utest.cpp
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00001 /*
00002  * test.cpp
00003  *
00004  *  Created on: May 5, 2012
00005  *      Author: vsu
00006  */
00007 
00008 #include <training.h>
00009 #include <sac_3dof.h>
00010 #include <pcl17/io/io.h>
00011 #include <gtest/gtest.h>
00012 
00013 // Runs clustering with the same number of points as number of clusters
00014 // Checks if returned cluster centers are the same as points
00015 TEST(ClusteringTest, SameNumberOfPointsAndClusters)
00016 {
00017 
00018   std::vector<featureType> features;
00019   std::vector<featureType> cluster_centers;
00020   std::vector<int> cluster_labels;
00021   int num_clusters = 10;
00022 
00023   for (int i = 0; i < num_clusters; i++)
00024   {
00025     featureType feature;
00026 
00027     for (int j = 0; j < featureLength; j++)
00028     {
00029       feature.histogram[j] = (float)rand() / (float)RAND_MAX;
00030     }
00031     features.push_back(feature);
00032   }
00033 
00034   cluster_features(features, num_clusters, cluster_centers, cluster_labels);
00035 
00036   for (int i = 0; i < num_clusters; i++)
00037   {
00038     featureType feature = features[i];
00039     featureType cluster_center = cluster_centers[cluster_labels[i]];
00040     for (int j = 0; j < featureLength; j++)
00041     {
00042       EXPECT_FLOAT_EQ(feature.histogram[j], cluster_center.histogram[j])
00043 <<        "Feature and Cluster Center differ at index " << j;
00044       }
00045     }
00046   }
00047 
00048   // Creates a vector of 6 points: 0, 0+delta, 0-delta, 1, 1+delta, 1-delta and runs clustering
00049   // Checks if first 3 points have centroids in 0 and last 3 in 1
00050 TEST(ClusteringTest, SymetricDelta)
00051 {
00052 
00053   std::vector<featureType> features;
00054   std::vector<featureType> cluster_centers;
00055   std::vector<int> cluster_labels;
00056   int num_clusters = 2;
00057   float delta = 0.01;
00058 
00059   featureType feature;
00060 
00061   for (int j = 0; j < featureLength; j++)
00062   {
00063     feature.histogram[j] = 0;
00064   }
00065   features.push_back(feature);
00066 
00067   for (int j = 0; j < featureLength; j++)
00068   {
00069     feature.histogram[j] = 0 - delta;
00070   }
00071   features.push_back(feature);
00072 
00073   for (int j = 0; j < featureLength; j++)
00074   {
00075     feature.histogram[j] = 0 + delta;
00076   }
00077   features.push_back(feature);
00078 
00079   for (int j = 0; j < featureLength; j++)
00080   {
00081     feature.histogram[j] = 1;
00082   }
00083   features.push_back(feature);
00084 
00085   for (int j = 0; j < featureLength; j++)
00086   {
00087     feature.histogram[j] = 1 - delta;
00088   }
00089   features.push_back(feature);
00090 
00091   for (int j = 0; j < featureLength; j++)
00092   {
00093     feature.histogram[j] = 1 + delta;
00094   }
00095   features.push_back(feature);
00096 
00097   cluster_features(features, num_clusters, cluster_centers, cluster_labels);
00098 
00099   for (int i = 0; i < 3; i++)
00100   {
00101     featureType cluster_center = cluster_centers[cluster_labels[i]];
00102     for (int j = 0; j < featureLength; j++)
00103     {
00104       EXPECT_FLOAT_EQ(0.0f, cluster_center.histogram[j])
00105 <<        "Cluster Center is not equal to 0 at index " << j;
00106       }
00107     }
00108 
00109     for (int i = 3; i < 6; i++)
00110     {
00111       featureType cluster_center = cluster_centers[cluster_labels[i]];
00112       for (int j = 0; j < featureLength; j++)
00113       {
00114         EXPECT_FLOAT_EQ(1.0f, cluster_center.histogram[j])
00115         << "Cluster Center is not equal to 1 at index " << j;
00116       }
00117     }
00118   }
00119 
00120 
00121 
00122 int main(int argc, char **argv)
00123 {
00124   testing::InitGoogleTest(&argc, argv);
00125   return RUN_ALL_TESTS();
00126 }
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furniture_classification
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autogenerated on Thu May 23 2013 18:32:29