def update(self, model):
self.model = model
self.sn2 = self.model.get_noise()
self.sample_representer_points()
mu, var = self.model.predict(np.array(self.zb), full_cov=True)
self.logP, self.dlogPdMu, self.dlogPdSigma, self.dlogPdMudMu = \
epmgp.joint_min(mu, var, with_derivatives=True)
self.W = scipy.stats.norm.ppf(
np.linspace(1. / (self.Np + 1), 1 - 1. / (self.Np + 1),
self.Np))[np.newaxis, :]
self.logP = np.reshape(self.logP, (self.logP.shape[0], 1))
def update(self, model):
self.model = model
self.sn2 = self.model.get_noise()
self.sample_representer_points()
mu, var = self.model.predict(np.array(self.zb), full_cov=True)
self.logP, self.dlogPdMu, self.dlogPdSigma, self.dlogPdMudMu = \
epmgp.joint_min(mu, var, with_derivatives=True)
self.W = scipy.stats.norm.ppf(np.linspace(1. / (self.Np + 1),
1 - 1. / (self.Np + 1),
self.Np))[np.newaxis, :]
self.logP = np.reshape(self.logP, (self.logP.shape[0], 1))
def test_compute_pmin(self):
# Uniform distribution
m = np.ones([self.acquisition_func.Nb, 1])
v = np.eye(self.acquisition_func.Nb)
pmin = epmgp.joint_min(m, v)
pmin = np.exp(pmin)
uprob = 1. / self.acquisition_func.Nb
assert pmin.shape[0] == self.acquisition_func.Nb
assert np.any(pmin < (uprob + 0.03)) and np.any(pmin > uprob - 0.01)
# Dirac delta
m = np.ones([self.acquisition_func.Nb, 1]) * 1000
m[0] = 1
v = np.eye(self.acquisition_func.Nb)
pmin = epmgp.joint_min(m, v)
pmin = np.exp(pmin)
uprob = 1. / self.acquisition_func.Nb
assert pmin[0] == 1.0
assert np.any(pmin[:1] > 1e-10)
def test_compute_pmin(self):
# Uniform distribution
m = np.ones([self.acquisition_func.Nb, 1])
v = np.eye(self.acquisition_func.Nb)
pmin = epmgp.joint_min(m, v)
pmin = np.exp(pmin)
uprob = 1. / self.acquisition_func.Nb
assert pmin.shape[0] == self.acquisition_func.Nb
assert np.any(pmin < (uprob + 0.03)) and np.any(pmin > uprob - 0.01)
# Dirac delta
m = np.ones([self.acquisition_func.Nb, 1]) * 1000
m[0] = 1
v = np.eye(self.acquisition_func.Nb)
pmin = epmgp.joint_min(m, v)
pmin = np.exp(pmin)
uprob = 1. / self.acquisition_func.Nb
assert pmin[0] == 1.0
assert np.any(pmin[:1] > 1e-10)
