def __init__(self, K, dt=1.0, dt_max=10.0,
B=5, basis=None,
sigma=np.inf,
lmbda=0):
"""
Initialize a discrete time network Hawkes model with K processes.
:param K: Number of processes
:param dt: Time bin size
:param dt_max:
"""
self.K = K
self.dt = dt
self.dt_max = dt_max
self.sigma = sigma
self.lmbda = lmbda
# Initialize the basis
if basis is None:
self.B = B
self.basis = CosineBasis(self.B, self.dt, self.dt_max, norm=True,
allow_instantaneous=False)
else:
self.basis = basis
self.B = basis.B
# Initialize nodes
self.nodes = \
[self._node_class(self.K, self.B, dt=self.dt,
sigma=self.sigma, lmbda=self.lmbda)
for _ in range(self.K)]
def __init__(self, K, dt=1.0, dt_max=10.0,
B=5, basis=None,
alpha=1.0, beta=1.0,
allow_instantaneous=False,
allow_self_connections=True):
"""
Initialize a discrete time network Hawkes model with K processes.
:param K: Number of processes
:param dt: Time bin size
:param dt_max:
"""
self.K = K
self.dt = dt
self.dt_max = dt_max
self.allow_self_connections = allow_self_connections
# Initialize the basis
if basis is None:
self.B = B
self.allow_instantaneous = allow_instantaneous
self.basis = CosineBasis(self.B, self.dt, self.dt_max, norm=True,
allow_instantaneous=allow_instantaneous)
else:
self.basis = basis
self.allow_instantaneous = basis.allow_instantaneous
self.B = basis.B
assert not (self.allow_instantaneous and self.allow_self_connections), \
"Cannot allow instantaneous self connections"
# Save the gamma prior
assert alpha >= 1.0, "Alpha must be greater than 1.0 to ensure log concavity"
self.alpha = alpha
self.beta = beta
# Initialize with sample from Gamma(alpha, beta)
# self.weights = np.random.gamma(self.alpha, 1.0/self.beta, size=(self.K, 1 + self.K*self.B))
# self.weights = self.alpha/self.beta * np.ones((self.K, 1 + self.K*self.B))
self.weights = 1e-3 * np.ones((self.K, 1 + self.K*self.B))
if not self.allow_self_connections:
self._remove_self_weights()
# Initialize the data list to empty
self.data_list = []
