a68ad3bda020d9ade2b33c58a3f1406de41b682c,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

                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) / \

Instances