coco
116 lines
4.8 KiB
C++
116 lines
4.8 KiB
C++
// Copyright (C) 2009 Davis E. King (davis@dlib.net)
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// License: Boost Software License See LICENSE.txt for the full license.
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#undef DLIB_SVm_KERNEL_MATRIX_ABSTRACT_
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#ifdef DLIB_SVm_KERNEL_MATRIX_ABSTRACT_
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#include <vector>
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#include "kernel_abstract.h"
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#include "../matrix/matrix_abstract.h"
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#include "../algs.h"
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namespace dlib
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{
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// ----------------------------------------------------------------------------------------
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template <
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typename kernel_type,
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typename V
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>
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const matrix_exp kernel_matrix (
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const kernel_type& kernel,
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const V& v
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);
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/*!
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requires
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- kernel == a kernel function object as defined by the file dlib/svm/kernel_abstract.h.
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This kernel must also be capable of operating on the contents of v.
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- V == dlib::matrix, std::vector, dlib::std_vector_c, dlib::random_subset_selector,
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dlib::linearly_independent_subset_finder, or kernel_type::sample_type.
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- if (V is a dlib::matrix) then
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- is_vector(v) == true
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ensures
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- if (V is of type kernel_type::sample_type) then
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- returns a matrix R such that:
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- R::type == kernel_type::scalar_type
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- R.size() == 1
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- R(0,0) == kernel(v,v)
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- else
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- returns a matrix R such that:
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- R::type == kernel_type::scalar_type
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- R is a square matrix of v.size() rows by v.size() columns
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- for all valid r and c:
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- R(r,c) == kernel(v(r), v(c))
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!*/
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// ----------------------------------------------------------------------------------------
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template <
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typename kernel_type,
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typename V1,
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typename V2
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>
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const matrix_exp kernel_matrix (
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const kernel_type& kernel,
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const V1& v1,
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const V2& v2
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);
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/*!
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requires
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- kernel == a kernel function object as defined by the file dlib/svm/kernel_abstract.h
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This kernel must also be capable of operating on the contents of v1 and v2.
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- V1 == dlib::matrix, std::vector, dlib::std_vector_c, dlib::random_subset_selector,
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dlib::linearly_independent_subset_finder, or kernel_type::sample_type.
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- V2 == dlib::matrix, std::vector, dlib::std_vector_c, dlib::random_subset_selector,
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dlib::linearly_independent_subset_finder, or kernel_type::sample_type.
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- if (V1 is a dlib::matrix) then
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- is_vector(v1) == true
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- if (V2 is a dlib::matrix) then
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- is_vector(v2) == true
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ensures
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- if (V1 and V2 are of type kernel_type::sample_type) then
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- returns a matrix R such that:
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- R::type == kernel_type::scalar_type
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- R.size() == 1
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- R(0,0) == kernel(v1,v2)
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- else if (V1 is of type kernel_type::sample_type) then
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- returns a matrix R such that:
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- R::type == kernel_type::scalar_type
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- R.nr() == 1
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- R.nc() == v2.size()
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- for all valid c:
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- R(0,c) == kernel(v1, v2(c))
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- else if (V2 is of type kernel_type::sample_type) then
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- returns a matrix R such that:
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- R::type == kernel_type::scalar_type
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- R.nr() == v1.size()
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- R.nc() == 1
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- for all valid r:
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- R(r,0) == kernel(v1(r), v2)
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- else
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- returns a matrix R such that:
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- R::type == kernel_type::scalar_type
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- R.nr() == v1.size()
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- R.nc() == v2.size()
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- for all valid r and c:
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- R(r,c) == kernel(v1(r), v2(c))
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A note about aliasing (see the examples/matrix_expressions_ex.cpp example program
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for a discussion of what aliasing is in the context of the dlib::matrix):
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kernel_matrix() expressions can detect aliasing of an argument if that
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argument is of type kernel_type::sample_type. However, it can't detect
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aliasing though std::vectors or other "list of sample type" container class
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arguments. This means that it is safe to assign a kernel_matrix() expression
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to a sample_type if V1 or V2 are of sample_type but not safe otherwise. However,
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since the latter case results in a general n by m matrix rather than a column
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or row vector you shouldn't ever be doing it anyway.
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!*/
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// ----------------------------------------------------------------------------------------
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}
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#endif // DLIB_SVm_KERNEL_MATRIX_ABSTRACT_
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