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set |

Set object properties

Syntax

    set(m,'AttrName1',AttrValue1,'AttrName2',AttrValue2,...)

    m_out = set(m,'AttrName1',AttrValue1,'AttrName2',AttrValue2,...)

Description

`set(m,'AttrName1',AttrValue1,'AttrName2',AttrValue2,...)` sets the value of the attribute `AttrName1`, `AttrName2`, ... associated to the object `m`.

`m_out = set(m,'AttrName1',AttrValue1,'AttrName2',AttrValue2,...)` sets the value of the attribute `AttrName1`, `AttrName2`, ... associated to the object `m` and return the modified model inside m_out. The original model is not modified. The following `AttrName` inherided from the mapping class are recognised:

- `name` sets the name of the object
- `n_in` sets the number of inputs of the model. This sets the saturation level of the inputs to -Inf, Inf.
- `n_out` sets the number of outputs of the model
- `userData` sets the 'userData' field
- `opt` sets the 'opt' field
- `date` sets the date of creation of the object
- `limits` sets the limits of the domain of definition of the lazy. The value must be a `2*n_in` matrix where the upper and lever saturation level are defined.
- `optimparams` sets the indices of the parameters to be optimised

The following codes specific to the lazy class are recognised:

- `id_par` identification parameters
- `cmb_par` model combination parameters

The identification parameter can assume the following forms:

              id_par[3,3]         | idm0  idM0 valM0 |
                                  | idm1  idM1 valM1 |
                                  | idm2  idM2 valM2 |

where [idmX,idMX] is the range in which the best number of neighbors is searched when identifying the local model of degree X and where valMX is the maximum number of neighbors used in validation for the model of degree X. This means that the model of degree X identified with k neighbors, is validated un the first v neighbors, where v=min(k,valMX).

              id_par[3,2]         | idm0  idM0 |
                                  | idm1  idM1 |
                                  | idm2  idM2 |

where idmX and idMX have the same role they have in point 1, and valMX is by default set to idMX: each model is validated on all the neighbors used in identification.

              id_par[3,1]         | c0 |
                                  | c1 |
                                  | c2 |

Here idmX and idMX are obtained according to the following fotmulas:

        idmX = 3 * no_parX * cX     idMX = 5 * no_parX * cX

where no_parX is the number of parameter of the model of degree X. Recommended choice: cX = 1. As far as the valMX are concerned, they get the default value as in point 2. The default value for cmb_par is 1 as it will be clear from what follows. If given, cmb_par can assume the following to forms:

              cmb_par[3,1]        | cmb0 |
                                  | cmb1 |
                                  | cmb2 |

where cmbX is the number of models of degree X that will be included in the local combination. Each local model will be therfore a combination of "the best cmb0 models of degree 0", "the best cmb1 models of degree 1", and "the best cmb2 models of degree 1" identified as specified by id_par.

              cmb_par[1,1]        | cmb |

where cmb is the number of models that will be combined, disregarding any constraint on the degree of the models that will be considered. Each local model will be therfore a combination of "the best cmb models", identified as specified by id_par.

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