pxmlw6n2f/Gazebo_Distributed_TCP/gazebo/math/Kmeans_TEST.cc

131 lines
4.1 KiB
C++

/*
* Copyright (C) 2014 Open Source Robotics Foundation
*
* 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.
*
*/
#include <vector>
#include <gtest/gtest.h>
#include "gazebo/math/Kmeans.hh"
using namespace gazebo;
class KmeansTest : public ::testing::Test { };
//////////////////////////////////////////////////
TEST_F(KmeansTest, Kmeans)
{
// Create some observations.
std::vector<math::Vector3> obs;
obs.push_back(math::Vector3(1.0, 1.0, 0.0));
obs.push_back(math::Vector3(1.1, 1.0, 0.0));
obs.push_back(math::Vector3(1.2, 1.0, 0.0));
obs.push_back(math::Vector3(1.3, 1.0, 0.0));
obs.push_back(math::Vector3(1.4, 1.0, 0.0));
obs.push_back(math::Vector3(5.0, 1.0, 0.0));
obs.push_back(math::Vector3(5.1, 1.0, 0.0));
obs.push_back(math::Vector3(5.2, 1.0, 0.0));
obs.push_back(math::Vector3(5.3, 1.0, 0.0));
obs.push_back(math::Vector3(5.4, 1.0, 0.0));
// Initialize Kmeans with two partitions.
math::Kmeans kmeans(obs);
// ::GetObservations()
std::vector<math::Vector3> obsCopy;
obsCopy = kmeans.Observations();
for (size_t i = 0; i < obsCopy.size(); ++i)
EXPECT_EQ(obsCopy[i], obs[i]);
// ::SetObservations()
for (size_t i = 0; i < obsCopy.size(); ++i)
obsCopy[i] += math::Vector3(0.1, 0.2, 0.0);
EXPECT_TRUE(kmeans.Observations(obsCopy));
obsCopy = kmeans.Observations();
for (size_t i = 0; i < obsCopy.size(); ++i)
EXPECT_EQ(obsCopy[i], obs[i] + math::Vector3(0.1, 0.2, 0.0));
EXPECT_TRUE(kmeans.Observations(obs));
// ::Cluster()
std::vector<math::Vector3> centroids;
std::vector<unsigned int> labels;
EXPECT_TRUE(kmeans.Cluster(2, centroids, labels));
// Check that there are two centroids.
EXPECT_EQ(centroids.size(), 2u);
// Check that the observations are clustered properly.
EXPECT_EQ(labels[0], labels[1]);
EXPECT_EQ(labels[1], labels[2]);
EXPECT_EQ(labels[2], labels[3]);
EXPECT_EQ(labels[3], labels[4]);
EXPECT_NE(labels[4], labels[5]);
EXPECT_EQ(labels[5], labels[6]);
EXPECT_EQ(labels[6], labels[7]);
EXPECT_EQ(labels[7], labels[8]);
EXPECT_EQ(labels[8], labels[9]);
// Check the centroids.
math::Vector3 expectedCentroid1(1.2, 1.0, 0.0);
math::Vector3 expectedCentroid2(5.2, 1.0, 0.0);
if (centroids[0] == expectedCentroid1)
EXPECT_EQ(centroids[1], expectedCentroid2);
else if (centroids[0] == expectedCentroid2)
EXPECT_EQ(centroids[1], expectedCentroid1);
else
FAIL();
// Try to use an empty observation vector.
obsCopy.clear();
EXPECT_FALSE(kmeans.Observations(obsCopy));
// Try to use a k > num_observations.
EXPECT_FALSE(kmeans.Cluster(obs.size() + 1, centroids, labels));
}
//////////////////////////////////////////////////
TEST_F(KmeansTest, Append)
{
// Create some observations.
std::vector<math::Vector3> obs, obs2, obsTotal;
obs.push_back(math::Vector3(1.0, 1.0, 0.0));
obs.push_back(math::Vector3(1.1, 1.0, 0.0));
obs.push_back(math::Vector3(1.2, 1.0, 0.0));
obs.push_back(math::Vector3(1.3, 1.0, 0.0));
obs.push_back(math::Vector3(1.4, 1.0, 0.0));
obs2.push_back(math::Vector3(5.0, 1.0, 0.0));
obs2.push_back(math::Vector3(5.1, 1.0, 0.0));
obs2.push_back(math::Vector3(5.2, 1.0, 0.0));
obs2.push_back(math::Vector3(5.3, 1.0, 0.0));
obs2.push_back(math::Vector3(5.4, 1.0, 0.0));
obsTotal.insert(obsTotal.end(), obs.begin(), obs.end());
obsTotal.insert(obsTotal.end(), obs2.begin(), obs2.end());
// Initialize Kmeans with two partitions.
math::Kmeans kmeans(obs);
kmeans.AppendObservations(obs2);
std::vector<math::Vector3> obsCopy;
obsCopy = kmeans.Observations();
for (unsigned int i = 0; i < obsTotal.size(); ++i)
EXPECT_EQ(obsTotal[i], obsCopy[i]);
}