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)