13#ifndef MLPACK_METHODS_SPARSE_CODING_SPARSE_CODING_HPP
14#define MLPACK_METHODS_SPARSE_CODING_SPARSE_CODING_HPP
25namespace sparse_coding {
145 template<
typename DictionaryInitializer = DataDependentRandomInitializer>
148 const double lambda1,
149 const double lambda2 = 0,
150 const size_t maxIterations = 0,
151 const double objTolerance = 0.01,
152 const double newtonTolerance = 1e-6,
153 const DictionaryInitializer& initializer =
154 DictionaryInitializer());
173 const double lambda1 = 0,
174 const double lambda2 = 0,
175 const size_t maxIterations = 0,
176 const double objTolerance = 0.01,
177 const double newtonTolerance = 1e-6);
183 template<
typename DictionaryInitializer = DataDependentRandomInitializer>
185 const DictionaryInitializer& initializer =
186 DictionaryInitializer());
195 void Encode(
const arma::mat& data, arma::mat& codes);
209 const arma::mat& codes,
210 const arma::uvec& adjacencies);
220 double Objective(
const arma::mat& data,
const arma::mat& codes)
const;
228 size_t Atoms()
const {
return atoms; }
258 template<
typename Archive>
266 arma::mat dictionary;
274 size_t maxIterations;
278 double newtonTolerance;
285#include "sparse_coding_impl.hpp"
An implementation of Sparse Coding with Dictionary Learning that achieves sparsity via an l1-norm reg...
double & ObjTolerance()
Modify the objective tolerance.
double Lambda1() const
Access the L1 regularization term.
double OptimizeDictionary(const arma::mat &data, const arma::mat &codes, const arma::uvec &adjacencies)
Learn dictionary via Newton method based on Lagrange dual.
arma::mat & Dictionary()
Modify the dictionary.
const arma::mat & Dictionary() const
Access the dictionary.
size_t MaxIterations() const
Get the maximum number of iterations.
double & NewtonTolerance()
Modify the tolerance for Newton's method (dictionary optimization step).
void Encode(const arma::mat &data, arma::mat &codes)
Sparse code each point in the given dataset via LARS, using the current dictionary and store the enco...
SparseCoding(const arma::mat &data, const size_t atoms, const double lambda1, const double lambda2=0, const size_t maxIterations=0, const double objTolerance=0.01, const double newtonTolerance=1e-6, const DictionaryInitializer &initializer=DictionaryInitializer())
Set the parameters to SparseCoding.
double Train(const arma::mat &data, const DictionaryInitializer &initializer=DictionaryInitializer())
Train the sparse coding model on the given dataset.
double NewtonTolerance() const
Get the tolerance for Newton's method (dictionary optimization step).
size_t & MaxIterations()
Modify the maximum number of iterations.
double & Lambda1()
Modify the L1 regularization term.
double Objective(const arma::mat &data, const arma::mat &codes) const
Compute the objective function.
double ObjTolerance() const
Get the objective tolerance.
double Lambda2() const
Access the L2 regularization term.
void ProjectDictionary()
Project each atom of the dictionary back onto the unit ball, if necessary.
size_t & Atoms()
Modify the number of atoms.
size_t Atoms() const
Access the number of atoms.
SparseCoding(const size_t atoms=0, const double lambda1=0, const double lambda2=0, const size_t maxIterations=0, const double objTolerance=0.01, const double newtonTolerance=1e-6)
Set the parameters to SparseCoding.
void serialize(Archive &ar, const unsigned int)
Serialize the sparse coding model.
double & Lambda2()
Modify the L2 regularization term.
Linear algebra utility functions, generally performed on matrices or vectors.
The core includes that mlpack expects; standard C++ includes and Armadillo.