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Added initial version of structural_svm_potts_problem object.

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Davis King 2012-04-29 15:45:11 -04:00
parent 9f607bc51c
commit 5e9df65355
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dlib/svm/structural_svm_potts_problem.h Normal file
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// Copyright (C) 2012 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_STRUCTURAL_SVM_POtTS_PROBLEM_H__
#define DLIB_STRUCTURAL_SVM_POtTS_PROBLEM_H__
#include "structural_svm_potts_problem_abstract.h"
#include "../graph_cuts.h"
#include "../matrix.h"
#include "../array.h"
#include <vector>
#include <iterator>
#include "structural_svm_problem_threaded.h"
#include "../graph.h"
// ----------------------------------------------------------------------------------------
namespace dlib
{
namespace impl
{
template <
typename T,
typename enable = void
>
struct fvect
{
// In this case type should be some sparse vector type
typedef typename T::type type;
};
template < typename T >
struct fvect<T, typename enable_if<is_matrix<typename T::type> >::type>
{
// The point of this stuff is to create the proper matrix
// type to represent the concatenation of an edge vector
// with an node vector.
typedef typename T::type node_mat;
typedef typename T::edge_type edge_mat;
const static long NRd = node_mat::NR;
const static long NRe = edge_mat::NR;
const static long NR = ((NRd!=0) && (NRe!=0)) ? (NRd+NRe) : 0;
typedef typename node_mat::value_type value_type;
typedef matrix<value_type,NR,1, typename node_mat::mem_manager_type, typename node_mat::layout_type> type;
};
}
// ----------------------------------------------------------------------------------------
template <
typename graph_type
>
class structural_svm_potts_problem : noncopyable,
public structural_svm_problem_threaded<matrix<double,0,1>,
typename dlib::impl::fvect<graph_type>::type >
{
public:
typedef matrix<double,0,1> matrix_type;
typedef typename dlib::impl::fvect<graph_type>::type feature_vector_type;
typedef graph_type sample_type;
typedef std::vector<node_label> label_type;
structural_svm_potts_problem(
const dlib::array<sample_type>& samples_,
const std::vector<label_type>& labels_,
unsigned long num_threads = 2
) :
structural_svm_problem_threaded<matrix_type,feature_vector_type>(num_threads),
samples(samples_),
labels(labels_)
{
// make sure requires clause is not broken
// TODO
// requires, at least one edge in the dataset. All edge vectors have same number
// of dims. All node vectors have same number of dims. none of the dims can be 0.
// all elements in a edge vector must be >= 0.
// Figure out how many dimensions are in a node vector. Just pick
// the first node we find and use it as the representative example.
node_dims = 0;
for (unsigned long i = 0; i < samples.size(); ++i)
{
if (samples[i].number_of_nodes() > 0)
{
node_dims = samples[i].node(0).data.size();
break;
}
}
// Figure out how many dimensions are in an edge vector. Just pick
// the first edge we find and use it as the representative example.
edge_dims = 0;
for (unsigned long i = 0; i < samples.size(); ++i)
{
for (unsigned long j = 0; j < samples[i].number_of_nodes(); ++j)
{
if (samples[i].node(j).number_of_neighbors() != 0)
{
edge_dims = samples[i].node(j).edge(0).size();
break;
}
}
// if we found an edge then stop
if (edge_dims != 0)
break;
}
}
long get_num_nonnegative_dimensions (
) const
/*!
ensures
- returns the number of dimensions of the solution vector which
are required to be non-negative. This is equal to the number of
dimensions for a feature vector on a graph edge.
!*/
{
return edge_dims;
}
private:
virtual long get_num_dimensions (
) const
{
// The psi/w vector will begin with all the edge dims and then follow with the node dims.
return edge_dims + node_dims;
}
virtual long get_num_samples (
) const
{
return samples.size();
}
template <typename psi_type>
typename enable_if<is_matrix<psi_type> >::type get_joint_feature_vector (
const sample_type& sample,
const label_type& label,
psi_type& psi
) const
{
psi.set_size(get_num_dimensions());
psi = 0;
for (unsigned long i = 0; i < sample.number_of_nodes(); ++i)
{
const bool label_i = (label[i]!=0);
// accumulate the node vectors
if (label_i == true)
set_rowm(psi, range(edge_dims, psi.size()-1)) = rowm(psi, range(edge_dims, psi.size()-1)) + sample.node(i).data;
for (unsigned long n = 0; n < sample.node(i).number_of_neighbors(); ++n)
{
const unsigned long j = sample.node(i).neighbor(n).index();
const bool label_j = (label[j]!=0);
// Don't double count edges. Also only include the vector if
// the labels disagree.
if (i < j && label_i != label_j)
{
set_rowm(psi, range(0, edge_dims-1)) = rowm(psi, range(0, edge_dims-1)) - sample.node(i).edge(n);
}
}
}
}
template <typename T>
void add_to_sparse_vect (
T& psi,
const T& vect,
unsigned long offset
) const
{
for (unsigned long i = 0; i < vect.size(); ++i)
{
psi.push_back(std::make_pair(vect[i].first+offset, vect[i].second));
}
}
template <typename T>
void subtract_from_sparse_vect (
T& psi,
const T& vect
) const
{
for (unsigned long i = 0; i < vect.size(); ++i)
{
psi.push_back(std::make_pair(vect[i].first, -vect[i].second));
}
}
template <typename psi_type>
typename disable_if<is_matrix<psi_type> >::type get_joint_feature_vector (
const sample_type& sample,
const label_type& label,
psi_type& psi
) const
{
psi.clear();
for (unsigned long i = 0; i < sample.number_of_nodes(); ++i)
{
const bool label_i = (label[i]!=0);
// accumulate the node vectors
if (label_i == true)
add_to_sparse_vect(psi, sample.node(i).data, edge_dims);
for (unsigned long n = 0; n < sample.node(i).number_of_neighbors(); ++n)
{
const unsigned long j = sample.node(i).neighbor(n).index();
const bool label_j = (label[j]!=0);
// Don't double count edges. Also only include the vector if
// the labels disagree.
if (i < j && label_i != label_j)
{
subtract_from_sparse_vect(psi, sample.node(i).edge(n));
}
}
}
}
virtual void get_truth_joint_feature_vector (
long idx,
feature_vector_type& psi
) const
{
get_joint_feature_vector(samples[idx], labels[idx], psi);
}
virtual void separation_oracle (
const long idx,
const matrix_type& current_solution,
double& loss,
feature_vector_type& psi
) const
{
using dlib::sparse_vector::dot;
using dlib::dot;
const sample_type& samp = samples[idx];
// setup the potts graph based on samples[idx] and current_solution.
graph<double,double>::kernel_1a_c g; // TODO, remove _c
copy_graph_structure(samp, g);
for (unsigned long i = 0; i < g.number_of_nodes(); ++i)
{
g.node(i).data = dot(rowm(current_solution,range(edge_dims,current_solution.size()-1)),
samp.node(i).data);
// Include a loss augmentation so that we will get the proper loss augmented
// max when we use find_max_factor_graph_potts() below.
const bool label_i = (labels[idx][i]!=0);
if (label_i)
g.node(i).data -= 1.0;
else
g.node(i).data += 1.0;
for (unsigned long n = 0; n < g.node(i).number_of_neighbors(); ++n)
{
const unsigned long j = g.node(i).neighbor(n).index();
// Don't compute an edge weight more than once.
if (i < j)
{
g.node(i).edge(n) = dot(rowm(current_solution,range(0,edge_dims-1)),
samp.node(i).edge(n));
}
}
}
std::vector<node_label> labeling;
find_max_factor_graph_potts(g, labeling);
// figure out the loss
loss = 0;
for (unsigned long i = 0; i < labeling.size(); ++i)
{
const bool true_label = (labels[idx][i]!= 0);
const bool pred_label = (labeling[i]!= 0);
if (true_label != pred_label)
++loss;
}
// compute psi
get_joint_feature_vector(samp, labeling, psi);
}
const dlib::array<sample_type>& samples;
const std::vector<label_type>& labels;
long node_dims;
long edge_dims;
};
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_STRUCTURAL_SVM_POtTS_PROBLEM_H__

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dlib/svm/structural_svm_potts_problem_abstract.h Normal file
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