IC_analysis_gauss.py

from circuits_IC import *

from IC_aux import compute_steps, find_eps_max
import numpy as np
from argparse import ArgumentParser
import pickle


parser = ArgumentParser()

parser.add_argument('--nqubits', type=int, default=10)
parser.add_argument('--nlayers', type=int, default = 6)
parser.add_argument('--model', type=int, default = 2)
parser.add_argument('--seed', type=int, default=0)


def main(nqubits, nlayers, model, seed):
    # Loading data
    nameX = f'data/q{nqubits}_l{nlayers}_model{model}_X_r{seed}.csv'
    nameGAUSS = f'data/q{nqubits}_l{nlayers}_gauss{model}_y_r{seed}.csv'
    theta = np.loadtxt(nameX)

    y_gauss = np.loadtxt(nameGAUSS)
    data_size = y_gauss.shape[-1]

    # Computation of Information Content for each value of x (E(Var(d f(x))))
    eps_gauss = np.zeros(data_size)
    for i in range(data_size):
        diff_gauss = compute_steps(theta, y_gauss[:, i])
        eps_gauss[i] = find_eps_max(diff_gauss)
        
    # IC for average functions (Var(E(d f(x))))
    eps_gauss_1 = find_eps_max(compute_steps(theta, np.mean(y_gauss, axis=1)))

    # save data
    nameEps = f'data_IC/q{nqubits}_l{nlayers}_model{model}_r{seed}.pkl'
    with open(nameEps, 'rb') as f:
        epsilons = pickle.load(f)
        
        
    epsilons['gauss']= eps_gauss
    epsilons['gauss1']= eps_gauss_1

    with open(nameEps, 'wb') as f:
            pickle.dump(epsilons, f)

if __name__ == '__main__':
    args = vars(parser.parse_args())
    main(**args)