def testParameterEstimation(self):
alpha = 0.1
beta = 0.1
data = numpy.random.gamma(alpha, 1. / beta, 10000)
pdf = Gamma(1, 1)
pdf.estimate(data)
self.assertAlmostEqual(pdf.alpha, alpha, places=2)
self.assertAlmostEqual(pdf.beta, beta, places=1)
def testParameterEstimation(self):
alpha = 0.1
beta = 0.1
data = numpy.random.gamma(alpha, 1. / beta, 10000)
pdf = Gamma(1, 1)
pdf.estimate(data)
self.assertAlmostEqual(pdf.alpha, alpha, places=2)
self.assertAlmostEqual(pdf.beta, beta, places=1)
def calculate_KL((posterior_distances, prior_distances, bins)):
from csb.statistics.pdf import Gamma
from csb.numeric import log
g = Gamma()
g.estimate(prior_distances)
posterior_hist = np.histogram(posterior_distances, bins=bins,
normed=True)[0]
return np.trapz(posterior_hist * log(posterior_hist / g(bins[:-1])),
bins[:-1])
