def run_optimal():
# Returns the current state of the agent
current_state = Monitor.monitor(None, agent)
# See what moves are available from the current positions
adj_states = Analyze.analyze(current_state)
# Get the next action
desired_action = Planning.optimalPolicy(adj_states, knowledge)
# Apply Environmental uncertainty
actual_action = knowledge.action_func(desired_action)
# Execute onto the environment
next_state = Execution.execute(adj_states + [current_state], actual_action)
agent.update(next_state, knowledge.state_value_dict[next_state])
def run(self):
# First the selected dataset needs to be loaded
dataset_name = self.data_selection.get()
if dataset_name == "Iris":
print("Selecting Iris!")
data = load_data.load_iris()
elif dataset_name == "Seeds":
data = load_data.load_seeds()
elif dataset_name == "Glass":
data = load_data.load_glass()
elif dataset_name == "Banknote":
data = load_data.load_banknote()
elif dataset_name == "Customers":
data = load_data.load_cust_data()
# Now run the selected clustering algorithm
score_list = [score_funcs.cluster_sse]
if self.alg_selection.get() == "K-Means":
Analyze.analyze(
data, dataset_name, 10,
self.build_kMeans_func(*kMeans_params[dataset_name]),
score_list)
elif self.alg_selection.get() == "DBSCAN":
Analyze.analyze(
data, dataset_name, 10,
self.build_dbscan_func(*dbscan_params[dataset_name]),
score_list)
elif self.alg_selection.get() == "Competitive Learning":
Analyze.analyze(data, dataset_name, 10,
self.build_cl_func(*cl_params[dataset_name]),
score_list)
elif self.alg_selection.get() == "PSO":
Analyze.analyze(data, dataset_name, 10,
self.build_pso_function(*pso_params[dataset_name]),
score_list)
elif self.alg_selection.get() == "ACO":
Analyze.analyze(data, dataset_name, 10,
self.build_aco_func(*aco_params[dataset_name]),
score_list)
if args.analyze is not None:
print('run...')
json_path = args.analyze[0]
# main_directory = os.path.dirname(os.path.realpath(__file__))
output_name = '../results/' + args.analyze[1]
f = open(json_path)
samples_names = json.loads(f.readline().strip('\n'))['samples']
stats_params_in_json = json.loads(f.readline().strip('\n'))['stats_params']
html = File_Output.HTML(output_name + ".html", samples_names, stats_params_in_json, analyze_params, misc_params)
tsv = File_Output.TSV(output_name + ".tsv", samples_names)
sites = Analyze.json_to_site(f)
my_sites = []
for site in sites:
Analyze.analyze(site)
if site.kind == '' and len(site.bulk) > 0 and site.bulk['SUM'] >= analyze_params["bulk_dp_interval"][0] and site.bulk['SUM'] <= analyze_params["bulk_dp_interval"][1]:
bulk_a1_ratio = float(site.bulk[site.alts['A1']])/site.bulk['SUM']
if bulk_a1_ratio <= (1 - stats_params_in_json["bulk_ref_limit"]):
nr_conflicting = 0
nr_c3_conflicting = 0
nr_a1 = 0
for sample in site.samples.values():
if sample.info == 'CONFLICT':
nr_conflicting += 1
elif sample.info == 'C3-CONFLICT':
nr_c3_conflicting += 1
elif (sample.info == 'HET-C1' or sample.info == 'HET-C2' or sample.info == 'H**O-A1'):
nr_a1 += 1
if nr_a1 >= analyze_params["a1_lower_limit"] and nr_conflicting <= analyze_params["conflicting_upper_limit"] and nr_c3_conflicting <= analyze_params["c3_conflicting_upper_limit"]:
