def testChunksizeResultsTica(self):
chunk = 40
lag = 100
np.random.seed(0)
X = np.random.randn(23000, 3)
# un-chunked
d = DataInMemory(X)
tica = TICA(lag=lag, output_dimension=1)
tica.data_producer = d
tica.parametrize()
cov = tica.cov.copy()
mean = tica.mu.copy()
# ------- run again with new chunksize -------
d = DataInMemory(X)
d.chunksize = chunk
tica = TICA(lag=lag, output_dimension=1)
tica.data_producer = d
tica.parametrize()
np.testing.assert_allclose(tica.mu, mean)
np.testing.assert_allclose(tica.cov, cov)
def test(self):
np.random.seed(0)
tica = TICA(lag=50, output_dimension=1)
data = np.random.randn(100, 10)
ds = DataInMemory(data)
tica.data_producer = ds
tica.parametrize()
Y = tica.map(data)
def test_singular_zeros(self):
tica = TICA(lag=1, output_dimension=1)
# make some data that has one column of all zeros
X = np.random.randn(100, 2)
X = np.hstack((X, np.zeros((100, 1))))
d = DataInMemory(X)
tica.data_producer = d
tica.parametrize()
assert tica.eigenvectors.dtype == np.float64
assert tica.eigenvalues.dtype == np.float64
def test_duplicated_data(self):
tica = TICA(lag=1, output_dimension=1)
# make some data that has one column repeated twice
X = np.random.randn(100, 2)
X = np.hstack((X, X[:, 0, np.newaxis]))
d = DataInMemory(X)
tica.data_producer = d
tica.parametrize()
assert tica.eigenvectors.dtype == np.float64
assert tica.eigenvalues.dtype == np.float64
