feat!: covariance replaced by covariance2, window altered to minimum of

the window of the two observables. Tests adjusted.
This commit is contained in:
Fabian Joswig 2021-12-13 17:06:03 +00:00
parent 06f4caf579
commit ec20ee38a6
4 changed files with 10 additions and 79 deletions

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@ -1334,76 +1334,6 @@ def covariance(obs1, obs2, correlation=False, **kwargs):
is constrained to the maximum value in order to make sure that covariance
matrices are positive semidefinite.
Parameters
----------
obs1 : Obs
First Obs
obs2 : Obs
Second Obs
correlation : bool
if true the correlation instead of the covariance is
returned (default False)
"""
if set(obs1.names).isdisjoint(set(obs2.names)):
return 0.
for name in sorted(set(obs1.names + obs2.names)):
if (obs1.shape.get(name) != obs2.shape.get(name)) and (obs1.shape.get(name) is not None) and (obs2.shape.get(name) is not None):
raise Exception('Shapes of ensemble', name, 'do not fit')
if (1 != len(set([len(idx) for idx in [obs1.idl[name], obs2.idl[name], _merge_idx([obs1.idl[name], obs2.idl[name]])]]))):
raise Exception('Shapes of ensemble', name, 'do not fit')
if not hasattr(obs1, 'e_dvalue') or not hasattr(obs2, 'e_dvalue'):
raise Exception('The gamma method has to be applied to both Obs first.')
dvalue = 0
for e_name in obs1.mc_names:
if e_name not in obs2.e_names:
continue
gamma = 0
r_length = []
for r_name in obs1.e_content[e_name]:
if r_name not in obs2.e_content[e_name]:
continue
r_length.append(len(obs1.deltas[r_name]))
gamma += np.sum(obs1.deltas[r_name] * obs2.deltas[r_name])
e_N = np.sum(r_length)
tau_combined = (obs1.e_tauint[e_name] + obs2.e_tauint[e_name]) / 2
dvalue += gamma / e_N * (1 + 1 / e_N) / e_N * 2 * tau_combined
for e_name in obs1.cov_names:
if e_name not in obs2.cov_names:
continue
dvalue += float(np.dot(np.transpose(obs1.covobs[e_name].grad), np.dot(obs1.covobs[e_name].cov, obs2.covobs[e_name].grad)))
if np.abs(dvalue / obs1.dvalue / obs2.dvalue) > 1.0:
dvalue = np.sign(dvalue) * obs1.dvalue * obs2.dvalue
if correlation:
dvalue = dvalue / obs1.dvalue / obs2.dvalue
return dvalue
def covariance2(obs1, obs2, correlation=False, **kwargs):
"""Alternative implementation of the covariance of two observables.
covariance(obs, obs) is equal to obs.dvalue ** 2
The gamma method has to be applied first to both observables.
If abs(covariance(obs1, obs2)) > obs1.dvalue * obs2.dvalue, the covariance
is constrained to the maximum value in order to make sure that covariance
matrices are positive semidefinite.
Keyword arguments
-----------------
correlation -- if true the correlation instead of the covariance is
@ -1503,7 +1433,7 @@ def covariance2(obs1, obs2, correlation=False, **kwargs):
# Make sure no entry of tauint is smaller than 0.5
e_n_tauint[e_name][e_n_tauint[e_name] < 0.5] = 0.500000000001
window = max(obs1.e_windowsize[e_name], obs2.e_windowsize[e_name])
window = min(obs1.e_windowsize[e_name], obs2.e_windowsize[e_name])
# Bias correction hep-lat/0306017 eq. (49)
e_dvalue[e_name] = 2 * (e_n_tauint[e_name][window] + obs1.tau_exp[e_name] * np.abs(e_rho[e_name][window + 1])) * (1 + (2 * window + 1) / e_N) * e_gamma[e_name][0] / e_N

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@ -40,7 +40,7 @@ def test_covobs():
[o.gamma_method() for o in cl]
assert(pe.covariance(cl[0], cl[1]) == cov[0][1])
assert(pe.covariance2(cl[0], cl[1]) == cov[1][0])
assert(pe.covariance(cl[0], cl[1]) == cov[1][0])
do = cl[0] * cl[1]
assert(np.array_equal(do.covobs['rAP'].grad, np.transpose([pi[1], pi[0]]).reshape(2, 1)))

View file

@ -83,6 +83,8 @@ def test_least_squares():
assert math.isclose(pcov[i, i], betac[i].dvalue ** 2, abs_tol=1e-3)
assert math.isclose(pe.covariance(betac[0], betac[1]), pcov[0, 1], abs_tol=1e-3)
def test_correlated_fit():
num_samples = 400
N = 10
@ -101,7 +103,6 @@ def test_least_squares():
c = cholesky(r, lower=True)
y = np.dot(c, x)
x = np.arange(N)
for linear in [True, False]:
data = []

View file

@ -555,7 +555,7 @@ def test_gamma_method_irregular():
assert((ae.e_tauint['a'] + 4 * ae.e_dtauint['a'] > ao.e_tauint['a']))
def test_covariance2_symmetry():
def test_covariance_symmetry():
value1 = np.random.normal(5, 10)
dvalue1 = np.abs(np.random.normal(0, 1))
test_obs1 = pe.pseudo_Obs(value1, dvalue1, 't')
@ -564,8 +564,8 @@ def test_covariance2_symmetry():
dvalue2 = np.abs(np.random.normal(0, 1))
test_obs2 = pe.pseudo_Obs(value2, dvalue2, 't')
test_obs2.gamma_method()
cov_ab = pe.covariance2(test_obs1, test_obs2)
cov_ba = pe.covariance2(test_obs2, test_obs1)
cov_ab = pe.covariance(test_obs1, test_obs2)
cov_ba = pe.covariance(test_obs2, test_obs1)
assert np.abs(cov_ab - cov_ba) <= 10 * np.finfo(np.float64).eps
assert np.abs(cov_ab) < test_obs1.dvalue * test_obs2.dvalue * (1 + 10 * np.finfo(np.float64).eps)
@ -578,10 +578,10 @@ def test_covariance2_symmetry():
idx = [i + 1 for i in range(len(configs)) if configs[i] == 1]
a = pe.Obs([zero_arr], ['t'], idl=[idx])
a.gamma_method()
assert np.isclose(a.dvalue**2, pe.covariance2(a, a), atol=100, rtol=1e-4)
assert np.isclose(a.dvalue**2, pe.covariance(a, a), atol=100, rtol=1e-4)
cov_ab = pe.covariance2(test_obs1, a)
cov_ba = pe.covariance2(a, test_obs1)
cov_ab = pe.covariance(test_obs1, a)
cov_ba = pe.covariance(a, test_obs1)
assert np.abs(cov_ab - cov_ba) <= 10 * np.finfo(np.float64).eps
assert np.abs(cov_ab) < test_obs1.dvalue * test_obs2.dvalue * (1 + 10 * np.finfo(np.float64).eps)