Add GaussianCoverSingleKDETest

src/mlpack/tests/kde_test.cpp

@@ -214,13 +214,48 @@ BOOST_AUTO_TEST_CASE(EpanechnikovCoverSingleKDETest)
   // Brute force KDE.
   EpanechnikovKernel kernel(kernelBandwidth);
   BruteForceKDE<EpanechnikovKernel>(reference,
+                                    query,
+                                    bfEstimations,
+                                    kernel);
+
+  // Optimized KDE.
+  metric::EuclideanDistance metric;
+  KDE<EpanechnikovKernel,
+      metric::EuclideanDistance,
+      arma::mat,
+      tree::StandardCoverTree>
+      kde(relError, 0.0, kernel, KDEMode::SINGLE_TREE_MODE, metric);
+  kde.Train(reference);
+  kde.Evaluate(query, treeEstimations);
+
+  // Check whether results are equal.
+  for (size_t i = 0; i < query.n_cols; ++i)
+    BOOST_REQUIRE_CLOSE(bfEstimations[i], treeEstimations[i], relError*100);
+}
+
+/**
+ * Test single-tree implementation results against brute force results using
+ * a cover-tree and Gaussian kernel.
+ */
+BOOST_AUTO_TEST_CASE(GaussianCoverSingleKDETest)
+{
+  arma::mat reference = arma::randu(2, 300);
+  arma::mat query = arma::randu(2, 100);
+  arma::vec bfEstimations = arma::vec(query.n_cols, arma::fill::zeros);
+  arma::vec treeEstimations = arma::vec(query.n_cols, arma::fill::zeros);
+  const double kernelBandwidth = 1.1;
+  const double relError = 0.08;
+
+  // Brute force KDE.
+  GaussianKernel kernel(kernelBandwidth);
+  BruteForceKDE<GaussianKernel>(reference,
                                 query,
                                 bfEstimations,
                                 kernel);
 
   // Optimized KDE.
   metric::EuclideanDistance metric;
-  KDE<EpanechnikovKernel,
+  KDE<GaussianKernel,
       metric::EuclideanDistance,
       arma::mat,
       tree::StandardCoverTree>