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@ -1,8 +1,9 @@ |
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import os |
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import time as tm |
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from line_profiler import LineProfiler |
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import numpy as np |
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import numba as nb |
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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 amalgamated_cims as acims |
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@ -13,7 +14,6 @@ class ParametersEstimator: |
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def __init__(self, sample_path, net_graph): |
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self.sample_path = sample_path |
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self.net_graph = net_graph |
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self.fancy_indexing_structure = self.net_graph.build_fancy_indexing_structure(1) |
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self.amalgamated_cims_struct = None |
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def init_amalgamated_cims_struct(self): |
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@ -21,67 +21,71 @@ class ParametersEstimator: |
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self.net_graph.get_nodes(), |
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self.net_graph.get_ordered_by_indx_parents_values_for_all_nodes()) |
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def parameters_estimation(self): |
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#print("Starting computing") |
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#t0 = tm.time() |
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for indx, trajectory in enumerate(self.sample_path.trajectories): |
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self.parameters_estimation_single_trajectory(trajectory.get_trajectory()) |
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#print("Finished Trajectory number", indx) |
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#t1 = tm.time() - t0 |
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#print("Elapsed Time ", t1) |
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def parameters_estimation_single_trajectory(self, trajectory): |
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tr_len = trajectory.shape[0] |
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row_length = trajectory.shape[1] |
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print(tr_len) |
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print(row_length) |
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t0 = tm.time() |
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for indx, row in enumerate(trajectory): |
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""" #if int(trajectory[indx][1]) == -1: |
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#break |
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if indx == tr_len - 2: |
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break |
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if trajectory[indx + 1][1] != -1: |
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transition = self.find_transition(trajectory[indx], trajectory[indx + 1], row_length) |
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which_node = transition[0] |
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# print(which_node) |
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which_matrix = self.which_matrix_to_update(row, transition[0]) |
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which_element = transition[1] |
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self.amalgamated_cims_struct.update_state_transition_for_matrix(which_node, which_matrix, which_element) |
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#changed_node = which_node |
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if int(trajectory[indx][0]) == 0: |
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time = trajectory[indx + 1][0] |
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#time = self.compute_time_delta(trajectory[indx], trajectory[indx + 1]) |
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which_element = transition[1][0] |
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self.amalgamated_cims_struct.update_state_residence_time_for_matrix(which_node, which_matrix, which_element, |
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time) |
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for node_indx in range(0, 3): |
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if node_indx != transition[0]: |
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# print(node) |
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which_node = node_indx |
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which_matrix = self.which_matrix_to_update(row, node_indx) |
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which_element = int(row[node_indx + 1]) |
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# print("State res time element " + str(which_element) + node) |
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# print("State res time matrix indx" + str(which_matrix)) |
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self.amalgamated_cims_struct.update_state_residence_time_for_matrix(which_node, which_matrix, |
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which_element, time) |
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t1 = tm.time() - t0 |
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print("Elapsed Time ", t1)""" |
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def find_transition(self, current_row, next_row, row_length): |
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for indx in range(1, row_length): |
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if current_row[indx] != next_row[indx]: |
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return [indx - 1, (current_row[indx], next_row[indx])] |
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def compute_time_delta(self, current_row, next_row): |
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return next_row[0] - current_row[0] |
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def which_matrix_to_update(self, current_row, node_indx): # produce strutture {'X':1, 'Y':2} dove X e Y sono i parent di node_id |
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return current_row[self.fancy_indexing_structure[node_indx]] |
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def compute_parameters(self): |
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for node_indx, set_of_cims in enumerate(self.amalgamated_cims_struct.sets_of_cims): |
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self.compute_state_res_time_for_node(node_indx, self.sample_path.trajectories.times, |
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self.sample_path.trajectories.trajectory, |
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self.net_graph.time_filtering[node_indx], |
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self.net_graph.time_scalar_indexing_strucure[node_indx], |
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set_of_cims.state_residence_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[node_indx], |
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self.net_graph.transition_scalar_indexing_structure[node_indx], |
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set_of_cims.transition_matrices) |
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set_of_cims.build_cims(set_of_cims.state_residence_times, set_of_cims.transition_matrices) |
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def compute_state_res_time_for_node(self, node_indx, times, trajectory, cols_filter, scalar_indexes_struct, T): |
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#print(times.size) |
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#print(trajectory) |
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#print(cols_filter) |
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#print(scalar_indexes_struct) |
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#print(T) |
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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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#print("Done This NODE", T) |
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def compute_state_residence_time_for_all_nodes(self): |
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for node_indx, set_of_cims in enumerate(self.amalgamated_cims_struct.sets_of_cims): |
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self.compute_state_res_time_for_node(node_indx, self.sample_path.trajectories[0].get_times(), |
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self.sample_path.trajectories[0].get_trajectory(), self.columns_filtering_structure[node_indx], |
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self.scalar_indexes_converter[node_indx], set_of_cims.state_residence_times) |
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def compute_state_transitions_for_a_node(self, node_indx, trajectory, cols_filter, scalar_indexing, M): |
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#print(node_indx) |
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#print(trajectory) |
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#print(cols_filter) |
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#print(scalar_indexing) |
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#print(M) |
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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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#print(trj_tmp) |
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#print("Summing", np.sum(trj_tmp[:, cols_filter] * scalar_indexing / scalar_indexing[0], axis=1).astype(np.int)) |
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#print(M.shape[1]) |
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#print(M.shape[2]) |
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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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#print(M_raveled) |
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M_raveled[diag_indices] = np.sum(M, axis=2).ravel() |
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#print(M_raveled) |
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#print(M) |
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def compute_state_transitions_for_all_nodes(self): |
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for node_indx, set_of_cims in enumerate(self.amalgamated_cims_struct.sets_of_cims): |
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self.compute_state_transitions_for_a_node(node_indx, self.sample_path.trajectories[0].get_complete_trajectory(), |
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self.transition_filtering[node_indx], |
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self.transition_scalar_index_converter[node_indx], set_of_cims.transition_matrices) |
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@ -99,18 +103,50 @@ g1.init_graph() |
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pe = ParametersEstimator(s1, g1) |
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pe.init_amalgamated_cims_struct() |
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print(pe.amalgamated_cims_struct.get_set_of_cims(0).get_cims_number()) |
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print(pe.amalgamated_cims_struct.get_set_of_cims(1).get_cims_number()) |
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print(pe.amalgamated_cims_struct.get_set_of_cims(2).get_cims_number()) |
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#pe.parameters_estimation_single_trajectory(pe.sample_path.trajectories[0].get_trajectory()) |
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lp = LineProfiler() |
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lp_wrapper = lp(pe.parameters_estimation_single_trajectory) |
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lp_wrapper(pe.sample_path.trajectories.get_trajectory()) |
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"""[[2999.2966 2749.2298 3301.5975] |
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[3797.1737 3187.8345 2939.2009] |
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[3432.224 3062.5402 4530.9028]] |
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[[ 827.6058 838.1515 686.1365] |
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[1426.384 2225.2093 1999.8528] |
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[ 745.3068 733.8129 746.2347] |
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[ 520.8113 690.9502 853.4022] |
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[1590.8609 1853.0021 1554.1874] |
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[ 637.5576 643.8822 654.9506] |
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[ 718.7632 742.2117 998.5844] |
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[1811.984 1598.0304 2547.988 ] |
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[ 770.8503 598.9588 984.3304]] |
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lp_wrapper = lp(pe.compute_state_residence_time_for_all_nodes) |
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lp_wrapper() |
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lp.print_stats() |
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#pe.compute_state_residence_time_for_all_nodes() |
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print(pe.amalgamated_cims_struct.sets_of_cims[0].state_residence_times) |
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[[[14472, 3552, 10920], |
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[12230, 25307, 13077], |
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[ 9707, 14408, 24115]], |
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[[22918, 6426, 16492], |
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[10608, 16072, 5464], |
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[10746, 11213, 21959]], |
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[[23305, 6816, 16489], |
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[ 3792, 19190, 15398], |
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[13718, 18243, 31961]]]) |
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Raveled [14472 3552 10920 12230 25307 13077 9707 14408 24115 22918 6426 16492 |
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10608 16072 5464 10746 11213 21959 23305 6816 16489 3792 19190 15398 |
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13718 18243 31961]""" |
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lp_wrapper = lp(pe.compute_parameters) |
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lp_wrapper() |
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#for variable in pe.amalgamated_cims_struct.sets_of_cims: |
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#for cond in variable.get_cims(): |
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#print(cond.cim) |
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print(pe.amalgamated_cims_struct.get_cims_of_node(1,[2])) |
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lp.print_stats() |
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#pe.parameters_estimation() |
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"""for matrix in pe.amalgamated_cims_struct.get_set_of_cims(1).actual_cims: |
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print(matrix.state_residence_times) |
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print(matrix.state_transition_matrix) |
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matrix.compute_cim_coefficients() |
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print(matrix.cim)""" |
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