def compute_log_za(b_list, pa_bias, marginalize_odd=True):
    log_za = 0.

    for i, b in enumerate(b_list):
        if i == (not marginalize_odd):
            log_za += numpy.sum(numpy.log(1 + numpy.exp(pa_bias)))
        else:
            log_za += numpy.log(2) * b.get_value().shape[0]

    return log_za
ecdf: x below all obs give 0.0
dataset/ETHPy150Open lisa-lab/pylearn2/pylearn2/scripts/dbm/dbm_metrics.py/compute_log_za--crossover with--dataset/ETHPy150Open QuantEcon/QuantEcon.py/quantecon/tests/test_ecdf.py/TestECDF.test_call_low