// Copyright (C) 2011 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_CREATE_RANDOM_PROJECTION_HAsH_Hh_
#define DLIB_CREATE_RANDOM_PROJECTION_HAsH_Hh_
#include "create_random_projection_hash_abstract.h"
#include "projection_hash.h"
#include "../matrix.h"
#include "../rand.h"
#include "../statistics.h"
#include "../svm.h"
#include <vector>
namespace dlib
{
// ----------------------------------------------------------------------------------------
template <
typename vector_type
>
projection_hash create_random_projection_hash (
const vector_type& v,
const int bits,
dlib::rand& rnd
)
{
// make sure requires clause is not broken
DLIB_ASSERT(0 < bits && bits <= 32 &&
v.size() > 1,
"\t projection_hash create_random_projection_hash()"
<< "\n\t Invalid arguments were given to this function."
<< "\n\t bits: " << bits
<< "\n\t v.size(): " << v.size()
);
#ifdef ENABLE_ASSERTS
for (unsigned long i = 0; i < v.size(); ++i)
{
DLIB_ASSERT(v[0].size() == v[i].size() && v[i].size() > 0 && is_col_vector(v[i]),
"\t projection_hash create_random_projection_hash()"
<< "\n\t Invalid arguments were given to this function."
<< "\n\t m(0).size(): " << v[0].size()
<< "\n\t m("<<i<<").size(): " << v[i].size()
<< "\n\t is_col_vector(v["<<i<<"]): " << is_col_vector(v[i])
);
}
#endif
running_covariance<matrix<double> > rc;
for (unsigned long i = 0; i < v.size(); ++i)
rc.add(matrix_cast<double>(v[i]));
// compute a whitening matrix
matrix<double> whiten = trans(chol(pinv(rc.covariance())));
// hashes
std::vector<unsigned long> h(v.size(),0);
std::vector<double> vals(v.size(),0);
// number of hits for each hash value
std::vector<unsigned long> counts;
std::vector<double> temp;
// build a random projection matrix
matrix<double> proj(bits, v[0].size());
for (long r = 0; r < proj.nr(); ++r)
for (long c = 0; c < proj.nc(); ++c)
proj(r,c) = rnd.get_random_gaussian();
// merge whitening matrix with projection matrix
proj = proj*whiten;
matrix<double,0,1> offset(bits);
// figure out what the offset values should be
for (int itr = 0; itr < offset.size(); ++itr)
{
counts.assign(static_cast<unsigned long>(std::pow(2.0,bits)), 0);
// count the popularity of each hash value
for (unsigned long i = 0; i < h.size(); ++i)
{
h[i] <<= 1;
counts[h[i]] += 1;
}
const unsigned long max_h = index_of_max(mat(counts));
temp.clear();
for (unsigned long i = 0; i < v.size(); ++i)
{
vals[i] = dot(rowm(proj,itr), matrix_cast<double>(v[i]));
if (h[i] == max_h)
temp.push_back(vals[i]);
}
// split down the middle
std::sort(temp.begin(), temp.end());
const double split = temp[temp.size()/2];
offset(itr) = -split;
for (unsigned long i = 0; i < vals.size(); ++i)
{
if (vals[i] - split > 0)
h[i] |= 1;
}
}
return projection_hash(proj, offset);
}
// ----------------------------------------------------------------------------------------
template <
typename vector_type
>
projection_hash create_random_projection_hash (
const vector_type& v,
const int bits
)
{
dlib::rand rnd;
return create_random_projection_hash(v,bits,rnd);
}
// ----------------------------------------------------------------------------------------
template <
typename vector_type
>
projection_hash create_max_margin_projection_hash (
const vector_type& v,
const int bits,
const double C,
dlib::rand& rnd
)
{
// make sure requires clause is not broken
DLIB_ASSERT(0 < bits && bits <= 32 &&
v.size() > 1,
"\t projection_hash create_max_margin_projection_hash()"
<< "\n\t Invalid arguments were given to this function."
<< "\n\t bits: " << bits
<< "\n\t v.size(): " << v.size()
);
#ifdef ENABLE_ASSERTS
for (unsigned long i = 0; i < v.size(); ++i)
{
DLIB_ASSERT(v[0].size() == v[i].size() && v[i].size() > 0 && is_col_vector(v[i]),
"\t projection_hash create_max_margin_projection_hash()"
<< "\n\t Invalid arguments were given to this function."
<< "\n\t m(0).size(): " << v[0].size()
<< "\n\t m("<<i<<").size(): " << v[i].size()
<< "\n\t is_col_vector(v["<<i<<"]): " << is_col_vector(v[i])
);
}
#endif
running_covariance<matrix<double> > rc;
for (unsigned long i = 0; i < v.size(); ++i)
rc.add(matrix_cast<double>(v[i]));
// compute a whitening matrix
matrix<double> whiten = trans(chol(pinv(rc.covariance())));
const matrix<double,0,1> meanval = whiten*rc.mean();
typedef matrix<double,0,1> sample_type;
random_subset_selector<sample_type> training_samples;
random_subset_selector<double> training_labels;
// We set this up to use enough samples to cover the vector space used by elements
// of v.
training_samples.set_max_size(v[0].size()*10);
training_labels.set_max_size(v[0].size()*10);
matrix<double> proj(bits, v[0].size());
matrix<double,0,1> offset(bits);
// learn the random planes and put them into proj and offset.
for (int itr = 0; itr < offset.size(); ++itr)
{
training_samples.make_empty();
training_labels.make_empty();
// pick random training data and give each sample a random label.
for (unsigned long i = 0; i < v.size(); ++i)
{
training_samples.add(whiten*v[i]-meanval);
if (rnd.get_random_double() > 0.5)
training_labels.add(+1);
else
training_labels.add(-1);
}
svm_c_linear_dcd_trainer<linear_kernel<sample_type> > trainer;
trainer.set_c(C);
decision_function<linear_kernel<sample_type> > df = trainer.train(training_samples, training_labels);
offset(itr) = -df.b;
set_rowm(proj,itr) = trans(df.basis_vectors(0));
}
return projection_hash(proj*whiten, offset-proj*meanval);
}
// ----------------------------------------------------------------------------------------
template <
typename vector_type
>
projection_hash create_max_margin_projection_hash (
const vector_type& v,
const int bits,
const double C = 10
)
{
dlib::rand rnd;
return create_max_margin_projection_hash(v,bits,C,rnd);
}
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_CREATE_RANDOM_PROJECTION_HAsH_Hh_