Python statistics.variance方法代码示例

# 需要导入模块: import statistics [as 别名]
# 或者: from statistics import variance [as 别名]
def mu(text):
    '''
    Muñoz Baquedano and Muñoz Urra's readability score (2006)
    '''
    n = count_words(text)
    # Delete all digits
    text = ''.join(filter(lambda x: not x.isdigit(), text))
    # Cleans it all
    clean = re.compile('\W+')
    text = clean.sub(' ', text).strip()
    text = text.split() # word list
    word_lengths = []
    for word in text:
        word_lengths.append(len(word))
    # The mean calculation needs at least 1 value on the list, and the variance, two. If somebody enters only one word or, what is worse, a figure, the calculation breaks, so this is a 'fix'
    try:
        mean = statistics.mean(word_lengths)
        variance = statistics.variance(word_lengths)
        mu = (n / (n - 1)) * (mean / variance) * 100
        return round(mu, 2)
    except:
        return 0 

# 需要导入模块: import statistics [as 别名]
# 或者: from statistics import variance [as 别名]
def GetStats(self, request: qrl_pb2.GetStatsReq, context) -> qrl_pb2.GetStatsResp:
        response = qrl_pb2.GetStatsResp()
        response.node_info.CopyFrom(self.qrlnode.get_node_info())

        response.epoch = self.qrlnode.epoch
        response.uptime_network = self.qrlnode.uptime_network
        response.block_last_reward = self.qrlnode.block_last_reward
        response.coins_total_supply = int(self.qrlnode.coin_supply_max)
        response.coins_emitted = int(self.qrlnode.coin_supply)

        response.block_time_mean = 0
        response.block_time_sd = 0

        if request.include_timeseries:
            tmp = list(self.qrlnode.get_block_timeseries(config.dev.block_timeseries_size))
            response.block_timeseries.extend(tmp)
            if len(tmp) > 2:
                vals = [v.time_last for v in tmp[1:]]
                response.block_time_mean = int(mean(vals))
                response.block_time_sd = int(variance(vals) ** 0.5)
        return response 

# 需要导入模块: import statistics [as 别名]
# 或者: from statistics import variance [as 别名]
def test_select_balanced_subset():
    categories = ['a', 'b', 'c']
    items = {
        'utt-1': {'a': 1, 'b': 0, 'c': 0},
        'utt-2': {'a': 1, 'c': 2},
        'utt-3': {'a': 1, 'b': 1, 'c': 1},
        'utt-4': {'a': 1, 'b': 1, 'c': 0},
        'utt-5': {'b': 1, 'c': 0},
        'utt-6': {'b': 2, 'c': 1},
        'utt-7': {'a': 1, 'b': 0, 'c': 1},
        'utt-8': {'a': 1, 'b': 1, 'c': 0},
        'utt-9': {'a': 1, 'b': 0, 'c': 0},
        'utt-10': {'c': 2},
        'utt-11': {'a': 1, 'b': 1, 'c': 1},
        'utt-12': {'b': 1, 'c': 0},
        'utt-13': {'b': 1, 'c': 0},
        'utt-14': {'b': 2, 'c': 1},
        'utt-15': {'a': 1, 'b': 0, 'c': 1},
        'utt-16': {'a': 1, 'b': 1, 'c': 0}
    }

    item_ids = utils.select_balanced_subset(items, 10, categories, seed=33)
    weights = [0 for __ in categories]

    for item_id in item_ids:
        for cat, weight in items[item_id].items():
            weights[categories.index(cat)] += weight

    assert len(item_ids) == 10
    assert statistics.variance(weights) <= 1 

# 需要导入模块: import statistics [as 别名]
# 或者: from statistics import variance [as 别名]
def crdb_stats(self, data):
        print('=> class_refresh_db fixture total: %s' % timedelta(
            seconds=sum(data)
        ))
        data = sorted(data)
        print('\tCalled %d times' % len(data))
        mu = statistics.mean(data)
        print('\tMean runtime: %s' % mu)
        print('\tMedian runtime: %s' % statistics.median(data))
        print('\tVariance: %s' % statistics.variance(data)) 

# 需要导入模块: import statistics [as 别名]
# 或者: from statistics import variance [as 别名]
def variance(self):

        return statistics.variance(self.price)
    # 标准差 

# 需要导入模块: import statistics [as 别名]
# 或者: from statistics import variance [as 别名]
def pvariance(self):
        '返回DataStruct.price的方差 variance'
        res = self.price.groupby(level=1
                                 ).apply(lambda x: statistics.pvariance(x))
        res.name = 'pvariance'
        return res

    # 方差 

# 需要导入模块: import statistics [as 别名]
# 或者: from statistics import variance [as 别名]
def variance(self):
        '返回DataStruct.price的方差 variance'
        res = self.price.groupby(level=1
                                 ).apply(lambda x: statistics.variance(x))
        res.name = 'variance'
        return res

    # 标准差 

# 需要导入模块: import statistics [as 别名]
# 或者: from statistics import variance [as 别名]
def test_domain_error_regression(self):
        # Regression test for a domain error exception.
        # (Thanks to Geremy Condra.)
        data = [0.123456789012345]*10000
        # All the items are identical, so variance should be exactly zero.
        # We allow some small round-off error, but not much.
        result = self.func(data)
        self.assertApproxEqual(result, 0.0, tol=5e-17)
        self.assertGreaterEqual(result, 0)  # A negative result must fail. 

# 需要导入模块: import statistics [as 别名]
# 或者: from statistics import variance [as 别名]
def test_shift_data(self):
        # Test that shifting the data by a constant amount does not affect
        # the variance or stdev. Or at least not much.

        # Due to rounding, this test should be considered an ideal. We allow
        # some tolerance away from "no change at all" by setting tol and/or rel
        # attributes. Subclasses may set tighter or looser error tolerances.
        raw = [1.03, 1.27, 1.94, 2.04, 2.58, 3.14, 4.75, 4.98, 5.42, 6.78]
        expected = self.func(raw)
        # Don't set shift too high, the bigger it is, the more rounding error.
        shift = 1e5
        data = [x + shift for x in raw]
        self.assertApproxEqual(self.func(data), expected) 

# 需要导入模块: import statistics [as 别名]
# 或者: from statistics import variance [as 别名]
def test_iter_list_same(self):
        # Test that iter data and list data give the same result.

        # This is an explicit test that iterators and lists are treated the
        # same; justification for this test over and above the similar test
        # in UnivariateCommonMixin is that an earlier design had variance and
        # friends swap between one- and two-pass algorithms, which would
        # sometimes give different results.
        data = [random.uniform(-3, 8) for _ in range(1000)]
        expected = self.func(data)
        self.assertEqual(self.func(iter(data)), expected) 

# 需要导入模块: import statistics [as 别名]
# 或者: from statistics import variance [as 别名]
def test_exact_uniform(self):
        # Test the variance against an exact result for uniform data.
        data = list(range(10000))
        random.shuffle(data)
        expected = (10000**2 - 1)/12  # Exact value.
        self.assertEqual(self.func(data), expected) 

# 需要导入模块: import statistics [as 别名]
# 或者: from statistics import variance [as 别名]
def test_ints(self):
        # Test population variance with int data.
        data = [4, 7, 13, 16]
        exact = 22.5
        self.assertEqual(self.func(data), exact) 

# 需要导入模块: import statistics [as 别名]
# 或者: from statistics import variance [as 别名]
def test_decimals(self):
        # Test population variance with Decimal data.
        D = Decimal
        data = [D("12.1"), D("12.2"), D("12.5"), D("12.9")]
        exact = D('0.096875')
        result = self.func(data)
        self.assertEqual(result, exact)
        self.assertIsInstance(result, Decimal) 

# 需要导入模块: import statistics [as 别名]
# 或者: from statistics import variance [as 别名]
def test_fractions(self):
        # Test sample variance with Fraction data.
        F = Fraction
        data = [F(1, 4), F(1, 4), F(3, 4), F(7, 4)]
        exact = F(1, 2)
        result = self.func(data)
        self.assertEqual(result, exact)
        self.assertIsInstance(result, Fraction) 

# 需要导入模块: import statistics [as 别名]
# 或者: from statistics import variance [as 别名]
def test_compare_to_variance(self):
        # Test that stdev is, in fact, the square root of variance.
        data = [random.uniform(-17, 24) for _ in range(1000)]
        expected = math.sqrt(statistics.pvariance(data))
        self.assertEqual(self.func(data), expected)