pmrf.sampling.samplers.UniformSampler
- class pmrf.sampling.samplers.UniformSampler(model)[source]
Bases:
BaseSamplerSampler using standard Uniform Random Sampling (Monte Carlo).
Samples are drawn independently from a uniform distribution over the unit hypercube.
- Parameters:
model (ModelT)
Methods
__class_getitem__(params)__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)Initialize the sampler.
__init_subclass__(*args, **kwargs)This method is called when a class is subclassed.
__iter__()Iterate over generated models.
__le__(value, /)Return self<=value.
__len__()Return the number of samples in the current iteration context.
__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 samples using uniform random sampling.
_generate_params(N)Internal method to generate parameter values in physical space.
generate_features(N, features, frequency[, ...])Generate feature vectors for N random samples.
generate_models(N)Generate N random model instances using the sampler's engine.
range(N)Configure the sampler for iteration over N samples.
Attributes
__abstractmethods____annotations____dict____doc____module____orig_bases____parameters____slots____weakref__list of weak references to the object (if defined)
_abc_impl_is_protocol