a243ad678a264c898e133bb1f97efed72703ee06,mvpa2/tests/test_rsa.py,,test_CDist,#,78

Before Change

    // check it doesn"t blow up without mean group samples
    for metric in metrics:
        pd_ = pdist(data, metric)
        cd_ = CDist(sattr=None, pairwise_metric=metric)

        assert_true(not cd_.is_trained)
        cd_.train(ds)

After Change

    // Some distance metrics
    metrics = ["euclidean", "correlation", "cityblock", "mahalanobis"]
    VI_mahalanobis = np.eye(5)
    for sattr in [["targets"], None]:
        for metric in metrics:
            metric_kwargs = {"VI": VI_mahalanobis} if metric == "mahalanobis" \
                else {}
            scipy_cdist = cdist(train_data.samples, test_data.samples,
                        metric, **metric_kwargs)
            pymvpa_cdist = CDist(pairwise_metric=metric,
                        pairwise_metric_kwargs=metric_kwargs,
                        sattr=sattr)

            assert_true(not pymvpa_cdist.is_trained)
            pymvpa_cdist.train(train_data)
            assert_true(pymvpa_cdist.is_trained)
            res = pymvpa_cdist(test_data)
            // Check to make sure the cdist results are close to CDist results
            assert_array_almost_equal(res.samples.ravel(),
                                      scipy_cdist.ravel())

def test_CDist_cval():
    targets = np.tile(range(3), 2)
    chunks = np.repeat(np.array((0,1)), 3)
    ds = dataset_wizard(samples=data, targets=targets, chunks=chunks)

Instances