def test_variance():
data = [4, 7, 13, 16]
def naive_var(data):
n = len(data)
return ((sum(di**2 for di in data) - sum(data)**2 / n) / (n - 1))
from pytools import variance
orig_variance = variance(data, entire_pop=False)
assert abs(naive_var(data) - orig_variance) < 1e-15
data = [1e9 + x for x in data]
assert abs(variance(data, entire_pop=False) - orig_variance) < 1e-15
def test_variance():
data = [4, 7, 13, 16]
def naive_var(data):
n = len(data)
return ((
sum(di**2 for di in data)
- sum(data)**2/n)
/(n-1))
from pytools import variance
orig_variance = variance(data, entire_pop=False)
assert abs(naive_var(data) - orig_variance) < 1e-15
data = [1e9 + x for x in data]
assert abs(variance(data, entire_pop=False) - orig_variance) < 1e-15
def test_variance():
def sg_variance(iterable, entire_pop):
from pyrticle._internal import StatsGatherer
sg = StatsGatherer()
for i in iterable:
sg.add(i)
if entire_pop:
return sg.variance()
else:
return sg.variance_sample()
for entire_pop in [False, True]:
data = [4, 7, 13, 16]
from pytools import variance
orig_variance = variance(data, entire_pop)
assert abs(sg_variance(data, entire_pop) - orig_variance) < 1e-15
data = [1e9 + x for x in data]
assert abs(sg_variance(data, entire_pop) - orig_variance) < 1e-15
def test_variance():
def sg_variance(iterable, entire_pop):
from pyrticle._internal import StatsGatherer
sg = StatsGatherer()
for i in iterable:
sg.add(i)
if entire_pop:
return sg.variance()
else:
return sg.variance_sample()
for entire_pop in [False, True]:
data = [4, 7, 13, 16]
from pytools import variance
orig_variance = variance(data, entire_pop)
assert abs(sg_variance(data, entire_pop)
- orig_variance) < 1e-15
data = [1e9 + x for x in data]
assert abs(sg_variance(data, entire_pop)
- orig_variance) < 1e-15
