Issue #27181 remove geometric_mean and defer for 3.7.
This commit is contained in:
Steven D'Aprano
parent
d3dbba4174
commit
fae2829c7a
|
|
@ -39,7 +39,6 @@ or sample.
|
|||
|
||||
======================= =============================================
|
||||
:func:`mean` Arithmetic mean ("average") of data.
|
||||
:func:`geometric_mean` Geometric mean of data.
|
||||
:func:`harmonic_mean` Harmonic mean of data.
|
||||
:func:`median` Median (middle value) of data.
|
||||
:func:`median_low` Low median of data.
|
||||
|
|
@ -113,34 +112,6 @@ However, for reading convenience, most of the examples show sorted sequences.
|
|||
``mean(data)`` is equivalent to calculating the true population mean μ.
|
||||
|
||||
|
||||
.. function:: geometric_mean(data)
|
||||
|
||||
Return the geometric mean of *data*, a sequence or iterator of
|
||||
real-valued numbers.
|
||||
|
||||
The geometric mean is the *n*-th root of the product of *n* data points.
|
||||
It is a type of average, a measure of the central location of the data.
|
||||
|
||||
The geometric mean is appropriate when averaging quantities which
|
||||
are multiplied together rather than added, for example growth rates.
|
||||
Suppose an investment grows by 10% in the first year, falls by 5% in
|
||||
the second, then grows by 12% in the third, what is the average rate
|
||||
of growth over the three years?
|
||||
|
||||
.. doctest::
|
||||
|
||||
>>> geometric_mean([1.10, 0.95, 1.12])
|
||||
1.0538483123382172
|
||||
|
||||
giving an average growth of 5.385%. Using the arithmetic mean will
|
||||
give approximately 5.667%, which is too high.
|
||||
|
||||
:exc:`StatisticsError` is raised if *data* is empty, or any
|
||||
element is less than zero.
|
||||
|
||||
.. versionadded:: 3.6
|
||||
|
||||
|
||||
.. function:: harmonic_mean(data)
|
||||
|
||||
Return the harmonic mean of *data*, a sequence or iterator of
|
||||
|
|
|
|||
|
|
@ -11,7 +11,6 @@ Calculating averages
|
|||
Function Description
|
||||
================== =============================================
|
||||
mean Arithmetic mean (average) of data.
|
||||
geometric_mean Geometric mean of data.
|
||||
harmonic_mean Harmonic mean of data.
|
||||
median Median (middle value) of data.
|
||||
median_low Low median of data.
|
||||
|
|
@ -80,7 +79,7 @@ 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', 'geometric_mean', 'harmonic_mean',
|
||||
'mean', 'mode', 'harmonic_mean',
|
||||
]
|
||||
|
||||
import collections
|
||||
|
|
@ -287,229 +286,6 @@ def _fail_neg(values, errmsg='negative value'):
|
|||
yield x
|
||||
|
||||
|
||||
class _nroot_NS:
|
||||
"""Hands off! Don't touch!
|
||||
|
||||
Everything inside this namespace (class) is an even-more-private
|
||||
implementation detail of the private _nth_root function.
|
||||
"""
|
||||
# This class exists only to be used as a namespace, for convenience
|
||||
# of being able to keep the related functions together, and to
|
||||
# collapse the group in an editor. If this were C# or C++, I would
|
||||
# use a Namespace, but the closest Python has is a class.
|
||||
#
|
||||
# FIXME possibly move this out into a separate module?
|
||||
# That feels like overkill, and may encourage people to treat it as
|
||||
# a public feature.
|
||||
def __init__(self):
|
||||
raise TypeError('namespace only, do not instantiate')
|
||||
|
||||
def nth_root(x, n):
|
||||
"""Return the positive nth root of numeric x.
|
||||
|
||||
This may be more accurate than ** or pow():
|
||||
|
||||
>>> math.pow(1000, 1.0/3) #doctest:+SKIP
|
||||
9.999999999999998
|
||||
|
||||
>>> _nth_root(1000, 3)
|
||||
10.0
|
||||
>>> _nth_root(11**5, 5)
|
||||
11.0
|
||||
>>> _nth_root(2, 12)
|
||||
1.0594630943592953
|
||||
|
||||
"""
|
||||
if not isinstance(n, int):
|
||||
raise TypeError('degree n must be an int')
|
||||
if n < 2:
|
||||
raise ValueError('degree n must be 2 or more')
|
||||
if isinstance(x, decimal.Decimal):
|
||||
return _nroot_NS.decimal_nroot(x, n)
|
||||
elif isinstance(x, numbers.Real):
|
||||
return _nroot_NS.float_nroot(x, n)
|
||||
else:
|
||||
raise TypeError('expected a number, got %s') % type(x).__name__
|
||||
|
||||
def float_nroot(x, n):
|
||||
"""Handle nth root of Reals, treated as a float."""
|
||||
assert isinstance(n, int) and n > 1
|
||||
if x < 0:
|
||||
raise ValueError('domain error: root of negative number')
|
||||
elif x == 0:
|
||||
return math.copysign(0.0, x)
|
||||
elif x > 0:
|
||||
try:
|
||||
isinfinity = math.isinf(x)
|
||||
except OverflowError:
|
||||
return _nroot_NS.bignum_nroot(x, n)
|
||||
else:
|
||||
if isinfinity:
|
||||
return float('inf')
|
||||
else:
|
||||
return _nroot_NS.nroot(x, n)
|
||||
else:
|
||||
assert math.isnan(x)
|
||||
return float('nan')
|
||||
|
||||
def nroot(x, n):
|
||||
"""Calculate x**(1/n), then improve the answer."""
|
||||
# This uses math.pow() to calculate an initial guess for the root,
|
||||
# then uses the iterated nroot algorithm to improve it.
|
||||
#
|
||||
# By my testing, about 8% of the time the iterated algorithm ends
|
||||
# up converging to a result which is less accurate than the initial
|
||||
# guess. [FIXME: is this still true?] In that case, we use the
|
||||
# guess instead of the "improved" value. This way, we're never
|
||||
# less accurate than math.pow().
|
||||
r1 = math.pow(x, 1.0/n)
|
||||
eps1 = abs(r1**n - x)
|
||||
if eps1 == 0.0:
|
||||
# r1 is the exact root, so we're done. By my testing, this
|
||||
# occurs about 80% of the time for x < 1 and 30% of the
|
||||
# time for x > 1.
|
||||
return r1
|
||||
else:
|
||||
try:
|
||||
r2 = _nroot_NS.iterated_nroot(x, n, r1)
|
||||
except RuntimeError:
|
||||
return r1
|
||||
else:
|
||||
eps2 = abs(r2**n - x)
|
||||
if eps1 < eps2:
|
||||
return r1
|
||||
return r2
|
||||
|
||||
def iterated_nroot(a, n, g):
|
||||
"""Return the nth root of a, starting with guess g.
|
||||
|
||||
This is a special case of Newton's Method.
|
||||
https://en.wikipedia.org/wiki/Nth_root_algorithm
|
||||
"""
|
||||
np = n - 1
|
||||
def iterate(r):
|
||||
try:
|
||||
return (np*r + a/math.pow(r, np))/n
|
||||
except OverflowError:
|
||||
# If r is large enough, r**np may overflow. If that
|
||||
# happens, r**-np will be small, but not necessarily zero.
|
||||
return (np*r + a*math.pow(r, -np))/n
|
||||
# With a good guess, such as g = a**(1/n), this will converge in
|
||||
# only a few iterations. However a poor guess can take thousands
|
||||
# of iterations to converge, if at all. We guard against poor
|
||||
# guesses by setting an upper limit to the number of iterations.
|
||||
r1 = g
|
||||
r2 = iterate(g)
|
||||
for i in range(1000):
|
||||
if r1 == r2:
|
||||
break
|
||||
# Use Floyd's cycle-finding algorithm to avoid being trapped
|
||||
# in a cycle.
|
||||
# https://en.wikipedia.org/wiki/Cycle_detection#Tortoise_and_hare
|
||||
r1 = iterate(r1)
|
||||
r2 = iterate(iterate(r2))
|
||||
else:
|
||||
# If the guess is particularly bad, the above may fail to
|
||||
# converge in any reasonable time.
|
||||
raise RuntimeError('nth-root failed to converge')
|
||||
return r2
|
||||
|
||||
def decimal_nroot(x, n):
|
||||
"""Handle nth root of Decimals."""
|
||||
assert isinstance(x, decimal.Decimal)
|
||||
assert isinstance(n, int)
|
||||
if x.is_snan():
|
||||
# Signalling NANs always raise.
|
||||
raise decimal.InvalidOperation('nth-root of snan')
|
||||
if x.is_qnan():
|
||||
# Quiet NANs only raise if the context is set to raise,
|
||||
# otherwise return a NAN.
|
||||
ctx = decimal.getcontext()
|
||||
if ctx.traps[decimal.InvalidOperation]:
|
||||
raise decimal.InvalidOperation('nth-root of nan')
|
||||
else:
|
||||
# Preserve the input NAN.
|
||||
return x
|
||||
if x < 0:
|
||||
raise ValueError('domain error: root of negative number')
|
||||
if x.is_infinite():
|
||||
return x
|
||||
# FIXME this hasn't had the extensive testing of the float
|
||||
# version _iterated_nroot so there's possibly some buggy
|
||||
# corner cases buried in here. Can it overflow? Fail to
|
||||
# converge or get trapped in a cycle? Converge to a less
|
||||
# accurate root?
|
||||
np = n - 1
|
||||
def iterate(r):
|
||||
return (np*r + x/r**np)/n
|
||||
r0 = x**(decimal.Decimal(1)/n)
|
||||
assert isinstance(r0, decimal.Decimal)
|
||||
r1 = iterate(r0)
|
||||
while True:
|
||||
if r1 == r0:
|
||||
return r1
|
||||
r0, r1 = r1, iterate(r1)
|
||||
|
||||
def bignum_nroot(x, n):
|
||||
"""Return the nth root of a positive huge number."""
|
||||
assert x > 0
|
||||
# I state without proof that ⁿ√x ≈ ⁿ√2·ⁿ√(x//2)
|
||||
# and that for sufficiently big x the error is acceptable.
|
||||
# We now halve x until it is small enough to get the root.
|
||||
m = 0
|
||||
while True:
|
||||
x //= 2
|
||||
m += 1
|
||||
try:
|
||||
y = float(x)
|
||||
except OverflowError:
|
||||
continue
|
||||
break
|
||||
a = _nroot_NS.nroot(y, n)
|
||||
# At this point, we want the nth-root of 2**m, or 2**(m/n).
|
||||
# We can write that as 2**(q + r/n) = 2**q * ⁿ√2**r where q = m//n.
|
||||
q, r = divmod(m, n)
|
||||
b = 2**q * _nroot_NS.nroot(2**r, n)
|
||||
return a * b
|
||||
|
||||
|
||||
# This is the (private) function for calculating nth roots:
|
||||
_nth_root = _nroot_NS.nth_root
|
||||
assert type(_nth_root) is type(lambda: None)
|
||||
|
||||
|
||||
def _product(values):
|
||||
"""Return product of values as (exponent, mantissa)."""
|
||||
errmsg = 'mixed Decimal and float is not supported'
|
||||
prod = 1
|
||||
for x in values:
|
||||
if isinstance(x, float):
|
||||
break
|
||||
prod *= x
|
||||
else:
|
||||
return (0, prod)
|
||||
if isinstance(prod, Decimal):
|
||||
raise TypeError(errmsg)
|
||||
# Since floats can overflow easily, we calculate the product as a
|
||||
# sort of poor-man's BigFloat. Given that:
|
||||
#
|
||||
# x = 2**p * m # p == power or exponent (scale), m = mantissa
|
||||
#
|
||||
# we can calculate the product of two (or more) x values as:
|
||||
#
|
||||
# x1*x2 = 2**p1*m1 * 2**p2*m2 = 2**(p1+p2)*(m1*m2)
|
||||
#
|
||||
mant, scale = 1, 0 #math.frexp(prod) # FIXME
|
||||
for y in chain([x], values):
|
||||
if isinstance(y, Decimal):
|
||||
raise TypeError(errmsg)
|
||||
m1, e1 = math.frexp(y)
|
||||
m2, e2 = math.frexp(mant)
|
||||
scale += (e1 + e2)
|
||||
mant = m1*m2
|
||||
return (scale, mant)
|
||||
|
||||
|
||||
# === Measures of central tendency (averages) ===
|
||||
|
||||
def mean(data):
|
||||
|
|
@ -538,49 +314,6 @@ def mean(data):
|
|||
return _convert(total/n, T)
|
||||
|
||||
|
||||
def geometric_mean(data):
|
||||
"""Return the geometric mean of data.
|
||||
|
||||
The geometric mean is appropriate when averaging quantities which
|
||||
are multiplied together rather than added, for example growth rates.
|
||||
Suppose an investment grows by 10% in the first year, falls by 5% in
|
||||
the second, then grows by 12% in the third, what is the average rate
|
||||
of growth over the three years?
|
||||
|
||||
>>> geometric_mean([1.10, 0.95, 1.12])
|
||||
1.0538483123382172
|
||||
|
||||
giving an average growth of 5.385%. Using the arithmetic mean will
|
||||
give approximately 5.667%, which is too high.
|
||||
|
||||
``StatisticsError`` will be raised if ``data`` is empty, or any
|
||||
element is less than zero.
|
||||
"""
|
||||
if iter(data) is data:
|
||||
data = list(data)
|
||||
errmsg = 'geometric mean does not support negative values'
|
||||
n = len(data)
|
||||
if n < 1:
|
||||
raise StatisticsError('geometric_mean requires at least one data point')
|
||||
elif n == 1:
|
||||
x = data[0]
|
||||
if isinstance(g, (numbers.Real, Decimal)):
|
||||
if x < 0:
|
||||
raise StatisticsError(errmsg)
|
||||
return x
|
||||
else:
|
||||
raise TypeError('unsupported type')
|
||||
else:
|
||||
scale, prod = _product(_fail_neg(data, errmsg))
|
||||
r = _nth_root(prod, n)
|
||||
if scale:
|
||||
p, q = divmod(scale, n)
|
||||
s = 2**p * _nth_root(2**q, n)
|
||||
else:
|
||||
s = 1
|
||||
return s*r
|
||||
|
||||
|
||||
def harmonic_mean(data):
|
||||
"""Return the harmonic mean of data.
|
||||
|
||||
|
|
|
|||
|
|
@ -1010,273 +1010,6 @@ class FailNegTest(unittest.TestCase):
|
|||
self.assertEqual(errmsg, msg)
|
||||
|
||||
|
||||
class Test_Product(NumericTestCase):
|
||||
"""Test the private _product function."""
|
||||
|
||||
def test_ints(self):
|
||||
data = [1, 2, 5, 7, 9]
|
||||
self.assertEqual(statistics._product(data), (0, 630))
|
||||
self.assertEqual(statistics._product(data*100), (0, 630**100))
|
||||
|
||||
def test_floats(self):
|
||||
data = [1.0, 2.0, 4.0, 8.0]
|
||||
self.assertEqual(statistics._product(data), (8, 0.25))
|
||||
|
||||
def test_overflow(self):
|
||||
# Test with floats that overflow.
|
||||
data = [1e300]*5
|
||||
self.assertEqual(statistics._product(data), (5980, 0.6928287951283193))
|
||||
|
||||
def test_fractions(self):
|
||||
F = Fraction
|
||||
data = [F(14, 23), F(69, 1), F(665, 529), F(299, 105), F(1683, 39)]
|
||||
exp, mant = statistics._product(data)
|
||||
self.assertEqual(exp, 0)
|
||||
self.assertEqual(mant, F(2*3*7*11*17*19, 23))
|
||||
self.assertTrue(isinstance(mant, F))
|
||||
# Mixed Fraction and int.
|
||||
data = [3, 25, F(2, 15)]
|
||||
exp, mant = statistics._product(data)
|
||||
self.assertEqual(exp, 0)
|
||||
self.assertEqual(mant, F(10))
|
||||
self.assertTrue(isinstance(mant, F))
|
||||
|
||||
def test_decimal(self):
|
||||
D = Decimal
|
||||
data = [D('24.5'), D('17.6'), D('0.025'), D('1.3')]
|
||||
expected = D('14.014000')
|
||||
self.assertEqual(statistics._product(data), (0, expected))
|
||||
|
||||
def test_mixed_decimal_float(self):
|
||||
# Test that mixed Decimal and float raises.
|
||||
self.assertRaises(TypeError, statistics._product, [1.0, Decimal(1)])
|
||||
self.assertRaises(TypeError, statistics._product, [Decimal(1), 1.0])
|
||||
|
||||
|
||||
@unittest.skipIf(True, "FIXME: tests known to fail, see issue #27181")
|
||||
class Test_Nth_Root(NumericTestCase):
|
||||
"""Test the functionality of the private _nth_root function."""
|
||||
|
||||
def setUp(self):
|
||||
self.nroot = statistics._nth_root
|
||||
|
||||
# --- Special values (infinities, NANs, zeroes) ---
|
||||
|
||||
def test_float_NAN(self):
|
||||
# Test that the root of a float NAN is a float NAN.
|
||||
NAN = float('nan')
|
||||
for n in range(2, 9):
|
||||
with self.subTest(n=n):
|
||||
result = self.nroot(NAN, n)
|
||||
self.assertTrue(math.isnan(result))
|
||||
|
||||
def test_decimal_QNAN(self):
|
||||
# Test the behaviour when taking the root of a Decimal quiet NAN.
|
||||
NAN = decimal.Decimal('nan')
|
||||
with decimal.localcontext() as ctx:
|
||||
ctx.traps[decimal.InvalidOperation] = 1
|
||||
self.assertRaises(decimal.InvalidOperation, self.nroot, NAN, 5)
|
||||
ctx.traps[decimal.InvalidOperation] = 0
|
||||
self.assertTrue(self.nroot(NAN, 5).is_qnan())
|
||||
|
||||
def test_decimal_SNAN(self):
|
||||
# Test that taking the root of a Decimal sNAN always raises.
|
||||
sNAN = decimal.Decimal('snan')
|
||||
with decimal.localcontext() as ctx:
|
||||
ctx.traps[decimal.InvalidOperation] = 1
|
||||
self.assertRaises(decimal.InvalidOperation, self.nroot, sNAN, 5)
|
||||
ctx.traps[decimal.InvalidOperation] = 0
|
||||
self.assertRaises(decimal.InvalidOperation, self.nroot, sNAN, 5)
|
||||
|
||||
def test_inf(self):
|
||||
# Test that the root of infinity is infinity.
|
||||
for INF in (float('inf'), decimal.Decimal('inf')):
|
||||
for n in range(2, 9):
|
||||
with self.subTest(n=n, inf=INF):
|
||||
self.assertEqual(self.nroot(INF, n), INF)
|
||||
|
||||
# FIXME: need to check Decimal zeroes too.
|
||||
def test_zero(self):
|
||||
# Test that the root of +0.0 is +0.0.
|
||||
for n in range(2, 11):
|
||||
with self.subTest(n=n):
|
||||
result = self.nroot(+0.0, n)
|
||||
self.assertEqual(result, 0.0)
|
||||
self.assertEqual(sign(result), +1)
|
||||
|
||||
# FIXME: need to check Decimal zeroes too.
|
||||
def test_neg_zero(self):
|
||||
# Test that the root of -0.0 is -0.0.
|
||||
for n in range(2, 11):
|
||||
with self.subTest(n=n):
|
||||
result = self.nroot(-0.0, n)
|
||||
self.assertEqual(result, 0.0)
|
||||
self.assertEqual(sign(result), -1)
|
||||
|
||||
# --- Test return types ---
|
||||
|
||||
def check_result_type(self, x, n, outtype):
|
||||
self.assertIsInstance(self.nroot(x, n), outtype)
|
||||
class MySubclass(type(x)):
|
||||
pass
|
||||
self.assertIsInstance(self.nroot(MySubclass(x), n), outtype)
|
||||
|
||||
def testDecimal(self):
|
||||
# Test that Decimal arguments return Decimal results.
|
||||
self.check_result_type(decimal.Decimal('33.3'), 3, decimal.Decimal)
|
||||
|
||||
def testFloat(self):
|
||||
# Test that other arguments return float results.
|
||||
for x in (0.2, Fraction(11, 7), 91):
|
||||
self.check_result_type(x, 6, float)
|
||||
|
||||
# --- Test bad input ---
|
||||
|
||||
def testBadOrderTypes(self):
|
||||
# Test that nroot raises correctly when n has the wrong type.
|
||||
for n in (5.0, 2j, None, 'x', b'x', [], {}, set(), sign):
|
||||
with self.subTest(n=n):
|
||||
self.assertRaises(TypeError, self.nroot, 2.5, n)
|
||||
|
||||
def testBadOrderValues(self):
|
||||
# Test that nroot raises correctly when n has a wrong value.
|
||||
for n in (1, 0, -1, -2, -87):
|
||||
with self.subTest(n=n):
|
||||
self.assertRaises(ValueError, self.nroot, 2.5, n)
|
||||
|
||||
def testBadTypes(self):
|
||||
# Test that nroot raises correctly when x has the wrong type.
|
||||
for x in (None, 'x', b'x', [], {}, set(), sign):
|
||||
with self.subTest(x=x):
|
||||
self.assertRaises(TypeError, self.nroot, x, 3)
|
||||
|
||||
def testNegativeError(self):
|
||||
# Test negative x raises correctly.
|
||||
x = random.uniform(-20.0, -0.1)
|
||||
assert x < 0
|
||||
for n in range(3, 7):
|
||||
with self.subTest(x=x, n=n):
|
||||
self.assertRaises(ValueError, self.nroot, x, n)
|
||||
# And Decimal.
|
||||
self.assertRaises(ValueError, self.nroot, Decimal(-27), 3)
|
||||
|
||||
# --- Test that nroot is never worse than calling math.pow() ---
|
||||
|
||||
def check_error_is_no_worse(self, x, n):
|
||||
y = math.pow(x, n)
|
||||
with self.subTest(x=x, n=n, y=y):
|
||||
err1 = abs(self.nroot(y, n) - x)
|
||||
err2 = abs(math.pow(y, 1.0/n) - x)
|
||||
self.assertLessEqual(err1, err2)
|
||||
|
||||
def testCompareWithPowSmall(self):
|
||||
# Compare nroot with pow for small values of x.
|
||||
for i in range(200):
|
||||
x = random.uniform(1e-9, 1.0-1e-9)
|
||||
n = random.choice(range(2, 16))
|
||||
self.check_error_is_no_worse(x, n)
|
||||
|
||||
def testCompareWithPowMedium(self):
|
||||
# Compare nroot with pow for medium-sized values of x.
|
||||
for i in range(200):
|
||||
x = random.uniform(1.0, 100.0)
|
||||
n = random.choice(range(2, 16))
|
||||
self.check_error_is_no_worse(x, n)
|
||||
|
||||
def testCompareWithPowLarge(self):
|
||||
# Compare nroot with pow for largish values of x.
|
||||
for i in range(200):
|
||||
x = random.uniform(100.0, 10000.0)
|
||||
n = random.choice(range(2, 16))
|
||||
self.check_error_is_no_worse(x, n)
|
||||
|
||||
def testCompareWithPowHuge(self):
|
||||
# Compare nroot with pow for huge values of x.
|
||||
for i in range(200):
|
||||
x = random.uniform(1e20, 1e50)
|
||||
# We restrict the order here to avoid an Overflow error.
|
||||
n = random.choice(range(2, 7))
|
||||
self.check_error_is_no_worse(x, n)
|
||||
|
||||
# --- Test for numerically correct answers ---
|
||||
|
||||
def testExactPowers(self):
|
||||
# Test that small integer powers are calculated exactly.
|
||||
for i in range(1, 51):
|
||||
for n in range(2, 16):
|
||||
if (i, n) == (35, 13):
|
||||
# See testExpectedFailure35p13
|
||||
continue
|
||||
with self.subTest(i=i, n=n):
|
||||
x = i**n
|
||||
self.assertEqual(self.nroot(x, n), i)
|
||||
|
||||
def testExpectedFailure35p13(self):
|
||||
# Test the expected failure 35**13 is almost exact.
|
||||
x = 35**13
|
||||
err = abs(self.nroot(x, 13) - 35)
|
||||
self.assertLessEqual(err, 0.000000001)
|
||||
|
||||
def testOne(self):
|
||||
# Test that the root of 1.0 is 1.0.
|
||||
for n in range(2, 11):
|
||||
with self.subTest(n=n):
|
||||
self.assertEqual(self.nroot(1.0, n), 1.0)
|
||||
|
||||
def testFraction(self):
|
||||
# Test Fraction results.
|
||||
x = Fraction(89, 75)
|
||||
self.assertEqual(self.nroot(x**12, 12), float(x))
|
||||
|
||||
def testInt(self):
|
||||
# Test int results.
|
||||
x = 276
|
||||
self.assertEqual(self.nroot(x**24, 24), x)
|
||||
|
||||
def testBigInt(self):
|
||||
# Test that ints too big to convert to floats work.
|
||||
bignum = 10**20 # That's not that big...
|
||||
self.assertEqual(self.nroot(bignum**280, 280), bignum)
|
||||
# Can we make it bigger?
|
||||
hugenum = bignum**50
|
||||
# Make sure that it is too big to convert to a float.
|
||||
try:
|
||||
y = float(hugenum)
|
||||
except OverflowError:
|
||||
pass
|
||||
else:
|
||||
raise AssertionError('hugenum is not big enough')
|
||||
self.assertEqual(self.nroot(hugenum, 50), float(bignum))
|
||||
|
||||
def testDecimal(self):
|
||||
# Test Decimal results.
|
||||
for s in '3.759 64.027 5234.338'.split():
|
||||
x = decimal.Decimal(s)
|
||||
with self.subTest(x=x):
|
||||
a = self.nroot(x**5, 5)
|
||||
self.assertEqual(a, x)
|
||||
a = self.nroot(x**17, 17)
|
||||
self.assertEqual(a, x)
|
||||
|
||||
def testFloat(self):
|
||||
# Test float results.
|
||||
for x in (3.04e-16, 18.25, 461.3, 1.9e17):
|
||||
with self.subTest(x=x):
|
||||
self.assertEqual(self.nroot(x**3, 3), x)
|
||||
self.assertEqual(self.nroot(x**8, 8), x)
|
||||
self.assertEqual(self.nroot(x**11, 11), x)
|
||||
|
||||
|
||||
class Test_NthRoot_NS(unittest.TestCase):
|
||||
"""Test internals of the nth_root function, hidden in _nroot_NS."""
|
||||
|
||||
def test_class_cannot_be_instantiated(self):
|
||||
# Test that _nroot_NS cannot be instantiated.
|
||||
# It should be a namespace, like in C++ or C#, but Python
|
||||
# lacks that feature and so we have to make do with a class.
|
||||
self.assertRaises(TypeError, statistics._nroot_NS)
|
||||
|
||||
|
||||
# === Tests for public functions ===
|
||||
|
||||
class UnivariateCommonMixin:
|
||||
|
|
|
|||
Loading…
Reference in New Issue