def run_algorithm(algorithm="buffer_kd_tree", tree_depth=None, leaf_size=None):
nbrs = NearestNeighbors(n_neighbors=n_neighbors, \
algorithm=algorithm, \
tree_depth=tree_depth, \
leaf_size=leaf_size, \
float_type=float_type, \
n_jobs=n_jobs, \
plat_dev_ids=plat_dev_ids, \
verbose=verbose)
start_time = time.time()
nbrs.fit(Xtrain)
end_time = time.time()
print("Fitting time: %f" % (end_time - start_time))
start_time = time.time()
dists, inds = nbrs.kneighbors(Xtest)
end_time = time.time()
print("Testing time: %f" % (end_time - start_time))
def run_algorithm(algorithm="buffer_kd_tree", tree_depth=None, leaf_size=None):
nbrs = NearestNeighbors(n_neighbors=n_neighbors,
algorithm=algorithm,
tree_depth=tree_depth,
leaf_size=leaf_size,
float_type=float_type,
n_jobs=n_jobs,
plat_dev_ids=plat_dev_ids,
verbose=verbose)
start_time = time.time()
nbrs.fit(Xtrain)
end_time = time.time()
print("Fitting time: %f" % (end_time - start_time))
start_time = time.time()
_, _ = nbrs.kneighbors(Xtest)
end_time = time.time()
print("Testing time: %f" % (end_time - start_time))
# Authors: Fabian Gieseke
# Licence: GNU GPL (v2)
import numpy
from bufferkdtree import NearestNeighbors
n_neighbors = 10
plat_dev_ids = {0: [0]}
n_jobs = 1
verbose = 1
X = numpy.random.uniform(low=-1, high=1, size=(10000, 10))
# (1) apply buffer k-d tree implementation
nbrs_buffer_kd_tree = NearestNeighbors(algorithm="buffer_kd_tree", \
tree_depth=9, \
plat_dev_ids=plat_dev_ids, \
verbose=verbose)
nbrs_buffer_kd_tree.fit(X)
dists, inds = nbrs_buffer_kd_tree.kneighbors(X, n_neighbors=n_neighbors)
print("\nbuffer_kd_tree output\n" + str(dists[0]))
# (2) apply brute-force implementation
nbrs_brute = NearestNeighbors(algorithm="brute", \
plat_dev_ids=plat_dev_ids, \
verbose=verbose)
nbrs_brute.fit(X)
dists, inds = nbrs_brute.kneighbors(X, n_neighbors=n_neighbors)
print("\nbrute output\n" + str(dists[0]))
# (3) apply k-d tree mplementation
nbrs_kd_tree = NearestNeighbors(algorithm="kd_tree", \
# https://github.com/gieseke/bufferkdtree/tree/master/examples
import time
from bufferkdtree import NearestNeighbors
import generate
Xtrain, Ytrain, Xtest = generate.get_data_set(data_set="psf_model_mag",
NUM_TRAIN=1000000,
NUM_TEST=1000000)
n_jobs = 1
print("Using n_jobs=%i" % n_jobs)
nbrs = NearestNeighbors(n_neighbors=10,
algorithm="kd_tree",
leaf_size=32,
n_jobs=n_jobs)
nbrs.fit(Xtrain)
start_time = time.time()
_, _ = nbrs.kneighbors(Xtest)
end_time = time.time()
print("Testing time: %f" % (end_time - start_time))
"""
print(__doc__)
import numpy
from bufferkdtree import NearestNeighbors
n_neighbors = 10
plat_dev_ids = {0:[0]}
n_jobs = 1
verbose = 1
X = numpy.random.uniform(low=-1, high=1, size=(10000,10))
# (1) apply buffer k-d tree implementation
nbrs_buffer_kd_tree = NearestNeighbors(algorithm="buffer_kd_tree",
tree_depth=9,
plat_dev_ids=plat_dev_ids,
verbose=verbose)
nbrs_buffer_kd_tree.fit(X)
dists, inds = nbrs_buffer_kd_tree.kneighbors(X, n_neighbors=n_neighbors)
print("\nbuffer_kd_tree output\n" + str(dists[0]))
# (2) apply brute-force implementation
nbrs_brute = NearestNeighbors(algorithm="brute",
plat_dev_ids=plat_dev_ids,
verbose=verbose)
nbrs_brute.fit(X)
dists, inds = nbrs_brute.kneighbors(X, n_neighbors=n_neighbors)
print("\nbrute output\n" + str(dists[0]))
# (3) apply k-d tree mplementation
nbrs_kd_tree = NearestNeighbors(algorithm="kd_tree",
