import sys sys.path.append("../") import numpy as np from scipy.stats import pearsonr from utils.parameters import PSlice, ParameterSpace, ParameterSpacePoint space = ParameterSpace(PSlice(300), PSlice(6), PSlice(2.), PSlice(4), PSlice(5.), PSlice(.1, 1.0, .1), PSlice(-30., 5., 5.), PSlice(120), PSlice(30), PSlice(10,80,10), PSlice(10), PSlice(20), PSlice(200), PSlice(40), PSlice(0.), PSlice(0), PSlice(5.), PSlice(2.)) space.load_analysis_results() mi_arr = space.get_nontrivial_subspace(('training_size', 40))._get_attribute_array('point_mi_qe') nm_arr = space.get_nontrivial_subspace(('training_size', 40))._get_attribute_array('new_measure') print(pearsonr(np.delete(mi_arr, 5, axis=0).flat, np.delete(nm_arr, 5, axis=0).flat))
multineuron_metric_mixing = psl(0.) linkage_method = psl(1) # 0: ward, 1: kmeans tau = psl(5) dt = psl(2) space = ParameterSpace(n_grc_dend, connectivity_rule, input_spatial_correlation_scale, active_mf_fraction, gaba_scale, dta, inh_cond_scaling, exc_cond_scaling, modulation_frequency, stim_rate_mu, stim_rate_sigma, noise_rate_mu, noise_rate_sigma, n_stim_patterns, n_trials, sim_duration, ana_duration, training_size, multineuron_metric_mixing, linkage_method, tau, dt) space.load_analysis_results() if plot_mi_heatmap: