Using shaman.js, dump trained model, and use it to do prediction.

regression_prediction.js

const { LinearRegression } = require('shaman');
require('sylvester');

LinearRegression.prototype.dump = function() {
  return {
    means: this.means,
    ranges: this.ranges,
    theta: this.theta.elements,
  }
};

function Predictor({means, ranges, theta}) {
  theta = $M(theta);
  return function predict(input) {
    if (!Array.isArray(input)) {
      input = [input];
    }

    input = input.map((val, i) => (val - means[i]) / ranges[i]);

    const xInput = $V([1]).augment(input);
    const output = theta.transpose().x(xInput);
    return output.e(1,1);
  }
}

// usage
var lr = new LinearRegression(X,Y, {
  algorithm: 'GradientDescent',
  numberOfIterations: 1000,
  learningRate: 0.5,
});
lr.train(err => {
  if(err) {
    throw err;
  }
  
  const model = lr.dump();
  const predict = Predictor(model);
  
  predict(1) === 10; // etc
});