def test_functional_cc(self):
"""Functional test for cc MI."""
# build the testing data
x = np.random.rand(n_times, n_mv, n_samples)
y = np.random.normal(size=(n_samples))
x[sl_effect, :, :] += y.reshape(1, 1, -1)
# functional test
c = BinMIEstimator(mi_type='cc')
mi = c.estimate(x, y)
self._compare_effects(effect, mi)
def test_functional_cd(self):
"""Functional test for cd MI."""
# build the testing data
x = np.random.rand(n_times, n_mv, n_samples)
x[sl_effect, :, 0:50] += 10.
x[sl_effect, :, 50::] -= 10.
y = np.array([0] * 50 + [1] * 50)
# functional test
c = BinMIEstimator(mi_type='cd')
mi = c.estimate(x, y)
self._compare_effects(effect, mi)
def test_functional_ccd(self):
"""Functional test for ccd MI."""
# build the testing data
x = np.random.rand(n_times, n_mv, n_samples)
y_pos = np.random.normal(size=(50, ))
y_neg = np.random.normal(size=(50, ))
x[sl_effect, :, 0:50] += y_pos.reshape(1, 1, -1)
x[sl_effect, :, 50::] -= y_neg.reshape(1, 1, -1)
y = np.r_[y_pos, y_neg]
z = np.array([0] * 50 + [1] * 50)
# functional test
c = BinMIEstimator(mi_type='ccd')
mi = c.estimate(x, y, z=z)
self._compare_effects(effect, mi)
def test_support_dim(self):
"""Test the support for different dimensions."""
c = BinMIEstimator(mi_type='cc')
# 1d
x = np.random.rand(100)
c = BinMIEstimator(mi_type='cc')
mi = c.estimate(x, y_i)
assert mi.shape == (1, 1)
# 2d
x = np.random.rand(1, 100)
mi = c.estimate(x, y_i)
assert mi.shape == (1, 1)
# Nd
x = np.random.rand(4, 5, 6, 1, 100)
mi = c.estimate(x, y_i)
assert mi.shape == (1, 4, 5, 6)
def test_smoke_ccd(self):
"""Smoke test for ccd MI."""
c = BinMIEstimator(mi_type='ccd')
c.estimate(x, y_f, z=z, categories=None)
c.estimate(x, y_f, z=z, categories=categories)