def test_sigma():
tg2 = truncated_gaussian(intervals, sigma=2.)
tg1 = truncated_gaussian(np.array(intervals)/2., sigma=1.)
Z = 3.5
nt.assert_equal(np.around(float(tg1.cdf(Z/2.)), 3),
np.around(float(tg2.cdf(Z)), 3))
np.testing.assert_equal(np.around(np.array(2 * tg1.equal_tailed_interval(Z/2,0.05)), 4),
np.around(np.array(tg2.equal_tailed_interval(Z,0.05)), 4))
def test_sigma():
tg2 = truncated_gaussian(intervals, sigma=2.)
tg1 = truncated_gaussian(np.array(intervals) / 2., sigma=1.)
Z = 3.5
nt.assert_equal(np.around(float(tg1.cdf(Z / 2.)), 3),
np.around(float(tg2.cdf(Z)), 3))
np.testing.assert_equal(
np.around(np.array(2 * tg1.equal_tailed_interval(Z / 2, 0.05)), 4),
np.around(np.array(tg2.equal_tailed_interval(Z, 0.05)), 4))
def test_UMAU_coverage():
alpha = 0.25
nsim = 1000
tg = truncated_gaussian([(2.3, np.inf)], sigma=2)
coverage = 0
for i in range(nsim):
while True:
Z = np.random.standard_normal() * 2
if Z > 2.3:
break
L, U = tg.UMAU_interval(Z, alpha)
coverage += (U > 0) * (L < 0)
SE = np.sqrt(alpha * (1 - alpha) * nsim)
print coverage
nt.assert_true(np.fabs(coverage - (1 - alpha) * nsim) < 2 * SE)
def test_UMAU_coverage():
alpha = 0.25
nsim = 1000
tg = truncated_gaussian([(2.3,np.inf)], sigma=2)
coverage = 0
for i in range(nsim):
while True:
Z = np.random.standard_normal()*2
if Z > 2.3:
break
L, U = tg.UMAU_interval(Z, alpha)
coverage += (U > 0) * (L < 0)
SE = np.sqrt(alpha*(1-alpha)*nsim)
print coverage
nt.assert_true(np.fabs(coverage - (1-alpha)*nsim) < 2*SE)
import nose.tools as nt
import numpy as np
from selection.truncated import truncated_gaussian
intervals = [(-np.inf, -4.), (3., np.inf)]
tg = truncated_gaussian(intervals)
X = np.linspace(-5, 5, 101)
F = [tg.cdf(x) for x in X]
def test_sigma():
tg2 = truncated_gaussian(intervals, sigma=2.)
tg1 = truncated_gaussian(np.array(intervals) / 2., sigma=1.)
Z = 3.5
nt.assert_equal(np.around(float(tg1.cdf(Z / 2.)), 3),
np.around(float(tg2.cdf(Z)), 3))
np.testing.assert_equal(
np.around(np.array(2 * tg1.equal_tailed_interval(Z / 2, 0.05)), 4),
np.around(np.array(tg2.equal_tailed_interval(Z, 0.05)), 4))
def test_equal_tailed_coverage():
alpha = 0.25
nsim = 1000
tg = truncated_gaussian([(2.3, np.inf)], sigma=2)
coverage = 0
for i in range(nsim):
import nose.tools as nt
import numpy as np
from selection.truncated import truncated_gaussian
intervals = [(-np.inf,-4.),(3.,np.inf)]
tg = truncated_gaussian(intervals)
X = np.linspace(-5,5,101)
F = [tg.cdf(x) for x in X]
def test_sigma():
tg2 = truncated_gaussian(intervals, sigma=2.)
tg1 = truncated_gaussian(np.array(intervals)/2., sigma=1.)
Z = 3.5
nt.assert_equal(np.around(float(tg1.cdf(Z/2.)), 3),
np.around(float(tg2.cdf(Z)), 3))
np.testing.assert_equal(np.around(np.array(2 * tg1.equal_tailed_interval(Z/2,0.05)), 4),
np.around(np.array(tg2.equal_tailed_interval(Z,0.05)), 4))
def test_equal_tailed_coverage():
alpha = 0.25
nsim = 1000
tg = truncated_gaussian([(2.3,np.inf)], sigma=2)
coverage = 0
for i in range(nsim):
while True:
Z = np.random.standard_normal() * 2
if Z > 2.3: