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))
dim = 2
N = 10
r = 2 + 2 * (np.arange(N) // (N / 2.))
th = np.linspace(0, 4 * np.pi, N, endpoint=False)
x = r * np.cos(th)
y = r * np.sin(th)
L = np.hstack((x[:, None], y[:, None]))
# Weight model
W = np.zeros((N, N))
# Distance matrix
D = ((L[:, None, :] - L[None, :, :])**2).sum(2)
sig = np.exp(-D / 2)
Sig = np.tile(sig[:, :, None, None], (1, 1, 1, 1))
Mu = expand_scalar(0, (N, N, 1))
for n in range(N):
for m in range(N):
W[n, m] = npr.multivariate_normal(Mu[n, m], Sig[n, m])
# Adjacency model
fig, axs = plt.subplots(4, 5)
P = [0.1, 0.3, 0.5, 0.7]
i0 = 3
import pickle
sm = pickle.load(
open('/Users/pillowlab/Dropbox/pyglm-master/Practices/model2.pkl', 'rb'))
A = 1 * (np.random.rand(N, N) < P[i0])
def rho(self, value):
self._rho = expand_scalar(value, (self.N,))
def mu_b(self, value):
self._mu_b = expand_scalar(value, (1,))
def mu_w(self, value):
N, B = self.N, self.B
self._mu_w = expand_scalar(value, (N, B))
def rho(self, value):
self._rho = expand_scalar(value, (self.N, ))
def mu_b(self, value):
self._mu_b = expand_scalar(value, (1, ))
def mu_w(self, value):
N, B = self.N, self.B
self._mu_w = expand_scalar(value, (N, B))
def __init__(self, N, B, rho=0.5, rho_self=None, **kwargs):
super(_FixedAdjacencyMixin, self).__init__(N, B)
self._rho = expand_scalar(rho, (N, N))
if rho_self is not None:
self._rho[np.diag_indices(N)] = rho_self