def test_truncated_scalar_gaussian_lb():
tn0_test = TruncatedScalarGaussian(lb=0)
tn0_true = truncnorm(0, np.Inf)
print "E[TN(0,inf)]:\t", tn0_test.expected_x()
print "E[TN(0,inf)]:\t", tn0_true.mean()
assert np.allclose(tn0_test.expected_x(), tn0_true.mean())
print "Var[TN(0,inf)]:\t", tn0_test.variance_x()
def test_truncated_scalar_gaussian_lb():
tn0_test = TruncatedScalarGaussian(lb=0)
tn0_true = truncnorm(0, np.Inf)
print "E[TN(0,inf)]:\t", tn0_test.expected_x()
print "E[TN(0,inf)]:\t", tn0_true.mean()
assert np.allclose(tn0_test.expected_x(), tn0_true.mean())
print "Var[TN(0,inf)]:\t", tn0_test.variance_x()
def test_truncated_scalar_gaussian():
lb = 0
ub = 10
mu = 3
sigma = 1
tn0_test = TruncatedScalarGaussian(mu=mu, sigmasq=np.sqrt(sigma), lb=lb, ub=ub)
tn0_true = truncnorm((lb-mu)/sigma, (ub-mu)/sigma, loc=mu)
print "E[TN(0,10)]:\t", tn0_test.expected_x()
print "E[TN(0,10)]:\t", tn0_true.mean()
assert np.allclose(tn0_test.expected_x(), tn0_true.mean())
print "Var[TN(0,10)]:\t", tn0_test.variance_x()
print "Var[TN(0,10)]:\t", tn0_true.var()
assert np.allclose(tn0_test.variance_x(), tn0_true.var())
print "E[-LN{TN(0,10)}]:\t", -tn0_test.negentropy()
print "E[-LN{TN(0,10)}]:\t", tn0_true.entropy()
assert np.allclose(-tn0_test.negentropy(), tn0_true.entropy())
def test_truncated_scalar_gaussian():
lb = 0
ub = 10
mu = 3
sigma = 1
tn0_test = TruncatedScalarGaussian(mu=mu,
sigmasq=np.sqrt(sigma),
lb=lb,
ub=ub)
tn0_true = truncnorm((lb - mu) / sigma, (ub - mu) / sigma, loc=mu)
print "E[TN(0,10)]:\t", tn0_test.expected_x()
print "E[TN(0,10)]:\t", tn0_true.mean()
assert np.allclose(tn0_test.expected_x(), tn0_true.mean())
print "Var[TN(0,10)]:\t", tn0_test.variance_x()
print "Var[TN(0,10)]:\t", tn0_true.var()
assert np.allclose(tn0_test.variance_x(), tn0_true.var())
print "E[-LN{TN(0,10)}]:\t", -tn0_test.negentropy()
print "E[-LN{TN(0,10)}]:\t", tn0_true.entropy()
assert np.allclose(-tn0_test.negentropy(), tn0_true.entropy())