add Huber loss

onn.py

@@ -71,7 +71,7 @@ class Confidence(Loss):
         categories = p.shape[-1]
         confidence = (np.max(p, axis=-1) - 1/categories) / (1 - 1/categories)
         # the exponent in softmax puts a maximum on confidence,
-        # but we don't compensate for that.  if necessary,
+        # but we don't compensate for that. if necessary,
         # it'd be better to use an activation that doesn't have this limit.
         return np.mean(confidence)
 
@@ -1102,6 +1102,8 @@ def lookup_loss(maybe_name):
         return Absolute()
     elif maybe_name == 'msee':
         return SomethingElse()
+    elif maybe_name == 'huber':
+        return Huber(delta=0.1)
     raise Exception('unknown objective', maybe_name)
 
 def ritual_from_config(config, learner):

onn_core.py

@@ -249,6 +249,20 @@ class Absolute(ResidualLoss):
     def df(self, r):
         return np.sign(r)
 
+class Huber(ResidualLoss):
+    def __init__(self, delta=1.0):
+        self.delta = _f(delta)
+
+    def f(self, r):
+        return np.where(r <= self.delta,
+                        np.square(r) / 2,
+                        self.delta * (np.abs(r) - self.delta / 2))
+
+    def df(self, r):
+        return np.where(r <= self.delta,
+                        r,
+                        self.delta * np.sign(r))
+
 # Regularizers {{{1
 
 class Regularizer: