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)
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)
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()
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()
arr = np.empty((0,2))
arr2 = np.empty((0,2))
for c in [1.0,1.3,2.0,3.0]:
print('c',c)
log_normal = logg(c)
for i in range(23):
print(i)
x= metropolis_hastings(log_normal, chain_size=500, thinning=15,x_prev=np.random.randn(2))
me = GaussianQuadraticTest(grad_log_dens)
qm = QuadraticMultiple(me)
qm2 = QuadraticMultiple2(me)
accept_null,p_val = qm.is_from_null(0.05, x, 0.1)
p_val2 = qm2.is_from_null(0.05, x, 0.1)
print(p_val2)
arr = np.vstack((arr, np.array([c,min(p_val)])))
arr2 = np.vstack((arr2, np.array([c,p_val2])))
df = DataFrame(arr)
pr = seaborn.boxplot(x=0,y=1,data=df)
seaborn.plt.show()
arr2 = np.empty((0, 2))
for c in [1.0, 1.3, 2.0, 3.0]:
print('c', c)
log_normal = logg(c)
for i in range(23):
print(i)
x = metropolis_hastings(log_normal,
chain_size=500,
thinning=15,
x_prev=np.random.randn(2))
me = GaussianQuadraticTest(grad_log_dens)
qm = QuadraticMultiple(me)
qm2 = QuadraticMultiple2(me)
accept_null, p_val = qm.is_from_null(0.05, x, 0.1)
p_val2 = qm2.is_from_null(0.05, x, 0.1)
print(p_val2)
arr = np.vstack((arr, np.array([c, min(p_val)])))
arr2 = np.vstack((arr2, np.array([c, p_val2])))
df = DataFrame(arr)
pr = seaborn.boxplot(x=0, y=1, data=df)
seaborn.plt.show()
df = DataFrame(arr2)
pr = seaborn.boxplot(x=0, y=1, data=df)
seaborn.plt.show()
