def runSimulation(start, solution):
"""
Returns minumum number of time steps needed to get to solution
"""
scoredict = {}
pathway = []
childrenlist = []
currentmin = 20
solution_found = False
pare_node = Node(start)
m = (0, pare_node)
heappush(queue, m)
tempnode = heappop(queue)
tempchildren = generateAllChildren(tempnode[1].cargo)
for k in range(len(tempchildren)):
tempscore = seriesScore(tempchildren[k])
initnode = Node(tempchildren[k], tempnode[1])
v = (tempscore, initnode)
heappush(queue, v)
# print queue[k][1]
print
while (queue != [] and not solution_found):
pare_node = heappop(queue)
# print pare_node[1]
children = generateAllChildren(pare_node[1].cargo)
c = selectChildren(children,10)
for i in range(len(c)):
score = seriesScore(c[i])
node = Node(c[i], pare_node[1])
l = (score, node)
heappush(queue, l)
if (c[i] == solution):
print "Solution: ", c[i]
solution_found = True
inversions = 0
while(node.prev != None):
print node
pathway.append(node.cargo)
node = node.prev
inversions += 1
print "Inversions: ", inversions
if (inversions < currentmin):
scoredict = {}
scoredict[inversions] = pathway
print scoredict
for key, val in scoredict.items():
currentmin = key
print currentmin
def runSimulation(start, solution):
"""
Returns minumum number of time steps needed to get to solution
"""
solution_found = False
pare_node = Node(start)
m = (0, pare_node)
heappush(queue, m)
while (queue != [] and not solution_found):
pare_node = heappop(queue)
children = generateAllChildren(pare_node[1].cargo)
c = selectChildren(children)
for i in range(len(c)):
score = seriesScore(c[i])
node = Node(c[i], pare_node[1])
l = (score, node)
heappush(queue, l)
if (c[i] == solution):
print "Solution: ", c[i]
solution_found = True
inversions = 0
while(node.prev != None):
print node
node = node.prev
inversions += 1
print "Inversions: ", inversions
def selectChildren(children):
scores = []
# calculate "fitness" scores
for i in range(len(children)):
s = seriesScore(children[i])
scores.append(s)
if (fittest == 1):
return children[scores.index(max(scores))]
def selectChildren(children):
scores = []
# calculate "fitness" scores
for i in range(len(children)):
s = seriesScore(children[i])
scores.append(s)
# check which 3 genomes have the best scores
dictionary = heapq.nlargest(3, zip(scores, children))
# put the best genomes in a list before returning
best_children = []
for j in range(len(dictionary)):
best_children.append(dictionary[j][1])
children = []
scores = []
return best_children
def runSimulation(start, solution):
"""
Returns minumum number of time steps needed to get to solution
"""
solutionNodes = []
lowest = 645758
pare_node = Node(start)
m = (0, pare_node)
heappush(queue, m)
while (queue != []):
pare_node = heappop(queue)
children = generateAllChildren(pare_node[1].cargo)
c = selectChildren(children)
for i in range(len(c)):
# create nodes
node = Node(c[i], pare_node[1])
if (c[i] == solution):
solutionNodes.append(node)
print "length of solutionNodes: ", len(solutionNodes)
else:
score = seriesScore(c[i])
l = (score, node)
heappush(queue, l)
inversions = 0
for j in range(len(solutionNodes)):
node = solutionNodes[j]
while((node.prev != None) and (inversions < lowest)):
print "Step", node
node = node.prev
inversions += 1
if ((inversions < lowest) and (node.prev == None)):
lowest = inversions
print "Inversions: ", inversions
j += 1
print "Start: ", start
def selectChildren(children):
scores = []
# calculate "fitness" scores
for i in range(len(children)):
s = 1 - (seriesScore(children[i])/len(start))
print "SERIESSCORE=: ", s
scores.append(s)
print "SCORES: ", scores
# check which 3 genomes have the best scores
dictionary = heapq.nlargest(4, zip(scores, children))
print
print dictionary
# put the best genomes in a list before returning
best_children = []
for j in range(len(dictionary)):
best_children.append(dictionary[j][1])
children = []
scores = []
return best_children
