def change_vartype(self, vartype, inplace=None):
vartype = to_cxxcimod(vartype)
if inplace == None or inplace == True:
return super().change_vartype(vartype)
elif inplace == False:
Model = self._model_selector()
if to_cxxcimod(self.vartype) == vartype:
return Model(self._get_keys(), self._get_values(), vartype)
else:
if vartype == cxxcimod.SPIN:
return Model(self.to_hising(), vartype)
elif vartype == cxxcimod.BINARY:
return Model(self.to_hubo(), vartype)
else:
raise Exception("Unknown vartype error")
else:
raise TypeError("Invalid inplace value")
def __init__(self,
linear,
quadratic,
offset=0.0,
var_type=dimod.SPIN,
**kwargs):
super().__init__(linear, quadratic, offset, to_cxxcimod(var_type))
self._init_process()
def __init__(self, *args, **kwargs):
# replace vartype with cxxcimod type
vartypes = [dimod.SPIN, dimod.BINARY, cimod.SPIN, cimod.BINARY, 'SPIN', 'BINARY']
args = [to_cxxcimod(elem) if type(elem) != np.ndarray and vartypes.count(elem) != 0 else elem for elem in args]
kwargs = {k: to_cxxcimod(v) if type(elem) != np.ndarray and vartypes.count(v) != 0 else v for k,v in kwargs.items()}
# if linear and quadratic are given and mode is dense
if len(args) >= 2 and type(args[0]) == dict and type(args[1]) == dict and sparse is False:
linear = args[0]
quadratic = args[1]
offset,vartype = extract_offset_and_vartype(*args[2:], **kwargs)
mat,idx_to_label = self._generate_mat(linear, quadratic, False)
super().__init__(mat, idx_to_label, offset, vartype, fix_format=False)
else:
super().__init__(*args, **kwargs)
def _BinaryPolynomialModel_from_list(keys: list, values: list, vartype):
if len(keys) == 0:
Model = make_BinaryPolynomialModel({})
return Model(keys, values, to_cxxcimod(vartype))
i = 0
label = None
while i < len(keys):
if len(keys[i]) > 0:
label = keys[i][0]
break
i += 1
if label == None:
Model = make_BinaryPolynomialModel({(): 1.0})
return Model(keys, values, to_cxxcimod(vartype))
else:
if isinstance(label, list):
label = tuple(label)
mock_polynomial = {(label, ): 1.0}
Model = make_BinaryPolynomialModel(mock_polynomial)
return Model(keys, values, to_cxxcimod(vartype))
def change_vartype(self, vartype, inplace=True):
"""
Create a binary quadratic model with the specified vartype
Args:
var_type (cimod.Vartype): SPIN or BINARY
Returns:
A new instance of the BinaryQuadraticModel class.
"""
cxxvartype = to_cxxcimod(vartype)
#FIXME: bottleneck: variable copies
bqm = super().change_vartype(cxxvartype, inplace)
self._re_calculate = True
return BinaryQuadraticModel(bqm.get_linear(), bqm.get_quadratic(),
bqm.get_offset(), vartype)
def _BinaryPolynomialModel_from_dict(polynomial: dict, vartype):
Model = make_BinaryPolynomialModel(polynomial)
return Model(polynomial, to_cxxcimod(vartype))
def BinaryPolynomialModel(*args, **kwargs):
if kwargs == {}:
if len(args) <= 1:
raise TypeError("Invalid argument for this function")
elif len(args) == 2:
if isinstance(args[0], dict):
return _BinaryPolynomialModel_from_dict(
args[0], to_cxxcimod(args[1]))
else:
raise TypeError("Invalid argument for this function")
elif len(args) == 3:
key_condition = isinstance(args[0], list) or isinstance(
args[0], tuple)
val_condition = isinstance(args[1], list) or isinstance(
args[1], tuple)
if key_condition and val_condition:
return _BinaryPolynomialModel_from_list(
args[0], args[1], to_cxxcimod(args[2]))
else:
raise TypeError("Invalid argument for this function")
else:
raise TypeError("Invalid argument for this function")
else:
if 'keys' in kwargs and 'values' in kwargs and 'vartype' in kwargs:
key_condition = isinstance(kwargs['keys'], list) or isinstance(
kwargs['keys'], tuple)
val_condition = isinstance(kwargs['values'], list) or isinstance(
kwargs['values'], tuple)
if key_condition and val_condition:
return _BinaryPolynomialModel_from_list(
kwargs['keys'], kwargs['values'],
to_cxxcimod(kwargs['vartype']))
else:
raise TypeError("Invalid argument for this function")
elif 'polynomial' in kwargs and 'vartype' in kwargs:
if isinstance(kwargs['polynomial'], dict):
return _BinaryPolynomialModel_from_dict(
kwargs['polynomial'], to_cxxcimod(kwargs['vartype']))
else:
raise TypeError("Invalid argument for this function")
elif 'values' in kwargs and 'vartype' in kwargs:
if len(args) != 1:
raise TypeError("Invalid argument for this function")
key_condition = isinstance(args[0], list) or isinstance(
args[0], tuple)
val_condition = isinstance(kwargs['values'], list) or isinstance(
kwargs['values'], tuple)
if key_condition and val_condition:
return _BinaryPolynomialModel_from_list(
args[0], kwargs['values'], to_cxxcimod(kwargs['vartype']))
else:
raise TypeError("Invalid argument for this function")
elif 'vartype' in kwargs:
if len(args) == 1:
if isinstance(args[0], dict):
return _BinaryPolynomialModel_from_dict(
args[0], to_cxxcimod(kwargs['vartype']))
else:
raise TypeError("Invalid argument for this function")
elif len(args) == 2:
key_condition = isinstance(args[0], list) or isinstance(
args[0], tuple)
val_condition = isinstance(args[1], list) or isinstance(
args[1], tuple)
if key_condition and val_condition:
return _BinaryPolynomialModel_from_list(
args[0], args[1], to_cxxcimod(kwargs['vartype']))
else:
raise TypeError("Invalid argument for this function")
else:
raise TypeError("Invalid argument for this function")
else:
raise TypeError("Invalid argument for this function")
def from_serializable(cls, obj):
if (obj["type"] != "BinaryPolynomialModel"):
raise Exception("Type must be \"BinaryPolynomialModel\"")
return cls(obj['variables'], obj['poly_key_distance_list'],
obj['poly_value_list'], to_cxxcimod(obj['vartype']))
def add_interactions_from(self, *args, **kwargs):
if kwargs == {}:
if len(args) == 0:
raise TypeError("Invalid argument for this function")
if len(args) == 1:
if isinstance(args[0], dict):
super().add_interactions_from(args[0])
else:
raise TypeError("Invalid argument for this function")
elif len(args) == 2:
key_condition = isinstance(args[0], list) or isinstance(
args[0], tuple)
val_condition = isinstance(args[1], list) or isinstance(
args[1], tuple)
if isinstance(args[0], dict):
super().add_interactions_from(args[0],
to_cxxcimod(args[1]))
elif key_condition and val_condition:
super().add_interactions_from(args[0], args[1])
else:
raise TypeError("Invalid argument for this function")
elif len(args) == 3:
key_condition = isinstance(args[0], list) or isinstance(
args[0], tuple)
val_condition = isinstance(args[1], list) or isinstance(
args[1], tuple)
if key_condition and val_condition:
super().add_interactions_from(args[0], args[1],
to_cxxcimod(args[2]))
else:
raise TypeError("Invalid argument for this function")
else:
raise TypeError("Invalid argument for this function")
else:
if 'keys' in kwargs and 'values' in kwargs and 'vartype' in kwargs:
if len(args) != 0:
raise TypeError("Invalid argument for this function")
super().add_interactions_from(
kwargs['keys'], kwargs['values'],
to_cxxcimod(kwargs['vartype']))
elif 'values' in kwargs and 'vartype' in kwargs:
if len(args) != 1:
raise TypeError("Invalid argument for this function")
key_condition = isinstance(args[0], list) or isinstance(
args[0], tuple)
val_condition = isinstance(kwargs['values'],
list) or isinstance(
kwargs['values'], tuple)
if key_condition and val_condition:
super().add_interactions_from(
args[0], kwargs['values'],
to_cxxcimod(kwargs['vartype']))
else:
raise TypeError("Invalid argument for this function")
elif 'polynomial' in kwargs and 'vartype' in kwargs:
if len(args) != 0:
raise TypeError("Invalid argument for this function")
if isinstance(kwargs['polynomial'], dict):
super().add_interactions_from(
kwargs['polynomial'],
to_cxxcimod(kwargs['vartype']))
else:
raise TypeError("Invalid argument for this function")
elif 'keys' in kwargs and 'values' in kwargs:
if len(args) != 0:
raise TypeError("Invalid argument for this function")
key_condition = isinstance(kwargs['keys'],
list) or isinstance(
kwargs['keys'], tuple)
val_condition = isinstance(kwargs['values'],
list) or isinstance(
kwargs['values'], tuple)
if key_condition and val_condition:
super().add_interactions_from(kwargs['keys'],
kwargs['values'])
else:
raise TypeError("Invalid argument for this function")
elif 'vartype' in kwargs:
if len(args) == 1:
if isinstance(args[0], dict):
super().add_interactions_from(
args[0], to_cxxcimod(kwargs['vartype']))
else:
raise TypeError(
"Invalid argument for this function")
elif len(args) == 2:
key_condition = isinstance(
args[0], list) or isinstance(args[0], tuple)
val_condition = isinstance(
args[1], list) or isinstance(args[1], tuple)
if key_condition and val_condition:
super().add_interactions_from(
args[0], args[1],
to_cxxcimod(kwargs['vartype']))
else:
raise TypeError(
"Invalid argument for this function")
elif 'values' in kwargs:
if len(args) != 1:
raise TypeError("Invalid argument for this function")
key_condition = isinstance(args[0], list) or isinstance(
args[0], tuple)
val_condition = isinstance(kwargs['values'],
list) or isinstance(
kwargs['values'], tuple)
if key_condition and val_condition:
super().add_interactions_from(args[0],
kwargs['values'])
else:
raise TypeError("Invalid argument for this function")
elif 'polynomial' in kwargs:
if len(args) != 0:
raise TypeError("Invalid argument for this function")
if isinstance(kwargs['polynomial'], dict):
super().add_interactions_from(kwargs['polynomial'])
else:
raise TypeError("Invalid argument for this function")
else:
raise TypeError("Invalid argument for this function")
def add_interaction(self,
key: list,
value,
vartype=cxxcimod.Vartype.NONE):
return super().add_interaction(key, value, to_cxxcimod(vartype))
def empty(self, vartype):
Model = self._model_selector()
return Model({}, to_cxxcimod(vartype))
def change_vartype(self, vartype, inplace=None):
if inplace is None:
super().change_vartype(to_cxxcimod(vartype))
else:
return self.__class__(super().change_vartype(to_cxxcimod(vartype), inplace))
def empty(self, vartype):
return self.__class__(super().empty(to_cxxcimod(vartype)))
