diff --git a/ccg-test.py b/ccg-test.py
index 501d54a..4d7b660 100644
--- a/ccg-test.py
+++ b/ccg-test.py
@@ -1,4 +1,5 @@
 from nltk.ccg import chart, lexicon
+from nltk.tree import Tree
 import pandas as pd
 import numpy as np
 
@@ -19,7 +20,7 @@ for i in range(n):
     for j in range(3):
         if isinstance(table['Cat'+str(j)][i],str):
             for mot in table['MOT'][i].split('/'):
-                lexstring+=mot+' => ' + table['Cat'+str(j)][i] + '\n'
+                lexstring+=mot+' => ' + table['Cat'+str(j)][i]  + '\n'
 
 # Pour inverser les slash dans le lexicon
 #lexstring = lexstring.replace('\\','#').replace('/','\\').replace('#','/')
@@ -38,8 +39,8 @@ printDerivations=not printTotal
 with open('phrases.txt') as f:
     lines = f.readlines()
 
-    lines.append("mon voisin lui donne le chat")
-
+    lines.append("le chat et la souris dorment")
+    
     for phrase in lines:
         # On met tout en minuscule
         phrase = phrase.lower().strip()
@@ -55,6 +56,106 @@ with open('phrases.txt') as f:
             i+=1
             if printDerivations:
                 chart.printCCGDerivation(parse)
-            
+        
         if printTotal:
             print(i,phrase)
+
+from nltk.ccg.chart import CCGChart,CCGLeafEdge
+
+
+valz = {
+    '>' : 0.8,
+    '<' : 0.7
+}
+def rweight(rule):
+    s = rule.__str__()
+    if s in valz:
+        return valz[s]
+    else:
+        return 1.0 # Base rules weight
+
+# Implements the CYK algorithm
+def cyk(parser, tokens, lex, rules):
+    chart = CCGChart(list(tokens))
+    chart2 = CCGChart(list(tokens))
+    
+    # Initialize leaf edges.
+    for index in range(chart.num_leaves()):
+        for token in lex.categories(chart.leaf(index)):
+            new_edge = CCGLeafEdge(index, token, chart.leaf(index))
+            new_edge.weight = 1.0
+            chart.insert(new_edge, ())
+            chart2.insert(new_edge, ())
+    print(chart.pretty_format())
+
+    # Select a span for the new edges
+    for span in range(2, chart.num_leaves() + 1):
+        for start in range(0, chart.num_leaves() - span + 1):
+            
+            bestedge = None
+            
+            # Try all possible pairs of edges that could generate
+            # an edge for that span
+            for part in range(1, span):
+                lstart = start
+                mid = start + part
+                rend = start + span
+
+                for left in chart.select(span=(lstart, mid)):
+                    for right in chart.select(span=(mid, rend)):
+                        # Generate all possible combinations of the two edges
+                        for rule in rules:
+                            edgez = list(rule.apply(chart, lex, left, right))
+                            if(len(edgez)==1):
+                                edge = edgez[0]
+                                edge.weight = rweight(rule) * left.weight * right.weight
+                                edge.triple = (rule,left,right)
+                                print(edge)
+                                if (bestedge == None) or (bestedge.weight < edge.weight):
+                                    bestedge = edge
+                            elif(len(edgez)!=0):
+                                print("Too many new edges")
+                                
+                        # end for rule loop
+                    # end for right loop
+                # end for left loop
+            # end for part loop
+            if bestedge != None:
+                print("|",bestedge.triple,rweight(bestedge.triple[0]) * bestedge.triple[1].weight * bestedge.triple[2].weight)
+                e = list(bestedge.triple[0].apply(chart2,lex,bestedge.triple[1],bestedge.triple[2]))[0]
+                e.triple = bestedge.triple
+                    
+        print("-"*20)
+    return chart
+
+def totree(edge):
+    if isinstance(edge,CCGLeafEdge):
+        return Tree((edge.token(),"Leaf"),[Tree(edge.token(),[edge.leaf()])])
+    else:
+        return Tree(
+            (chart.Token(None,edge.categ()),edge.triple[0].__str__()),
+            [totree(t) for t in (edge.triple[1:])])
+
+def viterbiCKY(mots):
+    n = len(mots)
+    t = np.zeros((n+1,n+1))
+    # t[s,k] is the probability of obtaining the word mots[s] mots[s+1] ... mots[s+n-1]
+    for i in range(0,n):
+        t[i][1] = 1.0
+    for l in range(2,len(mots)+1):
+        for s in range(0,n-l+1):
+            # We want to set t[s][l]
+            for k in range(1,l): # Partitionning of the sequence
+                pass
+            
+#T ← ∅
+#for 0 ≤ i ≤ n do
+#δ(〈wi , i, i + 1〉) ← 1.0
+#end for
+#for all 〈X , i, j〉 ∈ V following a topological order do
+#δ(〈X , i, j〉) ← 0
+#for 〈X , i, j〉 → 〈Y1, i, k〉 〈Y2, k, j〉 ∈ IE (v ) do
+#δ(〈X , i, j〉) ← max (δ(v ), ψ(e) × δ(〈Y1, i, k〉) × δ(〈Y2, k, j〉))
+#end for
+#end for
+#end function