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Diffstat (limited to 'ml/dlib/dlib/svm/rls.h')
| -rw-r--r-- | ml/dlib/dlib/svm/rls.h | 232 |
1 files changed, 0 insertions, 232 deletions
diff --git a/ml/dlib/dlib/svm/rls.h b/ml/dlib/dlib/svm/rls.h deleted file mode 100644 index edee6b062..000000000 --- a/ml/dlib/dlib/svm/rls.h +++ /dev/null @@ -1,232 +0,0 @@ -// Copyright (C) 2012 Davis E. King (davis@dlib.net) -// License: Boost Software License See LICENSE.txt for the full license. -#ifndef DLIB_RLs_Hh_ -#define DLIB_RLs_Hh_ - -#include "rls_abstract.h" -#include "../matrix.h" -#include "function.h" - -namespace dlib -{ - -// ---------------------------------------------------------------------------------------- - - class rls - { - - public: - - - explicit rls( - double forget_factor_, - double C_ = 1000, - bool apply_forget_factor_to_C_ = false - ) - { - // make sure requires clause is not broken - DLIB_ASSERT(0 < forget_factor_ && forget_factor_ <= 1 && - 0 < C_, - "\t rls::rls()" - << "\n\t invalid arguments were given to this function" - << "\n\t forget_factor_: " << forget_factor_ - << "\n\t C_: " << C_ - << "\n\t this: " << this - ); - - - C = C_; - forget_factor = forget_factor_; - apply_forget_factor_to_C = apply_forget_factor_to_C_; - } - - rls( - ) - { - C = 1000; - forget_factor = 1; - apply_forget_factor_to_C = false; - } - - double get_c( - ) const - { - return C; - } - - double get_forget_factor( - ) const - { - return forget_factor; - } - - bool should_apply_forget_factor_to_C ( - ) const - { - return apply_forget_factor_to_C; - } - - template <typename EXP> - void train ( - const matrix_exp<EXP>& x, - double y - ) - { - // make sure requires clause is not broken - DLIB_ASSERT(is_col_vector(x) && - (get_w().size() == 0 || get_w().size() == x.size()), - "\t void rls::train()" - << "\n\t invalid arguments were given to this function" - << "\n\t is_col_vector(x): " << is_col_vector(x) - << "\n\t x.size(): " << x.size() - << "\n\t get_w().size(): " << get_w().size() - << "\n\t this: " << this - ); - - if (R.size() == 0) - { - R = identity_matrix<double>(x.size())*C; - w.set_size(x.size()); - w = 0; - } - - // multiply by forget factor and incorporate x*trans(x) into R. - const double l = 1.0/forget_factor; - const double temp = 1 + l*trans(x)*R*x; - tmp = R*x; - R = l*R - l*l*(tmp*trans(tmp))/temp; - - // Since we multiplied by the forget factor, we need to add (1-forget_factor) of the - // identity matrix back in to keep the regularization alive. - if (forget_factor != 1 && !apply_forget_factor_to_C) - add_eye_to_inv(R, (1-forget_factor)/C); - - // R should always be symmetric. This line improves numeric stability of this algorithm. - if (cnt%10 == 0) - R = 0.5*(R + trans(R)); - ++cnt; - - w = w + R*x*(y - trans(x)*w); - - } - - - - const matrix<double,0,1>& get_w( - ) const - { - return w; - } - - template <typename EXP> - double operator() ( - const matrix_exp<EXP>& x - ) const - { - // make sure requires clause is not broken - DLIB_ASSERT(is_col_vector(x) && get_w().size() == x.size(), - "\t double rls::operator()()" - << "\n\t invalid arguments were given to this function" - << "\n\t is_col_vector(x): " << is_col_vector(x) - << "\n\t x.size(): " << x.size() - << "\n\t get_w().size(): " << get_w().size() - << "\n\t this: " << this - ); - - return dot(x,w); - } - - decision_function<linear_kernel<matrix<double,0,1> > > get_decision_function ( - ) const - { - // make sure requires clause is not broken - DLIB_ASSERT(get_w().size() != 0, - "\t decision_function rls::get_decision_function()" - << "\n\t invalid arguments were given to this function" - << "\n\t get_w().size(): " << get_w().size() - << "\n\t this: " << this - ); - - decision_function<linear_kernel<matrix<double,0,1> > > df; - df.alpha.set_size(1); - df.basis_vectors.set_size(1); - df.b = 0; - df.alpha = 1; - df.basis_vectors(0) = w; - - return df; - } - - friend inline void serialize(const rls& item, std::ostream& out) - { - int version = 2; - serialize(version, out); - serialize(item.w, out); - serialize(item.R, out); - serialize(item.C, out); - serialize(item.forget_factor, out); - serialize(item.cnt, out); - serialize(item.apply_forget_factor_to_C, out); - } - - friend inline void deserialize(rls& item, std::istream& in) - { - int version = 0; - deserialize(version, in); - if (!(1 <= version && version <= 2)) - throw dlib::serialization_error("Unknown version number found while deserializing rls object."); - - if (version >= 1) - { - deserialize(item.w, in); - deserialize(item.R, in); - deserialize(item.C, in); - deserialize(item.forget_factor, in); - } - item.cnt = 0; - item.apply_forget_factor_to_C = false; - if (version >= 2) - { - deserialize(item.cnt, in); - deserialize(item.apply_forget_factor_to_C, in); - } - } - - private: - - void add_eye_to_inv( - matrix<double>& m, - double C - ) - /*! - ensures - - Let m == inv(M) - - this function returns inv(M + C*identity_matrix<double>(m.nr())) - !*/ - { - for (long r = 0; r < m.nr(); ++r) - { - m = m - colm(m,r)*trans(colm(m,r))/(1/C + m(r,r)); - } - } - - - matrix<double,0,1> w; - matrix<double> R; - double C; - double forget_factor; - int cnt = 0; - bool apply_forget_factor_to_C; - - - // This object is here only to avoid reallocation during training. It don't - // logically contribute to the state of this object. - matrix<double,0,1> tmp; - }; - -// ---------------------------------------------------------------------------------------- - -} - -#endif // DLIB_RLs_Hh_ - |