coco
156 lines
4.7 KiB
C++
156 lines
4.7 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_REGRESSION_TRaINER_Hh_
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#define DLIB_CROSS_VALIDATE_REGRESSION_TRaINER_Hh_
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#include <vector>
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#include "../matrix.h"
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#include "../statistics.h"
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#include "cross_validate_regression_trainer_abstract.h"
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namespace dlib
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{
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// ----------------------------------------------------------------------------------------
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template <
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typename reg_funct_type,
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typename sample_type,
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typename label_type
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>
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matrix<double,1,4>
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test_regression_function (
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reg_funct_type& reg_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_regression_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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running_stats<double> rs, rs_mae;
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running_scalar_covariance<double> rc;
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for (unsigned long i = 0; i < x_test.size(); ++i)
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{
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// compute error
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const double output = reg_funct(x_test[i]);
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const double temp = output - y_test[i];
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rs_mae.add(std::abs(temp));
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rs.add(temp*temp);
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rc.add(output, y_test[i]);
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}
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matrix<double,1,4> result;
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result = rs.mean(), rc.correlation(), rs_mae.mean(), rs_mae.stddev();
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return result;
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}
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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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matrix<double,1,4>
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cross_validate_regression_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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// 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_regression_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 long num_in_test = x.size()/folds;
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const long num_in_train = x.size() - num_in_test;
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running_stats<double> rs, rs_mae;
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running_scalar_covariance<double> rc;
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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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long next_test_idx = 0;
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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 (long cnt = 0; cnt < num_in_test; ++cnt)
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{
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x_test.push_back(x[next_test_idx]);
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y_test.push_back(y[next_test_idx]);
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next_test_idx = (next_test_idx + 1)%x.size();
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}
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// load up the training samples
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long next = next_test_idx;
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for (long cnt = 0; cnt < num_in_train; ++cnt)
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{
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x_train.push_back(x[next]);
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y_train.push_back(y[next]);
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next = (next + 1)%x.size();
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}
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try
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{
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const typename trainer_type::trained_function_type& df = trainer.train(x_train,y_train);
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// do the training and testing
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for (unsigned long j = 0; j < x_test.size(); ++j)
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{
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// compute error
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const double output = df(x_test[j]);
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const double temp = output - y_test[j];
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rs_mae.add(std::abs(temp));
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rs.add(temp*temp);
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rc.add(output, y_test[j]);
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}
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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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matrix<double,1,4> result;
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result = rs.mean(), rc.correlation(), rs_mae.mean(), rs_mae.stddev();
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return result;
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}
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}
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// ----------------------------------------------------------------------------------------
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#endif // DLIB_CROSS_VALIDATE_REGRESSION_TRaINER_Hh_
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