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 | 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 3x 1x 3x 1x 1x 1x 1x 3x 3x 3x 3x 3x 3x | /**
* @license Apache-2.0
*
* Copyright (c) 2018 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 regularize = require( './../regularize.js' );
// MAIN //
/**
* Given a new observation `(x,y)`, updates the weights using the [Huber loss][1] function.
*
* ## Notes
*
* The Huber loss uses squared-error loss for observations with error smaller than epsilon in magnitude and linear loss above that in order to decrease the influence of outliers on the model fit.
*
* [1]: https://en.wikipedia.org/wiki/Huber_loss
*
* @private
* @param {WeightVector} weights - current model coefficients
* @param {NumericArray} x - feature vector
* @param {number} y - response value
* @param {PositiveNumber} eta - current learning rate
* @param {NonNegativeNumber} lambda - regularization parameter
* @param {PositiveNumber} epsilon - insensitivity parameter
*/
function huberLoss( weights, x, y, eta, lambda, epsilon ) {
var p = weights.innerProduct( x ) - y;
// Perform L2 regularization...
regularize( weights, lambda, eta );
if ( p > epsilon ) {
weights.add( x, -eta );
} else if ( p < -epsilon ) {
weights.add( x, +eta );
} else {
weights.add( x, -eta * p );
}
}
// EXPORTS //
module.exports = huberLoss;
|