class PathFinder:
def __init__(self, data):
self.data = data
def create_path(self):
""" Create an eligible solution for data.
We iteratively add sensors connected to the current solution which taps new targets.
"""
self.neighbours = []
self.detected = set([0])
self.solution = Solution(len(self.data.points))
self.add_neighbours(0)
while len(self.neighbours) != 0:
sum_norm = sum(
[len(n["neighbours_sens"]) for n in self.neighbours])
proba = [
len(n["neighbours_sens"]) / sum_norm for n in self.neighbours
]
neighbour = np.random.choice(self.neighbours, p=proba)
self.update_neighbours(deepcopy(neighbour))
self.add_neighbours(neighbour["id"])
self.solution.add_sensor(neighbour["id"])
return self.solution
def add_neighbours(self, target_id):
""" Update the neighbours list with the neighbours of target_id.
We add only positions that can tap new targets
"""
for n in self.data.get_neighbours_com(target_id):
set_sens = set(self.data.get_neighbours_sens(n) + [n])
set_reduced = set_sens.difference(self.detected)
if len(set_reduced) != 0:
self.neighbours.append({
"id": n,
"neighbours_sens": set_reduced
})
def update_neighbours(self, neighbour_added):
""" We update detected targets
We remove newly taped positions from the list of neighbours for potential sensors.
"""
self.detected = self.detected.union(neighbour_added["neighbours_sens"])
for n in self.neighbours:
n["neighbours_sens"] = n["neighbours_sens"].difference(
neighbour_added["neighbours_sens"])
self.neighbours = [
n for n in self.neighbours if n["id"] != neighbour_added["id"]
and len(n["neighbours_sens"]) != 0
]
