def test_ball_tree_query_radius_count(n_samples=100, n_features=10):
X = 2 * np.random.random(size=(n_samples, n_features)) - 1
dm = DistanceMetric()
D = dm.pdist(X, squareform=True)
r = np.mean(D)
bt = BallTree(X)
count1 = bt.query_radius(X, r, count_only=True)
count2 = (D <= r).sum(1)
assert_array_almost_equal(count1, count2)
def test_ball_tree_query_radius_count(n_samples=100, n_features=10):
X = 2 * np.random.random(size=(n_samples, n_features)) - 1
dm = DistanceMetric()
D = dm.pdist(X, squareform=True)
r = np.mean(D)
bt = BallTree(X)
count1 = bt.query_radius(X, r, count_only=True)
count2 = (D <= r).sum(1)
assert_array_almost_equal(count1, count2)
def test_query_radius_count(self):
# center the data
X = 2 * self.X - 1
dm = DistanceMetric()
D = dm.pdist(X, squareform=True)
r = np.mean(D)
bt = BallTree(X)
count1 = bt.query_radius(X, r, count_only=True)
count2 = (D <= r).sum(1)
assert_array_almost_equal(count1, count2)
def test_query_radius_count(self):
# center the data
X = 2 * self.X - 1
dm = DistanceMetric()
D = dm.pdist(X, squareform=True)
r = np.mean(D)
bt = BallTree(X)
count1 = bt.query_radius(X, r, count_only=True)
count2 = (D <= r).sum(1)
assert_array_almost_equal(count1, count2)
def test_query_radius_indices(self, n_queries=20):
# center the data
X = 2 * self.X - 1
dm = DistanceMetric()
D = dm.cdist(X[:n_queries], X)
r = np.mean(D)
bt = BallTree(X)
ind = bt.query_radius(X[:n_queries], r, return_distance=False)
ind2 = np.zeros(D.shape) + np.arange(D.shape[1])
ind = np.concatenate(map(np.sort, ind))
ind2 = ind2[D <= r]
assert_array_almost_equal(ind, ind2)
def test_query_radius_indices(self, n_queries=20):
# center the data
X = 2 * self.X - 1
dm = DistanceMetric()
D = dm.cdist(X[:n_queries], X)
r = np.mean(D)
bt = BallTree(X)
ind = bt.query_radius(X[:n_queries], r, return_distance=False)
ind2 = np.zeros(D.shape) + np.arange(D.shape[1])
ind = np.concatenate(map(np.sort, ind))
ind2 = ind2[D <= r]
assert_array_almost_equal(ind, ind2)
def test_ball_tree_query_radius(n_samples=100, n_features=10):
np.random.seed(0)
X = 2 * np.random.random(size=(n_samples, n_features)) - 1
query_pt = np.zeros(n_features, dtype=float)
eps = 1E-15 # roundoff error can cause test to fail
bt = BallTree(X, leaf_size=5)
rad = np.sqrt(((X - query_pt) ** 2).sum(1))
for r in np.linspace(rad[0], rad[-1], 100):
ind = bt.query_radius(query_pt, r + eps)[0]
i = np.where(rad <= r + eps)[0]
ind.sort()
i.sort()
assert_allclose(i, ind)
def test_ball_tree_query_radius_distance(n_samples=100, n_features=10):
X = 2 * np.random.random(size=(n_samples, n_features)) - 1
query_pt = np.zeros(n_features, dtype=float)
eps = 1e-15 # roundoff error can cause test to fail
bt = BallTree(X, leaf_size=5)
rad = np.sqrt(((X - query_pt) ** 2).sum(1))
for r in np.linspace(rad[0], rad[-1], 100):
ind, dist = bt.query_radius(query_pt, r + eps, return_distance=True)
ind = ind[0]
dist = dist[0]
d = np.sqrt(((query_pt - X[ind]) ** 2).sum(1))
assert_array_almost_equal(d, dist)
def test_ball_tree_query_radius(n_samples=100, n_features=10):
np.random.seed(0)
X = 2 * np.random.random(size=(n_samples, n_features)) - 1
query_pt = np.zeros(n_features, dtype=float)
eps = 1E-15 # roundoff error can cause test to fail
bt = BallTree(X, leaf_size=5)
rad = np.sqrt(((X - query_pt)**2).sum(1))
for r in np.linspace(rad[0], rad[-1], 100):
ind = bt.query_radius(query_pt, r + eps)[0]
i = np.where(rad <= r + eps)[0]
ind.sort()
i.sort()
assert_allclose(i, ind)
def test_ball_tree_query_radius_distance(n_samples=100, n_features=10):
X = 2 * np.random.random(size=(n_samples, n_features)) - 1
query_pt = np.zeros(n_features, dtype=float)
eps = 1E-15 # roundoff error can cause test to fail
bt = BallTree(X, leaf_size=5)
rad = np.sqrt(((X - query_pt)**2).sum(1))
for r in np.linspace(rad[0], rad[-1], 100):
ind, dist = bt.query_radius(query_pt, r + eps, return_distance=True)
ind = ind[0]
dist = dist[0]
d = np.sqrt(((query_pt - X[ind])**2).sum(1))
assert_array_almost_equal(d, dist)
def test_ball_tree_query_radius_indices(n_samples=100, n_features=10):
X = 2 * np.random.random(size=(n_samples, n_features)) - 1
dm = DistanceMetric()
D = dm.cdist(X[:10], X)
r = np.mean(D)
bt = BallTree(X)
ind = bt.query_radius(X[:10], r, return_distance=False)
for i in range(10):
ind1 = ind[i]
ind2 = np.where(D[i] <= r)[0]
ind1.sort()
ind2.sort()
assert_array_almost_equal(ind1, ind2)
def test_ball_tree_query_radius_indices(n_samples=100, n_features=10):
X = 2 * np.random.random(size=(n_samples, n_features)) - 1
dm = DistanceMetric()
D = dm.cdist(X[:10], X)
r = np.mean(D)
bt = BallTree(X)
ind = bt.query_radius(X[:10], r, return_distance=False)
for i in range(10):
ind1 = ind[i]
ind2 = np.where(D[i] <= r)[0]
ind1.sort()
ind2.sort()
assert_array_almost_equal(ind1, ind2)
from time import time
import numpy as np
from ball_tree import BallTree
X = np.random.random((10000, 3))
t0 = time()
BT = BallTree(X, 30)
t1 = time()
print "construction: %.2g sec" % (t1 - t0)
for k in [1, 2, 4, 8]:
for dual in (False, True):
t0 = time()
BT.query(X, k, dualtree=dual)
t1 = time()
if dual:
dual_str = ' (dual)'
else:
dual_str = ''
print "query %i in [%i, %i]%s: %.3g sec" % (k, X.shape[0], X.shape[1],
dual_str, t1 - t0)
for r in 0.1, 0.3, 0.5:
t0 = time()
BT.query_radius(X[:1000], r)
t1 = time()
print "query r<%.1f in [%i, %i]: %.3g sec" % (r, X.shape[0], X.shape[1],
t1 - t0)
import numpy as np
from ball_tree import BallTree
X = np.random.random((10000, 3))
t0 = time()
BT = BallTree(X, 30)
t1 = time()
print "construction: %.2g sec" % (t1 - t0)
for k in [1, 2, 4, 8]:
for dual in (False, True):
t0 = time()
BT.query(X, k, dualtree=dual)
t1 = time()
if dual:
dual_str = ' (dual)'
else:
dual_str = ''
print "query %i in [%i, %i]%s: %.3g sec" % (k, X.shape[0],
X.shape[1],
dual_str,
t1 - t0)
for r in 0.1, 0.3, 0.5:
t0 = time()
BT.query_radius(X[:1000], r)
t1 = time()
print "query r<%.1f in [%i, %i]: %.3g sec" % (r, X.shape[0], X.shape[1],
t1 - t0)
