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kgryte merged 3 commits into from
Aug 6, 2026
+5 −5
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chore: clean-up #13949

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f959bc5
style: remove erroneous `new` operator and fix annotations in `array/…
claude Aug 5, 2026
29f8d0d
docs: fix typo in `ml/base/loss/float64/hinge-gradient`
claude Aug 5, 2026
79fb418
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kgryte Aug 6, 2026
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8 changes: 4 additions & 4 deletions lib/node_modules/@stdlib/array/uint64/lib/main.js
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Original file line number Diff line number Diff line change
Expand Up @@ -621,13 +621,13 @@
*
* // Iterate over the key-value pairs...
* var v = it.next().value;
* // returns [ 0, <Uint>[ 1n ] ]
* // returns [ 0, <Uint64>[ 1n ] ]
*
* v = it.next().value;
* // returns [ 1, <Uint>[ 2n ] ]
* // returns [ 1, <Uint64>[ 2n ] ]
*
* v = it.next().value;
* // returns [ 2, <Uint>[ 3n ] ]
* // returns [ 2, <Uint64>[ 3n ] ]
*
* var bool = it.next().done;
* // returns true
Expand Down Expand Up @@ -673,7 +673,7 @@
};
}
return {
'value': [ i, new getUint64( buffer, i ) ],
'value': [ i, getUint64( buffer, i ) ],
'done': false
};
}
Expand Down Expand Up @@ -846,7 +846,7 @@
buf[ idx+indices.LOW ] = words[ 1 ];
return;
}
if ( isNonNegativeInteger( value ) || ( isBigInt( value ) && isNonNegativeInteger( Number( value ) ) ) ) {

Check warning on line 849 in lib/node_modules/@stdlib/array/uint64/lib/main.js

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This line has a length of 110. Maximum allowed is 80
if ( idx >= this._length ) {
throw new RangeError( format( 'invalid argument. Index argument is out-of-bounds. Value: `%u`.', idx ) );
}
Expand Down Expand Up @@ -909,7 +909,7 @@
// We need to copy source values...
tmp = new Uint32Array( sbuf.length );
for ( i = 0; i < sbuf.length; i++ ) {
tmp[ i ] = sbuf[ i ]; // TODO: handle accessor arrays

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Unexpected 'todo' comment: 'TODO: handle accessor arrays'
}
sbuf = tmp;
}
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2 changes: 1 addition & 1 deletion lib/node_modules/@stdlib/ml/base/loss/float64/hinge-gradient/README.md
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Expand Up @@ -99,7 +99,7 @@ v = hingeGradient( 2.4, 1.0, 0.453, 0.76 );

## Notes

- When `t = 1.0`, we get the loss used by SVM, where as when `t = 0.0`, we get the loss used by the Perceptron.
- When `t = 1.0`, we get the loss used by SVM; whereas, when `t = 0.0`, we get the loss used by the Perceptron.

</section>

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