Defines a data structure for a tensor and its operations.

You can construct a Tensor by calling Tensor.new/1 and passing it a list, or a list of lists, or a list of lists of...you get the idea.

Example

use Numerix.Tensor

x = Tensor.new([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

Once you have a Tensor (or three), you can then use it in normal math operations, e.g. elementwise matrix operations.

Example

x = Tensor.new([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y = Tensor.new([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
assert x / y == Tensor.new([[1, 1, 1], [1, 1, 1], [1, 1, 1]])

As it turns out, this is very handy when you need to implement complex math formulae as the code looks more like math functions than noisy code with a bunch of calls to Enum.map/2, Enum.zip/2 and the like.

Example

x = Tensor.new([[0, 0.1, 0.5, 0.9, 1.0]])
m = max(x)
e = exp(x - m)
s = sum(e)
assert e / s == Tensor.new([[0.1119598021340303, 0.12373471731203411, 0.18459050724175335, 0.2753766776533774, 0.30433829565880477]])

Oh, I should also mention that this API uses Flow to parallelize independent pieces of computation to speed things up! Depending on the type of calculations you're doing, the bigger the data and the more cores you have, the faster it gets.