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nmf_operations.hpp
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#ifndef VIENNACL_LINALG_HOST_BASED_NMF_OPERATIONS_HPP_
#define VIENNACL_LINALG_HOST_BASED_NMF_OPERATIONS_HPP_
/* =========================================================================
Copyright (c) 2010-2016, Institute for Microelectronics,
Institute for Analysis and Scientific Computing,
TU Wien.
Portions of this software are copyright by UChicago Argonne, LLC.
-----------------
ViennaCL - The Vienna Computing Library
-----------------
Project Head: Karl Rupp rupp@iue.tuwien.ac.at
(A list of authors and contributors can be found in the manual)
License: MIT (X11), see file LICENSE in the base directory
============================================================================= */
#include "viennacl/vector.hpp"
#include "viennacl/matrix.hpp"
#include "viennacl/linalg/prod.hpp"
#include "viennacl/linalg/norm_2.hpp"
#include "viennacl/linalg/norm_frobenius.hpp"
#include "viennacl/linalg/host_based/common.hpp"
namespace viennacl
{
namespace linalg
{
class nmf_config
{
public:
nmf_config(double val_epsilon = 1e-4, double val_epsilon_stagnation = 1e-5,
vcl_size_t num_max_iters = 10000, vcl_size_t num_check_iters = 100) :
eps_(val_epsilon), stagnation_eps_(val_epsilon_stagnation), max_iters_(num_max_iters), check_after_steps_(
(num_check_iters > 0) ? num_check_iters : 1), print_relative_error_(false), iters_(0)
{
}
double tolerance() const
{
return eps_;
}
void tolerance(double e)
{
eps_ = e;
}
double stagnation_tolerance() const
{
return stagnation_eps_;
}
void stagnation_tolerance(double e)
{
stagnation_eps_ = e;
}
vcl_size_t max_iterations() const
{
return max_iters_;
}
void max_iterations(vcl_size_t m)
{
max_iters_ = m;
}
vcl_size_t iters() const
{
return iters_;
}
vcl_size_t check_after_steps() const
{
return check_after_steps_;
}
void check_after_steps(vcl_size_t c)
{
if (c > 0)
check_after_steps_ = c;
}
bool print_relative_error() const
{
return print_relative_error_;
}
void print_relative_error(bool b)
{
print_relative_error_ = b;
}
template<typename ScalarType>
friend void nmf(viennacl::matrix_base<ScalarType> const & V,
viennacl::matrix_base<ScalarType> & W, viennacl::matrix_base<ScalarType> & H,
nmf_config const & conf);
private:
double eps_;
double stagnation_eps_;
vcl_size_t max_iters_;
vcl_size_t check_after_steps_;
bool print_relative_error_;
public:
mutable vcl_size_t iters_;
};
namespace host_based
{
template<typename NumericT>
void el_wise_mul_div(NumericT * matrix1,
NumericT const * matrix2,
NumericT const * matrix3, vcl_size_t size)
{
#ifdef VIENNACL_WITH_OPENMP
#pragma omp parallel for
#endif
for (long i2 = 0; i2 < long(size); i2++)
{
vcl_size_t i = vcl_size_t(i2);
NumericT val = matrix1[i] * matrix2[i];
NumericT divisor = matrix3[i];
matrix1[i] = (divisor > (NumericT) 0.00001) ? (val / divisor) : (NumericT) 0;
}
}
template<typename NumericT>
void nmf(viennacl::matrix_base<NumericT> const & V,
viennacl::matrix_base<NumericT> & W,
viennacl::matrix_base<NumericT> & H,
viennacl::linalg::nmf_config const & conf)
{
vcl_size_t k = W.size2();
conf.iters_ = 0;
if (viennacl::linalg::norm_frobenius(W) <= 0)
W = viennacl::scalar_matrix<NumericT>(W.size1(), W.size2(), NumericT(1.0));
if (viennacl::linalg::norm_frobenius(H) <= 0)
H = viennacl::scalar_matrix<NumericT>(H.size1(), H.size2(), NumericT(1.0));
viennacl::matrix_base<NumericT> wn(V.size1(), k, W.row_major());
viennacl::matrix_base<NumericT> wd(V.size1(), k, W.row_major());
viennacl::matrix_base<NumericT> wtmp(V.size1(), V.size2(), W.row_major());
viennacl::matrix_base<NumericT> hn(k, V.size2(), H.row_major());
viennacl::matrix_base<NumericT> hd(k, V.size2(), H.row_major());
viennacl::matrix_base<NumericT> htmp(k, k, H.row_major());
viennacl::matrix_base<NumericT> appr(V.size1(), V.size2(), V.row_major());
NumericT last_diff = 0;
NumericT diff_init = 0;
bool stagnation_flag = false;
for (vcl_size_t i = 0; i < conf.max_iterations(); i++)
{
conf.iters_ = i + 1;
hn = viennacl::linalg::prod(trans(W), V);
htmp = viennacl::linalg::prod(trans(W), W);
hd = viennacl::linalg::prod(htmp, H);
NumericT * data_H = detail::extract_raw_pointer<NumericT>(H);
NumericT * data_hn = detail::extract_raw_pointer<NumericT>(hn);
NumericT * data_hd = detail::extract_raw_pointer<NumericT>(hd);
viennacl::linalg::host_based::el_wise_mul_div(data_H, data_hn, data_hd, H.internal_size1() * H.internal_size2());
wn = viennacl::linalg::prod(V, trans(H));
wtmp = viennacl::linalg::prod(W, H);
wd = viennacl::linalg::prod(wtmp, trans(H));
NumericT * data_W = detail::extract_raw_pointer<NumericT>(W);
NumericT * data_wn = detail::extract_raw_pointer<NumericT>(wn);
NumericT * data_wd = detail::extract_raw_pointer<NumericT>(wd);
viennacl::linalg::host_based::el_wise_mul_div(data_W, data_wn, data_wd, W.internal_size1() * W.internal_size2());
if (i % conf.check_after_steps() == 0) //check for convergence
{
appr = viennacl::linalg::prod(W, H);
appr -= V;
NumericT diff_val = viennacl::linalg::norm_frobenius(appr);
if (i == 0)
diff_init = diff_val;
if (conf.print_relative_error())
std::cout << diff_val / diff_init << std::endl;
// Approximation check
if (diff_val / diff_init < conf.tolerance())
break;
// Stagnation check
if (std::fabs(diff_val - last_diff) / (diff_val * NumericT(conf.check_after_steps())) < conf.stagnation_tolerance()) //avoid situations where convergence stagnates
{
if (stagnation_flag) // iteration stagnates (two iterates with no notable progress)
break;
else
// record stagnation in this iteration
stagnation_flag = true;
} else
// good progress in this iteration, so unset stagnation flag
stagnation_flag = false;
// prepare for next iterate:
last_diff = diff_val;
}
}
}
} //namespace host_based
} //namespace linalg
} //namespace viennacl
#endif /* VIENNACL_LINALG_HOST_BASED_NMF_OPERATIONS_HPP_ */
norm_2.hpp
Generic interface for the l^2-norm. See viennacl/linalg/vector_operations.hpp for implementations...
viennacl::linalg::nmf_config::nmf
friend void nmf(viennacl::matrix_base< ScalarType > const &V, viennacl::matrix_base< ScalarType > &W, viennacl::matrix_base< ScalarType > &H, nmf_config const &conf)
The nonnegative matrix factorization (approximation) algorithm as suggested by Lee and Seung...
Definition: nmf_operations.hpp:59
prod.hpp
Generic interface for matrix-vector and matrix-matrix products. See viennacl/linalg/vector_operations...
matrix.hpp
Implementation of the dense matrix class.
viennacl::linalg::nmf_config::check_after_steps
vcl_size_t check_after_steps() const
Number of steps after which the convergence of NMF should be checked (again)
Definition: nmf_operations.hpp:91
viennacl::matrix_base
Definition: matrix_def.hpp:103
viennacl::linalg::nmf_config
Configuration class for the nonnegative-matrix-factorization algorithm. Specify tolerances, maximum iteration counts, etc., here.
Definition: nmf_operations.hpp:39
viennacl::linalg::host_based::nmf
void nmf(viennacl::matrix_base< NumericT > const &V, viennacl::matrix_base< NumericT > &W, viennacl::matrix_base< NumericT > &H, viennacl::linalg::nmf_config const &conf)
The nonnegative matrix factorization (approximation) algorithm as suggested by Lee and Seung...
Definition: nmf_operations.hpp:156
NumericT
float NumericT
Definition: bisect.cpp:40
viennacl::linalg::nmf_config::iters_
vcl_size_t iters_
Definition: nmf_operations.hpp:125
viennacl::linalg::prod
VectorT prod(std::vector< std::vector< T, A1 >, A2 > const &matrix, VectorT const &vector)
Definition: prod.hpp:102
viennacl::traits::size
vcl_size_t size(VectorType const &vec)
Generic routine for obtaining the size of a vector (ViennaCL, uBLAS, etc.)
Definition: size.hpp:239
norm_frobenius.hpp
Generic interface for the Frobenius norm.
viennacl::linalg::nmf_config::max_iterations
vcl_size_t max_iterations() const
Returns the maximum number of iterations for the NMF algorithm.
Definition: nmf_operations.hpp:74
viennacl::linalg::nmf_config::check_after_steps
void check_after_steps(vcl_size_t c)
Set the number of steps after which the convergence of NMF should be checked (again) ...
Definition: nmf_operations.hpp:96
viennacl::scalar_matrix
Represents a vector consisting of scalars 's' only, i.e. v[i] = s for all i. To be used as an initial...
Definition: matrix_def.hpp:93
viennacl::linalg::nmf_config::tolerance
double tolerance() const
Returns the relative tolerance for convergence.
Definition: nmf_operations.hpp:50
viennacl::linalg::nmf_config::print_relative_error
void print_relative_error(bool b)
Specify whether the relative error should be printed at each convergence check after 'num_check_iters...
Definition: nmf_operations.hpp:108
viennacl::vcl_size_t
std::size_t vcl_size_t
Definition: forwards.h:75
viennacl::matrix_base::size2
size_type size2() const
Returns the number of columns.
Definition: matrix_def.hpp:226
common.hpp
Common routines for single-threaded or OpenMP-enabled execution on CPU.
viennacl::linalg::nmf_config::tolerance
void tolerance(double e)
Sets the relative tolerance for convergence, i.e. norm(V - W * H) / norm(V - W_init * H_init) ...
Definition: nmf_operations.hpp:56
viennacl::linalg::host_based::el_wise_mul_div
void el_wise_mul_div(NumericT *matrix1, NumericT const *matrix2, NumericT const *matrix3, vcl_size_t size)
Missing OpenMP kernel for nonnegative matrix factorization of a dense matrices.
Definition: nmf_operations.hpp:132
viennacl::matrix_base::size1
size_type size1() const
Returns the number of rows.
Definition: matrix_def.hpp:224
viennacl::linalg::nmf_config::stagnation_tolerance
double stagnation_tolerance() const
Relative tolerance for the stagnation check.
Definition: nmf_operations.hpp:62
viennacl::linalg::nmf_config::nmf_config
nmf_config(double val_epsilon=1e-4, double val_epsilon_stagnation=1e-5, vcl_size_t num_max_iters=10000, vcl_size_t num_check_iters=100)
Definition: nmf_operations.hpp:42
viennacl::linalg::nmf_config::iters
vcl_size_t iters() const
Returns the number of iterations of the last NMF run using this configuration object.
Definition: nmf_operations.hpp:85
vector.hpp
The vector type with operator-overloads and proxy classes is defined here. Linear algebra operations ...
viennacl::matrix_base::row_major
bool row_major() const
Definition: matrix_def.hpp:248
viennacl::linalg::norm_frobenius
scalar_expression< const matrix_base< NumericT >, const matrix_base< NumericT >, op_norm_frobenius > norm_frobenius(const matrix_base< NumericT > &A)
Definition: norm_frobenius.hpp:61
viennacl::linalg::host_based::trans
void trans(const matrix_expression< const matrix_base< NumericT, SizeT, DistanceT >, const matrix_base< NumericT, SizeT, DistanceT >, op_trans > &proxy, matrix_base< NumericT > &temp_trans)
Definition: matrix_operations.hpp:111
viennacl::linalg::nmf_config::max_iterations
void max_iterations(vcl_size_t m)
Sets the maximum number of iterations for the NMF algorithm.
Definition: nmf_operations.hpp:79
viennacl::linalg::nmf_config::stagnation_tolerance
void stagnation_tolerance(double e)
Sets the tolerance for the stagnation check (i.e. the minimum required relative change of the residua...
Definition: nmf_operations.hpp:68
viennacl::matrix_base::internal_size2
size_type internal_size2() const
Returns the internal number of columns. Usually required for launching OpenCL kernels only...
Definition: matrix_def.hpp:240
viennacl::matrix_base::internal_size1
size_type internal_size1() const
Returns the internal number of rows. Usually required for launching OpenCL kernels only...
Definition: matrix_def.hpp:238
viennacl::linalg::nmf_config::print_relative_error
bool print_relative_error() const
Returns the flag specifying whether the relative tolerance should be printed in each iteration...
Definition: nmf_operations.hpp:103