34b73753f2967bf8a824fa0b869a992c86cdbaba,Orange/classification/logistic.py,LogisticRegressionLearner,fit,#LogisticRegressionLearner#Any#Any#Any#,31

Before Change

        theta, cost, ret = fmin_l_bfgs_b(
            self.cost_grad, theta, args=(X, Y.ravel()), **self.fmin_args)
        if ret["warnflag"] != 0:
            logging.info("L-BFGS failed to converge")

        return LogisticRegressionClassifier(theta, self.normalize, self.mean, self.std)

class LogisticRegressionClassifier(classification.Model):

After Change

            raise ValueError("Logistic regression does not support "
                "multi-label classification")

        if np.isnan(np.sum(X)) or np.isnan(np.sum(Y)):
            raise ValueError("Logistic regression does not support "
                "unknown values")

        self.mean = np.mean(X, axis=0)
        self.std = np.std(X, axis=0)
        if self.normalize:
            X = (X - self.mean) / self.std

Instances