Generic definition for NDArray

kmath-common/build.gradle

@@ -1,6 +1,8 @@
-description = "Platform-independent interfaces for kotlin maths"
+plugins{
+    id "kotlin-platform-common"
+}
 
-apply plugin: 'kotlin-platform-common'
+description = "Platform-independent interfaces for kotlin maths"
 
 repositories {
     mavenCentral()
@@ -12,3 +14,9 @@ dependencies {
     testCompile "org.jetbrains.kotlin:kotlin-test-common:$kotlin_version"
 }
 
+kotlin {
+    experimental {
+        coroutines "enable"
+    }
+}
+

kmath-common/src/main/kotlin/scientifik/kmath/structures/BufferNDField.kt

@@ -0,0 +1,93 @@
+package scientifik.kmath.structures
+
+import scientifik.kmath.operations.Field
+import kotlin.coroutines.experimental.buildSequence
+
+
+/**
+ * A generic buffer for both primitives and objects
+ */
+interface Buffer<T> {
+    operator fun get(index: Int): T
+    operator fun set(index: Int, value: T)
+}
+
+/**
+ * Generic implementation of NDField based on continuous buffer
+ */
+abstract class BufferNDField<T>(shape: List<Int>, field: Field<T>) : NDField<T>(shape, field) {
+
+    /**
+     * Strides for memory access
+     */
+    private val strides: List<Int> by lazy {
+        ArrayList<Int>(shape.size).apply {
+            var current = 1
+            add(1)
+            shape.forEach {
+                current *= it
+                add(current)
+            }
+        }
+    }
+
+    protected fun offset(index: List<Int>): Int {
+        return index.mapIndexed { i, value ->
+            if (value < 0 || value >= shape[i]) {
+                throw RuntimeException("Index out of shape bounds: ($i,$value)")
+            }
+            value * strides[i]
+        }.sum()
+    }
+
+    protected fun index(offset: Int): List<Int>{
+        return buildSequence {
+            var current = offset
+            var strideIndex = strides.size-2
+            while (strideIndex>=0){
+                yield(current / strides[strideIndex])
+                current %= strides[strideIndex]
+                strideIndex--
+            }
+        }.toList().reversed()
+    }
+
+    private val capacity: Int
+        get() = strides[shape.size]
+
+
+    protected abstract fun createBuffer(capacity: Int, initializer: (Int) -> T): Buffer<T>
+
+    override fun produce(initializer: (List<Int>) -> T): NDArray<T> {
+        val buffer = createBuffer(capacity){initializer(index(it))}
+        return BufferNDArray(this, buffer)
+    }
+
+
+    class BufferNDArray<T>(override val context: BufferNDField<T>, val data: Buffer<T>) : NDArray<T> {
+
+        override fun get(vararg index: Int): T {
+            return data[context.offset(index.asList())]
+        }
+
+        override fun equals(other: Any?): Boolean {
+            if (this === other) return true
+            if (other !is BufferNDArray<*>) return false
+
+            if (context != other.context) return false
+            if (data != other.data) return false
+
+            return true
+        }
+
+        override fun hashCode(): Int {
+            var result = context.hashCode()
+            result = 31 * result + data.hashCode()
+            return result
+        }
+
+        override val self: NDArray<T> get() = this
+    }
+}
+
+

kmath-common/src/main/kotlin/scientifik/kmath/structures/NDArray.kt

@@ -198,15 +198,3 @@ operator fun <T> T.div(arg: NDArray<T>): NDArray<T> = arg.transform { _, value -
     }
 }
 
-/**
- * Create a platform-specific NDArray of doubles
- */
-expect fun realNDArray(shape: List<Int>, initializer: (List<Int>) -> Double = { 0.0 }): NDArray<Double>
-
-fun real2DArray(dim1: Int, dim2: Int, initializer: (Int, Int) -> Double = { _, _ -> 0.0 }): NDArray<Double> {
-    return realNDArray(listOf(dim1, dim2)) { initializer(it[0], it[1]) }
-}
-
-fun real3DArray(dim1: Int, dim2: Int, dim3: Int, initializer: (Int, Int, Int) -> Double = { _, _, _ -> 0.0 }): NDArray<Double> {
-    return realNDArray(listOf(dim1, dim2, dim3)) { initializer(it[0], it[1], it[2]) }
-}

kmath-common/src/main/kotlin/scientifik/kmath/structures/NDArrayFactories.kt

@@ -0,0 +1,38 @@
+package scientifik.kmath.structures
+
+import scientifik.kmath.operations.Field
+
+/**
+ * Create a platform-optimized NDArray of doubles
+ */
+expect fun realNDArray(shape: List<Int>, initializer: (List<Int>) -> Double = { 0.0 }): NDArray<Double>
+
+fun real2DArray(dim1: Int, dim2: Int, initializer: (Int, Int) -> Double = { _, _ -> 0.0 }): NDArray<Double> {
+    return realNDArray(listOf(dim1, dim2)) { initializer(it[0], it[1]) }
+}
+
+fun real3DArray(dim1: Int, dim2: Int, dim3: Int, initializer: (Int, Int, Int) -> Double = { _, _, _ -> 0.0 }): NDArray<Double> {
+    return realNDArray(listOf(dim1, dim2, dim3)) { initializer(it[0], it[1], it[2]) }
+}
+
+
+class SimpleNDField<T: Any>(field: Field<T>, shape: List<Int>) : BufferNDField<T>(shape, field) {
+    override fun createBuffer(capacity: Int, initializer: (Int) -> T): Buffer<T> {
+        val array = ArrayList<T>(capacity)
+        (0 until capacity).forEach {
+            array.add(initializer(it))
+        }
+
+        return object : Buffer<T> {
+            override fun get(index: Int): T = array[index]
+
+            override fun set(index: Int, value: T) {
+                array[index] = initializer(index)
+            }
+        }
+    }
+}
+
+fun <T: Any> simpleNDArray(field: Field<T>, shape: List<Int>, initializer: (List<Int>) -> T): NDArray<T> {
+    return SimpleNDField(field, shape).produce { initializer(it) }
+}

kmath-common/src/test/kotlin/scientifik/kmath/structures/SimpleNDFieldTest.kt

@@ -0,0 +1,15 @@
+package scientifik.kmath.structures
+
+import scientifik.kmath.operations.DoubleField
+import kotlin.test.Test
+import kotlin.test.assertEquals
+
+
+class SimpleNDFieldTest{
+    @Test
+    fun testStrides(){
+        val ndArray = simpleNDArray(DoubleField, listOf(10,10)){(it[0]+it[1]).toDouble()}
+        assertEquals(ndArray[5,5], 10.0)
+    }
+
+}

kmath-jvm/build.gradle

@@ -1,4 +1,7 @@
-apply plugin: 'kotlin-platform-jvm'
+plugins{
+    id "kotlin-platform-jvm"
+    id "me.champeau.gradle.jmh" version "0.4.5"
+}
 
 repositories {
     mavenCentral()

kmath-jvm/src/jmh/kotlin/scietifik/kmath/structures/ArrayBenchmark.kt

@@ -0,0 +1,53 @@
+package scietifik.kmath.structures
+
+import org.openjdk.jmh.annotations.*
+import java.nio.IntBuffer
+
+
+@Fork(1)
+@Warmup(iterations = 2)
+@Measurement(iterations = 50)
+@State(Scope.Benchmark)
+open class ArrayBenchmark {
+
+    lateinit var array: IntArray
+    lateinit var arrayBuffer: IntBuffer
+    lateinit var nativeBuffer: IntBuffer
+
+    @Setup
+    fun setup() {
+        array = IntArray(10000) { it }
+        arrayBuffer = IntBuffer.wrap(array)
+        nativeBuffer = IntBuffer.allocate(10000)
+        for (i in 0 until 10000) {
+            nativeBuffer.put(i,i)
+        }
+    }
+
+    @Benchmark
+    fun benchmarkArrayRead() {
+        var res = 0
+        for (i in 1..10000) {
+            res += array[10000 - i]
+        }
+        print(res)
+    }
+
+    @Benchmark
+    fun benchmarkBufferRead() {
+        var res = 0
+        for (i in 1..10000) {
+            res += arrayBuffer.get(10000 - i)
+        }
+        print(res)
+    }
+
+    @Benchmark
+    fun nativeBufferRead() {
+        var res = 0
+        for (i in 1..10000) {
+            res += nativeBuffer.get(10000 - i)
+        }
+        print(res)
+    }
+}

kmath-jvm/src/main/kotlin/scientifik/kmath/structures/RealNDArray.kt

@@ -3,78 +3,22 @@ package scientifik.kmath.structures
 import scientifik.kmath.operations.DoubleField
 import java.nio.DoubleBuffer
 
-private class RealNDField(shape: List<Int>) : NDField<Double>(shape, DoubleField) {
+private class RealNDField(shape: List<Int>) : BufferNDField<Double>(shape, DoubleField) {
+    override fun createBuffer(capacity: Int, initializer: (Int) -> Double): Buffer<Double> {
+        val array = DoubleArray(capacity, initializer)
+        val buffer = DoubleBuffer.wrap(array)
+        return object : Buffer<Double> {
+            override fun get(index: Int): Double = buffer.get(index)
 
-    /**
+            override fun set(index: Int, value: Double) {
-     * Strides for memory access
+                buffer.put(index, value)
-     */
-    private val strides: List<Int> by lazy {
-        ArrayList<Int>(shape.size).apply {
-            var current = 1
-            add(1)
-            shape.forEach {
-                current *= it
-                add(current)
             }
         }
     }
-
-    fun offset(index: List<Int>): Int {
-        return index.mapIndexed { i, value ->
-            if (value < 0 || value >= shape[i]) {
-                throw RuntimeException("Index out of shape bounds: ($i,$value)")
 }
-            value * strides[i]
-        }.sum()
-    }
-
-    val capacity: Int
-        get() = strides[shape.size]
-
-
-    override fun produce(initializer: (List<Int>) -> Double): NDArray<Double> {
-        //TODO use sparse arrays for large capacities
-        val buffer = DoubleBuffer.allocate(capacity)
-        //FIXME there could be performance degradation due to iteration procedure. Replace by straight iteration
-        NDArray.iterateIndexes(shape).forEach {
-            buffer.put(offset(it), initializer(it))
-        }
-        return RealNDArray(this, buffer)
-    }
-
-    class RealNDArray(override val context: RealNDField, val data: DoubleBuffer) : NDArray<Double> {
-
-        override fun get(vararg index: Int): Double {
-            return data.get(context.offset(index.asList()))
-        }
-
-        override fun equals(other: Any?): Boolean {
-            if (this === other) return true
-            if (javaClass != other?.javaClass) return false
-
-            other as RealNDArray
-
-            if (context.shape != other.context.shape) return false
-            if (data != other.data) return false
-
-            return true
-        }
-
-        override fun hashCode(): Int {
-            var result = context.shape.hashCode()
-            result = 31 * result + data.hashCode()
-            return result
-        }
-
-        //TODO generate fixed hash code for quick comparison?
-
-
-        override val self: NDArray<Double> get() = this
-    }
-}
-
 
 actual fun realNDArray(shape: List<Int>, initializer: (List<Int>) -> Double): NDArray<Double> {
-    //TODO cache fields?
+    //TODO create a cache for fields to save time generating strides?
+
     return RealNDField(shape).produce { initializer(it) }
 }