-module(gleam_synapses@neural_network).
-compile(no_auto_import).

-export([init/1, init_with_seed/2, customized_init/3, prediction/2, errors/4, fit/4, to_json/1, of_json/1, to_svg/1]).

-spec seed_init(gleam@option:option(integer()), list(integer())) -> gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron())).
seed_init(Maybe_seed, Layers) ->
    Gen = gleam_synapses@model@net_elems@network:generator(
        gleam_zlists:of_list(Layers)
    ),
    case Maybe_seed of
        {some, I} ->
            minigen:run_with_seed(Gen, I);

        none ->
            minigen:run(Gen)
    end.

-spec fail_if_input_not_match(
    gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron())),
    list(float())
) -> nil.
fail_if_input_not_match(Network, Input_values) ->
    Num_of_input_vals = gleam@list:length(Input_values),
    {ok, First_neuron} = gleam@result:then(
        gleam_zlists:head(Network),
        fun gleam_zlists:head/1
    ),
    Input_layer_size = gleam_zlists:count(erlang:element(3, First_neuron)) - 1,
    Is_equal = Num_of_input_vals =:= Input_layer_size,
    {} = case Is_equal of
        true -> {};
        Gleam@Assert ->
            erlang:error(#{gleam_error => assert,
                           message => <<"Assertion pattern match failed"/utf8>>,
                           value => Gleam@Assert,
                           module => <<"gleam_synapses/neural_network"/utf8>>,
                           function => <<"fail_if_input_not_match"/utf8>>,
                           line => 36})
    end,
    nil.

-spec fail_if_expected_not_match(
    gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron())),
    list(float())
) -> nil.
fail_if_expected_not_match(Network, Expected_output) ->
    Num_of_expected_vals = gleam@list:length(Expected_output),
    {ok, Output_layer_size} = gleam@result:map(
        gleam_zlists:head(gleam_zlists:reverse(Network)),
        fun gleam_zlists:count/1
    ),
    Is_equal = Num_of_expected_vals =:= Output_layer_size,
    {} = case Is_equal of
        true -> {};
        Gleam@Assert ->
            erlang:error(#{gleam_error => assert,
                           message => <<"Assertion pattern match failed"/utf8>>,
                           value => Gleam@Assert,
                           module => <<"gleam_synapses/neural_network"/utf8>>,
                           function => <<"fail_if_expected_not_match"/utf8>>,
                           line => 52})
    end,
    nil.

-spec init(list(integer())) -> gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron())).
init(Layers) ->
    seed_init(none, Layers).

-spec init_with_seed(integer(), list(integer())) -> gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron())).
init_with_seed(Seed, Layers) ->
    seed_init({some, Seed}, Layers).

-spec customized_init(
    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())).
customized_init(Layers, Activation_f, Weight_init_f) ->
    gleam_synapses@model@net_elems@network:init(
        gleam_zlists:of_list(Layers),
        Activation_f,
        Weight_init_f
    ).

-spec prediction(
    gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron())),
    list(float())
) -> list(float()).
prediction(Network, Input_values) ->
    fail_if_input_not_match(Network, Input_values),
    Input = gleam_zlists:of_list(Input_values),
    gleam_zlists:to_list(
        gleam_synapses@model@net_elems@network:output(Network, Input)
    ).

-spec errors(
    gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron())),
    float(),
    list(float()),
    list(float())
) -> list(float()).
errors(Network, Learning_rate, Input_values, Expected_output) ->
    fail_if_input_not_match(Network, Input_values),
    fail_if_expected_not_match(Network, Expected_output),
    Input = gleam_zlists:of_list(Input_values),
    Expected = gleam_zlists:of_list(Expected_output),
    gleam_zlists:to_list(
        gleam_synapses@model@net_elems@network:errors(
            Network,
            Learning_rate,
            Input,
            Expected
        )
    ).

-spec fit(
    gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron())),
    float(),
    list(float()),
    list(float())
) -> gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron())).
fit(Network, Learning_rate, Input_values, Expected_output) ->
    fail_if_input_not_match(Network, Input_values),
    fail_if_expected_not_match(Network, Expected_output),
    Input = gleam_zlists:of_list(Input_values),
    Expected = gleam_zlists:of_list(Expected_output),
    gleam_synapses@model@net_elems@network:fit(
        Network,
        Learning_rate,
        Input,
        Expected
    ).

-spec to_json(
    gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron()))
) -> binary().
to_json(Network) ->
    gleam_synapses@model@net_elems@network:to_json(Network).

-spec of_json(binary()) -> gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron())).
of_json(Json) ->
    gleam_synapses@model@net_elems@network:of_json(Json).

-spec to_svg(
    gleam_zlists@interop:z_list(gleam_zlists@interop:z_list(gleam_synapses@model@net_elems@neuron:neuron()))
) -> binary().
to_svg(Network) ->
    gleam_synapses@model@draw:network_svg(Network).