def add_data(data, idle_data, algorithm, configuration=None):
labelsUpdated = False
if data: # not empty
gat_data.append(data)
if configuration is not None:
labels.append(
plotutils.translate_algorithm_name(
get_configuration_plot_name(algorithm, configuration)))
else:
labels.append(plotutils.translate_algorithm_name(algorithm))
labelsUpdated = True
if idle_data:
idle_planning_data.append(idle_data)
return labelsUpdated
def add_data(generated_subdata,
expanded_subdata,
algorithm,
configuration=None):
labelsUpdated = False
if generated_subdata: # not empty
generated_data.append(generated_subdata)
if configuration is not None:
labels.append(
plotutils.translate_algorithm_name(
get_configuration_plot_name(algorithm, configuration)))
else:
labels.append(plotutils.translate_algorithm_name(algorithm))
labelsUpdated = True
if expanded_subdata:
expanded_data.append(expanded_subdata)
return labelsUpdated
def add_data(data, idle_data, algorithm, configuration=None):
""" Adds the retrieved data to the collections to be returned.
:param data: GAT data for algorithm
:param idle_data: idle planning time data for algorithm
:param algorithm: algorithm for which the data belongs to
:param configuration: the configuration of the algorithm
:return: True if the labels list was updated with new data; False if the labels list was unchanged
"""
labels_updated = False
if data: # not empty
gat_data.append(data)
if configuration is not None:
labels.append(
plotutils.translate_algorithm_name(get_configuration_plot_name(algorithm, configuration)))
else:
labels.append(plotutils.translate_algorithm_name(algorithm))
labels_updated = True
if idle_data:
idle_planning_data.append(idle_data)
return labels_updated
def add_data(generated_subdata, expanded_subdata, algorithm, configuration=None):
""" Adds the retrieved data to the collections to be returned.
:param generated_subdata: generated nodes data for algorithm
:param expanded_subdata: expanded nodes data for algorithm
:param algorithm: algorithm for which the data belongs to
:param configuration: the configuration of the algorithm
:return: True if the labels list was updated with new data; False if the labels list was unchanged
"""
labels_updated = False
if generated_subdata: # not empty
generated_data.append(generated_subdata)
if configuration is not None:
labels.append(
plotutils.translate_algorithm_name(get_configuration_plot_name(algorithm, configuration)))
else:
labels.append(plotutils.translate_algorithm_name(algorithm))
labels_updated = True
if expanded_subdata:
expanded_data.append(expanded_subdata)
return labels_updated
def add_data(data, idle_data, algorithm, configuration=None):
""" Adds the retrieved data to the collections to be returned.
:param data: GAT data for algorithm
:param idle_data: idle planning time data for algorithm
:param algorithm: algorithm for which the data belongs to
:param configuration: the configuration of the algorithm
:return: True if the labels list was updated with new data; False if the labels list was unchanged
"""
labels_updated = False
if data: # not empty
gat_data.append(data)
if configuration is not None:
labels.append(
plotutils.translate_algorithm_name(
get_configuration_plot_name(algorithm, configuration)))
else:
labels.append(plotutils.translate_algorithm_name(algorithm))
labels_updated = True
if idle_data:
idle_planning_data.append(idle_data)
return labels_updated
def add_data(generated_subdata,
expanded_subdata,
algorithm,
configuration=None):
""" Adds the retrieved data to the collections to be returned.
:param generated_subdata: generated nodes data for algorithm
:param expanded_subdata: expanded nodes data for algorithm
:param algorithm: algorithm for which the data belongs to
:param configuration: the configuration of the algorithm
:return: True if the labels list was updated with new data; False if the labels list was unchanged
"""
labels_updated = False
if generated_subdata: # not empty
generated_data.append(generated_subdata)
if configuration is not None:
labels.append(
plotutils.translate_algorithm_name(
get_configuration_plot_name(algorithm, configuration)))
else:
labels.append(plotutils.translate_algorithm_name(algorithm))
labels_updated = True
if expanded_subdata:
expanded_data.append(expanded_subdata)
return labels_updated
data = times.values()
# print(data
# datadata = np.array(data)
# print(data
# print(datadata
# print(datadata.reshape(len(data[0]), len(data))
plt.ylabel("Goal Achievement Time (ms)")
labels = []
if domain_groups:
assert num_domains == 1
plt.xlabel("Algorithm")
plt.title(plotutils.translate_domain_name(next(iter(domain_counts.keys()))))
for key in times.keys(): # Assumes same order will be plotted
labels.append(plotutils.translate_algorithm_name(key[plotutils.Results.ALGORITHM]))
else:
assert num_algorithms == 1
plt.xlabel("Domain")
plt.title(plotutils.translate_algorithm_name(algorithm_counts.keys()[0]))
for key in times.keys():
labels.append(plotutils.translate_domain_name(key[plotutils.Results.DOMAIN]))
# print(len(data)
x = np.arange(1, len(times) + 1)
y = data
print(x)
print(y)
med, confidence_intervals_low, confidence_intervals_high = plotutils.median_confidence_intervals(y)
data = times.values()
# print(data
# datadata = np.array(data)
# print(data
# print(datadata
# print(datadata.reshape(len(data[0]), len(data))
plt.ylabel("Goal Achievement Time (ms)")
labels = []
if domain_groups:
assert num_domains == 1
plt.xlabel("Algorithm")
plt.title(plotutils.translate_domain_name(next(iter(domain_counts.keys()))))
for key in times.keys(): # Assumes same order will be plotted
labels.append(plotutils.translate_algorithm_name(key[plotutils.Results.ALGORITHM]))
else:
assert num_algorithms == 1
plt.xlabel("Domain")
plt.title(plotutils.translate_algorithm_name(algorithm_counts.keys()[0]))
for key in times.keys():
labels.append(plotutils.translate_domain_name(key[plotutils.Results.DOMAIN]))
# print(len(data)
x = np.arange(1, len(times) + 1)
y = data
print(x)
print(y)
med, confidence_intervals_low, confidence_intervals_high = plotutils.median_confidence_intervals(y)
