%DATASET Dataset class constructor%%    A = DATASET(DATA,LABELS,VARARGIN)%% A DATASET object is constructed from:%% DATA    size [M,K], a set of M datavectors of K features%                     a cell array of datasets will be concatenated. % LABELS  size [M,N]  array with labels for the M datavectors.%                     LABELS 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.%                     See also RENUMLAB.%% Other parameter fields may be set by%%    A = DATASET(DATA,LABELS,'field1',VALUE1,'field2',VALUE2, ...)%% The following parameter fields are possible:%% FEATLAB size [K,F]  array with labels for the K features% FEATDOM size [K]    cell array with domain description for the K features% 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%% These parameters are parsed and stored in the following fields:%% A.DATA    = data% A.NLAB    = numeric labels, index in lablist% A.FEATLAB = feature labels% A.FEATDOM = feature domains% A.PRIOR   = prior probabilities% A.COST    = classification cost matrix% A.LABLIST = labels of the classes% A.TARGETS = dataset with soft labels or targets% A.LABTYPE = label type: 'crisp','soft' or 'target'% A.OBJSIZE = number of objects or vector with its shape% A.FEATSIZE= number of features or vector with its shape% A.IDENT   = identifier for objects (integer)% A.VERSION = PRTools version used for creating dataset% A.NAME    = string with name of the dataset% A.USER    = user field%% Objects of type MEASUREMENT or old DATASET definitions given by% by a structure can be converted by the DATASET constructor.%% Data can be added or changed in an existing dataset by:% SET, SETDATA, SETFEATLAB, SETFEATDOM, SETFEATSIZE, SETIDENT,% SETLABELS, SETLABLIST, SETLABTYPE, SETNAME, SETNLAB, SETOBJSIZE,% SETPRIOR, SETCOST, SETTARGETS, SETUSER.%% Data can be retrieved from a dataset by:% GET, GETDATA, GETFEATLAB, GETFEATDOM, GETFEATSIZE, GETIDENT, GETLABELS,% GETLABLIST, GETLABTYPE, GETNAME, GETNLAB, GETOBJSIZE, GETPRIOR, GETCOST,% GETSIZE, GETTARGETS, GETUSER, GETVERSION, FINDIDENT, FINDLABELS,% FINDNLAB.%% Shortcuts for retrieving the datafield A.DATA are DOUBLE(A) and +A.