gleam_synapses

-module(gleam_synapses@model@net_elems@activation@activation).
-compile(no_auto_import).

-export([f/1, deriv/1, inverse/1, generator/0]).
-export_type([activation/0]).

-type activation() :: sigmoid | identity | tanh | leaky_re_lu.

-spec sigmoid_f(float()) -> float().
sigmoid_f(X) ->
    case (1.0 + gleam_synapses@model@mathematics:exp(0.0 - X)) of
        0.0 -> 0.0;
        Gleam@denominator -> 1.0 / Gleam@denominator
    end.

-spec f(activation()) -> fun((float()) -> float()).
f(Activation) ->
    case Activation of
        sigmoid ->
            fun sigmoid_f/1;

        identity ->
            fun gleam@function:identity/1;

        tanh ->
            fun gleam_synapses@model@mathematics:tanh/1;

        leaky_re_lu ->
            fun(X) -> case X < 0.0 of
                    true ->
                        0.01 * X;

                    false ->
                        X
                end end
    end.

-spec deriv(activation()) -> fun((float()) -> float()).
deriv(Activation) ->
    case Activation of
        sigmoid ->
            fun(D) -> sigmoid_f(D) * (1.0 - sigmoid_f(D)) end;

        identity ->
            gleam@function:constant(1.0);

        tanh ->
            fun(D@1) ->
                1.0
                - (gleam_synapses@model@mathematics:tanh(D@1)
                * gleam_synapses@model@mathematics:tanh(D@1))
            end;

        leaky_re_lu ->
            fun(D@2) -> case D@2 < 0.0 of
                    true ->
                        0.01;

                    false ->
                        1.0
                end end
    end.

-spec inverse(activation()) -> fun((float()) -> float()).
inverse(Activation) ->
    case Activation of
        sigmoid ->
            fun(Y) ->
                T = case (1.0
                - Y) of
                    0.0 -> 0.0;
                    Gleam@denominator -> Y
                    / Gleam@denominator
                end,
                gleam_synapses@model@mathematics:log(T)
            end;

        identity ->
            fun gleam@function:identity/1;

        tanh ->
            fun(Y@1) ->
                0.5
                * gleam_synapses@model@mathematics:log(case (1.0 - Y@1) of
                        0.0 -> 0.0;
                        Gleam@denominator@1 -> (1.0 + Y@1) / Gleam@denominator@1
                    end)
            end;

        leaky_re_lu ->
            fun(Y@2) -> case Y@2 < 0.0 of
                    true ->
                        Y@2 / 0.01;

                    false ->
                        Y@2
                end end
    end.

-spec generator() -> minigen:generator(activation()).
generator() ->
    minigen:always(sigmoid).