benhoob/HWW/network.C

#include "TArrow.h"
#include "TEllipse.h"
#include "TPaveLabel.h"
#include "TCanvas.h"
#include "TH2F.h"
#include "TFile.h"
#include "TString.h"
#include "TDirectory.h"
#include "TKey.h"
#include "TText.h"

#include "tmvaglob.C"

// this macro prints out a neural network generated by MethodMLP graphically
// @author: Matt Jachowski, jachowski@stanford.edu

TFile* Network_GFile = 0;

static Int_t c_DarkBackground = TColor::GetColor( "#6e7a85" );

void     draw_layer_labels( Int_t nLayers );
void     draw_layer       ( TCanvas* c, TH2F* h, Int_t iHist, Int_t nLayers, Double_t maxWeight );
void     draw_synapse     ( Double_t cx1, Double_t cy1, Double_t cx2, Double_t cy2,
                            Double_t  rad1, Double_t rad2, Double_t weightNormed );
TString* get_var_names    ( Int_t nVars );

Bool_t MovieMode = kFALSE;

void draw_network( TFile* f, TDirectory* d, const TString& hName = "weights_hist", 
                   Bool_t movieMode = kFALSE, const TString& epoch = "" )
{
   Bool_t __PRINT_LOGO__ = kTRUE;
   Network_GFile = f;

   MovieMode = movieMode;
   if (MovieMode) c_DarkBackground = TColor::GetColor( "#707F7F" );

   // create canvas
   TStyle* TMVAStyle = gROOT->GetStyle("TMVA"); // the TMVA style
   Int_t canvasColor = TMVAStyle->GetCanvasColor(); // backup
   TMVAStyle->SetCanvasColor( c_DarkBackground );

   Int_t titleFillColor = TMVAStyle->GetTitleFillColor();
   Int_t titleTextColor = TMVAStyle->GetTitleTextColor();
   Int_t borderSize     = TMVAStyle->GetTitleBorderSize();

   TMVAStyle->SetTitleFillColor( c_DarkBackground );
   TMVAStyle->SetTitleTextColor( TColor::GetColor( "#FFFFFF" ) );
   TMVAStyle->SetTitleBorderSize( 0 );
   
   static Int_t icanvas = -1;
   Int_t ixc = 100 + (icanvas)*40;
   Int_t iyc =   0 + (icanvas+1)*20;   
   if (MovieMode) ixc = iyc = 0;
   TString canvasnumber =  Form( "c%i", icanvas );
   TString canvastitle = Form("Neural Network Layout for: %s", d->GetName());
   TCanvas* c = new TCanvas( canvasnumber, canvastitle, 
                             ixc, 0 + (icanvas+1)*20, 1000, 650  );
   icanvas++;
   TIter next = d->GetListOfKeys();
   TKey *key( 0 );
   Int_t numHists = 0;

   // loop over all histograms with hName in name again
   next.Reset();
   Double_t maxWeight = 0;
   // find max weight
   while ((key = (TKey*)next())) {

      TClass *cl = gROOT->GetClass(key->GetClassName());
      if (!cl->InheritsFrom("TH2F")) continue;    
 
      TH2F* h = (TH2F*)key->ReadObj();    
      if (!h) {
         cout << "Big troubles in \"draw_network\" (1)" << endl;
         exit(1);
      }
      if (TString(h->GetName()).Contains( hName )){
         numHists++;
 
         Int_t n1 = h->GetNbinsX();
         Int_t n2 = h->GetNbinsY();
         for (Int_t i = 0; i < n1; i++) {
            for (Int_t j = 0; j < n2; j++) {
               Double_t weight = TMath::Abs(h->GetBinContent(i+1, j+1));
               if (maxWeight < weight) maxWeight = weight;
            }
         }
      }
   }
   if (numHists == 0) {
      cout << "Error: could not find histograms" << endl;
      //exit(1);
   }

   // draw network
   next.Reset();
   //cout << "check4a" << endl;

   Int_t count = 0;
   while ((key = (TKey*)next())) {
      //cout << "check4b" << endl;

      TClass *cl = gROOT->GetClass(key->GetClassName());
      if (!cl->InheritsFrom("TH2F")) continue;    
      //cout << "check4c" << endl;

      TH2F* h = (TH2F*)key->ReadObj();    
      //cout << (h->GetName()) << endl;
      if (!h) {
         cout << "Big troubles in \"draw_network\" (2)" << endl;
         exit(1);
      }
      //cout << (h->GetName()) << endl;
      if (TString(h->GetName()).Contains( hName )) {
         //cout << (h->GetName()) << endl;
         draw_layer(c, h, count++, numHists+1, maxWeight);
      }
      //cout << "check4d" << endl;

   }
   draw_layer_labels(numHists+1);

   // add epoch
   if (MovieMode) {
      TText* t = new TText();            
      t->SetTextSize( 0.04 );
      t->SetTextColor( 0 );
      t->SetTextAlign( 31 );      
      t->DrawTextNDC( 1 - c->GetRightMargin(), 1 - c->GetTopMargin() - 0.033, 
                      Form( "Epoch: %s", epoch.Data() ) );
   }

   // ============================================================
   if (__PRINT_LOGO__) TMVAGlob::plot_logo();
   // ============================================================  

   c->Update();
   if (MovieMode) {
      // save to file
      TString dirname  = "movieplots";
      TString foutname = dirname + "/" + hName;
      foutname.Resize( foutname.Length()-5 );
      foutname.ReplaceAll("epochmonitoring___","");
      foutname += ".gif";
      
      cout << "storing file: " << foutname << endl;
      c->Print(foutname);     
      c->Clear();
      delete c;
   }
   else {
      TString fname = "plots/network";
      TMVAGlob::imgconv( c, fname );
   }

   // reset global style changes so that it does not affect other plots
   TMVAStyle->SetCanvasColor    ( canvasColor );
   TMVAStyle->SetTitleFillColor ( titleFillColor );
   TMVAStyle->SetTitleTextColor ( titleTextColor );
   TMVAStyle->SetTitleBorderSize( borderSize );

}

void draw_layer_labels(Int_t nLayers)
{
   const Double_t LABEL_HEIGHT = 0.032;
   const Double_t LABEL_WIDTH  = 0.20;
   Double_t effWidth = 0.8*(1.0-LABEL_WIDTH)/nLayers;
   Double_t height = 0.8*LABEL_HEIGHT;
   Double_t margY = LABEL_HEIGHT - height;

   for (Int_t i = 0; i < nLayers; i++) {
      TString label = Form("Layer %i", i);
      if (i == nLayers-1) label = "Output layer";
      Double_t cx = i*(1.0-LABEL_WIDTH)/nLayers+1.0/(2.0*nLayers)+LABEL_WIDTH;
      Double_t x1 = cx-0.8*effWidth/2.0;
      Double_t x2 = cx+0.8*effWidth/2.0;
      Double_t y1 = margY;
      Double_t y2 = margY + height;

      TPaveLabel *p = new TPaveLabel(x1, y1, x2, y2, label+"", "br");
      p->SetFillColor(gStyle->GetTitleFillColor());
      p->SetTextColor(gStyle->GetTitleTextColor());
      p->SetFillStyle(1001);
      p->SetBorderSize( 0 );
      p->Draw();
   }
}

void draw_input_labels(Int_t nInputs, Double_t* cy, 
                       Double_t rad, Double_t layerWidth)
{
   const Double_t LABEL_HEIGHT = 0.04;
   const Double_t LABEL_WIDTH  = 0.20;
   Double_t width = LABEL_WIDTH + (layerWidth-4*rad);
   Double_t margX = 0.01;
   Double_t effHeight = 0.8*LABEL_HEIGHT;

   TString *varNames = get_var_names(nInputs);
   if (varNames == 0) exit(1);

   TString input;

   for (Int_t i = 0; i < nInputs; i++) {
      if (i != nInputs-1) input = varNames[i];
      else                input = "Bias node";
      Double_t x1 = margX;
      Double_t x2 = margX + width;
      Double_t y1 = cy[i] - effHeight;
      Double_t y2 = cy[i] + effHeight;

      TText* t = new TText();
      t->SetTextColor(gStyle->GetTitleTextColor());
      t->SetTextAlign(31);
      t->SetTextSize(LABEL_HEIGHT);
      if (i == nInputs-1) t->SetTextColor( TColor::GetColor( "#AFDCEC" ) );
      t->DrawText( x2, y1+0.018, input + " :");
   }

   delete[] varNames;
}

TString* get_var_names( Int_t nVars )
{
   const TString directories[6] = { "InputVariables_NoTransform",
                                    "InputVariables_DecorrTransform",
                                    "InputVariables_PCATransform",
                                    "InputVariables_Id",
                                    "InputVariables_Norm",
                                    "InputVariables_Deco"};
   
   TDirectory* dir = 0;
   for (Int_t i=0; i<6; i++) {
      dir = (TDirectory*)Network_GFile->Get( directories[i] );
      if (dir != 0) break;
   }
   if (dir==0) {
      cout << "*** Big troubles in macro \"network.C\": could not find directory for input variables, "
           << "and hence could not determine variable names --> abort" << endl;
      return 0;
   }
   dir->cd();

   TString* vars = new TString[nVars];
   Int_t ivar = 0;

   // loop over all objects in directory
   TIter next(dir->GetListOfKeys());
   TKey* key = 0;
   while ((key = (TKey*)next())) {
      if (key->GetCycle() != 1) continue;

      if (!TString(key->GetName()).Contains("__S") &&
          !TString(key->GetName()).Contains("__r") &&
          !TString(key->GetName()).Contains("Regression"))
         continue;
      if (TString(key->GetName()).Contains("target"))
         continue;

      // make sure, that we only look at histograms
      TClass *cl = gROOT->GetClass(key->GetClassName());
      if (!cl->InheritsFrom("TH1")) continue;
      TH1 *sig = (TH1*)key->ReadObj();
      TString hname = sig->GetTitle();
      
      vars[ivar] = hname; ivar++;

      if (ivar > nVars-1) break;
   }      
   
   if (ivar != nVars-1) { // bias layer and targets are also in nVars counts
      cout << "*** Troubles in \"network.C\": did not reproduce correct number of "
           << "input variables: " << ivar << " != " << nVars << endl;
   }

   return vars;
}

void draw_activation(TCanvas* c, Double_t cx, Double_t cy, 
                     Double_t radx, Double_t rady, Int_t whichActivation)
{
   TImage *activation = NULL;

   switch (whichActivation) {
   case 0:
      activation = TImage::Open("sigmoid-small.png");
      break;
   case 1:
      activation = TImage::Open("line-small.png");
      break;
   default:
      cout << "Activation index " << whichActivation << " is not known." << endl;
      cout << "You messed up or you need to modify network.C to introduce a new "
           << "activation function (and image) corresponding to this index" << endl;
   }

   if (activation == NULL) {
      cout << "Could not create an image... exit" << endl;
      return;
   }
  
   activation->SetConstRatio(kFALSE);

   radx *= 0.7;
   rady *= 0.7;
   TString name = Form("activation%f%f", cx, cy);
   TPad* p = new TPad(name+"", name+"", cx-radx, cy-rady, cx+radx, cy+rady);

   p->Draw();
   p->cd();

   activation->Draw();
   c->cd();
}

void draw_layer(TCanvas* c, TH2F* h, Int_t iHist, 
                Int_t nLayers, Double_t maxWeight)
{
   const Double_t MAX_NEURONS_NICE = 12;
   const Double_t LABEL_HEIGHT = 0.03;
   const Double_t LABEL_WIDTH  = 0.20;
   Double_t ratio = ((Double_t)(c->GetWindowHeight())) / c->GetWindowWidth();
   Double_t rad, cx1, *cy1, cx2, *cy2;

   // this is the smallest radius that will still display the activation images
   rad = 0.04*650/c->GetWindowHeight();

   Int_t nNeurons1 = h->GetNbinsX();
   cx1 = iHist*(1.0-LABEL_WIDTH)/nLayers + 1.0/(2.0*nLayers) + LABEL_WIDTH;
   cy1 = new Double_t[nNeurons1];

   Int_t nNeurons2 = h->GetNbinsY();
   cx2 = (iHist+1)*(1.0-LABEL_WIDTH)/nLayers + 1.0/(2.0*nLayers) + LABEL_WIDTH;
   cy2 = new Double_t[nNeurons2];

   Double_t effRad1 = rad;
   if (nNeurons1 > MAX_NEURONS_NICE)
      effRad1 = 0.8*(1.0-LABEL_HEIGHT)/(2.0*nNeurons1);

   for (Int_t i = 0; i < nNeurons1; i++) {
      cy1[nNeurons1-i-1] = i*(1.0-LABEL_HEIGHT)/nNeurons1 + 1.0/(2.0*nNeurons1) + LABEL_HEIGHT;

      if (iHist == 0) {

         TEllipse *ellipse = new TEllipse( cx1, cy1[nNeurons1-i-1], 
                                           effRad1*ratio, effRad1, 0, 360, 0 );
         ellipse->SetFillColor(TColor::GetColor( "#fffffd" ));
         ellipse->SetFillStyle(1001);
         ellipse->Draw();

         if (i == 0) ellipse->SetLineColor(9);

         if (nNeurons1 > MAX_NEURONS_NICE) continue;

         Int_t whichActivation = 0;
         if (iHist==0 || iHist==nLayers-1 || i==0) whichActivation = 1;
         draw_activation(c, cx1, cy1[nNeurons1-i-1], 
                         rad*ratio, rad, whichActivation);
      }
   }

   if (iHist == 0) draw_input_labels(nNeurons1, cy1, rad, (1.0-LABEL_WIDTH)/nLayers);

   Double_t effRad2 = rad;
   if (nNeurons2 > MAX_NEURONS_NICE)
      effRad2 = 0.8*(1.0-LABEL_HEIGHT)/(2.0*nNeurons2);

   for (Int_t i = 0; i < nNeurons2; i++) {
      cy2[nNeurons2-i-1] = i*(1.0-LABEL_HEIGHT)/nNeurons2 + 1.0/(2.0*nNeurons2) + LABEL_HEIGHT;

      TEllipse *ellipse = 
         new TEllipse(cx2, cy2[nNeurons2-i-1], effRad2*ratio, effRad2, 0, 360, 0);
      ellipse->SetFillColor(TColor::GetColor( "#fffffd" ));
      ellipse->SetFillStyle(1001);
      ellipse->Draw();

      if (i == 0 && nNeurons2 > 1) ellipse->SetLineColor(9);

      if (nNeurons2 > MAX_NEURONS_NICE) continue;

      Int_t whichActivation = 0;
      if (iHist+1==0 || iHist+1==nLayers-1 || i==0) whichActivation = 1;
      draw_activation(c, cx2, cy2[nNeurons2-i-1], rad*ratio, rad, whichActivation);
   }

   for (Int_t i = 0; i < nNeurons1; i++) {
      for (Int_t j = 0; j < nNeurons2; j++) {
         draw_synapse(cx1, cy1[i], cx2, cy2[j], effRad1*ratio, effRad2*ratio,
                      h->GetBinContent(i+1, j+1)/maxWeight);
      }
   }

   delete [] cy1;
   delete [] cy2;
}

void draw_synapse(Double_t cx1, Double_t cy1, Double_t cx2, Double_t cy2,
                  Double_t  rad1, Double_t rad2, Double_t weightNormed)
{
   const Double_t TIP_SIZE   = 0.01;
   const Double_t MAX_WEIGHT = 8;
   const Double_t MAX_COLOR  = 100;  // red
   const Double_t MIN_COLOR  = 60;   // blue

   if (weightNormed == 0) return;

   //   gStyle->SetPalette(100, NULL);

   TArrow *arrow = new TArrow(cx1+rad1, cy1, cx2-rad2, cy2, TIP_SIZE, ">");
   arrow->SetFillColor(1);
   arrow->SetFillStyle(1001);
   arrow->SetLineWidth((Int_t)(TMath::Abs(weightNormed)*MAX_WEIGHT+0.5));
   arrow->SetLineColor((Int_t)((weightNormed+1.0)/2.0*(MAX_COLOR-MIN_COLOR)+MIN_COLOR+0.5));
   arrow->Draw();
}

// input: - Input file (result from TMVA),
//        - use of TMVA plotting TStyle
void network( TString fin = "TMVA.root", Bool_t useTMVAStyle = kTRUE )
{
   // set style and remove existing canvas'
   TMVAGlob::Initialize( useTMVAStyle );

   // checks if file with name "fin" is already open, and if not opens one
   TFile* file = TMVAGlob::OpenFile( fin );  
   TIter next(file->GetListOfKeys());
   TKey *key(0);
   while( (key = (TKey*)next()) ) {      
      if (!TString(key->GetName()).BeginsWith("Method_MLP")) continue;
      if( ! gROOT->GetClass(key->GetClassName())->InheritsFrom("TDirectory") ) continue;

      cout << "--- Found directory: " << ((TDirectory*)key->ReadObj())->GetName() << endl;

      TDirectory* mDir = (TDirectory*)key->ReadObj();

      TIter keyIt(mDir->GetListOfKeys());
      TKey *titkey;
      while((titkey = (TKey*)keyIt())) {
        if( ! gROOT->GetClass(titkey->GetClassName())->InheritsFrom("TDirectory") ) continue;

        TDirectory* dir = (TDirectory *)titkey->ReadObj();
        dir->cd();  
        TList titles;
        UInt_t ni = TMVAGlob::GetListOfTitles( dir, titles );
        if (ni==0) {
           cout << "No titles found for Method_MLP" << endl;
           return;
        }
        draw_network( file, dir );
      }
   }

   return;
}

Revision:	1.1
Committed:	Mon Feb 14 12:39:14 2011 UTC (14 years, 3 months ago) by benhoob
Content type:	text/plain
Branch:	MAIN
CVS Tags:	HEAD
Log Message:	Initial commit
#	User	Rev	Content
1	benhoob	1.1	#include "TArrow.h"
2			#include "TEllipse.h"
3			#include "TPaveLabel.h"
4			#include "TCanvas.h"
5			#include "TH2F.h"
6			#include "TFile.h"
7			#include "TString.h"
8			#include "TDirectory.h"
9			#include "TKey.h"
10			#include "TText.h"
11
12			#include "tmvaglob.C"
13
14			// this macro prints out a neural network generated by MethodMLP graphically
15			// @author: Matt Jachowski, jachowski@stanford.edu
16
17			TFile* Network_GFile = 0;
18
19			static Int_t c_DarkBackground = TColor::GetColor( "#6e7a85" );
20
21			void draw_layer_labels( Int_t nLayers );
22			void draw_layer ( TCanvas* c, TH2F* h, Int_t iHist, Int_t nLayers, Double_t maxWeight );
23			void draw_synapse ( Double_t cx1, Double_t cy1, Double_t cx2, Double_t cy2,
24			Double_t rad1, Double_t rad2, Double_t weightNormed );
25			TString* get_var_names ( Int_t nVars );
26
27			Bool_t MovieMode = kFALSE;
28
29			void draw_network( TFile* f, TDirectory* d, const TString& hName = "weights_hist",
30			Bool_t movieMode = kFALSE, const TString& epoch = "" )
31			{
32			Bool_t __PRINT_LOGO__ = kTRUE;
33			Network_GFile = f;
34
35			MovieMode = movieMode;
36			if (MovieMode) c_DarkBackground = TColor::GetColor( "#707F7F" );
37
38			// create canvas
39			TStyle* TMVAStyle = gROOT->GetStyle("TMVA"); // the TMVA style
40			Int_t canvasColor = TMVAStyle->GetCanvasColor(); // backup
41			TMVAStyle->SetCanvasColor( c_DarkBackground );
42
43			Int_t titleFillColor = TMVAStyle->GetTitleFillColor();
44			Int_t titleTextColor = TMVAStyle->GetTitleTextColor();
45			Int_t borderSize = TMVAStyle->GetTitleBorderSize();
46
47			TMVAStyle->SetTitleFillColor( c_DarkBackground );
48			TMVAStyle->SetTitleTextColor( TColor::GetColor( "#FFFFFF" ) );
49			TMVAStyle->SetTitleBorderSize( 0 );
50
51			static Int_t icanvas = -1;
52			Int_t ixc = 100 + (icanvas)*40;
53			Int_t iyc = 0 + (icanvas+1)*20;
54			if (MovieMode) ixc = iyc = 0;
55			TString canvasnumber = Form( "c%i", icanvas );
56			TString canvastitle = Form("Neural Network Layout for: %s", d->GetName());
57			TCanvas* c = new TCanvas( canvasnumber, canvastitle,
58			ixc, 0 + (icanvas+1)*20, 1000, 650 );
59			icanvas++;
60			TIter next = d->GetListOfKeys();
61			TKey *key( 0 );
62			Int_t numHists = 0;
63
64			// loop over all histograms with hName in name again
65			next.Reset();
66			Double_t maxWeight = 0;
67			// find max weight
68			while ((key = (TKey*)next())) {
69
70			TClass *cl = gROOT->GetClass(key->GetClassName());
71			if (!cl->InheritsFrom("TH2F")) continue;
72
73			TH2F* h = (TH2F*)key->ReadObj();
74			if (!h) {
75			cout << "Big troubles in \"draw_network\" (1)" << endl;
76			exit(1);
77			}
78			if (TString(h->GetName()).Contains( hName )){
79			numHists++;
80
81			Int_t n1 = h->GetNbinsX();
82			Int_t n2 = h->GetNbinsY();
83			for (Int_t i = 0; i < n1; i++) {
84			for (Int_t j = 0; j < n2; j++) {
85			Double_t weight = TMath::Abs(h->GetBinContent(i+1, j+1));
86			if (maxWeight < weight) maxWeight = weight;
87			}
88			}
89			}
90			}
91			if (numHists == 0) {
92			cout << "Error: could not find histograms" << endl;
93			//exit(1);
94			}
95
96			// draw network
97			next.Reset();
98			//cout << "check4a" << endl;
99
100			Int_t count = 0;
101			while ((key = (TKey*)next())) {
102			//cout << "check4b" << endl;
103
104			TClass *cl = gROOT->GetClass(key->GetClassName());
105			if (!cl->InheritsFrom("TH2F")) continue;
106			//cout << "check4c" << endl;
107
108			TH2F* h = (TH2F*)key->ReadObj();
109			//cout << (h->GetName()) << endl;
110			if (!h) {
111			cout << "Big troubles in \"draw_network\" (2)" << endl;
112			exit(1);
113			}
114			//cout << (h->GetName()) << endl;
115			if (TString(h->GetName()).Contains( hName )) {
116			//cout << (h->GetName()) << endl;
117			draw_layer(c, h, count++, numHists+1, maxWeight);
118			}
119			//cout << "check4d" << endl;
120
121			}
122			draw_layer_labels(numHists+1);
123
124			// add epoch
125			if (MovieMode) {
126			TText* t = new TText();
127			t->SetTextSize( 0.04 );
128			t->SetTextColor( 0 );
129			t->SetTextAlign( 31 );
130			t->DrawTextNDC( 1 - c->GetRightMargin(), 1 - c->GetTopMargin() - 0.033,
131			Form( "Epoch: %s", epoch.Data() ) );
132			}
133
134			// ============================================================
135			if (__PRINT_LOGO__) TMVAGlob::plot_logo();
136			// ============================================================
137
138			c->Update();
139			if (MovieMode) {
140			// save to file
141			TString dirname = "movieplots";
142			TString foutname = dirname + "/" + hName;
143			foutname.Resize( foutname.Length()-5 );
144			foutname.ReplaceAll("epochmonitoring___","");
145			foutname += ".gif";
146
147			cout << "storing file: " << foutname << endl;
148			c->Print(foutname);
149			c->Clear();
150			delete c;
151			}
152			else {
153			TString fname = "plots/network";
154			TMVAGlob::imgconv( c, fname );
155			}
156
157			// reset global style changes so that it does not affect other plots
158			TMVAStyle->SetCanvasColor ( canvasColor );
159			TMVAStyle->SetTitleFillColor ( titleFillColor );
160			TMVAStyle->SetTitleTextColor ( titleTextColor );
161			TMVAStyle->SetTitleBorderSize( borderSize );
162
163			}
164
165			void draw_layer_labels(Int_t nLayers)
166			{
167			const Double_t LABEL_HEIGHT = 0.032;
168			const Double_t LABEL_WIDTH = 0.20;
169			Double_t effWidth = 0.8*(1.0-LABEL_WIDTH)/nLayers;
170			Double_t height = 0.8*LABEL_HEIGHT;
171			Double_t margY = LABEL_HEIGHT - height;
172
173			for (Int_t i = 0; i < nLayers; i++) {
174			TString label = Form("Layer %i", i);
175			if (i == nLayers-1) label = "Output layer";
176			Double_t cx = i(1.0-LABEL_WIDTH)/nLayers+1.0/(2.0nLayers)+LABEL_WIDTH;
177			Double_t x1 = cx-0.8*effWidth/2.0;
178			Double_t x2 = cx+0.8*effWidth/2.0;
179			Double_t y1 = margY;
180			Double_t y2 = margY + height;
181
182			TPaveLabel *p = new TPaveLabel(x1, y1, x2, y2, label+"", "br");
183			p->SetFillColor(gStyle->GetTitleFillColor());
184			p->SetTextColor(gStyle->GetTitleTextColor());
185			p->SetFillStyle(1001);
186			p->SetBorderSize( 0 );
187			p->Draw();
188			}
189			}
190
191			void draw_input_labels(Int_t nInputs, Double_t* cy,
192			Double_t rad, Double_t layerWidth)
193			{
194			const Double_t LABEL_HEIGHT = 0.04;
195			const Double_t LABEL_WIDTH = 0.20;
196			Double_t width = LABEL_WIDTH + (layerWidth-4*rad);
197			Double_t margX = 0.01;
198			Double_t effHeight = 0.8*LABEL_HEIGHT;
199
200			TString *varNames = get_var_names(nInputs);
201			if (varNames == 0) exit(1);
202
203			TString input;
204
205			for (Int_t i = 0; i < nInputs; i++) {
206			if (i != nInputs-1) input = varNames[i];
207			else input = "Bias node";
208			Double_t x1 = margX;
209			Double_t x2 = margX + width;
210			Double_t y1 = cy[i] - effHeight;
211			Double_t y2 = cy[i] + effHeight;
212
213			TText* t = new TText();
214			t->SetTextColor(gStyle->GetTitleTextColor());
215			t->SetTextAlign(31);
216			t->SetTextSize(LABEL_HEIGHT);
217			if (i == nInputs-1) t->SetTextColor( TColor::GetColor( "#AFDCEC" ) );
218			t->DrawText( x2, y1+0.018, input + " :");
219			}
220
221			delete[] varNames;
222			}
223
224			TString* get_var_names( Int_t nVars )
225			{
226			const TString directories[6] = { "InputVariables_NoTransform",
227			"InputVariables_DecorrTransform",
228			"InputVariables_PCATransform",
229			"InputVariables_Id",
230			"InputVariables_Norm",
231			"InputVariables_Deco"};
232
233			TDirectory* dir = 0;
234			for (Int_t i=0; i<6; i++) {
235			dir = (TDirectory*)Network_GFile->Get( directories[i] );
236			if (dir != 0) break;
237			}
238			if (dir==0) {
239			cout << "*** Big troubles in macro \"network.C\": could not find directory for input variables, "
240			<< "and hence could not determine variable names --> abort" << endl;
241			return 0;
242			}
243			dir->cd();
244
245			TString* vars = new TString[nVars];
246			Int_t ivar = 0;
247
248			// loop over all objects in directory
249			TIter next(dir->GetListOfKeys());
250			TKey* key = 0;
251			while ((key = (TKey*)next())) {
252			if (key->GetCycle() != 1) continue;
253
254			if (!TString(key->GetName()).Contains("__S") &&
255			!TString(key->GetName()).Contains("__r") &&
256			!TString(key->GetName()).Contains("Regression"))
257			continue;
258			if (TString(key->GetName()).Contains("target"))
259			continue;
260
261			// make sure, that we only look at histograms
262			TClass *cl = gROOT->GetClass(key->GetClassName());
263			if (!cl->InheritsFrom("TH1")) continue;
264			TH1 sig = (TH1)key->ReadObj();
265			TString hname = sig->GetTitle();
266
267			vars[ivar] = hname; ivar++;
268
269			if (ivar > nVars-1) break;
270			}
271
272			if (ivar != nVars-1) { // bias layer and targets are also in nVars counts
273			cout << "*** Troubles in \"network.C\": did not reproduce correct number of "
274			<< "input variables: " << ivar << " != " << nVars << endl;
275			}
276
277			return vars;
278			}
279
280			void draw_activation(TCanvas* c, Double_t cx, Double_t cy,
281			Double_t radx, Double_t rady, Int_t whichActivation)
282			{
283			TImage *activation = NULL;
284
285			switch (whichActivation) {
286			case 0:
287			activation = TImage::Open("sigmoid-small.png");
288			break;
289			case 1:
290			activation = TImage::Open("line-small.png");
291			break;
292			default:
293			cout << "Activation index " << whichActivation << " is not known." << endl;
294			cout << "You messed up or you need to modify network.C to introduce a new "
295			<< "activation function (and image) corresponding to this index" << endl;
296			}
297
298			if (activation == NULL) {
299			cout << "Could not create an image... exit" << endl;
300			return;
301			}
302
303			activation->SetConstRatio(kFALSE);
304
305			radx *= 0.7;
306			rady *= 0.7;
307			TString name = Form("activation%f%f", cx, cy);
308			TPad* p = new TPad(name+"", name+"", cx-radx, cy-rady, cx+radx, cy+rady);
309
310			p->Draw();
311			p->cd();
312
313			activation->Draw();
314			c->cd();
315			}
316
317			void draw_layer(TCanvas* c, TH2F* h, Int_t iHist,
318			Int_t nLayers, Double_t maxWeight)
319			{
320			const Double_t MAX_NEURONS_NICE = 12;
321			const Double_t LABEL_HEIGHT = 0.03;
322			const Double_t LABEL_WIDTH = 0.20;
323			Double_t ratio = ((Double_t)(c->GetWindowHeight())) / c->GetWindowWidth();
324			Double_t rad, cx1, cy1, cx2, cy2;
325
326			// this is the smallest radius that will still display the activation images
327			rad = 0.04*650/c->GetWindowHeight();
328
329			Int_t nNeurons1 = h->GetNbinsX();
330			cx1 = iHist(1.0-LABEL_WIDTH)/nLayers + 1.0/(2.0nLayers) + LABEL_WIDTH;
331			cy1 = new Double_t[nNeurons1];
332
333			Int_t nNeurons2 = h->GetNbinsY();
334			cx2 = (iHist+1)(1.0-LABEL_WIDTH)/nLayers + 1.0/(2.0nLayers) + LABEL_WIDTH;
335			cy2 = new Double_t[nNeurons2];
336
337			Double_t effRad1 = rad;
338			if (nNeurons1 > MAX_NEURONS_NICE)
339			effRad1 = 0.8(1.0-LABEL_HEIGHT)/(2.0nNeurons1);
340
341			for (Int_t i = 0; i < nNeurons1; i++) {
342			cy1[nNeurons1-i-1] = i(1.0-LABEL_HEIGHT)/nNeurons1 + 1.0/(2.0nNeurons1) + LABEL_HEIGHT;
343
344			if (iHist == 0) {
345
346			TEllipse *ellipse = new TEllipse( cx1, cy1[nNeurons1-i-1],
347			effRad1*ratio, effRad1, 0, 360, 0 );
348			ellipse->SetFillColor(TColor::GetColor( "#fffffd" ));
349			ellipse->SetFillStyle(1001);
350			ellipse->Draw();
351
352			if (i == 0) ellipse->SetLineColor(9);
353
354			if (nNeurons1 > MAX_NEURONS_NICE) continue;
355
356			Int_t whichActivation = 0;
357			if (iHist==0 \|\| iHist==nLayers-1 \|\| i==0) whichActivation = 1;
358			draw_activation(c, cx1, cy1[nNeurons1-i-1],
359			rad*ratio, rad, whichActivation);
360			}
361			}
362
363			if (iHist == 0) draw_input_labels(nNeurons1, cy1, rad, (1.0-LABEL_WIDTH)/nLayers);
364
365			Double_t effRad2 = rad;
366			if (nNeurons2 > MAX_NEURONS_NICE)
367			effRad2 = 0.8(1.0-LABEL_HEIGHT)/(2.0nNeurons2);
368
369			for (Int_t i = 0; i < nNeurons2; i++) {
370			cy2[nNeurons2-i-1] = i(1.0-LABEL_HEIGHT)/nNeurons2 + 1.0/(2.0nNeurons2) + LABEL_HEIGHT;
371
372			TEllipse *ellipse =
373			new TEllipse(cx2, cy2[nNeurons2-i-1], effRad2*ratio, effRad2, 0, 360, 0);
374			ellipse->SetFillColor(TColor::GetColor( "#fffffd" ));
375			ellipse->SetFillStyle(1001);
376			ellipse->Draw();
377
378			if (i == 0 && nNeurons2 > 1) ellipse->SetLineColor(9);
379
380			if (nNeurons2 > MAX_NEURONS_NICE) continue;
381
382			Int_t whichActivation = 0;
383			if (iHist+1==0 \|\| iHist+1==nLayers-1 \|\| i==0) whichActivation = 1;
384			draw_activation(c, cx2, cy2[nNeurons2-i-1], rad*ratio, rad, whichActivation);
385			}
386
387			for (Int_t i = 0; i < nNeurons1; i++) {
388			for (Int_t j = 0; j < nNeurons2; j++) {
389			draw_synapse(cx1, cy1[i], cx2, cy2[j], effRad1ratio, effRad2ratio,
390			h->GetBinContent(i+1, j+1)/maxWeight);
391			}
392			}
393
394			delete [] cy1;
395			delete [] cy2;
396			}
397
398			void draw_synapse(Double_t cx1, Double_t cy1, Double_t cx2, Double_t cy2,
399			Double_t rad1, Double_t rad2, Double_t weightNormed)
400			{
401			const Double_t TIP_SIZE = 0.01;
402			const Double_t MAX_WEIGHT = 8;
403			const Double_t MAX_COLOR = 100; // red
404			const Double_t MIN_COLOR = 60; // blue
405
406			if (weightNormed == 0) return;
407
408			// gStyle->SetPalette(100, NULL);
409
410			TArrow *arrow = new TArrow(cx1+rad1, cy1, cx2-rad2, cy2, TIP_SIZE, ">");
411			arrow->SetFillColor(1);
412			arrow->SetFillStyle(1001);
413			arrow->SetLineWidth((Int_t)(TMath::Abs(weightNormed)*MAX_WEIGHT+0.5));
414			arrow->SetLineColor((Int_t)((weightNormed+1.0)/2.0*(MAX_COLOR-MIN_COLOR)+MIN_COLOR+0.5));
415			arrow->Draw();
416			}
417
418			// input: - Input file (result from TMVA),
419			// - use of TMVA plotting TStyle
420			void network( TString fin = "TMVA.root", Bool_t useTMVAStyle = kTRUE )
421			{
422			// set style and remove existing canvas'
423			TMVAGlob::Initialize( useTMVAStyle );
424
425			// checks if file with name "fin" is already open, and if not opens one
426			TFile* file = TMVAGlob::OpenFile( fin );
427			TIter next(file->GetListOfKeys());
428			TKey *key(0);
429			while( (key = (TKey*)next()) ) {
430			if (!TString(key->GetName()).BeginsWith("Method_MLP")) continue;
431			if( ! gROOT->GetClass(key->GetClassName())->InheritsFrom("TDirectory") ) continue;
432
433			cout << "--- Found directory: " << ((TDirectory*)key->ReadObj())->GetName() << endl;
434
435			TDirectory* mDir = (TDirectory*)key->ReadObj();
436
437			TIter keyIt(mDir->GetListOfKeys());
438			TKey *titkey;
439			while((titkey = (TKey*)keyIt())) {
440			if( ! gROOT->GetClass(titkey->GetClassName())->InheritsFrom("TDirectory") ) continue;
441
442			TDirectory* dir = (TDirectory *)titkey->ReadObj();
443			dir->cd();
444			TList titles;
445			UInt_t ni = TMVAGlob::GetListOfTitles( dir, titles );
446			if (ni==0) {
447			cout << "No titles found for Method_MLP" << endl;
448			return;
449			}
450			draw_network( file, dir );
451			}
452			}
453
454			return;
455			}
456