def get_symb_mus(self, mus, sigmas, corxy, pis, prediction_method="pi"):
"""
Can be used to train an autoencoder that given location
trains a mixture density layer and then outputs the same
location
symbolycally predict the mu that maximizes the mixture model
either based on mixture probability of the component
with highest pi, see pred_sharedparams
"""
if prediction_method == "mixture":
"""
sigmainvs = 1.0 / sigmas
sigmainvprods = sigmainvs[:,:, 0] * sigmainvs[:,:, 1]
sigmas2 = sigmas ** 2
corxy2 = corxy **2
diff2 = diff ** 2
diffsigma = diff2 / sigmas2
diffsigmanorm = np.sum(diffsigma, axis=-1)
z = diffsigmanorm - 2 * corxy * diffprod * sigmainvprods
oneminuscorxy2inv = 1.0 / (1.0 - corxy2)
expterm = np.exp(-0.5 * z * oneminuscorxy2inv)
expterm = 1.0
probs = (0.5 / np.pi) * sigmainvprods * T.sqrt(oneminuscorxy2inv) * expterm
probs = pis * probs
"""
logging.fatal("not implemented!")
sys.exit()
elif prediction_method == "pi":
preds = T.argmax(pis, axis=1)
selected_mus = mus[T.arange(mus.shape[0]), preds, :]
return selected_mus
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