Mapping definition

Mapping definition

Mappings are typically defined inside functions that compute or apply a mapping, e.g. inside classifiers or routines for feature reduction. This section is thereby primarily of relevance for advanced PRTools users who want to extend the set of available mappings. Starting users or those who focus on applications applying given procedures may skip it, or just have a quick look at the below table which describes all fields of a mapping variable.

The mapping constructor looks like

    W = mapping(command,type,data,labels,size_in,size_out)

• command is a string with the name of the mapping (and thereby also the name of the m-file) that should be called to execute commands like B = A*W, in which A is usually a dataset, and occasionally another mapping.
• type is the mapping type, 'fixed', 'untrained', 'trained' or 'combiner'. See the description op mapping types.
• data is a structure or a cell array of all information that the specific procedure (name) expects and needs to perform the mapping.
• labels is a set of strings or numbers that will be used to annotate the resulting dataset B with feature labels (to be stored in its featlab field). The typical use is that labels stores the class names of A (its lablist field) in case of a classifier.
• size_in is the dimension of the input space (number of features of A).
• size_out is the dimension of the output space (number of features of B).

Consequently, if the dataset A has a size of [m,size_in] and W has a size of [size_in,size_out], then the mapping B will have a size of [m,size_out]. Thereby B = A*W is consistent with a Matlab expression in which B, A and W are matrices of the corresponding sizes.

PRTools uses the values of size_in and size_out mainly for error detection and returning appropriate error messages. In many cases the values may be replaced by 0, e.g. when the sizes are unknown at the moment of defining the mapping. This will not effect a correct execution, but may result in badly understandable error messages in case a dataset A of a wrong size is used for input.

A full list of all information stored in a mapping can be found by converting a mapping in a structure.

   a = iris;
   w = pca(a,2);
   struct(w)
%    mapping_file: 'affine'
%    mapping_type: 'trained'
%            data: [1x1 struct]
%          labels: [2x1 double]
%         size_in: 4
%        size_out: 2
%           scale: 1
%        out_conv: 0
%            cost: []
%            name: 'PCA to 2D'
%            user: []
%         version: {[1x1 struct]  '07-Apr-2011 15:21:01'}

All fields have a corresponding set-command (e.g. setdata) to store it and a get-command (e.g. getdata) to retrieve it. In some cases not the exact fields are retrieved but some derived data. In the table more information is given.

The mapping fields might also be accessed by structural indexing.

Mapping definition