chore: initial clean up

Author: Neerajpathak07

lib/node_modules/@stdlib/stats/base/dists/poisson/entropy/README.md

@@ -139,7 +139,7 @@ logEachMap( 'λ: %0.4f, H(X;λ): %0.4f', lambda, entropy );
 #include "stdlib/stats/base/dists/poisson/entropy.h"
 ```
 
-#### stdlib\_base\_dists\_poisson\_entropy( lambda )
+#### stdlib_base_dists_poisson_entropy( lambda )
 
 Returns the [entropy][entropy] of a [Poisson][poisson-distribution] distribution with mean parameter `lambda` (in [nats][nats]).
 

lib/node_modules/@stdlib/stats/base/dists/poisson/entropy/benchmark/benchmark.native.js

@@ -41,15 +41,15 @@ var opts = {
 // MAIN //
 
 bench( format( '%s::native', pkg ), opts, function benchmark( b ) {
+	var arrayOpts;
 	var lambda;
-	var opts;
 	var y;
 	var i;
 
-	opts = {
+	arrayOpts = {
 		'dtype': 'float64'
 	};
-	lambda = uniform( 100, EPS, 20.0, opts );
+	lambda = uniform( 100, EPS, 20.0, arrayOpts );
 
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {

lib/node_modules/@stdlib/stats/base/dists/poisson/entropy/benchmark/c/benchmark.c

@@ -93,8 +93,8 @@ static double random_uniform( const double min, const double max ) {
 * @return elapsed time in seconds
 */
 static double benchmark( void ) {
-	double elapsed;
 	double lambda[ 100 ];
+	double elapsed;
 	double y;
 	double t;
 	int i;

lib/node_modules/@stdlib/stats/base/dists/poisson/entropy/src/main.c

@@ -34,9 +34,10 @@
 * // returns ~2.508
 */
 double stdlib_base_dists_poisson_entropy( const double lambda ) {
+	double term;
 	double sum;
-	double lk;
 	double out;
+	double lk;
 	int k;
 
 	if ( stdlib_base_is_nan( lambda ) || lambda < 0.0 ) {
@@ -47,14 +48,18 @@ double stdlib_base_dists_poisson_entropy( const double lambda ) {
 	}
 	out = lambda * ( 1.0 - stdlib_base_ln( lambda ) );
 
-	// Compute the series: sum_{k=0}^{infty} lambda^k * ln(k!) / k!
-	// Starting from k=2, since ln(0!) = 0 and ln(1!) = 0:
+	// Compute: sum_{k=2}^{inf} lambda^k * ln(k!) / k!
+	// k starts at 1, lk = lambda^1, first iteration makes k=2, lk=lambda^2
 	sum = 0.0;
+	k = 1;
 	lk = lambda; // lambda^1
 	for ( k = 2; k < 1000; k++ ) {
 		lk *= lambda;
-		sum += lk * stdlib_base_factorialln( k ) / stdlib_base_factorial( k );
-		if ( lk / stdlib_base_factorial( k ) < 1.0e-16 ) {
+		term = lk * stdlib_base_factorialln( k ) / stdlib_base_factorial( k );
+		sum += term;
+
+		// Convergence: check relative size of term
+		if ( term < 1.0e-16 * sum ) {
 			break;
 		}
 	}

lib/node_modules/@stdlib/stats/base/dists/poisson/entropy/test/test.js

@@ -22,9 +22,8 @@
 
 var tape = require( 'tape' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
-var abs = require( '@stdlib/math/base/special/abs' );
+var isAlmostSameValue = require( '@stdlib/assert/is-almost-same-value' );
 var NINF = require( '@stdlib/constants/float64/ninf' );
-var EPS = require( '@stdlib/constants/float64/eps' );
 var entropy = require( './../lib' );
 
 
@@ -71,8 +70,6 @@ tape( 'if provided a `lambda` equal to `0`, the function returns `0`', function
 tape( 'the function returns the entropy of a Poisson distribution', function test( t ) {
 	var expected;
 	var lambda;
-	var delta;
-	var tol;
 	var i;
 	var y;
 
@@ -83,9 +80,7 @@ tape( 'the function returns the entropy of a Poisson distribution', function tes
 		if ( y === expected[i] ) {
 			t.strictEqual( y, expected[i], 'lambda: '+lambda[i]+', y: '+y+', expected: '+expected[i] );
 		} else {
-			delta = abs( y - expected[ i ] );
-			tol = 15.0 * EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( isAlmostSameValue( y, expected[i], 20 ), 'within tolerance. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
 		}
 	}
 	t.end();

lib/node_modules/@stdlib/stats/base/dists/poisson/entropy/test/test.native.js

@@ -89,7 +89,7 @@ tape( 'the function returns the entropy of a Poisson distribution', opts, functi
 		if ( y === expected[i] ) {
 			t.strictEqual( y, expected[i], 'lambda: '+lambda[i]+', y: '+y+', expected: '+expected[i] );
 		} else {
-			t.ok( isAlmostSameValue( y, expected[i], 1500 ), 'within tolerance. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
+			t.ok( isAlmostSameValue( y, expected[i], 20 ), 'within tolerance. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
 		}
 	}
 	t.end();