def fit(self,
events,
end_times=None,
baseline_start=None,
kernel_start=None):
"""Fit the model according to the given training data.
Parameters
----------
events : `list` of `list` of `np.ndarray`
List of Hawkes processes realizations.
Each realization of the Hawkes process is a list of n_node for
each component of the Hawkes. Namely `events[i][j]` contains a
one-dimensional `numpy.array` of the events' timestamps of
component j of realization i.
If only one realization is given, it will be wrapped into a list
end_times : `np.ndarray` or `float`, default = None
List of end time of all hawkes processes that will be given to the
model. If None, it will be set to each realization's latest time.
If only one realization is provided, then a float can be given.
baseline_start : `None` or `np.ndarray`, shape=(n_nodes), default=None
Used to force start values for baseline parameter
If `None` starts with uniform 1 values
kernel_start : `None` or `np.ndarray`, shape=(n_nodes, n_nodes, kernel_size), default=None
Used to force start values for kernel parameter
If `None` starts with random values
"""
LearnerHawkesNoParam.fit(self, events, end_times=end_times)
self.solve(baseline_start=baseline_start, kernel_start=kernel_start)
return self
def fit(self, events, end_times=None, adjacency_start=None, R_start=None):
"""Fit the model according to the given training data.
Parameters
----------
events : `list` of `list` of `np.ndarray`
List of Hawkes processes realizations.
Each realization of the Hawkes process is a list of n_node for
each component of the Hawkes. Namely `events[i][j]` contains a
one-dimensional `numpy.array` of the events' timestamps of
component j of realization i.
If only one realization is given, it will be wrapped into a list
end_times : `np.ndarray` or `float`, default = None
List of end time of all hawkes processes that will be given to the
model. If None, it will be set to each realization's latest time.
If only one realization is provided, then a float can be given.
adjacency_start : `str` or `np.ndarray, shape=(n_nodes + n_nodes * n_nodes,), default=`None`
Initial guess for the adjacency matrix. Will be used as
starting point in optimization.
If `None` and `R_start` is also `None`, a default starting point
is estimated from the estimated cumulants
If `"random"`, a starting point is estimated from estimated
cumulants with a bit a randomness
R_start : `np.ndarray`, shape=(n_nodes, n_nodes), default=None
R variable at which we start optimization. Superseded by
adjacency_start if adjacency_start is not `None`
"""
LearnerHawkesNoParam.fit(self, events, end_times=end_times)
self.solve(adjacency_start=adjacency_start, R_start=R_start)