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Deepbox
00beginner

Quick Start

A short tour of tensors, DataFrames and a first machine learning model. The script is about 50 lines.

What you will learn

  • Use deepbox/ndarray for tensor, chained add, mul, mean, item.
  • Use deepbox/dataframe for DataFrame creation and printing.
  • Use deepbox/ml for LinearRegression (fit, predict).
  • Use deepbox/preprocess for trainTestSplit.
  • A short tour of tensors, DataFrames and a first machine learning model. The script is about 50 lines.

Source

index.ts
/** * Quick Start * * A short tour of tensors, DataFrames and a first machine learning model. * Run this first to get a feel for the library. */ import { DataFrame } from "deepbox/dataframe";import { LinearRegression } from "deepbox/ml";import { tensor } from "deepbox/ndarray";import { trainTestSplit } from "deepbox/preprocess"; console.log("Deepbox quick start\n"); // 1. Tensors (N-dimensional arrays)console.log("1. Tensors:");const a = tensor([1, 2, 3, 4, 5]);const b = tensor([10, 20, 30, 40, 50]);// Tensor methods chain, so a.add(b).mul(2) reads left to right.console.log("   a + b =", a.add(b).toString());console.log("   (a + b) * 2 =", a.add(b).mul(2).toString());console.log(`   mean(a) = ${a.mean().item()}\n`); // 2. DataFrames (tabular data)console.log("2. DataFrames:");const df = new DataFrame({  name: ["Alice", "Bob", "Charlie"],  age: [25, 30, 35],  score: [85, 90, 78],});console.log(`${df.toString()}\n`); // 3. Machine learningconsole.log("3. Machine learning:"); // Generate simple data: y = 2x + 1const X = tensor([[1], [2], [3], [4], [5], [6], [7], [8]]);const y = tensor([3, 5, 7, 9, 11, 13, 15, 17]); const [XTrain, XTest, yTrain, yTest] = trainTestSplit(X, y, {  testSize: 0.25,  randomState: 42,}); const model = new LinearRegression();model.fit(XTrain, yTrain);const predictions = model.predict(XTest); console.log("   Trained a linear regression model");console.log("   Predictions:", predictions.toString());console.log("   Actual:     ", yTest.toString()); console.log("\nThe numbered examples (01 to 49) each cover one area in more detail.\n");

Output

Console output
Console output only: tensor arithmetic and a mean, a printed DataFrame, and the model's predictions next to the true values.