mirror of https://github.com/davisking/dlib.git
updated the svm_c_linear_trainer
--HG-- extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403504
This commit is contained in:
Davis King
parent
c2de9780a3
commit
ce42b721dd
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@ -41,13 +41,15 @@ namespace dlib
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const scalar_type C_neg,
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const in_sample_vector_type& samples_,
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const in_scalar_vector_type& labels_,
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bool be_verbose_
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const bool be_verbose_,
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const scalar_type eps_
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) :
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samples(samples_),
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labels(labels_),
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Cpos(C_pos),
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Cneg(C_neg),
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be_verbose(be_verbose_)
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be_verbose(be_verbose_),
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eps(eps_)
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{
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dot_prods.resize(samples.size());
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is_first_call = true;
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@ -83,10 +85,7 @@ namespace dlib
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cout << endl;
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}
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if (current_error_gap/current_objective_value < 0.001)
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return true;
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if (num_iterations > 10000)
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if (current_error_gap/current_objective_value < eps)
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return true;
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return false;
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@ -369,7 +368,8 @@ namespace dlib
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const scalar_type Cpos;
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const scalar_type Cneg;
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bool be_verbose;
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const bool be_verbose;
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const scalar_type eps;
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};
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@ -384,10 +384,11 @@ namespace dlib
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const scalar_type C_neg,
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const in_sample_vector_type& samples,
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const in_scalar_vector_type& labels,
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bool be_verbose
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const bool be_verbose,
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const scalar_type eps
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)
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{
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return oca_problem_c_svm<matrix_type, in_sample_vector_type, in_scalar_vector_type>(C_pos, C_neg, samples, labels, be_verbose);
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return oca_problem_c_svm<matrix_type, in_sample_vector_type, in_scalar_vector_type>(C_pos, C_neg, samples, labels, be_verbose, eps);
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}
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// ----------------------------------------------------------------------------------------
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@ -419,10 +420,14 @@ namespace dlib
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- #get_oca() == oca() (i.e. an instance of oca with default parameters)
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- #get_c_class1() == 1
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- #get_c_class2() == 1
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- #get_epsilon() == 0.001
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- this object will not be verbose unless be_verbose() is called
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!*/
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{
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Cpos = 1;
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Cneg = 1;
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verbose = false;
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eps = 0.001;
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}
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explicit svm_c_linear_trainer (
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@ -439,10 +444,69 @@ namespace dlib
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- #get_c_class2() == C
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!*/
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{
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// make sure requires clause is not broken
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DLIB_ASSERT(C > 0,
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"\t svm_c_linear_trainer::svm_c_linear_trainer()"
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<< "\n\t C must be greater than 0"
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<< "\n\t C: " << C
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<< "\n\t this: " << this
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);
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Cpos = C;
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Cneg = C;
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}
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void set_epsilon (
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scalar_type eps_
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)
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/*!
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requires
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- eps > 0
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ensures
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- #get_epsilon() == eps
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!*/
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{
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// make sure requires clause is not broken
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DLIB_ASSERT(eps_ > 0,
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"\t void svm_c_linear_trainer::set_epsilon()"
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<< "\n\t eps_ must be greater than 0"
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<< "\n\t eps_: " << eps_
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<< "\n\t this: " << this
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);
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eps = eps_;
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}
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const scalar_type get_epsilon (
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) const { return eps; }
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/*!
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ensures
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- returns the error epsilon that determines when training should stop.
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Smaller values may result in a more accurate solution but take longer
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to execute.
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!*/
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void be_verbose (
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)
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/*!
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ensures
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- This object will print status messages to standard out so that a
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user can observe the progress of the algorithm.
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!*/
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{
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verbose = true;
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}
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void be_quiet (
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)
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/*!
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ensures
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- this object will not print anything to standard out
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!*/
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{
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verbose = false;
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}
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void set_oca (
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const oca& item
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)
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@ -485,6 +549,14 @@ namespace dlib
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- #get_c_class2() == C
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!*/
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{
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// make sure requires clause is not broken
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DLIB_ASSERT(C > 0,
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"\t void svm_c_linear_trainer::set_c()"
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<< "\n\t C must be greater than 0"
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<< "\n\t C: " << C
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<< "\n\t this: " << this
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);
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Cpos = C;
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Cneg = C;
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}
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@ -529,6 +601,14 @@ namespace dlib
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- #get_c_class1() == C
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!*/
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{
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// make sure requires clause is not broken
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DLIB_ASSERT(C > 0,
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"\t void svm_c_linear_trainer::set_c_class1()"
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<< "\n\t C must be greater than 0"
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<< "\n\t C: " << C
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<< "\n\t this: " << this
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);
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Cpos = C;
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}
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@ -542,6 +622,14 @@ namespace dlib
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- #get_c_class2() == C
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!*/
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{
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// make sure requires clause is not broken
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DLIB_ASSERT(C > 0,
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"\t void svm_c_linear_trainer::set_c_class2()"
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<< "\n\t C must be greater than 0"
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<< "\n\t C: " << C
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<< "\n\t this: " << this
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);
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Cneg = C;
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}
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@ -570,12 +658,71 @@ namespace dlib
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- F(new_x) < 0
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!*/
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{
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scalar_type obj;
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return do_train(vector_to_matrix(x),vector_to_matrix(y),obj);
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}
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template <
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typename in_sample_vector_type,
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typename in_scalar_vector_type
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>
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const decision_function<kernel_type> train (
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const in_sample_vector_type& x,
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const in_scalar_vector_type& y,
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scalar_type& svm_objective
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) const
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/*!
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requires
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- is_binary_classification_problem(x,y) == true
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- x == a matrix or something convertible to a matrix via vector_to_matrix().
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Also, x should contain sample_type objects.
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- y == a matrix or something convertible to a matrix via vector_to_matrix().
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Also, y should contain scalar_type objects.
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ensures
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- trains a C support vector classifier given the training samples in x and
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labels in y.
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- #svm_objective == the final value of the SVM objective function
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- returns a decision function F with the following properties:
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- if (new_x is a sample predicted have +1 label) then
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- F(new_x) >= 0
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- else
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- F(new_x) < 0
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!*/
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{
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return do_train(vector_to_matrix(x),vector_to_matrix(y),svm_objective);
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}
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private:
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template <
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typename in_sample_vector_type,
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typename in_scalar_vector_type
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>
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const decision_function<kernel_type> do_train (
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const in_sample_vector_type& x,
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const in_scalar_vector_type& y,
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scalar_type& svm_objective
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) const
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{
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// make sure requires clause is not broken
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DLIB_ASSERT(is_binary_classification_problem(x,y) == true,
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"\t decision_function svm_c_linear_trainer::train(x,y)"
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<< "\n\t invalid inputs were given to this function"
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<< "\n\t x.nr(): " << x.nr()
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<< "\n\t y.nr(): " << y.nr()
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<< "\n\t x.nc(): " << x.nc()
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<< "\n\t y.nc(): " << y.nc()
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<< "\n\t is_binary_classification_problem(x,y): " << is_binary_classification_problem(x,y)
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);
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typedef matrix<scalar_type,0,1> w_type;
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w_type w;
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scalar_type obj = solver(make_oca_problem_c_svm<w_type>(Cpos, Cneg, vector_to_matrix(x), vector_to_matrix(y), true), w);
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std::cout << "final obj: "<< obj << std::endl;
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svm_objective = solver(
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make_oca_problem_c_svm<w_type>(Cpos, Cneg, x, y, verbose, eps),
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w);
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// put the solution into a decision function and then return it
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decision_function<kernel_type> df;
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@ -587,12 +734,12 @@ namespace dlib
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return df;
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}
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private:
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scalar_type Cpos;
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scalar_type Cneg;
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oca solver;
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scalar_type eps;
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bool verbose;
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};
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// ----------------------------------------------------------------------------------------
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