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bpo-36324: Make internal attributes for statistics.NormalDist() private. (GH-14871) (GH-14875)

This commit is contained in:
Miss Islington (bot) 2019-07-21 00:55:13 -07:00 committed by Raymond Hettinger
parent 3015191b29
commit c613c3319e
3 changed files with 56 additions and 49 deletions
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72
Lib/statistics.py
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@ -812,15 +812,15 @@ class NormalDist:
# 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'}
__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 = mu
self.sigma = sigma
self._mu = mu
self._sigma = sigma
@classmethod
def from_samples(cls, data):
@ -833,21 +833,21 @@ class NormalDist:
def samples(self, n, *, seed=None):
'Generate *n* samples for a given mean and standard deviation.'
gauss = random.gauss if seed is None else random.Random(seed).gauss
mu, sigma = self.mu, self.sigma
mu, sigma = self._mu, self._sigma
return [gauss(mu, sigma) for i in range(n)]
def pdf(self, x):
'Probability density function. P(x <= X < x+dx) / dx'
variance = self.sigma ** 2.0
variance = self._sigma ** 2.0
if not variance:
raise StatisticsError('pdf() not defined when sigma is zero')
return exp((x - self.mu)**2.0 / (-2.0*variance)) / sqrt(tau * variance)
return exp((x - self._mu)**2.0 / (-2.0*variance)) / sqrt(tau * variance)
def cdf(self, x):
'Cumulative distribution function. P(X <= x)'
if not self.sigma:
if not self._sigma:
raise StatisticsError('cdf() not defined when sigma is zero')
return 0.5 * (1.0 + erf((x - self.mu) / (self.sigma * sqrt(2.0))))
return 0.5 * (1.0 + erf((x - self._mu) / (self._sigma * sqrt(2.0))))
def inv_cdf(self, p):
'''Inverse cumulative distribution function. x : P(X <= x) = p
@ -859,7 +859,7 @@ class NormalDist:
'''
if (p <= 0.0 or p >= 1.0):
raise StatisticsError('p must be in the range 0.0 < p < 1.0')
if self.sigma <= 0.0:
if self._sigma <= 0.0:
raise StatisticsError('cdf() not defined when sigma at or below zero')
# There is no closed-form solution to the inverse CDF for the normal
@ -888,7 +888,7 @@ class NormalDist:
4.23133_30701_60091_1252e+1) * r +
1.0)
x = num / den
return self.mu + (x * self.sigma)
return self._mu + (x * self._sigma)
r = p if q <= 0.0 else 1.0 - p
r = sqrt(-log(r))
if r <= 5.0:
@ -930,7 +930,7 @@ class NormalDist:
x = num / den
if q < 0.0:
x = -x
return self.mu + (x * self.sigma)
return self._mu + (x * self._sigma)
def overlap(self, other):
'''Compute the overlapping coefficient (OVL) between two normal distributions.
@ -951,17 +951,17 @@ class NormalDist:
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
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)
dm = fabs(Y._mu - X._mu)
if not dv:
return 1.0 - erf(dm / (2.0 * X.sigma * sqrt(2.0)))
a = X.mu * Y_var - Y.mu * X_var
b = X.sigma * Y.sigma * sqrt(dm**2.0 + dv * log(Y_var / X_var))
return 1.0 - erf(dm / (2.0 * X._sigma * sqrt(2.0)))
a = X._mu * Y_var - Y._mu * X_var
b = X._sigma * Y._sigma * sqrt(dm**2.0 + 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)))
@ -969,17 +969,17 @@ class NormalDist:
@property
def mean(self):
'Arithmetic mean of the normal distribution.'
return self.mu
return self._mu
@property
def stdev(self):
'Standard deviation of the normal distribution.'
return self.sigma
return self._sigma
@property
def variance(self):
'Square of the standard deviation.'
return self.sigma ** 2.0
return self._sigma ** 2.0
def __add__(x1, x2):
'''Add a constant or another NormalDist instance.
@ -992,8 +992,8 @@ class NormalDist:
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)
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.
@ -1006,8 +1006,8 @@ class NormalDist:
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)
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.
@ -1015,7 +1015,7 @@ class NormalDist:
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))
return NormalDist(x1._mu * x2, x1._sigma * fabs(x2))
def __truediv__(x1, x2):
'''Divide both mu and sigma by a constant.
@ -1023,15 +1023,15 @@ class NormalDist:
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))
return NormalDist(x1._mu / x2, x1._sigma / fabs(x2))
def __pos__(x1):
'Return a copy of the instance.'
return NormalDist(x1.mu, x1.sigma)
return NormalDist(x1._mu, x1._sigma)
def __neg__(x1):
'Negates mu while keeping sigma the same.'
return NormalDist(-x1.mu, x1.sigma)
return NormalDist(-x1._mu, x1._sigma)
__radd__ = __add__
@ -1045,10 +1045,14 @@ class NormalDist:
'Two NormalDist objects are equal if their mu and sigma are both equal.'
if not isinstance(x2, NormalDist):
return NotImplemented
return (x1.mu, x2.sigma) == (x2.mu, x2.sigma)
return (x1._mu, x2._sigma) == (x2._mu, 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})'
return f'{type(self).__name__}(mu={self._mu!r}, sigma={self._sigma!r})'
if __name__ == '__main__':
@ -1065,8 +1069,8 @@ if __name__ == '__main__':
g2 = NormalDist(-5, 25)
# Test scaling by a constant
assert (g1 * 5 / 5).mu == g1.mu
assert (g1 * 5 / 5).sigma == g1.sigma
assert (g1 * 5 / 5).mean == g1.mean
assert (g1 * 5 / 5).stdev == g1.stdev
n = 100_000
G1 = g1.samples(n)
@ -1090,8 +1094,8 @@ if __name__ == '__main__':
print(NormalDist.from_samples(map(func, repeat(const), G1)))
def assert_close(G1, G2):
assert isclose(G1.mu, G1.mu, rel_tol=0.01), (G1, G2)
assert isclose(G1.sigma, G2.sigma, rel_tol=0.01), (G1, G2)
assert isclose(G1.mean, G1.mean, rel_tol=0.01), (G1, G2)
assert isclose(G1.stdev, G2.stdev, rel_tol=0.01), (G1, G2)
X = NormalDist(-105, 73)
Y = NormalDist(31, 47)

32
Lib/test/test_statistics.py
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@ -2326,18 +2326,18 @@ class TestNormalDist(unittest.TestCase):
nd = statistics.NormalDist(300, 23)
with self.assertRaises(TypeError):
vars(nd)
self.assertEqual(tuple(nd.__slots__), ('mu', 'sigma'))
self.assertEqual(tuple(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.mean, 500)
self.assertEqual(nd.stdev, 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.mean, 0)
self.assertEqual(nd.stdev, 1)
self.assertEqual(nd.variance, 1**2)
# error case: negative sigma
@ -2520,10 +2520,7 @@ class TestNormalDist(unittest.TestCase):
with self.assertRaises(statistics.StatisticsError):
iq.inv_cdf(1.1) # p over one
with self.assertRaises(statistics.StatisticsError):
iq.sigma = 0.0 # sigma is zero
iq.inv_cdf(0.5)
with self.assertRaises(statistics.StatisticsError):
iq.sigma = -0.1 # sigma under zero
iq = NormalDist(100, 0) # sigma is zero
iq.inv_cdf(0.5)
# Special values
@ -2544,8 +2541,8 @@ class TestNormalDist(unittest.TestCase):
def overlap_numeric(X, Y, *, steps=8_192, z=5):
'Numerical integration cross-check for overlap() '
fsum = math.fsum
center = (X.mu + Y.mu) / 2.0
width = z * max(X.sigma, Y.sigma)
center = (X.mean + Y.mean) / 2.0
width = z * max(X.stdev, Y.stdev)
start = center - width
dx = 2.0 * width / steps
x_arr = [start + i*dx for i in range(steps)]
@ -2626,12 +2623,12 @@ class TestNormalDist(unittest.TestCase):
X = NormalDist(100, 12)
Y = +X
self.assertIsNot(X, Y)
self.assertEqual(X.mu, Y.mu)
self.assertEqual(X.sigma, Y.sigma)
self.assertEqual(X.mean, Y.mean)
self.assertEqual(X.stdev, Y.stdev)
Y = -X
self.assertIsNot(X, Y)
self.assertEqual(X.mu, -Y.mu)
self.assertEqual(X.sigma, Y.sigma)
self.assertEqual(X.mean, -Y.mean)
self.assertEqual(X.stdev, Y.stdev)
def test_equality(self):
NormalDist = statistics.NormalDist
@ -2682,6 +2679,11 @@ class TestNormalDist(unittest.TestCase):
nd3 = pickle.loads(pickle.dumps(nd))
self.assertEqual(nd, nd3)
def test_hashability(self):
ND = statistics.NormalDist
s = {ND(100, 15), ND(100.0, 15.0), ND(100, 10), ND(95, 15), ND(100, 15)}
self.assertEqual(len(s), 3)
def test_repr(self):
nd = statistics.NormalDist(37.5, 5.625)
self.assertEqual(repr(nd), 'NormalDist(mu=37.5, sigma=5.625)')

1
Misc/NEWS.d/next/Library/2019-07-19-22-44-41.bpo-36324.1VjywS.rst Normal file
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@ -0,0 +1 @@
Make internal attributes for statistics.NormalDist() private.