def selector(algo,func_details,popSize,Iter,succ_rate,mean_feval):
function_name=func_details[0]
lb=func_details[1]
ub=func_details[2]
dim=func_details[3]
acc_err=func_details[4]
obj_val=func_details[5]
if(algo==0):
x,succ_rate,mean_feval=smo.main(getattr(benchmarks, function_name),lb,ub,dim,popSize,Iter,acc_err,obj_val,succ_rate,mean_feval)
return x,succ_rate,mean_feval
def selector(algo,func_details,popSize,Iter,succ_rate,mean_feval):
function_name=func_details[0]
lb=func_details[1]
ub=func_details[2]
dim=func_details[3]
acc_err=func_details[4]
obj_val=func_details[5]
#selection of different parameters
if(algo==0):
x,succ_rate,mean_feval=smo.main(getattr(benchmarks, function_name),lb,ub,dim,popSize,Iter,acc_err,obj_val,succ_rate,mean_feval) #getting attributes from different file
return x,succ_rate,mean_feval
def selector(algo, func_details, popSize, Iter, trainDataset, testDataset):
function_name = func_details[0]
lb = func_details[1]
ub = func_details[2]
DatasetSplitRatio = 2 / 3
dataTrain = "datasets/" + trainDataset
dataTest = "datasets/" + testDataset
Dataset_train = numpy.loadtxt(open(dataTrain, "rb"),
delimiter=",",
skiprows=0)
Dataset_test = numpy.loadtxt(open(dataTest, "rb"),
delimiter=",",
skiprows=0)
numRowsTrain = numpy.shape(Dataset_train)[
0] # number of instances in the train dataset
numInputsTrain = numpy.shape(
Dataset_train)[1] - 1 #number of features in the train dataset
numRowsTest = numpy.shape(Dataset_test)[
0] # number of instances in the test dataset
numInputsTest = numpy.shape(
Dataset_test)[1] - 1 #number of features in the test dataset
trainInput = Dataset_train[0:numRowsTrain, 0:-1]
trainOutput = Dataset_train[0:numRowsTrain, -1]
testInput = Dataset_test[0:numRowsTest, 0:-1]
testOutput = Dataset_test[0:numRowsTest, -1]
#number of hidden neurons
HiddenNeurons = numInputsTrain * 2 + 1
net = nl.net.newff([[0, 1]] * numInputsTrain, [HiddenNeurons, 1])
dim = (numInputsTrain * HiddenNeurons) + (2 * HiddenNeurons) + 1
if (algo == 0):
x = pso.PSO(getattr(costNN, function_name), lb, ub, dim, popSize, Iter,
trainInput, trainOutput, net)
if (algo == 1):
x = mvo.MVO(getattr(costNN, function_name), lb, ub, dim, popSize, Iter,
trainInput, trainOutput, net)
if (algo == 2):
x = gwo.GWO(getattr(costNN, function_name), lb, ub, dim, popSize, Iter,
trainInput, trainOutput, net)
if (algo == 3):
x = mfo.MFO(getattr(costNN, function_name), lb, ub, dim, popSize, Iter,
trainInput, trainOutput, net)
if (algo == 4):
x = cs.CS(getattr(costNN, function_name), lb, ub, dim, popSize, Iter,
trainInput, trainOutput, net)
if (algo == 5):
x = bat.BAT(getattr(costNN, function_name), lb, ub, dim, popSize, Iter,
trainInput, trainOutput, net)
if (algo == 6):
x = smo.main(getattr(costNN, function_name), lb, ub, dim, popSize,
Iter, 1.0e-5, trainInput, trainOutput, net)
# print(type(x),'------------------------------')
# Evaluate MLP classification model based on the training set
trainClassification_results = evalNet.evaluateNetClassifier(
x, trainInput, trainOutput, net)
x.trainAcc = trainClassification_results[0]
x.trainTP = trainClassification_results[1]
x.trainFN = trainClassification_results[2]
x.trainFP = trainClassification_results[3]
x.trainTN = trainClassification_results[4]
# Evaluate MLP classification model based on the testing set
testClassification_results = evalNet.evaluateNetClassifier(
x, testInput, testOutput, net)
x.testAcc = testClassification_results[0]
x.testTP = testClassification_results[1]
x.testFN = testClassification_results[2]
x.testFP = testClassification_results[3]
x.testTN = testClassification_results[4]
return x