MHS first implementation | STUD-7
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/*
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* Copyright 2018-2024 KMath contributors.
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* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
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*/
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package space.kscience.kmath.samplers
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import space.kscience.kmath.chains.BlockingDoubleChain
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import space.kscience.kmath.distributions.NormalDistribution
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import space.kscience.kmath.random.RandomGenerator
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import space.kscience.kmath.structures.Float64Buffer
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/**
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* [Metropolis–Hastings algorithm](https://en.wikipedia.org/wiki/Metropolis%E2%80%93Hastings_algorithm) for sampling
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* target distribution [distribution].
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*
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*/
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public class MetropolisHastingsSampler(
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public val distribution: (x : Double) -> Double,
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public val initState : Double = 0.0,
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) : BlockingDoubleSampler {
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override fun sample(generator: RandomGenerator): BlockingDoubleChain = object : BlockingDoubleChain {
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var currState = initState
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override fun nextBufferBlocking(size: Int): Float64Buffer {
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val u = generator.nextDoubleBuffer(size)
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return Float64Buffer(size) {index ->
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val proposalDist = NormalDistribution(currState, 0.01)
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val newState = proposalDist.sample(RandomGenerator.default(1)).nextBufferBlocking(1).get(0)
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val acceptanceRatio = distribution(newState) / distribution(currState)
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if (u[index] <= acceptanceRatio) {
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currState = newState
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}
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currState
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}
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}
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override suspend fun fork(): BlockingDoubleChain = BoxMullerSampler.sample(generator.fork())
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}
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}
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