dlib C++ Library - feature_ranking_abstract.h

    // 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_