jx/ext/math.js

/**
	 return max ;
    N = lxi.length ;
    mean = jx.math.mean(lxi,lni) ;

    sqr = [] ;
    for(var i=0; i < lxi.length ;i++){
		console.log(lxi[i])
       sqr[i] = jx.math.pow((Number(lxi[i])-mean),2 ) ;
    }

    total = jx.math.sum(sqr);

    return jx.math.sqrt(total/(N-1)) ;
} ;

/**
* Computes the factorial of a given value
*/
jx.math.factorial = function(value){
    r =value;
    for(var i =value-1; i > 0; i--){
        r *= i ;
    }

    return r;
} ;

/**
* Computes the fibonacci value of a given number using the golden ratio
*/
jx.math.fibonacci = function(value){
    r = (jx.math.pow(jx.math.PHI,value)/jx.math.sqrt(5)) + 0.5 ;
    return jx.math.floor(r) ;
} ;

/**
* computes the absolute difference of values in a list of observations
*/
jx.math.diff = function(lxi){
    var r = [] ;
    var x,y;
    for(var i=0; i < lxi.length-1; i++){
        x = lxi[i] ;
        y = lxi[i+1] ;
        r.push(y-x)
    }
    return r ;
};

/**
 * This section implements a few handlers based on sets
 */
jx.math.sets = {} ;
/**
 * This function will perform a unique operation of values/objects
 * @param list	list/vector of values or objects
 * @param equals	operator to be used, only provide this for complex objects
 */
jx.math.sets.unique = jx.utils.unique ;
/**
 * This function will perform the union of 2 sets (objects, or values)
 * @param list1		list/vector of values or objects
 * @param list2		list/vector of values or objects
 * @param equals	operator to be used to evaluate equality (use this for complex objects)
 */
jx.math.sets.union = function(list1,list2,equals){
	runion = [] ;
	runion = list1.concat(list2) ;
	runion = jx.math.sets.unique(runion,equals)
	return runion;
}

/**
* This function will normalize values within a vector
* By definition normalization is (x - u) / sd (assuming population parameters are known)
*/
jx.math.normalize = function(lvalues){
		mean = jx.math.mean(lvalues) ;
		sd = jx.math.sd(lvalues) ;
		return jx.utils.patterns.visitor(lvalues,function(x){
			return ((x - mean) / sd)
		})
}

/**
 * This function will scale a feature vector over it's range
 */
jx.math.scale = function(lvalues,percent){
	max = jx.math.max(lvalues) ;
	min = jx.math.min(lvalues) ;
	return jx.utils.patterns.visitor(lvalues,function(x){
		var value =  (x - min ) / max  ;
		if(percent == true){
			return (100*value).toFixed(2)
		}else{
			return value ;
		}
	})
}
/**
* This is a lightweight map reduce infrastructure
*/
jx.mr = {} ;
/**
* This function will perform a map on a given id in rec, then will call emit with the
*/
jx.mr.map = null
/**
* @param keys
* @param values array of values that were mapped
*/
jx.mr.reduce = null;
jx.mr.mapreduce = function(data,fn_map,fn_reduce){
	if (fn_map == null){
		throw new "Map function is not defined"
	}
	map = {} ;
	emit = function(id,values){
		if(map[id] == null){
			map[id]  = []
		}
		map[id].push(values);
	}
	if(data.constructor != Array){
		for (id in data){
			//rec = data[id] ;
			rec = {}
			rec['__id'] = id;
			rec['data'] = data[id] ;
			fn_map(rec,emit)

		}
	}else{
		for (var i=0; i < data.length; i++){
			rec = data[i];
			fn_map(rec,emit);
			//if(i == 2)break;
		}
	}
	if(fn_reduce != null){
		keys = jx.utils.keys(map) ;
		m = {}
		for(var i=0; i < keys.length; i++){
			id = keys[i] ;
			values = map[id] ;
			value = fn_reduce(id,values) ;
			id = keys[i] ;
			m[id] = value;

		}
		map = m

	}
	return map ;
}