feat: add ml/base/loss/float64/log-gradient

lib/node_modules/@stdlib/ml/base/loss/float64/log-gradient/README.md

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+<!--
+
+@license Apache-2.0
+
+Copyright (c) 2026 The Stdlib Authors.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+-->
+
+# logGradient
+
+> Compute the [log loss gradient][log-loss-gradient] with respect to the predicted value.
+
+<section class="intro">
+
+The [log loss gradient][log-loss-gradient] is defined as
+
+<!-- <equation class="equation" label="eq:log_loss_gradient" align="center" raw="\frac{\partial L}{\partial p} = -\frac{y}{1+e^{yp}}" alt="Equation for the log loss gradient."> -->
+
+```math
+\frac{\partial L}{\partial p} = -\frac{y}{1+e^{yp}}
+```
+
+<!-- </equation> -->
+
+</section>
+
+<!-- /.intro -->
+
+<section class="usage">
+
+## Usage
+
+```javascript
+var logGradient = require( '@stdlib/ml/base/loss/float64/log-gradient' );
+```
+
+#### logGradient( y, p )
+
+Computes the [log loss gradient][log-loss-gradient] with respect to the predicted value.
+
+```javascript
+var v = logGradient( 1.0, 0.782 );
+// returns ~-0.314
+
+v = logGradient( 1.0, -0.999 );
+// returns ~-0.731
+```
+
+If either argument is `NaN`, the function returns `NaN`.
+
+```javascript
+var v = logGradient( NaN, 0.782 );
+// returns NaN
+
+v = logGradient( 1.0, NaN );
+// returns NaN
+```
+
+If `y` is not +1 or -1, the function returns `NaN`.
+
+```javascript
+var v = logGradient( -0.9, 1.0 );
+// returns NaN
+
+v = logGradient( 0.453, 0.76 );
+// returns NaN
+```
+
+</section>
+
+<!-- /.usage -->
+
+<section class="examples">
+
+## Examples
+
+<!-- eslint no-undef: "error" -->
+
+```javascript
+var uniform = require( '@stdlib/random/array/uniform' );
+var sample = require( '@stdlib/random/sample' );
+var logEachMap = require( '@stdlib/console/log-each-map' );
+var logGradient = require( '@stdlib/ml/base/loss/float64/log-gradient' );
+
+var y = sample( [ -1.0, 1.0 ], {
+    'size': 100
+});
+var p = uniform( 100, -5.0, 5.0, {
+    'dtype': 'float64'
+});
+
+logEachMap( 'logGradient(%0.4f, %0.4f) = %0.4f', y, p, logGradient );
+
+```
+
+</section>
+
+<!-- /.examples -->
+
+<!-- C interface documentation. -->
+
+* * *
+
+<section class="c">
+
+## C APIs
+
+<!-- Section to include introductory text. Make sure to keep an empty line after the intro `section` element and another before the `/section` close. -->
+
+<section class="intro">
+
+</section>
+
+<!-- /.intro -->
+
+<!-- C usage documentation. -->
+
+<section class="usage">
+
+### Usage
+
+```c
+#include "stdlib/ml/base/loss/float64/log_gradient.h"
+```
+
+#### stdlib_base_float64_log_gradient( y, p )
+
+Computes the [log loss gradient][log-loss-gradient] with respect to the predicted value.
+
+```c
+double out = stdlib_base_float64_log_gradient( 1.0, 0.782 );
+// returns ~-0.314
+```
+
+The function accepts the following arguments:
+
+-   **y**: `[in] double` true target value.
+-   **p**: `[in] double` predicted value.
+
+```c
+double stdlib_base_float64_log_gradient( const double y, const double p );
+```
+
+</section>
+
+<!-- /.usage -->
+
+<!-- C API usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
+
+<section class="notes">
+
+</section>
+
+<!-- /.notes -->
+
+<!-- C API usage examples. -->
+
+<section class="examples">
+
+### Examples
+
+```c
+#include "stdlib/ml/base/loss/float64/log_gradient.h"
+#include <stdio.h>
+
+int main( void ) {
+    const double y[] = { -1.0, -1.0, -1.0, -1.0, -1.0, 1.0, 1.0, 1.0, 1.0, 1.0 };
+    const double p[] = { -5.0, -3.89, -2.78, -1.67, -0.56, 0.56, 1.67, 2.78, 3.89, 5.0 };
+
+    double v;
+    int i;
+    for ( i = 0; i < 10; i++ ) {
+        v = stdlib_base_float64_log_gradient( y[ i ], p[ i ] );
+        printf( "logGradient(%lf, %lf) = %lf\n", y[ i ], p[ i ], v );
+    }
+}
+```
+
+</section>
+
+<!-- /.examples -->
+
+</section>
+
+<!-- /.c -->
+
+<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->
+
+<section class="related">
+
+</section>
+
+<!-- /.related -->
+
+<!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
+
+<section class="links">
+
+[log-loss-gradient]: https://en.wikipedia.org/wiki/Cross-entropy#Relation_to_linear_regression
+
+<!-- <related-links> -->
+
+
+<!-- </related-links> -->
+
+</section>
+
+<!-- /.links -->

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

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

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

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