59 lines
2.0 KiB
CoffeeScript
59 lines
2.0 KiB
CoffeeScript
{ assert } = require 'chai'
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Chance = require 'chance'
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brain = require 'brain'
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_ = require 'lodash'
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d3 = require 'd3'
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chance = new Chance()
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module.exports =
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'neural net - detect sine chart type': (done) ->
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fns =
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# A "rough" sine wave.
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sine: (x) -> Math.sin chance.floating 'min': Math.max(x - 1, 0), 'max': x + 1
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# A linear function.
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linear: (x) -> ( chance.floating 'min': Math.max(x - 1, 0), 'max': x + 1 ) + 2
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# Generate sample data points.
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samples = 10
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data = _.map [ 0 ... 10 ], (i) ->
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fn = fns[ [ 'linear', 'sine' ][ sine = +(i < (samples / 2)) ] ]
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points = ( fn(j) for j in [ 0 ... 100 ] )
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{ points, sine }
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# Scale the data to just 6 points.
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for chart in data then do (chart) ->
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step = Math.round (len = chart.points.length) / 5
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chart.points = ( chart.points[ Math.min(i, len - 1) ] for i in _.range(0, len + step, step) )
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# Scale the values for each chart.
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for chart in data then do (chart) ->
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scale = d3.scale.linear().range([ 0, 1 ]).domain([ d3.min(chart.points), d3.max(chart.points) ])
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chart.points = _.map chart.points, scale
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# Split the dataset.
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training = data[ 1...9 ]
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testing = [ data[0] ].concat data[9]
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net = new brain.NeuralNetwork()
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keys = [ 0, 0.2, 0.4, 0.6, 0.8, 1 ]
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input = for chart in training then do (chart) ->
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'input': _.zipObject keys, chart.points
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'output':
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'sine': chart.sine
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net.train input,
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'errorThresh': 0.005 # error threshold to reach
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'iterations': 5e4 # maximum training iterations
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'log': no # console.log() progress periodically
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'logPeriod': 1e3 # number of iterations between logging
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'learningRate': 0.2 # learning rate
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# Now test it.
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for sample in testing
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out = net.run _.zipObject keys, sample.points
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assert sample.sine is Math.round(out.sine), "Network says #{out.sine} but sample says #{sample.sine}"
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do done |