mirror of
https://github.com/python/cpython.git
synced 2025-07-12 13:55:34 +00:00
bpo-36018: Add the NormalDist class to the statistics module (GH-11973)
This commit is contained in:
Raymond Hettinger
GitHub
parent
64d6cc826d
commit
11c7953165
5 changed files with 556 additions and 1 deletions
|
|
@ -5,9 +5,11 @@ approx_equal function.
|
|||
|
||||
import collections
|
||||
import collections.abc
|
||||
import copy
|
||||
import decimal
|
||||
import doctest
|
||||
import math
|
||||
import pickle
|
||||
import random
|
||||
import sys
|
||||
import unittest
|
||||
|
|
@ -2025,6 +2027,181 @@ class TestStdev(VarianceStdevMixin, NumericTestCase):
|
|||
expected = math.sqrt(statistics.variance(data))
|
||||
self.assertEqual(self.func(data), expected)
|
||||
|
||||
class TestNormalDist(unittest.TestCase):
|
||||
|
||||
def test_slots(self):
|
||||
nd = statistics.NormalDist(300, 23)
|
||||
with self.assertRaises(TypeError):
|
||||
vars(nd)
|
||||
self.assertEqual(nd.__slots__, ('mu', 'sigma'))
|
||||
|
||||
def test_instantiation_and_attributes(self):
|
||||
nd = statistics.NormalDist(500, 17)
|
||||
self.assertEqual(nd.mu, 500)
|
||||
self.assertEqual(nd.sigma, 17)
|
||||
self.assertEqual(nd.variance, 17**2)
|
||||
|
||||
# default arguments
|
||||
nd = statistics.NormalDist()
|
||||
self.assertEqual(nd.mu, 0)
|
||||
self.assertEqual(nd.sigma, 1)
|
||||
self.assertEqual(nd.variance, 1**2)
|
||||
|
||||
# error case: negative sigma
|
||||
with self.assertRaises(statistics.StatisticsError):
|
||||
statistics.NormalDist(500, -10)
|
||||
|
||||
def test_alternative_constructor(self):
|
||||
NormalDist = statistics.NormalDist
|
||||
data = [96, 107, 90, 92, 110]
|
||||
# list input
|
||||
self.assertEqual(NormalDist.from_samples(data), NormalDist(99, 9))
|
||||
# tuple input
|
||||
self.assertEqual(NormalDist.from_samples(tuple(data)), NormalDist(99, 9))
|
||||
# iterator input
|
||||
self.assertEqual(NormalDist.from_samples(iter(data)), NormalDist(99, 9))
|
||||
# error cases
|
||||
with self.assertRaises(statistics.StatisticsError):
|
||||
NormalDist.from_samples([]) # empty input
|
||||
with self.assertRaises(statistics.StatisticsError):
|
||||
NormalDist.from_samples([10]) # only one input
|
||||
|
||||
def test_sample_generation(self):
|
||||
NormalDist = statistics.NormalDist
|
||||
mu, sigma = 10_000, 3.0
|
||||
X = NormalDist(mu, sigma)
|
||||
n = 1_000
|
||||
data = X.samples(n)
|
||||
self.assertEqual(len(data), n)
|
||||
self.assertEqual(set(map(type, data)), {float})
|
||||
# mean(data) expected to fall within 8 standard deviations
|
||||
xbar = statistics.mean(data)
|
||||
self.assertTrue(mu - sigma*8 <= xbar <= mu + sigma*8)
|
||||
|
||||
# verify that seeding makes reproducible sequences
|
||||
n = 100
|
||||
data1 = X.samples(n, seed='happiness and joy')
|
||||
data2 = X.samples(n, seed='trouble and despair')
|
||||
data3 = X.samples(n, seed='happiness and joy')
|
||||
data4 = X.samples(n, seed='trouble and despair')
|
||||
self.assertEqual(data1, data3)
|
||||
self.assertEqual(data2, data4)
|
||||
self.assertNotEqual(data1, data2)
|
||||
|
||||
# verify that subclass type is honored
|
||||
class NewNormalDist(NormalDist):
|
||||
pass
|
||||
nnd = NewNormalDist(200, 5)
|
||||
self.assertEqual(type(nnd), NewNormalDist)
|
||||
|
||||
def test_pdf(self):
|
||||
NormalDist = statistics.NormalDist
|
||||
X = NormalDist(100, 15)
|
||||
# Verify peak around center
|
||||
self.assertLess(X.pdf(99), X.pdf(100))
|
||||
self.assertLess(X.pdf(101), X.pdf(100))
|
||||
# Test symmetry
|
||||
self.assertAlmostEqual(X.pdf(99), X.pdf(101))
|
||||
self.assertAlmostEqual(X.pdf(98), X.pdf(102))
|
||||
self.assertAlmostEqual(X.pdf(97), X.pdf(103))
|
||||
# Test vs CDF
|
||||
dx = 2.0 ** -10
|
||||
for x in range(90, 111):
|
||||
est_pdf = (X.cdf(x + dx) - X.cdf(x)) / dx
|
||||
self.assertAlmostEqual(X.pdf(x), est_pdf, places=4)
|
||||
# Error case: variance is zero
|
||||
Y = NormalDist(100, 0)
|
||||
with self.assertRaises(statistics.StatisticsError):
|
||||
Y.pdf(90)
|
||||
|
||||
def test_cdf(self):
|
||||
NormalDist = statistics.NormalDist
|
||||
X = NormalDist(100, 15)
|
||||
cdfs = [X.cdf(x) for x in range(1, 200)]
|
||||
self.assertEqual(set(map(type, cdfs)), {float})
|
||||
# Verify montonic
|
||||
self.assertEqual(cdfs, sorted(cdfs))
|
||||
# Verify center
|
||||
self.assertAlmostEqual(X.cdf(100), 0.50)
|
||||
# Error case: variance is zero
|
||||
Y = NormalDist(100, 0)
|
||||
with self.assertRaises(statistics.StatisticsError):
|
||||
Y.cdf(90)
|
||||
|
||||
def test_same_type_addition_and_subtraction(self):
|
||||
NormalDist = statistics.NormalDist
|
||||
X = NormalDist(100, 12)
|
||||
Y = NormalDist(40, 5)
|
||||
self.assertEqual(X + Y, NormalDist(140, 13)) # __add__
|
||||
self.assertEqual(X - Y, NormalDist(60, 13)) # __sub__
|
||||
|
||||
def test_translation_and_scaling(self):
|
||||
NormalDist = statistics.NormalDist
|
||||
X = NormalDist(100, 15)
|
||||
y = 10
|
||||
self.assertEqual(+X, NormalDist(100, 15)) # __pos__
|
||||
self.assertEqual(-X, NormalDist(-100, 15)) # __neg__
|
||||
self.assertEqual(X + y, NormalDist(110, 15)) # __add__
|
||||
self.assertEqual(y + X, NormalDist(110, 15)) # __radd__
|
||||
self.assertEqual(X - y, NormalDist(90, 15)) # __sub__
|
||||
self.assertEqual(y - X, NormalDist(-90, 15)) # __rsub__
|
||||
self.assertEqual(X * y, NormalDist(1000, 150)) # __mul__
|
||||
self.assertEqual(y * X, NormalDist(1000, 150)) # __rmul__
|
||||
self.assertEqual(X / y, NormalDist(10, 1.5)) # __truediv__
|
||||
with self.assertRaises(TypeError):
|
||||
y / X
|
||||
|
||||
def test_equality(self):
|
||||
NormalDist = statistics.NormalDist
|
||||
nd1 = NormalDist()
|
||||
nd2 = NormalDist(2, 4)
|
||||
nd3 = NormalDist()
|
||||
self.assertNotEqual(nd1, nd2)
|
||||
self.assertEqual(nd1, nd3)
|
||||
|
||||
# Test NotImplemented when types are different
|
||||
class A:
|
||||
def __eq__(self, other):
|
||||
return 10
|
||||
a = A()
|
||||
self.assertEqual(nd1.__eq__(a), NotImplemented)
|
||||
self.assertEqual(nd1 == a, 10)
|
||||
self.assertEqual(a == nd1, 10)
|
||||
|
||||
# All subclasses to compare equal giving the same behavior
|
||||
# as list, tuple, int, float, complex, str, dict, set, etc.
|
||||
class SizedNormalDist(NormalDist):
|
||||
def __init__(self, mu, sigma, n):
|
||||
super().__init__(mu, sigma)
|
||||
self.n = n
|
||||
s = SizedNormalDist(100, 15, 57)
|
||||
nd4 = NormalDist(100, 15)
|
||||
self.assertEqual(s, nd4)
|
||||
|
||||
# Don't allow duck type equality because we wouldn't
|
||||
# want a lognormal distribution to compare equal
|
||||
# to a normal distribution with the same parameters
|
||||
class LognormalDist:
|
||||
def __init__(self, mu, sigma):
|
||||
self.mu = mu
|
||||
self.sigma = sigma
|
||||
lnd = LognormalDist(100, 15)
|
||||
nd = NormalDist(100, 15)
|
||||
self.assertNotEqual(nd, lnd)
|
||||
|
||||
def test_pickle_and_copy(self):
|
||||
nd = statistics.NormalDist(37.5, 5.625)
|
||||
nd1 = copy.copy(nd)
|
||||
self.assertEqual(nd, nd1)
|
||||
nd2 = copy.deepcopy(nd)
|
||||
self.assertEqual(nd, nd2)
|
||||
nd3 = pickle.loads(pickle.dumps(nd))
|
||||
self.assertEqual(nd, nd3)
|
||||
|
||||
def test_repr(self):
|
||||
nd = statistics.NormalDist(37.5, 5.625)
|
||||
self.assertEqual(repr(nd), 'NormalDist(mu=37.5, sigma=5.625)')
|
||||
|
||||
|
||||
# === Run tests ===
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue