from args_parser import Args, parse
from graph import Graph
from page_rank import PageRank
if __name__ == "__main__":
args = parse("Graph Analyzer by Marcin Knap")
path = args[Args.ProjectName.value]
g = Graph(path)
g.analyze()
page_rank = PageRank(g.dig, path)
#page_rank.check_concur()
page_rank.calc_page_rank(d=0.85)
page_rank.print()
page_rank.save()
def main(argv):
opts = args_parser.parse(argv)
predict(opts)
if parsed_args.notebook_id == None:
# Start off in notebook_selection_scene
import notebook_selection_scene
notebook_selection_scene.init(stdscr)
scene_handler.scene = notebook_selection_scene
else:
# Start off in selected notebook_editing_scene
import notebook_editing_scene
notebook_editing_scene.init(stdscr, parsed_args.notebook_id)
scene_handler.scene = notebook_editing_scene
run = True
while run:
scene_handler.scene.redraw(stdscr)
stdscr.refresh()
c = stdscr.getch()
run = scene_handler.scene.handle_input(stdscr, c)
notebooks.save_notebooks()
if __name__ == "__main__":
args = sys.argv[1:len(sys.argv)]
parsed_args = args_parser.parse(args)
curses.wrapper(tsnb, parsed_args)
def main():
global VERBOSE
h, v, w, mu, mu_val = parse(' '.join(sys.argv[1:]))
if h:
print("- Use -v to activate Verbose")
print("- Use -w to exclude the genetic algorithm from the run")
print("- Use -mu value to set the value of mu in the graph generators; value should be in the range (0, 1)")
return
if v:
VERBOSE = True
else:
VERBOSE = False
if w:
algorithms = [clauset_newman_moore, louvain, reneel]
else:
algorithms = [clauset_newman_moore, louvain, reneel, gcm]
if VERBOSE:
print("Start process")
# small graphs
non_lfr_runs(algorithms)
with open('results/small_c1_test.json', 'w') as fs:
json.dump(RESULTS_S, fs)
# lfr benchmark graphs
sizes = [250, 500, 1000, 1500, 2000, 2500, 3000]
for n in sizes:
G, target_partition, target_communities = genrate_lfr_graph(size=n, mu=mu_val)
nodes_no = n
edges_no = G.number_of_edges()
avg_degree = sum([G.degree[i] for i in range(n)]) / nodes_no
print("========================================================")
print(nodes_no, edges_no, avg_degree)
print("========================================================")
pos = nx.spring_layout(G)
results = [alg(G) for alg in algorithms]
partitions = [r[0] for r in results]
metrics = [(coverage(G, r[1]), performance(G, r[1]),
normalized_mutual_info_score(convert_to_array(target_partition),
convert_to_array(r[0])
))
for r in results]
runtimes = [r[2] for r in results]
for idx in range(len(metrics)):
RESULTS_LFR[NAMES[idx]][n] = {
"coverage": metrics[idx][0],
"performance": metrics[idx][1],
"nmi": metrics[idx][2],
"runtime": runtimes[idx],
}
if VERBOSE:
print(
f"The coverage obtained by {algorithms[idx].__name__} was " + "%.4f" % metrics[idx][0])
print(
f"The performance obtained by {algorithms[idx].__name__} was " + "%.4f" % metrics[idx][1])
print(
f"The NMI score obtained by {algorithms[idx].__name__} was " + "%.4f" % metrics[idx][2])
print("========================================================")
parallel_display(algorithms, partitions, G, pos)
with open('results/lfr_c1_test.json', 'w') as fb:
json.dump(RESULTS_LFR, fb)
def main(argv):
opts = args_parser.parse(argv)
train.train(opts, export=True)
def __init__(self):
self.parser, self.args = args_parser.parse()
self.data = np.empty(shape=(0, self.args.channels),
dtype=self.args.audio_data_type)
self.buffer_max_samples = self.args.sample_rate / self.args.buffer_size
self.buffer_num_samples = 0
def main():
args = parse("WebCrawler by Marcin Knap")
crawler = ParallelCrawler(args[Args.HomePage.value], args[Args.ProjectName.value], args[Args.SaveFlag.value])
crawler.crawl()