print_result.py

def print_result(result):
    selection = sample_most_likely(result.eigenstate)
    value = portfolio.portfolio_value(selection, mu, sigma, risk_factor, budget, penalty)
    print('Optimal: selection {}, value {:.4f}'.format(selection, value))
    eigenvector = result.eigenstate if isinstance(result.eigenstate, np.ndarray) else result.eigenstate.to_matrix()
    probabilities = np.abs(eigenvector)**2
    i_sorted = reversed(np.argsort(probabilities))
    print('\n----------------- Full result ---------------------')
    print('selection\tvalue\t\tprobability')
    print('---------------------------------------------------')
    states, values, probs = [], [], []
    for i in i_sorted:
        x = index_to_selection(i, num_assets)
        value = portfolio.portfolio_value(x, mu, sigma, risk_factor, budget, penalty)
        probability = probabilities[i]
        print('%10s\t%.4f\t\t%.4f' %(x, value, probability))
        states.append(''.join(str(i) for i in x))
        values.append(value)
        probs.append(probability)
    return selection, states, values, probs