25.js

(function(jStat, Math) {

var isFunction = jStat.utils.isFunction;

// Ascending functions for sort
function ascNum(a, b) { return a - b; }

function clip(arg, min, max) {
  return Math.max(min, Math.min(arg, max));
}


// sum of an array
jStat.sum = function sum(arr) {
  var sum = 0;
  var i = arr.length;
  while (--i >= 0)
    sum += arr[i];
  return sum;
};


// sum squared
jStat.sumsqrd = function sumsqrd(arr) {
  var sum = 0;
  var i = arr.length;
  while (--i >= 0)
    sum += arr[i] * arr[i];
  return sum;
};


// sum of squared errors of prediction (SSE)
jStat.sumsqerr = function sumsqerr(arr) {
  var mean = jStat.mean(arr);
  var sum = 0;
  var i = arr.length;
  var tmp;
  while (--i >= 0) {
    tmp = arr[i] - mean;
    sum += tmp * tmp;
  }
  return sum;
};

// sum of an array in each row
jStat.sumrow = function sumrow(arr) {
  var sum = 0;
  var i = arr.length;
  while (--i >= 0)
    sum += arr[i];
  return sum;
};

// product of an array
jStat.product = function product(arr) {
  var prod = 1;
  var i = arr.length;
  while (--i >= 0)
    prod *= arr[i];
  return prod;
};


// minimum value of an array
jStat.min = function min(arr) {
  var low = arr[0];
  var i = 0;
  while (++i < arr.length)
    if (arr[i] < low)
      low = arr[i];
  return low;
};


// maximum value of an array
jStat.max = function max(arr) {
  var high = arr[0];
  var i = 0;
  while (++i < arr.length)
    if (arr[i] > high)
      high = arr[i];
  return high;
};


// unique values of an array
jStat.unique = function unique(arr) {
  var hash = {}, _arr = [];
  for(var i = 0; i < arr.length; i++) {
    if (!hash[arr[i]]) {
      hash[arr[i]] = true;
      _arr.push(arr[i]);
    }
  }
  return _arr;
};


// mean value of an array
jStat.mean = function mean(arr) {
  return jStat.sum(arr) / arr.length;
};


// mean squared error (MSE)
jStat.meansqerr = function meansqerr(arr) {
  return jStat.sumsqerr(arr) / arr.length;
};


// geometric mean of an array
jStat.geomean = function geomean(arr) {
  return Math.pow(jStat.product(arr), 1 / arr.length);
};


// median of an array
jStat.median = function median(arr) {
  var arrlen = arr.length;
  var _arr = arr.slice().sort(ascNum);
  // check if array is even or odd, then return the appropriate
  return !(arrlen & 1)
    ? (_arr[(arrlen / 2) - 1 ] + _arr[(arrlen / 2)]) / 2
    : _arr[(arrlen / 2) | 0 ];
};


// cumulative sum of an array
jStat.cumsum = function cumsum(arr) {
  return jStat.cumreduce(arr, function (a, b) { return a + b; });
};


// cumulative product of an array
jStat.cumprod = function cumprod(arr) {
  return jStat.cumreduce(arr, function (a, b) { return a * b; });
};


// successive differences of a sequence
jStat.diff = function diff(arr) {
  var diffs = [];
  var arrLen = arr.length;
  var i;
  for (var i = 1; i < arrLen; i++)
    diffs.push(arr[i] - arr[i - 1]);
  return diffs;
};


// ranks of an array
jStat.rank = function (arr) {
  var arrlen = arr.length;
  var sorted = arr.slice().sort(ascNum);
  var ranks = new Array(arrlen);
  for (var i = 0; i < arrlen; i++) {
    var first = sorted.indexOf(arr[i]);
    var last = sorted.lastIndexOf(arr[i]);
    if (first === last) {
      var val = first;
    } else {
      var val = (first + last) / 2;
    }
    ranks[i] = val + 1;
  }
  return ranks;
};


// mode of an array
// if there are multiple modes of an array, return all of them
// is this the appropriate way of handling it?
jStat.mode = function mode(arr) {
  var arrLen = arr.length;
  var _arr = arr.slice().sort(ascNum);
  var count = 1;
  var maxCount = 0;
  var numMaxCount = 0;
  var mode_arr = [];
  var i;

  for (var i = 0; i < arrLen; i++) {
    if (_arr[i] === _arr[i + 1]) {
      count++;
    } else {
      if (count > maxCount) {
        mode_arr = [_arr[i]];
        maxCount = count;
        numMaxCount = 0;
      }
      // are there multiple max counts
      else if (count === maxCount) {
        mode_arr.push(_arr[i]);
        numMaxCount++;
      }
      // resetting count for new value in array
      count = 1;
    }
  }

  return numMaxCount === 0 ? mode_arr[0] : mode_arr;
};


// range of an array
jStat.range = function range(arr) {
  return jStat.max(arr) - jStat.min(arr);
};

// variance of an array
// flag = true indicates sample instead of population
jStat.variance = function variance(arr, flag) {
  return jStat.sumsqerr(arr) / (arr.length - (flag ? 1 : 0));
};

// pooled variance of an array of arrays
jStat.pooledvariance = function pooledvariance(arr) {
  var sumsqerr = arr.reduce(function (a, samples) {return a + jStat.sumsqerr(samples);}, 0);
  var count = arr.reduce(function (a, samples) {return a + samples.length;}, 0);
  return sumsqerr / (count - arr.length);
};

// deviation of an array
jStat.deviation = function (arr) {
  var mean = jStat.mean(arr);
  var arrlen = arr.length;
  var dev = new Array(arrlen);
  for (var i = 0; i < arrlen; i++) {
    dev[i] = arr[i] - mean;
  }
  return dev;
};

// standard deviation of an array
// flag = true indicates sample instead of population
jStat.stdev = function stdev(arr, flag) {
  return Math.sqrt(jStat.variance(arr, flag));
};

// pooled standard deviation of an array of arrays
jStat.pooledstdev = function pooledstdev(arr) {
  return Math.sqrt(jStat.pooledvariance(arr));
};

// mean deviation (mean absolute deviation) of an array
jStat.meandev = function meandev(arr) {
  var mean = jStat.mean(arr);
  var a = [];
  for (var i = arr.length - 1; i >= 0; i--) {
    a.push(Math.abs(arr[i] - mean));
  }
  return jStat.mean(a);
};


// median deviation (median absolute deviation) of an array
jStat.meddev = function meddev(arr) {
  var median = jStat.median(arr);
  var a = [];
  for (var i = arr.length - 1; i >= 0; i--) {
    a.push(Math.abs(arr[i] - median));
  }
  return jStat.median(a);
};


// coefficient of variation
jStat.coeffvar = function coeffvar(arr) {
  return jStat.stdev(arr) / jStat.mean(arr);
};


// quartiles of an array
jStat.quartiles = function quartiles(arr) {
  var arrlen = arr.length;
  var _arr = arr.slice().sort(ascNum);
  return [
    _arr[ Math.round((arrlen) / 4) - 1 ],
    _arr[ Math.round((arrlen) / 2) - 1 ],
    _arr[ Math.round((arrlen) * 3 / 4) - 1 ]
  ];
};


// Arbitary quantiles of an array. Direct port of the scipy.stats
// implementation by Pierre GF Gerard-Marchant.
jStat.quantiles = function quantiles(arr, quantilesArray, alphap, betap) {
  var sortedArray = arr.slice().sort(ascNum);
  var quantileVals = [quantilesArray.length];
  var n = arr.length;
  var i, p, m, aleph, k, gamma;

  if (typeof alphap === 'undefined')
    alphap = 3 / 8;
  if (typeof betap === 'undefined')
    betap = 3 / 8;

  for (var i = 0; i < quantilesArray.length; i++) {
    p = quantilesArray[i];
    m = alphap + p * (1 - alphap - betap);
    aleph = n * p + m;
    k = Math.floor(clip(aleph, 1, n - 1));
    gamma = clip(aleph - k, 0, 1);
    quantileVals[i] = (1 - gamma) * sortedArray[k - 1] + gamma * sortedArray[k];
  }

  return quantileVals;
};

// Returns the k-th percentile of values in a range, where k is in the
// range 0..1, exclusive.
jStat.percentile = function percentile(arr, k) {
  var _arr = arr.slice().sort(ascNum);
  var realIndex = k * (_arr.length - 1);
  var index = parseInt(realIndex);
  var frac = realIndex - index;

  if (index + 1 < _arr.length) {
    return _arr[index] * (1 - frac) + _arr[index + 1] * frac;
  } else {
    return _arr[index];
  }
}


// The percentile rank of score in a given array. Returns the percentage
// of all values in the input array that are less than (kind='strict') or
// less or equal than (kind='weak') score. Default is weak.
jStat.percentileOfScore = function percentileOfScore(arr, score, kind) {
  var counter = 0;
  var len = arr.length;
  var strict = false;
  var value, i;

  if (kind === 'strict')
    strict = true;

  for (var i = 0; i < len; i++) {
    value = arr[i];
    if ((strict && value < score) ||
        (!strict && value <= score)) {
      counter++;
    }
  }

  return counter / len;
};


// Histogram (bin count) data
jStat.histogram = function histogram(arr, bins) {
  var first = jStat.min(arr);
  var binCnt = bins || 4;
  var binWidth = (jStat.max(arr) - first) / binCnt;
  var len = arr.length;
  var bins = [];
  var i;

  for (var i = 0; i < binCnt; i++)
    bins[i] = 0;
  for (var i = 0; i < len; i++)
    bins[Math.min(Math.floor(((arr[i] - first) / binWidth)), binCnt - 1)] += 1;

  return bins;
};


// covariance of two arrays
jStat.covariance = function covariance(arr1, arr2) {
  var u = jStat.mean(arr1);
  var v = jStat.mean(arr2);
  var arr1Len = arr1.length;
  var sq_dev = new Array(arr1Len);
  var i;

  for (var i = 0; i < arr1Len; i++)
    sq_dev[i] = (arr1[i] - u) * (arr2[i] - v);

  return jStat.sum(sq_dev) / (arr1Len - 1);
};


// (pearson's) population correlation coefficient, rho
jStat.corrcoeff = function corrcoeff(arr1, arr2) {
  return jStat.covariance(arr1, arr2) /
      jStat.stdev(arr1, 1) /
      jStat.stdev(arr2, 1);
};

  // (spearman's) rank correlation coefficient, sp
jStat.spearmancoeff =  function (arr1, arr2) {
  arr1 = jStat.rank(arr1);
  arr2 = jStat.rank(arr2);
  //return pearson's correlation of the ranks:
  return jStat.corrcoeff(arr1, arr2);
}


// statistical standardized moments (general form of skew/kurt)
jStat.stanMoment = function stanMoment(arr, n) {
  var mu = jStat.mean(arr);
  var sigma = jStat.stdev(arr);
  var len = arr.length;
  var skewSum = 0;

  for (var i = 0; i < len; i++)
    skewSum += Math.pow((arr[i] - mu) / sigma, n);

  return skewSum / arr.length;
};

// (pearson's) moment coefficient of skewness
jStat.skewness = function skewness(arr) {
  return jStat.stanMoment(arr, 3);
};

// (pearson's) (excess) kurtosis
jStat.kurtosis = function kurtosis(arr) {
  return jStat.stanMoment(arr, 4) - 3;
};


var jProto = jStat.prototype;


// Extend jProto with method for calculating cumulative sums and products.
// This differs from the similar extension below as cumsum and cumprod should
// not be run again in the case fullbool === true.
// If a matrix is passed, automatically assume operation should be done on the
// columns.
(function(funcs) {
  for (var i = 0; i < funcs.length; i++) (function(passfunc) {
    // If a matrix is passed, automatically assume operation should be done on
    // the columns.
    jProto[passfunc] = function(fullbool, func) {
      var arr = [];
      var i = 0;
      var tmpthis = this;
      // Assignment reassignation depending on how parameters were passed in.
      if (isFunction(fullbool)) {
        func = fullbool;
        fullbool = false;
      }
      // Check if a callback was passed with the function.
      if (func) {
        setTimeout(function() {
          func.call(tmpthis, jProto[passfunc].call(tmpthis, fullbool));
        });
        return this;
      }
      // Check if matrix and run calculations.
      if (this.length > 1) {
        tmpthis = fullbool === true ? this : this.transpose();
        for (; i < tmpthis.length; i++)
          arr[i] = jStat[passfunc](tmpthis[i]);
        return arr;
      }
      // Pass fullbool if only vector, not a matrix. for variance and stdev.
      return jStat[passfunc](this[0], fullbool);
    };
  })(funcs[i]);
})(('cumsum cumprod').split(' '));


// Extend jProto with methods which don't require arguments and work on columns.
(function(funcs) {
  for (var i = 0; i < funcs.length; i++) (function(passfunc) {
    // If a matrix is passed, automatically assume operation should be done on
    // the columns.
    jProto[passfunc] = function(fullbool, func) {
      var arr = [];
      var i = 0;
      var tmpthis = this;
      // Assignment reassignation depending on how parameters were passed in.
      if (isFunction(fullbool)) {
        func = fullbool;
        fullbool = false;
      }
      // Check if a callback was passed with the function.
      if (func) {
        setTimeout(function() {
          func.call(tmpthis, jProto[passfunc].call(tmpthis, fullbool));
        });
        return this;
      }
      // Check if matrix and run calculations.
      if (this.length > 1) {
        if (passfunc !== 'sumrow')
          tmpthis = fullbool === true ? this : this.transpose();
        for (; i < tmpthis.length; i++)
          arr[i] = jStat[passfunc](tmpthis[i]);
        return fullbool === true
            ? jStat[passfunc](jStat.utils.toVector(arr))
            : arr;
      }
      // Pass fullbool if only vector, not a matrix. for variance and stdev.
      return jStat[passfunc](this[0], fullbool);
    };
  })(funcs[i]);
})(('sum sumsqrd sumsqerr sumrow product min max unique mean meansqerr ' +
    'geomean median diff rank mode range variance deviation stdev meandev ' +
    'meddev coeffvar quartiles histogram skewness kurtosis').split(' '));


// Extend jProto with functions that take arguments. Operations on matrices are
// done on columns.
(function(funcs) {
  for (var i = 0; i < funcs.length; i++) (function(passfunc) {
    jProto[passfunc] = function() {
      var arr = [];
      var i = 0;
      var tmpthis = this;
      var args = Array.prototype.slice.call(arguments);

      // If the last argument is a function, we assume it's a callback; we
      // strip the callback out and call the function again.
      if (isFunction(args[args.length - 1])) {
        var callbackFunction = args[args.length - 1];
        var argsToPass = args.slice(0, args.length - 1);

        setTimeout(function() {
          callbackFunction.call(tmpthis,
                                jProto[passfunc].apply(tmpthis, argsToPass));
        });
        return this;

      // Otherwise we curry the function args and call normally.
      } else {
        var callbackFunction = undefined;
        var curriedFunction = function curriedFunction(vector) {
          return jStat[passfunc].apply(tmpthis, [vector].concat(args));
        }
      }

      // If this is a matrix, run column-by-column.
      if (this.length > 1) {
        tmpthis = tmpthis.transpose();
        for (; i < tmpthis.length; i++)
          arr[i] = curriedFunction(tmpthis[i]);
        return arr;
      }

      // Otherwise run on the vector.
      return curriedFunction(this[0]);
    };
  })(funcs[i]);
})('quantiles percentileOfScore'.split(' '));

}(jStat, Math));