def test_linspace():
a = linspace(2.0, 3.0, num=5, chunk_size=2)
assert a.shape == (5, )
a = tile(a)
assert a.nsplits == ((2, 2, 1), )
assert a.chunks[0].op.start == 2.
assert a.chunks[0].op.stop == 2.25
assert a.chunks[1].op.start == 2.5
assert a.chunks[1].op.stop == 2.75
assert a.chunks[2].op.start == 3.
assert a.chunks[2].op.stop == 3.
a = linspace(2.0, 3.0, num=5, endpoint=False, chunk_size=2)
assert a.shape == (5, )
a = tile(a)
assert a.nsplits == ((2, 2, 1), )
assert a.chunks[0].op.start == 2.
assert a.chunks[0].op.stop == 2.2
assert a.chunks[1].op.start == 2.4
assert a.chunks[1].op.stop == 2.6
assert a.chunks[2].op.start == 2.8
assert a.chunks[2].op.stop == 2.8
_, step = linspace(2.0, 3.0, num=5, chunk_size=2, retstep=True)
assert step == .25
def testLinspace(self):
a = linspace(2.0, 3.0, num=5, chunk_size=2)
self.assertEqual(a.shape, (5, ))
a = a.tiles()
self.assertEqual(a.nsplits, ((2, 2, 1), ))
self.assertEqual(a.chunks[0].op.start, 2.)
self.assertEqual(a.chunks[0].op.stop, 2.25)
self.assertEqual(a.chunks[1].op.start, 2.5)
self.assertEqual(a.chunks[1].op.stop, 2.75)
self.assertEqual(a.chunks[2].op.start, 3.)
self.assertEqual(a.chunks[2].op.stop, 3.)
a = linspace(2.0, 3.0, num=5, endpoint=False, chunk_size=2)
self.assertEqual(a.shape, (5, ))
a = a.tiles()
self.assertEqual(a.nsplits, ((2, 2, 1), ))
self.assertEqual(a.chunks[0].op.start, 2.)
self.assertEqual(a.chunks[0].op.stop, 2.2)
self.assertEqual(a.chunks[1].op.start, 2.4)
self.assertEqual(a.chunks[1].op.stop, 2.6)
self.assertEqual(a.chunks[2].op.start, 2.8)
self.assertEqual(a.chunks[2].op.stop, 2.8)
_, step = linspace(2.0, 3.0, num=5, chunk_size=2, retstep=True)
self.assertEqual(step, .25)
def testWhitening(self):
# Check that PCA output has unit-variance
rng = np.random.RandomState(0)
n_samples = 100
n_features = 80
n_components = 30
rank = 50
# some low rank data with correlated features
X = mt.dot(
rng.randn(n_samples, rank),
mt.dot(mt.diag(mt.linspace(10.0, 1.0, rank)),
rng.randn(rank, n_features)))
# the component-wise variance of the first 50 features is 3 times the
# mean component-wise variance of the remaining 30 features
X[:, :50] *= 3
self.assertEqual(X.shape, (n_samples, n_features))
# the component-wise variance is thus highly varying:
self.assertGreater(X.std(axis=0).std().to_numpy(), 43.8)
for solver, copy in product(self.solver_list, (True, False)):
# whiten the data while projecting to the lower dim subspace
X_ = X.copy() # make sure we keep an original across iterations.
pca = PCA(n_components=n_components,
whiten=True,
copy=copy,
svd_solver=solver,
random_state=0,
iterated_power=7)
# test fit_transform
X_whitened = pca.fit_transform(X_.copy())
self.assertEqual(X_whitened.shape, (n_samples, n_components))
X_whitened2 = pca.transform(X_)
assert_array_almost_equal(X_whitened.fetch(), X_whitened2.fetch())
assert_almost_equal(X_whitened.std(ddof=1, axis=0).to_numpy(),
np.ones(n_components),
decimal=6)
assert_almost_equal(
X_whitened.mean(axis=0).to_numpy(), np.zeros(n_components))
X_ = X.copy()
pca = PCA(n_components=n_components,
whiten=False,
copy=copy,
svd_solver=solver).fit(X_)
X_unwhitened = pca.transform(X_)
self.assertEqual(X_unwhitened.shape, (n_samples, n_components))
# in that case the output components still have varying variances
assert_almost_equal(
X_unwhitened.std(axis=0).std().to_numpy(), 74.1, 1)