def dfs_solver(csp):
"""depth-first search solver"""
path = Searcher(Search_from_CSP(csp)).search()
if path is not None:
return path.end()
else:
return None
def run(problem,name):
print("\n\n*******",name)
print("\nA*:")
asearcher = AStarSearcher(problem)
print("Path found:",asearcher.search()," cost=",asearcher.solution.cost)
print("there are",asearcher.frontier.count(asearcher.solution.cost),
"elements remaining on the queue with f-value=",asearcher.solution.cost)
print("\nA* with MPP:"),
msearcher = SearcherMPP(problem)
print("Path found:",msearcher.search()," cost=",msearcher.solution.cost)
print("there are",msearcher.frontier.count(msearcher.solution.cost),
"elements remaining on the queue with f-value=",msearcher.solution.cost)
bound = asearcher.solution.cost+0.01
print("\nBranch and bound (with too-good initial bound of", bound,")")
tbb = DF_branch_and_bound(problem,bound) # cheating!!!!
print("Path found:",tbb.search()," cost=",tbb.solution.cost)
print("Rerunning B&B")
print("Path found:",tbb.search())
bbound = asearcher.solution.cost*2+10
print("\nBranch and bound (with not-very-good initial bound of", bbound, ")")
tbb2 = DF_branch_and_bound(problem,bbound) # cheating!!!!
print("Path found:",tbb2.search()," cost=",tbb2.solution.cost)
print("Rerunning B&B")
print("Path found:",tbb2.search())
print("\nDepth-first search: (Use ^C if it goes on forever)")
tsearcher = Searcher(problem)
print("Path found:",tsearcher.search()," cost=",tsearcher.solution.cost)
def __init__(self,
problem,
pruning=None,
method='astar',
max_expanded=10000):
"""max_expanded is a bound on the number of paths expanded (to prevent infinite computation)"""
self.method = method
self.max_expanded = max_expanded
self.pruning = pruning
Searcher.__init__(self, problem, method)
if self.pruning == 'mpp':
self.explored = set()
def run(problem, name):
print("\n\n*******", name)
print("\nA*:")
tsearcher = Searcher(problem)
print("Path found:", tsearcher.search(), " cost=",
tsearcher.solution.cost)
print("there are", tsearcher.frontier.count(tsearcher.solution.cost),
"elements remaining on the queue with f-value=",
tsearcher.solution.cost)
print("\nA* with MPP:"),
msearcher = SearcherMPP(problem)
print("Path found:", msearcher.search(), " cost=",
msearcher.solution.cost)
print("there are", msearcher.frontier.count(msearcher.solution.cost),
"elements remaining on the queue with f-value=",
msearcher.solution.cost)
print("\nBranch and bound (with too-good initial bound):")
tbb = DF_branch_and_bound(problem,
tsearcher.solution.cost + 0.1) # cheating!!!!
print("Path found:", tbb.search(), " cost=", tbb.solution.cost)
if self.csp.consistent(new_env):
res.append(Arc(node, new_env))
return res
from cspExamples import csp1, csp2, test, crossword1, crossword1d
from searchGeneric import Searcher
def dfs_solver(csp):
"""depth-first search solver"""
path = Searcher(Search_from_CSP(csp)).search()
if path is not None:
return path.end()
else:
return None
if __name__ == "__main__":
test(dfs_solver)
## Test Solving CSPs with Search:
searcher1 = Searcher(Search_from_CSP(csp1))
#print(searcher1.search()) # get next solution
searcher2 = Searcher(Search_from_CSP(csp2))
#print(searcher2.search()) # get next solution
searcher3 = Searcher(Search_from_CSP(crossword1))
#print(searcher3.search()) # get next solution
searcher4 = Searcher(Search_from_CSP(crossword1d))
#print(searcher4.search()) # get next solution (warning: slow)
def ac_search_solver(csp):
"""arc consistency (search interface)"""
sol = Searcher(Search_with_AC_from_CSP(csp)).search()
if sol:
return {v:select(d) for (v,d) in sol.end().items()}
res.append(Arc(node, new_env))
return res
from cspExamples import csp1, csp2, test, crossword1, crossword1d
from searchGeneric import Searcher
def dfs_solver(csp):
"""depth-first search solver"""
path = Searcher(Search_from_CSP(csp)).search()
if path is not None:
return path.end()
else:
return None
if __name__ == "__main__":
test(dfs_solver)
## Test Solving CSPs with Search:
searcher1 = Searcher(Search_from_CSP(csp1))
#print(searcher1.search()) # get next solution
#searcher2 = Searcher(Search_from_CSP(csp2))
#print(searcher2.search()) # get next solution
#searcher3 = Searcher(Search_from_CSP(crossword1))
#print(searcher3.search()) # get next solution
#searcher4 = Searcher(Search_from_CSP(crossword1d))
#print(searcher4.search()) # get next solution (warning: slow)