From: yuanming_at_[hidden]
Date: 2008-05-25 02:52:58
Author: lakeeast
Date: 2008-05-25 02:52:58 EDT (Sun, 25 May 2008)
New Revision: 45739
URL: http://svn.boost.org/trac/boost/changeset/45739
Log:
Add unit test for k_means algorithm.
Added:
sandbox/cluster/libs/algorithm/cluster/test/
sandbox/cluster/libs/algorithm/cluster/test/Jamfile.v2 (contents, props changed)
sandbox/cluster/libs/algorithm/cluster/test/k_means_test.cpp (contents, props changed)
Added: sandbox/cluster/libs/algorithm/cluster/test/Jamfile.v2
==============================================================================
--- (empty file)
+++ sandbox/cluster/libs/algorithm/cluster/test/Jamfile.v2 2008-05-25 02:52:58 EDT (Sun, 25 May 2008)
@@ -0,0 +1,22 @@
+# Boost.Algorithm.Cluster Library
+
+# Copyright Yuanming Chen 2008. Use, modification and
+# distribution is subject to the Boost Software License, Version
+# 1.0. (See accompanying file LICENSE_1_0.txt or copy at
+# http://www.boost.org/LICENSE_1_0.txt)
+
+# For more information, see http://www.boost.org
+
+# bring in rules for testing
+import testing ;
+
+project
+ : source-location .
+ : requirements
+ ;
+{
+ test-suite cluster_test
+ :
+ [ run k_means_test.cpp ]
+ ;
+}
Added: sandbox/cluster/libs/algorithm/cluster/test/k_means_test.cpp
==============================================================================
--- (empty file)
+++ sandbox/cluster/libs/algorithm/cluster/test/k_means_test.cpp 2008-05-25 02:52:58 EDT (Sun, 25 May 2008)
@@ -0,0 +1,97 @@
+// Boost.Algorithm.Cluster Library
+// Copyright Yuanming Chen 2008. Use, modification and
+// distribution is subject to the Boost Software License, Version
+// 1.0. (See accompanying file LICENSE_1_0.txt or copy at
+// http://www.boost.org/LICENSE_1_0.txt)
+
+// For more information, see http://www.boost.org
+
+#include <boost/test/unit_test.hpp>
+#define BOOST_TEST_MODULE ClusterLib
+
+#include <boost/algorithm/cluster/k_means.hpp>
+#include <boost/array.hpp>
+#include <iostream>
+#include <fstream>
+#include <string>
+using namespace boost::algorithm::cluster;
+
+BOOST_AUTO_TEST_CASE( basic_k_means_test )
+{
+ //The example can be found at:
+ //http://people.revoledu.com/kardi/tutorial/kMean/NumericalExample.htm
+
+ //Attribute 1: weight index.
+ //Attribute 2: pH value
+ //"POINT" represents a data point in the multi-dimensional data base
+ typedef boost::array<double, 2> POINT;
+ ////////////////////////////////////////////////////////////////////////
+ //Four drugs. We need to get two clusters
+ std::vector<POINT> vecPoints;
+
+ POINT medicineA;
+ medicineA[0] = 1;
+ medicineA[1] = 1;
+ vecPoints.push_back(medicineA);
+
+ POINT medicineB;
+ medicineB[0] = 2;
+ medicineB[1] = 1;
+ vecPoints.push_back(medicineB);
+
+ POINT medicineC;
+ medicineC[0] = 4;
+ medicineC[1] = 3;
+ vecPoints.push_back(medicineC);
+
+ POINT medicineD;
+ medicineD[0] = 5;
+ medicineD[1] = 4;
+ vecPoints.push_back(medicineD);
+
+ const int k = 2; //2 groups of medicine
+
+ typedef KMeansCluster<POINT> ClusterType;
+ typedef KMeansClustering<ClusterType> ClusteringType;
+ size_t knDimension = POINT::size();
+
+ ClusteringType result = k_means(vecPoints.begin(), vecPoints.end(), k, 0.00001);
+ int nCentroidId = 0;
+ for(ClusteringType::type::const_iterator iter = result.clusters.begin();
+ iter != result.clusters.end();
+ iter++, nCentroidId++)
+ {
+ const ClusterType& cluster = *iter;
+ std::cout << "Cluster " << nCentroidId << std::endl;
+ std::cout << "Centroid point (";
+
+ for(unsigned int nAttribute = 0; nAttribute < knDimension; nAttribute++)
+ {
+ std::cout << cluster.centroid[nAttribute];
+ if(nAttribute != knDimension - 1)
+ {
+ std::cout << ", ";
+ }
+ }
+ std::cout << ")" << std::endl;
+ std::cout << "Number of points: " << cluster.points.size() << std::endl;
+
+ for(std::vector<int>::const_iterator iter = cluster.points.begin();
+ iter != cluster.points.end();
+ iter++)
+ {
+ std::cout << *iter << std::endl;
+ }
+ std::cout << std::endl << std::endl;
+ }
+ double centroid0_x = result.clusters[0].centroid[0];
+ double centroid0_y = result.clusters[0].centroid[1];
+ BOOST_CHECK_EQUAL( centroid0_x, 1.5 );
+ BOOST_CHECK_EQUAL( centroid0_y, 1 );
+
+
+ double centroid1_x = result.clusters[1].centroid[0];
+ double centroid1_y = result.clusters[1].centroid[1];
+ BOOST_CHECK_EQUAL( centroid1_x, 4.5 );
+ BOOST_CHECK_EQUAL( centroid1_y, 3.5 );
+}