def test_1():
x = generate_AR1(0.95, 1, 50**2 + 25, random_state=0)
val = integrated_autocorr5(x, size='sqrt')[0]
r.assign('x', x)
ref = r('(bm(x)$se)^2 * length(x) / var(x)')[0]
np.testing.assert_almost_equal(val, ref)
def test_1():
x = generate_AR1(0.95, 1, 50**2+25, random_state=0)
val = integrated_autocorr5(x, size='sqrt')[0]
r.assign('x', x)
ref = r('(bm(x)$se)^2 * length(x) / var(x)')[0]
np.testing.assert_almost_equal(val, ref)
def test_1():
x = generate_AR1(0.95, 1, 50**2 + 25, random_state=0)
x2 = np.vstack((x, x)).T
t1 = integrated_autocorr6(x)
t2 = integrated_autocorr6(x2)
assert t2.shape == (2, )
assert t2[0] == t1 and t2[1] == t1[0]
def test_1():
x = generate_AR1(0.95, 1, 50 ** 2 + 25, random_state=0)
x2 = np.vstack((x, x)).T
t1 = integrated_autocorr6(x)
t2 = integrated_autocorr6(x2)
assert t2.shape == (2,)
assert t2[0] == t1 and t2[1] == t1[0]
def test_1():
y = generate_AR1(0.95, 1, 10000)
tau = integrated_autocorr3(y)
r.assign('x', y)
r('popvar = (var(x)*(nrow(x)-1)/nrow(x))')
r('init = initseq(x)')
tau_ref = r('initseq(x)$var.pos / popvar')[0]
print(tau, tau_ref)
np.testing.assert_array_almost_equal(tau, tau_ref)
def test_1():
y = generate_AR1(0.95, 1, 10000)
tau = integrated_autocorr3(y)
r.assign('x', y)
r('popvar = (var(x)*(nrow(x)-1)/nrow(x))')
r('init = initseq(x)')
tau_ref = r('initseq(x)$var.pos / popvar')[0]
print(tau, tau_ref)
np.testing.assert_array_almost_equal(tau, tau_ref)
def test_2():
x = generate_AR1(0.95, 1, 50**2+25, random_state=0)
x2 = np.vstack((x, x)).T
val = integrated_autocorr5(x2, size='sqrt')
assert val.shape == (2,)
assert val[0] == val[1]
def test_2():
y = generate_AR1(0.95, 1, 10000)
y2 = np.vstack((y, y)).T
tau = integrated_autocorr3(y2)
assert tau.shape == (2, )
assert tau[0] == tau[1]
def test_2():
y = generate_AR1(0.95, 1, 10000)
y2 = np.vstack((y, y)).T
tau = integrated_autocorr3(y2)
assert tau.shape == (2,)
assert tau[0] == tau[1]
TRUE = 2 / (1 - PHI) - 1
n_steps = 1000000
grid = np.logspace(2, np.log10(n_steps), 10)
tau1 = []
tau2 = []
tau3 = []
tau4 = []
tau5 = []
tau6 = []
n_trials = 10
for i in range(n_trials):
y = generate_AR1(phi=PHI,
sigma=1,
n_steps=n_steps,
c=0,
y0=0,
random_state=None)
tau1.append([integrated_autocorr1(y[:n]) for n in grid])
tau2.append([integrated_autocorr2(y[:n]) for n in grid])
tau3.append([integrated_autocorr3(y[:n]) for n in grid])
tau4.append([integrated_autocorr4(y[:n]) for n in grid])
tau5.append([integrated_autocorr5(y[:n]) for n in grid])
tau6.append([integrated_autocorr6(y[:n]) for n in grid])
pp.errorbar(grid,
y=np.mean(tau1, axis=0),
yerr=np.std(tau1, axis=0),
c='b',
label='tau 1')
def test_2():
y = generate_AR1(phi=0.98, sigma=1, n_steps=1000, c=0, y0=0)
y2 = np.vstack((y, y)).T
val = integrated_autocorr4(y2)
print(val)
def test_1():
for i in range(10):
y = generate_AR1(phi=0.98, sigma=1, n_steps=10000, c=0, y0=0)
val = integrated_autocorr4(y)
rval = integrated_autocorr4r(y)
np.testing.assert_almost_equal(val, rval, decimal=2)
def setup():
global SAMPLES
SAMPLES = generate_AR1(PHI, 1, 10000, random_state=0).reshape(-1,1)
# = 2/(1-phi) - 1
PHI = 0.98
TRUE = 2/(1-PHI) - 1
n_steps = 1000000
grid = np.logspace(2, np.log10(n_steps), 10)
tau1 = []
tau2 = []
tau3 = []
tau4 = []
tau5 = []
tau6 = []
n_trials = 10
for i in range(n_trials):
y = generate_AR1(phi=PHI, sigma=1, n_steps=n_steps, c=0, y0=0, random_state=None)
tau1.append([integrated_autocorr1(y[:n]) for n in grid])
tau2.append([integrated_autocorr2(y[:n]) for n in grid])
tau3.append([integrated_autocorr3(y[:n]) for n in grid])
tau4.append([integrated_autocorr4(y[:n]) for n in grid])
tau5.append([integrated_autocorr5(y[:n]) for n in grid])
tau6.append([integrated_autocorr6(y[:n]) for n in grid])
pp.errorbar(grid, y=np.mean(tau1, axis=0), yerr=np.std(tau1, axis=0), c='b', label='tau 1')
pp.errorbar(grid-1, y=np.mean(tau2, axis=0), yerr=np.std(tau2, axis=0), c='r', label='tau 2')
pp.errorbar(grid-5, y=np.mean(tau3, axis=0), yerr=np.std(tau3, axis=0), c='g', label='tau 3')
pp.errorbar(grid-10, y=np.mean(tau4, axis=0), yerr=np.std(tau4, axis=0), c='gold', label='tau 4')
pp.errorbar(grid-20, y=np.mean(tau5, axis=0), yerr=np.std(tau5, axis=0), c='m', label='tau 5')
pp.errorbar(grid-30, y=np.mean(tau6, axis=0), yerr=np.std(tau6, axis=0), c='cyan', label='tau 6')
def test_2():
x = generate_AR1(0.95, 1, 50**2 + 25, random_state=0)
x2 = np.vstack((x, x)).T
val = integrated_autocorr5(x2, size='sqrt')
assert val.shape == (2, )
assert val[0] == val[1]
def setup():
global SAMPLES
SAMPLES = generate_AR1(PHI, 1, 10000, random_state=0).reshape(-1, 1)
