Esempio n. 1
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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])
Esempio n. 2
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    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)
Esempio n. 3
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    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"]: