A language model is predicting the next word. It has a vocabulary of { 'cat', 'dog', 'fox', 'hen' }. After processing a context, it retrieves the 5 nearest neighbors from its datastore, listed below with their distances and associated word tokens:

• Neighbor 1: (distance=0.2, token='dog')
• Neighbor 2: (distance=0.4, token='fox')
• Neighbor 3: (distance=0.5, token='cat')
• Neighbor 4: (distance=0.7, token='dog')
• Neighbor 5: (distance=0.9, token='fox')

Based on a method where the aggregated distance for a vocabulary token is the distance to its closest matching neighbor in the retrieved set, what are the aggregated distances for each token in the vocabulary? (Note: If a token does not appear in the neighbors, its distance is effectively infinite).