coco
209 lines
6.9 KiB
C++
209 lines
6.9 KiB
C++
// Copyright (C) 2010 Davis E. King (davis@dlib.net)
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// License: Boost Software License See LICENSE.txt for the full license.
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#ifndef DLIB_CROSS_VALIDATE_MULTICLASS_TRaINER_Hh_
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#define DLIB_CROSS_VALIDATE_MULTICLASS_TRaINER_Hh_
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#include <vector>
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#include "../matrix.h"
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#include "cross_validate_multiclass_trainer_abstract.h"
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#include <sstream>
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namespace dlib
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{
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// ----------------------------------------------------------------------------------------
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template <
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typename dec_funct_type,
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typename sample_type,
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typename label_type
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>
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const matrix<double> test_multiclass_decision_function (
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const dec_funct_type& dec_funct,
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const std::vector<sample_type>& x_test,
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const std::vector<label_type>& y_test
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)
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{
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// make sure requires clause is not broken
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DLIB_ASSERT( is_learning_problem(x_test,y_test) == true,
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"\tmatrix test_multiclass_decision_function()"
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<< "\n\t invalid inputs were given to this function"
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<< "\n\t is_learning_problem(x_test,y_test): "
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<< is_learning_problem(x_test,y_test));
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const std::vector<label_type> all_labels = dec_funct.get_labels();
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// make a lookup table that maps from labels to their index in all_labels
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std::map<label_type,unsigned long> label_to_int;
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for (unsigned long i = 0; i < all_labels.size(); ++i)
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label_to_int[all_labels[i]] = i;
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matrix<double, 0, 0, typename dec_funct_type::mem_manager_type> res;
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res.set_size(all_labels.size(), all_labels.size());
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res = 0;
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typename std::map<label_type,unsigned long>::const_iterator iter;
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// now test this trained object
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for (unsigned long i = 0; i < x_test.size(); ++i)
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{
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iter = label_to_int.find(y_test[i]);
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// ignore samples with labels that the decision function doesn't know about.
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if (iter == label_to_int.end())
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continue;
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const unsigned long truth = iter->second;
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const unsigned long pred = label_to_int[dec_funct(x_test[i])];
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res(truth,pred) += 1;
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}
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return res;
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}
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// ----------------------------------------------------------------------------------------
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class cross_validation_error : public dlib::error
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{
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public:
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cross_validation_error(const std::string& msg) : dlib::error(msg){};
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};
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template <
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typename trainer_type,
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typename sample_type,
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typename label_type
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>
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const matrix<double> cross_validate_multiclass_trainer (
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const trainer_type& trainer,
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const std::vector<sample_type>& x,
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const std::vector<label_type>& y,
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const long folds
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)
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{
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typedef typename trainer_type::mem_manager_type mem_manager_type;
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// make sure requires clause is not broken
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DLIB_ASSERT(is_learning_problem(x,y) == true &&
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1 < folds && folds <= static_cast<long>(x.size()),
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"\tmatrix cross_validate_multiclass_trainer()"
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<< "\n\t invalid inputs were given to this function"
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<< "\n\t x.size(): " << x.size()
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<< "\n\t folds: " << folds
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<< "\n\t is_learning_problem(x,y): " << is_learning_problem(x,y)
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);
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const std::vector<label_type> all_labels = select_all_distinct_labels(y);
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// count the number of times each label shows up
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std::map<label_type,long> label_counts;
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for (unsigned long i = 0; i < y.size(); ++i)
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label_counts[y[i]] += 1;
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// figure out how many samples from each class will be in the test and train splits
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std::map<label_type,long> num_in_test, num_in_train;
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for (typename std::map<label_type,long>::iterator i = label_counts.begin(); i != label_counts.end(); ++i)
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{
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const long in_test = i->second/folds;
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if (in_test == 0)
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{
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std::ostringstream sout;
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sout << "In dlib::cross_validate_multiclass_trainer(), the number of folds was larger" << std::endl;
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sout << "than the number of elements of one of the training classes." << std::endl;
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sout << " folds: "<< folds << std::endl;
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sout << " size of class " << i->first << ": "<< i->second << std::endl;
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throw cross_validation_error(sout.str());
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}
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num_in_test[i->first] = in_test;
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num_in_train[i->first] = i->second - in_test;
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}
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std::vector<sample_type> x_test, x_train;
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std::vector<label_type> y_test, y_train;
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matrix<double, 0, 0, mem_manager_type> res;
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std::map<label_type,long> next_test_idx;
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for (unsigned long i = 0; i < all_labels.size(); ++i)
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next_test_idx[all_labels[i]] = 0;
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label_type label;
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for (long i = 0; i < folds; ++i)
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{
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x_test.clear();
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y_test.clear();
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x_train.clear();
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y_train.clear();
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// load up the test samples
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for (unsigned long j = 0; j < all_labels.size(); ++j)
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{
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label = all_labels[j];
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long next = next_test_idx[label];
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long cur = 0;
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const long num_needed = num_in_test[label];
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while (cur < num_needed)
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{
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if (y[next] == label)
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{
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x_test.push_back(x[next]);
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y_test.push_back(label);
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++cur;
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}
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next = (next + 1)%x.size();
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}
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next_test_idx[label] = next;
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}
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// load up the training samples
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for (unsigned long j = 0; j < all_labels.size(); ++j)
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{
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label = all_labels[j];
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long next = next_test_idx[label];
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long cur = 0;
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const long num_needed = num_in_train[label];
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while (cur < num_needed)
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{
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if (y[next] == label)
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{
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x_train.push_back(x[next]);
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y_train.push_back(label);
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++cur;
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}
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next = (next + 1)%x.size();
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}
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}
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try
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{
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// do the training and testing
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res += test_multiclass_decision_function(trainer.train(x_train,y_train),x_test,y_test);
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}
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catch (invalid_nu_error&)
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{
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// just ignore cases which result in an invalid nu
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}
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} // for (long i = 0; i < folds; ++i)
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return res;
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}
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}
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// ----------------------------------------------------------------------------------------
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#endif // DLIB_CROSS_VALIDATE_MULTICLASS_TRaINER_Hh_
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