Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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141 lines
6.8 KiB
141 lines
6.8 KiB
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import numpy as np
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import network_graph as ng
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import sample_path as sp
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import set_of_cims as sofc
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import sets_of_cims_container as acims
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class ParametersEstimator:
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"""
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Has the task of computing the cims of particular node given the trajectories in samplepath and the net structure
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in the graph net_graph
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:sample_path: the container of the trajectories
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:net_graph: the net structure
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:single_set_of_cims: the set of cims object that will hold the cims of the node
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"""
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def __init__(self, sample_path: sp.SamplePath, net_graph: ng.NetworkGraph):
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self.sample_path = sample_path
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self.net_graph = net_graph
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self.sets_of_cims_struct = None
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self.single_set_of_cims = None
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def fast_init(self, node_id: str):
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"""
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Initializes all the necessary structures for the parameters estimation.
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Parameters:
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node_id: the node label
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Returns:
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void
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"""
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p_vals = self.net_graph.aggregated_info_about_nodes_parents[2]
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node_states_number = self.net_graph.get_states_number(node_id)
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self.single_set_of_cims = sofc.SetOfCims(node_id, p_vals, node_states_number, self.net_graph.p_combs)
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def compute_parameters_for_node(self, node_id: str) -> sofc.SetOfCims:
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"""
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Compute the CIMS of the node identified by the label node_id
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Parameters:
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node_id: the node label
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Returns:
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A setOfCims object filled with the computed CIMS
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"""
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node_indx = self.net_graph.get_node_indx(node_id)
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state_res_times = self.single_set_of_cims.state_residence_times
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transition_matrices = self.single_set_of_cims.transition_matrices
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trajectory = self.sample_path.trajectories.trajectory
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self.compute_state_res_time_for_node(node_indx, self.sample_path.trajectories.times,
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trajectory,
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self.net_graph.time_filtering,
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self.net_graph.time_scalar_indexing_strucure,
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state_res_times)
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self.compute_state_transitions_for_a_node(node_indx,
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self.sample_path.trajectories.complete_trajectory,
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self.net_graph.transition_filtering,
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self.net_graph.transition_scalar_indexing_structure,
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transition_matrices)
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self.single_set_of_cims.build_cims(state_res_times, transition_matrices)
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return self.single_set_of_cims
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def compute_state_res_time_for_node(self, node_indx: int, times: np.ndarray, trajectory: np.ndarray,
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cols_filter: np.ndarray, scalar_indexes_struct: np.ndarray, T: np.ndarray):
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"""
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Compute the state residence times for a node and fill the matrix T with the results
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Parameters:
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node_indx: the index of the node
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times: the times deltas vector
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trajectory: the trajectory
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cols_filter: the columns filtering structure
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scalar_indexes_struct: the indexing structure
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T: the state residence times vectors
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Returns:
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void
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"""
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T[:] = np.bincount(np.sum(trajectory[:, cols_filter] * scalar_indexes_struct / scalar_indexes_struct[0], axis=1)
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.astype(np.int), \
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times,
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minlength=scalar_indexes_struct[-1]).reshape(-1, T.shape[1])
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def compute_state_transitions_for_a_node(self, node_indx, trajectory, cols_filter, scalar_indexing, M):
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"""
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Compute the state residence times for a node and fill the matrices M with the results
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Parameters:
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node_indx: the index of the node
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times: the times deltas vector
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trajectory: the trajectory
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cols_filter: the columns filtering structure
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scalar_indexes: the indexing structure
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M: the state transition matrices
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Returns:
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void
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"""
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diag_indices = np.array([x * M.shape[1] + x % M.shape[1] for x in range(M.shape[0] * M.shape[1])],
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dtype=np.int64)
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trj_tmp = trajectory[trajectory[:, int(trajectory.shape[1] / 2) + node_indx].astype(np.int) >= 0]
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M[:] = np.bincount(np.sum(trj_tmp[:, cols_filter] * scalar_indexing / scalar_indexing[0], axis=1).astype(np.int),
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minlength=scalar_indexing[-1]).reshape(-1, M.shape[1], M.shape[2])
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M_raveled = M.ravel()
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M_raveled[diag_indices] = 0
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M_raveled[diag_indices] = np.sum(M, axis=2).ravel()
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def init_sets_cims_container(self):
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self.sets_of_cims_struct = acims.SetsOfCimsContainer(self.net_graph.nodes,
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self.net_graph.nodes_values,
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self.net_graph.get_ordered_by_indx_parents_values_for_all_nodes(),
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self.net_graph.p_combs)
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def compute_parameters(self):
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#print(self.net_graph.get_nodes())
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#print(self.amalgamated_cims_struct.sets_of_cims)
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#enumerate(zip(self.net_graph.get_nodes(), self.amalgamated_cims_struct.sets_of_cims))
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for indx, aggr in enumerate(zip(self.net_graph.nodes, self.sets_of_cims_struct.sets_of_cims)):
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#print(self.net_graph.time_filtering[indx])
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#print(self.net_graph.time_scalar_indexing_strucure[indx])
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self.compute_state_res_time_for_node(self.net_graph.get_node_indx(aggr[0]), self.sample_path.trajectories.times,
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self.sample_path.trajectories.trajectory,
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self.net_graph.time_filtering[indx],
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self.net_graph.time_scalar_indexing_strucure[indx],
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aggr[1].state_residence_times)
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#print(self.net_graph.transition_filtering[indx])
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#print(self.net_graph.transition_scalar_indexing_structure[indx])
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self.compute_state_transitions_for_a_node(self.net_graph.get_node_indx(aggr[0]),
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self.sample_path.trajectories.complete_trajectory,
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self.net_graph.transition_filtering[indx],
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self.net_graph.transition_scalar_indexing_structure[indx],
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aggr[1].transition_matrices)
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aggr[1].build_cims(aggr[1].state_residence_times, aggr[1].transition_matrices)
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