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fix: return NaN when the number of indexed elements is zero in stats/strided/distances/dcosine-similarity #9497

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Merged
kgryte merged 1 commit into from
Jan 5, 2026
Merged

fix: return NaN when the number of indexed elements is zero in stats/strided/distances/dcosine-similarity #9497

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2 changes: 1 addition & 1 deletion lib/node_modules/@stdlib/stats/strided/distances/dcosine-similarity/README.md
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Expand Up @@ -140,7 +140,7 @@ var z = dcosineSimilarity.ndarray( 3, x, 2, 1, y, -1, y.length-1 );

## Notes

- If `N <= 0`, both functions return `0.0`.
- If `N <= 0`, both functions return `NaN`.

</section>

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2 changes: 1 addition & 1 deletion lib/node_modules/@stdlib/stats/strided/distances/dcosine-similarity/docs/repl.txt
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Expand Up @@ -9,7 +9,7 @@
Indexing is relative to the first index. To introduce an offset, use a typed
array view.

If `N <= 0`, the function returns `0`.
If `N <= 0`, the function returns `NaN`.

Parameters
----------
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2 changes: 1 addition & 1 deletion lib/node_modules/@stdlib/stats/strided/distances/dcosine-similarity/lib/ndarray.js
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Expand Up @@ -53,7 +53,7 @@ function dcosineSimilarity( N, x, strideX, offsetX, y, strideY, offsetY ) {
var dot;

if ( N <= 0 ) {
return 0.0;
return NaN;
}
dot = ddot( N, x, strideX, offsetX, y, strideY, offsetY );
xnrm = dnrm2( N, x, strideX, offsetX );
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2 changes: 1 addition & 1 deletion lib/node_modules/@stdlib/stats/strided/distances/dcosine-similarity/src/main.c
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Expand Up @@ -56,7 +56,7 @@ double API_SUFFIX( stdlib_strided_dcosine_similarity_ndarray )( const CBLAS_INT
double dot;

if ( N <= 0 ) {
return 0.0;
return 0.0 / 0.0;
}
dot = c_ddot_ndarray( N, X, strideX, offsetX, Y, strideY, offsetY );
xnrm = c_dnrm2_ndarray( N, X, strideX, offsetX );
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6 changes: 3 additions & 3 deletions ...odules/@stdlib/stats/strided/distances/dcosine-similarity/test/test.dcosine_similarity.js
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Expand Up @@ -61,7 +61,7 @@ tape( 'the function calculates the cosine similarity of two strided arrays', fun
t.end();
});

tape( 'if provided an `N` parameter less than or equal to `0`, the function returns `0`', function test( t ) {
tape( 'if provided an `N` parameter less than or equal to `0`, the function returns `NaN`', function test( t ) {
var sim;
var x;
var y;
Expand All @@ -70,10 +70,10 @@ tape( 'if provided an `N` parameter less than or equal to `0`, the function retu
y = new Float64Array( [ 1.0, -2.0, 3.0 ] );

sim = dcosineSimilarity( 0, x, 1, y, 1 );
t.strictEqual( isAlmostSameValue( sim, 0.0, 1 ), true, 'returns expected value' );
t.strictEqual( isAlmostSameValue( sim, NaN, 1 ), true, 'returns expected value' );

sim = dcosineSimilarity( -4, x, 1, y, 1 );
t.strictEqual( isAlmostSameValue( sim, 0.0, 1 ), true, 'returns expected value' );
t.strictEqual( isAlmostSameValue( sim, NaN, 1 ), true, 'returns expected value' );
t.end();
});

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6 changes: 3 additions & 3 deletions ...@stdlib/stats/strided/distances/dcosine-similarity/test/test.dcosine_similarity.native.js
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Expand Up @@ -70,7 +70,7 @@ tape( 'the function calculates the cosine similarity of two strided arrays', opt
t.end();
});

tape( 'if provided an `N` parameter less than or equal to `0`, the function returns `0`', opts, function test( t ) {
tape( 'if provided an `N` parameter less than or equal to `0`, the function returns `NaN`', opts, function test( t ) {
var sim;
var x;
var y;
Expand All @@ -80,11 +80,11 @@ tape( 'if provided an `N` parameter less than or equal to `0`, the function retu

// Test against textbook algorithm for computing cosine similarity:
sim = dcosineSimilarity( 0, x, 1, y, 1 );
t.strictEqual( isAlmostSameValue( sim, 0.0, 1 ), true, 'returns expected value' );
t.strictEqual( isAlmostSameValue( sim, NaN, 1 ), true, 'returns expected value' );

// Test against textbook algorithm for computing cosine similarity:
sim = dcosineSimilarity( -4, x, 1, y, 1 );
t.strictEqual( isAlmostSameValue( sim, 0.0, 1 ), true, 'returns expected value' );
t.strictEqual( isAlmostSameValue( sim, NaN, 1 ), true, 'returns expected value' );
t.end();
});

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6 changes: 3 additions & 3 deletions lib/node_modules/@stdlib/stats/strided/distances/dcosine-similarity/test/test.ndarray.js
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Expand Up @@ -61,7 +61,7 @@ tape( 'the function calculates the cosine similarity of two strided arrays', fun
t.end();
});

tape( 'if provided an `N` parameter less than or equal to `0`, the function returns `0`', function test( t ) {
tape( 'if provided an `N` parameter less than or equal to `0`, the function returns `NaN`', function test( t ) {
var sim;
var x;
var y;
Expand All @@ -71,11 +71,11 @@ tape( 'if provided an `N` parameter less than or equal to `0`, the function retu

// Test against textbook algorithm for computing cosine similarity:
sim = dcosineSimilarity( 0, x, 1, 0, y, 1, 0 );
t.strictEqual( isAlmostSameValue( sim, 0.0, 1 ), true, 'returns expected value' );
t.strictEqual( isAlmostSameValue( sim, NaN, 1 ), true, 'returns expected value' );

// Test against textbook algorithm for computing cosine similarity:
sim = dcosineSimilarity( -4, x, 1, 0, y, 1, 0 );
t.strictEqual( isAlmostSameValue( sim, 0.0, 1 ), true, 'returns expected value' );
t.strictEqual( isAlmostSameValue( sim, NaN, 1 ), true, 'returns expected value' );
t.end();
});

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6 changes: 3 additions & 3 deletions ...de_modules/@stdlib/stats/strided/distances/dcosine-similarity/test/test.ndarray.native.js
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Original file line number Diff line number Diff line change
Expand Up @@ -70,7 +70,7 @@ tape( 'the function calculates the cosine similarity of two strided arrays', opt
t.end();
});

tape( 'if provided an `N` parameter less than or equal to `0`, the function returns `0`', opts, function test( t ) {
tape( 'if provided an `N` parameter less than or equal to `0`, the function returns `NaN`', opts, function test( t ) {
var sim;
var x;
var y;
Expand All @@ -80,11 +80,11 @@ tape( 'if provided an `N` parameter less than or equal to `0`, the function retu

// Test against textbook algorithm for computing cosine similarity:
sim = dcosineSimilarity( 0, x, 1, 0, y, 1, 0 );
t.strictEqual( isAlmostSameValue( sim, 0.0, 1 ), true, 'returns expected value' );
t.strictEqual( isAlmostSameValue( sim, NaN, 1 ), true, 'returns expected value' );

// Test against textbook algorithm for computing cosine similarity:
sim = dcosineSimilarity( -4, x, 1, 0, y, 1, 0 );
t.strictEqual( isAlmostSameValue( sim, 0.0, 1 ), true, 'returns expected value' );
t.strictEqual( isAlmostSameValue( sim, NaN, 1 ), true, 'returns expected value' );
t.end();
});

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