test: migrate stats/base/dists/bradford/entropy to ULP-based assertions

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

@@ -21,10 +21,9 @@
 // MODULES //
 
 var tape = require( 'tape' );
+var isAlmostSameValue = require( '@stdlib/assert/is-almost-same-value' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
-var abs = require( '@stdlib/math/base/special/abs' );
 var NINF = require( '@stdlib/constants/float64/ninf' );
-var EPS = require( '@stdlib/constants/float64/eps' );
 var entropy = require( './../lib' );
 
 
@@ -65,38 +64,28 @@ tape( 'if provided `c <= 0`, the function returns `NaN`', function test( t ) {
 
 tape( 'the function returns the differential entropy of a Bradford distribution given small parameter `c`', function test( t ) {
 	var expected;
-	var delta;
-	var tol;
 	var i;
 	var c;
 	var y;
 
 	expected = smallC.expected;
 	c = smallC.c;
+
+	/*
+	* NOTE: the ULP bound is set high in this case due to:
+	*
+	* 1. The shape parameter being very small which causes differences in the nested `ln` calculations when compared to the test fixtures by SciPy.
+	* 2. The expected values being very small.
+	*/
 	for ( i = 0; i < expected.length; i++ ) {
 		y = entropy( c[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'c: '+c[i]+', y: '+y+', expected: '+expected[i] );
-		} else {
-			delta = abs( y - expected[ i ] );
-
-			/*
-			* NOTE: the tolerance is set high in this case due to:
-			*
-			* 1. The shape parameter being very small which causes differences in the nested `ln` calculations when compared to the test fixtures by SciPy.
-			* 2. The expected values being very small.
-			*/
-			tol = 1523.0 * EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. c: '+c[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
-		}
+		t.strictEqual( isAlmostSameValue( y, expected[ i ], 1984 ), true, 'returns expected value' );
 	}
 	t.end();
 });
 
 tape( 'the function returns the differential entropy of a Bradford distribution given large parameter `c`', function test( t ) {
 	var expected;
-	var delta;
-	var tol;
 	var i;
 	var c;
 	var y;
@@ -105,13 +94,7 @@ tape( 'the function returns the differential entropy of a Bradford distribution
 	c = largeC.c;
 	for ( i = 0; i < expected.length; i++ ) {
 		y = entropy( c[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'c: '+c[i]+', y: '+y+', expected: '+expected[i] );
-		} else {
-			delta = abs( y - expected[ i ] );
-			tol = 45.0 * EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. c: '+c[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
-		}
+		t.strictEqual( isAlmostSameValue( y, expected[ i ], 64 ), true, 'returns expected value' );
 	}
 	t.end();
 });

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

@@ -22,10 +22,9 @@
 
 var resolve = require( 'path' ).resolve;
 var tape = require( 'tape' );
+var isAlmostSameValue = require( '@stdlib/assert/is-almost-same-value' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
-var abs = require( '@stdlib/math/base/special/abs' );
 var NINF = require( '@stdlib/constants/float64/ninf' );
-var EPS = require( '@stdlib/constants/float64/eps' );
 var tryRequire = require( '@stdlib/utils/try-require' );
 
 
@@ -74,31 +73,28 @@ tape( 'if provided `c <= 0`, the function returns `NaN`', opts, function test( t
 
 tape( 'the function returns the differential entropy of a Bradford distribution given small parameter `c`', opts, function test( t ) {
 	var expected;
-	var delta;
-	var tol;
 	var i;
 	var c;
 	var y;
 
 	expected = smallC.expected;
 	c = smallC.c;
+
+	/*
+	* NOTE: the ULP bound is set high in this case due to:
+	*
+	* 1. The shape parameter being very small which causes differences in the nested `ln` calculations when compared to the test fixtures by SciPy.
+	* 2. The expected values being very small.
+	*/
 	for ( i = 0; i < expected.length; i++ ) {
 		y = entropy( c[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'c: '+c[i]+', y: '+y+', expected: '+expected[i] );
-		} else {
-			delta = abs( y - expected[ i ] );
-			tol = 1523.0 * EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. c: '+c[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
-		}
+		t.strictEqual( isAlmostSameValue( y, expected[ i ], 1984 ), true, 'returns expected value' );
 	}
 	t.end();
 });
 
 tape( 'the function returns the differential entropy of a Bradford distribution given large parameter `c`', opts, function test( t ) {
 	var expected;
-	var delta;
-	var tol;
 	var i;
 	var c;
 	var y;
@@ -107,13 +103,7 @@ tape( 'the function returns the differential entropy of a Bradford distribution
 	c = largeC.c;
 	for ( i = 0; i < expected.length; i++ ) {
 		y = entropy( c[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'c: '+c[i]+', y: '+y+', expected: '+expected[i] );
-		} else {
-			delta = abs( y - expected[ i ] );
-			tol = 45.0 * EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. c: '+c[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
-		}
+		t.strictEqual( isAlmostSameValue( y, expected[ i ], 64 ), true, 'returns expected value' );
 	}
 	t.end();
 });