本文整理汇总了Python中scipy.spatial.KDTree方法的典型用法代码示例。如果您正苦于以下问题：Python spatial.KDTree方法的具体用法？Python spatial.KDTree怎么用？Python spatial.KDTree使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在模块scipy.spatial的用法示例。

在下文中一共展示了spatial.KDTree方法的23个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。

示例1: generate_icp_results

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# 需要导入模块: from scipy import spatial [as 别名]

# 或者: from scipy.spatial import KDTree [as 别名]

def generate_icp_results(self, gt_pose):

from icp import icp_test

from scipy.spatial import KDTree

M_given = self.templates[self.template_idx,:,:]

S_given = helper.apply_transformation(M_given.reshape((1,-1,3)), gt_pose)[0]

# S_given = S_given + np.random.normal(0,1,S_given.shape)# Noisy Data

M_given = M_given[0:self.NUM_POINT,:]# template data

S_given = S_given[0:self.NUM_POINT,:]# source data

tree_M = KDTree(M_given)

tree_M_sampled = KDTree(M_given[0:100,:])

final_pose, model_data, sensor_data, predicted_data, title, _, _ = icp_test(S_given[0:100,:], M_given, tree_M, M_given[0:100,:], tree_M_sampled, S_given, gt_pose.reshape((1,6)), self.MAX_LOOPS, self.ftol)

self.find_errors(gt_pose, final_pose)

helper.display_three_clouds(model_data, sensor_data, predicted_data, title)

开发者ID:vinits5，项目名称:pointnet-registration-framework，代码行数:19，

示例2: bench_build

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# 需要导入模块: from scipy import spatial [as 别名]

# 或者: from scipy.spatial import KDTree [as 别名]

def bench_build(self):

print()

print(' Constructing kd-tree')

print('=====================================')

print(' dim | # points | KDTree | cKDTree ')

for (m, n, repeat) in [(3,10000,3), (8,10000,3), (16,10000,3)]:

print('%4s | %7s ' % (m, n), end=' ')

sys.stdout.flush()

data = np.concatenate((np.random.randn(n//2,m),

np.random.randn(n-n//2,m)+np.ones(m)))

print('| %6.3fs ' % (measure('T1 = KDTree(data)', repeat) / repeat), end=' ')

sys.stdout.flush()

print('| %6.3fs' % (measure('T2 = cKDTree(data)', repeat) / repeat), end=' ')

sys.stdout.flush()

print('')

开发者ID:ktraunmueller，项目名称:Computable，代码行数:20，

示例3: setUp

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# 需要导入模块: from scipy import spatial [as 别名]

# 或者: from scipy.spatial import KDTree [as 别名]

def setUp(self):

self.data = np.array([[0,0,0],

[0,0,1],

[0,1,0],

[0,1,1],

[1,0,0],

[1,0,1],

[1,1,0],

[1,1,1]])

self.kdtree = KDTree(self.data)

self.n = self.kdtree.n

self.m = self.kdtree.m

np.random.seed(1234)

self.x = np.random.randn(3)

self.d = 0.5

self.k = 4

开发者ID:ktraunmueller，项目名称:Computable，代码行数:18，

示例4: test_onetree_query

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# 需要导入模块: from scipy import spatial [as 别名]

# 或者: from scipy.spatial import KDTree [as 别名]

def test_onetree_query():

np.random.seed(0)

n = 50

k = 4

points = np.random.randn(n,k)

T = KDTree(points)

yield check_onetree_query, T, 0.1

points = np.random.randn(3*n,k)

points[:n] *= 0.001

points[n:2*n] += 2

T = KDTree(points)

yield check_onetree_query, T, 0.1

yield check_onetree_query, T, 0.001

yield check_onetree_query, T, 0.00001

yield check_onetree_query, T, 1e-6

开发者ID:ktraunmueller，项目名称:Computable，代码行数:18，

示例5: exact_full_marker_data

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# 需要导入模块: from scipy import spatial [as 别名]

# 或者: from scipy.spatial import KDTree [as 别名]

def exact_full_marker_data(csv_path, marker_pkl):

all_data_arr = np.genfromtxt(csv_path, delimiter=",", skip_header=1)

marker_jdcs_collection = cPickle.load(open(marker_pkl[0], "rb"))

tmp_list = list()

for jdc in marker_jdcs_collection:

tmp_list.extend(jdc)

marker_pcd_arr = np.array(tmp_list)

tree = spatial.KDTree(all_data_arr[:, :3])

marker_full_data_ls = []

for i in xrange(marker_pcd_arr.shape[0]):

ret = tree.query(marker_pcd_arr[i])

marker_full_data_ls.append(all_data_arr[ret[1]])

marker_full_data_arr = np.array(marker_full_data_ls)

return marker_full_data_arr

# get the Hessian normal form of 3D plane

开发者ID:mfxox，项目名称:ILCC，代码行数:21，

示例6: build_index

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# 需要导入模块: from scipy import spatial [as 别名]

# 或者: from scipy.spatial import KDTree [as 别名]

def build_index(self):

midpoints = []

self.index = {}

for ct, fset in enumerate(self.sets.values()):

mp = []

for vr in self.explanatory_variables:

mp.append(fset.sets[vr.name].centroid)

midpoints.append(mp)

self.index[ct] = fset.name

import sys

sys.setrecursionlimit(100000)

self.kdtree = KDTree(midpoints)

sys.setrecursionlimit(1000)

开发者ID:PYFTS，项目名称:pyFTS，代码行数:21，

示例7: build_index

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# 需要导入模块: from scipy import spatial [as 别名]

# 或者: from scipy.spatial import KDTree [as 别名]

def build_index(self):

points = []

fset = self.sets[self.ordered_sets[0]]

points.append([fset.sets[1].lower, fset.sets[1].centroid, fset.sets[1].upper])

for ct, key in enumerate(self.ordered_sets[1:-1]):

fset = self.sets[key]

points.append([fset.lower, fset.centroid, fset.upper])

fset = self.sets[self.ordered_sets[-1]]

points.append([fset.sets[1].lower, fset.sets[1].centroid, fset.sets[1].upper])

import sys

sys.setrecursionlimit(100000)

self.kdtree = KDTree(points)

sys.setrecursionlimit(1000)

开发者ID:PYFTS，项目名称:pyFTS，代码行数:21，

示例8: build_index

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# 需要导入模块: from scipy import spatial [as 别名]

# 或者: from scipy.spatial import KDTree [as 别名]

def build_index(self):

points = []

#self.index = {}

for ct, key in enumerate(self.ordered_sets):

fset = self.sets[key]

points.append([fset.lower, fset.centroid, fset.upper])

#self.index[ct] = fset.name

import sys

sys.setrecursionlimit(100000)

self.kdtree = KDTree(points)

sys.setrecursionlimit(1000)

开发者ID:PYFTS，项目名称:pyFTS，代码行数:18，

示例9: triangulatePolygon

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# 需要导入模块: from scipy import spatial [as 别名]

# 或者: from scipy.spatial import KDTree [as 别名]

def triangulatePolygon(poly, hole=None):

# Triangulate poly with hole

cdt = CDT(poly.points)

if hole:

cdt.add_hole(hole)

triangles = cdt.triangulate()

# Frustratingly, CDT sometimes returns points that are not

# EXACTLY the same as the input points, so we use a KDTree

valid_points = [shapes.Point(p.x, p.y) for p in poly.points]

if hole:

valid_points += [shapes.Point(p.x, p.y) for p in hole]

tree = sp.KDTree(toNumpy(valid_points))

def convert(t):

def findClosest(point):

idx = tree.query(toNumpy([point]))[1]

return valid_points[idx]

A = findClosest(shapes.Point(t.a.x, t.a.y))

B = findClosest(shapes.Point(t.b.x, t.b.y))

C = findClosest(shapes.Point(t.c.x, t.c.y))

return shapes.Triangle(A, B, C)

return map(convert, triangles)

开发者ID:crm416，项目名称:point-location，代码行数:25，

示例10: __init__

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# 需要导入模块: from scipy import spatial [as 别名]

# 或者: from scipy.spatial import KDTree [as 别名]

def __init__(self, primaryData):

self.id = str(uuid.uuid4())

self.griddedCount = 0

self.griddedMatched = 0

self.insituCount = len(primaryData)

self.insituMatches = 0

self.primary = primaryData

for r in self.primary:

r["matches"] = []

self.data = []

for s in primaryData:

u = utm.from_latlon(s["y"], s["x"])

v = (u[0], u[1], 0.0)

self.data.append(v)

if len(self.data) > 0:

self.tree = spatial.KDTree(self.data)

else:

self.tree = None

开发者ID:apache，项目名称:incubator-sdap-nexus，代码行数:25，

示例11: _friends_leaveoneout_radius

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# 需要导入模块: from scipy import spatial [as 别名]

# 或者: from scipy.spatial import KDTree [as 别名]

def _friends_leaveoneout_radius(points, ftype):

"""Internal method used to compute the radius (half-side-length) for each

ball (cube) used in :class:`RadFriends` (:class:`SupFriends`) using

leave-one-out (LOO) cross-validation."""

# Construct KDTree to enable quick nearest-neighbor lookup for

# our resampled objects.

kdtree = spatial.KDTree(points)

if ftype == 'balls':

# Compute radius to two nearest neighbors (self + neighbor).

dists, ids = kdtree.query(points, k=2, eps=0, p=2)

elif ftype == 'cubes':

# Compute half-side-length to two nearest neighbors (self + neighbor).

dists, ids = kdtree.query(points, k=2, eps=0, p=np.inf)

dist = dists[:, 1] # distances to LOO nearest neighbor

return dist

开发者ID:joshspeagle，项目名称:dynesty，代码行数:21，

示例12: __init__

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# 需要导入模块: from scipy import spatial [as 别名]

# 或者: from scipy.spatial import KDTree [as 别名]

def __init__(self, p0, p1):

""" p0.shape == (N, 3)

p1.shape == (N, 3)

"""

self.p0 = p0

self.p1 = p1

leafsize = 1000

self.nearest = KDTree(self.p0, leafsize=leafsize)

self.g_series = None

开发者ID:vinits5，项目名称:pointnet-registration-framework，代码行数:11，

示例13: closest_building_distance_median

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# 需要导入模块: from scipy import spatial [as 别名]

# 或者: from scipy.spatial import KDTree [as 别名]

def closest_building_distance_median( point_ref, tree, df_closest_d, radius_search ):

"""

Dispersion metric at point_ref

Computes the median of the closest distance to another building for each building within a radius search

Uses the input KDTree to accelerate calculations

Parameters

----------

point_ref : shapely.Point

calculate index at input point

tree : scipy.spatial.KDTree

KDTree of buildings centroid

df : pandas.DataFrame

data frame of buildings with closest distance calculation

radius_search : float

circle radius to consider the dispersion calculation at a local point

Returns

----------

float

value of dispersion at input point

"""

# Query buildings within radius search

indices = tree.query_ball_point( point_ref, radius_search )

# No dispersion value

if (len(indices) == 0): return np.NaN

# Calculate median of closest distance values. If no information is available, NaN is set

return df_closest_d.loc[ indices ].median()

开发者ID:lgervasoni，项目名称:urbansprawl，代码行数:30，

示例14: closest_building_distance_average

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# 需要导入模块: from scipy import spatial [as 别名]

# 或者: from scipy.spatial import KDTree [as 别名]

def closest_building_distance_average( point_ref, tree, df_closest_d, radius_search ):

"""

Dispersion metric at point_ref

Computes the mean of the closest distance to another building for each building within a radius search

Uses the input KDTree to accelerate calculations

Parameters

----------

point_ref : shapely.Point

calculate index at input point

tree : scipy.spatial.KDTree

KDTree of buildings centroid

df : pandas.DataFrame

data frame of buildings with closest distance calculation

radius_search : int

circle radius to consider the dispersion calculation at a local point

Returns

----------

float

value of dispersion at input point

"""

# Query buildings within radius search

indices = tree.query_ball_point( point_ref, radius_search )

# No dispersion value

if (len(indices) == 0): return np.NaN

# Calculate mean of closest distance values. If no information is available, NaN is set

return df_closest_d.loc[ indices ].mean()

##############################################################

### Dispersion indices calculation

##############################################################

开发者ID:lgervasoni，项目名称:urbansprawl，代码行数:35，

示例15: _apply_polygon_closest_distance_neighbor

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# 需要导入模块: from scipy import spatial [as 别名]

# 或者: from scipy.spatial import KDTree [as 别名]

def _apply_polygon_closest_distance_neighbor(df_osm_built, K_nearest = 50):

"""

Computes for each polygon, the distance to the (approximated) nearest neighboring polygon

Approximation is done using distance between centroids to K nearest neighboring polygons, then evaluating the real polygon distance

A column `closest_d` is added in the data frame

Parameters

----------

df_osm_built: geopandas.GeoDataFrame

data frame containing the building's geometries

K_nearest: int

number of neighboring polygons to evaluate

Returns

----------

"""

def get_closest_indices(tree, x, K_nearest):

# Query the closest buidings considering their centroid

return tree.query( x.centroid.coords[0] , k=K_nearest+1)[1][1:]

def compute_closest_distance(x, buildings):

# Minimum distance of all distances between reference building 'x' and the other buildings

return (buildings.apply(lambda b: x.distance(b) ) ).min()

# Use all elements to get the exact closest neighbor?

if ( (K_nearest == -1) or (K_nearest >= len(df_osm_built)) ): K_nearest = len(df_osm_built)-1

# Get separate list for coordinates

coords_data = [ geom.centroid.coords[0] for geom in df_osm_built.geometry ]

# Create KD Tree using polygon's centroid

tree = spatial.KDTree( coords_data )

# Get the closest buildings indices

df_osm_built['closest_buildings'] = df_osm_built.geometry.apply(lambda x: get_closest_indices(tree, x, K_nearest) )

# Compute the minimum real distance for the closest buildings

df_osm_built['closest_d'] = df_osm_built.apply(lambda x: compute_closest_distance(x.geometry,df_osm_built.geometry.loc[x.closest_buildings]), axis=1 )

# Drop unnecessary column

df_osm_built.drop('closest_buildings', axis=1, inplace=True)

开发者ID:lgervasoni，项目名称:urbansprawl，代码行数:40，

示例16: associate_activities_closest_node

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# 需要导入模块: from scipy import spatial [as 别名]

# 或者: from scipy.spatial import KDTree [as 别名]

def associate_activities_closest_node(G, df_activities_built, df_activities_pois ):

"""

Associates the number of existing activities to their closest nodes in the graph

Parameters

----------

G : networkx multidigraph

input graph to calculate accessibility

df_activities_built : pandas.DataFrame

data selection of buildings with activity uses

df_activities_pois : pandas.DataFrame

data selection of points of interest with activity uses

Returns

----------

"""

# Initialize number of activity references

for u, data in G.nodes(data=True):

data["num_activities"] = 0

# Initialize KDTree of graph nodes

coords = np.array([[node, data['x'], data['y']] for node, data in G.nodes(data=True)])

df_nodes = pd.DataFrame(coords, columns=['node', 'x', 'y'])

# zip coordinates

data = list(zip( df_nodes["x"].ravel(), df_nodes["y"].ravel() ))

# Create input tree

tree = spatial.KDTree( data )

def associate_to_node(tree, point, G):

distance, idx_node = tree.query( (point.x,point.y) )

G.node[ df_nodes.loc[ idx_node, "node"] ]["num_activities"] += 1

# Associate each activity to its closest node

df_activities_built.apply(lambda x: associate_to_node(tree, x.geometry.centroid, G) , axis=1)

df_activities_pois.apply(lambda x: associate_to_node(tree, x.geometry.centroid, G) , axis=1)

开发者ID:lgervasoni，项目名称:urbansprawl，代码行数:38，

示例17: _generate_invariants

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# 需要导入模块: from scipy import spatial [as 别名]

# 或者: from scipy.spatial import KDTree [as 别名]

def _generate_invariants(sources):

"""Return an array of (unique) invariants derived from the array `sources`.

Return an array of the indices of `sources` that correspond to each invariant,

arranged as described in _arrangetriplet.

"""

from scipy.spatial import KDTree

from itertools import combinations

from functools import partial

arrange = partial(_arrangetriplet, sources=sources)

inv = []

triang_vrtx = []

coordtree = KDTree(sources)

# The number of nearest neighbors to request (to work with few sources)

knn = min(len(sources), NUM_NEAREST_NEIGHBORS)

for asrc in sources:

__, indx = coordtree.query(asrc, knn)

# Generate all possible triangles with the 5 indx provided, and store

# them with the order (a, b, c) defined in _arrangetriplet

all_asterism_triang = [

arrange(vertex_indices=list(cmb)) for cmb in combinations(indx, 3)

]

triang_vrtx.extend(all_asterism_triang)

inv.extend(

[

_invariantfeatures(*sources[triplet])

for triplet in all_asterism_triang

]

)

# Remove here all possible duplicate triangles

uniq_ind = [

pos for (pos, elem) in enumerate(inv) if elem not in inv[pos + 1 :]

]

inv_uniq = _np.array(inv)[uniq_ind]

triang_vrtx_uniq = _np.array(triang_vrtx)[uniq_ind]

return inv_uniq, triang_vrtx_uniq

开发者ID:toros-astro，项目名称:astroalign，代码行数:43，

示例18: test_register

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# 需要导入模块: from scipy import spatial [as 别名]

# 或者: from scipy.spatial import KDTree [as 别名]

def test_register(self):

def compare_image(the_image):

"""Return the fraction of sources found in the reference image"""

# pixel comparison is not good, doesn't work. Compare catalogs.

full_algn = the_image.astype("float32")

import sep

bkg = sep.Background(full_algn)

thresh = 3.0 * bkg.globalrms

allobjs = sep.extract(full_algn - bkg.back(), thresh)

allxy = np.array([[obj["x"], obj["y"]] for obj in allobjs])

from scipy.spatial import KDTree

ref_coordtree = KDTree(self.star_ref_pos)

# Compare here srcs list with self.star_ref_pos

num_sources = 0

for asrc in allxy:

found_source = ref_coordtree.query_ball_point(asrc, 3)

if found_source:

num_sources += 1

fraction_found = float(num_sources) / float(len(allxy))

return fraction_found

registered_img, footp = aa.register(

source=self.image, target=self.image_ref

)

self.assertIs(type(registered_img), np.ndarray)

self.assertIs(type(footp), np.ndarray)

self.assertIs(footp.dtype, np.dtype("bool"))

fraction = compare_image(registered_img)

self.assertGreater(fraction, 0.85)

开发者ID:toros-astro，项目名称:astroalign，代码行数:35，

示例19: test_find_sources

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# 需要导入模块: from scipy import spatial [as 别名]

# 或者: from scipy.spatial import KDTree [as 别名]

def test_find_sources(self):

srcs = aa._find_sources(self.image_ref)

from scipy.spatial import KDTree

ref_coordtree = KDTree(self.star_ref_pos)

# Compare here srcs list with self.star_ref_pos

num_sources = 0

for asrc in srcs:

found_source = ref_coordtree.query_ball_point(asrc, 3)

if found_source:

num_sources += 1

fraction_found = float(num_sources) / float(len(srcs))

self.assertGreater(fraction_found, 0.85)

开发者ID:toros-astro，项目名称:astroalign，代码行数:17，

示例20: bench_query

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# 需要导入模块: from scipy import spatial [as 别名]

# 或者: from scipy.spatial import KDTree [as 别名]

def bench_query(self):

print()

print(' Querying kd-tree')

print('===============================================================')

print(' dim | # points | # queries | KDTree | cKDTree | flat cKDTree')

for (m, n, r, repeat) in [(3,10000,1000,3),

(8,10000,1000,3),

(16,10000,1000,3)]:

print('%4s | %8s | %8s ' % (m, n, r), end=' ')

sys.stdout.flush()

data = np.concatenate((np.random.randn(n//2,m),

np.random.randn(n-n//2,m)+np.ones(m)))

queries = np.concatenate((np.random.randn(r//2,m),

np.random.randn(r-r//2,m)+np.ones(m)))

T1 = KDTree(data)

T2 = cKDTree(data)

T3 = cKDTree(data,leafsize=n)

print('| %6.3fs ' % (measure('T1.query(queries)', 1) / 1), end=' ')

sys.stdout.flush()

print('| %6.3fs' % (measure('T2.query(queries)', repeat) / repeat), end=' ')

sys.stdout.flush()

print('| %6.3fs' % (measure('T3.query(queries)', repeat) / repeat), end=' ')

sys.stdout.flush()

print('')

开发者ID:ktraunmueller，项目名称:Computable，代码行数:29，

示例21: bench_query_ball_point

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# 需要导入模块: from scipy import spatial [as 别名]

# 或者: from scipy.spatial import KDTree [as 别名]

def bench_query_ball_point(self):

print()

print(' Query ball point kd-tree')

print('===============================================================')

print(' dim | # points | # queries | probe radius | KDTree | cKDTree | flat cKDTree')

for (m, n, r, repeat) in [(3,10000,1000,3)]: # ,

# (8,10000,1000,3),

# (16,10000,1000,3)]:

for probe_radius in (0.2, 0.5):

print('%4s | %8s | %9s | %11.1f ' % (m, n, r, probe_radius), end=' ')

sys.stdout.flush()

data = np.concatenate((np.random.randn(n//2,m),

np.random.randn(n-n//2,m)+np.ones(m)))

queries = np.concatenate((np.random.randn(r//2,m),

np.random.randn(r-r//2,m)+np.ones(m)))

T1 = KDTree(data)

T2 = cKDTree(data)

T3 = cKDTree(data,leafsize=n)

print('| %6.3fs ' % (measure('T1.query_ball_point(queries, probe_radius)', 1) / 1), end=' ')

sys.stdout.flush()

print('| %6.3fs' % (measure('T2.query_ball_point(queries, probe_radius)', repeat) / repeat), end=' ')

sys.stdout.flush()

print('| %6.3fs' % (measure('T3.query_ball_point(queries, probe_radius)', repeat) / repeat), end=' ')

sys.stdout.flush()

print('')

开发者ID:ktraunmueller，项目名称:Computable，代码行数:30，

示例22: bench_query_pairs

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# 需要导入模块: from scipy import spatial [as 别名]

# 或者: from scipy.spatial import KDTree [as 别名]

def bench_query_pairs(self):

print()

print(' Query pairs kd-tree')

print('==================================================================')

print(' dim | # points | probe radius | KDTree | cKDTree | flat cKDTree')

for (m, n, repeat) in [(3,1000,30),

(8,1000,30),

(16,1000,30)]:

for probe_radius in (0.2, 0.5):

print('%4s | %8s | %11.1f ' % (m, n, probe_radius), end=' ')

sys.stdout.flush()

data = np.concatenate((np.random.randn(n//2,m),

np.random.randn(n-n//2,m)+np.ones(m)))

T1 = KDTree(data)

T2 = cKDTree(data)

T3 = cKDTree(data,leafsize=n)

print('| %6.3fs ' % (measure('T1.query_pairs(probe_radius)', 1) / 1), end=' ')

sys.stdout.flush()

print('| %6.3fs' % (measure('T2.query_pairs(probe_radius)', repeat) / repeat), end=' ')

sys.stdout.flush()

print('| %6.3fs' % (measure('T3.query_pairs(probe_radius)', repeat) / repeat), end=' ')

sys.stdout.flush()

print('')

开发者ID:ktraunmueller，项目名称:Computable，代码行数:28，

示例23: bench_sparse_distance_matrix

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# 需要导入模块: from scipy import spatial [as 别名]

# 或者: from scipy.spatial import KDTree [as 别名]

def bench_sparse_distance_matrix(self):

print()

print(' Sparse distance matrix kd-tree')

print('====================================================================')

print(' dim | # points T1 | # points T2 | probe radius | KDTree | cKDTree')

for (m, n1, n2, repeat) in [(3,1000,1000,30),

(8,1000,1000,30),

(16,1000,1000,30)]:

data1 = np.concatenate((np.random.randn(n1//2,m),

np.random.randn(n1-n1//2,m)+np.ones(m)))

data2 = np.concatenate((np.random.randn(n2//2,m),

np.random.randn(n2-n2//2,m)+np.ones(m)))

T1 = KDTree(data1)

T2 = KDTree(data2)

cT1 = cKDTree(data1)

cT2 = cKDTree(data2)

for probe_radius in (0.2, 0.5):

print('%4s | %11s | %11s | %11.1f ' % (m, n1, n2, probe_radius), end=' ')

sys.stdout.flush()

print('| %6.3fs ' % (measure('T1.sparse_distance_matrix(T2, probe_radius)', 1) / 1), end=' ')

sys.stdout.flush()

print('| %6.3fs ' % (measure('cT1.sparse_distance_matrix(cT2, probe_radius)', repeat) / repeat), end=' ')

sys.stdout.flush()

print('')

开发者ID:ktraunmueller，项目名称:Computable，代码行数:31，

注：本文中的scipy.spatial.KDTree方法示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。