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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)

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);
%    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 fields of the mapping structure


The name of the command (m-file) that executes the mapping.
mapping_type The type of the mapping: fixed, untrained, trained or combiner.
data A structure or a cell array with all information needed to execute the mapping.
labels Array with features used to annotate the features (featlab) of the output dataset.
size_in Input dimensionality. Number of features of the input dataset.
size_out Output dimensionality. Number of features of the output dataset.
scale A scalar or a vector to scale the output features.
out_conv Type of desired output conversion.
cost Classification costs in case the mapping defines a classifier.
name String with a name, just used for annotation of plots and other outputs.
user User defined field.
version PRTools version number and date of creating the mapping.

The mapping fields might also be accessed by structural indexing.

R.P.W. Duin, January 28, 2013

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