, i_node1, tree2, i_node2)
)
{{endfor}}
class BallTree(BallTree64):
__doc__ = CLASS_DOC.format(BinaryTree="BallTree")
pass
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