Expose distance metric and include L2 (euclidean), L1 (city-block, or "manhattan") and cosine.

[thorwhalen]

• cosine is useful when data is semantic (tfidf or word2vec). Cosine distance is 1 minus the cosine similarity.
• L1 pops up in when making a model more robust, or because it's quicker to compute on the edge.