Refactor random & gaussian API

index.js

@@ -2,19 +2,14 @@
 var gsl = require('./build/default/gsl');
 
 module.exports = {
+	Random: gsl.Random,
 	random: {
 		get: gsl.rngGet,
-		Get: gsl.RngGet,
 		min: gsl.rngMin,
 		max: gsl.rngMax,
 		uniform: gsl.rngUniform,
-		Uniform: gsl.RngUniform,
 		gaussian: gsl.ranGaussian,
-		Gaussian: gsl.RanGaussian,
 		gaussianZiggurat: gsl.ranGaussianZiggurat,
-		GaussianZiggurat: gsl.RanGaussianZiggurat,
-		gaussianRatioMethod: gsl.ranGaussianRatioMethod,
-		GaussianRatioMethod: gsl.RanGaussianRatioMethod
+		gaussianRatioMethod: gsl.ranGaussianRatioMethod
 	}
 };
-

readme.md

@@ -29,82 +29,70 @@ Via git (or downloaded tarball):
 Random API
 ----------
 
-Seed arguments are always optional and must be provided as unsigned integers. 
+The library takes two forms, functions and iterator objects. Both respect the same names with different conventions. For exemple, obtaining an uniform random name can be done as `random.get()` as well as `(new Random).get()`. If you wished to provide a seed, then you'll respectivelly call `random.get(seed)` and `(new Random(seed)).get()`
+
+Using an iterator objects make sense when performance is in consideration, specially when using seeds.
+
+Seeds are always optional and must be provided as unsigned integers. Deviations, used by gaussian functions, are float and default to 3.
+
+-	*Random([seed])*
+	Construct a new iterator, seel below for available random methods.
 
 ### [Sampling](http://www.gnu.org/software/gsl/manual/html_node/Sampling-from-a-random-number-generator.html)
 
 -	*random.get([seed])*
+	*Random.get()*
 	Returns a random integer. The minimum and maximum values depend on the algorithm used, but all integers in the range [min,max] are equally likely. The values of min and max can determined using the auxiliary functions `random.min()` and `random.max()`.
-		
--	*random.Get([seed])*
-	Creates an iterator. Take an optional seed argument as an unsigned int. Random number are obtained by calling `next`.
-	*random.Get.next()*
-	Same as `random.get` but called from a `random.Get` instance.  
 
 -	*random.min()*
+	*Random.min()*
 	Returns the smallest value that `random.get()` can return
 
 -	*random.max()*
+	*Random.max()*
 	Returns the largest value that `random.get()` can return
-
+	
 -	*random.uniform([seed])*
+	*Random.uniform([])*
 	Returns a double precision floating point number uniformly distributed in the range [0,1). The range includes 0.0 but excludes 1.0.
-		
--	*random.Uniform([seed])*
-	Creates an iterator. Take an optional seed argument as an unsigned int. Random number are obtained by calling `next`.
-	*random.Uniform.next()*
-	Same as `random.uniform` but called from a `random.Uniform` instance.  
 
 ### [Gaussian](http://www.gnu.org/software/gsl/manual/html_node/The-Gaussian-Distribution.html)
 
 -	*random.gaussian([seed], [deviation])*
+	*Random.gaussian([deviation])*
 	Returns a Gaussian random float with mean zero. Standart deviation is a float and default to 3.
-		
--	*random.Gaussian([seed])*
-	Creates an iterator. Take an optional seed argument as an unsigned int. Random number are obtained by calling `next` with an optional deviation.
-	Also, the `seed` property is accessible.
-	*random.Gaussian.next([deviation])*
-	Same as `random.gaussian` but called from a `random.Gaussian` instance. 
-	*random.Gaussian.seed*
-	Returns the seed used by the iterator.
 
 -	*random.gaussianZiggurat([seed], [deviation])*
+	*Random.gaussianZiggurat([deviation])*
 	Same as `random.gaussian` but using the alternative Marsaglia-Tsang ziggurat method.
-		
--	*random.GaussianZiggurat([seed])*
-	Same as `random.Gaussian` but using the alternative Marsaglia-Tsang ziggurat method.
 
 -	*random.gaussianRatioMethod([seed], [deviation])*
+	*Random.gaussianRatioMethod([deviation])*
 	Same as `random.gaussian` but using the alternative Kinderman-Monahan-Leva ratio method.
-		
--	*random.GaussianRatioMethod([seed])*
-	Same as `random.Gaussian` but using the alternative Kinderman-Monahan-Leva ratio method.
 
 Exemple
 
-	var random = require('gsl').random,
+	var gsl = require('gsl'),
 		seed = 50,
 		deviation = 0.5;
 
-	console.log( random.gaussian() );
-	console.log( random.gaussian(seed) );
-	console.log( random.gaussian(seed,deviation) );
+	console.log( gsl.random.gaussian() );
+	console.log( gsl.random.gaussian(seed) );
+	console.log( gsl.random.gaussian(seed, deviation) );
 
-	var it1 = new random.Gaussian();
-	console.log( it1.next() );
-	it1.seed;
+	var it1 = new gsl.Random();
+	console.log( it1.gaussian() );
 
-	var it1 = new random.Gaussian(seed);
-	console.log( it1.next(deviation) );
-	console.log( it1.seed );
+	var it1 = new gsl.Random(seed);
+	console.log( it1.gaussian(deviation) );
 
 Running the tests
 -----------------
 
-Tests are executed with expresso. To install it, simple use `npm install expresso`.
+Tests are executed with expresso. To install it, simply issue `npm install expresso`.
 
 To run the tests
-	expresso test
+	expresso
 
 Contributors
 ------------

samples/gaussian.js

@@ -1,16 +1,14 @@
 
-	var random = require('gsl').random,
+	var gsl = require('gsl'),
 		seed = 50,
 		deviation = 0.5;
+
+	console.log( gsl.random.gaussian() );
+	console.log( gsl.random.gaussian(seed) );
+	console.log( gsl.random.gaussian(seed, deviation) );
 
-	console.log( random.gaussian() );
-	console.log( random.gaussian(seed) );
-	console.log( random.gaussian(seed,deviation) );
-
-	var it1 = new random.Gaussian();
-	console.log( it1.next() );
-	it1.seed;
+	var it1 = new gsl.Random();
+	console.log( it1.gaussian() );
 
-	var it1 = new random.Gaussian(seed);
-	console.log( it1.next(deviation) );
-	console.log( it1.seed );
+	var it1 = new gsl.Random(seed);
+	console.log( it1.gaussian(deviation) );

samples/gaussian_speed.js

@@ -54,10 +54,10 @@ var assert = require('assert'),
 	console.log('C++ fn',(new Date).getTime() - time);
 
 	var time = (new Date).getTime();
-	var random = new gsl.random.Gaussian(1978);
+	var random = new gsl.Random(1978);
 	for(var i=0;i<1000000;i++){
 		for(var j=0;j<2;j++){
-			random.next();
+			random.gaussian();
 		}
 	}
 	console.log('C++ it',(new Date).getTime() - time);
@@ -71,10 +71,10 @@ var assert = require('assert'),
 	console.log('C++ fn Ziggurat',(new Date).getTime() - time);
 
 	var time = (new Date).getTime();
-	var random = new gsl.random.GaussianZiggurat(1978);
+	var random = new gsl.Random(1978);
 	for(var i=0;i<1000000;i++){
 		for(var j=0;j<2;j++){
-			random.next();
+			random.gaussianZiggurat();
 		}
 	}
 	console.log('C++ it Ziggurat',(new Date).getTime() - time);
@@ -88,10 +88,10 @@ var assert = require('assert'),
 	console.log('C++ fn RatioMethod',(new Date).getTime() - time);
 
 	var time = (new Date).getTime();
-	var random = new gsl.random.GaussianRatioMethod(1978);
+	var random = new gsl.Random(1978);
 	for(var i=0;i<1000000;i++){
 		for(var j=0;j<2;j++){
-			random.next();
+			random.gaussianRatioMethod();
 		}
 	}
 	console.log('C++ it RatioMethod',(new Date).getTime() - time);

samples/random_speed.js

@@ -39,10 +39,10 @@ var assert = require('assert'),
 	console.log('C++ get fn',(new Date).getTime() - time);
 
 	var time = (new Date).getTime();
-	var random = new gsl.random.Get(1978);
+	var random = new gsl.Random(1978);
 	for(var i=0;i<1000000;i++){
 		for(var j=0;j<2;j++){
-			random.next();
+			random.get();
 		}
 	}
 	console.log('C++ get it',(new Date).getTime() - time);
@@ -56,10 +56,10 @@ var assert = require('assert'),
 	console.log('C++ uniform fn',(new Date).getTime() - time);
 
 	var time = (new Date).getTime();
-	var random = new gsl.random.Uniform(1978);
+	var random = new gsl.Random(1978);
 	for(var i=0;i<1000000;i++){
 		for(var j=0;j<2;j++){
-			random.next();
+			random.uniform();
 		}
 	}
 	console.log('C++ uniform it',(new Date).getTime() - time);