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diff --git a/ml/dlib/dlib/svm/svm.h b/ml/dlib/dlib/svm/svm.h
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-// 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_
-
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