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
def main3():
#Testira nekaj algoritmov
X, Y = readData(trainFtrFile="data/trainFtrExtended_200f.csv",
trainClsFile= "data/trainClsExtended.csv", deleteFirstNFeatures=2)
# X, Y = readData(1000)
elmc = GenELMClassifier(hidden_layer = RandomLayer(n_hidden = 50, activation_func = 'multiquadric', alpha=1.0))
baggedElmc = BaggingClassifier(elmc, n_estimators=10)
ada = AdaBoostClassifier(n_estimators=30)
rf = sklearn.ensemble.RandomForestClassifier(n_estimators = 50, n_jobs = 1)
np.random.seed(np.random.randint(10000000))
adaRf = AdaBoostClassifier(rf, n_estimators=20)
print ("elmc: ", Helpers.cv(X,Y,elmc))
print ("baggedElmc: ", Helpers.cv(X,Y,baggedElmc))
print ("adaTree: ", Helpers.cv(X,Y,ada))
print ("rf: ", Helpers.cv(X,Y,rf))
print ("adaRf: ", Helpers.cv(X,Y,adaRf))
def main5():
#Cross validation 3 algoritmov
X, Y = readData()
tree = DecisionTreeClassifier()
elmc = GenELMClassifier(hidden_layer = RandomLayer(n_hidden = 100, activation_func = 'multiquadric', alpha=1.0))
baggedelmc = Bagging(elmc, n_estimators=20,ratioCutOffEstimators=0)
print ("baggedelmc: ", Helpers.cv(X,Y,baggedelmc))
def main1():
X, Y = readData(10000)
elmc = ELMClassifier(n_hidden=100, activation_func='gaussian')
baggedElmc = BaggingClassifier(elmc)
#baggedElmc.fit(X,Y)
rhl = RandomLayer(n_hidden=500, activation_func='gaussian')
genElmc = GenELMClassifier(rhl)
tr, ts, trRaw, tsRaw, prec, precTress, duration = Helpers.cv(X,Y,baggedElmc,3, printing=True)
plt.scatter(tr, ts, alpha=0.5, marker='D', c='r')
plt.scatter(trRaw, tsRaw, alpha=0.5, marker='D', c='b')
plt.show()
plt.scatter(prec, precTress,alpha=0.9, marker='D', c='r')
plt.show()
