def __init__( self, rho, sigma=None, tau=None, constant=False, init=Flat.dist(), sd=None, *args, **kwargs ): super().__init__(*args, **kwargs) if sd is not None: sigma = sd tau, sigma = get_tau_sigma(tau=tau, sigma=sigma) self.sigma = self.sd = tt.as_tensor_variable(sigma) self.tau = tt.as_tensor_variable(tau) self.mean = tt.as_tensor_variable(0.0) if isinstance(rho, list): p = len(rho) else: try: shape_ = rho.shape.tag.test_value except AttributeError: shape_ = rho.shape if hasattr(shape_, "size") and shape_.size == 0: p = 1 else: p = shape_[0] if constant: self.p = p - 1 else: self.p = p self.constant = constant self.rho = rho = tt.as_tensor_variable(rho) self.init = init
def __init__(self, tau=None, init=Flat.dist(), sigma=None, mu=0.0, sd=None, *args, **kwargs): kwargs.setdefault("shape", 1) super().__init__(*args, **kwargs) if sum(self.shape) == 0: raise TypeError( "GaussianRandomWalk must be supplied a non-zero shape argument!" ) if sd is not None: sigma = sd warnings.warn("sd is deprecated, use sigma instead", DeprecationWarning) tau, sigma = get_tau_sigma(tau=tau, sigma=sigma) self.tau = tt.as_tensor_variable(tau) sigma = tt.as_tensor_variable(sigma) self.sigma = self.sd = sigma self.mu = tt.as_tensor_variable(mu) self.init = init self.mean = tt.as_tensor_variable(0.0)
def __init__(self, w, mu, sigma=None, tau=None, sd=None, comp_shape=(), *args, **kwargs): if sd is not None: sigma = sd _, sigma = get_tau_sigma(tau=tau, sigma=sigma) self.mu = mu = at.as_tensor_variable(mu) self.sigma = self.sd = sigma = at.as_tensor_variable(sigma) super().__init__(w, Normal.dist(mu, sigma=sigma, shape=comp_shape), *args, **kwargs)
def __init__(self, w, mu, sigma=None, tau=None, sd=None, comp_shape=(), *args, **kwargs): if sd is not None: sigma = sd warnings.warn("sd is deprecated, use sigma instead", DeprecationWarning) _, sigma = get_tau_sigma(tau=tau, sigma=sigma) self.mu = mu = tt.as_tensor_variable(mu) self.sigma = self.sd = sigma = tt.as_tensor_variable(sigma) super().__init__(w, Normal.dist(mu, sigma=sigma, shape=comp_shape), *args, **kwargs)
def __init__(self, tau=None, init=Flat.dist(), sigma=None, mu=0., sd=None, *args, **kwargs): kwargs.setdefault('shape', 1) super().__init__(*args, **kwargs) if sum(self.shape) == 0: raise TypeError("RW2 must be supplied a non-zero shape argument!") if sd is not None: sigma = sd tau, sigma = get_tau_sigma(tau=tau, sigma=sigma) self.tau = tt.as_tensor_variable(tau) sigma = tt.as_tensor_variable(sigma) self.sigma = self.sd = sigma self.mu = tt.as_tensor_variable(mu) self.init = init self.mean = tt.as_tensor_variable(0.)