python类division()的实例源码

def __div__(self, other, *args):
        '''
        Integer division (//)
        '''
        return self._apply_operator(other, "__div__", *args)

def __truediv__(self, other, *args):
        '''
        Float division (/)
        '''
        return self._apply_operator(other, "__truediv__", *args)

def get_total_seconds(td):
        # integer division used here to emulate built-in total_seconds
        return ((86400 * td.days + td.seconds) * 10 ** 6 + td.microseconds) / 10 ** 6

def calculate_tf_idf(sentence, global_count_of_papers_words_occur_in, paper_bag_of_words):
    """
    Calculates the tf-idf score for a sentence based on all of the papers.
    :param sentence: the sentence to calculate the score for, as a list of words
    :param global_count_of_papers_words_occur_in: a dictionary of the form (word: number of papers the word occurs in)
    :param paper_bag_of_words: the bag of words representation for a paper
    :return: the tf-idf score of the sentence
    """
    bag_of_words = paper_bag_of_words

    sentence_tf_idf = 0

    length = 0

    tf_idfs = []

    for word in sentence:

        # Get the number of documents containing this word - the idf denominator (1 is added to prevent division by 0)
        docs_containing_word = global_count_of_papers_words_occur_in[word] + 1

        # Count of word in this paper - the tf score
        count_word = bag_of_words[word]

        idf = np.log(NUMBER_OF_PAPERS / docs_containing_word)

        word_tf_idf = count_word * idf

        tf_idfs.append(word_tf_idf)

    return [x for x in zip(sentence, tf_idfs)]

def calculate_tf_idf(sentence, global_count_of_papers_words_occur_in, paper_bag_of_words):
    """
    Calculates the tf-idf score for a sentence based on all of the papers.
    :param sentence: the sentence to calculate the score for, as a list of words
    :param global_count_of_papers_words_occur_in: a dictionary of the form (word: number of papers the word occurs in)
    :param paper_bag_of_words: the bag of words representation for a paper
    :return: the tf-idf score of the sentence
    """
    bag_of_words = paper_bag_of_words

    sentence_tf_idf = 0

    length = 0

    for word in sentence:

        if word in STOPWORDS:
            continue

        # Get the number of documents containing this word - the idf denominator (1 is added to prevent division by 0)
        docs_containing_word = global_count_of_papers_words_occur_in[word] + 1

        # Count of word in this paper - the tf score
        count_word = bag_of_words[word]

        idf = np.log(NUMBER_OF_PAPERS / docs_containing_word)

        #word_tf_idf = (1 + np.log(count_word)) * idf

        word_tf_idf = count_word * idf

        sentence_tf_idf += word_tf_idf

        length += 1

    if length == 0:
        return 0
    else:
        sentence_tf_idf = sentence_tf_idf / length
        return sentence_tf_idf

def download_speed(self):
        # Avoid zero division errors...
        if self.avg == 0.0:
            return "..."
        return format_size(1 / self.avg) + "/s"

def download_speed(self):
        # Avoid zero division errors...
        if self.avg == 0.0:
            return "..."
        return format_size(1 / self.avg) + "/s"

def download_speed(self):
        # Avoid zero division errors...
        if self.avg == 0.0:
            return "..."
        return format_size(1 / self.avg) + "/s"

def download_speed(self):
        # Avoid zero division errors...
        if self.avg == 0.0:
            return "..."
        return format_size(1 / self.avg) + "/s"

def download_speed(self):
        # Avoid zero division errors...
        if self.avg == 0.0:
            return "..."
        return format_size(1 / self.avg) + "/s"

def download_speed(self):
        # Avoid zero division errors...
        if self.avg == 0.0:
            return "..."
        return format_size(1 / self.avg) + "/s"

def weighted_avg_and_std(values, weights):
    """
    Return the weighted average and standard deviation.

    values, weights -- Numpy ndarrays with the same shape.

    References:
    - http://stackoverflow.com/questions/2413522/weighted-standard-deviation-in-numpy
    - https://en.wikipedia.org/wiki/Mean_square_weighted_deviation

    Note: The method is biased (division by N and not (N-1)).
    """
    average = NP.average(values, weights=weights)
    variance = NP.average((values-average)**2, weights=weights)  # Fast and numerically precise
    return (average, math.sqrt(variance))

def download_speed(self):
        # Avoid zero division errors...
        if self.avg == 0.0:
            return "..."
        return format_size(1 / self.avg) + "/s"

def get_total_seconds(td):
        # integer division used here to emulate built-in total_seconds
        return ((86400 * td.days + td.seconds) * 10 ** 6 + td.microseconds) / 10 ** 6

def download_speed(self):
        # Avoid zero division errors...
        if self.avg == 0.0:
            return "..."
        return format_size(1 / self.avg) + "/s"

def download_speed(self):
        # Avoid zero division errors...
        if self.avg == 0.0:
            return "..."
        return format_size(1 / self.avg) + "/s"

def __div__(self, other):
        """self / other without __future__ division

        May promote to float.
        """
        raise NotImplementedError

def __rdiv__(self, other):
        """other / self without __future__ division"""
        raise NotImplementedError

def __truediv__(self, other):
        """self / other with __future__ division.

        Should promote to float when necessary.
        """
        raise NotImplementedError

def __rtruediv__(self, other):
        """other / self with __future__ division"""
        raise NotImplementedError