import os from line_profiler import LineProfiler import numba as nb import numpy as np import network_graph as ng import sample_path as sp import sets_of_cims_container as acims class ParametersEstimator: def __init__(self, sample_path, net_graph): self.sample_path = sample_path self.net_graph = net_graph self.sets_of_cims_struct = None def init_amalgamated_cims_struct(self): self.sets_of_cims_struct = acims.SetsOfCimsContainer(self.net_graph.get_states_number_of_all_nodes_sorted(), self.net_graph.get_nodes(), self.net_graph.get_ordered_by_indx_parents_values_for_all_nodes()) def compute_parameters(self): #print(self.net_graph.get_nodes()) #print(self.amalgamated_cims_struct.sets_of_cims) #enumerate(zip(self.net_graph.get_nodes(), self.amalgamated_cims_struct.sets_of_cims)) for indx, aggr in enumerate(zip(self.net_graph.get_nodes(), self.amalgamated_cims_struct.sets_of_cims)): #print(self.net_graph.time_filtering[indx]) #print(self.net_graph.time_scalar_indexing_strucure[indx]) self.compute_state_res_time_for_node(self.net_graph.get_node_indx(aggr[0]), self.sample_path.trajectories.times, self.sample_path.trajectories.trajectory, self.net_graph.time_filtering[indx], self.net_graph.time_scalar_indexing_strucure[indx], aggr[1].state_residence_times) #print(self.net_graph.transition_filtering[indx]) #print(self.net_graph.transition_scalar_indexing_structure[indx]) self.compute_state_transitions_for_a_node(self.net_graph.get_node_indx(aggr[0]), self.sample_path.trajectories.complete_trajectory, self.net_graph.transition_filtering[indx], self.net_graph.transition_scalar_indexing_structure[indx], aggr[1].transition_matrices) aggr[1].build_cims(aggr[1].state_residence_times, aggr[1].transition_matrices) def compute_state_res_time_for_node(self, node_indx, times, trajectory, cols_filter, scalar_indexes_struct, T): #print(times.size) #print(trajectory) #print(cols_filter) #print(scalar_indexes_struct) #print(T) T[:] = np.bincount(np.sum(trajectory[:, cols_filter] * scalar_indexes_struct / scalar_indexes_struct[0], axis=1) .astype(np.int), \ times, minlength=scalar_indexes_struct[-1]).reshape(-1, T.shape[1]) #print("Done This NODE", T) def compute_state_residence_time_for_all_nodes(self): for node_indx, set_of_cims in enumerate(self.amalgamated_cims_struct.sets_of_cims): self.compute_state_res_time_for_node(node_indx, self.sample_path.trajectories[0].get_times(), self.sample_path.trajectories[0].get_trajectory(), self.columns_filtering_structure[node_indx], self.scalar_indexes_converter[node_indx], set_of_cims.state_residence_times) def compute_state_transitions_for_a_node(self, node_indx, trajectory, cols_filter, scalar_indexing, M): #print(node_indx) #print(trajectory) #print(cols_filter) #print(scalar_indexing) #print(M) diag_indices = np.array([x * M.shape[1] + x % M.shape[1] for x in range(M.shape[0] * M.shape[1])], dtype=np.int64) trj_tmp = trajectory[trajectory[:, int(trajectory.shape[1] / 2) + node_indx].astype(np.int) >= 0] #print(trj_tmp) #print("Summing", np.sum(trj_tmp[:, cols_filter] * scalar_indexing / scalar_indexing[0], axis=1).astype(np.int)) #print(M.shape[1]) #print(M.shape[2]) M[:] = np.bincount(np.sum(trj_tmp[:, cols_filter] * scalar_indexing / scalar_indexing[0], axis=1).astype(np.int), minlength=scalar_indexing[-1]).reshape(-1, M.shape[1], M.shape[2]) M_raveled = M.ravel() M_raveled[diag_indices] = 0 #print(M_raveled) M_raveled[diag_indices] = np.sum(M, axis=2).ravel() #print(M_raveled) #print(M) def compute_state_transitions_for_all_nodes(self): for node_indx, set_of_cims in enumerate(self.amalgamated_cims_struct.sets_of_cims): self.compute_state_transitions_for_a_node(node_indx, self.sample_path.trajectories[0].get_complete_trajectory(), self.transition_filtering[node_indx], self.transition_scalar_index_converter[node_indx], set_of_cims.transition_matrices) # Simple Test # """os.getcwd() os.chdir('..') path = os.getcwd() + '/data' s1 = sp.SamplePath(path) s1.build_trajectories() s1.build_structure() g1 = ng.NetworkGraph(s1.structure) g1.init_graph() pe = ParametersEstimator(s1, g1) pe.init_amalgamated_cims_struct() lp = LineProfiler() [[2999.2966 2749.2298 3301.5975] [3797.1737 3187.8345 2939.2009] [3432.224 3062.5402 4530.9028]] [[ 827.6058 838.1515 686.1365] [1426.384 2225.2093 1999.8528] [ 745.3068 733.8129 746.2347] [ 520.8113 690.9502 853.4022] [1590.8609 1853.0021 1554.1874] [ 637.5576 643.8822 654.9506] [ 718.7632 742.2117 998.5844] [1811.984 1598.0304 2547.988 ] [ 770.8503 598.9588 984.3304]] lp_wrapper = lp(pe.compute_state_residence_time_for_all_nodes) lp_wrapper() lp.print_stats() #pe.compute_state_residence_time_for_all_nodes() print(pe.amalgamated_cims_struct.sets_of_cims[0].state_residence_times) [[[14472, 3552, 10920], [12230, 25307, 13077], [ 9707, 14408, 24115]], [[22918, 6426, 16492], [10608, 16072, 5464], [10746, 11213, 21959]], [[23305, 6816, 16489], [ 3792, 19190, 15398], [13718, 18243, 31961]]]) Raveled [14472 3552 10920 12230 25307 13077 9707 14408 24115 22918 6426 16492 10608 16072 5464 10746 11213 21959 23305 6816 16489 3792 19190 15398 13718 18243 31961] lp_wrapper = lp(pe.compute_parameters) lp_wrapper() #for variable in pe.amalgamated_cims_struct.sets_of_cims: #for cond in variable.get_cims(): #print(cond.cim) print(pe.amalgamated_cims_struct.get_cims_of_node(1,[2])) lp.print_stats()"""