unimib-thesis-mad
/
PyCTBN
Archived
1
0
Fork 0
Code Releases Activity

Representing cims as flat arrays

Browse Source
parallel_struct_est
philpMartin 4 years ago
parent 0ebd040dfa
commit 3373f0f76e
2 changed files with 65 additions and 5 deletions
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Show Stats Download Patch File Download Diff File
  1. 3
      main_package/classes/conditional_intensity_matrix.py
  2. 67
      main_package/classes/parameters_estimator.py

3
main_package/classes/conditional_intensity_matrix.py
Unescape View File

@ -10,7 +10,8 @@ class ConditionalIntensityMatrix:
def update_state_transition_count(self, element_indx):
#print(element_indx)
self.state_transition_matrix[element_indx[0]][element_indx[1]] += 1
#self.state_transition_matrix[element_indx[0]][element_indx[1]] += 1
self.state_transition_matrix[element_indx] += 1
def update_state_residence_time_for_state(self, state, time):
#print("Time updating In state", state, time)

67
main_package/classes/parameters_estimator.py
Unescape View File

@ -76,13 +76,13 @@ class ParametersEstimator:
time = times[indx]
which_matrix = tuple(parents_values[indx]) # questo è un vettore
current_state = variable_values[indx]
"""if transitions[indx] == 1:
if transitions[indx] == 1:
prev_state = variable_values[indx - 1]
transition = [node_indx, (prev_state, current_state)]
#which_node = transition[0]
which_element = transition[1]
self.amalgamated_cims_struct.update_state_transition_for_matrix(node_indx, which_matrix, which_element)
#which_element = current_state"""
#which_element = current_state
self.amalgamated_cims_struct.update_state_residence_time_for_matrix(node_indx, which_matrix,
current_state,
time)
@ -125,6 +125,59 @@ class ParametersEstimator:
which_element,
time)
def parameters_estimation_for_variable_no_parent_in_place(self, node_indx, times, transitions, variable_values):
state_trans_matrix = np.zeros(shape=(3,3), dtype=np.int)
state_res_time_array = np.zeros(shape=(3), dtype=np.float)
for indx, row in enumerate(variable_values):
time = times[indx]
#which_matrix = 0
current_state = variable_values[indx]
if transitions[indx] == 1:
prev_state = variable_values[indx - 1]
#current_state = variable_values[indx]
transition = [node_indx, (prev_state, current_state)]
which_element = transition[1]
#self.amalgamated_cims_struct.update_state_transition_for_matrix(node_indx, which_matrix,
#which_element)
state_trans_matrix[which_element] += 1
which_element = current_state
#self.amalgamated_cims_struct.update_state_residence_time_for_matrix(node_indx, which_matrix,
#which_element,
#time)
state_res_time_array[which_element] += time
def parameters_estimation_for_variable_single_parent_in_place(self, node_indx, times, transitions, variable_values,
parents_values,values_tuple):
state_res_time_dim = values_tuple[1:]
state_trans_matricies = np.zeros(shape=27, dtype=np.int)
state_res_time_array = np.zeros(shape=9, dtype=np.float)
state_transition_indx = np.array(values_tuple, dtype=np.int)
for indx, row in enumerate(variable_values):
time = times[indx]
#which_matrix = np.ravel_multi_index(parents_values[indx], ) # Avendo un solo parent questo è uno scalare
#current_state = variable_values[indx]
#which_matrix = ParametersEstimator.taker(parents_values, indx)
state_transition_indx[0] = parents_values[indx]
state_transition_indx[1] = variable_values[indx]
# print(which_matrix.dtype)
if transitions[indx] == 1:
state_transition_indx[2] = variable_values[indx - 1]
#transition = [node_indx, (prev_state, current_state)]
#which_element = transition[1]
#self.amalgamated_cims_struct.update_state_transition_for_matrix(node_indx, which_matrix,
#which_element)
scalar_indx = np.ravel_multi_index(state_transition_indx, values_tuple)
print("State Transition", scalar_indx)
state_trans_matricies[scalar_indx] += 1
scalar_indx = np.ravel_multi_index(state_transition_indx[:-1], state_res_time_dim)
print("Res Time",scalar_indx)
state_res_time_array[scalar_indx] += time
#which_element = current_state
#self.amalgamated_cims_struct.update_state_residence_time_for_matrix(node_indx, which_matrix,
#which_element,time)
#t1 = tm.time() - t0
#print("Elapsed Time ", t1)
@ -178,15 +231,21 @@ for matrix in pe.amalgamated_cims_struct.get_set_of_cims(1).actual_cims:
matrix.compute_cim_coefficients()
print(matrix.cim)"""
"""lp_wrapper = lp(pe.parameters_estimation_for_variable_no_parent)
"""lp_wrapper = lp(pe.parameters_estimation_for_variable_no_parent_in_place)
lp_wrapper(0, pe.sample_path.trajectories[0].get_times(), pe.sample_path.trajectories[0].transitions[:, 0],
pe.sample_path.trajectories[0].get_trajectory()[:, 0] )
lp.print_stats()
lp_wrapper = lp(pe.parameters_estimation_for_variable_single_parent)
lp_wrapper(1, pe.sample_path.trajectories[0].get_times(), pe.sample_path.trajectories[0].transitions[:, 1],
pe.sample_path.trajectories[0].get_trajectory()[:,1], pe.sample_path.trajectories[0].get_trajectory()[:,2] )
lp.print_stats()"""
lp.print_stats()
lp_wrapper = lp(pe.parameters_estimation_for_variable_multiple_parents)
lp_wrapper(2, pe.sample_path.trajectories[0].get_times(), pe.sample_path.trajectories[0].transitions[:, 2],
pe.sample_path.trajectories[0].get_trajectory()[:,2], pe.sample_path.trajectories[0].get_trajectory()[:, [0,1]] )
lp.print_stats()"""
lp_wrapper = lp(pe.parameters_estimation_for_variable_single_parent_in_place)
lp_wrapper(1, pe.sample_path.trajectories[0].get_times(), pe.sample_path.trajectories[0].transitions[:, 1],
pe.sample_path.trajectories[0].get_trajectory()[:,1], pe.sample_path.trajectories[0].get_trajectory()[:,2], (3,3,3) )
lp.print_stats()