def __init__(self,
nu_0=None,
S_0=None,
M_0=None,
K_0=None,
affine=False,
A=None,
sigma=None):
self.affine = affine
self.diag_sigma = self._check_shapes(A, sigma, nu_0, S_0, M_0, K_0)
self.A = A
if self.diag_sigma:
self.sigma = np.diag(sigma)
self.dsigma = sigma
else:
self.sigma = sigma
self.dsigma = None
have_hypers = not any_none(nu_0, S_0, M_0, K_0)
if have_hypers:
self.natural_hypparam = self.mf_natural_hypparam = \
self._standard_to_natural(nu_0,S_0,M_0,K_0)
if A is sigma is None and have_hypers:
self.resample() # initialize from prior
def __init__(
self, nu_0=None,S_0=None,M_0=None,K_0=None,
affine=False,
A=None,sigma=None):
self.affine = affine
self.diag_sigma = self._check_shapes(A, sigma, nu_0, S_0, M_0, K_0)
self.A = A
if self.diag_sigma:
self.sigma = np.diag(sigma)
self.dsigma = sigma
else:
self.sigma = sigma
self.dsigma = None
have_hypers = not any_none(nu_0,S_0,M_0,K_0)
if have_hypers:
self.natural_hypparam = self.mf_natural_hypparam = \
self._standard_to_natural(nu_0,S_0,M_0,K_0)
if A is sigma is None and have_hypers:
self.resample() # initialize from prior
def __init__(
self,low=None,high=None,
x_m_low=None,alpha_low=None,x_m_high=None,alpha_high=None):
self.low = low
self.high = high
self.x_m_low = x_m_low
self.alpha_low = alpha_low
self.x_m_high = x_m_high
self.alpha_high = alpha_high
have_hypers = not any_none(x_m_low,alpha_low,x_m_high,alpha_high)
if low is high is None and have_hypers:
self.resample() # initialize from prior
def __init__(
self, nu_0=None,S_0=None,M_0=None,K_0=None,
affine=False,
A=None,sigma=None):
self.affine = affine
self.A = A
self.sigma = sigma
have_hypers = not any_none(nu_0,S_0,M_0,K_0)
if have_hypers:
self.natural_hypparam = self._standard_to_natural(nu_0,S_0,M_0,K_0)
if A is sigma is None and have_hypers:
self.resample() # initialize from prior
def __init__(self,
D_out,
D_in,
mu_0=None,
Sigma_0=None,
alpha_0=3.0,
beta_0=2.0,
A=None,
sigmasq=None,
niter=1):
self._D_out = D_out
self._D_in = D_in
self.A = A
self.sigmasq_flat = sigmasq
self.affine = False # We do not yet support affine
mu_0 = np.zeros(D_in) if mu_0 is None else mu_0
Sigma_0 = np.eye(D_in) if Sigma_0 is None else Sigma_0
assert mu_0.shape == (D_in, )
assert Sigma_0.shape == (D_in, D_in)
self.h_0 = np.linalg.solve(Sigma_0, mu_0)
self.J_0 = np.linalg.inv(Sigma_0)
self.alpha_0 = alpha_0
self.beta_0 = beta_0
self.niter = niter
if any_none(A, sigmasq):
self.A = np.zeros((D_out, D_in))
self.sigmasq_flat = np.ones((D_out, ))
self.resample(data=None) # initialize from prior
# Store the natural parameters and expose the standard versions as properties
self.mf_J_A = np.array([self.J_0.copy() for _ in range(D_out)])
# Initializing with mean zero is pathological. Break symmetry by starting with sampled A.
# self.mf_h_A = np.array([self.h_0.copy() for _ in range(D_out)])
self.mf_h_A = np.array(
[Jd.dot(Ad) for Jd, Ad in zip(self.mf_J_A, self.A)])
self.mf_alpha = self.alpha_0 * np.ones(D_out)
self.mf_beta = self.alpha_0 * self.sigmasq_flat
# Cache the standard parameters for A as well
self._mf_A_cache = {}
def __init__(self,
low=None,
high=None,
x_m_low=None,
alpha_low=None,
x_m_high=None,
alpha_high=None):
self.low = low
self.high = high
self.x_m_low = x_m_low
self.alpha_low = alpha_low
self.x_m_high = x_m_high
self.alpha_high = alpha_high
have_hypers = not any_none(x_m_low, alpha_low, x_m_high, alpha_high)
if low is high is None and have_hypers:
self.resample() # initialize from prior
def __init__(self, D_out, D_in, mu_0=None, Sigma_0=None, alpha_0=3.0, beta_0=2.0,
A=None, sigmasq=None, niter=1):
self._D_out = D_out
self._D_in = D_in
self.A = A
self.sigmasq_flat = sigmasq
self.affine = False # We do not yet support affine
mu_0 = np.zeros(D_in) if mu_0 is None else mu_0
Sigma_0 = np.eye(D_in) if Sigma_0 is None else Sigma_0
assert mu_0.shape == (D_in,)
assert Sigma_0.shape == (D_in, D_in)
self.h_0 = np.linalg.solve(Sigma_0, mu_0)
self.J_0 = np.linalg.inv(Sigma_0)
self.alpha_0 = alpha_0
self.beta_0 = beta_0
self.niter = niter
if any_none(A, sigmasq):
self.A = np.zeros((D_out, D_in))
self.sigmasq_flat = np.ones((D_out,))
self.resample(data=None) # initialize from prior
# Store the natural parameters and expose the standard versions as properties
self.mf_J_A = np.array([self.J_0.copy() for _ in range(D_out)])
# Initializing with mean zero is pathological. Break symmetry by starting with sampled A.
# self.mf_h_A = np.array([self.h_0.copy() for _ in range(D_out)])
self.mf_h_A = np.array([Jd.dot(Ad) for Jd,Ad in zip(self.mf_J_A, self.A)])
self.mf_alpha = self.alpha_0 * np.ones(D_out)
self.mf_beta = self.alpha_0 * self.sigmasq_flat
# Cache the standard parameters for A as well
self._mf_A_cache = {}
