interpretDiscreteGA: Interpreter of output of discrete genetic algorithm

Description

This function takes the integer string output by the discrete genetic algorithm for training a cFL model and generates the corresponding model based on the Fuzzy parameters.

Usage

interpretDiscreteGA(model,  paramsList, intString, bitString=NULL)

Arguments

model

PKN trained (same model input as to gaDiscrete).

paramsList

List containing parameters (see defaultParametersFuzzy). Only the fuzzy parameters are used.

intString

Integer string resulting from gaDiscrete (in bString field of gaDiscreteT1 output).

bitString

(optional) if you want to cut additional interactions from the model. Used in reduceFuzzy function.

Value

model: The selected initial model based on the provided bitstring.
simList: The corresponding data related to Model field
bitString: The bitstring corresponding to the Model field
cutModel: Same as Model but redundant reactions are also removed.
cutSimList: The corresponding data related to cutModel field
cutBitString: The corresponding bitstring related to cutModel field.

Details

After the discreteGA chooses transfer functions from a discrete set of transfer functions and removing interactions inconsistent with the data, this function interprets the output and returns an actual model using these transfer functions as well as a model from which logical redundancy was cut.

Examples

Run this code


    data(ToyModel, package="CellNOptR")
    data(CNOlistToy,package="CellNOptR")
    paramsList = defaultParametersFuzzy()
    ## Not run: 
# 
#         # preprocessing (see CNORwrapFuzzy or gaDiscreteT1)
#         T1opt = gaDiscreteT1(....)  # see CNORwrapFuzzy or gaDiscreteT1 for details
#         interpretDiscreteGA(ToyModel, paramsList, T1opt$bString)
# ## End(Not run)