chore: clean-up new examples

Author: Planeshifter

lib/node_modules/@stdlib/stats/base/dists/kumaraswamy/README.md

@@ -102,52 +102,41 @@ var y = dist.logpdf( 0.8 );
 
 ## Examples
 
-<!-- TODO: better examples -->
-
 <!-- eslint no-undef: "error" -->
 
 ```javascript
-var objectKeys = require( '@stdlib/utils/keys' );
 var kumaraswamy = require( '@stdlib/stats/base/dists/kumaraswamy' );
 
-console.log( objectKeys( kumaraswamy ) );
-// Create a Kumaraswamy distribution object
+// Create a Kumaraswamy distribution object:
 var a = 2.0;
 var b = 5.0;
 var dist = new kumaraswamy.Kumaraswamy( a, b );
 
-// Calculate basic distribution properties
+// Calculate basic distribution properties:
 console.log( 'Mean: %d', dist.mean );
 console.log( 'Median: %d', dist.median );
 console.log( 'Mode: %d', dist.mode );
 console.log( 'Variance: %d', dist.variance );
 
-// Evaluate the probability density function (PDF)
+// Evaluate the probability density function (PDF):
 var x = 0.5;
 var y = dist.pdf( x );
 console.log( 'PDF at x = %d: %d', x, y );
 
-// Evaluate the cumulative distribution function (CDF)
+// Evaluate the cumulative distribution function (CDF):
 y = dist.cdf( x );
 console.log( 'CDF at x = %d: %d', x, y );
 
-// Evaluate the natural logarithm of PDF and CDF
+// Evaluate the natural logarithm of PDF and CDF:
 console.log( 'Log PDF at x = %d: %d', x, dist.logpdf( x ) );
 console.log( 'Log CDF at x = %d: %d', x, dist.logcdf( x ) );
 
-// Calculate the quantile for a given probability
+// Calculate the quantile for a given probability:
 var p = 0.75;
 x = dist.quantile( p );
 console.log( 'Quantile at p = %d: %d', p, x );
 
-// Generate random numbers
-var r = new Array( 5 );
-for ( var i = 0; i < 5; i++ ) {
-    r[ i ] = kumaraswamy.random( a, b );
-}
-console.log( 'Random samples: %s', r.join( ', ' ) );
-
-// Use standalone functions
+// Use standalone distribution functions:
 x = 0.3;
 y = kumaraswamy.pdf( x, a, b );
 console.log( 'Standalone PDF at x = %d: %d', x, y );
@@ -158,23 +147,24 @@ console.log( 'Standalone CDF at x = %d: %d', x, y );
 y = kumaraswamy.quantile( 0.9, a, b );
 console.log( 'Standalone Quantile at p = 0.9: %d', y );
 
-// Calculate additional distribution properties
+// Calculate additional distribution properties:
 console.log( 'Kurtosis: %d', kumaraswamy.kurtosis( a, b ) );
 console.log( 'Skewness: %d', kumaraswamy.skewness( a, b ) );
 console.log( 'Standard Deviation: %d', kumaraswamy.stdev( a, b ) );
 
-// Demonstrate the effect of different shape parameters
+// Demonstrate the effect of different shape parameters:
 console.log( '\nEffect of shape parameters:' );
 var shapes = [
-    [0.5, 0.5],
-    [5.0, 1.0],
-    [1.0, 5.0],
-    [2.0, 2.0],
-    [10.0, 10.0]
+    [ 0.5, 0.5 ],
+    [ 5.0, 1.0 ],
+    [ 1.0, 5.0 ],
+    [ 2.0, 2.0 ],
+    [ 10.0, 10.0 ]
 ];
-
-for ( var i = 0; i < shapes.length; i++ ) {
-    var params = shapes[i];
+var params;
+var i;
+for ( i = 0; i < shapes.length; i++ ) {
+    params = shapes[i];
     console.log( '\na = %d, b = %d', params[0], params[1] );
     console.log( 'Mean: %d', kumaraswamy.mean( params[0], params[1] ) );
     console.log( 'Median: %d', kumaraswamy.median( params[0], params[1] ) );

lib/node_modules/@stdlib/stats/base/dists/kumaraswamy/examples/index.js

@@ -18,47 +18,38 @@
 
 'use strict';
 
-var objectKeys = require( '@stdlib/utils/keys' );
 var kumaraswamy = require( './../lib' );
 
-console.log( objectKeys( kumaraswamy ) );
-// Create a Kumaraswamy distribution object
+// Create a Kumaraswamy distribution object:
 var a = 2.0;
 var b = 5.0;
 var dist = new kumaraswamy.Kumaraswamy( a, b );
 
-// Calculate basic distribution properties
+// Calculate basic distribution properties:
 console.log( 'Mean: %d', dist.mean );
 console.log( 'Median: %d', dist.median );
 console.log( 'Mode: %d', dist.mode );
 console.log( 'Variance: %d', dist.variance );
 
-// Evaluate the probability density function (PDF)
+// Evaluate the probability density function (PDF):
 var x = 0.5;
 var y = dist.pdf( x );
 console.log( 'PDF at x = %d: %d', x, y );
 
-// Evaluate the cumulative distribution function (CDF)
+// Evaluate the cumulative distribution function (CDF):
 y = dist.cdf( x );
 console.log( 'CDF at x = %d: %d', x, y );
 
-// Evaluate the natural logarithm of PDF and CDF
+// Evaluate the natural logarithm of PDF and CDF:
 console.log( 'Log PDF at x = %d: %d', x, dist.logpdf( x ) );
 console.log( 'Log CDF at x = %d: %d', x, dist.logcdf( x ) );
 
-// Calculate the quantile for a given probability
+// Calculate the quantile for a given probability:
 var p = 0.75;
 x = dist.quantile( p );
 console.log( 'Quantile at p = %d: %d', p, x );
 
-// Generate random numbers
-var r = new Array( 5 );
-for ( var i = 0; i < 5; i++ ) {
-    r[ i ] = kumaraswamy.random( a, b );
-}
-console.log( 'Random samples: %s', r.join( ', ' ) );
-
-// Use standalone functions
+// Use standalone distribution functions:
 x = 0.3;
 y = kumaraswamy.pdf( x, a, b );
 console.log( 'Standalone PDF at x = %d: %d', x, y );
@@ -69,25 +60,27 @@ console.log( 'Standalone CDF at x = %d: %d', x, y );
 y = kumaraswamy.quantile( 0.9, a, b );
 console.log( 'Standalone Quantile at p = 0.9: %d', y );
 
-// Calculate additional distribution properties
+// Calculate additional distribution properties:
 console.log( 'Kurtosis: %d', kumaraswamy.kurtosis( a, b ) );
 console.log( 'Skewness: %d', kumaraswamy.skewness( a, b ) );
 console.log( 'Standard Deviation: %d', kumaraswamy.stdev( a, b ) );
 
-// Demonstrate the effect of different shape parameters
+// Demonstrate the effect of different shape parameters:
 console.log( '\nEffect of shape parameters:' );
 var shapes = [
-    [0.5, 0.5],
-    [5.0, 1.0],
-    [1.0, 5.0],
-    [2.0, 2.0],
-    [10.0, 10.0]
+	[ 0.5, 0.5 ],
+	[ 5.0, 1.0 ],
+	[ 1.0, 5.0 ],
+	[ 2.0, 2.0 ],
+	[ 10.0, 10.0 ]
 ];
-for ( var i = 0; i < shapes.length; i++ ) {
-    var params = shapes[i];
-    console.log( '\na = %d, b = %d', params[0], params[1] );
-    console.log( 'Mean: %d', kumaraswamy.mean( params[0], params[1] ) );
-    console.log( 'Median: %d', kumaraswamy.median( params[0], params[1] ) );
-    console.log( 'Mode: %d', kumaraswamy.mode( params[0], params[1] ) );
-    console.log( 'Skewness: %d', kumaraswamy.skewness( params[0], params[1] ) );
+var params;
+var i;
+for ( i = 0; i < shapes.length; i++ ) {
+	params = shapes[ i ];
+	console.log( '\na = %d, b = %d', params[0], params[1] );
+	console.log( 'Mean: %d', kumaraswamy.mean( params[0], params[1] ) );
+	console.log( 'Median: %d', kumaraswamy.median( params[0], params[1] ) );
+	console.log( 'Mode: %d', kumaraswamy.mode( params[0], params[1] ) );
+	console.log( 'Skewness: %d', kumaraswamy.skewness( params[0], params[1] ) );
 }