Auto-generated commit

Author: stdlib-bot

CHANGELOG.md

@@ -1257,6 +1257,7 @@
 
 ### Bug Fixes
 
+-   [`3af0a04`](https://github.com/stdlib-js/stdlib/commit/3af0a041f6ae95c251cbfa2acae82b30e3b4579b) - return `NaN` when the number of indexed elements is zero in `stats/strided/distances/dcosine-similarity` [(#9497)](https://github.com/stdlib-js/stdlib/pull/9497)
 -   [`bda1a5c`](https://github.com/stdlib-js/stdlib/commit/bda1a5c6cfa6777839ab5f941296b2008880b75a) - use correct types
 -   [`20d29ed`](https://github.com/stdlib-js/stdlib/commit/20d29ed3410f2ff8ff6bfadbfb0a1f5a4b7e5fde) - use `int8ndarray` type for alternative hypothesis param in `ndarray/sztest` and `ndarray/dztest`
 -   [`0d71e53`](https://github.com/stdlib-js/stdlib/commit/0d71e530c42bc8d7c98ad779e505fc01bd904a1f) - ensure correct `this` context when creating a new Module instance
@@ -3551,6 +3552,7 @@ A total of 562 issues were closed in this release:
 
 <details>
 
+-   [`3af0a04`](https://github.com/stdlib-js/stdlib/commit/3af0a041f6ae95c251cbfa2acae82b30e3b4579b) - **fix:** return `NaN` when the number of indexed elements is zero in `stats/strided/distances/dcosine-similarity` [(#9497)](https://github.com/stdlib-js/stdlib/pull/9497) _(by Nakul Krishnakumar)_
 -   [`c216b80`](https://github.com/stdlib-js/stdlib/commit/c216b80baf3efe0d21387a280368c44122328b76) - **feat:** add `stats/base/ndarray/smskmidrange` [(#9519)](https://github.com/stdlib-js/stdlib/pull/9519) _(by Sachin Pangal, Athan Reines)_
 -   [`bda1a5c`](https://github.com/stdlib-js/stdlib/commit/bda1a5c6cfa6777839ab5f941296b2008880b75a) - **fix:** use correct types _(by Athan Reines)_
 -   [`d681872`](https://github.com/stdlib-js/stdlib/commit/d681872a9b87321f4f66852f5aabfeec19e195fc) - **feat:** add `stats/base/ndarray/mskmidrange` [(#9511)](https://github.com/stdlib-js/stdlib/pull/9511) _(by Sachin Pangal)_

strided/distances/dcosine-similarity/README.md

@@ -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>
 

strided/distances/dcosine-similarity/docs/repl.txt

@@ -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
     ----------

strided/distances/dcosine-similarity/lib/ndarray.js

@@ -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 );

strided/distances/dcosine-similarity/src/main.c

@@ -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 );

strided/distances/dcosine-similarity/test/test.dcosine_similarity.js

@@ -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;
@@ -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();
 });
 

strided/distances/dcosine-similarity/test/test.dcosine_similarity.native.js

@@ -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;
@@ -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();
 });
 

strided/distances/dcosine-similarity/test/test.ndarray.js

@@ -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;
@@ -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();
 });
 

strided/distances/dcosine-similarity/test/test.ndarray.native.js

@@ -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;
@@ -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();
 });