def test_multipletargets_generic(
self, makemockrulelist,
makemockgaussianfree_multipletarget_generic):
mockgaussianfreestatistic = makemockgaussianfree_multipletarget_generic
mockrulelist = makemockrulelist
actual_length = length_rule_free_gaussian(mockrulelist,
mockgaussianfreestatistic)
n = 100000
var1 = 100
var2 = 150
expected = 1 + n/2*log2(pi) - gammaln(n/2)/log(2) + 0.5*log2(n+1) + n/2*log2(n*var1)+\
1 + n / 2 * log2(pi) - gammaln(n / 2) / log(2) + 0.5 * log2(n + 1) + n / 2 * log2(n * var2)
assert expected == pytest.approx(actual_length, rel=1e-3)
def test_all_large(self):
input_n = 100000
input_variance = 100000
input_log_gamma_n = gammaln(input_n / 2) / log(2)
actual_length = gaussian_bayesian_encoding(input_n, input_variance,
input_log_gamma_n)
expected_length = 1035207.3661371232
assert expected_length == pytest.approx(actual_length)
def test_variance_zero(self):
input_n = 2
input_variance = 0
input_log_gamma_n = gammaln(input_n / 2) / log(2)
actual_length = gaussian_bayesian_encoding(input_n, input_variance,
input_log_gamma_n)
expected_length = np.inf
assert expected_length == actual_length
def test_n_two(self):
input_n = 2
input_variance = 100
input_log_gamma_n = gammaln(input_n / 2) / log(2)
actual_length = gaussian_bayesian_encoding(input_n, input_variance,
input_log_gamma_n)
expected_length = 11.087833569607621
assert expected_length == pytest.approx(actual_length)
def test_multivariate_students_t(self, i: int):
mixture = self._init_mixture(self.D, self.prior)
mu = mixture.prior.m_0
v = mixture.prior.v_0 + i
cov = np.array([[5.5, -1.1], [-1.1, 6.6]])
logdet_cov = np.linalg.slogdet(cov)[1]
inv_cov = np.linalg.inv(cov)
delta = mixture.X[i] - mu
stud = (+gammaln((v + self.D) / 2.) - gammaln(v / 2.) -
self.D / 2. * math.log(v) - self.D / 2. * math.log(np.pi) -
0.5 * logdet_cov - (v + self.D) / 2. *
math.log(1 + 1. / v * np.dot(np.dot(delta, inv_cov), delta)))
assert_almost_equal(
mixture._multivariate_students_t(i, mu, logdet_cov, inv_cov, v),
stud)
def test_multipletargets_onetarget_simple(self, makemockrulelist,
makemockgaussianfree_onetarget):
mockgaussianfreestatistic = makemockgaussianfree_onetarget
mockrulelist = makemockrulelist
actual_length = length_rule_free_gaussian(mockrulelist,
mockgaussianfreestatistic)
n = 100000
var = 3
expected = 1 + n / 2 * log2(pi) - gammaln(
n / 2) / log(2) + 0.5 * log2(n + 1) + n / 2 * log2(n * var)
assert expected == pytest.approx(actual_length, rel=1e-3)
def _cache(self, N: int):
"""Execute pre-calculations"""
n = np.concatenate([[1], np.arange(1, self.prior.v_0 + N + 2)
]) # first element is dummy for indexing
self._log_v = np.log(n)
self._gammaln_by_2 = np.array([gammaln(n_i / 2) for n_i in n])