%MAPPING Mapping class constructor%%	W = MAPPING(MAPPING_FILE, MAPPING_TYPE, DATA, LABELS, SIZE_IN, SIZE_OUT)%% A map/classifier object is constructed. It may be used to map a dataset A% on another dataset B by B = mapd(A,W) or by training a mapping using an% untrained mapping W and a dataset A: V = mapd(A,W) or by modifying,% (or combining) a mapping W with another mapping V: Wnew = mapd(V,W);% These operations may also be written as B = A*W, V = A*W or Wnew = V*W.%% Note that mappings are usually not defined by PRTools users, but just by% PRTools programmers that like to add new tools for training or data% manupulation. See FISHERC or LDC for simple examples of a MAPPING construct.%% MAPPING_FILE       name of the routine used for learning or executing the%                    mapping. This routine (e.g. 'mapfile') should accept and%                    execute the following types of calls, depending on the%                    value of MAPPING_TYPE:%%    MAPPING_TYPE = 'untrained': V = mapfile(A,W) %                    for training the untrained mapping W by a dataset A,%                    resulting in a trained mapping V. This may be called as %                    V = A*W.%    MAPPING_TYPE = 'trained':   D = mapfile(B,W)%                    for mapping a dataset B by the mapping W resulting in a%                    dataset D. This may be called as D = B*W. W is the result%                    of training an untrained mapping V by a dataset A: %                    W = A*V. Consequently D = B*(A*V).%    MAPPING_TYPE = 'combiner: V2 = mapfile(V1,W), such that %                    D = B*V2 is consistent with D = B*V1*W and thereby %                    also with D = mapfile(B*V1,W).%    MAPPING_TYPE = 'fixed': D = mapfile(A,W) or D = A*W.%                    In practice there is not much difference between a%                    trained and a fixed mapping. The first is found from%                    data, the latter is defined directly by its parameters.%                   % MAPPING_TYPE       string defining the type of mapping:%                   'untrained', 'trained', "combiner' or 'fixed', see above.%                    Default is 'untrained'. MAPPING(MAPPING_FILE,DATA) is%                    equivalent to MAPPING(MAPPING_FILE,'untrained',DATA)%% DATA               Data, structure or cell array necessary for defining the%                    mapping, e.g. the weights of a neural network. DATA is%                    just used in the MAPPING_FILE for executing the mapping.% LABELS             Array with labels to be used as feature labels for the%                    dataset resulting by executing the mapping. So at least%                    as many labels as defined by SIZE_OUT has to be supplied.% SIZE_IN            Input dimensionality or size vector describing its shape,%                    e.g. in case the input space is derived from an image.%                    For a classifier SIZE_IN is the feature size.% SIZE_OUT           Output dimensionality or size vector describing its%                    shape, e.g. in case the output space should represent an%                    image. For a classifier SIZE_OUT is the number of%                    classes. Default is the number of labels in LABELS.%                    SIZE_IN and SIZE_OUT are just used for error checking.%                    If SIZE_IN is not supplied they are both set to 0 and %                    checking is skipped.%% Other parameter fields may be set to define the mapping further by%%	W = MAPPING(MAPPING_FILE, MAPPING_TYPE, DATA, LABELS, ...%                                             'field1',V1,'field2',V2, ...)% or by%%	W = MAPPING(MAPPING_FILE, MAPPING_TYPE, DATA, LABELS, SIZE_IN, ...%                                      SIZE_OUT,'field1',V1,'field2',V2, ...)%% The following fields are possible (if not set defaults are supplied):%% SCALE               Output multiplication factor. If SCALE is a scalar all%                     multiplied by it. SCALE may also be a vector with size%                     as defined by SIZE_OUT to set separate scalings for each%                     output.% OUT_CONV            0,1,2,3 for defining the desired output conversion:%                     0 - no(default), 1: SIGM, 2: NORMM or 3: SIGM and NORMM.%                     These values are set by cnormc in case of 2-class%                     discriminants (OUTCONV = 1) and by CLASSC%                     (OUT_CONV = OUT_CONV+2) to convert densities and%                     sigmoidal outputs to normalised posterior probabilities.% COST                Classification costs in case the mapping defines a%                     classifier. See SETCOST.% NAME                String with mapping name% USER                User definable variable%% All parameters are stored in fields corresponding to the above names.% Parameter fields of a given mapping may also be changed by:%%	W = SET(W,'field1',V1,'field2',V2, ...)%% They may also be set by the routines SETMAPPING_FILE, SETMAPPING_TYPE, % SETDATA, SETLABELS, SETSIZE_IN, SETSIZE_OUT, SETSIZE, SETSCALE, SETOUT_CONV,% SETCOST, SETNAME and SETUSER. Fields may be retrieved by%%	VARARGOUT = GET(W,'field1','field2', ...)%% or by the routines GETMAPPING_FILE, GETMAPPING_TYPE, GETDATA, GETSIZE_IN,% GETSIZE_OUT, GETSCALE, GETOUTCONV, GETCOST, GETNAME and GETUSER. %% See also DATASETS, MAPPINGS