def overproductionProcess(folder):
#metoda izdelana za testiranje metaDes, ne potrebujemo, ker pridobimo klasifikatorje od ostalih
XProd, YProd = Helpers.readData()
# divideDataForMeta(X, Y) #it divides data into Production, Meta and Selection
XMeta, YMeta, XSel, YSel, XTest, YTest = readForMeta2(folder)
overproductionELM(XProd,YProd, XMeta, XSel, XTest, folder=folder) #we generate classifiers and use them for responses
overproductionRf(XProd,YProd, XMeta, XSel, XTest, folder=folder)
for alpha in alphas:
for actFunction in activation_functions:
cls = GenELMClassifier(hidden_layer = RandomLayer(n_hidden = n_hidden, activation_func = actFunction, alpha=alpha))
parameter = Helpers.cv(X,Y,cls,5, printing = False)
parameter = parameter+ [n_hidden, alpha, actFunction, "normal"]
parameters.append(parameter)
print(parameter, "%d/%d" %(trial,nrOfTrials))
Helpers.pickleListAppend2(parameter, "parametersELM.p")
# parameter = Helpers.cv(X,Y,BaggingClassifier(cls,n_estimators=30),10, printing = False)
# parameter = parameter+ [n_hidden, alpha, actFunction, "bagged"]
# parameters.append(parameter)
# print(parameter, "%d/%d" %(trial,nrOfTrials))
trial = trial+1
# pickle.dump(parameters,open("parametersMultiQuadric.p","wb"))
return
def vrniMethod(self, parameter):
# acc,prec,precTress, n_hidden, rhl, actFunction = parameter
cls = GenELMClassifier(hidden_layer = RandomLayer(n_hidden = parameter[-3], activation_func = parameter[-1], alpha=parameter[-2]))
return cls
# tr, ts, trRaw, tsRaw, prec, precTress = res_dist(X,Y,cls,10)
if __name__ == "__main__":
X, Y = Helpers.readData()
ELMMethod().poisciParametre(X,Y)
# vrniMethod(pickle.load(open("parameter.p","rb")), X, Y)
def main1():
#method for testing MetaDES
X, Y = Helpers.readData()
divideDataForMeta(X,Y)
