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-module(ann).
-export([
    connect/2,
    dot_prod/2,
    feed_forward/3,
    perceptron/3,
    replace_input/2,
    sigmoid/1,
    run/0
]).

sigmoid(X) ->
    1 / (1 + math:exp(-X)).

sigmoid_deriv(X) ->
    math:exp(-X) / ((1 + math:exp(-X)) * (1 + math:exp(-X))).

dot_prod(X, Y) ->
    lists:foldl(fun({A, B}, Sum) -> A * B + Sum end, 0, lists:zip(X, Y)).

feed_forward(F, Weights, Inputs) ->
    F(dot_prod(Weights, Inputs)).

connect(Sender_PID,  Receiver_PID) ->
    Sender_PID ! {connect_to_output, {Receiver_PID, 0.0}},
    Receiver_PID ! {connect_to_input, {Sender_PID, 0.5}}.

replace_input(Inputs, Input) ->
    lists:keyreplace(element(1, Input), 1, Inputs, Input).

convert_to_keys(Tuple_list) ->
    lists:map(fun(Tup) -> element(1, Tup) end, Tuple_list).

convert_to_values(Tuple_list) ->
    lists:map(fun(Tup) -> element(2, Tup) end, Tuple_list).

vector_map(F, X, Y) -> lists:map(fun(A) -> F(element(1,A), element(2,A)) end, lists:zip(X, Y)).

% Adds the propagating sensitivity to the Sensitivities hash
add_sensitivity(Sensitivities, Backprop) when Sensitivities =/= [] ->
    replace_input(Sensitivities, Backprop);
add_sensitivity(Sensitivities, Backprop) when Sensitivities =:= [] ->
    [].

% Calculates the sensitivity of this particular node
calculate_sensitivity(Backprop, Inputs, Sensitivities, Output_value, Derv_value) 
    when Sensitivities =/= [], Inputs =:= [] -> % When the node is an input node:
    null;
calculate_sensitivity(Backprop, Inputs, Sensitivities, Output_value, Derv_value) 
    when Sensitivities =:= [], Inputs =/= [] -> % When the node is an output node:
    {_, Training_value} = Backprop,
    (Training_value - Output_value) * Derv_value;
calculate_sensitivity(Backprop, Inputs, Sensitivities, Output_value, Derv_value) 
    when Sensitivities =/= [], Inputs =/= [] -> % When the node is a hidden node:
    Derv_value * lists:foldl(fun(E, T) -> E + T end, 0, convert_to_values(Sensitivities)).

% Sensitivity = {Output_PID, Sensitivity_value}
perceptron(Weights, Inputs, Sensitivities) ->
receive
    % Backprop = {Input_PID, Training_value}
    {learn, Backprop} -> 
        Learning_rate = 0.5,

        % Calculate the correct sensitivities
        New_sensitivities = add_sensitivity(Sensitivities, Backprop),
        Output_value = feed_forward(fun(X) -> sigmoid(X) end, Weights, convert_to_values(Inputs)),
        Derv_value = feed_forward(fun(X) -> sigmoid_deriv(X) end, Weights, convert_to_values(Inputs)),
        Sensitivity = calculate_sensitivity(Backprop, Inputs, New_sensitivities,
                                            Output_value, Derv_value),
        io:format("(~w) New Sensitivities: ~w~n", [self(), New_sensitivities]),
        io:format("(~w) Calculated Sensitivity: ~w~n", [self(), Sensitivity]),

        % Adjust all the weights
        Weight_adjustments = lists:map(fun(Input) -> 
                                               Learning_rate * Sensitivity * Input 
                                       end,
                                       convert_to_values(Inputs)),
        New_weights = vector_map(fun(W, D) -> W + D end, Weights, Weight_adjustments),
        io:format("(~w) Adjusted Weights: ~w~n", [self(), Weights]),

        % propagate sensitivities and associated weights back to the previous layer
        vector_map(fun(Weight, Input_PID) ->
                           Input_PID ! {learn, {self(), Sensitivity * Weight}}
                   end,
                   New_weights,
                   convert_to_keys(Inputs)),

        perceptron(New_weights, Inputs, New_sensitivities);

    {stimulate, Input} ->
        % add Input to Inputs to get New_Inputs...
        New_inputs = replace_input(Inputs, Input),

        % calculate output of perceptron...
        Output = feed_forward(fun(X)-> sigmoid(X) end, Weights, convert_to_values(New_inputs)),

        % stimulate the perceptrons my output is connected to
        if
            Sensitivities =/= [] ->
                lists:foreach(fun(Output_PID) ->
                    Output_PID ! {stimulate, {self(), Output}}
                end,
                convert_to_keys(Sensitivities));

            Sensitivities =:= [] ->
                io:format("~n~w outputs: ~w", [self(), Output]),
                self() ! {learn, {self(), 1}}
        end,

        perceptron(Weights, New_inputs, Sensitivities);

    {connect_to_output, Sensitivity} ->
        Combined_output = [Sensitivity | Sensitivities],
        io:format("~w output connected to ~w: ~w~n", [self(), element(1, Sensitivity), Combined_output]),
        perceptron(Weights, Inputs, Combined_output);

    {connect_to_input, Sensitivity} ->
        Combined_input = [Sensitivity | Inputs],
        io:format("~w input connected from ~w: ~w~n", [self(), element(1, Sensitivity), Combined_input]),
        perceptron([0.5 | Weights], Combined_input, Sensitivities);

    {status} ->
        io:format("Status of Node(~w)~n W: ~w~n I: ~w~n S: ~w~n", [self(), Weights, Inputs, Sensitivities]),
        perceptron(Weights, Inputs, Sensitivities);

    {pass, Input_value} ->
        lists:foreach(fun(Output_PID) ->
                io:format("Stimulating ~w with ~w~n", [Output_PID, Input_value]),
                Output_PID ! {stimulate, {self(), Input_value}}
            end,
            convert_to_keys(Sensitivities)),
        perceptron(Weights, Inputs, Sensitivities)

end.

run() ->
    X1_pid = spawn(ann, perceptron, [[],[],[]]),
    X2_pid = spawn(ann, perceptron, [[],[],[]]),
    H1_pid = spawn(ann, perceptron, [[],[],[]]),
    H2_pid = spawn(ann, perceptron, [[],[],[]]),

    O_pid = spawn(ann, perceptron,  [[],[],[]]),

    % Connect input node X1 to hidden nodes H1 and H2
    ann:connect(X1_pid, H1_pid),
    ann:connect(X1_pid, H2_pid),

    % Connect input node X2 to hidden nodes H1 and H2
    ann:connect(X2_pid, H1_pid),
    ann:connect(X2_pid, H2_pid),

    % Connect input node H1 and H2 to output node O
    ann:connect(H1_pid, O_pid),
    ann:connect(H2_pid, O_pid),

    X1_pid ! {status},
    X2_pid ! {status},
    H1_pid ! {status},
    H2_pid ! {status},
    O_pid ! {status},

    X1_pid ! {pass, 1.8},
    X2_pid ! {pass, 1.3}.