coco
100 lines
4.0 KiB
C++
100 lines
4.0 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_MULTICLASS_TRaINER_ABSTRACT_Hh_
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#ifdef DLIB_CROSS_VALIDATE_MULTICLASS_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 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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requires
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- is_learning_problem(x_test, y_test)
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- dec_funct_type == some kind of multiclass decision function object
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(e.g. one_vs_one_decision_function)
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ensures
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- Tests dec_funct against the given samples in x_test and labels in y_test
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and returns a confusion matrix summarizing the results.
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- let L = dec_funct.get_labels(). Then the confusion matrix C returned
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by this function has the following properties.
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- C.nr() == C.nc() == L.size()
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- C(r,c) == the number of times a sample with label L(r) was predicted
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to have a label of L(c)
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- Any samples with a y_test value not in L are ignored. That is, samples
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with labels the decision function hasn't ever seen before are ignored.
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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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/*!
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This is the exception class used by the cross_validate_multiclass_trainer()
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routine.
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!*/
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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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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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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 multiclass classification trainer object (e.g. one_vs_one_trainer)
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ensures
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- performs k-fold cross validation by using the given trainer to solve the
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given multiclass classification problem for the given number of folds.
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Each fold is tested using the output of the trainer and the confusion
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matrix from all folds is summed and returned.
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- The total confusion matrix is computed by running test_binary_decision_function()
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on each fold and summing its output.
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- The number of folds used is given by the folds argument.
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- let L = select_all_distinct_labels(y). Then the confusion matrix C returned
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by this function has the following properties.
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- C.nr() == C.nc() == L.size()
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- C(r,c) == the number of times a sample with label L(r) was predicted
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to have a label of L(c)
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Note that sum(C) might be slightly less than x.size(). This happens if the number of
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samples in a class is not an even multiple of folds. This is because each fold has the
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same number of test samples in it and so if the number of samples in a class isn't a
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multiple of folds then a few are not tested.
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throws
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- cross_validation_error
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This exception is thrown if one of the classes has fewer samples than
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the number of requested folds.
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!*/
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}
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// ----------------------------------------------------------------------------------------
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#endif // DLIB_CROSS_VALIDATE_MULTICLASS_TRaINER_ABSTRACT_Hh_
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