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"""Represents optimization strategy for group in PSO.""" |
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# pylint: disable=redefined-variable-type |
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import numpy as np |
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from grortir.main.model.core.optimization_status import OptimizationStatus |
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from grortir.main.pso.group_optimization_strategy import \ |
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GroupOptimizationStrategy |
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class CallsGroupOptimizationStrategy(GroupOptimizationStrategy): |
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"""Represents optimization strategy for group in PSO.""" |
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def __init__(self, stages_in_group): |
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self.stages_in_group = stages_in_group |
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self.max_cost = 0 |
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self.expected_quality = np.inf |
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def initialize(self): |
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""" |
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Initialize strategy. |
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""" |
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max_cost = 0 |
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for stage in self.stages_in_group: |
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max_cost += stage.get_maximal_acceptable_cost() |
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if self.expected_quality > stage.maximum_acceptable_quality: |
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self.expected_quality = stage.maximum_acceptable_quality |
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self.max_cost = max_cost |
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def should_continue(self, best_particle): |
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""" |
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Return true if optimization should be continued for Calls Process. |
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Args: |
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best_particle Particle: best particle in swarm. |
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Returns: |
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bool: true if continuation is required. |
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""" |
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return self._is_safe_cost() and not self._is_enough_quality( |
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best_particle) |
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def finalize(self, best_particle): |
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""" |
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Set proper status after finished group optimization. |
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Args: |
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best_particle (Particle): best particle in Swarm |
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""" |
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optimization_status = OptimizationStatus.failed |
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if self._is_safe_cost() and self._is_enough_quality( |
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best_particle): |
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optimization_status = OptimizationStatus.success |
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for stage in self.stages_in_group: |
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stage.optimization_status = optimization_status |
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def _is_safe_cost(self): |
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current_cost = 0 |
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for stage in self.stages_in_group: |
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current_cost += stage.get_cost() |
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return current_cost < self.max_cost |
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def _is_enough_quality(self, best_particle): |
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return best_particle.best_quality <= self.expected_quality |
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qbahn /
grortir
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