@lala/classy@1.3.0

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]);
}