Press n or j to go to the next uncovered block, b, p or k for the previous block.
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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 isnanf = require( '@stdlib/math/base/assert/is-nanf' );
var expf = require( '@stdlib/math/base/special/expf' );
var f32 = require( '@stdlib/number/float64/base/to-float32' );
// MAIN //
/**
* Computes the log loss gradient with respect to a model parameter for a single-precision floating-point number.
*
* @param {number} x - input value
* @param {number} y - true target value
* @param {number} p - predicted value
* @returns {number} log loss gradient
*
* @example
* var v = logGradient( 2.3, 1.0, 0.782 );
* // returns ~-0.722
*
* @example
* var v = logGradient( 1.4, 1.0, 0.202 );
* // returns ~-0.63
*
* @example
* var v = logGradient( 1.0, 1.0, -0.999 );
* // returns ~-0.731
*
* @example
* var v = logGradient( 5.0, -1.0, 0.234 );
* // returns ~2.791
*
* @example
* var v = logGradient( -3.2, -1.0, 0.2 );
* // returns ~-1.759
*
* @example
* var v = logGradient( -4.5, 1.0, -0.9 );
* // returns ~3.199
*/
function logGradient( x, y, p ) {
x = f32( x );
y = f32( y );
p = f32( p );
if (
isnanf( x ) || isnanf( y ) || isnanf( p ) ||
( y !== -1.0 && y !== 1.0 )
) {
return NaN;
}
return f32( -( x * y ) / f32( 1.0 + expf( y * p ) ) );
}
// EXPORTS //
module.exports = logGradient;
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