opt.single_objective.comb.min_multi_cut_problem package

Submodules

opt.single_objective.comb.min_multi_cut_problem.min_multi_cut_problem module

class opt.single_objective.comb.min_multi_cut_problem.min_multi_cut_problem.MinMultiCutProblem(graph: Graph, source_terminal_pairs: list[tuple[int, int]])

Bases: Problem

Class representing the Minimum Multi Cut Problem.

This class inherits from the Problem class and is used to define and solve the Minimum Multi Cut Problem. The problem is defined by a graph and a list of source terminal pairs.

graph

Graph.

Type:: nx.Graph

source_terminal_pairs

list of pairs that represent source and terminal nodes

Type:: list[tuple[int,int]]

__init__(graph: nx.Graph, source_terminal_pairs: list): Initializes a new instance of the MinMultiCutProblem class.

__load_from_files__(graph_file_path: str, source_terminal_pairs_file_path: str) -> tuple: Static function that reads problem data from files.

from_input_files(graph_file_path: str, source_terminal_pairs_file_path: str): Creates a new MinMultiCutProblem instance when the input file and input format are specified.

__copy__() → MinMultiCutProblem: Internal copy of the MinMultiCutProblem problem.

copy() → MinMultiCutProblem: Copy the MinMultiCutProblem problem.

graph() → nx.Graph: Property getter for the graph of the target problem.

source_terminal_pairs() → list[tuple[int, int]]: Property getter for the source_terminal_pairs of the target problem.

string_rep(delimiter: str, indentation: int = 0, indentation_symbol: str = ‘’, group_start: str = ‘{’, group_end: str = ‘}’) -> str: String representation of the MinMultiCutProblem instance.

__str__() → str: String representation of the MinMultiCutProblem structure.

__repr__() → str: Representation of the MinMultiCutProblem instance.

__format__() → str: Formatted MinMultiCutProblem instance.

copy() → MinMultiCutProblem

Copy the MinMultiCutProblem problem

Returns:: new MinMultiCutProblem instance with the same properties
Return type:: MinMultiCutProblem

classmethod from_graph_and_source_terminal_pairs(graph: Graph, source_terminal_pairs: list)

Additional constructor. Create new MinMultiCutProblem instance when graph and source_terminal_pairs are specified

Parameters:

graph (nx.Graph) – graph of the problem
source_terminal_pairs (list[tuple[int,int]]) – source_terminal_pairs of the problem

classmethod from_input_files(graph_file_path: str, source_terminal_pairs_file_path: str) → MinMultiCutProblem

Additional constructor. Create new MinMultiCutProblem instance when input file and input format are specified

Parameters:

input_file_path (str) – path of the input file with problem graph
source_terminal_pairs_file_path (str) – path of the input file with problem source_terminal_pairs

Returns:

class instance

Return type:

MinMultiCutProblem

property graph: Graph

Property getter for graph of the target problem

Returns:: graph of the target problem instance
Return type:: nx.Graph

property source_terminal_pairs: list[tuple[int, int]]

Property getter for source_terminal_pairs of the target problem

Returns:: source_terminal_pairs of the target problem instance
Return type:: list

string_rep(delimiter: str, indentation: int = 0, indentation_symbol: str = '', group_start: str = '{', group_end: str = '}') → str

String representation of the MinMultiCutProblem instance

Parameters:

delimiter (str) – delimiter between fields
indentation (int, optional, default value 0) – level of indentation
indentation_symbol (str, optional, default value '') – indentation symbol
group_start (str, optional, default value '{') – group start string
group_end (str, optional, default value '}') – group end string

Returns:

string representation of instance that controls output

Return type:

str

opt.single_objective.comb.min_multi_cut_problem.min_multi_cut_problem_bit_array_solution module

The minimum_multi_cut_problem_bit_array_solution contains class MinMultiCutProblemBitArraySolution, that represents solution of the Minimum Multi Cut Problem, where BitArray representation of the problem has been used.

class opt.single_objective.comb.min_multi_cut_problem.min_multi_cut_problem_bit_array_solution.MinMultiCutProblemBitArraySolution(random_seed: int | None = None, evaluation_cache_is_used: bool = False, evaluation_cache_max_size: int = 0, distance_calculation_cache_is_used: bool = False, distance_calculation_cache_max_size: int = 0)

Bases: Solution[BitArray, str]

argument(representation: BitArray) → str

Argument of the target solution

Parameters:: representation (BitArray) – internal representation of the solution
Returns:: solution code
Return type:: str

calc_fitness(representation: BitArray, graph: Graph, source_terminal_pairs: list[tuple[int, int]]) → tuple[bool, float, float]

calculate_quality_directly(representation: BitArray, problem: MinMultiCutProblem) → QualityOfSolution

Fitness calculation of the minimum multi cut binary BitArray solution

Parameters:

representation (BitArray) – native representation of solution whose fitness is calculated
problem (Problem) – problem that is solved

Returns:

objective value, fitness value and feasibility of the solution instance

Return type:

QualityOfSolution

copy() → MinMultiCutProblemBitArraySolution

Copy the MinMultiCutProblemBitArraySolution

Returns:: new MinMultiCutProblemBitArraySolution instance with the same properties
Return type:: MinMultiCutProblemBitArraySolution

copy_from(original) → None: Copy all data from the original target solution

init_from(representation: BitArray, problem: Problem) → None

Initialization of the solution, by setting its native representation

Parameters:

representation (BitArray) – representation that will be ste to solution
problem (Problem) – problem which is solved by solution

init_random(problem: Problem) → None

Random initialization of the solution

Parameters:: problem (Problem) – problem which is solved by solution

is_feasible_sol(representation: BitArray, graph: Graph, source_terminal_pairs: list[tuple[int, int]]) → bool

native_representation(representation_str: str) → BitArray

Obtain BitArray representation from string representation of the BitArray binary solution of the Minimum Multi Cut problem

Parameters:: representation_str (str) – solution’s representation as string
Returns:: solution’s representation as BitArray
Return type:: BitArray

representation_distance_directly(solution_code_1: str, solution_code_2: str) → float

Calculating distance between two solutions determined by its code

Parameters:

solution_code_1 (str) – solution code for the first solution
solution_code_2 (str) – solution code for the second solution

Returns:

distance between two solutions represented by its code

Return type:

float

string_rep(delimiter: str = '\n', indentation: int = 0, indentation_symbol: str = ' ', group_start: str = '{', group_end: str = '}') → str

String representation of the solution instance

Parameters:

delimiter (str) – delimiter between fields
indentation (int, optional, default value 0) – level of indentation
indentation_symbol (str, optional, default value '') – indentation symbol
group_start (str, optional, default value '{') – group start string
group_end (str, optional, default value '}') – group end string

Returns:

string representation of instance that controls output

Return type:

str

opt.single_objective.comb.min_multi_cut_problem.min_multi_cut_problem_bit_array_solution.random() → x in the interval [0, 1).

opt.single_objective.comb.min_multi_cut_problem package

Submodules

opt.single_objective.comb.min_multi_cut_problem.min_multi_cut_problem module

opt.single_objective.comb.min_multi_cut_problem.min_multi_cut_problem_bit_array_solution module

Module contents