14 return sin((1.7+x)*(x-0.3)-2.3*(y+0.7));
23 for (
Int_t i=0; i<1000; i++) {
34 "Entry$%2",
"(Entry$%2)==0");
35 mlp->
Train(150,
"graph update=10");
58 mlpa->
GetIOTree()->
Draw(
"Out.Out0-True.True0:True.True0>>hDelta",
"",
"goff");
60 hDelta->
SetTitle(
"Difference between ANN output and truth vs. truth");
70 for (
Int_t ix=0; ix<15; ix++) {
72 for (
Int_t iy=0; iy<15; iy++) {
81 "ANN extrapolation, ANN output - truth",
83 g2Extrapolate->
Draw(
"TRI2");
TTree * GetIOTree() const
Double_t theUnknownFunction(Double_t x, Double_t y)
virtual void Draw(Option_t *option="")
Specific drawing options can be used to paint a TGraph2D:
void CheckNetwork()
Gives some information about the network in the terminal.
virtual Double_t Rndm(Int_t i=0)
Machine independent random number generator.
TVirtualPad * cd(Int_t subpadnumber=0)
Set current canvas & pad.
void GatherInformations()
Collect information about what is usefull in the network.
void DrawDInputs()
Draws the distribution (on the test sample) of the impact on the network output of a small variation ...
This is the base class for the ROOT Random number generators.
Double_t Evaluate(Int_t index, Double_t *params) const
Returns the Neural Net for a given set of input parameters #parameters must equal #input neurons...
A simple TTree restricted to a list of float variables only.
virtual void Draw(Option_t *option="")
Draw this histogram with options.
void Train(Int_t nEpoch, Option_t *option="text", Double_t minE=0)
Train the network.
2-D histogram with a float per channel (see TH1 documentation)}
virtual void Draw(Option_t *opt)
Default Draw method for all objects.
virtual Int_t Fill()
[fNvar] Array of variables
virtual void Divide(Int_t nx=1, Int_t ny=1, Float_t xmargin=0.01, Float_t ymargin=0.01, Int_t color=0)
Automatic pad generation by division.
THStack * DrawTruthDeviations(Option_t *option="")
Creates TProfiles of the difference of the MLP output minus the true value vs the true value...
virtual void SetTitle(const char *title)
Change (i.e.
Graphics object made of three arrays X, Y and Z with the same number of points each.
THStack * DrawTruthDeviationInsOut(Int_t outnode=0, Option_t *option="")
Creates a profile of the difference of the MLP output outnode minus the true value of outnode vs the ...