--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/hmm/tinySup.py	Mon Mar 09 16:48:09 2020 +0000
@@ -0,0 +1,66 @@
+'''Trivial test of unsupervised learning with full dictionary supplied
+See fnlp/lectures/12/hmmDNV.xlsx'''
+import nltk, random
+from nltk.tag.hmm import HiddenMarkovModelTagger, HiddenMarkovModelTrainer
+from nltk.probability import FreqDist,ConditionalFreqDist
+from nltk.probability import MLEProbDist, RandomProbDist, DictionaryConditionalProbDist
+
+tagset=['<s>','D','N','V','</s>']
+symbols=['<s>','the','sheep','run','</s>']
+sents=[[('<s>','<s>'),('the','D'),('sheep','N'),('run','V'),('</s>','</s>')],
+       [('<s>','<s>'),('sheep','N'),('run','V'),('the','D'),('sheep','N'),('</s>','</s>')],
+       [('<s>','<s>'),('run','V'),('the','D'),('sheep','N'),('</s>','</s>')]]
+
+taglists=[('<s>',[('<s>',1),('the',0),('sheep',0),('run',0),('</s>',0)]),
+         ('D',[('the',1),('sheep',0),('run',0),('<s>',0),('</s>',0)]),
+         ('N',[('the',0),('sheep',.5),('run',.5),('<s>',0),('</s>',0)]),
+         ('V',[('the',0),('sheep',.5),('run',.5),('<s>',0),('</s>',0)]),
+         ('</s>',[('<s>',0),('the',0),('sheep',0),('run',0),('</s>',1)])]
+
+tagdict=dict((k,MLEProbDist(FreqDist(dict(v)))) for k,v in taglists)
+  
+priors = MLEProbDist(FreqDist({'<s>':1,
+         'D':0,
+         'N':0,
+         'V':0,
+         '</s>':0}))
+
+transitions = DictionaryConditionalProbDist(
+                dict((state, RandomProbDist(tagset))
+                      for state in tagset))
+
+outputs = DictionaryConditionalProbDist(tagdict)
+
+
+for tag in tagset:
+  cp=outputs[tag]
+  print tag,sum(cp.prob(s) for s in symbols)
+
+model = HiddenMarkovModelTagger(symbols, tagset,
+                transitions, outputs, priors)
+
+for tag in tagset:
+  cp=model._outputs[tag]
+  print tag,sum(cp.prob(s) for s in symbols)
+
+nm=HiddenMarkovModelTrainer(states=tagset,symbols=symbols)
+
+# Note that contrary to naive reading of the documentation,
+#  train_unsupervised expects a sequence of sequences of word/tag pairs,
+#  it just ignores the tags
+nnm=nm.train_unsupervised(sents,model=model,max_iterations=10,updateOutputs=False)
+
+for tag in tagset:
+  if tag=='</s>':
+    break
+  cp=nnm._transitions[tag]
+  print(("    "+4*"%6s")%tuple(tagset[1:]))
+  print(("%3s: "+4*"%6.3f")%tuple([tag]+[cp.prob(s) for s in tagset[1:]]))
+
+for tag in tagset:
+  cp=nnm._outputs[tag]
+  print(("    "+5*"%6s")%tuple(symbols))
+  x=[cp.prob(s) for s in symbols]
+  print(("%3s: "+5*"%6.3f"+"%11.4e")%tuple([tag]+x+[sum(x)]))
+
+print nnm.evaluate(sents)