Fix Mean bug

benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/DotBenchmark.kt

@@ -14,7 +14,6 @@ import space.kscience.kmath.ejml.EjmlLinearSpaceDDRM
 import space.kscience.kmath.linear.invoke
 import space.kscience.kmath.linear.linearSpace
 import space.kscience.kmath.operations.DoubleField
-import space.kscience.kmath.operations.algebra
 import space.kscience.kmath.structures.Buffer
 import kotlin.random.Random
 
@@ -25,11 +24,11 @@ internal class DotBenchmark {
         const val dim = 1000
 
         //creating invertible matrix
-        val matrix1 = Double.algebra.linearSpace.buildMatrix(dim, dim) { i, j ->
-            if (i <= j) random.nextDouble() else 0.0
+        val matrix1 = DoubleField.linearSpace.buildMatrix(dim, dim) { _, _ ->
+            random.nextDouble()
         }
-        val matrix2 = Double.algebra.linearSpace.buildMatrix(dim, dim) { i, j ->
-            if (i <= j) random.nextDouble() else 0.0
+        val matrix2 = DoubleField.linearSpace.buildMatrix(dim, dim) { _, _ ->
+            random.nextDouble()
         }
 
         val cmMatrix1 = CMLinearSpace { matrix1.toCM() }
@@ -65,7 +64,7 @@ internal class DotBenchmark {
     }
 
     @Benchmark
-    fun doubleDot(blackhole: Blackhole) = with(Double.algebra.linearSpace) {
+    fun doubleDot(blackhole: Blackhole) = with(DoubleField.linearSpace) {
         blackhole.consume(matrix1 dot matrix2)
     }
 }

benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt

@@ -75,8 +75,9 @@ internal class ExpressionsInterpretersBenchmark {
         private val algebra = DoubleField
         private const val times = 1_000_000
 
-        private val functional = DoubleField.expressionInExtendedField {
-            bindSymbol(x) * number(2.0) + number(2.0) / bindSymbol(x) - number(16.0) / sin(bindSymbol(x))
+        private val functional = DoubleField.expression {
+            val x = bindSymbol(Symbol.x)
+            x * number(2.0) + 2.0 / x - 16.0 / sin(x)
         }
 
         private val node = MstExtendedField {

kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/FunctionalExpressionAlgebra.kt

@@ -192,3 +192,7 @@ public inline fun <T, A : Field<T>> A.expressionInField(
 public inline fun <T, A : ExtendedField<T>> A.expressionInExtendedField(
     block: FunctionalExpressionExtendedField<T, A>.() -> Expression<T>,
 ): Expression<T> = FunctionalExpressionExtendedField(this).block()
+
+public inline fun DoubleField.expression(
+    block: FunctionalExpressionExtendedField<Double, DoubleField>.() -> Expression<Double>,
+): Expression<Double> = FunctionalExpressionExtendedField(this).block()

kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/DoubleLinearSpace.kt

@@ -13,7 +13,6 @@ import space.kscience.kmath.operations.DoubleBufferOperations
 import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.structures.Buffer
 import space.kscience.kmath.structures.DoubleBuffer
-import space.kscience.kmath.structures.indices
 
 public object DoubleLinearSpace : LinearSpace<Double, DoubleField> {
 
@@ -30,7 +29,6 @@ public object DoubleLinearSpace : LinearSpace<Double, DoubleField> {
         initializer: DoubleField.(i: Int, j: Int) -> Double
     ): Matrix<Double> = ndRing(rows, columns).produce { (i, j) -> DoubleField.initializer(i, j) }.as2D()
 
-
     override fun buildVector(size: Int, initializer: DoubleField.(Int) -> Double): DoubleBuffer =
         DoubleBuffer(size) { DoubleField.initializer(it) }
 
@@ -50,9 +48,9 @@ public object DoubleLinearSpace : LinearSpace<Double, DoubleField> {
 
     // Create a continuous in-memory representation of this vector for better memory layout handling
     private fun Buffer<Double>.linearize() = if (this is DoubleBuffer) {
-        this
+        this.array
     } else {
-        DoubleBuffer(size) { get(it) }
+        DoubleArray(size) { get(it) }
     }
 
     @OptIn(PerformancePitfall::class)

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/Mean.kt

@@ -27,8 +27,13 @@ public class Mean<T>(
 
     override suspend fun evaluate(data: Buffer<T>): T = super<ComposableStatistic>.evaluate(data)
 
-    override suspend fun computeIntermediate(data: Buffer<T>): Pair<T, Int> =
-        evaluateBlocking(data) to data.size
+    override suspend fun computeIntermediate(data: Buffer<T>): Pair<T, Int> = group {
+        var res = zero
+        for (i in data.indices) {
+            res += data[i]
+        }
+        res to data.size
+    }
 
     override suspend fun composeIntermediate(first: Pair<T, Int>, second: Pair<T, Int>): Pair<T, Int> =
         group { first.first + second.first } to (first.second + second.second)
@@ -38,9 +43,11 @@ public class Mean<T>(
     }
 
     public companion object {
-        //TODO replace with optimized version which respects overflow
+        @Deprecated("Use Double.mean instead")
         public val double: Mean<Double> = Mean(DoubleField) { sum, count -> sum / count }
+        @Deprecated("Use Int.mean instead")
         public val int: Mean<Int> = Mean(IntRing) { sum, count -> sum / count }
+        @Deprecated("Use Long.mean instead")
         public val long: Mean<Long> = Mean(LongRing) { sum, count -> sum / count }
 
         public fun evaluate(buffer: Buffer<Double>): Double = double.evaluateBlocking(buffer)
@@ -48,3 +55,11 @@ public class Mean<T>(
         public fun evaluate(buffer: Buffer<Long>): Long = long.evaluateBlocking(buffer)
     }
 }
+
+
+//TODO replace with optimized version which respects overflow
+public val Double.Companion.mean: Mean<Double> get() = Mean(DoubleField) { sum, count -> sum / count }
+public val Int.Companion.mean: Mean<Int> get() = Mean(IntRing) { sum, count -> sum / count }
+public val Long.Companion.mean: Mean<Long> get() =  Mean(LongRing) { sum, count -> sum / count }
+
+

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/Statistic.kt

@@ -8,7 +8,6 @@ package space.kscience.kmath.stat
 import kotlinx.coroutines.CoroutineDispatcher
 import kotlinx.coroutines.Dispatchers
 import kotlinx.coroutines.ExperimentalCoroutinesApi
-import kotlinx.coroutines.FlowPreview
 import kotlinx.coroutines.flow.Flow
 import kotlinx.coroutines.flow.map
 import kotlinx.coroutines.flow.runningReduce
@@ -18,16 +17,23 @@ import space.kscience.kmath.structures.Buffer
 /**
  * A function, that transforms a buffer of random quantities to some resulting value
  */
-public interface Statistic<in T, out R> {
+public fun interface Statistic<in T, out R> {
     public suspend fun evaluate(data: Buffer<T>): R
 }
 
-public interface BlockingStatistic<in T, out R> : Statistic<T, R> {
+public suspend operator fun <T, R> Statistic<T, R>.invoke(data: Buffer<T>): R = evaluate(data)
+
+/**
+ * A statistic that is computed in a synchronous blocking mode
+ */
+public fun interface BlockingStatistic<in T, out R> : Statistic<T, R> {
     public fun evaluateBlocking(data: Buffer<T>): R
 
     override suspend fun evaluate(data: Buffer<T>): R = evaluateBlocking(data)
 }
 
+public operator fun <T, R> BlockingStatistic<T, R>.invoke(data: Buffer<T>): R = evaluateBlocking(data)
+
 /**
  * A statistic tha could be computed separately on different blocks of data and then composed
  *
@@ -48,8 +54,10 @@ public interface ComposableStatistic<in T, I, out R> : Statistic<T, R> {
     override suspend fun evaluate(data: Buffer<T>): R = toResult(computeIntermediate(data))
 }
 
-@FlowPreview
-@ExperimentalCoroutinesApi
+/**
+ * Flow intermediate state of the [ComposableStatistic]
+ */
+@OptIn(ExperimentalCoroutinesApi::class)
 private fun <T, I, R> ComposableStatistic<T, I, R>.flowIntermediate(
     flow: Flow<Buffer<T>>,
     dispatcher: CoroutineDispatcher = Dispatchers.Default,
@@ -64,7 +72,7 @@ private fun <T, I, R> ComposableStatistic<T, I, R>.flowIntermediate(
  *
  * The resulting flow contains values that include the whole previous statistics, not only the last chunk.
  */
-@OptIn(FlowPreview::class, ExperimentalCoroutinesApi::class)
+@OptIn(ExperimentalCoroutinesApi::class)
 public fun <T, I, R> ComposableStatistic<T, I, R>.flow(
     flow: Flow<Buffer<T>>,
     dispatcher: CoroutineDispatcher = Dispatchers.Default,

kmath-stat/src/jvmTest/kotlin/space/kscience/kmath/stat/StatisticTest.kt

@@ -5,11 +5,13 @@
 
 package space.kscience.kmath.stat
 
-import kotlinx.coroutines.flow.drop
 import kotlinx.coroutines.flow.first
+import kotlinx.coroutines.flow.last
+import kotlinx.coroutines.flow.take
 import kotlinx.coroutines.runBlocking
 import space.kscience.kmath.streaming.chunked
 import kotlin.test.Test
+import kotlin.test.assertEquals
 
 internal class StatisticTest {
     //create a random number generator.
@@ -22,12 +24,27 @@ internal class StatisticTest {
     val chunked = data.chunked(1000)
 
     @Test
-    fun testParallelMean() = runBlocking {
-        val average = Mean.double
-            .flow(chunked) //create a flow with results
-            .drop(99) // Skip first 99 values and use one with total data
-            .first() //get 1e5 data samples average
+    fun singleBlockingMean() {
+        val first = runBlocking { chunked.first()}
+        val res = Double.mean(first)
+        assertEquals(0.5,res, 1e-1)
+    }
 
-        println(average)
+    @Test
+    fun singleSuspendMean() = runBlocking {
+        val first = runBlocking { chunked.first()}
+        val res = Double.mean(first)
+        assertEquals(0.5,res, 1e-1)
     }
+
+    @Test
+    fun parallelMean() = runBlocking {
+        val average = Double.mean
+            .flow(chunked) //create a flow from evaluated results
+            .take(100) // Take 100 data chunks from the source and accumulate them
+            .last() //get 1e5 data samples average
+
+        assertEquals(0.5,average, 1e-3)
+    }
+
 }