Unsupervised machine learning with multivariate Gaussian mixture model which supports both offline data and real-time data stream.
Demo: https://lukapopijac.github.io/gaussian-mixture-model
npm install gaussian-mixture-model
import GMM from 'gaussian-mixture-model';
// initialize model
let gmm = new GMM({
weights: [0.5, 0.5],
means: [[-25, 40], [-60, -30]],
covariances: [
[[400,0],[0,400]],
[[400,0],[0,400]]
]
});
// create some data points
let data = [
[11,42],[19,45],[15,36],[25,38],[24,33],
[-24,3],[-31,-4],[-34,-14],[-25,-5],[-16,7]
];
// add data points to the model
data.forEach(p => gmm.addPoint(p));
// run 5 iterations of EM algorithm
gmm.runEM(5);
// predict cluster probabilities for point [-5, 25]
let prob = gmm.predict([-5, 25]); // [0.000009438559331418772, 0.000002126123537376676]
// predict and normalize cluster probabilities for point [-5, 25]
let probNorm = gmm.predictNormalize([-5, 25]); // [0.8161537535012295, 0.18384624649877046]This software is released under the MIT license.