def _forwardImplementation(self, inbuf, outbuf): """Calculate layer outputs (Gaussian parameters etc., not function values!) from given activations """ K = self.nGaussians # Mixing parameters and stddevs outbuf[0:K * 2] = safeExp(inbuf[0:K * 2]) outbuf[0:K] /= sum(outbuf[0:K]) # Means outbuf[K * 2:] = inbuf[K * 2:]
def _forwardImplementation(self, inbuf, outbuf): """Calculate layer outputs (Gaussian parameters etc., not function values!) from given activations """ K = self.nGaussians # Mixing parameters and stddevs outbuf[0:K*2] = safeExp(inbuf[0:K*2]) outbuf[0:K] /= sum(outbuf[0:K]) # Means outbuf[K*2:] = inbuf[K*2:]
def _forwardImplementation(self, inbuf, outbuf): outbuf[:] = safeExp(inbuf) outbuf.shape = scipy.size(outbuf) / self.slicelength, self.slicelength s = outbuf.sum(axis=1) outbuf = (outbuf.T / s).T.flatten()
def _forwardImplementation(self, inbuf, outbuf): outbuf[:] = safeExp(inbuf) outbuf /= sum(outbuf)
def transformAlphas(self, base): #print 'base', base return safeExp(base) / sum(safeExp(base))
def transformAlphas(self, base): #print 'base', base return safeExp(base)/sum(safeExp(base))