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Kotlin Linear Regression with Gradient Descent
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import java.net.URL | |
fun main(args: Array<String>) { | |
// Fit points to a line conforming to y = mx + b | |
class Point(val x: Double, val y: Double) | |
val points = URL("https://tinyurl.com/yaxgfjzt") | |
.readText().split(Regex("\\r?\\n")) | |
.asSequence() | |
.filter { it.isNotEmpty() } | |
.map { it.split(",") } | |
.map { Point(it[0].toDouble(), it[1].toDouble()) } | |
.toList() | |
// Points that exactly fit y = 2x + 100 | |
// You'll want epochs = 1000000 and learningRate = .00001 | |
// val points = (1..100).map { Point(it, (it * 2) + 100) } | |
val n = points.count().toDouble() | |
// partial derivative with respect to M | |
fun dM(expectedYs: List<Double>) = | |
(-2.0 / n) * points.mapIndexed { i, p -> p.x * (p.y - expectedYs[i]) }.sum() | |
// partial derivative with respect to B | |
fun dB(expectedYs: List<Double>) = | |
(-2.0 / n) * points.mapIndexed { i, p -> p.y - expectedYs[i] }.sum() | |
val learningRate = .000001 | |
var m = 0.0 | |
var b = 0.0 | |
val epochs = 1000 // epochs is a fancy name for # of iterations | |
(0..epochs).forEach { _ -> | |
val yPredictions = points.map { (m * it.x) + b } | |
val dM = dM(yPredictions) | |
val dB = dB(yPredictions) | |
m -= learningRate * dM | |
b -= learningRate * dB | |
} | |
println("f(x) = ${m}x + $b") | |
// RESULT: | |
// f(x) = 1.6633912038847907x + 0.003908633274244349 | |
// View Graph on Desmos | |
// https://www.desmos.com/calculator/ntomwigo6k | |
} | |
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