[SciPy-user] logexpdot

[Brian Lewis]

Hi all,

This is my first foray into np.newaxis.  I needed a dot product for
logarithm arrays and since I am always working with matrices, I just did
2D.  I'm looking for comments and suggestions for what I've implemented.
Also, is it easy to generalize this beyond 2D?

import numpy as np

def logdotexp2__1(x,y, out=None):
    # horrible
    r,c = x.shape[0], y.shape[1]
    if out is None:
        out = np.empty((r,c))
    for i in xrange(r):
        for j in xrange(c):
            out[i,j] = np.logaddexp2.reduce(x[i,:]+y[:,j])
    return out

def logdotexp2__2(x,y, out=None):
    # less horrible
    r,c = x.shape[0], y.shape[1]
    if out is None:
        out = np.empty((r,c))
    for i in xrange(r):
        np.logaddexp2.reduce(x[i,:,np.newaxis]+y, out=out[i,:])
    return out

def logdotexp2__3(x,y, out=None):
    # good?
    r,c = x.shape[0], y.shape[1]
    if out is None:
        out = np.empty((r,c))
    np.logaddexp2.reduce(x[:,:,np.newaxis]+y[np.newaxis,:,:], axis=1,
out=out)
    return out

if __name__ == '__main__':
    k = 10
    a = np.arange(k**2).reshape((k,k))
    b = np.arange(k**2,2*k**2).reshape((k,k))
#    timeit logdotexp2__1(a,b)
#    timeit logdotexp2__2(a,b)
#    timeit logdotexp2__3(a,b)


Thanks.
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