def __init__(self, N, K, A):
self.A = A
self.N = N
self.K = K
self.parameters = {
"noise": ParameterArray(1. * tf.ones((1, 1)))
}
self.nodes = {
"latent": GaussianArray.uniform((N, K)),
"heterogeneity": GaussianArray.uniform((N, 1)),
"product": GaussianArray.uniform((N, N, K)),
"vector": GaussianArray.uniform((N, N, K+2)),
"linear_predictor": GaussianArray.uniform((N, N)),
"noisy_linear_predictor": GaussianArray.uniform((N, N)),
"links": tf.zeros((N, N))
}
self.factors = {
"latent_prior": Prior(GaussianArray.from_shape((N, K), 0., 1.)),
"heterogeneity_prior": Prior(GaussianArray.from_shape((N, 1), 0., 1.)),
"product": Product((N, K), (N, N, K)),
"concatenate": Concatenate({"a_u": (N, N, 1), "a_v": (N, N, 1), "s_uv": (N, N, K)}, (N, N, K+2)),
"sum": Sum((N, N, K+2), (N, N)),
"noise": AddVariance((N, N)),
"adjacency": Probit((N, N))
}
self._current_iter = 0
self._break_symmetry()
def __init__(self, child: GaussianArray, mean: float = 0., variance: float = 1., initial=None, name=""):
super().__init__()
self._deterministic = False
self.mean = ParameterArray(mean, True, name=name+".mean")
self.variance = ParameterArrayLogScale(variance, True, name=name+".variance")
self._parameters = {"mean": self.mean, "variance": self.variance}
self.child = child
self.shape = child.shape()
self.prior = GaussianArray.from_shape(self.shape, self.mean.value(), self.variance.value())
# initialize child
self.message_to_child = GaussianArray.from_array(initial, tf.ones_like(initial) * variance * 0.1)
self.child.set_to(self.message_to_child)
