fuchsia / third_party / swift-cmark / refs/tags/swift-DEVELOPMENT-SNAPSHOT-2017-09-02-a / . / bench / statistics.py

## Module statistics.py | |

## | |

## Copyright (c) 2013 Steven D'Aprano <steve+python@pearwood.info>. | |

## | |

## Licensed under the Apache License, Version 2.0 (the "License"); | |

## you may not use this file except in compliance with the License. | |

## You may obtain a copy of the License at | |

## | |

## http://www.apache.org/licenses/LICENSE-2.0 | |

## | |

## Unless required by applicable law or agreed to in writing, software | |

## distributed under the License is distributed on an "AS IS" BASIS, | |

## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |

## See the License for the specific language governing permissions and | |

## limitations under the License. | |

""" | |

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. | |

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. | |

================== ============================================= | |

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 | |

Exceptions | |

---------- | |

A single exception is defined: StatisticsError is a subclass of ValueError. | |

""" | |

__all__ = [ 'StatisticsError', | |

'pstdev', 'pvariance', 'stdev', 'variance', | |

'median', 'median_low', 'median_high', 'median_grouped', | |

'mean', 'mode', | |

] | |

import collections | |

import math | |

from fractions import Fraction | |

from decimal import Decimal | |

# === Exceptions === | |

class StatisticsError(ValueError): | |

pass | |

# === Private utilities === | |

def _sum(data, start=0): | |

"""_sum(data [, start]) -> value | |

Return a high-precision sum of the given numeric data. If optional | |

argument ``start`` is given, it is added to the total. If ``data`` is | |

empty, ``start`` (defaulting to 0) is returned. | |

Examples | |

-------- | |

>>> _sum([3, 2.25, 4.5, -0.5, 1.0], 0.75) | |

11.0 | |

Some sources of round-off error will be avoided: | |

>>> _sum([1e50, 1, -1e50] * 1000) # Built-in sum returns zero. | |

1000.0 | |

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)]) | |

Fraction(63, 20) | |

>>> from decimal import Decimal as D | |

>>> data = [D("0.1375"), D("0.2108"), D("0.3061"), D("0.0419")] | |

>>> _sum(data) | |

Decimal('0.6963') | |

Mixed types are currently treated as an error, except that int is | |

allowed. | |

""" | |

# We fail as soon as we reach a value that is not an int or the type of | |

# the first value which is not an int. E.g. _sum([int, int, float, int]) | |

# is okay, but sum([int, int, float, Fraction]) is not. | |

allowed_types = set([int, type(start)]) | |

n, d = _exact_ratio(start) | |

partials = {d: n} # map {denominator: sum of numerators} | |

# Micro-optimizations. | |

exact_ratio = _exact_ratio | |

partials_get = partials.get | |

# Add numerators for each denominator. | |

for x in data: | |

_check_type(type(x), allowed_types) | |

n, d = exact_ratio(x) | |

partials[d] = partials_get(d, 0) + n | |

# Find the expected result type. If allowed_types has only one item, it | |

# will be int; if it has two, use the one which isn't int. | |

assert len(allowed_types) in (1, 2) | |

if len(allowed_types) == 1: | |

assert allowed_types.pop() is int | |

T = int | |

else: | |

T = (allowed_types - set([int])).pop() | |

if None in partials: | |

assert issubclass(T, (float, Decimal)) | |

assert not math.isfinite(partials[None]) | |

return T(partials[None]) | |

total = Fraction() | |

for d, n in sorted(partials.items()): | |

total += Fraction(n, d) | |

if issubclass(T, int): | |

assert total.denominator == 1 | |

return T(total.numerator) | |

if issubclass(T, Decimal): | |

return T(total.numerator)/total.denominator | |

return T(total) | |

def _check_type(T, allowed): | |

if T not in allowed: | |

if len(allowed) == 1: | |

allowed.add(T) | |

else: | |

types = ', '.join([t.__name__ for t in allowed] + [T.__name__]) | |

raise TypeError("unsupported mixed types: %s" % types) | |

def _exact_ratio(x): | |

"""Convert Real number x exactly to (numerator, denominator) pair. | |

>>> _exact_ratio(0.25) | |

(1, 4) | |

x is expected to be an int, Fraction, Decimal or float. | |

""" | |

try: | |

try: | |

# int, Fraction | |

return (x.numerator, x.denominator) | |

except AttributeError: | |

# float | |

try: | |

return x.as_integer_ratio() | |

except AttributeError: | |

# Decimal | |

try: | |

return _decimal_to_ratio(x) | |

except AttributeError: | |

msg = "can't convert type '{}' to numerator/denominator" | |

raise TypeError(msg.format(type(x).__name__)) from None | |

except (OverflowError, ValueError): | |

# INF or NAN | |

if __debug__: | |

# Decimal signalling NANs cannot be converted to float :-( | |

if isinstance(x, Decimal): | |

assert not x.is_finite() | |

else: | |

assert not math.isfinite(x) | |

return (x, None) | |

# FIXME This is faster than Fraction.from_decimal, but still too slow. | |

def _decimal_to_ratio(d): | |

"""Convert Decimal d to exact integer ratio (numerator, denominator). | |

>>> from decimal import Decimal | |

>>> _decimal_to_ratio(Decimal("2.6")) | |

(26, 10) | |

""" | |

sign, digits, exp = d.as_tuple() | |

if exp in ('F', 'n', 'N'): # INF, NAN, sNAN | |

assert not d.is_finite() | |

raise ValueError | |

num = 0 | |

for digit in digits: | |

num = num*10 + digit | |

if exp < 0: | |

den = 10**-exp | |

else: | |

num *= 10**exp | |

den = 1 | |

if sign: | |

num = -num | |

return (num, den) | |

def _counts(data): | |

# Generate a table of sorted (value, frequency) pairs. | |

table = collections.Counter(iter(data)).most_common() | |

if not table: | |

return table | |

# Extract the values with the highest frequency. | |

maxfreq = table[0][1] | |

for i in range(1, len(table)): | |

if table[i][1] != maxfreq: | |

table = table[:i] | |

break | |

return table | |

# === 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. | |

""" | |

if iter(data) is data: | |

data = list(data) | |

n = len(data) | |

if n < 1: | |

raise StatisticsError('mean requires at least one data point') | |

return _sum(data)/n | |

# FIXME: investigate ways to calculate medians without sorting? Quickselect? | |

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 | |

""" | |

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): | |

""""Return the 50th percentile (median) of grouped continuous data. | |

>>> median_grouped([1, 2, 2, 3, 4, 4, 4, 4, 4, 5]) | |

3.7 | |

>>> median_grouped([52, 52, 53, 54]) | |

52.5 | |

This calculates the median as the 50th percentile, and should be | |

used when your data is continuous and grouped. In the above example, | |

the values 1, 2, 3, etc. actually represent the midpoint of classes | |

0.5-1.5, 1.5-2.5, 2.5-3.5, etc. The middle value falls somewhere in | |

class 3.5-4.5, and interpolation is used to estimate it. | |

Optional argument ``interval`` represents the class interval, and | |

defaults to 1. Changing the class interval naturally will change the | |

interpolated 50th percentile value: | |

>>> median_grouped([1, 3, 3, 5, 7], interval=1) | |

3.25 | |

>>> median_grouped([1, 3, 3, 5, 7], interval=2) | |

3.5 | |

This function does not check whether the data points are at least | |

``interval`` apart. | |

""" | |

data = sorted(data) | |

n = len(data) | |

if n == 0: | |

raise StatisticsError("no median for empty data") | |

elif n == 1: | |

return data[0] | |

# Find the value at the midpoint. Remember this corresponds to the | |

# centre of the class interval. | |

x = data[n//2] | |

for obj in (x, interval): | |

if isinstance(obj, (str, bytes)): | |

raise TypeError('expected number but got %r' % obj) | |

try: | |

L = x - interval/2 # The lower limit of the median interval. | |

except TypeError: | |

# Mixed type. For now we just coerce to float. | |

L = float(x) - float(interval)/2 | |

cf = data.index(x) # Number of values below the median interval. | |

# FIXME The following line could be more efficient for big lists. | |

f = data.count(x) # Number of data points in the median interval. | |

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 is not exactly one most common value, ``mode`` will raise | |

StatisticsError. | |

""" | |

# Generate a table of sorted (value, frequency) pairs. | |

table = _counts(data) | |

if len(table) == 1: | |

return table[0][0] | |

elif table: | |

raise StatisticsError( | |

'no unique mode; found %d equally common values' % len(table) | |

) | |

else: | |

raise StatisticsError('no mode for empty data') | |

# === Measures of spread === | |

# See http://mathworld.wolfram.com/Variance.html | |

# http://mathworld.wolfram.com/SampleVariance.html | |

# http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance | |

# | |

# Under no circumstances use the so-called "computational formula for | |

# variance", as that is only suitable for hand calculations with a small | |

# amount of low-precision data. It has terrible numeric properties. | |

# | |

# See a comparison of three computational methods here: | |

# http://www.johndcook.com/blog/2008/09/26/comparing-three-methods-of-computing-standard-deviation/ | |

def _ss(data, c=None): | |

"""Return sum of square deviations of sequence data. | |

If ``c`` is None, the mean is calculated in one pass, and the deviations | |

from the mean are calculated in a second pass. Otherwise, deviations are | |

calculated from ``c`` as given. Use the second case with care, as it can | |

lead to garbage results. | |

""" | |

if c is None: | |

c = mean(data) | |

ss = _sum((x-c)**2 for x in data) | |

# The following sum should mathematically equal zero, but due to rounding | |

# error may not. | |

ss -= _sum((x-c) for x in data)**2/len(data) | |

assert not ss < 0, 'negative sum of square deviations: %f' % ss | |

return ss | |

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) | |

""" | |

if iter(data) is data: | |

data = list(data) | |

n = len(data) | |

if n < 2: | |

raise StatisticsError('variance requires at least two data points') | |

ss = _ss(data, xbar) | |

return ss/(n-1) | |

def pvariance(data, mu=None): | |

"""Return the population variance of ``data``. | |

data should be an 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 | |

This function does not check that ``mu`` is actually the mean of ``data``. | |

Giving arbitrary values for ``mu`` may lead to invalid or impossible | |

results. | |

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) | |

""" | |

if iter(data) is data: | |

data = list(data) | |

n = len(data) | |

if n < 1: | |

raise StatisticsError('pvariance requires at least one data point') | |

ss = _ss(data, mu) | |

return ss/n | |

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 | |

""" | |

var = variance(data, xbar) | |

try: | |

return var.sqrt() | |

except AttributeError: | |

return math.sqrt(var) | |

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 | |

""" | |

var = pvariance(data, mu) | |

try: | |

return var.sqrt() | |

except AttributeError: | |

return math.sqrt(var) |