def test_ssr(self):
# if x is a N x M array, then beta must be a M x P array, then y must be a N x P array
x = np.array([[1, 0, 4, 3, 2], [1, 1, 2, 2, 3]])
y = np.array([[1, 1], [1, 2]])
beta = np.array([[1, 2], [2, 3], [1, 1], [1, 2], [0, 1]])
result = bayes_stats.ssr(x, y, beta)
np.testing.assert_array_equal(result, 398)
def test_calc_rate_lin_alg_error(self):
x = np.array([[1, 2, 3], [2, 4, 6], [4, 8, 12], [8, 16, 24]])
y = np.array([[1, 2, 3], [0, 1, 1], [1, 1, 1], [1, 0, 1]])
xtx = np.dot(x.T, x) # [k x k]
xty = np.dot(x.T, y) # [k x 1]
gprior = np.array([[1, 1, 1, 1], [1, 0, 1, 0], [0, 0, 1, 1],
[1, 0, 1, 1]])
with self.assertRaises(np.linalg.LinAlgError):
model_beta = bayes_stats._solve_model(xtx, xty)
model_ssr = bayes_stats.ssr(x, y, model_beta)
scale_param = bayes_stats._calc_ig_scale(model_beta, model_ssr,
xtx, gprior)
def test_calc_rate(self):
x = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]])
y = np.array([[1, 2, 3], [0, 1, 1], [1, 1, 1], [1, 0, 1]])
xtx = np.dot(x.T, x) # [k x k]
xty = np.dot(x.T, y) # [k x 1]
gprior = np.array([[1, 1, 1, 1], [1, 0, 1, 0], [0, 0, 1, 1],
[1, 0, 1, 1]])
model_beta = bayes_stats._solve_model(xtx, xty)
model_ssr = bayes_stats.ssr(x, y, model_beta)
scale_param = bayes_stats._calc_ig_scale(model_beta, model_ssr, xtx,
gprior)
np.testing.assert_array_equal(
scale_param,
np.array([[1.5, 1.5, 2.5], [1.5, 2.5, 3.5], [2.5, 3.5, 5.5]]))
def test_ssr_negative(self):
x = np.array([[0, 1, -1, -2, 1], [-1, 2, 0, -1, 1]])
y = np.array([[2, -1], [1, 2]])
beta = np.array([[-1, -1], [1, 2], [2, 1], [-2, 1], [1, -1]])
result = bayes_stats.ssr(x, y, beta)
np.testing.assert_array_equal(result, 31)
def test_ssr_zeros(self):
x = np.array([[0, 0, 0, 0], [0, 0, 0, 0]])
y = np.array([[0, 0], [0, 0]])
beta = np.array([[0, 0], [0, 0], [0, 0], [0, 0]])
result = bayes_stats.ssr(x, y, beta)
np.testing.assert_array_equal(result, 0)