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 * xconst 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 inputconst 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 valueconst 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 1const 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