def test_density(b=1.0, c=0.0, N_smpls=10000, plot=False):
# Draw samples from the PG(1,0) distributions
ppg = pypolyagamma.PyPolyaGamma(np.random.randint(2**16))
smpls = np.zeros(N_smpls)
ppg.pgdrawv(np.ones(N_smpls), np.zeros(N_smpls), smpls)
# Compute the empirical PDF
bins = np.linspace(0, 2.0, 50)
centers = 0.5 * (bins[1:] + bins[:-1])
p_centers = pypolyagamma.pgpdf(centers, b, c)
empirical_pdf, _ = np.histogram(smpls, bins, normed=True)
# Check that the empirical pdf is close to the true pdf
err = (empirical_pdf - p_centers) / p_centers
assert np.all(np.abs(err) < 10.0), \
"Max error of {} exceeds tolerance of 5.0".format(abs(err).max())
if plot:
import matplotlib.pyplot as plt
plt.hist(smpls, bins=50, normed=True, alpha=0.5)
# Plot high resolution density
oms = np.linspace(1e-3, 2.0, 1000)
pdf = pypolyagamma.pgpdf(oms, b, c)
plt.plot(oms, pdf, '-b', lw=2)
plt.show()
return True
def test_density(b=1.0, c=0.0, N_smpls=10000, plot=False):
# Draw samples from the PG(1,0) distributions
ppg = pypolyagamma.PyPolyaGamma(np.random.randint(2 ** 16))
smpls = np.zeros(N_smpls)
ppg.pgdrawv(np.ones(N_smpls), np.zeros(N_smpls), smpls)
# Compute the empirical PDF
bins = np.linspace(0, 2.0, 50)
centers = 0.5 * (bins[1:] + bins[:-1])
p_centers = pypolyagamma.pgpdf(centers, b, c)
empirical_pdf, _ = np.histogram(smpls, bins, density=True)
# Check that the empirical pdf is close to the true pdf
err = (empirical_pdf - p_centers) / p_centers
assert np.all(np.abs(err) < 10.0), \
"Max error of {} exceeds tolerance of 5.0".format(abs(err).max())
if plot:
import matplotlib.pyplot as plt
plt.hist(smpls, bins=50, density=True, alpha=0.5)
# Plot high resolution density
oms = np.linspace(1e-3, 2.0, 1000)
pdf = pypolyagamma.pgpdf(oms, b, c)
plt.plot(oms, pdf, '-b', lw=2)
plt.show()
return True
def ks_test(b=1.0, c=0.0, N_smpls=10000, N_pts=10000):
"""
Kolmogorov-Smirnov test. We can't calculate the CDF exactly,
but we can do a pretty good job with numerical integration.
"""
# Estimate the true CDF
oms = np.linspace(1e-5, 3.0, N_pts)
pdf = pypolyagamma.pgpdf(oms, b, c, trunc=200)
cdf = lambda x: min(np.trapz(pdf[oms < x], oms[oms < x]), 1.0)
# Draw samples
ppg = pypolyagamma.PyPolyaGamma(np.random.randint(2**16))
smpls = 1e-3 * np.ones(N_smpls)
ppg.pgdrawv(b * np.ones(N_smpls), c * np.ones(N_smpls), smpls)
# TODO: Not sure why this always gives a p-value of zero
from scipy.stats import kstest
print(kstest(smpls, cdf))
def ks_test(b=1.0, c=0.0, N_smpls=10000, N_pts=10000):
"""
Kolmogorov-Smirnov test. We can't calculate the CDF exactly,
but we can do a pretty good job with numerical integration.
"""
# Estimate the true CDF
oms = np.linspace(1e-5, 3.0, N_pts)
pdf = pypolyagamma.pgpdf(oms, b, c, trunc=200)
cdf = lambda x: min(np.trapz(pdf[oms < x], oms[oms < x]), 1.0)
# Draw samples
ppg = pypolyagamma.PyPolyaGamma(np.random.randint(2 ** 16))
smpls = 1e-3 * np.ones(N_smpls)
ppg.pgdrawv(b * np.ones(N_smpls), c * np.ones(N_smpls), smpls)
# TODO: Not sure why this always gives a p-value of zero
from scipy.stats import kstest
print(kstest(smpls, cdf))
