hypney.estimators package

Submodules

hypney.estimators.confidence_interval module

class hypney.estimators.confidence_interval.CentralInterval(*args, cl=0.9, **kwargs)

Bases: object

Methods

__call__([data])

Call self as a function.
class hypney.estimators.confidence_interval.ConfidenceInterval(stat, poi='rate', cl=0.9, sign=1, anchors=None, use_cdf=False, ppf_fudge=0, ppf_interpolation=None)

Bases: object

Methods

__call__([data])

Call self as a function.
exception hypney.estimators.confidence_interval.EmptyIntervalError

Bases: Exception

Raised when empty interval would be returned (possible, but also possible anchors badly chosen)

exception hypney.estimators.confidence_interval.FullIntervalError

Bases: Exception

Raised when the whole real line would be returned (possible, but also possible anchors badly chosen)

class hypney.estimators.confidence_interval.LowerLimit(stat, poi='rate', cl=0.9, sign=1, anchors=None, use_cdf=False, ppf_fudge=0, ppf_interpolation=None)

Bases: ConfidenceInterval

Methods

__call__([data])

Call self as a function.
side = -1
class hypney.estimators.confidence_interval.UpperLimit(stat, poi='rate', cl=0.9, sign=1, anchors=None, use_cdf=False, ppf_fudge=0, ppf_interpolation=None)

Bases: ConfidenceInterval

Methods

__call__([data])

Call self as a function.
side = 1

hypney.estimators.optimizers module

class hypney.estimators.optimizers.FunctionLike

Bases: type

An function-like type implemented as a class FunctionLike’s return the result of some computation when called, just like a function, but are implemented as a class, and can thus use attributes, methods, inheritance, etc. Roughly, a call to a FunctionLike results in:

  • Making a new instance (self) as usual, including a call to self.__init__(*args, **kwargs)

  • return self(), rather than return self

Methods

__call__(*args, **kwargs)

Call self as a function.

mro(/)

Return a type's method resolution order.
class hypney.estimators.optimizers.Maximum(*args, **kwargs)

Bases: Minimum

Methods

__call__()

Call self as a function.

objective
sign = -1
class hypney.estimators.optimizers.MaximumAndValue(*args, **kwargs)

Bases: MinimumAndValue

Methods

__call__()

Call self as a function.

objective
sign = -1
class hypney.estimators.optimizers.Minimum(*args, **kwargs)

Bases: object

Methods

__call__()

Call self as a function.

objective
objective(params: Tensor, autograd)
return_kind = 'point'
sign = 1
class hypney.estimators.optimizers.MinimumAndValue(*args, **kwargs)

Bases: Minimum

Methods

__call__()

Call self as a function.

objective
return_kind = ('point', 'value')

Module contents