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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, 0 insertions, 136 deletions
diff --git a/ml/dlib/dlib/svm/feature_ranking_abstract.h b/ml/dlib/dlib/svm/feature_ranking_abstract.h deleted file mode 100644 index 5a6fd3bb9..000000000 --- a/ml/dlib/dlib/svm/feature_ranking_abstract.h +++ /dev/null @@ -1,136 +0,0 @@ -// 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_ - - - |