https://github.com/python/cpython/commit/120729d862f0ef9979508899f7f63a9f3d9623cb
commit: 120729d862f0ef9979508899f7f63a9f3d9623cb
branch: main
author: Raymond Hettinger <[email protected]>
committer: rhettinger <[email protected]>
date: 2024-10-01T15:55:36-05:00
summary:
Minor code beautifications in statistics.py (gh-124866)
files:
M Lib/statistics.py
diff --git a/Lib/statistics.py b/Lib/statistics.py
index f193fcdc241aa9..e2b59267f04f68 100644
--- a/Lib/statistics.py
+++ b/Lib/statistics.py
@@ -248,6 +248,7 @@ def count_positive(iterable):
found_zero = True
else:
raise StatisticsError('No negative inputs allowed', x)
+
total = fsum(map(log, count_positive(data)))
if not n:
@@ -710,6 +711,7 @@ def correlation(x, y, /, *, method='linear'):
start = (n - 1) / -2 # Center rankings around zero
x = _rank(x, start=start)
y = _rank(y, start=start)
+
else:
xbar = fsum(x) / n
ybar = fsum(y) / n
@@ -1213,91 +1215,6 @@ def quantiles(data, *, n=4, method='exclusive'):
## Normal Distribution #####################################################
-def _normal_dist_inv_cdf(p, mu, sigma):
- # There is no closed-form solution to the inverse CDF for the normal
- # distribution, so we use a rational approximation instead:
- # Wichura, M.J. (1988). "Algorithm AS241: The Percentage Points of the
- # Normal Distribution". Applied Statistics. Blackwell Publishing. 37
- # (3): 477–484. doi:10.2307/2347330. JSTOR 2347330.
- q = p - 0.5
-
- if fabs(q) <= 0.425:
- r = 0.180625 - q * q
- # Hash sum: 55.88319_28806_14901_4439
- num = (((((((2.50908_09287_30122_6727e+3 * r +
- 3.34305_75583_58812_8105e+4) * r +
- 6.72657_70927_00870_0853e+4) * r +
- 4.59219_53931_54987_1457e+4) * r +
- 1.37316_93765_50946_1125e+4) * r +
- 1.97159_09503_06551_4427e+3) * r +
- 1.33141_66789_17843_7745e+2) * r +
- 3.38713_28727_96366_6080e+0) * q
- den = (((((((5.22649_52788_52854_5610e+3 * r +
- 2.87290_85735_72194_2674e+4) * r +
- 3.93078_95800_09271_0610e+4) * r +
- 2.12137_94301_58659_5867e+4) * r +
- 5.39419_60214_24751_1077e+3) * r +
- 6.87187_00749_20579_0830e+2) * r +
- 4.23133_30701_60091_1252e+1) * r +
- 1.0)
- x = num / den
- return mu + (x * sigma)
-
- r = p if q <= 0.0 else 1.0 - p
- r = sqrt(-log(r))
- if r <= 5.0:
- r = r - 1.6
- # Hash sum: 49.33206_50330_16102_89036
- num = (((((((7.74545_01427_83414_07640e-4 * r +
- 2.27238_44989_26918_45833e-2) * r +
- 2.41780_72517_74506_11770e-1) * r +
- 1.27045_82524_52368_38258e+0) * r +
- 3.64784_83247_63204_60504e+0) * r +
- 5.76949_72214_60691_40550e+0) * r +
- 4.63033_78461_56545_29590e+0) * r +
- 1.42343_71107_49683_57734e+0)
- den = (((((((1.05075_00716_44416_84324e-9 * r +
- 5.47593_80849_95344_94600e-4) * r +
- 1.51986_66563_61645_71966e-2) * r +
- 1.48103_97642_74800_74590e-1) * r +
- 6.89767_33498_51000_04550e-1) * r +
- 1.67638_48301_83803_84940e+0) * r +
- 2.05319_16266_37758_82187e+0) * r +
- 1.0)
- else:
- r = r - 5.0
- # Hash sum: 47.52583_31754_92896_71629
- num = (((((((2.01033_43992_92288_13265e-7 * r +
- 2.71155_55687_43487_57815e-5) * r +
- 1.24266_09473_88078_43860e-3) * r +
- 2.65321_89526_57612_30930e-2) * r +
- 2.96560_57182_85048_91230e-1) * r +
- 1.78482_65399_17291_33580e+0) * r +
- 5.46378_49111_64114_36990e+0) * r +
- 6.65790_46435_01103_77720e+0)
- den = (((((((2.04426_31033_89939_78564e-15 * r +
- 1.42151_17583_16445_88870e-7) * r +
- 1.84631_83175_10054_68180e-5) * r +
- 7.86869_13114_56132_59100e-4) * r +
- 1.48753_61290_85061_48525e-2) * r +
- 1.36929_88092_27358_05310e-1) * r +
- 5.99832_20655_58879_37690e-1) * r +
- 1.0)
-
- x = num / den
- if q < 0.0:
- x = -x
-
- return mu + (x * sigma)
-
-
-# If available, use C implementation
-try:
- from _statistics import _normal_dist_inv_cdf
-except ImportError:
- pass
-
-
class NormalDist:
"Normal distribution of a random variable"
# https://en.wikipedia.org/wiki/Normal_distribution
@@ -1561,11 +1478,13 @@ def _sum(data):
types_add = types.add
partials = {}
partials_get = partials.get
+
for typ, values in groupby(data, type):
types_add(typ)
for n, d in map(_exact_ratio, values):
count += 1
partials[d] = partials_get(d, 0) + n
+
if None in partials:
# The sum will be a NAN or INF. We can ignore all the finite
# partials, and just look at this special one.
@@ -1574,6 +1493,7 @@ def _sum(data):
else:
# Sum all the partial sums using builtin sum.
total = sum(Fraction(n, d) for d, n in partials.items())
+
T = reduce(_coerce, types, int) # or raise TypeError
return (T, total, count)
@@ -1596,6 +1516,7 @@ def _ss(data, c=None):
types_add = types.add
sx_partials = defaultdict(int)
sxx_partials = defaultdict(int)
+
for typ, values in groupby(data, type):
types_add(typ)
for n, d in map(_exact_ratio, values):
@@ -1605,11 +1526,13 @@ def _ss(data, c=None):
if not count:
ssd = c = Fraction(0)
+
elif None in sx_partials:
# The sum will be a NAN or INF. We can ignore all the finite
# partials, and just look at this special one.
ssd = c = sx_partials[None]
assert not _isfinite(ssd)
+
else:
sx = sum(Fraction(n, d) for d, n in sx_partials.items())
sxx = sum(Fraction(n, d*d) for d, n in sxx_partials.items())
@@ -1693,8 +1616,10 @@ def _convert(value, T):
# This covers the cases where T is Fraction, or where value is
# a NAN or INF (Decimal or float).
return value
+
if issubclass(T, int) and value.denominator != 1:
T = float
+
try:
# FIXME: what do we do if this overflows?
return T(value)
@@ -1857,3 +1782,88 @@ def _sqrtprod(x: float, y: float) -> float:
#
https://www.wolframalpha.com/input/?i=Maclaurin+series+sqrt%28h**2+%2B+x%29+at+x%3D0
d = sumprod((x, h), (y, -h))
return h + d / (2.0 * h)
+
+
+def _normal_dist_inv_cdf(p, mu, sigma):
+ # There is no closed-form solution to the inverse CDF for the normal
+ # distribution, so we use a rational approximation instead:
+ # Wichura, M.J. (1988). "Algorithm AS241: The Percentage Points of the
+ # Normal Distribution". Applied Statistics. Blackwell Publishing. 37
+ # (3): 477–484. doi:10.2307/2347330. JSTOR 2347330.
+ q = p - 0.5
+
+ if fabs(q) <= 0.425:
+ r = 0.180625 - q * q
+ # Hash sum: 55.88319_28806_14901_4439
+ num = (((((((2.50908_09287_30122_6727e+3 * r +
+ 3.34305_75583_58812_8105e+4) * r +
+ 6.72657_70927_00870_0853e+4) * r +
+ 4.59219_53931_54987_1457e+4) * r +
+ 1.37316_93765_50946_1125e+4) * r +
+ 1.97159_09503_06551_4427e+3) * r +
+ 1.33141_66789_17843_7745e+2) * r +
+ 3.38713_28727_96366_6080e+0) * q
+ den = (((((((5.22649_52788_52854_5610e+3 * r +
+ 2.87290_85735_72194_2674e+4) * r +
+ 3.93078_95800_09271_0610e+4) * r +
+ 2.12137_94301_58659_5867e+4) * r +
+ 5.39419_60214_24751_1077e+3) * r +
+ 6.87187_00749_20579_0830e+2) * r +
+ 4.23133_30701_60091_1252e+1) * r +
+ 1.0)
+ x = num / den
+ return mu + (x * sigma)
+
+ r = p if q <= 0.0 else 1.0 - p
+ r = sqrt(-log(r))
+ if r <= 5.0:
+ r = r - 1.6
+ # Hash sum: 49.33206_50330_16102_89036
+ num = (((((((7.74545_01427_83414_07640e-4 * r +
+ 2.27238_44989_26918_45833e-2) * r +
+ 2.41780_72517_74506_11770e-1) * r +
+ 1.27045_82524_52368_38258e+0) * r +
+ 3.64784_83247_63204_60504e+0) * r +
+ 5.76949_72214_60691_40550e+0) * r +
+ 4.63033_78461_56545_29590e+0) * r +
+ 1.42343_71107_49683_57734e+0)
+ den = (((((((1.05075_00716_44416_84324e-9 * r +
+ 5.47593_80849_95344_94600e-4) * r +
+ 1.51986_66563_61645_71966e-2) * r +
+ 1.48103_97642_74800_74590e-1) * r +
+ 6.89767_33498_51000_04550e-1) * r +
+ 1.67638_48301_83803_84940e+0) * r +
+ 2.05319_16266_37758_82187e+0) * r +
+ 1.0)
+ else:
+ r = r - 5.0
+ # Hash sum: 47.52583_31754_92896_71629
+ num = (((((((2.01033_43992_92288_13265e-7 * r +
+ 2.71155_55687_43487_57815e-5) * r +
+ 1.24266_09473_88078_43860e-3) * r +
+ 2.65321_89526_57612_30930e-2) * r +
+ 2.96560_57182_85048_91230e-1) * r +
+ 1.78482_65399_17291_33580e+0) * r +
+ 5.46378_49111_64114_36990e+0) * r +
+ 6.65790_46435_01103_77720e+0)
+ den = (((((((2.04426_31033_89939_78564e-15 * r +
+ 1.42151_17583_16445_88870e-7) * r +
+ 1.84631_83175_10054_68180e-5) * r +
+ 7.86869_13114_56132_59100e-4) * r +
+ 1.48753_61290_85061_48525e-2) * r +
+ 1.36929_88092_27358_05310e-1) * r +
+ 5.99832_20655_58879_37690e-1) * r +
+ 1.0)
+
+ x = num / den
+ if q < 0.0:
+ x = -x
+
+ return mu + (x * sigma)
+
+
+# If available, use C implementation
+try:
+ from _statistics import _normal_dist_inv_cdf
+except ImportError:
+ pass
_______________________________________________
Python-checkins mailing list -- [email protected]
To unsubscribe send an email to [email protected]
https://mail.python.org/mailman3/lists/python-checkins.python.org/
Member address: [email protected]
