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Diffstat (limited to 'ml/dlib/dlib/svm/feature_ranking_abstract.h')
| -rw-r--r-- | ml/dlib/dlib/svm/feature_ranking_abstract.h | 136 |
1 files changed, 136 insertions, 0 deletions
diff --git a/ml/dlib/dlib/svm/feature_ranking_abstract.h b/ml/dlib/dlib/svm/feature_ranking_abstract.h new file mode 100644 index 000000000..5a6fd3bb9 --- /dev/null +++ b/ml/dlib/dlib/svm/feature_ranking_abstract.h @@ -0,0 +1,136 @@ +// Copyright (C) 2008 Davis E. King (davis@dlib.net) +// License: Boost Software License See LICENSE.txt for the full license. +#undef DLIB_KERNEL_FEATURE_RANKINg_ABSTRACT_H_ +#ifdef DLIB_KERNEL_FEATURE_RANKINg_ABSTRACT_H_ + +#include <vector> +#include <limits> + +#include "svm_abstract.h" +#include "kcentroid_abstract.h" +#include "../is_kind.h" + +namespace dlib +{ + +// ---------------------------------------------------------------------------------------- + + template < + typename kernel_type, + typename sample_matrix_type, + typename label_matrix_type + > + matrix<typename kernel_type::scalar_type> rank_features ( + const kcentroid<kernel_type>& kc, + const sample_matrix_type& samples, + const label_matrix_type& labels, + const long num_features = samples(0).nr() + ); + /*! + requires + - sample_matrix_type == a matrix or something convertible to a matrix via mat() + - label_matrix_type == a matrix or something convertible to a matrix via mat() + - is_binary_classification_problem(samples, labels) == true + - kc.train(samples(0)) must be a valid expression. This means that + kc must use a kernel type that is capable of operating on the + contents of the samples matrix + - 0 < num_features <= samples(0).nr() + ensures + - Let Class1 denote the centroid of all the samples with labels that are < 0 + - Let Class2 denote the centroid of all the samples with labels that are > 0 + - finds a ranking of the features where the best features come first. This + function does this by computing the distance between the centroid of the Class1 + samples and the Class2 samples in kernel defined feature space. + Good features are then ones that result in the biggest separation between + the two centroids of Class1 and Class2. + - Uses the kc object to compute the centroids of the two classes + - returns a ranking matrix R where: + - R.nr() == num_features + - r.nc() == 2 + - R(i,0) == the index of the ith best feature according to our ranking. + (e.g. samples(n)(R(0,0)) is the best feature from sample(n) and + samples(n)(R(1,0)) is the second best, samples(n)(R(2,0)) the + third best and so on) + - R(i,1) == a number that indicates how much separation exists between + the two centroids when features 0 through i are used. + !*/ + +// ---------------------------------------------------------------------------------------- + + template < + typename sample_matrix_type, + typename label_matrix_type + > + double find_gamma_with_big_centroid_gap ( + const sample_matrix_type& samples, + const label_matrix_type& labels, + double initial_gamma = 0.1, + unsigned long num_sv = 40 + ); + /*! + requires + - initial_gamma > 0 + - num_sv > 0 + - is_binary_classification_problem(samples, labels) == true + ensures + - This is a function that tries to pick a reasonable default value for the gamma + parameter of the radial_basis_kernel. It picks the parameter that gives the + largest separation between the centroids, in kernel feature space, of two classes + of data. It does this using the kcentroid object and it sets the kcentroid up + to use num_sv dictionary vectors. + - This function does a search for the best gamma and the search starts with + the value given by initial_gamma. Better initial guesses will give + better results since the routine may get stuck in a local minima. + - returns the value of gamma that results in the largest separation. + !*/ + +// ---------------------------------------------------------------------------------------- + + template < + typename sample_matrix_type, + typename label_matrix_type + > + double verbose_find_gamma_with_big_centroid_gap ( + const sample_matrix_type& samples, + const label_matrix_type& labels, + double initial_gamma = 0.1, + unsigned long num_sv = 40 + ); + /*! + requires + - initial_gamma > 0 + - num_sv > 0 + - is_binary_classification_problem(samples, labels) == true + ensures + - This function does the same exact thing as the above find_gamma_with_big_centroid_gap() + except that it is also verbose in the sense that it will print status messages to + standard out during its processing. + !*/ + +// ---------------------------------------------------------------------------------------- + + template < + typename vector_type + > + double compute_mean_squared_distance ( + const vector_type& samples + ); + /*! + requires + - vector_type is something with an interface compatible with std::vector. + Additionally, it must in turn contain dlib::matrix types which contain + scalars such as float or double values. + - for all valid i: is_vector(samples[i]) == true + ensures + - computes the average value of the squares of all the pairwise + distances between every element of samples. + !*/ + +// ---------------------------------------------------------------------------------------- + +} + +#endif // DLIB_KERNEL_FEATURE_RANKINg_ABSTRACT_H_ + + + |