本文整理汇总了Python中pyscipopt.Model.printStatistics方法的典型用法代码示例。如果您正苦于以下问题：Python Model.printStatistics方法的具体用法？Python Model.printStatistics怎么用？Python Model.printStatistics使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pyscipopt.Model的用法示例。
在下文中一共展示了Model.printStatistics方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_knapsack
# 需要导入模块: from pyscipopt import Model [as 别名]
# 或者: from pyscipopt.Model import printStatistics [as 别名]
def test_knapsack():
# create solver instance
s = Model("Knapsack")
s.hideOutput()
# setting the objective sense to maximise
s.setMaximize()
# item weights
weights = [4, 2, 6, 3, 7, 5]
# item costs
costs = [7, 2, 5, 4, 3, 4]
assert len(weights) == len(costs)
# knapsack size
knapsackSize = 15
# adding the knapsack variables
knapsackVars = []
varNames = []
varBaseName = "Item"
for i in range(len(weights)):
varNames.append(varBaseName + "_" + str(i))
knapsackVars.append(s.addVar(varNames[i], vtype='I', obj=costs[i], ub=1.0))
# adding a linear constraint for the knapsack constraint
coeffs = {knapsackVars[i]: weights[i] for i in range(len(weights))}
s.addCons(coeffs, lhs=None, rhs=knapsackSize)
# solve problem
s.optimize()
s.printStatistics()
# retrieving the best solution
solution = s.getBestSol()
# print solution
varSolutions = []
for i in range(len(weights)):
solValue = round(s.getVal(knapsackVars[i], solution))
varSolutions.append(solValue)
if solValue > 0:
print (varNames[i], "Times Selected:", solValue)
print ("\tIncluded Weight:", weights[i]*solValue, "\tItem Cost:", costs[i]*solValue)
s.releaseVar(knapsackVars[i])
includedWeight = sum([weights[i]*varSolutions[i] for i in range(len(weights))])
assert includedWeight > 0 and includedWeight <= knapsackSize示例2: test_heur
# 需要导入模块: from pyscipopt import Model [as 别名]
# 或者: from pyscipopt.Model import printStatistics [as 别名]
def test_heur():
# create solver instance
s = Model()
heuristic = MyHeur()
s.includeHeur(heuristic, "PyHeur", "custom heuristic implemented in python", "Y", timingmask=SCIP_HEURTIMING.BEFORENODE)
s.setPresolve(SCIP_PARAMSETTING.OFF)
# add some variables
x = s.addVar("x", obj=1.0)
y = s.addVar("y", obj=2.0)
# add some constraint
s.addCons(x + 2*y >= 5)
# solve problem
s.optimize()
# print solution
assert round(s.getVal(x)) == 5.0
assert round(s.getVal(y)) == 0.0
s.printStatistics()