// 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{classsammon_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, constlongnum_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>voidoperator() ( const std::vector<matrix_type>& data, constlongnum_dims, std::vector<matrix<double,0,1> >& result,double&err, constunsignedlongnum_iters = 1000, constdoubleerr_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_