.. highlight:: c .. _arr_map_fold: Functional constructs: map & fold ########################################### Two standard functional constructs are provided: * *map* that promotes a function acting on the array element to an array function, acting element by element. * *fold* is the reduction of a function on the array. .. _map: map ======================================================== * **Purpose** : map promotes any function into an `array function`, acting term by term. * **Synopsis** :: template auto map (F f); If `f` is a function, or a function object :: T2 f(T1) Then map(f) is a function:: template auto map(f) (A const &) with: * A::value_type == T1 * The returned type of map(f) models the :ref:`ImmutableCuboidArray` concept * with the same domain as A * with value_type == T2 * **Example**: .. triqs_example:: ./map_0.cpp fold ======================================================== * **Purpose** : fold implements the folding (or reduction) on the array. * **Syntax** : If `f` is a function, or a function object of synopsis (T, R being 2 types) :: R f (R , T) then :: auto F = fold(f); is a callable object which can fold any array of value_type T. So, if * A is a type which models the :ref:`ImmutableCuboidArray` concept (e.g. an array , a matrix, a vector, an expression, ...) * A::value_type is T then :: fold (f) ( A, R init = R() ) = f(f(f(f(init, a(0,0)), a(0,1)),a(0,2)),a(0,3), ....) Note that: * The order of traversal is the same as foreach. * The precise return type of fold is an implementation detail, depending on the precise type of f, use auto to keep it. * The function f will be inlined if possible, leading to efficient algorithms. * fold is implemented using a foreach loop, hence it is efficient. * **Example**: Many algorithms can be written in form of map/fold. The function :ref:`arr_fnt_sum` which returns the sum of all the elements of the array is implemented as :: template typename A::value_type sum(A const & a) { return fold ( std::plus<>()) (a); } or the Frobenius norm of a matrix, .. math:: \sum_{i=0}^{N-1} \sum_{j=0}^{N-1} | a_{ij} | ^2 reads : .. triqs_example:: ./map_1.cpp Note in this example: * the simplicity of the code * the genericity: it is valid for any dimension of array. * internally, the library will rewrite it as a series of for loop, ordered in the TraversalOrder of the array and inline the lambda.