def setUp(self):
self.cov = [[1, 0, 0], [0, 4, 0], [0, 0, 9]]
self.sigma = [1, 2, 3]
self.mean = [10, 11, 12]
self.likelihood = AnalyticalMultidimensionalCovariantGaussian(
mean=self.mean, cov=self.cov
)
[
-0.00334987648531921, 0.007465228985669282, -0.006204169580739178,
-0.005873218251875899, -0.009241221870695395, 0.003330357760641278,
-0.008466566781233205, 0.011126783289057604, -0.0031735521631824654,
-0.005619012077114915, -0.007137012700864866, -0.006482422704208912,
0.0033872675386130632, -0.000256550861960499, 0.05380987317762257
]
]
dim = 15
mean = np.zeros(dim)
label = "multidim_gaussian_unimodal"
outdir = "outdir"
likelihood = AnalyticalMultidimensionalCovariantGaussian(mean, cov)
priors = bilby.core.prior.PriorDict()
priors.update({
"x{0}".format(i): bilby.core.prior.Uniform(-5, 5, "x{0}".format(i))
for i in range(dim)
})
result = bilby.run_sampler(likelihood=likelihood,
priors=priors,
sampler="dynesty",
outdir=outdir,
label=label,
check_point_plot=True,
resume=True)
result.plot_corner(parameters={"x{0}".format(i): mean[i] for i in range(dim)})
def test_log_likelihood(self):
likelihood = AnalyticalMultidimensionalCovariantGaussian(mean=[0], cov=[1])
self.assertEqual(-np.log(2 * np.pi) / 2, likelihood.log_likelihood())