def issue_65_run_sim_for_clusters(dataset, n_clusters, load_from_file = False): from settings import get_fixed_parameters from fitness import evaluate_simulation_results import settings import os settings.PLOT_SAVE_PROB = 1 fit, par, gen, ids = IO.load_pickled_generation_dataframe(dataset) stats, pvals, kmeans = issue_55_calc_cluster_stats(dataset, n_clusters, load_from_file) graph_folder = '/Users/halfdan/Dropbox/Waseda/Research/MarketSimulation/Thesis/data_for_figures/issue_65/' for c, cluster in enumerate(kmeans.cluster_centers_): parameters = get_fixed_parameters() parameters.update(dict(zip(par.columns, map(int, cluster)))) print parameters #plot_name = '%scluster%s'%(graph_folder, c) folder = '%scluster_%s/'%(graph_folder,c) if not os.path.exists(folder): os.makedirs(folder) evaluate_simulation_results(folder, 0, parameters, range(4), autorun=True)
def helper(kwargs): evaluate_simulation_results(**kwargs)