def simulate():
random.seed(1234)
#num_neighbors_options = [3, 10, 20]
num_neighbors_options = [3, 10, 20]
# reverse order because of plot_error_history
#num_iterations_options = [1000, 200, 20]
num_iterations_options = [1000]
runs_per_config = 3
total_runs = len(num_neighbors_options) * len(num_iterations_options) * runs_per_config
runs_done = 0
rerr = [None]*(len(num_neighbors_options)*len(num_iterations_options))
error_history = [None]*(len(num_neighbors_options)*len(num_iterations_options))
conf = [None]*(len(num_neighbors_options)*len(num_iterations_options))
curr_conf = 0
move_length_history = [None]*(len(num_neighbors_options)*len(num_iterations_options))
for num_neighbors in num_neighbors_options:
for num_iterations in num_iterations_options:
c = Configuration(num_nodes, num_neighbors, num_iterations)
conf_rerr = [0]*num_nodes
conf_error_history = [0]*num_iterations
conf_move_len = [0]*num_iterations
conf[curr_conf] = "K: {}, i: {}".format(num_neighbors, num_iterations)
update_runs_completed(runs_done, total_runs)
for run in xrange(runs_per_config):
v = Vivaldi(init_graph, c)
v.run()
predicted = v.getRTTGraph()
temp_rerr = v.getRelativeError(predicted)
listadd(conf_rerr, temp_rerr)
listadd(conf_error_history, v.error_history)
listadd(conf_move_len, v.move_len_history)
runs_done += 1
update_runs_completed(runs_done, total_runs)
listdiv(conf_rerr, runs_per_config)
listdiv(conf_error_history, runs_per_config)
listdiv(conf_move_len, runs_per_config)
rerr[curr_conf] = conf_rerr
error_history[curr_conf] = conf_error_history
move_length_history[curr_conf] = conf_move_len
curr_conf += 1
finish_runs_completed(runs_per_config)
return {
'rerr' : rerr,
'error_history': error_history,
'move_length_history': move_length_history
}, conf
infile.close()
# These parameters are part of the Configuration.
# Modify them according to your need.
num_neighbors = 20
num_iterations = 200
# build a configuration and load the matrix into the graph
c = Configuration(num_nodes, num_neighbors, num_iterations)
init_graph = buildgraph(rows)
print "Running Vivaldi on a %d size matrix" % num_nodes
print "Num neighbors = %d " % num_neighbors
print "Num iterations = %d " % num_iterations
v = Vivaldi(init_graph, c)
v.run()
# get predicted graph
predicted = v.getRTTGraph()
# get relative errors (one value per node)
rerr = v.getRelativeError(predicted)
# average of node movements
average = sum(v.movements) / len(v.movements)
# variance of node movements
variance = sum((average - value) ** 2 for value in v.movements) / len(v.movements)
print "Position variation:"
print "Maximum: %.2f"%max(v.movements)
num_neighbors = 10 num_iterations = 200 num_dimension = 3 # build a configuration and load the matrix into the graph c = Configuration(num_nodes, num_neighbors, num_iterations, d=num_dimension) init_graph = buildgraph(rows) print "Running Vivaldi on a %d size matrix" % num_nodes print "Num dimensions = %d " % c.num_dimension print "Num neighbors = %d " % num_neighbors print "Num iterations = %d " % num_iterations # run vivaldi: here, only the CDF of the relative error is retrieved. # Modify to retrieve what's requested. v = Vivaldi(init_graph, c) v.run() predicted = v.getRTTGraph() rerr = v.getRelativeError(predicted) table([100*x for x in rerr], "RELATIVE ERROR (%)") # print predicted # print rerr # for i in v.positions: # print "%4d %4d" % (int(i[0]), int(i[1])) # Example (using pylab plotting function): # x = [i[0] for i in v.positions] # y = [i[1] for i in v.positions]
