# Copyright 2021 The NetKet Authors - All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any
from numba import jit
import numpy as np
from flax import struct
from netket.operator import DiscreteOperator
from netket.utils.types import Array
from .base import MetropolisRule
@struct.dataclass
class CustomRuleState:
sections: np.ndarray
rand_op_n: np.ndarray
weight_cumsum: np.ndarray
[docs]
class CustomRuleNumpy(MetropolisRule):
operator: Any = struct.field(pytree_node=False)
weight_list: Any = struct.field(pytree_node=False)
[docs]
def __init__(self, operator: DiscreteOperator, weight_list: Array = None):
"""
Construct a Custom Rule.
Args:
operator: a LocalOperator describing the possible moves.
weight_list: an optional list of probability for every move.
"""
if not isinstance(operator, DiscreteOperator):
raise TypeError(
"Argument to CustomRuleNumpy must be a valid operator, "
f"but operator is a {type(operator)}."
)
# Raise errors if hilbert is not valid
_check_operators(operator.operators)
if weight_list is None:
weight_list = np.ones(operator.n_operators, dtype=np.float32)
if weight_list is not None:
if weight_list.shape != (operator.n_operators,):
raise ValueError("move_weights have the wrong shape")
if weight_list.min() < 0:
raise ValueError("move_weights must be positive")
self.operator = operator
# normalise
self.weight_list = weight_list / weight_list.sum()
[docs]
def init_state(rule, sampler, machine, params, key):
return CustomRuleState(
sections=np.empty(sampler.n_batches, dtype=np.int32),
rand_op_n=np.empty(sampler.n_batches, dtype=np.int32),
weight_cumsum=rule.weight_list.cumsum(),
)
[docs]
def transition(rule, sampler, machine, parameters, state, rng, σ):
rule_state = state.rule_state
# numba does not support jitting np.random number generators
# so we have to generate the random numbers outside the jit
# block
rnd_uniform = rng.uniform(0.0, 1.0, size=σ.shape[0])
_pick_random_and_init(
σ.shape[0],
rule_state.weight_cumsum,
rnd_uniform=rnd_uniform,
out=rule_state.rand_op_n,
)
σ_conns, mels = rule.operator.get_conn_filtered(
state.σ, rule_state.sections, rule_state.rand_op_n
)
# numba does not support jitting np.random number generators
# so we have to generate the random numbers outside the jit
# block
rnd_uniform = rng.uniform(0.0, 1.0, size=state.σ1.shape[0])
_choose_and_return(
state.σ1,
σ_conns,
mels,
rule_state.sections,
state.log_prob_corr,
rnd_uniform,
)
@jit(nopython=True)
def _pick_random_and_init(batch_size, move_cumulative, rnd_uniform, out):
for i in range(batch_size):
p = rnd_uniform[i]
out[i] = np.searchsorted(move_cumulative, p)
# return out
@jit(nopython=True)
def _choose_and_return(σp, x_prime, mels, sections, log_prob_corr, rnd_uniform):
low = 0
for i in range(σp.shape[0]):
p = rnd_uniform[i]
exit_state = 0
cumulative_prob = mels[low].real
while p > cumulative_prob:
exit_state += 1
cumulative_prob += mels[low + exit_state].real
σp[i] = x_prime[low + exit_state]
low = sections[i]
log_prob_corr.fill(0.0)
def _check_operators(operators):
for op in operators:
op = op.todense()
assert op.imag.max() < 1.0e-10
assert op.min() >= 0
assert np.allclose(op.sum(axis=0), 1.0)
assert np.allclose(op.sum(axis=1), 1.0)
assert np.allclose(op, op.T)
