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)