def test_prec_to_partial():
"""Testing prec_to_partial function"""
shape = random.randint(1, 50)
prec = my_mfd.random_spd(shape)
partial = my_con.prec_to_partial(prec)
assert_true(my_mfd.is_spd(partial))
d = np.sqrt(np.diag(np.diag(prec)))
assert_array_almost_equal(
d.dot(partial).dot(d), -prec + 2 * np.diag(np.diag(prec)))
def test_transform(): # TODO : class test for class CovEmbedding
"""Testing fit_transform method for class CovEmbedding"""
n_subjects = random.randint(3, 50)
shape = random.randint(1, 10)
n_samples = 300
covs = []
signals = []
for k in xrange(n_subjects):
signal = np.random.randn(n_samples, shape)
signals.append(signal)
signal -= signal.mean(axis=0)
covs.append((signal.T).dot(signal) / n_samples)
for kind in ["correlation", "precision", "partial correlation", "tangent"]:
estimators = {'kind': kind, 'cov_estimator': None}
cov_embedding = my_con.CovEmbedding(**estimators)
covs_transformed = cov_embedding.fit_transform(signals)
# Generic
assert_is_instance(covs_transformed, np.ndarray)
assert_equal(len(covs_transformed), len(covs))
for k, vec in enumerate(covs_transformed):
assert_equal(vec.size, shape * (shape + 1) / 2)
cov_new = my_con.vec_to_sym(vec)
assert_true(my_mfd.is_spd(covs[k]))
if estimators["kind"] == "tangent":
assert_array_almost_equal(cov_new, cov_new.T)
fre_sqrt = my_mfd.sqrtm(cov_embedding.mean_cov_)
assert_true(my_mfd.is_spd(fre_sqrt))
assert_true(my_mfd.is_spd(cov_embedding.whitening_))
assert_array_almost_equal(
cov_embedding.whitening_.dot(fre_sqrt), np.eye(shape))
assert_array_almost_equal(
fre_sqrt.dot(my_mfd.expm(cov_new)).dot(fre_sqrt), covs[k])
if estimators["kind"] == "precision":
assert_true(my_mfd.is_spd(cov_new))
assert_array_almost_equal(cov_new.dot(covs[k]), np.eye(shape))
if estimators["kind"] == "correlation":
assert_true(my_mfd.is_spd(cov_new))
d = np.sqrt(np.diag(np.diag(covs[k])))
assert_array_almost_equal(d.dot(cov_new).dot(d), covs[k])
if estimators["kind"] == "partial correlation":
assert_true(my_mfd.is_spd(cov_new))
prec = linalg.inv(covs[k])
d = np.sqrt(np.diag(np.diag(prec)))
assert_array_almost_equal(
d.dot(cov_new).dot(d), -prec + 2 * np.diag(np.diag(prec)))