the integer-valued training data vectors stored as rows of a matrix
the training classification vector, where y_i = class for row i of the matrix x
the names for all features/variables
the number of classes
the names for all classes
Given a new discrete data vector z, determine which class it belongs to.
Given a new discrete data vector z, determine which class it belongs to.
the vector to classify
Given a set of data vectors and their classifications, build a classifier.
Given a set of data vectors and their classifications, build a classifier.
Given a new continuous data vector 'z', determine which class it belongs to, by first rounding it to an integer-valued vector.
Given a new continuous data vector 'z', determine which class it belongs to, by first rounding it to an integer-valued vector.
the vector to classify
Show the flaw by printing the error message.
Show the flaw by printing the error message.
the method where the error occurred
the error message
the number of data vectors in training-set (# rows)
the number of data vectors in training-set (# rows)
the training-set size as a Double
the training-set size as a Double
the number of features/variables (# columns)
the number of features/variables (# columns)
the feature-set size as a Double
the feature-set size as a Double
Test the quality of the training with a test-set and return the fraction of correct classifications.
Test the quality of the training with a test-set and return the fraction of correct classifications.
the integer-valued test vectors stored as rows of a matrix
the test classification vector, where yy_i = class for row i of xx
Return default values for binary input data (value count (vc) set to 2).
The
ClassifierIntabstract class provides a common foundation for several classifiers that operate on integer-valued data.