params = setup_params.setup_params() pool = multiprocessing.Pool(8) adjlist_file_name = "../synthetic_data.ajdlist0" feature_file_name = "../synthetic_data.feature0" num_graphs = 1 training_data_list = [{}] * num_graphs for i in range(num_graphs): training_data_list[i]["num_features"] = 2 training_data_list[i]["source"] = 0 training_data_list[i]["positives"] = range(200) training_data_list[i]["negatives"] = range(9800, 10000) training_data_list[i]["num_nodes"] = 10000 (training_data_list[i]["edge_ij"], training_data_list[i]["feature_stack"]) = problem_setup.get_edge_ij_and_feature_stack(adjlist_file_name, feature_file_name, training_data_list[i]["num_features"]) training_data_list[i]["diff_generating_mat"] = grad_one_source.build_diff_generating_mat(range(200), range(200, 9800), 10000) w0 = numpy.random.randn(training_data_list[0]["num_features"], 1) print(w0.shape) p_warm_start_list = [numpy.ones((10000, 1)) / 10000.0] * num_graphs p_grad_warm_start_list = [numpy.zeros((10000, 2))] * num_graphs #w_1 = numpy.copy(w0) #w_1[0] -= 1e-5 #w_2 = numpy.copy(w0) #w_2[0] += 1e-5 # #cost_1 = compute_cost.compute_cost(w_1, training_data_list, p_warm_start_list, p_grad_warm_start_list, params, pool) #grad_1 = compute_grad.compute_grad(w_1, training_data_list, p_warm_start_list, p_grad_warm_start_list, params, pool)
sigma_squared = float(sys.argv[2]) #num_graphs = 50 num_nodes = 2000 params = setup_params.setup_params() training_data_list = [] w_gt = numpy.array([[1.0], [-1.0]]) i = 0 indices = [] training_file = open("synthetic_trainers_mini.txt", "r") for line in training_file: index = int(line.rstrip("\n")) indices.append(index) (edge_ij, feature_stack, G) = problem_setup.get_edge_ij_and_feature_stack("../synthetic_data/synthetic_data.ajdlist%d"%(index), "../synthetic_data/synthetic_data.antisymmetric_feature%d"%(index), 2, num_nodes = num_nodes) source = index % 3 candidates = list(set(G.nodes()) - set(G.neighbors(source)) - set([source])) training_data_list.append({}) training_data_list[i]["num_features"] = 2 training_data_list[i]["source"] = source training_data_list[i]["candidates"] = candidates training_data_list[i]["edge_ij"] = edge_ij training_data_list[i]["feature_stack"] = feature_stack training_data_list[i]["num_nodes"] = num_nodes (training_data_list[i]["training_positives"], training_data_list[i]["training_negatives"]) = predict_one_source.predict_one_source(w_gt, training_data_list[i], params) problem_setup.write_spn_list("../synthetic_data/synthetic_data.spn%d"%(index), [(source, training_data_list[i]["training_positives"], training_data_list[i]["training_negatives"])]) i += 1 #x = range(num_graphs)