machine-learning

Important notes

This library has a dependency on the nblas package for fast matrix operations. It should work by default on OSX, but on Linux you may need to run apt-get install libblas-dev first. On Windows you may need to install LAPACK.

This library is in an early development phase and many breaking changes are to be expected.

The TypeScript source files can be found on GitHub and the JavaScript production files(including .ts.d files) can be found as an npm package.

Documentation

Below are some simple code usage examples. TypeDocs for all classes can be found here.

• Feedforward Neural Network
• Linear Regression
• Logistic Regression
• Multiclass Logistic Regression
• Nearest Neighbors

Feedforward Neural Network

import * as ml from 'machine-learning';

// Feedforward neural network: solve XNOR problem (opposite of XOR)
const inputs = new ml.Matrix([[0, 0], [0, 1], [1, 0], [1, 1]]);
const targets = new ml.Matrix([[1], [0], [0], [1]]);

const feedforwardNeuralNetwork = new ml.FeedforwardNeuralNetwork([2, 5, 1], 0);
feedforwardNeuralNetwork.setNumberOfEpochs(1000);
feedforwardNeuralNetwork.setLearningRate(1);

feedforwardNeuralNetwork.train(inputs, targets);
const predictions = feedforwardNeuralNetwork.predict(inputs);
console.log(predictions.toArray());
// [ [ 0.9943559154265011 ], [ 0.012148393118769857 ], [ 0.013640408487437417 ], [ 0.9816837627444868 ] ]

For more detailed information, access the FeedforwardNeuralNetwork class documentation

Linear Regression

import * as ml from 'machine-learning';

// Linear Regression: y = 1000 + 200 * x
const inputs = new ml.Matrix([[5], [7], [9], [11], [13]]);
const targets = new ml.Matrix([[2000], [2400], [2800], [3200], [3600]]);

const linearRegression = new ml.LinearRegression();
linearRegression.setNumberOfEpochs(10000);
linearRegression.setLearningRate(0.02);

linearRegression.train(inputs, targets);
const predictions = linearRegression.predict(inputs);
console.log(predictions.toArray());
// [ [ 1999.999991189672 ], [ 2399.9999948012005 ], [ 2799.999998412729 ], [ 3200.0000020242574 ], [ 3600.000005635786 ] ]

For more detailed information, access the LinearRegression class documentation

Logistic Regression

import * as ml from 'machine-learning';

// Logistic Regression: determine if second input is higher than first input
const inputs = new ml.Matrix([[1000, 1100], [4500, 3000], [700, 1300], [1150, 700], [1300, 1200], [600, 650]]);
const targets = new ml.Matrix([[1], [0], [1], [0], [0], [1]]);

const logisticRegression = new ml.LogisticRegression();
logisticRegression.setNumberOfEpochs(1000);
logisticRegression.setLearningRate(0.01);

logisticRegression.train(inputs, targets);
const predictions = logisticRegression.predict(inputs);
console.log(predictions.toArray());
// [ [ 1 ], [ 0 ], [ 1 ], [ 0 ], [ 0 ], [ 1 ] ]

For more detailed information, access the LogisticRegression class documentation

Multiclass Logistic Regression

import * as ml from 'machine-learning';

// Multiclass Logistic Regression: determine the highest value
const inputs = new ml.Matrix([[4500, 1200, 3000], [700, 890, 800], [700, 1200, 1300], [1150, 600, 700], [600, 1500, 1650], [400, 401, 400]]);
const targets = new ml.Matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 0, 0], [0, 0, 1], [0, 1, 0]]);

const multiclassLogisticRegression = new ml.MulticlassLogisticRegression();
multiclassLogisticRegression.setNumberOfEpochs(10000);
multiclassLogisticRegression.setLearningRate(0.1);

multiclassLogisticRegression.train(inputs, targets);
const predictions = multiclassLogisticRegression.predict(inputs);
console.log(predictions.toArray());
// [ [ 1, 0, 0 ], [ 0, 1, 0 ], [ 0, 0, 1 ], [ 1, 0, 0 ], [ 0, 0, 1 ], [ 0, 1, 0 ] ]

For more detailed information, access the MulticlassLogisticRegression class documentation

Nearest Neighbors

import * as ml from 'machine-learning';

// Nearest neighbors: Equidistant examples, breaks ties by considering multiple neighbors even though number set to 1
const inputs = new ml.Matrix([[0, 0], [0, 1], [1, 0], [1, 1], [1, 1], [2, 2]]);
const targets = new ml.Matrix([[0, 0, 0, 1], [0, 0, 1, 0], [0, 1, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0], [0, 0, 0, 1]]);

const nearestNeighbors = new ml.NearestNeighbors(inputs, targets);
nearestNeighbors.setNumberOfNeighbors(1);

const unknowns = new ml.Matrix([[0.5, 0.5], [1.5, 1.5], [1.75, 1.75]]);

const predictions = nearestNeighbors.predict(unknowns);
console.log(predictions.toArray());
// [ [ 0.4, 0.2, 0.2, 0.2 ], [ 0.6666666666666666, 0, 0, 0.3333333333333333 ], [ 0, 0, 0, 1 ] ]

For more detailed information, access the NearestNeighbors class documentation