TrainIBHM

Description

Trains an IBHM model using training data with input variables x and a single output variable y.

Usage

TrainIBHM(x, y, config = ConfigureIBHM())

Arguments

Value

The created model which is an object of class "IBHM".

References

Zawistowski, P. and Arabas, J.: "Benchmarking IBHM method using NN3 competition dataset." In Proc. 6th int. conf. on Hybrid artificial intelligent systems - Vol. 1, HAIS'11, pp 263–270, 2011. Springer-Verlag.

See Also

ConfigureIBHM,summary.IBHM,predict.IBHM

Examples

  # Training data
  x <- seq(-3,3,length.out=400)
  y <- tanh(x)
  
  # A held out validation set for the stop criterion
  x.val <- runif(50,min=-6,max=6)
  y.val <- tanh(x.val)
  
  
  # Training the model using the validation set to prevent overfitting
  m <- TrainIBHM(x,y, 
            ConfigureIBHM(stop.criterion = ValidationSC(x.val, y.val))
       )
                      
  summary(m)
  plot(y.val,predict(m,x.val),asp=1)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(IBHM)
Loading required package: compiler
Loading required package: DEoptim

DEoptim package
Differential Evolution algorithm in R
Authors: D. Ardia, K. Mullen, B. Peterson and J. Ulrich

Loading required package: cmaes
Loading required package: Rcpp
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/IBHM/TrainIBHM.Rd_%03d_medium.png", width=480, height=480)
> ### Name: TrainIBHM
> ### Title: TrainIBHM
> ### Aliases: TrainIBHM
> ### Keywords: ~models ~regression ~nonlinear
> 
> ### ** Examples
> 
>   # Training data
>   x <- seq(-3,3,length.out=400)
>   y <- tanh(x)
>   
>   # A held out validation set for the stop criterion
>   x.val <- runif(50,min=-6,max=6)
>   y.val <- tanh(x.val)
>   
>   
>   # Training the model using the validation set to prevent overfitting
>   m <- TrainIBHM(x,y, 
+             ConfigureIBHM(stop.criterion = ValidationSC(x.val, y.val))
+        )
Note: no visible binding for global variable '.refClassDef' 
Note: no visible binding for global variable '.refClassDef' 
Note: no visible binding for global variable '.pointer' 
Note: no visible binding for global variable '.pointer' 
Note: no visible binding for global variable '.pointer' 
>                       
>   summary(m)
Model equation:  1.00e+00  -2.00e+00 logsig  ( 2.73e+00 * dot.pr (x,[ 9.48e-01 7.32e-01 ]) + -2.59e+00 )  
 Model size:  1 

 Train set dim:  1  Train set size:  400 
 MSE:   3.754636e-14  Std. dev: 
 RMSE:  1.937688e-07 
 Pearson correlation coefficient:  1 
>   plot(y.val,predict(m,x.val),asp=1)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
> 
Note: no visible binding for global variable '.pointer'