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Diffstat (limited to 'ml/dlib/dlib/statistics/sammon_abstract.h')
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diff --git a/ml/dlib/dlib/statistics/sammon_abstract.h b/ml/dlib/dlib/statistics/sammon_abstract.h deleted file mode 100644 index 0e009729c..000000000 --- a/ml/dlib/dlib/statistics/sammon_abstract.h +++ /dev/null @@ -1,117 +0,0 @@ -// Copyright (C) 2012 Emanuele Cesena (emanuele.cesena@gmail.com), Davis E. King -// License: Boost Software License See LICENSE.txt for the full license. -#undef DLIB_SAMMoN_ABSTRACT_Hh_ -#ifdef DLIB_SAMMoN_ABSTRACT_Hh_ - -#include "../matrix/matrix_abstract.h" -#include <vector> - -namespace dlib -{ - - class sammon_projection - { - /*! - WHAT THIS OBJECT REPRESENTS - This is a function object that computes the Sammon projection of a set - of N points in a L-dimensional vector space onto a d-dimensional space - (d < L), according to the paper: - A Nonlinear Mapping for Data Structure Analysis (1969) by J.W. Sammon - - The current implementation is a vectorized version of the original algorithm. - !*/ - - public: - - sammon_projection( - ); - /*! - ensures - - this object is properly initialized - !*/ - - template <typename matrix_type> - std::vector<matrix<double,0,1> > operator() ( - const std::vector<matrix_type>& data, - const long num_dims - ); - /*! - requires - - num_dims > 0 - - matrix_type should be a kind of dlib::matrix of doubles capable - of representing column vectors. - - for all valid i: - - is_col_vector(data[i]) == true - - data[0].size() == data[i].size() - (i.e. all the vectors in data must have the same dimensionality) - - if (data.size() != 0) then - - 0 < num_dims <= data[0].size() - (i.e. you can't project into a higher dimension than the input data, - only to a lower dimension.) - ensures - - This routine computes Sammon's dimensionality reduction method based on the - given input data. It will attempt to project the contents of data into a - num_dims dimensional space that preserves relative distances between the - input data points. - - This function returns a std::vector, OUT, such that: - - OUT == a set of column vectors that represent the Sammon projection of - the input data vectors. - - OUT.size() == data.size() - - for all valid i: - - OUT[i].size() == num_dims - - OUT[i] == the Sammon projection of the input vector data[i] - !*/ - - template <typename matrix_type> - void operator() ( - const std::vector<matrix_type>& data, - const long num_dims, - std::vector<matrix<double,0,1> >& result, - double &err, - const unsigned long num_iters = 1000, - const double err_delta = 1.0e-9 - ); - /*! - requires - - num_iters > 0 - - err_delta > 0 - - num_dims > 0 - - matrix_type should be a kind of dlib::matrix of doubles capable - of representing column vectors. - - for all valid i: - - is_col_vector(data[i]) == true - - data[0].size() == data[i].size() - (i.e. all the vectors in data must have the same dimensionality) - - if (data.size() != 0) then - - 0 < num_dims <= data[0].size() - (i.e. you can't project into a higher dimension than the input data, - only to a lower dimension.) - ensures - - This routine computes Sammon's dimensionality reduction method based on the - given input data. It will attempt to project the contents of data into a - num_dims dimensional space that preserves relative distances between the - input data points. - - #err == the final error value at the end of the algorithm. The goal of Sammon's - algorithm is to find a lower dimensional projection of the input data that - preserves the relative distances between points. The value in #err is a measure - of the total error at the end of the algorithm. So smaller values indicate - a better projection was found than if a large value is returned via #err. - - Sammon's algorithm will run until either num_iters iterations has executed - or the change in error from one iteration to the next is less than err_delta. - - Upon completion, the output of Sammon's projection is stored into #result, in - particular, we will have: - - #result == a set of column vectors that represent the Sammon projection of - the input data vectors. - - #result.size() == data.size() - - for all valid i: - - #result[i].size() == num_dims - - #result[i] == the Sammon projection of the input vector data[i] - !*/ - - }; - -} - -#endif // DLIB_SAMMoN_ABSTRACT_Hh_ - - |