https://www.npmjs.com/package/encog
Encog is a NodeJs ES6 framework based on the Encog Machine Learning Framework by Jeff Heaton.
All credits of the framework should go to Jeff Heaton - http://www.heatonresearch.com/encog/
Based on the encog-java-core v3.4 - https://github.com/encog/encog-java-core
Full documentation and source code - https://github.com/redsoul/encog
npm install encog --save
Just require the library and all of Encog namespace will be available to you:
const Encog = require('encog');npm install --only=dev
npm test
-
Networks
- Basic Network
- Hopfield Network
- BAM (Bidirectional associative memory) Network
- Freeform Network
-
Training
- Back Propagation
- Manhattan Propagation
- Resilient Propagation
-
Stochastic Gradient Descent
- Momentum
- Nesterov
- RMS Prop
- AdaGrad
- Adam
- Levenberg Marquardt
- Neural Simulated Annealing
-
Patterns
- ADALINE
- Feed Forward (Perceptron)
- Elman Network
- Jordan Network
- Hopfield Network
- BAM Network
-
Activation Functions
- Elliott
- Symmetric Elliott
- Gaussian
- Linear
- Ramp
- ReLu
- Sigmoid
- Softmax
- Steepened Sigmoid
- Hyperbolic tangent
-
Error Functions
- Arctangent
- Cross Entropy
- Linear
- Output
const Encog = require('encog');
const XORdataset = Encog.Utils.Datasets.getXORDataSet();
//adjust the log level
Encog.Log.options.logLevel = 'info';
// create a neural network
const network = new Encog.Networks.Basic();
network.addLayer(new Encog.Layers.Basic(null, true, 2));
network.addLayer(new Encog.Layers.Basic(new Encog.ActivationFunctions.Sigmoid(), true, 4));
network.addLayer(new Encog.Layers.Basic(new Encog.ActivationFunctions.Sigmoid(), false, 1));
network.randomize();
const train = new Encog.Training.Propagation.Back(network, XORdataset.input, XORdataset.output);
Encog.Utils.Network.trainNetwork(train, {maxIterations: 250});
const accuracy = Encog.Utils.Network.validateNetwork(network, XORdataset.input, XORdataset.output);
console.log('Accuracy:', accuracy);Resilient Propagation example using Iris Flower Data Set (https://en.wikipedia.org/wiki/Iris_flower_data_set)
const Encog = require('encog');
const _ = require('lodash');
//adjust the log level
Encog.Log.options.logLevel = 'info';
const dataEncoder = new Encog.Preprocessing.DataEncoder();
let irisDataset = Encog.Utils.Datasets.getIrisDataSet();
irisDataset = _.shuffle(irisDataset);
irisDataset = Encog.Preprocessing.DataToolbox.trainTestSplit(irisDataset);
/******************/
//data normalization
/******************/
//apply a specific mapping to each column
const mappings = {
'Sepal.Length': new Encog.Preprocessing.DataMappers.MinMaxScaller(),
'Sepal.Width': new Encog.Preprocessing.DataMappers.MinMaxScaller(),
'Petal.Length': new Encog.Preprocessing.DataMappers.MinMaxScaller(),
'Petal.Width': new Encog.Preprocessing.DataMappers.MinMaxScaller(),
'Species': new Encog.Preprocessing.DataMappers.OneHot(),
};
//Fit to data, then transform it.
let trainData = dataEncoder.fit_transform(irisDataset.train, mappings);
//transform the test data based on the train data
let testData = dataEncoder.transform(irisDataset.test, mappings);
//slice the data in input and output
trainData = Encog.Preprocessing.DataToolbox.sliceOutput(trainData.values, 3);
testData = Encog.Preprocessing.DataToolbox.sliceOutput(testData.values, 3);
// create a neural network
const network = new Encog.Networks.Basic();
network.addLayer(new Encog.Layers.Basic(null, true, 4));
network.addLayer(new Encog.Layers.Basic(new Encog.ActivationFunctions.Sigmoid(), true, 10));
network.addLayer(new Encog.Layers.Basic(new Encog.ActivationFunctions.Sigmoid(), true, 5));
network.addLayer(new Encog.Layers.Basic(new Encog.ActivationFunctions.Sigmoid(), false, 3));
network.randomize();
// train the neural network
const train = new Encog.Training.Propagation.Resilient(network, trainData.input, trainData.output);
Encog.Utils.Network.trainNetwork(train, {minError: 0.01, minIterations: 5});
//validate the neural network
let accuracy = Encog.Utils.Network.validateNetwork(network, testData.input, testData.output);
console.log('Accuracy:', accuracy);
//save the trained network
Encog.Utils.File.saveNetwork(network, 'iris.dat');
//load a pretrained network
const newNetwork = Encog.Utils.File.loadNetwork('iris.dat');
//validate the neural network
accuracy = Encog.Utils.Network.validateNetwork(newNetwork, testData.input, testData.output);
console.log('accuracy: ', accuracy);const Encog = require('encog');
const _ = require('lodash');
const dataEncoder = new Encog.Preprocessing.DataEncoder();
//adjust the log level
Encog.Log.options.logLevel = 'info';
(async () => {
const dataset = await Encog.Preprocessing.DataToolbox.readTrainingCSV(
'./node_modules/encog/examples/data/data_banknote_authentication.csv'
);
const shuffledDataset = _.shuffle(dataset);
const splittedDataset = Encog.Preprocessing.DataToolbox.trainTestSplit(shuffledDataset);
/******************/
//data normalization
/******************/
//apply a specific mapping to each column
const mappings = {
'variance': new Encog.Preprocessing.DataMappers.MinMaxScaller(),
'skewness': new Encog.Preprocessing.DataMappers.MinMaxScaller(),
'curtosis': new Encog.Preprocessing.DataMappers.MinMaxScaller(),
'entropy': new Encog.Preprocessing.DataMappers.MinMaxScaller(),
'class': new Encog.Preprocessing.DataMappers.IntegerParser()
};
//Fit to data, then transform it.
let trainData = dataEncoder.fit_transform(splittedDataset.train, mappings);
//transform the test data based on the train data
let testData = dataEncoder.transform(splittedDataset.test, mappings);
//slice the data in input and output
trainData = Encog.Preprocessing.DataToolbox.sliceOutput(trainData.values, 1);
testData = Encog.Preprocessing.DataToolbox.sliceOutput(testData.values, 1);
// create a neural network
const network = new Encog.Networks.Basic();
network.addLayer(new Encog.Layers.Basic(null, true, 4));
network.addLayer(new Encog.Layers.Basic(new Encog.ActivationFunctions.Sigmoid(), true, 40));
network.addLayer(new Encog.Layers.Basic(new Encog.ActivationFunctions.Sigmoid(), true, 40));
network.addLayer(new Encog.Layers.Basic(new Encog.ActivationFunctions.Sigmoid(), true, 40));
network.addLayer(new Encog.Layers.Basic(new Encog.ActivationFunctions.Sigmoid(), false, 1));
network.randomize();
// train the neural network
const train = new Encog.Training.SGD.StochasticGradientDescent(network, trainData.input, trainData.output, new Encog.Training.SGD.Update.Adam());
Encog.Utils.Network.trainNetwork(train, {minError: 0.01, minIterations: 50, maxIterations: 200});
//validate the neural network
let accuracy = Encog.Utils.Network.validateNetwork(network, testData.input, testData.output);
console.log('Accuracy:', accuracy);
//save the trained network
Encog.Utils.File.saveNetwork(network, 'banknote_authentication.dat');
//load a pretrained network
const newNetwork = Encog.Utils.File.loadNetwork('banknote_authentication.dat');
//validate the neural network
accuracy = Encog.Utils.Network.validateNetwork(newNetwork, testData.input, testData.output);
console.log('accuracy: ', accuracy);
})();const Encog = require('encog');
const _ = require('lodash');
const hopfieldPatterns = Encog.Utils.Datasets.getHopfieldPatterns();
const HopfieldPattern = new Encog.Patterns.Hopfield();
//adjust the log level
Encog.Log.options.logLevel = 'info';
HopfieldPattern.setInputLayer(35);
const network = HopfieldPattern.generate();
_.each(hopfieldPatterns, function (pattern) {
network.addPattern(pattern);
});
network.runUntilStable(10);
const input = [
0, 0, 0, 0, 0,
0, 1, 1, 1, 0,
0, 0, 0, 0, 0,
0, 1, 1, 0, 0,
0, 0, 0, 0, 0,
0, 1, 1, 1, 0,
0, 0, 0, 0, 0
];
const result = network.compute(input);
console.log('Result:', result);
/*
Output:
0, 0, 0, 0, 0,
0, 1, 1, 1, 0,
0, 1, 0, 0, 0,
0, 1, 1, 0, 0,
0, 1, 0, 0, 0,
0, 1, 1, 1, 0,
0, 0, 0, 0, 0
*/8.0.0 or higher
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