Refactored unit tests

Author: mariusmuja

test/flann_hierarchical_test.cpp

@@ -17,7 +17,7 @@ class HierarchicalIndex_Brief100K : public FLANNTestFixture
 	typedef Distance::ResultType DistanceType;
 	flann::Matrix<unsigned char> data;
 	flann::Matrix<unsigned char> query;
-	flann::Matrix<size_t> match;
+	flann::Matrix<size_t> gt_indices;
 	flann::Matrix<DistanceType> dists;
 	flann::Matrix<DistanceType> gt_dists;
 	flann::Matrix<size_t> indices;
@@ -30,279 +30,79 @@ class HierarchicalIndex_Brief100K : public FLANNTestFixture
 		fflush(stdout);
 		flann::load_from_file(data, "brief100K.h5", "dataset");
 		flann::load_from_file(query, "brief100K.h5", "query");
-
-		dists = flann::Matrix<DistanceType>(new DistanceType[query.rows * k_nn_], query.rows, k_nn_);
-		indices = flann::Matrix<size_t>(new size_t[query.rows * k_nn_], query.rows, k_nn_);
-
 		printf("done\n");
 
-		// The matches are bogus so we compute them the hard way
-		//    flann::load_from_file(match,"brief100K.h5","indices");
-
 		flann::Index<Distance> index(data, flann::LinearIndexParams());
 		index.buildIndex();
 
 		start_timer("Searching KNN for ground truth...");
-		match = flann::Matrix<size_t>(new size_t[query.rows * k_nn_], query.rows, k_nn_);
+		gt_indices = flann::Matrix<size_t>(new size_t[query.rows * k_nn_], query.rows, k_nn_);
 		gt_dists = flann::Matrix<DistanceType>(new DistanceType[query.rows * k_nn_], query.rows, k_nn_);
-		index.knnSearch(query, match, gt_dists, k_nn_, flann::SearchParams(-1));
+		index.knnSearch(query, gt_indices, gt_dists, k_nn_, flann::SearchParams(-1));
 		printf("done (%g seconds)\n", stop_timer());
+
+		dists = flann::Matrix<DistanceType>(new DistanceType[query.rows * k_nn_], query.rows, k_nn_);
+		indices = flann::Matrix<size_t>(new size_t[query.rows * k_nn_], query.rows, k_nn_);
 	}
 
-  void TearDown()
-  {
-    delete[] data.ptr();
-    delete[] query.ptr();
-    delete[] match.ptr();
-    delete[] gt_dists.ptr();
-    delete[] dists.ptr();
-    delete[] indices.ptr();
-  }
+	void TearDown()
+	{
+		delete[] data.ptr();
+		delete[] query.ptr();
+		delete[] dists.ptr();
+		delete[] indices.ptr();
+		delete[] gt_indices.ptr();
+		delete[] gt_dists.ptr();
+	}
 };
 
 
-
 TEST_F(HierarchicalIndex_Brief100K, TestSearch)
 {
-    flann::Index<Distance> index(data, flann::HierarchicalClusteringIndexParams());
-    start_timer("Building hierarchical clustering index...");
-    index.buildIndex();
-    printf("done (%g seconds)\n", stop_timer());
-
-    start_timer("Searching KNN...");
-    index.knnSearch(query, indices, dists, k_nn_, flann::SearchParams(2000));
-    printf("done (%g seconds)\n", stop_timer());
-
-    float precision = computePrecisionDiscrete(gt_dists, dists);
-    EXPECT_GE(precision, 0.9);
-    printf("Precision: %g\n", precision);
+	TestSearch<Distance>(data, flann::HierarchicalClusteringIndexParams(),
+			query, indices, dists, k_nn_, flann::SearchParams(2000), 0.9, gt_indices, gt_dists);
 }
 
-TEST_F(HierarchicalIndex_Brief100K, TestIncremental)
+TEST_F(HierarchicalIndex_Brief100K, TestAddIncremental)
 {
-    size_t size1 = data.rows/2-1;
-    size_t size2 = data.rows-size1;
-    Matrix<ElementType> data1(data[0], size1, data.cols);
-    Matrix<ElementType> data2(data[size1], size2, data.cols);
-
-    flann::Index<Distance> index(data1, flann::HierarchicalClusteringIndexParams());
-    start_timer("Building hierarchical clustering index...");
-    index.buildIndex();
-    index.addPoints(data2);
-    printf("done (%g seconds)\n", stop_timer());
-
-    start_timer("Searching KNN...");
-    index.knnSearch(query, indices, dists, k_nn_, flann::SearchParams(2000));
-    printf("done (%g seconds)\n", stop_timer());
-
-    float precision = computePrecisionDiscrete(gt_dists, dists);
-    EXPECT_GE(precision, 0.87);
-    printf("Precision: %g\n", precision);
+	TestAddIncremental<Distance>(data, flann::HierarchicalClusteringIndexParams(),
+			query, indices, dists, k_nn_, flann::SearchParams(2000), 0.87, gt_indices, gt_dists);
 }
 
-TEST_F(HierarchicalIndex_Brief100K, TestIncremental2)
+TEST_F(HierarchicalIndex_Brief100K, TestAddIncremental2)
 {
-    size_t size1 = data.rows/2+1;
-    size_t size2 = data.rows-size1;
-    Matrix<ElementType> data1(data[0], size1, data.cols);
-    Matrix<ElementType> data2(data[size1], size2, data.cols);
-
-    flann::Index<Distance> index(data1, flann::HierarchicalClusteringIndexParams());
-    start_timer("Building hierarchical clustering index...");
-    index.buildIndex();
-    index.addPoints(data2);
-    printf("done (%g seconds)\n", stop_timer());
-
-    start_timer("Searching KNN...");
-    index.knnSearch(query, indices, dists, k_nn_, flann::SearchParams(2000));
-    printf("done (%g seconds)\n", stop_timer());
-
-    float precision = computePrecisionDiscrete(gt_dists, dists);
-    EXPECT_GE(precision, 0.87);
-    printf("Precision: %g\n", precision);
+	TestAddIncremental2<Distance>(data, flann::HierarchicalClusteringIndexParams(),
+			query, indices, dists, k_nn_, flann::SearchParams(2000), 0.87, gt_indices, gt_dists);
 }
 
 TEST_F(HierarchicalIndex_Brief100K, TestRemove)
 {
-    flann::Index<Distance> index(data, flann::HierarchicalClusteringIndexParams());
-    start_timer("Building hierarchical clustering index...");
-	index.buildIndex();
-	printf("done (%g seconds)\n", stop_timer());
-
-	start_timer("Searching KNN before removing points...");
-	index.knnSearch(query, indices, dists, k_nn_, flann::SearchParams(2000) );
-	printf("done (%g seconds)\n", stop_timer());
-
-	// remove about 50% of neighbors found
-	std::set<int> neighbors;
-	for (size_t i=0;i<indices.rows;++i) {
-		for (size_t j=0;j<indices.cols;++j) {
-			if (rand_double()<0.5) {
-				neighbors.insert(indices[i][j]);
-			}
-		}
-	}
-
-    flann::DynamicBitset removed(data.rows);
-
-    for (std::set<int>::iterator it = neighbors.begin(); it!=neighbors.end();++it) {
-    	index.removePoint(*it);
-    	removed.set(*it);
-    }
-
-    // also remove 10% of the initial points
-    size_t offset = data.rows/10;
-    for (size_t i=0;i<offset;++i) {
-    	index.removePoint(i);
-    	removed.set(i);
-    }
-
-    size_t new_size = 0;
-    for (size_t i=0;i<removed.size();++i) {
-    	if (!removed.test(i)) ++new_size;
-    }
-
-    EXPECT_EQ(index.size(), new_size);
-
-	start_timer("Searching KNN after remove points...");
-	index.knnSearch(query, indices, dists, k_nn_, flann::SearchParams(2000) );
-	printf("done (%g seconds)\n", stop_timer());
-
-	for (size_t i=0;i<indices.rows;++i) {
-		for (size_t j=0;j<indices.cols;++j) {
-			EXPECT_GE(indices[i][j], offset);
-			EXPECT_TRUE(neighbors.find(indices[i][j])==neighbors.end());
-		}
-	}
-
-	// rebuild index
-	index.buildIndex();
-
-	EXPECT_EQ(index.size(), new_size);
-
-	start_timer("Searching KNN after remove points and rebuild index...");
-	index.knnSearch(query, indices, dists, k_nn_, flann::SearchParams(2000) );
-	printf("done (%g seconds)\n", stop_timer());
-
-	for (size_t i=0;i<indices.rows;++i) {
-		for (size_t j=0;j<indices.cols;++j) {
-			EXPECT_GE(indices[i][j], offset);
-			EXPECT_TRUE(neighbors.find(indices[i][j])==neighbors.end());
-		}
-	}
+	TestRemove<Distance>(data, flann::HierarchicalClusteringIndexParams(),
+			query, indices, dists, k_nn_, flann::SearchParams(2000));
 }
 
 TEST_F(HierarchicalIndex_Brief100K, TestSave)
 {
-
-    flann::Index<Distance> index(data, flann::HierarchicalClusteringIndexParams());
-    start_timer("Building hierarchical clustering index...");
-    index.buildIndex();
-    printf("done (%g seconds)\n", stop_timer());
-
-    start_timer("Searching KNN...");
-    index.knnSearch(query, indices, dists, k_nn_, flann::SearchParams(2000));
-    printf("done (%g seconds)\n", stop_timer());
-
-    index.save("hierarchical_clustering_brief.idx");
-
-    printf("Loading hierarchical clustering index\n");
-    flann::Index<Distance> index_saved(data, flann::SavedIndexParams("hierarchical_clustering_brief.idx"));
-
-    start_timer("Searching KNN...");
-    index_saved.knnSearch(query, indices, dists, k_nn_, flann::SearchParams(2000));
-    printf("done (%g seconds)\n", stop_timer());
-
-
-    float precision = computePrecisionDiscrete(gt_dists, dists);
-    EXPECT_GE(precision, 0.9);
-    printf("Precision: %g\n", precision);
-
+	TestSave<Distance>(data, flann::HierarchicalClusteringIndexParams(),
+			query, indices, dists, k_nn_, flann::SearchParams(2000), 0.87, gt_indices, gt_dists);
 }
 
 
 TEST_F(HierarchicalIndex_Brief100K, TestCopy)
 {
-    flann::Index<Distance> index(data, flann::HierarchicalClusteringIndexParams());
-    start_timer("Building hierarchical clustering index...");
-    index.buildIndex();
-    printf("done (%g seconds)\n", stop_timer());
-
-    start_timer("Searching KNN...");
-    index.knnSearch(query, indices, dists, k_nn_, flann::SearchParams(2000));
-    printf("done (%g seconds)\n", stop_timer());
-
-    float precision = compute_precision(match, indices);
-    printf("Precision: %g\n", precision);
-    EXPECT_GE(precision, 0.75);
-
-    // test copy constructor
-    flann::Index<Distance> index2(index);
-
-    start_timer("Searching KNN...");
-    index2.knnSearch(query, indices, dists, k_nn_, flann::SearchParams(2000));
-    printf("done (%g seconds)\n", stop_timer());
-
-    float precision2 = compute_precision(match, indices);
-    printf("Precision: %g\n", precision2);
-    EXPECT_EQ(precision, precision2);
-
-    // test assignment operator
-    flann::Index<Distance> index3(data, flann::HierarchicalClusteringIndexParams());
-    index3 = index;
-
-    start_timer("Searching KNN...");
-    index3.knnSearch(query, indices, dists, k_nn_, flann::SearchParams(2000));
-    printf("done (%g seconds)\n", stop_timer());
-
-    float precision3 = compute_precision(match, indices);
-    printf("Precision: %g\n", precision3);
-    EXPECT_EQ(precision, precision3);
-
+	TestCopy<Distance>(data, flann::HierarchicalClusteringIndexParams(),
+			query, indices, dists, k_nn_, flann::SearchParams(2000), 0.87, gt_indices, gt_dists);
 }
 
 TEST_F(HierarchicalIndex_Brief100K, TestCopy2)
 {
-    flann::HierarchicalClusteringIndex<Distance> index(data, flann::HierarchicalClusteringIndexParams());
-    start_timer("Building hierarchical clustering index...");
-    index.buildIndex();
-    printf("done (%g seconds)\n", stop_timer());
-
-    start_timer("Searching KNN...");
-    index.knnSearch(query, indices, dists, k_nn_, flann::SearchParams(2000));
-    printf("done (%g seconds)\n", stop_timer());
-
-    float precision = compute_precision(match, indices);
-    printf("Precision: %g\n", precision);
-    EXPECT_GE(precision, 0.75);
-
-    // test copy constructor
-    flann::HierarchicalClusteringIndex<Distance > index2(index);
-
-    start_timer("Searching KNN...");
-    index2.knnSearch(query, indices, dists, k_nn_, flann::SearchParams(2000));
-    printf("done (%g seconds)\n", stop_timer());
-
-    float precision2 = compute_precision(match, indices);
-    printf("Precision: %g\n", precision2);
-    EXPECT_EQ(precision, precision2);
-
-    // test assignment operator
-    flann::HierarchicalClusteringIndex<Distance> index3(data, flann::HierarchicalClusteringIndexParams());
-    index3 = index;
-
-    start_timer("Searching KNN...");
-    index3.knnSearch(query, indices, dists, k_nn_, flann::SearchParams(2000));
-    printf("done (%g seconds)\n", stop_timer());
-
-    float precision3 = compute_precision(match, indices);
-    printf("Precision: %g\n", precision3);
-    EXPECT_EQ(precision, precision3);
+	TestCopy2<flann::HierarchicalClusteringIndex<Distance> >(data, flann::HierarchicalClusteringIndexParams(),
+			query, indices, dists, k_nn_, flann::SearchParams(2000), 0.87, gt_indices, gt_dists);
 }
 
 
 int main(int argc, char** argv)
 {
-    testing::InitGoogleTest(&argc, argv);
-    return RUN_ALL_TESTS();
+	testing::InitGoogleTest(&argc, argv);
+	return RUN_ALL_TESTS();
 }