8c3810bf74e7521ee3ff09b043d0c41619c93809,mvpa2/tests/test_mdp.py,,test_nodeargs,#,170

Before Change

       pcm = PCAMapper(alg="PCA", nodeargs={"svd": svd_val})
       assert_equal(pcm.node.svd, svd_val)
   for output_dim in [0.0, 0.5, 0.95, 0.99, 10, 50, 100]:
       pcm = PCAMapper(alg="PCA", nodeargs={"output_dim": output_dim})
       assert_equal(pcm.node.output_dim, output_dim)

After Change

        assert_equal(pcm.node.svd, svd_val)
    for output_dim in [0.5, 0.95, 0.99, 10, 50, 100]:
        pcm = PCAMapper(alg="PCA", output_dim=output_dim)
        for i in range(2):              // so we also test on trained one
            if isinstance(output_dim, float):
                assert_equal(pcm.node.desired_variance, output_dim)
            else:
                assert_equal(pcm.node.output_dim, output_dim)
            pcm.train(ds)
            if isinstance(output_dim, float):
                assert_not_equal(pcm.node.output_dim, output_dim)
                // some dimensions are chosen
                assert_true(pcm.node.output_dim > 0)

Instances