def importPhases(file, names=[], loglevel=0):
"""Import multiple phases from one file. The phase names should be
entered as a list of strings. See: importPhase """
s = []
for nm in names:
s.append(solution.Solution(src=file, id=nm, loglevel=loglevel))
return s
def run_solve(self, z3_context, elements_json, prev_solutions_json,
results, i):
""" Launch the solving process inside a thread to get a new design solution
Parameters:
z3_context: Z3 context objects
elements_json: JSOn string of the elements tree
previous_solutions_json: JSON string of the previous soluitons to avoid producing again
results: Results collection to store the checked solution
i: index to store in the results collection
"""
# Get the element shapes from the commmand line
elements = json.loads(elements_json)
prev_solutions = json.loads(prev_solutions_json)
# Construct the solver instance
solver = z3_solver.Solver(z3_context, elements, prev_solutions)
# Solve for a design solution
state = sln.Solution()
time_start = time.time()
solution = solver.branch_and_bound(state)
time_end = time.time()
logging.debug("Time in z3 " + str(i) + ": " + str(solver.time_z3))
logging.debug("Time to generate a solution " + str(i) + ": " +
str(time_end - time_start))
if solution is None:
logging.debug("----No solution found---")
results[i] = solution
def test_SingleNumber(): c = solution.Solution() lstA = [2,2,2,3,3,3,4,4,5,5,6,6,1,1,1,4,5,7,6] eq_(c.SingleNumber(lstA),7) lstB = [1,2,3,1,2,3,1,2,4,5,6,4,5,6,4,5,6,3,3] eq_(c.SingleNumber(lstB),3)
def canonical_solution(self, time_left):
sol = solution.Solution(self.instance)
t0 = time.clock()
route = [0]
q = 0
for i in range(1, len(self.instance.nodes)): # we bypass the depot 0
if q + self.instance.nodes[i]["rq"] <= self.instance.capacity:
q += self.instance.nodes[i]["rq"]
# Calculate distance between previous and current node added to the route
# > 0 is to ensure no IndexOutOfBounds Exception
if len(route) > 0:
self.total_distance += round(
abs(self.instance.nodes[i - 1]["pt"] -
self.instance.nodes[i]["pt"]), 0)
route += [i]
else:
self.total_distance += round(
abs(self.instance.nodes[i - 1]["pt"] -
self.instance.nodes[i]["pt"]), 0)
sol.routes += [route + [0]]
route = [0, i]
q = self.instance.nodes[i]["rq"]
if time.clock() - t0 > time_left:
sys.stdout.write("Time expired")
return sol
sol.routes += [route + [0]]
solution.routes = sol.routes
return sol
def canonical_solution(self):
self.solution = solution.Solution(self.instance)
self.solution = self.alg(self.instance,
self.solution,
seed=self.seed,
output_file=self.output_file)
return self.solution
def get_solution(self, source):
if source['file'] not in self.all_solutions:
s = solution.Solution(self, filename=source['file'])
self.all_solutions[source['file']] = s
self.drop_cache()
return self.all_solutions[source['file']]
def __init__(self, instance=None, algorithm=None, time_limit=60):
assert instance is not None
assert algorithm is not None
self.instance = instance
self.algorithm = algorithm
self.time_limit = time_limit
self.solution = solution.Solution(self.instance)
def Main(GeneticRules, extracting=0):
print "main"
start = timeit.default_timer()
#GeneticRules=[]
#flexible=[0.2,0.5,1]
flexible = [0.2]
dueDateParameter = [1.2, 1.5, 2, 0]
#dueDateParameter=[1.2]
jobAndMachine = [[10, 5], [20, 5], [50, 5], [20, 10], [50, 10], [100, 10],
[50, 15], [100, 15], [200, 15]]
'''
flexible=[0.5]
dueDateParameter=[1.5]
jobAndMachine=[[10,3]]
'''
data = [[0, 0, 0, 0] for i in xrange(len(GeneticRules))]
repetition = 1
for h in range(0, repetition):
for i in flexible:
for j in dueDateParameter:
for index, k in enumerate(jobAndMachine):
pr1 = SP.FJSSP(k[1], k[0], j, i) #generate problem
#pr1.printTable()#call print function from schedulingProblem file
s1 = SS.Solution(
pr1) #call solution function from solution file
cs1 = CS.createSolution(pr1, s1, index, GeneticRules,
extracting)
#s2 = cs1.randomSolution(s1)
#cs1.initialization()
Result = cs1.simulatedSolution()
for index, t in enumerate(data):
for l in range(0, 4):
if l == 0:
data[index][l] = Result[index][l]
else:
data[index][l] += Result[index][l]
#a=TS.tranformation(pr1,ab)#call transformation function from TranformPermutationToSolution file
#print s1.order, "\n"#print solution
#print timeit.default_timer()-start
for index, t in enumerate(
data): #it is used for calculate mean value of objectives
for l in range(0, 4):
if l == 0:
data[index][l] = data[index][l]
else:
data[index][l] = data[index][l] / (
len(flexible) * len(dueDateParameter) *
len(jobAndMachine) * repetition)
print data
# print "oldu bu is"
stop = timeit.default_timer()
print stop - start
return data
def main():
cases = ["A man, a plan, a canal: Panama", "race a car"]
op = solution.Solution().isPalindrome
for _ in xrange(10000):
for item in cases:
#print op(item), "\t", item
op(item)
def __init__(self, path, alert_file):
self.file_name = alert_file
self.path = path
self.observer = Observer()
self.text_operations = text_operations.TextOperations(self.file_name)
self.solution = solution.Solution()
self.end_program = end_program.EndProgram()
self.today_events = []
def test_singlenumber():
lstA = [1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 1] # output 7
lstB = [23, 25, 27, 29, 31, 29, 27, 25, 23] # output 31
c = solution.Solution()
eq_(c.SingleNumber(lstA), 7)
eq_(c.SingleNumber(lstB), 31)
def __init__(self, instance=None, alg=None, seed=4, output_file=None):
assert instance is not None
assert alg is not None
self.instance = instance
self.alg = alg
self.solution = solution.Solution(self.instance)
self.seed = seed
self.output_file = output_file
def __init__(self, file_name):
self.solution = solution.Solution()
self.file_name = file_name
self.alert_file = open(self.file_name, 'r')
self.alert_file.seek(0, 2)
self.high_sid_alert_number = 100000
self.alert_message = ''
self.ips_report_name = 'IPS_report_' + date.today().strftime('%d_%m_%y') + '.txt'
def main():
A = [
[[1, 2, 3], [4, 5, 6], [7, 8, 9]],
[[2, 3]],
]
op = solution.Solution().spiralOrder
for item in A:
print "-" * 20
print item
print op(item)
def main():
op = solution.Solution().candy
A = [1, 2, 3, 3, 4, 4, 3, 2, 1]
print A
for _ in xrange(10000):
op(A)
A = [4, 2, 3, 4, 1]
print A
for _ in xrange(10000):
op(A)
def main():
left = ListNode.build_num(99)
right = ListNode.build_num(1)
op = solution.Solution().addTwoNumbers
sum = op(left, right)
left.print_r()
print ""
right.print_r()
print ""
sum.print_r()
print ""
def __init__(self):
super().__init__()
# 记录所有模块
self.labels = []
self.order = [x for x in range(9)]
self.group = QSequentialAnimationGroup()
self.solutions = []
self.positions = []
# 求解器
self.sol = solution.Solution()
self.initUI()
def execute(nx_graph: nx.Graph, parameters: SimulatedAnnealingParameters):
observations = []
start_time = time.time()
# STEP 1
t = parameters.T_max
i_s2 = 0
i_s3 = 0
crnt_s = solution.Solution(nx_graph)
crnt_est = crnt_s.get_estimate()
while t > parameters.T_min:
i_s3 += 1
# STEP 2
while True:
i_s2 += 1
next_s = crnt_s.get_neighbour()
next_est = next_s.get_estimate()
if next_est < crnt_est:
crnt_s = next_s
crnt_est = next_est
elif random.random() > math.exp((next_est - crnt_est) / t):
crnt_s = next_s
crnt_est = next_est
if parameters.i_s2_observation_interval is not None and i_s2 % parameters.i_s2_observation_interval == 0:
observations.append(
SimulatedAnnealingObservation('s2', crnt_est,
time.time() - start_time,
i_s2, i_s3))
if i_s2 % parameters.k_t == 0:
break
# STEP 3
t = parameters.cooling_scheme.step(t, i_s3)
if parameters.i_s3_observation_interval is not None and i_s3 % parameters.i_s3_observation_interval == 0:
observations.append(
SimulatedAnnealingObservation('s3', crnt_est,
time.time() - start_time,
i_s2, i_s3))
end_time = time.time()
total_time = end_time - start_time
return SimulatedAnnealingResult(crnt_s, total_time, observations)
def create_feasible_solution(self):
"""Build a feasible, greedy solution for the problem."""
self._solution = solution.Solution(self._abstract_g, self._cache)
# While customers are not all served:
while self._solution.missing_customers():
self._customer = list(self._solution.missing_customers())
self._temp_route = solution.Route(self._cache, greenest=True)
self.create_feasible_route()
self._solution.routes.append(self._temp_route)
assert not self._solution.missing_customers(), 'self._customer is ' \
'empty even if sol.' \
'missing_customers(' \
') is not'
return self._solution
def algorithm(self, start_time, k=3):
sol = solution.Solution(self.instance)
route = [0] # Our route
capacity = 0 # Our capacity for each vehicle
unvisited_nodes = list(self.instance.nodes)
current_node = self.instance.nodes[0] # Starting a depot 0
unvisited_nodes.remove(current_node)
distances = {}
# While there are still nodes to visit, continue
while unvisited_nodes:
# Loop through the unvisited nodes
for index, p in enumerate(unvisited_nodes):
dist = sol.instance.distance(current_node['pt'], p['pt'])
distances.update({dist: p})
distances = {
k: v
for k, v in sorted(distances.items(), key=lambda item: item[0])
}
shortest_distances = dict(itertools.islice(distances.items(), k))
randomly_picked_node = random.choice(
list(shortest_distances.keys()))
node = distances.get(randomly_picked_node)
current_node = node
if capacity + node['rq'] <= self.instance.capacity:
capacity += node['rq']
route += [self.instance.nodes.index(node)]
unvisited_nodes.remove(current_node)
else:
sol.routes += [route + [0]]
route = [0, self.instance.nodes.index(node)]
capacity = node['rq']
unvisited_nodes.remove(node)
t1 = time.time() # End time
if t1 - start_time > self.time_limit:
sys.stdout.write("Time Expired\n")
return sol
distances = {}
node = None
randomly_picked_node = None
sol.routes += [route + [0]]
return sol
def solve(self, prob=None):
# Préparer l'exécution
self._prepare()
# Boucle d'exécution
while (self._continue()):
ampl_solver.FrpAmplMipSolver.solve()
# pass (je crois que ca devrais plus être là)
# Finaliser l'exécution
self._terminate()
# Retourner une solution
# TODO Le code ci-dessous est un code temporaire
return solution.Solution()
def canonical_solution(self, time_left):
sol = solution.Solution(self.instance)
t0 = time.clock()
route = [0]
q=0
for i in range(1,len(self.instance.nodes)): # we bypass the depot 0
if q+self.instance.nodes[i]["rq"] <= self.instance.capacity:
q+=self.instance.nodes[i]["rq"]
route += [i]
else:
sol.routes += [route+[0]]
route = [0,i]
q=self.instance.nodes[i]["rq"]
if time.clock() - t0 > time_left:
sys.stdout.write("Time expired")
return sol
sol.routes += [route+[0]]
return sol
def algorithm(self, start_time):
sol = solution.Solution(self.instance)
route = [0] # Our route
capacity = 0 # Our capacity for each vehicle
unvisited_nodes = list(self.instance.nodes)
current_node = self.instance.nodes[0] # Starting a depot 0
unvisited_nodes.remove(current_node)
shortest_distance = 0
closest_node = None
# While there are still nodes to visit, continue
while unvisited_nodes:
# Loop through the unvisited nodes
for index, p in enumerate(unvisited_nodes):
dist = sol.instance.distance(current_node['pt'], p['pt'])
if dist < shortest_distance or shortest_distance == 0:
shortest_distance = dist
closest_node = p
current_node = closest_node
if capacity + closest_node['rq'] <= self.instance.capacity:
capacity += closest_node['rq']
route += [self.instance.nodes.index(closest_node)]
unvisited_nodes.remove(current_node)
else:
sol.routes += [route + [0]]
route = [0, self.instance.nodes.index(closest_node)]
capacity = closest_node['rq']
unvisited_nodes.remove(closest_node)
t1 = time.time()
if t1 - start_time > self.time_limit:
sys.stdout.write("Time Expired\n")
return sol
shortest_distance = 0
closest_node = None
sol.routes += [route + [0]]
return sol
def clusterAlgorithm(self, time_left):
# To implement
"This heuristic algorithm makes a several clusters of points, where each cluster is <= maxCapacity. When clusters"
" have been found for all points, then we send a vehicle out to each cluster and find the routes."
"1 ) Pick a (random) point"
"2 ) Calculate the euclidean distance to all points to find a cluster, which does not exceed maximum capacity"
"3 ) Send a vehicle to the cluster and find the shortest route in the cluster"
"4 ) Repeat"
"Hvordan håndterer vi kunder, som ikke er i clusters?"
sol = solution.Solution(self.instance)
t0 = time.clock()
route = [0]
q = 0
visited = []
clusters = []
for i in range(len(self.instance.nodes) - 1):
for j in range(1, len(self.instance.nodes)):
temp = self.euclideanDistance(
self.instance.nodes[current]["pt"],
self.instance.nodes[j]["pt"])
if j not in visited:
if temp <= shortestDistance:
shortestDistance = temp
current = j
visited += [j]
if q + self.instance.nodes[current][
"rq"] <= self.instance.capacity:
q += self.instance.nodes[current]["rq"]
if len(route) > 0:
self.total_distance += shortestDistance
route += [current]
else:
self.total_distance += shortestDistance
sol.routes += [route + [0]]
route = [0, current]
q = self.instance.nodes[current]["rq"]
shortestDistance = 10000000
if time.clock() - t0 > time_left:
sys.stdout.write("Time expired")
return sol
sol.routes += [route + [0]]
solution.routes = sol.routes
return sol
def multiCrossOver(solutions, flag,number_of_guards, myMap):
guard_and_solutions=[]
for i in range(number_of_guards):
temp=[]
for s in solutions:
temp.append(s.guardsPath[i])
guard_and_solutions.append(temp)
check = zip(*guard_and_solutions)
res=[]
for elem in check:
newSol = solution.Solution(_map=myMap, _numberOfGuards=number_of_guards, _guardsPath=list(list(elem)))
res.append(newSol)
return res
def z3_solve(self):
# Solve for solutions only using Z3, rather than the branch and bound (experimental)
solutions = []
num_solutions = 1
slns_found = 0
while slns_found < num_solutions:
self.solver.push()
result = self.solver.check()
if str(result) == 'sat':
solution = sln.Solution()
model = self.solver.model()
json_sln = solution.convert_to_json(self.root, model)
solutions.append(json_sln)
self.encode_previous_solution_from_model(model, json_sln['id'])
slns_found += 1
return solutions
def run_reflow(self, z3_context, elements, solution, changed_element_id,
changed_property, changed_value, keep_or_prevent, results,
i):
""" Launch a reflow thread.
Parameters:
z3_context: A Z3 context object
elements: JSON string of the element tree
solution: The currrent solution tree from the previous soluiton with the values.
changed_element_id: ID of the changed element
changed_property: Name of the variable that was changed
changed_value: The value that the variable was changed to
keep_or_prevent: Whether the update was to "keep" or "prevent" the value.
results: collection for the results of reflow
i: Index to store in the results collection
"""
elements = json.loads(elements)
# Construct the solver instance
solver = z3_solver.Solver(z3_context, elements, prune_domains=False)
time_start = time.time()
# Solver class repairs the soluiton
repaired_solution = solver.repair_solution(solution,
changed_element_id,
changed_property,
changed_value,
keep_or_prevent)
if repaired_solution is None:
# This means the solution could not be repaired, so we request a new one to replace it.
print("Getting brand new solution")
solver = z3_solver.Solver(z3_context, elements)
# Get a new soltuion to replace the invalid one
state = sln.Solution()
time_start = time.time()
repaired_solution = solver.branch_and_bound(state)
time_end = time.time()
logging.debug("Time to check validity of solution " + str(i) + ": " +
str(time_end - time_start))
results[i] = repaired_solution
def __init__(self, **kwargs):
self.tests = {}
self.all_solutions = {}
self.name = 'NO NAME'
self.cached_solutions = None
self.skip_count = 0
self.clusters = {}
self.cluster_size = 0
self.dimentions = None
self.output = None
self.is_tl_hidden = True
self.is_rt_hidden = True
self.is_wa_hidden = True
self.is_changed = False
self.sources_path = ''
self.tests_path = ''
if 'file' in kwargs:
data = json.load(kwargs['file'])
self.clusters = dict(
(key, [cluster.Cluster(obj=c) for c in data['clusters'][key]])
for key in data['clusters'])
self.all_solutions = dict(
(e['name']['file'], solution.Solution(self, obj=e))
for e in data['solutions'])
self.tests = dict(
(e['name'], testf.Test(obj=e)) for e in data['tests'])
self.is_tl_hidden = data.get('is_tl_hidden', True)
self.is_rt_hidden = data.get('is_rt_hidden', True)
self.is_wa_hidden = data.get('is_wa_hidden', True)
self.cluster_size = data.get('cluster_size', 0)
self.name = data.get('name', 'NO NAME')
self.sources_path = data.get(
'sources_path',
os.path.split(list(
self.all_solutions.values())[0].filepath)[0])
self.tests_path = data.get(
'tests_path',
os.path.split(list(self.tests.values())[0].name)[0])
if 'output' in kwargs:
self.output = kwargs['output']
def test_evalRPN():
lstA = [
"10", "6", "9", "3", "+", "-11", "*", "/", "*", "17", "+", "5", "+"
] # output 22
lstB = [
"-78", "-33", "196", "+", "-19", "-", "115", "+", "-", "-99", "/",
"-18", "8", "*", "-86", "-", "-", "16", "/", "26", "-14", "-", "-",
"47", "-", "101", "-", "163", "*", "143", "-", "0", "-", "171", "+",
"120", "*", "-60", "+", "156", "/", "173", "/", "-24", "11", "+", "21",
"/", "*", "44", "*", "180", "70", "-40", "-", "*", "86", "132", "-84",
"+", "*", "-", "38", "/", "/", "21", "28", "/", "+", "83", "/", "-31",
"156", "-", "+", "28", "/", "95", "-", "120", "+", "8", "*", "90", "-",
"-94", "*", "-73", "/", "-62", "/", "93", "*", "196", "-", "-59", "+",
"187", "-", "143", "/", "-79", "-89", "+", "-"
] # output 165
c = solution.Solution()
eq_(c.evalRPN(lstA), 22)
eq_(c.evalRPN(lstB), 165)
def test_maxDepth():
root = leetcode.TreeNode(1)
leaf_1 = leetcode.TreeNode(2)
leaf_2 = leetcode.TreeNode(3)
leaf_3 = leetcode.TreeNode(4)
leaf_4 = leetcode.TreeNode(5)
leaf_5 = leetcode.TreeNode(6)
root.left = leaf_1
root.right = leaf_2
leaf_1.left = leaf_3
leaf_2.right = leaf_4
leaf_3.right = leaf_5
c = solution.Solution()
eq_(c.maxDepth(root), 4)
eq_(c.maxDepth(leaf_3), 2)