def test_cca_crossvalidate():
"""Test CCA with crossvalidation."""
# x = np.random.randn(1000, 11)
# y = np.random.randn(1000, 9)
# xx = [x, x, x]
# yy = [x[:, :9], y, y]
mat = loadmat('./tests/data/ccadata2.mat')
xx = mat['x']
yy = mat['y']
R1 = mat['R'] # no shifts
# Test with no shifts
A, B, R = cca_crossvalidate(xx, yy)
assert_almost_equal(R, R1, decimal=2)
# Create data where 1st comps should be uncorrelated, and 2nd and 3rd comps
# are very correlated
x = np.random.randn(1000, 10)
y = np.random.randn(1000, 10)
xx = [x, x, x]
yy = [y, x, x]
A, B, R = cca_crossvalidate(xx, yy)
assert_almost_equal(R[:, :, 0], 0, decimal=1)
assert_almost_equal(R[:, :, 1:], 1, decimal=1)
def test_cca_crossvalidate_shifts2():
"""Test CCA crossvalidation with shifts."""
mat = loadmat('./tests/data/ccacrossdata.mat')
xx = mat['xx2']
yy = mat['yy2']
R2 = mat['R'][:, ::-1, :] # shifts go in reverse direction in noisetools
# Test with shifts
A, B, R = cca_crossvalidate(xx, yy, shifts=[-3, -2, -1, 0, 1, 2, 3])
# correlations are ~ those of Matlab
assert_almost_equal(R, R2, decimal=3)
def test_cca_crossvalidate_shifts2():
"""Test CCA crossvalidation with shifts."""
mat = loadmat('./tests/data/ccacrossdata.mat')
xx = mat['xx2']
yy = mat['yy2']
R2 = mat['R']
# Test with shifts
A, B, R = cca_crossvalidate(xx, yy, shifts=[-3, -2, -1, 0, 1, 2, 3])
# correlations are ~ those of Maltab (very loose check, suspect numerical
# inaccuracy in normcol)
assert_almost_equal(R[:3, 1:-1, ...], R2[:3, 1:-1, ...], decimal=2)
assert_almost_equal(R, R2, decimal=1)
def test_cca_crossvalidate_shifts():
"""Test CCA crossvalidation with shifts."""
n_times, n_trials = 10000, 2
x = np.random.randn(n_times, 20, n_trials)
y = np.random.randn(n_times, 8, n_trials)
# perfectly correlated
y[:, :2, :] = x[:, :2, :]
# 1/2 correlated
y[:, 2:4, :] = x[:, 2:4, :] + np.random.randn(n_times, 2, n_trials)
# 1/4 correlated
y[:, 4:6, :] = x[:, 4:6, :] + np.random.randn(n_times, 2, n_trials) * 3
# uncorrelated
y[:, 6:8, :] = np.random.randn(n_times, 2, n_trials)
xx = multishift(x, -np.arange(1, 4), reshape=True, solution='valid')
yy = multishift(y, -np.arange(1, 4), reshape=True, solution='valid')
# Test with shifts
A, B, R = cca_crossvalidate(xx, yy, shifts=[-3, -2, -1, 0, 1, 2, 3])