-module(viva_math@attractor).
-compile([no_auto_import, nowarn_unused_vars, nowarn_unused_function, nowarn_nomatch, inline]).
-define(FILEPATH, "src/viva_math/attractor.gleam").
-export([emotional_attractors/0, nearest/2, basin_weights/3, analyze/3, classify_emotion/1, attractor_pull/3, weighted_pull/4, ou_mean_reversion/4, in_basin/3, nearby_attractors/3, blend_attractors/3, create/4, dominant_dimension/1]).
-export_type([attractor/0, attractor_result/0]).

-if(?OTP_RELEASE >= 27).
-define(MODULEDOC(Str), -moduledoc(Str)).
-define(DOC(Str), -doc(Str)).
-else.
-define(MODULEDOC(Str), -compile([])).
-define(DOC(Str), -compile([])).
-endif.

?MODULEDOC(
    " Attractor dynamics for emotional states.\n"
    "\n"
    " Based on Mehrabian's PAD model (1996) and dynamical systems theory.\n"
    " Emotions form attractors in PAD space - stable states the system tends toward.\n"
    "\n"
    " The 8 basic emotions correspond to the octants of the PAD cube:\n"
    " - Joy:     (+P, +A, +D)\n"
    " - Sadness: (-P, -A, -D)\n"
    " - Anger:   (-P, +A, +D)\n"
    " - Fear:    (-P, +A, -D)\n"
    " - Surprise: (+P, +A, -D) or (-P, +A, -D)\n"
    " - Disgust: (-P, -A, +D)\n"
    " - Trust:   (+P, -A, +D)\n"
    " - Anticipation: (+P, +A, +D)\n"
    "\n"
    " References:\n"
    " - Mehrabian (1996) \"Pleasure-arousal-dominance: A general framework\"\n"
    " - Russell (2003) \"Core affect and the psychological construction of emotion\"\n"
).

-type attractor() :: {attractor, binary(), viva_math@vector:vec3()}.

-type attractor_result() :: {attractor_result,
        attractor(),
        float(),
        list({attractor(), float()})}.

-file("src/viva_math/attractor.gleam", 44).
?DOC(
    " The 8 basic emotional attractors (Mehrabian octants).\n"
    " Values from empirical research on emotion self-reports.\n"
).
-spec emotional_attractors() -> list(attractor()).
emotional_attractors() ->
    [{attractor, <<"joy"/utf8>>, {vec3, 0.76, 0.48, 0.35}},
        {attractor, <<"excitement"/utf8>>, {vec3, 0.62, 0.75, 0.38}},
        {attractor, <<"trust"/utf8>>, {vec3, 0.58, -0.23, 0.42}},
        {attractor, <<"serenity"/utf8>>, {vec3, 0.45, -0.42, 0.21}},
        {attractor, <<"sadness"/utf8>>, {vec3, -0.63, -0.27, -0.33}},
        {attractor, <<"fear"/utf8>>, {vec3, -0.64, 0.6, -0.43}},
        {attractor, <<"anger"/utf8>>, {vec3, -0.51, 0.59, 0.25}},
        {attractor, <<"disgust"/utf8>>, {vec3, -0.6, 0.35, 0.11}}].

-file("src/viva_math/attractor.gleam", 60).
?DOC(" Find the nearest attractor to a given point.\n").
-spec nearest(viva_math@vector:vec3(), list(attractor())) -> {ok, attractor()} |
    {error, nil}.
nearest(Point, Attractors) ->
    case Attractors of
        [] ->
            {error, nil};

        [First | Rest] ->
            Initial = {First,
                viva_math@vector:distance(Point, erlang:element(3, First))},
            Result = gleam@list:fold(
                Rest,
                Initial,
                fun(Acc, Attr) ->
                    Dist = viva_math@vector:distance(
                        Point,
                        erlang:element(3, Attr)
                    ),
                    case Dist < erlang:element(2, Acc) of
                        true ->
                            {Attr, Dist};

                        false ->
                            Acc
                    end
                end
            ),
            {ok, erlang:element(1, Result)}
    end.

-file("src/viva_math/attractor.gleam", 91).
?DOC(
    " Calculate influence weights for all attractors using softmax of negative distances.\n"
    "\n"
    " CORRECTED per DeepSeek R1 validation:\n"
    " w_i = exp(-γ × d_i) / Σ_j exp(-γ × d_j)\n"
    "\n"
    " Where γ = 1/temperature (higher temp = softer weights, lower temp = sharper).\n"
    " This is more numerically stable than 1/d and matches Boltzmann distribution.\n"
    "\n"
    " Closer attractors have higher weights. The temperature parameter controls\n"
    " how \"sharp\" the weighting is (lower temp = more weight on nearest).\n"
).
-spec basin_weights(viva_math@vector:vec3(), list(attractor()), float()) -> list({attractor(),
    float()}).
basin_weights(Point, Attractors, Temperature) ->
    case Attractors of
        [] ->
            [];

        _ ->
            Gamma = case Temperature =< +0.0 of
                true ->
                    1.0;

                false ->
                    case Temperature of
                        +0.0 -> +0.0;
                        -0.0 -> -0.0;
                        Gleam@denominator -> 1.0 / Gleam@denominator
                    end
            end,
            Neg_gamma_distances = gleam@list:map(
                Attractors,
                fun(Attr) ->
                    Dist = viva_math@vector:distance(
                        Point,
                        erlang:element(3, Attr)
                    ),
                    {Attr, +0.0 - (Gamma * Dist)}
                end
            ),
            Max_val = gleam@list:fold(
                Neg_gamma_distances,
                -1000.0,
                fun(Acc, Pair) ->
                    gleam@float:max(Acc, erlang:element(2, Pair))
                end
            ),
            Exps = gleam@list:map(
                Neg_gamma_distances,
                fun(Pair@1) ->
                    {erlang:element(1, Pair@1),
                        gleam_community@maths:exponential(
                            erlang:element(2, Pair@1) - Max_val
                        )}
                end
            ),
            Sum = gleam@list:fold(
                Exps,
                +0.0,
                fun(Acc@1, Pair@2) -> Acc@1 + erlang:element(2, Pair@2) end
            ),
            case Sum =:= +0.0 of
                true ->
                    gleam@list:map(Attractors, fun(A) -> {A, +0.0} end);

                false ->
                    gleam@list:map(
                        Exps,
                        fun(Pair@3) -> {erlang:element(1, Pair@3), case Sum of
                                    +0.0 -> +0.0;
                                    -0.0 -> -0.0;
                                    Gleam@denominator@1 -> erlang:element(
                                        2,
                                        Pair@3
                                    )
                                    / Gleam@denominator@1
                                end} end
                    )
            end
    end.

-file("src/viva_math/attractor.gleam", 134).
?DOC(" Comprehensive attractor analysis for a point.\n").
-spec analyze(viva_math@vector:vec3(), list(attractor()), float()) -> {ok,
        attractor_result()} |
    {error, nil}.
analyze(Point, Attractors, Temperature) ->
    case nearest(Point, Attractors) of
        {error, nil} ->
            {error, nil};

        {ok, Near} ->
            Dist = viva_math@vector:distance(Point, erlang:element(3, Near)),
            Weights = basin_weights(Point, Attractors, Temperature),
            {ok, {attractor_result, Near, Dist, Weights}}
    end.

-file("src/viva_math/attractor.gleam", 150).
?DOC(" Classify emotional state by nearest attractor name.\n").
-spec classify_emotion(viva_math@vector:vec3()) -> binary().
classify_emotion(Point) ->
    case nearest(Point, emotional_attractors()) of
        {ok, Attr} ->
            erlang:element(2, Attr);

        {error, nil} ->
            <<"neutral"/utf8>>
    end.

-file("src/viva_math/attractor.gleam", 161).
?DOC(
    " Compute attractor pull - force vector toward nearest attractor.\n"
    "\n"
    " The pull strength increases with distance from attractor (spring-like).\n"
    " strength parameter controls overall force magnitude.\n"
).
-spec attractor_pull(viva_math@vector:vec3(), attractor(), float()) -> viva_math@vector:vec3().
attractor_pull(Point, Attractor, Strength) ->
    Diff = viva_math@vector:sub(erlang:element(3, Attractor), Point),
    Dist = viva_math@vector:length(Diff),
    case Dist =:= +0.0 of
        true ->
            viva_math@vector:zero();

        false ->
            Normalized = viva_math@vector:scale(Diff, case Dist of
                    +0.0 -> +0.0;
                    -0.0 -> -0.0;
                    Gleam@denominator -> 1.0 / Gleam@denominator
                end),
            viva_math@vector:scale(Normalized, Strength * Dist)
    end.

-file("src/viva_math/attractor.gleam", 181).
?DOC(
    " Compute weighted pull from all attractors.\n"
    "\n"
    " Each attractor pulls proportionally to its basin weight.\n"
).
-spec weighted_pull(
    viva_math@vector:vec3(),
    list(attractor()),
    float(),
    float()
) -> viva_math@vector:vec3().
weighted_pull(Point, Attractors, Strength, Temperature) ->
    Weights = basin_weights(Point, Attractors, Temperature),
    gleam@list:fold(
        Weights,
        viva_math@vector:zero(),
        fun(Acc, Pair) ->
            {Attr, Weight} = Pair,
            Pull = attractor_pull(Point, Attr, Strength * Weight),
            viva_math@vector:add(Acc, Pull)
        end
    ).

-file("src/viva_math/attractor.gleam", 201).
?DOC(
    " Ornstein-Uhlenbeck mean reversion toward attractor.\n"
    "\n"
    " dx = theta * (attractor - x) * dt\n"
    "\n"
    " This is the deterministic part of O-U process.\n"
    " theta controls reversion speed (higher = faster return to attractor).\n"
).
-spec ou_mean_reversion(
    viva_math@vector:vec3(),
    viva_math@vector:vec3(),
    float(),
    float()
) -> viva_math@vector:vec3().
ou_mean_reversion(Current, Attractor, Theta, Dt) ->
    Diff = viva_math@vector:sub(Attractor, Current),
    Delta = viva_math@vector:scale(Diff, Theta * Dt),
    viva_math@vector:add(Current, Delta).

-file("src/viva_math/attractor.gleam", 215).
?DOC(
    " Check if point is in basin of an attractor.\n"
    "\n"
    " A point is \"in\" a basin if that attractor has the highest weight.\n"
).
-spec in_basin(viva_math@vector:vec3(), attractor(), list(attractor())) -> boolean().
in_basin(Point, Attractor, All) ->
    case nearest(Point, All) of
        {ok, Near} ->
            erlang:element(2, Near) =:= erlang:element(2, Attractor);

        {error, nil} ->
            false
    end.

-file("src/viva_math/attractor.gleam", 227).
?DOC(" Find all attractors within a given distance.\n").
-spec nearby_attractors(viva_math@vector:vec3(), list(attractor()), float()) -> list(attractor()).
nearby_attractors(Point, Attractors, Radius) ->
    gleam@list:filter(
        Attractors,
        fun(Attr) ->
            viva_math@vector:distance(Point, erlang:element(3, Attr)) =< Radius
        end
    ).

-file("src/viva_math/attractor.gleam", 240).
?DOC(
    " Interpolate between two attractors based on a blend factor.\n"
    "\n"
    " t=0 gives first attractor, t=1 gives second.\n"
).
-spec blend_attractors(attractor(), attractor(), float()) -> attractor().
blend_attractors(A, B, T) ->
    Pos = viva_math@vector:lerp(erlang:element(3, A), erlang:element(3, B), T),
    Name = <<<<(erlang:element(2, A))/binary, "_"/utf8>>/binary,
        (erlang:element(2, B))/binary>>,
    {attractor, Name, Pos}.

-file("src/viva_math/attractor.gleam", 247).
?DOC(" Create a custom attractor from name and PAD values.\n").
-spec create(binary(), float(), float(), float()) -> attractor().
create(Name, Pleasure, Arousal, Dominance) ->
    {attractor, Name, viva_math@vector:pad(Pleasure, Arousal, Dominance)}.

-file("src/viva_math/attractor.gleam", 257).
?DOC(" Get the dominant emotion component (P, A, or D) for an attractor.\n").
-spec dominant_dimension(attractor()) -> binary().
dominant_dimension(Attractor) ->
    P = gleam@float:absolute_value(
        erlang:element(2, erlang:element(3, Attractor))
    ),
    A = gleam@float:absolute_value(
        erlang:element(3, erlang:element(3, Attractor))
    ),
    D = gleam@float:absolute_value(
        erlang:element(4, erlang:element(3, Attractor))
    ),
    case (P >= A) andalso (P >= D) of
        true ->
            <<"pleasure"/utf8>>;

        false ->
            case A >= D of
                true ->
                    <<"arousal"/utf8>>;

                false ->
                    <<"dominance"/utf8>>
            end
    end.