import differentialevolution_par_scinet as df import numpy as np version_num = 5 c_mat = np.load("Anat Data/c_mat{}g.npy".format(version_num)) tract_mat = np.load("Anat Data/tract_mat{}g.npy".format(version_num)) w_mat = np.load("Anat Data/w_mat{}g.npy".format(version_num)) ue_array = np.load("Anat Data/ue_array{}g.npy".format(version_num)) #%% TARGET DATA nodes = 10 num_dim = int((((nodes**2) - nodes) / 2)) skip = 200 targ_data = hf.plot_cor_mat(ue_array, nodes, skip) np.fill_diagonal(targ_data, 0) #%% DIFF EVOLUTION PARAMS bounds = [] lower = 0 upper = 1 for n in range(num_dim): bounds.append((lower, upper)) evol_params = { 'strategy': 'best2bin', 'maxiter': 400, 'popsize': 15, 'tol': 0.5, 'mut': 0.5,
'init': 'latinhypercube', 'atol': 0, 'mse': 0, 'bound_l': 0, 'bound_u': 4000 } # SIGNAL PROPERTIES _Dt = wc_params['dt'] _alpha = 10 _dt = _Dt / _alpha # time step is 1ms: _dt = 0.001 fs = 1 / _dt # Sampling rate, or number of measurements per second # WEIGHTS AND TRACT # sick kids catmatrix = np.load("Anat Data/catmatrix{}.npy".format(file_num)) tract_mat = np.load("Anat Data/tract_mat{}_r.npy".format(file_num)) # TARGET DATA all_ts = [] for i in range(10): all_ts.append(catmatrix[i, 3, :]) skip = 200 targ_data = hf.plot_cor_mat(np.array(all_ts), nodes, skip) np.fill_diagonal(targ_data, 0) # ARGS FOR RESIDUAL FXN IN DIFF EVOLUTION ALROGITHM args = (wc_params, targ_data, nodes, tract_mat, skip, wc_seed)