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src/o/r/Orange-2.7.2/docs/reference/rst/code/lasso-example.py   Orange(Download)
housing = Orange.data.Table("housing")
learner = Orange.regression.lasso.LassoRegressionLearner(
    lasso_lambda=1, n_boot=100, n_perm=100)
classifier = learner(housing)
 
# prediction for five data instances
for ins in housing[:5]:
    print "Actual: %3.2f, predicted: %3.2f" % (
        ins.get_class(), classifier(ins))

src/o/r/Orange-2.7.2/Orange/OrangeWidgets/Regression/OWLinearRegression.py   Orange(Download)
    def apply_lasso(self):
        learner = lasso.LassoRegressionLearner(
            lasso_lambda=self.lasso_lambda, eps=self.eps,
            n_boot=0, n_perm=0,
            name=self.name
            if self.data is not None:
                ll = lasso.LassoRegressionLearner(
                    lasso_lambda=self.lasso_lambda, eps=self.eps,
                    n_boot=10, n_perm=10
                    )
                predictor = ll(self.data)

src/o/r/Orange-2.7.2/Orange/testing/unit/tests/test_lasso.py   Orange(Download)
    def setUp(self):
        self.learner = lasso.LassoRegressionLearner(n_boot=2, n_perm=2)
 
    @test_on_data
    def test_learner_on(self, dataset):