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Diffstat (limited to 'ml/dlib/dlib/svm/krr_trainer_abstract.h')
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diff --git a/ml/dlib/dlib/svm/krr_trainer_abstract.h b/ml/dlib/dlib/svm/krr_trainer_abstract.h deleted file mode 100644 index 399802f6b..000000000 --- a/ml/dlib/dlib/svm/krr_trainer_abstract.h +++ /dev/null @@ -1,322 +0,0 @@ -// Copyright (C) 2010 Davis E. King (davis@dlib.net) -// License: Boost Software License See LICENSE.txt for the full license. -#undef DLIB_KRR_TRAInER_ABSTRACT_Hh_ -#ifdef DLIB_KRR_TRAInER_ABSTRACT_Hh_ - -#include "../algs.h" -#include "function_abstract.h" -#include "kernel_abstract.h" -#include "empirical_kernel_map_abstract.h" - -namespace dlib -{ - template < - typename K - > - class krr_trainer - { - /*! - REQUIREMENTS ON K - is a kernel function object as defined in dlib/svm/kernel_abstract.h - - INITIAL VALUE - - get_lambda() == 0 - - basis_loaded() == false - - get_max_basis_size() == 400 - - will_use_regression_loss_for_loo_cv() == true - - get_search_lambdas() == logspace(-9, 2, 50) - - this object will not be verbose unless be_verbose() is called - - WHAT THIS OBJECT REPRESENTS - This object represents a tool for performing kernel ridge regression - (This basic algorithm is also known my many other names, e.g. regularized - least squares or least squares SVM). - - The exact definition of what this algorithm does is this: - Find w and b that minimizes the following (x_i are input samples and y_i are target values): - lambda*dot(w,w) + sum_over_i( (f(x_i) - y_i)^2 ) - where f(x) == dot(x,w) - b - - Except the dot products are replaced by kernel functions. So this - algorithm is just regular old least squares regression but with the - addition of a regularization term which encourages small w and the - application of the kernel trick. - - - It is implemented using the empirical_kernel_map and thus allows you - to run the algorithm on large datasets and obtain sparse outputs. It is also - capable of estimating the lambda parameter using leave-one-out cross-validation. - - - The leave-one-out cross-validation implementation is based on the techniques - discussed in this paper: - Notes on Regularized Least Squares by Ryan M. Rifkin and Ross A. Lippert. - !*/ - - public: - typedef K kernel_type; - typedef typename kernel_type::scalar_type scalar_type; - typedef typename kernel_type::sample_type sample_type; - typedef typename kernel_type::mem_manager_type mem_manager_type; - typedef decision_function<kernel_type> trained_function_type; - - krr_trainer ( - ); - /*! - ensures - - This object is properly initialized and ready to be used. - !*/ - - void be_verbose ( - ); - /*! - ensures - - This object will print status messages to standard out. - !*/ - - void be_quiet ( - ); - /*! - ensures - - this object will not print anything to standard out - !*/ - - const kernel_type get_kernel ( - ) const; - /*! - ensures - - returns a copy of the kernel function in use by this object - !*/ - - void set_kernel ( - const kernel_type& k - ); - /*! - ensures - - #get_kernel() == k - !*/ - - template <typename T> - void set_basis ( - const T& basis_samples - ); - /*! - requires - - T must be a dlib::matrix type or something convertible to a matrix via mat() - (e.g. a std::vector) - - is_vector(basis_samples) == true - - basis_samples.size() > 0 - - get_kernel() must be capable of operating on the elements of basis_samples. That is, - expressions such as get_kernel()(basis_samples(0), basis_samples(0)) should make sense. - ensures - - #basis_loaded() == true - - training will be carried out in the span of the given basis_samples - !*/ - - bool basis_loaded ( - ) const; - /*! - ensures - - returns true if this object has been loaded with user supplied basis vectors and false otherwise. - !*/ - - void clear_basis ( - ); - /*! - ensures - - #basis_loaded() == false - !*/ - - unsigned long get_max_basis_size ( - ) const; - /*! - ensures - - returns the maximum number of basis vectors this object is allowed - to use. This parameter only matters when the user has not supplied - a basis via set_basis(). - !*/ - - void set_max_basis_size ( - unsigned long max_basis_size - ); - /*! - requires - - max_basis_size > 0 - ensures - - #get_max_basis_size() == max_basis_size - !*/ - - void set_lambda ( - scalar_type lambda - ); - /*! - requires - - lambda >= 0 - ensures - - #get_lambda() == lambda - !*/ - - const scalar_type get_lambda ( - ) const; - /*! - ensures - - returns the regularization parameter. It is the parameter that - determines the trade off between trying to fit the training data - exactly or allowing more errors but hopefully improving the - generalization ability of the resulting function. Smaller values - encourage exact fitting while larger values of lambda may encourage - better generalization. - - Note that a lambda of 0 has a special meaning. It indicates to this - object that it should automatically determine an appropriate lambda - value. This is done using leave-one-out cross-validation. - !*/ - - void use_regression_loss_for_loo_cv ( - ); - /*! - ensures - - #will_use_regression_loss_for_loo_cv() == true - !*/ - - void use_classification_loss_for_loo_cv ( - ); - /*! - ensures - - #will_use_regression_loss_for_loo_cv() == false - !*/ - - bool will_use_regression_loss_for_loo_cv ( - ) const; - /*! - ensures - - returns true if the automatic lambda estimation will attempt to estimate a lambda - appropriate for a regression task. Otherwise it will try and find one which - minimizes the number of classification errors. - !*/ - - template <typename EXP> - void set_search_lambdas ( - const matrix_exp<EXP>& lambdas - ); - /*! - requires - - is_vector(lambdas) == true - - lambdas.size() > 0 - - min(lambdas) > 0 - - lambdas must contain floating point numbers - ensures - - #get_search_lambdas() == lambdas - !*/ - - const matrix<scalar_type,0,0,mem_manager_type>& get_search_lambdas ( - ) const; - /*! - ensures - - returns a matrix M such that: - - is_vector(M) == true - - M == a list of all the lambda values which will be tried when performing - LOO cross-validation for determining the best lambda. - !*/ - - template < - typename in_sample_vector_type, - typename in_scalar_vector_type - > - const decision_function<kernel_type> train ( - const in_sample_vector_type& x, - const in_scalar_vector_type& y - ) const; - /*! - requires - - x == a matrix or something convertible to a matrix via mat(). - Also, x should contain sample_type objects. - - y == a matrix or something convertible to a matrix via mat(). - Also, y should contain scalar_type objects. - - is_learning_problem(x,y) == true - - if (get_lambda() == 0 && will_use_regression_loss_for_loo_cv() == false) then - - is_binary_classification_problem(x,y) == true - (i.e. if you want this algorithm to estimate a lambda appropriate for - classification functions then you had better give a valid classification - problem) - ensures - - performs kernel ridge regression given the training samples in x and target values in y. - - returns a decision_function F with the following properties: - - F(new_x) == predicted y value - - - if (basis_loaded()) then - - training will be carried out in the span of the user supplied basis vectors - - else - - this object will attempt to automatically select an appropriate basis - - - if (get_lambda() == 0) then - - This object will perform internal leave-one-out cross-validation to determine an - appropriate lambda automatically. It will compute the LOO error for each lambda - in get_search_lambdas() and select the best one. - - if (will_use_regression_loss_for_loo_cv()) then - - the lambda selected will be the one that minimizes the mean squared error. - - else - - the lambda selected will be the one that minimizes the number classification - mistakes. We say a point is classified correctly if the output of the - decision_function has the same sign as its label. - - #get_lambda() == 0 - (i.e. we don't change the get_lambda() value. If you want to know what the - automatically selected lambda value was then call the version of train() - defined below) - - else - - The user supplied value of get_lambda() will be used to perform the kernel - ridge regression. - !*/ - - template < - typename in_sample_vector_type, - typename in_scalar_vector_type - > - const decision_function<kernel_type> train ( - const in_sample_vector_type& x, - const in_scalar_vector_type& y, - std::vector<scalar_type>& loo_values - ) const; - /*! - requires - - all the requirements for train(x,y) must be satisfied - ensures - - returns train(x,y) - (i.e. executes train(x,y) and returns its result) - - #loo_values.size() == y.size() - - for all valid i: - - #loo_values[i] == leave-one-out prediction for the value of y(i) based - on all the training samples other than (x(i),y(i)). - !*/ - - template < - typename in_sample_vector_type, - typename in_scalar_vector_type - > - const decision_function<kernel_type> train ( - const in_sample_vector_type& x, - const in_scalar_vector_type& y, - std::vector<scalar_type>& loo_values, - scalar_type& lambda_used - ) const; - /*! - requires - - all the requirements for train(x,y) must be satisfied - ensures - - returns train(x,y) - (i.e. executes train(x,y) and returns its result) - - #loo_values.size() == y.size() - - for all valid i: - - #loo_values[i] == leave-one-out prediction for the value of y(i) based - on all the training samples other than (x(i),y(i)). - - #lambda_used == the value of lambda used to generate the - decision_function. Note that this lambda value is always - equal to get_lambda() if get_lambda() isn't 0. - !*/ - - }; - -} - -#endif // DLIB_KRR_TRAInER_ABSTRACT_Hh_ - |