322 const char* extF,
const char* extD)
385 const char* extF,
const char* extD)
447 const char * training,
450 const char* extF,
const char* extD)
473 if(testcut==
"") testcut =
Form(
"!(%s)",training);
482 data->Draw(
Form(
">>fTrainingList_%zu",(
size_t)
this),training,
"goff");
483 data->Draw(
Form(
">>fTestList_%zu",(
size_t)
this),(
const char *)testcut,
"goff");
487 Warning(
"TMultiLayerPerceptron::TMultiLayerPerceptron",
"Data not set. Cannot define datasets");
526 const char * training,
529 const char* extF,
const char* extD)
552 if(testcut==
"") testcut =
Form(
"!(%s)",training);
561 data->Draw(
Form(
">>fTrainingList_%zu",(
size_t)
this),training,
"goff");
562 data->Draw(
Form(
">>fTestList_%zu",(
size_t)
this),(
const char *)testcut,
"goff");
566 Warning(
"TMultiLayerPerceptron::TMultiLayerPerceptron",
"Data not set. Cannot define datasets");
593 std::cerr <<
"Error: data already defined." << std::endl;
654 fData->
Draw(
Form(
">>fTrainingList_%zu",(
size_t)
this),train,
"goff");
657 Warning(
"TMultiLayerPerceptron::TMultiLayerPerceptron",
"Data not set. Cannot define datasets");
676 fData->
Draw(
Form(
">>fTestList_%zu",(
size_t)
this),test,
"goff");
679 Warning(
"TMultiLayerPerceptron::TMultiLayerPerceptron",
"Data not set. Cannot define datasets");
796 Bool_t minE_Train =
false;
802 Int_t displayStepping = 1;
806 displayStepping = atoi(out.Data() + 7);
816 TGraph *train_residual_plot =
nullptr;
817 TGraph *test_residual_plot =
nullptr;
819 Error(
"Train",
"Training/Test samples still not defined. Cannot train the neural network");
822 Info(
"Train",
"Using %d train and %d test entries.",
826 std::cout <<
"Training the Neural Network" << std::endl;
830 canvas =
new TCanvas(
"NNtraining",
"Neural Net training");
833 if(!canvas) canvas =
new TCanvas(
"NNtraining",
"Neural Net training");
835 train_residual_plot =
new TGraph(nEpoch);
836 test_residual_plot =
new TGraph(nEpoch);
840 residual_plot->
Add(train_residual_plot);
841 residual_plot->
Add(test_residual_plot);
842 residual_plot->
Draw(
"LA");
854 for (i = 0; i < els; i++)
857 TMatrixD bfgsh(matrix_size, matrix_size);
863 for (
Int_t iepoch = 0; (iepoch < nEpoch) && (!minE_Train || training_E>minE) && (!minE_Test || test_E>minE) ; iepoch++) {
892 for (i = 0; i < els; i++)
893 onorm += dir[i] * dir[i];
901 prod -= dir[idx++] * neuron->
GetDEDw();
907 prod -= dir[idx++] * synapse->
GetDEDw();
924 for (i = 0; i < els; i++)
925 onorm += dir[i] * dir[i];
951 if (
GetBFGSH(bfgsh, gamma, delta)) {
966 Error(
"TMultiLayerPerceptron::Train()",
"Line search fail");
976 Error(
"TMultiLayerPerceptron::Train()",
"Stop.");
985 if ((verbosity % 2) && ((!(iepoch % displayStepping)) || (iepoch == nEpoch - 1))) {
986 std::cout <<
"Epoch: " << iepoch
987 <<
" learn=" << training_E
988 <<
" test=" << test_E
992 train_residual_plot->
SetPoint(iepoch, iepoch,training_E);
993 test_residual_plot->
SetPoint(iepoch, iepoch,test_E);
997 for (i = 1; i < nEpoch; i++) {
998 train_residual_plot->
SetPoint(i, i, trp);
999 test_residual_plot->
SetPoint(i, i, tep);
1002 if ((!(iepoch % displayStepping)) || (iepoch == nEpoch - 1)) {
1018 std::cout <<
"Training done." << std::endl;
1019 if (verbosity / 2) {
1022 "Training sample",
"L");
1024 "Test sample",
"L");
1038 return out->GetValue();
1052 if (nEntries == 0)
return 0.0;
1083 for (i = 0; i < nEvents; i++) {
1088 for (i = 0; i < nEvents; i++) {
1105 return (error / 2.);
1118 if (
target < DBL_EPSILON) {
1124 if ((1 -
target) < DBL_EPSILON) {
1149 if (
target > DBL_EPSILON) {
1181 for (i = 0; i < nEvents; i++) {
1208 for (i = 0; i < nEvents; i++) {
1270 Bool_t normalize =
false;
1274 Int_t maxop, maxpar, maxconst;
1336 for (i = 0; i<nneurons; i++) {
1340 if(
f.GetMultiplicity()==1 &&
f.GetNdata()>1) {
1341 Warning(
"TMultiLayerPerceptron::ExpandStructure()",
"Variable size arrays cannot be used to build implicitly an input layer. The index 0 will be assumed.");
1348 else if(
f.GetNdata()>1) {
1349 for(
Int_t j=0; j<
f.GetNdata(); j++) {
1350 if(i||j) newInput +=
",";
1358 if(i) newInput +=
",";
1364 fStructure = newInput +
":" + hiddenAndOutput;
1381 hidden(hidden.
Last(
':') + 1,
1383 if (
input.Length() == 0) {
1384 Error(
"BuildNetwork()",
"malformed structure. No input layer.");
1387 if (
output.Length() == 0) {
1388 Error(
"BuildNetwork()",
"malformed structure. No output layer.");
1407 for (i = 0; i<nneurons; i++) {
1423 Int_t prevStart = 0;
1429 end = hidden.
Index(
":", beg + 1);
1446 Error(
"BuildOneHiddenLayer",
1447 "The specification '%s' for hidden layer %d must contain only numbers!",
1448 sNumNodes.
Data(), layer - 1);
1451 for (
Int_t i = 0; i < num; i++) {
1452 name.Form(
"HiddenL%d:N%d",layer,i);
1455 for (
Int_t j = prevStart; j < prevStop; j++) {
1464 for (
Int_t i = prevStop; i < nEntries; i++) {
1466 for (
Int_t j = prevStop; j < nEntries; j++)
1471 prevStart = prevStop;
1494 Int_t prevStart = prevStop - prev;
1500 for (i = 0; i<nneurons; i++) {
1507 for (j = prevStart; j < prevStop; j++) {
1516 for (i = prevStop; i < nEntries; i++) {
1518 for (j = prevStop; j < nEntries; j++)
1539 Error(
"DrawResult()",
"no such output.");
1544 new TCanvas(
"NNresult",
"Neural Net output");
1545 const Double_t *norm = out->GetNormalisation();
1556 if ((!
fData) || (!events)) {
1557 Error(
"DrawResult()",
"no dataset.");
1562 TString title =
"Neural Net Output control. ";
1564 setname =
"MLP_" + setname +
"_comp";
1567 hist =
new TH2D(setname.
Data(), title.
Data(), 50, -1, 1, 50, -1, 1);
1570 for (i = 0; i < nEvents; i++) {
1572 hist->
Fill(out->GetValue(), (out->GetBranch() - norm[1]) / norm[0]);
1577 TString title =
"Neural Net Output. ";
1579 setname =
"MLP_" + setname;
1582 hist =
new TH1D(setname, title, 50, 1, -1);
1585 for (i = 0; i < nEvents; i++)
1593 hist =
new TH1D(setname, title, 50, 1, -1);
1595 nEvents = events->
GetN();
1596 for (i = 0; i < nEvents; i++)
1612 Error(
"TMultiLayerPerceptron::DumpWeights()",
"Invalid file name");
1620 *
output <<
"#input normalization" << std::endl;
1628 *
output <<
"#output normalization" << std::endl;
1635 *
output <<
"#neurons weights" << std::endl;
1642 *
output <<
"#synapses weights" << std::endl;
1647 ((std::ofstream *)
output)->close();
1662 Error(
"TMultiLayerPerceptron::LoadWeights()",
"Invalid file name");
1665 char *buff =
new char[100];
1668 input.getline(buff, 100);
1676 input.getline(buff, 100);
1678 input.getline(buff, 100);
1685 input.getline(buff, 100);
1687 input.getline(buff, 100);
1695 input.getline(buff, 100);
1697 input.getline(buff, 100);
1727 return out->GetValue();
1744 Warning(
"TMultiLayerPerceptron::Export",
"Request to export a network using an external function");
1751 TString classname = basefilename;
1756 std::ofstream headerfile(header);
1757 std::ofstream sourcefile(source);
1758 headerfile <<
"#ifndef " << basefilename <<
"_h" << std::endl;
1759 headerfile <<
"#define " << basefilename <<
"_h" << std::endl << std::endl;
1760 headerfile <<
"class " << classname <<
" { " << std::endl;
1761 headerfile <<
"public:" << std::endl;
1762 headerfile <<
" " << classname <<
"() {}" << std::endl;
1763 headerfile <<
" ~" << classname <<
"() {}" << std::endl;
1764 sourcefile <<
"#include \"" << header <<
"\"" << std::endl;
1765 sourcefile <<
"#include <cmath>" << std::endl << std::endl;
1766 headerfile <<
" double Value(int index";
1767 sourcefile <<
"double " << classname <<
"::Value(int index";
1769 headerfile <<
",double in" << i;
1770 sourcefile <<
",double in" << i;
1772 headerfile <<
");" << std::endl;
1773 sourcefile <<
") {" << std::endl;
1775 sourcefile <<
" input" << i <<
" = (in" << i <<
" - "
1779 sourcefile <<
" switch(index) {" << std::endl;
1784 sourcefile <<
" case " << idx++ <<
":" << std::endl
1785 <<
" return neuron" << neuron <<
"();" << std::endl;
1786 sourcefile <<
" default:" << std::endl
1787 <<
" return 0.;" << std::endl <<
" }"
1789 sourcefile <<
"}" << std::endl << std::endl;
1790 headerfile <<
" double Value(int index, double* input);" << std::endl;
1791 sourcefile <<
"double " << classname <<
"::Value(int index, double* input) {" << std::endl;
1793 sourcefile <<
" input" << i <<
" = (input[" << i <<
"] - "
1797 sourcefile <<
" switch(index) {" << std::endl;
1802 sourcefile <<
" case " << idx++ <<
":" << std::endl
1803 <<
" return neuron" << neuron <<
"();" << std::endl;
1804 sourcefile <<
" default:" << std::endl
1805 <<
" return 0.;" << std::endl <<
" }"
1807 sourcefile <<
"}" << std::endl << std::endl;
1808 headerfile <<
"private:" << std::endl;
1810 headerfile <<
" double input" << i <<
";" << std::endl;
1815 if (!neuron->
GetPre(0)) {
1816 headerfile <<
" double neuron" << neuron <<
"();" << std::endl;
1817 sourcefile <<
"double " << classname <<
"::neuron" << neuron
1818 <<
"() {" << std::endl;
1819 sourcefile <<
" return input" << idx++ <<
";" << std::endl;
1820 sourcefile <<
"}" << std::endl << std::endl;
1822 headerfile <<
" double input" << neuron <<
"();" << std::endl;
1823 sourcefile <<
"double " << classname <<
"::input" << neuron
1824 <<
"() {" << std::endl;
1825 sourcefile <<
" double input = " << neuron->
GetWeight()
1826 <<
";" << std::endl;
1829 while ((syn = neuron->
GetPre(
n++))) {
1830 sourcefile <<
" input += synapse" << syn <<
"();" << std::endl;
1832 sourcefile <<
" return input;" << std::endl;
1833 sourcefile <<
"}" << std::endl << std::endl;
1835 headerfile <<
" double neuron" << neuron <<
"();" << std::endl;
1836 sourcefile <<
"double " << classname <<
"::neuron" << neuron <<
"() {" << std::endl;
1837 sourcefile <<
" double input = input" << neuron <<
"();" << std::endl;
1841 sourcefile <<
" return ((input < -709. ? 0. : (1/(1+exp(-input)))) * ";
1846 sourcefile <<
" return (input * ";
1851 sourcefile <<
" return (tanh(input) * ";
1856 sourcefile <<
" return (exp(-input*input) * ";
1861 sourcefile <<
" return (exp(input) / (";
1864 sourcefile <<
"exp(input" << side <<
"())";
1866 sourcefile <<
" + exp(input" << side <<
"())";
1867 sourcefile <<
") * ";
1872 sourcefile <<
" return (0.0 * ";
1877 sourcefile <<
"}" << std::endl << std::endl;
1884 headerfile <<
" double synapse" << synapse <<
"();" << std::endl;
1885 sourcefile <<
"double " << classname <<
"::synapse"
1886 << synapse <<
"() {" << std::endl;
1887 sourcefile <<
" return (neuron" << synapse->
GetPre()
1888 <<
"()*" << synapse->
GetWeight() <<
");" << std::endl;
1889 sourcefile <<
"}" << std::endl << std::endl;
1892 headerfile <<
"};" << std::endl << std::endl;
1893 headerfile <<
"#endif // " << basefilename <<
"_h" << std::endl << std::endl;
1896 std::cout << header <<
" and " << source <<
" created." << std::endl;
1898 else if(lg ==
"FORTRAN") {
1899 TString implicit =
" implicit double precision (a-h,n-z)\n";
1900 std::ofstream sigmoid(
"sigmoid.f");
1901 sigmoid <<
" double precision FUNCTION SIGMOID(X)" << std::endl
1903 <<
" IF(X.GT.37.) THEN" << std::endl
1904 <<
" SIGMOID = 1." << std::endl
1905 <<
" ELSE IF(X.LT.-709.) THEN" << std::endl
1906 <<
" SIGMOID = 0." << std::endl
1907 <<
" ELSE" << std::endl
1908 <<
" SIGMOID = 1./(1.+EXP(-X))" << std::endl
1909 <<
" ENDIF" << std::endl
1910 <<
" END" << std::endl;
1914 std::ofstream sourcefile(source);
1917 sourcefile <<
" double precision function " <<
filename
1918 <<
"(x, index)" << std::endl;
1919 sourcefile << implicit;
1920 sourcefile <<
" double precision x(" <<
1924 sourcefile <<
"C --- Last Layer" << std::endl;
1928 TString ifelseif =
" if (index.eq.";
1930 sourcefile << ifelseif.
Data() << idx++ <<
") then" << std::endl
1932 <<
"=neuron" << neuron <<
"(x);" << std::endl;
1933 ifelseif =
" else if (index.eq.";
1935 sourcefile <<
" else" << std::endl
1936 <<
" " <<
filename <<
"=0.d0" << std::endl
1937 <<
" endif" << std::endl;
1938 sourcefile <<
" end" << std::endl;
1941 sourcefile <<
"C --- First and Hidden layers" << std::endl;
1946 sourcefile <<
" double precision function neuron"
1947 << neuron <<
"(x)" << std::endl
1949 sourcefile <<
" double precision x("
1951 if (!neuron->
GetPre(0)) {
1952 sourcefile <<
" neuron" << neuron
1953 <<
" = (x(" << idx+1 <<
") - "
1957 <<
"d0" << std::endl;
1960 sourcefile <<
" neuron" << neuron
1961 <<
" = " << neuron->
GetWeight() <<
"d0" << std::endl;
1964 while ((syn = neuron->
GetPre(
n++)))
1965 sourcefile <<
" neuron" << neuron
1966 <<
" = neuron" << neuron
1967 <<
" + synapse" << syn <<
"(x)" << std::endl;
1971 sourcefile <<
" neuron" << neuron
1972 <<
"= (sigmoid(neuron" << neuron <<
")*";
1981 sourcefile <<
" neuron" << neuron
1982 <<
"= (tanh(neuron" << neuron <<
")*";
1987 sourcefile <<
" neuron" << neuron
1988 <<
"= (exp(-neuron" << neuron <<
"*neuron"
1996 sourcefile <<
" div = exp(neuron" << side <<
"())" << std::endl;
1998 sourcefile <<
" div = div + exp(neuron" << side <<
"())" << std::endl;
1999 sourcefile <<
" neuron" << neuron ;
2000 sourcefile <<
"= (exp(neuron" << neuron <<
") / div * ";
2005 sourcefile <<
" neuron " << neuron <<
"= 0.";
2011 sourcefile <<
" end" << std::endl;
2016 sourcefile <<
"C --- Synapses" << std::endl;
2020 sourcefile <<
" double precision function " <<
"synapse"
2021 << synapse <<
"(x)\n" << implicit;
2022 sourcefile <<
" double precision x("
2024 sourcefile <<
" synapse" << synapse
2025 <<
"=neuron" << synapse->
GetPre()
2026 <<
"(x)*" << synapse->
GetWeight() <<
"d0" << std::endl;
2027 sourcefile <<
" end" << std::endl << std::endl;
2031 std::cout << source <<
" created." << std::endl;
2033 else if(lg ==
"PYTHON") {
2037 std::ofstream pythonfile(pyfile);
2038 pythonfile <<
"from math import exp" << std::endl << std::endl;
2039 pythonfile <<
"from math import tanh" << std::endl << std::endl;
2040 pythonfile <<
"class " << classname <<
":" << std::endl;
2041 pythonfile <<
"\tdef value(self,index";
2043 pythonfile <<
",in" << i;
2045 pythonfile <<
"):" << std::endl;
2047 pythonfile <<
"\t\tself.input" << i <<
" = (in" << i <<
" - "
2054 pythonfile <<
"\t\tif index==" << idx++
2055 <<
": return self.neuron" << neuron <<
"();" << std::endl;
2056 pythonfile <<
"\t\treturn 0." << std::endl;
2061 pythonfile <<
"\tdef neuron" << neuron <<
"(self):" << std::endl;
2063 pythonfile <<
"\t\treturn self.input" << idx++ << std::endl;
2065 pythonfile <<
"\t\tinput = " << neuron->
GetWeight() << std::endl;
2068 while ((syn = neuron->
GetPre(
n++)))
2069 pythonfile <<
"\t\tinput = input + self.synapse"
2070 << syn <<
"()" << std::endl;
2074 pythonfile <<
"\t\tif input<-709. : return " << neuron->
GetNormalisation()[1] << std::endl;
2075 pythonfile <<
"\t\treturn ((1/(1+exp(-input)))*";
2080 pythonfile <<
"\t\treturn (input*";
2085 pythonfile <<
"\t\treturn (tanh(input)*";
2090 pythonfile <<
"\t\treturn (exp(-input*input)*";
2095 pythonfile <<
"\t\treturn (exp(input) / (";
2098 pythonfile <<
"exp(self.neuron" << side <<
"())";
2100 pythonfile <<
" + exp(self.neuron" << side <<
"())";
2101 pythonfile <<
") * ";
2106 pythonfile <<
"\t\treturn 0.";
2117 pythonfile <<
"\tdef synapse" << synapse <<
"(self):" << std::endl;
2118 pythonfile <<
"\t\treturn (self.neuron" << synapse->
GetPre()
2119 <<
"()*" << synapse->
GetWeight() <<
")" << std::endl;
2123 std::cout << pyfile <<
" created." << std::endl;
2145 for (
Int_t i = 0; i <
n; i++) {
2163 for (i = 0; i < nEvents; i++)
2169 for (i = 0; i < nEvents; i++) {
2238 neuron->
SetWeight(origin[idx] + (dir[idx] * dist));
2244 synapse->
SetWeight(origin[idx] + (dir[idx] * dist));
2260 dir[idx++] = -neuron->
GetDEDw();
2264 dir[idx++] = -synapse->
GetDEDw();
2303 MLP_Line(origin, direction, alpha2);
2309 for (icount = 0; icount < 100; icount++) {
2311 MLP_Line(origin, direction, alpha3);
2328 for (icount = 0; icount < 100; icount++) {
2330 MLP_Line(origin, direction, alpha2);
2348 (err3 - err1) / ((err3 - err2) / (alpha3 - alpha2)
2349 - (err2 - err1) / (alpha2 - alpha1)));
2358 buffer[idx] = neuron->
GetWeight() - origin[idx];
2364 buffer[idx] = synapse->
GetWeight() - origin[idx];
2388 dir[idx] = -neuron->
GetDEDw() + beta * dir[idx];
2394 dir[idx] = -synapse->
GetDEDw() + beta * dir[idx];
2442 gamma[idx++][0] = -neuron->
GetDEDw();
2447 gamma[idx++][0] = -synapse->
GetDEDw();
2449 for (
Int_t i = 0; i < els; i++)
2450 delta[i].Assign(buffer[i]);
2457 gamma[idx++][0] += neuron->
GetDEDw();
2462 gamma[idx++][0] += synapse->
GetDEDw();
2505 dedw[idx++][0] = neuron->
GetDEDw();
2510 dedw[idx++][0] = synapse->
GetDEDw();
2513 for (
Int_t i = 0; i < els; i++)
2514 dir[i] = -direction[i][0];
2526#define NeuronSize 2.5
2529 Float_t xStep = 1./(nLayers+1.);
2531 for(layer=0; layer< nLayers-1; layer++) {
2535 nNeurons_this =
input.CountChar(
',')+1;
2543 Int_t num = atoi(
TString(hidden(beg, end - beg)).Data());
2546 end = hidden.
Index(
":", beg + 1);
2547 if(layer==cnt) nNeurons_this = num;
2551 if(layer==cnt) nNeurons_this = num;
2554 if(layer==nLayers-2) {
2556 nNeurons_next =
output.CountChar(
',')+1;
2564 Int_t num = atoi(
TString(hidden(beg, end - beg)).Data());
2567 end = hidden.
Index(
":", beg + 1);
2568 if(layer+1==cnt) nNeurons_next = num;
2572 if(layer+1==cnt) nNeurons_next = num;
2574 Float_t yStep_this = 1./(nNeurons_this+1.);
2575 Float_t yStep_next = 1./(nNeurons_next+1.);
2580 maxWeight = maxWeight < theSynapse->
GetWeight() ? theSynapse->
GetWeight() : maxWeight;
2583 for(
Int_t neuron1=0; neuron1<nNeurons_this; neuron1++) {
2584 for(
Int_t neuron2=0; neuron2<nNeurons_next; neuron2++) {
2585 TLine* synapse =
new TLine(xStep*(layer+1),yStep_this*(neuron1+1),xStep*(layer+2),yStep_next*(neuron2+1));
2588 if (!theSynapse)
continue;
2597 for(layer=0; layer< nLayers; layer++) {
2601 nNeurons =
input.CountChar(
',')+1;
2603 else if(layer==nLayers-1) {
2605 nNeurons =
output.CountChar(
',')+1;
2613 Int_t num = atoi(
TString(hidden(beg, end - beg)).Data());
2616 end = hidden.
Index(
":", beg + 1);
2617 if(layer==cnt) nNeurons = num;
2621 if(layer==cnt) nNeurons = num;
2623 Float_t yStep = 1./(nNeurons+1.);
2624 for(
Int_t neuron=0; neuron<nNeurons; neuron++) {
2634 Float_t yStep = 1./(nrItems+1);
2635 for (
Int_t item = 0; item < nrItems; item++) {
2637 TText* label =
new TText(0.5*xStep,yStep*(item+1),brName.
Data());
2643 yStep=1./(numOutNodes+1);
2644 for (
Int_t outnode=0; outnode<numOutNodes; outnode++) {
2646 if (neuron && neuron->
GetName()) {
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void input
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t Int_t Int_t Window_t TString Int_t GCValues_t GetPrimarySelectionOwner GetDisplay GetScreen GetColormap GetNativeEvent const char const char dpyName wid window const char font_name cursor keysym reg const char only_if_exist regb h Point_t winding char text const char depth char const char Int_t count const char ColorStruct_t color const char filename
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t Int_t Int_t Window_t TString Int_t GCValues_t GetPrimarySelectionOwner GetDisplay GetScreen GetColormap GetNativeEvent const char const char dpyName wid window const char font_name cursor keysym reg const char only_if_exist regb h Point_t winding char text const char depth char const char Int_t count const char ColorStruct_t color const char Pixmap_t Pixmap_t PictureAttributes_t attr const char char ret_data h unsigned char height h Atom_t Int_t ULong_t ULong_t unsigned char prop_list Atom_t Atom_t target
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void data
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t index
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t Int_t Int_t Window_t TString Int_t GCValues_t GetPrimarySelectionOwner GetDisplay GetScreen GetColormap GetNativeEvent const char const char dpyName wid window const char font_name cursor keysym reg const char only_if_exist regb h Point_t winding char text const char depth char const char Int_t count const char ColorStruct_t color const char Pixmap_t Pixmap_t PictureAttributes_t attr const char char ret_data h unsigned char height h Atom_t Int_t ULong_t ULong_t unsigned char prop_list Atom_t Atom_t Atom_t Time_t type
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void reg
TMatrixT< Double_t > TMatrixD
char * Form(const char *fmt,...)
Formats a string in a circular formatting buffer.
R__EXTERN TSystem * gSystem
virtual void SetTitleOffset(Float_t offset=1)
Set distance between the axis and the axis title.
virtual void SetLineStyle(Style_t lstyle)
Set the line style.
virtual void SetLineWidth(Width_t lwidth)
Set the line width.
virtual void SetLineColor(Color_t lcolor)
Set the line color.
virtual void SetMarkerColor(Color_t mcolor=1)
Set the marker color.
virtual void SetMarkerSize(Size_t msize=1)
Set the marker size.
virtual void SetLeftMargin(Float_t leftmargin)
Set Pad left margin in fraction of the pad width.
void SetDecimals(Bool_t dot=kTRUE)
Sets the decimals flag By default, blank characters are stripped, and then the label is correctly ali...
virtual void UnZoom()
Reset first & last bin to the full range.
static TClass * GetClass(const char *name, Bool_t load=kTRUE, Bool_t silent=kFALSE)
Static method returning pointer to TClass of the specified class name.
virtual void SetOwner(Bool_t enable=kTRUE)
Set whether this collection is the owner (enable==true) of its content.
TDirectory::TContext keeps track and restore the current directory.
<div class="legacybox"><h2>Legacy Code</h2> TEventList is a legacy interface: there will be no bug fi...
virtual Long64_t GetEntry(Int_t index) const
Return value of entry at index in the list.
virtual Int_t GetN() const
A TGraph is an object made of two arrays X and Y with npoints each.
virtual void SetPoint(Int_t i, Double_t x, Double_t y)
Set x and y values for point number i.
1-D histogram with a double per channel (see TH1 documentation)
void Reset(Option_t *option="") override
Reset.
virtual Int_t Fill(Double_t x)
Increment bin with abscissa X by 1.
void Draw(Option_t *option="") override
Draw this histogram with options.
2-D histogram with a double per channel (see TH1 documentation)
void Reset(Option_t *option="") override
Reset this histogram: contents, errors, etc.
Int_t Fill(Double_t) override
Invalid Fill method.
This class displays a legend box (TPaveText) containing several legend entries.
TLegendEntry * AddEntry(const TObject *obj, const char *label="", Option_t *option="lpf")
Add a new entry to this legend.
void Draw(Option_t *option="") override
Draw this legend with its current attributes.
Use the TLine constructor to create a simple line.
TObject * At(Int_t idx) const override
Returns the object at position idx. Returns 0 if idx is out of range.
void Draw(Option_t *option="") override
Draw this marker with its current attributes.
virtual TMatrixTBase< Element > & UnitMatrix()
Make a unit matrix (matrix need not be a square one).
A TMultiGraph is a collection of TGraph (or derived) objects.
TList * GetListOfGraphs() const
virtual void Add(TGraph *graph, Option_t *chopt="")
Add a new graph to the list of graphs.
void Draw(Option_t *chopt="") override
Draw this multigraph with its current attributes.
TAxis * GetYaxis()
Get y axis of the graph.
TAxis * GetXaxis()
Get x axis of the graph.
This class describes a neural network.
TTreeFormula * fEventWeight
! formula representing the event weight
void BuildOneHiddenLayer(const TString &sNumNodes, Int_t &layer, Int_t &prevStart, Int_t &prevStop, Bool_t lastLayer)
Builds a hidden layer, updates the number of layers.
void SteepestDir(Double_t *)
Sets the search direction to steepest descent.
void BuildNetwork()
Instantiates the network from the description.
TObjArray fNetwork
Collection of all the neurons in the network.
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.
TEventList * fTest
! EventList defining the events in the test dataset
bool GetBFGSH(TMatrixD &, TMatrixD &, TMatrixD &)
Computes the hessian matrix using the BFGS update algorithm.
void BuildHiddenLayers(TString &)
Builds hidden layers.
void BuildFirstLayer(TString &)
Instantiates the neurons in input Inputs are normalised and the type is set to kOff (simple forward o...
void SetTau(Double_t tau)
Sets Tau - used in line search (look at the constructor for the complete description of learning meth...
TMultiLayerPerceptron()
Default constructor.
Double_t GetSumSquareError() const
Error on the output for a given event.
void ConjugateGradientsDir(Double_t *, Double_t)
Sets the search direction to conjugate gradient direction beta should be:
Double_t fTau
! Tau - used in line search - Default=3.
TTree * fData
! pointer to the tree used as datasource
Double_t Result(Int_t event, Int_t index=0) const
Computes the output for a given event.
void SetGammaDelta(TMatrixD &, TMatrixD &, Double_t *)
Sets the gamma and delta vectors Gamma is computed here, so ComputeDEDw cannot have been called bef...
TEventList * fTraining
! EventList defining the events in the training dataset
TString fStructure
String containing the network structure.
Int_t fReset
! number of epochs between two resets of the search direction to the steepest descent - Default=50
Bool_t LoadWeights(Option_t *filename="")
Loads the weights from a text file conforming to the format defined by DumpWeights.
void MLP_Batch(Double_t *)
One step for the batch (stochastic) method.
TNeuron::ENeuronType fOutType
Type of output neurons.
Double_t fCurrentTreeWeight
! weight of the current tree in a chain
ELearningMethod fLearningMethod
! The Learning Method
Double_t fLastAlpha
! internal parameter used in line search
Int_t fCurrentTree
! index of the current tree in a chain
void Export(Option_t *filename="NNfunction", Option_t *language="C++") const
Exports the NN as a function for any non-ROOT-dependant code Supported languages are: only C++ ,...
Double_t fEpsilon
! Epsilon - used in stochastic minimisation - Default=0.
void Train(Int_t nEpoch, Option_t *option="text", Double_t minE=0)
Train the network.
TNeuron::ENeuronType GetType() const
void BFGSDir(TMatrixD &, Double_t *)
Computes the direction for the BFGS algorithm as the product between the Hessian estimate (bfgsh) and...
void SetTestDataSet(TEventList *test)
Sets the Test dataset.
Bool_t fTrainingOwner
! internal flag whether one has to delete fTraining or not
void SetLearningMethod(TMultiLayerPerceptron::ELearningMethod method)
Sets the learning method.
void SetTrainingDataSet(TEventList *train)
Sets the Training dataset.
void BuildLastLayer(TString &, Int_t)
Builds the output layer Neurons are linear combinations of input, by default.
Double_t fDelta
! Delta - used in stochastic minimisation - Default=0.
TTreeFormulaManager * fManager
! TTreeFormulaManager for the weight and neurons
void Randomize() const
Randomize the weights.
Bool_t LineSearch(Double_t *, Double_t *)
Search along the line defined by direction.
void ExpandStructure()
Expand the structure of the first layer.
Double_t fEta
! Eta - used in stochastic minimisation - Default=0.1
Double_t GetError(Int_t event) const
Error on the output for a given event.
Double_t fEtaDecay
! EtaDecay - Eta *= EtaDecay at each epoch - Default=1.
void SetEtaDecay(Double_t ed)
Sets EtaDecay - Eta *= EtaDecay at each epoch (look at the constructor for the complete description o...
void AttachData()
Connects the TTree to Neurons in input and output layers.
void SetData(TTree *)
Set the data source.
void SetEventWeight(const char *)
Set the event weight.
Bool_t DumpWeights(Option_t *filename="-") const
Dumps the weights to a text file.
TString fWeight
String containing the event weight.
void SetDelta(Double_t delta)
Sets Delta - used in stochastic minimisation (look at the constructor for the complete description of...
~TMultiLayerPerceptron() override
Destructor.
Double_t GetCrossEntropy() const
Cross entropy error for a softmax output neuron, for a given event.
void SetReset(Int_t reset)
Sets number of epochs between two resets of the search direction to the steepest descent.
Bool_t fTestOwner
! internal flag whether one has to delete fTest or not
void Shuffle(Int_t *, Int_t) const
Shuffle the Int_t index[n] in input.
Double_t DerivDir(Double_t *)
scalar product between gradient and direction = derivative along direction
void MLP_Stochastic(Double_t *)
One step for the stochastic method buffer should contain the previous dw vector and will be updated.
void Draw(Option_t *option="") override
Draws the network structure.
TObjArray fSynapses
Collection of all the synapses in the network.
void MLP_Line(Double_t *, Double_t *, Double_t)
Sets the weights to a point along a line Weights are set to [origin + (dist * dir)].
TNeuron::ENeuronType fType
Type of hidden neurons.
TObjArray fLastLayer
Collection of the output neurons; subset of fNetwork.
TString fextD
String containing the derivative name.
void ComputeDEDw() const
Compute the DEDw = sum on all training events of dedw for each weight normalized by the number of eve...
Double_t GetCrossEntropyBinary() const
Cross entropy error for sigmoid output neurons, for a given event.
void DrawResult(Int_t index=0, Option_t *option="test") const
Draws the neural net output It produces an histogram with the output for the two datasets.
void SetEta(Double_t eta)
Sets Eta - used in stochastic minimisation (look at the constructor for the complete description of l...
TObjArray fFirstLayer
Collection of the input neurons; subset of fNetwork.
void GetEntry(Int_t) const
Load an entry into the network.
void SetEpsilon(Double_t eps)
Sets Epsilon - used in stochastic minimisation (look at the constructor for the complete description ...
TString fextF
String containing the function name.
virtual void SetTitle(const char *title="")
Set the title of the TNamed.
const char * GetName() const override
Returns name of object.
This class describes an elementary neuron, which is the basic element for a Neural Network.
Double_t GetWeight() const
void SetWeight(Double_t w)
Sets the neuron weight to w.
Double_t GetValue() const
Computes the output using the appropriate function and all the weighted inputs, or uses the branch as...
void SetDEDw(Double_t in)
Sets the derivative of the total error wrt the neuron weight.
Double_t GetDeDw() const
Computes the derivative of the error wrt the neuron weight.
TNeuron * GetInLayer(Int_t n) const
Double_t GetError() const
Computes the error for output neurons.
TTreeFormula * UseBranch(TTree *, const char *)
Sets a formula that can be used to make the neuron an input.
TSynapse * GetPre(Int_t n) const
void ForceExternalValue(Double_t value)
Uses the branch type to force an external value.
Double_t GetTarget() const
Computes the normalized target pattern for output neurons.
const Double_t * GetNormalisation() const
ENeuronType GetType() const
Returns the neuron type.
void SetNewEvent() const
Inform the neuron that inputs of the network have changed, so that the buffered values have to be rec...
void SetNormalisation(Double_t mean, Double_t RMS)
Sets the normalization variables.
void AddInLayer(TNeuron *)
Tells a neuron which neurons form its layer (including itself).
Iterator of object array.
TObject * Next() override
Return next object in array. Returns 0 when no more objects in array.
Int_t GetEntriesFast() const
TIterator * MakeIterator(Bool_t dir=kIterForward) const override
Returns an array iterator.
TObject * At(Int_t idx) const override
void AddLast(TObject *obj) override
Add object in the next empty slot in the array.
TObject * UncheckedAt(Int_t i) const
Int_t GetLast() const override
Return index of last object in array.
Collectable string class.
virtual void Warning(const char *method, const char *msgfmt,...) const
Issue warning message.
virtual void Error(const char *method, const char *msgfmt,...) const
Issue error message.
virtual void Draw(Option_t *option="")
Default Draw method for all objects.
virtual void Info(const char *method, const char *msgfmt,...) const
Issue info message.
Random number generator class based on M.
Double_t Rndm() override
Machine independent random number generator.
Regular expression class.
void ToLower()
Change string to lower-case.
Bool_t EndsWith(const char *pat, ECaseCompare cmp=kExact) const
Return true if string ends with the specified string.
Ssiz_t First(char c) const
Find first occurrence of a character c.
const char * Data() const
Bool_t IsAlpha() const
Returns true if all characters in string are alphabetic.
Ssiz_t Last(char c) const
Find last occurrence of a character c.
void ToUpper()
Change string to upper case.
Int_t CountChar(Int_t c) const
Return number of times character c occurs in the string.
Bool_t Contains(const char *pat, ECaseCompare cmp=kExact) const
Bool_t IsAlnum() const
Returns true if all characters in string are alphanumeric.
Ssiz_t Index(const char *pat, Ssiz_t i=0, ECaseCompare cmp=kExact) const
This is a simple weighted bidirectional connection between two neurons.
Double_t GetDeDw() const
Computes the derivative of the error wrt the synapse weight.
void SetWeight(Double_t w)
Sets the weight of the synapse.
Double_t GetWeight() const
void SetDEDw(Double_t in)
Sets the derivative of the total error wrt the synapse weight.
virtual int Load(const char *module, const char *entry="", Bool_t system=kFALSE)
Load a shared library.
virtual Bool_t ProcessEvents()
Process pending events (GUI, timers, sockets).
Base class for several text objects.
The TTimeStamp encapsulates seconds and ns since EPOCH.
A TTree represents a columnar dataset.
virtual Int_t GetEntry(Long64_t entry, Int_t getall=0)
Read all branches of entry and return total number of bytes read.
virtual Double_t GetWeight() const
void Draw(Option_t *opt) override
Default Draw method for all objects.
virtual Long64_t GetEntries() const
virtual Int_t GetTreeNumber() const
TVirtualPad is an abstract base class for the Pad and Canvas classes.
virtual void Modified(Bool_t flag=1)=0
Double_t Log(Double_t x)
Returns the natural logarithm of x.
Double_t Sqrt(Double_t x)
Returns the square root of x.
Short_t Abs(Short_t d)
Returns the absolute value of parameter Short_t d.