def test_negative_loglikelihood(self):
t0 = [2.0]
m = self.model(self.ps.freq[1:], t0)
loglike = np.sum(self.ps.power[1:]/m + np.log(m))
lpost = PSDPosterior(self.ps, self.model)
lpost.logprior = set_logprior(lpost, self.priors)
loglike_test = lpost.loglikelihood(t0, neg=True)
assert np.isclose(loglike, loglike_test)
def test_negative_loglikelihood(self):
t0 = [2.0]
m = self.model(self.ps.freq[1:], t0)
loglike = np.sum(self.ps.power[1:]/m + np.log(m))
lpost = PSDPosterior(self.ps.freq, self.ps.power,
self.model, m=self.ps.m)
lpost.logprior = set_logprior(lpost, self.priors)
loglike_test = lpost.loglikelihood(t0, neg=True)
assert np.isclose(loglike, loglike_test)
def test_loglikelihood(self):
t0 = [2.0]
self.model.amplitude = t0[0]
mean_model = self.model(self.ps.freq)
loglike = -np.sum(np.log(mean_model)) - np.sum(self.ps.power/mean_model)
lpost = PSDPosterior(self.ps, self.model)
lpost.logprior = set_logprior(lpost, self.priors)
loglike_test = lpost.loglikelihood(t0, neg=False)
assert np.isclose(loglike, loglike_test)
def test_loglikelihood(self):
t0 = [2.0]
self.model.amplitude = t0[0]
mean_model = self.model(self.ps.freq)
loglike = -np.sum(np.log(mean_model)) - np.sum(self.ps.power/mean_model)
lpost = PSDPosterior(self.ps.freq, self.ps.power,
self.model, m=self.ps.m)
lpost.logprior = set_logprior(lpost, self.priors)
loglike_test = lpost.loglikelihood(t0, neg=False)
assert np.isclose(loglike, loglike_test)
def test_negative_loglikelihood(self):
t0 = [2.0]
self.model.amplitude = t0[0]
mean_model = self.model(self.ps.freq)
loglike = 2.0 * self.m * (np.sum(np.log(mean_model)) + np.sum(
self.ps.power / mean_model) + np.sum(
(2.0 / (2. * self.m) - 1.0) * np.log(self.ps.power)))
lpost = PSDPosterior(self.ps, self.model)
lpost.logprior = set_logprior(lpost, self.priors)
loglike_test = lpost.loglikelihood(t0, neg=True)
assert np.isclose(loglike, loglike_test)
def test_negative_loglikelihood(self):
t0 = [2.0]
self.model.amplitude = t0[0]
mean_model = self.model(self.ps.freq)
loglike = 2.0*self.m*(np.sum(np.log(mean_model)) +
np.sum(self.ps.power/mean_model) +
np.sum((2.0 / (2. * self.m) - 1.0) *
np.log(self.ps.power)))
lpost = PSDPosterior(self.ps.freq, self.ps.power,
self.model, m=self.ps.m)
lpost.logprior = set_logprior(lpost, self.priors)
loglike_test = lpost.loglikelihood(t0, neg=True)
assert np.isclose(loglike, loglike_test)
