def test_ScaledArray_dot():
for N in range(2, 5):
for P in range(2, 5):
array = np.random.rand(N, P) + 1
std = np.diag(1/np.std(array, axis=0))
mu = np.mean(array, axis=0)
for K in range(1, 5):
for squeeze in [True, False]:
x = np.random.rand(P, K)
if squeeze:
x = np.squeeze(x)
for fit_x in [x, None]:
# With No Scale or Center
# x = A'Ax
result = array.dot(x)
sarray = ScaledCenterArray(scale=False, center=False)
sarray.fit(da.array(array), x=fit_x)
np.testing.assert_array_almost_equal(result, sarray.dot(x))
# With Scale but No Center
# B = AD
b_array = array.dot(std)
result = b_array.dot(x)
sarray = ScaledCenterArray(scale=True, center=False)
sarray.fit(da.array(array), x=fit_x)
np.testing.assert_array_almost_equal(result, sarray.dot(x))
# With Center but No Scale:
# B = (A - U)
b_array = array - mu
result = b_array.dot(x)
sarray = ScaledCenterArray(scale=False, center=True)
sarray.fit(da.array(array), x=fit_x)
np.testing.assert_array_almost_equal(result, sarray.dot(x))
# With Center and Scale:
# (A - U)'D'D(A - U)x
b_array = (array - mu).dot(std)
result = b_array.dot(x)
sarray = ScaledCenterArray(scale=True, center=True)
sarray.fit(da.array(array), x=fit_x)
np.testing.assert_array_almost_equal(result, sarray.dot(x))