jimapps

gar.fun<-function(out.var,mod.in,bar.plot=T,struct=NULL,x.lab=NULL,
                  y.lab=NULL, wts.only = F){
  
  require(ggplot2)
  require(plyr)
  
  # function works with neural networks from neuralnet, nnet, and RSNNS package
  # manual input vector of weights also okay
  
  #sanity checks

jimapps

plot.nnet<-function(mod.in,nid=T,all.out=T,all.in=T,bias=T,wts.only=F,rel.rsc=5,
                    circle.cex=5,node.labs=T,var.labs=T,x.lab=NULL,y.lab=NULL,
                    line.stag=NULL,struct=NULL,cex.val=1,alpha.val=1,
                    circle.col='lightblue',pos.col='black',neg.col='grey',
                    bord.col='lightblue', max.sp = F,...){

  require(scales)
  
  #sanity checks
  if('mlp' %in% class(mod.in)) warning('Bias layer not applicable for rsnns object')

jimapps

#!/usr/bin/perl

# Copyright (c) 2015, Paul Ford, ford@ftrain.com
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# 1. Redistributions of source code must retain the above copyright