def test_update_sufficient_statistics(self):
"""Test sufficient statistics correctly updated."""
n = 10
for i in range(10):
x = np.random.uniform(-100, 100, n)
jx = 2 * np.ones_like(x)
sumx = x.sum()
sumx2 = (x**2).sum()
logJX = np.log(jx).sum()
f = FStudentT(x, jx, 1., 10., 10., "test")
stats = f.get_sufficient_statistics()
self.assertAlmostEqual(stats[0], sumx, delta=1e-4)
self.assertAlmostEqual(stats[1], sumx2, delta=1e-2)
self.assertAlmostEqual(stats[2], n, delta=1e-6)
self.assertAlmostEqual(f.get_LogJX(), logJX, delta=1e-6)
x = np.random.uniform(-100, 100, n)
jx = 3 * np.ones_like(x)
sumx = x.sum()
sumx2 = (x**2).sum()
logJX = np.log(jx).sum()
f.update_sufficient_statistics(x, jx)
stats = f.get_sufficient_statistics()
self.assertAlmostEqual(stats[0], sumx, delta=1e-4)
self.assertAlmostEqual(stats[1], sumx2, delta=1e-2)
self.assertAlmostEqual(stats[2], n, delta=1e-6)
self.assertAlmostEqual(f.get_LogJX(), logJX, delta=1e-6)
def test_create_from_sufficient_statistics(self):
"""Test constructor with sufficient stats."""
n = 10
for i in range(10):
x = np.random.uniform(-100, 100, n)
jx = 2 * np.ones_like(x)
sumx = x.sum()
sumx2 = (x**2).sum()
logJX = np.log(jx).sum()
f = FStudentT(sumx, sumx2, n, logJX, 1., 10., 10., "test")
stats = f.get_sufficient_statistics()
self.assertAlmostEqual(stats[0], sumx, delta=1e-4)
self.assertAlmostEqual(stats[1], sumx2, delta=1e-2)
self.assertAlmostEqual(stats[2], n, delta=1e-6)
self.assertAlmostEqual(f.get_LogJX(), logJX, delta=1e-6)