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)

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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.