import numpy
def masked_spectral_distance(true, pred):
# Note, fragment ions that cannot exists (i.e. y20 for a 7mer) must have the value -1.
import tensorflow
import keras.backend as k
epsilon = k.epsilon()
pred_masked = ((true + 1) * pred) / (true + 1 + epsilon)
true_masked = ((true + 1) * true) / (true + 1 + epsilon)
pred_norm = k.l2_normalize(true_masked, axis=-1)
true_norm = k.l2_normalize(pred_masked, axis=-1)
product = k.sum(pred_norm * true_norm, axis=1)
arccos = tensorflow.acos(product)
return 2 * arccos / numpy.pi
losses = {"masked_spectral_distance": masked_spectral_distance}
def get(loss_name):
if loss_name in losses:
return losses[loss_name]
else:
return loss_name
