"""Knapsack evaluators classes
"""
# main imports
from ..base import Evaluator
[docs]class KnapsackEvaluator(Evaluator):
"""Knapsack evaluator class which enables to compute solution using specific `_data`
- stores into its `_data` dictionary attritute required measures when computing a knapsack solution
- `_data['worths']` stores knapsack objects worths information
- `compute` method enables to compute and associate a score to a given knapsack solution
Example:
>>> import random
>>> # binary solution import
>>> from macop.solutions.discrete import BinarySolution
>>> # evaluator import
>>> from macop.evaluators.discrete.mono import KnapsackEvaluator
>>> solution_data = [1, 0, 0, 1, 1, 0, 1, 0]
>>> size = len(solution_data)
>>> solution = BinarySolution(solution_data, size)
>>> # evaluator initialization (worths objects passed into data)
>>> worths = [ random.randint(5, 20) for i in range(size) ]
>>> evaluator = KnapsackEvaluator(data={'worths': worths})
>>> # compute solution score
>>> evaluator.compute(solution)
40
"""
[docs] def compute(self, solution):
"""Apply the computation of fitness from solution
Args:
solution: {Solution} -- Solution instance
Returns:
{float} -- fitness score of solution
"""
fitness = 0
for index, elem in enumerate(solution._data):
fitness += self._data['worths'][index] * elem
return fitness