class Backtracking_Search:
def __init__(self, cspArg):
# the csp problem we are trying to solve
self.csp = copy.deepcopy(cspArg)
# a root node for our 'tree'. The root nodes' parent is 'none', that's how we know it's the root
self.root = Node()
# currentNode is initially set to root and we will generate the tree as we go along searching
self.currentNode = self.root
self.currentNode.currentcsp = copy.deepcopy(cspArg)
# we initially start with the entire set of variables, but remove them and add them back depending on where we
# are in the search.as we go down the tree, we eliminate variables, if we backtrack back up the tree, we add
# them back
self.remainingVariables = self.csp.variables
self.bsh = Backtracking_Search_Heuristics()
def backtrack(self, currentNode):
print "call"
print "current csp vars"
for var in currentNode.currentcsp.variables:
print var.name
# if the assignment of the current node is valid then we return it as the solution
if self.csp.isCompleteAndValid(self.currentNode.assignment):
return self.currentNode.assignment
# let's select the next variable that we want to test the values for
if len(currentNode.currentcsp.variables) is 0:
return False
else:
currentVariable = self.bsh.chooseNextVariable(currentNode.currentcsp)
print "currentVar is : " + currentVariable.name
# and remove it from the list of available variables
currentNode.currentcsp.variables.remove(currentVariable)
# get the values from this variables domain and put them in the order that we want to test them
sortedVarValues = self.bsh.getOrderedValues(currentVariable, self.currentNode)
newChildren = self.bsh.createChildNodes(sortedVarValues, currentNode, currentVariable)
for child in newChildren:
print "current child assn: " + str(child.assignment)
if self.csp.isCompleteAndValid(child.assignment):
print "finished with : "
child.printAssignment
return True
currentNode = child
#print "currentNode assn: " + str(currentNode.assignment)
result = self.backtrack(currentNode)
return False
class Backtracking_Search:
def __init__(self, cspArg):
# the csp problem we are trying to solve
self.csp = copy.deepcopy(cspArg)
# a root node for our 'tree'. The root nodes' parent is 'none', that's how we know it's the root
self.root = Node()
# currentNode is initially set to root and we will generate the tree as we go along searching
self.currentNode = self.root
self.currentNode.currentcsp = copy.deepcopy(cspArg)
# we initially start with the entire set of variables, but remove them and add them back depending on where we
# are in the search.as we go down the tree, we eliminate variables, if we backtrack back up the tree, we add
# them back
self.remainingVariables = self.csp.variables
self.solutions = []
self.bsh = Backtracking_Search_Heuristics()
def backtrack(self, currentNode):
# if the assignment of the current node is valid then we return it as the solution
if self.csp.isCompleteAndValid(self.currentNode.assignment):
return self.currentNode.assignment
# let's select the next variable that we want to test the values for
if len(currentNode.currentcsp.variables) is 0:
return False
else:
currentVariable = self.bsh.chooseNextVariable(currentNode.currentcsp)
print "currentVar is : " + currentVariable.name
# and remove it from the list of available variables
currentNode.currentcsp.variables.remove(currentVariable)
# get the values from this variables domain and put them in the order that we want to test them
sortedVarValues = self.bsh.getOrderedValues(currentVariable, self.currentNode)
newChildren = self.bsh.createChildNodes(sortedVarValues, currentNode, currentVariable)
print "children created: "
for child in newChildren:
print child.assignment
# check all generated children to see if they are valid solutions
for child in newChildren:
if self.csp.isCompleteAndValid(child.assignment):
self.solutions.append(child.assignment)
print "valid solution: " + str(child.assignment)
currentNode = child
self.backtrack(currentNode)
def __init__(self, cspArg):
# the csp problem we are trying to solve
self.csp = copy.deepcopy(cspArg)
# a root node for our 'tree'. The root nodes' parent is 'none', that's how we know it's the root
self.root = Node()
# currentNode is initially set to root and we will generate the tree as we go along searching
self.currentNode = self.root
self.currentNode.currentcsp = copy.deepcopy(cspArg)
# we initially start with the entire set of variables, but remove them and add them back depending on where we
# are in the search.as we go down the tree, we eliminate variables, if we backtrack back up the tree, we add
# them back
self.remainingVariables = self.csp.variables
self.solutions = []
self.bsh = Backtracking_Search_Heuristics()
def __init__(self, cspArg):
# the csp problem we are trying to solve
self.csp = copy.deepcopy(cspArg)
# a root node for our 'tree'. The root nodes' parent is 'none', that's how we know it's the root
self.root = Node()
# currentNode is initially set to root and we will generate the tree as we go along searching
self.currentNode = self.root
self.currentNode.currentcsp = copy.deepcopy(cspArg)
# we initially start with the entire set of variables, but remove them and add them back depending on where we
# are in the search.as we go down the tree, we eliminate variables, if we backtrack back up the tree, we add
# them back
self.remainingVariables = self.csp.variables
self.bsh = Backtracking_Search_Heuristics()
