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Diffstat (limited to 'ml/dlib/dlib/lsh/create_random_projection_hash_abstract.h')
| -rw-r--r-- | ml/dlib/dlib/lsh/create_random_projection_hash_abstract.h | 148 |
1 files changed, 0 insertions, 148 deletions
diff --git a/ml/dlib/dlib/lsh/create_random_projection_hash_abstract.h b/ml/dlib/dlib/lsh/create_random_projection_hash_abstract.h deleted file mode 100644 index cff55b9a5..000000000 --- a/ml/dlib/dlib/lsh/create_random_projection_hash_abstract.h +++ /dev/null @@ -1,148 +0,0 @@ -// Copyright (C) 2011 Davis E. King (davis@dlib.net) -// License: Boost Software License See LICENSE.txt for the full license. -#undef DLIB_CREATE_RANDOM_PROJECTION_HAsH_ABSTRACT_Hh_ -#ifdef DLIB_CREATE_RANDOM_PROJECTION_HAsH_ABSTRACT_Hh_ - -#include "projection_hash_abstract.h" -#include "../rand.h" - -namespace dlib -{ - -// ---------------------------------------------------------------------------------------- - - template < - typename vector_type - > - projection_hash create_random_projection_hash ( - const vector_type& v, - const int bits, - dlib::rand& rnd - ); - /*! - requires - - 0 < bits <= 32 - - v.size() > 1 - - vector_type == a std::vector or compatible type containing dlib::matrix - objects, each representing a column vector of the same size. - - for all valid i, j: - - is_col_vector(v[i]) == true - - v[i].size() > 0 - - v[i].size() == v[j].size() - - i.e. v contains only column vectors and all the column vectors - have the same non-zero length - - rand_type == a type that implements the dlib/rand/rand_kernel_abstract.h interface - ensures - - returns a hash function H such that: - - H.num_hash_bins() == pow(2,bits) - - H will be setup so that it hashes the contents of v such that each bin - ends up with roughly the same number of elements in it. This is - accomplished by picking random hyperplanes passing though the data. In - particular, each plane normal vector is filled with Gaussian random - numbers and we also perform basic centering to ensure the plane passes - though the data. - - This function uses the supplied random number generator, rnd, to drive part - of it's processing. Therefore, giving different random number generators - will produce different outputs. - !*/ - -// ---------------------------------------------------------------------------------------- - - template < - typename vector_type - > - projection_hash create_random_projection_hash ( - const vector_type& v, - const int bits - ); - /*! - requires - - 0 < bits <= 32 - - v.size() > 1 - - vector_type == a std::vector or compatible type containing dlib::matrix - objects, each representing a column vector of the same size. - - for all valid i, j: - - is_col_vector(v[i]) == true - - v[i].size() > 0 - - v[i].size() == v[j].size() - - i.e. v contains only column vectors and all the column vectors - have the same non-zero length - ensures - - returns create_random_projection_hash(v,bits,dlib::rand()) - (i.e. calls the above function with a default initialized random number generator) - !*/ - -// ---------------------------------------------------------------------------------------- - - template < - typename vector_type - > - projection_hash create_max_margin_projection_hash ( - const vector_type& v, - const int bits, - const double C, - dlib::rand& rnd - ); - /*! - requires - - 0 < bits <= 32 - - v.size() > 1 - - vector_type == a std::vector or compatible type containing dlib::matrix - objects, each representing a column vector of the same size. - - for all valid i, j: - - is_col_vector(v[i]) == true - - v[i].size() > 0 - - v[i].size() == v[j].size() - - i.e. v contains only column vectors and all the column vectors - have the same non-zero length - - rand_type == a type that implements the dlib/rand/rand_kernel_abstract.h interface - ensures - - returns a hash function H such that: - - H.num_hash_bins() == pow(2,bits) - - H will be setup so that it hashes the contents of v such that - each bin ends up with roughly the same number of elements - in it. This is accomplished using a variation on the random hyperplane - generation technique from the paper: - Random Maximum Margin Hashing by Alexis Joly and Olivier Buisson - In particular, we use the svm_c_linear_dcd_trainer to generate planes. - We train it on randomly selected and randomly labeled points from v. - The C SVM parameter is set to the given C argument. - - This function uses the supplied random number generator, rnd, to drive part - of it's processing. Therefore, giving different random number generators - will produce different outputs. - !*/ - -// ---------------------------------------------------------------------------------------- - - template < - typename vector_type - > - projection_hash create_max_margin_projection_hash ( - const vector_type& v, - const int bits, - const double C = 10 - ); - /*! - requires - - 0 < bits <= 32 - - v.size() > 1 - - vector_type == a std::vector or compatible type containing dlib::matrix - objects, each representing a column vector of the same size. - - for all valid i, j: - - is_col_vector(v[i]) == true - - v[i].size() > 0 - - v[i].size() == v[j].size() - - i.e. v contains only column vectors and all the column vectors - have the same non-zero length - ensures - - returns create_max_margin_projection_hash(v,bits,C,dlib::rand()) - (i.e. calls the above function with a default initialized random number generator) - !*/ - -// ---------------------------------------------------------------------------------------- - -} - -#endif // DLIB_CREATE_RANDOM_PROJECTION_HAsH_ABSTRACT_Hh_ - - |