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Diffstat (limited to 'ml/dlib/dlib/statistics/dpca.h')
| -rw-r--r-- | ml/dlib/dlib/statistics/dpca.h | 541 |
1 files changed, 0 insertions, 541 deletions
diff --git a/ml/dlib/dlib/statistics/dpca.h b/ml/dlib/dlib/statistics/dpca.h deleted file mode 100644 index cae784682..000000000 --- a/ml/dlib/dlib/statistics/dpca.h +++ /dev/null @@ -1,541 +0,0 @@ -// Copyright (C) 2009 Davis E. King (davis@dlib.net) -// License: Boost Software License See LICENSE.txt for the full license. -#ifndef DLIB_DPCA_h_ -#define DLIB_DPCA_h_ - -#include "dpca_abstract.h" -#include <limits> -#include <cmath> -#include "../algs.h" -#include "../matrix.h" -#include <iostream> - -namespace dlib -{ - -// ---------------------------------------------------------------------------------------- - - template < - typename matrix_type - > - class discriminant_pca - { - /*! - INITIAL VALUE - - vect_size == 0 - - total_count == 0 - - between_count == 0 - - within_count == 0 - - between_weight == 1 - - within_weight == 1 - - CONVENTION - - vect_size == in_vector_size() - - total_count == the number of times add_to_total_variance() has been called. - - within_count == the number of times add_to_within_class_variance() has been called. - - between_count == the number of times add_to_between_class_variance() has been called. - - between_weight == between_class_weight() - - within_weight == within_class_weight() - - - if (total_count != 0) - - total_sum == the sum of all vectors given to add_to_total_variance() - - the covariance of all the elements given to add_to_total_variance() is given - by: - - let avg == total_sum/total_count - - covariance == total_cov/total_count - avg*trans(avg) - - if (within_count != 0) - - within_cov/within_count == the normalized within class scatter matrix - - if (between_count != 0) - - between_cov/between_count == the normalized between class scatter matrix - !*/ - - public: - - struct discriminant_pca_error : public error - { - discriminant_pca_error(const std::string& message): error(message) {} - }; - - typedef typename matrix_type::mem_manager_type mem_manager_type; - typedef typename matrix_type::type scalar_type; - typedef typename matrix_type::layout_type layout_type; - typedef matrix<scalar_type,0,0,mem_manager_type,layout_type> general_matrix; - typedef matrix<scalar_type,0,1,mem_manager_type,layout_type> column_matrix; - - discriminant_pca ( - ) - { - clear(); - } - - void clear( - ) - { - total_count = 0; - between_count = 0; - within_count = 0; - - vect_size = 0; - - - between_weight = 1; - within_weight = 1; - - - total_sum.set_size(0); - between_cov.set_size(0,0); - total_cov.set_size(0,0); - within_cov.set_size(0,0); - } - - long in_vector_size ( - ) const - { - return vect_size; - } - - void set_within_class_weight ( - scalar_type weight - ) - { - // make sure requires clause is not broken - DLIB_ASSERT(weight >= 0, - "\t void discriminant_pca::set_within_class_weight()" - << "\n\t You can't use negative weight values" - << "\n\t weight: " << weight - << "\n\t this: " << this - ); - - within_weight = weight; - } - - scalar_type within_class_weight ( - ) const - { - return within_weight; - } - - void set_between_class_weight ( - scalar_type weight - ) - { - // make sure requires clause is not broken - DLIB_ASSERT(weight >= 0, - "\t void discriminant_pca::set_between_class_weight()" - << "\n\t You can't use negative weight values" - << "\n\t weight: " << weight - << "\n\t this: " << this - ); - - between_weight = weight; - } - - scalar_type between_class_weight ( - ) const - { - return between_weight; - } - - template <typename EXP1, typename EXP2> - void add_to_within_class_variance( - const matrix_exp<EXP1>& x, - const matrix_exp<EXP2>& y - ) - { - // make sure requires clause is not broken - DLIB_ASSERT(is_col_vector(x) && is_col_vector(y) && - x.size() == y.size() && - (in_vector_size() == 0 || x.size() == in_vector_size()), - "\t void discriminant_pca::add_to_within_class_variance()" - << "\n\t Invalid inputs were given to this function" - << "\n\t is_col_vector(x): " << is_col_vector(x) - << "\n\t is_col_vector(y): " << is_col_vector(y) - << "\n\t x.size(): " << x.size() - << "\n\t y.size(): " << y.size() - << "\n\t in_vector_size(): " << in_vector_size() - << "\n\t this: " << this - ); - - vect_size = x.size(); - if (within_count == 0) - { - within_cov = (x-y)*trans(x-y); - } - else - { - within_cov += (x-y)*trans(x-y); - } - ++within_count; - } - - template <typename EXP1, typename EXP2> - void add_to_between_class_variance( - const matrix_exp<EXP1>& x, - const matrix_exp<EXP2>& y - ) - { - // make sure requires clause is not broken - DLIB_ASSERT(is_col_vector(x) && is_col_vector(y) && - x.size() == y.size() && - (in_vector_size() == 0 || x.size() == in_vector_size()), - "\t void discriminant_pca::add_to_between_class_variance()" - << "\n\t Invalid inputs were given to this function" - << "\n\t is_col_vector(x): " << is_col_vector(x) - << "\n\t is_col_vector(y): " << is_col_vector(y) - << "\n\t x.size(): " << x.size() - << "\n\t y.size(): " << y.size() - << "\n\t in_vector_size(): " << in_vector_size() - << "\n\t this: " << this - ); - - vect_size = x.size(); - if (between_count == 0) - { - between_cov = (x-y)*trans(x-y); - } - else - { - between_cov += (x-y)*trans(x-y); - } - ++between_count; - } - - template <typename EXP> - void add_to_total_variance( - const matrix_exp<EXP>& x - ) - { - // make sure requires clause is not broken - DLIB_ASSERT(is_col_vector(x) && (in_vector_size() == 0 || x.size() == in_vector_size()), - "\t void discriminant_pca::add_to_total_variance()" - << "\n\t Invalid inputs were given to this function" - << "\n\t is_col_vector(x): " << is_col_vector(x) - << "\n\t in_vector_size(): " << in_vector_size() - << "\n\t x.size(): " << x.size() - << "\n\t this: " << this - ); - - vect_size = x.size(); - if (total_count == 0) - { - total_cov = x*trans(x); - total_sum = x; - } - else - { - total_cov += x*trans(x); - total_sum += x; - } - ++total_count; - } - - const general_matrix dpca_matrix ( - const double eps = 0.99 - ) const - { - general_matrix dpca_mat; - general_matrix eigenvalues; - dpca_matrix(dpca_mat, eigenvalues, eps); - return dpca_mat; - } - - const general_matrix dpca_matrix_of_size ( - const long num_rows - ) - { - // make sure requires clause is not broken - DLIB_ASSERT(0 < num_rows && num_rows <= in_vector_size(), - "\t general_matrix discriminant_pca::dpca_matrix_of_size()" - << "\n\t Invalid inputs were given to this function" - << "\n\t num_rows: " << num_rows - << "\n\t in_vector_size(): " << in_vector_size() - << "\n\t this: " << this - ); - - general_matrix dpca_mat; - general_matrix eigenvalues; - dpca_matrix_of_size(dpca_mat, eigenvalues, num_rows); - return dpca_mat; - } - - void dpca_matrix ( - general_matrix& dpca_mat, - general_matrix& eigenvalues, - const double eps = 0.99 - ) const - { - // make sure requires clause is not broken - DLIB_ASSERT(0 < eps && eps <= 1 && in_vector_size() != 0, - "\t void discriminant_pca::dpca_matrix()" - << "\n\t Invalid inputs were given to this function" - << "\n\t eps: " << eps - << "\n\t in_vector_size(): " << in_vector_size() - << "\n\t this: " << this - ); - - compute_dpca_matrix(dpca_mat, eigenvalues, eps, 0); - } - - void dpca_matrix_of_size ( - general_matrix& dpca_mat, - general_matrix& eigenvalues, - const long num_rows - ) - { - // make sure requires clause is not broken - DLIB_ASSERT(0 < num_rows && num_rows <= in_vector_size(), - "\t general_matrix discriminant_pca::dpca_matrix_of_size()" - << "\n\t Invalid inputs were given to this function" - << "\n\t num_rows: " << num_rows - << "\n\t in_vector_size(): " << in_vector_size() - << "\n\t this: " << this - ); - - compute_dpca_matrix(dpca_mat, eigenvalues, 1, num_rows); - } - - void swap ( - discriminant_pca& item - ) - { - using std::swap; - swap(total_cov, item.total_cov); - swap(total_sum, item.total_sum); - swap(total_count, item.total_count); - swap(vect_size, item.vect_size); - swap(between_cov, item.between_cov); - - swap(between_count, item.between_count); - swap(between_weight, item.between_weight); - swap(within_cov, item.within_cov); - swap(within_count, item.within_count); - swap(within_weight, item.within_weight); - } - - friend void deserialize ( - discriminant_pca& item, - std::istream& in - ) - { - deserialize( item.total_cov, in); - deserialize( item.total_sum, in); - deserialize( item.total_count, in); - deserialize( item.vect_size, in); - deserialize( item.between_cov, in); - deserialize( item.between_count, in); - deserialize( item.between_weight, in); - deserialize( item.within_cov, in); - deserialize( item.within_count, in); - deserialize( item.within_weight, in); - } - - friend void serialize ( - const discriminant_pca& item, - std::ostream& out - ) - { - serialize( item.total_cov, out); - serialize( item.total_sum, out); - serialize( item.total_count, out); - serialize( item.vect_size, out); - serialize( item.between_cov, out); - serialize( item.between_count, out); - serialize( item.between_weight, out); - serialize( item.within_cov, out); - serialize( item.within_count, out); - serialize( item.within_weight, out); - } - - discriminant_pca operator+ ( - const discriminant_pca& item - ) const - { - // make sure requires clause is not broken - DLIB_ASSERT((in_vector_size() == 0 || item.in_vector_size() == 0 || in_vector_size() == item.in_vector_size()) && - between_class_weight() == item.between_class_weight() && - within_class_weight() == item.within_class_weight(), - "\t discriminant_pca discriminant_pca::operator+()" - << "\n\t The two discriminant_pca objects being added must have compatible parameters" - << "\n\t in_vector_size(): " << in_vector_size() - << "\n\t item.in_vector_size(): " << item.in_vector_size() - << "\n\t between_class_weight(): " << between_class_weight() - << "\n\t item.between_class_weight(): " << item.between_class_weight() - << "\n\t within_class_weight(): " << within_class_weight() - << "\n\t item.within_class_weight(): " << item.within_class_weight() - << "\n\t this: " << this - ); - - discriminant_pca temp(item); - - // We need to make sure to ignore empty matrices. That's what these if statements - // are for. - - if (total_count != 0 && temp.total_count != 0) - { - temp.total_cov += total_cov; - temp.total_sum += total_sum; - temp.total_count += total_count; - } - else if (total_count != 0) - { - temp.total_cov = total_cov; - temp.total_sum = total_sum; - temp.total_count = total_count; - } - - if (between_count != 0 && temp.between_count != 0) - { - temp.between_cov += between_cov; - temp.between_count += between_count; - } - else if (between_count != 0) - { - temp.between_cov = between_cov; - temp.between_count = between_count; - } - - if (within_count != 0 && temp.within_count != 0) - { - temp.within_cov += within_cov; - temp.within_count += within_count; - } - else if (within_count != 0) - { - temp.within_cov = within_cov; - temp.within_count = within_count; - } - - return temp; - } - - discriminant_pca& operator+= ( - const discriminant_pca& rhs - ) - { - (*this + rhs).swap(*this); - return *this; - } - - private: - - void compute_dpca_matrix ( - general_matrix& dpca_mat, - general_matrix& eigenvalues, - const double eps, - long num_rows - ) const - { - general_matrix cov; - - // now combine the three measures of variance into a single matrix by using the - // within_weight and between_weight weights. - cov = get_total_covariance_matrix(); - if (within_count != 0) - cov -= within_weight*within_cov/within_count; - if (between_count != 0) - cov += between_weight*between_cov/between_count; - - - eigenvalue_decomposition<general_matrix> eig(make_symmetric(cov)); - - eigenvalues = eig.get_real_eigenvalues(); - dpca_mat = eig.get_pseudo_v(); - - // sort the eigenvalues and eigenvectors so that the biggest eigenvalues come first - rsort_columns(dpca_mat, eigenvalues); - - long num_vectors = 0; - if (num_rows == 0) - { - // Some of the eigenvalues might be negative. So first lets zero those out - // so they won't get considered. - eigenvalues = pointwise_multiply(eigenvalues > 0, eigenvalues); - // figure out how many eigenvectors we want in our dpca matrix - const double thresh = sum(eigenvalues)*eps; - double total = 0; - for (long r = 0; r < eigenvalues.size() && total < thresh; ++r) - { - // Don't even think about looking at eigenvalues that are 0. If we go this - // far then we have all we need. - if (eigenvalues(r) == 0) - break; - - ++num_vectors; - total += eigenvalues(r); - } - - if (num_vectors == 0) - throw discriminant_pca_error("While performing discriminant_pca, all eigenvalues were negative or 0"); - } - else - { - num_vectors = num_rows; - } - - - // So now we know we want to use num_vectors of the first eigenvectors. So - // pull those out and discard the rest. - dpca_mat = trans(colm(dpca_mat,range(0,num_vectors-1))); - - // also clip off the eigenvalues we aren't using - eigenvalues = rowm(eigenvalues, range(0,num_vectors-1)); - - } - - general_matrix get_total_covariance_matrix ( - ) const - /*! - ensures - - returns the covariance matrix of all the data given to the add_to_total_variance() - !*/ - { - // if we don't even know the dimensionality of the vectors we are dealing - // with then just return an empty matrix - if (vect_size == 0) - return general_matrix(); - - // we know the vector size but we have zero total covariance. - if (total_count == 0) - { - general_matrix temp(vect_size,vect_size); - temp = 0; - return temp; - } - - // In this case we actually have something to make a total covariance matrix out of. - // So do that. - column_matrix avg = total_sum/total_count; - - return total_cov/total_count - avg*trans(avg); - } - - general_matrix total_cov; - column_matrix total_sum; - scalar_type total_count; - - long vect_size; - - general_matrix between_cov; - scalar_type between_count; - scalar_type between_weight; - - general_matrix within_cov; - scalar_type within_count; - scalar_type within_weight; - }; - - template < - typename matrix_type - > - inline void swap ( - discriminant_pca<matrix_type>& a, - discriminant_pca<matrix_type>& b - ) { a.swap(b); } - -// ---------------------------------------------------------------------------------------- - -} - -#endif // DLIB_DPCA_h_ - - |