block_size = args.block_size engine = ccc.get_CrossCatClient('hadoop', seed = inf_seed) if filename is not None: # Load the data from table and sub-sample entities to max_rows T, M_r, M_c = du.read_model_data_from_csv(filename, max_rows, gen_seed) truth_flag = 0 else: T, M_r, M_c, data_inverse_permutation_indices = \ du.gen_factorial_data_objects(gen_seed, num_clusters, num_cols, max_rows, num_views, max_mean=100, max_std=1, send_data_inverse_permutation_indices=True) view_assignment_truth, X_D_truth = ctu.truth_from_permute_indices(data_inverse_permutation_indices, max_rows,num_cols,num_views, num_clusters) truth_flag = 1 num_rows = len(T) num_cols = len(T[0]) ari_table = [] ari_views = [] print 'Initializing ...' # Call Initialize and Analyze M_c, M_r, X_L_list, X_D_list = engine.initialize(M_c, M_r, T, n_chains = numChains) if truth_flag: tmp_ari_table, tmp_ari_views = ctu.multi_chain_ARI(X_L_list,X_D_list, view_assignment_truth, X_D_truth) ari_table.append(tmp_ari_table)
num_cols = 32 num_rows = 400 num_views = 2 n_steps = 1 n_times = 5 n_chains = 3 n_test = 100 CT_KERNEL = 1 get_next_seed = make_get_next_seed(gen_seed) # generate some data T, M_r, M_c, data_inverse_permutation_indices = du.gen_factorial_data_objects( get_next_seed(), num_clusters, num_cols, num_rows, num_views, max_mean=100, max_std=1, send_data_inverse_permutation_indices=True) view_assignment_truth, X_D_truth = ctu.truth_from_permute_indices( data_inverse_permutation_indices, num_rows, num_cols, num_views, num_clusters) # run some tests engine = LocalEngine() 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)) 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, CT_KERNEL=CT_KERNEL) multi_state_ARIs.append(