pygsti.objectivefns.objectivefns.LogLWildcardFunction

class LogLWildcardFunction(logl_objective_fn, base_pt, wildcard)

Bases: ObjectiveFunction

A wildcard-budget bolt-on to an existing objective function.

Currently, this existing function must be a log-likelihood type function because the computational logic assumes this. The resulting object is an objective function over the space of wildcard budget parameter vectors (not model parameters).

Parameters:

• logl_objective_fn (PoissonPicDeltaLogLFunction) – The bare log-likelihood function.

• base_pt (numpy.ndarray) – Unused. The model-parameter vector where this objective function is based.

• wildcard (WildcardBudget) – The wildcard budget that adjusts the “bare” probabilities of logl_objective_fn before evaluating the rest of the objective function.

Methods

__init__(logl_objective_fn, base_pt, wildcard)
chi2k_distributed_qty(objective_function_value)	Convert a value of this objective function to one that is expected to be chi2_k distributed.
dlsvec(wvec)	The derivative (jacobian) of the least-squares vector.
fn([wvec])	Evaluate this objective function.
lsvec([wvec])	Compute the least-squares vector of the objective function.
terms([wvec])	Compute the terms of the objective function.

chi2k_distributed_qty(objective_function_value)

Convert a value of this objective function to one that is expected to be chi2_k distributed.

Parameters:

objective_function_value (float) – A value of this objective function, i.e. one returned from self.fn(…).

Return type:

float

dlsvec(wvec)

The derivative (jacobian) of the least-squares vector.

Derivatives are taken with respect to wildcard budget parameters.

Parameters:

wvec (numpy.ndarray, optional) – The vector of (wildcard budget) parameters to evaluate the objective function at. If None, then the budget’s current parameter vector is used (held internally).

Returns:

An array of shape (nElements,nParams) where nElements is the number of circuit outcomes and nParams is the number of wildcard budget parameters.

Return type:

numpy.ndarray

fn(wvec=None)

Evaluate this objective function.

Parameters:

wvec (numpy.ndarray, optional) – The vector of (wildcard budget) parameters to evaluate the objective function at. If None, then the budget’s current parameter vector is used (held internally).

Return type:

float

lsvec(wvec=None)

Compute the least-squares vector of the objective function.

This is the square-root of the terms-vector returned from terms(). This vector is the objective function value used by a least-squares optimizer when optimizing this objective function. Note that the existence of this quantity requires that the terms be non-negative. If this is not the case, an error is raised.

Parameters:

wvec (numpy.ndarray, optional) – The vector of (wildcard budget) parameters to evaluate the objective function at. If None, then the budget’s current parameter vector is used (held internally).

Returns:

An array of shape (nElements,) where nElements is the number of circuit outcomes.

Return type:

numpy.ndarray

terms(wvec=None)

Compute the terms of the objective function.

The “terms” are the per-circuit-outcome values that get summed together to result in the objective function value.

Parameters:

wvec (numpy.ndarray, optional) – The vector of (wildcard budget) parameters to evaluate the objective function at. If None, then the budget’s current parameter vector is used (held internally).

Returns:

An array of shape (nElements,) where nElements is the number of circuit outcomes.

Return type:

numpy.ndarray