// Copyright (C) 2010  Davis E. King (davis@dlib.net)
#undef DLIB_CROSS_VALIDATE_REGRESSION_TRaINER_ABSTRACT_Hh_
#ifdef DLIB_CROSS_VALIDATE_REGRESSION_TRaINER_ABSTRACT_Hh_

#include <vector>
#include "../matrix.h"

namespace dlib
{

// ----------------------------------------------------------------------------------------

template <
typename reg_funct_type,
typename sample_type,
typename label_type
>
matrix<double,1,4>
test_regression_function (
reg_funct_type& reg_funct,
const std::vector<sample_type>& x_test,
const std::vector<label_type>& y_test
);
/*!
requires
- is_learning_problem(x_test, y_test)
- reg_funct_type == some kind of regression function object
(e.g. a decision_function created by the svr_trainer )
ensures
- Tests reg_funct against the given samples in x_test and target values in
y_test and returns a matrix M summarizing the results.  Specifically:
- M(0) == the mean squared error.
The MSE is given by: sum over i: pow(reg_funct(x_test[i]) - y_test[i], 2.0)
- M(1) == the correlation between reg_funct(x_test[i]) and y_test[i].
This is a number between -1 and 1.
- M(2) == the mean absolute error.
This is given by: sum over i: abs(reg_funct(x_test[i]) - y_test[i])
- M(3) == the standard deviation of the absolute error.
!*/

// ----------------------------------------------------------------------------------------

template <
typename trainer_type,
typename sample_type,
typename label_type
>
matrix<double,1,4>
cross_validate_regression_trainer (
const trainer_type& trainer,
const std::vector<sample_type>& x,
const std::vector<label_type>& y,
const long folds
);
/*!
requires
- is_learning_problem(x,y)
- 1 < folds <= x.size()
- trainer_type == some kind of regression trainer object (e.g. svr_trainer)
ensures
- Performs k-fold cross validation by using the given trainer to solve a
regression problem for the given number of folds.  Each fold is tested using
the output of the trainer.  A matrix M summarizing the results is returned.
Specifically:
- M(0) == the mean squared error.
The MSE is given by: sum over i: pow(reg_funct(x[i]) - y[i], 2.0)
- M(1) == the correlation between a predicted y value and its true value.
This is a number between -1 and 1.
- M(2) == the mean absolute error.
This is given by: sum over i: abs(reg_funct(x_test[i]) - y_test[i])
- M(3) == the standard deviation of the absolute error.
!*/

}

// ----------------------------------------------------------------------------------------

#endif // DLIB_CROSS_VALIDATE_REGRESSION_TRaINER_ABSTRACT_Hh_