def __init__(self, graph, domain_size):
self.graph = graph
self.domain_size = domain_size
self.astar = None
self.csp = None
self.astar = AStarCSP(self.graph)
self.csp = CSP(self.graph)
self.nodes_checked = set()
self.goal_node = None
def __init__(self, graph, domain_size):
self.graph = graph
self.domain_size = domain_size
self.astar = None
self.csp = None
self.astar = AStarCSP(self.graph)
self.csp = CSP(self.graph)
self.nodes_checked = set()
self.goal_node = None
class AStarGAC:
def __init__(self, graph, domain_size):
self.graph = graph
self.domain_size = domain_size
self.astar = None
self.csp = None
self.astar = AStarCSP(self.graph)
self.csp = CSP(self.graph)
self.nodes_checked = set()
self.goal_node = None
def makefunc(self, var_names, expression, envir=globals()):
args = ""
for n in var_names: args = args + "," + n
return eval("(lambda " + args[1:] + ": " + expression + ")"
, envir)
# Return
# "MOD" if we modified the domain
# "SOL" if we solved the puzzle
# "NON" if no change has happened
def increment(self):
# run astar
#if self.csp.is_solved():
# print "Solution found!"
# return "SOL"
# From the pseudocode in the task description
astar_result = self.astar.incremental_solver()
if astar_result[0].startswith("SUCCESS"):
current_csp_domains = deepcopy(self.csp.domains)
found_better = False
for node in self.astar.open_heap:
if node not in self.nodes_checked: self.nodes_checked.add(node)
else: continue
self.csp.contradictory = False
# rerun csp and find best guess
self.csp.domains = node.domains
csp_rerun_result = self.csp.rerun()
if node.h == 0:
print "Solution found"
self.goal_node = node
return "SOL"
if self.csp.contradictory:
self.astar.open_heap.remove(node)
self.astar.open_set.remove(node)
continue
else:
# fix node
node.domains = deepcopy(self.csp.domains)
found_better = True
node.set_f(g=0, h=self.astar.calculate_h(node))
if found_better:
node = min(self.astar.open_heap, key=attrgetter('f'))
#node = self.astar.open_heap[0]
#print "best f", node.f
self.csp.domains = node.domains
self.goal_node = node
return "MOD"
else:
self.csp.domains = current_csp_domains
heapq.heapify(self.astar.open_heap)
return "NON"
else: return "NON"
class AStarGAC:
def __init__(self, graph, domain_size):
self.graph = graph
self.domain_size = domain_size
self.astar = None
self.csp = None
self.astar = AStarCSP(self.graph)
self.csp = CSP(self.graph)
self.nodes_checked = set()
self.goal_node = None
def makefunc(self, var_names, expression, envir=globals()):
args = ""
for n in var_names:
args = args + "," + n
return eval("(lambda " + args[1:] + ": " + expression + ")", envir)
# Return
# "MOD" if we modified the domain
# "SOL" if we solved the puzzle
# "NON" if no change has happened
def increment(self):
# run astar
#if self.csp.is_solved():
# print "Solution found!"
# return "SOL"
# From the pseudocode in the task description
astar_result = self.astar.incremental_solver()
if astar_result[0].startswith("SUCCESS"):
current_csp_domains = deepcopy(self.csp.domains)
found_better = False
for node in self.astar.open_heap:
if node not in self.nodes_checked: self.nodes_checked.add(node)
else: continue
self.csp.contradictory = False
# rerun csp and find best guess
self.csp.domains = node.domains
csp_rerun_result = self.csp.rerun()
if node.h == 0:
print "Solution found"
self.goal_node = node
return "SOL"
if self.csp.contradictory:
self.astar.open_heap.remove(node)
self.astar.open_set.remove(node)
continue
else:
# fix node
node.domains = deepcopy(self.csp.domains)
found_better = True
node.set_f(g=0, h=self.astar.calculate_h(node))
if found_better:
node = min(self.astar.open_heap, key=attrgetter('f'))
#node = self.astar.open_heap[0]
#print "best f", node.f
self.csp.domains = node.domains
self.goal_node = node
return "MOD"
else:
self.csp.domains = current_csp_domains
heapq.heapify(self.astar.open_heap)
return "NON"
else:
return "NON"
