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// Copyright (C) 2008 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_SVm_THREADED_
#define DLIB_SVm_THREADED_
#include <cmath>
#include <iostream>
#include <limits>
#include <sstream>
#include <vector>
#include "svm_threaded_abstract.h"
#include "svm.h"
#include "../matrix.h"
#include "../algs.h"
#include "../serialize.h"
#include "function.h"
#include "kernel.h"
#include "../threads.h"
#include "../pipe.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
namespace cvtti_helpers
{
template <typename trainer_type, typename in_sample_vector_type>
struct job
{
typedef typename trainer_type::scalar_type scalar_type;
typedef typename trainer_type::sample_type sample_type;
typedef typename trainer_type::mem_manager_type mem_manager_type;
typedef matrix<sample_type,0,1,mem_manager_type> sample_vector_type;
typedef matrix<scalar_type,0,1,mem_manager_type> scalar_vector_type;
job() : x(0) {}
trainer_type trainer;
matrix<long,0,1> x_test, x_train;
scalar_vector_type y_test, y_train;
const in_sample_vector_type* x;
};
struct task
{
template <
typename trainer_type,
typename mem_manager_type,
typename in_sample_vector_type
>
void operator()(
job<trainer_type,in_sample_vector_type>& j,
matrix<double,1,2,mem_manager_type>& result
)
{
try
{
result = test_binary_decision_function(j.trainer.train(rowm(*j.x,j.x_train), j.y_train), rowm(*j.x,j.x_test), j.y_test);
// Do this just to make j release it's memory since people might run threaded cross validation
// on very large datasets. Every bit of freed memory helps out.
j = job<trainer_type,in_sample_vector_type>();
}
catch (invalid_nu_error&)
{
// If this is a svm_nu_trainer then we might get this exception if the nu is
// invalid. In this case just return a cross validation score of 0.
result = 0;
}
catch (std::bad_alloc&)
{
std::cerr << "\nstd::bad_alloc thrown while running cross_validate_trainer_threaded(). Not enough memory.\n" << std::endl;
throw;
}
}
};
}
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_threaded_impl (
const trainer_type& trainer,
const in_sample_vector_type& x,
const in_scalar_vector_type& y,
const long folds,
const long num_threads
)
{
using namespace dlib::cvtti_helpers;
typedef typename trainer_type::mem_manager_type mem_manager_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)) &&
num_threads > 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 num_threads: " << num_threads
<< "\n\t is_binary_classification_problem(x,y): " << ((is_binary_classification_problem(x,y))? "true":"false")
);
task mytask;
thread_pool tp(num_threads);
// 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;
long pos_idx = 0;
long neg_idx = 0;
std::vector<future<job<trainer_type,in_sample_vector_type> > > jobs(folds);
std::vector<future<matrix<double, 1, 2, mem_manager_type> > > results(folds);
for (long i = 0; i < folds; ++i)
{
job<trainer_type,in_sample_vector_type>& j = jobs[i].get();
j.x = &x;
j.x_test.set_size (num_pos_test_samples + num_neg_test_samples);
j.y_test.set_size (num_pos_test_samples + num_neg_test_samples);
j.x_train.set_size(num_pos_train_samples + num_neg_train_samples);
j.y_train.set_size(num_pos_train_samples + num_neg_train_samples);
j.trainer = trainer;
long cur = 0;
// load up our positive test samples
while (cur < num_pos_test_samples)
{
if (y(pos_idx) == +1.0)
{
j.x_test(cur) = pos_idx;
j.y_test(cur) = +1.0;
++cur;
}
pos_idx = (pos_idx+1)%x.nr();
}
// load up our negative test samples
while (cur < j.x_test.nr())
{
if (y(neg_idx) == -1.0)
{
j.x_test(cur) = neg_idx;
j.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)
{
j.x_train(cur) = train_pos_idx;
j.y_train(cur) = +1.0;
++cur;
}
train_pos_idx = (train_pos_idx+1)%x.nr();
}
// load up our negative train samples
while (cur < j.x_train.nr())
{
if (y(train_neg_idx) == -1.0)
{
j.x_train(cur) = train_neg_idx;
j.y_train(cur) = -1.0;
++cur;
}
train_neg_idx = (train_neg_idx+1)%x.nr();
}
// finally spawn a task to process this job
tp.add_task(mytask, jobs[i], results[i]);
} // for (long i = 0; i < folds; ++i)
matrix<double, 1, 2, mem_manager_type> res;
set_all_elements(res,0);
// now compute the total results
for (long i = 0; i < folds; ++i)
{
res += results[i].get();
}
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_threaded (
const trainer_type& trainer,
const in_sample_vector_type& x,
const in_scalar_vector_type& y,
const long folds,
const long num_threads
)
{
return cross_validate_trainer_threaded_impl(trainer,
mat(x),
mat(y),
folds,
num_threads);
}
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_SVm_THREADED_
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