"use strict";

/** @fileOverview AI modules. */

/** @module */

var ai = {};

/** Directions. @constant */

ai.dirs = ["up", "right", "down", "left"];

/** Possible direction function names. @constant */

ai.canDirs = ["canUp", "canRight", "canDown", "canLeft"];

/** Create empty 'to' if argument missing. */

ai.copyObj = function(from, to) {

    if (to == null || typeof to !== "object")

        to = {};

    if (from == null || typeof from !== "object")

        return to;

    for (var attr in from) {

        if (from.hasOwnProperty(attr))

            to[attr] = from[attr];

    }

    return to;

}

// Each strategy is a function that except current board position as 2d array and context from

// previous call to share state/precomputed values between calls.

////////////////////////////////////////////////////////////////

// Blind random AI.

////////////////////////////////////////////////////////////////

/** Blind random AI.

 * @param {Board} brdEngine  board engine from board.js

 * @constructor */

ai.BlindRandom = function(brdEngine) {

    this.brdEngine = brdEngine;

}

ai.BlindRandom.prototype.analyse = function(brd) {

    var origBrd = new this.brdEngine(brd);

    while (true) {

        var rnd = Math.floor(Math.random()*4);

        if (origBrd[ai.canDirs[rnd]]())

            return ai.dirs[rnd];

    }

}

/* Mark that next board will be unrelated to previous, so any stored precompution can be cleared. */

ai.BlindRandom.prototype.cleanup = function() { }

////////////////////////////////////////////////////////////////

// Blind weight random AI.

////////////////////////////////////////////////////////////////

/**

 * @name ai.BlindWeightRandom.cfg

 * @namespace

 * @property {number} left   weight

 * @property {number} right  weight

 * @property {number} up     weight

 * @property {number} down   weight

 */

/** Blind weight random AI.

 * @param {Board} brdEngine  board engine from board.js

 * @param {ai.BlindWeightRandom.cfg} cfg  configuration settings

 * @constructor */

ai.BlindWeightRandom = function(brdEngine, cfg) {

    this.brdEngine = brdEngine;

    this.cfg = ai.copyObj(ai.BlindWeightRandom.bestCfg);

    ai.copyObj(cfg, this.cfg);

    var total = this.cfg.left + this.cfg.right + this.cfg.up + this.cfg.down;

    this.threshold1 = this.cfg.left/total;

    this.threshold2 = (this.cfg.left + this.cfg.down)/total;

    this.threshold3 = (this.cfg.left + this.cfg.down + this.cfg.right)/total;

}

ai.BlindWeightRandom.bestCfg = { left: 1, right: 16, up: 4, down: 8 };

ai.BlindWeightRandom.prototype.analyse = function(brd) {

    var origBrd = new this.brdEngine(brd);

    while (true) {

        var rnd = Math.random();

        if (rnd < this.threshold1)

            var dir = 0;

        else if (rnd < this.threshold2)

            var dir = 1;

        else if (rnd < this.threshold3)

            var dir = 2;

        else

            var dir = 3;

        if (origBrd[ai.canDirs[dir]]())

            return ai.dirs[dir];

    }

}

/* Mark that next board will be unrelated to previous, so any stored precompution can be cleared. */

ai.BlindWeightRandom.prototype.cleanup = function() { }

////////////////////////////////////////////////////////////////

// Blind cycle AI.

////////////////////////////////////////////////////////////////

/**

 * @name ai.BlindCycle.cfg

 * @namespace

 * @property {boolean} whilePossible  move in one direction while possible

 * @property {boolean} down           switch direction clockwise

 */

/** Blind cycle AI.

 * @param {Board} brdEngine  board engine from board.js

 * @param {ai.BlindCycle.cfg} cfg  configuration settings

 * @constructor */

ai.BlindCycle = function(brdEngine, cfg) {

    this.brdEngine = brdEngine;

    this.cfg = cfg || {};

    this.cfg.whilePossible = this.cfg.whilePossible || false;

    this.cfg.clockwise = this.cfg.clockwise || false;

}

ai.BlindCycle.dirs = ["left", "down", "right", "up"];

ai.BlindCycle.canDirs = ["canLeft", "canDown", "canRight", "canUp"];

ai.BlindCycle.prototype.nextDir = function(dir) {

    if (this.cfg.clockwise)

        return (dir + (4-1)) % 4;

    else

        return (dir + 1) % 4;

}

ai.BlindCycle.prototype.analyse = function(brd) {

    var origBrd = new this.brdEngine(brd);

    this.prevDir = this.prevDir || 0;

    if (!this.cfg.whilePossible)

        this.prevDir = this.nextDir(this.prevDir);

    while (true) {

        if (origBrd[ai.BlindCycle.canDirs[this.prevDir]]())

            return ai.BlindCycle.dirs[this.prevDir];

        this.prevDir = this.nextDir(this.prevDir);

    }

}

/* Mark that next board will be unrelated to previous, so any stored precompution can be cleared. */

ai.BlindCycle.prototype.cleanup = function() {

    delete this.prevDir;

}

////////////////////////////////////////////////////////////////

// 1 step deep with linear weight function on score, max value,

// bonuses for max value stay at corner or edge and bonuses

// for each free field.

////////////////////////////////////////////////////////////////

/**

 * Defines coefficient for linear resulted weight function.

 * @name ai.OneStepAhead.cfg

 * @namespace

 * @property {number} scoreCoef    multiplicator for score

 * @property {number} maxValCoef   multiplicator for max value

 * @property {number} cornerBonus  bonus for max value at board corner

 * @property {number} edgeBonus    bonus for max value at board edge

 * @property {number} freeBonus    bonus for each free cell

 */

/** 1 step deep with * AI.

 * @param {Board} brdEngine  board engine from board.js

 * @param {ai.OneStepAhead.cfg} cfg  configuration settings

 * @constructor */

ai.OneStepAhead = function(brdEngine, cfg) {

    this.brdEngine = brdEngine;

    this.cfg = ai.copyObj(ai.OneStepAhead.bestCfg);

    ai.copyObj(cfg, this.cfg);

}

ai.OneStepAhead.bestCfg = {scoreCoef: 1, maxValCoef: 0, cornerBonus: 0, edgeBonus: 0, freeBonus: 0};

ai.OneStepAhead.prototype.weight = function(brd) {

    var weight = 0;

    if (this.cfg.scoreCoef > 0)

        weight += this.cfg.scoreCoef * brd.score();

    var max = brd.max();

    if (this.cfg.maxValCoef > 0)

        weight += this.cfg.maxValCoef * max;

    if (this.cfg.cornerBonus > 0 && brd.atCorner(max))

        weight += this.cfg.cornerBonus;

    if (this.cfg.edgeBonus > 0 && brd.atEdge(max))

        weight += this.cfg.edgeBonus;

    if (this.cfg.freeBonus > 0)

        weight += this.cfg.freeBonus * brd.free();

    return weight;

}

ai.OneStepAhead.prototype.analyse = function(brd) {

    var origBrd = new this.brdEngine(brd);

    var nextBrd = new this.brdEngine();

    var maxWeight = -1;

    var bestDir;

    for (var i = 0; i < ai.dirs.length; i++) {

        var dir = ai.dirs[i];

        if (origBrd[dir](nextBrd)) {

            var weight = this.weight(nextBrd);

            if (maxWeight < weight) {

                bestDir = dir;

                maxWeight = weight;

            }

        }

    }

    return bestDir;

}

/* Mark that next board will be unrelated to previous, so any stored precompution can be cleared. */

ai.OneStepAhead.prototype.cleanup = function() { }

////////////////////////////////////////////////////////////////

// N level deep on score value without random simulation.

////////////////////////////////////////////////////////////////

/**

 * Defines coefficient for linear resulted weight function.

 * @name ai.StaticDeepMerges.cfg

 * @namespace

 * @property {number} scoreCoef    multiplicator for score

 * @property {number} maxValCoef   multiplicator for max value

 * @property {number} cornerBonus  bonus for max value at board corner

 * @property {number} edgeBonus    bonus for max value at board edge

 * @property {number} freeBonus    bonus for each free cell

 */

/** Deep merges AI without random simulation.

 * @param {Board} brdEngine  board engine from board.js

 * @param {Object} cfg  configuration settings

 * @constructor */

ai.StaticDeepMerges = function(brdEngine, cfg) {

    this.brdEngine = brdEngine;

    this.cfg = ai.copyObj(ai.OneStepAhead.bestCfg);

    ai.copyObj(cfg, this.cfg);

}

ai.StaticDeepMerges.bestCfg = {scoreCoef: 1, maxValCoef: 0, cornerBonus: 0, edgeBonus: 0, freeBonus: 0, weightThreshold: 10};

ai.StaticDeepMerges.prototype.weight = function(brd) {

    var weight = 0;

    if (this.cfg.scoreCoef > 0)

        weight += this.cfg.scoreCoef * brd.score();

    var max = brd.max();

    if (this.cfg.maxValCoef > 0)

        weight += this.cfg.maxValCoef * max;

    if (this.cfg.cornerBonus > 0 && brd.atCorner(max))

        weight += this.cfg.cornerBonus;

    if (this.cfg.edgeBonus > 0 && brd.atEdge(max))

        weight += this.cfg.edgeBonus;

    if (this.cfg.freeBonus > 0)

        weight += this.cfg.freeBonus * brd.free();

    return weight;

}

ai.StaticDeepMerges.prototype.analyse = function(brd) {

    var origBrd = new this.brdEngine(brd);

    var nextBrd = new this.brdEngine();

    var prevScore = -1, nextScore = -1;

    var maxWeight = -1;

    var bestDir;

    for (var i = 0; i < ai.dirs.length; i++) {

        var dir = ai.dirs[i];

        if (origBrd[dir](nextBrd)) {

            var weight = this.evalFn(nextBrd);

            var ok = (weight - maxWeight) > this.cfg.weightThreshold;

            if ( ! ok && maxWeight <= weight) {

                nextScore = this.weight(nextBrd);

                ok = prevScore < nextScore;

            }

            if (ok) {

                prevScore = nextScore;

                maxWeight = weight;

                bestDir = dir;

            }

        }

    }

    return bestDir;

}

ai.StaticDeepMerges.prototype.evalFn = function(brd) {

    var currScore = brd.score();

    var maxWeight = currScore;

    var nextBrd = new this.brdEngine();

    for (var i = 0; i < ai.dirs.length; i++) {

        if (brd[ai.dirs[i]](nextBrd)) {

            var score = nextBrd.score();

            if (score > currScore)

                maxWeight = Math.max(maxWeight, this.evalFn(nextBrd));

        }

    }

    return maxWeight;

}

/* Mark that next board will be unrelated to previous, so any stored precompution can be cleared. */

ai.StaticDeepMerges.prototype.cleanup = function() { }

////////////////////////////////////////////////////////////////

// N level deep with random simulation.

////////////////////////////////////////////////////////////////

/**

 * Defines coefficient for linear resulted weight function.

 * @name ai.expectimax.cfg

 * @namespace

 * @property {number} scoreCoef    multiplicator for score

 * @property {number} maxValCoef   multiplicator for max value

 * @property {number} cornerBonus  bonus for max value at board corner

 * @property {number} edgeBonus    bonus for max value at board edge

 * @property {number} freeBonus    bonus for each free cell

 */

/** N level deep with random simulation.

 * @param {Board} brdEngine  board engine from board.js

 * @param {ai.expectimax.cfg} cfg  configuration settings

 * @constructor */

ai.expectimax = function(brdEngine, cfg) {

    this.brdEngine = brdEngine;

    this.cfg = ai.copyObj(ai.expectimax.bestCfg);

    ai.copyObj(cfg, this.cfg);

    if (this.cfg.balance <= 0)

        this.cfg.balance = ai.expectimax.bestCfg.balance;

    if ( this.cfg.balance > 1)

        this.cfg.balance = 1;

    if (!this.cfg.depth || this.cfg.depth < 0 || 9 <= this.cfg.depth)

        this.cfg.depth = ai.expectimax.bestCfg.depth;

}

ai.expectimax.bestCfg = {balance: .9, depth: 5, scoreCoef: 1, maxValCoef: 0, cornerBonus: 0, edgeBonus: 0, freeBonus: 0};

ai.expectimax.prototype.weight = function(brd) {

    var score = 0;

    var cfg = this.cfg;

    if (cfg.scoreCoef > 0)

        score += cfg.scoreCoef * brd.score();

    if (cfg.maxValCoef > 0 || cfg.cornerBonus > 0 || cfg.edgeBonus > 0) {

        var max = brd.max();

        if (cfg.maxValCoef > 0)

            score += cfg.maxValCoef * max;

        if (cfg.cornerBonus > 0)

            if (brd.atCorner(max))

                score += cfg.cornerBonus;

        if (cfg.edgeBonus > 0)

            if (brd.atEdge(max))

                score += cfg.edgeBonus;

        if (cfg.freeBonus > 0)

            score += cfg.freeBonus * brd.free();

    }

    return score;

}

ai.expectimax.prototype.analyse = function(brd) {

    var origBrd = new this.brdEngine(brd);

    var nextBrd = new this.brdEngine();

    var maxW = -1;

    var bestDir;

    this.cleanup();

    this.depthLimit = this.cfg.depth;

    var free = origBrd.free();

    if (free >= 6)

        this.depthLimit = Math.min(this.depthLimit, 6 - free/3);

    for (var i = 0; i < ai.dirs.length; i++) {

        var dir = ai.dirs[i];

        if (origBrd[dir](nextBrd)) {

            var w = this.evalFn(nextBrd, 1);

            if (w > maxW) {

                maxW = w;

                bestDir = dir;

            }

        }

    }

    this.cleanup();

    return bestDir;

}

ai.expectimax.prototype.evalFn = function(brd, depth) {

    if (depth >= this.depthLimit)

        return this.weight(brd);

    if (this.cache[depth]) {

        var cache = this.cache[depth];

        for (var i = cache.length-1; i >= 0; i--) {

            if (brd.equals(cache[i].brd))

                return cache[i].weight;

        }

    } else {

        this.cache[depth] = [];

    }

    var wMin = +Infinity;

    for (var i = 0; i < 3; i++) {

        for (var j = 0; j < 3; j++) {

            if (brd.get(i, j) === 0) {

                var randBoard = brd.copy();

                randBoard.set(i, j, 1);

                var nextBrd = new this.brdEngine();

                var wMax2 = 0;

                for (var diri = 0; diri < ai.dirs.length; diri++) {

                    if (randBoard[ai.dirs[diri]](nextBrd))

                        wMax2 = Math.max(wMax2, this.evalFn(nextBrd, depth+1));

                }

                var wMax4 = 0;

                if (this.cfg.balance < 1) {

                    randBoard.set(i, j, 2);

                    for (var diri = 0; diri < ai.dirs.length; diri++) {

                        if (randBoard[ai.dirs[diri]](nextBrd))

                            wMax4 = Math.max(wMax4, this.evalFn(nextBrd, depth+1));

                    }

                    var balance = this.cfg.balance;

                }

                wMin = Math.min(wMin, balance * wMax2 + (1 - balance) * wMax4);

            }

        }

    }

    this.cache[depth].push({brd: brd, weight: wMin});

    return wMin;

}

/* Mark that next board will be unrelated to previous, so any stored precompution can be cleared. */

ai.expectimax.prototype.cleanup = function() {

    this.cache = [];

}