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Change from to From in tests and core

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Signed-off-by: Omar Shrit <omar@avontech.fr>
This commit is contained in:
Omar Shrit
2024-02-11 16:33:32 +01:00
parent e88c62355a
commit 4f77f2feda
8 changed files with 45 additions and 45 deletions
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src/mlpack/core/util/conv_to.hpp
+2 -2
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@@ -35,7 +35,7 @@ class ConvTo
* @param input The input that is converted.
*/
template<typename InputType>
inline static OutputType from(const InputType& input,
inline static OutputType From(const InputType& input,
const typename std::enable_if_t<
coot::is_coot_type<InputType>::value ||
coot::is_coot_type<OutputType>::value>* = 0)
@@ -51,7 +51,7 @@ class ConvTo
* @param input The input that is converted.
*/
template<typename InputType>
inline static OutputType from(const InputType& input,
inline static OutputType From(const InputType& input,
const typename std::enable_if_t<
arma::is_arma_type<InputType>::value ||
arma::is_arma_type<OutputType>::value>* = 0)
src/mlpack/tests/ann/not_adapted/ann_layer_test.cpp
+1 -1
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@@ -1791,7 +1791,7 @@ TEST_CASE("SimpleLookupLayerTest", "[ANNLayerTest]")
{
// The Lookup module uses index - 1 for the cols.
const double outputSum = arma::accu(module.Parameters().cols(
ConvTo<arma::uvec>::from(input.col(i)) - 1));
ConvTo<arma::uvec>::From(input.col(i)) - 1));
REQUIRE(std::fabs(outputSum - arma::accu(output.col(i))) <= 1e-5);
}
src/mlpack/tests/image_load_test.cpp
+1 -1
View File
@@ -115,7 +115,7 @@ TEST_CASE("SaveImageMatAPITest", "[ImageLoadTest]")
arma::Mat<unsigned char> im1;
size_t dimension = info.Width() * info.Height() * info.Channels();
im1 = arma::randi<arma::Mat<unsigned char>>(dimension, 1);
arma::mat input = ConvTo<arma::mat>::from(im1);
arma::mat input = ConvTo<arma::mat>::From(im1);
REQUIRE(Save("APITest.bmp", input, info, false) == true);
arma::mat output;
src/mlpack/tests/linear_svm_test.cpp
+20 -20
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@@ -930,27 +930,27 @@ TEMPLATE_TEST_CASE("LinearSVMLBFGSMultipleClasses", "[LinearSVMTest]", float,
{
for (size_t i = 0; i < points / 5; ++i)
{
data.col(i) = ConvTo<VecType>::from(g1.Random());
data.col(i) = ConvTo<VecType>::From(g1.Random());
labels(i) = 0;
}
for (size_t i = points / 5; i < (2 * points) / 5; ++i)
{
data.col(i) = ConvTo<VecType>::from(g2.Random());
data.col(i) = ConvTo<VecType>::From(g2.Random());
labels(i) = 1;
}
for (size_t i = (2 * points) / 5; i < (3 * points) / 5; ++i)
{
data.col(i) = ConvTo<VecType>::from(g3.Random());
data.col(i) = ConvTo<VecType>::From(g3.Random());
labels(i) = 2;
}
for (size_t i = (3 * points) / 5; i < (4 * points) / 5; ++i)
{
data.col(i) = ConvTo<VecType>::from(g4.Random());
data.col(i) = ConvTo<VecType>::From(g4.Random());
labels(i) = 3;
}
for (size_t i = (4 * points) / 5; i < points; ++i)
{
data.col(i) = ConvTo<VecType>::from(g5.Random());
data.col(i) = ConvTo<VecType>::From(g5.Random());
labels(i) = 4;
}
@@ -965,27 +965,27 @@ TEMPLATE_TEST_CASE("LinearSVMLBFGSMultipleClasses", "[LinearSVMTest]", float,
// Create test dataset.
for (size_t i = 0; i < points / 5; ++i)
{
data.col(i) = ConvTo<VecType>::from(g1.Random());
data.col(i) = ConvTo<VecType>::From(g1.Random());
labels(i) = 0;
}
for (size_t i = points / 5; i < (2 * points) / 5; ++i)
{
data.col(i) = ConvTo<VecType>::from(g2.Random());
data.col(i) = ConvTo<VecType>::From(g2.Random());
labels(i) = 1;
}
for (size_t i = (2 * points) / 5; i < (3 * points) / 5; ++i)
{
data.col(i) = ConvTo<VecType>::from(g3.Random());
data.col(i) = ConvTo<VecType>::From(g3.Random());
labels(i) = 2;
}
for (size_t i = (3 * points) / 5; i < (4 * points) / 5; ++i)
{
data.col(i) = ConvTo<VecType>::from(g4.Random());
data.col(i) = ConvTo<VecType>::From(g4.Random());
labels(i) = 3;
}
for (size_t i = (4 * points) / 5; i < points; ++i)
{
data.col(i) = ConvTo<VecType>::from(g5.Random());
data.col(i) = ConvTo<VecType>::From(g5.Random());
labels(i) = 4;
}
@@ -1029,27 +1029,27 @@ TEMPLATE_TEST_CASE("LinearSVMClassifySinglePointTest", "[LinearSVMTest]", float,
for (size_t i = 0; i < points / 5; ++i)
{
data.col(i) = ConvTo<VecType>::from(g1.Random());
data.col(i) = ConvTo<VecType>::From(g1.Random());
labels(i) = 0;
}
for (size_t i = points / 5; i < (2 * points) / 5; ++i)
{
data.col(i) = ConvTo<VecType>::from(g2.Random());
data.col(i) = ConvTo<VecType>::From(g2.Random());
labels(i) = 1;
}
for (size_t i = (2 * points) / 5; i < (3 * points) / 5; ++i)
{
data.col(i) = ConvTo<VecType>::from(g3.Random());
data.col(i) = ConvTo<VecType>::From(g3.Random());
labels(i) = 2;
}
for (size_t i = (3 * points) / 5; i < (4 * points) / 5; ++i)
{
data.col(i) = ConvTo<VecType>::from(g4.Random());
data.col(i) = ConvTo<VecType>::From(g4.Random());
labels(i) = 3;
}
for (size_t i = (4 * points) / 5; i < points; ++i)
{
data.col(i) = ConvTo<VecType>::from(g5.Random());
data.col(i) = ConvTo<VecType>::From(g5.Random());
labels(i) = 4;
}
@@ -1059,27 +1059,27 @@ TEMPLATE_TEST_CASE("LinearSVMClassifySinglePointTest", "[LinearSVMTest]", float,
// Create test dataset.
for (size_t i = 0; i < points / 5; ++i)
{
data.col(i) = ConvTo<VecType>::from(g1.Random());
data.col(i) = ConvTo<VecType>::From(g1.Random());
labels(i) = 0;
}
for (size_t i = points / 5; i < (2 * points) / 5; ++i)
{
data.col(i) = ConvTo<VecType>::from(g2.Random());
data.col(i) = ConvTo<VecType>::From(g2.Random());
labels(i) = 1;
}
for (size_t i = (2 * points) / 5; i < (3 * points) / 5; ++i)
{
data.col(i) = ConvTo<VecType>::from(g3.Random());
data.col(i) = ConvTo<VecType>::From(g3.Random());
labels(i) = 2;
}
for (size_t i = (3 * points) / 5; i < (4 * points) / 5; ++i)
{
data.col(i) = ConvTo<VecType>::from(g4.Random());
data.col(i) = ConvTo<VecType>::From(g4.Random());
labels(i) = 3;
}
for (size_t i = (4 * points) / 5; i < points; ++i)
{
data.col(i) = ConvTo<VecType>::from(g5.Random());
data.col(i) = ConvTo<VecType>::From(g5.Random());
labels(i) = 4;
}
src/mlpack/tests/lmnn_test.cpp
+1 -1
View File
@@ -411,7 +411,7 @@ double KnnAccuracy(const arma::mat& dataset,
Map(labels(neighbors(j, i))) +=
1 / std::pow(distances(j, i) + 1, 2);
size_t index = ConvTo<size_t>::from(arma::find(Map
size_t index = ConvTo<size_t>::From(arma::find(Map
== arma::max(Map)));
// Increase count if labels match.
src/mlpack/tests/main_tests/hmm_test_utils.hpp
+1 -1
View File
@@ -49,7 +49,7 @@ struct InitHMMModel
++it)
{
arma::Col<size_t> maxSeqs =
ConvTo<arma::Col<size_t>>::from(arma::max(*it, 1)) + 1;
ConvTo<arma::Col<size_t>>::From(arma::max(*it, 1)) + 1;
maxEmissions = arma::max(maxEmissions, maxSeqs);
}
src/mlpack/tests/main_tests/image_converter_test.cpp
+5 -5
View File
@@ -44,7 +44,7 @@ TEST_CASE_METHOD(ImageConverterTestFixture, "LoadImageTest",
TEST_CASE_METHOD(ImageConverterTestFixture, "SaveImageTest",
"[ImageConverterMainTest][BindingTests]")
{
arma::mat testimage = ConvTo<arma::mat>::from(
arma::mat testimage = ConvTo<arma::mat>::From(
arma::randi<arma::Mat<unsigned char>>((5 * 5 * 3), 2));
SetInputParam<vector<string>>("input", {"test_image777.png",
"test_image999.png"});
@@ -81,7 +81,7 @@ TEST_CASE_METHOD(ImageConverterTestFixture, "SaveImageTest",
TEST_CASE_METHOD(ImageConverterTestFixture, "IncompleteTest",
"[ImageConverterMainTest][BindingTests]")
{
arma::mat testimage = ConvTo<arma::mat>::from(
arma::mat testimage = ConvTo<arma::mat>::From(
arma::randi<arma::Mat<unsigned char>>((5 * 5 * 3), 2));
SetInputParam<vector<string>>("input", {"test_image777.png",
"test_image999.png"});
@@ -99,7 +99,7 @@ TEST_CASE_METHOD(ImageConverterTestFixture, "IncompleteTest",
TEST_CASE_METHOD(ImageConverterTestFixture, "InvalidInputTest",
"[ImageConverterMainTest][BindingTests]")
{
arma::mat testimage = ConvTo<arma::mat>::from(
arma::mat testimage = ConvTo<arma::mat>::From(
arma::randi<arma::Mat<unsigned char>>((5 * 5 * 3), 2));
SetInputParam<vector<string>>("input", {"test_image777.png",
"test_image999.png"});
@@ -119,7 +119,7 @@ TEST_CASE_METHOD(ImageConverterTestFixture, "InvalidInputTest",
TEST_CASE_METHOD(ImageConverterTestFixture, "InvalidWidthTest",
"[ImageConverterMainTest][BindingTests]")
{
arma::mat testimage = ConvTo<arma::mat>::from(
arma::mat testimage = ConvTo<arma::mat>::From(
arma::randi<arma::Mat<unsigned char>>((5 * 5 * 3), 2));
SetInputParam<vector<string>>("input", {"test_image777.png",
"test_image999.png"});
@@ -138,7 +138,7 @@ TEST_CASE_METHOD(ImageConverterTestFixture, "InvalidWidthTest",
TEST_CASE_METHOD(ImageConverterTestFixture, "InvalidChannelTest",
"[ImageConverterMainTest][BindingTests]")
{
arma::mat testimage = ConvTo<arma::mat>::from(
arma::mat testimage = ConvTo<arma::mat>::From(
arma::randi<arma::Mat<unsigned char>>((5 * 5 * 3), 2));
SetInputParam<vector<string>>("input", {"test_image777.png",
"test_image999.png"});
src/mlpack/tests/softmax_regression_test.cpp
+14 -14
View File
@@ -232,12 +232,12 @@ TEMPLATE_TEST_CASE("SoftmaxRegressionFitIntercept", "[SoftmaxRegressionTest]",
arma::Row<size_t> responses(1000);
for (size_t i = 0; i < 500; ++i)
{
data.col(i) = ConvTo<MatType>::from(g1.Random());
data.col(i) = ConvTo<MatType>::From(g1.Random());
responses[i] = 0;
}
for (size_t i = 500; i < 1000; ++i)
{
data.col(i) = ConvTo<MatType>::from(g2.Random());
data.col(i) = ConvTo<MatType>::From(g2.Random());
responses[i] = 1;
}
@@ -251,12 +251,12 @@ TEMPLATE_TEST_CASE("SoftmaxRegressionFitIntercept", "[SoftmaxRegressionTest]",
// Create a test set.
for (size_t i = 0; i < 500; ++i)
{
data.col(i) = ConvTo<MatType>::from(g1.Random());
data.col(i) = ConvTo<MatType>::From(g1.Random());
responses[i] = 0;
}
for (size_t i = 500; i < 1000; ++i)
{
data.col(i) = ConvTo<MatType>::from(g2.Random());
data.col(i) = ConvTo<MatType>::From(g2.Random());
responses[i] = 1;
}
@@ -289,27 +289,27 @@ TEMPLATE_TEST_CASE("SoftmaxRegressionMultipleClasses",
for (size_t i = 0; i < points / 5; ++i)
{
// TODO: when GaussianDistribution is templatized, remove the conv_to.
data.col(i) = ConvTo<MatType>::from(g1.Random());
data.col(i) = ConvTo<MatType>::From(g1.Random());
labels(i) = 0;
}
for (size_t i = points / 5; i < (2 * points) / 5; ++i)
{
data.col(i) = ConvTo<MatType>::from(g2.Random());
data.col(i) = ConvTo<MatType>::From(g2.Random());
labels(i) = 1;
}
for (size_t i = (2 * points) / 5; i < (3 * points) / 5; ++i)
{
data.col(i) = ConvTo<MatType>::from(g3.Random());
data.col(i) = ConvTo<MatType>::From(g3.Random());
labels(i) = 2;
}
for (size_t i = (3 * points) / 5; i < (4 * points) / 5; ++i)
{
data.col(i) = ConvTo<MatType>::from(g4.Random());
data.col(i) = ConvTo<MatType>::From(g4.Random());
labels(i) = 3;
}
for (size_t i = (4 * points) / 5; i < points; ++i)
{
data.col(i) = ConvTo<MatType>::from(g5.Random());
data.col(i) = ConvTo<MatType>::From(g5.Random());
labels(i) = 4;
}
@@ -323,27 +323,27 @@ TEMPLATE_TEST_CASE("SoftmaxRegressionMultipleClasses",
// Create test dataset.
for (size_t i = 0; i < points / 5; ++i)
{
data.col(i) = ConvTo<MatType>::from(g1.Random());
data.col(i) = ConvTo<MatType>::From(g1.Random());
labels(i) = 0;
}
for (size_t i = points / 5; i < (2 * points) / 5; ++i)
{
data.col(i) = ConvTo<MatType>::from(g2.Random());
data.col(i) = ConvTo<MatType>::From(g2.Random());
labels(i) = 1;
}
for (size_t i = (2 * points) / 5; i < (3 * points) / 5; ++i)
{
data.col(i) = ConvTo<MatType>::from(g3.Random());
data.col(i) = ConvTo<MatType>::From(g3.Random());
labels(i) = 2;
}
for (size_t i = (3 * points) / 5; i < (4 * points) / 5; ++i)
{
data.col(i) = ConvTo<MatType>::from(g4.Random());
data.col(i) = ConvTo<MatType>::From(g4.Random());
labels(i) = 3;
}
for (size_t i = (4 * points) / 5; i < points; ++i)
{
data.col(i) = ConvTo<MatType>::from(g5.Random());
data.col(i) = ConvTo<MatType>::From(g5.Random());
labels(i) = 4;
}