def test_no_gatestrings(self):
ds = pygsti.objects.DataSet(fileToLoadFrom=compare_files + "/analysis.dataset")
L1 = pygsti.logl(std.gs_target, ds,
probClipInterval=(-1e6,1e6), countVecMx=None,
poissonPicture=True, check=False)
self.assertAlmostEqual(L1, -4531934.43735, 2)
L2 = pygsti.logl_max(ds)
self.assertAlmostEqual(L2, -1329179.7675, 5)
def test_no_gatestrings(self):
ds = pygsti.objects.DataSet(fileToLoadFrom=compare_files + "/analysis.dataset")
gateset = std.gs_target #could use pygsti.io.load_gateset(compare_files + "/analysis.gateset"), but then change hardcoded #'s
L1 = pygsti.logl(gateset, ds,
probClipInterval=(-1e6,1e6), countVecMx=None,
poissonPicture=True, check=False)
self.assertAlmostEqual(L1, -4531934.43735, 2)
L2 = pygsti.logl_max(ds)
self.assertAlmostEqual(L2, -1329179.7675, 5)
def test_logl_fn(self):
ds = pygsti.objects.DataSet(fileToLoadFrom=compare_files + "/analysis.dataset")
gatestrings = pygsti.construction.gatestring_list( [ ('Gx',), ('Gy',), ('Gx','Gx') ] )
spam_labels = std.gs_target.get_spam_labels()
pygsti.create_count_vec_dict( spam_labels, ds, gatestrings )
gateset = pygsti.io.load_gateset(compare_files + "/analysis.gateset")
L1 = pygsti.logl(gateset, ds, gatestrings,
probClipInterval=(-1e6,1e6), countVecMx=None,
poissonPicture=True, check=False)
L2 = pygsti.logl(gateset, ds, gatestrings,
probClipInterval=(-1e6,1e6), countVecMx=None,
poissonPicture=False, check=False) #Non-poisson-picture
dL1 = pygsti.logl_jacobian(gateset, ds, gatestrings,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=True, check=False)
dL2 = pygsti.logl_jacobian(gateset, ds, gatestrings,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=False, check=False)
dL2b = pygsti.logl_jacobian(gateset, ds, None,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=False, check=False) #test None as gs list
hL1 = pygsti.logl_hessian(gateset, ds, gatestrings,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=True, check=False)
hL2 = pygsti.logl_hessian(gateset, ds, gatestrings,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=False, check=False)
hL2b = pygsti.logl_hessian(gateset, ds, None,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=False, check=False) #test None as gs list
maxL1 = pygsti.logl_max(ds, gatestrings, poissonPicture=True, check=True)
maxL2 = pygsti.logl_max(ds, gatestrings, poissonPicture=False, check=True)
pygsti.cptp_penalty(gateset, include_spam_penalty=True)
twoDelta1 = pygsti.two_delta_loglfn(N=100, p=0.5, f=0.6, minProbClip=1e-6, poissonPicture=True)
twoDelta2 = pygsti.two_delta_loglfn(N=100, p=0.5, f=0.6, minProbClip=1e-6, poissonPicture=False)
def test_logl_fn(self):
ds = pygsti.objects.DataSet(fileToLoadFrom=compare_files + "/analysis.dataset%s" % self.versionsuffix)
circuits = pygsti.construction.circuit_list( [ ('Gx',), ('Gy',), ('Gx','Gx') ] )
#OLD spam_labels = std.target_model().get_spam_labels()
#OLD pygsti.create_count_vec_dict( spam_labels, ds, circuits )
model = pygsti.io.load_model(compare_files + "/analysis.model")
L1 = pygsti.logl(model, ds, circuits,
probClipInterval=(-1e6,1e6),
poissonPicture=True, check=False)
L2 = pygsti.logl(model, ds, circuits,
probClipInterval=(-1e6,1e6),
poissonPicture=False, check=False) #Non-poisson-picture
dL1 = pygsti.logl_jacobian(model, ds, circuits,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=True, check=False)
dL2 = pygsti.logl_jacobian(model, ds, circuits,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=False, check=False)
dL2b = pygsti.logl_jacobian(model, ds, None,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=False, check=False) #test None as mdl list
hL1 = pygsti.logl_hessian(model, ds, circuits,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=True, check=False)
hL2 = pygsti.logl_hessian(model, ds, circuits,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=False, check=False)
hL2b = pygsti.logl_hessian(model, ds, None,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=False, check=False) #test None as mdl list
maxL1 = pygsti.logl_max(model, ds, circuits, poissonPicture=True, check=True)
maxL2 = pygsti.logl_max(model, ds, circuits, poissonPicture=False, check=True)
pygsti.cptp_penalty(model, include_spam_penalty=True)
twoDelta1 = pygsti.two_delta_loglfn(N=100, p=0.5, f=0.6, minProbClip=1e-6, poissonPicture=True)
twoDelta2 = pygsti.two_delta_loglfn(N=100, p=0.5, f=0.6, minProbClip=1e-6, poissonPicture=False)
def test_logl_fn(self):
ds = pygsti.objects.DataSet(fileToLoadFrom=compare_files + "/analysis.dataset")
gatestrings = pygsti.construction.gatestring_list( [ ('Gx',), ('Gy',), ('Gx','Gx') ] )
spam_labels = std.gs_target.get_spam_labels()
pygsti.create_count_vec_dict( spam_labels, ds, gatestrings )
L1 = pygsti.logl(std.gs_target, ds, gatestrings,
probClipInterval=(-1e6,1e6), countVecMx=None,
poissonPicture=True, check=False)
L2 = pygsti.logl(std.gs_target, ds, gatestrings,
probClipInterval=(-1e6,1e6), countVecMx=None,
poissonPicture=False, check=False) #Non-poisson-picture
dL1 = pygsti.logl_jacobian(std.gs_target, ds, gatestrings,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=True, check=False)
dL2 = pygsti.logl_jacobian(std.gs_target, ds, gatestrings,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=False, check=False)
dL2b = pygsti.logl_jacobian(std.gs_target, ds, None,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=False, check=False) #test None as gs list
hL1 = pygsti.logl_hessian(std.gs_target, ds, gatestrings,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=True, check=False)
hL2 = pygsti.logl_hessian(std.gs_target, ds, gatestrings,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=False, check=False)
hL2b = pygsti.logl_hessian(std.gs_target, ds, None,
probClipInterval=(-1e6,1e6), radius=1e-4,
poissonPicture=False, check=False) #test None as gs list
maxL1 = pygsti.logl_max(ds, gatestrings, poissonPicture=True, check=True)
maxL2 = pygsti.logl_max(ds, gatestrings, poissonPicture=False, check=True)
pygsti.cptp_penalty(std.gs_target, include_spam_penalty=True)
twoDelta1 = pygsti.two_delta_loglfn(N=100, p=0.5, f=0.6, minProbClip=1e-6, poissonPicture=True)
twoDelta2 = pygsti.two_delta_loglfn(N=100, p=0.5, f=0.6, minProbClip=1e-6, poissonPicture=False)
def test_no_gatestrings(self):
ds = pygsti.objects.DataSet(fileToLoadFrom=compare_files + "/analysis.dataset%s" % self.versionsuffix)
gateset = std.gs_target #could use pygsti.io.load_gateset(compare_files + "/analysis.gateset"), but then change hardcoded #'s
L1 = pygsti.logl(gateset, ds,
probClipInterval=(-1e6,1e6),
poissonPicture=True, check=False)
self.assertAlmostEqual(L1,-21393568.52986, 2)
#self.assertAlmostEqual(L1,-21579292.1837, 2) #OLD2
#self.assertAlmostEqual(L1, -4531934.43735, 2) #OLD
L2 = pygsti.logl_max(gateset, ds)
self.assertAlmostEqual(L2, -14028782.1039, 2)
