def likelihood(self, outcomes, modelparams, expparams):
r"""
Calculates the probability of each given outcome, conditioned on each
given model parameter vector and each given experimental control setting.
:param np.ndarray modelparams: A shape ``(n_models, n_modelparams)``
array of model parameter vectors describing the hypotheses for
which the likelihood function is to be calculated.
:param np.ndarray expparams: A shape ``(n_experiments, )`` array of
experimental control settings, with ``dtype`` given by
:attr:`~qinfer.Simulatable.expparams_dtype`, describing the
experiments from which the given outcomes were drawn.
:rtype: np.ndarray
:return: A three-index tensor ``L[i, j, k]``, where ``i`` is the outcome
being considered, ``j`` indexes which vector of model parameters was used,
and where ``k`` indexes which experimental parameters where used.
Each element ``L[i, j, k]`` then corresponds to the likelihood
:math:`\Pr(d_i | \vec{x}_j; e_k)`.
"""
# Count the number of times the inner-most loop is called.
self._call_count += (
safe_shape(outcomes) * safe_shape(modelparams) * safe_shape(expparams)
)
def likelihood(self, outcomes, modelparams, expparams):
# TODO: document
# Count the number of times the inner-most loop is called.
self._call_count += (
safe_shape(outcomes) * safe_shape(modelparams) * safe_shape(expparams)
)
def likelihood(self, outcomes, modelparams, expparams):
r"""
Calculates the probability of each given outcome, conditioned on each
given model parameter vector and each given experimental control setting.
:param np.ndarray modelparams: A shape ``(n_models, n_modelparams)``
array of model parameter vectors describing the hypotheses for
which the likelihood function is to be calculated.
:param np.ndarray expparams: A shape ``(n_experiments, )`` array of
experimental control settings, with ``dtype`` given by
:attr:`~qinfer.Simulatable.expparams_dtype`, describing the
experiments from which the given outcomes were drawn.
:rtype: np.ndarray
:return: A three-index tensor ``L[i, j, k]``, where ``i`` is the outcome
being considered, ``j`` indexes which vector of model parameters was used,
and where ``k`` indexes which experimental parameters where used.
Each element ``L[i, j, k]`` then corresponds to the likelihood
:math:`\Pr(d_i | \vec{x}_j; e_k)`.
"""
# Count the number of times the inner-most loop is called.
self._call_count += (safe_shape(outcomes) * safe_shape(modelparams) *
safe_shape(expparams))
def pr0_to_likelihood_array(outcomes, pr0):
"""
Assuming a two-outcome measurement with probabilities given by the
array ``pr0``, returns an array of the form expected to be returned by
``likelihood`` method.
:param numpy.ndarray outcomes: Array of integers indexing outcomes.
:param numpy.ndarray pr0: Array of shape ``(n_models, n_experiments)``
describing the probability of obtaining outcome ``0`` from each
set of model parameters and experiment parameters.
"""
pr0 = pr0[np.newaxis, ...]
pr1 = 1 - pr0
if len(np.shape(outcomes)) == 0:
outcomes = np.array(outcomes)[None]
return np.concatenate([
pr0 if outcomes[idx] == 0 else pr1
for idx in range(safe_shape(outcomes))
])
def pr0_to_likelihood_array(outcomes, pr0):
"""
Assuming a two-outcome measurement with probabilities given by the
array ``pr0``, returns an array of the form expected to be returned by
``likelihood`` method.
:param numpy.ndarray outcomes: Array of integers indexing outcomes.
:param numpy.ndarray pr0: Array of shape ``(n_models, n_experiments)``
describing the probability of obtaining outcome ``0`` from each
set of model parameters and experiment parameters.
"""
pr0 = pr0[np.newaxis, ...]
pr1 = 1 - pr0
if len(np.shape(outcomes)) == 0:
outcomes = np.array(outcomes)[None]
return np.concatenate([
pr0 if outcomes[idx] == 0 else pr1
for idx in range(safe_shape(outcomes))
])
