const xmm = require('xmmjs');

// Create a training set to host the training data
const ts = xmm.TrainingSet({ inputDimension: 3 });

// Add a new phrase to the training set, and record data frames
const phrase1 = ts.push(0, 'one');
for (let i = 0; i < 1000; i += 1) {
  const frame = Array.from(Array(3), () => Math.random()); // get data from somewhere
  phrase1.push(frame);
}
const phrase2 = ts.push(1, 'two');
for (let i = 0; i < 1000; i += 1) {
  const frame = Array.from(Array(3), () => 1 + Math.random()); // get data from somewhere
  phrase2.push(frame);
}

// Train the GMM with the given configuration
const configuration = {
  gaussians: 3,
  regularization: {
    absolute: 1e-1,
    relative: 1e-10,
  },
  covarianceMode: 'full',
};
const gmmParams = xmm.trainMulticlassGMM(ts, configuration);

// Create a predictor to perform real-time recognition
const predictor = xmm.MulticlassGMMPredictor(gmmParams);
predictor.reset();

predictor.predict([0.5, 0.5, 0.5]);
console.log('results (0.5)', predictor.results);

predictor.predict([1.5, 1.5, 1.5]);
console.log('results (1.5)', predictor.results);