def optimise():
testCases = testcollection.readTests()
#grouping.redefineGroupingConditions(dataselect.conditionBreakHigh)
#grouping.redefineGroupingConditions(dataselect.conditionDoubleTopsFiltered)
### Objective Function.
### An input for pyswarm.pso. Returns a float utility score from testing with a set of weights.
def testWithWeights(weights):
arrLen = len(parametersToOptimise)
weightDict = {}
for i in range(0,arrLen):
weightDict[parametersToOptimise[i]] = weights[i]
result = testAlgorithmsForAverageScore(testCases, weightDict)
print(str(result) + ' <- ' + str(weights)) # print as we go.
return (result - 1)*100
### Uncomment this to test the running time of weight optimisation.
# testWeightOptimisationRunningTime(testWithWeights)
ub = [1]*5 # Upper bound = 1
lb = [0]*5 # Lower bound = 0
xopt, fopt = pyswarm.pso(testWithWeights, lb, ub, maxiter=20)
# If we reach this point, it means the weight optimisation process is complete.
print('Final Weights:')
print(xopt)
print('Score = ' + str(fopt))
def runTests():
#grouping.redefineGroupingConditions(dataselect.conditionBreakHigh, dataselect.conditionDoubleTops, dataselect.conditionDoubleTopsFiltered)
#grouping.redefineGroupingConditions(dataselect.conditionBreakHigh)
#grouping.redefineGroupingConditions(dataselect.conditionDoubleTopsFiltered)
#grouping.changeToShiftedMonthsGroups()
testCases = testcollection.readTests()
testalgos.compareAlgorithmsWithData(testCases)
