shared_params

internal

Provides and extends the 'Fuzzy Coco' algorithm by wrapping the
'FuzzyCoCo' 'C++' Library, cf
<https://github.com/Lonza-RND-Data-Science/fuzzycoco>. 'Fuzzy Coco'
constructs systems that predict the outcome of a human decision-making
process while providing an understandable explanation of a possible
reasoning leading to it. The constructed fuzzy systems are composed
of rules and linguistic variables. This package provides a 'S3' classic
interface (fit_xy()/fit()/predict()/evaluate()) and a 'tidymodels'/'parsnip'
interface, a custom engine with custom iteration stop criterion and
progress bar support as well as a systematic implementation that do
not rely on genetic programming but rather explore all possible
combinations.

Karl Forner

Rfuzzycoco

Provides an R Interface to the 'FuzzyCoCo' C++ Library and
Extends It

shared_params function

<dl><dt>data</dt>
<dd>the data to fit as a data frame. The output variables must be grouped AFTER the input variables</dd>
<dt>engine</dt>
<dd>the fuzzy coco fit engine to use, one of rcpp and hybrid</dd>
<dt>formula</dt>
<dd>the fuzzy coco model as a formula</dd>
<dt>fuzzy_system_desc</dt>
<dd>a fuzzy system description as a named list</dd>
<dt>max_generations</dt>
<dd>The maximum number of iterations of the algorithm.
Each iteration produces a new generation of the rules and membership functions populations.</dd>
<dt>max_fitness</dt>
<dd>a stop condition: the iterations stop as soon as a generated fuzzy system fitness
reaches that threshold.</dd>
<dt>mode</dt>
<dd>the type of model, either classification or regression</dd>
<dt>model</dt>
<dd>a Fuzzy Coco model as a <code>fuzzy_coco</code> object</dd>
<dt>params</dt>
<dd>fuzzy coco parameters, as a recursive named list, cf <code>params()</code></dd>
<dt>progress</dt>
<dd>whether to display the computation progress (using progressr, if available)</dd>
<dt>seed</dt>
<dd>the RNG seed to use (to fit the model)</dd>
<dt>until</dt>
<dd>function that takes an <code>engine</code> and returns TRUE if and only if the evolution must stop.
It is a way for the user to customize the stop conditions of the algorithm.</dd>
<dt>verbose</dt>
<dd>whether to be verbose</dd>
<dt>x</dt>
<dd>the input variables data (usually to fit) as a dataframe</dd>
<dt>y</dt>
<dd>the output variables data (usually to fit) as a dataframe</dd></dl>

Arguments

shared params — shared_params

<dl>

<dt>data</dt>
<dd>the data to fit as a data frame. The output variables must be grouped AFTER the input variables</dd>


<dt>engine</dt>
<dd>the fuzzy coco fit engine to use, one of rcpp and hybrid</dd>


<dt>formula</dt>
<dd>the fuzzy coco model as a formula</dd>


<dt>fuzzy_system_desc</dt>
<dd>a fuzzy system description as a named list</dd>


<dt>max_generations</dt>
<dd>The maximum number of iterations of the algorithm.
Each iteration produces a new generation of the rules and membership functions populations.</dd>


<dt>max_fitness</dt>
<dd>a stop condition: the iterations stop as soon as a generated fuzzy system fitness
reaches that threshold.</dd>


<dt>mode</dt>
<dd>the type of model, either classification or regression</dd>


<dt>model</dt>
<dd>a Fuzzy Coco model as a <code>fuzzy_coco</code> object</dd>


<dt>params</dt>
<dd>fuzzy coco parameters, as a recursive named list, cf <code>params()</code></dd>


<dt>progress</dt>
<dd>whether to display the computation progress (using progressr, if available)</dd>


<dt>seed</dt>
<dd>the RNG seed to use (to fit the model)</dd>


<dt>until</dt>
<dd>function that takes an <code>engine</code> and returns TRUE if and only if the evolution must stop.
It is a way for the user to customize the stop conditions of the algorithm.</dd>


<dt>verbose</dt>
<dd>whether to be verbose</dd>


<dt>x</dt>
<dd>the input variables data (usually to fit) as a dataframe</dd>


<dt>y</dt>
<dd>the output variables data (usually to fit) as a dataframe</dd>

</dl>

shared_params: shared params

Description

Arguments