def setUp(self):
super(TestSandhisplitter, self).setUp()
self.model = Model(depth=3, skip=1)
self.SS = Sandhisplitter()
testcases = resource_filename("sandhisplitter.tests",
"resources/samples.txt")
self.entries = open(testcases, "r", encoding='utf-8')
class TestSandhisplitter(TestCase):
def setUp(self):
super(TestSandhisplitter, self).setUp()
self.model = Model(depth=3, skip=1)
self.SS = Sandhisplitter()
testcases = resource_filename("sandhisplitter.tests",
"resources/samples.txt")
self.entries = open(testcases, "r", encoding='utf-8')
def test_splits(self):
count = 0
entries = map(lambda x: x.strip(), self.entries.readlines())
for line in entries:
count += 1
(word, splits, locs) = extract(line)
self.model.add_entry(word, splits, locs)
m = self.model.serialize()
self.SS.set_model(m)
for line in entries:
(word, splits, locs) = extract(line)
obtained, pos = self.SS.split(word)
self.assertEqual(locs, pos)
self.assertEqual(splits, obtained)
def test_details(self):
self.assertEqual(self.SS.get_module_name(), "Sandhi-Splitter")
self.assertEqual(self.SS.get_info(), "Sandhi-splitter for malayalam")
def test_instance(self):
self.assertEqual(isinstance(getInstance(), Sandhisplitter), True)
def sandhi_split(token_words):
#print("\n Splitted using Sandhi!!\n--------------------------")
temp=[]
s = Sandhisplitter()
for word in token_words:
ss=s.split(word)
out=ss[0]
temp.append(out)
print(out)
return temp
def __init__(self):
super(Malayalam, self).__init__()
# Let's give the spellchecker a boost.
self.sandhi = Sandhisplitter()
class Malayalam(BaseMalayalam, object):
def __init__(self):
super(Malayalam, self).__init__()
# Let's give the spellchecker a boost.
self.sandhi = Sandhisplitter()
def check(self, word):
# Trivial case, word is in corpus
if super(Malayalam, self).check(word):
return True
# Sandhisplitter additions
# Check for each split word if word exists in corpus
# Increases True Positives, Reduces False Negatives
words, splits = self.sandhi.split(word)
for w in words:
if not super(Malayalam, self).check(w):
return False
return True
def suggest(self, word, n=5):
# Start with bases suggestions
suggestions = super(Malayalam, self).suggest(word, n)
# Sandhisplitter additions
words, splits = self.sandhi.split(word)
corrections = []
for w in words:
# Word in dictionary
if super(Malayalam, self).check(w):
corrections.append([w])
# Word not in dictionary
else:
corrections.append(super(Malayalam, self).suggest(w, n))
# Cross product to get all possibilities
candidates = product(*corrections)
# Apply joiner on possibile tuples.
for group in candidates:
joined = self.sandhi.join(group)
suggestions.append(joined)
# Scoring via levenstein, sort by levenshtein
scores = []
for suggestion in suggestions:
score = super(Malayalam,
self).levenshtein_distance(suggestion, word)
scores.append(score)
paired = list(zip(scores, suggestions))
paired.sort()
sorted_suggestions = []
for (score, suggestion) in paired:
sorted_suggestions.append(suggestion)
# Trim off to match n
if (len(sorted_suggestions) > n):
sorted_suggestions = sorted_suggestions[:n]
# And tadaa!!!
return sorted_suggestions
def __init__(self):
super(Malayalam, self).__init__()
# Let's give the spellchecker a boost.
self.sandhi = Sandhisplitter()
class Malayalam(BaseMalayalam, object):
def __init__(self):
super(Malayalam, self).__init__()
# Let's give the spellchecker a boost.
self.sandhi = Sandhisplitter()
def check(self, word):
# Trivial case, word is in corpus
if super(Malayalam, self).check(word):
return True
# Sandhisplitter additions
# Check for each split word if word exists in corpus
# Increases True Positives, Reduces False Negatives
words, splits = self.sandhi.split(word)
for w in words:
if not super(Malayalam, self).check(w):
return False
return True
def suggest(self, word, n=5):
# Start with bases suggestions
suggestions = super(Malayalam, self).suggest(word, n)
# Sandhisplitter additions
words, splits = self.sandhi.split(word)
corrections = []
for w in words:
# Word in dictionary
if super(Malayalam, self).check(w):
corrections.append([w])
# Word not in dictionary
else:
corrections.append(super(Malayalam, self).suggest(w, n))
# Cross product to get all possibilities
candidates = product(*corrections)
# Apply joiner on possibile tuples.
for group in candidates:
joined = self.sandhi.join(group)
suggestions.append(joined)
# Scoring via levenstein, sort by levenshtein
scores = []
for suggestion in suggestions:
score = super(Malayalam, self).levenshtein_distance(
suggestion, word)
scores.append(score)
paired = list(zip(scores, suggestions))
paired.sort()
sorted_suggestions = []
for (score, suggestion) in paired:
sorted_suggestions.append(suggestion)
# Trim off to match n
if (len(sorted_suggestions) > n):
sorted_suggestions = sorted_suggestions[:n]
# And tadaa!!!
return sorted_suggestions
