def mazeMaker(dim, p):
finalPath = []
arr = [['0' for i in range(dim)] for j in range(dim)]
arr[0][0] = "s"
arr[dim-1][dim-1] = "g"
count = 0
for row in arr:
for i, ele in enumerate(row):
if ele != "s" and ele != "g" and int(ele) == 0:
if random.randrange(0, 100, 1)/100 < p:
row[i] = "x"
count += 1
sol = Solution(arr, (0, 0), "strat1", fireStartLoc)
algoResult = sol.dfs()
backtrack_info = algoResult[0]
if backtrack_info != {}:
finalPath = sol.create_solution(backtrack_info, (0, 0))
if finalPath == []:
arr = mazeMaker(dim, p)
writeGame(arr, GAMEFILE)
writeGame(arr, CLEANFILE)
writeGame(arr, FIREFILE)
return arr
def optimizedTabou(self,start,size):
tabou = []
ltabou = []
s = self.glouton1(start)
i = 0
while i != 100:
i = i + 1
neighbors,fct = self.generateNeighborsTabou(s.getChemin(),ltabou)
index = fct.index(min(fct))
newSol = neighbors[index]
tabou.append(Solution(newSol,fct[index]))
ltabou.append(newSol)
if len(ltabou) == size:
lst = [tabou[j].getFct() for j in range(len(tabou))]
index = lst.index(min(lst))
x = tabou[index]
y = tabou[index].getChemin()
tabou = []
ltabou = []
tabou.append(x)
ltabou.append(y)
s = Solution(newSol,fct[index])
lst = [tabou[j].getFct() for j in range(len(tabou))]
index = lst.index(min(lst))
return tabou[index]
def main():
root = TreeNode(1)
root.left = TreeNode(2)
root.left.left = TreeNode(3)
root.left.right = TreeNode(4)
root.right = TreeNode(5)
root.right.right = TreeNode(6)
solution = Solution()
solution.flatten(root)
printFlattenTree(root)
print '----'
root2 = TreeNode(1)
solution.flatten(root2)
printFlattenTree(root2)
print '----'
root3 = TreeNode(1)
root3.right = TreeNode(2)
root3.right.left = TreeNode(3)
root3.right.right = TreeNode(4)
root3.right.right.right = TreeNode(5)
solution.flatten(root3)
printFlattenTree(root3)
print '----'
solution.flatten(None)
printFlattenTree(None)
def runIteration(self):
if self.current_temperature > 1.0:
# Create a new solution depending on the current solution,
# i.e. a neighbour
neighbour = Solution(
self.objective_function,
1, # Minimisation
self.getRandomNeighbour(self.current_solution))
# Get its energy (cost function)
self.getEnergy(neighbour)
# Accept the neighbour or not depending on the acceptance probability
if self.acceptanceProbability(
neighbour.getObjective()) > self.system_random.uniform(
0, 1):
self.current_solution = copy.deepcopy(neighbour)
# The neighbour is better thant the current element
if self.best_solution.getObjective(
) > self.current_solution.getObjective():
self.best_solution = copy.deepcopy(self.current_solution)
# Log the current states
self.logCurrentState()
# Cool the system
self.current_temperature = self.cooling_schedule()
def greedy(self,r):
for i in range(len(C)):
l = Solution([Population.CO[i]])
l.seed_greedy(0,r)
self.pop.append(l)
self.pop.sort(key = lambda l : l.dis)
self.rm_dup()
def HookeJeevesMove(self):
currentPoint = self.history[self.iIter]
bestPoints = list()
for i in range(len(currentPoint.DV)):
newDV = list()
for j in range(len(currentPoint.DV)):
if i == j:
newDV.append(currentPoint.DV[j] + self.stepSize[j])
else:
newDV.append(currentPoint.DV[j])
newPoint = Solution(Solution.getName(),newDV)
if (not self.isTabu(newPoint)) and (not newPoint.isValid):
self.addIfNotDominated(newPoint,bestPoints)
self.removeDominatedPoints(bestPoints)
newDV = list()
for j in range(len(currentPoint.DV)):
if i == j:
newDV.append(currentPoint.DV[j] - self.stepSize[j])
else:
newDV.append(currentPoint.DV[j])
newPoint = Solution(Solution.getName(),newDV)
if (not self.isTabu(newPoint)) and (not newPoint.isValid):
self.addIfNotDominated(newPoint,bestPoints)
self.removeDominatedPoints(bestPoints)
self.iIter += 1
self.iLocal += 1
nextPoint = self.selectRandom(bestPoints)
self.history.append(nextPoint)
bestPoints.remove(nextPoint)
for remainingPoints in bestPoints:
self.addIfNotDominated(remainingPoint,self.IM)
self.removeDominatedPoints(self.IM)
self.nextMoveHookeJeeves = False
def test_clear(self):
poissonForm = PoissonFormulation.PoissonFormulation(2, True)
poissonBF = poissonForm.bf()
mesh = MeshFactory.MeshFactory_rectilinearMesh(poissonBF, [1.0, 1.0],
[2, 3], 4)
s = Solution.Solution_solution(mesh)
x = Function.Function_xn(1)
one = Function.Function_constant(1)
zero = Function.Function_constant(0)
phi = poissonForm.phi(
) # VarPtr for main, scalar-valued variable in Poisson problem
psi = poissonForm.psi() # VarPtr for gradient of psi, vector-valued
s = Solution.Solution_solution(mesh)
s.projectOntoMesh({
phi.ID(): x,
psi.ID(): Function.Function_vectorize(one, zero)
})
s.addSolution(s, 1.0, [phi.ID()])
self.assertNotEqual(
0.0, s.L2NormOfSolution(0)) # not zero since addSolution
s.clear()
self.assertIsNotNone(
s) #make sure some object still exists since should be in tact
self.assertEqual(
0, s.L2NormOfSolution(0)) # zero since zero solutions now
def test_case1(self):
testclass = Solution.Solution()
spec = Solution.TreeNode(5)
spec.left = Solution.TreeNode(2)
spec.right = Solution.TreeNode(-3)
#testclass.findFrequentTreeSum(spec)
self.assertEqual(testclass.findFrequentTreeSum(spec), [2, -3, 4])
def tabou(self, a):
tabou = []
ltabou = []
s = self.glouton1()
i = 0
while i != 100:
i = i + 1
neighbors, fct = self.generateNeighborsTabou(
s.getInstance(), ltabou)
if (len(fct) == 0):
break
index = fct.index(max(fct))
newSol = neighbors[index]
tabou.append(Solution(newSol, fct[index]))
ltabou.append(newSol)
if len(ltabou) == a:
lst = [tabou[j].getValeur() for j in range(a)]
index = lst.index(max(lst))
x = tabou[index]
tabou = []
ltabou = []
tabou.append(x)
ltabou.append(x.getInstance())
s = Solution(newSol, fct[index])
lst = [tabou[j].getValeur() for j in range(len(tabou))]
index = lst.index(max(lst))
return tabou[index]
def main():
root = TreeNode(1)
root.left = TreeNode(2)
root.right = TreeNode(2)
root.left.right = TreeNode(3)
root.right.right = TreeNode(3)
s = Solution()
print s.isSymmetric(root)
def main():
solution = Solution()
height = [1, 1]
print(solution.maxArea(height))
height = [1, 2]
print(solution.maxArea(height))
def main():
root = TreeNode(1)
root.left = TreeNode(2)
root.right = TreeNode(2)
root.left.right = TreeNode(3)
root.right.right = TreeNode(3)
s = Solution()
print s.isSymmetric(root)
def buildSolution(self, branchInfo):
# 验证 编译 锁
lock_file = self.__checkLock()
if lock_file == '':
logger.error('另一个打包脚本正在运行...')
return {'success': False, 'error': '另一个打包脚本正在运行...'}
logger.info(branchInfo.programName + '/' + branchInfo.branchTag + '编译开始')
logger.info("Name: %s" % branchInfo.programName)
logger.info("Tag: %s" % branchInfo.branchTag)
working_dir = getLocalDir(branchInfo.programName) + '/' + branchInfo.branchTag
if branchInfo.programName == 'updater':
try:
sol = Solution.Solution(working_dir, 'Updater.sln', False, 'Release')
sol.buildSolution(True, ['tlupdater'])
os.remove(lock_file)
if sol.status() == 3: # error
return {'success': False, 'error': 'build error %s/%s' % (branchInfo.programName, branchInfo.branchTag)}
elif sol.status() == 2: # finished
return {'success': True, 'error': 'build successfully %s/%s' % (branchInfo.programName, branchInfo.branchTag)}
except Exception as e:
os.remove(lock_file)
return {'success':False, 'error': e.message}
elif branchInfo.programName == 'daemon':
try:
sol = Solution.Solution(working_dir, 'daemon.sln', False, "Deploy_Release")
sol.buildSolution(True, ['daemon'])
os.remove(lock_file)
if sol.status() == 3: # error
return {'success': False, 'error': 'build error %s/%s' % (branchInfo.programName, branchInfo.branchTag)}
except Exception as e:
os.remove(lock_file)
return {'success':False, 'error': e.message}
elif branchInfo.programName == 'quoter':
try:
sol = Solution.Solution(working_dir, 'quoter.sln', False, "Deploy_Release")
sol.buildSolution(True, ['XtQuoter'])
os.remove(lock_file)
if sol.status() == 3: # error
return {'success': False, 'error': 'build error %s/%s' % (branchInfo.programName, branchInfo.branchTag)}
except Exception as e:
os.remove(lock_file)
return {'success':False, 'error': e.message}
elif branchInfo.programName == 'XtTradeClient':
try:
sol = Solution.Solution(working_dir, 'XtTradeClient.full.sln', False, "Deploy_Release")
sol.buildSolution(True, ['XtTradeClient'])
os.remove(lock_file)
if sol.status() == 3: # error
return {'success': False, 'error': 'build error %s/%s' % (branchInfo.programName, branchInfo.branchTag)}
except Exception as e:
os.remove(lock_file)
return {'success':False, 'error': e.message}
logger.info(branchInfo.programName + '/' + branchInfo.branchTag + '编译完成')
return {'success': True, 'error': 'build successfully %s/%s' % (branchInfo.programName, branchInfo.branchTag)}
def main():
solution = Solution()
root5 = TreeNode(1)
root5.left = TreeNode(2)
root5.right = TreeNode(3)
root5.left.left = TreeNode(4)
root5.left.right = TreeNode(5)
print 'The count is :', solution.countNodes(root5)
root6 = TreeNode(1)
root6.left = TreeNode(2)
root6.right = TreeNode(3)
root6.left.left = TreeNode(4)
root6.left.right = TreeNode(5)
root6.right.left = TreeNode(6)
print 'The count is :', solution.countNodes(root6)
root7 = TreeNode(1)
root7.left = TreeNode(2)
root7.right = TreeNode(3)
root7.left.left = TreeNode(4)
root7.left.right = TreeNode(5)
root7.right.left = TreeNode(6)
root7.right.right = TreeNode(7)
print 'The count is :', solution.countNodes(root7)
root8 = TreeNode(1)
root8.left = TreeNode(2)
root8.right = TreeNode(3)
root8.left.left = TreeNode(4)
root8.left.right = TreeNode(5)
root8.right.left = TreeNode(6)
root8.right.right = TreeNode(7)
root8.left.left.left = TreeNode(8)
root8.left.left.right = TreeNode(9)
root8.left.right.left = TreeNode(10)
root8.left.right.right = TreeNode(11)
print 'The count is :', solution.countNodes(root8)
root = TreeNode(1)
print 'The count is :', solution.countNodes(root)
root2 = TreeNode(1)
root2.left = TreeNode(2)
print 'The count is :', solution.countNodes(root2)
root3 = TreeNode(1)
root3.left = TreeNode(2)
root3.right = TreeNode(3)
print 'The count is :', solution.countNodes(root3)
root4 = TreeNode(1)
root4.left = TreeNode(2)
root4.right = TreeNode(3)
root4.left.left = TreeNode(4)
print 'The count is :', solution.countNodes(root4)
def psuedogreedy(self, r):
for i in range(len(C)):
l = Solution([Population.CO[i]])
l.seed_psuedogreedy(0, r)
self.pop.append(l)
self.pop.sort(key = lambda l : l.dis)
self.rm_dup()
for j in range(len(self.pop)):
print self.pop[j]
def read(self, data_type):
directory = os.getcwd() + r'/csv/'
filename = r'priority_queue.csv'
access_type = 'r'
serial_list = read(directory, filename, access_type)
for element in serial_list:
if data_type is Solution:
new_solution = Solution()
new_solution.process_csv(element[0])
self.insert(new_solution, eval(element[1]))
def process_csv(self, module):
# print(module)
self.anomaly_type = module[0]
self.train_size = module[1]
self.solution_tuple_list = []
for solution_tuple in eval(module[2]):
slt = Solution()
slt.process_csv(solution_tuple[0])
self.solution_tuple_list.append(
(slt, solution_tuple[1], solution_tuple[2]))
def cleanSolution(self, branchInfo):
# 验证 编译 锁
lock_file = self.__checkLock()
if lock_file == '':
logger.error('另一个打包脚本正在运行...')
return {'success': False, 'error': '另一个打包脚本正在运行...'}
sln_file = ''
root_path = getLocalDir(branchInfo.programName) + '/' + branchInfo.branchTag
if branchInfo.programName == 'updater':
sln_file = 'Updater.sln'
elif branchInfo.programName == 'daemon':
sln_file = 'daemon.sln'
elif branchInfo.programName == 'quoter':
sln_file = 'quoter.sln'
elif branchInfo.programName == 'XtTradeClient':
sln_file = 'XtTradeClient.full.sln'
if sln_file == '':
if branchInfo.programName != '':
logger.error('program \"', branchInfo.programName, '\" does not exists')
os.remove(lock_file)
return {'success': False, 'error': 'program \"' + branchInfo.programName + '\" does not exists'}
else:
os.remove(lock_file)
return {'success': False, 'error': 'program name is not set'}
logger.info('%s/%s clean start' % (branchInfo.programName, branchInfo.branchTag))
try:
if branchInfo.programName == 'updater':
try:
sol = Solution.Solution(root_path, sln_file, True, "Release")
os.remove(lock_file)
if sol.status() == 3: # error
return {'success': False, 'error': 'clean error %s/%s' % (branchInfo.programName, branchInfo.branchTag)}
except Exception as e:
os.remove(lock_file)
return {'success': False, 'error': e.message}
else:
try:
sol = Solution.Solution(root_path, sln_file, True, self.build_configuration)
os.remove(lock_file)
if sol.status() == 3: # error
return {'success': False, 'error': 'clean error %s' % (branchInfo.programName, branchInfo.branchTag)}
except Exception as e:
os.remove(lock_file)
return {'success': False, 'error': e.message}
logger.info('%s/%s clean end' % (branchInfo.programName, branchInfo.branchTag))
return {'success': True, 'error': 'Clean %s succeeds' % sln_file}
except Exception as e:
os.remove(lock_file)
logger.error('%s/%s: %s' %(branchInfo.programName, branchInfo.branchTag, e.message))
return {'success': False, 'error': 'clean %s fails' % sln_file}
def main():
solution = Solution()
testCases = [
[],
[1, 2],
[1, 2, 3],
[1, 2, 3, 4],
[1, 2, 3, 4, 5],
[1, 2, 3, 4, 5, 6],
]
for t in testCases:
p = solution.swapPairs(makeList(t))
printList(p)
def Vk(self,s,k):
if k == 0:
x0 = self.two_opt(s.getChemin())
# print("2-opt")
return Solution(x0,self.fonctionObjectif(x0))
if k == 1:
x1 = self.three_opt(s.getChemin())
# print("3-opt")
return Solution(x1,self.fonctionObjectif(x1))
if k == 2:
x2 = self.four_opt(s.getChemin())
# print("4-opt")
return Solution(x2,self.fonctionObjectif(x2))
def main():
if (len(sys.argv) < 2):
print("Please provide the input filepath as the argument")
sys.exit()
input_file = sys.argv[1]
input = readFromFile(input_file)
#Get answer
solution = Solution(input)
print(solution.findClosestPair())
def main():
solution = Solution()
print(listProduct([""], ["a", "b", "c"]))
digits = "111111"
print(solution.letterCombinations(digits))
digits = "111112"
print(solution.letterCombinations(digits))
digits = "234"
print(solution.letterCombinations(digits))
def mutation(self,ind,fct,typeOfMutation):
n = len(ind)
if typeOfMutation == "descente":
return self.descenteVPop(Solution(ind,fct))
if typeOfMutation == "inversion":
l = self.two_opt(ind)
return Solution(l,self.fonctionObjectif(l))
if typeOfMutation == "permutation":
i = random.randint(0, n-2)
j = random.randint(i+1, n-1)
lst = ind.copy()
lst[i],lst[j] = lst[j],lst[i]
return Solution(lst,self.fonctionObjectif(lst))
def tabou(self,start):
tabou = [] #Solution
ltabou = [] #chemin
s = self.glouton1(start)
i = 0
while i != 100:
i = i + 1
neighbors,fct = self.generateNeighborsTabou(s.getChemin(),ltabou)
index = fct.index(min(fct))
newSol = neighbors[index]
tabou.append(Solution(newSol,fct[index]))
ltabou.append(newSol)
s = Solution(newSol,fct[index])
lst = [tabou[j].getFct() for j in range(len(tabou))]
index = lst.index(min(lst))
return tabou[index]
def main():
with open('testcase.txt', "r") as f:
lines = f.readlines()
i = 0
passall = True
while i < len(lines) :
line = lines[i]
nums = stringToIntegerList(line)
if (nums == "null") :
nums = None
#print nums
line = lines[i+1]
#print line
expected = int(line.replace("\n",""))
ret = Solution.Solution().singleNumber(nums)
#print expected
#print ret
if (expected != ret) :
if (nums is None) :
strnums = 'null'
else:
strnums = integerListToString(nums)
print "[Fail]" + strnums + ";" + str(ret) + ";" + str(expected)
passall = False
break
i = i + 2
#print out
if passall == True :
print "[Success]Your solution passed all " + str(len(lines)/2) + " test cases!"
def recuit(self,start):
T0 = 10
Tmin = 1e-2
kmax = 1e4
T = T0
k = 0
s = self.glouton1(start).getChemin()
# calcul de l'énergie initiale du système (la distance initiale à minimiser)
Ec = self.fonctionObjectif(s)
N = len(s)
while T > Tmin:
# choix de deux villes différentes au hasard
i = random.randint(1,N-1)
j = random.randint(1,N -1)
if i == j: continue
# création de la fluctuation et mesure de l'énergie
s = self.fluctuation(s,i,j)
Ef = self.fonctionObjectif(s)
Ec, s = self.metropolis(s,T,Ef,Ec,i,j)
# application de la loi de refroidissement
k += 1
T = T0*np.exp(-k/kmax)
return Solution(s,self.fonctionObjectif(s))
def Vk(self, s, k):
if k == 0:
x0 = self.hamming1(s.getInstance())
while self.poids(x0) > self.capacite:
x0 = self.hamming1(s.getInstance())
return Solution(x0, self.fctObj(x0))
if k == 1:
x1 = self.hamming2(s.getInstance())
while self.poids(x1) > self.capacite:
x1 = self.hamming1(s.getInstance())
return Solution(x1, self.fctObj(x1))
if k == 2:
x2 = self.hamming3(s.getInstance())
while self.poids(x2) > self.capacite:
x2 = self.hamming1(s.getInstance())
return Solution(x2, self.fctObj(x2))
def mutation(self, ind, fct, typeOfMutation):
n = len(ind)
if typeOfMutation == "descente":
return self.descenteVNS(Solution(ind, fct))
if typeOfMutation == "permutation":
i = random.randint(0, n - 2)
j = random.randint(i + 1, n - 1)
lst = ind.copy()
lst[i], lst[j] = lst[j], lst[i]
while self.poids(lst) > self.capacite:
i = random.randint(0, n - 2)
j = random.randint(i + 1, n - 1)
lst = ind.copy()
lst[i], lst[j] = lst[j], lst[i]
return Solution(lst, self.fctObj(lst))
def __init__(self):
self.design = Solution.Solution()
self.design.convert_design_to_string()
self.design.save_current_design_as_input()
self.design.evaluate_design()
self.candidate_design = copy.deepcopy(self.design)
def geneticAlgorithm(self,mutationType):
p0 = self.generatePopulation()
p = p0
t = 0
while t != 50:
crossOverList = self.selectAnyType(p[0], p[1], "crossOver")
b = len(crossOverList[0])
childs = []
for i in range(0,len(crossOverList[0])-1,2):
childs.append(self.crossOver(crossOverList[0][i], crossOverList[0][i+1])[0])
childs.append(self.crossOver(crossOverList[0][i], crossOverList[0][i+1])[1])
mutationList = self.selectAnyType(p[0], p[1], "mutation")
a = len(mutationList[0])
# print("before adding mut " + str(p[0]))
for i in range(a):
mutated = self.mutation(mutationList[0][i], mutationList[1][i], mutationType)
p[0].append(mutated.getChemin())
p[1].append(mutated.getFct())
for i in range(b):
p[0].append(childs[i])
p[1].append(self.fonctionObjectif(childs[i]))
p = self.selectAnyType(p[0], p[1])
t += 1
index = p[1].index(min(p[1]))
return Solution(p[0][index], p[1][index])
def rk4(theta0, omega0, step=0.1, stop=10, start=0, name="runge_kutta"):
def omega_prime(theta):
"""
:return: derivative of omega with respect to time at some point theta
"""
return -(g / l) * theta
def theta_prime(omega):
"""
:return: derivative of theta with respect to time at some point omega
"""
return omega
t = np.arange(start, stop, step)
n = int((stop - start)/step)
omega = np.zeros(n)
theta = np.zeros(n)
omega[0], theta[0] = omega0, theta0
for i in range(n-1):
# calculate next omega
o1 = omega_prime(theta[i])
o2 = omega_prime(theta[i] + o1 * step / 2)
o3 = omega_prime(theta[i] + o2 * step / 2)
o4 = omega_prime(theta[i] + o3 * step)
omega[i + 1] = omega[i] + (step/6)*(o1 + 2*o2 + 2*o3 + o4)
# calculate next theta
t1 = theta_prime(omega[i])
t2 = theta_prime(omega[i] + t1 * step / 2)
t3 = theta_prime(omega[i] + t2 * step / 2)
t4 = theta_prime(omega[i] + t3 * step)
theta[i + 1] = theta[i] + (step/6)*(t1 + 2 * t2 + 2 * t3 + t4)
# calculate energy
e = 0.5*m*l*l*(omega**2 + (g/l)*theta**2)
return Solution(t, theta, omega, e, name=name)
def setSolution(self, end, iterations, branchingSum, expandedNodes, visited, time_elapsed): itr = self.end path = [] c = 0 while True: if itr: path.append(itr) itr = itr.getFather() else: path.reverse() if(len(path) == 1 and end == path[0]): path.pop() cost = 0 for j in range(0, len(path)-1): for i in range(0, len(self.graph[path[j]])): if self.graph[path[j]][i][0] == path[j + 1]: cost = cost + self.graph[path[j]][i][1] if iterations == 0: branchFactor = 0 else: branchFactor = branchingSum/iterations self.solution = Solution(path, cost, expandedNodes, branchFactor, len(visited), time_elapsed, iterations) break return self.solution
def test_setUseCondensedSolve(self):
poissonForm = PoissonFormulation.PoissonFormulation(2, True)
poissonBF = poissonForm.bf()
mesh = MeshFactory.MeshFactory_rectilinearMesh(poissonBF, [1.0, 1.0],
[2, 3], 4)
s = Solution.Solution_solution(mesh)
s.setUseCondensedSolve(False) # if no exception, then successful test
def test_Solution(self):
poissonForm = PoissonFormulation.PoissonFormulation(2, True)
poissonBF = poissonForm.bf()
mesh2 = MeshFactory.MeshFactory_rectilinearMesh(
poissonBF, [1.0, 1.0], [2, 3], 4)
s = Solution.Solution_solution(mesh2)
self.assertIsNotNone(s) #make sure some object exists
def main():
solution = Solution()
strs = [-1, 0, 1, 2, -1, -4]
print(solution.threeSum(strs))
strs = [3, 0, -2, -1, 1, 2]
print(solution.threeSum(strs))
strs = [0, 0, 0, 0]
print(solution.threeSum(strs))
strs = [0, 0, 0, 0, 0, 0]
print(solution.threeSum(strs))
strs = [7, -10, 7, 3, 14, 3, -2, -15, 7, -1, -7, 6, -5, -1, 3, -13, 6, -15, -10, 14, 8, 5, -10, -1, 1, 1, 11, 6, 8,
5, -4, 0, 3, 10, -12, -6, -2, -6, -6, -10, 8, -5, 12, 10, 1, -8, 4, -8, -8, 2, -9, -15, 14, -11, -1, -8, 5,
-13, 14, -2, 0, -13, 14, -12, 12, -13, -3, -13, -12, -2, -15, 4, 8, 4, -1, -6, 11, 11, -7, -12, -2, -8, 10,
-3, -4, -6, 4, -14, -12, -5, 0, 3, -3, -9, -2, -6, -15, 2, -11, -11, 8, -11, 8, -7, 8, 14, -5, 4, 10, 3, -1,
-15, 10, -6, -11, 13, -5, 1, -15]
def main():
solution = Solution()
l = ListNode.generate([1, 2, 3, 4, 5])
res = solution.removeNthFromEnd(l, 2)
res.tranverseListNoe()
print("******")
l = ListNode.generate([1, 2])
res = solution.removeNthFromEnd(l, 1)
res.tranverseListNoe()
print("******")
l = ListNode.generate([1, 2])
res = solution.removeNthFromEnd(l, 2)
res.tranverseListNoe()
print("******")
l = ListNode.generate([1, 2, 3])
res = solution.removeNthFromEnd(l, 3)
res.tranverseListNoe()
def PatternMove(self):
currentPoint = self.history[self.iIter]
lastPoint = self.history[self.iIter-1]
# find out last move
lastMove = list()
for i in range(len(currentPoint.DV)):
lastMove.append(currentPoint.DV[i] - lastPoint.DV[i])
# recreate last move to current design vector
nextDV = list()
for i in range(len(currentPoint.DV)):
nextDV.append(currentPoint.DV[i] + lastMove[i])
newPoint = Solution(Solution.getName(),nextDV)
newPoint.evaluate()
if newPoint.dominate(currentPoint):
self.iIter += 1
self.iLocal += 1
self.history.append(newPoint)
return True
return False
def createChild(self,parent):
""" create a candidate child """
i = self.P.index(parent)
a = i
b = i
c = i
while i == a or i == b or i == c or a == b or a == c or b == c:
a = random.randint(0,len(self.P)-1)
b = random.randint(0,len(self.P)-1)
c = random.randint(0,len(self.P)-1)
mateDV = self.mate(self.P[a],self.P[b],self.P[c])
if self.debug:
for DV in mateDV:
print str(DV)
childDV = self.crossover(self.P[i],DV)
return Solution(Solution.getName(),childDV)
def main():
solution = Solution()
strs = ["123456", "124546"]
print(solution.longestCommonPrefix(strs))
def main():
solution = Solution()
strs = [-1, 2, 1, -4]
target = 1
print(solution.threeSumClosest(strs, target))
def main(maxiter,featureset):
import sys
print 'Number of arguments:', len(sys.argv), 'arguments.'
print 'Argument List:', str(sys.argv)
maxiter = int(sys.argv[-2])
featureset = int(sys.argv[-1])
sys.path
sys.path.append(".")
import Solution as sl
import numpy as np
learner = sl.perceptron(maxiter,featureset)
labels = []
labels.append(learner['otrain'][:,0])
labels.append(learner['ptrain'])
match = sum(learner['otrain'][:,0] == learner['ptrain'])
#print '*******************'
#print 'Training accuracy is: {0:.3f}%'.format(match*1.0/len(learner['otrain'])*100.0)
#print '*******************'
#########################################
##### now run validation
vdata = np.array(readfile("./validation.txt"))
nvdata = vdata.shape[0]
# get rid of ? missing data
contatr = np.array([1,2,7,10,13,14])
attrlist = np.array(range(len(vdata[0])))
for i in contatr:
dum = vdata[vdata[:,i]!=np.array('?'),i]
vdata[vdata[:,i]==np.array('?'),i] = np.mean(dum.astype(np.float))
disatr = list(set(attrlist)-set(contatr))
for i in disatr:
dum = max(set(list(vdata[:,i])), key=list(vdata[:,i]).count)
vdata[vdata[:,i]==np.array('?'),i] = np.array(dum)
print "is there missing data: ", sum(sum(vdata[:,:15]==np.array('?')))
ori_v_label = vdata[:,-1];
ori_v_label_bool = np.zeros((vdata.shape[0],1))-1
ori_v_label_bool[ori_v_label==np.array('+')] = 1
newdatafeatset = sl.getfeature(learner['thres'],vdata[:,:15],featureset,disatr,contatr) # no label col
predv = np.sign(np.sum(np.tile(learner['alphaa']*learner['y'],(1,nvdata))*np.dot(learner['X'],newdatafeatset.T),axis=0))
match = sum(predv == ori_v_label_bool[:,0])
labels.append(ori_v_label_bool[:,0])
labels.append(predv)
#print '*******************'
#print 'validation accuracy is: {0:.3f}%'.format(match*1.0/nvdata*100.0)
#print '*******************'
##########################################
##### now run test
tdata = np.array(readfile("./test.txt"))
ntdata = tdata.shape[0]
# get rid of ? missing data
contatr = np.array([1,2,7,10,13,14])
attrlist = np.array(range(len(tdata[0])))
for i in contatr:
dum = tdata[tdata[:,i]!=np.array('?'),i]
tdata[tdata[:,i]==np.array('?'),i] = np.mean(dum.astype(np.float))
disatr = list(set(attrlist)-set(contatr))
for i in disatr:
dum = max(set(list(tdata[:,i])), key=list(tdata[:,i]).count)
tdata[tdata[:,i]==np.array('?'),i] = np.array(dum)
print "is there missing data: ", sum(sum(tdata[:,:15]==np.array('?')))
ori_t_label = tdata[:,-1];
ori_t_label_bool = np.zeros((tdata.shape[0],1))-1
ori_t_label_bool[ori_v_label==np.array('+')] = 1
newdatafeatset = sl.getfeature(learner['thres'],tdata[:,:15],featureset,disatr,contatr) # no label col
predt = np.sign(np.sum(np.tile(learner['alphaa']*learner['y'],(1,ntdata))*np.dot(learner['X'],newdatafeatset.T),axis=0))
match = sum(predt == ori_t_label_bool[:,0])
#print '*******************'
#print 'test accuracy is: {0:.3f}%'.format(match*1.0/ntdata*100.0)
#print '*******************'
labels.append(ori_t_label_bool[:,0])
labels.append(predt)
if labels == None or len(labels) != 6:
print '\nError: Perceptron Return Value.\n'
else:
eval(labels[0],labels[1],labels[2],labels[3],labels[4],labels[5])
from Solution import * sol = Solution() print sol.canWinNim(4)
import sys
sys.path
sys.path.append("/home/he72/STAT590/hw2")
import Solution as sl
import numpy as np
learner = sl.perceptron(10,1)
labels = []
labels.append(learner['otrain'])
labels.append(learner['ptrain'])
sys.argv=['5','2']
execfile('./Check.py')
import os
import sys
sys.path.append("/home/he72/STAT590/hw2")
import numpy as np
import Check as Ck
data = np.array(Ck.readfile("/home/he72/STAT590/hw2/train.txt"))
# get rid of ? missing data
contatr = np.array([1,2,7,10,13,14])
attrlist = np.array(range(len(data[0])))
for i in contatr:
dum = data[data[:,i]!=np.array('?'),i]
data[data[:,i]==np.array('?'),i] = np.mean(dum.astype(np.float))
disatr = list(set(attrlist)-set(contatr))
for i in disatr:
dum = max(set(list(data[:,i])), key=list(data[:,i]).count)
data[data[:,i]==np.array('?'),i] = np.array(dum)
print "is there missing data: ", sum(sum(data[:,:15]==np.array('?')))
def main():
solution = Solution()
s = "([{}]))"
print(solution.isValid(s))
def main():
solution = Solution()
strs = [1, 0, -1, 0, -2, 2]
target = 0
print(solution.fourSum(strs, target))
def test_answers_1(self): self.assertEqual(Solution.answer(2,3,14), "Ambiguous") self.assertEqual(Solution.answer(19,19,3), "03/19/19") #needed to return the string, not just print it
def Create_Solution(App, Row, Col, Unrolling_Vec, Blocking_Vec, IO_Buffers, DFG_Lat):
Possible_Solution = Solution()
Possible_Solution.Loop_Vec = Get_Loop_Vec(App)
Possible_Solution.SCGRA_Row = Row
Possible_Solution.SCGRA_Col = Col
Possible_Solution.SCGRA_Size = Row * Col
Possible_Solution.Loop_Unrolling_Vec = copy.copy(Unrolling_Vec)
Possible_Solution.DFG_Lat = DFG_Lat
Possible_Solution.In_Buffer = IO_Buffers[0]
Possible_Solution.Out_Buffer = IO_Buffers[1]
Possible_Solution.In_Addr_Buffer = IO_Buffers[2]
Possible_Solution.Out_Addr_Buffer = IO_Buffers[3]
Possible_Solution.Inst_Mem = Get_Min_Inst_Mem(DFG_Lat)
Possible_Solution.Loop_Blocking_Vec = copy.copy(Blocking_Vec)
Possible_Solution.Update_HW_Cost()
DFG_Num = Get_SB_Num(Unrolling_Vec, Blocking_Vec)
Block_Num = Get_SB_Num(Blocking_Vec, Get_Loop_Vec(App))
Loop_Transfer_Vec = Get_Loop_Transfer_Vec(App, Unrolling_Vec, Blocking_Vec)
Possible_Solution.Update_Lat(DFG_Num, Block_Num, Loop_Transfer_Vec)
Possible_Solution.Update_Power()
Possible_Solution.Update_EDP()
Solution_Str = Possible_Solution.Get_Syllabus()
return Possible_Solution