7b20a10273955a6b33a8563a878aadd9c0dfe0a6,mvpa2/tests/test_rsa.py,,test_CDist,#,76

Before Change

    chunks = np.repeat(np.array((0,1)),3)
    ds = dataset_wizard(samples=data, targets=targets, chunks=chunks)
    // Some distance metrics
    euc = pdist(data, "euclidean")
    pear = pdist(data, "correlation")
    city = pdist(data, "cityblock")
    cdist_euc = CDist(pairwise_metric="euclidean")
    cdist_pear = CDist(pairwise_metric="correlation")
    cdist_city = CDist(pairwise_metric="cityblock")
    for pd_, cd_ in zip([euc, pear, city], [cdist_euc, cdist_pear, cdist_city]):
        cd_.train(ds[ds.sa.chunks == 0, ])
        res = cd_(ds[ds.sa.chunks == 1, ])
        // Check to make sure the pdist results are close to CDist results
        assert_array_almost_equal(res.samples.ravel(), squareform(pd_)[:3, 3:].ravel())


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

After Change

    chunks = np.repeat(np.array((0,1)), 3)
    ds = dataset_wizard(samples=data, targets=targets, chunks=chunks)
    // Some distance metrics
    metrics = ["euclidean", "correlation", "cityblock"]
    for metric in metrics:
        pd_ = pdist(data, metric)
        cd_ = CDist(pairwise_metric=metric)

        cd_.train(ds[ds.sa.chunks == 0, ])
        res = cd_(ds[ds.sa.chunks == 1, ])
        // Check to make sure the pdist results are close to CDist results
        assert_array_almost_equal(res.samples.ravel(),
                                  squareform(pd_)[:3, 3:].ravel())


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

Instances