691ab45b091efada84b851e100f93452ab99f11f,pyglmnet/pyglmnet.py,,_logL,#Any#Any#Any#Any#,57

Before Change

        else:
            logL = np.sum(y * np.log(y_hat) + (1 - y) * np.log(1 - y_hat))
    elif distr == "probit":
        logL = np.sum(y * np.log(y_hat) + (1 - y) * np.log(1 - y_hat))
    elif distr == "gamma":
        // see
        // https://www.statistics.ma.tum.de/fileadmin/w00bdb/www/czado/lec8.pdf
        nu = 1.  // shape parameter, exponential for now

After Change

        logL = np.sum(y * np.log(y_hat + eps) - y_hat)
    elif distr == "gaussian":
        logL = -0.5 * np.sum((y - y_hat)**2)
    elif distr in ["binomial", "probit"]:

        // prevents underflow
        if z is not None:

Instances