// Copyright (C) 2007 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_SVm_
#define DLIB_SVm_
#include "svm_abstract.h"
#include <cmath>
#include <limits>
#include <sstream>
#include "../matrix.h"
#include "../algs.h"
#include "../serialize.h"
#include "../rand.h"
#include "../std_allocator.h"
#include "function.h"
#include "kernel.h"
#include "../enable_if.h"
#include "../optimization.h"
#include "svm_nu_trainer.h"
#include <vector>
#include <set>
namespace dlib
{
// ----------------------------------------------------------------------------------------
template <
typename T,
typename U
>
inline bool is_learning_problem_impl (
const T& x,
const U& x_labels
)
{
return is_col_vector(x) &&
is_col_vector(x_labels) &&
x.size() == x_labels.size() &&
x.size() > 0;
}
template <
typename T,
typename U
>
inline bool is_learning_problem (
const T& x,
const U& x_labels
)
{
return is_learning_problem_impl(mat(x), mat(x_labels));
}
// ----------------------------------------------------------------------------------------
template <
typename T,
typename U
>
bool is_binary_classification_problem_impl (
const T& x,
const U& x_labels
)
{
bool seen_neg_class = false;
bool seen_pos_class = false;
if (is_learning_problem_impl(x,x_labels) == false)
return false;
if (x.size() <= 1) return false;
for (long r = 0; r < x_labels.nr(); ++r)
{
if (x_labels(r) != -1 && x_labels(r) != 1)
return false;
if (x_labels(r) == 1)
seen_pos_class = true;
if (x_labels(r) == -1)
seen_neg_class = true;
}
return seen_pos_class && seen_neg_class;
}
template <
typename T,
typename U
>
bool is_binary_classification_problem (
const T& x,
const U& x_labels
)
{
return is_binary_classification_problem_impl(mat(x), mat(x_labels));
}
// ----------------------------------------------------------------------------------------
template <
typename dec_funct_type,
typename in_sample_vector_type,
typename in_scalar_vector_type
>
const matrix<double,1,2> test_binary_decision_function_impl (
const dec_funct_type& dec_funct,
const in_sample_vector_type& x_test,
const in_scalar_vector_type& y_test
)
{
// make sure requires clause is not broken
DLIB_ASSERT( is_binary_classification_problem(x_test,y_test) == true,
"\tmatrix test_binary_decision_function()"
<< "\n\t invalid inputs were given to this function"
<< "\n\t is_binary_classification_problem(x_test,y_test): "
<< ((is_binary_classification_problem(x_test,y_test))? "true":"false"));
// count the number of positive and negative examples
long num_pos = 0;
long num_neg = 0;
long num_pos_correct = 0;
long num_neg_correct = 0;
// now test this trained object
for (long i = 0; i < x_test.nr(); ++i)
{
// if this is a positive example
if (y_test(i) == +1.0)
{
++num_pos;
if (dec_funct(x_test(i)) >= 0)
++num_pos_correct;
}
else if (y_test(i) == -1.0)
{
++num_neg;
if (dec_funct(x_test(i)) < 0)
++num_neg_correct;
}
else
{
throw dlib::error("invalid input labels to the test_binary_decision_function() function");
}
}
matrix<double, 1, 2> res;
res(0) = (double)num_pos_correct/(double)(num_pos);
res(1) = (double)num_neg_correct/(double)(num_neg);
return res;
}
template <
typename dec_funct_type,
typename in_sample_vector_type,
typename in_scalar_vector_type
>
const matrix<double,1,2> test_binary_decision_function (
const dec_funct_type& dec_funct,
const in_sample_vector_type& x_test,
const in_scalar_vector_type& y_test
)
{
return test_binary_decision_function_impl(dec_funct,
mat(x_test),
mat(y_test));
}
// ----------------------------------------------------------------------------------------
template <
typename sequence_type
>
bool is_sequence_labeling_problem (
const std::vector<sequence_type>& samples,
const std::vector<std::vector<unsigned long> >& labels
)
{
if (is_learning_problem(samples, labels))
{
for (unsigned long i = 0; i < samples.size(); ++i)
{
if (samples[i].size() != labels[i].size())
return false;
}
return true;
}
return false;
}
// ----------------------------------------------------------------------------------------
template <
typename sequence_type
>
bool is_sequence_segmentation_problem (
const std::vector<sequence_type>& samples,
const std::vector<std::vector<std::pair<unsigned long,unsigned long> > >& segments
)
{
if (is_learning_problem(samples, segments))
{
for (unsigned long i = 0; i < samples.size(); ++i)
{
// Make sure the segments are inside samples[i] and don't overlap with each
// other.
std::vector<bool> hits(samples[i].size(), false);
for (unsigned long j = 0; j < segments[i].size(); ++j)
{
const unsigned long begin = segments[i][j].first;
const unsigned long end = segments[i][j].second;
// if the segment is outside the sequence
if (end > samples[i].size())
return false;
if (begin >= end)
return false;
// check for overlap
for (unsigned long k = begin; k < end; ++k)
{
if (hits[k])
return false;
hits[k] = true;
}
}
}
return true;
}
return false;
}
// ----------------------------------------------------------------------------------------
template <
typename lhs_type,
typename rhs_type
>
bool is_assignment_problem (
const std::vector<std::pair<std::vector<lhs_type>, std::vector<rhs_type> > >& samples,
const std::vector<std::vector<long> >& labels
)
{
std::vector<bool> seen_label;
if (is_learning_problem(samples, labels))
{
for (unsigned long i = 0; i < samples.size(); ++i)
{
if (samples[i].first.size() != labels[i].size())
return false;
seen_label.assign(samples[i].second.size(), false);
for (unsigned long j = 0; j < labels[i].size(); ++j)
{
if (!(-1 <= labels[i][j] && labels[i][j] < (long)samples[i].second.size()))
return false;
if (labels[i][j] != -1)
{
// check label uniqueness
if (seen_label[labels[i][j]])
return false;
seen_label[labels[i][j]] = true;
}
}
}
return true;
}
return false;
}
// ----------------------------------------------------------------------------------------
template <
typename lhs_type,
typename rhs_type
>
bool is_forced_assignment_problem (
const std::vector<std::pair<std::vector<lhs_type>, std::vector<rhs_type> > >& samples,
const std::vector<std::vector<long> >& labels
)
{
if (is_assignment_problem(samples, labels))
{
for (unsigned long i = 0; i < samples.size(); ++i)
{
const unsigned long N = sum(mat(labels[i]) != -1);
if (std::min(samples[i].first.size(), samples[i].second.size()) != N)
return false;
}
return true;
}
return false;
}
// ----------------------------------------------------------------------------------------
template <
typename detection_type_,
typename label_type_ = long
>
struct labeled_detection
{
typedef detection_type_ detection_type;
typedef label_type_ label_type;
detection_type det;
label_type label;
};
template <
typename detection_type_,
typename label_type_
>
inline void serialize ( const labeled_detection<detection_type_,label_type_>& item, std::ostream& out)
{
serialize(item.det, out);
serialize(item.label, out);
}
template <
typename detection_type_,
typename label_type_
>
inline void deserialize (labeled_detection<detection_type_,label_type_>& item, std::istream& in)
{
deserialize(item.det, in);
deserialize(item.label, in);
}
// ----------------------------------------------------------------------------------------
template <
typename detection_type,
typename label_type
>
bool is_track_association_problem (
const std::vector<std::vector<labeled_detection<detection_type,label_type> > >& samples
)
{
if (samples.size() == 0)
return false;
unsigned long num_nonzero_elements = 0;
for (unsigned long i = 0; i < samples.size(); ++i)
{
if (samples.size() > 0)
++num_nonzero_elements;
}
if (num_nonzero_elements < 2)
return false;
// now make sure the label_type values are unique within each time step.
for (unsigned long i = 0; i < samples.size(); ++i)
{
std::set<label_type> vals;
for (unsigned long j = 0; j < samples[i].size(); ++j)
vals.insert(samples[i][j].label);
if (vals.size() != samples[i].size())
return false;
}
// passed all tests so it's good
return true;
}
// ----------------------------------------------------------------------------------------
template <
typename detection_type,
typename label_type
>
bool is_track_association_problem (
const std::vector<std::vector<std::vector<labeled_detection<detection_type,label_type> > > >& samples
)
{
for (unsigned long i = 0; i < samples.size(); ++i)
{
if (!is_track_association_problem(samples[i]))
return false;
}
// passed all tests so it's good
return true;
}
// ----------------------------------------------------------------------------------------
template <
typename trainer_type,
typename in_sample_vector_type,
typename in_scalar_vector_type
>
const matrix<double, 1, 2, typename trainer_type::mem_manager_type>
cross_validate_trainer_impl (
const trainer_type& trainer,
const in_sample_vector_type& x,
const in_scalar_vector_type& y,
const long folds
)
{
typedef typename in_scalar_vector_type::value_type scalar_type;
typedef typename trainer_type::mem_manager_type mem_manager_type;
typedef matrix<scalar_type,0,1,mem_manager_type> scalar_vector_type;
// make sure requires clause is not broken
DLIB_ASSERT(is_binary_classification_problem(x,y) == true &&
1 < folds && folds <= std::min(sum(y>0),sum(y<0)),
"\tmatrix cross_validate_trainer()"
<< "\n\t invalid inputs were given to this function"
<< "\n\t std::min(sum(y>0),sum(y<0)): " << std::min(sum(y>0),sum(y<0))
<< "\n\t folds: " << folds
<< "\n\t is_binary_classification_problem(x,y): " << ((is_binary_classification_problem(x,y))? "true":"false")
);
// count the number of positive and negative examples
long num_pos = 0;
long num_neg = 0;
for (long r = 0; r < y.nr(); ++r)
{
if (y(r) == +1.0)
++num_pos;
else
++num_neg;
}
// figure out how many positive and negative examples we will have in each fold
const long num_pos_test_samples = num_pos/folds;
const long num_pos_train_samples = num_pos - num_pos_test_samples;
const long num_neg_test_samples = num_neg/folds;
const long num_neg_train_samples = num_neg - num_neg_test_samples;
matrix<long,0,1> x_test, x_train;
scalar_vector_type y_test, y_train;
x_test.set_size (num_pos_test_samples + num_neg_test_samples);
y_test.set_size (num_pos_test_samples + num_neg_test_samples);
x_train.set_size(num_pos_train_samples + num_neg_train_samples);
y_train.set_size(num_pos_train_samples + num_neg_train_samples);
long pos_idx = 0;
long neg_idx = 0;
matrix<double, 1, 2, mem_manager_type> res;
set_all_elements(res,0);
for (long i = 0; i < folds; ++i)
{
long cur = 0;
// load up our positive test samples
while (cur < num_pos_test_samples)
{
if (y(pos_idx) == +1.0)
{
x_test(cur) = pos_idx;
y_test(cur) = +1.0;
++cur;
}
pos_idx = (pos_idx+1)%x.nr();
}
// load up our negative test samples
while (cur < x_test.nr())
{
if (y(neg_idx) == -1.0)
{
x_test(cur) = neg_idx;
y_test(cur) = -1.0;
++cur;
}
neg_idx = (neg_idx+1)%x.nr();
}
// load the training data from the data following whatever we loaded
// as the testing data
long train_pos_idx = pos_idx;
long train_neg_idx = neg_idx;
cur = 0;
// load up our positive train samples
while (cur < num_pos_train_samples)
{
if (y(train_pos_idx) == +1.0)
{
x_train(cur) = train_pos_idx;
y_train(cur) = +1.0;
++cur;
}
train_pos_idx = (train_pos_idx+1)%x.nr();
}
// load up our negative train samples
while (cur < x_train.nr())
{
if (y(train_neg_idx) == -1.0)
{
x_train(cur) = train_neg_idx;
y_train(cur) = -1.0;
++cur;
}
train_neg_idx = (train_neg_idx+1)%x.nr();
}
try
{
// do the training and testing
res += test_binary_decision_function(trainer.train(rowm(x,x_train),y_train),rowm(x,x_test),y_test);
}
catch (invalid_nu_error&)
{
// Just ignore the error in this case since we are going to
// interpret an invalid nu value the same as generating a decision
// function that miss-classifies everything.
}
} // for (long i = 0; i < folds; ++i)
return res/(double)folds;
}
template <
typename trainer_type,
typename in_sample_vector_type,
typename in_scalar_vector_type
>
const matrix<double, 1, 2, typename trainer_type::mem_manager_type>
cross_validate_trainer (
const trainer_type& trainer,
const in_sample_vector_type& x,
const in_scalar_vector_type& y,
const long folds
)
{
return cross_validate_trainer_impl(trainer,
mat(x),
mat(y),
folds);
}
// ----------------------------------------------------------------------------------------
namespace prob_impl
{
template <typename vect_type>
struct objective
{
objective (
const vect_type& f_,
const vect_type& t_
) : f(f_), t(t_) {}
double operator() (
const matrix<double,2,1>& x
) const
{
const double A = x(0);
const double B = x(1);
double res = 0;
for (unsigned long i = 0; i < f.size(); ++i)
{
const double val = A*f[i]+B;
// See the paper "A Note on Platt's Probabilistic Outputs for Support Vector Machines"
// for an explanation of why this code looks the way it does (rather than being the
// obvious formula).
if (val < 0)
res += (t[i] - 1)*val + std::log(1 + std::exp(val));
else
res += t[i]*val + std::log(1 + std::exp(-val));
}
return res;
}
const vect_type& f;
const vect_type& t;
};
template <typename vect_type>
struct der
{
der (
const vect_type& f_,
const vect_type& t_
) : f(f_), t(t_) {}
matrix<double,2,1> operator() (
const matrix<double,2,1>& x
) const
{
const double A = x(0);
const double B = x(1);
double derA = 0;
double derB = 0;
for (unsigned long i = 0; i < f.size(); ++i)
{
const double val = A*f[i]+B;
double p;
// compute p = 1/(1+exp(val))
// but do so in a way that avoids numerical overflow.
if (val < 0)
p = 1.0/(1 + std::exp(val));
else
p = std::exp(-val)/(1 + std::exp(-val));
derA += f[i]*(t[i] - p);
derB += (t[i] - p);
}
matrix<double,2,1> res;
res = derA, derB;
return res;
}
const vect_type& f;
const vect_type& t;
};
template <typename vect_type>
struct hessian
{
hessian (
const vect_type& f_,
const vect_type& t_
) : f(f_), t(t_) {}
matrix<double,2,2> operator() (
const matrix<double,2,1>& x
) const
{
const double A = x(0);
const double B = x(1);
matrix<double,2,2> h;
h = 0;
for (unsigned long i = 0; i < f.size(); ++i)
{
const double val = A*f[i]+B;
// compute pp = 1/(1+exp(val)) and
// compute pn = 1 - pp
// but do so in a way that avoids numerical overflow and catastrophic cancellation.
double pp, pn;
if (val < 0)
{
const double temp = std::exp(val);
pp = 1.0/(1 + temp);
pn = temp*pp;
}
else
{
const double temp = std::exp(-val);
pn = 1.0/(1 + temp);
pp = temp*pn;
}
h(0,0) += f[i]*f[i]*pp*pn;
const double temp2 = f[i]*pp*pn;
h(0,1) += temp2;
h(1,0) += temp2;
h(1,1) += pp*pn;
}
return h;
}
const vect_type& f;
const vect_type& t;
};
}
// ----------------------------------------------------------------------------------------
inline double platt_scale (
const std::pair<double,double>& params,
const double score
)
{
return 1/(1 + std::exp(params.first*score + params.second));
}
// ----------------------------------------------------------------------------------------
template <typename T, typename alloc>
std::pair<double,double> learn_platt_scaling (
const std::vector<T,alloc>& scores,
const std::vector<T,alloc>& labels
)
{
// make sure requires clause is not broken
DLIB_ASSERT(is_binary_classification_problem(scores,labels) == true,
"\t std::pair<T,T> learn_platt_scaling()"
<< "\n\t invalid inputs were given to this function"
<< "\n\t scores.size(): " << scores.size()
<< "\n\t labels.size(): " << labels.size()
<< "\n\t is_binary_classification_problem(scores,labels): " << is_binary_classification_problem(scores,labels)
);
const T num_pos = sum(mat(labels)>0);
const T num_neg = sum(mat(labels)<0);
const T hi_target = (num_pos+1)/(num_pos+2);
const T lo_target = 1.0/(num_neg+2);
std::vector<T,alloc> target;
for (unsigned long i = 0; i < labels.size(); ++i)
{
// if this was a positive example
if (labels[i] == +1.0)
{
target.push_back(hi_target);
}
else if (labels[i] == -1.0)
{
target.push_back(lo_target);
}
else
{
throw dlib::error("invalid input labels to the learn_platt_scaling() function.");
}
}
// Now find the maximum likelihood parameters of the sigmoid.
prob_impl::objective<std::vector<T,alloc> > obj(scores, target);
prob_impl::der<std::vector<T,alloc> > obj_der(scores, target);
prob_impl::hessian<std::vector<T,alloc> > obj_hessian(scores, target);
matrix<double,2,1> val;
val = 0;
find_min(newton_search_strategy(obj_hessian),
objective_delta_stop_strategy(),
obj,
obj_der,
val,
0);
const double A = val(0);
const double B = val(1);
return std::make_pair(A,B);
}
// ----------------------------------------------------------------------------------------
template <
typename trainer_type,
typename sample_vector_type,
typename label_vector_type
>
const probabilistic_function<typename trainer_type::trained_function_type>
train_probabilistic_decision_function (
const trainer_type& trainer,
const sample_vector_type& x,
const label_vector_type& y,
const long folds
)
{
typedef typename sample_vector_type::value_type sample_type;
typedef typename label_vector_type::value_type scalar_type;
/*
This function fits a sigmoid function to the output of the
svm trained by svm_nu_trainer or a similar trainer. The
technique used is the one described in the papers:
Probabilistic Outputs for Support Vector Machines and
Comparisons to Regularized Likelihood Methods by
John C. Platt. March 26, 1999
A Note on Platt's Probabilistic Outputs for Support Vector Machines
by Hsuan-Tien Lin, Chih-Jen Lin, and Ruby C. Weng
*/
// make sure requires clause is not broken
DLIB_ASSERT(is_binary_classification_problem(x,y) == true &&
1 < folds && folds <= (long)x.size(),
"\tprobabilistic_decision_function train_probabilistic_decision_function()"
<< "\n\t invalid inputs were given to this function"
<< "\n\t x.size(): " << x.size()
<< "\n\t y.size(): " << y.size()
<< "\n\t folds: " << folds
<< "\n\t is_binary_classification_problem(x,y): " << is_binary_classification_problem(x,y)
);
// count the number of positive and negative examples
const long num_pos = (long)sum(mat(y) > 0);
const long num_neg = (long)sum(mat(y) < 0);
// figure out how many positive and negative examples we will have in each fold
const long num_pos_test_samples = num_pos/folds;
const long num_pos_train_samples = num_pos - num_pos_test_samples;
const long num_neg_test_samples = num_neg/folds;
const long num_neg_train_samples = num_neg - num_neg_test_samples;
typename trainer_type::trained_function_type d;
std::vector<sample_type> x_test, x_train;
std::vector<scalar_type> y_test, y_train;
x_test.resize (num_pos_test_samples + num_neg_test_samples);
y_test.resize (num_pos_test_samples + num_neg_test_samples);
x_train.resize(num_pos_train_samples + num_neg_train_samples);
y_train.resize(num_pos_train_samples + num_neg_train_samples);
std::vector<scalar_type> out, out_label;
long pos_idx = 0;
long neg_idx = 0;
for (long i = 0; i < folds; ++i)
{
long cur = 0;
// load up our positive test samples
while (cur < num_pos_test_samples)
{
if (y[pos_idx] == +1.0)
{
x_test[cur] = x[pos_idx];
y_test[cur] = +1.0;
++cur;
}
pos_idx = (pos_idx+1)%x.size();
}
// load up our negative test samples
while (cur < (long)x_test.size())
{
if (y[neg_idx] == -1.0)
{
x_test[cur] = x[neg_idx];
y_test[cur] = -1.0;
++cur;
}
neg_idx = (neg_idx+1)%x.size();
}
// load the training data from the data following whatever we loaded
// as the testing data
long train_pos_idx = pos_idx;
long train_neg_idx = neg_idx;
cur = 0;
// load up our positive train samples
while (cur < num_pos_train_samples)
{
if (y[train_pos_idx] == +1.0)
{
x_train[cur] = x[train_pos_idx];
y_train[cur] = +1.0;
++cur;
}
train_pos_idx = (train_pos_idx+1)%x.size();
}
// load up our negative train samples
while (cur < (long)x_train.size())
{
if (y[train_neg_idx] == -1.0)
{
x_train[cur] = x[train_neg_idx];
y_train[cur] = -1.0;
++cur;
}
train_neg_idx = (train_neg_idx+1)%x.size();
}
// do the training
d = trainer.train (x_train,y_train);
// now test this fold
for (unsigned long i = 0; i < x_test.size(); ++i)
{
out.push_back(d(x_test[i]));
out_label.push_back(y_test[i]);
}
} // for (long i = 0; i < folds; ++i)
std::pair<double,double> params = learn_platt_scaling(out, out_label);
const double A = params.first;
const double B = params.second;
return probabilistic_function<typename trainer_type::trained_function_type>( A, B, trainer.train(x,y) );
}
// ----------------------------------------------------------------------------------------
template <typename trainer_type>
struct trainer_adapter_probabilistic
{
typedef probabilistic_function<typename trainer_type::trained_function_type> trained_function_type;
const trainer_type trainer;
const long folds;
trainer_adapter_probabilistic (
const trainer_type& trainer_,
const long folds_
) : trainer(trainer_),folds(folds_) {}
template <
typename T,
typename U
>
const trained_function_type train (
const T& samples,
const U& labels
) const
{
return train_probabilistic_decision_function(trainer, samples, labels, folds);
}
};
template <
typename trainer_type
>
trainer_adapter_probabilistic<trainer_type> probabilistic (
const trainer_type& trainer,
const long folds
)
{
return trainer_adapter_probabilistic<trainer_type>(trainer,folds);
}
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
template <
typename T,
typename U,
typename V,
typename rand_type
>
typename enable_if<is_matrix<T>,void>::type randomize_samples (
T& t,
U& u,
V& v,
rand_type& r
)
{
// make sure requires clause is not broken
DLIB_ASSERT(is_vector(t) && is_vector(u) && is_vector(v) && u.size() == t.size() &&
u.size() == v.size(),
"\t randomize_samples(t,u,v)"
<< "\n\t invalid inputs were given to this function"
<< "\n\t t.size(): " << t.size()
<< "\n\t u.size(): " << u.size()
<< "\n\t v.size(): " << v.size()
<< "\n\t is_vector(t): " << is_vector(t)
<< "\n\t is_vector(u): " << is_vector(u)
<< "\n\t is_vector(v): " << is_vector(v)
);
long n = t.size()-1;
while (n > 0)
{
// pick a random index to swap into t[n]
const unsigned long idx = r.get_random_32bit_number()%(n+1);
// swap our randomly selected index into the n position
exchange(t(idx), t(n));
exchange(u(idx), u(n));
exchange(v(idx), v(n));
--n;
}
}
// ----------------------------------------------------------------------------------------
template <
typename T,
typename U,
typename V,
typename rand_type
>
typename disable_if<is_matrix<T>,void>::type randomize_samples (
T& t,
U& u,
V& v,
rand_type& r
)
{
// make sure requires clause is not broken
DLIB_ASSERT(u.size() == t.size() && u.size() == v.size(),
"\t randomize_samples(t,u,v)"
<< "\n\t invalid inputs were given to this function"
<< "\n\t t.size(): " << t.size()
<< "\n\t u.size(): " << u.size()
<< "\n\t v.size(): " << v.size()
);
long n = t.size()-1;
while (n > 0)
{
// pick a random index to swap into t[n]
const unsigned long idx = r.get_random_32bit_number()%(n+1);
// swap our randomly selected index into the n position
exchange(t[idx], t[n]);
exchange(u[idx], u[n]);
exchange(v[idx], v[n]);
--n;
}
}
// ----------------------------------------------------------------------------------------
template <
typename T,
typename U,
typename V
>
typename disable_if<is_rand<V>,void>::type randomize_samples (
T& t,
U& u,
V& v
)
{
rand r;
randomize_samples(t,u,v,r);
}
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
template <
typename T,
typename U,
typename rand_type
>
typename enable_if_c<is_matrix<T>::value && is_rand<rand_type>::value,void>::type randomize_samples (
T& t,
U& u,
rand_type& r
)
{
// make sure requires clause is not broken
DLIB_ASSERT(is_vector(t) && is_vector(u) && u.size() == t.size(),
"\t randomize_samples(t,u)"
<< "\n\t invalid inputs were given to this function"
<< "\n\t t.size(): " << t.size()
<< "\n\t u.size(): " << u.size()
<< "\n\t is_vector(t): " << (is_vector(t)? "true" : "false")
<< "\n\t is_vector(u): " << (is_vector(u)? "true" : "false")
);
long n = t.size()-1;
while (n > 0)
{
// pick a random index to swap into t[n]
const unsigned long idx = r.get_random_32bit_number()%(n+1);
// swap our randomly selected index into the n position
exchange(t(idx), t(n));
exchange(u(idx), u(n));
--n;
}
}
// ----------------------------------------------------------------------------------------
template <
typename T,
typename U,
typename rand_type
>
typename disable_if_c<is_matrix<T>::value || !is_rand<rand_type>::value,void>::type randomize_samples (
T& t,
U& u,
rand_type& r
)
{
// make sure requires clause is not broken
DLIB_ASSERT(u.size() == t.size(),
"\t randomize_samples(t,u)"
<< "\n\t invalid inputs were given to this function"
<< "\n\t t.size(): " << t.size()
<< "\n\t u.size(): " << u.size()
);
long n = t.size()-1;
while (n > 0)
{
// pick a random index to swap into t[n]
const unsigned long idx = r.get_random_32bit_number()%(n+1);
// swap our randomly selected index into the n position
exchange(t[idx], t[n]);
exchange(u[idx], u[n]);
--n;
}
}
// ----------------------------------------------------------------------------------------
template <
typename T,
typename U
>
typename disable_if<is_rand<U>,void>::type randomize_samples (
T& t,
U& u
)
{
rand r;
randomize_samples(t,u,r);
}
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
template <
typename T,
typename rand_type
>
typename enable_if_c<is_matrix<T>::value && is_rand<rand_type>::value,void>::type randomize_samples (
T& t,
rand_type& r
)
{
// make sure requires clause is not broken
DLIB_ASSERT(is_vector(t),
"\t randomize_samples(t)"
<< "\n\t invalid inputs were given to this function"
<< "\n\t is_vector(t): " << (is_vector(t)? "true" : "false")
);
long n = t.size()-1;
while (n > 0)
{
// pick a random index to swap into t[n]
const unsigned long idx = r.get_random_32bit_number()%(n+1);
// swap our randomly selected index into the n position
exchange(t(idx), t(n));
--n;
}
}
// ----------------------------------------------------------------------------------------
template <
typename T,
typename rand_type
>
typename disable_if_c<(is_matrix<T>::value==true)||(is_rand<rand_type>::value==false),void>::type randomize_samples (
T& t,
rand_type& r
)
{
long n = t.size()-1;
while (n > 0)
{
// pick a random index to swap into t[n]
const unsigned long idx = r.get_random_32bit_number()%(n+1);
// swap our randomly selected index into the n position
exchange(t[idx], t[n]);
--n;
}
}
// ----------------------------------------------------------------------------------------
template <
typename T
>
void randomize_samples (
T& t
)
{
rand r;
randomize_samples(t,r);
}
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_SVm_