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Added WarmStart template parameter

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This commit is contained in:
Rishabh Garg
2021-03-17 18:43:20 +05:30
parent 3d0e439d49
commit fd43668cda
2 changed files with 10 additions and 10 deletions
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src/mlpack/methods/random_forest/random_forest.hpp
+1 -1
View File
@@ -359,7 +359,7 @@ class RandomForest
* @tparam MatType The type of data matrix (i.e. arma::mat).
* @return The average entropy of all the decision trees trained under forest.
*/
template<bool UseWeights, bool UseDatasetInfo, typename MatType>
template<bool UseWeights, bool UseDatasetInfo, bool WarmStart = false, typename MatType>
double Train(const MatType& data,
const data::DatasetInfo& datasetInfo,
const arma::Row<size_t>& labels,
src/mlpack/methods/random_forest/random_forest_impl.hpp
+9 -9
View File
@@ -44,7 +44,7 @@ RandomForest<
// Pass off work to the Train() method.
data::DatasetInfo info; // Ignored.
arma::rowvec weights; // Fake weights, not used.
Train<false, false>(dataset, info, labels, numClasses, weights, numTrees,
Train<false, false, false>(dataset, info, labels, numClasses, weights, numTrees,
minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
}
@@ -74,7 +74,7 @@ RandomForest<
{
// Pass off work to the Train() method.
arma::rowvec weights; // Fake weights, not used.
Train<false, true>(dataset, datasetInfo, labels, numClasses, weights,
Train<false, true, false>(dataset, datasetInfo, labels, numClasses, weights,
numTrees, minimumLeafSize, minimumGainSplit, maximumDepth,
dimensionSelector);
}
@@ -105,7 +105,7 @@ RandomForest<
{
// Pass off work to the Train() method.
data::DatasetInfo info; // Ignored by Train().
Train<true, false>(dataset, info, labels, numClasses, weights, numTrees,
Train<true, false, false>(dataset, info, labels, numClasses, weights, numTrees,
minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
}
@@ -135,7 +135,7 @@ RandomForest<
DimensionSelectionType dimensionSelector)
{
// Pass off work to the Train() method.
Train<true, true>(dataset, datasetInfo, labels, numClasses, weights, numTrees,
Train<true, true, false>(dataset, datasetInfo, labels, numClasses, weights, numTrees,
minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
}
@@ -165,7 +165,7 @@ double RandomForest<
// Pass off to Train().
data::DatasetInfo info; // Ignored by Train().
arma::rowvec weights; // Ignored by Train().
return Train<false, false>(dataset, info, labels, numClasses, weights,
return Train<false, false, false>(dataset, info, labels, numClasses, weights,
numTrees, minimumLeafSize, minimumGainSplit, maximumDepth,
dimensionSelector);
}
@@ -196,7 +196,7 @@ double RandomForest<
{
// Pass off to Train().
arma::rowvec weights; // Ignored by Train().
return Train<false, true>(dataset, datasetInfo, labels, numClasses, weights,
return Train<false, true, false>(dataset, datasetInfo, labels, numClasses, weights,
numTrees, minimumLeafSize, minimumGainSplit, maximumDepth,
dimensionSelector);
}
@@ -227,7 +227,7 @@ double RandomForest<
{
// Pass off to Train().
data::DatasetInfo info; // Ignored by Train().
return Train<false, false>(dataset, info, labels, numClasses, weights,
return Train<false, false, false>(dataset, info, labels, numClasses, weights,
numTrees, minimumLeafSize, minimumGainSplit, maximumDepth,
dimensionSelector);
}
@@ -258,7 +258,7 @@ double RandomForest<
DimensionSelectionType dimensionSelector)
{
// Pass off to Train().
return Train<true, true>(dataset, datasetInfo, labels, numClasses, weights,
return Train<true, true, false>(dataset, datasetInfo, labels, numClasses, weights,
numTrees, minimumLeafSize, minimumGainSplit, maximumDepth,
dimensionSelector);
}
@@ -445,7 +445,7 @@ template<
template<typename> class CategoricalSplitType,
typename ElemType
>
template<bool UseWeights, bool UseDatasetInfo, typename MatType>
template<bool UseWeights, bool UseDatasetInfo, bool WarmStart, typename MatType>
double RandomForest<
FitnessFunction,
DimensionSelectionType,