retraigo / la-classy

Machine Learning Module for Single Layer Perceptron ML models, written in Rust for Typescript.

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 { Matrix } from "jsr:@lala/appraisal@0.7.5";
import {
  GradientDescentSolver,
  adamOptimizer,
  huber,
} from "jsr:@lala/classy@1.2.1";

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

const solver = new GradientDescentSolver({
  // Huber loss is a mix of MSE and MAE
  loss: huber(),
  // ADAM optimizer with 1 + 1 input for intercept, 2 outputs.
  optimizer: adamOptimizer(2, 2),
});

// Train for 700 epochs in 2 minibatches
solver.train(
  new Matrix(
    x.map((n) => [n]),
    "f32"
  ),
  new Matrix(y, "f32"),
  { silent: false, fit_intercept: true, epochs: 700, n_batches: 2 }
);

const res = solver.predict(
  new Matrix(
    x.map((n) => [n]),
    "f32"
  )
);

for (let i = 0; i < res.nRows; i += 1) {
  console.log(Array.from(res.row(i)), y[i]);
}

There are other examples in retraigo/deno-ml.

Documentation

JSR

Maintainers

Pranev (retraigo)

Discord: Kuro's Chaos Abyss Graveyard