@@ -0,0 +1,889 @@
+import numpy as np%0Afrom numpy.testing import assert_equal, assert_allclose%0Afrom sklearn.datasets import make_blobs%0A%0Afrom .. import LocalityPreservingProjection%0A%0Adef test_lpp_transform():%0A    # just a smoketest%0A    X = np.random.rand(100, 2)%0A%0A    lpp = LocalityPreservingProjection(n_components=1)%0A    lpp = lpp.fit(X)%0A    Xproj = lpp.transform(X)%0A    assert_equal(Xproj.shape, X%5B:, :1%5D.shape)%0A%0A    Xproj = lpp.fit_transform(X)%0A    assert_equal(Xproj.shape, X%5B:, :1%5D.shape)%0A%0A%0Adef test_line_plus_outlier():%0A    # test that a basic line plus outlier is projected as expected%0A    # cf. figure 1 in the LPP paper%0A    X = np.zeros((100, 2))%0A    X%5B:, 0%5D = np.arange(100)%0A    X%5B0, 1%5D = 30%0A%0A    lpp = LocalityPreservingProjection(n_components=2)%0A    Xlpp = lpp.fit_transform(X)%0A    assert_allclose(Xlpp%5B1:, 1%5D, 0, atol=1E-3)%0A%0A    ratio = Xlpp%5B1:, 0%5D / X%5B1:, 0%5D%0A    assert_allclose(ratio, ratio%5B0%5D)%0A

add basic LPP tests