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Implement Commons RNG-like samplers in kmath-prob module for Multiplatform #164

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CommanderTvis merged 44 commits from feature/mp-samplers into dev 2021-03-31 09:25:44 +03:00
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examples/src/main/kotlin/kscience/kmath/commons/fit/fitWithAutoDiff.kt
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@ -9,6 +9,7 @@ import kscience.kmath.real.RealVector
import kscience.kmath.real.map import kscience.kmath.real.map
import kscience.kmath.real.step import kscience.kmath.real.step
import kscience.kmath.stat.* import kscience.kmath.stat.*
import kscience.kmath.stat.distributions.NormalDistribution
import kscience.kmath.structures.asIterable import kscience.kmath.structures.asIterable
import kscience.kmath.structures.toList import kscience.kmath.structures.toList
import kscience.plotly.* import kscience.plotly.*
@ -33,10 +34,9 @@ operator fun TraceValues.invoke(vector: RealVector) {
/** /**
* Least squares fie with auto-differentiation. Uses `kmath-commons` and `kmath-for-real` modules. * Least squares fie with auto-differentiation. Uses `kmath-commons` and `kmath-for-real` modules.
*/ */
fun main() { suspend fun main() {
//A generator for a normally distributed values //A generator for a normally distributed values
val generator = Distribution.normal() val generator = NormalDistribution(2.0, 7.0)
//A chain/flow of random values with the given seed //A chain/flow of random values with the given seed
val chain = generator.sample(RandomGenerator.default(112667)) val chain = generator.sample(RandomGenerator.default(112667))
@ -49,7 +49,7 @@ fun main() {
//Perform an operation on each x value (much more effective, than numpy) //Perform an operation on each x value (much more effective, than numpy)
val y = x.map { val y = x.map {
val value = it.pow(2) + it + 1 val value = it.pow(2) + it + 1
value + chain.nextDouble() * sqrt(value) value + chain.next() * sqrt(value)
} }
// this will also work, but less effective: // this will also work, but less effective:
// val y = x.pow(2)+ x + 1 + chain.nextDouble() // val y = x.pow(2)+ x + 1 + chain.nextDouble()