La Classy

Single Layer Perceptron (SLP) library for Deno.

This library is written TypeScript and Rust and it uses FFI.

Why Classy?

• It’s fast.
• It gives you some freedom to experiment with different combinations of loss functions, activation functions, etc.
• It’s easy to use.

Features

• Optimization Algorithms:
  • Gradient Descent
  • Stochastic Average Gradients
  • Ordinary Least Squares
• Optimizers for updating weights:
  • RMSProp
  • ADAM
• Schedulers for learning rate:
  • One-cycle Scheduler
  • Decay
• Regularization
• Activation Functions:
  • Linear (regression, SVM, etc.)
  • Sigmoid (logistic regression)
  • Softmax (multinomial logistic regression)
  • Tanh (it’s just there)
• Loss Functions:
  • Mean Squared Error (regression)
  • Mean Absolute Error (regression)
  • Cross-Entropy (multinomial classification)
  • Binary Cross-Entropy / Logistic Loss (binary classification)
  • Hinge Loss (binary classification, SVM)

Quick Example

Regression

import { OLSSolver } from "https://deno.land/x/classylala/mod.ts";

const x = [100, 23, 53, 56, 12, 98, 75];
const y = x.map((a) => [a * 6 + 13, a * 4 + 2]);

const solver = new OLSSolver();

solver.train(
  { data: Float64Array.from(x), shape: [x.length, 1] },
  { data: Float64Array.from(y.flat()), shape: [y.length, 2] },
  { silent: false, fit_intercept: true }
);

const res = solver.predict({
  data: Float64Array.from(x),
  shape: [x.length, 1],
});
for (const pred of res.rows()) {
  console.log(pred);
}

There are other examples in /examples

Documentation

Deno /x

Maintainers

Pranev (retraigo)

Check out deno-ml for examples! Discord: Kuro’s Chaos Abyss Graveyard