Apply a unary function in an element-wise manner on TensorT, in-place.

Input:

• a var Tensor
• An in-place function that returns no value

Result:

• Nothing, the var Tensor is modified in-place

Usage with Nim's sugar module:

• The sugar module has a functional programming paradigm, anonymous functions cannot mutate the arguments

Usage with named functions: .. code:: nim proc pluseqone[T](x: var T) = x += 1 a.apply(pluseqone) # Apply the in-place function pluseqone apply is especially useful to do multiple element-wise operations on a tensor in a single loop over the data.

Apply a unary function in an element-wise manner on TensorT, in-place.

Input:

• a var Tensor
• A function or anonymous function that returns a value

Result:

• Nothing, the var Tensor is modified in-place

Usage with Nim's sugar module: .. code:: nim var a = newTensor(5,5, int) # a must be var a.apply(x => x+1) # Map the anonymous function x => x+1 Usage with named functions: .. code:: nim proc plusone[T](x: T): T = x + 1 a.apply(plusone) # Apply the function plusone in-place

Apply a binary in-place function in an element-wise manner on two TensorT, returning a new Tensor. Overload for types that are not mem-copyable.

The function is applied on the elements with the same coordinates.

Input:

• A var tensor
• A function
• A tensor

Result:

• Nothing, the varTensor is modified in-place

Usage with named functions: .. code:: nim proc **=T = # We create a new in-place power **= function that works on 2 scalars x = pow(x, y) a.apply2(**=, b) # Or apply2(a, **=, b)

apply2 is especially useful to do multiple element-wise operations on a two tensors in a single loop over the data. for example A += alpha * sin(A) + B

Apply a binary in-place function in an element-wise manner on two TensorT, returning a new Tensor. Overload for types that are not mem-copyable.

The function is applied on the elements with the same coordinates.

Input:

• A var tensor
• A function
• A tensor

Result:

• Nothing, the varTensor is modified in-place

Usage with named functions: .. code:: nim proc **=T = # We create a new in-place power **= function that works on 2 scalars x = pow(x, y) a.apply2(**=, b) # Or apply2(a, **=, b) apply2 is especially useful to do multiple element-wise operations on a two tensors in a single loop over the data. for example A += alpha * sin(A) + B

Apply a unary function in an element-wise manner on TensorT, returning a new Tensor. Usage with Nim's sugar module: .. code:: nim a.map(x => x+1) # Map the anonymous function x => x+1 Usage with named functions: .. code:: nim proc plusone[T](x: T): T = x + 1 a.map(plusone) # Map the function plusone Note: for basic operation, you can use implicit broadcasting instead with operators prefixed by a dot : .. code:: nim a +. 1 map is especially useful to do multiple element-wise operations on a tensor in a single loop over the data.

For types that are not mem-copyable types (ref, string, etc.) a non OpenMP accelerated version of apply2_inline is used internally!

Apply a binary function in an element-wise manner on two TensorT, returning a new Tensor.

The function is applied on the elements with the same coordinates.

Input:

• A tensor
• A function
• A tensor

Result:

• A new tensor

Usage with named functions: .. code:: nim proc **T: T = # We create a new power ** function that works on 2 scalars pow(x, y) a.map2(**, b) # Or map2(a, **, b) map2 is especially useful to do multiple element-wise operations on a two tensors in a single loop over the data. for example alpha * sin(A) + B

For OpenMP compatibility, this map2 doesn't allow ref types as result like seq or string

Apply a binary function in an element-wise manner on two TensorT, returning a new Tensor.

This is a fallback for ref types as OpenMP will not work with if the results allocate memory managed by GC.