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chromium/external/github.com/python/cpython/769d44c87227c44d21ded11b06fb89cdebc64f2f/./Lib/statistics.py
blob: eee3e6eb94e990bbe21d1e37cb09d54c82dd2a24 [file]
"""
Basic statistics module.
This module provides functions for calculating statistics of data, including
averages, variance, and standard deviation.
Calculating averages
--------------------
================== ==================================================
Function Description
================== ==================================================
mean Arithmetic mean (average) of data.
fmean Fast, floating-point arithmetic mean.
geometric_mean Geometric mean of data.
harmonic_mean Harmonic mean of data.
median Median (middle value) of data.
median_low Low median of data.
median_high High median of data.
median_grouped Median, or 50th percentile, of grouped data.
mode Mode (most common value) of data.
multimode List of modes (most common values of data).
quantiles Divide data into intervals with equal probability.
================== ==================================================
Calculate the arithmetic mean ("the average") of data:
>>> mean([-1.0, 2.5, 3.25, 5.75])
2.625
Calculate the standard median of discrete data:
>>> median([2, 3, 4, 5])
3.5
Calculate the median, or 50th percentile, of data grouped into class intervals
centred on the data values provided. E.g. if your data points are rounded to
the nearest whole number:
>>> median_grouped([2, 2, 3, 3, 3, 4]) #doctest: +ELLIPSIS
2.8333333333...
This should be interpreted in this way: you have two data points in the class
interval 1.5-2.5, three data points in the class interval 2.5-3.5, and one in
the class interval 3.5-4.5. The median of these data points is 2.8333...
Calculating variability or spread
---------------------------------
================== =============================================
Function Description
================== =============================================
pvariance Population variance of data.
variance Sample variance of data.
pstdev Population standard deviation of data.
stdev Sample standard deviation of data.
================== =============================================
Calculate the standard deviation of sample data:
>>> stdev([2.5, 3.25, 5.5, 11.25, 11.75]) #doctest: +ELLIPSIS
4.38961843444...
If you have previously calculated the mean, you can pass it as the optional
second argument to the four "spread" functions to avoid recalculating it:
>>> data = [1, 2, 2, 4, 4, 4, 5, 6]
>>> mu = mean(data)
>>> pvariance(data, mu)
2.5
Statistics for relations between two inputs
-------------------------------------------
================== ====================================================
Function Description
================== ====================================================
covariance Sample covariance for two variables.
correlation Pearson's correlation coefficient for two variables.
linear_regression Intercept and slope for simple linear regression.
================== ====================================================
Calculate covariance, Pearson's correlation, and simple linear regression
for two inputs:
>>> x = [1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> y = [1, 2, 3, 1, 2, 3, 1, 2, 3]
>>> covariance(x, y)
0.75
>>> correlation(x, y) #doctest: +ELLIPSIS
0.31622776601...
>>> linear_regression(x, y) #doctest:
LinearRegression(slope=0.1, intercept=1.5)
Exceptions
----------
A single exception is defined: StatisticsError is a subclass of ValueError.
"""
__all__ = [
'NormalDist',
'StatisticsError',
'correlation',
'covariance',
'fmean',
'geometric_mean',
'harmonic_mean',
'kde',
'kde_random',
'linear_regression',
'mean',
'median',
'median_grouped',
'median_high',
'median_low',
'mode',
'multimode',
'pstdev',
'pvariance',
'quantiles',
'stdev',
'variance',
]
import math
import numbers
import random
import sys
from fractions import Fraction
from decimal import Decimal
from itertools import compress, count, groupby, repeat
from bisect import bisect_left, bisect_right
from math import hypot, sqrt, fabs, exp, erfc, log, fsum, sumprod
from math import isfinite, isinf, pi, sin, cosh
from math import sinpi, cospi, tanpi, asinpi, acospi, atanpi
from functools import reduce
from operator import itemgetter
from collections import Counter, namedtuple, defaultdict
_SQRT2 = sqrt(2.0)
_SQRT2PI = float.fromhex('0x1.40d931ff62706p+1') # Correctly rounded sqrt(2*pi)
_random = random
## Exceptions ##############################################################
class StatisticsError(ValueError):
pass
## Measures of central tendency (averages) #################################
def mean(data):
"""Return the sample arithmetic mean of data.
>>> mean([1, 2, 3, 4, 4])
2.8
>>> from fractions import Fraction as F
>>> mean([F(3, 7), F(1, 21), F(5, 3), F(1, 3)])
Fraction(13, 21)
>>> from decimal import Decimal as D
>>> mean([D("0.5"), D("0.75"), D("0.625"), D("0.375")])
Decimal('0.5625')
If ``data`` is empty, StatisticsError will be raised.
"""
T, total, n = _sum(data)
if n < 1:
raise StatisticsError('mean requires at least one data point')
return _convert(total / n, T)
def fmean(data, weights=None):
"""Convert data to floats and compute the arithmetic mean.
This runs faster than the mean() function and it always returns a float.
If the input dataset is empty, it raises a StatisticsError.
>>> fmean([3.5, 4.0, 5.25])
4.25
"""
if weights is None:
try:
n = len(data)
except TypeError:
# Handle iterators that do not define __len__().
counter = count(1)
total = fsum(compress(data, counter))
n = next(counter) - 1
else:
total = fsum(data)
if not n:
raise StatisticsError('fmean requires at least one data point')
return total / n
if not isinstance(weights, (list, tuple)):
weights = list(weights)
try:
num = sumprod(data, weights)
except ValueError:
raise StatisticsError('data and weights must be the same length')
den = fsum(weights)
if not den:
raise StatisticsError('sum of weights must be non-zero')
return num / den
def geometric_mean(data):
"""Convert data to floats and compute the geometric mean.
Raises a StatisticsError if the input dataset is empty
or if it contains a negative value.
Returns zero if the product of inputs is zero.
No special efforts are made to achieve exact results.
(However, this may change in the future.)
>>> round(geometric_mean([54, 24, 36]), 9)
36.0
"""
n = 0
found_zero = False
def count_positive(iterable):
nonlocal n, found_zero
for n, x in enumerate(iterable, start=1):
if x > 0.0 or math.isnan(x):
yield x
elif x == 0.0:
found_zero = True
else:
raise StatisticsError(f'No negative inputs allowed: {x!r}')
total = fsum(map(log, count_positive(data)))
if not n:
raise StatisticsError('Must have a non-empty dataset')
if math.isnan(total):
return math.nan
if found_zero:
return math.nan if total == math.inf else 0.0
return exp(total / n)
def harmonic_mean(data, weights=None):
"""Return the harmonic mean of data.
The harmonic mean is the reciprocal of the arithmetic mean of the
reciprocals of the data. It can be used for averaging ratios or
rates, for example speeds.
Suppose a car travels 40 km/hr for 5 km and then speeds-up to
60 km/hr for another 5 km. What is the average speed?
>>> harmonic_mean([40, 60])
48.0
Suppose a car travels 40 km/hr for 5 km, and when traffic clears,
speeds-up to 60 km/hr for the remaining 30 km of the journey. What
is the average speed?
>>> harmonic_mean([40, 60], weights=[5, 30])
56.0
If ``data`` is empty, or any element is less than zero,
``harmonic_mean`` will raise ``StatisticsError``.
"""
if iter(data) is data:
data = list(data)
errmsg = 'harmonic mean does not support negative values'
n = len(data)
if n < 1:
raise StatisticsError('harmonic_mean requires at least one data point')
elif n == 1 and weights is None:
x = data[0]
if isinstance(x, (numbers.Real, Decimal)):
if x < 0:
raise StatisticsError(errmsg)
return x
else:
raise TypeError('unsupported type')
if weights is None:
weights = repeat(1, n)
sum_weights = n
else:
if iter(weights) is weights:
weights = list(weights)
if len(weights) != n:
raise StatisticsError('Number of weights does not match data size')
_, sum_weights, _ = _sum(w for w in _fail_neg(weights, errmsg))
try:
data = _fail_neg(data, errmsg)
T, total, count = _sum(w / x if w else 0 for w, x in zip(weights, data))
except ZeroDivisionError:
return 0
if total <= 0:
raise StatisticsError('Weighted sum must be positive')
return _convert(sum_weights / total, T)
def median(data):
"""Return the median (middle value) of numeric data.
When the number of data points is odd, return the middle data point.
When the number of data points is even, the median is interpolated by
taking the average of the two middle values:
>>> median([1, 3, 5])
3
>>> median([1, 3, 5, 7])
4.0
"""
data = sorted(data)
n = len(data)
if n == 0:
raise StatisticsError("no median for empty data")
if n % 2 == 1:
return data[n // 2]
else:
i = n // 2
return (data[i - 1] + data[i]) / 2
def median_low(data):
"""Return the low median of numeric data.
When the number of data points is odd, the middle value is returned.
When it is even, the smaller of the two middle values is returned.
>>> median_low([1, 3, 5])
3
>>> median_low([1, 3, 5, 7])
3
"""
# Potentially the sorting step could be replaced with a quickselect.
# However, it would require an excellent implementation to beat our
# highly optimized builtin sort.
data = sorted(data)
n = len(data)
if n == 0:
raise StatisticsError("no median for empty data")
if n % 2 == 1:
return data[n // 2]
else:
return data[n // 2 - 1]
def median_high(data):
"""Return the high median of data.
When the number of data points is odd, the middle value is returned.
When it is even, the larger of the two middle values is returned.
>>> median_high([1, 3, 5])
3
>>> median_high([1, 3, 5, 7])
5
"""
data = sorted(data)
n = len(data)
if n == 0:
raise StatisticsError("no median for empty data")
return data[n // 2]
def median_grouped(data, interval=1.0):
"""Estimates the median for numeric data binned around the midpoints
of consecutive, fixed-width intervals.
The *data* can be any iterable of numeric data with each value being
exactly the midpoint of a bin. At least one value must be present.
The *interval* is width of each bin.
For example, demographic information may have been summarized into
consecutive ten-year age groups with each group being represented
by the 5-year midpoints of the intervals:
>>> demographics = Counter({
... 25: 172, # 20 to 30 years old
... 35: 484, # 30 to 40 years old
... 45: 387, # 40 to 50 years old
... 55: 22, # 50 to 60 years old
... 65: 6, # 60 to 70 years old
... })
The 50th percentile (median) is the 536th person out of the 1071
member cohort. That person is in the 30 to 40 year old age group.
The regular median() function would assume that everyone in the
tricenarian age group was exactly 35 years old. A more tenable
assumption is that the 484 members of that age group are evenly
distributed between 30 and 40. For that, we use median_grouped().
>>> data = list(demographics.elements())
>>> median(data)
35
>>> round(median_grouped(data, interval=10), 1)
37.5
The caller is responsible for making sure the data points are separated
by exact multiples of *interval*. This is essential for getting a
correct result. The function does not check this precondition.
Inputs may be any numeric type that can be coerced to a float during
the interpolation step.
"""
data = sorted(data)
n = len(data)
if not n:
raise StatisticsError("no median for empty data")
# Find the value at the midpoint. Remember this corresponds to the
# midpoint of the class interval.
x = data[n // 2]
# Using O(log n) bisection, find where all the x values occur in the data.
# All x will lie within data[i:j].
i = bisect_left(data, x)
j = bisect_right(data, x, lo=i)
# Coerce to floats, raising a TypeError if not possible
try:
interval = float(interval)
x = float(x)
except ValueError:
raise TypeError(f'Value cannot be converted to a float')
# Interpolate the median using the formula found at:
# https://www.cuemath.com/data/median-of-grouped-data/
L = x - interval / 2.0 # Lower limit of the median interval
cf = i # Cumulative frequency of the preceding interval
f = j - i # Number of elements in the median internal
return L + interval * (n / 2 - cf) / f
def mode(data):
"""Return the most common data point from discrete or nominal data.
``mode`` assumes discrete data, and returns a single value. This is the
standard treatment of the mode as commonly taught in schools:
>>> mode([1, 1, 2, 3, 3, 3, 3, 4])
3
This also works with nominal (non-numeric) data:
>>> mode(["red", "blue", "blue", "red", "green", "red", "red"])
'red'
If there are multiple modes with same frequency, return the first one
encountered:
>>> mode(['red', 'red', 'green', 'blue', 'blue'])
'red'
If *data* is empty, ``mode``, raises StatisticsError.
"""
pairs = Counter(iter(data)).most_common(1)
try:
return pairs[0][0]
except IndexError:
raise StatisticsError('no mode for empty data') from None
def multimode(data):
"""Return a list of the most frequently occurring values.
Will return more than one result if there are multiple modes
or an empty list if *data* is empty.
>>> multimode('aabbbbbbbbcc')
['b']
>>> multimode('aabbbbccddddeeffffgg')
['b', 'd', 'f']
>>> multimode('')
[]
"""
counts = Counter(iter(data))
if not counts:
return []
maxcount = max(counts.values())
return [value for value, count in counts.items() if count == maxcount]
## Measures of spread ######################################################
def variance(data, xbar=None):
"""Return the sample variance of data.
data should be an iterable of Real-valued numbers, with at least two
values. The optional argument xbar, if given, should be the mean of
the data. If it is missing or None, the mean is automatically calculated.
Use this function when your data is a sample from a population. To
calculate the variance from the entire population, see ``pvariance``.
Examples:
>>> data = [2.75, 1.75, 1.25, 0.25, 0.5, 1.25, 3.5]
>>> variance(data)
1.3720238095238095
If you have already calculated the mean of your data, you can pass it as
the optional second argument ``xbar`` to avoid recalculating it:
>>> m = mean(data)
>>> variance(data, m)
1.3720238095238095
This function does not check that ``xbar`` is actually the mean of
``data``. Giving arbitrary values for ``xbar`` may lead to invalid or
impossible results.
Decimals and Fractions are supported:
>>> from decimal import Decimal as D
>>> variance([D("27.5"), D("30.25"), D("30.25"), D("34.5"), D("41.75")])
Decimal('31.01875')
>>> from fractions import Fraction as F
>>> variance([F(1, 6), F(1, 2), F(5, 3)])
Fraction(67, 108)
"""
# http://mathworld.wolfram.com/SampleVariance.html
T, ss, c, n = _ss(data, xbar)
if n < 2:
raise StatisticsError('variance requires at least two data points')
return _convert(ss / (n - 1), T)
def pvariance(data, mu=None):
"""Return the population variance of ``data``.
data should be a sequence or iterable of Real-valued numbers, with at least one
value. The optional argument mu, if given, should be the mean of
the data. If it is missing or None, the mean is automatically calculated.
Use this function to calculate the variance from the entire population.
To estimate the variance from a sample, the ``variance`` function is
usually a better choice.
Examples:
>>> data = [0.0, 0.25, 0.25, 1.25, 1.5, 1.75, 2.75, 3.25]
>>> pvariance(data)
1.25
If you have already calculated the mean of the data, you can pass it as
the optional second argument to avoid recalculating it:
>>> mu = mean(data)
>>> pvariance(data, mu)
1.25
Decimals and Fractions are supported:
>>> from decimal import Decimal as D
>>> pvariance([D("27.5"), D("30.25"), D("30.25"), D("34.5"), D("41.75")])
Decimal('24.815')
>>> from fractions import Fraction as F
>>> pvariance([F(1, 4), F(5, 4), F(1, 2)])
Fraction(13, 72)
"""
# http://mathworld.wolfram.com/Variance.html
T, ss, c, n = _ss(data, mu)
if n < 1:
raise StatisticsError('pvariance requires at least one data point')
return _convert(ss / n, T)
def stdev(data, xbar=None):
"""Return the square root of the sample variance.
See ``variance`` for arguments and other details.
>>> stdev([1.5, 2.5, 2.5, 2.75, 3.25, 4.75])
1.0810874155219827
"""
T, ss, c, n = _ss(data, xbar)
if n < 2:
raise StatisticsError('stdev requires at least two data points')
mss = ss / (n - 1)
try:
mss_numerator = mss.numerator
mss_denominator = mss.denominator
except AttributeError:
raise ValueError('inf or nan encountered in data')
if issubclass(T, Decimal):
return _decimal_sqrt_of_frac(mss_numerator, mss_denominator)
return _float_sqrt_of_frac(mss_numerator, mss_denominator)
def pstdev(data, mu=None):
"""Return the square root of the population variance.
See ``pvariance`` for arguments and other details.
>>> pstdev([1.5, 2.5, 2.5, 2.75, 3.25, 4.75])
0.986893273527251
"""
T, ss, c, n = _ss(data, mu)
if n < 1:
raise StatisticsError('pstdev requires at least one data point')
mss = ss / n
try:
mss_numerator = mss.numerator
mss_denominator = mss.denominator
except AttributeError:
raise ValueError('inf or nan encountered in data')
if issubclass(T, Decimal):
return _decimal_sqrt_of_frac(mss_numerator, mss_denominator)
return _float_sqrt_of_frac(mss_numerator, mss_denominator)
## Statistics for relations between two inputs #############################
def covariance(x, y, /):
"""Covariance
Return the sample covariance of two inputs *x* and *y*. Covariance
is a measure of the joint variability of two inputs.
>>> x = [1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> y = [1, 2, 3, 1, 2, 3, 1, 2, 3]
>>> covariance(x, y)
0.75
>>> z = [9, 8, 7, 6, 5, 4, 3, 2, 1]
>>> covariance(x, z)
-7.5
>>> covariance(z, x)
-7.5
"""
# https://en.wikipedia.org/wiki/Covariance
n = len(x)
if len(y) != n:
raise StatisticsError('covariance requires that both inputs have same number of data points')
if n < 2:
raise StatisticsError('covariance requires at least two data points')
xbar = fsum(x) / n
ybar = fsum(y) / n
sxy = sumprod((xi - xbar for xi in x), (yi - ybar for yi in y))
return sxy / (n - 1)
def correlation(x, y, /, *, method='linear'):
"""Pearson's correlation coefficient
Return the Pearson's correlation coefficient for two inputs. Pearson's
correlation coefficient *r* takes values between -1 and +1. It measures
the strength and direction of a linear relationship.
>>> x = [1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> y = [9, 8, 7, 6, 5, 4, 3, 2, 1]
>>> correlation(x, x)
1.0
>>> correlation(x, y)
-1.0
If *method* is "ranked", computes Spearman's rank correlation coefficient
for two inputs. The data is replaced by ranks. Ties are averaged
so that equal values receive the same rank. The resulting coefficient
measures the strength of a monotonic relationship.
Spearman's rank correlation coefficient is appropriate for ordinal
data or for continuous data that doesn't meet the linear proportion
requirement for Pearson's correlation coefficient.
"""
# https://en.wikipedia.org/wiki/Pearson_correlation_coefficient
# https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient
n = len(x)
if len(y) != n:
raise StatisticsError('correlation requires that both inputs have same number of data points')
if n < 2:
raise StatisticsError('correlation requires at least two data points')
if method not in {'linear', 'ranked'}:
raise ValueError(f'Unknown method: {method!r}')
if method == 'ranked':
start = (n - 1) / -2 # Center rankings around zero
x = _rank(x, start=start)
y = _rank(y, start=start)
else:
xbar = fsum(x) / n
ybar = fsum(y) / n
x = [xi - xbar for xi in x]
y = [yi - ybar for yi in y]
sxy = sumprod(x, y)
sxx = sumprod(x, x)
syy = sumprod(y, y)
try:
return sxy / _sqrtprod(sxx, syy)
except ZeroDivisionError:
raise StatisticsError('at least one of the inputs is constant')
LinearRegression = namedtuple('LinearRegression', ('slope', 'intercept'))
def linear_regression(x, y, /, *, proportional=False):
"""Slope and intercept for simple linear regression.
Return the slope and intercept of simple linear regression
parameters estimated using ordinary least squares. Simple linear
regression describes relationship between an independent variable
*x* and a dependent variable *y* in terms of a linear function:
y = slope * x + intercept + noise
where *slope* and *intercept* are the regression parameters that are
estimated, and noise represents the variability of the data that was
not explained by the linear regression (it is equal to the
difference between predicted and actual values of the dependent
variable).
The parameters are returned as a named tuple.
>>> x = [1, 2, 3, 4, 5]
>>> noise = NormalDist().samples(5, seed=42)
>>> y = [3 * x[i] + 2 + noise[i] for i in range(5)]
>>> linear_regression(x, y) #doctest: +ELLIPSIS
LinearRegression(slope=3.17495..., intercept=1.00925...)
If *proportional* is true, the independent variable *x* and the
dependent variable *y* are assumed to be directly proportional.
The data is fit to a line passing through the origin.
Since the *intercept* will always be 0.0, the underlying linear
function simplifies to:
y = slope * x + noise
>>> y = [3 * x[i] + noise[i] for i in range(5)]
>>> linear_regression(x, y, proportional=True) #doctest: +ELLIPSIS
LinearRegression(slope=2.90475..., intercept=0.0)
"""
# https://en.wikipedia.org/wiki/Simple_linear_regression
n = len(x)
if len(y) != n:
raise StatisticsError('linear regression requires that both inputs have same number of data points')
if n < 2:
raise StatisticsError('linear regression requires at least two data points')
if not proportional:
xbar = fsum(x) / n
ybar = fsum(y) / n
x = [xi - xbar for xi in x] # List because used three times below
y = (yi - ybar for yi in y) # Generator because only used once below
sxy = sumprod(x, y) + 0.0 # Add zero to coerce result to a float
sxx = sumprod(x, x)
try:
slope = sxy / sxx # equivalent to: covariance(x, y) / variance(x)
except ZeroDivisionError:
raise StatisticsError('x is constant')
intercept = 0.0 if proportional else ybar - slope * xbar
return LinearRegression(slope=slope, intercept=intercept)
## Kernel Density Estimation ###############################################
_kernel_specs = {}
def register(*kernels):
"Load the kernel's pdf, cdf, invcdf, and support into _kernel_specs."
def deco(builder):
spec = dict(zip(('pdf', 'cdf', 'invcdf', 'support'), builder()))
for kernel in kernels:
_kernel_specs[kernel] = spec
return builder
return deco
@register('normal', 'gauss')
def normal_kernel():
sqrt2pi = _SQRT2PI
neg_sqrt2 = -_SQRT2
pdf = lambda t: exp(-1/2 * t * t) / sqrt2pi
cdf = lambda t: 1/2 * erfc(t / neg_sqrt2)
invcdf = lambda t: _normal_dist_inv_cdf(t, 0.0, 1.0)
support = None
return pdf, cdf, invcdf, support
@register('logistic')
def logistic_kernel():
# 1.0 / (exp(t) + 2.0 + exp(-t))
pdf = lambda t: 1/2 / (1.0 + cosh(t))
cdf = lambda t: 1.0 - 1.0 / (exp(t) + 1.0)
invcdf = lambda p: log(p / (1.0 - p))
support = None
return pdf, cdf, invcdf, support
@register('sigmoid')
def sigmoid_kernel():
# (2/pi) / (exp(t) + exp(-t))
recip_pi = 1 / pi
pdf = lambda t: recip_pi / cosh(t)
cdf = lambda t: 2.0 * atanpi(exp(t))
invcdf = lambda p: log(tanpi(p * 0.5))
support = None
return pdf, cdf, invcdf, support
@register('rectangular', 'uniform')
def rectangular_kernel():
pdf = lambda t: 1/2
cdf = lambda t: 1/2 * t + 1/2
invcdf = lambda p: 2.0 * p - 1.0
support = 1.0
return pdf, cdf, invcdf, support
@register('triangular')
def triangular_kernel():
pdf = lambda t: 1.0 - abs(t)
cdf = lambda t: t*t * (1/2 if t < 0.0 else -1/2) + t + 1/2
invcdf = lambda p: sqrt(2.0*p) - 1.0 if p < 1/2 else 1.0 - sqrt(2.0 - 2.0*p)
support = 1.0
return pdf, cdf, invcdf, support
@register('parabolic', 'epanechnikov')
def parabolic_kernel():
pdf = lambda t: 3/4 * (1.0 - t * t)
cdf = lambda t: sumprod((-1/4, 3/4, 1/2), (t**3, t, 1.0))
invcdf = lambda p: 2.0 * cospi((acospi(2.0 * p - 1.0) + 1.0) / 3.0)
support = 1.0
return pdf, cdf, invcdf, support
def _newton_raphson(f_inv_estimate, f, f_prime, tolerance=1e-12):
def f_inv(y):
"Return x such that f(x) ≈ y within the specified tolerance."
x = f_inv_estimate(y)
while abs(diff := f(x) - y) > tolerance:
x -= diff / f_prime(x)
return x
return f_inv
def _quartic_invcdf_estimate(p):
# A handrolled piecewise approximation. There is no magic here.
sign, p = (1.0, p) if p <= 1/2 else (-1.0, 1.0 - p)
if p < 0.0106:
return ((2.0 * p) ** 0.3838 - 1.0) * sign
x = (2.0 * p) ** 0.4258865685331 - 1.0
if p < 0.499:
x += 0.026818732 * sin(7.101753784 * p + 2.73230839482953)
return x * sign
@register('quartic', 'biweight')
def quartic_kernel():
pdf = lambda t: 15/16 * (u := 1.0 - t * t) * u
cdf = lambda t: sumprod((3/16, -5/8, 15/16, 1/2),
(t**5, t**3, t, 1.0))
invcdf = _newton_raphson(_quartic_invcdf_estimate, f=cdf, f_prime=pdf)
support = 1.0
return pdf, cdf, invcdf, support
def _triweight_invcdf_estimate(p):
# A handrolled piecewise approximation. There is no magic here.
sign, p = (1.0, p) if p <= 1/2 else (-1.0, 1.0 - p)
x = (2.0 * p) ** 0.3400218741872791 - 1.0
if 0.00001 < p < 0.499:
x -= 0.033 * sinpi(2.14 * (p - 0.035))
return x * sign
@register('triweight')
def triweight_kernel():
pdf = lambda t: 35/32 * (1.0 - t * t) ** 3
cdf = lambda t: sumprod((-5/32, 21/32, -35/32, 35/32, 1/2),
(t**7, t**5, t**3, t, 1.0))
invcdf = _newton_raphson(_triweight_invcdf_estimate, f=cdf, f_prime=pdf)
support = 1.0
return pdf, cdf, invcdf, support
@register('cosine')
def cosine_kernel():
c1 = pi / 4
pdf = lambda t: c1 * cospi(0.5 * t)
cdf = lambda t: 1/2 * sinpi(0.5 * t) + 1/2
invcdf = lambda p: 2.0 * asinpi(2.0 * p - 1.0)
support = 1.0
return pdf, cdf, invcdf, support
del register, normal_kernel, logistic_kernel, sigmoid_kernel
del rectangular_kernel, triangular_kernel, parabolic_kernel
del quartic_kernel, triweight_kernel, cosine_kernel
def kde(data, h, kernel='normal', *, cumulative=False):
"""Kernel Density Estimation: Create a continuous probability density
function or cumulative distribution function from discrete samples.
The basic idea is to smooth the data using a kernel function
to help draw inferences about a population from a sample.
The degree of smoothing is controlled by the scaling parameter h
which is called the bandwidth. Smaller values emphasize local
features while larger values give smoother results.
The kernel determines the relative weights of the sample data
points. Generally, the choice of kernel shape does not matter
as much as the more influential bandwidth smoothing parameter.
Kernels that give some weight to every sample point:
normal (gauss)
logistic
sigmoid
Kernels that only give weight to sample points within
the bandwidth:
rectangular (uniform)
triangular
parabolic (epanechnikov)
quartic (biweight)
triweight
cosine
If *cumulative* is true, will return a cumulative distribution function.
A StatisticsError will be raised if the data sequence is empty.
Example
-------
Given a sample of six data points, construct a continuous
function that estimates the underlying probability density:
>>> sample = [-2.1, -1.3, -0.4, 1.9, 5.1, 6.2]
>>> f_hat = kde(sample, h=1.5)
Compute the area under the curve:
>>> area = sum(f_hat(x) for x in range(-20, 20))
>>> round(area, 4)
1.0
Plot the estimated probability density function at
evenly spaced points from -6 to 10:
>>> for x in range(-6, 11):
... density = f_hat(x)
... plot = ' ' * int(density * 400) + 'x'
... print(f'{x:2}: {density:.3f} {plot}')
...
-6: 0.002 x
-5: 0.009 x
-4: 0.031 x
-3: 0.070 x
-2: 0.111 x
-1: 0.125 x
0: 0.110 x
1: 0.086 x
2: 0.068 x
3: 0.059 x
4: 0.066 x
5: 0.082 x
6: 0.082 x
7: 0.058 x
8: 0.028 x
9: 0.009 x
10: 0.002 x
Estimate P(4.5 < X <= 7.5), the probability that a new sample value
will be between 4.5 and 7.5:
>>> cdf = kde(sample, h=1.5, cumulative=True)
>>> round(cdf(7.5) - cdf(4.5), 2)
0.22
References
----------
Kernel density estimation and its application:
https://www.itm-conferences.org/articles/itmconf/pdf/2018/08/itmconf_sam2018_00037.pdf
Kernel functions in common use:
https://en.wikipedia.org/wiki/Kernel_(statistics)#kernel_functions_in_common_use
Interactive graphical demonstration and exploration:
https://demonstrations.wolfram.com/KernelDensityEstimation/
Kernel estimation of cumulative distribution function of a random variable with bounded support
https://www.econstor.eu/bitstream/10419/207829/1/10.21307_stattrans-2016-037.pdf
"""
n = len(data)
if not n:
raise StatisticsError('Empty data sequence')
if not isinstance(data[0], (int, float)):
raise TypeError('Data sequence must contain ints or floats')
if h <= 0.0:
raise StatisticsError(f'Bandwidth h must be positive, not {h=!r}')
kernel_spec = _kernel_specs.get(kernel)
if kernel_spec is None:
raise StatisticsError(f'Unknown kernel name: {kernel!r}')
K = kernel_spec['pdf']
W = kernel_spec['cdf']
support = kernel_spec['support']
if support is None:
def pdf(x):
return sum(K((x - x_i) / h) for x_i in data) / (len(data) * h)
def cdf(x):
return sum(W((x - x_i) / h) for x_i in data) / len(data)
else:
sample = sorted(data)
bandwidth = h * support
def pdf(x):
nonlocal n, sample
if len(data) != n:
sample = sorted(data)
n = len(data)
i = bisect_left(sample, x - bandwidth)
j = bisect_right(sample, x + bandwidth)
supported = sample[i : j]
return sum(K((x - x_i) / h) for x_i in supported) / (n * h)
def cdf(x):
nonlocal n, sample
if len(data) != n:
sample = sorted(data)
n = len(data)
i = bisect_left(sample, x - bandwidth)
j = bisect_right(sample, x + bandwidth)
supported = sample[i : j]
return sum((W((x - x_i) / h) for x_i in supported), i) / n
if cumulative:
cdf.__doc__ = f'CDF estimate with {h=!r} and {kernel=!r}'
return cdf
else:
pdf.__doc__ = f'PDF estimate with {h=!r} and {kernel=!r}'
return pdf
def kde_random(data, h, kernel='normal', *, seed=None):
"""Return a function that makes a random selection from the estimated
probability density function created by kde(data, h, kernel).
Providing a *seed* allows reproducible selections within a single
thread. The seed may be an integer, float, str, or bytes.
A StatisticsError will be raised if the *data* sequence is empty.
Example:
>>> data = [-2.1, -1.3, -0.4, 1.9, 5.1, 6.2]
>>> rand = kde_random(data, h=1.5, seed=8675309)
>>> new_selections = [rand() for i in range(10)]
>>> [round(x, 1) for x in new_selections]
[0.7, 6.2, 1.2, 6.9, 7.0, 1.8, 2.5, -0.5, -1.8, 5.6]
"""
n = len(data)
if not n:
raise StatisticsError('Empty data sequence')
if not isinstance(data[0], (int, float)):
raise TypeError('Data sequence must contain ints or floats')
if h <= 0.0:
raise StatisticsError(f'Bandwidth h must be positive, not {h=!r}')
kernel_spec = _kernel_specs.get(kernel)
if kernel_spec is None:
raise StatisticsError(f'Unknown kernel name: {kernel!r}')
invcdf = kernel_spec['invcdf']
prng = _random.Random(seed)
random = prng.random
choice = prng.choice
def rand():
return choice(data) + h * invcdf(random())
rand.__doc__ = f'Random KDE selection with {h=!r} and {kernel=!r}'
return rand
## Quantiles ###############################################################
# There is no one perfect way to compute quantiles. Here we offer
# two methods that serve common needs. Most other packages
# surveyed offered at least one or both of these two, making them
# "standard" in the sense of "widely-adopted and reproducible".
# They are also easy to explain, easy to compute manually, and have
# straight-forward interpretations that aren't surprising.
# The default method is known as "R6", "PERCENTILE.EXC", or "expected
# value of rank order statistics". The alternative method is known as
# "R7", "PERCENTILE.INC", or "mode of rank order statistics".
# For sample data where there is a positive probability for values
# beyond the range of the data, the R6 exclusive method is a
# reasonable choice. Consider a random sample of nine values from a
# population with a uniform distribution from 0.0 to 1.0. The
# distribution of the third ranked sample point is described by
# betavariate(alpha=3, beta=7) which has mode=0.250, median=0.286, and
# mean=0.300. Only the latter (which corresponds with R6) gives the
# desired cut point with 30% of the population falling below that
# value, making it comparable to a result from an inv_cdf() function.
# The R6 exclusive method is also idempotent.
# For describing population data where the end points are known to
# be included in the data, the R7 inclusive method is a reasonable
# choice. Instead of the mean, it uses the mode of the beta
# distribution for the interior points. Per Hyndman & Fan, "One nice
# property is that the vertices of Q7(p) divide the range into n - 1
# intervals, and exactly 100p% of the intervals lie to the left of
# Q7(p) and 100(1 - p)% of the intervals lie to the right of Q7(p)."
# If needed, other methods could be added. However, for now, the
# position is that fewer options make for easier choices and that
# external packages can be used for anything more advanced.
def quantiles(data, *, n=4, method='exclusive'):
"""Divide *data* into *n* continuous intervals with equal probability.
Returns a list of (n - 1) cut points separating the intervals.
Set *n* to 4 for quartiles (the default). Set *n* to 10 for deciles.
Set *n* to 100 for percentiles which gives the 99 cuts points that
separate *data* in to 100 equal sized groups.
The *data* can be any iterable containing sample.
The cut points are linearly interpolated between data points.
If *method* is set to *inclusive*, *data* is treated as population
data. The minimum value is treated as the 0th percentile and the
maximum value is treated as the 100th percentile.
"""
if n < 1:
raise StatisticsError('n must be at least 1')
data = sorted(data)
ld = len(data)
if ld < 2:
if ld == 1:
return data * (n - 1)
raise StatisticsError('must have at least one data point')
if method == 'inclusive':
m = ld - 1
result = []
for i in range(1, n):
j, delta = divmod(i * m, n)
interpolated = (data[j] * (n - delta) + data[j + 1] * delta) / n
result.append(interpolated)
return result
if method == 'exclusive':
m = ld + 1
result = []
for i in range(1, n):
j = i * m // n # rescale i to m/n
j = 1 if j < 1 else ld-1 if j > ld-1 else j # clamp to 1 .. ld-1
delta = i*m - j*n # exact integer math
interpolated = (data[j - 1] * (n - delta) + data[j] * delta) / n
result.append(interpolated)
return result
raise ValueError(f'Unknown method: {method!r}')
## Normal Distribution #####################################################
class NormalDist:
"Normal distribution of a random variable"
# https://en.wikipedia.org/wiki/Normal_distribution
# https://en.wikipedia.org/wiki/Variance#Properties
__slots__ = {
'_mu': 'Arithmetic mean of a normal distribution',
'_sigma': 'Standard deviation of a normal distribution',
}
def __init__(self, mu=0.0, sigma=1.0):
"NormalDist where mu is the mean and sigma is the standard deviation."
if sigma < 0.0:
raise StatisticsError('sigma must be non-negative')
self._mu = float(mu)
self._sigma = float(sigma)
@classmethod
def from_samples(cls, data):
"Make a normal distribution instance from sample data."
return cls(*_mean_stdev(data))
def samples(self, n, *, seed=None):
"Generate *n* samples for a given mean and standard deviation."
rnd = random.random if seed is None else random.Random(seed).random
inv_cdf = _normal_dist_inv_cdf
mu = self._mu
sigma = self._sigma
return [inv_cdf(rnd(), mu, sigma) for _ in repeat(None, n)]
def pdf(self, x):
"Probability density function. P(x <= X < x+dx) / dx"
sigma = self._sigma
if not sigma:
raise StatisticsError('pdf() not defined when sigma is zero')
z = (x - self._mu) / sigma
return exp(-0.5 * z * z) / (_SQRT2PI * sigma)
def cdf(self, x):
"Cumulative distribution function. P(X <= x)"
if not self._sigma:
raise StatisticsError('cdf() not defined when sigma is zero')
return 0.5 * erfc((self._mu - x) / (self._sigma * _SQRT2))
def inv_cdf(self, p):
"""Inverse cumulative distribution function. x : P(X <= x) = p
Finds the value of the random variable such that the probability of
the variable being less than or equal to that value equals the given
probability.
This function is also called the percent point function or quantile
function.
"""
if p <= 0.0 or p >= 1.0:
raise StatisticsError('p must be in the range 0.0 < p < 1.0')
return _normal_dist_inv_cdf(p, self._mu, self._sigma)
def quantiles(self, n=4):
"""Divide into *n* continuous intervals with equal probability.
Returns a list of (n - 1) cut points separating the intervals.
Set *n* to 4 for quartiles (the default). Set *n* to 10 for deciles.
Set *n* to 100 for percentiles which gives the 99 cuts points that
separate the normal distribution in to 100 equal sized groups.
"""
return [self.inv_cdf(i / n) for i in range(1, n)]
def overlap(self, other):
"""Compute the overlapping coefficient (OVL) between two normal distributions.
Measures the agreement between two normal probability distributions.
Returns a value between 0.0 and 1.0 giving the overlapping area in
the two underlying probability density functions.
>>> N1 = NormalDist(2.4, 1.6)
>>> N2 = NormalDist(3.2, 2.0)
>>> N1.overlap(N2)
0.8035050657330205
"""
# See: "The overlapping coefficient as a measure of agreement between
# probability distributions and point estimation of the overlap of two
# normal densities" -- Henry F. Inman and Edwin L. Bradley Jr
# http://dx.doi.org/10.1080/03610928908830127
if not isinstance(other, NormalDist):
raise TypeError('Expected another NormalDist instance')
X, Y = self, other
if (Y._sigma, Y._mu) < (X._sigma, X._mu): # sort to assure commutativity
X, Y = Y, X
X_var, Y_var = X.variance, Y.variance
if not X_var or not Y_var:
raise StatisticsError('overlap() not defined when sigma is zero')
dv = Y_var - X_var
dm = fabs(Y._mu - X._mu)
if not dv:
return erfc(dm / (2.0 * X._sigma * _SQRT2))
a = X._mu * Y_var - Y._mu * X_var
b = X._sigma * Y._sigma * sqrt(dm * dm + dv * log(Y_var / X_var))
x1 = (a + b) / dv
x2 = (a - b) / dv
return 1.0 - (fabs(Y.cdf(x1) - X.cdf(x1)) + fabs(Y.cdf(x2) - X.cdf(x2)))
def zscore(self, x):
"""Compute the Standard Score. (x - mean) / stdev
Describes *x* in terms of the number of standard deviations
above or below the mean of the normal distribution.
"""
# https://www.statisticshowto.com/probability-and-statistics/z-score/
if not self._sigma:
raise StatisticsError('zscore() not defined when sigma is zero')
return (x - self._mu) / self._sigma
@property
def mean(self):
"Arithmetic mean of the normal distribution."
return self._mu
@property
def median(self):
"Return the median of the normal distribution"
return self._mu
@property
def mode(self):
"""Return the mode of the normal distribution
The mode is the value x where which the probability density
function (pdf) takes its maximum value.
"""
return self._mu
@property
def stdev(self):
"Standard deviation of the normal distribution."
return self._sigma
@property
def variance(self):
"Square of the standard deviation."
return self._sigma * self._sigma
def __add__(x1, x2):
"""Add a constant or another NormalDist instance.
If *other* is a constant, translate mu by the constant,
leaving sigma unchanged.
If *other* is a NormalDist, add both the means and the variances.
Mathematically, this works only if the two distributions are
independent or if they are jointly normally distributed.
"""
if isinstance(x2, NormalDist):
return NormalDist(x1._mu + x2._mu, hypot(x1._sigma, x2._sigma))
return NormalDist(x1._mu + x2, x1._sigma)
def __sub__(x1, x2):
"""Subtract a constant or another NormalDist instance.
If *other* is a constant, translate by the constant mu,
leaving sigma unchanged.
If *other* is a NormalDist, subtract the means and add the variances.
Mathematically, this works only if the two distributions are
independent or if they are jointly normally distributed.
"""
if isinstance(x2, NormalDist):
return NormalDist(x1._mu - x2._mu, hypot(x1._sigma, x2._sigma))
return NormalDist(x1._mu - x2, x1._sigma)
def __mul__(x1, x2):
"""Multiply both mu and sigma by a constant.
Used for rescaling, perhaps to change measurement units.
Sigma is scaled with the absolute value of the constant.
"""
return NormalDist(x1._mu * x2, x1._sigma * fabs(x2))
def __truediv__(x1, x2):
"""Divide both mu and sigma by a constant.
Used for rescaling, perhaps to change measurement units.
Sigma is scaled with the absolute value of the constant.
"""
return NormalDist(x1._mu / x2, x1._sigma / fabs(x2))
def __pos__(x1):
"Return a copy of the instance."
return NormalDist(x1._mu, x1._sigma)
def __neg__(x1):
"Negates mu while keeping sigma the same."
return NormalDist(-x1._mu, x1._sigma)
__radd__ = __add__
def __rsub__(x1, x2):
"Subtract a NormalDist from a constant or another NormalDist."
return -(x1 - x2)
__rmul__ = __mul__
def __eq__(x1, x2):
"Two NormalDist objects are equal if their mu and sigma are both equal."
if not isinstance(x2, NormalDist):
return NotImplemented
return x1._mu == x2._mu and x1._sigma == x2._sigma
def __hash__(self):
"NormalDist objects hash equal if their mu and sigma are both equal."
return hash((self._mu, self._sigma))
def __repr__(self):
return f'{type(self).__name__}(mu={self._mu!r}, sigma={self._sigma!r})'
def __getstate__(self):
return self._mu, self._sigma
def __setstate__(self, state):
self._mu, self._sigma = state
## Private utilities #######################################################
def _sum(data):
"""_sum(data) -> (type, sum, count)
Return a high-precision sum of the given numeric data as a fraction,
together with the type to be converted to and the count of items.
Examples
--------
>>> _sum([3, 2.25, 4.5, -0.5, 0.25])
(<class 'float'>, Fraction(19, 2), 5)
Some sources of round-off error will be avoided:
# Built-in sum returns zero.
>>> _sum([1e50, 1, -1e50] * 1000)
(<class 'float'>, Fraction(1000, 1), 3000)
Fractions and Decimals are also supported:
>>> from fractions import Fraction as F
>>> _sum([F(2, 3), F(7, 5), F(1, 4), F(5, 6)])
(<class 'fractions.Fraction'>, Fraction(63, 20), 4)
>>> from decimal import Decimal as D
>>> data = [D("0.1375"), D("0.2108"), D("0.3061"), D("0.0419")]
>>> _sum(data)
(<class 'decimal.Decimal'>, Fraction(6963, 10000), 4)
Mixed types are currently treated as an error, except that int is
allowed.
"""
count = 0
types = set()
partials = {}
for typ, values in groupby(data, type):
types.add(typ)
for n, d in map(_exact_ratio, values):
count += 1
partials[d] = partials.get(d, 0) + n
if None in partials:
# The sum will be a NAN or INF. We can ignore all the finite
# partials, and just look at this special one.
total = partials[None]
assert not _isfinite(total)
else:
# Sum all the partial sums using builtin sum.
total = sum(Fraction(n, d) for d, n in partials.items())
T = reduce(_coerce, types, int) # or raise TypeError
return (T, total, count)
def _ss(data, c=None):
"""Return the exact mean and sum of square deviations of sequence data.
Calculations are done in a single pass, allowing the input to be an iterator.
If given *c* is used the mean; otherwise, it is calculated from the data.
Use the *c* argument with care, as it can lead to garbage results.
"""
if c is not None:
T, ssd, count = _sum((d := x - c) * d for x in data)
return (T, ssd, c, count)
count = 0
types = set()
sx_partials = defaultdict(int)
sxx_partials = defaultdict(int)
for typ, values in groupby(data, type):
types.add(typ)
for n, d in map(_exact_ratio, values):
count += 1
sx_partials[d] += n
sxx_partials[d] += n * n
if not count:
ssd = c = Fraction(0)
elif None in sx_partials:
# The sum will be a NAN or INF. We can ignore all the finite
# partials, and just look at this special one.
ssd = c = sx_partials[None]
assert not _isfinite(ssd)
else:
sx = sum(Fraction(n, d) for d, n in sx_partials.items())
sxx = sum(Fraction(n, d*d) for d, n in sxx_partials.items())
# This formula has poor numeric properties for floats,
# but with fractions it is exact.
ssd = (count * sxx - sx * sx) / count
c = sx / count
T = reduce(_coerce, types, int) # or raise TypeError
return (T, ssd, c, count)
def _isfinite(x):
try:
return x.is_finite() # Likely a Decimal.
except AttributeError:
return math.isfinite(x) # Coerces to float first.
def _coerce(T, S):
"""Coerce types T and S to a common type, or raise TypeError.
Coercion rules are currently an implementation detail. See the CoerceTest
test class in test_statistics for details.
"""
# See http://bugs.python.org/issue24068.
assert T is not bool, "initial type T is bool"
# If the types are the same, no need to coerce anything. Put this
# first, so that the usual case (no coercion needed) happens as soon
# as possible.
if T is S: return T
# Mixed int & other coerce to the other type.
if S is int or S is bool: return T
if T is int: return S
# If one is a (strict) subclass of the other, coerce to the subclass.
if issubclass(S, T): return S
if issubclass(T, S): return T
# Ints coerce to the other type.
if issubclass(T, int): return S
if issubclass(S, int): return T
# Mixed fraction & float coerces to float (or float subclass).
if issubclass(T, Fraction) and issubclass(S, float):
return S
if issubclass(T, float) and issubclass(S, Fraction):
return T
# Any other combination is disallowed.
msg = "don't know how to coerce %s and %s"
raise TypeError(msg % (T.__name__, S.__name__))
def _exact_ratio(x):
"""Return Real number x to exact (numerator, denominator) pair.
>>> _exact_ratio(0.25)
(1, 4)
x is expected to be an int, Fraction, Decimal or float.
"""
try:
return x.as_integer_ratio()
except AttributeError:
pass
except (OverflowError, ValueError):
# float NAN or INF.
assert not _isfinite(x)
return (x, None)
try:
# x may be an Integral ABC.
return (x.numerator, x.denominator)
except AttributeError:
msg = f"can't convert type '{type(x).__name__}' to numerator/denominator"
raise TypeError(msg)
def _convert(value, T):
"""Convert value to given numeric type T."""
if type(value) is T:
# This covers the cases where T is Fraction, or where value is
# a NAN or INF (Decimal or float).
return value
if issubclass(T, int) and value.denominator != 1:
T = float
try:
# FIXME: what do we do if this overflows?
return T(value)
except TypeError:
if issubclass(T, Decimal):
return T(value.numerator) / T(value.denominator)
else:
raise
def _fail_neg(values, errmsg='negative value'):
"""Iterate over values, failing if any are less than zero."""
for x in values:
if x < 0:
raise StatisticsError(errmsg)
yield x
def _rank(data, /, *, key=None, reverse=False, ties='average', start=1) -> list[float]:
"""Rank order a dataset. The lowest value has rank 1.
Ties are averaged so that equal values receive the same rank:
>>> data = [31, 56, 31, 25, 75, 18]
>>> _rank(data)
[3.5, 5.0, 3.5, 2.0, 6.0, 1.0]
The operation is idempotent:
>>> _rank([3.5, 5.0, 3.5, 2.0, 6.0, 1.0])
[3.5, 5.0, 3.5, 2.0, 6.0, 1.0]
It is possible to rank the data in reverse order so that the
highest value has rank 1. Also, a key-function can extract
the field to be ranked:
>>> goals = [('eagles', 45), ('bears', 48), ('lions', 44)]
>>> _rank(goals, key=itemgetter(1), reverse=True)
[2.0, 1.0, 3.0]
Ranks are conventionally numbered starting from one; however,
setting *start* to zero allows the ranks to be used as array indices:
>>> prize = ['Gold', 'Silver', 'Bronze', 'Certificate']
>>> scores = [8.1, 7.3, 9.4, 8.3]
>>> [prize[int(i)] for i in _rank(scores, start=0, reverse=True)]
['Bronze', 'Certificate', 'Gold', 'Silver']
"""
# If this function becomes public at some point, more thought
# needs to be given to the signature. A list of ints is
# plausible when ties is "min" or "max". When ties is "average",
# either list[float] or list[Fraction] is plausible.
# Default handling of ties matches scipy.stats.mstats.spearmanr.
if ties != 'average':
raise ValueError(f'Unknown tie resolution method: {ties!r}')
if key is not None:
data = map(key, data)
val_pos = sorted(zip(data, count()), reverse=reverse)
i = start - 1
result = [0] * len(val_pos)
for _, g in groupby(val_pos, key=itemgetter(0)):
group = list(g)
size = len(group)
rank = i + (size + 1) / 2
for value, orig_pos in group:
result[orig_pos] = rank
i += size
return result
def _integer_sqrt_of_frac_rto(n: int, m: int) -> int:
"""Square root of n/m, rounded to the nearest integer using round-to-odd."""
# Reference: https://www.lri.fr/~melquion/doc/05-imacs17_1-expose.pdf
a = math.isqrt(n // m)
return a | (a*a*m != n)
# For 53 bit precision floats, the bit width used in
# _float_sqrt_of_frac() is 109.
_sqrt_bit_width: int = 2 * sys.float_info.mant_dig + 3
def _float_sqrt_of_frac(n: int, m: int) -> float:
"""Square root of n/m as a float, correctly rounded."""
# See principle and proof sketch at: https://bugs.python.org/msg407078
q = (n.bit_length() - m.bit_length() - _sqrt_bit_width) // 2
if q >= 0:
numerator = _integer_sqrt_of_frac_rto(n, m << 2 * q) << q
denominator = 1
else:
numerator = _integer_sqrt_of_frac_rto(n << -2 * q, m)
denominator = 1 << -q
return numerator / denominator # Convert to float
def _decimal_sqrt_of_frac(n: int, m: int) -> Decimal:
"""Square root of n/m as a Decimal, correctly rounded."""
# Premise: For decimal, computing (n/m).sqrt() can be off
# by 1 ulp from the correctly rounded result.
# Method: Check the result, moving up or down a step if needed.
if n <= 0:
if not n:
return Decimal('0.0')
n, m = -n, -m
root = (Decimal(n) / Decimal(m)).sqrt()
nr, dr = root.as_integer_ratio()
plus = root.next_plus()
np, dp = plus.as_integer_ratio()
# test: n / m > ((root + plus) / 2) ** 2
if 4 * n * (dr*dp)**2 > m * (dr*np + dp*nr)**2:
return plus
minus = root.next_minus()
nm, dm = minus.as_integer_ratio()
# test: n / m < ((root + minus) / 2) ** 2
if 4 * n * (dr*dm)**2 < m * (dr*nm + dm*nr)**2:
return minus
return root
def _mean_stdev(data):
"""In one pass, compute the mean and sample standard deviation as floats."""
T, ss, xbar, n = _ss(data)
if n < 2:
raise StatisticsError('stdev requires at least two data points')
mss = ss / (n - 1)
try:
return float(xbar), _float_sqrt_of_frac(mss.numerator, mss.denominator)
except AttributeError:
# Handle Nans and Infs gracefully
return float(xbar), float(xbar) / float(ss)
def _sqrtprod(x: float, y: float) -> float:
"Return sqrt(x * y) computed with improved accuracy and without overflow/underflow."
h = sqrt(x * y)
if not isfinite(h):
if isinf(h) and not isinf(x) and not isinf(y):
# Finite inputs overflowed, so scale down, and recompute.
scale = 2.0 ** -512 # sqrt(1 / sys.float_info.max)
return _sqrtprod(scale * x, scale * y) / scale
return h
if not h:
if x and y:
# Non-zero inputs underflowed, so scale up, and recompute.
# Scale: 1 / sqrt(sys.float_info.min * sys.float_info.epsilon)
scale = 2.0 ** 537
return _sqrtprod(scale * x, scale * y) / scale
return h
# Improve accuracy with a differential correction.
# https://www.wolframalpha.com/input/?i=Maclaurin+series+sqrt%28h**2+%2B+x%29+at+x%3D0
d = sumprod((x, h), (y, -h))
return h + d / (2.0 * h)
def _normal_dist_inv_cdf(p, mu, sigma):
# There is no closed-form solution to the inverse CDF for the normal
# distribution, so we use a rational approximation instead:
# Wichura, M.J. (1988). "Algorithm AS241: The Percentage Points of the
# Normal Distribution". Applied Statistics. Blackwell Publishing. 37
# (3): 477–484. doi:10.2307/2347330. JSTOR 2347330.
q = p - 0.5
if fabs(q) <= 0.425:
r = 0.180625 - q * q
# Hash sum: 55.88319_28806_14901_4439
num = (((((((2.50908_09287_30122_6727e+3 * r +
3.34305_75583_58812_8105e+4) * r +
6.72657_70927_00870_0853e+4) * r +
4.59219_53931_54987_1457e+4) * r +
1.37316_93765_50946_1125e+4) * r +
1.97159_09503_06551_4427e+3) * r +
1.33141_66789_17843_7745e+2) * r +
3.38713_28727_96366_6080e+0) * q
den = (((((((5.22649_52788_52854_5610e+3 * r +
2.87290_85735_72194_2674e+4) * r +
3.93078_95800_09271_0610e+4) * r +
2.12137_94301_58659_5867e+4) * r +
5.39419_60214_24751_1077e+3) * r +
6.87187_00749_20579_0830e+2) * r +
4.23133_30701_60091_1252e+1) * r +
1.0)
x = num / den
return mu + (x * sigma)
r = p if q <= 0.0 else 1.0 - p
r = sqrt(-log(r))
if r <= 5.0:
r = r - 1.6
# Hash sum: 49.33206_50330_16102_89036
num = (((((((7.74545_01427_83414_07640e-4 * r +
2.27238_44989_26918_45833e-2) * r +
2.41780_72517_74506_11770e-1) * r +
1.27045_82524_52368_38258e+0) * r +
3.64784_83247_63204_60504e+0) * r +
5.76949_72214_60691_40550e+0) * r +
4.63033_78461_56545_29590e+0) * r +
1.42343_71107_49683_57734e+0)
den = (((((((1.05075_00716_44416_84324e-9 * r +
5.47593_80849_95344_94600e-4) * r +
1.51986_66563_61645_71966e-2) * r +
1.48103_97642_74800_74590e-1) * r +
6.89767_33498_51000_04550e-1) * r +
1.67638_48301_83803_84940e+0) * r +
2.05319_16266_37758_82187e+0) * r +
1.0)
else:
r = r - 5.0
# Hash sum: 47.52583_31754_92896_71629
num = (((((((2.01033_43992_92288_13265e-7 * r +
2.71155_55687_43487_57815e-5) * r +
1.24266_09473_88078_43860e-3) * r +
2.65321_89526_57612_30930e-2) * r +
2.96560_57182_85048_91230e-1) * r +
1.78482_65399_17291_33580e+0) * r +
5.46378_49111_64114_36990e+0) * r +
6.65790_46435_01103_77720e+0)
den = (((((((2.04426_31033_89939_78564e-15 * r +
1.42151_17583_16445_88870e-7) * r +
1.84631_83175_10054_68180e-5) * r +
7.86869_13114_56132_59100e-4) * r +
1.48753_61290_85061_48525e-2) * r +
1.36929_88092_27358_05310e-1) * r +
5.99832_20655_58879_37690e-1) * r +
1.0)
x = num / den
if q < 0.0:
x = -x
return mu + (x * sigma)
# If available, use C implementation
try:
from _statistics import _normal_dist_inv_cdf
except ImportError:
pass