gleam_synapses

-module(gleam_synapses@model@mathematics).
-compile(no_auto_import).

-export([dot_product/2, root_mean_square_error/1]).

-spec dot_product(
    gleam_zlists@interop:z_list(float()),
    gleam_zlists@interop:z_list(float())
) -> float().
dot_product(Left, Right) ->
    gleam_zlists:sum(
        gleam_zlists:map(gleam_zlists:zip(Left, Right), fun(X) -> {A, B} = X,
                A * B end)
    ).

-spec euclidean_distance(
    gleam_zlists@interop:z_list(float()),
    gleam_zlists@interop:z_list(float())
) -> float().
euclidean_distance(Xs, Ys) ->
    {ok, Res} = gleam@float:square_root(
        gleam_zlists:sum(
            gleam_zlists:map(gleam_zlists:zip(Xs, Ys), fun(T) -> {X, Y} = T,
                    Diff = X - Y,
                    Diff * Diff end)
        )
    ),
    Res.

-spec root_mean_square_error(
    gleam_zlists@interop:z_list({gleam_zlists@interop:z_list(float()),
                                 gleam_zlists@interop:z_list(float())})
) -> float().
root_mean_square_error(Y_hats_with_ys) ->
    {N, S} = gleam_zlists:reduce(
        gleam_zlists:map(Y_hats_with_ys, fun(T) -> {Y_hat, Y} = T,
                D = euclidean_distance(Y_hat, Y),
                D * D end),
        {0, 0.0},
        fun(X, Acc) -> {Acc_n, Acc_s} = Acc,
            {Acc_n + 1, Acc_s + X} end
    ),
    Avg = case gleam@int:to_float(N) of
        0.0 -> 0.0;
        Gleam@denominator -> S / Gleam@denominator
    end,
    {ok, Res} = gleam@float:square_root(Avg),
    Res.