ECOCPAK v0.9
Functions
Fn_loss_weighted_decoding

Functions

u32 ecocpak::linear_loss_weighted_decoding (const vector< Classifier * > &classifiers_vector, const vector< ClassData > &classes_vector, const imat &ecoc_matrix, const mat &test_set_samples, const uvec &test_set_labels, uvec &results, umat &confussion)
u32 ecocpak::exponential_loss_weighted_decoding (const vector< Classifier * > &classifiers_vector, const vector< ClassData > &classes_vector, const imat &ecoc_matrix, const mat &test_set_samples, const uvec &test_set_labels, uvec &results, umat &confussion)
u32 linear_loss_weighted_decoding (const vector< Classifier * > &classifiers_vector, const vector< ClassData > &classes_vector, const imat &ecoc_matrix, const mat &test_set_samples, const uvec &test_set_labels, uvec &results, umat &confussion)
u32 exponential_loss_weighted_decoding (const vector< Classifier * > &classifiers_vector, const vector< ClassData > &classes_vector, const imat &ecoc_matrix, const mat &test_set_samples, const uvec &test_set_labels, uvec &results, umat &confussion)

Function Documentation

u32 ecocpak::exponential_loss_weighted_decoding ( const vector< Classifier * > &  classifiers_vector,
const vector< ClassData > &  classes_vector,
const imat &  ecoc_matrix,
const mat &  test_set_samples,
const uvec &  test_set_labels,
uvec &  results,
umat &  confussion 
)
  • Decodes the coding matrix and evaluates the testing error using Exponential Loss Weighted Decoding.
  • Input Arguments:
    • classifiers_vector : Vector of classifiers.
    • classes_vector : Vector of classes.
    • ecoc_matrix : Coding matrix.
    • test_set_samples : 2D array of test samples.
    • test_set_labels : Labels vector of test samples.
    • classifiers_option : Type of classifier.
  • Outputs Arguments:
    • Void.
  • Return Argument:
    • Integral value which denotes the number of misclassified test samples.
u32 exponential_loss_weighted_decoding ( const vector< Classifier * > &  classifiers_vector,
const vector< ClassData > &  classes_vector,
const imat &  ecoc_matrix,
const mat &  test_set_samples,
const uvec &  test_set_labels,
uvec &  results,
umat &  confussion 
)
  • Decodes the coding matrix and evaluates the testing error using Exponential Loss Weighted Decoding.
  • Input Arguments:
    • classifiers_vector : Vector of classifiers.
    • classes_vector : Vector of classes.
    • ecoc_matrix : Coding matrix.
    • test_set_samples : 2D array of test samples.
    • test_set_labels : Labels vector of test samples.
    • classifiers_option : Type of classifier.
  • Outputs Arguments:
    • Void.
  • Return Argument:
    • Integral value which denotes the number of misclassified test samples.
u32 linear_loss_weighted_decoding ( const vector< Classifier * > &  classifiers_vector,
const vector< ClassData > &  classes_vector,
const imat &  ecoc_matrix,
const mat &  test_set_samples,
const uvec &  test_set_labels,
uvec &  results,
umat &  confussion 
)
  • Decodes the coding matrix and evaluates the testing error using Linear Loss Weighted Decoding.
  • Input Arguments:
    • classifiers_vector : Vector of classifiers.
    • classes_vector : Vector of classes.
    • ecoc_matrix : Coding matrix.
    • test_set_samples : 2D matrix of test samples
    • test_set_labels : Labels vector of test samples.
    • classifiers_option : Type of classifier.
  • Outputs Arguments:
    • Void.
  • Return Argument:
    • Integral value which denotes the number of misclassified test samples.
u32 ecocpak::linear_loss_weighted_decoding ( const vector< Classifier * > &  classifiers_vector,
const vector< ClassData > &  classes_vector,
const imat &  ecoc_matrix,
const mat &  test_set_samples,
const uvec &  test_set_labels,
uvec &  results,
umat &  confussion 
)
  • Decodes the coding matrix and evaluates the testing error using Linear Loss Weighted Decoding.
  • Input Arguments:
    • classifiers_vector : Vector of classifiers.
    • classes_vector : Vector of classes.
    • ecoc_matrix : Coding matrix.
    • test_set_samples : 2D matrix of test samples
    • test_set_labels : Labels vector of test samples.
    • classifiers_option : Type of classifier.
  • Outputs Arguments:
    • Void.
  • Return Argument:
    • Integral value which denotes the number of misclassified test samples.
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