Press n or j to go to the next uncovered block, b, p or k for the previous block.
| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 2019x 2019x 2019x 2019x 2019x 2019x 7x 7x 2019x 672x 672x 2019x 804x 804x 536x 2019x 1x 1x 1x 1x 1x | /**
* @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 f32 = require( '@stdlib/number/float64/base/to-float32' );
// MAIN //
/**
* Computes the epsilon insensitive loss gradient with respect to a model parameter for a single-precision floating-point number.
*
* @param {number} x - input value
* @param {number} e - insensitivity parameter
* @param {number} y - true target value
* @param {number} p - predicted value
* @returns {number} epsilon insensitive loss gradient
*
* @example
* var v = epsilonInsensitiveGradient( 3.0, 5.0, 10.2, 0.782 );
* // returns -3.0
*
* @example
* var v = epsilonInsensitiveGradient( 2.5, 2.0, 8.0, 0.202 );
* // returns -2.5
*
* @example
* var v = epsilonInsensitiveGradient( -1.3, 1.0, 23.2, -0.999 );
* // returns ~1.3
*
* @example
* var v = epsilonInsensitiveGradient( -2.0, 11.0, -12.2, 0.234 );
* // returns -2.0
*
* @example
* var v = epsilonInsensitiveGradient( -2.0, 2.0, -21.0, 0.2 );
* // returns -2.0
*
* @example
* var v = epsilonInsensitiveGradient( -1.3, 9.0, 4.0, -0.9 );
* // returns 0.0
*/
function epsilonInsensitiveGradient( x, e, y, p ) {
x = f32( x );
e = f32( e );
y = f32( y );
p = f32( p );
if ( isnanf( x ) || isnanf( e ) || isnanf( y ) || isnanf( p ) ) {
return NaN;
}
if ( f32( y - p ) > e ) {
return f32( -x );
}
if ( f32( p - y ) > e ) {
return x;
}
return 0.0;
}
// EXPORTS //
module.exports = epsilonInsensitiveGradient;
|