%PRDATASET Dataset class constructor
%
% A = PRDATASET(DATA,LABELS)
%
% INPUT
% DATA size [M,K] a set of M datavectors (objects) of length K.
% a cell array of datasets will be concatenated.
% LABELS size [M,N] array with labels for the M datavectors.
% They should be either integers or character strings.
% Choose single characters for the fastest implementation.
% Numeric labels with value NaN or character labels
% with value CHAR(0) are interpreted as missing labels.
%
% OUTPUT
% A Dataset
%
% DESCRIPTION
% This command is the class constructor for datasets. In addition to the object labels
% various other types of information can be stored in the fields of A.
% These fields are:
%
% DATA size [M,K] array (doubles) with M K-dimensional feature vectors (objects)
% FEATLAB size [K,F] array with labels for the K features
% FEATDOM size [K] cell array with domain description for the K features
% TARGETS size [M,C] dataset with soft labels or targets
% PRIOR size [C,1] prior probabilities for each of the C classes
% - PRIOR = 0: all classes have equal probability 1/C
% - PRIOR = []: all datavectors are equally probable
% COST size [C,C+1] Classification cost matrix. COST(I,J) are the costs
% of classifying an object from class I as class J.
% Column C+1 generates an alternative reject class and
% may be omitted, yielding a size of [C,C].
% An empty cost matrix, COST = [] (default) is interpreted
% as COST = ONES(C) - EYE(C) (identical costs of
% misclassification).
% LABLIST size [C,N] class labels corresponding to the unique labels found
% in LABELS and thereby to the classes in the dataset.
% The order of the items in LABLIST corresponds to the
% apriori probablities stored in PRIOR. LABLIST should
% only be given explicitely if PRIOR is given and if it
% is not equal to 0 and not empty.
% LABTYPE String defining the label type,
% 'crisp' for defining classes by integers or strings
% 'soft' for defining memberships to classes. In this
% case LABELS should be a MxC array with numbers
% between 0 and 1.
% 'targets' for defining regression type target values.
% Labels should be a MxN numeric array for
% defining N targets per object.
% OBJSIZE number of objects, or vector with its shape. This is
% useful if the set of objects can be interpreted as an
% image (objects are pixels).
% FEATSIZE number of features, or vector with its shape. This is
% useful if the set of features can be interpreted as an
% image (features are pixels).
% IDENT [M,1] Cell array, identifier for objects.
% NAME String with dataset name
% USER User definable variable
% VERSION Date and PRTOOLS version at creation
%
% The fields LABLIST, OBJSIZE, FEATSIZE, IDENT and VERSION are preset by PRTOOLS.
% The other fields can be set by the user by the below SET commands.
% All fields can be read by GET commands. By STRUCT(A) a dataset A can be
% converted to a structure. By DOUBLE(A) or +A the data can be retrieved.
% HELP DATASETS lists more information.
%
% SEE ALSO (<a href="http://37steps.com/prtools">PRTools Guide</a>)
% DATASETS, MAPPINGS