single_state_ARIs.append(ctu.get_column_ARI(X_L, view_assignment_truth))
single_state_mean_test_lls.append(
ctu.calc_mean_test_log_likelihood(M_c, T, X_L, X_D, T_test)
)
for time_i in range(n_times):
X_L, X_D = engine.analyze(M_c, T, X_L, X_D, n_steps=n_steps)
single_state_ARIs.append(ctu.get_column_ARI(X_L, view_assignment_truth))
single_state_mean_test_lls.append(
ctu.calc_mean_test_log_likelihood(M_c, T, X_L, X_D, T_test)
)
# multistate test
multi_state_ARIs = []
multi_state_mean_test_lls = []
X_L_list, X_D_list = engine.initialize(M_c, M_r, T, n_chains=n_chains)
multi_state_ARIs.append(ctu.get_column_ARIs(X_L_list, view_assignment_truth))
multi_state_mean_test_lls.append(ctu.calc_mean_test_log_likelihoods(M_c, T,
X_L_list, X_D_list, T_test))
for time_i in range(n_times):
X_L_list, X_D_list = engine.analyze(M_c, T, X_L_list, X_D_list, n_steps=n_steps)
multi_state_ARIs.append(ctu.get_column_ARIs(X_L_list, view_assignment_truth))
multi_state_mean_test_lls.append(ctu.calc_mean_test_log_likelihoods(M_c, T,
X_L_list, X_D_list, T_test))
# print results
print('generative_mean_test_log_likelihood')
print(generative_mean_test_log_likelihood)
#
print('single_state_mean_test_lls:')
print(single_state_mean_test_lls)
#
print('single_state_ARIs:')
print(single_state_ARIs)
single_state_ARIs.append(ctu.get_column_ARI(X_L,
view_assignment_truth))
single_state_mean_test_lls.append(
ctu.calc_mean_test_log_likelihood(M_c, T, X_L, X_D, T_test))
# multistate test
multi_state_ARIs = []
multi_state_mean_test_lls = []
X_L_list, X_D_list = engine.initialize(M_c,
M_r,
T,
get_next_seed(),
n_chains=n_chains)
multi_state_ARIs.append(
ctu.get_column_ARIs(X_L_list, view_assignment_truth))
multi_state_mean_test_lls.append(
ctu.calc_mean_test_log_likelihoods(M_c, T, X_L_list, X_D_list, T_test))
for time_i in range(n_times):
X_L_list, X_D_list = engine.analyze(M_c,
T,
X_L_list,
X_D_list,
get_next_seed(),
n_steps=n_steps)
multi_state_ARIs.append(
ctu.get_column_ARIs(X_L_list, view_assignment_truth))
multi_state_mean_test_lls.append(
ctu.calc_mean_test_log_likelihoods(M_c, T, X_L_list, X_D_list,
T_test))
# print results
print('generative_mean_test_log_likelihood')