Make the weights of the CombinedLoss optimizable in a QuaPy gridsearc…

…h (and bump the version number to a new release)

qunfold/losses.py

@@ -157,10 +157,11 @@ class CombinedLoss(AbstractLoss):
   """
   def __init__(self, *losses, weights=None):
     self.losses = losses
-    if weights is None:
-      weights = jnp.ones(len(losses))
     self.weights = weights
   def _instantiate(self, q, M, N):
+    weights = self.weights
+    if weights is None:
+      weights = jnp.ones(len(self.losses))
     return lambda p: _combine_losses(self.losses, self.weights, q, M, p, N)
 
 # helpers for TikhonovRegularization

setup.py

@@ -6,7 +6,7 @@ def readme():
 
 setup(
     name="qunfold",
-    version="0.1.4-rc",
+    version="0.1.4",
     description="Composable quantification and unfolding methods",
     long_description=readme(),
     classifiers=[