def sample_search_space(self, random_state):
#get_timestamp
ts = str(time.time())
ts = re.sub(r'[.]', '', ts)[:10]
#create memories for points and lines in workspace
points, line = self.create_initial_route()
solutionpoints = arcpy.CopyFeatures_management(
points, "threedpoints" + "_" + str(ts))
arcpy.CopyFeatures_management(line, "threedline" + "_" + str(ts))
solution = Solution(
uls.random_pointmove_in_boundary(solutionpoints, self.IDW,
random_state, self.x_y_limits,
self.z_sigma))
solution.PointFCName = "threedpoints" + "_" + str(ts)
uls.pointsToLine(solution.PointFCName, "threedline" + "_" + str(ts))
solution.LineFCName = "threedline" + "_" + str(ts)
return solution
def __should_accept(self, candidate: Solution, temperature: float):
"""decides if candidate solution should substitute the current solution"""
ncost = candidate.cost()
ccost = self.current_solution.cost()
if ncost <= ccost:
###################### START DATA COLLECTION ########################
if settings.options.collect_data:
settings.collector.add_data("should_accept", True)
###################### END DATA COLLECTION ##########################
return True
else:
result = math.exp((ccost - ncost) / temperature) >= random.random()
###################### START DATA COLLECTION ########################
if settings.options.collect_data:
settings.collector.add_data("should_accept", result)
settings.collector.add_data("prob_acceptance", math.exp((ccost - ncost) / temperature))
###################### END DATA COLLECTION ##########################
return result
def sample_search_space(self, random_state):
return Solution(
random_boolean_1D_array(self.dimensionality, random_state))
def sample_search_space(self, random_state):
return Solution(random_state.uniform(low=self.search_space[0], high=self.search_space[1], size=self.search_space[2]))
def _mutation(self, individual):
mutant = self.mutation(individual.representation, self._random_state)
mutant = Solution(mutant)
return mutant
def _crossover(self, p1, p2):
off1, off2 = self.crossover(p1.representation, p2.representation,
self._random_state)
off1 = Solution(off1)
off2 = Solution(off2)
return off1, off2
def _mutation(self, chromosome):
mutant = self.mutation(chromosome, self._random_state)
mutant = Solution(mutant)
return mutant
def _crossover(self, p1, p2):
off1, off2 = self.crossover(p1, p2, self._random_state)
off1, off2 = Solution(off1), Solution(off2)
return off1, off2
def _choose_random_neighbor(self, solution):
neighbor = Solution(
self.neighborhood_function(solution.representation,
self._random_state))
self.problem_instance.evaluate(neighbor)
return neighbor
def _crossover1(self, p1, p2):
off11 = self.crossover2(p1.representation, p2.representation,
self._random_state)
off11 = Solution(off11)
return off11
def sample_search_space(self, random_state):
return Solution(
uls.random_float_1D_array(self.search_space, random_state))
