Maths library
etch
mathematics and machine learning operations are supported by a headeronly fullytemplated C++ library.
Detailed developer documentation for the C++ implementation of the maths libraries will be available in the Fetch Ledger section in due course. Developers should be comfortable with SFINAE.
Architecture
A core component of the maths library is the tensor.hpp
class which handles Ndimensional array mathematics. This is crucial for the machine learning library but can also be used for any generalised matrix algebra.
The remainder of the library contains templated free functions that can be called with the following types:
 Builtin C++ types such as
double
,int
,float
, etc.  C++ tensors of builtin types such as
Tensor<double>
. etch
types such asFixed32
.etch
tensors types such asTensor<Fixed32>
.
The header file fundamental_operators.hpp
contains common operations Add
, Subtract
, Multiply
, and Divide
.
The file matrix_operations.hpp
contains Max
, ArgMax
, Product
, Sum
, Dot
, etc.
The standard_functions
directory contains header files for additional standard operations.
The following block diagram gives a rough indication of the library structure.
Advice
This is work in progress.
Tensor
A Tensor
is a wrapper for a data_
object which is, by default, a SharedArray
managed by the vectorise
library. This library manages the vectorisation/SIMD on the underlying data.
Tensor
objects provide interfaces for manipulating arrays at a mathematical/algebraic level while allowing implementations to be efficient and vectorisable.
Tensors have related helper classes such as TensorIterator
, TensorBroadcast
, and TensorSlice
that permit efficient and convenient manipulation such as accessing, transposing, and slicing.
Check out the available mathematical functions in etch
here.
Working with the maths library
When working with the C++ maths library, take note of the following:

Functions should have two interfaces: one that takes a reference to the return object, and one that creates the return object internally.

Function design decisions should follow Numpy conventions where possible.