coco
83 lines
3.1 KiB
C++
83 lines
3.1 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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#undef DLIB_CROSS_VALIDATE_REGRESSION_TRaINER_ABSTRACT_Hh_
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#ifdef DLIB_CROSS_VALIDATE_REGRESSION_TRaINER_ABSTRACT_Hh_
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#include <vector>
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#include "../matrix.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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requires
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- is_learning_problem(x_test, y_test)
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- reg_funct_type == some kind of regression function object
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(e.g. a decision_function created by the svr_trainer )
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ensures
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- Tests reg_funct against the given samples in x_test and target values in
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y_test and returns a matrix M summarizing the results. Specifically:
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- M(0) == the mean squared error.
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The MSE is given by: sum over i: pow(reg_funct(x_test[i]) - y_test[i], 2.0)
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- M(1) == the correlation between reg_funct(x_test[i]) and y_test[i].
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This is a number between -1 and 1.
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- M(2) == the mean absolute error.
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This is given by: sum over i: abs(reg_funct(x_test[i]) - y_test[i])
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- M(3) == the standard deviation of the absolute error.
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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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requires
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- is_learning_problem(x,y)
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- 1 < folds <= x.size()
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- trainer_type == some kind of regression trainer object (e.g. svr_trainer)
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ensures
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- Performs k-fold cross validation by using the given trainer to solve a
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regression problem for the given number of folds. Each fold is tested using
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the output of the trainer. A matrix M summarizing the results is returned.
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Specifically:
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- M(0) == the mean squared error.
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The MSE is given by: sum over i: pow(reg_funct(x[i]) - y[i], 2.0)
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- M(1) == the correlation between a predicted y value and its true value.
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This is a number between -1 and 1.
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- M(2) == the mean absolute error.
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This is given by: sum over i: abs(reg_funct(x_test[i]) - y_test[i])
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- M(3) == the standard deviation of the absolute error.
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!*/
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}
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// ----------------------------------------------------------------------------------------
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#endif // DLIB_CROSS_VALIDATE_REGRESSION_TRaINER_ABSTRACT_Hh_
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