func function(config *config.Config) error {
output, err := database.Create(*outputFile)
if err != nil {
return err
}
defer output.Close()
system, err := system.New(&config.System)
if err != nil {
return err
}
var anuncertainty uncertainty.Uncertainty
if config.Solution.Aleatory {
anuncertainty, err = uncertainty.NewAleatory(system, &config.Uncertainty)
} else {
anuncertainty, err = uncertainty.NewEpistemic(system, &config.Uncertainty)
}
if err != nil {
return err
}
aquantity, err := quantity.New(system, anuncertainty, &config.Quantity)
if err != nil {
return err
}
ni, no := aquantity.Dimensions()
index, err := detect(ni, *parameterIndex)
if err != nil {
return err
}
points, err := generate(ni, *nodeCount, config.Solution.Rule, index)
if err != nil {
return err
}
np := uint(len(points)) / ni
log.Println(system)
log.Println(aquantity)
var values []float64
if len(*approximateFile) > 0 {
approximate, err := database.Open(*approximateFile)
if err != nil {
return err
}
defer approximate.Close()
asolution, err := solution.New(ni, no, &config.Solution)
if err != nil {
return err
}
surrogate := new(solution.Surrogate)
if err = approximate.Get("surrogate", surrogate); err != nil {
return err
}
log.Printf("Evaluating the approximation at %d points...\n", np)
values = asolution.Evaluate(surrogate, points)
} else {
log.Printf("Evaluating the original model at %d points...\n", np)
values = quantity.Invoke(aquantity, points)
}
if err := output.Put("values", values, no, np); err != nil {
return err
}
if err := output.Put("points", points, ni, np); err != nil {
return err
}
return nil
}
func function(config *config.Config) error {
if len(*sampleSeed) > 0 {
if number, err := strconv.ParseInt(*sampleSeed, 0, 64); err != nil {
return err
} else {
config.Assessment.Seed = number
}
}
if len(*sampleCount) > 0 {
if number, err := strconv.ParseUint(*sampleCount, 0, 64); err != nil {
return err
} else {
config.Assessment.Samples = uint(number)
}
}
if config.Assessment.Samples == 0 {
return errors.New("the number of samples should be positive")
}
approximate, err := database.Open(*approximateFile)
if err != nil {
return err
}
defer approximate.Close()
output, err := database.Create(*outputFile)
if err != nil {
return err
}
defer output.Close()
system, err := system.New(&config.System)
if err != nil {
return err
}
auncertainty, err := uncertainty.NewAleatory(system, &config.Uncertainty)
if err != nil {
return err
}
aquantity, err := quantity.New(system, auncertainty, &config.Quantity)
if err != nil {
return err
}
euncertainty, err := uncertainty.NewEpistemic(system, &config.Uncertainty)
if err != nil {
return err
}
equantity, err := quantity.New(system, euncertainty, &config.Quantity)
if err != nil {
return err
}
var target, proxy quantity.Quantity
if config.Solution.Aleatory {
target, proxy = aquantity, aquantity // noop
} else {
target, proxy = equantity, aquantity
}
ni, no := target.Dimensions()
ns := config.Assessment.Samples
surrogate := new(solution.Surrogate)
if err = approximate.Get("surrogate", surrogate); err != nil {
return err
}
solution, err := solution.New(ni, no, &config.Solution)
if err != nil {
return err
}
points := generate(target, proxy, ns, config.Assessment.Seed)
log.Printf("Evaluating the surrogate model at %d points...\n", ns)
log.Printf("%5s %15s\n", "Step", "Nodes")
nk := uint(len(surrogate.Active))
cumsum := append([]uint(nil), surrogate.Active...)
for i := uint(1); i < nk; i++ {
cumsum[i] += cumsum[i-1]
}
indices := choose.UniformUint(cumsum, maxSteps)
nk = uint(len(indices))
active := make([]uint, nk)
for i := uint(0); i < nk; i++ {
active[i] = cumsum[indices[i]]
}
values := make([]float64, 0, ns*no)
for i := uint(0); i < nk; i++ {
log.Printf("%5d %15d\n", i, active[i])
s := *surrogate
s.Nodes = active[i]
s.Indices = s.Indices[:active[i]*ni]
s.Surpluses = s.Surpluses[:active[i]*no]
if !solution.Validate(&s) {
panic("something went wrong")
}
values = append(values, solution.Evaluate(&s, points)...)
}
if err := output.Put("surrogate", *surrogate); err != nil {
return err
}
if err := output.Put("points", points, ni, ns); err != nil {
return err
}
if err := output.Put("values", values, no, ns, nk); err != nil {
return err
}
if err := output.Put("active", active); err != nil {
return err
}
return nil
}
func function(_ *config.Config) error {
reference, err := database.Open(*referenceFile)
if err != nil {
return err
}
defer reference.Close()
observe, err := database.Open(*observeFile)
if err != nil {
return err
}
defer observe.Close()
predict, err := database.Open(*predictFile)
if err != nil {
return err
}
defer predict.Close()
output, err := database.Create(*outputFile)
if err != nil {
return err
}
defer output.Close()
rvalues := []float64{}
if err := reference.Get("values", &rvalues); err != nil {
return err
}
ovalues := []float64{}
if err := observe.Get("values", &ovalues); err != nil {
return err
}
pvalues := []float64{}
if err := predict.Get("values", &pvalues); err != nil {
return err
}
active := []uint{}
if err := predict.Get("active", &active); err != nil {
return err
}
surrogate := new(solution.Surrogate)
if err := predict.Get("surrogate", surrogate); err != nil {
return err
}
no := surrogate.Outputs
nq := no / momentCount
nk := uint(len(active))
if ne := active[nk-1]; uint(len(ovalues))/no < ne {
return errors.New(fmt.Sprintf("the number of observations should be at least %d", ne))
}
εo := make([]float64, 0, nq*nk*metricCount)
εp := make([]float64, 0, nq*nk*metricCount)
for i := uint(0); i < nq; i++ {
r := slice(rvalues, no, i*momentCount, 1)
o := cumulate(slice(ovalues, no, i*momentCount, 1), active)
for j := uint(0); j < nk; j++ {
εo = append(εo, assess(r, o[j])...)
}
p := divide(slice(pvalues, no, i*momentCount, 1), nk)
for j := uint(0); j < nk; j++ {
εp = append(εp, assess(r, p[j])...)
}
}
if err := output.Put("active", active); err != nil {
return err
}
if err := output.Put("observe", εo, metricCount, nk, nq); err != nil {
return err
}
if err := output.Put("predict", εp, metricCount, nk, nq); err != nil {
return err
}
return nil
}