Esempio n. 1
0
def count_ps_from_beta(N, beta, verbose=False, A=4, log_cols=None, entropies=None):
    iterator = (lambda x:tqdm(x, total=partitions(N, A))) if verbose else (lambda x:x)
    if log_cols is None or entropies is None:
        log_ws = np.array([log_counts_to_cols(count, A=A) + (-beta*entropy_from_counts(count))
                           for count in iterator(enumerate_counts_iter(N, A=A))])
    else:
        log_ws = log_cols + -beta*entropies
    return np.exp(np_log_normalize(log_ws))
Esempio n. 2
0
def count_ps_from_beta(N,
                       beta,
                       verbose=False,
                       A=4,
                       log_cols=None,
                       entropies=None):
    iterator = (lambda x: tqdm(x, total=partitions(N, A))) if verbose else (
        lambda x: x)
    if log_cols is None or entropies is None:
        log_ws = np.array([
            log_counts_to_cols(count, A=A) +
            (-beta * entropy_from_counts(count))
            for count in iterator(enumerate_counts_iter(N, A=A))
        ])
    else:
        log_ws = log_cols + -beta * entropies
    return np.exp(np_log_normalize(log_ws))
Esempio n. 3
0
 def f2(beta):
     log_phats = np_log_normalize(log_cols + -beta * entropies)
     expected_entropy = np.exp(log_phats).dot(entropies)
     return log2(A) - expected_entropy - desired_ic_per_col
Esempio n. 4
0
 def f2(beta):
     log_phats = np_log_normalize(log_cols + -beta*entropies)
     expected_entropy = np.exp(log_phats).dot(entropies)
     return log2(A) - expected_entropy - desired_ic_per_col