a68ad3bda020d9ade2b33c58a3f1406de41b682c,pyglmnet/pyglmnet.py,GLM,fit,#GLM#Any#Any#,661

Before Change

            z = beta[0] + np.dot(X, beta[1:])       // cache z

        // Initialize loss accumulators
        L, DL = list(), list()
        for t in range(0, self.max_iter):
            if self.solver == "batch-gradient":
                grad = _grad_L2loss(self.distr,
                                    alpha, self.Tau,

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) / \
                        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