Removing extra test

mlpack · Jun 12, 2020 · 47b544c · 47b544c
1 parent 6ee4b3a
commit 47b544c
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Showing 2 changed files with 3 additions and 84 deletions.
diff --git a/src/mlpack/methods/ann/layer/radial_basis_function_impl.hpp b/src/mlpack/methods/ann/layer/radial_basis_function_impl.hpp
@@ -21,7 +21,9 @@ template<typename InputDataType, typename OutputDataType,
          typename Activation>
 RBF<InputDataType, OutputDataType, Activation>::RBF() :
     inSize(0),
-    outSize(0)
+    outSize(0),
+    sigmas(0),
+    betas(0),
 {
   // Nothing to do here.
 }

diff --git a/src/mlpack/tests/feedforward_network_test.cpp b/src/mlpack/tests/feedforward_network_test.cpp
@@ -706,87 +706,4 @@ BOOST_AUTO_TEST_CASE(RBFNetworkTest)
   TestNetwork<>(model1, dataset, labels1, dataset, labels, 10, 0.1);
 }
 
-/**
- * Train the RBF network on a larger dataset.
- */
-BOOST_AUTO_TEST_CASE(RBFNetworkTest)
-{
-  mlpack::math::RandomSeed(std::time(NULL));
-  // Load the dataset.
-  arma::mat trainData;
-  data::Load("thyroid_train.csv", trainData, true);
-
-  arma::mat trainLabels = trainData.row(trainData.n_rows - 1);
-  trainData.shed_row(trainData.n_rows - 1);
-
-  arma::mat trainLabels1 = arma::zeros(3, trainData.n_cols);
-  for(size_t i = 0; i < trainData.n_cols; i++)
-  {
-    trainLabels1.col(i).row((trainLabels(i) - 1)) = 1;
-  }
-
-  arma::mat testData;
-  data::Load("thyroid_test.csv", testData, true);
-
-  arma::mat testLabels = testData.row(testData.n_rows - 1);
-  testData.shed_row(testData.n_rows - 1);
-
-  /*
-   * Construct a feed forward network with trainData.n_rows input nodes,
-   * hiddenLayerSize hidden nodes and trainLabels.n_rows output nodes. The
-   * network structure looks like:
-   *
-   *  Input         RBF          Activation    Output
-   *  Layer         Layer         Layer        Layer
-   * +-----+       +-----+       +-----+       +-----+
-   * |     |       |     |       |     |       |     |
-   * |     +------>|     +------>|     +------>|     |
-   * |     |       |     |       |     |       |     |
-   * +-----+       +--+--+       +-----+       +-----+
-   */
-
-  arma::mat centroids;
-  KMeans<> kmeans;
-  kmeans.Cluster(trainData, 8, centroids);
-
-  FFN<MeanSquaredError<> > model;
-  model.Add<RBF<> >(trainData.n_rows, 8, centroids);
-  model.Add<Linear<> >(8, 3);
-
-  // RBFN neural net with MeanSquaredError.
-  TestNetwork<>(model, trainData, trainLabels1, testData, testLabels, 10, 0.1);
-
-  arma::mat dataset;
-  dataset.load("mnist_first250_training_4s_and_9s.arm");
-
-  // Normalize each point since these are images.
-  for (size_t i = 0; i < dataset.n_cols; ++i)
-  {
-    dataset.col(i) /= norm(dataset.col(i), 2);
-  }
-
-  arma::mat labels = arma::zeros(1, dataset.n_cols);
-  labels.submat(0, labels.n_cols / 2, 0, labels.n_cols - 1).fill(1);
-
-  arma::mat labels1 = arma::zeros(2, dataset.n_cols);
-  for(size_t i = 0; i < dataset.n_cols; i++)
-  {
-    labels1.col(i).row(labels(i)) = 1;
-  }
-  labels += 1;
-
-
-  arma::mat centroids1;
-  arma::Row<size_t> assignments;
-  KMeans<> kmeans1;
-  kmeans1.Cluster(dataset, 60, centroids1);
-
-  FFN<MeanSquaredError<> > model1;
-  model1.Add<RBF<> >(dataset.n_rows, 60, centroids1, 0.34493);
-  model1.Add<Linear<> >(60, 2);
-
-  // RBFN neural net with MeanSquaredError.
-  TestNetwork<>(model1, dataset, labels1, dataset, labels, 10, 0.1);
-}
-
 BOOST_AUTO_TEST_SUITE_END();