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 Superseded by adjacency_start if adjacency_start is not `None` """ LearnerHawkesNoParam.fit(self, events, end_times=end_times) self.solve(adjacency_start=adjacency_start,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)