Predict future values with a simple but powerful linear regression algorithm, with JavaScript and TypeScript!
Predict future values with a simple but powerful linear regression algorithm, with JavaScript and TypeScript!
Use this package if you want to make your code greener and need to predict values with linear regression. Instead of
using Tensorflow.js and getting all kind of useless and heavy functions and classes, you only get what you need with linear-regression-ts!
- Generate train and test data.
- Train a one-variable & multi-variable linear regression model with gradient descent.
- Evaluate the performance of the model.
- Predict unknown values.
- Automate the optimization of hyperparameters, also learning rate and the amount of iterations.
- Set custom intercept and slope, and use them to make the predictions.
- JSDoc included to help make the use of this library as downright as possible.
``bash`
npm install linear-regression-ts
`typescript
import { LinearRegression, trainAndTestSets } from "linear-regression-ts"
// Create train and test sets. One column for one feature.
const x = [[0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [7, 8], [9, 10], [10, 11]]
const y = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
const { trainValues, testValues, trainLabels, testLabels } = trainAndTestSets({ inputs: x, labels: y, ratio: 50 })
// Initialize the untrained model.
const lr = new LinearRegression({ inputs: trainValues, labels: trainLabels })
// Train the model.
const iterations = 100000
const learningRate1 = 0.00001
const logging = true
const optimizeStartingWeights = true
const { intercept, slopes, error } = lr.fit({ iterations, learningRate: learningRate1, logging, optimizeStartingWeights })
// Set custom intercept and slope. Let's set the ones we got previously.
lr.setModel({ slopes, intercept })
// Test the model, and get the evaluation of its performance.
const { mse, mae } = lr.scores({ testValues, testLabels })
console.log("MSE", mse, "MAE " + mae)
// Predict unknown values.
const predictions = lr.predict({ inputs: testValues })
let a = 0
for (const i of predictions) {
console.log("Prediction:", i, "Real value:", testLabels[a])
a++
}
// Optimize the hyperparameters. To be fixed.
// const iterationAlternatives = [0, 1, 2, 5, 10, 20, 100, 1000, 5000, 10000]
// const learningRateAlternatives = [0.00001, 0.0001, 0.001, 0.01, 0.1, 1]
// const { iteration, learningRate } = lr.optimizeHyperparams({ iterations: iterationAlternatives, learningRates: learningRateAlternatives })
// console.log("Best iteration amount:", iteration, " and best learning rate:", learningRate)
``