This sampler generates i.i.d. samples from . In order to perform exact sampling, is precomputed an all the possible values of the quantum numbers . This sampler has thus an exponential cost with the number of degrees of freedom, and cannot be used for large systems, where Metropolis-based sampling are instead a viable option.

Class Constructor

Constructs a new ExactSampler given a machine.

Argument Type Description
machine netket.machine.Machine A machine used for the sampling. The probability distribution being sampled from is .


Exact sampling from a RBM machine in a 1D lattice of spin 1/2

>>> import netket as nk
>>> g=nk.graph.Hypercube(length=8,n_dim=1,pbc=True)
>>> hi=nk.hilbert.Spin(s=0.5,graph=g)
>>> # RBM Spin Machine
>>> ma = nk.machine.RbmSpin(alpha=1, hilbert=hi)
>>> sa = nk.sampler.ExactSampler(machine=ma)

Class Methods


Resets the state of the sampler, including the acceptance rate statistics and optionally initializing at random the visible units being sampled.

Argument Type Description
init_random bool=False If True the quantum numbers (visible units)


Seeds the random number generator used by the Sampler.

Argument Type Description
base_seed int The base seed for the random number generator


Performs a sampling sweep. Typically a single sweep consists of an extensive number of local moves.


Property Type Description
acceptance numpy.array The measured acceptance rate for the sampling. In the case of rejection-free sampling this is always equal to 1.
hilbert netket.hilbert The Hilbert space used for the sampling.
machine netket.machine The machine used for the sampling.
visible numpy.array The quantum numbers being sampled, and distributed according to