def __pre_init__(self, hilbert, rule, **kwargs):
r"""
Constructs a Metropolis Sampler.
Args:
hilbert: The hilbert space to sample
rule: A `MetropolisRule` to generate random transitions from a given state as
well as uniform random states.
n_sweeps: The number of exchanges that compose a single sweep.
If None, sweep_size is equal to the number of degrees of freedom being sampled
(the size of the input vector s to the machine).
reset_chains: If False the state configuration is not resetted when reset() is called.
n_chains: The number of Markov Chain to be run in parallel on a single process.
machine_pow: The power to which the machine should be exponentiated to generate the pdf (default = 2).
dtype: The dtype of the statees sampled (default = np.float32).
"""
# process arguments in the base
args, kwargs = super().__pre_init__(hilbert=hilbert, **kwargs)
kwargs["rule"] = rule
# deprecation warnings
if "reset_chain" in kwargs:
warn_deprecation(
"The keyword argument `reset_chain` is deprecated in favour of `reset_chains`"
)
kwargs["reset_chains"] = kwargs.pop("reset_chain")
return args, kwargs
def edges(
self,
color=None,
*,
return_color: bool = False,
filter_color: Optional[int] = None,
) -> EdgeSequence:
if color is not None:
warn_deprecation(
"The color option has been split into return_color and filter_color."
)
# need to check for bool first, because bool is a subclass of int
if isinstance(color, bool):
return_color = color
elif isinstance(color, int):
filter_color = color
else:
raise TypeError("Incorrect type for 'color'")
if not return_color and filter_color is None:
return self._igraph.get_edgelist()
edges_with_color = zip(self._igraph.get_edgelist(), self.edge_colors)
if filter_color is not None:
edges_with_color = filter(lambda x: x[1] == filter_color,
edges_with_color)
if return_color:
return [(*e, c) for (e, c) in edges_with_color]
else:
return [e for (e, _) in edges_with_color]
def __pre_init__(self, hilbert, rule, **kwargs):
"""
Constructs a Metropolis Sampler.
Args:
hilbert: The Hilbert space to sample.
rule: A `MetropolisRule` to generate random transitions from a given state as
well as uniform random states.
n_chains: The total number of independent Markov chains across all MPI ranks. Either specify this or `n_chains_per_rank`.
n_chains_per_rank: Number of independent chains on every MPI rank (default = 16).
n_sweeps: Number of sweeps for each step along the chain. Defaults to the number of sites in the Hilbert space.
This is equivalent to subsampling the Markov chain.
reset_chains: If True, resets the chain state when `reset` is called on every new sampling (default = False).
machine_pow: The power to which the machine should be exponentiated to generate the pdf (default = 2).
dtype: The dtype of the states sampled (default = np.float64).
"""
if "n_chains" not in kwargs and "n_chains_per_rank" not in kwargs:
kwargs["n_chains_per_rank"] = 16
# process arguments in the base
args, kwargs = super().__pre_init__(hilbert=hilbert, **kwargs)
kwargs["rule"] = rule
# deprecation warnings
if "reset_chain" in kwargs:
warn_deprecation(
"The keyword argument `reset_chain` is deprecated in favour of `reset_chains`"
)
kwargs["reset_chains"] = kwargs.pop("reset_chain")
return args, kwargs
def __pre_init__(self, *args, **kwargs):
"""
Construct an exact sampler.
Args:
hilbert: The Hilbert space to sample.
machine_pow: The power to which the machine should be exponentiated to generate the pdf (default = 2).
dtype: The dtype of the states sampled (default = np.float64).
"""
if "n_chains" in kwargs or "n_chains_per_rank" in kwargs:
warn_deprecation(
"Specifying `n_chains` or `n_chains_per_rank` when constructing exact samplers is deprecated."
)
return super().__pre_init__(*args, **kwargs)
def __pre_init__(self, *args, **kwargs):
"""
Construct an autoregressive direct sampler.
Args:
hilbert: The Hilbert space to sample.
dtype: The dtype of the states sampled (default = np.float64).
Note:
`ARDirectSampler.machine_pow` has no effect. Please set the model's `machine_pow` instead.
"""
if "n_chains" in kwargs or "n_chains_per_rank" in kwargs:
warn_deprecation(
"Specifying `n_chains` or `n_chains_per_rank` when constructing exact samplers is deprecated."
)
return super().__pre_init__(*args, **kwargs)
def __pre_init__(self, hilbert: AbstractHilbert, rule: MetropolisRule, **kwargs):
"""
Constructs a Metropolis Sampler.
Args:
hilbert: The Hilbert space to sample.
rule: A `MetropolisRule` to generate random transitions from a given state as
well as uniform random states.
n_chains: The total number of independent Markov chains across all MPI ranks.
Either specify this or `n_chains_per_rank`. If MPI is disabled, the two are equivalent;
if MPI is enabled and `n_chains` is specified, then every MPI rank will run
`n_chains/mpi.n_nodes` chains. In general, we recommend specifying `n_chains_per_rank`
as it is more portable.
n_chains_per_rank: Number of independent chains on every MPI rank (default = 16).
n_sweeps: Number of sweeps for each step along the chain.
This is equivalent to subsampling the Markov chain. (Defaults to the number of sites
in the Hilbert space.)
reset_chains: If True, resets the chain state when `reset` is called on every
new sampling (default = False).
machine_pow: The power to which the machine should be exponentiated to generate
the pdf (default = 2).
dtype: The dtype of the states sampled (default = np.float64).
"""
# Validate the inputs
if not isinstance(rule, MetropolisRule):
raise TypeError(
f"The second positional argument, rule, must be a MetropolisRule but "
f"`type(rule)={type(rule)}`."
)
if "n_chains" not in kwargs and "n_chains_per_rank" not in kwargs:
kwargs["n_chains_per_rank"] = 16
# process arguments in the base
args, kwargs = super().__pre_init__(hilbert=hilbert, **kwargs)
kwargs["rule"] = rule
# deprecation warnings
if "reset_chain" in kwargs:
warn_deprecation(
"The keyword argument `reset_chain` is deprecated in favour of `reset_chains`"
)
kwargs["reset_chains"] = kwargs.pop("reset_chain")
return args, kwargs