def testTruncatedGamma(self):
alpha = 2.
beta = 1.
x_min = 0.1
x_max = 5.
x = truncated_gamma(10000, alpha, beta, x_min, x_max)
self.assertTrue((x <= x_max).all())
self.assertTrue((x >= x_min).all())
hy, hx = density(x, 100)
hx = 0.5 * (hx[1:] + hx[:-1])
hy = hy.astype('d')
with warnings.catch_warnings(record=True) as warning:
warnings.simplefilter("always")
hy /= (hx[1] - hx[0]) * hy.sum()
self.assertLessEqual(len(warning), 1)
if len(warning) == 1:
warning = warning[0]
self.assertEqual(warning.category, RuntimeWarning)
self.assertTrue(
str(warning.message).startswith(
'divide by zero encountered'))
x = numpy.linspace(x_min, x_max, 1000)
p = (alpha - 1) * log(x) - beta * x
p -= log_sum_exp(p)
p = exp(p) / (x[1] - x[0])
def testTruncatedGamma(self):
alpha = 2.
beta = 1.
x_min = 0.1
x_max = 5.
x = truncated_gamma(10000, alpha, beta, x_min, x_max)
self.assertTrue((x <= x_max).all())
self.assertTrue((x >= x_min).all())
hy, hx = density(x, 100)
hx = 0.5 * (hx[1:] + hx[:-1])
hy = hy.astype('d')
with warnings.catch_warnings(record=True) as warning:
warnings.simplefilter("always")
hy /= (hx[1] - hx[0]) * hy.sum()
self.assertLessEqual(len(warning), 1)
if len(warning) == 1:
warning = warning[0]
self.assertEqual(warning.category, RuntimeWarning)
self.assertTrue(str(warning.message).startswith('divide by zero encountered'))
x = numpy.linspace(x_min, x_max, 1000)
p = (alpha - 1) * log(x) - beta * x
p -= log_sum_exp(p)
p = exp(p) / (x[1] - x[0])
def testSampleFromHistogram(self):
mu = 5.
sigma = 1.
normal = Normal(mu, sigma)
x = normal.random(10000)
hx, p = density(x, 100)
samples = hx[sample_from_histogram(p, n_samples=10000)]
self.assertAlmostEqual(mu, numpy.mean(samples), delta=0.5)
self.assertAlmostEqual(sigma, numpy.std(samples), delta=0.5)
def testSampleFromHistogram(self):
mu = 5.
sigma = 1.
normal = Normal(mu, sigma)
x = normal.random(10000)
hx, p = density(x, 100)
samples = hx[sample_from_histogram(p, n_samples=10000)]
self.assertAlmostEqual(mu, numpy.mean(samples), delta=0.5)
self.assertAlmostEqual(sigma, numpy.std(samples), delta=0.5)
def testTruncatedNormal(self):
mu = 2.
sigma = 1.
x_min = -1.
x_max = 5.
x = truncated_normal(10000, mu, sigma, x_min, x_max)
self.assertAlmostEqual(numpy.mean(x), mu, delta=1e-1)
self.assertAlmostEqual(numpy.var(x), sigma, delta=1e-1)
self.assertTrue((x <= x_max).all())
self.assertTrue((x >= x_min).all())
hy, hx = density(x, 100)
hx = 0.5 * (hx[1:] + hx[:-1])
hy = hy.astype('d')
with warnings.catch_warnings(record=True) as warning:
warnings.simplefilter("always")
hy /= (hx[1] - hx[0]) * hy.sum()
self.assertLessEqual(len(warning), 1)
if len(warning) == 1:
warning = warning[0]
self.assertEqual(warning.category, RuntimeWarning)
self.assertTrue(
str(warning.message).startswith(
'divide by zero encountered'))
x = numpy.linspace(mu - 5 * sigma, mu + 5 * sigma, 1000)
p = -0.5 * (x - mu)**2 / sigma**2
p -= log_sum_exp(p)
p = exp(p) / (x[1] - x[0])
def testTruncatedNormal(self):
mu = 2.
sigma = 1.
x_min = -1.
x_max = 5.
x = truncated_normal(10000, mu, sigma, x_min, x_max)
self.assertAlmostEqual(numpy.mean(x), mu, delta=1e-1)
self.assertAlmostEqual(numpy.var(x), sigma, delta=1e-1)
self.assertTrue((x <= x_max).all())
self.assertTrue((x >= x_min).all())
hy, hx = density(x, 100)
hx = 0.5 * (hx[1:] + hx[:-1])
hy = hy.astype('d')
with warnings.catch_warnings(record=True) as warning:
warnings.simplefilter("always")
hy /= (hx[1] - hx[0]) * hy.sum()
self.assertLessEqual(len(warning), 1)
if len(warning) == 1:
warning = warning[0]
self.assertEqual(warning.category, RuntimeWarning)
self.assertTrue(str(warning.message).startswith('divide by zero encountered'))
x = numpy.linspace(mu - 5 * sigma, mu + 5 * sigma, 1000)
p = -0.5 * (x - mu) ** 2 / sigma ** 2
p -= log_sum_exp(p)
p = exp(p) / (x[1] - x[0])
