fafe8422549c1809ad66f124e842431009bbddba,skbio/stats/ordination/tests/test_ordination.py,TestCAResults,test_scaling1,#TestCAResults#,285

Before Change

        V = np.array([[1.31871, -0.34374],
                      [-0.37215,  1.48150],
                      [-0.99972, -0.92612]])
        npt.assert_almost_equal(*normalize_signs(V, scores.species), decimal=5)
        F = np.array([[-0.26322, -0.17862],
                      [-0.06835,  0.27211],
                      [0.51685, -0.09517]])
        npt.assert_almost_equal(*normalize_signs(F, scores.site), decimal=5)

After Change

    def test_scaling1(self):
        eigvals = pd.Series(np.array([0.09613302, 0.04094181]), ["CA1", "CA2"])
        // p. 458
        features = pd.DataFrame(np.array([[1.31871, -0.34374], // V
                                          [-0.37215, 1.48150],
                                          [-0.99972, -0.92612]]),
                                ["Species1", "Species2", "Species3"],
                                ["CA1", "CA2"])
        samples = pd.DataFrame(np.array([[-0.26322, -0.17862], // F
                                         [-0.06835, 0.27211],
                                         [0.51685, -0.09517]]),
                               ["Site1", "Site2", "Site3"],
                               ["CA1", "CA2"])
        exp = OrdinationResults("CA", "Correspondance Analysis",
                                eigvals=eigvals, features=features,
                                samples=samples)
        scores = ca(self.contingency, 1)

        assert_ordination_results_equal(exp, scores,
                                        ignore_directionality=True)

Instances