def poisciParametre(self, X,Y):
activation_functions = ['multiquadric' , 'softlim', 'inv_multiquadric', 'gaussian', 'tanh', 'sine', 'tribas', 'inv_tribas', 'sigmoid']
n_hiddens = [50, 100,200, 500,800, 900, 1000, 1500, 2000, 3000, 5000, 10000]#3, 30,50,100]
parameters = []
alphas = [1.0,0.7,0.5,0.0]#0.0,0.2,0.4,0.5,0.7,0.9,1.0]
nrOfTrials = len(activation_functions)*len(alphas) * len(n_hiddens)
trial = 1
np.random.seed(np.random.randint(10000000))
for n_hidden in n_hiddens:
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
