1 files changed, 59 insertions, 0 deletions
diff --git a/sourcecodes/bnt-master/SLP/misc/discretization.m b/sourcecodes/bnt-master/SLP/misc/discretization.m
new file mode 100644
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--- /dev/null
+++ b/sourcecodes/bnt-master/SLP/misc/discretization.m
@@ -0,0 +1,59 @@
+function [appD, testD, bornes] = discretization(critere, continues, miss, app, test)
+% [appD, testD, bornes] = discretization(critere, continious, miss, app, test)
+%
+% Inputs :
+%    critere = 1, 2, 3 or 4 (see hict_ic for details)
+%    continious = vector of continious variables to discretize
+%    miss ~= 0 if it exists missing values coded by 'miss' value
+%    app = Learning base
+%    test = Test base (only the learning base is used to make the discretization rules) [optionnal]
+%
+% Outputs :
+%    appD = Learning base with discretized entries on 'continious' variables
+%    testD = Test base with discretized entries on 'continious' variables
+%    bornes = limits of discretization intervals found by hist_ic
+%
+
+tt=1;
+if nargin<5, test=[]; tt=0; end
+app = app';
+test = test';
+[ma, Na] = size(app);
+[mt, Nt] = size(test);
+I = [];
+testD = [];
+
+if miss,
+ [I J]=find(app==miss);
+ [I2 J2]=find(test==miss);
+end
+completes=setdiff(1:ma,I);
+donnees_continue=app(completes,continues);
+
+% echantillonnage
+[n,bornes,nbbornes,xx]=hist_ic(donnees_continue,critere);
+
+% on re-distribue l'ensemble des donnees d'apprentissage continues
+[n2,appD_continues]=histc_ic(app(:,continues),bornes);
+if tt, [n2test,testD_continues]=histc_ic(test(:,continues),bornes); end
+
+% on insere les donnees continues discretisees dans les matrices
+appD=app;
+if tt, testD=test; end
+for i=1:length(continues)
+    appD(:,continues(i))=appD_continues(:,i);
+    if tt, testD(:,continues(i))=testD_continues(:,i); end
+end
+
+if miss,
+ for k=1:length(I)
+  app(I(k),J(k))=miss;
+ end
+ if tt, 
+  for l=1:length(I2)
+   testD(I2(l),J2(l))=miss;
+  end
+ end
+end
+appD = appD';
+testD = testD';
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