Apparatus Machine Learning Routines

Common errors

Warnings

Install

Imports

• BayesClassifier
  wrong

  import { BayesClassifier } from 'apparatus';
  import { BayesClassifier } from 'apparatus/lib/classifier/bayes_classifier';

  correct

  const bayes = require('apparatus/lib/classifier/bayes_classifier');
  const classifier = new bayes.BayesClassifier();

  Apparatus uses deep CommonJS imports. The main `apparatus` module does not export algorithms directly.

• KMeans
  wrong

  import { KMeans } from 'apparatus/lib/clustering/k_means';
  const KMeans = require('apparatus/lib/clustering/k_means');

  correct

  const KMeans = require('apparatus/lib/clustering/k_means').KMeans;
  const k_means = new KMeans();

  Some modules export a named property, `KMeans` in this case. ESM imports are not supported.

• LogisticRegression
  wrong

  import LogisticRegression from 'apparatus/lib/regression/logistic_regression';

  correct

  const LogisticRegression = require('apparatus/lib/regression/logistic_regression').LogisticRegression;
  const regression = new LogisticRegression();

  Similar to KMeans, LogisticRegression is a named export from a deep path within the CommonJS module structure.

Quickstart

Demonstrates training a K-Means clustering model with sample 2D data, including setup and output of clusters and centroids.

const KMeans = require('apparatus/lib/clustering/k_means').KMeans;

// Sample data: each inner array is a data point (e.g., [x, y])
const data = [
  [1, 1], [1.5, 2],
  [5, 7], [8, 8],
  [1, 0.8], [9, 11]
];

const k_means = new KMeans();

// Configure K-Means algorithm
// k: number of clusters
// iterations: max iterations
// threshold: convergence threshold
k_means.k = 2;
k_means.iterations = 100;
k_means.threshold = 0.001;

// Train the K-Means model
k_means.train(data, (error, result) => {
  if (error) {
    console.error('K-Means training error:', error);
    return;
  }
  console.log('K-Means Clusters:');
  result.clusters.forEach((cluster, index) => {
    console.log(`  Cluster ${index + 1} Centroid: [${cluster.centroid.join(', ')}]`);
    console.log(`  Points in Cluster ${index + 1}:`, cluster.points);
  });
});