break
for next in _get_neighbors(current, theMap):
if theMap[next.pos[0]][next.pos[1]] >= 0:
theMap[next.pos[0]][next.pos[1]] = next.depth
self.searchedNodes += 1
self.queue.sort_add(next)
return self.path
if __name__ == "__main__":
# map_object, info = pp.generateMap2d([60,60])
map_object = pp.generateMap2d([60, 60])
start = Node(
[np.where(map_object == -2)[0][0],
np.where(map_object == -2)[1][0]], None, 0, 0)
goal = Node(
[np.where(map_object == -3)[0][0],
np.where(map_object == -3)[1][0]], None, 0, 0)
searcher = AstarM()
searcher.search(map_object, start, goal)
print("Number of visited nodes: {}".format(searcher.searchedNodes))
print("Length of path: {}".format(
len(searcher.path[0]) + len(searcher.path[1])))
pp.plotMap(map_object, searcher.path)
for next in _get_neighbors(current, theMap):
if theMap[next.pos[0]][next.pos[1]] == 0:
theMap[next.pos[0]][next.pos[1]] = next.depth
self.searchedNodes += 1
self.queue.add(next)
return self.path
if __name__ == "__main__":
# map_object, info = pp.generateMap2d([60,60])
#np.random.seed(2)
map_object = pp.generateMap2d([50, 50])
start = Node(
[np.where(map_object == -2)[0][0],
np.where(map_object == -2)[1][0]], None, 0, 0)
goal = Node(
[np.where(map_object == -3)[0][0],
np.where(map_object == -3)[1][0]], None, 0, 0)
searcher = DFSearch()
searcher.search(map_object, start, goal)
print("Number of visited nodes: {}".format(searcher.searchedNodes))
print("Length of path: {}".format(
len(searcher.path[0]) + len(searcher.path[1])))
pp.plotMap(map_object, searcher.path)
from task1_ISA_GreedySearch_Euclidean import GreedySearchE
from task1_ISA_GreedySearch_Manhattan import GreedySearchM
from task1_ISA_Astar_Euclidean import AstarE
from task1_ISA_Astar_Manhattan import AstarM
BFS_Data = []
Random_Data = []
DFS_Data = []
for i in range(100):
print(i)
BFSAgent = BFSearch()
RandomAgent = RandomSearch()
DFSAgent = DFSearch()
map_object = pp.generateMap2d([100, 100])
map_object2 = np.copy(map_object)
map_object3 = np.copy(map_object)
start = Node(
[np.where(map_object == -2)[0][0],
np.where(map_object == -2)[1][0]], None, 0, 0)
goal = Node(
[np.where(map_object == -3)[0][0],
np.where(map_object == -3)[1][0]], None, 0, 0)
BFSAgent.search(map_object, start, goal)
RandomAgent.search(map_object2, start, goal)
DFSAgent.search(map_object3, start, goal)
BFS_Data.append({
"PathLenght": 0,
"Expanded": 0
}
for result in resultList:
for key in result.keys():
if key != "AgentType" and key in analysedResults.keys():
analysedResults[key] += result[key]
for key in analysedResults.keys():
if key != "AgentType" and key != "Wins":
analysedResults[key] = analysedResults.get(key) / len(resultList)
return analysedResults
for roundIndex in range(10):
searchMap = generateMap2d([100, 100])
for agent in agents:
searchMapCopy = copy(searchMap)
startNodeMask = np.where(searchMap == -2)
startNode = [startNodeMask[0][0], startNodeMask[1][0]]
goalNodeMask = np.where(searchMap == -3)
goalNode = [goalNodeMask[0][0], goalNodeMask[1][0]]
agent.__init__(searchMap=searchMapCopy,
startPosition=startNode,
goalPosition=goalNode)
agentResults = agent.search()
resultData[type(agent).__name__].append(agentResults)
for agentKey in resultData.keys():
analysedResultData.append(analyseResults(resultData[agentKey]))
