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

-export([init/3, output/2, errors/4, fit/4, to_json/1, of_json/1, generator/1]).

-spec lazy_realization(
    gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron()))
) -> gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron())).
lazy_realization(Network) ->
    serialized(Network),
    Network.

-spec init(
    gleam_zlists@interop:z_list(integer()),
    fun((integer()) -> gleam_synapses@model@net_elems@activation:activation()),
    fun((integer()) -> float())
) -> gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron())).
init(Layer_sizes, Activation_f, Weight_init_f) ->
    {ok, Tl} = gleam_zlists:tail(Layer_sizes),
    lazy_realization(
        gleam_zlists:map(
            gleam_zlists:with_index(gleam_zlists:zip(Layer_sizes, Tl)),
            fun(T) -> {{Lr_sz, Next_lr_sz}, Index} = T,
                gleam_synapses@model@net_elems@layer:init(
                    Lr_sz,
                    Next_lr_sz,
                    Activation_f(Index),
                    fun() -> fun() -> Weight_init_f(Index) end end
                ) end
        )
    ).

-spec output(
    gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron())),
    gleam_zlists@interop:z_list(float())
) -> gleam_zlists@interop:z_list(float()).
output(Network, Input_val) ->
    gleam_zlists:reduce(
        Network,
        Input_val,
        fun(X, Acc) -> gleam_synapses@model@net_elems@layer:output(X, Acc) end
    ).

-spec fed_forward_acc_f(
    gleam_zlists@interop:z_list({gleam_zlists@interop:z_list(float()),
                                 gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron())}),
    gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron())
) -> gleam_zlists@interop:z_list({gleam_zlists@interop:z_list(float()),
                                  gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron())}).
fed_forward_acc_f(Already_fed, Next_layer) ->
    {ok, {Errors_val, Layer_val}} = gleam_zlists:head(Already_fed),
    Next_input = gleam_synapses@model@net_elems@layer:output(
        Layer_val,
        Errors_val
    ),
    gleam_zlists:cons(Already_fed, {Next_input, Next_layer}).

-spec fed_forward(
    gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron())),
    gleam_zlists@interop:z_list(float())
) -> gleam_zlists@interop:z_list({gleam_zlists@interop:z_list(float()),
                                  gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron())}).
fed_forward(Network, Input_val) ->
    {ok, {Net_hd, Net_tl}} = gleam_zlists:uncons(Network),
    Init_feed = gleam_zlists:singleton({Input_val, Net_hd}),
    gleam_zlists:reduce(
        Net_tl,
        Init_feed,
        fun(X, Acc) -> fed_forward_acc_f(Acc, X) end
    ).

-spec back_propagated_acc_f(
    float(),
    {gleam_zlists@interop:z_list(float()),
     gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron()))},
    {gleam_zlists@interop:z_list(float()),
     gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron())}
) -> {gleam_zlists@interop:z_list(float()),
      gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron()))}.
back_propagated_acc_f(
    Learning_rate,
    Errors_with_already_propagated,
    Input_with_layer
) ->
    {Errors_val, Already_propagated} = Errors_with_already_propagated,
    {Last_input, Last_layer} = Input_with_layer,
    Last_output_with_errors = gleam_zlists:zip(
        gleam_synapses@model@net_elems@layer:output(Last_layer, Last_input),
        Errors_val
    ),
    {Next_errors, Propagated_layer} = gleam_synapses@model@net_elems@layer:back_propagated(
        Last_layer,
        Learning_rate,
        Last_input,
        Last_output_with_errors
    ),
    Next_already_propagated = gleam_zlists:cons(
        Already_propagated,
        Propagated_layer
    ),
    {Next_errors, Next_already_propagated}.

-spec back_propagated(
    float(),
    gleam_zlists@interop:z_list(float()),
    gleam_zlists@interop:z_list({gleam_zlists@interop:z_list(float()),
                                 gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron())})
) -> {gleam_zlists@interop:z_list(float()),
      gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron()))}.
back_propagated(Learning_rate, Expected_output, Reversed_inputs_with_layers) ->
    {ok, {{Last_input, Last_layer}, Reversed_inputs_with_layers_tl}} = gleam_zlists:uncons(
        Reversed_inputs_with_layers
    ),
    Output_val = gleam_synapses@model@net_elems@layer:output(
        Last_layer,
        Last_input
    ),
    Errors_val = gleam_zlists:map(
        gleam_zlists:zip(Output_val, Expected_output),
        fun(T) -> {A, B} = T,
            A - B end
    ),
    Output_with_errors = gleam_zlists:zip(Output_val, Errors_val),
    {Init_errors, First_propagated} = gleam_synapses@model@net_elems@layer:back_propagated(
        Last_layer,
        Learning_rate,
        Last_input,
        Output_with_errors
    ),
    Init_acc = {Init_errors, gleam_zlists:singleton(First_propagated)},
    gleam_zlists:reduce(
        Reversed_inputs_with_layers_tl,
        Init_acc,
        fun(X, Acc) -> back_propagated_acc_f(Learning_rate, Acc, X) end
    ).

-spec errors_with_fit_net(
    gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron())),
    float(),
    gleam_zlists@interop:z_list(float()),
    gleam_zlists@interop:z_list(float())
) -> {gleam_zlists@interop:z_list(float()),
      gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron()))}.
errors_with_fit_net(Network, Learning_rate, Input_val, Expected_output) ->
    back_propagated(
        Learning_rate,
        Expected_output,
        fed_forward(Network, Input_val)
    ).

-spec errors(
    gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron())),
    float(),
    gleam_zlists@interop:z_list(float()),
    gleam_zlists@interop:z_list(float())
) -> gleam_zlists@interop:z_list(float()).
errors(Network, Learning_rate, Input_val, Expected_output) ->
    {ok, Last_layer} = gleam_zlists:head(gleam_zlists:reverse(Network)),
    Restricted_output = gleam_zlists:map(
        gleam_zlists:zip(Last_layer, Expected_output),
        fun(T) -> {A, B} = T,
            gleam_synapses@model@net_elems@activation:restricted_output(
                erlang:element(2, A),
                B
            ) end
    ),
    gleam@pair:first(
        errors_with_fit_net(
            Network,
            Learning_rate,
            Input_val,
            Restricted_output
        )
    ).

-spec fit(
    gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron())),
    float(),
    gleam_zlists@interop:z_list(float()),
    gleam_zlists@interop:z_list(float())
) -> gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron())).
fit(Network, Learning_rate, Input_val, Expected_output) ->
    {ok, Last_layer} = gleam_zlists:head(gleam_zlists:reverse(Network)),
    Restricted_output = gleam_zlists:map(
        gleam_zlists:zip(Last_layer, Expected_output),
        fun(T) -> {A, B} = T,
            gleam_synapses@model@net_elems@activation:restricted_output(
                erlang:element(2, A),
                B
            ) end
    ),
    lazy_realization(
        gleam@pair:second(
            errors_with_fit_net(
                Network,
                Learning_rate,
                Input_val,
                Restricted_output
            )
        )
    ).

-spec serialized(
    gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron()))
) -> list(list(gleam_synapses@model@net_elems@neuron:neuron_serialized())).
serialized(Network) ->
    gleam_zlists:to_list(
        gleam_zlists:map(
            Network,
            fun gleam_synapses@model@net_elems@layer:serialized/1
        )
    ).

-spec deserialized(
    list(list(gleam_synapses@model@net_elems@neuron:neuron_serialized()))
) -> gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron())).
deserialized(Network_serialized) ->
    gleam_zlists:map(
        gleam_zlists:of_list(Network_serialized),
        fun gleam_synapses@model@net_elems@layer:deserialized/1
    ).

-spec json_encoded(
    list(list(gleam_synapses@model@net_elems@neuron:neuron_serialized()))
) -> gleam_synapses@model@edited_jsone:json_value().
json_encoded(Network_serialized) ->
    gleam_synapses@model@edited_jsone:array(
        Network_serialized,
        fun gleam_synapses@model@net_elems@layer:json_encoded/1
    ).

-spec json_decoder() -> decode:decoder(list(list(gleam_synapses@model@net_elems@neuron:neuron_serialized()))).
json_decoder() ->
    decode:list(gleam_synapses@model@net_elems@layer:json_decoder()).

-spec to_json(
    gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron()))
) -> binary().
to_json(Network) ->
    {ok, Dyn} = gleam_synapses@model@edited_jsone:encode(
        json_encoded(serialized(Network))
    ),
    {ok, Res} = decode:decode_dynamic(Dyn, decode:string()),
    Res.

-spec of_json(binary()) -> gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron())).
of_json(S) ->
    {ok, Dyn} = gleam_synapses@model@edited_jsone:decode(S),
    {ok, Res} = decode:decode_dynamic(Dyn, json_decoder()),
    lazy_realization(deserialized(Res)).

-spec generator(gleam_zlists@interop:z_list(integer())) -> minigen:generator(gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron()))).
generator(Layer_sizes) ->
    {ok, Tl} = gleam_zlists:tail(Layer_sizes),
    minigen:map(
        minigen:map(
            gleam_zlists:reduce(
                gleam_zlists:zip(Layer_sizes, Tl),
                minigen:always(gleam_zlists:new()),
                fun(T, Acc_gen) -> {Lr_sz, Next_lr_sz} = T,
                    minigen:then(
                        Acc_gen,
                        fun(Acc_zls) ->
                            minigen:map(
                                gleam_synapses@model@net_elems@layer:generator(
                                    Lr_sz,
                                    Next_lr_sz
                                ),
                                fun(Layer) ->
                                    gleam_zlists:cons(Acc_zls, Layer)
                                end
                            )
                        end
                    ) end
            ),
            fun gleam_zlists:reverse/1
        ),
        fun lazy_realization/1
    ).