apaam/mlpack
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Checking

Browse Source
This commit is contained in:
Heisenbuug
2021-02-22 19:08:50 +05:30
parent 075d6c4beb
commit 1b2ffe201f
1 changed files with 18 additions and 15 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/mlpack/tests/feedforward_network_2_test.cpp
+18 -15
View File
@@ -31,12 +31,12 @@ using namespace mlpack::kmeans;
/**
* Train and evaluate a model with the specified structure.
*/
template<typename MatType = arma::mat, typename ModelType>
void TestNetwork(ModelType& model,
MatType& trainData,
MatType& trainLabels,
MatType& testData,
MatType& testLabels,
template <typename MatType = arma::mat, typename ModelType>
void TestNetwork(ModelType &model,
MatType &trainData,
MatType &trainLabels,
MatType &testData,
MatType &testLabels,
const size_t maxEpochs,
const double classificationErrorThreshold)
{
@@ -50,7 +50,8 @@ void TestNetwork(ModelType& model,
for (size_t i = 0; i < predictionTemp.n_cols; ++i)
{
prediction(i) = arma::as_scalar(arma::find(
arma::max(predictionTemp.col(i)) == predictionTemp.col(i), 1)) + 1;
arma::max(predictionTemp.col(i)) == predictionTemp.col(i), 1)) +
1;
}
size_t correct = arma::accu(prediction == testLabels);
@@ -65,7 +66,8 @@ TEST_CASE("RBFNetworkTest", "[FeedForwardNetworkTest]")
{
// Load the dataset.
arma::mat trainData;
data::Load("thyroid_train.csv", trainData, true);
if (!data::Load("thyroid_train.csv", trainData))
FAIL("Cannot load dataset thyroid_train.csv")
arma::mat trainLabels = trainData.row(trainData.n_rows - 1);
trainData.shed_row(trainData.n_rows - 1);
@@ -77,7 +79,8 @@ TEST_CASE("RBFNetworkTest", "[FeedForwardNetworkTest]")
}
arma::mat testData;
data::Load("thyroid_test.csv", testData, true);
if (!data::Load("thyroid_test.csv", testData))
FAIL("Cannot load dataset thyroid_test.csv")
arma::mat testLabels = testData.row(testData.n_rows - 1);
testData.shed_row(testData.n_rows - 1);
@@ -99,9 +102,9 @@ TEST_CASE("RBFNetworkTest", "[FeedForwardNetworkTest]")
KMeans<> kmeans;
kmeans.Cluster(trainData, 8, centroids);
FFN<MeanSquaredError<> > model;
model.Add<RBF<> >(trainData.n_rows, 8, centroids);
model.Add<Linear<> >(8, 3);
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);
@@ -131,9 +134,9 @@ TEST_CASE("RBFNetworkTest", "[FeedForwardNetworkTest]")
KMeans<> kmeans1;
kmeans1.Cluster(dataset, 140, centroids1);
FFN<MeanSquaredError<> > model1;
model1.Add<RBF<> >(dataset.n_rows, 140, centroids1, 4.1);
model1.Add<Linear<> >(140, 2);
FFN<MeanSquaredError<>> model1;
model1.Add<RBF<>>(dataset.n_rows, 140, centroids1, 4.1);
model1.Add<Linear<>>(140, 2);
// RBFN neural net with MeanSquaredError.
TestNetwork<>(model1, dataset, labels1, dataset, labels, 10, 0.1);