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Diffstat (limited to 'python/examples/test.py')
-rw-r--r-- | python/examples/test.py | 70 |
1 files changed, 70 insertions, 0 deletions
diff --git a/python/examples/test.py b/python/examples/test.py new file mode 100644 index 00000000..eb9e6a95 --- /dev/null +++ b/python/examples/test.py @@ -0,0 +1,70 @@ +#coding: utf8 +import cdec +import gzip + +weights = '../tests/system_tests/australia/weights' +grammar_file = '../tests/system_tests/australia/australia.scfg.gz' + +# Load decoder width configuration +decoder = cdec.Decoder(formalism='scfg') +# Read weights +decoder.read_weights(weights) + +print dict(decoder.weights) + +# Read grammar +with gzip.open(grammar_file) as f: + grammar = f.read() + +# Input sentence +sentence = u'澳洲 是 与 北韩 有 邦交 的 少数 国家 之一 。' +print ' Input:', sentence.encode('utf8') + +# Decode +forest = decoder.translate(sentence, grammar=grammar) + +# Get viterbi translation +print 'Output[0]:', forest.viterbi().encode('utf8') +f_tree, e_tree = forest.viterbi_trees() +print ' FTree[0]:', f_tree.encode('utf8') +print ' ETree[0]:', e_tree.encode('utf8') +print 'LgProb[0]:', forest.viterbi_features().dot(decoder.weights) + +# Get k-best translations +kbest = zip(forest.kbest(5), forest.kbest_trees(5), forest.kbest_features(5)) +for i, (sentence, (f_tree, e_tree), features) in enumerate(kbest, 1): + print 'Output[%d]:' % i, sentence.encode('utf8') + print ' FTree[%d]:' % i, f_tree.encode('utf8') + print ' ETree[%d]:' % i, e_tree.encode('utf8') + print ' FVect[%d]:' % i, dict(features) + +# Sample translations from the forest +for sentence in forest.sample(5): + print 'Sample:', sentence.encode('utf8') + +# Get feature vector for 1best +fsrc = forest.viterbi_features() + +# Feature expectations +print 'Feature expectations:', dict(forest.inside_outside()) + +# Reference lattice +lattice = ((('australia',0,1),),(('is',0,1),),(('one',0,1),),(('of',0,1),),(('the',0,4),('a',0,4),('a',0,1),('the',0,1),),(('small',0,1),('tiny',0,1),('miniscule',0,1),('handful',0,2),),(('number',0,1),('group',0,1),),(('of',0,2),),(('few',0,1),),(('countries',0,1),),(('that',0,1),),(('has',0,1),('have',0,1),),(('diplomatic',0,1),),(('relations',0,1),),(('with',0,1),),(('north',0,1),),(('korea',0,1),),(('.',0,1),),) + +lat = cdec.Lattice(lattice) +assert (lattice == tuple(lat)) + +# Intersect forest and lattice +assert forest.intersect(lat) + +# Get best synchronous parse +f_tree, e_tree = forest.viterbi_trees() +print 'FTree:', f_tree.encode('utf8') +print 'ETree:', e_tree.encode('utf8') + +# Compare 1best and reference feature vectors +fref = forest.viterbi_features() +print dict(fsrc - fref) + +# Prune hypergraph +forest.prune(density=100) |