macop.solutions.discrete

Discrete solution classes implementations

Classes

BinarySolution(data, size)	Binary integer solution class
CombinatoryIntegerSolution(data, size)	Combinatory integer solution class
IntegerSolution(data, size)	Integer solution class

class macop.solutions.discrete.BinarySolution(data, size)

Binary integer solution class

• store solution as a binary array (example: [0, 1, 0, 1, 1])

• associated size is the size of the array

• mainly use for selecting or not an element in a list of valuable objects

data

{ndarray} – array of binary values

size

{int} – size of binary array values

score

{float} – fitness score value

static random(size, validator=None)

Intialize binary array with use of validator to generate valid random solution

Parameters

• size – {int} – expected solution size to generate

• validator – {function} – specific function which validates or not a solution (if None, not validation is applied)

Returns

{BinarySolution} – new generated binary solution

Example:

>>> from macop.solutions.discrete import BinarySolution
>>> validator = lambda solution: True if sum(solution._data) > 5 else False
>>> solution = BinarySolution.random(10, validator)
>>> sum(solution._data) > 5
True

class macop.solutions.discrete.CombinatoryIntegerSolution(data, size)

Combinatory integer solution class

• store solution as a binary array (example: [0, 1, 0, 1, 1])

• associated size is the size of the array

• mainly use for selecting or not an element in a list of valuable objects

data

{ndarray} – array of integer values

size

{int} – size of integer array values

score

{float} – fitness score value

static random(size, validator=None)

Intialize combinatory integer array with use of validator to generate valid random solution

Parameters

• size – {int} – expected solution size to generate

• validator – {function} – specific function which validates or not a solution (if None, not validation is applied)

Returns

{CombinatoryIntegerSolution} – new generated combinatory integer solution

Example:

>>> from macop.solutions.discrete import CombinatoryIntegerSolution
>>> validator = lambda solution: True if sum(solution._data) > 5 else False
>>> solution = CombinatoryIntegerSolution.random(5, validator)
>>> sum(solution._data) > 5
True

class macop.solutions.discrete.IntegerSolution(data, size)

Integer solution class

data

{ndarray} – array of binary values

size

{int} – size of binary array values

score

{float} – fitness score value

static random(size, validator=None)

Intialize integer array with use of validator to generate valid random solution

Parameters

• size – {int} – expected solution size to generate

• validator – {function} – specific function which validates or not a solution (if None, not validation is applied)

Returns

{IntegerSolution} – new generated integer solution

Example:

>>> from macop.solutions.discrete import IntegerSolution
>>> import numpy as np
>>> np.random.seed(42)
>>> validator = lambda solution: True if sum(solution._data) > 5 else False
>>> solution = IntegerSolution.random(5, validator)
>>> sum(solution._data) > 10
True