full - sepal.js

var brain = require('brain.js')
var fs = require('fs')

// configuration to be used in the brain
const config = {
    binaryThresh: 0.5, // arbitary value
    hiddenLayers: [3], // the size of the hidden layers in the network
    activation: 'sigmoid' // activation function
}

// array to hold the training data after formatting
var trainingData = []

// initialise a new backpropogating neural network
const net = new brain.NeuralNetwork(config)

// load each line of the training data into a new index of an array
var trainingFile = fs.readFileSync("data.csv").toString().split("\n")

// iterate over each line of the training data, adding it to a new array for training later
for (var i = 0; i < trainingFile.length; i++) {
    var entry = trainingFile[i]

    var values = entry.split(",")
    var points = [parseInt(values[0]), parseInt(values[1]), parseInt(values[2]), parseInt(values[3])]
   
    if (i <= 49) {
        trainingData.push({input: points, output: {setosa: 1}})
    }
    else if (i > 49 && i <= 99) {
        trainingData.push({input: points, output: {versicolor: 1}})
    }
    else if (i > 99 && i <= 149) {
        trainingData.push({input: points, output: {virginica: 1}})
    }
}

// console log the output of each iteration
net.train(trainingData, {
    log: true
})

// test data - actually virginica
var output = net.run([6,3,5,2])
console.log(output)