def likelihood(self, outcomes, modelparams, expparams):
# We first call the superclass method, which basically
# just makes sure that call count diagnostics are properly
# logged.
super(MultiCosModel, self).likelihood(outcomes, modelparams, expparams)
# Next, since we have a two-outcome model, everything is defined by
# Pr(0 | modelparams; expparams), so we find the probability of 0
# for each model and each experiment.
#
# We do so by taking a product along the modelparam index (len 2,
# indicating omega_1 or omega_2), then squaring the result.
pr0 = np.prod(
np.cos(
# shape (n_models, 1, 2)
modelparams[:, np.newaxis, :] *
# shape (n_experiments, 2)
expparams['ts']
), # <- broadcasts to shape (n_models, n_experiments, 2).
axis=2 # <- product over the final index (len 2)
) ** 2 # square each element
# Now we use pr0_to_likelihood_array to turn this two index array
# above into the form expected by SMCUpdater and other consumers
# of likelihood().
return FiniteOutcomeModel.pr0_to_likelihood_array(outcomes, pr0)
def likelihood(self, outcomes, modelparams, expparams):
super(TomographyModel, self).likelihood(outcomes, modelparams, expparams)
pr1 = np.empty((modelparams.shape[0], expparams.shape[0]))
pr1[:, :] = np.einsum(
'ei,mi->me',
# This should be the Hermitian conjugate, but since
# expparams['meas'] is real (that is, since the measurement)
# is Hermitian, then that's not needed here.
expparams['meas'],
modelparams
)
np.clip(pr1, 0, 1, out=pr1)
return FiniteOutcomeModel.pr0_to_likelihood_array(outcomes, 1 - pr1)
def likelihood(self, outcomes, modelparams, expparams):
super(TomographyModel, self).likelihood(outcomes, modelparams,
expparams)
pr1 = np.empty((modelparams.shape[0], expparams.shape[0]))
pr1[:, :] = np.einsum(
'ei,mi->me',
# This should be the Hermitian conjugate, but since
# expparams['meas'] is real (that is, since the measurement)
# is Hermitian, then that's not needed here.
expparams['meas'],
modelparams)
np.clip(pr1, 0, 1, out=pr1)
return FiniteOutcomeModel.pr0_to_likelihood_array(outcomes, 1 - pr1)
def likelihood(self, outcomes, modelparams, expparams):
# modelparams give particles
# outcomes should be a length 1 list with a single outcome for the single experiment performed
time = expparams["t"]
print(
f"Likelihood fnc given \nOutcomes:\n {outcomes} \nParticles:\n {modelparams} \nExperiment:\n {expparams}"
)
prob_measuring_zero = compute_prob_measuring_zero(
modelparams,
time=expparams["t"] # particles
)
likelihood = FiniteOutcomeModel.pr0_to_likelihood_array(
outcomes=outcomes,
pr0=prob_measuring_zero,
)
return likelihood
def likelihood(self, outcomes, modelparams, expparams):
super(MockModel, self).likelihood(outcomes, modelparams, expparams)
pr0 = np.ones((modelparams.shape[0], expparams.shape[0])) / 2
return FiniteOutcomeModel.pr0_to_likelihood_array(outcomes, pr0)
