def __init__(self, N, B,
mu=0.0, sigma=1.0,
mu_self=None, sigma_self=None,
**kwargs):
super(_FixedWeightsMixin, self).__init__(N, B)
self._mu = expand_scalar(mu, (N, N, B))
self._sigma = expand_cov(mu, (N, N, B, B))
if (mu_self is not None) and (sigma_self is not None):
self._mu[np.arange(N), np.arange(N), :] = expand_scalar(mu_self, (N, B))
self._sigma[np.arange(N), np.arange(N), :] = expand_cov(sigma_self, (N, B, B))
def __init__(self, N, B,
mu_0=0.0, sigma_0=1.0, kappa_0=1.0, nu_0=3.0,
is_diagonal_weight_special=True,
**kwargs):
super(_IndependentGaussianMixin, self).__init__(N, B)
mu_0 = expand_scalar(mu_0, (B,))
sigma_0 = expand_cov(sigma_0, (B,B))
self._gaussian = Gaussian(mu_0=mu_0, sigma_0=sigma_0, kappa_0=kappa_0, nu_0=max(nu_0, B+2.))
self.is_diagonal_weight_special = is_diagonal_weight_special
if is_diagonal_weight_special:
self._self_gaussian = \
Gaussian(mu_0=mu_0, sigma_0=sigma_0, kappa_0=kappa_0, nu_0=nu_0)
def __init__(self,
T,
N,
B,
mu=0.0,
sigma=0.001,
mu_self=None,
sigma_self=None,
**kwargs):
super(_FixedWeightsMixin, self).__init__(T, N, B)
self._mu = expand_scalar(mu, (N, T, N, B))
self._sigma = expand_cov(sigma, (N, T, N, B, B))
# initialize sigma
self._sigma[0, :, 0, :, :] = 0.001
self._sigma[1, :, 1, :, :] = 0.001
self._sigma[1, :, 0, :, :] = 0.001
self._sigma[0, :, 1, :, :] = 0.001
if (mu_self is not None) and (sigma_self is not None):
self._mu[np.arange(N), :,
np.arange(N), :] = expand_scalar(mu_self, (N, T, B))
self._sigma[np.arange(N), :,
np.arange(N), :] = expand_cov(sigma_self, (N, T, B, B))
def S_b(self, value):
assert np.isscalar(value)
self._S_b = expand_cov(value, (1, 1))
def S_w(self, value):
N, B = self.N, self.B
self._S_w = expand_cov(value, (N, B, B))
def S_b(self, value):
assert np.isscalar(value)
self._S_b = expand_cov(value, (1, 1))
def S_w(self, value):
N, B = self.N, self.B
self._S_w = expand_cov(value, (N, B, B))