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Merge remote-tracking branch 'origin/master' into knn-kfn-binding-load-output-fix

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This commit is contained in:
Ryan Curtin
2024-12-04 09:51:57 -05:00
parent de0ba147e4 96fd2ad2b2
commit a4b32ec328
19 changed files with 42 additions and 54 deletions
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COPYRIGHT.txt
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@@ -83,7 +83,7 @@ Copyright:
Copyright 2017, Samikshya Chand <samikshya289@gmail.com>
Copyright 2017, N Rajiv Vaidyanathan <rajivvaidyanathan4@gmail.com>
Copyright 2017, Kartik Nighania <kartiknighania@gmail.com>
Copyright 2017-2023, Dirk Eddelbuettel <edd@debian.org>
Copyright 2017-2024, Dirk Eddelbuettel <edd@debian.org>
Copyright 2017-2018, Eugene Freyman <evg.freyman@gmail.com>
Copyright 2017-2019, Manish Kumar <manish887kr@gmail.com>
Copyright 2017-2018, Haritha Sreedharan Nair <haritha1313@gmail.com>
@@ -151,7 +151,7 @@ Copyright:
Copyright 2021, Roshan Nrusing Swain <swainroshan001@gmail.com>
Copyright 2021, Suvarsha Chennareddy <suvarshachennareddy@gmail.com>
Copyright 2021, Shubham Agrawal <shubham.agra1206@gmail.com>
Copyright 2020-2022, James Joseph Balamuta <balamut2@illinois.edu>
Copyright 2020-2024, James Balamuta <james.balamuta@gmail.com>
Copyright 2022, Sri Madhan M <srimadhan11@gmail.com>
Copyright 2022, Zhuojin Liu <zhuojinliu.cs@gmail.com>
Copyright 2022, Richèl Bilderbeek <richel@richelbilderbeek.nl>
doc/developer/community.md
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@@ -12,9 +12,9 @@ information, see [this page](gsoc.md).
All mlpack development is done on [GitHub](https://github.com/mlpack/mlpack).
Commits and issue comments can be tracked via the
[mlpack-git](https://freelists.org/list/mlpack-git) list (graciously hosted by
[FreeLists](https://freelists.org). Communication is generally either via
issues on GitHub, or via chat:
[mlpack-git](https://www.freelists.org/list/mlpack-git) list (graciously hosted
by [FreeLists](https://www.freelists.org). Communication is generally either
via issues on GitHub, or via chat:
## Real-time chat
doc/developer/gsoc.md
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@@ -51,7 +51,7 @@ project. A student should ideally be familiar with
mlpack are SFINAE ([example in mlpack, see std::enable_if usages](https://github.com/mlpack/mlpack/blob/565cfd3aad22deec0656b86e801052593a937723/src/mlpack/methods/mean_shift/mean_shift.hpp)),
[policy-based design](https://www.drdobbs.com/policy-based-design-in-the-real-world/184401861),
and [compile-time class traits](https://accu.org/index.php/journals/442).
Here are some [other useful resources](https://www.codeproject.com/Articles/3743/A-gentle-introduction-to-Template-Metaprogramming)
Here are some [other useful resources](https://en.wikipedia.org/wiki/Template_metaprogramming)
for learning template metaprogramming, and some useful
[reference books](https://www.aristeia.com/books.html).
If some of this sounds new to you, don’t feel overwhelmed; it’s not a
src/mlpack/bindings/R/mlpack/src/Makevars
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@@ -1,2 +1,3 @@
CXX_STD = CXX17
PKG_CXXFLAGS = -I. -I../inst/include $(SHLIB_OPENMP_CXXFLAGS)
PKG_LIBS = $(SHLIB_OPENMP_CXXFLAGS) $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)
src/mlpack/methods/adaboost/adaboost_impl.hpp
+2 -3
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@@ -163,9 +163,8 @@ void AdaBoost<WeakLearnerType, MatType>::Classify(
probabilities(prediction) += alpha[i];
}
arma::uword maxIndex = 0;
probabilities /= accu(probabilities);
probabilities.max(maxIndex);
arma::uword maxIndex = probabilities.index_max();
prediction = (size_t) maxIndex;
}
@@ -204,7 +203,7 @@ void AdaBoost<WeakLearnerType, MatType>::Classify(
for (size_t i = 0; i < predictedLabels.n_cols; ++i)
{
probabilities.col(i) /= accu(probabilities.col(i));
probabilities.col(i).max(maxIndex);
maxIndex = probabilities.col(i).index_max();
predictedLabels(i) = maxIndex;
}
}
src/mlpack/methods/ann/loss_functions/vr_class_reward_impl.hpp
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@@ -49,7 +49,7 @@ typename MatType::elem_type VRClassRewardType<MatType>::Forward(
for (size_t i = 0; i < input.n_cols - 1; ++i)
{
input.unsafe_col(i).max(index);
index = input.unsafe_col(i).index_max();
reward = (index == target(i)) * scale;
}
src/mlpack/methods/approx_kfn/drusilla_select_impl.hpp
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@@ -94,8 +94,7 @@ void DrusillaSelect<MatType>::Train(
for (size_t i = 0; i < l; ++i)
{
// Pick best index.
arma::uword maxIndex = 0;
norms.max(maxIndex);
arma::uword maxIndex = norms.index_max();
arma::vec line(refCopy.col(maxIndex) / norm(refCopy.col(maxIndex)));
src/mlpack/methods/decision_tree/decision_tree_impl.hpp
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@@ -1159,8 +1159,7 @@ void DecisionTree<FitnessFunction,
// Now normalize into probabilities.
classProbabilities /= UseWeights ? sumWeights : labels.n_elem;
arma::uword maxIndex = 0;
classProbabilities.max(maxIndex);
arma::uword maxIndex = classProbabilities.index_max();
majorityClass = (size_t) maxIndex;
}
src/mlpack/methods/hmm/hmm_impl.hpp
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@@ -528,13 +528,14 @@ double HMM<Distribution>::Predict(const arma::mat& dataSeq,
for (size_t j = 0; j < logTransition.n_rows; j++)
{
arma::vec prob = logStateProb.col(t - 1) + logTransition.row(j).t();
logStateProb(j, t) = prob.max(index) + logProbs(t, j);
index = prob.index_max();
logStateProb(j, t) = prob[index] + logProbs(t, j);
stateSeqBack(j, t) = index;
}
}
// Backtrack to find the most probable state sequence.
logStateProb.unsafe_col(dataSeq.n_cols - 1).max(index);
index = logStateProb.unsafe_col(dataSeq.n_cols - 1).index_max();
stateSeq[dataSeq.n_cols - 1] = index;
for (size_t t = 2; t <= dataSeq.n_cols; t++)
{
src/mlpack/methods/hoeffding_trees/binary_numeric_split_impl.hpp
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@@ -141,10 +141,9 @@ void BinaryNumericSplit<FitnessFunction, ObservationType>::Split(
}
// Calculate the majority classes of the children.
arma::uword maxIndex;
counts.unsafe_col(0).max(maxIndex);
arma::uword maxIndex = counts.unsafe_col(0).index_max();
childMajorities[0] = size_t(maxIndex);
counts.unsafe_col(1).max(maxIndex);
maxIndex = counts.unsafe_col(1).index_max();
childMajorities[1] = size_t(maxIndex);
// Create the according SplitInfo object.
@@ -155,8 +154,7 @@ template<typename FitnessFunction, typename ObservationType>
size_t BinaryNumericSplit<FitnessFunction, ObservationType>::MajorityClass()
const
{
arma::uword maxIndex;
classCounts.max(maxIndex);
arma::uword maxIndex = classCounts.index_max();
return size_t(maxIndex);
}
src/mlpack/methods/hoeffding_trees/hoeffding_categorical_split_impl.hpp
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@@ -64,8 +64,7 @@ void HoeffdingCategoricalSplit<FitnessFunction>::Split(
childMajorities.set_size(sufficientStatistics.n_cols);
for (size_t i = 0; i < sufficientStatistics.n_cols; ++i)
{
arma::uword maxIndex = 0;
sufficientStatistics.unsafe_col(i).max(maxIndex);
arma::uword maxIndex = sufficientStatistics.unsafe_col(i).index_max();
childMajorities[i] = size_t(maxIndex);
}
@@ -79,8 +78,7 @@ size_t HoeffdingCategoricalSplit<FitnessFunction>::MajorityClass() const
// Calculate the class that we have seen the most of.
arma::Col<size_t> classCounts = sum(sufficientStatistics, 1);
arma::uword maxIndex = 0;
classCounts.max(maxIndex);
arma::uword maxIndex = classCounts.index_max();
return size_t(maxIndex);
}
src/mlpack/methods/hoeffding_trees/hoeffding_numeric_split_impl.hpp
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@@ -122,8 +122,7 @@ void HoeffdingNumericSplit<FitnessFunction, ObservationType>::Split(
childMajorities.set_size(sufficientStatistics.n_cols);
for (size_t i = 0; i < sufficientStatistics.n_cols; ++i)
{
arma::uword maxIndex = 0;
sufficientStatistics.unsafe_col(i).max(maxIndex);
arma::uword maxIndex = sufficientStatistics.unsafe_col(i).index_max();
childMajorities[i] = size_t(maxIndex);
}
@@ -144,8 +143,7 @@ size_t HoeffdingNumericSplit<FitnessFunction, ObservationType>::
for (size_t i = 0; i < samplesSeen; ++i)
classes[labels[i]]++;
arma::uword majorityClass;
classes.max(majorityClass);
arma::uword majorityClass = classes.index_max();
return size_t(majorityClass);
}
else
@@ -154,8 +152,7 @@ size_t HoeffdingNumericSplit<FitnessFunction, ObservationType>::
// statistics.
arma::Col<size_t> classCounts = sum(sufficientStatistics, 1);
arma::uword maxIndex = 0;
classCounts.max(maxIndex);
arma::uword maxIndex = classCounts.index_max();
return size_t(maxIndex);
}
}
src/mlpack/methods/kmeans/max_variance_new_cluster_impl.hpp
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@@ -35,8 +35,7 @@ void MaxVarianceNewCluster::EmptyCluster(const MatType& data,
this->iteration = iteration;
// Now find the cluster with maximum variance.
arma::uword maxVarCluster = 0;
variances.max(maxVarCluster);
arma::uword maxVarCluster = variances.index_max();
// If the cluster with maximum variance has variance of 0, then we can't
// continue. All the points are the same.
src/mlpack/methods/naive_bayes/naive_bayes_classifier_impl.hpp
+4 -8
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@@ -258,8 +258,7 @@ size_t NaiveBayesClassifier<ModelMatType>::Classify(const VecType& point) const
ModelMatType logLikelihoods;
LogLikelihood(point, logLikelihoods);
arma::uword maxIndex = 0;
logLikelihoods.max(maxIndex);
arma::uword maxIndex = logLikelihoods.index_max();
return maxIndex;
}
@@ -301,8 +300,7 @@ void NaiveBayesClassifier<ModelMatType>::Classify(
maxValue;
probabilities = exp(logLikelihoods - logProbX); // log(exp(value)) == value.
arma::uword maxIndex = 0;
logLikelihoods.max(maxIndex);
arma::uword maxIndex = logLikelihoods.index_max();
prediction = (size_t) maxIndex;
}
@@ -332,8 +330,7 @@ void NaiveBayesClassifier<ModelMatType>::Classify(
for (size_t i = 0; i < data.n_cols; ++i)
{
arma::uword maxIndex = 0;
logLikelihoods.unsafe_col(i).max(maxIndex);
arma::uword maxIndex = logLikelihoods.unsafe_col(i).index_max();
predictions[i] = maxIndex;
}
}
@@ -384,8 +381,7 @@ void NaiveBayesClassifier<ModelMatType>::Classify(
// Now calculate maximum probabilities for each point.
for (size_t i = 0; i < data.n_cols; ++i)
{
arma::uword maxIndex = 0;
logLikelihoods.unsafe_col(i).max(maxIndex);
arma::uword maxIndex = logLikelihoods.unsafe_col(i).index_max();
predictions[i] = maxIndex;
}
}
src/mlpack/methods/perceptron/perceptron_impl.hpp
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@@ -225,7 +225,7 @@ void Perceptron<
size_t j, i = 0;
bool converged = false;
size_t tempLabel;
arma::uword maxIndexRow = 0, maxIndexCol = 0;
arma::uword maxIndexRow = 0;
arma::Mat<ElemType> tempLabelMat;
LearnPolicy LP;
@@ -244,7 +244,8 @@ void Perceptron<
// correctly classifies this.
tempLabelMat = weights.t() * data.col(j) + biases;
tempLabelMat.max(maxIndexRow, maxIndexCol);
maxIndexRow = arma::ind2sub(arma::size(tempLabelMat),
tempLabelMat.index_max())(0);
// Check whether prediction is correct.
if (maxIndexRow != labels(0, j))
@@ -289,7 +290,7 @@ size_t Perceptron<LearnPolicy, WeightInitializationPolicy, MatType>::Classify(
arma::uword maxIndex = 0;
tempLabelVec = weights.t() * point + biases;
tempLabelVec.max(maxIndex);
maxIndex = tempLabelVec.index_max();
return size_t(maxIndex);
}
@@ -322,7 +323,7 @@ void Perceptron<LearnPolicy, WeightInitializationPolicy, MatType>::Classify(
for (size_t i = 0; i < test.n_cols; ++i)
{
tempLabelMat = weights.t() * test.col(i) + biases;
tempLabelMat.max(maxIndex);
maxIndex = tempLabelMat.index_max();
predictedLabels(i) = maxIndex;
}
}
src/mlpack/methods/radical/radical_impl.hpp
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@@ -103,8 +103,7 @@ inline typename MatType::elem_type Radical::Apply2D(const MatType& matX,
values(i) = Vasicek(candidateY1, m) + Vasicek(candidateY2, m);
}
arma::uword indOpt = 0;
values.min(indOpt); // we ignore the return value; we don't care about it
arma::uword indOpt = values.index_min();
return (indOpt / (ElemType) angles) * M_PI / 2.0;
}
src/mlpack/methods/random_forest/random_forest_impl.hpp
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@@ -356,8 +356,7 @@ void RandomForest<
// Find maximum element after renormalizing probabilities.
probabilities /= trees.size();
arma::uword maxIndex = 0;
probabilities.max(maxIndex);
arma::uword maxIndex = probabilities.index_max();
// Set prediction.
prediction = (size_t) maxIndex;
src/mlpack/tests/adaboost_test.cpp
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@@ -644,7 +644,7 @@ TEMPLATE_TEST_CASE("ClassifyTest_VERTEBRALCOL", "[AdaBoostTest]", mat, fmat)
for (size_t i = 0; i < predictedLabels1.n_cols; ++i)
{
pRow = probabilities.unsafe_col(i);
pRow.max(maxIndex);
maxIndex = pRow.index_max();
REQUIRE(predictedLabels1(i) == maxIndex);
REQUIRE(accu(probabilities.col(i)) == Approx(1));
}
@@ -714,7 +714,7 @@ TEMPLATE_TEST_CASE("ClassifyTest_NONLINSEP", "[AdaBoostTest]", mat, fmat)
for (size_t i = 0; i < predictedLabels1.n_cols; ++i)
{
pRow = probabilities.unsafe_col(i);
pRow.max(maxIndex);
maxIndex = pRow.index_max();
REQUIRE(predictedLabels1(i) == maxIndex);
REQUIRE(accu(probabilities.col(i)) == Approx(1).epsilon(1e-7));
}
@@ -787,7 +787,7 @@ TEMPLATE_TEST_CASE("ClassifyTest_IRIS", "[AdaBoostTest]", mat, fmat)
for (size_t i = 0; i < predictedLabels1.n_cols; ++i)
{
pRow = probabilities.unsafe_col(i);
pRow.max(maxIndex);
maxIndex = pRow.index_max();
REQUIRE(predictedLabels1(i) == maxIndex);
REQUIRE(accu(probabilities.col(i)) == Approx(1).epsilon(1e-7));
}
src/mlpack/tests/mean_shift_test.cpp
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@@ -163,7 +163,8 @@ TEMPLATE_TEST_CASE("GaussianClustering", "[MeanShiftTest]", float, double)
centroids.col(i));
// Are we near a centroid of a Gaussian?
const ElemType minVal = centroidDistances.min(minIndices[i]);
minIndices[i] = centroidDistances.index_min();
const ElemType minVal = centroidDistances(minIndices[i]);
success = (std::abs(minVal) <= 0.65);
if (!success)
break;
@@ -240,7 +241,8 @@ TEMPLATE_TEST_CASE("GaussianClusteringCentroidsOnly", "[MeanShiftTest]", float,
centroids.col(i));
// Are we near a centroid of a Gaussian?
const ElemType minVal = centroidDistances.min(minIndices[i]);
minIndices[i] = centroidDistances.index_min();
const ElemType minVal = centroidDistances(minIndices[i]);
success = (std::abs(minVal) <= 0.65);
if (!success)
break;