def multiple_memory_random_wd(node_list=[10, 20, 50, 100, 200, 500],
weights="random",
positive_cycle_check=None,
verbose=False,
plot=True):
"""
Compute and plots memory benchmarks for algorithm wd with a list of graphs
:param node_list: list of nodes of the graphs on which the benchmark will be run
:param weights: random | positive
:param positive_cycle_check: True or False, to enable or skip the check for null cycle weight
:param verbose: True [False] to enable [disable] verbosity
:param plot: True to plot on a graph the memory usages as the number of nodes grows
:return: dictionary {n_nodes: memory_usage} with memory usages for each different run and n_nodes as key
"""
memory_usage_dictionary = {}
for n in node_list:
memory_usage_dictionary[n] = memory_random_wd(
n_nodes=n,
weights=weights,
positive_cycle_check=positive_cycle_check,
verbose=verbose)
if plot:
plot_dictionary(memory_usage_dictionary)
if verbose:
print(memory_usage_dictionary)
return memory_usage_dictionary
def multiple_memory_random_graph_instantiation(
node_list=[10, 20, 50, 100, 200, 500],
p=0.5,
weights="random",
positive_cycle_check=True,
verbose=False,
plot=True):
"""
Compute and plots time benchmarks for algorithm opt1 with a list of graphs
:param node_list: list of nodes of the graphs on which the benchmark will be run
:param plot: True to plot on a graph the delta values as the number of nodes grows
:param p: edge probability
:param weights: random | positive
:param positive_cycle_check: True or False, to enable or skip the check for null cycle weight
:param verbose: True [False] to enable [disable] verbosity
:return: dictionary {n_nodes: memory_usage} with memory usages for each different run and n_nodes as key
"""
assert weights == "random" or weights == "positive"
memory_usage_dictionary = {}
for n in node_list:
memory_usage_dictionary[n] = random_graph_instantiation_memory(
n_nodes=n,
p=p,
weights=weights,
positive_cycle_check=positive_cycle_check,
verbose=verbose)
if plot:
plot_dictionary(memory_usage_dictionary)
if verbose:
print(memory_usage_dictionary)
return memory_usage_dictionary
def multiple_time_random_wd(node_list=[10, 20, 50, 100, 200, 500],
weights="random",
positive_cycle_check=None,
verbose=False,
plot=True):
"""
Compute and plots time benchmarks for algorithm wd with a list of graphs
:param node_list: list of nodes of the graphs on which the benchmark will be run
:param weights: random | positive
:param positive_cycle_check: True or False, to enable or skip the check for null cycle weight
:param verbose: True [False] to enable [disable] verbosity
:param plot: True to plot on a graph the memory usages as the number of nodes grows
:return:
"""
delta_times = {}
for n in node_list:
delta_times[n] = time_random_wd(
n_nodes=n,
weights=weights,
positive_cycle_check=positive_cycle_check,
verbose=verbose)
if plot:
plot_dictionary(delta_times)
return delta_times