Take a look as einsum, it is quite good for such things. Chuck
On Tue, Nov 25, 2014 at 9:06 PM, Yuxiang Wang <yw...@virginia.edu> wrote: > Dear all, > > I have been doing tensor algebra recently (for continuum mechanics) > and was looking into two common operations: tensor product & tensor > contraction. > > 1. Tensor product > > One common usage is: > a[i1, i2, i3, ..., iN, j1, j2, j3, ..., jM] = b[i1, i2, i3, ..., iN] * > c[j1, j2, j3, ..., jM] > > I looked into the current np.outer(), and the only difference is that > it always flattens the input array. So actually, the function for > tensor product is simply > > np.outer(a, b, out=out).reshape(a.shape+b.shape) <-- I think I got > this right but please do correct me if I am wrong > > Would anyone think it helpful or harmful to add such a function, > np.tensorprod()? it will simply be like > def tensorprod(a, b, out=None): > return outer(a, b, out=out).reshape(a.shape+b.shape) > > > 2. Tensor contraction > > It is currently the np.tensordot(a, b) and it will do np.tensordot(a, > b, axes=2) by default. I think this is all great, but it would be even > better if we specify in the doc, that: > i) say explicitly that by default it will be the double-dot or > contraction operator, and > ii) explain that in cases where axes is an integer-like scalar, > which axes were selected from the two array and in what order. Like: > if axes is an integer-like scalar, it is the number axes to sum over, > equivalent to axes=(list(range(-axes, 0)), list(range(0, axes))) (or > something like this) > > > It'd be great to hear what you would think about it, > > Shawn > > > -- > Yuxiang "Shawn" Wang > Gerling Research Lab > University of Virginia > yw...@virginia.edu > +1 (434) 284-0836 > https://sites.google.com/a/virginia.edu/yw5aj/ > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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