Minor edits. Tests added. | STUD-7

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

@@ -6,33 +6,39 @@
 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%E2%80%93Hastings_algorithm) for sampling
- * target distribution [distribution].
+ * target distribution [targetDist].
  *
+ *      params:
+ *          - targetDist: function close to the density of the sampled distribution;
+ *          - initialState: initial value of the chain of sampled values.
  */
 public class MetropolisHastingsSampler(
-    public val distribution: (x : Double) -> Double,
+    public val targetDist: (arg : Double) -> Double,
-    public val initState : Double = 0.0,
+    public val initialState : Double = 0.0,
 ) : BlockingDoubleSampler {
     override fun sample(generator: RandomGenerator): BlockingDoubleChain = object : BlockingDoubleChain {
-        var currState = initState
+        var currentState = initialState
+        fun proposalDist(arg : Double) = NormalDistribution(arg, 0.01)
 
         override fun nextBufferBlocking(size: Int): Float64Buffer {
-            val u = generator.nextDoubleBuffer(size)
+            val acceptanceProb = generator.nextDoubleBuffer(size)
 
             return Float64Buffer(size) {index ->
-                val proposalDist = NormalDistribution(currState, 0.01)
+                val newState = proposalDist(currentState).sample(RandomGenerator.default(0)).nextBufferBlocking(5).get(4)
-                val newState = proposalDist.sample(RandomGenerator.default(1)).nextBufferBlocking(1).get(0)
+                val firstComp = targetDist(newState) / targetDist(currentState)
-                val acceptanceRatio = distribution(newState) / distribution(currState)
+                val secondComp = proposalDist(newState).probability(currentState) / proposalDist(currentState).probability(newState)
-                if (u[index] <= acceptanceRatio) {
+                val acceptanceRatio = min(1.0, firstComp * secondComp)
-                    currState = newState
+
-                }
+                currentState = if (acceptanceProb[index] <= acceptanceRatio) newState else currentState
-                currState
+                currentState
             }
         }
 

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

@@ -0,0 +1,26 @@
+/*
+ * 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.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)
+
+
+        val sampledValues = sampler.sample(DefaultGenerator()).nextBufferBlocking(10)
+        assertEquals(0.05, Float64Field.mean(sampledValues), 0.01)
+    }
+}