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Merge pull request #12 from mlpack/master

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updating boost_to_catch2_2 with mlpack/master
This commit is contained in:
Aakash kaushik
2020-11-05 22:16:44 +05:30
committed by GitHub
parent 73bfe6eb45 58870ad8fe
commit 57bd40e722
41 changed files with 6239 additions and 2687 deletions
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.github/workflows/update-catch.yaml
+48
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@@ -0,0 +1,48 @@
name: Update Catch
on:
workflow_dispatch:
schedule:
- cron: '0 10 1/16 * *'
jobs:
updateCatch:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Get Latest Catch Tagged Release
id: catch-header
run: |
# Ping version information upstream.
CATCH_RELEASE_JSON=$(curl -sL https://api.github.com/repos/catchorg/Catch2/releases/latest)
CATCH_RELEASE_VERSION=$(jq -r ".tag_name" <<< "$CATCH_RELEASE_JSON" | tr -d v)
echo ::set-output name=release_tag::$(echo $CATCH_RELEASE_VERSION)
# Extract out version information from git repository.
CATCH_VERSION_MAJOR=$(grep -i ".*#define CATCH_VERSION_MAJOR.*" src/mlpack/tests/catch.hpp | grep -o "[0-9]*")
CATCH_VERSION_MINOR=$(grep -i ".*#define CATCH_VERSION_MINOR.*" src/mlpack/tests/catch.hpp | grep -o "[0-9]*")
CATCH_VERSION_PATCH=$(grep -i ".*#define CATCH_VERSION_PATCH.*" src/mlpack/tests/catch.hpp | grep -o "[0-9]*")
# Combine values to match release tag information.
CATCH_VERSION_VALUE=${CATCH_VERSION_MAJOR}.${CATCH_VERSION_MINOR}.${CATCH_VERSION_PATCH}
# Set the current release tag.
echo ::set-output name=current_tag::$(echo $CATCH_VERSION_VALUE)
- name: Update Catch
if: steps.catch-header.outputs.current_tag != steps.catch-header.outputs.release_tag
env:
CURRENT_TAG: ${{ steps.catch-header.outputs.current_tag }}
RELEASE_TAG: ${{ steps.catch-header.outputs.release_tag }}
run: |
# Delete the catch.hpp.
rm -f src/mlpack/tests/catch.hpp
# Download the release.
curl -sL https://github.com/catchorg/Catch2/releases/latest/download/catch.hpp -o src/mlpack/tests/catch.hpp
- name: Create Pull Request For Catch
if: steps.catch-header.outputs.current_tag != steps.catch-header.outputs.release_tag
uses: peter-evans/create-pull-request@v3
with:
commit-message: Upgrade Catch to ${{ steps.catch-header.outputs.release_tag }}
title: Upgrade Catch to ${{ steps.catch-header.outputs.release_tag }}
body: |
Updates [catchorg/Catch2](https://github.com/catchorg/Catch2) to ${{ steps.catch-header.outputs.release_tag }}.
Auto-generated by [create-pull-request](https://github.com/peter-evans/create-pull-request).
labels: update dependencies, automated PR
branch: catch-header-updates-${{ steps.catch-header.outputs.release_tag }}
.github/workflows/update-cli11.yaml
+44
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@@ -0,0 +1,44 @@
name: Update CLI11
on:
workflow_dispatch:
schedule:
- cron: '0 10 1/16 * *'
jobs:
updateCLI11:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Get Latest CLI11 Tagged Release
id: cli11-header
run: |
# Ping version information upstream.
CLI11_RELEASE_JSON=$(curl -sL https://api.github.com/repos/CLIUtils/CLI11/releases/latest)
CLI11_RELEASE_VERSION=$(jq -r ".tag_name" <<< "$CLI11_RELEASE_JSON" | tr -d v)
echo ::set-output name=release_tag::$(echo $CLI11_RELEASE_VERSION)
# Extract out version information from git repository.
CLI11_VERSION_VALUE=$(grep -i ".*#define CLI11_VERSION.*" src/mlpack/bindings/cli/third_party/CLI/CLI11.hpp | grep -Po "(\d+\.)+\d+")
# Set the current release tag.
echo ::set-output name=current_tag::$(echo $CLI11_VERSION_VALUE)
- name: Update CLI11
if: steps.cli11-header.outputs.current_tag != steps.cli11-header.outputs.release_tag
env:
CURRENT_TAG: ${{ steps.cli11-header.outputs.current_tag }}
RELEASE_TAG: ${{ steps.cli11-header.outputs.release_tag }}
run: |
# Delete the CLI11.hpp.
rm -f src/mlpack/bindings/cli/third_party/CLI/CLI11.hpp
# Download the release.
curl -sL https://github.com/CLIUtils/CLI11/releases/latest/download/CLI11.hpp -o src/mlpack/bindings/cli/third_party/CLI/CLI11.hpp
- name: Create Pull Request For CLI11
if: steps.cli11-header.outputs.current_tag != steps.cli11-header.outputs.release_tag
uses: peter-evans/create-pull-request@v3
with:
commit-message: Upgrade CLI11 to ${{ steps.cli11-header.outputs.release_tag }}
title: Upgrade CLI11 to ${{ steps.cli11-header.outputs.release_tag }}
body: |
Updates [CLIUtils/CLI11](https://github.com/CLIUtils/CLI11) to ${{ steps.cli11-header.outputs.release_tag }}.
Auto-generated by [create-pull-request](https://github.com/peter-evans/create-pull-request).
labels: update dependencies, automated PR
branch: cli11-header-updates-${{ steps.cli11-header.outputs.release_tag }}
src/mlpack/bindings/cli/third_party/CLI/CLI11.hpp
Vendored
+276 -807
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File diff suppressed because it is too large Load Diff
src/mlpack/bindings/markdown/print_docs.cpp
+1 -1
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@@ -180,7 +180,7 @@ void PrintDocs(const std::string& bindingName,
}
if (hasOutputOptions)
{
{
// Next, iterate through the list of output options.
cout << "### Output options" << endl;
cout << endl;
src/mlpack/core/cereal/array_wrapper.hpp
+2 -2
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@@ -21,7 +21,7 @@
namespace cereal {
/**
/**
* This class is used as a shim for cereal to be able to serialize a raw pointer array.
*/
template<class T>
@@ -84,7 +84,7 @@ ArrayWrapper<T> make_array(T*& t, S& s)
* @param T C Style array.
* @param S Size of the array.
*/
#define CEREAL_POINTER_ARRAY(T,S) cereal::make_array(T, S)
#define CEREAL_POINTER_ARRAY(T, S) cereal::make_array(T, S)
} // namespace cereal
src/mlpack/core/cereal/is_loading.hpp
+8 -7
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@@ -5,7 +5,7 @@
* Implementation of is_loading function.
*
* This implementation provides backward compatibilty with older
* version of cereal that does not have Archive::is_loading struct.
* version of cereal that does not have Archive::is_loading struct.
*
* mlpack is free software; you may redistribute it and/or modify it under the
* terms of the 3-clause BSD license. You should have received a copy of the
@@ -28,11 +28,12 @@ struct is_cereal_archive
{
// Archive::is_loading is not implemented yet, so we can use std::is_same<>
// to check if it is a loading archive.
constexpr static bool value = std::is_same<Archive, cereal::BinaryInputArchive>::value ||
//#if (BINDING_TYPE != BINDING_TYPE_R)
std::is_same<Archive, cereal::JSONInputArchive>::value ||
//#endif
std::is_same<Archive, cereal::XMLInputArchive>::value;
constexpr static bool value = std::is_same<Archive,
cereal::BinaryInputArchive>::value ||
// #if (BINDING_TYPE != BINDING_TYPE_R)
std::is_same<Archive, cereal::JSONInputArchive>::value ||
// #endif
std::is_same<Archive, cereal::XMLInputArchive>::value;
};
template<typename Archive>
@@ -40,7 +41,7 @@ bool is_loading(
const typename std::enable_if<
is_cereal_archive<Archive>::value, Archive>::type* = 0)
{
return true;
return true;
}
template<typename Archive>
src/mlpack/core/cereal/is_saving.hpp
+8 -7
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@@ -6,7 +6,7 @@
* Implementation of is_saving function.
*
* This implementation provides backward compatibilty with older
* version of cereal that does not have Archive::is_saving struct.
* version of cereal that does not have Archive::is_saving struct.
*
* mlpack is free software; you may redistribute it and/or modify it under the
* terms of the 3-clause BSD license. You should have received a copy of the
@@ -29,11 +29,12 @@ struct is_cereal_archive_saving
{
// Archive::is_saving is not implemented yet, so we can use std::is_same<>
// to check if it is a loading archive.
constexpr static bool value = std::is_same<Archive, cereal::BinaryOutputArchive>::value ||
//#if (BINDING_TYPE != BINDING_TYPE_R)
std::is_same<Archive, cereal::JSONOutputArchive>::value ||
//#endif
std::is_same<Archive, cereal::XMLOutputArchive>::value;
constexpr static bool value = std::is_same<Archive,
cereal::BinaryOutputArchive>::value ||
// #if (BINDING_TYPE != BINDING_TYPE_R)
std::is_same<Archive, cereal::JSONOutputArchive>::value ||
// #endif
std::is_same<Archive, cereal::XMLOutputArchive>::value;
};
template<typename Archive>
@@ -41,7 +42,7 @@ bool is_saving(
const typename std::enable_if<
is_cereal_archive_saving<Archive>::value, Archive>::type* = 0)
{
return true;
return true;
}
template<typename Archive>
src/mlpack/core/data/has_serialize.hpp
+5 -2
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@@ -32,9 +32,12 @@ template<typename T>
struct HasSerializeFunction
{
template<typename C>
using NonStaticSerialize = void(C::*)(cereal::XMLOutputArchive&, const uint32_t version);
using NonStaticSerialize = void(C::*)(cereal::XMLOutputArchive&,
const uint32_t version);
template<typename /* C */>
using StaticSerialize = void(*)(cereal::XMLOutputArchive&, const uint32_t version);
using StaticSerialize = void(*)(cereal::XMLOutputArchive&,
const uint32_t version);
static const bool value = HasSerializeCheck<T, NonStaticSerialize>::value ||
HasSerializeCheck<T, StaticSerialize>::value;
src/mlpack/core/metrics/bleu_impl.hpp
+1 -1
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@@ -187,7 +187,7 @@ ElemType BLEU<ElemType, PrecisionType>::Evaluate(
template <typename ElemType, typename PrecisionType>
template <typename Archive>
void BLEU<ElemType, PrecisionType>::serialize(Archive& ar,
void BLEU<ElemType, PrecisionType>::serialize(Archive& ar,
const uint32_t version)
{
ar(CEREAL_NVP(maxOrder));
src/mlpack/core/tree/binary_space_tree/binary_space_tree_impl.hpp
+1 -1
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@@ -1131,7 +1131,7 @@ void BinarySpaceTree<MetricType, StatisticType, MatType, BoundType, SplitType>::
if (node->left)
stack.push(node->left);
if (node->right)
stack.push(node->right);
stack.push(node->right);
}
}
}
src/mlpack/core/tree/rectangle_tree/rectangle_tree_impl.hpp
+1 -1
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@@ -1431,7 +1431,7 @@ void RectangleTree<MetricType, StatisticType, MatType, SplitType, DescentType,
{
MatType*& datasetTemp = const_cast<MatType*&>(dataset);
ar(CEREAL_POINTER(datasetTemp));
}
}
ar(CEREAL_NVP(points));
ar(CEREAL_NVP(auxiliaryInfo));
src/mlpack/methods/ann/layer/add_impl.hpp
+1 -1
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@@ -23,7 +23,7 @@ template<typename InputDataType, typename OutputDataType>
Add<InputDataType, OutputDataType>::Add(const size_t outSize) :
outSize(outSize)
{
weights.set_size(outSize, 1);
weights.set_size(WeightSize(), 1);
}
template<typename InputDataType, typename OutputDataType>
src/mlpack/methods/ann/layer/atrous_convolution_impl.hpp
+1 -2
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@@ -122,8 +122,7 @@ AtrousConvolution<
dilationWidth(dilationWidth),
dilationHeight(dilationHeight)
{
weights.set_size((outSize * inSize * kernelWidth * kernelHeight) + outSize,
1);
weights.set_size(WeightSize(), 1);
// Transform paddingType to lowercase.
std::string paddingTypeLow = paddingType;
src/mlpack/methods/ann/layer/concat.hpp
+7 -4
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@@ -165,9 +165,9 @@ class Concat
}
//! Return the initial point for the optimization.
const arma::mat& Parameters() const { return parameters; }
const arma::mat& Parameters() const { return weights; }
//! Modify the initial point for the optimization.
arma::mat& Parameters() { return parameters; }
arma::mat& Parameters() { return weights; }
//! Get the value of run parameter.
bool Run() const { return run; }
@@ -196,6 +196,9 @@ class Concat
//! Get the axis of concatenation.
size_t const& ConcatAxis() const { return axis; }
//! Get the size of the weight matrix.
size_t WeightSize() const { return 0; }
/**
* Serialize the layer
*/
@@ -225,8 +228,8 @@ class Concat
//! Locally-stored network modules.
std::vector<LayerTypes<CustomLayers...> > network;
//! Locally-stored model parameters.
arma::mat parameters;
//! Locally-stored model weights.
OutputDataType weights;
//! Locally-stored delta visitor.
DeltaVisitor deltaVisitor;
src/mlpack/methods/ann/layer/concat_impl.hpp
+2 -2
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@@ -33,7 +33,7 @@ Concat<InputDataType, OutputDataType, CustomLayers...>::Concat(
run(run),
channels(1)
{
parameters.set_size(0, 0);
weights.set_size(0, 0);
}
template<typename InputDataType, typename OutputDataType,
@@ -49,7 +49,7 @@ Concat<InputDataType, OutputDataType, CustomLayers...>::Concat(
model(model),
run(run)
{
parameters.set_size(0, 0);
weights.set_size(0, 0);
// Parameters to help calculate the number of channels.
size_t oldColSize = 1, newColSize = 1;
src/mlpack/methods/ann/layer/dropconnect.hpp
+6 -3
View File
@@ -115,9 +115,9 @@ class DropConnect
std::vector<LayerTypes<> >& Model() { return network; }
//! Get the parameters.
OutputDataType const& Parameters() const { return parameters; }
OutputDataType const& Parameters() const { return weights; }
//! Modify the parameters.
OutputDataType& Parameters() { return parameters; }
OutputDataType& Parameters() { return weights; }
//! Get the output parameter.
OutputDataType const& OutputParameter() const { return outputParameter; }
@@ -150,6 +150,9 @@ class DropConnect
scale = 1.0 / (1.0 - ratio);
}
//! Return the size of the weight matrix.
size_t WeightSize() const { return 0; }
/**
* Serialize the layer.
*/
@@ -164,7 +167,7 @@ class DropConnect
double scale;
//! Locally-stored weight object.
OutputDataType parameters;
OutputDataType weights;
//! Locally-stored delta object.
OutputDataType delta;
src/mlpack/methods/ann/layer/dropconnect_impl.hpp
+1 -1
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@@ -108,7 +108,7 @@ template<typename Archive>
void DropConnect<InputDataType, OutputDataType>::serialize(
Archive& ar, const uint32_t /* version */)
{
// Delete the old network first, if needed.
// Delete the old network first, if needed.
if (cereal::is_loading<Archive>())
{
boost::apply_visitor(DeleteVisitor(), baseLayer);
src/mlpack/methods/ann/layer/fast_lstm.hpp
+6
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@@ -164,6 +164,12 @@ class FastLSTM
//! Get the number of output units.
size_t OutSize() const { return outSize; }
//! Get the size of the weight matrix.
size_t WeightSize() const
{
return 4 * outSize * inSize + 4 * outSize + 4 * outSize * outSize;
}
/**
* Serialize the layer
*/
src/mlpack/methods/ann/layer/fast_lstm_impl.hpp
+1 -2
View File
@@ -42,8 +42,7 @@ FastLSTM<InputDataType, OutputDataType>::FastLSTM(
{
// Weights for: input to gate layer (4 * outsize * inSize + 4 * outsize)
// and output to gate (4 * outSize).
weights.set_size(
4 * outSize * inSize + 4 * outSize + 4 * outSize * outSize, 1);
weights.set_size(WeightSize(), 1);
}
template<typename InputDataType, typename OutputDataType>
src/mlpack/methods/ann/layer/linear_impl.hpp
+3 -3
View File
@@ -38,7 +38,7 @@ Linear<InputDataType, OutputDataType, RegularizerType>::Linear(
outSize(outSize),
regularizer(regularizer)
{
weights.set_size(outSize * inSize + outSize, 1);
weights.set_size(WeightSize(), 1);
}
template<typename InputDataType, typename OutputDataType,
@@ -70,7 +70,7 @@ template<typename InputDataType, typename OutputDataType,
Linear<InputDataType, OutputDataType, RegularizerType>&
Linear<InputDataType, OutputDataType, RegularizerType>::
operator=(const Linear& layer)
{
{
if (this != &layer)
{
inSize = layer.inSize;
@@ -86,7 +86,7 @@ template<typename InputDataType, typename OutputDataType,
Linear<InputDataType, OutputDataType, RegularizerType>&
Linear<InputDataType, OutputDataType, RegularizerType>::
operator=(Linear&& layer)
{
{
if (this != &layer)
{
inSize = layer.inSize;
src/mlpack/methods/ann/layer/softmin_impl.hpp
+2 -2
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@@ -29,7 +29,7 @@ template<typename InputType, typename OutputType>
void Softmin<InputDataType, OutputDataType>::Forward(
const InputType& input,
OutputType& output)
{
{
InputType softminInput = arma::exp(-(input.each_row() -
arma::min(input, 0)));
output = softminInput.each_row() / sum(softminInput, 0);
@@ -53,7 +53,7 @@ void Softmin<InputDataType, OutputDataType>::serialize(
{
// Nothing to do here.
}
} // namespace ann
} // namespace mlpack
src/mlpack/methods/ann/loss_functions/mean_absolute_percentage_error_impl.hpp
+3 -3
View File
@@ -32,7 +32,7 @@ MeanAbsolutePercentageError<InputDataType, OutputDataType>::Forward(
const InputType& input,
const TargetType& target)
{
InputType loss = arma::abs((input - target) / target);
InputType loss = arma::abs((input - target) / target);
return arma::accu(loss) * (100 / target.n_cols);
}
@@ -43,9 +43,9 @@ void MeanAbsolutePercentageError<InputDataType, OutputDataType>::Backward(
const TargetType& target,
OutputType& output)
{
{
output = (((arma::conv_to<arma::mat>::from(input < target) * -2) + 1) /
target) * (100 / target.n_cols) ;
target) * (100 / target.n_cols);
}
template<typename InputDataType, typename OutputDataType>
src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression_impl.hpp
+2 -1
View File
@@ -21,7 +21,8 @@ namespace regression {
* Serialize the Bayesian linear regression model.
*/
template<typename Archive>
void BayesianLinearRegression::serialize(Archive& ar, const uint32_t /* version */)
void BayesianLinearRegression::serialize(Archive& ar,
const uint32_t /* version */)
{
ar(CEREAL_NVP(centerData));
ar(CEREAL_NVP(scaleData));
src/mlpack/methods/hmm/hmm_util_impl.hpp
+14 -3
View File
@@ -41,14 +41,25 @@ void LoadHMMAndPerformAction(const std::string& modelFile,
{
const std::string extension = data::Extension(modelFile);
if (extension == "xml")
LoadHMMAndPerformActionHelper<ActionType, cereal::XMLInputArchive>(modelFile, x);
{
LoadHMMAndPerformActionHelper<ActionType, cereal::XMLInputArchive>(
modelFile, x);
}
else if (extension == "bin")
LoadHMMAndPerformActionHelper<ActionType, cereal::BinaryInputArchive>(modelFile, x);
{
LoadHMMAndPerformActionHelper<ActionType, cereal::BinaryInputArchive>(
modelFile, x);
}
else if (extension == "json")
LoadHMMAndPerformActionHelper<ActionType, cereal::JSONInputArchive>(modelFile, x);
{
LoadHMMAndPerformActionHelper<ActionType, cereal::JSONInputArchive>(
modelFile, x);
}
else
{
Log::Fatal << "Unknown extension '" << extension << "' for HMM model file "
<< "(known: 'xml', 'json', 'bin')." << std::endl;
}
}
template<typename ActionType,
src/mlpack/tests/activation_functions_test.cpp
+2 -3
View File
@@ -573,7 +573,7 @@ void CheckSoftminActivationCorrect(const arma::colvec input,
// Test the activation function using the entire vector as input.
arma::colvec activations;
softmin.Forward(input,activations);
softmin.Forward(input, activations);
for (size_t i = 0; i < activations.n_elem; ++i)
{
REQUIRE(activations.at(i) == Approx(target.at(i)).epsilon(1e-5));
@@ -606,8 +606,7 @@ void CheckSoftminDerivativeCorrect(const arma::colvec input,
for (size_t i = 0; i < derivatives.n_elem; ++i)
{
REQUIRE(derivatives.at(i) == Approx(target.at(i)).epsilon(1e-5));
}
}
}
/**
src/mlpack/tests/ann_visitor_test.cpp
+68 -5
View File
@@ -86,15 +86,78 @@ TEST_CASE("WeightSetVisitorTest", "[ANNVisitorTest]")
}
/**
* Test that WeightSizeVisitor works properly.
* Test that WeightSizeVisitor works properly for linear layer.
*/
TEST_CASE("WeightSizeVisitorTest", "[ANNVisitorTest]")
TEST_CASE("WeightSizeVisitorTestForLinearLayer", "[ANNVisitorTest]")
{
size_t randomSize = arma::randi(arma::distr_param(1, 100));
size_t randomInSize = arma::randi(arma::distr_param(1, 100));
size_t randomOutSize = arma::randi(arma::distr_param(1, 100));
LayerTypes<> linear = new Linear<>(randomSize, randomSize);
LayerTypes<> linearLayer = new Linear<>(randomInSize, randomOutSize);
CheckCorrectnessOfWeightSize(linear);
size_t weightSize = boost::apply_visitor(WeightSizeVisitor(), linearLayer);
CheckCorrectnessOfWeightSize(linearLayer);
}
/**
* Test that WeightSizeVisitor works properly for concat layer.
*/
TEST_CASE("WeightSizeVisitorTestForConcatLayer", "[ANNVisitorTest]")
{
LayerTypes<> concatLayer = new Concat<>();
size_t weightSize = boost::apply_visitor(WeightSizeVisitor(), concatLayer);
CheckCorrectnessOfWeightSize(concatLayer);
}
/**
* Test that WeightSizeVisitor works properly for fast lstm layer.
*/
TEST_CASE("WeightSizeVisitorTestForFastLSTMLayer", "[ANNVisitorTest]")
{
size_t randomInSize = arma::randi(arma::distr_param(1, 100));
size_t randomOutSize = arma::randi(arma::distr_param(1, 100));
LayerTypes<> fastLSTMLayer = new FastLSTM<>(randomInSize, randomOutSize);
size_t weightSize = boost::apply_visitor(WeightSizeVisitor(), fastLSTMLayer);
CheckCorrectnessOfWeightSize(fastLSTMLayer);
}
/**
* Test that WeightSizeVisitor works properly for Add layer.
*/
TEST_CASE("WeightSizeVisitorTestForAddLayer", "[ANNVisitorTest]")
{
size_t randomOutSize = arma::randi(arma::distr_param(1, 100));
LayerTypes<> addLayer = new Add<>(randomOutSize);
size_t weightSize = boost::apply_visitor(WeightSizeVisitor(), addLayer);
CheckCorrectnessOfWeightSize(addLayer);
}
/**
* Test that WeightSizeVisitor works properly for Atrous Convolution Layer.
*/
TEST_CASE("WeightSizeVisitorTestForAtrousConvolutionLayer", "[ANNVisitorTest]")
{
size_t randomInSize = arma::randi(arma::distr_param(1, 100));
size_t randomOutSize = arma::randi(arma::distr_param(1, 100));
size_t randomKernelWidth = arma::randi(arma::distr_param(1, 100));
size_t randomKernelHeight = arma::randi(arma::distr_param(1, 100));
LayerTypes<> atrousConvLayer = new AtrousConvolution<>(randomInSize,
randomOutSize, randomKernelWidth, randomKernelHeight);
size_t weightSize = boost::apply_visitor(WeightSizeVisitor(),
atrousConvLayer);
CheckCorrectnessOfWeightSize(atrousConvLayer);
}
src/mlpack/tests/catch.hpp
+5603 -1745
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File diff suppressed because it is too large Load Diff
src/mlpack/tests/distribution_test.cpp
+2 -1
View File
@@ -1518,7 +1518,8 @@ TEST_CASE("DiagonalGaussianUnbiasedEstimatorTest", "[DistributionTest]")
* the weighted mean and covariance reduce to the unweighted sample mean and
* covariance.
*/
TEST_CASE("DiagonalGaussianWeightedParametersReductionTest", "[DistributionTest]")
TEST_CASE("DiagonalGaussianWeightedParametersReductionTest",
"[DistributionTest]")
{
arma::vec mean("2.5 1.5 8.2 3.1");
arma::vec cov("1.2 3.1 8.3 4.3");
src/mlpack/tests/hoeffding_tree_test.cpp
+20 -10
View File
@@ -173,7 +173,8 @@ TEST_CASE("HoeffdingInformationGainBadSplitTest", "[HoeffdingTreeTest]")
counts(1, 0) = 5;
counts(1, 1) = 5;
REQUIRE(HoeffdingInformationGain::Evaluate(counts) == Approx(0.0).margin(1e-10));
REQUIRE(HoeffdingInformationGain::Evaluate(counts) ==
Approx(0.0).margin(1e-10));
}
/**
@@ -216,7 +217,8 @@ TEST_CASE("HoeffdingInformationGainZeroTest", "[HoeffdingTreeTest]")
// When nothing has been seen, the information gain should be zero.
arma::Mat<size_t> counts = arma::zeros<arma::Mat<size_t>>(10, 10);
REQUIRE(HoeffdingInformationGain::Evaluate(counts) == Approx(0.0).margin(1e-10));
REQUIRE(HoeffdingInformationGain::Evaluate(counts) ==
Approx(0.0).margin(1e-10));
}
/**
@@ -225,14 +227,22 @@ TEST_CASE("HoeffdingInformationGainZeroTest", "[HoeffdingTreeTest]")
*/
TEST_CASE("HoeffdingInformationGainRangeTest", "[HoeffdingTreeTest]")
{
REQUIRE(HoeffdingInformationGain::Range(1) == Approx(0).epsilon(1e-7));
REQUIRE(HoeffdingInformationGain::Range(2) == Approx(1.0).epsilon(1e-7));
REQUIRE(HoeffdingInformationGain::Range(3) == Approx(1.5849625).epsilon(1e-7));
REQUIRE(HoeffdingInformationGain::Range(4) == Approx(2).epsilon(1e-7));
REQUIRE(HoeffdingInformationGain::Range(5) == Approx(2.32192809).epsilon(1e-7));
REQUIRE(HoeffdingInformationGain::Range(10) == Approx(3.32192809).epsilon(1e-7));
REQUIRE(HoeffdingInformationGain::Range(100) == Approx(6.64385619).epsilon(1e-7));
REQUIRE(HoeffdingInformationGain::Range(1000) == Approx(9.96578428).epsilon(1e-7));
REQUIRE(HoeffdingInformationGain::Range(1) ==
Approx(0).epsilon(1e-7));
REQUIRE(HoeffdingInformationGain::Range(2) ==
Approx(1.0).epsilon(1e-7));
REQUIRE(HoeffdingInformationGain::Range(3) ==
Approx(1.5849625).epsilon(1e-7));
REQUIRE(HoeffdingInformationGain::Range(4) ==
Approx(2).epsilon(1e-7));
REQUIRE(HoeffdingInformationGain::Range(5) ==
Approx(2.32192809).epsilon(1e-7));
REQUIRE(HoeffdingInformationGain::Range(10) ==
Approx(3.32192809).epsilon(1e-7));
REQUIRE(HoeffdingInformationGain::Range(100) ==
Approx(6.64385619).epsilon(1e-7));
REQUIRE(HoeffdingInformationGain::Range(1000) ==
Approx(9.96578428).epsilon(1e-7));
}
/**
src/mlpack/tests/io_test.cpp
+2 -1
View File
@@ -795,7 +795,8 @@ TEST_CASE_METHOD(IOTestDestroyer, "OutputMatrixParamTest",
remove("test.csv");
}
TEST_CASE_METHOD(IOTestDestroyer, "OutputMatrixNoTransposeParamTest", "[IOTest]")
TEST_CASE_METHOD(IOTestDestroyer, "OutputMatrixNoTransposeParamTest",
"[IOTest]")
{
AddRequiredCLIOptions();
src/mlpack/tests/lin_alg_test.cpp
-1
View File
@@ -190,7 +190,6 @@ TEST_CASE("TestSvecSmat", "[LinAlgTest]")
for (size_t i = 0; i < 3; ++i)
for (size_t j = 0; j < 3; ++j)
REQUIRE(X(i, j) == Approx(Xtest(i, j)).epsilon(1e-9));
}
TEST_CASE("TestSparseSvec", "[LinAlgTest]")
src/mlpack/tests/lmnn_test.cpp
+14 -14
View File
@@ -120,7 +120,7 @@ TEST_CASE("LMNNInitialPointTest", "[LMNNTest]")
for (int col = 0; col < 5; col++)
{
if (row == col)
REQUIRE(initialPoint(row, col) == Approx( 1.0).epsilon(1e-7));
REQUIRE(initialPoint(row, col) == Approx(1.0).epsilon(1e-7));
else
REQUIRE(initialPoint(row, col) == Approx(0.0).margin(1e-5));
}
@@ -207,8 +207,8 @@ TEST_CASE("LMNNSeparableObjectiveTest", "[LMNNTest]")
// Result calculated by hand.
arma::mat coordinates = arma::eye<arma::mat>(2, 2);
REQUIRE(lmnnfn.Evaluate(coordinates, 0, 1) == Approx( 1.576).epsilon(1e-7));
REQUIRE(lmnnfn.Evaluate(coordinates, 1, 1) == Approx( 1.576).epsilon(1e-7));
REQUIRE(lmnnfn.Evaluate(coordinates, 0, 1) == Approx(1.576).epsilon(1e-7));
REQUIRE(lmnnfn.Evaluate(coordinates, 1, 1) == Approx(1.576).epsilon(1e-7));
REQUIRE(lmnnfn.Evaluate(coordinates, 2, 1) == Approx(1.576).epsilon(1e-7));
REQUIRE(lmnnfn.Evaluate(coordinates, 3, 1) == Approx(1.576).epsilon(1e-7));
REQUIRE(lmnnfn.Evaluate(coordinates, 4, 1) == Approx(1.576).epsilon(1e-7));
@@ -326,21 +326,21 @@ TEST_CASE("LMNNSeparableEvaluateWithGradientTest", "[LMNNTest]")
objective = lmnnfn.EvaluateWithGradient(coordinates, 4, gradient, 1);
REQUIRE(objective == Approx( 1.576).epsilon(1e-7));
REQUIRE(objective == Approx(1.576).epsilon(1e-7));
REQUIRE(gradient(0, 0) == Approx( -0.048).epsilon(1e-7));
REQUIRE(gradient(0, 1) == Approx( 0.0).epsilon(1e-7));
REQUIRE(gradient(1, 0) == Approx( 0.0).epsilon(1e-7));
REQUIRE(gradient(1, 1) == Approx( 2.0).epsilon(1e-7));
REQUIRE(gradient(0, 0) == Approx(-0.048).epsilon(1e-7));
REQUIRE(gradient(0, 1) == Approx(0.0).epsilon(1e-7));
REQUIRE(gradient(1, 0) == Approx(0.0).epsilon(1e-7));
REQUIRE(gradient(1, 1) == Approx(2.0).epsilon(1e-7));
objective = lmnnfn.EvaluateWithGradient(coordinates, 5, gradient, 1);
REQUIRE(objective == Approx( 1.576).epsilon(1e-7));
REQUIRE(objective == Approx(1.576).epsilon(1e-7));
REQUIRE(gradient(0, 0) == Approx( -0.048).epsilon(1e-7));
REQUIRE(gradient(0, 1) == Approx( 0.0).epsilon(1e-7));
REQUIRE(gradient(1, 0) == Approx( 0.0).epsilon(1e-7));
REQUIRE(gradient(1, 1) == Approx( 2.0).epsilon(1e-7));
REQUIRE(gradient(0, 0) == Approx(-0.048).epsilon(1e-7));
REQUIRE(gradient(0, 1) == Approx(0.0).epsilon(1e-7));
REQUIRE(gradient(1, 0) == Approx(0.0).epsilon(1e-7));
REQUIRE(gradient(1, 1) == Approx(2.0).epsilon(1e-7));
}
// Check that final objective value using SGD optimizer is optimal.
@@ -450,7 +450,7 @@ TEST_CASE("LMNNAccuracyTest", "[LMNNTest]")
REQUIRE(initAccuracy < finalAccuracy);
// Since this is a very simple dataset final accuracy should be around 100%.
REQUIRE(finalAccuracy == Approx( 100.0).epsilon(1e-7));
REQUIRE(finalAccuracy == Approx(100.0).epsilon(1e-7));
}
// Check that accuracy while learning square distance matrix is the same as when
src/mlpack/tests/loss_functions_test.cpp
+9 -6
View File
@@ -201,7 +201,7 @@ TEST_CASE("KLDivergenceMeanTest", "[LossFunctionsTest]")
target = arma::exp(arma::mat("2 1 1 1 1 1 1 1 1 1"));
loss = module.Forward(input, target);
REQUIRE(loss == Approx(-1.1 ).epsilon(1e-5));
REQUIRE(loss == Approx(-1.1).epsilon(1e-5));
// Test the Backward function.
module.Backward(input, target, output);
@@ -846,7 +846,8 @@ TEST_CASE("SoftMarginLossTest", "[LossFunctionsTest]")
// Test the Backward function.
module1.Backward(input, target, output);
REQUIRE(arma::as_scalar(arma::accu(output)) == Approx(-1.48227).epsilon(1e-3));
REQUIRE(arma::as_scalar(arma::accu(output)) ==
Approx(-1.48227).epsilon(1e-3));
REQUIRE(output.n_rows == input.n_rows);
REQUIRE(output.n_cols == input.n_cols);
CheckMatrices(output, expectedOutput, 0.1);
@@ -865,7 +866,8 @@ TEST_CASE("SoftMarginLossTest", "[LossFunctionsTest]")
// Test the Backward function.
module2.Backward(input, target, output);
REQUIRE(arma::as_scalar(arma::accu(output)) == Approx(-0.164697).epsilon(1e-3));
REQUIRE(arma::as_scalar(arma::accu(output)) ==
Approx(-0.164697).epsilon(1e-3));
REQUIRE(output.n_rows == input.n_rows);
REQUIRE(output.n_cols == input.n_cols);
CheckMatrices(output, expectedOutput, 0.1);
@@ -884,12 +886,13 @@ TEST_CASE("MeanAbsolutePercentageErrorTest", "[LossFunctionsTest]")
// Test the Forward function. Loss should be 95.625.
// Loss value calculated manually.
double loss = module.Forward(input,target);
REQUIRE(loss == Approx(95.625).epsilon(1e-1));
double loss = module.Forward(input, target);
REQUIRE(loss == Approx(95.625).epsilon(1e-1));
// Test the Backward function.
module.Backward(input, target, output);
REQUIRE(arma::as_scalar(arma::accu(output)) == Approx(-105.625).epsilon(1e-3));
REQUIRE(arma::as_scalar(arma::accu(output)) ==
Approx(-105.625).epsilon(1e-3));
REQUIRE(output.n_rows == input.n_rows);
REQUIRE(output.n_cols == input.n_cols);
CheckMatrices(output, expectedOutput, 0.1);
src/mlpack/tests/main_tests/kde_test.cpp
+5 -5
View File
@@ -87,7 +87,7 @@ TEST_CASE_METHOD(KDETestFixture, "KDEGaussianRTreeResultsMain",
// Check whether results are equal.
for (size_t i = 0; i < query.n_cols; ++i)
REQUIRE(kdeEstimations[i] == Approx( mainEstimations[i]).epsilon(relError));
REQUIRE(kdeEstimations[i] == Approx(mainEstimations[i]).epsilon(relError));
}
/**
@@ -128,7 +128,7 @@ TEST_CASE_METHOD(KDETestFixture, "KDETriangularBallTreeResultsMain",
// Check whether results are equal.
for (size_t i = 0; i < query.n_cols; ++i)
REQUIRE(kdeEstimations[i] == Approx( mainEstimations[i]).epsilon(relError));
REQUIRE(kdeEstimations[i] == Approx(mainEstimations[i]).epsilon(relError));
}
/**
@@ -170,7 +170,7 @@ TEST_CASE_METHOD(KDETestFixture, "KDEMonoResultsMain",
// Check whether results are equal.
for (size_t i = 0; i < reference.n_cols; ++i)
REQUIRE(kdeEstimations[i] == Approx( mainEstimations[i]).epsilon(relError));
REQUIRE(kdeEstimations[i] == Approx(mainEstimations[i]).epsilon(relError));
}
/**
@@ -239,7 +239,7 @@ TEST_CASE_METHOD(KDETestFixture, "KDEModelReuse",
// Check estimations are the same.
for (size_t i = 0; i < samples; ++i)
REQUIRE(oldEstimations[i] == Approx( newEstimations[i]).epsilon(relError));
REQUIRE(oldEstimations[i] == Approx(newEstimations[i]).epsilon(relError));
}
/**
@@ -282,7 +282,7 @@ TEST_CASE_METHOD(KDETestFixture, "KDEGaussianSingleKDTreeResultsMain",
// Check whether results are equal.
for (size_t i = 0; i < query.n_cols; ++i)
REQUIRE(kdeEstimations[i] == Approx( mainEstimations[i]).epsilon(relError));
REQUIRE(kdeEstimations[i] == Approx(mainEstimations[i]).epsilon(relError));
}
/**
src/mlpack/tests/main_tests/krann_test.cpp
+4 -2
View File
@@ -453,7 +453,8 @@ TEST_CASE_METHOD(KRANNTestFixture, "KRANNDifferentTreeType",
// Check that initial output matrices and the output matrices using
// saved model are equal
CHECK(output_model->TreeType() == 0);
const bool check = output_model->TreeType() == 0;
CHECK(check == true);
CHECK(IO::GetParam<RANNModel*>("output_model")->TreeType() ==
8);
delete output_model;
@@ -491,7 +492,8 @@ TEST_CASE_METHOD(KRANNTestFixture, "KRANNDifferentSingleSampleLimit",
// Check that initial output matrices and the output matrices using
// saved model are equal.
CHECK( IO::GetParam<RANNModel*>("output_model")->SingleSampleLimit() == (int) 15);
CHECK(IO::GetParam<RANNModel*>("output_model")->SingleSampleLimit() ==
(int) 15);
CHECK(output_model->SingleSampleLimit() == (int) 20);
delete output_model;
}
src/mlpack/tests/nbc_test.cpp
-1
View File
@@ -81,7 +81,6 @@ TEST_CASE("NaiveBayesClassifierTest", "[NBCTest]")
{
for (size_t j = 0; j < testResProbs.n_rows; ++j)
{
REQUIRE(testResProbs(j, i) + 0.0001 ==
Approx(calcProbs(j, i) + 0.0001).epsilon(0.0001));
}
src/mlpack/tests/pca_test.cpp
+4 -2
View File
@@ -316,7 +316,8 @@ TEST_CASE("PCAScalingTest", "[PCATest]")
// zero. There is noise, of course...
REQUIRE(std::abs(eigvec(0, 0)) == Approx(sqrt(2) / 2).epsilon(0.0035));
REQUIRE(std::abs(eigvec(1, 0)) == Approx(sqrt(2) / 2).epsilon(0.0035));
REQUIRE(eigvec(2, 0) == Approx(0.0).margin(0.1)); // Large tolerance for noise.
// Large tolerance for noise.
REQUIRE(eigvec(2, 0) == Approx(0.0).margin(0.1));
// The second component should be focused almost entirely in the third
// dimension.
@@ -328,7 +329,8 @@ TEST_CASE("PCAScalingTest", "[PCATest]")
// the first (plus tolerance).
REQUIRE(std::abs(eigvec(0, 0)) == Approx(sqrt(2) / 2).epsilon(0.0035));
REQUIRE(std::abs(eigvec(1, 0)) == Approx(sqrt(2) / 2).epsilon(0.0035));
REQUIRE(eigvec(2, 0) == Approx(0.0).margin(0.1)); // Large tolerance for noise.
// Large tolerance for noise.
REQUIRE(eigvec(2, 0) == Approx(0.0).margin(0.1));
// The eigenvalues should sum to three.
REQUIRE(accu(eigval) == Approx(3.0).epsilon(0.001));
src/mlpack/tests/range_search_test.cpp
+33 -17
View File
@@ -206,13 +206,20 @@ TEST_CASE("ExhaustiveSyntheticTest", "[RangeSearchTest]")
// Neighbors of point 10.
REQUIRE(sortedOutput[newFromOld[10]].size() == 4);
REQUIRE(sortedOutput[newFromOld[10]][0].second == newFromOld[9]);
REQUIRE(sortedOutput[newFromOld[10]][0].first == Approx(0.10).epsilon(1e-7));
REQUIRE(sortedOutput[newFromOld[10]][1].second == newFromOld[3]);
REQUIRE(sortedOutput[newFromOld[10]][1].first == Approx(0.25).epsilon(1e-7));
REQUIRE(sortedOutput[newFromOld[10]][2].second == newFromOld[8]);
REQUIRE(sortedOutput[newFromOld[10]][2].first == Approx(0.55).epsilon(1e-7));
REQUIRE(sortedOutput[newFromOld[10]][3].second == newFromOld[1]);
REQUIRE(sortedOutput[newFromOld[10]][3].first == Approx(0.65).epsilon(1e-7));
REQUIRE(sortedOutput[newFromOld[10]][0].first ==
Approx(0.10).epsilon(1e-7));
REQUIRE(sortedOutput[newFromOld[10]][1].second ==
newFromOld[3]);
REQUIRE(sortedOutput[newFromOld[10]][1].first ==
Approx(0.25).epsilon(1e-7));
REQUIRE(sortedOutput[newFromOld[10]][2].second ==
newFromOld[8]);
REQUIRE(sortedOutput[newFromOld[10]][2].first ==
Approx(0.55).epsilon(1e-7));
REQUIRE(sortedOutput[newFromOld[10]][3].second ==
newFromOld[1]);
REQUIRE(sortedOutput[newFromOld[10]][3].first ==
Approx(0.65).epsilon(1e-7));
// Now do it again with a different range: [sqrt(0.5) 1.0].
if (rs->ReferenceTree())
@@ -273,9 +280,11 @@ TEST_CASE("ExhaustiveSyntheticTest", "[RangeSearchTest]")
// Neighbors of point 10.
REQUIRE(sortedOutput[newFromOld[10]].size() == 2);
REQUIRE(sortedOutput[newFromOld[10]][0].second == newFromOld[2]);
REQUIRE(sortedOutput[newFromOld[10]][0].first == Approx(0.85).epsilon(1e-7));
REQUIRE(sortedOutput[newFromOld[10]][0].first ==
Approx(0.85).epsilon(1e-7));
REQUIRE(sortedOutput[newFromOld[10]][1].second == newFromOld[0]);
REQUIRE(sortedOutput[newFromOld[10]][1].first == Approx(0.95).epsilon(1e-7));
REQUIRE(sortedOutput[newFromOld[10]][1].first ==
Approx(0.95).epsilon(1e-7));
// Now do it again with a different range: [1.0 inf].
if (rs->ReferenceTree())
@@ -433,13 +442,20 @@ TEST_CASE("ExhaustiveSyntheticTest", "[RangeSearchTest]")
// Neighbors of point 10.
REQUIRE(sortedOutput[newFromOld[10]].size() == 4);
REQUIRE(sortedOutput[newFromOld[10]][0].second == newFromOld[5]);
REQUIRE(sortedOutput[newFromOld[10]][0].first == Approx(1.22).epsilon(1e-7));
REQUIRE(sortedOutput[newFromOld[10]][1].second == newFromOld[7]);
REQUIRE(sortedOutput[newFromOld[10]][1].first == Approx(2.30).epsilon(1e-7));
REQUIRE(sortedOutput[newFromOld[10]][2].second == newFromOld[6]);
REQUIRE(sortedOutput[newFromOld[10]][2].first == Approx(3.00).epsilon(1e-7));
REQUIRE(sortedOutput[newFromOld[10]][3].second == newFromOld[4]);
REQUIRE(sortedOutput[newFromOld[10]][3].first == Approx(4.05).epsilon(1e-7));
REQUIRE(sortedOutput[newFromOld[10]][0].first ==
Approx(1.22).epsilon(1e-7));
REQUIRE(sortedOutput[newFromOld[10]][1].second ==
newFromOld[7]);
REQUIRE(sortedOutput[newFromOld[10]][1].first ==
Approx(2.30).epsilon(1e-7));
REQUIRE(sortedOutput[newFromOld[10]][2].second ==
newFromOld[6]);
REQUIRE(sortedOutput[newFromOld[10]][2].first ==
Approx(3.00).epsilon(1e-7));
REQUIRE(sortedOutput[newFromOld[10]][3].second ==
newFromOld[4]);
REQUIRE(sortedOutput[newFromOld[10]][3].first ==
Approx(4.05).epsilon(1e-7));
// Clean the memory.
delete rs;
@@ -1042,7 +1058,7 @@ TEST_CASE("DualBallTreeTest2", "[RangeSearchTest]")
{
REQUIRE(kdSorted[i][j].second == ballSorted[i][j].second);
REQUIRE(kdSorted[i][j].first ==
Approx(ballSorted[i][j].first).epsilon (1e-7));
Approx(ballSorted[i][j].first).epsilon(1e-7));
}
}
}
src/mlpack/tests/rl_components_test.cpp
+1 -2
View File
@@ -196,7 +196,7 @@ TEST_CASE("DoublePoleCartTest", "[RLComponentsTest]")
}
/**
* Constructs a ContinuousDoublePoleCart instance and check if the main
* Constructs a ContinuousDoublePoleCart instance and check if the main
* routine works as it should be.
*/
TEST_CASE("ContinuousDoublePoleCartTest", "[RLComponentsTest]")
@@ -285,5 +285,4 @@ TEST_CASE("GreedyPolicyTest", "[RLComponentsTest]")
CartPole::Action action = policy.Sample(actionValue);
REQUIRE(actionValue[action.action] ==
Approx(actionValue.max()).epsilon(1e-7));
}
src/mlpack/tests/sort_policy_test.cpp
+2 -2
View File
@@ -107,7 +107,7 @@ TEST_CASE("NnsNodeToNodeDistance", "[SortPolicyTest]")
utility[0] = 0.5;
nodeTwo.Bound() |= utility;
REQUIRE(NearestNeighborSort::BestNodeToNodeDistance(&nodeOne, &nodeTwo) ==
REQUIRE(NearestNeighborSort::BestNodeToNodeDistance(&nodeOne, &nodeTwo) ==
Approx(0.0).margin(1e-5));
}
@@ -146,7 +146,7 @@ TEST_CASE("NnsPointToNodeDistance", "[SortPolicyTest]")
// And now when the point is inside the bound.
point[0] = 0.5;
REQUIRE(NearestNeighborSort::BestPointToNodeDistance(point, &node) ==
REQUIRE(NearestNeighborSort::BestPointToNodeDistance(point, &node) ==
Approx(0.0).margin(1e-5));
}
src/mlpack/tests/string_encoding_test.cpp
+26 -14
View File
@@ -269,7 +269,8 @@ TEST_CASE("DictionaryEncodingIndividualCharactersTest", "[StringEncodingTest]")
* Test the one pass modification of the dictionary encoding algorithm
* in case of individual character encoding.
*/
TEST_CASE("OnePassDictionaryEncodingIndividualCharactersTest", "[StringEncodingTest]")
TEST_CASE("OnePassDictionaryEncodingIndividualCharactersTest",
"[StringEncodingTest]")
{
std::vector<string> input = {
"GACCA",
@@ -542,7 +543,7 @@ TEST_CASE("CharExtractDictionaryEncodingSerialization", "[StringEncodingTest]")
/**
* Test the Bag of Words encoding algorithm.
*/
*/
TEST_CASE("BagOfWordsEncodingTest", "[StringEncodingTest]")
{
using DictionaryType = StringEncodingDictionary<boost::string_view>;
@@ -617,7 +618,7 @@ TEST_CASE("BagOfWordsEncodingTest", "[StringEncodingTest]")
/**
* Test the Bag of Words encoding algorithm. The output is saved into a vector.
*/
*/
TEST_CASE("VectorBagOfWordsEncodingTest", "[StringEncodingTest]")
{
using DictionaryType = StringEncodingDictionary<boost::string_view>;
@@ -684,7 +685,8 @@ TEST_CASE("BagOfWordsEncodingIndividualCharactersTest", "[StringEncodingTest]")
* Test the Bag of Words encoding algorithm in case of individual
* characters encoding. The output type is vector<vector<size_t>>.
*/
TEST_CASE("VectorBagOfWordsEncodingIndividualCharactersTest", "[StringEncodingTest]")
TEST_CASE("VectorBagOfWordsEncodingIndividualCharactersTest",
"[StringEncodingTest]")
{
std::vector<string> input = {
"GACCA",
@@ -861,7 +863,8 @@ TEST_CASE("VectorRawCountSmoothIdfEncodingTest", "[StringEncodingTest]")
* raw count term frequency type and the smooth inverse document frequency type.
* These parameters are the default ones.
*/
TEST_CASE("RawCountSmoothIdfEncodingIndividualCharactersTest", "[StringEncodingTest]")
TEST_CASE("RawCountSmoothIdfEncodingIndividualCharactersTest",
"[StringEncodingTest]")
{
vector<string> input = {
"GACCA",
@@ -942,7 +945,8 @@ TEST_CASE("RawCountSmoothIdfEncodingIndividualCharactersTest", "[StringEncodingT
* These parameters are the default ones. The output type is
* vector<vector<double>>.
*/
TEST_CASE("VectorRawCountSmoothIdfEncodingIndividualCharactersTest", "[StringEncodingTest]")
TEST_CASE("VectorRawCountSmoothIdfEncodingIndividualCharactersTest",
"[StringEncodingTest]")
{
std::vector<string> input = {
"GACCA",
@@ -1068,7 +1072,8 @@ TEST_CASE("VectorTfIdfRawCountEncodingTest", "[StringEncodingTest]")
* raw count term frequency type and the non-smooth inverse document frequency
* type.
*/
TEST_CASE("RawCountTfIdfEncodingIndividualCharactersTest", "[StringEncodingTest]")
TEST_CASE("RawCountTfIdfEncodingIndividualCharactersTest",
"[StringEncodingTest]")
{
vector<string> input = {
"GACCA",
@@ -1099,7 +1104,8 @@ TEST_CASE("RawCountTfIdfEncodingIndividualCharactersTest", "[StringEncodingTest]
* raw count term frequency type and the non-smooth inverse document frequency
* type. The output type is vector<vector<double>>.
*/
TEST_CASE("VectorRawCountTfIdfEncodingIndividualCharactersTest", "[StringEncodingTest]")
TEST_CASE("VectorRawCountTfIdfEncodingIndividualCharactersTest",
"[StringEncodingTest]")
{
std::vector<string> input = {
"GACCA",
@@ -1129,7 +1135,8 @@ TEST_CASE("VectorRawCountTfIdfEncodingIndividualCharactersTest", "[StringEncodin
* Test the Tf-Idf encoding algorithm for individual characters with the
* binary term frequency type and the smooth inverse document frequency type.
*/
TEST_CASE("BinarySmoothIdfEncodingIndividualCharactersTest", "[StringEncodingTest]")
TEST_CASE("BinarySmoothIdfEncodingIndividualCharactersTest",
"[StringEncodingTest]")
{
vector<string> input = {
"GACCA",
@@ -1160,7 +1167,8 @@ TEST_CASE("BinarySmoothIdfEncodingIndividualCharactersTest", "[StringEncodingTes
* binary term frequency type and the smooth inverse document frequency type.
* The output type is vector<vector<double>>.
*/
TEST_CASE("VectorBinarySmoothIdfEncodingIndividualCharactersTest", "[StringEncodingTest]")
TEST_CASE("VectorBinarySmoothIdfEncodingIndividualCharactersTest",
"[StringEncodingTest]")
{
std::vector<string> input = {
"GACCA",
@@ -1222,7 +1230,8 @@ TEST_CASE("BinaryTfIdfEncodingIndividualCharactersTest", "[StringEncodingTest]")
* sublinear term frequency type and the smooth inverse document frequency
* type.
*/
TEST_CASE("SublinearSmoothIdfEncodingIndividualCharactersTest", "[StringEncodingTest]")
TEST_CASE("SublinearSmoothIdfEncodingIndividualCharactersTest",
"[StringEncodingTest]")
{
vector<string> input = {
"GACCA",
@@ -1254,7 +1263,8 @@ TEST_CASE("SublinearSmoothIdfEncodingIndividualCharactersTest", "[StringEncoding
* sublinear term frequency type and the non-smooth inverse document frequency
* type.
*/
TEST_CASE("SublinearTfIdfEncodingIndividualCharactersTest", "[StringEncodingTest]")
TEST_CASE("SublinearTfIdfEncodingIndividualCharactersTest",
"[StringEncodingTest]")
{
vector<string> input = {
"GACCA",
@@ -1286,7 +1296,8 @@ TEST_CASE("SublinearTfIdfEncodingIndividualCharactersTest", "[StringEncodingTest
* standard term frequency type and the smooth inverse document frequency
* type.
*/
TEST_CASE("TermFrequencySmoothIdfEncodingIndividualCharactersTest", "[StringEncodingTest]")
TEST_CASE("TermFrequencySmoothIdfEncodingIndividualCharactersTest",
"[StringEncodingTest]")
{
vector<string> input = {
"GACCA",
@@ -1367,7 +1378,8 @@ TEST_CASE("TermFrequencySmoothIdfEncodingIndividualCharactersTest", "[StringEnco
* standard term frequency type and the non-smooth inverse document frequency
* type.
*/
TEST_CASE("TermFrequencyTfIdfEncodingIndividualCharactersTest", "[StringEncodingTest]")
TEST_CASE("TermFrequencyTfIdfEncodingIndividualCharactersTest",
"[StringEncodingTest]")
{
vector<string> input = {
"GACCA",