def testLogConditionalProbability(self):
"""Test log_conditional_probability()"""
obtained = direct_confirmation_measure.log_conditional_probability(
self.segmentation, self.accumulator)[0]
# Answer should be ~ ln(1 / 2) = -0.693147181
expected = -0.693147181
self.assertAlmostEqual(expected, obtained)
mean, std = direct_confirmation_measure.log_conditional_probability(
self.segmentation, self.accumulator, with_std=True)[0]
self.assertAlmostEqual(expected, mean)
self.assertEqual(0.0, std)
def testLogConditionalProbability(self):
"""Test log_conditional_probability()"""
obtained = direct_confirmation_measure.log_conditional_probability(
self.segmentation, self.accumulator)[0]
# Answer should be ~ ln(1 / 2) = -0.693147181
expected = -0.693147181
self.assertAlmostEqual(expected, obtained)
mean, std = direct_confirmation_measure.log_conditional_probability(
self.segmentation, self.accumulator, with_std=True)[0]
self.assertAlmostEqual(expected, mean)
self.assertEqual(0.0, std)
def testLogConditionalProbability(self):
"""Test log_conditional_probability()"""
obtained = direct_confirmation_measure.log_conditional_probability(
self.segmentation, self.posting_list, self.num_docs)[0]
# Answer should be ~ ln(1 / 2) = -0.693147181
expected = -0.693147181
self.assertAlmostEqual(obtained, expected)
continue
tupl = (w1, w2)
unsegmented_topic.append(w1)
segmented_topic.append(tupl)
segmented_topics.append(segmented_topic)
unsegmented_topics.append(unsegmented_topic)
segmented_topic = []
unsegmented_topic = []
#Make accumulator
accumulator = probability_estimation.p_boolean_document(
corpus, segmented_topics)
#Perform the measurements and print results
lcp = direct_confirmation_measure.log_conditional_probability(
segmented_topics, accumulator)
with codecs.open(outputfile1, encoding='utf-8', mode='w',
errors='ignore') as outputFile:
for item in lcp:
outputFile.write('%s \n' % (item))
pmi = direct_confirmation_measure.log_ratio_measure(
segmented_topics, accumulator)
with codecs.open(outputfile2, encoding='utf-8', mode='w',
errors='ignore') as outputFile:
for item in pmi:
outputFile.write('%s \n' % (item))
cosim = indirect_confirmation_measure.cosine_similarity(
def testLogConditionalProbability(self):
"""Test log_conditional_probability()"""
obtained = direct_confirmation_measure.log_conditional_probability(self.segmentation, self.posting_list, self.num_docs)[0]
# Answer should be ~ ln(1 / 2) = -0.693147181
expected = -0.693147181
self.assertAlmostEqual(obtained, expected)