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## iof-tools / networkxMiCe / networkx-master / networkx / tests / test_all_random_functions.py @ 5cef0f13

 1 ```from __future__ import print_function ``` ```import random ``` ```from nose import SkipTest ``` ```from nose.tools import assert_equal ``` ```try: ``` ``` import numpy as np ``` ```except ImportError: ``` ``` raise SkipTest('Numpy not available') ``` ```import networkx as nx ``` ```from networkx.algorithms import approximation as approx ``` ```from networkx.algorithms import threshold ``` ```progress = 0 ``` ```# store the random numbers after setting a global seed ``` ```np.random.seed(42) ``` ```np_rv = np.random.rand() ``` ```random.seed(42) ``` ```py_rv = random.random() ``` ```def t(f, *args, **kwds): ``` ``` """call one function and check if global RNG changed""" ``` ``` global progress ``` ``` progress += 1 ``` ``` print(progress, ",", end="") ``` ``` f(*args, **kwds) ``` ``` after_np_rv = np.random.rand() ``` ``` # if np_rv != after_np_rv: ``` ``` # print(np_rv, after_np_rv, "don't match np!") ``` ``` assert_equal(np_rv, after_np_rv) ``` ``` np.random.seed(42) ``` ``` after_py_rv = random.random() ``` ``` # if py_rv != after_py_rv: ``` ``` # print(py_rv, after_py_rv, "don't match py!") ``` ``` assert_equal(py_rv, after_py_rv) ``` ``` random.seed(42) ``` ```def run_all_random_functions(seed): ``` ``` n = 20 ``` ``` m = 10 ``` ``` k = l = 2 ``` ``` s = v = 10 ``` ``` p = q = p1 = p2 = p_in = p_out = 0.4 ``` ``` alpha = radius = theta = 0.75 ``` ``` sizes = (20, 20, 10) ``` ``` colors = [1, 2, 3] ``` ``` G = nx.barbell_graph(12, 20) ``` ``` deg_sequence = in_degree_sequence = w = sequence = aseq = bseq = \ ``` ``` [3, 2, 1, 3, 2, 1, 3, 2, 1, 2, 1, 2, 1] ``` ``` # print("starting...") ``` ``` t(nx.maximal_independent_set, G, seed=seed) ``` ``` t(nx.rich_club_coefficient, G, seed=seed, normalized=False) ``` ``` t(nx.random_reference, G, seed=seed) ``` ``` t(nx.lattice_reference, G, seed=seed) ``` ``` t(nx.sigma, G, 1, 2, seed=seed) ``` ``` t(nx.omega, G, 1, 2, seed=seed) ``` ``` # print("out of smallworld.py") ``` ``` t(nx.double_edge_swap, G, seed=seed) ``` ``` # print("starting connected_double_edge_swap") ``` ``` t(nx.connected_double_edge_swap, nx.complete_graph(9), seed=seed) ``` ``` # print("ending connected_double_edge_swap") ``` ``` t(nx.random_layout, G, seed=seed) ``` ``` t(nx.fruchterman_reingold_layout, G, seed=seed) ``` ``` t(nx.algebraic_connectivity, G, seed=seed) ``` ``` t(nx.fiedler_vector, G, seed=seed) ``` ``` t(nx.spectral_ordering, G, seed=seed) ``` ``` # print('starting average_clustering') ``` ``` t(approx.average_clustering, G, seed=seed) ``` ``` t(nx.betweenness_centrality, G, seed=seed) ``` ``` t(nx.edge_betweenness_centrality, G, seed=seed) ``` ``` t(nx.edge_betweenness, G, seed=seed) ``` ``` t(nx.approximate_current_flow_betweenness_centrality, G, seed=seed) ``` ``` # print("kernighan") ``` ``` t(nx.algorithms.community.kernighan_lin_bisection, G, seed=seed) ``` ``` # nx.algorithms.community.asyn_lpa_communities(G, seed=seed) ``` ``` t(nx.algorithms.tree.greedy_branching, G, seed=seed) ``` ``` t(nx.algorithms.tree.Edmonds, G, seed=seed) ``` ``` # print('done with graph argument functions') ``` ``` t(nx.spectral_graph_forge, G, alpha, seed=seed) ``` ``` t(nx.algorithms.community.asyn_fluidc, G, k, max_iter=1, seed=seed) ``` ``` t(nx.algorithms.connectivity.edge_augmentation.greedy_k_edge_augmentation, ``` ``` G, k, seed=seed) ``` ``` t(nx.algorithms.coloring.strategy_random_sequential, G, colors, seed=seed) ``` ``` cs = ['d', 'i', 'i', 'd', 'd', 'i'] ``` ``` t(threshold.swap_d, cs, seed=seed) ``` ``` t(nx.configuration_model, deg_sequence, seed=seed) ``` ``` t(nx.directed_configuration_model, ``` ``` in_degree_sequence, in_degree_sequence, seed=seed) ``` ``` t(nx.expected_degree_graph, w, seed=seed) ``` ``` t(nx.random_degree_sequence_graph, sequence, seed=seed) ``` ``` joint_degrees = {1: {4: 1}, ``` ``` 2: {2: 2, 3: 2, 4: 2}, ``` ``` 3: {2: 2, 4: 1}, ``` ``` 4: {1: 1, 2: 2, 3: 1}} ``` ``` t(nx.joint_degree_graph, joint_degrees, seed=seed) ``` ``` joint_degree_sequence = [(1, 0), (1, 0), (1, 0), (2, 0), (1, 0), (2, 1), ``` ``` (0, 1), (0, 1)] ``` ``` t(nx.random_clustered_graph, joint_degree_sequence, seed=seed) ``` ``` constructor = [(3, 3, .5), (10, 10, .7)] ``` ``` t(nx.random_shell_graph, constructor, seed=seed) ``` ``` mapping = {1: 0.4, 2: 0.3, 3: 0.3} ``` ``` t(nx.utils.random_weighted_sample, mapping, k, seed=seed) ``` ``` t(nx.utils.weighted_choice, mapping, seed=seed) ``` ``` t(nx.algorithms.bipartite.configuration_model, aseq, bseq, seed=seed) ``` ``` t(nx.algorithms.bipartite.preferential_attachment_graph, ``` ``` aseq, p, seed=seed) ``` ``` def kernel_integral(u, w, z): ``` ``` return (z - w) ``` ``` t(nx.random_kernel_graph, n, kernel_integral, seed=seed) ``` ``` sizes = [75, 75, 300] ``` ``` probs = [[0.25, 0.05, 0.02], ``` ``` [0.05, 0.35, 0.07], ``` ``` [0.02, 0.07, 0.40]] ``` ``` t(nx.stochastic_block_model, sizes, probs, seed=seed) ``` ``` t(nx.random_partition_graph, sizes, p_in, p_out, seed=seed) ``` ``` # print("starting generator functions") ``` ``` t(threshold.random_threshold_sequence, n, p, seed=seed) ``` ``` t(nx.tournament.random_tournament, n, seed=seed) ``` ``` t(nx.relaxed_caveman_graph, l, k, p, seed=seed) ``` ``` t(nx.planted_partition_graph, l, k, p_in, p_out, seed=seed) ``` ``` t(nx.gaussian_random_partition_graph, n, s, v, p_in, p_out, seed=seed) ``` ``` t(nx.gn_graph, n, seed=seed) ``` ``` t(nx.gnr_graph, n, p, seed=seed) ``` ``` t(nx.gnc_graph, n, seed=seed) ``` ``` t(nx.scale_free_graph, n, seed=seed) ``` ``` t(nx.directed.random_uniform_k_out_graph, n, k, seed=seed) ``` ``` t(nx.random_k_out_graph, n, k, alpha, seed=seed) ``` ``` N = 1000 ``` ``` t(nx.partial_duplication_graph, N, n, p, q, seed=seed) ``` ``` t(nx.duplication_divergence_graph, n, p, seed=seed) ``` ``` t(nx.random_geometric_graph, n, radius, seed=seed) ``` ``` t(nx.soft_random_geometric_graph, n, radius, seed=seed) ``` ``` t(nx.geographical_threshold_graph, n, theta, seed=seed) ``` ``` t(nx.waxman_graph, n, seed=seed) ``` ``` t(nx.navigable_small_world_graph, n, seed=seed) ``` ``` t(nx.thresholded_random_geometric_graph, n, radius, theta, seed=seed) ``` ``` t(nx.uniform_random_intersection_graph, n, m, p, seed=seed) ``` ``` t(nx.k_random_intersection_graph, n, m, k, seed=seed) ``` ``` t(nx.general_random_intersection_graph, n, 2, [0.1, 0.5], seed=seed) ``` ``` t(nx.fast_gnp_random_graph, n, p, seed=seed) ``` ``` t(nx.gnp_random_graph, n, p, seed=seed) ``` ``` t(nx.dense_gnm_random_graph, n, m, seed=seed) ``` ``` t(nx.gnm_random_graph, n, m, seed=seed) ``` ``` t(nx.newman_watts_strogatz_graph, n, k, p, seed=seed) ``` ``` t(nx.watts_strogatz_graph, n, k, p, seed=seed) ``` ``` t(nx.connected_watts_strogatz_graph, n, k, p, seed=seed) ``` ``` t(nx.random_regular_graph, 3, n, seed=seed) ``` ``` t(nx.barabasi_albert_graph, n, m, seed=seed) ``` ``` t(nx.extended_barabasi_albert_graph, n, m, p, q, seed=seed) ``` ``` t(nx.powerlaw_cluster_graph, n, m, p, seed=seed) ``` ``` t(nx.random_lobster, n, p1, p2, seed=seed) ``` ``` t(nx.random_powerlaw_tree, n, seed=seed, tries=5000) ``` ``` t(nx.random_powerlaw_tree_sequence, 10, seed=seed, tries=5000) ``` ``` t(nx.random_tree, n, seed=seed) ``` ``` t(nx.utils.powerlaw_sequence, n, seed=seed) ``` ``` t(nx.utils.zipf_rv, 2.3, seed=seed) ``` ``` cdist = [.2, .4, .5, .7, .9, 1.0] ``` ``` t(nx.utils.discrete_sequence, n, cdistribution=cdist, seed=seed) ``` ``` t(nx.algorithms.bipartite.random_graph, n, m, p, seed=seed) ``` ``` t(nx.algorithms.bipartite.gnmk_random_graph, n, m, k, seed=seed) ``` ``` LFR = nx.generators.LFR_benchmark_graph ``` ``` t(LFR, 25, 3, 1.5, 0.1, average_degree=3, min_community=10, ``` ``` seed=seed, max_community=20) ``` ``` # print("done") ``` ```# choose to test an integer seed, or whether a single RNG can be everywhere ``` ```# np_rng = np.random.RandomState(14) ``` ```# seed = np_rng ``` ```# seed = 14 ``` ```# print("NetworkX Version:", nx.__version__) ``` ```def test_rng_interface(): ``` ``` global progress ``` ``` # try different kinds of seeds ``` ``` for seed in [14, np.random.RandomState(14)]: ``` ``` np.random.seed(42) ``` ``` random.seed(42) ``` ``` run_all_random_functions(seed) ``` ``` progress = 0 ``` ``` # check that both global RNGs are unaffected ``` ``` after_np_rv = np.random.rand() ``` ```# if np_rv != after_np_rv: ``` ```# print(np_rv, after_np_rv, "don't match np!") ``` ``` assert_equal(np_rv, after_np_rv) ``` ``` after_py_rv = random.random() ``` ```# if py_rv != after_py_rv: ``` ```# print(py_rv, after_py_rv, "don't match py!") ``` ``` assert_equal(py_rv, after_py_rv) ``` ```# print("\nDone testing seed:", seed) ``` ```# test_rng_interface() ```