def sim(A):
while True:
y = np.random.standard_normal(L.shape[1])
if con(y):
break
return chisq.quadratic_test(y, np.identity(con.dim), con)
def test_chisq_central(nsim=None, burnin=8000, ndraw=2000):
n, p = 4, 10
A, b = np.random.standard_normal((n, p)), np.zeros(n)
con = AC.constraints(A,b)
while True:
z = np.random.standard_normal(p)
if con(z):
break
S = np.identity(p)[:3]
Z = AC.sample_from_constraints(con, z, ndraw=ndraw, burnin=burnin)
P = []
for i in range(Z.shape[0]/10):
P.append(chisq.quadratic_test(Z[10*i], S, con))
# no plots in the test!
# ecdf = sm.distributions.ECDF(P)
# plt.clf()
# x = np.linspace(0,1,101)
# plt.plot(x, ecdf(x), c='red')
# plt.plot([0,1],[0,1], c='blue', linewidth=2)
nt.assert_true(np.fabs(np.mean(P)-0.5) < 0.03)
nt.assert_true(np.fabs(np.std(P)-1/np.sqrt(12)) < 0.03)
def test_chisq_central(nsim=None, burnin=8000, ndraw=2000):
n, p = 4, 10
A, b = np.random.standard_normal((n, p)), np.zeros(n)
con = AC.constraints(A, b)
while True:
z = np.random.standard_normal(p)
if con(z):
break
S = np.identity(p)[:3]
Z = AC.sample_from_constraints(con, z, ndraw=ndraw, burnin=burnin)
P = []
for i in range(Z.shape[0] / 10):
P.append(chisq.quadratic_test(Z[10 * i], S, con))
# no plots in the test!
# ecdf = sm.distributions.ECDF(P)
# plt.clf()
# x = np.linspace(0,1,101)
# plt.plot(x, ecdf(x), c='red')
# plt.plot([0,1],[0,1], c='blue', linewidth=2)
nt.assert_true(np.fabs(np.mean(P) - 0.5) < 0.03)
nt.assert_true(np.fabs(np.std(P) - 1 / np.sqrt(12)) < 0.03)
def sim(A):
while True:
y = np.random.standard_normal(L.shape[1])
if con(y):
break
return chisq.quadratic_test(y, np.identity(con.dim),
con)
def test_chisq_central(nsim=None, burnin=8000, ndraw=2000):
n, p = 4, 10
A, b = np.random.standard_normal((n, p)), np.zeros(n)
con = AC.constraints(A, b)
while True:
z = np.random.standard_normal(p)
if con(z):
break
S = np.identity(p)[:3]
Z = AC.sample_from_constraints(con, z, ndraw=ndraw, burnin=burnin)
P = []
for i in range(int(Z.shape[0] / 10)):
P.append(chisq.quadratic_test(Z[10 * i], S, con))
nt.assert_true(np.fabs(np.mean(P) - 0.5) < 0.03)
nt.assert_true(np.fabs(np.std(P) - 1 / np.sqrt(12)) < 0.03)