Python log_conditional_probability示例

示例#1

文件： test_direct_confirmation.py 项目： chitang1990/Gensim-3.1.0

    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)

示例#2

文件： test_direct_confirmation.py 项目： RaRe-Technologies/gensim

    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)

示例#3

文件： test_direct_confirmation.py 项目： YantianZha/Distr2Vec

 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)

示例#4

文件： coherence_measures_unrefined.py 项目： Directorman9/cross_collection_opinion_mining

                    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(

示例#5

文件： test_direct_confirmation.py 项目： ArifAhmed1995/gensim

 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)