/*
* Copyright (c) AXA Shared Services Spain S.A.
*
* Permission is hereby granted, free of charge, to any person obtaining
* a copy of this software and associated documentation files (the
* "Software"), to deal in the Software without restriction, including
* without limitation the rights to use, copy, modify, merge, publish,
* distribute, sublicense, and/or sell copies of the Software, and to
* permit persons to whom the Software is furnished to do so, subject to
* the following conditions:
*
* The above copyright notice and this permission notice shall be
* included in all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
* NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
* LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
* OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
* WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
*/
const { NeuralNetwork } = require('brain.js');
/**
* Classifier using Brain.js Neural Network
*/
class BrainClassifier {
/**
* Constructor of the class.
* @param {Object} settings Settings for the instance.
*/
constructor(settings) {
this.settings = settings || {};
if (!this.settings.config) {
this.settings.config = {
activation: 'leaky-relu',
hiddenLayers: [],
learningRate: 0.1,
errorThresh: 0.0005,
};
}
this.settings.config.timeout = this.settings.timeout || 2 * 60 * 1000;
this.labels = [];
this.network = new NeuralNetwork(this.settings.config);
}
/**
* Train the classifier given a dataset.
* @param {Object} dataset Dataset with features and outputs.
*/
async trainBatch(dataset) {
const netDataset = [];
dataset.forEach(item => {
const netItem = {
input: item.input,
output: {},
};
netItem.output[item.output] = 1;
netDataset.push(netItem);
});
return this.network.train(netDataset);
}
/**
* Given a sample, return the classification.
* @param {Object} sample Input sample.
* @returns {Object} Classification output.
*/
classify(sample) {
const scores = [];
if (Object.keys(sample).length > 0) {
const result = this.network.run(sample);
Object.keys(result).forEach(key => {
scores.push({ label: key, value: result[key] });
});
}
if (scores.length > 0) {
return scores.sort((x, y) => y.value - x.value);
}
return [{ label: 'None', value: 1 }];
}
}
module.exports = BrainClassifier;
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