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Author: Daniel Sedra

modules/tdIDF.py

@@ -5,7 +5,8 @@
 
 # constructs a vector, for each word in question the tf-idf score with article
 def main(question, article):
-  ddict = get_counts()
+  ddict = {}
+  counts = get_counts()
   for tok in nltk.word_tokenize(article):
     ddict[tok] = ddict.get(tok, 0) + 1
 
@@ -16,21 +17,27 @@ def main(question, article):
     tf = ddict.get(tok, 0) 
 
     # total articles is 108 / number that have current token
-    idf = math.log(float(108)/len(filter(lambda x:tok in x.keys(),dicts)) + 1)
+    idf = math.log(float(108)/len(filter(lambda x:tok in x.keys(),counts)) + 1)
     vec.append(tf*idf)
 
   largest = max(vec)
   normalized = map(lambda y: y/largest, vec)
-  return normalized
+
+  finDic = {}
+  for word,i in enumerate(nltk.word_tokenize(question)):
+    finDic[word] = normalized[i]
+
+  print finDic
+  return finDic
 
 articles_per_set = 9
 
 # goes through sample wiki articles and gets word counts
 def get_counts():
-  counts = {}
+  counts = []
   sets_per_year = 4
   for year in ("S08", "S09", "S10"):
-    for i in xrange(1, num_sets):
+    for i in xrange(1, sets_per_year):
       for j in xrange(1, articles_per_set+1):
         path = "../Question_Answer_Dataset_v1.1/"+year+"/data/set"+str(i)+"/a"+str(j)+".txt"
         cfile = open(path).read()
@@ -39,4 +46,7 @@ def get_counts():
           partic[tok] = partic.get(tok, 0) + 1
         counts.append(partic)
     sets_per_year += 1
-  return counts
+  return counts
+
+
+main('and kangaroo','and and kangaroo')