teldufalsari/kmath
2
0
Fork 0
forked from kscience/kmath
Code Pull Requests 1 Activity

Minor edits. Tests added. | STUD-7

Browse Source
This commit is contained in:
vasilev.ilya 2024-05-20 01:12:27 +03:00
parent dbc5488eb2
commit 3417d8cdc1
2 changed files with 65 additions and 13 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

18
kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/MetropolisHastingsSampler.kt
View File

@ -13,36 +13,38 @@ import space.kscience.kmath.structures.Float64Buffer
import kotlin.math.*
/**
* [Metropolis–Hastings algorithm](https://en.wikipedia.org/wiki/Metropolis%E2%80%93Hastings_algorithm) for sampling
* [Metropolis–Hastings algorithm](https://en.wikipedia.org/wiki/Metropolis-Hastings_algorithm) for sampling
* target distribution [targetDist].
*
* The normal distribution is used as the proposal function.
*
* params:
* - targetDist: function close to the density of the sampled distribution;
* - initialState: initial value of the chain of sampled values.
* - initialState: initial value of the chain of sampled values;
* - proposalStd: standard deviation of the proposal function.
*/
public class MetropolisHastingsSampler(
public val targetDist: (arg : Double) -> Double,
public val initialState : Double = 0.0,
public val proposalStd : Double = 1.0,
) : BlockingDoubleSampler {
override fun sample(generator: RandomGenerator): BlockingDoubleChain = object : BlockingDoubleChain {
var currentState = initialState
fun proposalDist(arg : Double) = NormalDistribution(arg, 0.01)
fun proposalDist(arg : Double) = NormalDistribution(arg, proposalStd)
override fun nextBufferBlocking(size: Int): Float64Buffer {
val acceptanceProb = generator.nextDoubleBuffer(size)
return Float64Buffer(size) {index ->
val newState = proposalDist(currentState).sample(RandomGenerator.default(0)).nextBufferBlocking(5).get(4)
val firstComp = targetDist(newState) / targetDist(currentState)
val secondComp = proposalDist(newState).probability(currentState) / proposalDist(currentState).probability(newState)
val acceptanceRatio = min(1.0, firstComp * secondComp)
val newState = proposalDist(currentState).sample(generator).nextBufferBlocking(1).get(0)
val acceptanceRatio = min(1.0, targetDist(newState) / targetDist(currentState))
currentState = if (acceptanceProb[index] <= acceptanceRatio) newState else currentState
currentState
}
}
override suspend fun fork(): BlockingDoubleChain = BoxMullerSampler.sample(generator.fork())
override suspend fun fork(): BlockingDoubleChain = sample(generator.fork())
}
}

60
kmath-stat/src/commonTest/kotlin/space/kscience/kmath/samplers/TestMetropolisHastingsSampler.kt
View File

@ -9,18 +9,68 @@ import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.random.DefaultGenerator
import space.kscience.kmath.stat.invoke
import space.kscience.kmath.stat.mean
import kotlin.math.exp
import kotlin.math.pow
import kotlin.test.Test
import kotlin.test.assertEquals
class TestMetropolisHastingsSampler {
@Test
fun samplingNormalTest1() {
fun myDist(arg : Double) = NormalDistribution(0.0, 1.0).probability(arg)
val sampler = MetropolisHastingsSampler(::myDist)
fun samplingNormalTest() {
fun normalDist1(arg : Double) = NormalDistribution(0.5, 1.0).probability(arg)
var sampler = MetropolisHastingsSampler(::normalDist1, proposalStd = 1.0)
var sampledValues = sampler.sample(DefaultGenerator()).nextBufferBlocking(1_000_000)
assertEquals(0.5, Float64Field.mean(sampledValues), 1e-2)
val sampledValues = sampler.sample(DefaultGenerator()).nextBufferBlocking(10)
assertEquals(0.05, Float64Field.mean(sampledValues), 0.01)
fun normalDist2(arg : Double) = NormalDistribution(68.13, 1.0).probability(arg)
sampler = MetropolisHastingsSampler(::normalDist2, initialState = 63.0, proposalStd = 1.0)
sampledValues = sampler.sample(DefaultGenerator()).nextBufferBlocking(1_000_000)
assertEquals(68.13, Float64Field.mean(sampledValues), 1e-2)
}
@Test
fun samplingExponentialTest() {
fun expDist(arg : Double, param : Double) : Double {
if (arg < 0.0) { return 0.0 }
return param * exp(-param * arg)
}
fun expDist1(arg : Double) = expDist(arg, 0.5)
var sampler = MetropolisHastingsSampler(::expDist1, initialState = 2.0, proposalStd = 1.0)
var sampledValues = sampler.sample(DefaultGenerator()).nextBufferBlocking(1_000_000)
assertEquals(2.0, Float64Field.mean(sampledValues), 1e-2)
fun expDist2(arg : Double) = expDist(arg, 2.0)
sampler = MetropolisHastingsSampler(::expDist2, initialState = 9.0, proposalStd = 1.0)
sampledValues = sampler.sample(DefaultGenerator()).nextBufferBlocking(1_000_000)
assertEquals(0.5, Float64Field.mean(sampledValues), 1e-2)
}
@Test
fun samplingRayleighTest() {
fun rayleighDist(arg : Double, sigma : Double) : Double {
if (arg < 0.0) { return 0.0 }
val expArg = (arg / sigma).pow(2)
return arg * exp(-expArg / 2.0) / sigma.pow(2)
}
fun rayleighDist1(arg : Double) = rayleighDist(arg, 1.0)
var sampler = MetropolisHastingsSampler(::rayleighDist1, initialState = 2.0, proposalStd = 1.0)
var sampledValues = sampler.sample(DefaultGenerator()).nextBufferBlocking(1_000_000)
assertEquals(1.25, Float64Field.mean(sampledValues), 1e-2)
fun rayleighDist2(arg : Double) = rayleighDist(arg, 2.0)
sampler = MetropolisHastingsSampler(::rayleighDist2, proposalStd = 1.0)
sampledValues = sampler.sample(DefaultGenerator()).nextBufferBlocking(10_000_000)
assertEquals(2.5, Float64Field.mean(sampledValues), 1e-2)
}
}