Hi all!
I just wrote a simple gradient regressor in Go. Gradient boosting is a
statistical learning method. Given a number of samples it returns a
function that fits the those data and can be used to predict previously
unseen data. The usage is simple, here's an example:
trainSamples := []sample.Sample{
sample.DefaultSample{Xs: map[string]float64{"x": 0}, Y: 10},
sample.DefaultSample{Xs: map[string]float64{"x": 1}, Y: 10},
sample.DefaultSample{Xs: map[string]float64{"x": 2}, Y: 20},
sample.DefaultSample{Xs: map[string]float64{"x": 3}, Y: 20},
sample.DefaultSample{Xs: map[string]float64{"x": 4}, Y: 5},
sample.DefaultSample{Xs: map[string]float64{"x": 5}, Y: 5},
}
predictFunc := gradboostreg.Learn(trainSamples, 0.5, 10)
testSamples := []sample.Sample{
sample.DefaultSample{Xs: map[string]float64{"x": 0.0}, Y: 10},
sample.DefaultSample{Xs: map[string]float64{"x": 0.5}, Y: 10},
sample.DefaultSample{Xs: map[string]float64{"x": 2.5}, Y: 20},
sample.DefaultSample{Xs: map[string]float64{"x": 2.0}, Y: 20},
sample.DefaultSample{Xs: map[string]float64{"x": 4.5}, Y: 5},
}
for i := range testSamples {
predicted, actual := predictFunc(testSamples[i]), testSamples[i].GetY()
fmt.Printf("predicted=%.1f actual=%.1f\n", predicted, actual)
}
// Output:
// predicted=10.0 actual=10.0
// predicted=10.0 actual=10.0
// predicted=20.0 actual=20.0
// predicted=20.0 actual=20.0
// predicted=5.0 actual=5.0
Here's the source: https://github.com/siadat/gradboostreg
Let me know what you think! :)
Thanks,
Sina
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