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Merge branch 'master' into msle

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This commit is contained in:
Saksham Rastogi
2020-02-23 05:06:14 +05:30
committed by GitHub
parent ae897db406 cfaab7877b
commit 1d2ea64020
43 changed files with 2157 additions and 281 deletions
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.ci/macos-steps.yaml
+1
View File
@@ -15,6 +15,7 @@ steps:
sudo xcode-select --switch /Applications/Xcode_10.1.app/Contents/Developer
unset BOOST_ROOT
pip install cython numpy pandas zipp
brew update
brew install openblas armadillo boost
if [ "a$(julia.version)" != "a" ]; then
COPYRIGHT.txt
+2
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@@ -125,6 +125,8 @@ Copyright:
Copyright 2019, Rohit Kartik <rohit.audrey@gmail.com>
Copyright 2019, Aditya Viki <adityaviki01@gmail.com>
Copyright 2019, Kartik Dutt <kartikdutt@live.in>
Copyright 2020, Sriram S K <sriramsk1999@gmail.com>
Copyright 2020, Manoranjan Kumar Bharti ( Nakul Bharti ) <knakul853@gmail.com>
License: BSD-3-clause
All rights reserved.
HISTORY.md
+10 -1
View File
@@ -2,7 +2,9 @@
###### ????-??-??
* Added `mean squared logarithmic error` loss function for neural networks
(#2210).
* Added `mean bias loss function` for neural networks (#2210).
* The DecisionStump class has been marked deprecated; use the `DecisionTree`
class with `NoRecursion=true` or use `ID3DecisionStump` instead (#2099).
@@ -33,10 +35,17 @@
* Add Mish activation function (#2158).
* Update `init_rules` in AMF to allow users to merge two initialization
rules (#2151).
* Add GELU activation function (#2183).
* Better error handling of eigendecompositions and Cholesky decompositions
(#2088, #1840).
* Add LiSHT activation function (#2182).
* Add Valid and Same Padding for Transposed Convolution layer (#2163).
### mlpack 3.2.2
###### 2019-11-26
doc/guide/sample_ml_app.hpp
+8 -8
View File
@@ -27,20 +27,20 @@ mlpack and dependencies in Release Mode).
- Right click on the project and select Properties, select the x64 Debug profile
- Under C/C++ > General > Additional Include Directories add:
@code
- C:\boost\boost_1_66_0
- C:\mlpack\armadillo-8.500.1\include
- C:\mlpack\mlpack-3.2.1\build\include
- C:\boost\boost_1_71_0\lib\native\include
- C:\mlpack\armadillo-9.800.3\include
- C:\mlpack\mlpack-3.2.2\build\include
@endcode
- Under Linker > Input > Additional Dependencies add:
@code
- C:\mlpack\mlpack-3.2.1\build\Debug\mlpack.lib
- C:\boost\boost_1_66_0\lib64-msvc-14.1\libboost_serialization-vc141-mt-gd-x64-1_66.lib
- C:\boost\boost_1_66_0\lib64-msvc-14.1\libboost_program_options-vc141-mt-gd-x64-1_66.lib
- C:\mlpack\mlpack-3.2.2\build\Debug\mlpack.lib
- C:\boost\boost_1_71_0\lib64-msvc-14.2\libboost_serialization-vc142-mt-gd-x64-1_71.lib
- C:\boost\boost_1_71_0\lib64-msvc-14.2\libboost_program_options-vc142-mt-gd-x64-1_71.lib
@endcode
- Under Build Events > Post-Build Event > Command Line add:
@code
- xcopy /y "C:\mlpack\mlpack-3.2.1\build\Debug\mlpack.dll" $(OutDir)
- xcopy /y "C:\mlpack\mlpack-3.2.1\packages\OpenBLAS.0.2.14.1\lib\native\bin\x64\*.dll" $(OutDir)
- xcopy /y "C:\mlpack\mlpack-3.2.2\build\Debug\mlpack.dll" $(OutDir)
- xcopy /y "C:\mlpack\mlpack-3.2.2\packages\OpenBLAS.0.2.14.1\lib\native\bin\x64\*.dll" $(OutDir)
@endcode
@note Recent versions of Visual Studio set "Conformance Mode" enabled by default. This causes some issues with
src/mlpack/core/tree/binary_space_tree/binary_space_tree.hpp
+15 -1
View File
@@ -273,7 +273,7 @@ class BinarySpaceTree
* Create a binary space tree by copying the other tree. Be careful! This
* can take a long time and use a lot of memory.
*
* @param other Tree to be replicated.
* @param other Tree to be copied.
*/
BinarySpaceTree(const BinarySpaceTree& other);
@@ -283,6 +283,20 @@ class BinarySpaceTree
*/
BinarySpaceTree(BinarySpaceTree&& other);
/**
* Copy the given BinarySaceTree.
*
* @param other The tree to be copied.
*/
BinarySpaceTree& operator=(const BinarySpaceTree& other);
/**
* Take ownership of the given BinarySpaceTree.
*
* @param other The tree to take ownership of.
*/
BinarySpaceTree& operator=(BinarySpaceTree&& other);
/**
* Initialize the tree from a boost::serialization archive.
*
src/mlpack/core/tree/binary_space_tree/binary_space_tree_impl.hpp
+122
View File
@@ -341,6 +341,7 @@ BinarySpaceTree(
stat(other.stat),
parentDistance(other.parentDistance),
furthestDescendantDistance(other.furthestDescendantDistance),
minimumBoundDistance(other.minimumBoundDistance),
// Copy matrix, but only if we are the root.
dataset((other.parent == NULL) ? new MatType(*other.dataset) : NULL)
{
@@ -379,6 +380,126 @@ BinarySpaceTree(
}
}
/**
* Copy assignment operator: copy the given other tree.
*/
template<typename MetricType,
typename StatisticType,
typename MatType,
template<typename BoundMetricType, typename...> class BoundType,
template<typename SplitBoundType, typename SplitMatType>
class SplitType>
BinarySpaceTree<MetricType, StatisticType, MatType, BoundType, SplitType>&
BinarySpaceTree<MetricType, StatisticType, MatType, BoundType, SplitType>::
operator=(const BinarySpaceTree& other)
{
// Return if it's the same tree.
if (this == &other)
return *this;
// Freeing memory that will not be used anymore.
delete dataset;
delete left;
delete right;
left = NULL;
right = NULL;
parent = other.Parent();
begin = other.Begin();
count = other.Count();
bound = other.bound;
stat = other.stat;
parentDistance = other.ParentDistance();
furthestDescendantDistance = other.FurthestDescendantDistance();
minimumBoundDistance = other.MinimumBoundDistance();
// Copy matrix, but only if we are the root.
dataset = ((other.parent == NULL) ? new MatType(*other.dataset) : NULL);
// Create left and right children (if any).
if (other.Left())
{
left = new BinarySpaceTree(*other.Left());
left->Parent() = this; // Set parent to this, not other tree.
}
if (other.Right())
{
right = new BinarySpaceTree(*other.Right());
right->Parent() = this; // Set parent to this, not other tree.
}
// Propagate matrix, but only if we are the root.
if (parent == NULL)
{
std::queue<BinarySpaceTree*> queue;
if (left)
queue.push(left);
if (right)
queue.push(right);
while (!queue.empty())
{
BinarySpaceTree* node = queue.front();
queue.pop();
node->dataset = dataset;
if (node->left)
queue.push(node->left);
if (node->right)
queue.push(node->right);
}
}
return *this;
}
/**
* Move assignment operator: take ownership of the given tree.
*/
template<typename MetricType,
typename StatisticType,
typename MatType,
template<typename BoundMetricType, typename...> class BoundType,
template<typename SplitBoundType, typename SplitMatType>
class SplitType>
BinarySpaceTree<MetricType, StatisticType, MatType, BoundType, SplitType>&
BinarySpaceTree<MetricType, StatisticType, MatType, BoundType, SplitType>::
operator=(BinarySpaceTree&& other)
{
// Return if it's the same tree.
if (this == &other)
return *this;
// Freeing memory that will not be used anymore.
delete dataset;
delete left;
delete right;
parent = other.Parent();
left = other.Left();
right = other.Right();
begin = other.Begin();
count = other.Count();
bound = std::move(other.bound);
stat = std::move(other.stat);
parentDistance = other.ParentDistance();
furthestDescendantDistance = other.FurthestDescendantDistance();
minimumBoundDistance = other.MinimumBoundDistance();
dataset = other.dataset;
other.left = NULL;
other.right = NULL;
other.parent = NULL;
other.begin = 0;
other.count = 0;
other.parentDistance = 0.0;
other.furthestDescendantDistance = 0.0;
other.minimumBoundDistance = 0.0;
other.dataset = NULL;
return *this;
}
/**
* Move constructor.
*/
@@ -406,6 +527,7 @@ BinarySpaceTree(BinarySpaceTree&& other) :
// tree's contents, so it doesn't delete anything when it is destructed.
other.left = NULL;
other.right = NULL;
other.parent = NULL;
other.begin = 0;
other.count = 0;
other.parentDistance = 0.0;
src/mlpack/core/tree/cosine_tree/cosine_tree.cpp
+221 -12
View File
@@ -18,11 +18,12 @@ namespace mlpack {
namespace tree {
CosineTree::CosineTree(const arma::mat& dataset) :
dataset(dataset),
dataset(&dataset),
parent(NULL),
left(NULL),
right(NULL),
numColumns(dataset.n_cols)
numColumns(dataset.n_cols),
localDataset(false)
{
// Initialize sizes of column indices and l2 norms.
indices.resize(numColumns);
@@ -47,11 +48,12 @@ CosineTree::CosineTree(const arma::mat& dataset) :
CosineTree::CosineTree(CosineTree& parentNode,
const std::vector<size_t>& subIndices) :
dataset(parentNode.GetDataset()),
dataset(&parentNode.GetDataset()),
parent(&parentNode),
left(NULL),
right(NULL),
numColumns(subIndices.size())
numColumns(subIndices.size()),
localDataset(false)
{
// Initialize sizes of column indices and l2 norms.
indices.resize(numColumns);
@@ -76,10 +78,11 @@ CosineTree::CosineTree(CosineTree& parentNode,
CosineTree::CosineTree(const arma::mat& dataset,
const double epsilon,
const double delta) :
dataset(dataset),
dataset(&dataset),
delta(delta),
left(NULL),
right(NULL)
right(NULL),
localDataset(false)
{
// Declare the cosine tree priority queue.
CosineNodeQueue treeQueue;
@@ -150,8 +153,214 @@ CosineTree::CosineTree(const arma::mat& dataset,
ConstructBasis(treeQueue);
}
//! Copy the given tree.
CosineTree::CosineTree(const CosineTree& other) :
// Copy matrix, but only if we are the root.
dataset((other.parent == NULL) ? new arma::mat(*other.dataset) : NULL),
delta(other.delta),
parent(NULL),
left(NULL),
right(NULL),
indices(other.indices),
l2NormsSquared(other.l2NormsSquared),
centroid(other.centroid),
basisVector(other.basisVector),
splitPointIndex(other.SplitPointIndex()),
numColumns(other.NumColumns()),
l2Error(other.L2Error()),
frobNormSquared(other.FrobNormSquared()),
localDataset(other.parent == NULL)
{
// Create left and right children (if any).
if (other.Left())
{
left = new CosineTree(*other.Left());
left->Parent() = this; // Set parent to this, not other tree.
}
if (other.Right())
{
right = new CosineTree(*other.Right());
right->Parent() = this; // Set parent to this, not other tree.
}
// Propagate matrix, but only if we are the root.
if (parent == NULL && localDataset)
{
std::queue<CosineTree*> queue;
if (left)
queue.push(left);
if (right)
queue.push(right);
while (!queue.empty())
{
CosineTree* node = queue.front();
queue.pop();
node->dataset = dataset;
if (node->left)
queue.push(node->left);
if (node->right)
queue.push(node->right);
}
}
}
//! Copy assignment operator: copy the given other tree.
CosineTree& CosineTree::operator=(const CosineTree& other)
{
// Return if it's the same tree.
if (this == &other)
return *this;
// Freeing memory that will not be used anymore.
if (localDataset)
delete dataset;
delete left;
delete right;
// Performing a deep copy of the dataset.
dataset = (other.parent == NULL) ? new arma::mat(*other.dataset) : NULL;
delta = other.delta;
parent = other.Parent();
left = other.Left();
right = other.Right();
indices = other.indices;
l2NormsSquared = other.l2NormsSquared;
centroid = other.centroid;
basisVector = other.basisVector;
splitPointIndex = other.SplitPointIndex();
numColumns = other.NumColumns();
l2Error = other.L2Error();
localDataset = (other.parent == NULL) ? true : false;
frobNormSquared = other.FrobNormSquared();
// Create left and right children (if any).
if (other.Left())
{
left = new CosineTree(*other.Left());
left->Parent() = this; // Set parent to this, not other tree.
}
if (other.Right())
{
right = new CosineTree(*other.Right());
right->Parent() = this; // Set parent to this, not other tree.
}
// Propagate matrix, but only if we are the root.
if (parent == NULL && localDataset)
{
std::queue<CosineTree*> queue;
if (left)
queue.push(left);
if (right)
queue.push(right);
while (!queue.empty())
{
CosineTree* node = queue.front();
queue.pop();
node->dataset = dataset;
if (node->left)
queue.push(node->left);
if (node->right)
queue.push(node->right);
}
}
return *this;
}
//! Move the given tree.
CosineTree::CosineTree(CosineTree&& other) :
dataset(other.dataset),
delta(std::move(other.delta)),
parent(other.parent),
left(other.left),
right(other.right),
indices(std::move(other.indices)),
l2NormsSquared(std::move(other.l2NormsSquared)),
centroid(std::move(other.centroid)),
basisVector(std::move(other.basisVector)),
splitPointIndex(other.splitPointIndex),
numColumns(other.numColumns),
l2Error(other.l2Error),
frobNormSquared(other.frobNormSquared),
localDataset(other.localDataset)
{
// Now we are a clone of the other tree. But we must also clear the other
// tree's contents, so it doesn't delete anything when it is destructed.
other.dataset = NULL;
other.parent = NULL;
other.left = NULL;
other.right = NULL;
other.splitPointIndex = 0;
other.numColumns = 0;
other.l2Error = -1;
other.localDataset = false;
other.frobNormSquared = 0;
// Set new parent.
if (left)
left->parent = this;
if (right)
right->parent = this;
}
//! Move assignment operator: take ownership of the given tree.
CosineTree& CosineTree::operator=(CosineTree&& other)
{
// Return if it's the same tree.
if (this == &other)
return *this;
// Freeing memory that will not be used anymore.
if (localDataset)
delete dataset;
delete left;
delete right;
dataset = other.dataset;
delta = std::move(other.delta);
parent = other.Parent();
left = other.Left();
right = other.Right();
indices = std::move(other.indices);
l2NormsSquared = std::move(other.l2NormsSquared);
centroid = std::move(other.centroid);
basisVector = std::move(other.basisVector);
splitPointIndex = other.SplitPointIndex();
numColumns = other.NumColumns();
l2Error = other.L2Error();
localDataset = other.localDataset;
frobNormSquared = other.FrobNormSquared();
// Now we are a clone of the other tree. But we must also clear the other
// tree's contents, so it doesn't delete anything when it is destructed.
other.dataset = NULL;
other.parent = NULL;
other.left = NULL;
other.right = NULL;
other.splitPointIndex = 0;
other.numColumns = 0;
other.l2Error = -1;
other.localDataset = false;
other.frobNormSquared = 0;
// Set new parent.
if (left)
left->parent = this;
if (right)
right->parent = this;
return *this;
}
CosineTree::~CosineTree()
{
if (localDataset)
delete dataset;
if (left)
delete left;
if (right)
@@ -206,7 +415,7 @@ double CosineTree::MonteCarloError(CosineTree* node,
node->ColumnSamplesLS(sampledIndices, probabilities, numSamples);
// Get pointer to the original dataset.
arma::mat dataset = node->GetDataset();
const arma::mat& dataset = node->GetDataset();
// Initialize weighted projection magnitudes as zeros.
arma::vec weightedMagnitudes;
@@ -280,7 +489,7 @@ double CosineTree::MonteCarloError(CosineTree* node,
void CosineTree::ConstructBasis(CosineNodeQueue& treeQueue)
{
// Initialize basis as matrix of zeros.
basis.zeros(dataset.n_rows, treeQueue.size());
basis.zeros(dataset->n_rows, treeQueue.size());
// Variables for iterating through the priority queue.
CosineTree *currentNode;
@@ -435,8 +644,8 @@ void CosineTree::CalculateCosines(arma::vec& cosines)
else
{
cosines(i) =
std::abs(arma::norm_dot(dataset.col(indices[splitPointIndex]),
dataset.col(indices[i])));
std::abs(arma::norm_dot(dataset->col(indices[splitPointIndex]),
dataset->col(indices[i])));
}
}
}
@@ -444,12 +653,12 @@ void CosineTree::CalculateCosines(arma::vec& cosines)
void CosineTree::CalculateCentroid()
{
// Initialize centroid as vector of zeros.
centroid.zeros(dataset.n_rows);
centroid.zeros(dataset->n_rows);
// Calculate centroid of columns in the node.
for (size_t i = 0; i < numColumns; i++)
{
centroid += dataset.col(indices[i]);
centroid += dataset->col(indices[i]);
}
centroid /= numColumns;
}
src/mlpack/core/tree/cosine_tree/cosine_tree.hpp
+33 -2
View File
@@ -68,6 +68,35 @@ class CosineTree
const double epsilon,
const double delta);
/**
* Copy the given tree. Be careful! This may use a lot of memory.
*
* @param other Tree to copy from.
*/
CosineTree(const CosineTree& other);
/**
* Move the given tree. The tree passed as a parameter will be emptied and
* will not be usable after this call.
*
* @param other Tree to move.
*/
CosineTree(CosineTree&& other);
/**
* Copy the given Cosine Tree.
*
* @param other The tree to be copied.
*/
CosineTree& operator=(const CosineTree& other);
/**
* Take ownership of the given Cosine Tree.
*
* @param other The tree to take ownership of.
*/
CosineTree& operator=(CosineTree&& other);
/**
* Clean up the CosineTree: release allocated memory (including children).
*/
@@ -169,7 +198,7 @@ class CosineTree
void GetFinalBasis(arma::mat& finalBasis) { finalBasis = basis; }
//! Get pointer to the dataset matrix.
const arma::mat& GetDataset() const { return dataset; }
const arma::mat& GetDataset() const { return *dataset; }
//! Get the indices of columns in the node.
std::vector<size_t>& VectorIndices() { return indices; }
@@ -214,7 +243,7 @@ class CosineTree
private:
//! Matrix for which cosine tree is constructed.
const arma::mat& dataset;
const arma::mat* dataset;
//! Cumulative probability for Monte Carlo error lower bound.
double delta;
//! Subspace basis of the input dataset.
@@ -241,6 +270,8 @@ class CosineTree
double l2Error;
//! Frobenius norm squared of columns in the node.
double frobNormSquared;
//! If true, we own the dataset and need to destroy it in the destructor.
bool localDataset;
};
class CompareCosineNode
src/mlpack/core/tree/cover_tree/cover_tree_impl.hpp
+2 -2
View File
@@ -548,7 +548,7 @@ CoverTree<MetricType, StatisticType, MatType, RootPointPolicy>::CoverTree(
}
}
// Copy Assignment.
// Copy assignment operator: copy the given other tree.
template<
typename MetricType,
typename StatisticType,
@@ -658,7 +658,7 @@ CoverTree<MetricType, StatisticType, MatType, RootPointPolicy>::CoverTree(
other.metric = NULL;
}
// Move Assignment.
// Move assignment operator: take ownership of the given tree.
template<
typename MetricType,
typename StatisticType,
src/mlpack/core/tree/octree/octree.hpp
+14
View File
@@ -220,6 +220,20 @@ class Octree
*/
Octree(Octree&& other);
/**
* Copy the given Octree.
*
* @param other The tree to be copied.
*/
Octree& operator=(const Octree& other);
/**
* Take ownership of the given Octree.
*
* @param other The tree to take ownership of.
*/
Octree& operator=(Octree&& other);
/**
* Initialize the tree from a boost::serialization archive.
*
src/mlpack/core/tree/octree/octree_impl.hpp
+79
View File
@@ -363,6 +363,43 @@ Octree<MetricType, StatisticType, MatType>::Octree(const Octree& other) :
}
}
//! Copy assignment operator: copy the given other tree.
template<typename MetricType, typename StatisticType, typename MatType>
Octree<MetricType, StatisticType, MatType>&
Octree<MetricType, StatisticType, MatType>::
operator=(const Octree& other)
{
// Return if it's the same tree.
if (this == &other)
return *this;
// Freeing memory that will not be used anymore.
delete dataset;
for (size_t i = 0; i < children.size(); ++i)
delete children[i];
children.clear();
begin = other.Begin();
count = other.Count();
bound = other.bound;
dataset = ((other.parent == NULL) ? new MatType(*other.dataset) : NULL);
parent = NULL;
stat = other.stat;
parentDistance = other.ParentDistance();
furthestDescendantDistance = other.FurthestDescendantDistance();
metric = other.metric;
// If we have any children, we need to create them, and then ensure that their
// parent links are set right.
for (size_t i = 0; i < other.NumChildren(); ++i)
{
children.push_back(new Octree(other.Child(i)));
children[i]->parent = this;
children[i]->dataset = this->dataset;
}
return *this;
}
//! Move the given tree.
template<typename MetricType, typename StatisticType, typename MatType>
Octree<MetricType, StatisticType, MatType>::Octree(Octree&& other) :
@@ -389,6 +426,48 @@ Octree<MetricType, StatisticType, MatType>::Octree(Octree&& other) :
other.parent = NULL;
}
//! Move assignment operator: take ownership of the given tree.
template<typename MetricType, typename StatisticType, typename MatType>
Octree<MetricType, StatisticType, MatType>&
Octree<MetricType, StatisticType, MatType>::
operator=(Octree&& other)
{
// Return if it's the same tree.
if (this == &other)
return *this;
// Freeing memory that will not be used anymore.
delete dataset;
for (size_t i = 0; i < children.size(); ++i)
delete children[i];
children.clear();
children = std::move(other.children);
begin = other.Begin();
count = other.Count();
bound = std::move(other.bound);
dataset = other.dataset;
parent = other.Parent();
stat = std::move(other.stat);
parentDistance = other.ParentDistance();
furthestDescendantDistance = other.furthestDescendantDistance();
metric = std::move(other.metric);
// Update the parent pointers of the direct children.
for (size_t i = 0; i < children.size(); ++i)
children[i]->parent = this;
other.begin = 0;
other.count = 0;
other.dataset = new MatType();
other.parentDistance = 0.0;
other.numDescendants = 0;
other.furthestDescendantDistance = 0.0;
other.parent = NULL;
return *this;
}
template<typename MetricType, typename StatisticType, typename MatType>
Octree<MetricType, StatisticType, MatType>::Octree() :
begin(0),
src/mlpack/core/tree/rectangle_tree/rectangle_tree_impl.hpp
+32
View File
@@ -183,6 +183,7 @@ RectangleTree(
maxLeafSize(other.MaxLeafSize()),
minLeafSize(other.MinLeafSize()),
bound(other.bound),
stat(other.stat),
parentDistance(other.ParentDistance()),
dataset(deepCopy ?
(parent ? parent->dataset : new MatType(*other.dataset)) :
@@ -203,6 +204,9 @@ RectangleTree(
children = other.children;
}
/**
* Move constructor.
*/
template<typename MetricType,
typename StatisticType,
typename MatType,
@@ -223,6 +227,7 @@ RectangleTree(RectangleTree&& other) :
maxLeafSize(other.MaxLeafSize()),
minLeafSize(other.MinLeafSize()),
bound(std::move(other.bound)),
stat(std::move(other.stat)),
parentDistance(other.ParentDistance()),
dataset(other.dataset),
ownsDataset(other.ownsDataset),
@@ -242,6 +247,8 @@ RectangleTree(RectangleTree&& other) :
for (size_t i = 0; i < numChildren; i++)
children[i]->parent = this;
}
// Now we are a clone of the other tree. But we must also clear the other
// tree's contents, so it doesn't delete anything when it is destructed.
other.maxNumChildren = 0;
other.minNumChildren = 0;
other.numChildren = 0;
@@ -256,6 +263,9 @@ RectangleTree(RectangleTree&& other) :
other.ownsDataset = false;
}
/**
* Copy assignment operator: copy the given other tree.
*/
template<typename MetricType,
typename StatisticType,
typename MatType,
@@ -272,6 +282,7 @@ operator=(const RectangleTree& other)
if (this == &other)
return *this;
// Freeing memory that will not be used anymore.
for (size_t i = 0; i < numChildren; i++)
delete children[i];
@@ -289,6 +300,7 @@ operator=(const RectangleTree& other)
maxLeafSize = other.MaxLeafSize();
minLeafSize = other.MinLeafSize();
bound = other.bound;
stat = other.stat;
parentDistance = other.ParentDistance();
dataset = new MatType(*other.dataset);
ownsDataset = true;
@@ -304,6 +316,9 @@ operator=(const RectangleTree& other)
return *this;
}
/**
* Move assignment operator: take ownership of the given tree.
*/
template<typename MetricType,
typename StatisticType,
typename MatType,
@@ -320,6 +335,7 @@ operator=(RectangleTree&& other)
if (this == &other)
return *this;
// Freeing memory that will not be used anymore.
for (size_t i = 0; i < numChildren; i++)
delete children[i];
@@ -337,12 +353,28 @@ operator=(RectangleTree&& other)
maxLeafSize = other.MaxLeafSize();
minLeafSize = other.MinLeafSize();
bound = std::move(other.bound);
stat = std::move(other.stat);
parentDistance = other.ParentDistance();
dataset = other.dataset;
ownsDataset = other.ownsDataset;
points = std::move(other.points);
auxiliaryInfo = std::move(other.auxiliaryInfo);
// Now we are a clone of the other tree. But we must also clear the other
// tree's contents, so it doesn't delete anything when it is destructed.
other.maxNumChildren = 0;
other.minNumChildren = 0;
other.numChildren = 0;
other.parent = NULL;
other.begin = 0;
other.count = 0;
other.numDescendants = 0;
other.maxLeafSize = 0;
other.minLeafSize = 0;
other.parentDistance = 0;
other.dataset = NULL;
other.ownsDataset = false;
return *this;
}
src/mlpack/core/tree/spill_tree/spill_tree_impl.hpp
+2 -2
View File
@@ -193,7 +193,7 @@ SpillTree(const SpillTree& other) :
}
/**
* Copy Assignment.
* Copy assignment operator: copy the given other tree.
*/
template<typename MetricType,
typename StatisticType,
@@ -320,7 +320,7 @@ SpillTree(SpillTree&& other) :
}
/**
* Move Assignment.
* Move assignment operator: take ownership of the given tree.
*/
template<typename MetricType,
typename StatisticType,
src/mlpack/methods/amf/init_rules/average_init.hpp
+48 -5
View File
@@ -1,5 +1,5 @@
/**
* @file averge_init.hpp
* @file average_init.hpp
* @author Sumedh Ghaisas
*
* Initialization rule for Alternating Matrix Factorization.
@@ -49,7 +49,6 @@ class AverageInitialization
const size_t m = V.n_cols;
double avgV = 0;
size_t count = 0;
double min = DBL_MAX;
// Iterate over all elements in the matrix (for sparse matrices, this only
@@ -57,7 +56,6 @@ class AverageInitialization
for (typename MatType::const_row_col_iterator it = V.begin();
it != V.end(); ++it)
{
++count;
avgV += *it;
// Track the minimum value.
if (*it < min)
@@ -70,8 +68,53 @@ class AverageInitialization
W.randu(n, r);
H.randu(r, m);
W = W + avgV;
H = H + avgV;
W += avgV;
H += + avgV;
}
/**
* Initialize the matrix W or H to the average value of V with uniform
* random noise added.
*
* @param V Input matrix.
* @param r Rank of matrix.
* @param M W or H matrix, to be initialized to the average value of V
* with uniform random noise added.
* @param whichMatrix If true, initialize W. Otherwise, initialize H.
*/
template<typename MatType>
inline static void InitializeOne(const MatType& V,
const size_t r,
arma::mat& M,
const bool whichMatrix = true)
{
const size_t n = V.n_rows;
const size_t m = V.n_cols;
double avgV = 0;
double min = DBL_MAX;
// Iterate over all elements in the matrix (for sparse matrices, this only
// iterates over nonzeros).
for (typename MatType::const_row_col_iterator it = V.begin();
it != V.end(); ++it)
{
avgV += *it;
// Track the minimum value.
if (*it < min)
min = *it;
}
if (whichMatrix)
{
// Initialize W to random values
M.randu(n, r);
}
else
{
// Initialize H to random values
M.randu(r, m);
}
M += sqrt(((avgV / (n * m)) - min) / r);
}
//! Serialize the object (in this case, there is nothing to do).
src/mlpack/methods/amf/init_rules/given_init.hpp
+124 -7
View File
@@ -1,5 +1,5 @@
/**
* @file given_initialization.hpp
* @file given_init.hpp
* @author Ryan Curtin
*
* Initialization rule for alternating matrix factorization (AMF). This simple
@@ -28,25 +28,62 @@ class GivenInitialization
{
public:
// Empty constructor required for the InitializeRule template.
GivenInitialization() { }
GivenInitialization() : wIsGiven(false), hIsGiven(false) { }
// Initialize the GivenInitialization object with the given matrices.
GivenInitialization(const arma::mat& w, const arma::mat& h) : w(w), h(h) { }
GivenInitialization(const arma::mat& w, const arma::mat& h) :
w(w), h(h), wIsGiven(true), hIsGiven(true) { }
// Initialize the GivenInitialization object, taking control of the given
// matrices.
GivenInitialization(const arma::mat&& w, const arma::mat&& h) :
w(std::move(w)),
h(std::move(h))
h(std::move(h)),
wIsGiven(true),
hIsGiven(true)
{ }
// Initialize either H or W with the given matrix.
GivenInitialization(const arma::mat& m, const bool whichMatrix = true)
{
if (whichMatrix)
{
w = m;
wIsGiven = true;
hIsGiven = false;
}
else
{
h = m;
wIsGiven = false;
hIsGiven = true;
}
}
// Initialize either H or W, taking control of the given matrix.
GivenInitialization(const arma::mat&& m, const bool whichMatrix = true)
{
if (whichMatrix)
{
w = std::move(m);
wIsGiven = true;
hIsGiven = false;
}
else
{
h = std::move(m);
wIsGiven = false;
hIsGiven = true;
}
}
/**
* Fill W and H with random uniform noise.
* Fill W and H with given matrices.
*
* @param V Input matrix.
* @param r Rank of decomposition.
* @param W W matrix, to be filled with random noise.
* @param H H matrix, to be filled with random noise.
* @param W W matrix, to be initialized to given matrix.
* @param H H matrix, to be initialized to given matrix.
*/
template<typename MatType>
inline void Initialize(const MatType& V,
@@ -54,6 +91,16 @@ class GivenInitialization
arma::mat& W,
arma::mat& H)
{
// Make sure the initial W, H matrices are given
if (!wIsGiven)
{
Log::Fatal << "Initial W matrix is not given!" << std::endl;
}
if (!hIsGiven)
{
Log::Fatal << "Initial H matrix is not given!" << std::endl;
}
// Make sure the initial W, H matrices have correct size.
if (w.n_rows != V.n_rows)
{
@@ -85,6 +132,72 @@ class GivenInitialization
H = h;
}
/**
* Fill W or H with given matrix.
*
* @param V Input matrix.
* @param r Rank of decomposition.
* @param M W or H matrix, to be initialized to given matrix.
* @param whichMatrix If true, initialize W. Otherwise, initialize H.
*/
template<typename MatType>
inline void InitializeOne(const MatType& V,
const size_t r,
arma::mat& M,
const bool whichMatrix = true)
{
if (whichMatrix)
{
// Make sure the initial W matrix is given.
if (!wIsGiven)
{
Log::Fatal << "Initial W matrix is not given!" << std::endl;
}
// Make sure the initial W matrix has correct size.
if (w.n_rows != V.n_rows)
{
Log::Fatal << "The number of rows in given W (" << w.n_rows
<< ") doesn't equal the number of rows in V (" << V.n_rows
<< ") !" << std::endl;
}
if (w.n_cols != r)
{
Log::Fatal << "The number of columns in given W (" << w.n_cols
<< ") doesn't equal the rank of factorization (" << r
<< ") !" << std::endl;
}
// Initialize W to the given matrix.
M = w;
}
else
{
// Make sure the initial H matrix is given.
if (!hIsGiven)
{
Log::Fatal << "Initial H matrix is not given!" << std::endl;
}
// Make sure the initial H matrix has correct size.
if (h.n_cols != V.n_cols)
{
Log::Fatal << "The number of columns in given H (" << h.n_cols
<< ") doesn't equal the number of columns in V (" << V.n_cols
<< ") !" << std::endl;
}
if (h.n_rows != r)
{
Log::Fatal << "The number of rows in given H (" << h.n_rows
<< ") doesn't equal the rank of factorization (" << r
<< ") !"<< std::endl;
}
// Initialize H to the given matrix.
M = h;
}
}
//! Serialize the object (in this case, there is nothing to serialize).
template<typename Archive>
void serialize(Archive& ar, const unsigned int /* version */)
@@ -98,6 +211,10 @@ class GivenInitialization
arma::mat w;
//! The H matrix for initialization.
arma::mat h;
//! Whether initial W is given.
bool wIsGiven;
//! Whether initial H is given.
bool hIsGiven;
};
} // namespace amf
src/mlpack/methods/amf/init_rules/merge_init.hpp
+69
View File
@@ -0,0 +1,69 @@
/**
* @file merge_init.hpp
* @author Ziyang Jiang
*
* Initialization rule for alternating matrix factorization (AMF). This simple
* initialization is performed by assigning a given matrix to W or H and a
* random matrix to another one.
*
* 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
* 3-clause BSD license along with mlpack. If not, see
* http://www.opensource.org/licenses/BSD-3-Clause for more information.
*/
#ifndef MLPACK_METHODS_AMF_MERGE_INIT_HPP
#define MLPACK_METHODS_AMF_MERGE_INIT_HPP
#include <mlpack/prereqs.hpp>
namespace mlpack {
namespace amf {
/**
* This initialization rule for AMF simply takes in two initialization rules,
* and initialize W with the first rule and H with the second rule.
*/
template<typename WInitializationRuleType, typename HInitializationRuleType>
class MergeInitialization
{
public:
// Empty constructor required for the InitializeRule template
MergeInitialization() { }
// Initialize the MergeInitialization object with existing initialization
// rules.
MergeInitialization(const WInitializationRuleType& wInitRule,
const HInitializationRuleType& hInitRule) :
wInitializationRule(wInitRule),
hInitializationRule(hInitRule)
{ }
/**
* Initialize W and H with the corresponding initialization rules.
*
* @param V Input matrix.
* @param r Rank of decomposition.
* @param W W matrix, to be initialized to given matrix.
* @param H H matrix, to be initialized to given matrix.
*/
template<typename MatType>
inline void Initialize(const MatType& V,
const size_t r,
arma::mat& W,
arma::mat& H)
{
wInitializationRule.InitializeOne(V, r, W);
hInitializationRule.InitializeOne(V, r, H, false);
}
private:
// Initialization rule for W matrix
WInitializationRuleType wInitializationRule;
// Initialization rule for H matrix
HInitializationRuleType hInitializationRule;
};
} // namespace amf
} // namespace mlpack
#endif
src/mlpack/methods/amf/init_rules/random_init.hpp
+29
View File
@@ -51,6 +51,35 @@ class RandomInitialization
H.randu(r, m);
}
/**
* Fill W or H with random uniform noise.
*
* @param V Input matrix.
* @param r Rank of decomposition.
* @param M W or H matrix, to be filled with random noise.
* @param whichMatrix If true, initialize W. Otherwise, initialize H.
*/
template<typename MatType>
inline void InitializeOne(const MatType& V,
const size_t r,
arma::mat& M,
const bool whichMatrix = true)
{
// Simple implementation (left in the header file due to its simplicity).
const size_t n = V.n_rows;
const size_t m = V.n_cols;
// Initialize W or H to random values
if (whichMatrix)
{
M.randu(n, r);
}
else
{
M.randu(r, m);
}
}
//! Serialize the object (in this case, there is nothing to serialize).
template<typename Archive>
void serialize(Archive& /* ar */, const unsigned int /* version */) { }
src/mlpack/methods/ann/activation_functions/CMakeLists.txt
+1
View File
@@ -9,6 +9,7 @@ set(SOURCES
softplus_function.hpp
swish_function.hpp
mish_function.hpp
lisht_function.hpp
gelu_function.hpp
)
src/mlpack/methods/ann/activation_functions/lisht_function.hpp
+95
View File
@@ -0,0 +1,95 @@
/**
* @file lisht_function.hpp
* @author Kartik Dutt
*
* Definition and implementation of the LiSHT function as described by
* Swalpa K. Roy, Suvojit Manna, Shiv Ram Dubey and Bidyut B. Chaudhuri.
*
* For more information, see the following paper.
*
* @code
* @misc{
* author = {Swalpa K. Roy, Suvojit Manna, Shiv R. Dubey and
* Bidyut B. Chaudhuri},
* title = {LiSHT: Non-Parametric Linearly Scaled Hyperbolic Tangent
* Activation Function for Neural Networks},
* year = {2019}
* }
* @endcode
*
* 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
* 3-clause BSD license along with mlpack. If not, see
* http://www.opensource.org/licenses/BSD-3-Clause for more information.
*/
#ifndef MLPACK_METHODS_ANN_ACTIVATION_FUNCTIONS_LISHT_FUNCTION_HPP
#define MLPACK_METHODS_ANN_ACTIVATION_FUNCTIONS_LISHT_FUNCTION_HPP
#include <mlpack/prereqs.hpp>
#include <algorithm>
namespace mlpack {
namespace ann /** Artificial Neural Network. */ {
/**
* The LiSHT function, defined by
*
* @f{eqnarray*}{
* f(x) = x * tanh(x)
* f'(x) = tanh(x) + x * (1 - tanh^{2}(x))
* @f}
*/
class LiSHTFunction
{
public:
/**
* Computes the LiSHT function.
*
* @param x Input data.
* @return f(x).
*/
static double Fn(const double x)
{
return x * std::tanh(x);
}
/**
* Computes the LiSHT function.
*
* @param x Input data.
* @param y The resulting output activation.
*/
template <typename InputVecType, typename OutputVecType>
static void Fn(const InputVecType &x, OutputVecType &y)
{
y = x % arma::tanh(x);
}
/**
* Computes the first derivative of the LiSHT function.
*
* @param y Input data.
* @return f'(x)
*/
static double Deriv(const double y)
{
return std::tanh(y) + y * (1 - std::pow(std::tanh(y), 2));
}
/**
* Computes the first derivatives of the LiSHT function.
*
* @param y Input activations.
* @param x The resulting derivatives.
*/
template <typename InputVecType, typename OutputVecType>
static void Deriv(const InputVecType &y, OutputVecType &x)
{
x = arma::tanh(y) + y % (1 - arma::pow(arma::tanh(y), 2));
}
}; // class LishtFunction
} // namespace ann
} // namespace mlpack
#endif
src/mlpack/methods/ann/layer/atrous_convolution.hpp
+4 -4
View File
@@ -85,7 +85,7 @@ class AtrousConvolution
const size_t inputHeight = 0,
const size_t dilationWidth = 1,
const size_t dilationHeight = 1,
const std::string paddingType = "None");
const std::string& paddingType = "None");
/**
* Create the AtrousConvolution object using the specified number of
@@ -116,13 +116,13 @@ class AtrousConvolution
const size_t kernelHeight,
const size_t strideWidth,
const size_t strideHeight,
const std::tuple<size_t, size_t> padW,
const std::tuple<size_t, size_t> padH,
const std::tuple<size_t, size_t>& padW,
const std::tuple<size_t, size_t>& padH,
const size_t inputWidth = 0,
const size_t inputHeight = 0,
const size_t dilationWidth = 1,
const size_t dilationHeight = 1,
const std::string paddingType = "None");
const std::string& paddingType = "None");
/*
* Set the weight and bias term.
src/mlpack/methods/ann/layer/atrous_convolution_impl.hpp
+19 -41
View File
@@ -63,45 +63,23 @@ AtrousConvolution<
const size_t inputHeight,
const size_t dilationWidth,
const size_t dilationHeight,
const std::string paddingType) :
inSize(inSize),
outSize(outSize),
kernelWidth(kernelWidth),
kernelHeight(kernelHeight),
strideWidth(strideWidth),
strideHeight(strideHeight),
inputWidth(inputWidth),
inputHeight(inputHeight),
outputWidth(0),
outputHeight(0),
dilationWidth(dilationWidth),
dilationHeight(dilationHeight)
const std::string& paddingType) :
AtrousConvolution(
inSize,
outSize,
kernelWidth,
kernelHeight,
strideWidth,
strideHeight,
std::tuple<size_t, size_t>(padW, padW),
std::tuple<size_t, size_t>(padH, padH),
inputWidth,
inputHeight,
dilationWidth,
dilationHeight,
paddingType)
{
weights.set_size((outSize * inSize * kernelWidth * kernelHeight) + outSize,
1);
// Transform paddingType to lowercase.
std::string paddingTypeLow = paddingType;
std::transform(paddingType.begin(), paddingType.end(), paddingTypeLow.begin(),
[](unsigned char c){ return std::tolower(c); });
size_t padWLeft = padW;
size_t padWRight = padW;
size_t padHBottom = padH;
size_t padHTop = padH;
if (paddingTypeLow == "valid")
{
padWLeft = 0;
padWRight = 0;
padHTop = 0;
padHBottom = 0;
}
else if (paddingTypeLow == "same")
{
InitializeSamePadding(padWLeft, padWRight, padHTop, padHBottom);
}
padding = ann::Padding<>(padWLeft, padWRight, padHTop, padHBottom);
// Nothing to do here.
}
template<
@@ -124,13 +102,13 @@ AtrousConvolution<
const size_t kernelHeight,
const size_t strideWidth,
const size_t strideHeight,
const std::tuple<size_t, size_t> padW,
const std::tuple<size_t, size_t> padH,
const std::tuple<size_t, size_t>& padW,
const std::tuple<size_t, size_t>& padH,
const size_t inputWidth,
const size_t inputHeight,
const size_t dilationWidth,
const size_t dilationHeight,
const std::string paddingType) :
const std::string& paddingType) :
inSize(inSize),
outSize(outSize),
kernelWidth(kernelWidth),
src/mlpack/methods/ann/layer/base_layer.hpp
+12
View File
@@ -22,6 +22,7 @@
#include <mlpack/methods/ann/activation_functions/hard_sigmoid_function.hpp>
#include <mlpack/methods/ann/activation_functions/swish_function.hpp>
#include <mlpack/methods/ann/activation_functions/mish_function.hpp>
#include <mlpack/methods/ann/activation_functions/lisht_function.hpp>
#include <mlpack/methods/ann/activation_functions/gelu_function.hpp>
namespace mlpack {
@@ -209,6 +210,17 @@ template <
using MishFunctionLayer = BaseLayer<
ActivationFunction, InputDataType, OutputDataType>;
/**
* Standard LiSHT-Layer using the LiSHT activation function.
*/
template <
class ActivationFunction = LiSHTFunction,
typename InputDataType = arma::mat,
typename OutputDataType = arma::mat
>
using LiSHTFunctionLayer = BaseLayer<
ActivationFunction, InputDataType, OutputDataType>;
/**
* Standard GELU-Layer using the GELU activation function.
*/
src/mlpack/methods/ann/layer/convolution.hpp
+4 -4
View File
@@ -76,7 +76,7 @@ class Convolution
const size_t padH = 0,
const size_t inputWidth = 0,
const size_t inputHeight = 0,
const std::string paddingType = "None");
const std::string& paddingType = "None");
/**
* Create the Convolution object using the specified number of input maps,
@@ -104,11 +104,11 @@ class Convolution
const size_t kernelHeight,
const size_t strideWidth,
const size_t strideHeight,
const std::tuple<size_t, size_t> padW,
const std::tuple<size_t, size_t> padH,
const std::tuple<size_t, size_t>& padW,
const std::tuple<size_t, size_t>& padH,
const size_t inputWidth = 0,
const size_t inputHeight = 0,
const std::string paddingType = "None");
const std::string& paddingType = "None");
/*
* Set the weight and bias term.
src/mlpack/methods/ann/layer/convolution_impl.hpp
+16 -39
View File
@@ -60,43 +60,20 @@ Convolution<
const size_t padH,
const size_t inputWidth,
const size_t inputHeight,
const std::string paddingType) :
inSize(inSize),
outSize(outSize),
kernelWidth(kernelWidth),
kernelHeight(kernelHeight),
strideWidth(strideWidth),
strideHeight(strideHeight),
padWLeft(padW),
padWRight(padW),
padHBottom(padH),
padHTop(padH),
inputWidth(inputWidth),
inputHeight(inputHeight),
outputWidth(0),
outputHeight(0)
const std::string& paddingType) :
Convolution(
inSize,
outSize,
kernelWidth,
kernelHeight,
strideWidth,
strideHeight,
std::tuple<size_t, size_t>(padW, padW),
std::tuple<size_t, size_t>(padH, padH),
inputWidth,
inputHeight)
{
weights.set_size((outSize * inSize * kernelWidth * kernelHeight) + outSize,
1);
// Transform paddingType to lowercase.
std::string paddingTypeLow = paddingType;
std::transform(paddingType.begin(), paddingType.end(), paddingTypeLow.begin(),
[](unsigned char c){ return std::tolower(c); });
if (paddingTypeLow == "valid")
{
padWLeft = 0;
padWRight = 0;
padHTop = 0;
padHBottom = 0;
}
else if (paddingTypeLow == "same")
{
InitializeSamePadding();
}
padding = ann::Padding<>(padWLeft, padWRight, padHTop, padHBottom);
// Nothing to do here.
}
template<
@@ -119,11 +96,11 @@ Convolution<
const size_t kernelHeight,
const size_t strideWidth,
const size_t strideHeight,
const std::tuple<size_t, size_t> padW,
const std::tuple<size_t, size_t> padH,
const std::tuple<size_t, size_t>& padW,
const std::tuple<size_t, size_t>& padH,
const size_t inputWidth,
const size_t inputHeight,
const std::string paddingType) :
const std::string& paddingType) :
inSize(inSize),
outSize(outSize),
kernelWidth(kernelWidth),
src/mlpack/methods/ann/layer/transposed_convolution.hpp
+82 -17
View File
@@ -67,12 +67,13 @@ class TransposedConvolution
* @param kernelHeight Height of the filter/kernel.
* @param strideWidth Stride of filter application in the x direction.
* @param strideHeight Stride of filter application in the y direction.
* @param padWidth Padding width of the input.
* @param padHeight Padding height of the input.
* @param padW Padding width of the input.
* @param padH Padding height of the input.
* @param inputWidth The width of the input data.
* @param inputHeight The height of the input data.
* @param outputWidth The width of the output data.
* @param outputHeight The height of the output data.
* @param paddingType The type of padding (Valid or Same). Defaults to None.
*/
TransposedConvolution(const size_t inSize,
const size_t outSize,
@@ -80,12 +81,55 @@ class TransposedConvolution
const size_t kernelHeight,
const size_t strideWidth = 1,
const size_t strideHeight = 1,
const size_t padWidth = 0,
const size_t padHeight = 0,
const size_t padW = 0,
const size_t padH = 0,
const size_t inputWidth = 0,
const size_t inputHeight = 0,
const size_t outputWidth = 0,
const size_t outputHeight = 0);
const size_t outputHeight = 0,
const std::string& paddingType = "None");
/**
* Create the Transposed Convolution object using the specified number of
* input maps, output maps, filter size, stride and padding parameter.
*
* Note: The equivalent stride of a transposed convolution operation is always
* equal to 1. In this implementation, stride of filter represents the stride
* of the associated convolution operation.
* Note: Padding of input represents padding of associated convolution
* operation.
*
* @param inSize The number of input maps.
* @param outSize The number of output maps.
* @param kernelWidth Width of the filter/kernel.
* @param kernelHeight Height of the filter/kernel.
* @param strideWidth Stride of filter application in the x direction.
* @param strideHeight Stride of filter application in the y direction.
* @param padW A two-value tuple indicating padding widths of the input.
* First value is padding at left side. Second value is padding on
* right side.
* @param padH A two-value tuple indicating padding heights of the input.
* First value is padding at top. Second value is padding on
* bottom.
* @param inputWidth The width of the input data.
* @param inputHeight The height of the input data.
* @param outputWidth The width of the output data.
* @param outputHeight The height of the output data.
* @param paddingType The type of padding (Valid or Same). Defaults to None.
*/
TransposedConvolution(const size_t inSize,
const size_t outSize,
const size_t kernelWidth,
const size_t kernelHeight,
const size_t strideWidth,
const size_t strideHeight,
const std::tuple<size_t, size_t>& padW,
const std::tuple<size_t, size_t>& padH,
const size_t inputWidth = 0,
const size_t inputHeight = 0,
const size_t outputWidth = 0,
const size_t outputHeight = 0,
const std::string& paddingType = "None");
/*
* Set the weight and bias term.
@@ -199,15 +243,25 @@ class TransposedConvolution
//! Modify the stride height.
size_t& StrideHeight() { return strideHeight; }
//! Get the padding width.
size_t PadWidth() const { return padWidth; }
//! Modify the padding width.
size_t& PadWidth() { return padWidth; }
//! Get the top padding height.
size_t PadHTop() const { return padHTop; }
//! Modify the top padding height.
size_t& PadHTop() { return padHTop; }
//! Get the padding height.
size_t PadHeight() const { return padHeight; }
//! Modify the padding height.
size_t& PadHeight() { return padHeight; }
//! Get the bottom padding height.
size_t PadHBottom() const { return padHBottom; }
//! Modify the bottom padding height.
size_t& PadHBottom() { return padHBottom; }
//! Get the left padding width.
size_t PadWLeft() const { return padWLeft; }
//! Modify the left padding width.
size_t& PadWLeft() { return padWLeft; }
//! Get the right padding width.
size_t PadWRight() const { return padWRight; }
//! Modify the right padding width.
size_t& PadWRight() { return padWRight; }
//! Modify the bias weights of the layer.
arma::mat& Bias() { return bias; }
@@ -235,6 +289,11 @@ class TransposedConvolution
output.slice(s) = arma::fliplr(arma::flipud(input.slice(s)));
}
/*
* Function to assign padding such that output size is same as input size.
*/
void InitializeSamePadding();
/*
* Rotates a dense matrix counterclockwise by 180 degrees.
*
@@ -328,11 +387,17 @@ class TransposedConvolution
//! Locally-stored stride of the filter in y-direction.
size_t strideHeight;
//! Locally-stored padding width.
size_t padWidth;
//! Locally-stored left-side padding width.
size_t padWLeft;
//! Locally-stored padding height.
size_t padHeight;
//! Locally-stored right-side padding width.
size_t padWRight;
//! Locally-stored bottom padding height.
size_t padHBottom;
//! Locally-stored top padding height.
size_t padHTop;
//! Locally-stored number of zeros added to the right of input.
size_t aW;
src/mlpack/methods/ann/layer/transposed_convolution_impl.hpp
+153 -31
View File
@@ -57,18 +57,68 @@ TransposedConvolution<
const size_t kernelHeight,
const size_t strideWidth,
const size_t strideHeight,
const size_t padWidth,
const size_t padHeight,
const size_t padW,
const size_t padH,
const size_t inputWidth,
const size_t inputHeight,
const size_t outputWidth,
const size_t outputHeight) :
const size_t outputHeight,
const std::string& paddingType) :
TransposedConvolution(
inSize,
outSize,
kernelWidth,
kernelHeight,
strideWidth,
strideHeight,
std::tuple<size_t, size_t>(padW, padW),
std::tuple<size_t, size_t>(padH, padH),
inputWidth,
inputHeight,
outputWidth,
outputHeight,
paddingType)
{
// Nothing to do here.
}
template<
typename ForwardConvolutionRule,
typename BackwardConvolutionRule,
typename GradientConvolutionRule,
typename InputDataType,
typename OutputDataType
>
TransposedConvolution<
ForwardConvolutionRule,
BackwardConvolutionRule,
GradientConvolutionRule,
InputDataType,
OutputDataType
>::TransposedConvolution(
const size_t inSize,
const size_t outSize,
const size_t kernelWidth,
const size_t kernelHeight,
const size_t strideWidth,
const size_t strideHeight,
const std::tuple<size_t, size_t>& padW,
const std::tuple<size_t, size_t>& padH,
const size_t inputWidth,
const size_t inputHeight,
const size_t outputWidth,
const size_t outputHeight,
const std::string& paddingType) :
inSize(inSize),
outSize(outSize),
kernelWidth(kernelWidth),
kernelHeight(kernelHeight),
strideWidth(strideWidth),
strideHeight(strideHeight),
padWLeft(std::get<0>(padW)),
padWRight(std::get<1>(padW)),
padHBottom(std::get<1>(padH)),
padHTop(std::get<0>(padH)),
inputWidth(inputWidth),
inputHeight(inputHeight),
outputWidth(outputWidth),
@@ -76,26 +126,49 @@ TransposedConvolution<
{
weights.set_size((outSize * inSize * kernelWidth * kernelHeight) + outSize,
1);
// Transform paddingType to lowercase.
std::string paddingTypeLow = paddingType;
std::transform(paddingType.begin(), paddingType.end(), paddingTypeLow.begin(),
[](unsigned char c){ return std::tolower(c); });
aW = (outputWidth + 2 * padWidth - kernelWidth) % strideWidth;
aH = (outputHeight + 2 * padHeight - kernelHeight) % strideHeight;
if (paddingTypeLow == "valid")
{
// Set Padding to 0.
padWLeft = 0;
padWRight = 0;
padHTop = 0;
padHBottom = 0;
}
else if (paddingTypeLow == "same")
{
InitializeSamePadding();
}
const size_t padWidthForward = kernelWidth - padWidth - 1;
const size_t padHeightForward = kernelHeight - padHeight - 1;
const size_t totalPadWidth = padWLeft + padWRight;
const size_t totalPadHeight = padHTop + padHBottom;
paddingForward = ann::Padding<>(padWidthForward, padWidthForward + aW,
padHeightForward, padHeightForward + aH);
paddingBackward = ann::Padding<>(padWidth, padWidth, padHeight, padHeight);
aW = (outputWidth + totalPadWidth - kernelWidth) % strideWidth;
aH = (outputHeight + totalPadHeight - kernelHeight) % strideHeight;
const size_t padWidthLeftForward = kernelWidth - padWLeft - 1;
const size_t padHeightTopForward = kernelHeight - padHTop - 1;
const size_t padWidthRightForward = kernelWidth - padWRight - 1;
const size_t padHeightBottomtForward = kernelHeight - padHBottom - 1;
paddingForward = ann::Padding<>(padWidthLeftForward,
padWidthRightForward + aW, padHeightTopForward,
padHeightBottomtForward + aH);
paddingBackward = ann::Padding<>(padWLeft, padWRight, padHTop, padHBottom);
// Check if the output height and width are possible given the other
// parameters of the layer.
if (outputWidth != strideWidth * (inputWidth - 1) +
aW + kernelWidth - 2 * padWidth ||
aW + kernelWidth - totalPadWidth ||
outputHeight != strideHeight * (inputHeight - 1) +
aH + kernelHeight - 2 * padHeight)
aH + kernelHeight - totalPadHeight)
{
Log::Fatal << "The output width / output height is not possible given "
<< "the other parameters of the layer." << std::endl;
<< "the other parameters of the layer." << std::endl;
}
}
@@ -144,12 +217,13 @@ void TransposedConvolution<
{
InsertZeros(inputTemp, strideWidth, strideHeight, inputExpandedTemp);
if (paddingForward.PadWLeft() != 0 || paddingForward.PadHTop() != 0 ||
aW != 0 || aH != 0)
if (paddingForward.PadWLeft() != 0 || paddingForward.PadWRight() != 0 ||
paddingForward.PadHTop() != 0 || paddingForward.PadHBottom() != 0)
{
inputPaddedTemp.set_size(inputExpandedTemp.n_rows +
paddingForward.PadWLeft() * 2 + aW, inputExpandedTemp.n_cols +
paddingForward.PadHTop() * 2 + aH, inputExpandedTemp.n_slices);
paddingForward.PadWLeft() + paddingForward.PadWRight(),
inputExpandedTemp.n_cols + paddingForward.PadHTop() +
paddingForward.PadHBottom(), inputExpandedTemp.n_slices);
for (size_t i = 0; i < inputExpandedTemp.n_slices; ++i)
{
@@ -165,12 +239,13 @@ void TransposedConvolution<
}
}
else if (paddingForward.PadWLeft() != 0 ||
paddingForward.PadWRight() != 0 ||
paddingForward.PadHTop() != 0 ||
aW != 0 ||
aH != 0)
paddingForward.PadHBottom() != 0)
{
inputPaddedTemp.set_size(inputTemp.n_rows + paddingForward.PadWLeft() * 2 +
aW, inputTemp.n_cols + paddingForward.PadHTop() * 2 + aH,
inputPaddedTemp.set_size(inputTemp.n_rows + paddingForward.PadWLeft() +
paddingForward.PadWRight(), inputTemp.n_cols +
paddingForward.PadHTop() + paddingForward.PadHBottom(),
inputTemp.n_slices);
for (size_t i = 0; i < inputTemp.n_slices; ++i)
@@ -202,9 +277,9 @@ void TransposedConvolution<
if (strideWidth > 1 ||
strideHeight > 1 ||
paddingForward.PadWLeft() != 0 ||
paddingForward.PadWRight() != 0 ||
paddingForward.PadHTop() != 0 ||
aW != 0 ||
aH != 0)
paddingForward.PadHBottom() != 0)
{
ForwardConvolutionRule::Convolution(inputPaddedTemp.slice(inMap +
batchCount * inSize), rotatedFilter, convOutput, 1, 1);
@@ -242,11 +317,13 @@ void TransposedConvolution<
arma::Cube<eT> mappedError(gy.memptr(), outputWidth, outputHeight,
outSize * batchSize, false, false);
arma::Cube<eT> mappedErrorPadded;
if (paddingBackward.PadWLeft() != 0 || paddingBackward.PadHTop() != 0)
if (paddingBackward.PadWLeft() != 0 || paddingBackward.PadWRight() != 0 ||
paddingBackward.PadHTop() != 0 || paddingBackward.PadHBottom() != 0)
{
mappedErrorPadded.set_size(mappedError.n_rows +
paddingBackward.PadWLeft() * 2, mappedError.n_cols +
paddingBackward.PadHTop() * 2, mappedError.n_slices);
paddingBackward.PadWLeft() + paddingBackward.PadWRight(),
mappedError.n_cols + paddingBackward.PadHTop() +
paddingBackward.PadHBottom(), mappedError.n_slices);
for (size_t i = 0; i < mappedError.n_slices; ++i)
{
@@ -273,7 +350,8 @@ void TransposedConvolution<
{
arma::Mat<eT> output;
if (paddingBackward.PadWLeft() != 0 || paddingBackward.PadHTop() != 0)
if (paddingBackward.PadWLeft() != 0 || paddingBackward.PadWRight() != 0 ||
paddingBackward.PadHTop() != 0 || paddingBackward.PadHBottom() != 0)
{
BackwardConvolutionRule::Convolution(mappedErrorPadded.slice(outMap),
weight.slice(outMapIdx), output, strideWidth, strideHeight);
@@ -334,9 +412,9 @@ void TransposedConvolution<
if (strideWidth > 1 ||
strideHeight > 1 ||
paddingForward.PadWLeft() != 0 ||
paddingForward.PadWRight() != 0 ||
paddingForward.PadHTop() != 0 ||
aW != 0 ||
aH != 0)
paddingForward.PadHBottom() != 0)
{
inputSlice = inputPaddedTemp.slice(inMap + batchCount * inSize);
}
@@ -387,6 +465,10 @@ void TransposedConvolution<
ar & BOOST_SERIALIZATION_NVP(padWidth);
ar & BOOST_SERIALIZATION_NVP(padHeight);
}
ar &BOOST_SERIALIZATION_NVP(padWLeft);
ar &BOOST_SERIALIZATION_NVP(padWRight);
ar &BOOST_SERIALIZATION_NVP(padHBottom);
ar &BOOST_SERIALIZATION_NVP(padHTop);
ar & BOOST_SERIALIZATION_NVP(inputWidth);
ar & BOOST_SERIALIZATION_NVP(inputHeight);
ar & BOOST_SERIALIZATION_NVP(outputWidth);
@@ -402,9 +484,49 @@ void TransposedConvolution<
{
weights.set_size((outSize * inSize * kernelWidth * kernelHeight) + outSize,
1);
size_t totalPadWidth = padWLeft + padWRight;
size_t totalPadHeight = padHTop + padHBottom;
aW = (outputWidth + kernelWidth - totalPadWidth - 2) % strideWidth;
aH = (outputHeight + kernelHeight - totalPadHeight - 2) % strideHeight;
}
}
template<
typename ForwardConvolutionRule,
typename BackwardConvolutionRule,
typename GradientConvolutionRule,
typename InputDataType,
typename OutputDataType
>
void TransposedConvolution<
ForwardConvolutionRule,
BackwardConvolutionRule,
GradientConvolutionRule,
InputDataType,
OutputDataType
>::InitializeSamePadding(){
/**
* Using O=s*(I-1) + K -2P + A
* where
* s=stride
* I=Input Shape
* K=Kernel Size
* P=Padding
*/
const size_t totalHorizontalPadding = (strideWidth - 1) * inputWidth +
kernelWidth - strideWidth;
const size_t totalVerticalPadding = (strideHeight - 1) * inputHeight +
kernelHeight - strideHeight;
aW = (outputWidth + kernelWidth - 2 * padWidth - 2) % strideWidth;
aH = (outputHeight + kernelHeight - 2 * padHeight - 2) % strideHeight;
padWLeft = totalVerticalPadding / 2;
padWRight = totalVerticalPadding - totalVerticalPadding / 2;
padHTop = totalHorizontalPadding / 2;
padHBottom = totalHorizontalPadding - totalHorizontalPadding / 2;
// If Padding is negative throw a fatal error.
if (totalHorizontalPadding < 0 || totalVerticalPadding < 0)
{
Log::Fatal << "The output width / output height is not possible given "
<< "same padding for the layer." << std::endl;
}
}
src/mlpack/methods/ann/loss_functions/CMakeLists.txt
+2
View File
@@ -9,6 +9,8 @@ set(SOURCES
earth_mover_distance_impl.hpp
kl_divergence.hpp
kl_divergence_impl.hpp
mean_bias_error.hpp
mean_bias_error_impl.hpp
mean_squared_error.hpp
mean_squared_error_impl.hpp
mean_squared_logarithmic_error.hpp
src/mlpack/methods/ann/loss_functions/mean_bias_error.hpp
+84
View File
@@ -0,0 +1,84 @@
/**
* @file mean_bias_error.hpp
* @author Saksham Rastogi
*
* Definition of the mean bias error performance function.
*
* 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
* 3-clause BSD license along with mlpack. If not, see
* http://www.opensource.org/licenses/BSD-3-Clause for more information.
*/
#ifndef MLPACK_METHODS_ANN_LOSS_FUNCTION_MEAN_BIAS_ERROR_HPP
#define MLPACK_METHODS_ANN_LOSS_FUNCTION_MEAN_BIAS_ERROR_HPP
#include <mlpack/prereqs.hpp>
namespace mlpack {
namespace ann /** Artificial Neural Network. */ {
/**
* The mean bias error performance function measures the network's
* performance according to the mean of errors.
*
* @tparam InputDataType Type of the input data (arma::colvec, arma::mat,
* arma::sp_mat or arma::cube).
* @tparam OutputDataType Type of the output data (arma::colvec, arma::mat,
* arma::sp_mat or arma::cube).
*/
template <
typename InputDataType = arma::mat,
typename OutputDataType = arma::mat
>
class MeanBiasError
{
public:
/**
* Create the MeanBiasError object.
*/
MeanBiasError();
/**
* Computes the mean bias error function.
*
* @param input Input data used for evaluating the specified function.
* @param target The target vector.
*/
template<typename InputType, typename TargetType>
double Forward(const InputType&& input, const TargetType&& target);
/**
* Ordinary feed backward pass of a neural network.
*
* @param input The propagated input activation.
* @param target The target vector.
* @param output The calculated error.
*/
template<typename InputType, typename TargetType, typename OutputType>
void Backward(const InputType&& input,
const TargetType&& target,
OutputType&& output);
//! Get the output parameter.
OutputDataType& OutputParameter() const { return outputParameter; }
//! Modify the output parameter.
OutputDataType& OutputParameter() { return outputParameter; }
/**
* Serialize the layer.
*/
template<typename Archive>
void serialize(Archive& ar, const unsigned int /* version */);
private:
//! Locally-stored output parameter object.
OutputDataType outputParameter;
}; // class MeanBiasError
} // namespace ann
} // namespace mlpack
// Include implementation.
#include "mean_bias_error_impl.hpp"
#endif
src/mlpack/methods/ann/loss_functions/mean_bias_error_impl.hpp
+59
View File
@@ -0,0 +1,59 @@
/**
* @file mean_bias_error_impl.hpp
* @author Saksham Rastogi
*
* Implementation of the mean bias error performance function.
*
* 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
* 3-clause BSD license along with mlpack. If not, see
* http://www.opensource.org/licenses/BSD-3-Clause for more information.
*/
#ifndef MLPACK_METHODS_ANN_LOSS_FUNCTION_MEAN_BIAS_ERROR_IMPL_HPP
#define MLPACK_METHODS_ANN_LOSS_FUNCTION_MEAN_BIAS_ERROR_IMPL_HPP
// In case it hasn't yet been included.
#include "mean_bias_error.hpp"
namespace mlpack {
namespace ann /** Artificial Neural Network. */ {
template<typename InputDataType, typename OutputDataType>
MeanBiasError<InputDataType, OutputDataType>::MeanBiasError()
{
// Nothing to do here.
}
template<typename InputDataType, typename OutputDataType>
template<typename InputType, typename TargetType>
double MeanBiasError<InputDataType, OutputDataType>::Forward(
const InputType&& input, const TargetType&& target)
{
return arma::accu(target - input) / target.n_cols;
}
template<typename InputDataType, typename OutputDataType>
template<typename InputType, typename TargetType, typename OutputType>
void MeanBiasError<InputDataType, OutputDataType>::Backward(
const InputType&& input,
const TargetType&& target,
OutputType&& output)
{
output.set_size(arma::size(input));
output.fill(-1.0);
}
template<typename InputDataType, typename OutputDataType>
template<typename Archive>
void MeanBiasError<InputDataType, OutputDataType>::serialize(
Archive& /* ar */,
const unsigned int /* version */)
{
// Nothing to do here.
}
} // namespace ann
} // namespace mlpack
#endif
src/mlpack/methods/nmf/nmf_main.cpp
+66 -66
View File
@@ -14,9 +14,9 @@
#include <mlpack/core/util/mlpack_main.hpp>
#include <mlpack/methods/amf/amf.hpp>
#include <mlpack/methods/amf/init_rules/random_init.hpp>
#include <mlpack/methods/amf/init_rules/given_init.hpp>
#include <mlpack/methods/amf/init_rules/merge_init.hpp>
#include <mlpack/methods/amf/update_rules/nmf_mult_dist.hpp>
#include <mlpack/methods/amf/update_rules/nmf_mult_div.hpp>
#include <mlpack/methods/amf/update_rules/nmf_als.hpp>
@@ -130,6 +130,68 @@ void SaveWH(const bool bindingTransposed, arma::mat&& w, arma::mat&& h)
}
}
template<typename UpdateRuleType>
void ApplyFactorization(const arma::mat& V,
const size_t r,
arma::mat& W,
arma::mat& H)
{
const size_t maxIterations = CLI::GetParam<int>("max_iterations");
const double minResidue = CLI::GetParam<double>("min_residue");
SimpleResidueTermination srt(minResidue, maxIterations);
// Load input dataset. We know if the data is transposed based on the
// BINDING_MATRIX_TRANSPOSED macro, which will be 'true' or 'false'.
arma::mat initialW, initialH;
LoadInitialWH(BINDING_MATRIX_TRANSPOSED, initialW, initialH);
if (CLI::HasParam("initial_w") && CLI::HasParam("initial_h"))
{
// Initialize W and H with given matrices
GivenInitialization ginit = GivenInitialization(initialW, initialH);
AMF<SimpleResidueTermination,
GivenInitialization,
UpdateRuleType> amf(srt, ginit);
amf.Apply(V, r, W, H);
}
else if (CLI::HasParam("initial_w"))
{
// Merge GivenInitialization and RandomInitialization rules
// to initialize W with the given matrix, and H with random noise
GivenInitialization ginit = GivenInitialization(initialW);
RandomInitialization rinit = RandomInitialization();
MergeInitialization<GivenInitialization, RandomInitialization> minit =
MergeInitialization<GivenInitialization, RandomInitialization>
(ginit, rinit);
AMF<SimpleResidueTermination,
MergeInitialization<GivenInitialization, RandomInitialization>,
UpdateRuleType> amf(srt, minit);
amf.Apply(V, r, W, H);
}
else if (CLI::HasParam("initial_h"))
{
// Merge GivenInitialization and RandomInitialization rules
// to initialize H with the given matrix, and W with random noise
GivenInitialization ginit = GivenInitialization(initialH, false);
RandomInitialization rinit = RandomInitialization();
MergeInitialization<RandomInitialization, GivenInitialization> minit =
MergeInitialization<RandomInitialization, GivenInitialization>
(rinit, ginit);
AMF<SimpleResidueTermination,
MergeInitialization<RandomInitialization, GivenInitialization>,
UpdateRuleType> amf(srt, minit);
amf.Apply(V, r, W, H);
}
else
{
// Use random initialization
AMF<SimpleResidueTermination,
RandomInitialization,
UpdateRuleType> amf(srt);
amf.Apply(V, r, W, H);
}
}
static void mlpackMain()
{
// Initialize random seed.
@@ -140,8 +202,6 @@ static void mlpackMain()
// Gather parameters.
const size_t r = CLI::GetParam<int>("rank");
const size_t maxIterations = CLI::GetParam<int>("max_iterations");
const double minResidue = CLI::GetParam<double>("min_residue");
const string updateRules = CLI::GetParam<string>("update_rules");
// Validate parameters.
@@ -153,10 +213,7 @@ static void mlpackMain()
true, "max_iterations must be non-negative");
RequireAtLeastOnePassed({ "h", "w" }, false, "no output will be saved");
RequireNoneOrAllPassed({"initial_w", "initial_h"}, true);
// Load input dataset. We know if the data is transposed based on the
// BINDING_MATRIX_TRANSPOSED macro, which will be 'true' or 'false'.
arma::mat V = std::move(CLI::GetParam<arma::mat>("input"));
arma::mat W;
@@ -167,76 +224,19 @@ static void mlpackMain()
{
Log::Info << "Performing NMF with multiplicative distance-based update "
<< "rules." << std::endl;
SimpleResidueTermination srt(minResidue, maxIterations);
if (CLI::HasParam("initial_w"))
{
// Initialization with given W, H matrices.
arma::mat initialW, initialH;
LoadInitialWH(BINDING_MATRIX_TRANSPOSED, initialW, initialH);
GivenInitialization ginit = GivenInitialization(initialW, initialH);
AMF<SimpleResidueTermination,
GivenInitialization> amf(srt, ginit);
amf.Apply(V, r, W, H);
}
else
{
AMF<> amf(srt);
amf.Apply(V, r, W, H);
}
ApplyFactorization<NMFMultiplicativeDistanceUpdate>(V, r, W, H);
}
else if (updateRules == "multdiv")
{
Log::Info << "Performing NMF with multiplicative divergence-based update "
<< "rules." << std::endl;
SimpleResidueTermination srt(minResidue, maxIterations);
if (CLI::HasParam("initial_w"))
{
// Initialization with given W, H matrices.
arma::mat initialW, initialH;
LoadInitialWH(BINDING_MATRIX_TRANSPOSED, initialW, initialH);
GivenInitialization ginit = GivenInitialization(initialW, initialH);
AMF<SimpleResidueTermination,
GivenInitialization,
NMFMultiplicativeDivergenceUpdate> amf(srt, ginit);
amf.Apply(V, r, W, H);
}
else
{
AMF<SimpleResidueTermination,
RandomInitialization,
NMFMultiplicativeDivergenceUpdate> amf(srt);
amf.Apply(V, r, W, H);
}
ApplyFactorization<NMFMultiplicativeDivergenceUpdate>(V, r, W, H);
}
else if (updateRules == "als")
{
Log::Info << "Performing NMF with alternating least squared update rules."
<< std::endl;
SimpleResidueTermination srt(minResidue, maxIterations);
if (CLI::HasParam("initial_w"))
{
// Initialization with given W, H matrices.
arma::mat initialW, initialH;
LoadInitialWH(BINDING_MATRIX_TRANSPOSED, initialW, initialH);
GivenInitialization ginit = GivenInitialization(initialW, initialH);
AMF<SimpleResidueTermination,
GivenInitialization,
NMFALSUpdate> amf(srt, ginit);
amf.Apply(V, r, W, H);
}
else
{
AMF<SimpleResidueTermination,
RandomInitialization,
NMFALSUpdate> amf(srt);
amf.Apply(V, r, W, H);
}
ApplyFactorization<NMFALSUpdate>(V, r, W, H);
}
// Save results. Remember from our discussion in the comments earlier that we
src/mlpack/methods/softmax_regression/softmax_regression.hpp
+46 -7
View File
@@ -71,7 +71,6 @@ class SoftmaxRegression
SoftmaxRegression(const size_t inputSize = 0,
const size_t numClasses = 0,
const bool fitIntercept = false);
/**
* Construct the SoftmaxRegression class with the provided data and labels.
* This will train the model. Optionally, the parameter 'lambda' can be
@@ -94,23 +93,45 @@ class SoftmaxRegression
const double lambda = 0.0001,
const bool fitIntercept = false,
OptimizerType optimizer = OptimizerType());
/**
* Construct the SoftmaxRegression class with the provided data and labels.
* This will train the model. Optionally, the parameter 'lambda' can be
* passed, which controls the amount of L2-regularization in the objective
* function. By default, the model takes a small value.
*
* @tparam OptimizerType Desired optimizer type.
* @tparam CallbackTypes Types of Callback Functions.
* @param data Input training features. Each column associate with one sample
* @param labels Labels associated with the feature data.
* @param inputSize Size of the input feature vector.
* @param numClasses Number of classes for classification.
* @param lambda L2-regularization constant.
* @param fitIntercept add intercept term or not.
* @param optimizer Desired optimizer.
* @param callbacks Callback function for ensmallen optimizer `OptimizerType`.
* See https://www.ensmallen.org/docs.html#callback-documentation.
*/
template<typename OptimizerType, typename... CallbackTypes>
SoftmaxRegression(const arma::mat& data,
const arma::Row<size_t>& labels,
const size_t numClasses,
const double lambda,
const bool fitIntercept,
OptimizerType optimizer,
CallbackTypes&&... callbacks);
/**
* Classify the given points, returning the predicted labels for each point.
* The function calculates the probabilities for every class, given a data
* point. It then chooses the class which has the highest probability among
* all.
*
* @param dataset Set of points to classify.
* @param labels Predicted labels for each point.
*/
void Classify(const arma::mat& dataset, arma::Row<size_t>& labels) const;
/**
* Classify the given point. The predicted class label is returned.
* The function calculates the probabilites for every class, given the point.
* It then chooses the class which has the highest probability among all.
*
* @param point Point to be classified.
* @return Predicted class label of the point.
*/
@@ -151,7 +172,6 @@ class SoftmaxRegression
*/
double ComputeAccuracy(const arma::mat& testData,
const arma::Row<size_t>& labels) const;
/**
* Train the softmax regression with the given training data.
*
@@ -167,6 +187,25 @@ class SoftmaxRegression
const arma::Row<size_t>& labels,
const size_t numClasses,
OptimizerType optimizer = OptimizerType());
/**
* Train the softmax regression with the given training data.
*
* @tparam OptimizerType Desired optimizer type.
* @tparam CallbackTypes Types of Callback Functions.
* @param data Input data with each column as one example.
* @param labels Labels associated with the feature data.
* @param numClasses Number of classes for classification.
* @param optimizer Desired optimizer.
* @param callbacks Callback function for ensmallen optimizer `OptimizerType`.
* See https://www.ensmallen.org/docs.html#callback-documentation.
* @return Objective value of the final point.
*/
template<typename OptimizerType = ens::L_BFGS, typename... CallbackTypes>
double Train(const arma::mat& data,
const arma::Row<size_t>& labels,
const size_t numClasses,
OptimizerType optimizer,
CallbackTypes&&... callbacks);
//! Sets the number of classes.
size_t& NumClasses() { return numClasses; }
@@ -188,7 +227,7 @@ class SoftmaxRegression
//! Gets the features size of the training data
size_t FeatureSize() const
{ return fitIntercept ? parameters.n_cols - 1 :
{ return fitIntercept ? parameters.n_cols - 1:
parameters.n_cols; }
/**
src/mlpack/methods/softmax_regression/softmax_regression_function.hpp
+5
View File
@@ -175,6 +175,11 @@ class SoftmaxRegressionFunction
{
return initialPoint.n_cols;
}
/**
* Return the number of separable functions
(the number of predictor points).
*/
size_t NumFunctions() const { return data.n_cols; }
//! Sets the regularization parameter.
double& Lambda() { return lambda; }
src/mlpack/methods/softmax_regression/softmax_regression_impl.hpp
+41 -2
View File
@@ -33,6 +33,22 @@ SoftmaxRegression::SoftmaxRegression(
Train(data, labels, numClasses, optimizer);
}
template<typename OptimizerType, typename... CallbackTypes>
SoftmaxRegression::SoftmaxRegression(
const arma::mat& data,
const arma::Row<size_t>& labels,
const size_t numClasses,
const double lambda,
const bool fitIntercept,
OptimizerType optimizer,
CallbackTypes&&... callbacks) :
numClasses(numClasses),
lambda(lambda),
fitIntercept(fitIntercept)
{
Train(data, labels, numClasses, optimizer, callbacks...);
}
template<typename VecType>
size_t SoftmaxRegression::Classify(const VecType& point) const
{
@@ -47,8 +63,8 @@ double SoftmaxRegression::Train(const arma::mat& data,
const size_t numClasses,
OptimizerType optimizer)
{
SoftmaxRegressionFunction regressor(data, labels, numClasses,
lambda, fitIntercept);
SoftmaxRegressionFunction regressor(data, labels, numClasses, lambda,
fitIntercept);
if (parameters.is_empty())
parameters = regressor.GetInitialPoint();
@@ -63,6 +79,29 @@ double SoftmaxRegression::Train(const arma::mat& data,
return out;
}
template<typename OptimizerType, typename... CallbackTypes>
double SoftmaxRegression::Train(const arma::mat& data,
const arma::Row<size_t>& labels,
const size_t numClasses,
OptimizerType optimizer,
CallbackTypes&&... callbacks)
{
SoftmaxRegressionFunction regressor(data, labels, numClasses, lambda,
fitIntercept);
if (parameters.is_empty())
parameters = regressor.GetInitialPoint();
// Train the model.
Timer::Start("softmax_regression_optimization");
const double out = optimizer.Optimize(regressor, parameters, callbacks...);
Timer::Stop("softmax_regression_optimization");
Log::Info << "SoftmaxRegression::SoftmaxRegression(): final objective of "
<< "trained model is " << out << "." << std::endl;
return out;
}
} // namespace regression
} // namespace mlpack
src/mlpack/methods/softmax_regression/softmax_regression_main.cpp
-1
View File
@@ -273,6 +273,5 @@ Model* TrainSoftmax(const size_t maxIterations)
sm = new Model(trainData, trainLabels, numClasses,
CLI::GetParam<double>("lambda"), intercept, std::move(optimizer));
}
return sm;
}
src/mlpack/tests/activation_functions_test.cpp
+28
View File
@@ -22,6 +22,7 @@
#include <mlpack/methods/ann/activation_functions/swish_function.hpp>
#include <mlpack/methods/ann/activation_functions/hard_sigmoid_function.hpp>
#include <mlpack/methods/ann/activation_functions/mish_function.hpp>
#include <mlpack/methods/ann/activation_functions/lisht_function.hpp>
#include <mlpack/methods/ann/activation_functions/gelu_function.hpp>
#include <boost/test/unit_test.hpp>
@@ -635,6 +636,7 @@ BOOST_AUTO_TEST_CASE(HardSigmoidFunctionTest)
CheckDerivativeCorrect<HardSigmoidFunction>(desiredActivations,
desiredDerivatives);
}
/**
* Basic test of the Mish function.
*/
@@ -657,6 +659,31 @@ BOOST_AUTO_TEST_CASE(MishFunctionTest)
desiredDerivatives);
}
/**
* Basic test of the LiSHT function.
*/
BOOST_AUTO_TEST_CASE(LiSHTFunctionTest)
{
// Calculated using tfa.activations.LiSHT().
// where tfa is tensorflow_addons.
const arma::colvec desiredActivations("1.928055 3.189384 \
4.4988894 100.2 0.7615942 \
0.7615942 1.9280552 0");
const arma::colvec desiredDerivatives("1.1150033 1.0181904 \
1.001978 1.0 \
1.0896928 1.0896928 \
1.1150033 0.0");
CheckActivationCorrect<LiSHTFunction>(activationData,
desiredActivations);
CheckDerivativeCorrect<LiSHTFunction>(desiredActivations,
desiredDerivatives);
}
/**
* Basic test of the GELU function.
*/
BOOST_AUTO_TEST_CASE(GELUFunctionTest)
{
// Calculated using torch.nn.gelu().
@@ -682,4 +709,5 @@ BOOST_AUTO_TEST_CASE(GELUFunctionTest)
CheckDerivativeCorrect<GELUFunction>(desiredActivations,
desiredDerivatives);
}
BOOST_AUTO_TEST_SUITE_END();
src/mlpack/tests/ann_layer_test.cpp
+101
View File
@@ -2931,4 +2931,105 @@ BOOST_AUTO_TEST_CASE(ConvolutionLayerPaddingTest)
module2.Backward(std::move(input), std::move(output), std::move(delta));
}
/**
* Test that the padding options in Transposed Convolution layer.
*/
BOOST_AUTO_TEST_CASE(TransposedConvolutionLayerPaddingTest)
{
arma::mat output, input, delta;
TransposedConvolution<> module1(1, 1, 3, 3, 1, 1, 0, 0, 4, 4, 6, 6, "VALID");
// Test the forward function.
// Valid Should give the same result.
input = arma::linspace<arma::colvec>(0, 15, 16);
module1.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros);
module1.Reset();
module1.Forward(std::move(input), std::move(output));
// Value calculated using tensorflow.nn.conv2d_transpose().
BOOST_REQUIRE_EQUAL(arma::accu(output), 0.0);
// Test the Backward Function.
module1.Backward(std::move(input), std::move(output), std::move(delta));
BOOST_REQUIRE_EQUAL(arma::accu(delta), 0.0);
// Test Valid for non zero padding.
TransposedConvolution<> module2(1, 1, 3, 3, 2, 2,
std::tuple<size_t, size_t>(0, 0), std::tuple<size_t, size_t>(0, 0),
2, 2, 5, 5, "VALID");
// Test the forward function.
input = arma::linspace<arma::colvec>(0, 3, 4);
module2.Parameters() = arma::mat(25 + 1, 1, arma::fill::zeros);
module2.Parameters()(2) = 8.0;
module2.Parameters()(4) = 6.0;
module2.Parameters()(6) = 4.0;
module2.Parameters()(8) = 2.0;
module2.Reset();
module2.Forward(std::move(input), std::move(output));
// Value calculated using torch.nn.functional.conv_transpose2d().
BOOST_REQUIRE_EQUAL(arma::accu(output), 120.0);
// Test the Backward Function.
module2.Backward(std::move(input), std::move(output), std::move(delta));
BOOST_REQUIRE_EQUAL(arma::accu(delta), 960.0);
// Test for same padding type.
TransposedConvolution<> module3(1, 1, 3, 3, 2, 2, 0, 0, 3, 3, 3, 3, "SAME");
// Test the forward function.
input = arma::linspace<arma::colvec>(0, 8, 9);
module3.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros);
module3.Reset();
module3.Forward(std::move(input), std::move(output));
BOOST_REQUIRE_EQUAL(arma::accu(output), 0);
BOOST_REQUIRE_EQUAL(output.n_rows, input.n_rows);
BOOST_REQUIRE_EQUAL(output.n_cols, input.n_cols);
// Test the Backward Function.
module3.Backward(std::move(input), std::move(output), std::move(delta));
BOOST_REQUIRE_EQUAL(arma::accu(delta), 0.0);
// Output shape should equal input.
TransposedConvolution<> module4(1, 1, 3, 3, 1, 1,
std::tuple<size_t, size_t>(2, 2), std::tuple<size_t, size_t>(2, 2),
5, 5, 5, 5, "SAME");
// Test the forward function.
input = arma::linspace<arma::colvec>(0, 24, 25);
module4.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros);
module4.Reset();
module4.Forward(std::move(input), std::move(output));
BOOST_REQUIRE_EQUAL(arma::accu(output), 0);
BOOST_REQUIRE_EQUAL(output.n_rows, input.n_rows);
BOOST_REQUIRE_EQUAL(output.n_cols, input.n_cols);
// Test the Backward Function.
module4.Backward(std::move(input), std::move(output), std::move(delta));
BOOST_REQUIRE_EQUAL(arma::accu(delta), 0.0);
TransposedConvolution<> module5(1, 1, 3, 3, 2, 2, 0, 0, 2, 2, 2, 2, "SAME");
// Test the forward function.
input = arma::linspace<arma::colvec>(0, 3, 4);
module5.Parameters() = arma::mat(25 + 1, 1, arma::fill::zeros);
module5.Reset();
module5.Forward(std::move(input), std::move(output));
BOOST_REQUIRE_EQUAL(arma::accu(output), 0);
BOOST_REQUIRE_EQUAL(output.n_rows, input.n_rows);
BOOST_REQUIRE_EQUAL(output.n_cols, input.n_cols);
// Test the Backward Function.
module5.Backward(std::move(input), std::move(output), std::move(delta));
BOOST_REQUIRE_EQUAL(arma::accu(delta), 0.0);
TransposedConvolution<> module6(1, 1, 4, 4, 1, 1, 1, 1, 5, 5, 5, 5, "SAME");
// Test the forward function.
input = arma::linspace<arma::colvec>(0, 24, 25);
module6.Parameters() = arma::mat(16 + 1, 1, arma::fill::zeros);
module6.Reset();
module6.Forward(std::move(input), std::move(output));
BOOST_REQUIRE_EQUAL(arma::accu(output), 0);
BOOST_REQUIRE_EQUAL(output.n_rows, input.n_rows);
BOOST_REQUIRE_EQUAL(output.n_cols, input.n_cols);
// Test the Backward Function.
module6.Backward(std::move(input), std::move(output), std::move(delta));
BOOST_REQUIRE_EQUAL(arma::accu(delta), 0.0);
}
BOOST_AUTO_TEST_SUITE_END();
src/mlpack/tests/callback_test.cpp
+63 -23
View File
@@ -19,8 +19,9 @@
#include <mlpack/methods/lmnn/lmnn.hpp>
#include <mlpack/methods/nca/nca.hpp>
#include <mlpack/core/metrics/lmetric.hpp>
#include <mlpack/methods/softmax_regression/softmax_regression.hpp>
#include <mlpack/methods/softmax_regression/softmax_regression_impl.hpp>
#include <mlpack/methods/ann/init_rules/gaussian_init.hpp>
#include <boost/test/unit_test.hpp>
using namespace mlpack;
@@ -29,6 +30,7 @@ using namespace mlpack::regression;
using namespace mlpack::lmnn;
using namespace mlpack::metric;
using namespace mlpack::nca;
using namespace mlpack::distribution;
BOOST_AUTO_TEST_SUITE(CallbackTest);
@@ -94,12 +96,12 @@ BOOST_AUTO_TEST_CASE(RNNCallbackTest)
// Create model with user defined rho parameter.
RNN<NegativeLogLikelihood<>, RandomInitialization> model(
rho, false, NegativeLogLikelihood<>(), init);
model.Add<IdentityLayer<> >();
model.Add<Linear<> >(1, 10);
model.Add<IdentityLayer<>>();
model.Add<Linear<>>(1, 10);
// Use LSTM layer with rho.
model.Add<LSTM<> >(10, 3, rho);
model.Add<LogSoftMax<> >();
model.Add<LSTM<>>(10, 3, rho);
model.Add<LogSoftMax<>>();
std::stringstream stream;
model.Train(input, target, ens::PrintLoss(stream));
@@ -120,12 +122,12 @@ BOOST_AUTO_TEST_CASE(RNNWithOptimizerCallbackTest)
// Create model with user defined rho parameter.
RNN<NegativeLogLikelihood<>, RandomInitialization> model(
rho, false, NegativeLogLikelihood<>(), init);
model.Add<IdentityLayer<> >();
model.Add<Linear<> >(1, 10);
model.Add<IdentityLayer<>>();
model.Add<Linear<>>(1, 10);
// Use LSTM layer with rho.
model.Add<LSTM<> >(10, 3, rho);
model.Add<LogSoftMax<> >();
model.Add<LSTM<>>(10, 3, rho);
model.Add<LogSoftMax<>>();
std::stringstream stream;
ens::StandardSGD opt(0.1, 1, 5);
@@ -139,17 +141,17 @@ BOOST_AUTO_TEST_CASE(RNNWithOptimizerCallbackTest)
*/
BOOST_AUTO_TEST_CASE(LRWithOptimizerCallback)
{
arma::mat data("1 2 3;"
"1 2 3");
arma::Row<size_t> responses("1 1 0");
arma::mat data("1 2 3;"
"1 2 3");
arma::Row<size_t> responses("1 1 0");
ens::StandardSGD sgd(0.1, 1, 5);
LogisticRegression<> logisticRegression(data, responses, sgd, 0.001);
std::stringstream stream;
logisticRegression.Train<ens::StandardSGD>(data, responses, sgd,
ens::PrintLoss(stream));
ens::StandardSGD sgd(0.1, 1, 5);
LogisticRegression<> logisticRegression(data, responses, sgd, 0.001);
std::stringstream stream;
logisticRegression.Train<ens::StandardSGD>(data, responses, sgd,
ens::PrintLoss(stream));
BOOST_REQUIRE_GT(stream.str().length(), 0);
BOOST_REQUIRE_GT(stream.str().length(), 0);
}
/**
@@ -158,8 +160,8 @@ BOOST_AUTO_TEST_CASE(LRWithOptimizerCallback)
BOOST_AUTO_TEST_CASE(LMNNWithOptimizerCallback)
{
// Useful but simple dataset with six points and two classes.
arma::mat dataset = "-0.1 -0.1 -0.1 0.1 0.1 0.1;"
" 1.0 0.0 -1.0 1.0 0.0 -1.0 ";
arma::mat dataset = "-0.1 -0.1 -0.1 0.1 0.1 0.1;"
" 1.0 0.0 -1.0 1.0 0.0 -1.0 ";
arma::Row<size_t> labels = " 0 0 0 1 1 1 ";
LMNN<> lmnn(dataset, labels, 1);
@@ -177,8 +179,8 @@ BOOST_AUTO_TEST_CASE(LMNNWithOptimizerCallback)
BOOST_AUTO_TEST_CASE(NCAWithOptimizerCallback)
{
// Useful but simple dataset with six points and two classes.
arma::mat data = "-0.1 -0.1 -0.1 0.1 0.1 0.1;"
" 1.0 0.0 -1.0 1.0 0.0 -1.0 ";
arma::mat data = "-0.1 -0.1 -0.1 0.1 0.1 0.1;"
" 1.0 0.0 -1.0 1.0 0.0 -1.0 ";
arma::Row<size_t> labels = " 0 0 0 1 1 1 ";
NCA<SquaredEuclideanDistance> nca(data, labels);
@@ -190,6 +192,41 @@ BOOST_AUTO_TEST_CASE(NCAWithOptimizerCallback)
BOOST_REQUIRE_GT(stream.str().length(), 0);
}
/**
* Test softmax_regression implementation with PrintLoss callback.
*/
BOOST_AUTO_TEST_CASE(SRWithOptimizerCallback)
{
const size_t points = 1000;
const size_t inputSize = 3;
const size_t numClasses = 3;
const double lambda = 0.5;
// Generate two-Gaussian dataset.
GaussianDistribution g1(arma::vec("1.0 9.0 1.0"), arma::eye<arma::mat>(3, 3));
GaussianDistribution g2(arma::vec("4.0 3.0 4.0"), arma::eye<arma::mat>(3, 3));
arma::mat data(inputSize, points);
arma::Row<size_t> labels(points);
for (size_t i = 0; i < points / 2; i++)
{
data.col(i) = g1.Random();
labels(i) = 0;
}
for (size_t i = points / 2; i < points; i++)
{
data.col(i) = g2.Random();
labels(i) = 1;
}
ens::StandardSGD sgd(0.1, 1, 5);
std::stringstream stream;
// Train softmax regression object.
SoftmaxRegression sr(data, labels, numClasses, lambda);
sr.Train(data, labels, numClasses, sgd, ens::ProgressBar(70, stream));
BOOST_REQUIRE_GT(stream.str().length(), 0);
}
/*
* Tests the RBM Implementation with PrintLoss callback.
@@ -205,7 +242,10 @@ BOOST_AUTO_TEST_CASE(RBMCallbackTest)
GaussianInitialization gaussian(0, 0.1);
RBM<GaussianInitialization> model(trainData,
gaussian, trainData.n_rows, hiddenLayerSize, batchSize);
gaussian,
trainData.n_rows,
hiddenLayerSize,
batchSize);
size_t numRBMIterations = 10;
ens::StandardSGD msgd(0.03, batchSize, numRBMIterations, 0, true);
src/mlpack/tests/cosine_tree_test.cpp
+214
View File
@@ -222,4 +222,218 @@ BOOST_AUTO_TEST_CASE(CosineTreeModifiedGramSchmidt)
}
}
/**
* Test the copy constructor & copy assignment using Cosine trees.
*/
BOOST_AUTO_TEST_CASE(CopyConstructorAndOperatorCosineTreeTest)
{
// Initialize constants required for the test.
const size_t numRows = 10;
const size_t numCols = 15;
// Vectors to hold depth-first traversal
// of the number of columns in each node.
std::vector<int> v1, v2, v3;
// Make a random dataset.
arma::mat* data = new arma::mat(numRows, numCols, arma::fill::randu);
// Make a cosine tree, with the generated dataset.
CosineTree* ctree1 = new CosineTree(*data);
// Stacks for depth first search of the tree.
std::vector<CosineTree*> nodeStack1, nodeStack2, nodeStack3;
nodeStack1.push_back(ctree1);
// While stack is not empty.
while (nodeStack1.size())
{
// Pop a node from the stack and split it.
CosineTree *currentNode1, *currentLeft1, *currentRight1;
currentNode1 = nodeStack1.back();
currentNode1->CosineNodeSplit();
nodeStack1.pop_back();
// Obtain pointers to the children of the node.
currentLeft1 = currentNode1->Left();
currentRight1 = currentNode1->Right();
// If children exist.
if (currentLeft1 && currentRight1)
{
// Push the child nodes on to the stack.
nodeStack1.push_back(currentLeft1);
nodeStack1.push_back(currentRight1);
v1.push_back(currentNode1->NumColumns());
}
}
// Copy constructor and operator.
CosineTree ctree2(*ctree1);
CosineTree ctree3 = *ctree1;
delete ctree1;
delete data;
nodeStack2.push_back(&ctree2);
nodeStack3.push_back(&ctree3);
// While stacks are not empty.
while (nodeStack2.size() && nodeStack3.size())
{
// Pop a node from the stack and split it.
CosineTree *currentNode2, *currentLeft2, *currentRight2;
CosineTree *currentNode3, *currentLeft3, *currentRight3;
currentNode2 = nodeStack2.back();
nodeStack2.pop_back();
currentNode3 = nodeStack3.back();
nodeStack3.pop_back();
// Obtain pointers to the children of the node.
currentLeft2 = currentNode2->Left();
currentRight2 = currentNode2->Right();
currentLeft3 = currentNode3->Left();
currentRight3 = currentNode3->Right();
// If children exist.
if (currentLeft2 && currentRight2 && currentLeft3 && currentRight3)
{
// Push the child nodes on to the stack.
nodeStack2.push_back(currentLeft2);
nodeStack2.push_back(currentRight2);
v2.push_back(currentNode2->NumColumns());
nodeStack3.push_back(currentLeft3);
nodeStack3.push_back(currentRight3);
v3.push_back(currentNode3->NumColumns());
}
}
for (size_t i = 0; i < v1.size(); i++)
{
BOOST_REQUIRE_EQUAL(v1.at(i), v2.at(i));
BOOST_REQUIRE_EQUAL(v1.at(i), v3.at(i));
}
}
/**
* Test the move constructor & move assignment using Cosine trees.
*/
BOOST_AUTO_TEST_CASE(MoveConstructorAndOperatorCosineTreeTest)
{
// Initialize constants required for the test.
const size_t numRows = 10;
const size_t numCols = 15;
// Vectors to hold depth-first traversal
// of the number of columns in each node.
std::vector<int> v1, v2, v3;
// Make a random dataset.
arma::mat data = arma::randu(numRows, numCols);
// Make a cosine tree, with the generated dataset.
CosineTree ctree1(data);
// Stacks for depth first search of the tree.
std::vector<CosineTree*> nodeStack1, nodeStack2, nodeStack3;
nodeStack1.push_back(&ctree1);
// While stack is not empty.
while (nodeStack1.size())
{
// Pop a node from the stack and split it.
CosineTree *currentNode1, *currentLeft1, *currentRight1;
currentNode1 = nodeStack1.back();
currentNode1->CosineNodeSplit();
nodeStack1.pop_back();
// Obtain pointers to the children of the node.
currentLeft1 = currentNode1->Left();
currentRight1 = currentNode1->Right();
// If children exist.
if (currentLeft1 && currentRight1)
{
// Push the child nodes on to the stack.
nodeStack1.push_back(currentLeft1);
nodeStack1.push_back(currentRight1);
v1.push_back(currentNode1->NumColumns());
}
}
// Move constructor.
CosineTree ctree2(std::move(ctree1));
nodeStack2.push_back(&ctree2);
// While stacks are not empty.
while (nodeStack2.size())
{
// Pop a node from the stack and split it.
CosineTree *currentNode2, *currentLeft2, *currentRight2;
currentNode2 = nodeStack2.back();
nodeStack2.pop_back();
// Obtain pointers to the children of the node.
currentLeft2 = currentNode2->Left();
currentRight2 = currentNode2->Right();
// If children exist.
if (currentLeft2 && currentRight2)
{
// Push the child nodes on to the stack.
nodeStack2.push_back(currentLeft2);
nodeStack2.push_back(currentRight2);
v2.push_back(currentNode2->NumColumns());
}
}
// Move operator.
CosineTree ctree3 = std::move(ctree2);
nodeStack3.push_back(&ctree3);
// While stacks are not empty.
while (nodeStack3.size())
{
// Pop a node from the stack and split it.
CosineTree *currentNode3, *currentLeft3, *currentRight3;
currentNode3 = nodeStack3.back();
nodeStack3.pop_back();
// Obtain pointers to the children of the node.
currentLeft3 = currentNode3->Left();
currentRight3 = currentNode3->Right();
// If children exist.
if (currentLeft3 && currentRight3)
{
// Push the child nodes on to the stack.
nodeStack3.push_back(currentLeft3);
nodeStack3.push_back(currentRight3);
v3.push_back(currentNode3->NumColumns());
}
}
for (size_t i = 0; i < v1.size(); i++)
{
BOOST_REQUIRE_EQUAL(v1.at(i), v2.at(i));
BOOST_REQUIRE_EQUAL(v1.at(i), v3.at(i));
}
}
BOOST_AUTO_TEST_SUITE_END();
src/mlpack/tests/knn_test.cpp
+131 -3
View File
@@ -1357,6 +1357,34 @@ BOOST_AUTO_TEST_CASE(CopyConstructorAndOperatorCoverTreeTest)
CheckMatrices(distances, distances3);
}
/**
* Test the copy constructor and copy operator using the BinarySpaceTree.
*/
BOOST_AUTO_TEST_CASE(CopyConstructorAndOperatorBinarySpaceTreeTest)
{
arma::mat dataset = arma::randu<arma::mat>(5, 500);
typedef NeighborSearch<NearestNeighborSort, EuclideanDistance, arma::mat,
KDTree> NeighborSearchType;
NeighborSearchType knn(std::move(dataset));
// Copy constructor and operator.
NeighborSearchType knn2(knn);
NeighborSearchType knn3 = knn;
// Get results.
arma::mat distances, distances2, distances3;
arma::Mat<size_t> neighbors, neighbors2, neighbors3;
knn.Search(3, neighbors, distances);
knn2.Search(3, neighbors2, distances2);
knn3.Search(3, neighbors3, distances3);
CheckMatrices(neighbors, neighbors2);
CheckMatrices(neighbors, neighbors3);
CheckMatrices(distances, distances2);
CheckMatrices(distances, distances3);
}
/**
* Test the copy constructor and copy operator using the Spill Tree.
*/
@@ -1385,6 +1413,34 @@ BOOST_AUTO_TEST_CASE(CopyConstructorAndOperatorSpillTreeTest)
CheckMatrices(distances, distances3);
}
/**
* Test the copy constructor and copy operator using the Octree.
*/
BOOST_AUTO_TEST_CASE(CopyConstructorAndOperatorOctreeTest)
{
arma::mat dataset = arma::randu<arma::mat>(5, 500);
typedef NeighborSearch<NearestNeighborSort, EuclideanDistance, arma::mat,
Octree> NeighborSearchType;
NeighborSearchType knn(std::move(dataset));
// Copy constructor and operator.
NeighborSearchType knn2(knn);
NeighborSearchType knn3 = knn;
// Get results.
arma::mat distances, distances2, distances3;
arma::Mat<size_t> neighbors, neighbors2, neighbors3;
knn.Search(3, neighbors, distances);
knn2.Search(3, neighbors2, distances2);
knn3.Search(3, neighbors3, distances3);
CheckMatrices(neighbors, neighbors2);
CheckMatrices(neighbors, neighbors3);
CheckMatrices(distances, distances2);
CheckMatrices(distances, distances3);
}
/**
* Test the move constructor.
*/
@@ -1411,7 +1467,7 @@ BOOST_AUTO_TEST_CASE(MoveConstructorTest)
}
/**
* Test the move constructor using R trees.
* Test the move constructor & move assignment using R trees.
*/
BOOST_AUTO_TEST_CASE(MoveConstructorRTreeTest)
{
@@ -1421,8 +1477,8 @@ BOOST_AUTO_TEST_CASE(MoveConstructorRTreeTest)
NeighborSearchType* knn = new NeighborSearchType(std::move(dataset));
// Get predictions.
arma::mat distances, distances2;
arma::Mat<size_t> neighbors, neighbors2;
arma::mat distances, distances2, distances3;
arma::Mat<size_t> neighbors, neighbors2, neighbors3;
knn->Search(3, neighbors, distances);
@@ -1433,11 +1489,83 @@ BOOST_AUTO_TEST_CASE(MoveConstructorRTreeTest)
knn2.Search(3, neighbors2, distances2);
// Use move assignment.
NeighborSearchType knn3 = std::move(knn2);
knn3.Search(3, neighbors3, distances3);
CheckMatrices(neighbors, neighbors2);
CheckMatrices(neighbors, neighbors3);
CheckMatrices(distances, distances2);
CheckMatrices(distances, distances3);
}
/**
* Test the move constructor & move assignment using Binary Space trees.
*/
BOOST_AUTO_TEST_CASE(MoveConstructorBinarySpaceTreeTest)
{
arma::mat dataset = arma::randu<arma::mat>(5, 500);
typedef NeighborSearch<NearestNeighborSort, EuclideanDistance, arma::mat,
KDTree> NeighborSearchType;
NeighborSearchType* knn = new NeighborSearchType(std::move(dataset));
// Get predictions.
arma::mat distances, distances2, distances3;
arma::Mat<size_t> neighbors, neighbors2, neighbors3;
knn->Search(3, neighbors, distances);
// Use move constructor.
NeighborSearchType knn2(std::move(*knn));
delete knn;
knn2.Search(3, neighbors2, distances2);
// Use move assignment.
NeighborSearchType knn3 = std::move(knn2);
knn3.Search(3, neighbors3, distances3);
CheckMatrices(neighbors, neighbors2);
CheckMatrices(neighbors, neighbors3);
CheckMatrices(distances, distances2);
CheckMatrices(distances, distances3);
}
/**
* Test the move constructor & move assignment using Octree.
*/
BOOST_AUTO_TEST_CASE(MoveConstructorOctreeTest)
{
arma::mat dataset = arma::randu<arma::mat>(5, 500);
typedef NeighborSearch<NearestNeighborSort, EuclideanDistance, arma::mat,
Octree> NeighborSearchType;
NeighborSearchType* knn = new NeighborSearchType(std::move(dataset));
// Get predictions.
arma::mat distances, distances2, distances3;
arma::Mat<size_t> neighbors, neighbors2, neighbors3;
knn->Search(3, neighbors, distances);
// Use move constructor.
NeighborSearchType knn2(std::move(*knn));
delete knn;
knn2.Search(3, neighbors2, distances2);
// Use move assignment.
NeighborSearchType knn3 = std::move(knn2);
knn3.Search(3, neighbors3, distances3);
CheckMatrices(neighbors, neighbors2);
CheckMatrices(neighbors, neighbors3);
CheckMatrices(distances, distances2);
CheckMatrices(distances, distances3);
}
/**
* Test the move constructor & move assignment using Cover Tree.
*/
src/mlpack/tests/loss_functions_test.cpp
+40
View File
@@ -3,6 +3,7 @@
* @author Dakshit Agrawal
* @author Sourabh Varshney
* @author Atharva Khandait
* @author Saksham Rastogi
*
* Tests for loss functions in mlpack::methods::ann:loss_functions.
*
@@ -21,6 +22,7 @@
#include <mlpack/methods/ann/loss_functions/cross_entropy_error.hpp>
#include <mlpack/methods/ann/loss_functions/reconstruction_loss.hpp>
#include <mlpack/methods/ann/loss_functions/mean_squared_logarithmic_error.hpp>
#include <mlpack/methods/ann/loss_functions/mean_bias_error.hpp>
#include <mlpack/methods/ann/loss_functions/dice_loss.hpp>
#include <mlpack/methods/ann/init_rules/nguyen_widrow_init.hpp>
#include <mlpack/methods/ann/ffn.hpp>
@@ -431,4 +433,42 @@ BOOST_AUTO_TEST_CASE(DiceLossTest)
BOOST_REQUIRE_EQUAL(output.n_cols, input2.n_cols);
}
/*
* Simple test for the mean bias error performance function.
*/
BOOST_AUTO_TEST_CASE(SimpleMeanBiasErrorTest)
{
arma::mat input, output, target;
MeanBiasError<> module;
// Test the Forward function on a user generator input and compare it against
// the manually calculated result.
input = arma::mat("1.0 0.0 1.0 -1.0 -1.0 0.0 -1.0 0.0");
target = arma::zeros(1, 8);
double error = module.Forward(std::move(input), std::move(target));
BOOST_REQUIRE_EQUAL(error, 0.125);
// Test the Backward function.
module.Backward(std::move(input), std::move(target), std::move(output));
// We should get a vector with -1 everywhere.
for (double el : output)
{
BOOST_REQUIRE_EQUAL(el, -1);
}
BOOST_REQUIRE_EQUAL(output.n_rows, input.n_rows);
BOOST_REQUIRE_EQUAL(output.n_cols, input.n_cols);
// Test the error function on a single input.
input = arma::mat("2");
target = arma::mat("3");
error = module.Forward(std::move(input), std::move(target));
BOOST_REQUIRE_EQUAL(error, 1.0);
// Test the Backward function on a single input.
module.Backward(std::move(input), std::move(target), std::move(output));
// Test whether the output is negative.
BOOST_REQUIRE_EQUAL(arma::accu(output), -1);
BOOST_REQUIRE_EQUAL(output.n_elem, 1);
}
BOOST_AUTO_TEST_SUITE_END();
src/mlpack/tests/main_tests/nmf_test.cpp
+79
View File
@@ -284,4 +284,83 @@ BOOST_AUTO_TEST_CASE(NMFMaxIterationTest)
BOOST_REQUIRE_GT(arma::norm(h1 - h2), 1e-5);
}
/**
* Test NMF with given initial_w and initial_h.
*/
BOOST_AUTO_TEST_CASE(NMFWHGivenInitTest)
{
mat v = arma::randu(10, 10);
mat initialW = arma::randu(10, 5);
mat initialH = arma::randu(5, 10);
int r = 5;
SetInputParam("input", v);
SetInputParam("rank", r);
SetInputParam("initial_w", initialW);
SetInputParam("initial_h", initialH);
mlpackMain();
const mat w = CLI::GetParam<mat>("w");
const mat h = CLI::GetParam<mat>("h");
// Check the shapes of W and H.
BOOST_REQUIRE_EQUAL(w.n_rows, 10);
BOOST_REQUIRE_EQUAL(w.n_cols, 5);
BOOST_REQUIRE_EQUAL(h.n_rows, 5);
BOOST_REQUIRE_EQUAL(h.n_cols, 10);
}
/**
* Test NMF with given initial_w.
*/
BOOST_AUTO_TEST_CASE(NMFWGivenInitTest)
{
mat v = arma::randu(10, 10);
mat initialW = arma::randu(10, 5);
int r = 5;
SetInputParam("input", v);
SetInputParam("rank", r);
SetInputParam("initial_w", initialW);
mlpackMain();
const mat w = CLI::GetParam<mat>("w");
const mat h = CLI::GetParam<mat>("h");
// Check the shapes of W and H.
BOOST_REQUIRE_EQUAL(w.n_rows, 10);
BOOST_REQUIRE_EQUAL(w.n_cols, 5);
BOOST_REQUIRE_EQUAL(h.n_rows, 5);
BOOST_REQUIRE_EQUAL(h.n_cols, 10);
}
/**
* Test NMF with given initial_h.
*/
BOOST_AUTO_TEST_CASE(NMFHGivenInitTest)
{
mat v = arma::randu(10, 10);
mat initialH = arma::randu(5, 10);
int r = 5;
SetInputParam("input", v);
SetInputParam("rank", r);
SetInputParam("initial_h", initialH);
mlpackMain();
const mat w = CLI::GetParam<mat>("w");
const mat h = CLI::GetParam<mat>("h");
// Check the shapes of W and H.
BOOST_REQUIRE_EQUAL(w.n_rows, 10);
BOOST_REQUIRE_EQUAL(w.n_cols, 5);
BOOST_REQUIRE_EQUAL(h.n_rows, 5);
BOOST_REQUIRE_EQUAL(h.n_cols, 10);
}
BOOST_AUTO_TEST_SUITE_END();
src/mlpack/tests/serialization_test.cpp
-2
View File
@@ -666,9 +666,7 @@ BOOST_AUTO_TEST_CASE(SoftmaxRegressionTest)
labels[i] = 0;
for (size_t i = 500; i < 1000; ++i)
labels[i] = 1;
SoftmaxRegression sr(dataset, labels, 2);
SoftmaxRegression srXml(dataset.n_rows, 2);
SoftmaxRegression srText(dataset.n_rows, 2);
SoftmaxRegression srBinary(dataset.n_rows, 2);
src/mlpack/tests/softmax_regression_test.cpp
+1 -1
View File
@@ -360,8 +360,8 @@ BOOST_AUTO_TEST_CASE(SoftmaxRegressionTrainTest)
SoftmaxRegression sr(dataset.n_rows, 2);
SoftmaxRegression sr2(dataset.n_rows, 2);
sr.Parameters() = sr2.Parameters();
sr.Train(dataset, labels, 2);
ens::L_BFGS lbfgs;
sr.Train(dataset, labels, 2, std::move(lbfgs));
sr2.Train(dataset, labels, 2, std::move(lbfgs));
// Ensure that the parameters are the same.