def logp(self, F, Y):
return logdensities.gaussian(Y, F, self.variance)
def predict_density(self, Fmu, Fvar, Y):
return logdensities.gaussian(Y, Fmu, Fvar + self.variance)
def predict_density(self, Fmu, Fvar, Y, Y_var):
return logdensities.gaussian(Y, Fmu * self.tec_scale,
Fvar * self.tec_scale**2 + Y_var)
def logp(self, F, Y, Y_var):
tec = F * self.tec_scale
return logdensities.gaussian(Y, tec, Y_var)
def test_gaussian(x, mu, var):
gpf = logdensities.gaussian(x, mu, var).numpy()
sps = scipy.stats.norm(loc=mu, scale=np.sqrt(var)).logpdf(x)
np.testing.assert_allclose(gpf, sps)
def logp(self, F, Y, hetero_variance=None,**unused_args):
"""The log-likelihood function."""
return densities.gaussian(F, Y, hetero_variance)
def logp(self, F, Y):
import pdb; pdb.set_trace()
return logdensities.gaussian(Y, F, self.variance)
