def test_tga_median():
# TGA on dense arrays
tga = TGA(n_components=2, random_state=1, centering='median')
X = iris.data
X_r = tga.fit(X).transform(X)
assert_equal(X_r.shape[1], 2)
tga = TGA(n_components=2, random_state=1, centering='median')
X_r2 = tga.fit_transform(X)
assert_array_almost_equal(X_r, X_r2)
tga = TGA(random_state=1, centering='median')
tga.fit(X)
X_r = tga.transform(X)
tga = TGA(random_state=1, centering='median')
X_r2 = tga.fit_transform(X)
assert_array_almost_equal(X_r, X_r2)
def test_pca_error():
X = [[0, 1], [1, 0]]
for n_components in [-1, 3]:
assert_raises(ValueError, TGA(n_components).fit, X)
for trim_p in [-0.1, 0.6]:
assert_raises(ValueError, TGA(trim_proportion=trim_p).fit, X)
assert_raises(ValueError, TGA(centering='invalid').fit, X)
def test_tga_check_projection():
# Test that the projection by RandomizedPCA on dense data is correct
rng = np.random.RandomState(0)
n, p = 100, 3
X = rng.randn(n, p) * .1
X[:10] += np.array([3, 4, 5])
Xt = 0.1 * rng.randn(1, p) + np.array([3, 4, 5])
Yt = TGA(n_components=2, random_state=0).fit(X).transform(Xt)
Yt /= np.sqrt((Yt ** 2).sum())
assert_almost_equal(np.abs(Yt[0][0]), 1., 1)
