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pythonnumpymatrixscipy
sonovice
sonovice

Reputation: 831

Faster way of calculating a distance matrix with numpy?

I am calculating a matrix with numpy/scipy like this:

cost = np.empty([chroma1.data.shape[1], chroma2.data.shape[1]])

for x, cx in enumerate(chroma1.transpose()):
    for y, cy in enumerate(chroma2.transpose()):
        cost[x, y] = sp.distance.euclidean(cx, cy)

This takes quite an amount of time. Is there any numpy/scipy function that would allow me to get rid of the two nested loops?

Upvotes: 1

Views: 1492

Answers (1)

Joe Kington
Joe Kington

Reputation: 284552

It looks like you're calculating a distance matrix. scipy.spatial.distance contains several specialized, optimized functions for doing exactly that.

In your case:

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

should do exactly what you want.

Upvotes: 3

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