From c47c661fe9ab36319d5faacbc91bf39317797a8c Mon Sep 17 00:00:00 2001 From: Claude Date: Wed, 7 Oct 2026 10:29:16 +0000 Subject: [PATCH] test: migrate `stats/base/dists/negative-binomial/variance` to ULP-based assertions Migrate the native test suite from relative tolerance assertions to ULP difference assertions via `@stdlib/assert/is-almost-same-value`. Ref: https://github.com/stdlib-js/stdlib/issues/11352 Co-Authored-By: Claude Opus 5 Claude-Session: https://claude.ai/code/session_01TDyxNxcdwVVi7jiuRDFZCo --- .../negative-binomial/variance/test/test.native.js | 13 ++----------- 1 file changed, 2 insertions(+), 11 deletions(-) diff --git a/lib/node_modules/@stdlib/stats/base/dists/negative-binomial/variance/test/test.native.js b/lib/node_modules/@stdlib/stats/base/dists/negative-binomial/variance/test/test.native.js index 90fda0c314e3..7323baa2b3f0 100644 --- a/lib/node_modules/@stdlib/stats/base/dists/negative-binomial/variance/test/test.native.js +++ b/lib/node_modules/@stdlib/stats/base/dists/negative-binomial/variance/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 tryRequire = require( '@stdlib/utils/try-require' ); var isnan = require( '@stdlib/math/base/assert/is-nan' ); -var abs = require( '@stdlib/math/base/special/abs' ); -var EPS = require( '@stdlib/constants/float64/eps' ); // FIXTURES // @@ -77,8 +76,6 @@ tape( 'if provided `r <= 0` or `p <= 0` or `p > 1`, the function returns `NaN`', tape( 'the function returns the variance of a negative binomial distribution', opts, function test( t ) { var expected; - var delta; - var tol; var r; var p; var y; @@ -89,13 +86,7 @@ tape( 'the function returns the variance of a negative binomial distribution', o p = data.p; for ( i = 0; i < r.length; i++ ) { y = variance( r[ i ], p[ i ] ); - if ( y === expected[ i ] ) { - t.strictEqual( y, expected[ i ], 'r: ' + r[ i ] + ', p: ' + p[ i ] + ', y: ' + y + ', expected: ' + expected[ i ] ); - } else { - delta = abs( y - expected[ i ] ); - tol = 2.0 * EPS * abs( expected[ i ] ); - t.ok( delta <= tol, 'within tolerance. r: ' + r[ i ] + '. p: ' + p[ i ] + '. y: ' + y + '. E: ' + expected[ i ] + '. Δ: ' + delta + '. tol: ' + tol + '.' ); - } + t.strictEqual( isAlmostSameValue( y, expected[ i ], 0 ), true, 'returns expected value' ); } t.end(); });