def __init__(self, numOGaus=10, alphaA=0.02, alphaM=0.02, alphaS=0.02):
        self.alphaA = alphaA
        self.alphaM = alphaM
        self.alphaS = alphaS
        self.minSig = 0.000001
        self.numOGaus = numOGaus #Number of Gaussians
        self.rangeMin = -20.0
        self.rangeMax = 20.0
        self.epsilon = (self.rangeMax - self.rangeMin) / (sqrt(2.0) * float(self.numOGaus - 1)) #Initial value of sigmas

        self.propFakt = 1.0 / float(self.numOGaus)
        self.distFakt = 1.0 / float(self.numOGaus - 1)
        self.distRange = self.rangeMax - self.rangeMin

        self.sigma = ones(self.numOGaus)
        self.mue = zeros(self.numOGaus)
        self.alpha = ones(self.numOGaus)
        self.sigma *= self.epsilon
        self.alpha /= float(self.numOGaus)
        for i in range(self.numOGaus):
            self.mue[i] = self.distRange * float(i) * self.distFakt + self.rangeMin
        self.baseline = 0.0
        self.best = 0.000001
dataset/ETHPy150Open pybrain/pybrain/pybrain/tools/mixtures/mogpuremax.py/MixtureOfGaussians.__init__/original