'use strict';

var ArgumentsError = require('../../error/ArgumentsError');
var isCollection = require('../../utils/collection/isCollection');
var isNumber = require('../../utils/number').isNumber;

// TODO: rethink math.distribution
// TODO: rework to a typed function
function factory (type, config, load, typed, math) {
  var matrix = load(require('../../type/matrix/function/matrix'));
  var array = require('../../utils/array');

  // seeded pseudo random number generator
  var rng = load(require('./seededRNG'));

  /**
   * Create a distribution object with a set of random functions for given
   * random distribution.
   *
   * Syntax:
   *
   *     math.distribution(name)
   *
   * Examples:
   *
   *     var normalDist = math.distribution('normal'); // create a normal distribution
   *     normalDist.random(0, 10);                     // get a random value between 0 and 10
   *
   * See also:
   *
   *     random, randomInt, pickRandom
   *
   * @param {string} name   Name of a distribution. Choose from 'uniform', 'normal'.
   * @return {Object}       Returns a distribution object containing functions:
   *                        `random([size] [, min] [, max])`,
   *                        `randomInt([min] [, max])`,
   *                        `pickRandom(array)`
   */
  function distribution(name) {
    if (!distributions.hasOwnProperty(name))
      throw new Error('Unknown distribution ' + name);

    var args = Array.prototype.slice.call(arguments, 1),
        distribution = distributions[name].apply(this, args);

    return (function(distribution) {

      // This is the public API for all distributions
      var randFunctions = {

        random: function(arg1, arg2, arg3) {
          var size, min, max;

          if (arguments.length > 3) {
            throw new ArgumentsError('random', arguments.length, 0, 3);
          } else if (arguments.length === 1) {
            // `random(max)` or `random(size)`
            if (isCollection(arg1)) {
              size = arg1;
            } else {
              max = arg1;
            }
          } else if (arguments.length === 2) {
            // `random(min, max)` or `random(size, max)`
            if (isCollection(arg1)) {
              size = arg1;
              max = arg2;
            } else {
              min = arg1;
              max = arg2;
            }
          } else {
            // `random(size, min, max)`
            size = arg1;
            min = arg2;
            max = arg3;
          }

          // TODO: validate type of size
          if ((min !== undefined && !isNumber(min)) || (max !== undefined && !isNumber(max))) {
            throw new TypeError('Invalid argument in function random');
          }

          if (max === undefined) max = 1;
          if (min === undefined) min = 0;
          if (size !== undefined) {
            var res = _randomDataForMatrix(size.valueOf(), min, max, _random);
            return type.isMatrix(size) ? matrix(res) : res;
          }
          return _random(min, max);
        },

        randomInt: typed({
          'number | Array': function(arg) {
            var min = 0;

            if (isCollection(arg)) {
              var size = arg;
              var max = 1;
              var res = _randomDataForMatrix(size.valueOf(), min, max, _randomInt);
              return type.isMatrix(size) ? matrix(res) : res;
            } else {
              var max = arg;
              return _randomInt(min, max);
            }
          },
          'number | Array, number': function(arg1, arg2) {
            if (isCollection(arg1)) {
              var size = arg1;
              var max = arg2;
              var min = 0;
              var res = _randomDataForMatrix(size.valueOf(), min, max, _randomInt);
              return type.isMatrix(size) ? matrix(res) : res;
            }
            else {
              var min = arg1;
              var max = arg2;
              return _randomInt(min, max);
            }
          },
          'Array, number, number': function(size, min, max) {
            var res = _randomDataForMatrix(size.valueOf(), min, max, _randomInt);
            return (size && size.isMatrix === true) ? matrix(res) : res;
          }
        }),

        pickRandom: typed({
          'Array': function(possibles) {
            return _pickRandom(possibles);
          },
          'Array, number | Array': function(possibles, arg2) {
            var number, weights;

            if (Array.isArray(arg2)) {
              weights = arg2;
            } else if (isNumber(arg2)) {
              number = arg2;
            } else {
              throw new TypeError('Invalid argument in function pickRandom')
            }

            return _pickRandom(possibles, number, weights);
          },
          'Array, number | Array, Array | number': function(possibles, arg2, arg3) {
            var number, weights;

            if (Array.isArray(arg2)) {
              weights = arg2;
              number = arg3;
            } else {
              weights = arg3;
              number = arg2;
            }

            if (!Array.isArray(weights) || !isNumber(number)) {
              throw new TypeError('Invalid argument in function pickRandom');
            }

            return _pickRandom(possibles, number, weights);
          }
        })
      }

      var _pickRandom = function(possibles, number, weights) {
        var single = (typeof number === 'undefined');

        if (single) {
          number = 1;
        }

        if (type.isMatrix(possibles)) {
          possibles = possibles.valueOf(); // get Array
        } else if (!Array.isArray(possibles)) {
          throw new TypeError('Unsupported type of value in function pickRandom');
        }

        if (array.size(possibles).length > 1) {
          throw new Error('Only one dimensional vectors supported');
        }

        if (typeof weights !== 'undefined') {
          if (weights.length != possibles.length) {
            throw new Error('Weights must have the same length as possibles');
          }

          var totalWeights = 0;

          for (var i = 0, len = weights.length; i < len; i++) {
            if (!isNumber(weights[i]) || weights[i] < 0) {
              throw new Error('Weights must be an array of positive numbers');
            }

            totalWeights += weights[i];
          }
        }

        var length = possibles.length;

        if (length == 0) {
          return [];
        } else if (number >= length) {
          return possibles;
        }

        var result = [];
        var pick;

        while (result.length < number) {
          if (typeof weights === 'undefined') {
            pick = possibles[Math.floor(rng() * length)];
          } else {
            var randKey = rng() * totalWeights;

            for (var i = 0, len = possibles.length; i < len; i++) {
              randKey -= weights[i];

              if (randKey < 0) {
                pick = possibles[i];
                break;
              }
            }
          }

          if (result.indexOf(pick) == -1) {
            result.push(pick);
          }
        }

        return single ? result[0] : result;

        // TODO: add support for multi dimensional matrices
      }

      var _random = function(min, max) {
        return min + distribution() * (max - min);
      };

      var _randomInt = function(min, max) {
        return Math.floor(min + distribution() * (max - min));
      };

      // This is a function for generating a random matrix recursively.
      var _randomDataForMatrix = function(size, min, max, randFunc) {
        var data = [], length, i;
        size = size.slice(0);

        if (size.length > 1) {
          for (var i = 0, length = size.shift(); i < length; i++) {
            data.push(_randomDataForMatrix(size, min, max, randFunc));
          }
        } else {
          for (var i = 0, length = size.shift(); i < length; i++) {
            data.push(randFunc(min, max));
          }
        }

        return data;
      };

      return randFunctions;

    })(distribution);
  }

  // Each distribution is a function that takes no argument and when called returns
  // a number between 0 and 1.
  var distributions = {

    uniform: function() {
      return rng;
    },

    // Implementation of normal distribution using Box-Muller transform
    // ref : http://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform
    // We take : mean = 0.5, standard deviation = 1/6
    // so that 99.7% values are in [0, 1].
    normal: function() {
      return function() {
        var u1, u2,
            picked = -1;
        // We reject values outside of the interval [0, 1]
        // TODO: check if it is ok to do that?
        while (picked < 0 || picked > 1) {
          u1 = rng();
          u2 = rng();
          picked = 1/6 * Math.pow(-2 * Math.log(u1), 0.5) * Math.cos(2 * Math.PI * u2) + 0.5;
        }
        return picked;
      }
    }
  };

  distribution.toTex = undefined; // use default template

  return distribution;
}

exports.name = 'distribution';
exports.factory = factory;