Proba oracle and random per state

Train.py

@@ -214,8 +214,6 @@ def trainModelRl(debug, networkName, modelDir, filename, nbIter, batchSize, devF
   sentIndex = 0
 
   for epoch in range(1,nbIter+1) :
-    probaRandom = round((probas[0][0]-probas[0][2])*math.exp((-epoch+1)/probas[0][1])+probas[0][2], 2)
-    probaOracle = round((probas[1][0]-probas[1][2])*math.exp((-epoch+1)/probas[1][1])+probas[1][2], 2)
     i = 0
     totalLoss = 0.0
     while True :
@@ -237,6 +235,9 @@ def trainModelRl(debug, networkName, modelDir, filename, nbIter, batchSize, devF
         transitionSet = transitionSets[sentence.state]
         fromState = sentence.state
         toState = sentence.state
+        probaRandom = round((probas[fromState][0][0]-probas[fromState][0][2])*math.exp((-epoch+1)/probas[fromState][0][1])+probas[fromState][0][2], 2)
+        probaOracle = round((probas[fromState][1][0]-probas[fromState][1][2])*math.exp((-epoch+1)/probas[fromState][1][1])+probas[fromState][1][2], 2)
+        
 
         if debug :
           sentence.printForDebug(sys.stderr)

main.py

@@ -89,6 +89,7 @@ if __name__ == "__main__" :
     args.states = ["tagger"]
     strategy = {"TAG" : (1,0)}
     args.network = "tagger"
+    args.probas = [[[0.6,4,0.1],[0.3,2,0.0]]]
   elif args.transitions == "taggerbt" :
     tmpDicts = Dicts()
     tmpDicts.readConllu(args.corpus, ["UPOS"], 0)
@@ -98,11 +99,13 @@ if __name__ == "__main__" :
     args.states = ["tagger", "backer"]
     strategy = {"TAG" : (1,1), "NOBACK" : (0,0)}
     args.network = "tagger"
+    args.probas = [[[0.6,4,0.1],[0.3,2,0.0]],[[0.6,4,0.1],[0.3,2,0.0]]]
   elif args.transitions == "eager" :
     transitionSets = [[Transition(elem) for elem in (["SHIFT","REDUCE","LEFT","RIGHT"]+args.ts.split(',')) if len(elem) > 0]]
     args.predictedStr = "HEAD"
     args.states = ["parser"]
     strategy = {"RIGHT" : (1,0), "SHIFT" : (1,0), "LEFT" : (0,0), "REDUCE" : (0,0)}
+    args.probas = [[[0.6,4,0.1],[0.3,2,0.0]]]
   elif args.transitions == "tagparser" :
     tmpDicts = Dicts()
     tmpDicts.readConllu(args.corpus, ["UPOS"], 0)
@@ -111,6 +114,7 @@ if __name__ == "__main__" :
     args.predictedStr = "HEAD,UPOS"
     args.states = ["tagger", "parser"]
     strategy = {"RIGHT" : (1,0), "SHIFT" : (1,0), "LEFT" : (0,1), "REDUCE" : (0,1), "TAG" : (0,1)}
+    args.probas = [[[0.6,4,0.1],[0.3,2,0.0]],[[0.6,4,0.1],[0.3,2,0.0]]]
   elif args.transitions == "tagparserbt" :
     tmpDicts = Dicts()
     tmpDicts.readConllu(args.corpus, ["UPOS"], 0)
@@ -119,6 +123,7 @@ if __name__ == "__main__" :
     args.predictedStr = "HEAD,UPOS"
     args.states = ["tagger", "parser", "backer"]
     strategy = {"RIGHT" : (1,2), "SHIFT" : (1,2), "LEFT" : (0,1), "REDUCE" : (0,1), "TAG" : (0,1), "NOBACK" : (0,0)}
+    args.probas = [[[0.6,4,0.1],[0.3,2,0.0]],[[0.6,4,0.1],[0.3,2,0.0]],[[0.0,25,1.0],[1.0,25,0.0]]]
   elif args.transitions == "swift" :
     transitionSets = [[Transition(elem) for elem in (["SHIFT"]+["LEFT "+str(n) for n in range(1,6)]+["RIGHT "+str(n) for n in range(1,6)]+args.ts.split(',')) if len(elem) > 0]]
     args.predictedStr = "HEAD"
@@ -133,7 +138,7 @@ if __name__ == "__main__" :
     json.dump(strategy, open(args.model+"/strategy.json", "w"))
     printTS(transitionSets, sys.stderr)
     probas = [list(map(float, args.probaRandom.split(','))), list(map(float, args.probaOracle.split(',')))]
-    Train.trainMode(args.debug, args.network, args.corpus, args.type, transitionSets, strategy, args.model, int(args.iter), int(args.batchSize), args.dev, args.bootstrap, args.incr, args.reward, float(args.lr), float(args.gamma), probas, int(args.countBreak), args.predicted, args.silent)
+    Train.trainMode(args.debug, args.network, args.corpus, args.type, transitionSets, strategy, args.model, int(args.iter), int(args.batchSize), args.dev, args.bootstrap, args.incr, args.reward, float(args.lr), float(args.gamma), args.probas, int(args.countBreak), args.predicted, args.silent)
   elif args.mode == "decode" :
     transInfos = json.load(open(args.model+"/transitions.json", "r"))
     transNames = json.load(open(args.model+"/transitions.json", "r"))[1]