Chapter 10 computing

Today, I did computing of Chapter 10.

Codes:
modelPrediction=function(x, mod)
{
  if(x[1] < 0 | x[1] > 1) return(10^38)
  if(x[2] < 0 | x[2] > 1) return(10^38)
  if(x[3] < 0 | x[3] > 1) return(10^38)
  if(x[4] < 0 | x[4] > 1) return(10^38)
  if(x[5] < 0 | x[5] > 1) return(10^38)
  if(x[6] < 0 | x[6] > 1) return(10^38)
  x=c(x, 1 - sum(x))
  if(x[7] < 0.05) return(10^38)
  tmp <- as.data.frame(t(x))
  names(tmp)=c('Cement','BlastFurnaceSlag','FlyAsh',
               'Superplasticizer','CoarseAggregate',
               'FineAggregate', 'Water')
  tmp$Age=28
  -predict(mod, tmp)
}

# Cubist mdoel
cbresults=startingvalues
cbresults$Water=NA
cbresults$Prediction=NA
cbresults
for(i in 1:nrow(cbresults))
{
  results=optim(unlist(cbresults[i,1:6]),
                     modelPrediction,
                     method = "Nelder-Mead",
                    control=list(maxit=5000),
                      mod = cbmodel)
  cbresults$Prediction[i]=-results$value
  # save the final mixture values
  cbresults[i,1:6]=results$par
}
#col.sums <- apply(x, 2, sum)  row.sums <- apply(x, 1, sum)  rbind  cbind
cbresults$Water=1 - apply(cbresults[,1:6], 1, sum)
# Keep the top three mixtures, -: decreasing order
cbresults=cbresults[order(-cbresults$Prediction),][1:3,]
cbresults$Model="Cubist"
cbresults

       Cement BlastFurnaceSlag      FlyAsh Superplasticizer CoarseAggregate FineAggregate      Water Prediction  Model
523 0.1747206       0.17969922 0.012880890     0.0027363548       0.3200175     0.2532112 0.05673427  102.44370 Cubist
495 0.2098331       0.05295034 0.006751067     0.0003692619       0.3538669     0.3238973 0.05233208   86.01767 Cubist
477 0.2110388       0.04514574 0.062017524     0.0023323792       0.3178446     0.3077021 0.05391885   85.51025 Cubist

# neural network model
nnetresults=startingvalues
nnetresults$Water=NA
nnetresults$Prediction=NA
nnetresults
for(i in 1:nrow(nnetresults))
{
  results=optim(unlist(nnetresults[i,1:6]),
                modelPrediction,
                method = "Nelder-Mead",
                control=list(maxit=5000),
                mod = nnetmodel)
  nnetresults$Prediction[i]=-results$value
  nnetresults[i,1:6]=results$par
}
nnetresults$Water=1 - apply(nnetresults[,1:6], 1, sum)
nnetresults=nnetresults[order(-nnetresults$Prediction),][1:3,]
nnetresults$Model="NNet"
nnetresults

       Cement BlastFurnaceSlag       FlyAsh Superplasticizer CoarseAggregate FineAggregate      Water Prediction Model
69  0.1617514       0.13291706 5.509921e-10      0.007644831       0.3905959     0.2564540 0.05063673   81.92797  NNet
357 0.1366392       0.04989215 2.395829e-08      0.002012726       0.4513948     0.3012054 0.05885566   80.16362  NNet
477 0.2145572       0.04306283 4.614330e-02      0.001330241       0.2993219     0.3299018 0.06568276   79.59500  NNet
(I am faced with some problems of drawing graphs. Tomorrow, I will try to fix them.)

Tomorrow, I will fix some problems of the codes.