openjij.cxxjij.graph

cxxjij submodule for graph

class openjij.cxxjij.graph.BinaryPolynomialModel

Bases: pybind11_object

__init__(self: openjij.cxxjij.graph.BinaryPolynomialModel, key_list: list[list[Union[int, str, list[Union[int, str]]]]], value_list: list[float]) -> None

__init__(self: BinaryPolynomialModel, key_list: list[list[int | str | list[int | str]]], value_list: list[float]) → None

__new__(**kwargs)

calculate_energy(self: BinaryPolynomialModel, arg0: list[int]) → float

get_adjacency_list(self: BinaryPolynomialModel) → list[list[int]]

get_degree(self: BinaryPolynomialModel) → int

get_estimated_max_energy_difference(self: BinaryPolynomialModel) → float

get_estimated_min_energy_difference(self: BinaryPolynomialModel) → float

get_index_list(self: BinaryPolynomialModel) → list[int | str | list[int | str]]

get_index_map(self: BinaryPolynomialModel) → dict[int | str | list[int | str], int]

get_key_value_list(self: BinaryPolynomialModel) → list[tuple[list[int], float]]

get_system_size(self: BinaryPolynomialModel) → int

class openjij.cxxjij.graph.CSRSparse

Bases: Graph

__init__(*args, **kwargs) Overloaded function.

1. __init__(self: openjij.cxxjij.graph.CSRSparse, interaction: scipy.sparse.csr_matrix[numpy.float64]) -> None

2. __init__(self: openjij.cxxjij.graph.CSRSparse, other: openjij.cxxjij.graph.CSRSparse) -> None

__init__(*args, **kwargs)

Overloaded function.

1. __init__(self: openjij.cxxjij.graph.CSRSparse, interaction: scipy.sparse.csr_matrix[numpy.float64]) -> None

2. __init__(self: openjij.cxxjij.graph.CSRSparse, other: openjij.cxxjij.graph.CSRSparse) -> None

__new__(**kwargs)

calc_energy(*args, **kwargs)

Overloaded function.

1. calc_energy(self: openjij.cxxjij.graph.CSRSparse, spins: numpy.ndarray[numpy.float64[m, 1]]) -> float

2. calc_energy(self: openjij.cxxjij.graph.CSRSparse, spins: list[int]) -> float

gen_binary(*args, **kwargs)

Overloaded function.

1. gen_binary(self: openjij.cxxjij.graph.Graph, seed: int) -> list[int]

2. gen_binary(self: openjij.cxxjij.graph.Graph) -> list[int]

gen_spin(*args, **kwargs)

Overloaded function.

1. gen_spin(self: openjij.cxxjij.graph.Graph, seed: int) -> list[int]

2. gen_spin(self: openjij.cxxjij.graph.Graph) -> list[int]

get_interactions(self: CSRSparse) → csr_matrix[numpy.float64]

size(self: Graph) → int

class openjij.cxxjij.graph.Chimera

Bases: Sparse

__init__(*args, **kwargs) Overloaded function.

1. __init__(self: openjij.cxxjij.graph.Chimera, num_row: int, num_column: int, init_val: float = 0) -> None

2. __init__(self: openjij.cxxjij.graph.Chimera, other: openjij.cxxjij.graph.Chimera) -> None

3. __init__(self: openjij.cxxjij.graph.Chimera, obj: object, num_row: int, num_column: int, init_val: float = 0) -> None

__init__(*args, **kwargs)

Overloaded function.

1. __init__(self: openjij.cxxjij.graph.Chimera, num_row: int, num_column: int, init_val: float = 0) -> None

2. __init__(self: openjij.cxxjij.graph.Chimera, other: openjij.cxxjij.graph.Chimera) -> None

3. __init__(self: openjij.cxxjij.graph.Chimera, obj: object, num_row: int, num_column: int, init_val: float = 0) -> None

__new__(**kwargs)

adj_nodes(self: Sparse, arg0: int) → list[int]

calc_energy(*args, **kwargs)

Overloaded function.

1. calc_energy(self: openjij.cxxjij.graph.Sparse, spins: numpy.ndarray[numpy.float64[m, 1]]) -> float

2. calc_energy(self: openjij.cxxjij.graph.Sparse, spins: list[int]) -> float

gen_binary(*args, **kwargs)

Overloaded function.

1. gen_binary(self: openjij.cxxjij.graph.Graph, seed: int) -> list[int]

2. gen_binary(self: openjij.cxxjij.graph.Graph) -> list[int]

gen_spin(*args, **kwargs)

Overloaded function.

1. gen_spin(self: openjij.cxxjij.graph.Graph, seed: int) -> list[int]

2. gen_spin(self: openjij.cxxjij.graph.Graph) -> list[int]

get_num_column(self: Chimera) → int

get_num_edges(self: Sparse) → int

get_num_in_chimera(self: Chimera) → int

get_num_row(self: Chimera) → int

size(self: Graph) → int

to_ind(self: Chimera, arg0: int, arg1: int, arg2: int) → int

to_rci(self: Chimera, arg0: int) → tuple[int, int, int]

class openjij.cxxjij.graph.ChimeraDir

Bases: pybind11_object

Members:

PLUS_R

MINUS_R

PLUS_C

MINUS_C

IN_0or4

IN_1or5

IN_2or6

IN_3or7

__init__(self: openjij.cxxjij.graph.ChimeraDir, value: int) -> None

__init__(self: ChimeraDir, value: int) → None

__new__(**kwargs)

IN_0or4 = <ChimeraDir.IN_0or4: 4>

IN_1or5 = <ChimeraDir.IN_1or5: 5>

IN_2or6 = <ChimeraDir.IN_2or6: 6>

IN_3or7 = <ChimeraDir.IN_3or7: 7>

MINUS_C = <ChimeraDir.MINUS_C: 3>

MINUS_R = <ChimeraDir.MINUS_R: 1>

PLUS_C = <ChimeraDir.PLUS_C: 2>

PLUS_R = <ChimeraDir.PLUS_R: 0>

property name

property value

class openjij.cxxjij.graph.Dense

Bases: Graph

__init__(*args, **kwargs) Overloaded function.

1. __init__(self: openjij.cxxjij.graph.Dense, num_spins: int) -> None

2. __init__(self: openjij.cxxjij.graph.Dense, obj: object) -> None

3. __init__(self: openjij.cxxjij.graph.Dense, other: openjij.cxxjij.graph.Dense) -> None

__init__(*args, **kwargs)

Overloaded function.

1. __init__(self: openjij.cxxjij.graph.Dense, num_spins: int) -> None

2. __init__(self: openjij.cxxjij.graph.Dense, obj: object) -> None

3. __init__(self: openjij.cxxjij.graph.Dense, other: openjij.cxxjij.graph.Dense) -> None

__new__(**kwargs)

calc_energy(*args, **kwargs)

Overloaded function.

1. calc_energy(self: openjij.cxxjij.graph.Dense, spins: numpy.ndarray[numpy.float64[m, 1]]) -> float

2. calc_energy(self: openjij.cxxjij.graph.Dense, spins: list[int]) -> float

gen_binary(*args, **kwargs)

Overloaded function.

1. gen_binary(self: openjij.cxxjij.graph.Graph, seed: int) -> list[int]

2. gen_binary(self: openjij.cxxjij.graph.Graph) -> list[int]

gen_spin(*args, **kwargs)

Overloaded function.

1. gen_spin(self: openjij.cxxjij.graph.Graph, seed: int) -> list[int]

2. gen_spin(self: openjij.cxxjij.graph.Graph) -> list[int]

get_interactions(self: Dense) → ndarray[numpy.float64[m, n]]

set_interaction_matrix(self: Dense, interaction: ndarray[numpy.float64[m, n]]) → None

size(self: Graph) → int

class openjij.cxxjij.graph.Dir

Bases: pybind11_object

Members:

PLUS_R

MINUS_R

PLUS_C

MINUS_C

__init__(self: openjij.cxxjij.graph.Dir, value: int) -> None

__init__(self: Dir, value: int) → None

__new__(**kwargs)

MINUS_C = <Dir.MINUS_C: 3>

MINUS_R = <Dir.MINUS_R: 1>

PLUS_C = <Dir.PLUS_C: 2>

PLUS_R = <Dir.PLUS_R: 0>

property name

property value

class openjij.cxxjij.graph.Graph

Bases: pybind11_object

__init__(self: openjij.cxxjij.graph.Graph, num_spins: int) -> None

__init__(self: Graph, num_spins: int) → None

__new__(**kwargs)

gen_binary(*args, **kwargs)

Overloaded function.

1. gen_binary(self: openjij.cxxjij.graph.Graph, seed: int) -> list[int]

2. gen_binary(self: openjij.cxxjij.graph.Graph) -> list[int]

gen_spin(*args, **kwargs)

Overloaded function.

1. gen_spin(self: openjij.cxxjij.graph.Graph, seed: int) -> list[int]

2. gen_spin(self: openjij.cxxjij.graph.Graph) -> list[int]

size(self: Graph) → int

class openjij.cxxjij.graph.IsingPolynomialModel

Bases: pybind11_object

__init__(self: openjij.cxxjij.graph.IsingPolynomialModel, key_list: list[list[Union[int, str, list[Union[int, str]]]]], value_list: list[float]) -> None

__init__(self: IsingPolynomialModel, key_list: list[list[int | str | list[int | str]]], value_list: list[float]) → None

__new__(**kwargs)

calculate_energy(self: IsingPolynomialModel, arg0: list[int]) → float

get_adjacency_list(self: IsingPolynomialModel) → list[list[int]]

get_degree(self: IsingPolynomialModel) → int

get_estimated_max_energy_difference(self: IsingPolynomialModel) → float

get_estimated_min_energy_difference(self: IsingPolynomialModel) → float

get_index_list(self: IsingPolynomialModel) → list[int | str | list[int | str]]

get_index_map(self: IsingPolynomialModel) → dict[int | str | list[int | str], int]

get_key_value_list(self: IsingPolynomialModel) → list[tuple[list[int], float]]

get_system_size(self: IsingPolynomialModel) → int

class openjij.cxxjij.graph.Polynomial

Bases: Graph

__init__(*args, **kwargs) Overloaded function.

1. __init__(self: openjij.cxxjij.graph.Polynomial, num_variables: int) -> None

2. __init__(self: openjij.cxxjij.graph.Polynomial, obj: object) -> None

__init__(*args, **kwargs)

Overloaded function.

1. __init__(self: openjij.cxxjij.graph.Polynomial, num_variables: int) -> None

2. __init__(self: openjij.cxxjij.graph.Polynomial, obj: object) -> None

__new__(**kwargs)

calc_energy(self: Polynomial, spins: list[int], omp_flag: bool = True) → float

energy(self: Polynomial, spins: list[int], omp_flag: bool = True) → float

gen_binary(*args, **kwargs)

Overloaded function.

1. gen_binary(self: openjij.cxxjij.graph.Graph, seed: int) -> list[int]

2. gen_binary(self: openjij.cxxjij.graph.Graph) -> list[int]

gen_spin(*args, **kwargs)

Overloaded function.

1. gen_spin(self: openjij.cxxjij.graph.Graph, seed: int) -> list[int]

2. gen_spin(self: openjij.cxxjij.graph.Graph) -> list[int]

get_num_interactions(self: Polynomial) → int

get_polynomial(self: Polynomial) → dict

size(self: Graph) → int

class openjij.cxxjij.graph.Sparse

Bases: Graph

__init__(*args, **kwargs) Overloaded function.

1. __init__(self: openjij.cxxjij.graph.Sparse, num_spins: int, num_edges: int) -> None

2. __init__(self: openjij.cxxjij.graph.Sparse, num_spins: int) -> None

3. __init__(self: openjij.cxxjij.graph.Sparse, obj: object, num_edges: int) -> None

4. __init__(self: openjij.cxxjij.graph.Sparse, obj: object) -> None

5. __init__(self: openjij.cxxjij.graph.Sparse, other: openjij.cxxjij.graph.Sparse) -> None

__init__(*args, **kwargs)

Overloaded function.

1. __init__(self: openjij.cxxjij.graph.Sparse, num_spins: int, num_edges: int) -> None

2. __init__(self: openjij.cxxjij.graph.Sparse, num_spins: int) -> None

3. __init__(self: openjij.cxxjij.graph.Sparse, obj: object, num_edges: int) -> None

4. __init__(self: openjij.cxxjij.graph.Sparse, obj: object) -> None

5. __init__(self: openjij.cxxjij.graph.Sparse, other: openjij.cxxjij.graph.Sparse) -> None

__new__(**kwargs)

adj_nodes(self: Sparse, arg0: int) → list[int]

calc_energy(*args, **kwargs)

Overloaded function.

1. calc_energy(self: openjij.cxxjij.graph.Sparse, spins: numpy.ndarray[numpy.float64[m, 1]]) -> float

2. calc_energy(self: openjij.cxxjij.graph.Sparse, spins: list[int]) -> float

gen_binary(*args, **kwargs)

Overloaded function.

1. gen_binary(self: openjij.cxxjij.graph.Graph, seed: int) -> list[int]

2. gen_binary(self: openjij.cxxjij.graph.Graph) -> list[int]

gen_spin(*args, **kwargs)

Overloaded function.

1. gen_spin(self: openjij.cxxjij.graph.Graph, seed: int) -> list[int]

2. gen_spin(self: openjij.cxxjij.graph.Graph) -> list[int]

get_num_edges(self: Sparse) → int

size(self: Graph) → int

class openjij.cxxjij.graph.Square

Bases: Sparse

__init__(*args, **kwargs) Overloaded function.

1. __init__(self: openjij.cxxjij.graph.Square, num_row: int, num_column: int, init_val: float = 0) -> None

2. __init__(self: openjij.cxxjij.graph.Square, other: openjij.cxxjij.graph.Square) -> None

3. __init__(self: openjij.cxxjij.graph.Square, obj: object, num_row: int, num_column: int, init_val: float = 0) -> None

__init__(*args, **kwargs)

Overloaded function.

1. __init__(self: openjij.cxxjij.graph.Square, num_row: int, num_column: int, init_val: float = 0) -> None

2. __init__(self: openjij.cxxjij.graph.Square, other: openjij.cxxjij.graph.Square) -> None

3. __init__(self: openjij.cxxjij.graph.Square, obj: object, num_row: int, num_column: int, init_val: float = 0) -> None

__new__(**kwargs)

adj_nodes(self: Sparse, arg0: int) → list[int]

calc_energy(*args, **kwargs)

Overloaded function.

1. calc_energy(self: openjij.cxxjij.graph.Sparse, spins: numpy.ndarray[numpy.float64[m, 1]]) -> float

2. calc_energy(self: openjij.cxxjij.graph.Sparse, spins: list[int]) -> float

gen_binary(*args, **kwargs)

Overloaded function.

1. gen_binary(self: openjij.cxxjij.graph.Graph, seed: int) -> list[int]

2. gen_binary(self: openjij.cxxjij.graph.Graph) -> list[int]

gen_spin(*args, **kwargs)

Overloaded function.

1. gen_spin(self: openjij.cxxjij.graph.Graph, seed: int) -> list[int]

2. gen_spin(self: openjij.cxxjij.graph.Graph) -> list[int]

get_num_column(self: Square) → int

get_num_edges(self: Sparse) → int

get_num_row(self: Square) → int

size(self: Graph) → int

to_ind(self: Square, arg0: int, arg1: int) → int

to_rc(self: Square, arg0: int) → tuple[int, int]