log_loss.py 文件源码

def log_probs(Z, rho):
    """
    compute the probabilities of the logistic loss function in a way that is numerically stable

    see also: http://stackoverflow.com/questions/20085768/
    Parameters
    ----------
    Z           numpy.array containing training data with shape = (n_rows, n_cols)
    rho         numpy.array of coefficients with shape = (n_cols,)

    Returns
    -------
    log_probs   numpy.array of probabilities under the logit model
    """

    scores = Z.dot(rho)
    pos_idx = scores > 0
    log_probs = np.empty_like(scores)
    log_probs[pos_idx]  = 1.0 / (1.0 + np.exp(-scores[pos_idx]))
    log_probs[~pos_idx] = np.exp(scores[~pos_idx]) / (1.0 + np.exp(scores[~pos_idx]))
    return log_probs