# FixedValuePrior¶

class lightkurve.prf.FixedValuePrior(value, name=None)[source]

Bases: oktopus.prior.Prior

An improper prior with a negative log probability of 0 at a single fixed value and inf elsewhere. This is similar to a Dirac Delta function, except this function does not peak at infinity so that it can be used in numerical optimization functions. It does not integrate to one as a result and is therefore an “improper distribution”.

Examples

>>> fp = FixedValuePrior(1)
>>> fp(1)
-0.0
>>> fp(0.5)
inf

Attributes: value : int or array-like of ints The fixed value.

Attributes Summary

 mean Returns the fixed value. name A name associated with the prior variance Returns zero.

Methods Summary

 __call__(params) Calls evaluate() evaluate(params) Returns the negative log pdf. fit([optimizer]) Minimizes the evaluate() function using scipy.optimize.minimize(), scipy.optimize.differential_evolution(), scipy.optimize.basinhopping(), or skopt.gp.gp_minimize(). gradient(params) Returns the gradient of the loss function evaluated at params hessian(params) Returns the Hessian matrix of the loss function evaluated at params

Attributes Documentation

mean

Returns the fixed value.

name

A name associated with the prior

variance

Returns zero.

Methods Documentation

__call__(params)

Calls evaluate()

evaluate(params)[source]

Returns the negative log pdf.

fit(optimizer='minimize', **kwargs)

Minimizes the evaluate() function using scipy.optimize.minimize(), scipy.optimize.differential_evolution(), scipy.optimize.basinhopping(), or skopt.gp.gp_minimize().

Parameters: optimizer : str Optimization algorithm. Options are: - 'minimize' uses :func:scipy.optimize.minimize - 'differential_evolution' uses :func:scipy.optimize.differential_evolution - 'basinhopping' uses :func:scipy.optimize.basinhopping - 'gp_minimize' uses :func:skopt.gp.gp_minimize  ‘minimize’ is usually robust enough and therefore recommended whenever a good initial guess can be provided. The remaining options are global optimizers which might provide better results precisely in cases where a close engouh initial guess cannot be obtained trivially. kwargs : dict Dictionary for additional arguments. opt_result : Object containing the results of the optimization process. Note: this is also stored in self.opt_result.
gradient(params)[source]

Returns the gradient of the loss function evaluated at params

Parameters: params : ndarray parameter vector of the model
hessian(params)

Returns the Hessian matrix of the loss function evaluated at params

Parameters: params : ndarray parameter vector of the model