python - Faster way of calculating a distance matrix with numpy? -


i calculating matrix numpy/scipy this:

cost = np.empty([chroma1.data.shape[1], chroma2.data.shape[1]])  x, cx in enumerate(chroma1.transpose()):     y, cy in enumerate(chroma2.transpose()):         cost[x, y] = sp.distance.euclidean(cx, cy)

this takes quite amount of time. there numpy/scipy function allow me rid of 2 nested loops?

it looks you're calculating distance matrix. scipy.spatial.distance contains several specialized, optimized functions doing that.

in case:

cost = scipy.spatial.distance.cdist(chroma1.t, chroma2.t)

should want.


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