AsterMind-ELM Documentation
A modular Extreme Learning Machine (ELM) library for JS/TS (browser + Node)
What you can build — and why this is groundbreaking
Instant, tiny, on-device ML for the web — train in milliseconds, predict with microsecond latency
AsterMind brings instant, tiny, on-device ML to the web. It lets you ship models that train in milliseconds, predict with microsecond latency, and run entirely in the browser — no GPU, no server, no tracking. With Kernel ELMs, Online ELM, DeepELM, and Web Worker offloading, you can create:
- Private, on-device classifiers (language, intent, toxicity, spam) that retrain on user feedback
- Real-time retrieval & reranking with compact embeddings (ELM, KernelELM, Nyström whitening) for search and RAG
- Interactive creative tools (music/drum generators, autocompletes) that respond instantly
- Edge analytics: regressors/classifiers from data that never leaves the page
- Deep ELM chains: stack encoders → embedders → classifiers for powerful pipelines, still tiny and transparent
Why it matters: ELMs give you closed-form training (no heavy SGD), interpretable structure, and tiny memory footprints. AsterMind modernizes ELM with kernels, online learning, workerized training, robust preprocessing, and deep chaining — making seriously fast ML practical for every web app.
New in this release
- •Kernel ELMs (KELMs) — exact and Nyström kernels (RBF/Linear/Poly/Laplacian/Custom) with ridge solve
- •Whitened Nyström — optional Kmm-1/2 whitening via symmetric eigendecomposition
- •Online ELM (OS-ELM) — streaming RLS updates with forgetting factor (no full retrain)
- •DeepELM — multi-layer stacked ELM with non-linear projections
- •Web Worker adapter — off-main-thread training/prediction for ELM and KELM
- •Matrix upgrades — Jacobi eigendecomp, invSqrtSym, improved Cholesky
- •EmbeddingStore 2.0 — unit-norm vectors, ring buffer capacity, metadata filters
- •ELMChain+Embeddings — safer chaining with dimension checks, JSON I/O
- •Activations — added linear and gelu; centralized registry
- •Configs — split into Numeric and Text configs; stronger typing
- •UMD exports — window.astermind exposes ELM, OnlineELM, KernelELM, DeepELM, etc.
- •Robust preprocessing — safer encoder path, improved error handling
AsterMind: Decentralized ELM Framework Inspired by Nature
Welcome to AsterMind, a modular, decentralized ML framework built around cooperating Extreme Learning Machines (ELMs) that self-train, self-evaluate, and self-repair — like the nervous system of a starfish.
How This ELM Library Differs from a Traditional ELM
This library preserves the core Extreme Learning Machine idea — random hidden layer, nonlinear activation, closed-form output solve — but extends it with:
- • Multiple activations (ReLU, LeakyReLU, Sigmoid, Linear, GELU)
- • Xavier/Uniform/He initialization
- • Dropout on hidden activations
- • Sample weighting
- • Metrics gate (RMSE, MAE, Accuracy, F1, Cross-Entropy, R²)
- • JSON export/import
- • Model lifecycle management
- • UniversalEncoder for text (char/token)
- • Data augmentation utilities
- • Chaining (ELMChain) for stacked embeddings
- • Weight reuse (simulated fine-tuning)
- • Logging utilities
AsterMind is designed for:
- • Lightweight, in-browser ML pipelines
- • Transparent, interpretable predictions
- • Continuous, incremental learning
- • Resilient systems with no single point of failure
Core Features
Architecture
- ✅ Modular Architecture
- ✅ Closed-form training (ridge/pseudoinverse)
- ✅ JSON import/export
- ✅ Self-governing training
- ✅ Flexible preprocessing
Activations & Kernels
- ✅ relu, leakyrelu, sigmoid, tanh, linear, gelu
- ✅ Initializers: uniform, xavier, he
- ✅ Kernel ELM with Nyström + whitening
- ✅ Online ELM (RLS) with forgetting factor
Advanced Features
- ✅ DeepELM (stacked layers)
- ✅ Web Worker adapter
- ✅ Embeddings & Chains for retrieval and deep pipelines
- ✅ Retrieval and classification utilities
Deployment
- ✅ Lightweight (ESM + UMD)
- ✅ Zero server/GPU required
- ✅ Private, on-device ML
- ✅ Numeric + Text configs
Installation
NPM (scoped package):
npm install @astermind/astermind-elm
# or
pnpm add @astermind/astermind-elm
# or
yarn add @astermind/astermind-elmCDN / <script> (UMD global astermind):
<!-- jsDelivr -->
<script src="https://cdn.jsdelivr.net/npm/@astermind/astermind-elm/dist/astermind.umd.js"></script>
<!-- or unpkg -->
<script src="https://unpkg.com/@astermind/astermind-elm/dist/astermind.umd.js"></script>
<script>
const { ELM, KernelELM } = window.astermind;
</script>Repository:
Usage Examples
Basic ELM Classifier
import { ELM } from "@astermind/astermind-elm";
const config = { categories: ['English', 'French'], hiddenUnits: 128 };
const elm = new ELM(config);
// Load or train logic here
const results = elm.predict("bonjour");
console.log(results);CommonJS / Node:
const { ELM } = require("@astermind/astermind-elm");Why Use AsterMind?
Because you can build AI systems that:
- Are decentralized
- Self-heal and retrain independently
- Run in the browser
- Are transparent and interpretable
Suggested Experiments
- •Compare retrieval performance with Sentence-BERT and TFIDF
- •Experiment with activations and token vs char encoding
- •Deploy in-browser retraining workflows
"AsterMind doesn't just mimic a brain—it functions more like a starfish: fully decentralized, self-evaluating, and self-repairing."