def test_indicators(self, dimensions, indicator):
"""
Test if indicator matrix is built correctly.
"""
dct = {
'intercept':
[indicator[j] == dimensions[j] for j in range(len(dimensions))]
}
y = np.random.randn(np.prod(dimensions))
model = LME(dimensions, 0, y, {}, dct, {}, False, {})
Z = rutils.kronecker(indicator, dimensions, 0)
x = np.random.randn(np.prod(indicator))
assert (np.linalg.norm(model.X(x) - Z.dot(x)) < 1e-10) and \
(np.linalg.norm(model.XT(y) - np.transpose(Z).dot(y)) < 1e-10)
def test_repeat_covariate(self, dimensions, cov_dim):
N = np.prod(dimensions)
X = np.ones((N, 2)) # 1st column is intercept
cov = np.random.randn(np.prod(cov_dim))
cov_dim_bool = [
cov_dim[i] == dimensions[i] for i in range(len(dimensions))
]
Z = rutils.kronecker(cov_dim, dimensions, 0)
X[:, 1] = Z.dot(cov)
beta_true = [1., -0.6] # beta_0 for intercept
Y = X.dot(beta_true)
model = LME(dimensions, 0, Y, {'cov1': (cov, cov_dim_bool)}, {},
{'cov1': [-float('inf'), float('inf')]}, True, {})
beta = np.random.randn(2)
assert np.linalg.norm(model.X(beta) - X.dot(beta)) < 1e-10
y = np.random.randn(N)
assert np.linalg.norm(model.XT(y) - np.transpose(X).dot(y)) < 1e-10
model._buildX()
assert np.linalg.norm(model.Xm - X) < 1e-10