coco
149 lines
6.0 KiB
C++
149 lines
6.0 KiB
C++
// Copyright (C) 2011 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_CREATE_RANDOM_PROJECTION_HAsH_ABSTRACT_Hh_
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#ifdef DLIB_CREATE_RANDOM_PROJECTION_HAsH_ABSTRACT_Hh_
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#include "projection_hash_abstract.h"
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#include "../rand.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 vector_type
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>
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projection_hash create_random_projection_hash (
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const vector_type& v,
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const int bits,
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dlib::rand& rnd
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);
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/*!
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requires
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- 0 < bits <= 32
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- v.size() > 1
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- vector_type == a std::vector or compatible type containing dlib::matrix
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objects, each representing a column vector of the same size.
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- for all valid i, j:
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- is_col_vector(v[i]) == true
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- v[i].size() > 0
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- v[i].size() == v[j].size()
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- i.e. v contains only column vectors and all the column vectors
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have the same non-zero length
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- rand_type == a type that implements the dlib/rand/rand_kernel_abstract.h interface
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ensures
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- returns a hash function H such that:
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- H.num_hash_bins() == pow(2,bits)
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- H will be setup so that it hashes the contents of v such that each bin
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ends up with roughly the same number of elements in it. This is
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accomplished by picking random hyperplanes passing though the data. In
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particular, each plane normal vector is filled with Gaussian random
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numbers and we also perform basic centering to ensure the plane passes
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though the data.
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- This function uses the supplied random number generator, rnd, to drive part
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of its processing. Therefore, giving different random number generators
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will produce different outputs.
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!*/
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// ----------------------------------------------------------------------------------------
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template <
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typename vector_type
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>
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projection_hash create_random_projection_hash (
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const vector_type& v,
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const int bits
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);
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/*!
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requires
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- 0 < bits <= 32
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- v.size() > 1
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- vector_type == a std::vector or compatible type containing dlib::matrix
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objects, each representing a column vector of the same size.
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- for all valid i, j:
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- is_col_vector(v[i]) == true
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- v[i].size() > 0
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- v[i].size() == v[j].size()
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- i.e. v contains only column vectors and all the column vectors
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have the same non-zero length
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ensures
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- returns create_random_projection_hash(v,bits,dlib::rand())
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(i.e. calls the above function with a default initialized random number generator)
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!*/
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// ----------------------------------------------------------------------------------------
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template <
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typename vector_type
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>
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projection_hash create_max_margin_projection_hash (
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const vector_type& v,
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const int bits,
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const double C,
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dlib::rand& rnd
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);
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/*!
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requires
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- 0 < bits <= 32
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- v.size() > 1
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- vector_type == a std::vector or compatible type containing dlib::matrix
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objects, each representing a column vector of the same size.
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- for all valid i, j:
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- is_col_vector(v[i]) == true
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- v[i].size() > 0
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- v[i].size() == v[j].size()
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- i.e. v contains only column vectors and all the column vectors
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have the same non-zero length
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- rand_type == a type that implements the dlib/rand/rand_kernel_abstract.h interface
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ensures
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- returns a hash function H such that:
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- H.num_hash_bins() == pow(2,bits)
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- H will be setup so that it hashes the contents of v such that
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each bin ends up with roughly the same number of elements
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in it. This is accomplished using a variation on the random hyperplane
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generation technique from the paper:
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Random Maximum Margin Hashing by Alexis Joly and Olivier Buisson
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In particular, we use the svm_c_linear_dcd_trainer to generate planes.
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We train it on randomly selected and randomly labeled points from v.
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The C SVM parameter is set to the given C argument.
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- This function uses the supplied random number generator, rnd, to drive part
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of its processing. Therefore, giving different random number generators
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will produce different outputs.
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!*/
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// ----------------------------------------------------------------------------------------
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template <
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typename vector_type
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>
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projection_hash create_max_margin_projection_hash (
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const vector_type& v,
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const int bits,
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const double C = 10
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);
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/*!
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requires
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- 0 < bits <= 32
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- v.size() > 1
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- vector_type == a std::vector or compatible type containing dlib::matrix
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objects, each representing a column vector of the same size.
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- for all valid i, j:
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- is_col_vector(v[i]) == true
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- v[i].size() > 0
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- v[i].size() == v[j].size()
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- i.e. v contains only column vectors and all the column vectors
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have the same non-zero length
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ensures
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- returns create_max_margin_projection_hash(v,bits,C,dlib::rand())
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(i.e. calls the above function with a default initialized random number generator)
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!*/
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// ----------------------------------------------------------------------------------------
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}
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#endif // DLIB_CREATE_RANDOM_PROJECTION_HAsH_ABSTRACT_Hh_
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