Hey Tim,
Yep, sure does. For those who find this later (i.e. future me), here are a couple ways to replicate the example in the numpy docs using TensorOperations.jl: using TensorOperations, IndexNotation # NOTE: we build it inside-out and then permute to match numpy's row major # reshape with Julia's column major reshape. a = permutedims(reshape(0:59.0, (5, 4, 3)), (3, 2, 1)) b = permutedims(reshape(0:23.0, (2, 3, 4)), (3, 2, 1)) # first method -- using IndexNotation c1 = a[l"a,b,c"]*b[l"b,a,d"] # second method -- calling tensorcontract c2 = tensorcontract(a, ["a", "b", "c"], b, ["b", "a", "d"]) # third method -- using inlace operations c3 = Array(Float64, 5, 2) TensorOperations.tensorcontract!(1,a,["a", "b", "c"],'N',b,["b", "a", "d"],'N',0.0, c3,["c", "d"]) On Sunday, February 22, 2015 at 6:34:12 AM UTC-5, Tim Holy wrote: Does TensorOperations.jl do what you want? > > --Tim > > On Sunday, February 22, 2015 12:32:34 AM Viral Shah wrote: > > Best to file an issue. > > > > -viral > > > > On Sunday, February 22, 2015 at 12:52:17 PM UTC+5:30, Spencer Lyon > wrote: > > > Bump on this thread. I need this functionality again... > > > > > > On Friday, December 6, 2013 at 11:04:44 AM UTC-5, Spencer Lyon wrote: > > >> I am working with tensors with more than 2 dimensions. I would like > to > > >> find a Julia equivalent to the numpy function tensordot. The > docstring > > >> for the function explains its use well, so I will just copy/paste it > > >> here: > > >> > > >> def tensordot(a, b, axes=2): > > >> """ > > >> Compute tensor dot product along specified axes for arrays >= > 1-D. > > >> > > >> Given two tensors (arrays of dimension greater than or equal to > one), > > >> ``a`` and ``b``, and an array_like object containing two > array_like > > >> objects, ``(a_axes, b_axes)``, sum the products of ``a``'s and > > >> ``b``'s > > >> elements (components) over the axes specified by ``a_axes`` and > > >> ``b_axes``. The third argument can be a single non-negative > > >> integer_like scalar, ``N``; if it is such, then the last ``N`` > > >> dimensions of ``a`` and the first ``N`` dimensions of ``b`` are > > >> summed > > >> over. > > >> > > >> Parameters > > >> ---------- > > >> a, b : array_like, len(shape) >= 1 > > >> > > >> Tensors to "dot". > > >> > > >> axes : variable type > > >> > > >> * integer_like scalar > > >> > > >> Number of axes to sum over (applies to both arrays); or > > >> > > >> * array_like, shape = (2,), both elements array_like > > >> > > >> Axes to be summed over, first sequence applying to ``a``, > second > > >> to ``b``. > > >> > > >> See Also > > >> -------- > > >> dot, einsum > > >> > > >> Notes > > >> ----- > > >> When there is more than one axis to sum over - and they are not > the > > >> last > > >> (first) axes of ``a`` (``b``) - the argument ``axes`` should > consist > > >> of > > >> two sequences of the same length, with the first axis to sum over > > >> given > > >> first in both sequences, the second axis second, and so forth. > > >> > > >> Examples > > >> -------- > > >> > > >> A "traditional" example: > > >> >>> a = np.arange(60.).reshape(3,4,5) > > >> >>> b = np.arange(24.).reshape(4,3,2) > > >> >>> c = np.tensordot(a,b, axes=([1,0],[0,1])) > > >> >>> c.shape > > >> > > >> (5, 2) > > >> > > >> >>> c > > >> > > >> array([[ 4400., 4730.], > > >> > > >> [ 4532., 4874.], > > >> [ 4664., 5018.], > > >> [ 4796., 5162.], > > >> [ 4928., 5306.]]) > > >> >>> > > >> >>> # A slower but equivalent way of computing the same... > > >> >>> d = np.zeros((5,2)) > > >> > > >> >>> for i in range(5): > > >> ... for j in range(2): > > >> ... for k in range(3): > > >> ... for n in range(4): > > >> ... d[i,j] += a[k,n,i] * b[n,k,j] > > >> > > >> >>> c == d > > >> > > >> array([[ True, True], > > >> > > >> [ True, True], > > >> [ True, True], > > >> [ True, True], > > >> [ True, True]], dtype=bool) > > >> > > >> """ > > >> > > >> I think the functionality laid out in the explicit for loops would be > > >> quite easy to replicate using the @nloops and @nref macros in > Cartesian, > > >> but I am not sure if that will be most performant way to implement > such a > > >> function. Also, I wanted to check here to see if this has already > been > > >> done > > >> before I spend some time working on this. > > >> > > >> Thanks, > >
