How to use the openjij.model.BinaryQuadraticModel function in openjij

# 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.

import openjij
from openjij.model import BinaryQuadraticModel
import cxxjij as cj


class ChimeraModel(BinaryQuadraticModel):
    """Binary quadnratic model dealing with chimera graph
    This model deal with chimera graph.
    ChimeraModel provide methods to verify whether a given interaction graph matches a Chimera graph and to convert it to cxxjij.graph.Chimera.

    Examples:
        # This interactions satisfy chimera topology.
        >>> Q={(0, 4): -1, (4, 12): -1}
        >>> chimera_model = ChimeraModel(Q, unit_num_L=2)  # make
        >>> chimera_self.validate_chimera()
    """

    def __init__(self, linear=None, quadratic=None,
                 offset=0.0, var_type=openjij.SPIN,
                 unit_num_L=None, model=None,
                 gpu=False):
        self.gpu = gpu

def sampling(self, model,
                 initial_state=None,
                 reinitialize_state=True, seed=None,
                 **kwargs):
        # Check the system for GPU is compiled
        try:
            self.system_class = cxxjij.system.ChimeraClassicalGPU
        except AttributeError:
            raise AttributeError(
                'Does the computer you are running have a GPU? Compilation for the GPU has not been done. Please reinstall or compile.')

        # convert to ChimeraModel from normal BQM
        if isinstance(model, BinaryQuadraticModel):
            if 'unit_num_L' in kwargs:
                self.unit_num_L = kwargs['unit_num_L']
            elif not self.unit_num_L:
                raise ValueError(
                    'Input "unit_num_L" to the argument or the constructor of GPUSASampler.')
            chimera_model = ChimeraModel(
                model=model, unit_num_L=self.unit_num_L, gpu=True)
        else:
            chimera_model = model

        if chimera_model.unit_num_L % 2 != 0:
            raise ValueError('unit_num_L should be even number.')

        self.unit_num_L = chimera_model.unit_num_L
        self._set_model(chimera_model)

def _make_dense_graph(self, h=None, J=None, Q=None, var_type='SPIN'):
        if var_type == 'BINARY':
            if Q is None:
                raise ValueError('Input QUBO matrix: Q')
            model = BinaryQuadraticModel(Q=Q, var_type='BINARY')
        elif var_type == 'SPIN':
            if h is None or J is None:
                raise ValueError('Input h and J')
            model = BinaryQuadraticModel(h=h, J=J, var_type='SPIN')
        self.indices = model.indices
        self.N = len(model.indices)
        self.energy_bias = model.energy_bias

        self.var_type = model.var_type

        dense_graph = model.convert_to_dense_graph()
        self.model = model
        return dense_graph

def _make_dense_graph(self, h=None, J=None, Q=None, spin_type='ising'):
        if spin_type=='qubo':
            if Q is None:
                raise ValueError('Input QUBO matrix: Q')
            model = BinaryQuadraticModel(Q=Q, spin_type='qubo')
        elif spin_type=='ising':
            if h is None or J is None:
                raise ValueError('Input h and J')
            model = BinaryQuadraticModel(h=h, J=J, spin_type='ising')
        self.indices = model.indices
        self.N = len(model.indices)
        self.energy_bias = model.energy_bias

        self.spin_type = model.spin_type

        dense_graph = model.convert_to_dense_graph()
        return dense_graph

# 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 openjij.model import BinaryQuadraticModel

class ChimeraModel(BinaryQuadraticModel):
    def __init__(self, h=None, J=None, Q=None, unit_num_L=None, model=None, var_type='SPIN'):
        if model:
            self.linear = model.linear
            self.quad = model.quad
            self.indices = model.indices
            self.energy_bias = model.energy_bias
            self.var_type = model.var_type
            self._interaction_matrix = None
        else:
            super().__init__(h=h, J=J, Q=Q, var_type=var_type)

        if not unit_num_L:
            raise ValueError('Input unit_num_L which is the length of the side of the two-dimensional grid where chimera unit cells are arranged.')
        self.unit_num_L = unit_num_L
        
        self.coordinate = self._validate_indices(self.indices)

def _make_dense_graph(self, h=None, J=None, Q=None, var_type='SPIN'):
        if var_type == 'BINARY':
            if Q is None:
                raise ValueError('Input QUBO matrix: Q')
            model = BinaryQuadraticModel(Q=Q, var_type='BINARY')
        elif var_type == 'SPIN':
            if h is None or J is None:
                raise ValueError('Input h and J')
            model = BinaryQuadraticModel(h=h, J=J, var_type='SPIN')
        self.indices = model.indices
        self.N = len(model.indices)
        self.energy_bias = model.energy_bias

        self.var_type = model.var_type

        dense_graph = model.convert_to_dense_graph()
        self.model = model
        return dense_graph

def _make_dense_graph(self, h=None, J=None, Q=None, spin_type='ising'):
        if spin_type=='qubo':
            if Q is None:
                raise ValueError('Input QUBO matrix: Q')
            model = BinaryQuadraticModel(Q=Q, spin_type='qubo')
        elif spin_type=='ising':
            if h is None or J is None:
                raise ValueError('Input h and J')
            model = BinaryQuadraticModel(h=h, J=J, spin_type='ising')
        self.indices = model.indices
        self.N = len(model.indices)
        self.energy_bias = model.energy_bias

        self.spin_type = model.spin_type

        dense_graph = model.convert_to_dense_graph()
        return dense_graph

def sampling(self, model,
                 initial_state=None,
                 reinitialize_state=True, seed=None,
                 **kwargs):
        # Check the system for GPU is compiled
        try:
            self.system_class = cxxjij.system.ChimeraTransverseGPU
        except AttributeError:
            raise AttributeError(
                'Does the computer you are running have a GPU? Compilation for the GPU has not been done. Please reinstall or compile.')

        # convert to ChimeraModel from normal BQM
        if isinstance(model, BinaryQuadraticModel):
            if 'unit_num_L' in kwargs:
                self.unit_num_L = kwargs['unit_num_L']
            elif not self.unit_num_L:
                raise ValueError(
                    'Input "unit_num_L" to the argument or the constructor of GPUSQASampler.')
            chimera_model = ChimeraModel(
                model=model, unit_num_L=self.unit_num_L, gpu=True)
        else:
            chimera_model = model

        if chimera_model.unit_num_L % 2 != 0:
            raise ValueError('unit_num_L should be even number.')

        self.unit_num_L = chimera_model.unit_num_L
        self._set_model(chimera_model)