[Python-checkins] bpo-36324: Make internal attributes for statistics.NormalDist() private. (GH-14871)
Raymond Hettinger
webhook-mailer at python.org
Sun Jul 21 03:34:58 EDT 2019
https://github.com/python/cpython/commit/02c91f59b6f6e720a9e89635e00c55bcf7f932a8
commit: 02c91f59b6f6e720a9e89635e00c55bcf7f932a8
branch: master
author: Raymond Hettinger <rhettinger at users.noreply.github.com>
committer: GitHub <noreply at github.com>
date: 2019-07-21T00:34:47-07:00
summary:
bpo-36324: Make internal attributes for statistics.NormalDist() private. (GH-14871)
* Make internals private
* Finish making mu and sigma private
* Add missing __hash__() method
* Add blurb
files:
A Misc/NEWS.d/next/Library/2019-07-19-22-44-41.bpo-36324.1VjywS.rst
M Lib/statistics.py
M Lib/test/test_statistics.py
diff --git a/Lib/statistics.py b/Lib/statistics.py
index f09f7be354c2..ff07dc4a6b55 100644
--- a/Lib/statistics.py
+++ b/Lib/statistics.py
@@ -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 @@ def from_samples(cls, data):
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 @@ def inv_cdf(self, p):
'''
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 @@ def inv_cdf(self, p):
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 @@ def inv_cdf(self, p):
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 @@ def overlap(self, other):
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 @@ def overlap(self, other):
@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 @@ def __add__(x1, x2):
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 @@ def __sub__(x1, x2):
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 @@ def __mul__(x1, x2):
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 @@ def __truediv__(x1, x2):
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 @@ 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.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 @@ def __repr__(self):
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 @@ def __repr__(self):
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)
diff --git a/Lib/test/test_statistics.py b/Lib/test/test_statistics.py
index 946c7428c613..ed2f6579b0b9 100644
--- a/Lib/test/test_statistics.py
+++ b/Lib/test/test_statistics.py
@@ -2326,18 +2326,18 @@ def test_slots(self):
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 @@ def test_inv_cdf(self):
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 @@ def test_overlap(self):
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 @@ def test_unary_operations(self):
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 @@ def test_pickle_and_copy(self):
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)')
diff --git a/Misc/NEWS.d/next/Library/2019-07-19-22-44-41.bpo-36324.1VjywS.rst b/Misc/NEWS.d/next/Library/2019-07-19-22-44-41.bpo-36324.1VjywS.rst
new file mode 100644
index 000000000000..2e41211c685e
--- /dev/null
+++ b/Misc/NEWS.d/next/Library/2019-07-19-22-44-41.bpo-36324.1VjywS.rst
@@ -0,0 +1 @@
+Make internal attributes for statistics.NormalDist() private.
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