Merge pull request #533 from Vasilev-Ilya/STUD-7_metropolis_hastings_…

…sampler

Draft: STUD-7: Metropolis-Hastings sampler implementation

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

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+/*
+ * 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())
+    }
+
+}

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

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+/*
+ * 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)
+    }
+}