def test_corr(self):
np.random.seed(43)
sigma = np.array([[1, 0.5], [0.5, 1]])
def grad_log_correleted(x):
sigmaInv = np.linalg.inv(sigma)
return -np.dot(sigmaInv.T + sigmaInv, x) / 2.0
me = GaussianQuadraticTest(grad_log_correleted)
qm = QuadraticMultiple(me)
X = np.random.multivariate_normal([0, 0], sigma, 200)
reject, p_val = qm.is_from_null(0.05, X, 0.1)
np.testing.assert_almost_equal([0.465, 0.465], p_val)
arr = []
me = GaussianSteinTest(grad_log_pob, 1)
for time in times_we_look_at:
chain_at_time = samples[:, time]
# print(time)
# pval = me.compute_pvalue(chain_at_time)
# arr.append(pval)
def grad_log_pob(t):
a = np.sum(manual_grad(t[0], t[1], X), axis=0) + grad_log_prior(t)
return a
P_CHANGE = 0.1
me = GaussianQuadraticTest(grad_log_pob)
qm = QuadraticMultiple(me)
reject, p = qm.is_from_null(0.05, chain_at_time, 0.1)
print(reject)
# import matplotlib.pyplot as plt
#
# print(arr)
#
# plt.plot(arr)
#
# plt.show()
