pmrf.sampling.samplers.LatinHypercubeSampler

class pmrf.sampling.samplers.LatinHypercubeSampler(model)[source]

__init__(model)

Methods

`__delattr__`(name, /)	Implement delattr(self, name).
`__dir__`()	Default dir() implementation.
`__eq__`(value, /)	Return self==value.
`__format__`(format_spec, /)	Default object formatter.
`__ge__`(value, /)	Return self>=value.
`__getattribute__`(name, /)	Return getattr(self, name).
`__gt__`(value, /)	Return self>value.
`__hash__`()	Return hash(self).
`__init__`(model)
`__init_subclass__`	This method is called when a class is subclassed.
`__iter__`()
`__le__`(value, /)	Return self<=value.
`__len__`()
`__lt__`(value, /)	Return self<value.
`__ne__`(value, /)	Return self!=value.
`__new__`(**kwargs)
`__reduce__`()	Helper for pickle.
`__reduce_ex__`(protocol, /)	Helper for pickle.
`__repr__`()	Return repr(self).
`__setattr__`(name, value, /)	Implement setattr(self, name, value).
`__sizeof__`()	Size of object in memory, in bytes.
`__str__`()	Return str(self).
`__subclasshook__`	Abstract classes can override this to customize issubclass().
`_generate_hypercube_samples`(N, D)
`_generate_params`(N)
`generate_features`(N, features, frequency[, ...])
`generate_models`(N)	Generates N random models using the sampler's engine.
`range`(N)	Allows the CircuitSampler to be used as an iterable. To use, call e.g:

Attributes

`__abstractmethods__`
`__annotations__`
`__dict__`
`__doc__`
`__module__`
`__slots__`
`__weakref__`	list of weak references to the object (if defined)
`_abc_impl`