diff --git a/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/MetropolisHastingsSampler.kt b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/MetropolisHastingsSampler.kt new file mode 100644 index 000000000..d8ded96eb --- /dev/null +++ b/kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/MetropolisHastingsSampler.kt @@ -0,0 +1,50 @@ +/* + * Copyright 2018-2024 KMath contributors. + * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file. + */ + +package space.kscience.kmath.samplers + +import space.kscience.kmath.chains.BlockingDoubleChain +import space.kscience.kmath.distributions.Distribution1D +import space.kscience.kmath.distributions.NormalDistribution +import space.kscience.kmath.random.RandomGenerator +import space.kscience.kmath.structures.Float64Buffer +import kotlin.math.* + +/** + * [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; + * - 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, proposalStd) + + override fun nextBufferBlocking(size: Int): Float64Buffer { + val acceptanceProb = generator.nextDoubleBuffer(size) + + return Float64Buffer(size) {index -> + 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 = sample(generator.fork()) + } + +} \ No newline at end of file diff --git a/kmath-stat/src/commonTest/kotlin/space/kscience/kmath/samplers/TestMetropolisHastingsSampler.kt b/kmath-stat/src/commonTest/kotlin/space/kscience/kmath/samplers/TestMetropolisHastingsSampler.kt new file mode 100644 index 000000000..9ab44f87d --- /dev/null +++ b/kmath-stat/src/commonTest/kotlin/space/kscience/kmath/samplers/TestMetropolisHastingsSampler.kt @@ -0,0 +1,76 @@ +/* + * Copyright 2018-2024 KMath contributors. + * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file. + */ + +package space.kscience.kmath.samplers +import space.kscience.kmath.distributions.NormalDistribution +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 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) + + 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) + } +} \ No newline at end of file