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switch to serialize() and use boost::serialization

Browse Source 
The commit replaces Serialize() with serialize()
for files inside mlpack/methods/(r* or s*). This
also replaces data::CreateNVP(member, "member") with
BOOST_SERIALIZATION_NVP(member). The current changes
are required since we have moved to using boost
serialization and plan to drop serialization shim.
This commit is contained in:
Rohit Kaushik
2017-10-04 23:18:51 +05:30
parent b8ee319818
commit 494a52f4e8
106 changed files with 380 additions and 463 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/mlpack/core/metrics/lmetric.hpp
+1 -1
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@@ -85,7 +85,7 @@ class LMetric
//! Serialize the metric (nothing to do).
template<typename Archive>
void Serialize(Archive& /* ar */, const unsigned int /* version */) { }
void serialize(Archive& /* ar */, const unsigned int /* version */) { }
//! The power of the metric.
static const int Power = TPower;
src/mlpack/core/tree/binary_space_tree/binary_space_tree.hpp
+1 -1
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@@ -548,7 +548,7 @@ class BinarySpaceTree
* Serialize the tree.
*/
template<typename Archive>
void Serialize(Archive& ar, const unsigned int version);
void serialize(Archive& ar, const unsigned int version);
};
} // namespace tree
src/mlpack/core/tree/binary_space_tree/binary_space_tree_impl.hpp
+1 -1
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@@ -942,7 +942,7 @@ template<typename MetricType,
class SplitType>
template<typename Archive>
void BinarySpaceTree<MetricType, StatisticType, MatType, BoundType, SplitType>::
Serialize(Archive& ar, const unsigned int /* version */)
serialize(Archive& ar, const unsigned int /* version */)
{
using data::CreateNVP;
src/mlpack/methods/adaboost/adaboost.hpp
+1 -1
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@@ -161,7 +161,7 @@ class AdaBoost
* Serialize the AdaBoost model.
*/
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */);
void serialize(Archive& ar, const unsigned int /* version */);
private:
//! The number of classes in the model.
src/mlpack/methods/adaboost/adaboost_impl.hpp
+6 -11
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@@ -238,13 +238,13 @@ void AdaBoost<WeakLearnerType, MatType>::Classify(
*/
template<typename WeakLearnerType, typename MatType>
template<typename Archive>
void AdaBoost<WeakLearnerType, MatType>::Serialize(Archive& ar,
void AdaBoost<WeakLearnerType, MatType>::serialize(Archive& ar,
const unsigned int /* version */)
{
ar & data::CreateNVP(numClasses, "classes");
ar & data::CreateNVP(tolerance, "tolerance");
ar & data::CreateNVP(ztProduct, "ztProduct");
ar & data::CreateNVP(alpha, "alpha");
ar & BOOST_SERIALIZATION_NVP(numClasses);
ar & BOOST_SERIALIZATION_NVP(tolerance);
ar & BOOST_SERIALIZATION_NVP(ztProduct);
ar & BOOST_SERIALIZATION_NVP(alpha);
// Now serialize each weak learner.
if (Archive::is_loading::value)
@@ -252,12 +252,7 @@ void AdaBoost<WeakLearnerType, MatType>::Serialize(Archive& ar,
wl.clear();
wl.resize(alpha.size());
}
for (size_t i = 0; i < wl.size(); ++i)
{
std::ostringstream oss;
oss << "weakLearner" << i;
ar & data::CreateNVP(wl[i], oss.str());
}
ar & BOOST_SERIALIZATION_NVP(wl);
}
} // namespace adaboost
src/mlpack/methods/adaboost/adaboost_model.hpp
+6 -6
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@@ -86,7 +86,7 @@ class AdaBoostModel
//! Serialize the model.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */)
void serialize(Archive& ar, const unsigned int /* version */)
{
if (Archive::is_loading::value)
{
@@ -99,13 +99,13 @@ class AdaBoostModel
pBoost = NULL;
}
ar & data::CreateNVP(mappings, "mappings");
ar & data::CreateNVP(weakLearnerType, "weakLearnerType");
ar & BOOST_SERIALIZATION_NVP(mappings);
ar & BOOST_SERIALIZATION_NVP(weakLearnerType);
if (weakLearnerType == WeakLearnerTypes::DECISION_STUMP)
ar & data::CreateNVP(dsBoost, "adaboost_ds");
ar & BOOST_SERIALIZATION_NVP(dsBoost);
else if (weakLearnerType == WeakLearnerTypes::PERCEPTRON)
ar & data::CreateNVP(pBoost, "adaboost_p");
ar & data::CreateNVP(dimensionality, "dimensionality");
ar & BOOST_SERIALIZATION_NVP(pBoost);
ar & BOOST_SERIALIZATION_NVP(dimensionality);
}
};
src/mlpack/methods/amf/init_rules/average_init.hpp
+1 -1
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@@ -76,7 +76,7 @@ class AverageInitialization
//! Serialize the object (in this case, there is nothing to do).
template<typename Archive>
void Serialize(Archive& /* ar */, const unsigned int /* version */) { }
void serialize(Archive& /* ar */, const unsigned int /* version */) { }
};
} // namespace amf
src/mlpack/methods/amf/init_rules/given_init.hpp
+3 -3
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@@ -61,10 +61,10 @@ class GivenInitialization
//! Serialize the object (in this case, there is nothing to serialize).
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */)
void serialize(Archive& ar, const unsigned int /* version */)
{
ar & data::CreateNVP(w, "w");
ar & data::CreateNVP(h, "h");
ar & BOOST_SERIALIZATION_NVP(w);
ar & BOOST_SERIALIZATION_NVP(h);
}
private:
src/mlpack/methods/amf/init_rules/random_acol_init.hpp
+1 -1
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@@ -85,7 +85,7 @@ class RandomAcolInitialization
//! Serialize the object (in this case, there is nothing to serialize).
template<typename Archive>
void Serialize(Archive& /* ar */, const unsigned int /* version */) { }
void serialize(Archive& /* ar */, const unsigned int /* version */) { }
};
} // namespace amf
src/mlpack/methods/amf/init_rules/random_init.hpp
+1 -1
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@@ -53,7 +53,7 @@ class RandomInitialization
//! Serialize the object (in this case, there is nothing to serialize).
template<typename Archive>
void Serialize(Archive& /* ar */, const unsigned int /* version */) { }
void serialize(Archive& /* ar */, const unsigned int /* version */) { }
};
} // namespace amf
src/mlpack/methods/amf/update_rules/nmf_als.hpp
+1 -1
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@@ -120,7 +120,7 @@ class NMFALSUpdate
//! Serialize the object (in this case, there is nothing to serialize).
template<typename Archive>
void Serialize(Archive& /* ar */, const unsigned int /* version */) { }
void serialize(Archive& /* ar */, const unsigned int /* version */) { }
}; // class NMFALSUpdate
} // namespace amf
src/mlpack/methods/amf/update_rules/nmf_mult_dist.hpp
+1 -1
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@@ -98,7 +98,7 @@ class NMFMultiplicativeDistanceUpdate
//! Serialize the object (in this case, there is nothing to serialize).
template<typename Archive>
void Serialize(Archive& /* ar */, const unsigned int /* version */) { }
void serialize(Archive& /* ar */, const unsigned int /* version */) { }
};
} // namespace amf
src/mlpack/methods/amf/update_rules/nmf_mult_div.hpp
+1 -1
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@@ -151,7 +151,7 @@ class NMFMultiplicativeDivergenceUpdate
//! Serialize the object (in this case, there is nothing to serialize).
template<typename Archive>
void Serialize(Archive& /* ar */, const unsigned int /* version */) { }
void serialize(Archive& /* ar */, const unsigned int /* version */) { }
};
} // namespace amf
src/mlpack/methods/amf/update_rules/svd_batch_learning.hpp
+7 -8
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@@ -166,15 +166,14 @@ class SVDBatchLearning
//! Serialize the SVDBatch object.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */)
void serialize(Archive& ar, const unsigned int /* version */)
{
using data::CreateNVP;
ar & CreateNVP(u, "u");
ar & CreateNVP(kw, "kw");
ar & CreateNVP(kh, "kh");
ar & CreateNVP(momentum, "momentum");
ar & CreateNVP(mW, "mW");
ar & CreateNVP(mH, "mH");
ar & BOOST_SERIALIZATION_NVP(u);
ar & BOOST_SERIALIZATION_NVP(kw);
ar & BOOST_SERIALIZATION_NVP(kh);
ar & BOOST_SERIALIZATION_NVP(momentum);
ar & BOOST_SERIALIZATION_NVP(mW);
ar & BOOST_SERIALIZATION_NVP(mH);
}
private:
src/mlpack/methods/ann/ffn.hpp
+1 -1
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@@ -217,7 +217,7 @@ class FFN
//! Serialize the model.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */);
void serialize(Archive& ar, const unsigned int /* version */);
/**
* Perform the forward pass of the data in real batch mode.
src/mlpack/methods/ann/ffn_impl.hpp
+6 -6
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@@ -394,14 +394,14 @@ void FFN<OutputLayerType, InitializationRuleType>::Gradient()
template<typename OutputLayerType, typename InitializationRuleType>
template<typename Archive>
void FFN<OutputLayerType, InitializationRuleType>::Serialize(
void FFN<OutputLayerType, InitializationRuleType>::serialize(
Archive& ar, const unsigned int /* version */)
{
ar & data::CreateNVP(parameter, "parameter");
ar & data::CreateNVP(width, "width");
ar & data::CreateNVP(height, "height");
ar & data::CreateNVP(currentInput, "currentInput");
ar & data::CreateNVP(currentTarget, "currentTarget");
ar & BOOST_SERIALIZATION_NVP(parameter);
ar & BOOST_SERIALIZATION_NVP(width);
ar & BOOST_SERIALIZATION_NVP(height);
ar & BOOST_SERIALIZATION_NVP(currentInput);
ar & BOOST_SERIALIZATION_NVP(currentTarget);
// Be sure to clear other layers before loading.
if (Archive::is_loading::value)
src/mlpack/methods/ann/rnn.hpp
+1 -1
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@@ -209,7 +209,7 @@ class RNN
//! Serialize the model.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */);
void serialize(Archive& ar, const unsigned int /* version */);
private:
// Helper functions.
/**
src/mlpack/methods/ann/rnn_impl.hpp
+8 -8
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@@ -409,16 +409,16 @@ void RNN<OutputLayerType, InitializationRuleType>::Gradient()
template<typename OutputLayerType, typename InitializationRuleType>
template<typename Archive>
void RNN<OutputLayerType, InitializationRuleType>::Serialize(
void RNN<OutputLayerType, InitializationRuleType>::serialize(
Archive& ar, const unsigned int /* version */)
{
ar & data::CreateNVP(parameter, "parameter");
ar & data::CreateNVP(rho, "rho");
ar & data::CreateNVP(single, "single");
ar & data::CreateNVP(inputSize, "inputSize");
ar & data::CreateNVP(outputSize, "outputSize");
ar & data::CreateNVP(targetSize, "targetSize");
ar & data::CreateNVP(currentInput, "currentInput");
ar & BOOST_SERIALIZATION_NVP(parameter);
ar & BOOST_SERIALIZATION_NVP(rho);
ar & BOOST_SERIALIZATION_NVP(single);
ar & BOOST_SERIALIZATION_NVP(inputSize);
ar & BOOST_SERIALIZATION_NVP(outputSize);
ar & BOOST_SERIALIZATION_NVP(targetSize);
ar & BOOST_SERIALIZATION_NVP(currentInput);
if (Archive::is_loading::value)
{
src/mlpack/methods/approx_kfn/approx_kfn_main.cpp
+4 -4
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@@ -120,16 +120,16 @@ class ApproxKFNModel
//! Serialize the model.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */)
void serialize(Archive& ar, const unsigned int /* version */)
{
ar & data::CreateNVP(type, "type");
ar & BOOST_SERIALIZATION_NVP(type);
if (type == 0)
{
ar & data::CreateNVP(ds, "model");
ar & BOOST_SERIALIZATION_NVP(ds);
}
else
{
ar & data::CreateNVP(qdafn, "model");
ar & BOOST_SERIALIZATION_NVP(qdafn);
}
}
};
src/mlpack/methods/approx_kfn/drusilla_select.hpp
+1 -1
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@@ -97,7 +97,7 @@ class DrusillaSelect
* Serialize the model.
*/
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */);
void serialize(Archive& ar, const unsigned int /* version */);
//! Access the candidate set.
const MatType& CandidateSet() const { return candidateSet; }
src/mlpack/methods/approx_kfn/drusilla_select_impl.hpp
+5 -7
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@@ -201,15 +201,13 @@ void DrusillaSelect<MatType>::Search(const MatType& querySet,
//! Serialize the model.
template<typename MatType>
template<typename Archive>
void DrusillaSelect<MatType>::Serialize(Archive& ar,
void DrusillaSelect<MatType>::serialize(Archive& ar,
const unsigned int /* version */)
{
using data::CreateNVP;
ar & CreateNVP(candidateSet, "candidateSet");
ar & CreateNVP(candidateIndices, "candidateIndices");
ar & CreateNVP(l, "l");
ar & CreateNVP(m, "m");
ar & BOOST_SERIALIZATION_NVP(candidateSet);
ar & BOOST_SERIALIZATION_NVP(candidateIndices);
ar & BOOST_SERIALIZATION_NVP(l);
ar & BOOST_SERIALIZATION_NVP(m);
}
} // namespace neighbor
src/mlpack/methods/approx_kfn/qdafn.hpp
+1 -1
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@@ -81,7 +81,7 @@ class QDAFN
//! Serialize the model.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */);
void serialize(Archive& ar, const unsigned int /* version */);
//! Get the number of projections.
size_t NumProjections() const { return candidateSet.size(); }
src/mlpack/methods/approx_kfn/qdafn_impl.hpp
+8 -10
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@@ -171,19 +171,17 @@ void QDAFN<MatType>::Search(const MatType& querySet,
template<typename MatType>
template<typename Archive>
void QDAFN<MatType>::Serialize(Archive& ar, const unsigned int /* version */)
void QDAFN<MatType>::serialize(Archive& ar, const unsigned int /* version */)
{
using data::CreateNVP;
ar & CreateNVP(l, "l");
ar & CreateNVP(m, "m");
ar & CreateNVP(lines, "lines");
ar & CreateNVP(projections, "projections");
ar & CreateNVP(sIndices, "sIndices");
ar & CreateNVP(sValues, "sValues");
ar & BOOST_SERIALIZATION_NVP(l);
ar & BOOST_SERIALIZATION_NVP(m);
ar & BOOST_SERIALIZATION_NVP(lines);
ar & BOOST_SERIALIZATION_NVP(projections);
ar & BOOST_SERIALIZATION_NVP(sIndices);
ar & BOOST_SERIALIZATION_NVP(sValues);
if (Archive::is_loading::value)
candidateSet.clear();
ar & CreateNVP(candidateSet, "candidateSet");
ar & BOOST_SERIALIZATION_NVP(candidateSet);
}
} // namespace neighbor
src/mlpack/methods/cf/cf.hpp
+1 -1
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@@ -248,7 +248,7 @@ class CF
* Serialize the CF model to the given archive.
*/
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */);
void serialize(Archive& ar, const unsigned int /* version */);
private:
//! Number of users for similarity.
src/mlpack/methods/cf/cf_impl.hpp
+6 -8
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@@ -155,17 +155,15 @@ void CF::Train(const arma::sp_mat& data,
//! Serialize the model.
template<typename Archive>
void CF::Serialize(Archive& ar, const unsigned int /* version */)
void CF::serialize(Archive& ar, const unsigned int /* version */)
{
// This model is simple; just serialize all the members. No special handling
// required.
using data::CreateNVP;
ar & CreateNVP(numUsersForSimilarity, "numUsersForSimilarity");
ar & CreateNVP(rank, "rank");
ar & CreateNVP(w, "w");
ar & CreateNVP(h, "h");
ar & CreateNVP(cleanedData, "cleanedData");
ar & BOOST_SERIALIZATION_NVP(numUsersForSimilarity);
ar & BOOST_SERIALIZATION_NVP(rank);
ar & BOOST_SERIALIZATION_NVP(w);
ar & BOOST_SERIALIZATION_NVP(h);
ar & BOOST_SERIALIZATION_NVP(cleanedData);
}
} // namespace cf
src/mlpack/methods/decision_stump/decision_stump.hpp
+1 -1
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@@ -131,7 +131,7 @@ class DecisionStump
//! Serialize the decision stump.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */);
void serialize(Archive& ar, const unsigned int /* version */);
private:
//! The number of classes (we must store this for boosting).
src/mlpack/methods/decision_stump/decision_stump_impl.hpp
+6 -8
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@@ -198,18 +198,16 @@ DecisionStump<MatType>::DecisionStump(const DecisionStump<>& other,
*/
template<typename MatType>
template<typename Archive>
void DecisionStump<MatType>::Serialize(Archive& ar,
void DecisionStump<MatType>::serialize(Archive& ar,
const unsigned int /* version */)
{
using data::CreateNVP;
// This is straightforward; just serialize all of the members of the class.
// None need special handling.
ar & CreateNVP(numClasses, "classes");
ar & CreateNVP(bucketSize, "bucketSize");
ar & CreateNVP(splitDimension, "splitDimension");
ar & CreateNVP(split, "split");
ar & CreateNVP(binLabels, "binLabels");
ar & BOOST_SERIALIZATION_NVP(numClasses);
ar & BOOST_SERIALIZATION_NVP(bucketSize);
ar & BOOST_SERIALIZATION_NVP(splitDimension);
ar & BOOST_SERIALIZATION_NVP(split);
ar & BOOST_SERIALIZATION_NVP(binLabels);
}
/**
src/mlpack/methods/decision_stump/decision_stump_main.cpp
+3 -3
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@@ -86,10 +86,10 @@ struct DSModel
//! Serialize the model.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */)
void serialize(Archive& ar, const unsigned int /* version */)
{
ar & data::CreateNVP(mappings, "mappings");
ar & data::CreateNVP(stump, "stump");
ar & BOOST_SERIALIZATION_NVP(mappings);
ar & BOOST_SERIALIZATION_NVP(stump);
}
};
src/mlpack/methods/decision_tree/decision_tree.hpp
+1 -1
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@@ -312,7 +312,7 @@ class DecisionTree :
* Serialize the tree.
*/
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */);
void serialize(Archive& ar, const unsigned int /* version */);
//! Get the number of children.
size_t NumChildren() const { return children.size(); }
src/mlpack/methods/decision_tree/decision_tree_impl.hpp
+6 -8
View File
@@ -932,11 +932,9 @@ void DecisionTree<FitnessFunction,
CategoricalSplitType,
DimensionSelectionType,
ElemType,
NoRecursion>::Serialize(Archive& ar,
NoRecursion>::serialize(Archive& ar,
const unsigned int /* version */)
{
using data::CreateNVP;
// Clean memory if needed.
if (Archive::is_loading::value)
{
@@ -947,7 +945,7 @@ void DecisionTree<FitnessFunction,
// Serialize the children first.
size_t numChildren = children.size();
ar & CreateNVP(numChildren, "numChildren");
ar & BOOST_SERIALIZATION_NVP(numChildren);
if (Archive::is_loading::value)
{
children.resize(numChildren, NULL);
@@ -959,13 +957,13 @@ void DecisionTree<FitnessFunction,
{
std::ostringstream name;
name << "child" << i;
ar & CreateNVP(*children[i], name.str());
ar & BOOST_SERIALIZATION_NVP(*children[i]);
}
// Now serialize the rest of the object.
ar & CreateNVP(splitDimension, "splitDimension");
ar & CreateNVP(dimensionTypeOrMajorityClass, "dimensionTypeOrMajorityClass");
ar & CreateNVP(classProbabilities, "classProbabilities");
ar & BOOST_SERIALIZATION_NVP(splitDimension);
ar & BOOST_SERIALIZATION_NVP(dimensionTypeOrMajorityClass);
ar & BOOST_SERIALIZATION_NVP(classProbabilities);
}
template<typename FitnessFunction,
src/mlpack/methods/decision_tree/decision_tree_main.cpp
+2 -2
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@@ -102,9 +102,9 @@ class DecisionTreeModel
// Serialize the model.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */)
void serialize(Archive& ar, const unsigned int /* version */)
{
ar & data::CreateNVP(tree, "tree");
ar & BOOST_SERIALIZATION_NVP(tree);
}
};
src/mlpack/methods/det/dtree.hpp
+1 -1
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@@ -313,7 +313,7 @@ class DTree
* Serialize the density estimation tree.
*/
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */);
void serialize(Archive& ar, const unsigned int /* version */);
private:
// Utility methods.
src/mlpack/methods/det/dtree_impl.hpp
+17 -19
View File
@@ -946,25 +946,23 @@ DTree<MatType, TagType>::ComputeVariableImportance(arma::vec& importances) const
template <typename MatType, typename TagType>
template <typename Archive>
void DTree<MatType, TagType>::Serialize(Archive& ar,
void DTree<MatType, TagType>::serialize(Archive& ar,
const unsigned int /* version */)
{
using data::CreateNVP;
ar & CreateNVP(start, "start");
ar & CreateNVP(end, "end");
ar & CreateNVP(maxVals, "maxVals");
ar & CreateNVP(minVals, "minVals");
ar & CreateNVP(splitDim, "splitDim");
ar & CreateNVP(splitValue, "splitValue");
ar & CreateNVP(logNegError, "logNegError");
ar & CreateNVP(subtreeLeavesLogNegError, "subtreeLeavesLogNegError");
ar & CreateNVP(subtreeLeaves, "subtreeLeaves");
ar & CreateNVP(root, "root");
ar & CreateNVP(ratio, "ratio");
ar & CreateNVP(logVolume, "logVolume");
ar & CreateNVP(bucketTag, "bucketTag");
ar & CreateNVP(alphaUpper, "alphaUpper");
ar & BOOST_SERIALIZATION_NVP(start);
ar & BOOST_SERIALIZATION_NVP(end);
ar & BOOST_SERIALIZATION_NVP(maxVals);
ar & BOOST_SERIALIZATION_NVP(minVals);
ar & BOOST_SERIALIZATION_NVP(splitDim);
ar & BOOST_SERIALIZATION_NVP(splitValue);
ar & BOOST_SERIALIZATION_NVP(logNegError);
ar & BOOST_SERIALIZATION_NVP(subtreeLeavesLogNegError);
ar & BOOST_SERIALIZATION_NVP(subtreeLeaves);
ar & BOOST_SERIALIZATION_NVP(root);
ar & BOOST_SERIALIZATION_NVP(ratio);
ar & BOOST_SERIALIZATION_NVP(logVolume);
ar & BOOST_SERIALIZATION_NVP(bucketTag);
ar & BOOST_SERIALIZATION_NVP(alphaUpper);
if (Archive::is_loading::value)
{
@@ -974,7 +972,7 @@ void DTree<MatType, TagType>::Serialize(Archive& ar,
delete right;
}
ar & CreateNVP(left, "left");
ar & CreateNVP(right, "right");
ar & BOOST_SERIALIZATION_NVP(left);
ar & BOOST_SERIALIZATION_NVP(right);
}
src/mlpack/methods/fastmks/fastmks.hpp
+1 -1
View File
@@ -250,7 +250,7 @@ class FastMKS
//! Serialize the model.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */);
void serialize(Archive& ar, const unsigned int /* version */);
private:
//! The reference dataset. We never own this; only the tree or a higher level
src/mlpack/methods/fastmks/fastmks_impl.hpp
+6 -8
View File
@@ -509,15 +509,13 @@ template<typename KernelType,
typename TreeStatType,
typename TreeMatType> class TreeType>
template<typename Archive>
void FastMKS<KernelType, MatType, TreeType>::Serialize(
void FastMKS<KernelType, MatType, TreeType>::serialize(
Archive& ar,
const unsigned int /* version */)
{
using data::CreateNVP;
// Serialize preferences for search.
ar & CreateNVP(naive, "naive");
ar & CreateNVP(singleMode, "singleMode");
ar & BOOST_SERIALIZATION_NVP(naive);
ar & BOOST_SERIALIZATION_NVP(singleMode);
// If we are doing naive search, serialize the dataset. Otherwise we
// serialize the tree.
@@ -531,8 +529,8 @@ void FastMKS<KernelType, MatType, TreeType>::Serialize(
setOwner = true;
}
ar & CreateNVP(referenceSet, "referenceSet");
ar & CreateNVP(metric, "metric");
ar & BOOST_SERIALIZATION_NVP(referenceSet);
ar & BOOST_SERIALIZATION_NVP(metric);
}
else
{
@@ -545,7 +543,7 @@ void FastMKS<KernelType, MatType, TreeType>::Serialize(
treeOwner = true;
}
ar & CreateNVP(referenceTree, "referenceTree");
ar & BOOST_SERIALIZATION_NVP(referenceTree);
if (Archive::is_loading::value)
{
src/mlpack/methods/fastmks/fastmks_model.hpp
+1 -1
View File
@@ -126,7 +126,7 @@ class FastMKSModel
* Serialize the model.
*/
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */);
void serialize(Archive& ar, const unsigned int /* version */);
private:
//! The type of kernel we are using.
src/mlpack/methods/fastmks/fastmks_model_impl.hpp
+9 -11
View File
@@ -126,11 +126,9 @@ void FastMKSModel::BuildModel(const arma::mat& referenceData,
}
template<typename Archive>
void FastMKSModel::Serialize(Archive& ar, const unsigned int /* version */)
void FastMKSModel::serialize(Archive& ar, const unsigned int /* version */)
{
using data::CreateNVP;
ar & CreateNVP(kernelType, "kernelType");
ar & BOOST_SERIALIZATION_NVP(kernelType);
if (Archive::is_loading::value)
{
@@ -163,31 +161,31 @@ void FastMKSModel::Serialize(Archive& ar, const unsigned int /* version */)
switch (kernelType)
{
case LINEAR_KERNEL:
ar & CreateNVP(linear, "linear_fastmks");
ar & BOOST_SERIALIZATION_NVP(linear);
break;
case POLYNOMIAL_KERNEL:
ar & CreateNVP(polynomial, "polynomial_fastmks");
ar & BOOST_SERIALIZATION_NVP(polynomial);
break;
case COSINE_DISTANCE:
ar & CreateNVP(cosine, "cosine_fastmks");
ar & BOOST_SERIALIZATION_NVP(cosine);
break;
case GAUSSIAN_KERNEL:
ar & CreateNVP(gaussian, "gaussian_fastmks");
ar & BOOST_SERIALIZATION_NVP(gaussian);
break;
case EPANECHNIKOV_KERNEL:
ar & CreateNVP(epan, "epan_fastmks");
ar & BOOST_SERIALIZATION_NVP(epan);
break;
case TRIANGULAR_KERNEL:
ar & CreateNVP(triangular, "triangular_fastmks");
ar & BOOST_SERIALIZATION_NVP(triangular);
break;
case HYPTAN_KERNEL:
ar & CreateNVP(hyptan, "hyptan_fastmks");
ar & BOOST_SERIALIZATION_NVP(hyptan);
break;
}
}
src/mlpack/methods/fastmks/fastmks_stat.hpp
+3 -3
View File
@@ -100,10 +100,10 @@ class FastMKSStat
//! Serialize the statistic.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */)
void serialize(Archive& ar, const unsigned int /* version */)
{
ar & data::CreateNVP(bound, "bound");
ar & data::CreateNVP(selfKernel, "selfKernel");
ar & BOOST_SERIALIZATION_NVP(bound);
ar & BOOST_SERIALIZATION_NVP(selfKernel);
// Void out last kernel information on load.
if (Archive::is_loading::value)
src/mlpack/methods/gmm/diagonal_constraint.hpp
+1 -1
View File
@@ -32,7 +32,7 @@ class DiagonalConstraint
//! Serialize the constraint (which holds nothing, so, nothing to do).
template<typename Archive>
static void Serialize(Archive& /* ar */, const unsigned int /* version */) { }
static void serialize(Archive& /* ar */, const unsigned int /* version */) { }
};
} // namespace gmm
src/mlpack/methods/gmm/eigenvalue_ratio_constraint.hpp
+2 -2
View File
@@ -78,11 +78,11 @@ class EigenvalueRatioConstraint
//! Serialize the constraint.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */)
void serialize(Archive& ar, const unsigned int /* version */)
{
// Strip the const for the sake of loading/saving. This is the only time it
// is modified (other than the constructor).
ar & data::CreateNVP(const_cast<arma::vec&>(ratios), "ratios");
ar & BOOST_SERIALIZATION_NVP(const_cast<arma::vec&>(ratios));
}
private:
src/mlpack/methods/gmm/em_fit.hpp
+1 -1
View File
@@ -130,7 +130,7 @@ class EMFit
//! Serialize the fitter.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int version);
void serialize(Archive& ar, const unsigned int version);
private:
/**
src/mlpack/methods/gmm/em_fit_impl.hpp
+5 -7
View File
@@ -316,16 +316,14 @@ double EMFit<InitialClusteringType, CovarianceConstraintPolicy>::LogLikelihood(
template<typename InitialClusteringType, typename CovarianceConstraintPolicy>
template<typename Archive>
void EMFit<InitialClusteringType, CovarianceConstraintPolicy>::Serialize(
void EMFit<InitialClusteringType, CovarianceConstraintPolicy>::serialize(
Archive& ar,
const unsigned int /* version */)
{
using data::CreateNVP;
ar & CreateNVP(maxIterations, "maxIterations");
ar & CreateNVP(tolerance, "tolerance");
ar & CreateNVP(clusterer, "clusterer");
ar & CreateNVP(constraint, "constraint");
ar & BOOST_SERIALIZATION_NVP(maxIterations);
ar & BOOST_SERIALIZATION_NVP(tolerance);
ar & BOOST_SERIALIZATION_NVP(clusterer);
ar & BOOST_SERIALIZATION_NVP(constraint);
}
// Armadillo uses uword internally as an OpenMP index type, which crashes Visual
src/mlpack/methods/gmm/gmm.hpp
+1 -1
View File
@@ -265,7 +265,7 @@ class GMM
* Serialize the GMM.
*/
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */);
void serialize(Archive& ar, const unsigned int /* version */);
private:
/**
src/mlpack/methods/gmm/gmm_impl.hpp
+6 -13
View File
@@ -191,27 +191,20 @@ double GMM::Train(const arma::mat& observations,
* Serialize the object.
*/
template<typename Archive>
void GMM::Serialize(Archive& ar, const unsigned int /* version */)
void GMM::serialize(Archive& ar, const unsigned int /* version */)
{
using data::CreateNVP;
ar & CreateNVP(gaussians, "gaussians");
ar & CreateNVP(dimensionality, "dimensionality");
ar & BOOST_SERIALIZATION_NVP(gaussians);
ar & BOOST_SERIALIZATION_NVP(dimensionality);
// Load (or save) the gaussians. Not going to use the default std::vector
// serialize here because it won't call out correctly to Serialize() for each
// serialize here because it won't call out correctly to serialize() for each
// Gaussian distribution.
if (Archive::is_loading::value)
dists.resize(gaussians);
for (size_t i = 0; i < gaussians; ++i)
{
std::ostringstream oss;
oss << "dist" << i;
ar & CreateNVP(dists[i], oss.str());
}
ar & BOOST_SERIALIZATION_NVP(dists);
ar & CreateNVP(weights, "weights");
ar & BOOST_SERIALIZATION_NVP(weights);
}
} // namespace gmm
src/mlpack/methods/gmm/no_constraint.hpp
+1 -1
View File
@@ -30,7 +30,7 @@ class NoConstraint
//! Serialize the object (nothing to do).
template<typename Archive>
static void Serialize(Archive& /* ar */, const unsigned int /* version */) { }
static void serialize(Archive& /* ar */, const unsigned int /* version */) { }
};
} // namespace gmm
src/mlpack/methods/gmm/positive_definite_constraint.hpp
+1 -1
View File
@@ -64,7 +64,7 @@ class PositiveDefiniteConstraint
//! Serialize the constraint (which stores nothing, so, nothing to do).
template<typename Archive>
static void Serialize(Archive& /* ar */, const unsigned int /* version */) { }
static void serialize(Archive& /* ar */, const unsigned int /* version */) { }
};
} // namespace gmm
src/mlpack/methods/hmm/hmm.hpp
+1 -1
View File
@@ -326,7 +326,7 @@ class HMM
* Serialize the object.
*/
template<typename Archive>
void Serialize(Archive& ar, const unsigned int version);
void serialize(Archive& ar, const unsigned int version);
protected:
// Helper functions.
src/mlpack/methods/hmm/hmm_impl.hpp
+6 -11
View File
@@ -587,12 +587,12 @@ void HMM<Distribution>::Backward(const arma::mat& dataSeq,
//! Serialize the HMM.
template<typename Distribution>
template<typename Archive>
void HMM<Distribution>::Serialize(Archive& ar, const unsigned int /* version */)
void HMM<Distribution>::serialize(Archive& ar, const unsigned int /* version */)
{
ar & data::CreateNVP(dimensionality, "dimensionality");
ar & data::CreateNVP(tolerance, "tolerance");
ar & data::CreateNVP(transition, "transition");
ar & data::CreateNVP(initial, "initial");
ar & BOOST_SERIALIZATION_NVP(dimensionality);
ar & BOOST_SERIALIZATION_NVP(tolerance);
ar & BOOST_SERIALIZATION_NVP(transition);
ar & BOOST_SERIALIZATION_NVP(initial);
// Now serialize each emission. If we are loading, we must resize the vector
// of emissions correctly.
@@ -600,12 +600,7 @@ void HMM<Distribution>::Serialize(Archive& ar, const unsigned int /* version */)
emission.resize(transition.n_rows);
// Load the emissions; generate the correct name for each one.
for (size_t i = 0; i < emission.size(); ++i)
{
std::ostringstream oss;
oss << "emission" << i;
ar & data::CreateNVP(emission[i], oss.str());
}
ar & BOOST_SERIALIZATION_NVP(emission);
}
} // namespace hmm
src/mlpack/methods/hmm/hmm_model.hpp
+5 -5
View File
@@ -141,9 +141,9 @@ class HMMModel
//! Serialize the model.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */)
void serialize(Archive& ar, const unsigned int /* version */)
{
ar & data::CreateNVP(type, "type");
ar & BOOST_SERIALIZATION_NVP(type);
// If necessary, clean memory.
if (Archive::is_loading::value)
@@ -158,11 +158,11 @@ class HMMModel
}
if (type == HMMType::DiscreteHMM)
ar & data::CreateNVP(discreteHMM, "discreteHMM");
ar & BOOST_SERIALIZATION_NVP(discreteHMM);
else if (type == HMMType::GaussianHMM)
ar & data::CreateNVP(gaussianHMM, "gaussianHMM");
ar & BOOST_SERIALIZATION_NVP(gaussianHMM);
else if (type == HMMType::GaussianMixtureModelHMM)
ar & data::CreateNVP(gmmHMM, "gmmHMM");
ar & BOOST_SERIALIZATION_NVP(gmmHMM);
}
};
src/mlpack/methods/hmm/hmm_util_impl.hpp
+4 -4
View File
@@ -66,7 +66,7 @@ void LoadHMMAndPerformActionHelper(const std::string& modelFile,
// Read in the unsigned integer that denotes the type of the model.
char type;
ar >> data::CreateNVP(type, "hmm_type");
ar >> BOOST_SERIALIZATION_NVP(type);
using namespace mlpack::distribution;
@@ -101,7 +101,7 @@ void DeserializeHMMAndPerformAction(ArchiveType& ar, ExtraInfoType* x)
{
// Extract the HMM and perform the action.
HMMType hmm;
ar >> data::CreateNVP(hmm, "hmm");
ar >> BOOST_SERIALIZATION_NVP(hmm);
ActionType::Apply(hmm, x);
}
@@ -143,8 +143,8 @@ void SaveHMMHelper(HMMType& hmm, const std::string& modelFile)
if (type == char(-1))
Log::Fatal << "Unknown HMM type given to SaveHMM()!" << std::endl;
ar << data::CreateNVP(type, "hmm_type");
ar << data::CreateNVP(hmm, "hmm");
ar << BOOST_SERIALIZATION_NVP(type);
ar << BOOST_SERIALIZATION_NVP(hmm);
}
// Utility functions to turn a type into something we can store.
src/mlpack/methods/hoeffding_trees/binary_numeric_split.hpp
+1 -1
View File
@@ -108,7 +108,7 @@ class BinaryNumericSplit
//! Serialize the object.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */);
void serialize(Archive& ar, const unsigned int /* version */);
private:
//! The elements seen so far, in sorted order.
src/mlpack/methods/hoeffding_trees/binary_numeric_split_impl.hpp
+3 -3
View File
@@ -170,13 +170,13 @@ double BinaryNumericSplit<FitnessFunction, ObservationType>::
template<typename FitnessFunction, typename ObservationType>
template<typename Archive>
void BinaryNumericSplit<FitnessFunction, ObservationType>::Serialize(
void BinaryNumericSplit<FitnessFunction, ObservationType>::serialize(
Archive& ar,
const unsigned int /* version */)
{
// Serialize.
ar & data::CreateNVP(sortedElements, "sortedElements");
ar & data::CreateNVP(classCounts, "classCounts");
ar & BOOST_SERIALIZATION_NVP(sortedElements);
ar & BOOST_SERIALIZATION_NVP(classCounts);
}
src/mlpack/methods/hoeffding_trees/binary_numeric_split_info.hpp
+2 -2
View File
@@ -34,9 +34,9 @@ class BinaryNumericSplitInfo
//! Serialize the split (save/load the split points).
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */)
void serialize(Archive& ar, const unsigned int /* version */)
{
ar & data::CreateNVP(splitPoint, "splitPoint");
ar & BOOST_SERIALIZATION_NVP(splitPoint);
}
private:
src/mlpack/methods/hoeffding_trees/categorical_split_info.hpp
+1 -1
View File
@@ -32,7 +32,7 @@ class CategoricalSplitInfo
//! Serialize the object. (Nothing needs to be saved.)
template<typename Archive>
void Serialize(Archive& /* ar */, const unsigned int /* version */) { }
void serialize(Archive& /* ar */, const unsigned int /* version */) { }
};
} // namespace tree
src/mlpack/methods/hoeffding_trees/hoeffding_categorical_split.hpp
+2 -2
View File
@@ -108,9 +108,9 @@ class HoeffdingCategoricalSplit
//! Serialize the categorical split.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */)
void serialize(Archive& ar, const unsigned int /* version */)
{
ar & data::CreateNVP(sufficientStatistics, "sufficientStatistics");
ar & BOOST_SERIALIZATION_NVP(sufficientStatistics);
}
private:
src/mlpack/methods/hoeffding_trees/hoeffding_numeric_split.hpp
+1 -1
View File
@@ -121,7 +121,7 @@ class HoeffdingNumericSplit
//! Serialize the object.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */);
void serialize(Archive& ar, const unsigned int /* version */);
private:
//! Before binning, this holds the points we have seen so far.
src/mlpack/methods/hoeffding_trees/hoeffding_numeric_split_impl.hpp
+9 -20
View File
@@ -188,21 +188,19 @@ double HoeffdingNumericSplit<FitnessFunction, ObservationType>::
template<typename FitnessFunction, typename ObservationType>
template<typename Archive>
void HoeffdingNumericSplit<FitnessFunction, ObservationType>::Serialize(
void HoeffdingNumericSplit<FitnessFunction, ObservationType>::serialize(
Archive& ar,
const unsigned int /* version */)
{
using data::CreateNVP;
ar & CreateNVP(samplesSeen, "samplesSeen");
ar & CreateNVP(observationsBeforeBinning, "observationsBeforeBinning");
ar & CreateNVP(bins, "bins");
ar & BOOST_SERIALIZATION_NVP(samplesSeen);
ar & BOOST_SERIALIZATION_NVP(observationsBeforeBinning);
ar & BOOST_SERIALIZATION_NVP(bins);
if (samplesSeen >= observationsBeforeBinning)
{
// The binning has happened, so we only need to save the resulting bins.
ar & CreateNVP(splitPoints, "splitPoints");
ar & CreateNVP(sufficientStatistics, "sufficientStatistics");
ar & BOOST_SERIALIZATION_NVP(splitPoints);
ar & BOOST_SERIALIZATION_NVP(sufficientStatistics);
if (Archive::is_loading::value)
{
@@ -225,18 +223,9 @@ void HoeffdingNumericSplit<FitnessFunction, ObservationType>::Serialize(
size_t numClasses;
if (Archive::is_saving::value)
numClasses = sufficientStatistics.n_rows;
ar & data::CreateNVP(numClasses, "numClasses");
for (size_t i = 0; i < samplesSeen; ++i)
{
std::ostringstream oss;
oss << "obs" << i;
ar & CreateNVP(observations[i], oss.str());
std::ostringstream oss2;
oss2 << "label" << i;
ar & CreateNVP(labels[i], oss2.str());
}
ar & BOOST_SERIALIZATION_NVP(numClasses);
ar & BOOST_SERIALIZATION_NVP(observations);
ar & BOOST_SERIALIZATION_NVP(labels);
if (Archive::is_loading::value)
{
src/mlpack/methods/hoeffding_trees/hoeffding_tree.hpp
+1 -1
View File
@@ -289,7 +289,7 @@ class HoeffdingTree
//! Serialize the split.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */);
void serialize(Archive& ar, const unsigned int /* version */);
private:
// We need to keep some information for before we have split.
src/mlpack/methods/hoeffding_trees/hoeffding_tree_impl.hpp
+16 -28
View File
@@ -692,23 +692,21 @@ void HoeffdingTree<
FitnessFunction,
NumericSplitType,
CategoricalSplitType
>::Serialize(Archive& ar, const unsigned int /* version */)
>::serialize(Archive& ar, const unsigned int /* version */)
{
using data::CreateNVP;
ar & CreateNVP(splitDimension, "splitDimension");
ar & BOOST_SERIALIZATION_NVP(splitDimension);
// Clear memory for the mappings if necessary.
if (Archive::is_loading::value && ownsMappings && dimensionMappings)
delete dimensionMappings;
ar & CreateNVP(dimensionMappings, "dimensionMappings");
ar & BOOST_SERIALIZATION_NVP(dimensionMappings);
// Special handling for const object.
data::DatasetInfo* d = NULL;
if (Archive::is_saving::value)
d = const_cast<data::DatasetInfo*>(datasetInfo);
ar & CreateNVP(d, "datasetInfo");
ar & BOOST_SERIALIZATION_NVP(d);
if (Archive::is_loading::value)
{
if (datasetInfo && ownsInfo)
@@ -724,18 +722,18 @@ void HoeffdingTree<
children.clear();
}
ar & CreateNVP(majorityClass, "majorityClass");
ar & CreateNVP(majorityProbability, "majorityProbability");
ar & BOOST_SERIALIZATION_NVP(majorityClass);
ar & BOOST_SERIALIZATION_NVP(majorityProbability);
// Depending on whether or not we have split yet, we may need to save
// different things.
if (splitDimension == size_t(-1))
{
// We have not yet split. So we have to serialize the splits.
ar & CreateNVP(numSamples, "numSamples");
ar & CreateNVP(numClasses, "numClasses");
ar & CreateNVP(maxSamples, "maxSamples");
ar & CreateNVP(successProbability, "successProbability");
ar & BOOST_SERIALIZATION_NVP(numSamples);
ar & BOOST_SERIALIZATION_NVP(numClasses);
ar & BOOST_SERIALIZATION_NVP(maxSamples);
ar & BOOST_SERIALIZATION_NVP(successProbability);
// Serialize the splits, but not if we haven't seen any samples yet (in
// which case we can just reinitialize).
@@ -766,34 +764,24 @@ void HoeffdingTree<
return;
// Serialize numeric splits.
for (size_t i = 0; i < numericSplits.size(); ++i)
{
std::ostringstream name;
name << "numericSplit" << i;
ar & CreateNVP(numericSplits[i], name.str());
}
ar & BOOST_SERIALIZATION_NVP(numericSplits);
// Serialize categorical splits.
for (size_t i = 0; i < categoricalSplits.size(); ++i)
{
std::ostringstream name;
name << "categoricalSplit" << i;
ar & CreateNVP(categoricalSplits[i], name.str());
}
ar & BOOST_SERIALIZATION_NVP(categoricalSplits);
}
else
{
// We have split, so we only need to save the split and the children.
if (datasetInfo->Type(splitDimension) == data::Datatype::categorical)
ar & CreateNVP(categoricalSplit, "categoricalSplit");
ar & BOOST_SERIALIZATION_NVP(categoricalSplit);
else
ar & CreateNVP(numericSplit, "numericSplit");
ar & BOOST_SERIALIZATION_NVP(numericSplit);
// Serialize the children, because we have split.
size_t numChildren;
if (Archive::is_saving::value)
numChildren = children.size();
ar & CreateNVP(numChildren, "numChildren");
ar & BOOST_SERIALIZATION_NVP(numChildren);
if (Archive::is_loading::value) // If needed, allocate space.
{
children.resize(numChildren, NULL);
@@ -805,7 +793,7 @@ void HoeffdingTree<
{
std::ostringstream name;
name << "child" << i;
ar & data::CreateNVP(*children[i], name.str());
ar & BOOST_SERIALIZATION_NVP(*children[i]);
// The child doesn't actually own its own DatasetInfo. We do. The same
// applies for the dimension mappings.
src/mlpack/methods/hoeffding_trees/hoeffding_tree_model.hpp
+5 -7
View File
@@ -161,10 +161,8 @@ class HoeffdingTreeModel
* Serialize the model.
*/
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */)
void serialize(Archive& ar, const unsigned int /* version */)
{
ar & data::CreateNVP(type, "type");
// Clear memory if needed.
if (Archive::is_loading::value)
{
@@ -186,28 +184,28 @@ class HoeffdingTreeModel
// Create fake tree to load into if needed.
if (Archive::is_loading::value)
giniHoeffdingTree = new GiniHoeffdingTreeType(info, 1, 1);
ar & data::CreateNVP(*giniHoeffdingTree, "giniHoeffdingTree");
ar & BOOST_SERIALIZATION_NVP(*giniHoeffdingTree);
}
else if (type == GINI_BINARY)
{
// Create fake tree to load into if needed.
if (Archive::is_loading::value)
giniBinaryTree = new GiniBinaryTreeType(info, 1, 1);
ar & data::CreateNVP(*giniBinaryTree, "giniBinaryTree");
ar & BOOST_SERIALIZATION_NVP(*giniBinaryTree);
}
else if (type == INFO_HOEFFDING)
{
// Create fake tree to load into if needed.
if (Archive::is_loading::value)
infoHoeffdingTree = new InfoHoeffdingTreeType(info, 1, 1);
ar & data::CreateNVP(*infoHoeffdingTree, "infoHoeffdingTree");
ar & BOOST_SERIALIZATION_NVP(*infoHoeffdingTree);
}
else if (type == INFO_BINARY)
{
// Create fake tree to load into if needed.
if (Archive::is_loading::value)
infoBinaryTree = new InfoBinaryTreeType(info, 1, 1);
ar & data::CreateNVP(*infoBinaryTree, "infoBinaryTree");
ar & BOOST_SERIALIZATION_NVP(*infoBinaryTree);
}
}
src/mlpack/methods/hoeffding_trees/numeric_split_info.hpp
+2 -2
View File
@@ -38,9 +38,9 @@ class NumericSplitInfo
//! Serialize the split (save/load the split points).
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */)
void serialize(Archive& ar, const unsigned int /* version */)
{
ar & data::CreateNVP(splitPoints, "splitPoints");
ar & BOOST_SERIALIZATION_NVP(splitPoints);
}
private:
src/mlpack/methods/kmeans/allow_empty_clusters.hpp
+1 -1
View File
@@ -62,7 +62,7 @@ class AllowEmptyClusters
//! Serialize the empty cluster policy (nothing to do).
template<typename Archive>
void Serialize(Archive& /* ar */, const unsigned int /* version */) { }
void serialize(Archive& /* ar */, const unsigned int /* version */) { }
};
} // namespace kmeans
src/mlpack/methods/kmeans/kill_empty_clusters.hpp
+1 -1
View File
@@ -62,7 +62,7 @@ class KillEmptyClusters
//! Serialize the empty cluster policy (nothing to do).
template<typename Archive>
void Serialize(Archive& /* ar */, const unsigned int /* version */) { }
void serialize(Archive& /* ar */, const unsigned int /* version */) { }
};
} // namespace kmeans
src/mlpack/methods/kmeans/kmeans.hpp
+1 -1
View File
@@ -178,7 +178,7 @@ class KMeans
//! Serialize the k-means object.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int version);
void serialize(Archive& ar, const unsigned int version);
private:
//! Maximum number of iterations before giving up.
src/mlpack/methods/kmeans/kmeans_impl.hpp
+5 -5
View File
@@ -347,12 +347,12 @@ void KMeans<MetricType,
InitialPartitionPolicy,
EmptyClusterPolicy,
LloydStepType,
MatType>::Serialize(Archive& ar, const unsigned int /* version */)
MatType>::serialize(Archive& ar, const unsigned int /* version */)
{
ar & data::CreateNVP(maxIterations, "max_iterations");
ar & data::CreateNVP(metric, "metric");
ar & data::CreateNVP(partitioner, "partitioner");
ar & data::CreateNVP(emptyClusterAction, "emptyClusterAction");
ar & BOOST_SERIALIZATION_NVP(maxIterations);
ar & BOOST_SERIALIZATION_NVP(metric);
ar & BOOST_SERIALIZATION_NVP(partitioner);
ar & BOOST_SERIALIZATION_NVP(emptyClusterAction);
}
} // namespace kmeans
src/mlpack/methods/kmeans/max_variance_new_cluster.hpp
+1 -1
View File
@@ -56,7 +56,7 @@ class MaxVarianceNewCluster
//! Serialize the object.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int version);
void serialize(Archive& ar, const unsigned int version);
private:
//! Index of iteration for which variance is cached.
src/mlpack/methods/kmeans/max_variance_new_cluster_impl.hpp
+1 -1
View File
@@ -100,7 +100,7 @@ size_t MaxVarianceNewCluster::EmptyCluster(const MatType& data,
//! Serialize the object.
template<typename Archive>
void MaxVarianceNewCluster::Serialize(Archive& /* ar */,
void MaxVarianceNewCluster::serialize(Archive& /* ar */,
const unsigned int /* version */)
{
// Serialization is useless here, because the only thing we store is
src/mlpack/methods/kmeans/random_partition.hpp
+1 -1
View File
@@ -52,7 +52,7 @@ class RandomPartition
//! Serialize the partitioner (nothing to do).
template<typename Archive>
void Serialize(Archive& /* ar */, const unsigned int /* version */) { }
void serialize(Archive& /* ar */, const unsigned int /* version */) { }
};
} // namespace kmeans
src/mlpack/methods/kmeans/refined_start.hpp
+3 -3
View File
@@ -89,10 +89,10 @@ class RefinedStart
//! Serialize the object.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */)
void serialize(Archive& ar, const unsigned int /* version */)
{
ar & data::CreateNVP(samplings, "samplings");
ar & data::CreateNVP(percentage, "percentage");
ar & BOOST_SERIALIZATION_NVP(samplings);
ar & BOOST_SERIALIZATION_NVP(percentage);
}
private:
src/mlpack/methods/lars/lars.hpp
+2 -2
View File
@@ -337,7 +337,7 @@ class LARS
* Serialize the LARS model.
*/
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */);
void serialize(Archive& ar, const unsigned int /* version */);
private:
//! Gram matrix.
@@ -428,7 +428,7 @@ class LARS
} // namespace regression
} // namespace mlpack
// Include implementation of Serialize().
// Include implementation of serialize().
#include "lars_impl.hpp"
#endif
src/mlpack/methods/lars/lars_impl.hpp
+16 -18
View File
@@ -22,34 +22,32 @@ namespace regression {
* Serialize the LARS model.
*/
template<typename Archive>
void LARS::Serialize(Archive& ar, const unsigned int /* version */)
void LARS::serialize(Archive& ar, const unsigned int /* version */)
{
using data::CreateNVP;
// If we're loading, we have to use the internal storage.
if (Archive::is_loading::value)
{
matGram = &matGramInternal;
ar & CreateNVP(matGramInternal, "matGramInternal");
ar & BOOST_SERIALIZATION_NVP(matGramInternal);
}
else
{
ar & CreateNVP(const_cast<arma::mat&>(*matGram), "matGramInternal");
ar & BOOST_SERIALIZATION_NVP(const_cast<arma::mat&>(*matGram));
}
ar & CreateNVP(matUtriCholFactor, "matUtriCholFactor");
ar & CreateNVP(useCholesky, "useCholesky");
ar & CreateNVP(lasso, "lasso");
ar & CreateNVP(lambda1, "lambda1");
ar & CreateNVP(elasticNet, "elasticNet");
ar & CreateNVP(lambda2, "lambda2");
ar & CreateNVP(tolerance, "tolerance");
ar & CreateNVP(betaPath, "betaPath");
ar & CreateNVP(lambdaPath, "lambdaPath");
ar & CreateNVP(activeSet, "activeSet");
ar & CreateNVP(isActive, "isActive");
ar & CreateNVP(ignoreSet, "ignoreSet");
ar & CreateNVP(isIgnored, "isIgnored");
ar & BOOST_SERIALIZATION_NVP(matUtriCholFactor);
ar & BOOST_SERIALIZATION_NVP(useCholesky);
ar & BOOST_SERIALIZATION_NVP(lasso);
ar & BOOST_SERIALIZATION_NVP(lambda1);
ar & BOOST_SERIALIZATION_NVP(elasticNet);
ar & BOOST_SERIALIZATION_NVP(lambda2);
ar & BOOST_SERIALIZATION_NVP(tolerance);
ar & BOOST_SERIALIZATION_NVP(betaPath);
ar & BOOST_SERIALIZATION_NVP(lambdaPath);
ar & BOOST_SERIALIZATION_NVP(activeSet);
ar & BOOST_SERIALIZATION_NVP(isActive);
ar & BOOST_SERIALIZATION_NVP(ignoreSet);
ar & BOOST_SERIALIZATION_NVP(isIgnored);
}
} // namespace regression
src/mlpack/methods/linear_regression/linear_regression.hpp
+4 -4
View File
@@ -199,11 +199,11 @@ class LinearRegression
* Serialize the model.
*/
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */)
void serialize(Archive& ar, const unsigned int /* version */)
{
ar & data::CreateNVP(parameters, "parameters");
ar & data::CreateNVP(lambda, "lambda");
ar & data::CreateNVP(intercept, "intercept");
ar & BOOST_SERIALIZATION_NVP(parameters);
ar & BOOST_SERIALIZATION_NVP(lambda);
ar & BOOST_SERIALIZATION_NVP(intercept);
}
private:
src/mlpack/methods/local_coordinate_coding/lcc.hpp
+1 -1
View File
@@ -197,7 +197,7 @@ class LocalCoordinateCoding
//! Serialize the model.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */);
void serialize(Archive& ar, const unsigned int /* version */);
private:
//! Number of atoms in dictionary.
src/mlpack/methods/local_coordinate_coding/lcc_impl.hpp
+6 -6
View File
@@ -106,14 +106,14 @@ void LocalCoordinateCoding::Train(
}
template<typename Archive>
void LocalCoordinateCoding::Serialize(Archive& ar,
void LocalCoordinateCoding::serialize(Archive& ar,
const unsigned int /* version */)
{
ar & data::CreateNVP(atoms, "atoms");
ar & data::CreateNVP(dictionary, "dictionary");
ar & data::CreateNVP(lambda, "lambda");
ar & data::CreateNVP(maxIterations, "maxIterations");
ar & data::CreateNVP(tolerance, "tolerance");
ar & BOOST_SERIALIZATION_NVP(atoms);
ar & BOOST_SERIALIZATION_NVP(dictionary);
ar & BOOST_SERIALIZATION_NVP(lambda);
ar & BOOST_SERIALIZATION_NVP(maxIterations);
ar & BOOST_SERIALIZATION_NVP(tolerance);
}
} // namespace lcc
src/mlpack/methods/logistic_regression/logistic_regression.hpp
+1 -1
View File
@@ -248,7 +248,7 @@ class LogisticRegression
//! Serialize the model.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */);
void serialize(Archive& ar, const unsigned int /* version */);
private:
//! Vector of trained parameters (size: dimensionality plus one).
src/mlpack/methods/logistic_regression/logistic_regression_impl.hpp
+3 -3
View File
@@ -175,12 +175,12 @@ double LogisticRegression<MatType>::ComputeAccuracy(
template<typename MatType>
template<typename Archive>
void LogisticRegression<MatType>::Serialize(
void LogisticRegression<MatType>::serialize(
Archive& ar,
const unsigned int /* version */)
{
ar & data::CreateNVP(parameters, "parameters");
ar & data::CreateNVP(lambda, "lambda");
ar & BOOST_SERIALIZATION_NVP(parameters);
ar & BOOST_SERIALIZATION_NVP(lambda);
}
} // namespace regression
src/mlpack/methods/lsh/lsh_search.hpp
+1 -1
View File
@@ -259,7 +259,7 @@ class LSHSearch
* @param ar Archive to serialize to.
*/
template<typename Archive>
void Serialize(Archive& ar, const unsigned int version);
void serialize(Archive& ar, const unsigned int version);
//! Return the number of distance evaluations performed.
size_t DistanceEvaluations() const { return distanceEvaluations; }
src/mlpack/methods/lsh/lsh_search_impl.hpp
+19 -26
View File
@@ -1062,11 +1062,9 @@ double LSHSearch<SortPolicy>::ComputeRecall(
template<typename SortPolicy>
template<typename Archive>
void LSHSearch<SortPolicy>::Serialize(Archive& ar,
void LSHSearch<SortPolicy>::serialize(Archive& ar,
const unsigned int version)
{
using data::CreateNVP;
// If we are loading, we are going to own the reference set.
if (Archive::is_loading::value)
{
@@ -1074,10 +1072,10 @@ void LSHSearch<SortPolicy>::Serialize(Archive& ar,
delete referenceSet;
ownsSet = true;
}
ar & CreateNVP(referenceSet, "referenceSet");
ar & BOOST_SERIALIZATION_NVP(referenceSet);
ar & CreateNVP(numProj, "numProj");
ar & CreateNVP(numTables, "numTables");
ar & BOOST_SERIALIZATION_NVP(numProj);
ar & BOOST_SERIALIZATION_NVP(numTables);
// Delete existing projections, if necessary.
if (Archive::is_loading::value)
@@ -1088,7 +1086,7 @@ void LSHSearch<SortPolicy>::Serialize(Archive& ar,
if (version == 0)
{
std::vector<arma::mat> tmpProj;
ar & CreateNVP(tmpProj, "projections");
ar & BOOST_SERIALIZATION_NVP(tmpProj);
projections.set_size(tmpProj[0].n_rows, tmpProj[0].n_cols, tmpProj.size());
for (size_t i = 0; i < tmpProj.size(); ++i)
@@ -1096,14 +1094,14 @@ void LSHSearch<SortPolicy>::Serialize(Archive& ar,
}
else
{
ar & CreateNVP(projections, "projections");
ar & BOOST_SERIALIZATION_NVP(projections);
}
ar & CreateNVP(offsets, "offsets");
ar & CreateNVP(hashWidth, "hashWidth");
ar & CreateNVP(secondHashSize, "secondHashSize");
ar & CreateNVP(secondHashWeights, "secondHashWeights");
ar & CreateNVP(bucketSize, "bucketSize");
ar & BOOST_SERIALIZATION_NVP(offsets);
ar & BOOST_SERIALIZATION_NVP(hashWidth);
ar & BOOST_SERIALIZATION_NVP(secondHashSize);
ar & BOOST_SERIALIZATION_NVP(secondHashWeights);
ar & BOOST_SERIALIZATION_NVP(bucketSize);
// needs specific handling for new version
// Backward compatibility: in older versions of LSHSearch, the secondHashTable
@@ -1112,7 +1110,7 @@ void LSHSearch<SortPolicy>::Serialize(Archive& ar,
if (version == 0)
{
arma::Mat<size_t> tmpSecondHashTable;
ar & CreateNVP(tmpSecondHashTable, "secondHashTable");
ar & BOOST_SERIALIZATION_NVP(tmpSecondHashTable);
// The old secondHashTable was stored in row-major format, so we transpose
// it.
@@ -1140,7 +1138,7 @@ void LSHSearch<SortPolicy>::Serialize(Archive& ar,
size_t tables;
if (Archive::is_saving::value)
tables = secondHashTable.size();
ar & CreateNVP(tables, "numSecondHashTables");
ar & BOOST_SERIALIZATION_NVP(tables);
// Set size of second hash table if needed.
if (Archive::is_loading::value)
@@ -1149,12 +1147,7 @@ void LSHSearch<SortPolicy>::Serialize(Archive& ar,
secondHashTable.resize(tables);
}
for (size_t i = 0; i < secondHashTable.size(); ++i)
{
std::ostringstream oss;
oss << "secondHashTable" << i;
ar & CreateNVP(secondHashTable[i], oss.str());
}
ar & BOOST_SERIALIZATION_NVP(secondHashTable);
}
// Backward compatibility: old versions of LSHSearch held bucketContentSize
@@ -1166,8 +1159,8 @@ void LSHSearch<SortPolicy>::Serialize(Archive& ar,
// it. But we can't do that until we have bucketRowInHashTable, so we also
// have to load that.
arma::Col<size_t> tmpBucketContentSize;
ar & CreateNVP(tmpBucketContentSize, "bucketContentSize");
ar & CreateNVP(bucketRowInHashTable, "bucketRowInHashTable");
ar & BOOST_SERIALIZATION_NVP(tmpBucketContentSize);
ar & BOOST_SERIALIZATION_NVP(bucketRowInHashTable);
// Compress into a smaller vector by just dropping all of the zeros.
bucketContentSize.set_size(secondHashTable.size());
@@ -1177,11 +1170,11 @@ void LSHSearch<SortPolicy>::Serialize(Archive& ar,
}
else
{
ar & CreateNVP(bucketContentSize, "bucketContentSize");
ar & CreateNVP(bucketRowInHashTable, "bucketRowInHashTable");
ar & BOOST_SERIALIZATION_NVP(bucketContentSize);
ar & BOOST_SERIALIZATION_NVP(bucketRowInHashTable);
}
ar & CreateNVP(distanceEvaluations, "distanceEvaluations");
ar & BOOST_SERIALIZATION_NVP(distanceEvaluations);
}
} // namespace neighbor
src/mlpack/methods/naive_bayes/naive_bayes_classifier.hpp
+1 -1
View File
@@ -213,7 +213,7 @@ class NaiveBayesClassifier
//! Serialize the classifier.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */);
void serialize(Archive& ar, const unsigned int /* version */);
private:
//! Sample mean for each class.
src/mlpack/methods/naive_bayes/naive_bayes_classifier_impl.hpp
+4 -4
View File
@@ -339,13 +339,13 @@ void NaiveBayesClassifier<ModelMatType>::Classify(
template<typename ModelMatType>
template<typename Archive>
void NaiveBayesClassifier<ModelMatType>::Serialize(
void NaiveBayesClassifier<ModelMatType>::serialize(
Archive& ar,
const unsigned int /* version */)
{
ar & data::CreateNVP(means, "means");
ar & data::CreateNVP(variances, "variances");
ar & data::CreateNVP(probabilities, "probabilities");
ar & BOOST_SERIALIZATION_NVP(means);
ar & BOOST_SERIALIZATION_NVP(variances);
ar & BOOST_SERIALIZATION_NVP(probabilities);
}
} // namespace naive_bayes
src/mlpack/methods/naive_bayes/nbc_main.cpp
+3 -3
View File
@@ -77,10 +77,10 @@ struct NBCModel
//! Serialize the model.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */)
void serialize(Archive& ar, const unsigned int /* version */)
{
ar & data::CreateNVP(nbc, "nbc");
ar & data::CreateNVP(mappings, "mappings");
ar & BOOST_SERIALIZATION_NVP(nbc);
ar & BOOST_SERIALIZATION_NVP(mappings);
}
};
src/mlpack/methods/neighbor_search/neighbor_search.hpp
+1 -1
View File
@@ -402,7 +402,7 @@ class NeighborSearch
//! Serialize the NeighborSearch model.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */);
void serialize(Archive& ar, const unsigned int /* version */);
private:
//! Permutations of reference points during tree building.
src/mlpack/methods/neighbor_search/neighbor_search_impl.hpp
+7 -9
View File
@@ -1082,15 +1082,13 @@ template<typename SortPolicy,
template<typename> class SingleTreeTraversalType>
template<typename Archive>
void NeighborSearch<SortPolicy, MetricType, MatType, TreeType,
DualTreeTraversalType, SingleTreeTraversalType>::Serialize(
DualTreeTraversalType, SingleTreeTraversalType>::serialize(
Archive& ar,
const unsigned int /* version */)
{
using data::CreateNVP;
// Serialize preferences for search.
ar & CreateNVP(searchMode, "searchMode");
ar & CreateNVP(treeNeedsReset, "treeNeedsReset");
ar & BOOST_SERIALIZATION_NVP(searchMode);
ar & BOOST_SERIALIZATION_NVP(treeNeedsReset);
// If we are doing naive search, we serialize the dataset. Otherwise we
// serialize the tree.
@@ -1105,8 +1103,8 @@ DualTreeTraversalType, SingleTreeTraversalType>::Serialize(
setOwner = true; // We will own the reference set when we load it.
}
ar & CreateNVP(referenceSet, "referenceSet");
ar & CreateNVP(metric, "metric");
ar & BOOST_SERIALIZATION_NVP(referenceSet);
ar & BOOST_SERIALIZATION_NVP(metric);
// If we are loading, set the tree to NULL and clean up memory if necessary.
if (Archive::is_loading::value)
@@ -1131,8 +1129,8 @@ DualTreeTraversalType, SingleTreeTraversalType>::Serialize(
treeOwner = true;
}
ar & CreateNVP(referenceTree, "referenceTree");
ar & CreateNVP(oldFromNewReferences, "oldFromNewReferences");
ar & BOOST_SERIALIZATION_NVP(referenceTree);
ar & BOOST_SERIALIZATION_NVP(oldFromNewReferences);
// If we are loading, set the dataset accordingly and clean up memory if
// necessary.
src/mlpack/methods/neighbor_search/neighbor_search_stat.hpp
+5 -7
View File
@@ -92,14 +92,12 @@ class NeighborSearchStat
//! Serialize the statistic to/from an archive.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */)
void serialize(Archive& ar, const unsigned int /* version */)
{
using data::CreateNVP;
ar & CreateNVP(firstBound, "firstBound");
ar & CreateNVP(secondBound, "secondBound");
ar & CreateNVP(auxBound, "auxBound");
ar & CreateNVP(lastDistance, "lastDistance");
ar & BOOST_SERIALIZATION_NVP(firstBound);
ar & BOOST_SERIALIZATION_NVP(secondBound);
ar & BOOST_SERIALIZATION_NVP(auxBound);
ar & BOOST_SERIALIZATION_NVP(lastDistance);
}
};
src/mlpack/methods/neighbor_search/ns_model.hpp
+1 -1
View File
@@ -368,7 +368,7 @@ class NSModel
//! Serialize the neighbor search model.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */);
void serialize(Archive& ar, const unsigned int /* version */);
//! Expose the dataset.
const arma::mat& Dataset() const;
src/mlpack/methods/neighbor_search/ns_model_impl.hpp
+9 -9
View File
@@ -377,32 +377,32 @@ void serialize(
SingleTreeTraversalType>& ns,
const unsigned int version)
{
ns.Serialize(ar, version);
ns.serialize(ar, version);
}
//! Serialize the kNN model.
template<typename SortPolicy>
template<typename Archive>
void NSModel<SortPolicy>::Serialize(Archive& ar, const unsigned int version)
void NSModel<SortPolicy>::serialize(Archive& ar, const unsigned int version)
{
ar & data::CreateNVP(treeType, "treeType");
ar & BOOST_SERIALIZATION_NVP(treeType);
// Backward compatibility: older versions of NSModel didn't include these
// parameters.
if (version > 0)
{
ar & data::CreateNVP(leafSize, "leafSize");
ar & data::CreateNVP(tau, "tau");
ar & data::CreateNVP(rho, "rho");
ar & BOOST_SERIALIZATION_NVP(leafSize);
ar & BOOST_SERIALIZATION_NVP(tau);
ar & BOOST_SERIALIZATION_NVP(rho);
}
ar & data::CreateNVP(randomBasis, "randomBasis");
ar & data::CreateNVP(q, "q");
ar & BOOST_SERIALIZATION_NVP(randomBasis);
ar & BOOST_SERIALIZATION_NVP(q);
// This should never happen, but just in case, be clean with memory.
if (Archive::is_loading::value)
boost::apply_visitor(DeleteVisitor(), nSearch);
const std::string& name = NSModelName<SortPolicy>::Name();
ar & data::CreateNVP(nSearch, name);
ar & BOOST_SERIALIZATION_NVP(nSearch);
}
//! Expose the dataset.
src/mlpack/methods/perceptron/perceptron.hpp
+1 -1
View File
@@ -120,7 +120,7 @@ class Perceptron
* Serialize the perceptron.
*/
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */);
void serialize(Archive& ar, const unsigned int /* version */);
//! Get the maximum number of iterations.
size_t MaxIterations() const { return maxIterations; }
src/mlpack/methods/perceptron/perceptron_impl.hpp
+4 -4
View File
@@ -197,15 +197,15 @@ template<typename LearnPolicy,
typename WeightInitializationPolicy,
typename MatType>
template<typename Archive>
void Perceptron<LearnPolicy, WeightInitializationPolicy, MatType>::Serialize(
void Perceptron<LearnPolicy, WeightInitializationPolicy, MatType>::serialize(
Archive& ar,
const unsigned int /* version */)
{
// We just need to serialize the maximum number of iterations, the weights,
// and the biases.
ar & data::CreateNVP(maxIterations, "maxIterations");
ar & data::CreateNVP(weights, "weights");
ar & data::CreateNVP(biases, "biases");
ar & BOOST_SERIALIZATION_NVP(maxIterations);
ar & BOOST_SERIALIZATION_NVP(weights);
ar & BOOST_SERIALIZATION_NVP(biases);
}
} // namespace perceptron
src/mlpack/methods/perceptron/perceptron_main.cpp
+3 -3
View File
@@ -93,10 +93,10 @@ class PerceptronModel
const Col<size_t>& Map() const { return map; }
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */)
void serialize(Archive& ar, const unsigned int /* version */)
{
ar & data::CreateNVP(p, "perceptron");
ar & data::CreateNVP(map, "mappings");
ar & BOOST_SERIALIZATION_NVP(p);
ar & BOOST_SERIALIZATION_NVP(map);
}
};
src/mlpack/methods/random_forest/random_forest.hpp
+1 -1
View File
@@ -256,7 +256,7 @@ class RandomForest
* Serialize the random forest.
*/
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */);
void serialize(Archive& ar, const unsigned int /* version */);
private:
/**
src/mlpack/methods/random_forest/random_forest_impl.hpp
+3 -8
View File
@@ -385,7 +385,7 @@ void RandomForest<
NumericSplitType,
CategoricalSplitType,
ElemType
>::Serialize(Archive& ar, const unsigned int /* version */)
>::serialize(Archive& ar, const unsigned int /* version */)
{
size_t numTrees;
if (Archive::is_loading::value)
@@ -393,18 +393,13 @@ void RandomForest<
else
numTrees = trees.size();
ar & data::CreateNVP(numTrees, "numTrees");
ar & BOOST_SERIALIZATION_NVP(numTrees);
// Allocate space if needed.
if (Archive::is_loading::value)
trees.resize(numTrees);
for (size_t i = 0; i < numTrees; ++i)
{
std::ostringstream oss;
oss << "tree" << i;
ar & data::CreateNVP(trees[i], oss.str());
}
ar & BOOST_SERIALIZATION_NVP(trees);
}
template<
src/mlpack/methods/random_forest/random_forest_main.cpp
+2 -2
View File
@@ -56,9 +56,9 @@ class RandomForestModel
// Serialize the model.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */)
void serialize(Archive& ar, const unsigned int /* version */)
{
ar & data::CreateNVP(rf, "random_forest");
ar & BOOST_SERIALIZATION_NVP(rf);
}
};
src/mlpack/methods/range_search/range_search.hpp
+1 -1
View File
@@ -316,7 +316,7 @@ class RangeSearch
//! Serialize the model.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int version);
void serialize(Archive& ar, const unsigned int version);
//! Return the reference set.
const MatType& ReferenceSet() const { return *referenceSet; }
src/mlpack/methods/range_search/range_search_impl.hpp
+7 -9
View File
@@ -738,15 +738,13 @@ template<typename MetricType,
typename TreeStatType,
typename TreeMatType> class TreeType>
template<typename Archive>
void RangeSearch<MetricType, MatType, TreeType>::Serialize(
void RangeSearch<MetricType, MatType, TreeType>::serialize(
Archive& ar,
const unsigned int /* version */)
{
using data::CreateNVP;
// Serialize preferences for search.
ar & CreateNVP(naive, "naive");
ar & CreateNVP(singleMode, "singleMode");
ar & BOOST_SERIALIZATION_NVP(naive);
ar & BOOST_SERIALIZATION_NVP(singleMode);
// Reset base cases and scores if we are loading.
if (Archive::is_loading::value)
@@ -767,8 +765,8 @@ void RangeSearch<MetricType, MatType, TreeType>::Serialize(
setOwner = true;
}
ar & CreateNVP(referenceSet, "referenceSet");
ar & CreateNVP(metric, "metric");
ar & BOOST_SERIALIZATION_NVP(referenceSet);
ar & BOOST_SERIALIZATION_NVP(metric);
// If we are loading, set the tree to NULL and clean up memory if necessary.
if (Archive::is_loading::value)
@@ -793,8 +791,8 @@ void RangeSearch<MetricType, MatType, TreeType>::Serialize(
treeOwner = true;
}
ar & CreateNVP(referenceTree, "referenceTree");
ar & CreateNVP(oldFromNewReferences, "oldFromNewReferences");
ar & BOOST_SERIALIZATION_NVP(referenceTree);
ar & BOOST_SERIALIZATION_NVP(oldFromNewReferences);
// If we are loading, set the dataset accordingly and clean up memory if
// necessary.
src/mlpack/methods/range_search/range_search_stat.hpp
+2 -2
View File
@@ -46,9 +46,9 @@ class RangeSearchStat
//! Serialize the statistic.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */)
void serialize(Archive& ar, const unsigned int /* version */)
{
ar & data::CreateNVP(lastDistance, "lastDistance");
ar & BOOST_SERIALIZATION_NVP(lastDistance);
}
private:
src/mlpack/methods/range_search/rs_model.hpp
+2 -2
View File
@@ -331,7 +331,7 @@ class RSModel
//! Serialize the range search model.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */);
void serialize(Archive& ar, const unsigned int /* version */);
//! Expose the dataset.
const arma::mat& Dataset() const;
@@ -420,7 +420,7 @@ class RSModel
} // namespace range
} // namespace mlpack
// Include implementation (of Serialize() and inline functions).
// Include implementation (of serialize() and inline functions).
#include "rs_model_impl.hpp"
#endif
src/mlpack/methods/range_search/rs_model_impl.hpp
+6 -8
View File
@@ -2,7 +2,7 @@
* @file rs_model_impl.hpp
* @author Ryan Curtin
*
* Implementation of Serialize() and inline functions for RSModel.
* Implementation of serialize() and inline functions for RSModel.
*
* 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
@@ -458,7 +458,7 @@ template<typename Archive>
template<typename RSType>
void SerializeVisitor<Archive>::operator()(RSType* rs) const
{
ar & data::CreateNVP(rs, name);
ar & BOOST_SERIALIZATION_NVP(rs);
}
//! Return whether single mode enabled
@@ -481,13 +481,11 @@ bool& NaiveVisitor::operator()(RSType* rs) const
// Serialize the model.
template<typename Archive>
void RSModel::Serialize(Archive& ar, const unsigned int /* version */)
void RSModel::serialize(Archive& ar, const unsigned int /* version */)
{
using data::CreateNVP;
ar & CreateNVP(treeType, "treeType");
ar & CreateNVP(randomBasis, "randomBasis");
ar & CreateNVP(q, "q");
ar & BOOST_SERIALIZATION_NVP(treeType);
ar & BOOST_SERIALIZATION_NVP(randomBasis);
ar & BOOST_SERIALIZATION_NVP(q);
// This should never happen, but just in case...
if (Archive::is_loading::value)
src/mlpack/methods/rann/ra_model.hpp
+1 -1
View File
@@ -379,7 +379,7 @@ class RAModel
//! Serialize the model.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */);
void serialize(Archive& ar, const unsigned int /* version */);
//! Expose the dataset.
const arma::mat& Dataset() const;
src/mlpack/methods/rann/ra_model_impl.hpp
+5 -5
View File
@@ -246,7 +246,7 @@ template<typename Archive>
template<typename RAType>
void SerializeVisitor<Archive>::operator()(RAType*& ra) const
{
ar & data::CreateNVP(ra, name);
ar & BOOST_SERIALIZATION_NVP(ra);
}
//! Exposes the Naive() method of the given RAType instance.
@@ -344,12 +344,12 @@ RAModel<SortPolicy>::~RAModel()
template<typename SortPolicy>
template<typename Archive>
void RAModel<SortPolicy>::Serialize(Archive& ar,
void RAModel<SortPolicy>::serialize(Archive& ar,
const unsigned int /* version */)
{
ar & data::CreateNVP(treeType, "treeType");
ar & data::CreateNVP(randomBasis, "randomBasis");
ar & data::CreateNVP(q, "q");
ar & BOOST_SERIALIZATION_NVP(treeType);
ar & BOOST_SERIALIZATION_NVP(randomBasis);
ar & BOOST_SERIALIZATION_NVP(q);
// This should never happen, but just in case, be clean with memory.
if (Archive::is_loading::value)
src/mlpack/methods/rann/ra_query_stat.hpp
+3 -3
View File
@@ -62,10 +62,10 @@ class RAQueryStat
//! Serialize the statistic.
template<typename Archive>
void Serialize(Archive& ar, const unsigned int /* version */)
void serialize(Archive& ar, const unsigned int /* version */)
{
ar & data::CreateNVP(bound, "bound");
ar & data::CreateNVP(numSamplesMade, "numSamplesMade");
ar & BOOST_SERIALIZATION_NVP(bound);
ar & BOOST_SERIALIZATION_NVP(numSamplesMade);
}
private:

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