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