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feat: add ml/base/loss/float64/hinge-gradient
PR-URL: #12216 Co-authored-by: Athan Reines <kgryte@gmail.com> Reviewed-by: Athan Reines <kgryte@gmail.com> Signed-off-by: Nakul Krishnakumar <nakulkrishnakumar86@gmail.com> Signed-off-by: Athan Reines <kgryte@gmail.com>
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‎lib/node_modules/@stdlib/ml/base/loss/float64/hinge-gradient/README.md‎

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<!--
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@license Apache-2.0
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Copyright (c) 2026 The Stdlib Authors.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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-->
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# hingeGradient
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> Compute the [hinge loss gradient][hinge-loss-gradient] with respect to a model parameter.
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<section class="intro">
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The [hinge loss gradient][hinge-loss-gradient] is defined as
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<!-- <equation class="equation" label="eq:hinge_loss_gradient" align="center" raw="\frac{\partial \ell}{\partial w_i} = \begin{cases} -yx_i & \text{if } yp \leq t \\ 0 & \text{otherwise} \end{cases}" alt="Equation for the hinge loss gradient."> -->
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```math
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\frac{\partial \ell}{\partial w_i} = \begin{cases} -yx_i & \text{if } yp \leq t \\ 0 & \text{otherwise} \end{cases}
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```
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<!-- </equation> -->
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</section>
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<!-- /.intro -->
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<section class="usage">
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## Usage
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```javascript
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var hingeGradient = require( '@stdlib/ml/base/loss/float64/hinge-gradient' );
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```
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#### hingeGradient( x, t, y, p )
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Computes the [hinge loss gradient][hinge-loss-gradient] with respect to a model parameter.
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```javascript
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var v = hingeGradient( 3.0, 1.0, 1.0, 0.782 );
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// returns -3.0
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v = hingeGradient( -1.3, 1.0, 1.0, -0.999 );
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// returns 1.3
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```
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The function accepts the following arguments:
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- **x**: input value.
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- **t**: margin threshold.
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- **y**: true target value.
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- **p**: predicted value.
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If any argument is `NaN`, the function returns `NaN`.
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```javascript
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var v = hingeGradient( NaN, 1.0, 1.0, 0.782 );
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// returns NaN
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v = hingeGradient( 1.0, 1.0, NaN, 0.782 );
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// returns NaN
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v = hingeGradient( NaN, NaN, 1.0, 0.782 );
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// returns NaN
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v = hingeGradient( NaN, NaN, NaN, NaN );
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// returns NaN
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```
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If `y` is not +1 or -1, the function returns `NaN`.
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```javascript
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var v = hingeGradient( 3.0, 1.0, -0.9, 1.0 );
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// returns NaN
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v = hingeGradient( 2.4, 1.0, 0.453, 0.76 );
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// returns NaN
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```
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</section>
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<!-- /.usage -->
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<section class="notes">
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## Notes
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- 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.
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</section>
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<!-- /.notes -->
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<section class="examples">
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## Examples
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<!-- eslint no-undef: "error" -->
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```javascript
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var uniform = require( '@stdlib/random/array/uniform' );
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var sample = require( '@stdlib/random/sample' );
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var logEachMap = require( '@stdlib/console/log-each-map' );
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var hingeGradient = require( '@stdlib/ml/base/loss/float64/hinge-gradient' );
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var y = sample( [ -1.0, 1.0 ], {
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'size': 100
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});
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var opts = {
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'dtype': 'float64'
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};
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var x = uniform( 100, -100.0, 100.0, opts );
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var t = uniform( 100, 0.0, 5.0, opts );
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var p = uniform( 100, -5.0, 5.0, opts );
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logEachMap( 'hingeGradient(%0.4f, %0.4f, %0.4f, %0.4f) = %0.4f', x, t, y, p, hingeGradient );
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```
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</section>
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<!-- /.examples -->
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<!-- C interface documentation. -->
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* * *
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<section class="c">
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## C APIs
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<!-- Section to include introductory text. Make sure to keep an empty line after the intro `section` element and another before the `/section` close. -->
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<section class="intro">
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</section>
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<!-- /.intro -->
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<!-- C usage documentation. -->
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<section class="usage">
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### Usage
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```c
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#include "stdlib/ml/base/loss/float64/hinge_gradient.h"
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```
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#### stdlib_base_float64_hinge_gradient( x, t, y, p )
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Computes the [hinge loss gradient][hinge-loss-gradient] with respect to a model parameter.
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```c
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double out = stdlib_base_float64_hinge_gradient( 3.0, 1.0, 1.0, 0.782 );
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// returns -3.0
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```
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The function accepts the following arguments:
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- **x**: `[in] double` input value.
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- **t**: `[in] double` margin threshold.
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- **y**: `[in] double` true target value.
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- **p**: `[in] double` predicted value.
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```c
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double stdlib_base_float64_hinge_gradient( const double x, const double t, const double y, const double p );
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```
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</section>
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<!-- /.usage -->
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<!-- C API usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
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<section class="notes">
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</section>
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<!-- /.notes -->
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<!-- C API usage examples. -->
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<section class="examples">
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### Examples
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```c
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#include "stdlib/ml/base/loss/float64/hinge_gradient.h"
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#include <stdio.h>
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int main( void ) {
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const double x[] = { -10.0, -9.56, -8.67, -7.78, -6.89, 6.89, 7.78, 8.67, 9.56, 10.0 };
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const double t[] = { 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 };
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const double y[] = { -1.0, -1.0, -1.0, -1.0, -1.0, 1.0, 1.0, 1.0, 1.0, 1.0 };
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const double p[] = { -5.0, -3.89, -2.78, -1.67, -0.56, 0.56, 1.67, 2.78, 3.89, 5.0 };
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double v;
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int i;
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for ( i = 0; i < 10; i++ ) {
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v = stdlib_base_float64_hinge_gradient( x[ i ], t[ i ], y[ i ], p[ i ] );
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printf( "hingeGradient(%lf, %lf, %lf, %lf) = %lf\n", x[ i ], t[ i ], y[ i ], p[ i ], v );
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}
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}
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```
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</section>
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<!-- /.examples -->
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</section>
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<!-- /.c -->
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<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->
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<section class="related">
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</section>
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<!-- /.related -->
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<!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
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<section class="links">
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[hinge-loss-gradient]: https://en.wikipedia.org/wiki/Hinge_loss#Optimization
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</section>
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<!-- /.links -->

‎lib/node_modules/@stdlib/ml/base/loss/float64/hinge-gradient/benchmark/benchmark.js‎

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/**
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* @license Apache-2.0
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*
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* Copyright (c) 2026 The Stdlib Authors.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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'use strict';
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// MODULES //
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var bench = require( '@stdlib/bench' );
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var uniform = require( '@stdlib/random/array/uniform' );
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var sample = require( '@stdlib/random/sample' );
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var isnan = require( '@stdlib/math/base/assert/is-nan' );
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var pkg = require( './../package.json' ).name;
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var hingeGradient = require( './../lib' );
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// MAIN //
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bench( pkg, function benchmark( b ) {
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var len;
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var x;
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var y;
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var p;
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var v;
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var i;
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len = 100;
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y = sample( [ -1.0, 1.0 ], {
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'size': len
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});
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x = uniform( len, -100.0, 100.0 );
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p = uniform( len, -2.0, 2.0 );
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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v = hingeGradient( x[ i%x.length ], 1.0, y[ i%y.length ], p[ i%p.length ] );
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if ( isnan( v ) ) {
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b.fail( 'should not return NaN' );
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}
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}
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b.toc();
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if ( isnan( v ) ) {
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b.fail( 'should not return NaN' );
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}
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b.pass( 'benchmark finished' );
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b.end();
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});

‎lib/node_modules/@stdlib/ml/base/loss/float64/hinge-gradient/benchmark/benchmark.native.js‎

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/**
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* @license Apache-2.0
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*
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* Copyright (c) 2026 The Stdlib Authors.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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'use strict';
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// MODULES //
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var resolve = require( 'path' ).resolve;
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var bench = require( '@stdlib/bench' );
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var uniform = require( '@stdlib/random/array/uniform' );
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var sample = require( '@stdlib/random/sample' );
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var isnan = require( '@stdlib/math/base/assert/is-nan' );
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var tryRequire = require( '@stdlib/utils/try-require' );
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var format = require( '@stdlib/string/format' );
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var pkg = require( './../package.json' ).name;
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// VARIABLES //
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var hingeGradient = tryRequire( resolve( __dirname, './../lib/native.js' ) );
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var opts = {
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'skip': ( hingeGradient instanceof Error )
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};
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// MAIN //
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bench( format( '%s::native', pkg ), opts, function benchmark( b ) {
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var len;
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var x;
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var y;
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var p;
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var v;
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var i;
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len = 100;
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y = sample( [ -1.0, 1.0 ], {
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'size': len
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});
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x = uniform( len, -100.0, 100.0 );
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p = uniform( len, -2.0, 2.0 );
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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v = hingeGradient( x[ i%x.length ], 1.0, y[ i%y.length ], p[ i%p.length ] );
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if ( isnan( v ) ) {
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b.fail( 'should not return NaN' );
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}
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}
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b.toc();
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if ( isnan( v ) ) {
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b.fail( 'should not return NaN' );
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}
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b.pass( 'benchmark finished' );
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b.end();
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});

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