Python statistics.StatisticsError方法代碼示例

# 需要導入模塊: import statistics [as 別名]
# 或者: from statistics import StatisticsError [as 別名]
def diagnosticity(evaluations):
    """Return the diagnosticity of a piece of evidence given its evaluations against a set of hypotheses.

    :param evaluations: an iterable of iterables of Eval for a piece of evidence
    """
    # The "diagnosticity" needs to capture how well the evidence separates/distinguishes the hypotheses. If we don't
    # show a preference between consistent/inconsistent, STDDEV captures this intuition OK. However, in the future,
    # we may want to favor evidence for which hypotheses are inconsistent. Additionally, we may want to calculate
    # "marginal diagnosticity" which takes into the rest of the evidence.
    # (1) calculate the consensus for each hypothesis
    # (2) map N/A to neutral because N/A doesn't help determine consistency of the evidence
    # (3) calculate the population standard deviation of the evidence. It's more reasonable to consider the set of
    #     hypotheses at a given time to be the population of hypotheses than as a "sample" (although it doesn't matter
    #     much because we're comparing across hypothesis sets of the same size)
    na_neutral = map(mean_na_neutral_vote, evaluations)  # pylint: disable=bad-builtin
    try:
        return statistics.pstdev(filter(None.__ne__, na_neutral))  # pylint: disable=bad-builtin
    except statistics.StatisticsError:
        return 0.0 

# 需要導入模塊: import statistics [as 別名]
# 或者: from statistics import StatisticsError [as 別名]
def _get_center_coords(
    locations: Iterable[Tuple[float, float]], mode: str = "median"
) -> Tuple[float, float]:
    """Return the center (median) of the coordinates."""
    if not locations:
        return 0, 0
    locs = list(locations)
    if mode == "median":
        try:
            return (
                stats.median([loc[0] for loc in locs if not math.isnan(loc[0])]),
                stats.median([loc[1] for loc in locs if not math.isnan(loc[1])]),
            )
        except stats.StatisticsError:
            pass
    return (
        stats.mean([loc[0] for loc in locs if not math.isnan(loc[0])]),
        stats.mean([loc[1] for loc in locs if not math.isnan(loc[1])]),
    ) 

# 需要導入模塊: import statistics [as 別名]
# 或者: from statistics import StatisticsError [as 別名]
def mode(data):
    """Return the mode, the most common data point from discrete or nominal data.

    If there are multiple modes with the same frequency, the first one encountered
    in data is returned.

    If data is empty, StatisticsError is raised.

    To speed up the computation, the bit length of the sample range max(data) - min(data)
    is revealed, provided this range is not too small.
    """
    if iter(data) is data:
        x = list(data)
    else:
        x = data[:]
    n = len(x)
    if not n:
        raise statistics.StatisticsError('mode requires at least one data point')

    if isinstance(x[0], sectypes.SecureObject):
        return _mode(x, PRIV=runtime.options.sec_param//6)

    return statistics.mode(x)  # NB: raises StatisticsError in Python < 3.8 if x is multimodal 

# 需要導入模塊: import statistics [as 別名]
# 或者: from statistics import StatisticsError [as 別名]
def append_data_statistics(meta_data):
    # get data statistics
    for char_cnt in meta_data:
        data = meta_data[char_cnt]["data"]
        audio_len_list = [d["audio_len"] for d in data]
        mean_audio_len = mean(audio_len_list)
        try:
            mode_audio_list = [round(d["audio_len"], 2) for d in data]
            mode_audio_len = mode(mode_audio_list)
        except StatisticsError:
            mode_audio_len = audio_len_list[0]
        median_audio_len = median(audio_len_list)

        try:
            std = stdev(
                d["audio_len"] for d in data
            )
        except StatisticsError:
            std = 0

        meta_data[char_cnt]["mean"] = mean_audio_len
        meta_data[char_cnt]["median"] = median_audio_len
        meta_data[char_cnt]["mode"] = mode_audio_len
        meta_data[char_cnt]["std"] = std
    return meta_data