Reimplement random-forking chain

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

@@ -8,35 +8,51 @@
 package space.kscience.kmath.samplers
 
 import kotlinx.coroutines.CoroutineScope
+import kotlinx.coroutines.Deferred
 import kotlinx.coroutines.ExperimentalCoroutinesApi
+import kotlinx.coroutines.async
 import kotlinx.coroutines.channels.Channel
 import kotlinx.coroutines.isActive
 import kotlinx.coroutines.launch
+import space.kscience.kmath.UnstableKMathAPI
 import space.kscience.kmath.chains.Chain
+import space.kscience.kmath.operations.Group
+import space.kscience.kmath.operations.ScaleOperations
 import space.kscience.kmath.random.RandomGenerator
 import space.kscience.kmath.stat.Sampler
+import space.kscience.kmath.structures.Float64
 import kotlin.coroutines.coroutineContext
 
+@UnstableKMathAPI
+public data class RandomForkingSample<T>(
+    val value: Deferred<T>,
+    val generation: Int,
+    val energy: Float64,
+    val stepChain: Chain<T>
+)
 
 /**
  * A sampler that creates a chain that could be split at each computation
  */
-public class RandomForkingSampler<T: Any>(
+@UnstableKMathAPI
+public class RandomForkingSampler<T : Any>(
     private val scope: CoroutineScope,
-    private val initialValue: T,
+    private val initialValue: suspend (RandomGenerator) -> T,
     private val makeStep: suspend RandomGenerator.(T) -> List<T>
 ) : Sampler<T?> {
 
-    override fun sample(generator: RandomGenerator): Chain<T?> = buildChain(scope, initialValue) { generator.makeStep(it) }
+    override fun sample(generator: RandomGenerator): Chain<T?> =
+        buildChain(scope, initial = { initialValue(generator) }) { generator.makeStep(it) }
 
     public companion object {
         private suspend fun <T> Channel<T>.receiveEvents(
             initial: T,
+            buffer: Int = 50,
             makeStep: suspend (T) -> List<T>
         ) {
             send(initial)
             //inner dispatch queue
-            val innerChannel = Channel<T>(50)
+            val innerChannel = Channel<T>(buffer)
             innerChannel.send(initial)
             while (coroutineContext.isActive && !innerChannel.isEmpty) {
                 val current = innerChannel.receive()
@@ -51,21 +67,71 @@ public class RandomForkingSampler<T: Any>(
         }
 
 
-        public fun <T: Any> buildChain(
+        internal fun <T : Any> buildChain(
             scope: CoroutineScope,
-            initial: T,
+            initial: suspend () -> T,
             makeStep: suspend (T) -> List<T>
         ): Chain<T?> {
             val channel = Channel<T>(Channel.RENDEZVOUS)
             scope.launch {
-                channel.receiveEvents(initial, makeStep)
+                channel.receiveEvents(initial(), makeStep = makeStep)
             }
 
             return object : Chain<T?> {
                 override suspend fun next(): T? = channel.receiveCatching().getOrNull()
 
-                override suspend fun fork(): Chain<T?> = buildChain(scope, channel.receive(), makeStep)
+                override suspend fun fork(): Chain<T?> = buildChain(scope, { channel.receive() }, makeStep)
             }
         }
+
+
+        public fun <T : Any, A> metropolisHastings(
+            scope: CoroutineScope,
+            algebra: A,
+            startPoint: suspend (RandomGenerator) -> T,
+            stepSampler: Sampler<T>,
+            initialEnergy: Float64,
+            energySplitRule: suspend RandomForkingSample<T>.() -> List<Float64>,
+            stepScaleRule: suspend (Float64) -> Float64 = { 1.0 },
+            targetPdf: suspend (T) -> Float64,
+        ): RandomForkingSampler<RandomForkingSample<T>> where A : Group<T>, A : ScaleOperations<T> =
+            RandomForkingSampler<RandomForkingSample<T>>(
+                scope = scope,
+                initialValue = { generator ->
+                    RandomForkingSample<T>(
+                        value = scope.async { startPoint(generator) },
+                        generation = 0,
+                        energy = initialEnergy,
+                        stepChain = stepSampler.sample(generator)
+                    )
+                }
+            ) { previousSample: RandomForkingSample<T> ->
+                val value = previousSample.value.await()
+                previousSample.energySplitRule().map { energy ->
+                    RandomForkingSample<T>(
+                        value = scope.async<T> {
+                            val proposalPoint = with(algebra) {
+                                value + previousSample.stepChain.next() * stepScaleRule(previousSample.energy)
+                            }
+                            val ratio = targetPdf(proposalPoint) / targetPdf(value)
+
+                            if (ratio >= 1.0) {
+                                proposalPoint
+                            } else {
+                                val acceptanceProbability = nextDouble()
+                                if (acceptanceProbability <= ratio) {
+                                    proposalPoint
+                                } else {
+                                    value
+                                }
+                            }
+                        },
+                        generation = previousSample.generation + 1,
+                        energy = 0.0,
+                        stepChain = previousSample.stepChain
+                    )
+                }
+            }
+
     }
 }