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import os |
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import os |
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import time as tm |
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import time as tm |
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from line_profiler import LineProfiler |
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from line_profiler import LineProfiler |
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from multiprocessing import Process |
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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 network_graph as ng |
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import sample_path as sp |
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import sample_path as sp |
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import amalgamated_cims as acims |
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import amalgamated_cims as acims |
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@ -58,20 +60,73 @@ class ParametersEstimator: |
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self.amalgamated_cims_struct.update_state_residence_time_for_matrix( |
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self.amalgamated_cims_struct.update_state_residence_time_for_matrix( |
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which_node, which_matrix, which_element, time) |
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which_node, which_matrix, which_element, time) |
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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): |
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def which_matrix_to_update(self, current_row, node_indx): |
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return tuple(current_row.take(self.fancy_indexing_structure[node_indx])) |
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#print(type(self.fancy_indexing_structure[node_indx])) |
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return tuple(current_row.take(self.fancy_indexing_structure[node_indx])) |
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#return tuple(ParametersEstimator.taker(current_row, self.fancy_indexing_structure[node_indx])) |
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def parameters_estimation_for_variable_multiple_parents(self, node_indx, times, transitions ,variable_values, parents_values): |
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#print(times) |
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#print(variable_values) |
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#print(parents_values) |
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#print("Starting computing") |
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#t0 = tm.time() |
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for indx, row in enumerate(variable_values): |
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time = times[indx] |
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which_matrix = tuple(parents_values[indx]) # questo è un vettore |
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current_state = variable_values[indx] |
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"""if transitions[indx] == 1: |
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prev_state = variable_values[indx - 1] |
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transition = [node_indx, (prev_state, current_state)] |
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#which_node = transition[0] |
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which_element = transition[1] |
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self.amalgamated_cims_struct.update_state_transition_for_matrix(node_indx, which_matrix, which_element) |
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#which_element = current_state""" |
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self.amalgamated_cims_struct.update_state_residence_time_for_matrix(node_indx, which_matrix, |
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current_state, |
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time) |
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def parameters_estimation_for_variable_single_parent(self, node_indx, times, transitions, variable_values, |
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parents_values): |
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for indx, row in enumerate(variable_values): |
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time = times[indx] |
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which_matrix = parents_values[indx] # Avendo un solo parent questo è uno scalare |
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current_state = variable_values[indx] |
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#which_matrix = ParametersEstimator.taker(parents_values, indx) |
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# print(which_matrix.dtype) |
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if transitions[indx] == 1: |
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prev_state = variable_values[indx - 1] |
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transition = [node_indx, (prev_state, current_state)] |
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which_element = transition[1] |
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self.amalgamated_cims_struct.update_state_transition_for_matrix(node_indx, which_matrix, |
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which_element) |
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which_element = current_state |
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self.amalgamated_cims_struct.update_state_residence_time_for_matrix(node_indx, which_matrix, |
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which_element,time) |
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def parameters_estimation_for_variable_no_parent(self, node_indx, times, transitions,variable_values): |
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for indx, row in enumerate(variable_values): |
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time = times[indx] |
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which_matrix = 0 |
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current_state = variable_values[indx] |
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"""if transitions[indx] == 1: |
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prev_state = variable_values[indx - 1] |
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#current_state = variable_values[indx] |
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transition = [node_indx, (prev_state, current_state)] |
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which_element = transition[1] |
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self.amalgamated_cims_struct.update_state_transition_for_matrix(node_indx, which_matrix, |
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which_element)""" |
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which_element = current_state |
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self.amalgamated_cims_struct.update_state_residence_time_for_matrix(node_indx, which_matrix, |
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which_element, |
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time) |
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#t1 = tm.time() - t0 |
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#print("Elapsed Time ", t1) |
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@ -92,17 +147,46 @@ 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(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(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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print(pe.amalgamated_cims_struct.get_set_of_cims(2).get_cims_number()) |
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print(np.shape(s1.trajectories[0].transitions)[0]) |
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#pe.parameters_estimation_for_variable(0, pe.sample_path.trajectories[0].get_trajectory()[:, 0], |
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# pe.sample_path.trajectories[0].get_trajectory()[:, 1], []) |
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#pe.parameters_estimation_single_trajectory(pe.sample_path.trajectories[0].get_trajectory()) |
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#pe.parameters_estimation_single_trajectory(pe.sample_path.trajectories[0].get_trajectory()) |
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pe.parameters_estimation() |
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#pe.parameters_estimation() |
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"""lp = LineProfiler() |
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lp = LineProfiler() |
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lp.add_function(pe.compute_sufficient_statistics_for_row) # add additional function to profile |
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#lp.add_function(pe.compute_sufficient_statistics_for_row) # add additional function to profile |
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lp_wrapper = lp(pe.parameters_estimation_single_trajectory) |
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#lp_wrapper = lp(pe.parameters_estimation_single_trajectory) |
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#lp_wrapper = lp(pe.parameters_estimation) |
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#lp_wrapper = lp(pe.parameters_estimation) |
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lp_wrapper(pe.sample_path.trajectories[0].get_trajectory()) |
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#lp_wrapper(pe.sample_path.trajectories[0].get_trajectory()) |
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lp.print_stats()""" |
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#lp.print_stats() |
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#lp_wrapper = lp(pe.parameters_estimation_for_variable) |
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#lp_wrapper(2, pe.sample_path.trajectories[0].get_times(), |
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#pe.sample_path.trajectories[0].get_trajectory()[:, 2], |
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#pe.sample_path.trajectories[0].get_trajectory()[:, [0,1]]) |
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"""lp_wrapper = lp(pe.parameters_estimation_for_variable_single_parent) |
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lp_wrapper(1, pe.sample_path.trajectories[0].get_times(), |
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pe.sample_path.trajectories[0].get_trajectory()[:, 1], |
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pe.sample_path.trajectories[0].get_trajectory()[:, 2]) |
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lp.print_stats() |
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#print( pe.sample_path.trajectories[0].get_trajectory()[:, [1,2]]) |
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for matrix in pe.amalgamated_cims_struct.get_set_of_cims(1).actual_cims: |
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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_residence_times) |
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print(matrix.state_transition_matrix) |
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print(matrix.state_transition_matrix) |
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matrix.compute_cim_coefficients() |
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matrix.compute_cim_coefficients() |
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print(matrix.cim) |
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print(matrix.cim)""" |
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"""lp_wrapper = lp(pe.parameters_estimation_for_variable_no_parent) |
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lp_wrapper(0, pe.sample_path.trajectories[0].get_times(), pe.sample_path.trajectories[0].transitions[:, 0], |
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pe.sample_path.trajectories[0].get_trajectory()[:, 0] ) |
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lp.print_stats() |
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lp_wrapper = lp(pe.parameters_estimation_for_variable_single_parent) |
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lp_wrapper(1, pe.sample_path.trajectories[0].get_times(), pe.sample_path.trajectories[0].transitions[:, 1], |
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pe.sample_path.trajectories[0].get_trajectory()[:,1], pe.sample_path.trajectories[0].get_trajectory()[:,2] ) |
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lp.print_stats()""" |
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lp_wrapper = lp(pe.parameters_estimation_for_variable_multiple_parents) |
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lp_wrapper(2, pe.sample_path.trajectories[0].get_times(), pe.sample_path.trajectories[0].transitions[:, 2], |
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pe.sample_path.trajectories[0].get_trajectory()[:,2], pe.sample_path.trajectories[0].get_trajectory()[:, [0,1]] ) |
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lp.print_stats() |