8dc523d9bd92011caca608833221916d9e1b8130,Orange/classification/logistic_regression.py,LogisticRegressionLearner,fit,#LogisticRegressionLearner#Any#Any#Any#,48

Before Change

        theta = np.zeros(X.shape[1])
        theta, cost, ret = fmin_l_bfgs_b(self.cost_grad, theta,
                                         args=(X, Y.ravel()), **self.fmin_args)

        return LogisticRegressionClassifier(theta)

After Change

        self.random_state = random_state

    def fit(self, X, Y, W):
        lr = linear_model.LogisticRegression(
            penalty=self.penalty,
            dual=self.dual,
            tol=self.tol,
            C=self.C,
            fit_intercept=self.fit_intercept,
            intercept_scaling=self.intercept_scaling,
            random_state=self.random_state
        )
        clsf = lr.fit(X, Y.ravel())

        return LogisticRegressionClassifier(clsf)

Instances