ECOCPAK v0.9
Functions
Fn_ecoc_one

Functions

void ecocpak::one_vs_all_ecocone (vector< ClassData > &classes_vector, const int classifiers_type, imat &coding_matrix, vector< Classifier * > &classifiers_vector)
void ecocpak::one_vs_one_ecocone (vector< ClassData > &classes_vector, const int classifiers_type, imat &coding_matrix, vector< Classifier * > &classifiers_vector)
void ecocpak::decoc_ecocone (vector< ClassData > &classes_vector, const int classifiers_type, const int criterion_option, imat &coding_matrix, vector< Classifier * > &classifiers_vector)
void ecocpak::subdecoc_ecocone (vector< ClassData > &classes_vector, const int classifiers_type, const int criterion_option, const Threshold &thres, imat &coding_matrix, vector< Classifier * > &classifiers_vector)
void ecocpak::dense_random_ecocone (vector< ClassData > &classes_vector, const int classifiers_type, const u32 n_matrices, const u32 n_desired_classifiers, imat &coding_matrix, vector< Classifier * > &classifiers_vector)
void ecocpak::sparse_random_ecocone (vector< ClassData > &classes_vector, const int classifiers_type, const u32 n_matrices, const u32 n_desired_classifiers, imat &coding_matrix, vector< Classifier * > &classifiers_vector)
u32 ecocpak::ecoc_one (const mat &training_samples, const icolvec &training_labels, const mat &testing_samples, const icolvec &testing_labels, const Threshold &thres, const int decoding_strategy, const int classifiers_type, const int criterion_option, const u32 n_matrices, const u32 n_desired_classifiers, const double validation, const int init_coding_strategy, const int ecocone_mode, const u32 max_iter, const double epsilon, const double wv, const bool verbose, ofstream &verbose_output, double &elapsed_time)
 ECOC One training and testing procedure.
void one_vs_all_ecocone (vector< ClassData > &classes_vector, const int classifiers_type, imat &coding_matrix, vector< Classifier * > &classifiers_vector)
void one_vs_one_ecocone (vector< ClassData > &classes_vector, const int classifiers_type, imat &coding_matrix, vector< Classifier * > &classifiers_vector)
void decoc_ecocone (vector< ClassData > &classes_vector, const int classifiers_type, const int criterion_option, imat &coding_matrix, vector< Classifier * > &classifiers_vector)
void subdecoc_ecocone (vector< ClassData > &classes_vector, const int classifiers_type, const int criterion_option, const Threshold &thres, imat &coding_matrix, vector< Classifier * > &classifiers_vector)
void dense_random_ecocone (vector< ClassData > &classes_vector, const int classifiers_type, const u32 n_matrices, const u32 n_desired_classifiers, imat &coding_matrix, vector< Classifier * > &classifiers_vector)
void sparse_random_ecocone (vector< ClassData > &classes_vector, const int classifiers_type, const u32 n_matrices, const u32 n_desired_classifiers, imat &coding_matrix, vector< Classifier * > &classifiers_vector)
u32 ecoc_one (const mat &training_samples, const icolvec &training_labels, const mat &testing_samples, const icolvec &testing_labels, const Threshold &thres, const int decoding_strategy, const int classifiers_type, const int criterion_option, const u32 n_matrices, const u32 n_desired_classifiers, const double validation, const int init_coding_strategy, const int ecocone_mode, const u32 max_iter, const double epsilon, const double wv, const bool verbose, ofstream &verbose_output, double &elapsed_time)
 ECOC One training and testing procedure.

Function Documentation

void ecocpak::decoc_ecocone ( vector< ClassData > &  classes_vector,
const int  classifiers_type,
const int  criterion_option,
imat &  coding_matrix,
vector< Classifier * > &  classifiers_vector 
)
  • ECOC One DECOC initial coding.
  • Input Arguments:
    • classes_vector : Vector of ClassData objects.
    • classifiers_type : Type of classifier.
    • criterion_option : Criterion option.
  • Output Arguments:
    • coding_matrix : Coding matrix.
    • classifiers_vector : Vector of classifiers objects.
  • Return Arguments:
    • Void.
void decoc_ecocone ( vector< ClassData > &  classes_vector,
const int  classifiers_type,
const int  criterion_option,
imat &  coding_matrix,
vector< Classifier * > &  classifiers_vector 
)
  • ECOC One DECOC initial coding.
  • Input Arguments:
    • classes_vector : Vector of ClassData objects.
    • classifiers_type : Type of classifier.
    • criterion_option : Criterion option.
  • Output Arguments:
    • coding_matrix : Coding matrix.
    • classifiers_vector : Vector of classifiers objects.
  • Return Arguments:
    • Void.
void ecocpak::dense_random_ecocone ( vector< ClassData > &  classes_vector,
const int  classifiers_type,
const u32  n_matrices,
const u32  n_desired_classifiers,
imat &  coding_matrix,
vector< Classifier * > &  classifiers_vector 
)
  • ECOC One Dense random initial coding.
  • Input Arguments:
    • classes_vector : Vector of ClassData objects.
    • classifiers_type : Type of classifier.
    • n_matrices : Number of valid dense random coding matrices to be produced.
    • n_desired_classifiers : Number of desired classifiers (i.e., number of coding matrix columns).
  • Output Arguments:
    • coding_matrix : Coding matrix.
    • classifiers_vector : Vector of classifier objects.
  • Return Arguments:
    • Void.
void dense_random_ecocone ( vector< ClassData > &  classes_vector,
const int  classifiers_type,
const u32  n_matrices,
const u32  n_desired_classifiers,
imat &  coding_matrix,
vector< Classifier * > &  classifiers_vector 
)
  • ECOC One Dense random initial coding.
  • Input Arguments:
    • classes_vector : Vector of ClassData objects.
    • classifiers_type : Type of classifier.
    • n_matrices : Number of valid dense random coding matrices to be produced.
    • n_desired_classifiers : Number of desired classifiers (i.e., number of coding matrix columns).
  • Output Arguments:
    • coding_matrix : Coding matrix.
    • classifiers_vector : Vector of classifier objects.
  • Return Arguments:
    • Void.
u32 ecocpak::ecoc_one ( const mat &  training_samples,
const icolvec &  training_labels,
const mat &  testing_samples,
const icolvec &  testing_labels,
const Threshold &  thres,
const int  decoding_strategy,
const int  classifiers_type,
const int  criterion_option,
const u32  n_matrices,
const u32  n_desired_classifiers,
const double  validation,
const int  init_coding_strategy,
const int  ecocone_mode,
const u32  max_iter,
const double  epsilon,
const double  wv,
const bool  verbose,
ofstream &  verbose_output,
double &  elapsed_time 
)

ECOC One training and testing procedure.

  • Input Arguments:
    • training_samples : A 2D input matrix where its rows represent the training sample vectors.
    • training_labels : The respective class labels of the training_samples input matrix.
    • testing_samples : A 2D input matrix where its rows represent the testing sample vectors.
    • testing_labels : The respective class labels of the testing_samples input matrix.
    • thres : Threshold object.
    • decoding_strategy : User's option specifying the type of decoding strategy that will be used.
    • classifiers_type : Type of classifier.
    • criterion_option : Criterion option (FQMI, FLDR or Custom).
    • n_matrices : Number of valid sparse random coding matrices to be produced.
    • n_desired_classifiers : Number of desired classifiers (i.e., number of coding matrix columns).
    • validation : Percentage of validation set in (0,1).
    • init_coding_strategy : Initial coding strategy.
    • ecocone_mode : ECOC One mode.
    • max_iter : Maximum number of column added.
    • epsilon : Epsilon.
    • wv : Validation error weight.
  • Output Arguments:
    • coding_matrix : Coding matrix.
    • classifiers_vector : Vector of classifiers objects.
    • execution_time : Execution time.
  • Return Argument:
    • Number of misclassified test samples.
u32 ecoc_one ( const mat &  training_samples,
const icolvec &  training_labels,
const mat &  testing_samples,
const icolvec &  testing_labels,
const Threshold &  thres,
const int  decoding_strategy,
const int  classifiers_type,
const int  criterion_option,
const u32  n_matrices,
const u32  n_desired_classifiers,
const double  validation,
const int  init_coding_strategy,
const int  ecocone_mode,
const u32  max_iter,
const double  epsilon,
const double  wv,
const bool  verbose,
ofstream &  verbose_output,
double &  elapsed_time 
)

ECOC One training and testing procedure.

  • Input Arguments:
    • training_samples : A 2D input matrix where its rows represent the training sample vectors.
    • training_labels : The respective class labels of the training_samples input matrix.
    • testing_samples : A 2D input matrix where its rows represent the testing sample vectors.
    • testing_labels : The respective class labels of the testing_samples input matrix.
    • thres : Threshold object.
    • decoding_strategy : User's option specifying the type of decoding strategy that will be used.
    • classifiers_type : Type of classifier.
    • criterion_option : Criterion option (FQMI, FLDR or Custom).
    • n_matrices : Number of valid sparse random coding matrices to be produced.
    • n_desired_classifiers : Number of desired classifiers (i.e., number of coding matrix columns).
    • validation : Percentage of validation set in (0,1).
    • init_coding_strategy : Initial coding strategy.
    • ecocone_mode : ECOC One mode.
    • max_iter : Maximum number of column added.
    • epsilon : Epsilon.
    • wv : Validation error weight.
  • Output Arguments:
    • coding_matrix : Coding matrix.
    • classifiers_vector : Vector of classifiers objects.
    • execution_time : Execution time.
  • Return Argument:
    • Number of misclassified test samples.
void one_vs_all_ecocone ( vector< ClassData > &  classes_vector,
const int  classifiers_type,
imat &  coding_matrix,
vector< Classifier * > &  classifiers_vector 
)
  • ECOC One one versus all initial coding.
  • Input Arguments:
    • classes_vector : Vector of ClassData objects.
    • classifiers_type : Type of classifier.
  • Output Arguments:
    • coding_matrix : Coding matrix.
    • classifiers_vector : Vector of classifier objects.
  • Return Arguments:
    • Void.
void ecocpak::one_vs_all_ecocone ( vector< ClassData > &  classes_vector,
const int  classifiers_type,
imat &  coding_matrix,
vector< Classifier * > &  classifiers_vector 
)
  • ECOC One one versus all initial coding.
  • Input Arguments:
    • classes_vector : Vector of ClassData objects.
    • classifiers_type : Type of classifier.
  • Output Arguments:
    • coding_matrix : Coding matrix.
    • classifiers_vector : Vector of classifier objects.
  • Return Arguments:
    • Void.
void one_vs_one_ecocone ( vector< ClassData > &  classes_vector,
const int  classifiers_type,
imat &  coding_matrix,
vector< Classifier * > &  classifiers_vector 
)
  • ECOC One one versus one initial coding.
  • Input Arguments:
    • classes_vector : Vector of ClassData objects.
    • classifiers_type : Type of classifier.
  • Output Arguments:
    • coding_matrix : Coding matrix.
    • classifiers_vector : Vector of classifier objects.
  • Return Arguments:
    • Void.
void ecocpak::one_vs_one_ecocone ( vector< ClassData > &  classes_vector,
const int  classifiers_type,
imat &  coding_matrix,
vector< Classifier * > &  classifiers_vector 
)
  • ECOC One one versus one initial coding.
  • Input Arguments:
    • classes_vector : Vector of ClassData objects.
    • classifiers_type : Type of classifier.
  • Output Arguments:
    • coding_matrix : Coding matrix.
    • classifiers_vector : Vector of classifier objects.
  • Return Arguments:
    • Void.
void sparse_random_ecocone ( vector< ClassData > &  classes_vector,
const int  classifiers_type,
const u32  n_matrices,
const u32  n_desired_classifiers,
imat &  coding_matrix,
vector< Classifier * > &  classifiers_vector 
)
  • Ecoc One Sparse random initial coding.
  • Input Arguments:
    • classes_vector : Vector of ClassData objects.
    • classifiers_type : Type of classifier.
    • n_matrices : Number of valid sparse random coding matrices to be produced.
    • n_desired_classifiers : Number of desired classifiers (i.e., number of coding matrix columns).
  • Output Arguments:
    • coding_matrix : Coding matrix.
    • classifiers_vector : Vector of classifiers objects.
  • Return Arguments:
    • Void.
void ecocpak::sparse_random_ecocone ( vector< ClassData > &  classes_vector,
const int  classifiers_type,
const u32  n_matrices,
const u32  n_desired_classifiers,
imat &  coding_matrix,
vector< Classifier * > &  classifiers_vector 
)
  • Ecoc One Sparse random initial coding.
  • Input Arguments:
    • classes_vector : Vector of ClassData objects.
    • classifiers_type : Type of classifier.
    • n_matrices : Number of valid sparse random coding matrices to be produced.
    • n_desired_classifiers : Number of desired classifiers (i.e., number of coding matrix columns).
  • Output Arguments:
    • coding_matrix : Coding matrix.
    • classifiers_vector : Vector of classifiers objects.
  • Return Arguments:
    • Void.
void ecocpak::subdecoc_ecocone ( vector< ClassData > &  classes_vector,
const int  classifiers_type,
const int  criterion_option,
const Threshold &  thres,
imat &  coding_matrix,
vector< Classifier * > &  classifiers_vector 
)
  • ECOC One subDECOC initial coding.
  • Input Arguments:
    • classes_vector : Vector of ClassData objects.
    • classifiers_type : Type of classifier.
    • criterion_option : Criterion option.
    • thres : Threshold object.
  • Output Arguments:
    • coding_matrix : Coding matrix.
    • classifiers_vector : Vector of classifier objects.
  • Return Arguments:
    • Void.
void subdecoc_ecocone ( vector< ClassData > &  classes_vector,
const int  classifiers_type,
const int  criterion_option,
const Threshold &  thres,
imat &  coding_matrix,
vector< Classifier * > &  classifiers_vector 
)
  • ECOC One subDECOC initial coding.
  • Input Arguments:
    • classes_vector : Vector of ClassData objects.
    • classifiers_type : Type of classifier.
    • criterion_option : Criterion option.
    • thres : Threshold object.
  • Output Arguments:
    • coding_matrix : Coding matrix.
    • classifiers_vector : Vector of classifier objects.
  • Return Arguments:
    • Void.
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