def random_state(
self,
key=NoneType(),
size: Optional[int] = NoneType(),
dtype=np.float32,
out: Optional[np.ndarray] = None,
rgen=None,
) -> jnp.ndarray:
r"""Generates either a single or a batch of uniformly distributed random states.
Runs as :code:`random_state(self, key, size=None, dtype=np.float32)` by default.
Args:
key: rng state from a jax-style functional generator.
size: If provided, returns a batch of configurations of the form :code:`(size, N)` if size
is an integer or :code:`(*size, N)` if it is a tuple and where :math:`N` is the Hilbert space size.
By default, a single random configuration with shape :code:`(#,)` is returned.
dtype: DType of the resulting vector.
out: Deprecated. Will be removed in v3.1
rgen: Deprecated. Will be removed in v3.1
Returns:
A state or batch of states sampled from the uniform distribution on the hilbert space.
Example:
>>> import netket, jax
>>> hi = netket.hilbert.Qubit(N=2)
>>> k1, k2 = jax.random.split(jax.random.PRNGKey(1))
>>> print(hi.random_state(key=k1))
[1. 0.]
>>> print(hi.random_state(key=k2, size=2))
[[0. 0.]
[0. 1.]]
"""
# legacy support
# TODO: Remove in 3.1
# if no positional arguments, and key is unspecified -> legacy
if isinstance(key, NoneType):
warn_deprecation(legacy_warn_str)
# legacy sure
if isinstance(size, NoneType):
return self._random_state_legacy(size=None, out=out, rgen=rgen)
else:
return self._random_state_legacy(size=size, out=out, rgen=rgen)
elif (isinstance(key, tuple)
or isinstance(key, int) and isinstance(size, NoneType)):
# if one positional argument legacy typee...
warn_deprecation(legacy_warn_str)
return self._random_state_legacy(size=key, out=out, rgen=rgen)
else:
from netket.hilbert import random
size = size if not isinstance(size, NoneType) else None
return random.random_state(self, key, size, dtype=dtype)
def random_state(
self,
key=NoneType(),
size: Optional[int] = NoneType(),
dtype=np.float32,
out: Optional[np.ndarray] = None,
rgen=None,
) -> jnp.ndarray:
r"""Generates either a single or a batch of uniformly distributed random states.
random_state(self, key, size=None, dtype=np.float32)
Args:
key: rng state from a jax-style functional generator.
size: If provided, returns a batch of configurations of the form (size, #) if size
is an integer or (*size, #) if it is a tuple and where # is the Hilbert space size.
By default, a single random configuration with shape (#,) is returned.
dtype: Dtype of the resulting vector.
out: Deprecated. Will be removed in v3.1
rgen: Deprecated. Will be removed in v3.1
Returns:
A state or batch of states sampled from the uniform distribution on the hilbert space.
Example:
>>> hi = netket.hilbert.Qubit(N=2)
>>> hi.random_state(jax.random.PRNGKey(0))
array([0., 1.])
>>> hi.random_state(size=2)
array([[0., 0.], [1., 0.]])
"""
# legacy support
# TODO: Remove in 3.1
# if no positional arguments, and key is unspecified -> legacy
if isinstance(key, NoneType):
# legacy sure
if isinstance(size, NoneType):
return self._random_state_legacy(size=None, out=out, rgen=rgen)
else:
return self._random_state_legacy(size=size, out=out, rgen=rgen)
elif (isinstance(key, tuple)
or isinstance(key, int) and isinstance(size, NoneType)):
# if one positional argument legacy typee...
return self._random_state_legacy(size=key, out=out, rgen=rgen)
else:
from netket.hilbert import random
size = size if not isinstance(size, NoneType) else None
return random.random_state(self, key, size, dtype=dtype)
def random_state(
self,
key=None,
size: Optional[int] = None,
dtype=np.float32,
) -> jnp.ndarray:
r"""Generates either a single or a batch of uniformly distributed random states.
Runs as :code:`random_state(self, key, size=None, dtype=np.float32)` by default.
Args:
key: rng state from a jax-style functional generator.
size: If provided, returns a batch of configurations of the form
:code:`(size, N)` if size is an integer or :code:`(*size, N)` if it is
a tuple and where :math:`N` is the Hilbert space size.
By default, a single random configuration with shape
:code:`(#,)` is returned.
dtype: DType of the resulting vector.
Returns:
A state or batch of states sampled from the uniform distribution on the
hilbert space.
Example:
>>> import netket, jax
>>> hi = netket.hilbert.Qubit(N=2)
>>> k1, k2 = jax.random.split(jax.random.PRNGKey(1))
>>> print(hi.random_state(key=k1))
[1. 0.]
>>> print(hi.random_state(key=k2, size=2))
[[0. 0.]
[0. 1.]]
"""
from netket.hilbert import random
return random.random_state(self, key, size, dtype=dtype)