4367785c9131771d2dd80b45d4bbb4ca00bd24ac,pyglmnet/pyglmnet.py,GLM,fit,#GLM#Any#Any#,661

Before Change

            if t > 1:
                DL.append(L[-1] - L[-2])
                if np.abs(DL[-1] / L[-1]) < tol:
                    msg = ("\tConverged. Loss function:"
                           " {0:.2f}").format(L[-1])
                    logger.info(msg)
                    msg = ("\tdL/L: {0:.6f}\n".format(DL[-1] / L[-1]))
                    logger.info(msg)
                    break

        // Update the estimated variables
        self.beta0_ = beta[0]
        self.beta_ = beta[1:]
        self.ynull_ = np.mean(y)
        self._loss = L

After Change

                                    reg_lambda, X, y, self.eta,
                                    beta)
                if t > 1:
                    if np.linalg.norm(grad) / np.linalg.norm(beta) < tol / lr:
                        msg = ("\tConverged in {0:d} iterations".format(t))
                        logger.info(msg)
                        break
                beta = beta - self.learning_rate * grad

            elif self.solver == "cdfast":
                beta_old = deepcopy(beta)
                beta, z = \
                    self._cdfast(X, y, z, ActiveSet, beta, reg_lambda)
                if t > 1:
                    if np.linalg.norm(beta - beta_old) / \
                        np.linalg.norm(beta_old) < tol / lr:
                        msg = ("\tConverged in {0:d} iterations".format(t))
                        logger.info(msg)
                        break
            // Apply proximal operator
            beta[1:] = self._prox(beta[1:], reg_lambda * alpha)

            // Update active set
            if self.solver == "cdfast":

Instances