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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may not
# use this file except in compliance with the License. You may obtain a copy of
# the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
# ==============================================================================
"""Tests for clustering_ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
# TODO: #6568 Remove this hack that makes dlopen() not crash.
if hasattr(sys, "getdlopenflags") and hasattr(sys, "setdlopenflags"):
import ctypes
sys.setdlopenflags(sys.getdlopenflags() | ctypes.RTLD_GLOBAL)
import numpy as np
from tensorflow.contrib.factorization.python.ops import clustering_ops
from tensorflow.python.platform import test
class KmeansPlusPlusInitializationTest(test.TestCase):
# All but one input point are close to (101, 1). With uniform random sampling,
# it is highly improbable for (-1, -1) to be selected.
def setUp(self):
self._points = np.array([[100., 0.],
[101., 2.],
[102., 0.],
[100., 1.],
[100., 2.],
[101., 0.],
[101., 0.],
[101., 1.],
[102., 0.],
[-1., -1.]]).astype(np.float32)
def runTestWithSeed(self, seed):
with self.test_session():
sampled_points = clustering_ops.kmeans_plus_plus_initialization(
self._points, 3, seed, (seed % 5) - 1)
self.assertAllClose(
sorted(sampled_points.eval().tolist()), [[-1., -1.],
[101., 1.],
[101., 1.]],
atol=1.0)
def testBasic(self):
for seed in range(100):
self.runTestWithSeed(seed)
# A simple test that can be verified by hand.
class NearestCentersTest(test.TestCase):
def setUp(self):
self._points = np.array([[100., 0.],
[101., 2.],
[99., 2.],
[1., 1.]]).astype(np.float32)
self._centers = np.array([[100., 0.],
[99., 1.],
[50., 50.],
[0., 0.],
[1., 1.]]).astype(np.float32)
def testNearest1(self):
with self.test_session():
[indices, distances] = clustering_ops.nearest_neighbors(self._points,
self._centers, 1)
self.assertAllClose(indices.eval(), [[0], [0], [1], [4]])
self.assertAllClose(distances.eval(), [[0.], [5.], [1.], [0.]])
def testNearest2(self):
with self.test_session():
[indices, distances] = clustering_ops.nearest_neighbors(self._points,
self._centers, 2)
self.assertAllClose(indices.eval(), [[0, 1], [0, 1], [1, 0], [4, 3]])
self.assertAllClose(distances.eval(),
[[0., 2.], [5., 5.], [1., 5.], [0., 2.]])
# A test with large inputs.
class NearestCentersLargeTest(test.TestCase):
def setUp(self):
num_points = 1000
num_centers = 2000
num_dim = 100
max_k = 5
# Construct a small number of random points and later tile them.
points_per_tile = 10
assert num_points % points_per_tile == 0
points = np.random.standard_normal(
[points_per_tile, num_dim]).astype(np.float32)
# Construct random centers.
self._centers = np.random.standard_normal(
[num_centers, num_dim]).astype(np.float32)
# Exhaustively compute expected nearest neighbors.
def squared_distance(x, y):
return np.linalg.norm(x - y, ord=2)**2
nearest_neighbors = [
sorted([(squared_distance(point, self._centers[j]), j)
for j in range(num_centers)])[:max_k] for point in points
]
expected_nearest_neighbor_indices = np.array(
[[i for _, i in nn] for nn in nearest_neighbors])
expected_nearest_neighbor_squared_distances = np.array(
[[dist for dist, _ in nn] for nn in nearest_neighbors])
# Tile points and expected results to reach requested size (num_points)
(self._points, self._expected_nearest_neighbor_indices,
self._expected_nearest_neighbor_squared_distances) = (
np.tile(x, (num_points / points_per_tile, 1))
for x in (points, expected_nearest_neighbor_indices,
expected_nearest_neighbor_squared_distances))
def testNearest1(self):
with self.test_session():
[indices, distances] = clustering_ops.nearest_neighbors(self._points,
self._centers, 1)
self.assertAllClose(indices.eval(),
self._expected_nearest_neighbor_indices[:, [0]])
self.assertAllClose(
distances.eval(),
self._expected_nearest_neighbor_squared_distances[:, [0]])
def testNearest5(self):
with self.test_session():
[indices, distances] = clustering_ops.nearest_neighbors(self._points,
self._centers, 5)
self.assertAllClose(indices.eval(),
self._expected_nearest_neighbor_indices[:, 0:5])
self.assertAllClose(
distances.eval(),
self._expected_nearest_neighbor_squared_distances[:, 0:5])
if __name__ == "__main__":
np.random.seed(0)
test.main()
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