teldufalsari/kmath
2
0
Fork 0
forked from kscience/kmath
Code Pull Requests 1 Activity

Safe shapes

Browse Source
This commit is contained in:
Alexander Nozik 2022-10-14 12:47:57 +03:00
parent c653052d8c
commit b0abcf2d0c
No known key found for this signature in database
GPG Key ID: F7FCF2DD25C71357
72 changed files with 860 additions and 517 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
CHANGELOG.md
View File

@ -7,6 +7,7 @@
- Algebra now has an obligatory `bufferFactory` (#477).
### Changed
- Shape is read-only
- Major refactor of tensors (only minor API changes)
- Kotlin 1.7.20
- `LazyStructure` `deffered` -> `async` to comply with coroutines code style

11
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/NDFieldBenchmark.kt
View File

@ -13,10 +13,8 @@ import org.jetbrains.kotlinx.multik.api.Multik
import org.jetbrains.kotlinx.multik.api.ones
import org.jetbrains.kotlinx.multik.ndarray.data.DN
import org.jetbrains.kotlinx.multik.ndarray.data.DataType
import space.kscience.kmath.nd.BufferedFieldOpsND
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.ndAlgebra
import space.kscience.kmath.nd.one
import space.kscience.kmath.misc.UnsafeKMathAPI
import space.kscience.kmath.nd.*
import space.kscience.kmath.nd4j.nd4j
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.tensors.core.DoubleTensor
@ -69,9 +67,10 @@ internal class NDFieldBenchmark {
blackhole.consume(res)
}
@OptIn(UnsafeKMathAPI::class)
@Benchmark
fun multikInPlaceAdd(blackhole: Blackhole) = with(multikAlgebra) {
val res = Multik.ones<Double, DN>(shape, DataType.DoubleDataType).wrap()
val res = Multik.ones<Double, DN>(shape.asArray(), DataType.DoubleDataType).wrap()
repeat(n) { res += 1.0 }
blackhole.consume(res)
}
@ -86,7 +85,7 @@ internal class NDFieldBenchmark {
private companion object {
private const val dim = 1000
private const val n = 100
private val shape = intArrayOf(dim, dim)
private val shape = Shape(dim, dim)
private val specializedField = DoubleField.ndAlgebra
private val genericField = BufferedFieldOpsND(DoubleField)
private val nd4jField = DoubleField.nd4j

14
build.gradle.kts
View File

@ -1,4 +1,3 @@
import space.kscience.gradle.isInDevelopment
import space.kscience.gradle.useApache2Licence
import space.kscience.gradle.useSPCTeam
@ -15,7 +14,7 @@ allprojects {
}
group = "space.kscience"
version = "0.3.1-dev-4"
version = "0.3.1-dev-5"
}
subprojects {
@ -78,11 +77,12 @@ ksciencePublish {
}
github("kmath", "SciProgCentre")
space(
if (isInDevelopment) {
"https://maven.pkg.jetbrains.space/mipt-npm/p/sci/dev"
} else {
"https://maven.pkg.jetbrains.space/mipt-npm/p/sci/release"
}
"https://maven.pkg.jetbrains.space/spc/p/sci/maven"
// if (isInDevelopment) {
// "https://maven.pkg.jetbrains.space/spc/p/sci/dev"
// } else {
// "https://maven.pkg.jetbrains.space/spc/p/sci/release"
// }
)
sonatype()
}

5
examples/src/main/kotlin/space/kscience/kmath/structures/StreamDoubleFieldND.kt
View File

@ -17,7 +17,7 @@ import java.util.stream.IntStream
* A demonstration implementation of NDField over Real using Java [java.util.stream.DoubleStream] for parallel
* execution.
*/
class StreamDoubleFieldND(override val shape: IntArray) : FieldND<Double, DoubleField>,
class StreamDoubleFieldND(override val shape: Shape) : FieldND<Double, DoubleField>,
NumbersAddOps<StructureND<Double>>,
ExtendedField<StructureND<Double>> {
@ -31,6 +31,7 @@ class StreamDoubleFieldND(override val shape: IntArray) : FieldND<Double, Double
return structureND(shape) { d }
}
@OptIn(PerformancePitfall::class)
private val StructureND<Double>.buffer: DoubleBuffer
get() = when {
!shape.contentEquals(this@StreamDoubleFieldND.shape) -> throw ShapeMismatchException(
@ -110,4 +111,4 @@ class StreamDoubleFieldND(override val shape: IntArray) : FieldND<Double, Double
override fun atanh(arg: StructureND<Double>): BufferND<Double> = arg.map { atanh(it) }
}
fun DoubleField.ndStreaming(vararg shape: Int): StreamDoubleFieldND = StreamDoubleFieldND(shape)
fun DoubleField.ndStreaming(vararg shape: Int): StreamDoubleFieldND = StreamDoubleFieldND(Shape(shape))

5
examples/src/main/kotlin/space/kscience/kmath/structures/StructureReadBenchmark.kt
View File

@ -5,16 +5,19 @@
package space.kscience.kmath.structures
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.nd.BufferND
import space.kscience.kmath.nd.ColumnStrides
import space.kscience.kmath.nd.Shape
import kotlin.system.measureTimeMillis
@Suppress("ASSIGNED_BUT_NEVER_ACCESSED_VARIABLE")
@OptIn(PerformancePitfall::class)
fun main() {
val n = 6000
val array = DoubleArray(n * n) { 1.0 }
val buffer = DoubleBuffer(array)
val strides = ColumnStrides(intArrayOf(n, n))
val strides = ColumnStrides(Shape(n, n))
val structure = BufferND(strides, buffer)
measureTimeMillis {

12
examples/src/main/kotlin/space/kscience/kmath/structures/StructureWriteBenchmark.kt
View File

@ -5,16 +5,20 @@
package space.kscience.kmath.structures
import space.kscience.kmath.nd.BufferND
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.mapToBuffer
import space.kscience.kmath.operations.map
import kotlin.system.measureTimeMillis
private inline fun <T, reified R: Any> BufferND<T>.map(block: (T) -> R): BufferND<R> = BufferND(indices, buffer.map(block))
@Suppress("UNUSED_VARIABLE")
fun main() {
val n = 6000
val structure = StructureND.buffered(intArrayOf(n, n), Buffer.Companion::auto) { 1.0 }
structure.mapToBuffer { it + 1 } // warm-up
val time1 = measureTimeMillis { val res = structure.mapToBuffer { it + 1 } }
val structure = StructureND.buffered(Shape(n, n), Buffer.Companion::auto) { 1.0 }
structure.map { it + 1 } // warm-up
val time1 = measureTimeMillis { val res = structure.map { it + 1 } }
println("Structure mapping finished in $time1 millis")
val array = DoubleArray(n * n) { 1.0 }

16
examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
View File

@ -6,6 +6,8 @@
package space.kscience.kmath.tensors
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.nd.contentEquals
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.tensors.core.DoubleTensor
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
@ -23,10 +25,10 @@ fun main() {
DoubleTensorAlgebra {
// take coefficient vector from normal distribution
val alpha = randomNormal(
intArrayOf(5),
Shape(5),
randSeed
) + fromArray(
intArrayOf(5),
Shape(5),
doubleArrayOf(1.0, 2.5, 3.4, 5.0, 10.1)
)
@ -34,7 +36,7 @@ fun main() {
// also take sample of size 20 from normal distribution for x
val x = randomNormal(
intArrayOf(20, 5),
Shape(20, 5),
randSeed
)
@ -50,11 +52,13 @@ fun main() {
// inverse Sigma matrix can be restored from singular values with diagonalEmbedding function
val sigma = diagonalEmbedding(singValues.map{ if (abs(it) < 1e-3) 0.0 else 1.0/it })
val sigma = diagonalEmbedding(singValues.map { if (abs(it) < 1e-3) 0.0 else 1.0 / it })
val alphaOLS = v dot sigma dot u.transposed() dot y
println("Estimated alpha:\n" +
"$alphaOLS")
println(
"Estimated alpha:\n" +
"$alphaOLS"
)
// figure out MSE of approximation
fun mse(yTrue: DoubleTensor, yPred: DoubleTensor): Double {

9
examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt
View File

@ -5,6 +5,7 @@
package space.kscience.kmath.tensors
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.tensors.core.tensorAlgebra
import space.kscience.kmath.tensors.core.withBroadcast
@ -16,7 +17,7 @@ fun main(): Unit = Double.tensorAlgebra.withBroadcast { // work in context with
// assume x is range from 0 until 10
val x = fromArray(
intArrayOf(10),
Shape(10),
DoubleArray(10) { it.toDouble() }
)
@ -41,13 +42,13 @@ fun main(): Unit = Double.tensorAlgebra.withBroadcast { // work in context with
// save means ans standard deviations for further recovery
val mean = fromArray(
intArrayOf(2),
Shape(2),
doubleArrayOf(xMean, yMean)
)
println("Means:\n$mean")
val std = fromArray(
intArrayOf(2),
Shape(2),
doubleArrayOf(xStd, yStd)
)
println("Standard deviations:\n$std")
@ -68,7 +69,7 @@ fun main(): Unit = Double.tensorAlgebra.withBroadcast { // work in context with
// we can restore original data from reduced data;
// for example, find 7th element of dataset.
val n = 7
val restored = (datasetReduced.getTensor(n) dot v.view(intArrayOf(1, 2))) * std + mean
val restored = (datasetReduced.getTensor(n) dot v.view(Shape(1, 2))) * std + mean
println("Original value:\n${dataset.getTensor(n)}")
println("Restored value:\n$restored")
}

5
examples/src/main/kotlin/space/kscience/kmath/tensors/dataSetNormalization.kt
View File

@ -5,6 +5,7 @@
package space.kscience.kmath.tensors
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.tensors.core.tensorAlgebra
import space.kscience.kmath.tensors.core.withBroadcast
@ -13,10 +14,10 @@ import space.kscience.kmath.tensors.core.withBroadcast
fun main() = Double.tensorAlgebra.withBroadcast { // work in context with broadcast methods
// take dataset of 5-element vectors from normal distribution
val dataset = randomNormal(intArrayOf(100, 5)) * 1.5 // all elements from N(0, 1.5)
val dataset = randomNormal(Shape(100, 5)) * 1.5 // all elements from N(0, 1.5)
dataset += fromArray(
intArrayOf(5),
Shape(5),
doubleArrayOf(0.0, 1.0, 1.5, 3.0, 5.0) // row means
)

7
examples/src/main/kotlin/space/kscience/kmath/tensors/linearSystemSolvingWithLUP.kt
View File

@ -5,6 +5,7 @@
package space.kscience.kmath.tensors
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.tensors.core.DoubleTensor
import space.kscience.kmath.tensors.core.tensorAlgebra
import space.kscience.kmath.tensors.core.withBroadcast
@ -15,13 +16,13 @@ fun main() = Double.tensorAlgebra.withBroadcast {// work in context with linear
// set true value of x
val trueX = fromArray(
intArrayOf(4),
Shape(4),
doubleArrayOf(-2.0, 1.5, 6.8, -2.4)
)
// and A matrix
val a = fromArray(
intArrayOf(4, 4),
Shape(4, 4),
doubleArrayOf(
0.5, 10.5, 4.5, 1.0,
8.5, 0.9, 12.8, 0.1,
@ -64,7 +65,7 @@ fun main() = Double.tensorAlgebra.withBroadcast {// work in context with linear
// this function returns solution x of a system lx = b, l should be lower triangular
fun solveLT(l: DoubleTensor, b: DoubleTensor): DoubleTensor {
val n = l.shape[0]
val x = zeros(intArrayOf(n))
val x = zeros(Shape(n))
for (i in 0 until n) {
x[intArrayOf(i)] = (b[intArrayOf(i)] - l.getTensor(i).dot(x).value()) / l[intArrayOf(i, i)]
}

12
examples/src/main/kotlin/space/kscience/kmath/tensors/neuralNetwork.kt
View File

@ -5,6 +5,8 @@
package space.kscience.kmath.tensors
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.nd.contentEquals
import space.kscience.kmath.operations.asIterable
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
@ -68,12 +70,12 @@ class Dense(
private val weights: DoubleTensor = DoubleTensorAlgebra {
randomNormal(
intArrayOf(inputUnits, outputUnits),
Shape(inputUnits, outputUnits),
seed
) * sqrt(2.0 / (inputUnits + outputUnits))
}
private val bias: DoubleTensor = DoubleTensorAlgebra { zeros(intArrayOf(outputUnits)) }
private val bias: DoubleTensor = DoubleTensorAlgebra { zeros(Shape(outputUnits)) }
override fun forward(input: DoubleTensor): DoubleTensor = BroadcastDoubleTensorAlgebra {
(input dot weights) + bias
@ -182,17 +184,17 @@ fun main() = BroadcastDoubleTensorAlgebra {
//val testSize = sampleSize - trainSize
// take sample of features from normal distribution
val x = randomNormal(intArrayOf(sampleSize, features), seed) * 2.5
val x = randomNormal(Shape(sampleSize, features), seed) * 2.5
x += fromArray(
intArrayOf(5),
Shape(5),
doubleArrayOf(0.0, -1.0, -2.5, -3.0, 5.5) // row means
)
// define class like '1' if the sum of features > 0 and '0' otherwise
val y = fromArray(
intArrayOf(sampleSize, 1),
Shape(sampleSize, 1),
DoubleArray(sampleSize) { i ->
if (x.getTensor(i).sum() > 0.0) {
1.0

4
gradle.properties
View File

@ -3,13 +3,11 @@
# Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
#
kotlin.code.style=official
kotlin.jupyter.add.scanner=false
kotlin.mpp.stability.nowarn=true
kotlin.native.ignoreDisabledTargets=true
kotlin.incremental.js.ir=true
org.gradle.configureondemand=true
org.gradle.parallel=true
org.gradle.jvmargs=-Xmx4096m
toolsVersion=0.13.0-kotlin-1.7.20-Beta
toolsVersion=0.13.1-kotlin-1.7.20

6
kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/ComplexFieldND.kt
View File

@ -5,6 +5,7 @@
package space.kscience.kmath.complex
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.nd.*
import space.kscience.kmath.operations.*
@ -20,6 +21,7 @@ import kotlin.contracts.contract
public sealed class ComplexFieldOpsND : BufferedFieldOpsND<Complex, ComplexField>(ComplexField.bufferAlgebra),
ScaleOperations<StructureND<Complex>>, ExtendedFieldOps<StructureND<Complex>>, PowerOperations<StructureND<Complex>> {
@OptIn(PerformancePitfall::class)
override fun StructureND<Complex>.toBufferND(): BufferND<Complex> = when (this) {
is BufferND -> this
else -> {
@ -69,12 +71,12 @@ public class ComplexFieldND(override val shape: Shape) :
public val ComplexField.ndAlgebra: ComplexFieldOpsND get() = ComplexFieldOpsND
public fun ComplexField.ndAlgebra(vararg shape: Int): ComplexFieldND = ComplexFieldND(shape)
public fun ComplexField.ndAlgebra(vararg shape: Int): ComplexFieldND = ComplexFieldND(Shape(shape))
/**
* Produce a context for n-dimensional operations inside this real field
*/
public inline fun <R> ComplexField.withNdAlgebra(vararg shape: Int, action: ComplexFieldND.() -> R): R {
contract { callsInPlace(action, InvocationKind.EXACTLY_ONCE) }
return ComplexFieldND(shape).action()
return ComplexFieldND(Shape(shape)).action()
}

6
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/BufferedLinearSpace.kt
View File

@ -6,9 +6,7 @@
package space.kscience.kmath.linear
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.nd.BufferedRingOpsND
import space.kscience.kmath.nd.as2D
import space.kscience.kmath.nd.asND
import space.kscience.kmath.nd.*
import space.kscience.kmath.operations.*
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.VirtualBuffer
@ -23,7 +21,7 @@ public class BufferedLinearSpace<T, out A : Ring<T>>(
private val ndAlgebra = BufferedRingOpsND(bufferAlgebra)
override fun buildMatrix(rows: Int, columns: Int, initializer: A.(i: Int, j: Int) -> T): Matrix<T> =
ndAlgebra.structureND(intArrayOf(rows, columns)) { (i, j) -> elementAlgebra.initializer(i, j) }.as2D()
ndAlgebra.structureND(Shape(rows, columns)) { (i, j) -> elementAlgebra.initializer(i, j) }.as2D()
override fun buildVector(size: Int, initializer: A.(Int) -> T): Point<T> =
bufferAlgebra.buffer(size) { elementAlgebra.initializer(it) }

6
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/DoubleLinearSpace.kt
View File

@ -6,9 +6,7 @@
package space.kscience.kmath.linear
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.nd.DoubleFieldOpsND
import space.kscience.kmath.nd.as2D
import space.kscience.kmath.nd.asND
import space.kscience.kmath.nd.*
import space.kscience.kmath.operations.DoubleBufferOps
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.invoke
@ -23,7 +21,7 @@ public object DoubleLinearSpace : LinearSpace<Double, DoubleField> {
rows: Int,
columns: Int,
initializer: DoubleField.(i: Int, j: Int) -> Double
): Matrix<Double> = DoubleFieldOpsND.structureND(intArrayOf(rows, columns)) { (i, j) ->
): Matrix<Double> = DoubleFieldOpsND.structureND(Shape(rows, columns)) { (i, j) ->
DoubleField.initializer(i, j)
}.as2D()

4
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/VirtualMatrix.kt
View File

@ -5,6 +5,8 @@
package space.kscience.kmath.linear
import space.kscience.kmath.nd.Shape
/**
* The matrix where each element is evaluated each time when is being accessed.
*
@ -16,7 +18,7 @@ public class VirtualMatrix<out T : Any>(
public val generator: (i: Int, j: Int) -> T,
) : Matrix<T> {
override val shape: IntArray get() = intArrayOf(rowNum, colNum)
override val shape: Shape get() = Shape(rowNum, colNum)
override operator fun get(i: Int, j: Int): T = generator(i, j)
}

13
kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/annotations.kt
View File

@ -29,3 +29,16 @@ public annotation class UnstableKMathAPI
public annotation class PerformancePitfall(
val message: String = "Potential performance problem",
)
/**
* Marks API that is public, but should not be used without clear understanding what it does.
*/
@MustBeDocumented
@Retention(value = AnnotationRetention.BINARY)
@RequiresOptIn(
"This API is unsafe and should be used carefully",
RequiresOptIn.Level.ERROR,
)
public annotation class UnsafeKMathAPI(
val message: String = "Unsafe API",
)

15
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/BufferAlgebraND.kt
View File

@ -12,7 +12,7 @@ import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.operations.*
public interface BufferAlgebraND<T, out A : Algebra<T>> : AlgebraND<T, A> {
public val indexerBuilder: (IntArray) -> ShapeIndexer
public val indexerBuilder: (Shape) -> ShapeIndexer
public val bufferAlgebra: BufferAlgebra<T, A>
override val elementAlgebra: A get() = bufferAlgebra.elementAlgebra
@ -26,6 +26,7 @@ public interface BufferAlgebraND<T, out A : Algebra<T>> : AlgebraND<T, A> {
)
}
@OptIn(PerformancePitfall::class)
public fun StructureND<T>.toBufferND(): BufferND<T> = when (this) {
is BufferND -> this
else -> {
@ -46,7 +47,7 @@ public interface BufferAlgebraND<T, out A : Algebra<T>> : AlgebraND<T, A> {
zipInline(left.toBufferND(), right.toBufferND(), transform)
public companion object {
public val defaultIndexerBuilder: (IntArray) -> ShapeIndexer = ::Strides
public val defaultIndexerBuilder: (Shape) -> ShapeIndexer = ::Strides
}
}
@ -98,24 +99,24 @@ internal inline fun <T, A : Algebra<T>> BufferAlgebraND<T, A>.zipInline(
@OptIn(PerformancePitfall::class)
public open class BufferedGroupNDOps<T, out A : Group<T>>(
override val bufferAlgebra: BufferAlgebra<T, A>,
override val indexerBuilder: (IntArray) -> ShapeIndexer = BufferAlgebraND.defaultIndexerBuilder,
override val indexerBuilder: (Shape) -> ShapeIndexer = BufferAlgebraND.defaultIndexerBuilder,
) : GroupOpsND<T, A>, BufferAlgebraND<T, A> {
override fun StructureND<T>.unaryMinus(): StructureND<T> = map { -it }
}
public open class BufferedRingOpsND<T, out A : Ring<T>>(
bufferAlgebra: BufferAlgebra<T, A>,
indexerBuilder: (IntArray) -> ShapeIndexer = BufferAlgebraND.defaultIndexerBuilder,
indexerBuilder: (Shape) -> ShapeIndexer = BufferAlgebraND.defaultIndexerBuilder,
) : BufferedGroupNDOps<T, A>(bufferAlgebra, indexerBuilder), RingOpsND<T, A>
public open class BufferedFieldOpsND<T, out A : Field<T>>(
bufferAlgebra: BufferAlgebra<T, A>,
indexerBuilder: (IntArray) -> ShapeIndexer = BufferAlgebraND.defaultIndexerBuilder,
indexerBuilder: (Shape) -> ShapeIndexer = BufferAlgebraND.defaultIndexerBuilder,
) : BufferedRingOpsND<T, A>(bufferAlgebra, indexerBuilder), FieldOpsND<T, A> {
public constructor(
elementAlgebra: A,
indexerBuilder: (IntArray) -> ShapeIndexer = BufferAlgebraND.defaultIndexerBuilder,
indexerBuilder: (Shape) -> ShapeIndexer = BufferAlgebraND.defaultIndexerBuilder,
) : this(BufferFieldOps(elementAlgebra), indexerBuilder)
@OptIn(PerformancePitfall::class)
@ -130,7 +131,7 @@ public val <T, A : Field<T>> BufferAlgebra<T, A>.nd: BufferedFieldOpsND<T, A> ge
public fun <T, A : Algebra<T>> BufferAlgebraND<T, A>.structureND(
vararg shape: Int,
initializer: A.(IntArray) -> T,
): BufferND<T> = structureND(shape, initializer)
): BufferND<T> = structureND(Shape(shape), initializer)
public fun <T, EA : Algebra<T>, A> A.structureND(
initializer: EA.(IntArray) -> T,

74
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/BufferND.kt
View File

@ -5,10 +5,9 @@
package space.kscience.kmath.nd
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.BufferFactory
import space.kscience.kmath.structures.MutableBuffer
import space.kscience.kmath.structures.MutableBufferFactory
/**
* Represents [StructureND] over [Buffer].
@ -22,32 +21,33 @@ public open class BufferND<out T>(
public open val buffer: Buffer<T>,
) : StructureND<T> {
@PerformancePitfall
override operator fun get(index: IntArray): T = buffer[indices.offset(index)]
override val shape: IntArray get() = indices.shape
override val shape: Shape get() = indices.shape
override fun toString(): String = StructureND.toString(this)
}
/**
* Transform structure to a new structure using provided [BufferFactory] and optimizing if argument is [BufferND]
*/
public inline fun <T, R : Any> StructureND<T>.mapToBuffer(
factory: BufferFactory<R>,
crossinline transform: (T) -> R,
): BufferND<R> = if (this is BufferND<T>)
BufferND(this.indices, factory.invoke(indices.linearSize) { transform(buffer[it]) })
else {
val strides = ColumnStrides(shape)
BufferND(strides, factory.invoke(strides.linearSize) { transform(get(strides.index(it))) })
}
/**
* Transform structure to a new structure using inferred [BufferFactory]
*/
public inline fun <T, reified R : Any> StructureND<T>.mapToBuffer(
crossinline transform: (T) -> R,
): BufferND<R> = mapToBuffer(Buffer.Companion::auto, transform)
///**
// * Transform structure to a new structure using provided [BufferFactory] and optimizing if argument is [BufferND]
// */
//public inline fun <T, R : Any> StructureND<T>.mapToBuffer(
// factory: BufferFactory<R>,
// crossinline transform: (T) -> R,
//): BufferND<R> = if (this is BufferND<T>)
// BufferND(this.indices, factory.invoke(indices.linearSize) { transform(buffer[it]) })
//else {
// val strides = ColumnStrides(shape)
// BufferND(strides, factory.invoke(strides.linearSize) { transform(get(strides.index(it))) })
//}
//
///**
// * Transform structure to a new structure using inferred [BufferFactory]
// */
//public inline fun <T, reified R : Any> StructureND<T>.mapToBuffer(
// crossinline transform: (T) -> R,
//): BufferND<R> = mapToBuffer(Buffer.Companion::auto, transform)
/**
* Represents [MutableStructureND] over [MutableBuffer].
@ -60,22 +60,24 @@ public open class MutableBufferND<T>(
strides: ShapeIndexer,
override val buffer: MutableBuffer<T>,
) : MutableStructureND<T>, BufferND<T>(strides, buffer) {
@PerformancePitfall
override fun set(index: IntArray, value: T) {
buffer[indices.offset(index)] = value
}
}
/**
* Transform structure to a new structure using provided [MutableBufferFactory] and optimizing if argument is [MutableBufferND]
*/
public inline fun <T, reified R : Any> MutableStructureND<T>.mapToMutableBuffer(
factory: MutableBufferFactory<R> = MutableBufferFactory(MutableBuffer.Companion::auto),
crossinline transform: (T) -> R,
): MutableBufferND<R> {
return if (this is MutableBufferND<T>)
MutableBufferND(this.indices, factory.invoke(indices.linearSize) { transform(buffer[it]) })
else {
val strides = ColumnStrides(shape)
MutableBufferND(strides, factory.invoke(strides.linearSize) { transform(get(strides.index(it))) })
}
}
///**
// * Transform structure to a new structure using provided [MutableBufferFactory] and optimizing if argument is [MutableBufferND]
// */
//public inline fun <T, reified R : Any> MutableStructureND<T>.mapToMutableBuffer(
// factory: MutableBufferFactory<R> = MutableBufferFactory(MutableBuffer.Companion::auto),
// crossinline transform: (T) -> R,
//): MutableBufferND<R> {
// return if (this is MutableBufferND<T>)
// MutableBufferND(this.indices, factory.invoke(indices.linearSize) { transform(buffer[it]) })
// else {
// val strides = ColumnStrides(shape)
// MutableBufferND(strides, factory.invoke(strides.linearSize) { transform(get(strides.index(it))) })
// }
//}

17
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/DoubleFieldND.kt
View File

@ -14,15 +14,25 @@ import kotlin.contracts.contract
import kotlin.math.pow
import kotlin.math.pow as kpow
/**
* A simple mutable [StructureND] of doubles
*/
public class DoubleBufferND(
indexes: ShapeIndexer,
override val buffer: DoubleBuffer,
) : MutableBufferND<Double>(indexes, buffer)
) : MutableBufferND<Double>(indexes, buffer), MutableStructureNDOfDouble{
override fun getDouble(index: IntArray): Double = buffer[indices.offset(index)]
override fun setDouble(index: IntArray, value: Double) {
buffer[indices.offset(index)] = value
}
}
public sealed class DoubleFieldOpsND : BufferedFieldOpsND<Double, DoubleField>(DoubleField.bufferAlgebra),
ScaleOperations<StructureND<Double>>, ExtendedFieldOps<StructureND<Double>> {
@OptIn(PerformancePitfall::class)
override fun StructureND<Double>.toBufferND(): DoubleBufferND = when (this) {
is DoubleBufferND -> this
else -> {
@ -221,7 +231,8 @@ public class DoubleFieldND(override val shape: Shape) :
public val DoubleField.ndAlgebra: DoubleFieldOpsND get() = DoubleFieldOpsND
public fun DoubleField.ndAlgebra(vararg shape: Int): DoubleFieldND = DoubleFieldND(shape)
public fun DoubleField.ndAlgebra(vararg shape: Int): DoubleFieldND = DoubleFieldND(Shape(shape))
public fun DoubleField.ndAlgebra(shape: Shape): DoubleFieldND = DoubleFieldND(shape)
/**
* Produce a context for n-dimensional operations inside this real field
@ -229,5 +240,5 @@ public fun DoubleField.ndAlgebra(vararg shape: Int): DoubleFieldND = DoubleField
@UnstableKMathAPI
public inline fun <R> DoubleField.withNdAlgebra(vararg shape: Int, action: DoubleFieldND.() -> R): R {
contract { callsInPlace(action, InvocationKind.EXACTLY_ONCE) }
return DoubleFieldND(shape).run(action)
return DoubleFieldND(Shape(shape)).run(action)
}

2
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/IntRingND.kt
View File

@ -46,5 +46,5 @@ public class IntRingND(
public inline fun <R> IntRing.withNdAlgebra(vararg shape: Int, action: IntRingND.() -> R): R {
contract { callsInPlace(action, InvocationKind.EXACTLY_ONCE) }
return IntRingND(shape).run(action)
return IntRingND(Shape(shape)).run(action)
}

50
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/PermutedStructureND.kt Normal file
View File

@ -0,0 +1,50 @@
/*
* Copyright 2018-2022 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.nd
import space.kscience.kmath.misc.PerformancePitfall
public class PermutedStructureND<T>(
public val origin: StructureND<T>,
public val permutation: (IntArray) -> IntArray,
) : StructureND<T> {
override val shape: Shape
get() = origin.shape
@OptIn(PerformancePitfall::class)
override fun get(index: IntArray): T {
return origin[permutation(index)]
}
}
public fun <T> StructureND<T>.permute(
permutation: (IntArray) -> IntArray,
): PermutedStructureND<T> = PermutedStructureND(this, permutation)
public class PermutedMutableStructureND<T>(
public val origin: MutableStructureND<T>,
override val shape: Shape = origin.shape,
public val permutation: (IntArray) -> IntArray,
) : MutableStructureND<T> {
@OptIn(PerformancePitfall::class)
override fun set(index: IntArray, value: T) {
origin[permutation(index)] = value
}
@OptIn(PerformancePitfall::class)
override fun get(index: IntArray): T {
return origin[permutation(index)]
}
}
public fun <T> MutableStructureND<T>.permute(
newShape: Shape = shape,
permutation: (IntArray) -> IntArray,
): PermutedMutableStructureND<T> = PermutedMutableStructureND(this, newShape, permutation)

83
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Shape.kt
View File

@ -5,21 +5,88 @@
package space.kscience.kmath.nd
import space.kscience.kmath.misc.UnsafeKMathAPI
import kotlin.jvm.JvmInline
/**
* A read-only ND shape
*/
@JvmInline
public value class Shape(@PublishedApi internal val array: IntArray) {
public val size: Int get() = array.size
public operator fun get(index: Int): Int = array[index]
override fun toString(): String = array.contentToString()
}
public inline fun Shape.forEach(block: (value: Int) -> Unit): Unit = array.forEach(block)
public inline fun Shape.forEachIndexed(block: (index: Int, value: Int) -> Unit): Unit = array.forEachIndexed(block)
public infix fun Shape.contentEquals(other: Shape): Boolean = array.contentEquals(other.array)
public fun Shape.contentHashCode(): Int = array.contentHashCode()
public val Shape.indices: IntRange get() = array.indices
public val Shape.linearSize: Int get() = array.reduce(Int::times)
public fun Shape.slice(range: IntRange): Shape = Shape(array.sliceArray(range))
public fun Shape.last(): Int = array.last()
/**
* A shape including last [n] dimensions of this shape
*/
public fun Shape.last(n: Int): Shape = Shape(array.copyOfRange(size - n, size))
public fun Shape.first(): Int = array.first()
/**
* A shape including first [n] dimensions of this shape
*/
public fun Shape.first(n: Int): Shape = Shape(array.copyOfRange(0, n))
public operator fun Shape.plus(add: IntArray): Shape = Shape(array + add)
public operator fun Shape.plus(add: Shape): Shape = Shape(array + add.array)
public fun Shape.isEmpty(): Boolean = size == 0
public fun Shape.isNotEmpty(): Boolean = size > 0
public fun Shape.transposed(i: Int, j: Int): Shape = Shape(array.copyOf().apply {
val ith = get(i)
val jth = get(j)
set(i, jth)
set(j, ith)
})
public operator fun Shape.component1(): Int = get(0)
public operator fun Shape.component2(): Int = get(1)
public operator fun Shape.component3(): Int = get(2)
/**
* Convert to array with protective copy
*/
public fun Shape.toArray(): IntArray = array.copyOf()
@UnsafeKMathAPI
public fun Shape.asArray(): IntArray = array
public fun Shape.asList(): List<Int> = array.asList()
/**
* An exception is thrown when the expected and actual shape of NDArray differ.
*
* @property expected the expected shape.
* @property actual the actual shape.
*/
public class ShapeMismatchException(public val expected: IntArray, public val actual: IntArray) :
RuntimeException("Shape ${actual.contentToString()} doesn't fit in expected shape ${expected.contentToString()}.")
public class ShapeMismatchException(public val expected: Shape, public val actual: Shape) :
RuntimeException("Shape $actual doesn't fit in expected shape ${expected}.")
public class IndexOutOfShapeException(public val shape: Shape, public val index: IntArray) :
RuntimeException("Index ${index.contentToString()} is out of shape ${shape.contentToString()}")
RuntimeException("Index ${index.contentToString()} is out of shape ${shape}")
public typealias Shape = IntArray
public fun Shape(shapeFirst: Int, vararg shapeRest: Int): Shape = intArrayOf(shapeFirst, *shapeRest)
public fun Shape(shapeFirst: Int, vararg shapeRest: Int): Shape = Shape(intArrayOf(shapeFirst, *shapeRest))
public interface WithShape {
public val shape: Shape
@ -28,8 +95,8 @@ public interface WithShape {
}
internal fun requireIndexInShape(index: IntArray, shape: Shape) {
if (index.size != shape.size) throw IndexOutOfShapeException(index, shape)
if (index.size != shape.size) throw IndexOutOfShapeException(shape, index)
shape.forEachIndexed { axis, axisShape ->
if (index[axis] !in 0 until axisShape) throw IndexOutOfShapeException(index, shape)
if (index[axis] !in 0 until axisShape) throw IndexOutOfShapeException(shape, index)
}
}

17
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/ShapeIndices.kt
View File

@ -49,10 +49,10 @@ public abstract class Strides : ShapeIndexer {
/**
* Array strides
*/
public abstract val strides: IntArray
internal abstract val strides: IntArray
public override fun offset(index: IntArray): Int = index.mapIndexed { i, value ->
if (value < 0 || value >= shape[i]) throw IndexOutOfBoundsException("Index $value out of shape bounds: (0,${this.shape[i]})")
if (value !in 0 until shape[i]) throw IndexOutOfBoundsException("Index $value out of shape bounds: (0, ${this.shape[i]})")
value * strides[i]
}.sum()
@ -63,15 +63,12 @@ public abstract class Strides : ShapeIndexer {
*/
public override fun asSequence(): Sequence<IntArray> = (0 until linearSize).asSequence().map(::index)
public companion object{
public fun linearSizeOf(shape: IntArray): Int = shape.reduce(Int::times)
}
}
/**
* Column-first [Strides]. Columns are represented as continuous arrays
*/
public class ColumnStrides(override val shape: IntArray) : Strides() {
public class ColumnStrides(override val shape: Shape) : Strides() {
override val linearSize: Int get() = strides[shape.size]
/**
@ -121,7 +118,7 @@ public class ColumnStrides(override val shape: IntArray) : Strides() {
*
* @param shape the shape of the tensor.
*/
public class RowStrides(override val shape: IntArray) : Strides() {
public class RowStrides(override val shape: Shape) : Strides() {
override val strides: IntArray by lazy {
val nDim = shape.size
@ -151,7 +148,7 @@ public class RowStrides(override val shape: IntArray) : Strides() {
return res
}
override val linearSize: Int get() = linearSizeOf(shape)
override val linearSize: Int get() = shape.linearSize
override fun equals(other: Any?): Boolean {
if (this === other) return true
@ -166,9 +163,9 @@ public class RowStrides(override val shape: IntArray) : Strides() {
}
@ThreadLocal
private val defaultStridesCache = HashMap<IntArray, Strides>()
private val defaultStridesCache = HashMap<Shape, Strides>()
/**
* Cached builder for default strides
*/
public fun Strides(shape: IntArray): Strides = defaultStridesCache.getOrPut(shape) { RowStrides(shape) }
public fun Strides(shape: Shape): Strides = defaultStridesCache.getOrPut(shape) { RowStrides(shape) }

2
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/ShortRingND.kt
View File

@ -30,5 +30,5 @@ public class ShortRingND(
public inline fun <R> ShortRing.withNdAlgebra(vararg shape: Int, action: ShortRingND.() -> R): R {
contract { callsInPlace(action, InvocationKind.EXACTLY_ONCE) }
return ShortRingND(shape).run(action)
return ShortRingND(Shape(shape)).run(action)
}

15
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure1D.kt
View File

@ -18,6 +18,7 @@ import kotlin.jvm.JvmInline
public interface Structure1D<out T> : StructureND<T>, Buffer<T> {
override val dimension: Int get() = 1
@PerformancePitfall
override operator fun get(index: IntArray): T {
require(index.size == 1) { "Index dimension mismatch. Expected 1 but found ${index.size}" }
return get(index[0])
@ -32,6 +33,8 @@ public interface Structure1D<out T> : StructureND<T>, Buffer<T> {
* A mutable structure that is guaranteed to be one-dimensional
*/
public interface MutableStructure1D<T> : Structure1D<T>, MutableStructureND<T>, MutableBuffer<T> {
@PerformancePitfall
override operator fun set(index: IntArray, value: T) {
require(index.size == 1) { "Index dimension mismatch. Expected 1 but found ${index.size}" }
set(index[0], value)
@ -43,9 +46,10 @@ public interface MutableStructure1D<T> : Structure1D<T>, MutableStructureND<T>,
*/
@JvmInline
private value class Structure1DWrapper<out T>(val structure: StructureND<T>) : Structure1D<T> {
override val shape: IntArray get() = structure.shape
override val shape: Shape get() = structure.shape
override val size: Int get() = structure.shape[0]
@PerformancePitfall
override operator fun get(index: Int): T = structure[index]
@PerformancePitfall
@ -56,13 +60,16 @@ private value class Structure1DWrapper<out T>(val structure: StructureND<T>) : S
* A 1D wrapper for a mutable nd-structure
*/
private class MutableStructure1DWrapper<T>(val structure: MutableStructureND<T>) : MutableStructure1D<T> {
override val shape: IntArray get() = structure.shape
override val shape: Shape get() = structure.shape
override val size: Int get() = structure.shape[0]
@PerformancePitfall
override fun elements(): Sequence<Pair<IntArray, T>> = structure.elements()
@PerformancePitfall
override fun get(index: Int): T = structure[index]
@PerformancePitfall
override fun set(index: Int, value: T) {
structure[intArrayOf(index)] = value
}
@ -83,7 +90,7 @@ private class MutableStructure1DWrapper<T>(val structure: MutableStructureND<T>)
*/
@JvmInline
private value class Buffer1DWrapper<out T>(val buffer: Buffer<T>) : Structure1D<T> {
override val shape: IntArray get() = intArrayOf(buffer.size)
override val shape: Shape get() = Shape(buffer.size)
override val size: Int get() = buffer.size
@PerformancePitfall
@ -95,7 +102,7 @@ private value class Buffer1DWrapper<out T>(val buffer: Buffer<T>) : Structure1D<
}
internal class MutableBuffer1DWrapper<T>(val buffer: MutableBuffer<T>) : MutableStructure1D<T> {
override val shape: IntArray get() = intArrayOf(buffer.size)
override val shape: Shape get() = Shape(buffer.size)
override val size: Int get() = buffer.size
@PerformancePitfall

7
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt
View File

@ -29,7 +29,7 @@ public interface Structure2D<out T> : StructureND<T> {
*/
public val colNum: Int
override val shape: IntArray get() = intArrayOf(rowNum, colNum)
override val shape: Shape get() = Shape(rowNum, colNum)
/**
* The buffer of rows of this structure. It gets elements from the structure dynamically.
@ -54,6 +54,7 @@ public interface Structure2D<out T> : StructureND<T> {
*/
public operator fun get(i: Int, j: Int): T
@PerformancePitfall
override operator fun get(index: IntArray): T {
require(index.size == 2) { "Index dimension mismatch. Expected 2 but found ${index.size}" }
return get(index[0], index[1])
@ -106,6 +107,7 @@ private value class Structure2DWrapper<out T>(val structure: StructureND<T>) : S
override val rowNum: Int get() = shape[0]
override val colNum: Int get() = shape[1]
@PerformancePitfall
override operator fun get(i: Int, j: Int): T = structure[i, j]
override fun <F : StructureFeature> getFeature(type: KClass<out F>): F? = structure.getFeature(type)
@ -123,12 +125,15 @@ private class MutableStructure2DWrapper<T>(val structure: MutableStructureND<T>)
override val rowNum: Int get() = shape[0]
override val colNum: Int get() = shape[1]
@PerformancePitfall
override operator fun get(i: Int, j: Int): T = structure[i, j]
@PerformancePitfall
override fun set(index: IntArray, value: T) {
structure[index] = value
}
@PerformancePitfall
override operator fun set(i: Int, j: Int, value: T) {
structure[intArrayOf(i, j)] = value
}

19
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt
View File

@ -46,6 +46,7 @@ public interface StructureND<out T> : Featured<StructureFeature>, WithShape {
* @param index the indices.
* @return the value.
*/
@PerformancePitfall
public operator fun get(index: IntArray): T
/**
@ -97,6 +98,7 @@ public interface StructureND<out T> : Featured<StructureFeature>, WithShape {
/**
* Debug output to string
*/
@OptIn(PerformancePitfall::class)
public fun toString(structure: StructureND<*>): String {
val bufferRepr: String = when (structure.shape.size) {
1 -> (0 until structure.shape[0]).map { structure[it] }
@ -116,7 +118,7 @@ public interface StructureND<out T> : Featured<StructureFeature>, WithShape {
}
val className = structure::class.simpleName ?: "StructureND"
return "$className(shape=${structure.shape.contentToString()}, buffer=$bufferRepr)"
return "$className(shape=${structure.shape}, buffer=$bufferRepr)"
}
/**
@ -145,13 +147,13 @@ public interface StructureND<out T> : Featured<StructureFeature>, WithShape {
): BufferND<T> = BufferND(strides, Buffer.auto(type, strides.linearSize) { i -> initializer(strides.index(i)) })
public fun <T> buffered(
shape: IntArray,
shape: Shape,
bufferFactory: BufferFactory<T> = BufferFactory.boxing(),
initializer: (IntArray) -> T,
): BufferND<T> = buffered(ColumnStrides(shape), bufferFactory, initializer)
public inline fun <reified T : Any> auto(
shape: IntArray,
shape: Shape,
crossinline initializer: (IntArray) -> T,
): BufferND<T> = auto(ColumnStrides(shape), initializer)
@ -160,13 +162,13 @@ public interface StructureND<out T> : Featured<StructureFeature>, WithShape {
vararg shape: Int,
crossinline initializer: (IntArray) -> T,
): BufferND<T> =
auto(ColumnStrides(shape), initializer)
auto(ColumnStrides(Shape(shape)), initializer)
public inline fun <T : Any> auto(
type: KClass<T>,
vararg shape: Int,
crossinline initializer: (IntArray) -> T,
): BufferND<T> = auto(type, ColumnStrides(shape), initializer)
): BufferND<T> = auto(type, ColumnStrides(Shape(shape)), initializer)
}
}
@ -214,8 +216,13 @@ public fun <T : Comparable<T>> LinearSpace<T, Ring<T>>.contentEquals(
* @param index the indices.
* @return the value.
*/
@PerformancePitfall
public operator fun <T> StructureND<T>.get(vararg index: Int): T = get(index)
public operator fun StructureND<Double>.get(vararg index: Int): Double = getDouble(index)
public operator fun StructureND<Int>.get(vararg index: Int): Int = getInt(index)
//@UnstableKMathAPI
//public inline fun <reified T : StructureFeature> StructureND<*>.getFeature(): T? = getFeature(T::class)
@ -229,12 +236,14 @@ public interface MutableStructureND<T> : StructureND<T> {
* @param index the indices.
* @param value the value.
*/
@PerformancePitfall
public operator fun set(index: IntArray, value: T)
}
/**
* Set value at specified indices
*/
@PerformancePitfall
public operator fun <T> MutableStructureND<T>.set(vararg index: Int, value: T) {
set(index, value)
}

3
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/VirtualStructureND.kt
View File

@ -5,12 +5,15 @@
package space.kscience.kmath.nd
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.misc.UnstableKMathAPI
public open class VirtualStructureND<T>(
override val shape: Shape,
public val producer: (IntArray) -> T,
) : StructureND<T> {
@PerformancePitfall
override fun get(index: IntArray): T {
requireIndexInShape(index, shape)
return producer(index)

4
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/operationsND.kt
View File

@ -5,6 +5,9 @@
package space.kscience.kmath.nd
import space.kscience.kmath.misc.PerformancePitfall
@OptIn(PerformancePitfall::class)
public fun <T> StructureND<T>.roll(axis: Int, step: Int = 1): StructureND<T> {
require(axis in shape.indices) { "Axis $axis is outside of shape dimensions: [0, ${shape.size})" }
return VirtualStructureND(shape) { index ->
@ -19,6 +22,7 @@ public fun <T> StructureND<T>.roll(axis: Int, step: Int = 1): StructureND<T> {
}
}
@OptIn(PerformancePitfall::class)
public fun <T> StructureND<T>.roll(pair: Pair<Int, Int>, vararg others: Pair<Int, Int>): StructureND<T> {
val axisMap: Map<Int, Int> = mapOf(pair, *others)
require(axisMap.keys.all { it in shape.indices }) { "Some of axes ${axisMap.keys} is outside of shape dimensions: [0, ${shape.size})" }

45
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/primitiveStructureND.kt Normal file
View File

@ -0,0 +1,45 @@
/*
* Copyright 2018-2022 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.nd
import space.kscience.kmath.misc.PerformancePitfall
public interface StructureNDOfDouble : StructureND<Double> {
/**
* Guaranteed non-blocking access to content
*/
public fun getDouble(index: IntArray): Double
}
/**
* Optimized method to access primitive without boxing if possible
*/
@OptIn(PerformancePitfall::class)
public fun StructureND<Double>.getDouble(index: IntArray): Double =
if (this is StructureNDOfDouble) getDouble(index) else get(index)
public interface MutableStructureNDOfDouble : StructureNDOfDouble, MutableStructureND<Double> {
/**
* Guaranteed non-blocking access to content
*/
public fun setDouble(index: IntArray, value: Double)
}
@OptIn(PerformancePitfall::class)
public fun MutableStructureND<Double>.getDouble(index: IntArray): Double =
if (this is StructureNDOfDouble) getDouble(index) else get(index)
public interface StructureNDOfInt : StructureND<Int> {
/**
* Guaranteed non-blocking access to content
*/
public fun getInt(index: IntArray): Int
}
@OptIn(PerformancePitfall::class)
public fun StructureND<Int>.getInt(index: IntArray): Int =
if (this is StructureNDOfInt) getInt(index) else get(index)

7
kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/BufferAccessor2D.kt
View File

@ -5,10 +5,7 @@
package space.kscience.kmath.structures
import space.kscience.kmath.nd.ColumnStrides
import space.kscience.kmath.nd.Structure2D
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.as2D
import space.kscience.kmath.nd.*
/**
* A context that allows to operate on a [MutableBuffer] as on 2d array
@ -31,7 +28,7 @@ internal class BufferAccessor2D<T>(
//TODO optimize wrapper
fun MutableBuffer<T>.collect(): Structure2D<T> = StructureND.buffered(
ColumnStrides(intArrayOf(rowNum, colNum)),
ColumnStrides(Shape(rowNum, colNum)),
factory
) { (i, j) ->
get(i, j)

4
kmath-core/src/commonTest/kotlin/space/kscience/kmath/nd/StridesTest.kt
View File

@ -10,7 +10,7 @@ import kotlin.test.Test
class StridesTest {
@Test
fun checkRowBasedStrides() {
val strides = RowStrides(intArrayOf(3, 3))
val strides = RowStrides(Shape(3, 3))
var counter = 0
for(i in 0..2){
for(j in 0..2){
@ -24,7 +24,7 @@ class StridesTest {
@Test
fun checkColumnBasedStrides() {
val strides = ColumnStrides(intArrayOf(3, 3))
val strides = ColumnStrides(Shape(3, 3))
var counter = 0
for(i in 0..2){
for(j in 0..2){

2
kmath-core/src/commonTest/kotlin/space/kscience/kmath/structures/NumberNDFieldTest.kt
View File

@ -88,7 +88,7 @@ class NumberNDFieldTest {
@Test
fun testInternalContext() {
algebra {
(DoubleField.ndAlgebra(*array1.shape)) { with(L2Norm) { 1 + norm(array1) + exp(array2) } }
(DoubleField.ndAlgebra(array1.shape)) { with(L2Norm) { 1 + norm(array1) + exp(array2) } }
}
}
}

4
kmath-coroutines/src/jvmMain/kotlin/space/kscience/kmath/structures/LazyStructureND.kt
View File

@ -9,11 +9,12 @@ import kotlinx.coroutines.*
import space.kscience.kmath.coroutines.Math
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.nd.ColumnStrides
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.nd.StructureND
public class LazyStructureND<out T>(
public val scope: CoroutineScope,
override val shape: IntArray,
override val shape: Shape,
public val function: suspend (IntArray) -> T,
) : StructureND<T> {
private val cache: MutableMap<IntArray, Deferred<T>> = HashMap()
@ -23,6 +24,7 @@ public class LazyStructureND<out T>(
}
public suspend fun await(index: IntArray): T = async(index).await()
@PerformancePitfall
override operator fun get(index: IntArray): T = runBlocking { async(index).await() }
@OptIn(PerformancePitfall::class)

3
kmath-dimensions/src/commonMain/kotlin/space/kscience/kmath/dimensions/Wrappers.kt
View File

@ -6,6 +6,7 @@
package space.kscience.kmath.dimensions
import space.kscience.kmath.linear.*
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.nd.Structure2D
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.Ring
@ -47,7 +48,7 @@ public interface DMatrix<out T, R : Dimension, C : Dimension> : Structure2D<T> {
public value class DMatrixWrapper<out T, R : Dimension, C : Dimension>(
private val structure: Structure2D<T>,
) : DMatrix<T, R, C> {
override val shape: IntArray get() = structure.shape
override val shape: Shape get() = structure.shape
override val rowNum: Int get() = shape[0]
override val colNum: Int get() = shape[1]
override operator fun get(i: Int, j: Int): T = structure[i, j]

3
kmath-ejml/src/test/kotlin/space/kscience/kmath/ejml/EjmlMatrixTest.kt
View File

@ -15,6 +15,7 @@ import space.kscience.kmath.linear.*
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.toArray
import space.kscience.kmath.operations.algebra
import kotlin.random.Random
import kotlin.random.asJavaRandom
@ -52,7 +53,7 @@ internal class EjmlMatrixTest {
fun shape() {
val m = randomMatrix
val w = EjmlDoubleMatrix(m)
assertContentEquals(intArrayOf(m.numRows, m.numCols), w.shape)
assertContentEquals(intArrayOf(m.numRows, m.numCols), w.shape.toArray())
}
@OptIn(UnstableKMathAPI::class)

3
kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/HistogramND.kt
View File

@ -7,6 +7,7 @@ package space.kscience.kmath.histogram
import space.kscience.kmath.domains.Domain
import space.kscience.kmath.linear.Point
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.nd.ColumnStrides
import space.kscience.kmath.nd.FieldOpsND
import space.kscience.kmath.nd.Shape
@ -24,6 +25,7 @@ public class HistogramND<T : Comparable<T>, D : Domain<T>, V : Any>(
internal val values: StructureND<V>,
) : Histogram<T, V, DomainBin<T, D, V>> {
@OptIn(PerformancePitfall::class)
override fun get(point: Point<T>): DomainBin<T, D, V>? {
val index = group.getIndexOrNull(point) ?: return null
return group.produceBin(index, values[index])
@ -31,6 +33,7 @@ public class HistogramND<T : Comparable<T>, D : Domain<T>, V : Any>(
override val dimension: Int get() = group.shape.size
@OptIn(PerformancePitfall::class)
override val bins: Iterable<DomainBin<T, D, V>>
get() = ColumnStrides(group.shape).asSequence().map {
group.produceBin(it, values[it])

18
kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/UniformHistogramGroupND.kt
View File

@ -9,11 +9,10 @@ package space.kscience.kmath.histogram
import space.kscience.kmath.domains.HyperSquareDomain
import space.kscience.kmath.linear.Point
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.nd.*
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.Field
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.operations.*
import space.kscience.kmath.structures.*
import kotlin.math.floor
@ -40,7 +39,7 @@ public class UniformHistogramGroupND<V : Any, A : Field<V>>(
public val dimension: Int get() = lower.size
override val shape: IntArray = IntArray(binNums.size) { binNums[it] + 2 }
override val shape: Shape = Shape(IntArray(binNums.size) { binNums[it] + 2 })
private val binSize = DoubleBuffer(dimension) { (upper[it] - lower[it]) / binNums[it] }
@ -83,8 +82,12 @@ public class UniformHistogramGroupND<V : Any, A : Field<V>>(
}
override fun produce(builder: HistogramBuilder<Double, V>.() -> Unit): HistogramND<Double, HyperSquareDomain, V> {
val ndCounter = StructureND.buffered(shape) { Counter.of(valueAlgebraND.elementAlgebra) }
@OptIn(PerformancePitfall::class)
override fun produce(
builder: HistogramBuilder<Double, V>.() -> Unit,
): HistogramND<Double, HyperSquareDomain, V> {
val ndCounter: BufferND<ObjectCounter<V>> =
StructureND.buffered(shape) { Counter.of(valueAlgebraND.elementAlgebra) }
val hBuilder = object : HistogramBuilder<Double, V> {
override val defaultValue: V get() = valueAlgebraND.elementAlgebra.one
@ -94,7 +97,8 @@ public class UniformHistogramGroupND<V : Any, A : Field<V>>(
}
}
hBuilder.apply(builder)
val values: BufferND<V> = ndCounter.mapToBuffer(valueBufferFactory) { it.value }
val values: BufferND<V> = BufferND(ndCounter.indices, ndCounter.buffer.map(valueBufferFactory) { it.value })
return HistogramND(this, values)
}

2
kmath-histograms/src/commonTest/kotlin/space/kscience/kmath/histogram/MultivariateHistogramTest.kt
View File

@ -7,6 +7,7 @@
package space.kscience.kmath.histogram
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.nd.ColumnStrides
import space.kscience.kmath.operations.invoke
@ -50,6 +51,7 @@ internal class MultivariateHistogramTest {
assertEquals(n, histogram.bins.sumOf { it.binValue.toInt() })
}
@OptIn(PerformancePitfall::class)
@Test
fun testHistogramAlgebra() {
Histogram.uniformDoubleNDFromRanges(

4
kmath-multik/src/commonMain/kotlin/space/kscience/kmath/multik/MultikTensor.kt
View File

@ -13,14 +13,16 @@ import kotlin.jvm.JvmInline
@JvmInline
public value class MultikTensor<T>(public val array: MutableMultiArray<T, DN>) : Tensor<T> {
override val shape: Shape get() = array.shape
override val shape: Shape get() = Shape(array.shape)
@PerformancePitfall
override fun get(index: IntArray): T = array[index]
@PerformancePitfall
override fun elements(): Sequence<Pair<IntArray, T>> =
array.multiIndices.iterator().asSequence().map { it to get(it) }
@PerformancePitfall
override fun set(index: IntArray, value: T) {
array[index] = value
}

39
kmath-multik/src/commonMain/kotlin/space/kscience/kmath/multik/MultikTensorAlgebra.kt
View File

@ -14,6 +14,7 @@ import org.jetbrains.kotlinx.multik.api.stat.Statistics
import org.jetbrains.kotlinx.multik.ndarray.data.*
import org.jetbrains.kotlinx.multik.ndarray.operations.*
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.misc.UnsafeKMathAPI
import space.kscience.kmath.nd.*
import space.kscience.kmath.operations.*
import space.kscience.kmath.tensors.api.Tensor
@ -30,21 +31,22 @@ public abstract class MultikTensorAlgebra<T, A : Ring<T>>(
protected val multikLinAl: LinAlg = multikEngine.getLinAlg()
protected val multikStat: Statistics = multikEngine.getStatistics()
@OptIn(UnsafeKMathAPI::class)
override fun structureND(shape: Shape, initializer: A.(IntArray) -> T): MultikTensor<T> {
val strides = ColumnStrides(shape)
val memoryView = initMemoryView<T>(strides.linearSize, type)
strides.asSequence().forEachIndexed { linearIndex, tensorIndex ->
memoryView[linearIndex] = elementAlgebra.initializer(tensorIndex)
}
return MultikTensor(NDArray(memoryView, shape = shape, dim = DN(shape.size)))
return MultikTensor(NDArray(memoryView, shape = shape.asArray(), dim = DN(shape.size)))
}
@OptIn(PerformancePitfall::class)
@OptIn(PerformancePitfall::class, UnsafeKMathAPI::class)
override fun StructureND<T>.map(transform: A.(T) -> T): MultikTensor<T> = if (this is MultikTensor) {
val data = initMemoryView<T>(array.size, type)
var count = 0
for (el in array) data[count++] = elementAlgebra.transform(el)
NDArray(data, shape = shape, dim = array.dim).wrap()
NDArray(data, shape = shape.asArray(), dim = array.dim).wrap()
} else {
structureND(shape) { index ->
transform(get(index))
@ -75,6 +77,7 @@ public abstract class MultikTensorAlgebra<T, A : Ring<T>>(
/**
* Transform a structure element-by element in place.
*/
@OptIn(PerformancePitfall::class)
public inline fun <T> MutableStructureND<T>.mapIndexedInPlace(operation: (index: IntArray, t: T) -> T): Unit {
if (this is MultikTensor) {
array.multiIndices.iterator().forEach {
@ -106,10 +109,11 @@ public abstract class MultikTensorAlgebra<T, A : Ring<T>>(
* Convert a tensor to [MultikTensor] if necessary. If tensor is converted, changes on the resulting tensor
* are not reflected back onto the source
*/
@OptIn(UnsafeKMathAPI::class, PerformancePitfall::class)
public fun StructureND<T>.asMultik(): MultikTensor<T> = if (this is MultikTensor) {
this
} else {
val res = mk.zeros<T, DN>(shape, type).asDNArray()
val res = mk.zeros<T, DN>(shape.asArray(), type).asDNArray()
for (index in res.multiIndices) {
res[index] = this[index]
}
@ -118,7 +122,8 @@ public abstract class MultikTensorAlgebra<T, A : Ring<T>>(
public fun MutableMultiArray<T, *>.wrap(): MultikTensor<T> = MultikTensor(this.asDNArray())
override fun StructureND<T>.valueOrNull(): T? = if (shape contentEquals intArrayOf(1)) {
@OptIn(PerformancePitfall::class)
override fun StructureND<T>.valueOrNull(): T? = if (shape contentEquals Shape(1)) {
get(intArrayOf(0))
} else null
@ -139,6 +144,7 @@ public abstract class MultikTensorAlgebra<T, A : Ring<T>>(
}
}
@OptIn(PerformancePitfall::class)
override fun Tensor<T>.plusAssign(arg: StructureND<T>) {
if (this is MultikTensor) {
array.plusAssign(arg.asMultik().array)
@ -163,6 +169,7 @@ public abstract class MultikTensorAlgebra<T, A : Ring<T>>(
}
}
@OptIn(PerformancePitfall::class)
override fun Tensor<T>.minusAssign(arg: StructureND<T>) {
if (this is MultikTensor) {
array.minusAssign(arg.asMultik().array)
@ -188,6 +195,7 @@ public abstract class MultikTensorAlgebra<T, A : Ring<T>>(
}
}
@OptIn(PerformancePitfall::class)
override fun Tensor<T>.timesAssign(arg: StructureND<T>) {
if (this is MultikTensor) {
array.timesAssign(arg.asMultik().array)
@ -201,13 +209,13 @@ public abstract class MultikTensorAlgebra<T, A : Ring<T>>(
override fun Tensor<T>.getTensor(i: Int): MultikTensor<T> = asMultik().array.mutableView(i).wrap()
override fun Tensor<T>.transposed(i: Int, j: Int): MultikTensor<T> = asMultik().array.transpose(i, j).wrap()
override fun StructureND<T>.transposed(i: Int, j: Int): MultikTensor<T> = asMultik().array.transpose(i, j).wrap()
override fun Tensor<T>.view(shape: IntArray): MultikTensor<T> {
require(shape.all { it > 0 })
require(shape.fold(1, Int::times) == this.shape.size) {
override fun Tensor<T>.view(shape: Shape): MultikTensor<T> {
require(shape.asList().all { it > 0 })
require(shape.linearSize == this.shape.size) {
"Cannot reshape array of size ${this.shape.size} into a new shape ${
shape.joinToString(
shape.asList().joinToString(
prefix = "(",
postfix = ")"
)
@ -215,10 +223,11 @@ public abstract class MultikTensorAlgebra<T, A : Ring<T>>(
}
val mt = asMultik().array
return if (mt.shape.contentEquals(shape)) {
return if (Shape(mt.shape).contentEquals(shape)) {
mt
} else {
NDArray(mt.data, mt.offset, shape, dim = DN(shape.size), base = mt.base ?: mt)
@OptIn(UnsafeKMathAPI::class)
NDArray(mt.data, mt.offset, shape.asArray(), dim = DN(shape.size), base = mt.base ?: mt)
}.wrap()
}
@ -241,7 +250,7 @@ public abstract class MultikTensorAlgebra<T, A : Ring<T>>(
TODO("Not implemented for broadcasting")
}
override fun diagonalEmbedding(diagonalEntries: Tensor<T>, offset: Int, dim1: Int, dim2: Int): MultikTensor<T> {
override fun diagonalEmbedding(diagonalEntries: StructureND<T>, offset: Int, dim1: Int, dim2: Int): MultikTensor<T> {
TODO("Diagonal embedding not implemented")
}
@ -284,8 +293,9 @@ public abstract class MultikDivisionTensorAlgebra<T, A : Field<T>>(
multikEngine: Engine,
) : MultikTensorAlgebra<T, A>(multikEngine), TensorPartialDivisionAlgebra<T, A> where T : Number, T : Comparable<T> {
@OptIn(UnsafeKMathAPI::class)
override fun T.div(arg: StructureND<T>): MultikTensor<T> =
Multik.ones<T, DN>(arg.shape, type).apply { divAssign(arg.asMultik().array) }.wrap()
Multik.ones<T, DN>(arg.shape.asArray(), type).apply { divAssign(arg.asMultik().array) }.wrap()
override fun StructureND<T>.div(arg: T): MultikTensor<T> =
asMultik().array.div(arg).wrap()
@ -301,6 +311,7 @@ public abstract class MultikDivisionTensorAlgebra<T, A : Field<T>>(
}
}
@OptIn(PerformancePitfall::class)
override fun Tensor<T>.divAssign(arg: StructureND<T>) {
if (this is MultikTensor) {
array.divAssign(arg.asMultik().array)

5
kmath-multik/src/commonTest/kotlin/space/kscience/kmath/multik/MultikNDTest.kt
View File

@ -7,6 +7,7 @@ package space.kscience.kmath.multik
import org.jetbrains.kotlinx.multik.default.DefaultEngine
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.one
import space.kscience.kmath.operations.DoubleField
@ -28,8 +29,8 @@ internal class MultikNDTest {
fun dotResult() {
val dim = 100
val tensor1 = DoubleTensorAlgebra.randomNormal(shape = intArrayOf(dim, dim), 12224)
val tensor2 = DoubleTensorAlgebra.randomNormal(shape = intArrayOf(dim, dim), 12225)
val tensor1 = DoubleTensorAlgebra.randomNormal(shape = Shape(dim, dim), 12224)
val tensor2 = DoubleTensorAlgebra.randomNormal(shape = Shape(dim, dim), 12225)
val multikResult = with(multikAlgebra) {
tensor1 dot tensor2

30
kmath-nd4j/src/main/kotlin/space/kscience/kmath/nd4j/Nd4jArrayAlgebra.kt
View File

@ -11,6 +11,7 @@ import org.nd4j.linalg.api.ops.impl.transforms.strict.ASinh
import org.nd4j.linalg.factory.Nd4j
import org.nd4j.linalg.ops.transforms.Transforms
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.misc.UnsafeKMathAPI
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.nd.*
import space.kscience.kmath.operations.*
@ -33,7 +34,8 @@ public sealed interface Nd4jArrayAlgebra<T, out C : Algebra<T>> : AlgebraND<T, C
public val StructureND<T>.ndArray: INDArray
override fun structureND(shape: Shape, initializer: C.(IntArray) -> T): Nd4jArrayStructure<T> {
val struct = Nd4j.create(*shape)!!.wrap()
@OptIn(UnsafeKMathAPI::class)
val struct: Nd4jArrayStructure<T> = Nd4j.create(*shape.asArray())!!.wrap()
struct.indicesIterator().forEach { struct[it] = elementAlgebra.initializer(it) }
return struct
}
@ -45,23 +47,23 @@ public sealed interface Nd4jArrayAlgebra<T, out C : Algebra<T>> : AlgebraND<T, C
return newStruct
}
@OptIn(PerformancePitfall::class)
@OptIn(PerformancePitfall::class, UnsafeKMathAPI::class)
override fun StructureND<T>.mapIndexed(
transform: C.(index: IntArray, T) -> T,
): Nd4jArrayStructure<T> {
val new = Nd4j.create(*shape).wrap()
val new = Nd4j.create(*shape.asArray()).wrap()
new.indicesIterator().forEach { idx -> new[idx] = elementAlgebra.transform(idx, this[idx]) }
return new
}
@OptIn(PerformancePitfall::class)
@OptIn(PerformancePitfall::class, UnsafeKMathAPI::class)
override fun zip(
left: StructureND<T>,
right: StructureND<T>,
transform: C.(T, T) -> T,
): Nd4jArrayStructure<T> {
require(left.shape.contentEquals(right.shape)) { "Can't zip tow structures of shape ${left.shape} and ${right.shape}" }
val new = Nd4j.create(*left.shape).wrap()
val new = Nd4j.create(*left.shape.asArray()).wrap()
new.indicesIterator().forEach { idx -> new[idx] = elementAlgebra.transform(left[idx], right[idx]) }
return new
}
@ -192,11 +194,11 @@ public open class DoubleNd4jArrayFieldOps : Nd4jArrayExtendedFieldOps<Double, Do
override fun INDArray.wrap(): Nd4jArrayStructure<Double> = asDoubleStructure()
@OptIn(PerformancePitfall::class)
@OptIn(PerformancePitfall::class, UnsafeKMathAPI::class)
override val StructureND<Double>.ndArray: INDArray
get() = when (this) {
is Nd4jArrayStructure<Double> -> ndArray
else -> Nd4j.zeros(*shape).also {
else -> Nd4j.zeros(*shape.asArray()).also {
elements().forEach { (idx, value) -> it.putScalar(idx, value) }
}
}
@ -225,7 +227,7 @@ public val DoubleField.nd4j: DoubleNd4jArrayFieldOps get() = DoubleNd4jArrayFiel
public class DoubleNd4jArrayField(override val shape: Shape) : DoubleNd4jArrayFieldOps(), FieldND<Double, DoubleField>
public fun DoubleField.nd4j(shapeFirst: Int, vararg shapeRest: Int): DoubleNd4jArrayField =
DoubleNd4jArrayField(intArrayOf(shapeFirst, * shapeRest))
DoubleNd4jArrayField(Shape(shapeFirst, * shapeRest))
/**
@ -236,11 +238,11 @@ public open class FloatNd4jArrayFieldOps : Nd4jArrayExtendedFieldOps<Float, Floa
override fun INDArray.wrap(): Nd4jArrayStructure<Float> = asFloatStructure()
@OptIn(PerformancePitfall::class)
@OptIn(PerformancePitfall::class, UnsafeKMathAPI::class)
override val StructureND<Float>.ndArray: INDArray
get() = when (this) {
is Nd4jArrayStructure<Float> -> ndArray
else -> Nd4j.zeros(*shape).also {
else -> Nd4j.zeros(*shape.asArray()).also {
elements().forEach { (idx, value) -> it.putScalar(idx, value) }
}
}
@ -274,7 +276,7 @@ public class FloatNd4jArrayField(override val shape: Shape) : FloatNd4jArrayFiel
public val FloatField.nd4j: FloatNd4jArrayFieldOps get() = FloatNd4jArrayFieldOps
public fun FloatField.nd4j(shapeFirst: Int, vararg shapeRest: Int): FloatNd4jArrayField =
FloatNd4jArrayField(intArrayOf(shapeFirst, * shapeRest))
FloatNd4jArrayField(Shape(shapeFirst, * shapeRest))
/**
* Represents [RingND] over [Nd4jArrayIntStructure].
@ -284,11 +286,11 @@ public open class IntNd4jArrayRingOps : Nd4jArrayRingOps<Int, IntRing> {
override fun INDArray.wrap(): Nd4jArrayStructure<Int> = asIntStructure()
@OptIn(PerformancePitfall::class)
@OptIn(PerformancePitfall::class, UnsafeKMathAPI::class)
override val StructureND<Int>.ndArray: INDArray
get() = when (this) {
is Nd4jArrayStructure<Int> -> ndArray
else -> Nd4j.zeros(*shape).also {
else -> Nd4j.zeros(*shape.asArray()).also {
elements().forEach { (idx, value) -> it.putScalar(idx, value) }
}
}
@ -313,4 +315,4 @@ public val IntRing.nd4j: IntNd4jArrayRingOps get() = IntNd4jArrayRingOps
public class IntNd4jArrayRing(override val shape: Shape) : IntNd4jArrayRingOps(), RingND<Int, IntRing>
public fun IntRing.nd4j(shapeFirst: Int, vararg shapeRest: Int): IntNd4jArrayRing =
IntNd4jArrayRing(intArrayOf(shapeFirst, * shapeRest))
IntNd4jArrayRing(Shape(shapeFirst, * shapeRest))

27
kmath-nd4j/src/main/kotlin/space/kscience/kmath/nd4j/Nd4jArrayStructure.kt
View File

@ -7,8 +7,7 @@ package space.kscience.kmath.nd4j
import org.nd4j.linalg.api.ndarray.INDArray
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.nd.MutableStructureND
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.*
/**
* Represents a [StructureND] wrapping an [INDArray] object.
@ -22,7 +21,7 @@ public sealed class Nd4jArrayStructure<T> : MutableStructureND<T> {
*/
public abstract val ndArray: INDArray
override val shape: IntArray get() = ndArray.shape().toIntArray()
override val shape: Shape get() = Shape(ndArray.shape().toIntArray())
internal abstract fun elementsIterator(): Iterator<Pair<IntArray, T>>
internal fun indicesIterator(): Iterator<IntArray> = ndArray.indicesIterator()
@ -31,20 +30,31 @@ public sealed class Nd4jArrayStructure<T> : MutableStructureND<T> {
override fun elements(): Sequence<Pair<IntArray, T>> = Sequence(::elementsIterator)
}
private data class Nd4jArrayIntStructure(override val ndArray: INDArray) : Nd4jArrayStructure<Int>() {
public data class Nd4jArrayIntStructure(override val ndArray: INDArray) : Nd4jArrayStructure<Int>(), StructureNDOfInt {
override fun elementsIterator(): Iterator<Pair<IntArray, Int>> = ndArray.intIterator()
@OptIn(PerformancePitfall::class)
override fun get(index: IntArray): Int = ndArray.getInt(*index)
override fun getInt(index: IntArray): Int = ndArray.getInt(*index)
@OptIn(PerformancePitfall::class)
override fun set(index: IntArray, value: Int): Unit = run { ndArray.putScalar(index, value) }
}
/**
* Wraps this [INDArray] to [Nd4jArrayStructure].
*/
public fun INDArray.asIntStructure(): Nd4jArrayStructure<Int> = Nd4jArrayIntStructure(this)
public fun INDArray.asIntStructure(): Nd4jArrayIntStructure = Nd4jArrayIntStructure(this)
private data class Nd4jArrayDoubleStructure(override val ndArray: INDArray) : Nd4jArrayStructure<Double>() {
public data class Nd4jArrayDoubleStructure(override val ndArray: INDArray) : Nd4jArrayStructure<Double>(), StructureNDOfDouble {
override fun elementsIterator(): Iterator<Pair<IntArray, Double>> = ndArray.realIterator()
@OptIn(PerformancePitfall::class)
override fun get(index: IntArray): Double = ndArray.getDouble(*index)
override fun getDouble(index: IntArray): Double = ndArray.getDouble(*index)
@OptIn(PerformancePitfall::class)
override fun set(index: IntArray, value: Double): Unit = run { ndArray.putScalar(index, value) }
}
@ -53,9 +63,12 @@ private data class Nd4jArrayDoubleStructure(override val ndArray: INDArray) : Nd
*/
public fun INDArray.asDoubleStructure(): Nd4jArrayStructure<Double> = Nd4jArrayDoubleStructure(this)
private data class Nd4jArrayFloatStructure(override val ndArray: INDArray) : Nd4jArrayStructure<Float>() {
public data class Nd4jArrayFloatStructure(override val ndArray: INDArray) : Nd4jArrayStructure<Float>() {
override fun elementsIterator(): Iterator<Pair<IntArray, Float>> = ndArray.floatIterator()
@PerformancePitfall
override fun get(index: IntArray): Float = ndArray.getFloat(*index)
@PerformancePitfall
override fun set(index: IntArray, value: Float): Unit = run { ndArray.putScalar(index, value) }
}

22
kmath-nd4j/src/main/kotlin/space/kscience/kmath/nd4j/Nd4jTensorAlgebra.kt
View File

@ -13,9 +13,8 @@ import org.nd4j.linalg.factory.Nd4j
import org.nd4j.linalg.factory.ops.NDBase
import org.nd4j.linalg.ops.transforms.Transforms
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.nd.ColumnStrides
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.misc.UnsafeKMathAPI
import space.kscience.kmath.nd.*
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.Field
import space.kscience.kmath.tensors.api.AnalyticTensorAlgebra
@ -96,7 +95,7 @@ public sealed interface Nd4jTensorAlgebra<T : Number, A : Field<T>> : AnalyticTe
override fun StructureND<T>.unaryMinus(): Nd4jArrayStructure<T> = ndArray.neg().wrap()
override fun Tensor<T>.getTensor(i: Int): Nd4jArrayStructure<T> = ndArray.slice(i.toLong()).wrap()
override fun Tensor<T>.transposed(i: Int, j: Int): Nd4jArrayStructure<T> = ndArray.swapAxes(i, j).wrap()
override fun StructureND<T>.transposed(i: Int, j: Int): Nd4jArrayStructure<T> = ndArray.swapAxes(i, j).wrap()
override fun StructureND<T>.dot(other: StructureND<T>): Nd4jArrayStructure<T> = ndArray.mmul(other.ndArray).wrap()
override fun StructureND<T>.min(dim: Int, keepDim: Boolean): Nd4jArrayStructure<T> =
@ -108,7 +107,9 @@ public sealed interface Nd4jTensorAlgebra<T : Number, A : Field<T>> : AnalyticTe
override fun StructureND<T>.max(dim: Int, keepDim: Boolean): Nd4jArrayStructure<T> =
ndArray.max(keepDim, dim).wrap()
override fun Tensor<T>.view(shape: IntArray): Nd4jArrayStructure<T> = ndArray.reshape(shape).wrap()
@OptIn(UnsafeKMathAPI::class)
override fun Tensor<T>.view(shape: Shape): Nd4jArrayStructure<T> = ndArray.reshape(shape.asArray()).wrap()
override fun Tensor<T>.viewAs(other: StructureND<T>): Nd4jArrayStructure<T> = view(other.shape)
override fun StructureND<T>.argMin(dim: Int, keepDim: Boolean): Tensor<Int> =
@ -176,8 +177,9 @@ public object DoubleNd4jTensorAlgebra : Nd4jTensorAlgebra<Double, DoubleField> {
override fun INDArray.wrap(): Nd4jArrayStructure<Double> = asDoubleStructure()
@OptIn(UnsafeKMathAPI::class)
override fun structureND(shape: Shape, initializer: DoubleField.(IntArray) -> Double): Nd4jArrayStructure<Double> {
val array: INDArray = Nd4j.zeros(*shape)
val array: INDArray = Nd4j.zeros(*shape.asArray())
val indices = ColumnStrides(shape)
indices.asSequence().forEach { index ->
array.putScalar(index, elementAlgebra.initializer(index))
@ -186,21 +188,21 @@ public object DoubleNd4jTensorAlgebra : Nd4jTensorAlgebra<Double, DoubleField> {
}
@OptIn(PerformancePitfall::class)
@OptIn(PerformancePitfall::class, UnsafeKMathAPI::class)
override val StructureND<Double>.ndArray: INDArray
get() = when (this) {
is Nd4jArrayStructure<Double> -> ndArray
else -> Nd4j.zeros(*shape).also {
else -> Nd4j.zeros(*shape.asArray()).also {
elements().forEach { (idx, value) -> it.putScalar(idx, value) }
}
}
override fun StructureND<Double>.valueOrNull(): Double? =
if (shape contentEquals intArrayOf(1)) ndArray.getDouble(0) else null
if (shape contentEquals Shape(1)) ndArray.getDouble(0) else null
// TODO rewrite
override fun diagonalEmbedding(
diagonalEntries: Tensor<Double>,
diagonalEntries: StructureND<Double>,
offset: Int,
dim1: Int,
dim2: Int,

3
kmath-nd4j/src/test/kotlin/space/kscience/kmath/nd4j/Nd4jArrayStructureTest.kt
View File

@ -7,6 +7,7 @@ package space.kscience.kmath.nd4j
import org.nd4j.linalg.factory.Nd4j
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.nd.asList
import space.kscience.kmath.nd.get
import kotlin.test.Test
import kotlin.test.assertEquals
@ -27,7 +28,7 @@ internal class Nd4jArrayStructureTest {
fun testShape() {
val nd = Nd4j.rand(10, 2, 3, 6) ?: fail()
val struct = nd.asDoubleStructure()
assertEquals(intArrayOf(10, 2, 3, 6).toList(), struct.shape.toList())
assertEquals(intArrayOf(10, 2, 3, 6).toList(), struct.shape.asList())
}
@Test

2
kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/DoubleTensorFlowAlgebra.kt
View File

@ -28,6 +28,8 @@ public class DoubleTensorFlowOutput(
}
internal fun Shape.toLongArray(): LongArray = LongArray(size) { get(it).toLong() }
public class DoubleTensorFlowAlgebra internal constructor(
graph: Graph,
) : TensorFlowAlgebra<Double, TFloat64, DoubleField>(graph), PowerOperations<StructureND<Double>> {

20
kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/TensorFlowAlgebra.kt
View File

@ -18,9 +18,12 @@ import org.tensorflow.types.TInt32
import org.tensorflow.types.family.TNumber
import org.tensorflow.types.family.TType
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.misc.UnsafeKMathAPI
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.asArray
import space.kscience.kmath.nd.contentEquals
import space.kscience.kmath.operations.Ring
import space.kscience.kmath.tensors.api.Tensor
import space.kscience.kmath.tensors.api.TensorAlgebra
@ -38,7 +41,7 @@ public sealed interface TensorFlowTensor<T> : Tensor<T>
*/
@JvmInline
public value class TensorFlowArray<T>(public val tensor: NdArray<T>) : Tensor<T> {
override val shape: Shape get() = tensor.shape().asArray().toIntArray()
override val shape: Shape get() = Shape(tensor.shape().asArray().toIntArray())
override fun get(index: IntArray): T = tensor.getObject(*index.toLongArray())
@ -62,7 +65,7 @@ public abstract class TensorFlowOutput<T, TT : TType>(
public var output: Output<TT> = output
internal set
override val shape: Shape get() = output.shape().asArray().toIntArray()
override val shape: Shape get() = Shape(output.shape().asArray().toIntArray())
protected abstract fun org.tensorflow.Tensor.actualizeTensor(): NdArray<T>
@ -96,8 +99,8 @@ public abstract class TensorFlowAlgebra<T, TT : TNumber, A : Ring<T>> internal c
protected abstract fun const(value: T): Constant<TT>
override fun StructureND<T>.valueOrNull(): T? = if (shape contentEquals intArrayOf(1))
get(Shape(0)) else null
override fun StructureND<T>.valueOrNull(): T? = if (shape contentEquals Shape(1))
get(intArrayOf(0)) else null
/**
* Perform binary lazy operation on tensor. Both arguments are implicitly converted
@ -188,12 +191,13 @@ public abstract class TensorFlowAlgebra<T, TT : TNumber, A : Ring<T>> internal c
StridedSliceHelper.stridedSlice(ops.scope(), it, Indices.at(i.toLong()))
}
override fun Tensor<T>.transposed(i: Int, j: Int): Tensor<T> = operate {
override fun StructureND<T>.transposed(i: Int, j: Int): Tensor<T> = operate {
ops.linalg.transpose(it, ops.constant(intArrayOf(i, j)))
}
override fun Tensor<T>.view(shape: IntArray): Tensor<T> = operate {
ops.reshape(it, ops.constant(shape))
override fun Tensor<T>.view(shape: Shape): Tensor<T> = operate {
@OptIn(UnsafeKMathAPI::class)
ops.reshape(it, ops.constant(shape.asArray()))
}
override fun Tensor<T>.viewAs(other: StructureND<T>): Tensor<T> = operate(other) { l, r ->
@ -208,7 +212,7 @@ public abstract class TensorFlowAlgebra<T, TT : TNumber, A : Ring<T>> internal c
}
override fun diagonalEmbedding(
diagonalEntries: Tensor<T>,
diagonalEntries: StructureND<T>,
offset: Int,
dim1: Int,
dim2: Int,

5
kmath-tensorflow/src/test/kotlin/space/kscience/kmath/tensorflow/DoubleTensorFlowOps.kt
View File

@ -7,6 +7,7 @@ package space.kscience.kmath.tensorflow
import org.junit.jupiter.api.Test
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.nd.get
import space.kscience.kmath.nd.structureND
import space.kscience.kmath.operations.DoubleField
@ -31,8 +32,8 @@ class DoubleTensorFlowOps {
fun dot(){
val dim = 1000
val tensor1 = DoubleTensorAlgebra.randomNormal(shape = intArrayOf(dim, dim), 12224)
val tensor2 = DoubleTensorAlgebra.randomNormal(shape = intArrayOf(dim, dim), 12225)
val tensor1 = DoubleTensorAlgebra.randomNormal(shape = Shape(dim, dim), 12224)
val tensor2 = DoubleTensorAlgebra.randomNormal(shape = Shape(dim, dim), 12225)
DoubleField.produceWithTF {
tensor1 dot tensor2

14
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
View File

@ -21,7 +21,7 @@ public interface LinearOpsTensorAlgebra<T, A : Field<T>> : TensorPartialDivision
*
* @return the determinant.
*/
public fun StructureND<T>.det(): Tensor<T>
public fun StructureND<T>.det(): StructureND<T>
/**
* Computes the multiplicative inverse matrix of a square matrix input, or of each square matrix in a batched input.
@ -31,7 +31,7 @@ public interface LinearOpsTensorAlgebra<T, A : Field<T>> : TensorPartialDivision
*
* @return the multiplicative inverse of a matrix.
*/
public fun StructureND<T>.inv(): Tensor<T>
public fun StructureND<T>.inv(): StructureND<T>
/**
* Cholesky decomposition.
@ -47,7 +47,7 @@ public interface LinearOpsTensorAlgebra<T, A : Field<T>> : TensorPartialDivision
* @receiver the `input`.
* @return the batch of `L` matrices.
*/
public fun StructureND<T>.cholesky(): Tensor<T>
public fun StructureND<T>.cholesky(): StructureND<T>
/**
* QR decomposition.
@ -61,7 +61,7 @@ public interface LinearOpsTensorAlgebra<T, A : Field<T>> : TensorPartialDivision
* @receiver the `input`.
* @return pair of `Q` and `R` tensors.
*/
public fun StructureND<T>.qr(): Pair<Tensor<T>, Tensor<T>>
public fun StructureND<T>.qr(): Pair<StructureND<T>, StructureND<T>>
/**
* LUP decomposition
@ -75,7 +75,7 @@ public interface LinearOpsTensorAlgebra<T, A : Field<T>> : TensorPartialDivision
* @receiver the `input`.
* @return triple of P, L and U tensors
*/
public fun StructureND<T>.lu(): Triple<Tensor<T>, Tensor<T>, Tensor<T>>
public fun StructureND<T>.lu(): Triple<StructureND<T>, StructureND<T>, StructureND<T>>
/**
* Singular Value Decomposition.
@ -91,7 +91,7 @@ public interface LinearOpsTensorAlgebra<T, A : Field<T>> : TensorPartialDivision
* @receiver the `input`.
* @return triple `Triple(U, S, V)`.
*/
public fun StructureND<T>.svd(): Triple<Tensor<T>, Tensor<T>, Tensor<T>>
public fun StructureND<T>.svd(): Triple<StructureND<T>, StructureND<T>, StructureND<T>>
/**
* Returns eigenvalues and eigenvectors of a real symmetric matrix `input` or a batch of real symmetric matrices,
@ -101,6 +101,6 @@ public interface LinearOpsTensorAlgebra<T, A : Field<T>> : TensorPartialDivision
* @receiver the `input`.
* @return a pair `eigenvalues to eigenvectors`
*/
public fun StructureND<T>.symEig(): Pair<Tensor<T>, Tensor<T>>
public fun StructureND<T>.symEig(): Pair<StructureND<T>, StructureND<T>>
}

9
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
View File

@ -6,6 +6,7 @@
package space.kscience.kmath.tensors.api
import space.kscience.kmath.nd.RingOpsND
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.operations.Ring
@ -176,11 +177,13 @@ public interface TensorAlgebra<T, A : Ring<T>> : RingOpsND<T, A> {
* Returns a tensor that is a transposed version of this tensor. The given dimensions [i] and [j] are swapped.
* For more information: https://pytorch.org/docs/stable/generated/torch.transpose.html
*
* If axis indices are negative, they are counted from shape end.
*
* @param i the first dimension to be transposed
* @param j the second dimension to be transposed
* @return transposed tensor
*/
public fun Tensor<T>.transposed(i: Int = -2, j: Int = -1): Tensor<T>
public fun StructureND<T>.transposed(i: Int = shape.size - 2, j: Int = shape.size - 1): Tensor<T>
/**
* Returns a new tensor with the same data as the self tensor but of a different shape.
@ -190,7 +193,7 @@ public interface TensorAlgebra<T, A : Ring<T>> : RingOpsND<T, A> {
* @param shape the desired size
* @return tensor with new shape
*/
public fun Tensor<T>.view(shape: IntArray): Tensor<T>
public fun Tensor<T>.view(shape: Shape): Tensor<T>
/**
* View this tensor as the same size as [other].
@ -248,7 +251,7 @@ public interface TensorAlgebra<T, A : Ring<T>> : RingOpsND<T, A> {
* are filled by [diagonalEntries]
*/
public fun diagonalEmbedding(
diagonalEntries: Tensor<T>,
diagonalEntries: StructureND<T>,
offset: Int = 0,
dim1: Int = -2,
dim2: Int = -1,

3
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
View File

@ -7,6 +7,7 @@ package space.kscience.kmath.tensors.core
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.nd.RowStrides
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.nd.Strides
import space.kscience.kmath.structures.MutableBuffer
import space.kscience.kmath.tensors.api.Tensor
@ -15,7 +16,7 @@ import space.kscience.kmath.tensors.api.Tensor
* Represents [Tensor] over a [MutableBuffer] intended to be used through [DoubleTensor] and [IntTensor]
*/
public abstract class BufferedTensor<T>(
override val shape: IntArray,
override val shape: Shape,
) : Tensor<T> {
public abstract val source: MutableBuffer<T>

30
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensor.kt
View File

@ -7,10 +7,7 @@ package space.kscience.kmath.tensors.core
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.nd.MutableStructure2D
import space.kscience.kmath.nd.MutableStructureND
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.nd.Strides
import space.kscience.kmath.nd.*
import space.kscience.kmath.structures.*
import space.kscience.kmath.tensors.core.internal.toPrettyString
import kotlin.jvm.JvmInline
@ -87,22 +84,30 @@ public inline fun OffsetDoubleBuffer.mapInPlace(operation: (Double) -> Double) {
* [DoubleTensor] always uses row-based strides
*/
public class DoubleTensor(
shape: IntArray,
shape: Shape,
override val source: OffsetDoubleBuffer,
) : BufferedTensor<Double>(shape) {
) : BufferedTensor<Double>(shape), MutableStructureNDOfDouble {
init {
require(linearSize == source.size) { "Source buffer size must be equal tensor size" }
}
public constructor(shape: IntArray, buffer: DoubleBuffer) : this(shape, OffsetDoubleBuffer(buffer, 0, buffer.size))
public constructor(shape: Shape, buffer: DoubleBuffer) : this(shape, OffsetDoubleBuffer(buffer, 0, buffer.size))
override fun get(index: IntArray): Double = this.source[indices.offset(index)]
@OptIn(PerformancePitfall::class)
override fun get(index: IntArray): Double = source[indices.offset(index)]
@OptIn(PerformancePitfall::class)
override fun set(index: IntArray, value: Double) {
source[indices.offset(index)] = value
}
override fun getDouble(index: IntArray): Double = get(index)
override fun setDouble(index: IntArray, value: Double) {
set(index, value)
}
override fun toString(): String = toPrettyString()
}
@ -140,25 +145,26 @@ public value class DoubleTensor2D(public val tensor: DoubleTensor) : MutableStru
@PerformancePitfall
override fun elements(): Sequence<Pair<IntArray, Double>> = tensor.elements()
@OptIn(PerformancePitfall::class)
override fun get(index: IntArray): Double = tensor[index]
override val shape: Shape get() = tensor.shape
}
public fun DoubleTensor.asDoubleTensor2D(): DoubleTensor2D = DoubleTensor2D(this)
public fun DoubleTensor.asDoubleBuffer(): OffsetDoubleBuffer = if(shape.size == 1){
public fun DoubleTensor.asDoubleBuffer(): OffsetDoubleBuffer = if (shape.size == 1) {
source
} else {
error("Only 1D tensors could be cast to 1D" )
error("Only 1D tensors could be cast to 1D")
}
public inline fun DoubleTensor.forEachMatrix(block: (index: IntArray, matrix: DoubleTensor2D) -> Unit) {
val n = shape.size
check(n >= 2) { "Expected tensor with 2 or more dimensions, got size $n" }
val matrixOffset = shape[n - 1] * shape[n - 2]
val matrixShape = intArrayOf(shape[n - 2], shape[n - 1])
val matrixShape = Shape(shape[n - 2], shape[n - 1])
val size = Strides.linearSizeOf(matrixShape)
val size = matrixShape.linearSize
for (i in 0 until linearSize / matrixOffset) {
val offset = i * matrixOffset
val index = indices.index(offset).sliceArray(0 until (shape.size - 2))

162
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
View File

@ -11,7 +11,6 @@ package space.kscience.kmath.tensors.core
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.nd.*
import space.kscience.kmath.nd.Strides.Companion.linearSizeOf
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.structures.*
import space.kscience.kmath.tensors.api.AnalyticTensorAlgebra
@ -93,7 +92,7 @@ public open class DoubleTensorAlgebra :
override fun StructureND<Double>.valueOrNull(): Double? {
val dt = asDoubleTensor()
return if (dt.shape contentEquals intArrayOf(1)) dt.source[0] else null
return if (dt.shape contentEquals Shape(1)) dt.source[0] else null
}
override fun StructureND<Double>.value(): Double = valueOrNull()
@ -106,7 +105,7 @@ public open class DoubleTensorAlgebra :
* @param array one-dimensional data array.
* @return tensor with the [shape] shape and [array] data.
*/
public fun fromArray(shape: IntArray, array: DoubleArray): DoubleTensor {
public fun fromArray(shape: Shape, array: DoubleArray): DoubleTensor {
checkNotEmptyShape(shape)
checkEmptyDoubleBuffer(array)
checkBufferShapeConsistency(shape, array)
@ -120,18 +119,18 @@ public open class DoubleTensorAlgebra :
* @param initializer mapping tensor indices to values.
* @return tensor with the [shape] shape and data generated by the [initializer].
*/
override fun structureND(shape: IntArray, initializer: DoubleField.(IntArray) -> Double): DoubleTensor = fromArray(
override fun structureND(shape: Shape, initializer: DoubleField.(IntArray) -> Double): DoubleTensor = fromArray(
shape,
RowStrides(shape).asSequence().map { DoubleField.initializer(it) }.toMutableList().toDoubleArray()
)
override fun Tensor<Double>.getTensor(i: Int): DoubleTensor {
val dt = asDoubleTensor()
val lastShape = shape.drop(1).toIntArray()
val newShape = if (lastShape.isNotEmpty()) lastShape else intArrayOf(1)
val lastShape = shape.last(shape.size - 1)
val newShape: Shape = if (lastShape.isNotEmpty()) lastShape else Shape(1)
return DoubleTensor(
newShape,
dt.source.view(newShape.reduce(Int::times) * i, linearSizeOf(newShape))
dt.source.view(newShape.linearSize * i, newShape.linearSize)
)
}
@ -142,9 +141,9 @@ public open class DoubleTensorAlgebra :
* @param shape array of integers defining the shape of the output tensor.
* @return tensor with the [shape] shape and filled with [value].
*/
public fun full(value: Double, shape: IntArray): DoubleTensor {
public fun full(value: Double, shape: Shape): DoubleTensor {
checkNotEmptyShape(shape)
val buffer = DoubleBuffer(shape.reduce(Int::times)) { value }
val buffer = DoubleBuffer(shape.linearSize) { value }
return DoubleTensor(shape, buffer)
}
@ -166,7 +165,7 @@ public open class DoubleTensorAlgebra :
* @param shape array of integers defining the shape of the output tensor.
* @return tensor filled with the scalar value `0.0`, with the [shape] shape.
*/
public fun zeros(shape: IntArray): DoubleTensor = full(0.0, shape)
public fun zeros(shape: Shape): DoubleTensor = full(0.0, shape)
/**
* Returns a tensor filled with the scalar value `0.0`, with the same shape as a given array.
@ -181,7 +180,7 @@ public open class DoubleTensorAlgebra :
* @param shape array of integers defining the shape of the output tensor.
* @return tensor filled with the scalar value `1.0`, with the [shape] shape.
*/
public fun ones(shape: IntArray): DoubleTensor = full(1.0, shape)
public fun ones(shape: Shape): DoubleTensor = full(1.0, shape)
/**
* Returns a tensor filled with the scalar value `1.0`, with the same shape as a given array.
@ -197,7 +196,7 @@ public open class DoubleTensorAlgebra :
* @return a 2-D tensor with ones on the diagonal and zeros elsewhere.
*/
public fun eye(n: Int): DoubleTensor {
val shape = intArrayOf(n, n)
val shape = Shape(n, n)
val buffer = DoubleBuffer(n * n) { 0.0 }
val res = DoubleTensor(shape, buffer)
for (i in 0 until n) {
@ -235,7 +234,7 @@ public open class DoubleTensorAlgebra :
override fun Tensor<Double>.minusAssign(arg: StructureND<Double>) {
checkShapesCompatible(this, arg)
mapIndexedInPlace { index, value -> value - arg[index] }
mapIndexedInPlace { index, value -> value - arg.getDouble(index) }
}
override fun Double.times(arg: StructureND<Double>): DoubleTensor = arg.map { this@times * it }
@ -270,32 +269,44 @@ public open class DoubleTensorAlgebra :
override fun StructureND<Double>.unaryMinus(): DoubleTensor = map { -it }
override fun Tensor<Double>.transposed(i: Int, j: Int): DoubleTensor {
// TODO change strides instead of changing content
val dt = asDoubleTensor()
val ii = dt.minusIndex(i)
val jj = dt.minusIndex(j)
checkTranspose(dt.dimension, ii, jj)
val n = dt.linearSize
val resBuffer = DoubleArray(n)
val resShape = dt.shape.copyOf()
resShape[ii] = resShape[jj].also { resShape[jj] = resShape[ii] }
val resTensor = DoubleTensor(resShape, resBuffer.asBuffer())
for (offset in 0 until n) {
val oldMultiIndex = dt.indices.index(offset)
val newMultiIndex = oldMultiIndex.copyOf()
newMultiIndex[ii] = newMultiIndex[jj].also { newMultiIndex[jj] = newMultiIndex[ii] }
val linearIndex = resTensor.indices.offset(newMultiIndex)
resTensor.source[linearIndex] = dt.source[offset]
override fun StructureND<Double>.transposed(i: Int, j: Int): Tensor<Double> {
val actualI = if (i >= 0) i else shape.size + i
val actualJ = if(j>=0) j else shape.size + j
return asDoubleTensor().permute(
shape.transposed(actualI, actualJ)
) { originIndex ->
originIndex.copyOf().apply {
val ith = get(actualI)
val jth = get(actualJ)
set(actualI, jth)
set(actualJ, ith)
}
}
return resTensor
// // TODO change strides instead of changing content
// val dt = asDoubleTensor()
// val ii = dt.minusIndex(i)
// val jj = dt.minusIndex(j)
// checkTranspose(dt.dimension, ii, jj)
// val n = dt.linearSize
// val resBuffer = DoubleArray(n)
//
// val resShape = dt.shape.copyOf()
// resShape[ii] = resShape[jj].also { resShape[jj] = resShape[ii] }
//
// val resTensor = DoubleTensor(resShape, resBuffer.asBuffer())
//
// for (offset in 0 until n) {
// val oldMultiIndex = dt.indices.index(offset)
// val newMultiIndex = oldMultiIndex.copyOf()
// newMultiIndex[ii] = newMultiIndex[jj].also { newMultiIndex[jj] = newMultiIndex[ii] }
//
// val linearIndex = resTensor.indices.offset(newMultiIndex)
// resTensor.source[linearIndex] = dt.source[offset]
// }
// return resTensor
}
override fun Tensor<Double>.view(shape: IntArray): DoubleTensor {
override fun Tensor<Double>.view(shape: Shape): DoubleTensor {
checkView(asDoubleTensor(), shape)
return DoubleTensor(shape, asDoubleTensor().source)
}
@ -335,7 +346,7 @@ public open class DoubleTensorAlgebra :
@UnstableKMathAPI
public infix fun StructureND<Double>.matmul(other: StructureND<Double>): DoubleTensor {
if (shape.size == 1 && other.shape.size == 1) {
return DoubleTensor(intArrayOf(1), DoubleBuffer(times(other).sum()))
return DoubleTensor(Shape(1), DoubleBuffer(times(other).sum()))
}
var penultimateDim = false
@ -347,7 +358,7 @@ public open class DoubleTensorAlgebra :
if (shape.size == 1) {
penultimateDim = true
newThis = newThis.view(intArrayOf(1) + shape)
newThis = newThis.view(Shape(1) + shape)
}
if (other.shape.size == 1) {
@ -367,8 +378,8 @@ public open class DoubleTensorAlgebra :
"Tensors dot operation dimension mismatch: ($l, $m1) x ($m2, $n)"
}
val resShape = newThis.shape.sliceArray(0..(newThis.shape.size - 2)) + intArrayOf(newOther.shape.last())
val resSize = resShape.reduce { acc, i -> acc * i }
val resShape = newThis.shape.slice(0..(newThis.shape.size - 2)) + intArrayOf(newOther.shape.last())
val resSize = resShape.linearSize
val resTensor = DoubleTensor(resShape, DoubleArray(resSize).asBuffer())
val resMatrices = resTensor.matrices
@ -385,9 +396,9 @@ public open class DoubleTensorAlgebra :
// }
return if (penultimateDim) {
resTensor.view(resTensor.shape.dropLast(2).toIntArray() + intArrayOf(resTensor.shape.last()))
resTensor.view(resTensor.shape.first(resTensor.shape.size - 2) + Shape(resTensor.shape.last()))
} else if (lastDim) {
resTensor.view(resTensor.shape.dropLast(1).toIntArray())
resTensor.view(resTensor.shape.first(resTensor.shape.size - 1))
} else {
resTensor
}
@ -399,7 +410,7 @@ public open class DoubleTensorAlgebra :
}
override fun diagonalEmbedding(
diagonalEntries: Tensor<Double>,
diagonalEntries: StructureND<Double>,
offset: Int,
dim1: Int,
dim2: Int,
@ -423,11 +434,11 @@ public open class DoubleTensorAlgebra :
lessDim = greaterDim.also { greaterDim = lessDim }
}
val resShape = diagonalEntries.shape.slice(0 until lessDim).toIntArray() +
val resShape = diagonalEntries.shape.slice(0 until lessDim) +
intArrayOf(diagonalEntries.shape[n - 1] + abs(realOffset)) +
diagonalEntries.shape.slice(lessDim until greaterDim - 1).toIntArray() +
diagonalEntries.shape.slice(lessDim until greaterDim - 1) +
intArrayOf(diagonalEntries.shape[n - 1] + abs(realOffset)) +
diagonalEntries.shape.slice(greaterDim - 1 until n - 1).toIntArray()
diagonalEntries.shape.slice(greaterDim - 1 until n - 1)
val resTensor: DoubleTensor = zeros(resShape)
for (i in 0 until diagonalEntries.indices.linearSize) {
@ -495,8 +506,8 @@ public open class DoubleTensorAlgebra :
* @return tensor of a given shape filled with numbers from the normal distribution
* with `0.0` mean and `1.0` standard deviation.
*/
public fun randomNormal(shape: IntArray, seed: Long = 0): DoubleTensor =
DoubleTensor(shape, DoubleBuffer.randomNormals(shape.reduce(Int::times), seed))
public fun randomNormal(shape: Shape, seed: Long = 0): DoubleTensor =
DoubleTensor(shape, DoubleBuffer.randomNormals(shape.linearSize, seed))
/**
* Returns a tensor with the same shape as `input` of random numbers drawn from normal distributions
@ -508,7 +519,7 @@ public open class DoubleTensorAlgebra :
* with `0.0` mean and `1.0` standard deviation.
*/
public fun Tensor<Double>.randomNormalLike(seed: Long = 0): DoubleTensor =
DoubleTensor(shape, DoubleBuffer.randomNormals(shape.reduce(Int::times), seed))
DoubleTensor(shape, DoubleBuffer.randomNormals(shape.linearSize, seed))
/**
* Concatenates a sequence of tensors with equal shapes along the first dimension.
@ -520,7 +531,7 @@ public open class DoubleTensorAlgebra :
check(tensors.isNotEmpty()) { "List must have at least 1 element" }
val shape = tensors[0].shape
check(tensors.all { it.shape contentEquals shape }) { "Tensors must have same shapes" }
val resShape = intArrayOf(tensors.size) + shape
val resShape = Shape(tensors.size) + shape
// val resBuffer: List<Double> = tensors.flatMap {
// it.asDoubleTensor().source.array.drop(it.asDoubleTensor().bufferStart)
// .take(it.asDoubleTensor().linearSize)
@ -545,11 +556,11 @@ public open class DoubleTensorAlgebra :
): DoubleTensor {
check(dim < dimension) { "Dimension $dim out of range $dimension" }
val resShape = if (keepDim) {
shape.take(dim).toIntArray() + intArrayOf(1) + shape.takeLast(dimension - dim - 1).toIntArray()
shape.first(dim) + intArrayOf(1) + shape.last(dimension - dim - 1)
} else {
shape.take(dim).toIntArray() + shape.takeLast(dimension - dim - 1).toIntArray()
shape.first(dim) + shape.last(dimension - dim - 1)
}
val resNumElements = resShape.reduce(Int::times)
val resNumElements = resShape.linearSize
val init = foldFunction(DoubleArray(1) { 0.0 })
val resTensor = DoubleTensor(
resShape,
@ -573,11 +584,11 @@ public open class DoubleTensorAlgebra :
): IntTensor {
check(dim < dimension) { "Dimension $dim out of range $dimension" }
val resShape = if (keepDim) {
shape.take(dim).toIntArray() + intArrayOf(1) + shape.takeLast(dimension - dim - 1).toIntArray()
shape.first(dim) + intArrayOf(1) + shape.last(dimension - dim - 1)
} else {
shape.take(dim).toIntArray() + shape.takeLast(dimension - dim - 1).toIntArray()
shape.first(dim) + shape.last(dimension - dim - 1)
}
val resNumElements = resShape.reduce(Int::times)
val resNumElements = resShape.linearSize
val init = foldFunction(DoubleArray(1) { 0.0 })
val resTensor = IntTensor(
resShape,
@ -674,9 +685,9 @@ public open class DoubleTensorAlgebra :
check(tensors.isNotEmpty()) { "List must have at least 1 element" }
val n = tensors.size
val m = tensors[0].shape[0]
check(tensors.all { it.shape contentEquals intArrayOf(m) }) { "Tensors must have same shapes" }
check(tensors.all { it.shape contentEquals Shape(m) }) { "Tensors must have same shapes" }
val resTensor = DoubleTensor(
intArrayOf(n, n),
Shape(n, n),
DoubleBuffer(n * n) { 0.0 }
)
for (i in 0 until n) {
@ -772,7 +783,7 @@ public open class DoubleTensorAlgebra :
): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
checkSquareMatrix(luTensor.shape)
check(
luTensor.shape.dropLast(2).toIntArray() contentEquals pivotsTensor.shape.dropLast(1).toIntArray() ||
luTensor.shape.first(luTensor.shape.size - 2) contentEquals pivotsTensor.shape.first(pivotsTensor.shape.size - 1) ||
luTensor.shape.last() == pivotsTensor.shape.last() - 1
) { "Inappropriate shapes of input tensors" }
@ -843,9 +854,10 @@ public open class DoubleTensorAlgebra :
return qTensor to rTensor
}
override fun StructureND<Double>.svd(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> =
override fun StructureND<Double>.svd(): Triple<StructureND<Double>, StructureND<Double>, StructureND<Double>> =
svd(epsilon = 1e-10)
/**
* Singular Value Decomposition.
*
@ -859,13 +871,13 @@ public open class DoubleTensorAlgebra :
* i.e., the precision with which the cosine approaches 1 in an iterative algorithm.
* @return a triple `Triple(U, S, V)`.
*/
public fun StructureND<Double>.svd(epsilon: Double): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
public fun StructureND<Double>.svd(epsilon: Double): Triple<StructureND<Double>, StructureND<Double>, StructureND<Double>> {
val size = dimension
val commonShape = shape.sliceArray(0 until size - 2)
val (n, m) = shape.sliceArray(size - 2 until size)
val uTensor = zeros(commonShape + intArrayOf(min(n, m), n))
val sTensor = zeros(commonShape + intArrayOf(min(n, m)))
val vTensor = zeros(commonShape + intArrayOf(min(n, m), m))
val commonShape = shape.slice(0 until size - 2)
val (n, m) = shape.slice(size - 2 until size)
val uTensor = zeros(commonShape + Shape(min(n, m), n))
val sTensor = zeros(commonShape + Shape(min(n, m)))
val vTensor = zeros(commonShape + Shape(min(n, m), m))
val matrices = asDoubleTensor().matrices
val uTensors = uTensor.matrices
@ -879,7 +891,7 @@ public open class DoubleTensorAlgebra :
sTensorVectors[index],
vTensors[index]
)
val matrixSize = matrix.shape.reduce { acc, i -> acc * i }
val matrixSize = matrix.shape.linearSize
val curMatrix = DoubleTensor(
matrix.shape,
matrix.source.view(0, matrixSize)
@ -901,7 +913,7 @@ public open class DoubleTensorAlgebra :
* and when the cosine approaches 1 in the SVD algorithm.
* @return a pair `eigenvalues to eigenvectors`.
*/
public fun StructureND<Double>.symEigSvd(epsilon: Double): Pair<DoubleTensor, DoubleTensor> {
public fun StructureND<Double>.symEigSvd(epsilon: Double): Pair<DoubleTensor, StructureND<Double>> {
//TODO optimize conversion
checkSymmetric(asDoubleTensor(), epsilon)
@ -925,7 +937,7 @@ public open class DoubleTensorAlgebra :
matrix.asDoubleTensor2D().cleanSym(n)
}
val eig = (utv dot s.view(shp)).view(s.shape)
val eig = (utv dot s.asDoubleTensor().view(shp)).view(s.shape)
return eig to v
}
@ -934,8 +946,8 @@ public open class DoubleTensorAlgebra :
checkSymmetric(asDoubleTensor(), epsilon)
val size = this.dimension
val eigenvectors = zeros(this.shape)
val eigenvalues = zeros(this.shape.sliceArray(0 until size - 1))
val eigenvectors = zeros(shape)
val eigenvalues = zeros(shape.slice(0 until size - 1))
var eigenvalueStart = 0
var eigenvectorStart = 0
@ -976,9 +988,11 @@ public open class DoubleTensorAlgebra :
val n = shape.size
val detTensorShape = IntArray(n - 1) { i -> shape[i] }
detTensorShape[n - 2] = 1
val resBuffer = DoubleBuffer(detTensorShape.reduce(Int::times)) { 0.0 }
val detTensorShape = Shape(IntArray(n - 1) { i -> shape[i] }.apply {
set(n - 2, 1)
})
val resBuffer = DoubleBuffer(detTensorShape.linearSize) { 0.0 }
val detTensor = DoubleTensor(
detTensorShape,

8
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/IntTensor.kt
View File

@ -5,6 +5,8 @@
package space.kscience.kmath.tensors.core
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.structures.*
/**
@ -73,7 +75,7 @@ public inline fun OffsetIntBuffer.mapInPlace(operation: (Int) -> Int) {
* Default [BufferedTensor] implementation for [Int] values
*/
public class IntTensor(
shape: IntArray,
shape: Shape,
override val source: OffsetIntBuffer,
) : BufferedTensor<Int>(shape) {
@ -81,10 +83,12 @@ public class IntTensor(
require(linearSize == source.size) { "Source buffer size must be equal tensor size" }
}
public constructor(shape: IntArray, buffer: IntBuffer) : this(shape, OffsetIntBuffer(buffer, 0, buffer.size))
public constructor(shape: Shape, buffer: IntBuffer) : this(shape, OffsetIntBuffer(buffer, 0, buffer.size))
@OptIn(PerformancePitfall::class)
override fun get(index: IntArray): Int = this.source[indices.offset(index)]
@OptIn(PerformancePitfall::class)
override fun set(index: IntArray, value: Int) {
source[indices.offset(index)] = value
}

101
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/IntTensorAlgebra.kt
View File

@ -23,6 +23,7 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, IntRing> {
public companion object : IntTensorAlgebra()
override val elementAlgebra: IntRing get() = IntRing
@ -88,7 +89,7 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, IntRing> {
override fun StructureND<Int>.valueOrNull(): Int? {
val dt = asIntTensor()
return if (dt.shape contentEquals intArrayOf(1)) dt.source[0] else null
return if (dt.shape contentEquals Shape(1)) dt.source[0] else null
}
override fun StructureND<Int>.value(): Int = valueOrNull()
@ -101,11 +102,11 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, IntRing> {
* @param array one-dimensional data array.
* @return tensor with the [shape] shape and [array] data.
*/
public fun fromArray(shape: IntArray, array: IntArray): IntTensor {
public fun fromArray(shape: Shape, array: IntArray): IntTensor {
checkNotEmptyShape(shape)
check(array.isNotEmpty()) { "Illegal empty buffer provided" }
check(array.size == shape.reduce(Int::times)) {
"Inconsistent shape ${shape.toList()} for buffer of size ${array.size} provided"
check(array.size == shape.linearSize) {
"Inconsistent shape ${shape} for buffer of size ${array.size} provided"
}
return IntTensor(shape, array.asBuffer())
}
@ -117,16 +118,16 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, IntRing> {
* @param initializer mapping tensor indices to values.
* @return tensor with the [shape] shape and data generated by the [initializer].
*/
override fun structureND(shape: IntArray, initializer: IntRing.(IntArray) -> Int): IntTensor = fromArray(
override fun structureND(shape: Shape, initializer: IntRing.(IntArray) -> Int): IntTensor = fromArray(
shape,
RowStrides(shape).asSequence().map { IntRing.initializer(it) }.toMutableList().toIntArray()
)
override fun Tensor<Int>.getTensor(i: Int): IntTensor {
val dt = asIntTensor()
val lastShape = shape.drop(1).toIntArray()
val newShape = if (lastShape.isNotEmpty()) lastShape else intArrayOf(1)
return IntTensor(newShape, dt.source.view(newShape.reduce(Int::times) * i))
val lastShape = shape.last(shape.size - 1)
val newShape = if (lastShape.isNotEmpty()) lastShape else Shape(1)
return IntTensor(newShape, dt.source.view(newShape.linearSize * i))
}
/**
@ -136,9 +137,9 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, IntRing> {
* @param shape array of integers defining the shape of the output tensor.
* @return tensor with the [shape] shape and filled with [value].
*/
public fun full(value: Int, shape: IntArray): IntTensor {
public fun full(value: Int, shape: Shape): IntTensor {
checkNotEmptyShape(shape)
val buffer = IntBuffer(shape.reduce(Int::times)) { value }
val buffer = IntBuffer(shape.linearSize) { value }
return IntTensor(shape, buffer)
}
@ -160,7 +161,7 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, IntRing> {
* @param shape array of integers defining the shape of the output tensor.
* @return tensor filled with the scalar value `0`, with the [shape] shape.
*/
public fun zeros(shape: IntArray): IntTensor = full(0, shape)
public fun zeros(shape: Shape): IntTensor = full(0, shape)
/**
* Returns a tensor filled with the scalar value `0`, with the same shape as a given array.
@ -175,7 +176,7 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, IntRing> {
* @param shape array of integers defining the shape of the output tensor.
* @return tensor filled with the scalar value `1`, with the [shape] shape.
*/
public fun ones(shape: IntArray): IntTensor = full(1, shape)
public fun ones(shape: Shape): IntTensor = full(1, shape)
/**
* Returns a tensor filled with the scalar value `1`, with the same shape as a given array.
@ -191,7 +192,7 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, IntRing> {
* @return a 2-D tensor with ones on the diagonal and zeros elsewhere.
*/
public fun eye(n: Int): IntTensor {
val shape = intArrayOf(n, n)
val shape = Shape(n, n)
val buffer = IntBuffer(n * n) { 0 }
val res = IntTensor(shape, buffer)
for (i in 0 until n) {
@ -249,32 +250,44 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, IntRing> {
override fun StructureND<Int>.unaryMinus(): IntTensor = map { -it }
override fun Tensor<Int>.transposed(i: Int, j: Int): IntTensor {
// TODO change strides instead of changing content
val dt = asIntTensor()
val ii = dt.minusIndex(i)
val jj = dt.minusIndex(j)
checkTranspose(dt.dimension, ii, jj)
val n = dt.linearSize
val resBuffer = IntArray(n)
val resShape = dt.shape.copyOf()
resShape[ii] = resShape[jj].also { resShape[jj] = resShape[ii] }
val resTensor = IntTensor(resShape, resBuffer.asBuffer())
for (offset in 0 until n) {
val oldMultiIndex = dt.indices.index(offset)
val newMultiIndex = oldMultiIndex.copyOf()
newMultiIndex[ii] = newMultiIndex[jj].also { newMultiIndex[jj] = newMultiIndex[ii] }
val linearIndex = resTensor.indices.offset(newMultiIndex)
resTensor.source[linearIndex] = dt.source[offset]
override fun StructureND<Int>.transposed(i: Int, j: Int): Tensor<Int> {
val actualI = if (i >= 0) i else shape.size + i
val actualJ = if(j>=0) j else shape.size + j
return asIntTensor().permute(
shape.transposed(actualI, actualJ)
) { originIndex ->
originIndex.copyOf().apply {
val ith = get(actualI)
val jth = get(actualJ)
set(actualI, jth)
set(actualJ, ith)
}
}
return resTensor
// // TODO change strides instead of changing content
// val dt = asIntTensor()
// val ii = dt.minusIndex(i)
// val jj = dt.minusIndex(j)
// checkTranspose(dt.dimension, ii, jj)
// val n = dt.linearSize
// val resBuffer = IntArray(n)
//
// val resShape = dt.shape.toArray()
// resShape[ii] = resShape[jj].also { resShape[jj] = resShape[ii] }
//
// val resTensor = IntTensor(Shape(resShape), resBuffer.asBuffer())
//
// for (offset in 0 until n) {
// val oldMultiIndex = dt.indices.index(offset)
// val newMultiIndex = oldMultiIndex.copyOf()
// newMultiIndex[ii] = newMultiIndex[jj].also { newMultiIndex[jj] = newMultiIndex[ii] }
//
// val linearIndex = resTensor.indices.offset(newMultiIndex)
// resTensor.source[linearIndex] = dt.source[offset]
// }
// return resTensor
}
override fun Tensor<Int>.view(shape: IntArray): IntTensor {
override fun Tensor<Int>.view(shape: Shape): IntTensor {
checkView(asIntTensor(), shape)
return IntTensor(shape, asIntTensor().source)
}
@ -287,7 +300,7 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, IntRing> {
}
override fun diagonalEmbedding(
diagonalEntries: Tensor<Int>,
diagonalEntries: StructureND<Int>,
offset: Int,
dim1: Int,
dim2: Int,
@ -311,11 +324,11 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, IntRing> {
lessDim = greaterDim.also { greaterDim = lessDim }
}
val resShape = diagonalEntries.shape.slice(0 until lessDim).toIntArray() +
val resShape = diagonalEntries.shape.slice(0 until lessDim) +
intArrayOf(diagonalEntries.shape[n - 1] + abs(realOffset)) +
diagonalEntries.shape.slice(lessDim until greaterDim - 1).toIntArray() +
diagonalEntries.shape.slice(lessDim until greaterDim - 1) +
intArrayOf(diagonalEntries.shape[n - 1] + abs(realOffset)) +
diagonalEntries.shape.slice(greaterDim - 1 until n - 1).toIntArray()
diagonalEntries.shape.slice(greaterDim - 1 until n - 1)
val resTensor = zeros(resShape)
for (i in 0 until diagonalEntries.asIntTensor().linearSize) {
@ -375,7 +388,7 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, IntRing> {
check(tensors.isNotEmpty()) { "List must have at least 1 element" }
val shape = tensors[0].shape
check(tensors.all { it.shape contentEquals shape }) { "Tensors must have same shapes" }
val resShape = intArrayOf(tensors.size) + shape
val resShape = Shape(tensors.size) + shape
// val resBuffer: List<Int> = tensors.flatMap {
// it.asIntTensor().source.array.drop(it.asIntTensor().bufferStart)
// .take(it.asIntTensor().linearSize)
@ -399,11 +412,11 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, IntRing> {
): IntTensor {
check(dim < dimension) { "Dimension $dim out of range $dimension" }
val resShape = if (keepDim) {
shape.take(dim).toIntArray() + intArrayOf(1) + shape.takeLast(dimension - dim - 1).toIntArray()
shape.first(dim) + intArrayOf(1) + shape.last(dimension - dim - 1)
} else {
shape.take(dim).toIntArray() + shape.takeLast(dimension - dim - 1).toIntArray()
shape.first(dim) + shape.last(dimension - dim - 1)
}
val resNumElements = resShape.reduce(Int::times)
val resNumElements = resShape.linearSize
val init = foldFunction(IntArray(1) { 0 })
val resTensor = IntTensor(
resShape,

27
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/broadcastUtils.kt
View File

@ -5,6 +5,8 @@
package space.kscience.kmath.tensors.core.internal
import space.kscience.kmath.misc.UnsafeKMathAPI
import space.kscience.kmath.nd.*
import space.kscience.kmath.structures.asBuffer
import space.kscience.kmath.tensors.core.DoubleTensor
import kotlin.math.max
@ -12,7 +14,7 @@ import kotlin.math.max
internal fun multiIndexBroadCasting(tensor: DoubleTensor, resTensor: DoubleTensor, linearSize: Int) {
for (linearIndex in 0 until linearSize) {
val totalMultiIndex = resTensor.indices.index(linearIndex)
val curMultiIndex = tensor.shape.copyOf()
val curMultiIndex = tensor.shape.toArray()
val offset = totalMultiIndex.size - curMultiIndex.size
@ -30,7 +32,7 @@ internal fun multiIndexBroadCasting(tensor: DoubleTensor, resTensor: DoubleTenso
}
}
internal fun broadcastShapes(vararg shapes: IntArray): IntArray {
internal fun broadcastShapes(shapes: List<Shape>): Shape {
var totalDim = 0
for (shape in shapes) {
totalDim = max(totalDim, shape.size)
@ -55,15 +57,15 @@ internal fun broadcastShapes(vararg shapes: IntArray): IntArray {
}
}
return totalShape
return Shape(totalShape)
}
internal fun broadcastTo(tensor: DoubleTensor, newShape: IntArray): DoubleTensor {
internal fun broadcastTo(tensor: DoubleTensor, newShape: Shape): DoubleTensor {
require(tensor.shape.size <= newShape.size) {
"Tensor is not compatible with the new shape"
}
val n = newShape.reduce { acc, i -> acc * i }
val n = newShape.linearSize
val resTensor = DoubleTensor(newShape, DoubleArray(n).asBuffer())
for (i in tensor.shape.indices) {
@ -79,8 +81,8 @@ internal fun broadcastTo(tensor: DoubleTensor, newShape: IntArray): DoubleTensor
}
internal fun broadcastTensors(vararg tensors: DoubleTensor): List<DoubleTensor> {
val totalShape = broadcastShapes(*(tensors.map { it.shape }).toTypedArray())
val n = totalShape.reduce { acc, i -> acc * i }
val totalShape = broadcastShapes(tensors.map { it.shape })
val n = totalShape.linearSize
return tensors.map { tensor ->
val resTensor = DoubleTensor(totalShape, DoubleArray(n).asBuffer())
@ -100,12 +102,12 @@ internal fun broadcastOuterTensors(vararg tensors: DoubleTensor): List<DoubleTen
return tensors.asList()
}
val totalShape = broadcastShapes(*(tensors.map { it.shape.sliceArray(0..it.shape.size - 3) }).toTypedArray())
val n = totalShape.reduce { acc, i -> acc * i }
val totalShape = broadcastShapes(tensors.map { it.shape.slice(0..it.shape.size - 3) })
val n = totalShape.linearSize
return buildList {
for (tensor in tensors) {
val matrixShape = tensor.shape.sliceArray(tensor.shape.size - 2 until tensor.shape.size).copyOf()
val matrixShape = tensor.shape.slice(tensor.shape.size - 2 until tensor.shape.size)
val matrixSize = matrixShape[0] * matrixShape[1]
val matrix = DoubleTensor(matrixShape, DoubleArray(matrixSize).asBuffer())
@ -114,10 +116,11 @@ internal fun broadcastOuterTensors(vararg tensors: DoubleTensor): List<DoubleTen
for (linearIndex in 0 until n) {
val totalMultiIndex = outerTensor.indices.index(linearIndex)
var curMultiIndex = tensor.shape.sliceArray(0..tensor.shape.size - 3).copyOf()
@OptIn(UnsafeKMathAPI::class)
var curMultiIndex = tensor.shape.slice(0..tensor.shape.size - 3).asArray()
curMultiIndex = IntArray(totalMultiIndex.size - curMultiIndex.size) { 1 } + curMultiIndex
val newTensor = DoubleTensor(curMultiIndex + matrixShape, tensor.source)
val newTensor = DoubleTensor(Shape(curMultiIndex) + matrixShape, tensor.source)
for (i in curMultiIndex.indices) {
if (curMultiIndex[i] != 1) {

21
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/checks.kt
View File

@ -5,15 +5,18 @@
package space.kscience.kmath.tensors.core.internal
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.contentEquals
import space.kscience.kmath.nd.linearSize
import space.kscience.kmath.tensors.api.Tensor
import space.kscience.kmath.tensors.core.DoubleTensor
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
import space.kscience.kmath.tensors.core.asDoubleTensor
internal fun checkNotEmptyShape(shape: IntArray) =
check(shape.isNotEmpty()) {
internal fun checkNotEmptyShape(shape: Shape) =
check(shape.size > 0) {
"Illegal empty shape provided"
}
@ -21,15 +24,15 @@ internal fun checkEmptyDoubleBuffer(buffer: DoubleArray) = check(buffer.isNotEmp
"Illegal empty buffer provided"
}
internal fun checkBufferShapeConsistency(shape: IntArray, buffer: DoubleArray) =
check(buffer.size == shape.reduce(Int::times)) {
"Inconsistent shape ${shape.toList()} for buffer of size ${buffer.size} provided"
internal fun checkBufferShapeConsistency(shape: Shape, buffer: DoubleArray) =
check(buffer.size == shape.linearSize) {
"Inconsistent shape ${shape} for buffer of size ${buffer.size} provided"
}
@PublishedApi
internal fun <T> checkShapesCompatible(a: StructureND<T>, b: StructureND<T>): Unit =
check(a.shape contentEquals b.shape) {
"Incompatible shapes ${a.shape.toList()} and ${b.shape.toList()} "
"Incompatible shapes ${a.shape} and ${b.shape} "
}
internal fun checkTranspose(dim: Int, i: Int, j: Int) =
@ -37,10 +40,10 @@ internal fun checkTranspose(dim: Int, i: Int, j: Int) =
"Cannot transpose $i to $j for a tensor of dim $dim"
}
internal fun <T> checkView(a: Tensor<T>, shape: IntArray) =
check(a.shape.reduce(Int::times) == shape.reduce(Int::times))
internal fun <T> checkView(a: Tensor<T>, shape: Shape) =
check(a.shape.linearSize == shape.linearSize)
internal fun checkSquareMatrix(shape: IntArray) {
internal fun checkSquareMatrix(shape: Shape) {
val n = shape.size
check(n >= 2) {
"Expected tensor with 2 or more dimensions, got size $n instead"

15
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/doubleTensorHelpers.kt
View File

@ -6,7 +6,6 @@
package space.kscience.kmath.tensors.core.internal
import space.kscience.kmath.nd.*
import space.kscience.kmath.nd.Strides.Companion.linearSizeOf
import space.kscience.kmath.structures.DoubleBuffer
import space.kscience.kmath.structures.asBuffer
import space.kscience.kmath.structures.indices
@ -40,7 +39,7 @@ internal fun MutableStructure2D<Double>.jacobiHelper(
source[i * shape[0] + j] = value
}
fun maxOffDiagonal(matrix: BufferedTensor<Double>): Double {
fun maxOffDiagonal(matrix: DoubleTensor): Double {
var maxOffDiagonalElement = 0.0
for (i in 0 until n - 1) {
for (j in i + 1 until n) {
@ -50,7 +49,7 @@ internal fun MutableStructure2D<Double>.jacobiHelper(
return maxOffDiagonalElement
}
fun rotate(a: BufferedTensor<Double>, s: Double, tau: Double, i: Int, j: Int, k: Int, l: Int) {
fun rotate(a: DoubleTensor, s: Double, tau: Double, i: Int, j: Int, k: Int, l: Int) {
val g = a[i, j]
val h = a[k, l]
a[i, j] = g - s * (h + g * tau)
@ -58,8 +57,8 @@ internal fun MutableStructure2D<Double>.jacobiHelper(
}
fun jacobiIteration(
a: BufferedTensor<Double>,
v: BufferedTensor<Double>,
a: DoubleTensor,
v: DoubleTensor,
d: DoubleBuffer,
z: DoubleBuffer,
) {
@ -157,7 +156,7 @@ internal val DoubleTensor.vectors: List<DoubleTensor>
get() {
val n = shape.size
val vectorOffset = shape[n - 1]
val vectorShape = intArrayOf(shape.last())
val vectorShape = Shape(shape.last())
return List(linearSize / vectorOffset) { index ->
val offset = index * vectorOffset
@ -174,9 +173,9 @@ internal val DoubleTensor.matrices: List<DoubleTensor>
val n = shape.size
check(n >= 2) { "Expected tensor with 2 or more dimensions, got size $n" }
val matrixOffset = shape[n - 1] * shape[n - 2]
val matrixShape = intArrayOf(shape[n - 2], shape[n - 1])
val matrixShape = Shape(shape[n - 2], shape[n - 1])
val size = linearSizeOf(matrixShape)
val size = matrixShape.linearSize
return List(linearSize / matrixOffset) { index ->
val offset = index * matrixOffset

7
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/intTensorHelpers.kt
View File

@ -5,6 +5,9 @@
package space.kscience.kmath.tensors.core.internal
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.nd.first
import space.kscience.kmath.nd.last
import space.kscience.kmath.operations.asSequence
import space.kscience.kmath.structures.IntBuffer
import space.kscience.kmath.structures.VirtualBuffer
@ -35,7 +38,7 @@ internal fun List<OffsetIntBuffer>.concat(): IntBuffer {
internal fun IntTensor.vectors(): VirtualBuffer<IntTensor> {
val n = shape.size
val vectorOffset = shape[n - 1]
val vectorShape = intArrayOf(shape.last())
val vectorShape = shape.last(1)
return VirtualBuffer(linearSize / vectorOffset) { index ->
val offset = index * vectorOffset
@ -52,7 +55,7 @@ internal val IntTensor.matrices: VirtualBuffer<IntTensor>
val n = shape.size
check(n >= 2) { "Expected tensor with 2 or more dimensions, got size $n" }
val matrixOffset = shape[n - 1] * shape[n - 2]
val matrixShape = intArrayOf(shape[n - 2], shape[n - 1])
val matrixShape = Shape(shape[n - 2], shape[n - 1])
return VirtualBuffer(linearSize / matrixOffset) { index ->
val offset = index * matrixOffset

13
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/linUtils.kt
View File

@ -5,10 +5,7 @@
package space.kscience.kmath.tensors.core.internal
import space.kscience.kmath.nd.MutableStructure1D
import space.kscience.kmath.nd.MutableStructure2D
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.as1D
import space.kscience.kmath.nd.*
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.DoubleBuffer
import space.kscience.kmath.structures.IntBuffer
@ -98,7 +95,7 @@ internal fun <T> StructureND<T>.setUpPivots(): IntTensor {
pivotsShape[n - 2] = m + 1
return IntTensor(
pivotsShape,
Shape(pivotsShape),
IntBuffer(pivotsShape.reduce(Int::times)) { 0 }
)
}
@ -243,10 +240,10 @@ internal fun DoubleTensorAlgebra.svd1d(a: DoubleTensor, epsilon: Double = 1e-10)
val b: DoubleTensor
if (n > m) {
b = a.transposed(0, 1).dot(a)
v = DoubleTensor(intArrayOf(m), DoubleBuffer.randomUnitVector(m, 0))
v = DoubleTensor(Shape(m), DoubleBuffer.randomUnitVector(m, 0))
} else {
b = a.dot(a.transposed(0, 1))
v = DoubleTensor(intArrayOf(n), DoubleBuffer.randomUnitVector(n, 0))
v = DoubleTensor(Shape(n), DoubleBuffer.randomUnitVector(n, 0))
}
var lastV: DoubleTensor
@ -278,7 +275,7 @@ internal fun DoubleTensorAlgebra.svdHelper(
outerProduct[i * v.shape[0] + j] = u.getTensor(i).value() * v.getTensor(j).value()
}
}
a = a - singularValue.times(DoubleTensor(intArrayOf(u.shape[0], v.shape[0]), outerProduct.asBuffer()))
a = a - singularValue.times(DoubleTensor(Shape(u.shape[0], v.shape[0]), outerProduct.asBuffer()))
}
var v: DoubleTensor
var u: DoubleTensor

4
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/utils.kt
View File

@ -6,6 +6,8 @@
package space.kscience.kmath.tensors.core.internal
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.nd.asList
import space.kscience.kmath.nd.last
import space.kscience.kmath.operations.DoubleBufferOps.Companion.map
import space.kscience.kmath.random.RandomGenerator
import space.kscience.kmath.samplers.GaussianSampler
@ -92,7 +94,7 @@ public fun DoubleTensor.toPrettyString(): String = buildString {
append(']')
charOffset -= 1
index.reversed().zip(shape.reversed()).drop(1).forEach { (ind, maxInd) ->
index.reversed().zip(shape.asList().reversed()).drop(1).forEach { (ind, maxInd) ->
if (ind != maxInd - 1) {
return@forEach
}

4
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorAlgebraExtensions.kt
View File

@ -12,11 +12,11 @@ import space.kscience.kmath.nd.Shape
import kotlin.jvm.JvmName
@JvmName("varArgOne")
public fun DoubleTensorAlgebra.one(vararg shape: Int): DoubleTensor = ones(intArrayOf(*shape))
public fun DoubleTensorAlgebra.one(vararg shape: Int): DoubleTensor = ones(Shape(shape))
public fun DoubleTensorAlgebra.one(shape: Shape): DoubleTensor = ones(shape)
@JvmName("varArgZero")
public fun DoubleTensorAlgebra.zero(vararg shape: Int): DoubleTensor = zeros(intArrayOf(*shape))
public fun DoubleTensorAlgebra.zero(vararg shape: Int): DoubleTensor = zeros(Shape(shape))
public fun DoubleTensorAlgebra.zero(shape: Shape): DoubleTensor = zeros(shape)

12
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorTransform.kt
View File

@ -5,9 +5,7 @@
package space.kscience.kmath.tensors.core
import space.kscience.kmath.nd.DoubleBufferND
import space.kscience.kmath.nd.RowStrides
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.*
import space.kscience.kmath.structures.DoubleBuffer
import space.kscience.kmath.structures.asBuffer
import space.kscience.kmath.tensors.api.Tensor
@ -23,7 +21,7 @@ public fun StructureND<Double>.copyToTensor(): DoubleTensor = if (this is Double
} else {
DoubleTensor(
shape,
RowStrides(this.shape).map(this::get).toDoubleArray().asBuffer(),
RowStrides(this.shape).map(this::getDouble).toDoubleArray().asBuffer(),
)
}
@ -36,7 +34,7 @@ public fun StructureND<Int>.toDoubleTensor(): DoubleTensor {
} else {
val tensor = DoubleTensorAlgebra.zeroesLike(this)
indices.forEach {
tensor[it] = get(it).toDouble()
tensor[it] = getInt(it).toDouble()
}
return tensor
}
@ -59,7 +57,7 @@ public fun StructureND<Double>.asDoubleTensor(): DoubleTensor = if (this is Doub
public fun StructureND<Int>.asIntTensor(): IntTensor = when (this) {
is IntTensor -> this
else -> IntTensor(
this.shape,
RowStrides(this.shape).map(this::get).toIntArray().asBuffer()
shape,
RowStrides(shape).map(this::getInt).toIntArray().asBuffer()
)
}

64
kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt
View File

@ -5,6 +5,8 @@
package space.kscience.kmath.tensors.core
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.nd.contentEquals
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.tensors.core.internal.broadcastOuterTensors
import space.kscience.kmath.tensors.core.internal.broadcastShapes
@ -19,38 +21,38 @@ internal class TestBroadcasting {
fun testBroadcastShapes() = DoubleTensorAlgebra {
assertTrue(
broadcastShapes(
intArrayOf(2, 3), intArrayOf(1, 3), intArrayOf(1, 1, 1)
) contentEquals intArrayOf(1, 2, 3)
listOf(Shape(2, 3), Shape(1, 3), Shape(1, 1, 1))
) contentEquals Shape(1, 2, 3)
)
assertTrue(
broadcastShapes(
intArrayOf(6, 7), intArrayOf(5, 6, 1), intArrayOf(7), intArrayOf(5, 1, 7)
) contentEquals intArrayOf(5, 6, 7)
listOf(Shape(6, 7), Shape(5, 6, 1), Shape(7), Shape(5, 1, 7))
) contentEquals Shape(5, 6, 7)
)
}
@Test
fun testBroadcastTo() = DoubleTensorAlgebra {
val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
val tensor1 = fromArray(Shape(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
val tensor2 = fromArray(Shape(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
val res = broadcastTo(tensor2, tensor1.shape)
assertTrue(res.shape contentEquals intArrayOf(2, 3))
assertTrue(res.shape contentEquals Shape(2, 3))
assertTrue(res.source contentEquals doubleArrayOf(10.0, 20.0, 30.0, 10.0, 20.0, 30.0))
}
@Test
fun testBroadcastTensors() = DoubleTensorAlgebra {
val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
val tensor3 = fromArray(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
val tensor1 = fromArray(Shape(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
val tensor2 = fromArray(Shape(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
val tensor3 = fromArray(Shape(1, 1, 1), doubleArrayOf(500.0))
val res = broadcastTensors(tensor1, tensor2, tensor3)
assertTrue(res[0].shape contentEquals intArrayOf(1, 2, 3))
assertTrue(res[1].shape contentEquals intArrayOf(1, 2, 3))
assertTrue(res[2].shape contentEquals intArrayOf(1, 2, 3))
assertTrue(res[0].shape contentEquals Shape(1, 2, 3))
assertTrue(res[1].shape contentEquals Shape(1, 2, 3))
assertTrue(res[2].shape contentEquals Shape(1, 2, 3))
assertTrue(res[0].source contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
assertTrue(res[1].source contentEquals doubleArrayOf(10.0, 20.0, 30.0, 10.0, 20.0, 30.0))
@ -59,15 +61,15 @@ internal class TestBroadcasting {
@Test
fun testBroadcastOuterTensors() = DoubleTensorAlgebra {
val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
val tensor3 = fromArray(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
val tensor1 = fromArray(Shape(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
val tensor2 = fromArray(Shape(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
val tensor3 = fromArray(Shape(1, 1, 1), doubleArrayOf(500.0))
val res = broadcastOuterTensors(tensor1, tensor2, tensor3)
assertTrue(res[0].shape contentEquals intArrayOf(1, 2, 3))
assertTrue(res[1].shape contentEquals intArrayOf(1, 1, 3))
assertTrue(res[2].shape contentEquals intArrayOf(1, 1, 1))
assertTrue(res[0].shape contentEquals Shape(1, 2, 3))
assertTrue(res[1].shape contentEquals Shape(1, 1, 3))
assertTrue(res[2].shape contentEquals Shape(1, 1, 1))
assertTrue(res[0].source contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
assertTrue(res[1].source contentEquals doubleArrayOf(10.0, 20.0, 30.0))
@ -76,37 +78,37 @@ internal class TestBroadcasting {
@Test
fun testBroadcastOuterTensorsShapes() = DoubleTensorAlgebra {
val tensor1 = fromArray(intArrayOf(2, 1, 3, 2, 3), DoubleArray(2 * 1 * 3 * 2 * 3) { 0.0 })
val tensor2 = fromArray(intArrayOf(4, 2, 5, 1, 3, 3), DoubleArray(4 * 2 * 5 * 1 * 3 * 3) { 0.0 })
val tensor3 = fromArray(intArrayOf(1, 1), doubleArrayOf(500.0))
val tensor1 = fromArray(Shape(2, 1, 3, 2, 3), DoubleArray(2 * 1 * 3 * 2 * 3) { 0.0 })
val tensor2 = fromArray(Shape(4, 2, 5, 1, 3, 3), DoubleArray(4 * 2 * 5 * 1 * 3 * 3) { 0.0 })
val tensor3 = fromArray(Shape(1, 1), doubleArrayOf(500.0))
val res = broadcastOuterTensors(tensor1, tensor2, tensor3)
assertTrue(res[0].shape contentEquals intArrayOf(4, 2, 5, 3, 2, 3))
assertTrue(res[1].shape contentEquals intArrayOf(4, 2, 5, 3, 3, 3))
assertTrue(res[2].shape contentEquals intArrayOf(4, 2, 5, 3, 1, 1))
assertTrue(res[0].shape contentEquals Shape(4, 2, 5, 3, 2, 3))
assertTrue(res[1].shape contentEquals Shape(4, 2, 5, 3, 3, 3))
assertTrue(res[2].shape contentEquals Shape(4, 2, 5, 3, 1, 1))
}
@Test
fun testMinusTensor() = BroadcastDoubleTensorAlgebra.invoke {
val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
val tensor3 = fromArray(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
val tensor1 = fromArray(Shape(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
val tensor2 = fromArray(Shape(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
val tensor3 = fromArray(Shape(1, 1, 1), doubleArrayOf(500.0))
val tensor21 = tensor2 - tensor1
val tensor31 = tensor3 - tensor1
val tensor32 = tensor3 - tensor2
assertTrue(tensor21.shape contentEquals intArrayOf(2, 3))
assertTrue(tensor21.shape contentEquals Shape(2, 3))
assertTrue(tensor21.source contentEquals doubleArrayOf(9.0, 18.0, 27.0, 6.0, 15.0, 24.0))
assertTrue(tensor31.shape contentEquals intArrayOf(1, 2, 3))
assertTrue(tensor31.shape contentEquals Shape(1, 2, 3))
assertTrue(
tensor31.source
contentEquals doubleArrayOf(499.0, 498.0, 497.0, 496.0, 495.0, 494.0)
)
assertTrue(tensor32.shape contentEquals intArrayOf(1, 1, 3))
assertTrue(tensor32.shape contentEquals Shape(1, 1, 3))
assertTrue(tensor32.source contentEquals doubleArrayOf(490.0, 480.0, 470.0))
}

21
kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleAnalyticTensorAlgebra.kt
View File

@ -5,6 +5,7 @@
package space.kscience.kmath.tensors.core
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.asBuffer
import kotlin.math.*
@ -13,7 +14,7 @@ import kotlin.test.assertTrue
internal class TestDoubleAnalyticTensorAlgebra {
val shape = intArrayOf(2, 1, 3, 2)
val shape = Shape(2, 1, 3, 2)
val buffer = doubleArrayOf(
27.1, 20.0, 19.84,
23.123, 3.0, 2.0,
@ -102,7 +103,7 @@ internal class TestDoubleAnalyticTensorAlgebra {
assertTrue { tensor.floor() eq expectedTensor(::floor) }
}
val shape2 = intArrayOf(2, 2)
val shape2 = Shape(2, 2)
val buffer2 = doubleArrayOf(
1.0, 2.0,
-3.0, 4.0
@ -114,13 +115,13 @@ internal class TestDoubleAnalyticTensorAlgebra {
assertTrue { tensor2.min() == -3.0 }
assertTrue {
tensor2.min(0, true) eq fromArray(
intArrayOf(1, 2),
Shape(1, 2),
doubleArrayOf(-3.0, 2.0)
)
}
assertTrue {
tensor2.min(1, false) eq fromArray(
intArrayOf(2),
Shape(2),
doubleArrayOf(1.0, -3.0)
)
}
@ -131,13 +132,13 @@ internal class TestDoubleAnalyticTensorAlgebra {
assertTrue { tensor2.max() == 4.0 }
assertTrue {
tensor2.max(0, true) eq fromArray(
intArrayOf(1, 2),
Shape(1, 2),
doubleArrayOf(1.0, 4.0)
)
}
assertTrue {
tensor2.max(1, false) eq fromArray(
intArrayOf(2),
Shape(2),
doubleArrayOf(2.0, 4.0)
)
}
@ -148,13 +149,13 @@ internal class TestDoubleAnalyticTensorAlgebra {
assertTrue { tensor2.sum() == 4.0 }
assertTrue {
tensor2.sum(0, true) eq fromArray(
intArrayOf(1, 2),
Shape(1, 2),
doubleArrayOf(-2.0, 6.0)
)
}
assertTrue {
tensor2.sum(1, false) eq fromArray(
intArrayOf(2),
Shape(2),
doubleArrayOf(3.0, 1.0)
)
}
@ -165,13 +166,13 @@ internal class TestDoubleAnalyticTensorAlgebra {
assertTrue { tensor2.mean() == 1.0 }
assertTrue {
tensor2.mean(0, true) eq fromArray(
intArrayOf(1, 2),
Shape(1, 2),
doubleArrayOf(-1.0, 3.0)
)
}
assertTrue {
tensor2.mean(1, false) eq fromArray(
intArrayOf(2),
Shape(2),
doubleArrayOf(1.5, 0.5)
)
}

38
kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt
View File

@ -5,6 +5,8 @@
package space.kscience.kmath.tensors.core
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.nd.contentEquals
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.tensors.core.internal.svd1d
import kotlin.math.abs
@ -17,7 +19,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
@Test
fun testDetLU() = DoubleTensorAlgebra {
val tensor = fromArray(
intArrayOf(2, 2, 2),
Shape(2, 2, 2),
doubleArrayOf(
1.0, 3.0,
1.0, 2.0,
@ -27,7 +29,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
)
val expectedTensor = fromArray(
intArrayOf(2, 1),
Shape(2, 1),
doubleArrayOf(
-1.0,
-7.0
@ -43,7 +45,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
fun testDet() = DoubleTensorAlgebra {
val expectedValue = 0.019827417
val m = fromArray(
intArrayOf(3, 3), doubleArrayOf(
Shape(3, 3), doubleArrayOf(
2.1843, 1.4391, -0.4845,
1.4391, 1.7772, 0.4055,
-0.4845, 0.4055, 0.7519
@ -57,7 +59,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
fun testDetSingle() = DoubleTensorAlgebra {
val expectedValue = 48.151623
val m = fromArray(
intArrayOf(1, 1), doubleArrayOf(
Shape(1, 1), doubleArrayOf(
expectedValue
)
)
@ -68,7 +70,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
@Test
fun testInvLU() = DoubleTensorAlgebra {
val tensor = fromArray(
intArrayOf(2, 2, 2),
Shape(2, 2, 2),
doubleArrayOf(
1.0, 0.0,
0.0, 2.0,
@ -78,7 +80,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
)
val expectedTensor = fromArray(
intArrayOf(2, 2, 2), doubleArrayOf(
Shape(2, 2, 2), doubleArrayOf(
1.0, 0.0,
0.0, 0.5,
0.0, 1.0,
@ -92,14 +94,14 @@ internal class TestDoubleLinearOpsTensorAlgebra {
@Test
fun testScalarProduct() = DoubleTensorAlgebra {
val a = fromArray(intArrayOf(3), doubleArrayOf(1.8, 2.5, 6.8))
val b = fromArray(intArrayOf(3), doubleArrayOf(5.5, 2.6, 6.4))
val a = fromArray(Shape(3), doubleArrayOf(1.8, 2.5, 6.8))
val b = fromArray(Shape(3), doubleArrayOf(5.5, 2.6, 6.4))
assertEquals(a.dot(b).value(), 59.92)
}
@Test
fun testQR() = DoubleTensorAlgebra {
val shape = intArrayOf(2, 2, 2)
val shape = Shape(2, 2, 2)
val buffer = doubleArrayOf(
1.0, 3.0,
1.0, 2.0,
@ -120,7 +122,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
@Test
fun testLU() = DoubleTensorAlgebra {
val shape = intArrayOf(2, 2, 2)
val shape = Shape(2, 2, 2)
val buffer = doubleArrayOf(
1.0, 3.0,
1.0, 2.0,
@ -140,9 +142,9 @@ internal class TestDoubleLinearOpsTensorAlgebra {
@Test
fun testCholesky() = DoubleTensorAlgebra {
val tensor = randomNormal(intArrayOf(2, 5, 5), 0)
val tensor = randomNormal(Shape(2, 5, 5), 0)
val sigma = (tensor matmul tensor.transposed()) + diagonalEmbedding(
fromArray(intArrayOf(2, 5), DoubleArray(10) { 0.1 })
fromArray(Shape(2, 5), DoubleArray(10) { 0.1 })
)
val low = sigma.cholesky()
val sigmChol = low matmul low.transposed()
@ -151,24 +153,24 @@ internal class TestDoubleLinearOpsTensorAlgebra {
@Test
fun testSVD1D() = DoubleTensorAlgebra {
val tensor2 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
val tensor2 = fromArray(Shape(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
val res = svd1d(tensor2)
assertTrue(res.shape contentEquals intArrayOf(2))
assertTrue(res.shape contentEquals Shape(2))
assertTrue { abs(abs(res.source[0]) - 0.386) < 0.01 }
assertTrue { abs(abs(res.source[1]) - 0.922) < 0.01 }
}
@Test
fun testSVD() = DoubleTensorAlgebra {
testSVDFor(fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)))
testSVDFor(fromArray(intArrayOf(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0)))
testSVDFor(fromArray(Shape(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)))
testSVDFor(fromArray(Shape(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0)))
}
@Test
fun testBatchedSVD() = DoubleTensorAlgebra {
val tensor = randomNormal(intArrayOf(2, 5, 3), 0)
val tensor = randomNormal(Shape(2, 5, 3), 0)
val (tensorU, tensorS, tensorV) = tensor.svd()
val tensorSVD = tensorU matmul (diagonalEmbedding(tensorS) matmul tensorV.transposed())
assertTrue(tensor.eq(tensorSVD))
@ -176,7 +178,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
@Test
fun testBatchedSymEig() = DoubleTensorAlgebra {
val tensor = randomNormal(shape = intArrayOf(2, 3, 3), 0)
val tensor = randomNormal(shape = Shape(2, 3, 3), 0)
val tensorSigma = tensor + tensor.transposed()
val (tensorS, tensorV) = tensorSigma.symEig()
val tensorSigmaCalc = tensorV matmul (diagonalEmbedding(tensorS) matmul tensorV.transposed())

12
kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensor.kt
View File

@ -21,14 +21,14 @@ internal class TestDoubleTensor {
@Test
fun testValue() = DoubleTensorAlgebra {
val value = 12.5
val tensor = fromArray(intArrayOf(1), doubleArrayOf(value))
val tensor = fromArray(Shape(1), doubleArrayOf(value))
assertEquals(tensor.value(), value)
}
@OptIn(PerformancePitfall::class)
@Test
fun testStrides() = DoubleTensorAlgebra {
val tensor = fromArray(intArrayOf(2, 2), doubleArrayOf(3.5, 5.8, 58.4, 2.4))
val tensor = fromArray(Shape(2, 2), doubleArrayOf(3.5, 5.8, 58.4, 2.4))
assertEquals(tensor[intArrayOf(0, 1)], 5.8)
assertTrue(
tensor.elements().map { it.second }.toList()
@ -38,7 +38,7 @@ internal class TestDoubleTensor {
@Test
fun testGet() = DoubleTensorAlgebra {
val tensor = fromArray(intArrayOf(1, 2, 2), doubleArrayOf(3.5, 5.8, 58.4, 2.4))
val tensor = fromArray(Shape(1, 2, 2), doubleArrayOf(3.5, 5.8, 58.4, 2.4))
val matrix = tensor.getTensor(0).asDoubleTensor2D()
assertEquals(matrix[0, 1], 5.8)
@ -67,7 +67,7 @@ internal class TestDoubleTensor {
val doubleArray = DoubleBuffer(1.0, 2.0, 3.0)
// create ND buffers, no data is copied
val ndArray: MutableBufferND<Double> = DoubleBufferND(ColumnStrides(intArrayOf(3)), doubleArray)
val ndArray: MutableBufferND<Double> = DoubleBufferND(ColumnStrides(Shape(3)), doubleArray)
// map to tensors
val tensorArray = ndArray.asDoubleTensor() // Data is copied because of strides change.
@ -91,7 +91,7 @@ internal class TestDoubleTensor {
@Test
fun test2D() = with(DoubleTensorAlgebra) {
val tensor: DoubleTensor = structureND(intArrayOf(3, 3)) { (i, j) -> (i - j).toDouble() }
val tensor: DoubleTensor = structureND(Shape(3, 3)) { (i, j) -> (i - j).toDouble() }
//println(tensor.toPrettyString())
val tensor2d = tensor.asDoubleTensor2D()
assertBufferEquals(DoubleBuffer(1.0, 0.0, -1.0), tensor2d.rows[1])
@ -100,7 +100,7 @@ internal class TestDoubleTensor {
@Test
fun testMatrixIteration() = with(DoubleTensorAlgebra) {
val tensor = structureND(intArrayOf(3, 3, 3, 3)) { index -> index.sum().toDouble() }
val tensor = structureND(Shape(3, 3, 3, 3)) { index -> index.sum().toDouble() }
tensor.forEachMatrix { index, matrix ->
println(index.joinToString { it.toString() })
println(matrix)

84
kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
View File

@ -6,6 +6,8 @@
package space.kscience.kmath.tensors.core
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.nd.contentEquals
import space.kscience.kmath.nd.get
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.testutils.assertBufferEquals
@ -18,62 +20,62 @@ internal class TestDoubleTensorAlgebra {
@Test
fun testDoublePlus() = DoubleTensorAlgebra {
val tensor = fromArray(intArrayOf(2), doubleArrayOf(1.0, 2.0))
val tensor = fromArray(Shape(2), doubleArrayOf(1.0, 2.0))
val res = 10.0 + tensor
assertTrue(res.source contentEquals doubleArrayOf(11.0, 12.0))
}
@Test
fun testDoubleDiv() = DoubleTensorAlgebra {
val tensor = fromArray(intArrayOf(2), doubleArrayOf(2.0, 4.0))
val tensor = fromArray(Shape(2), doubleArrayOf(2.0, 4.0))
val res = 2.0 / tensor
assertTrue(res.source contentEquals doubleArrayOf(1.0, 0.5))
}
@Test
fun testDivDouble() = DoubleTensorAlgebra {
val tensor = fromArray(intArrayOf(2), doubleArrayOf(10.0, 5.0))
val tensor = fromArray(Shape(2), doubleArrayOf(10.0, 5.0))
val res = tensor / 2.5
assertTrue(res.source contentEquals doubleArrayOf(4.0, 2.0))
}
@Test
fun testTranspose1x1() = DoubleTensorAlgebra {
val tensor = fromArray(intArrayOf(1), doubleArrayOf(0.0))
val tensor = fromArray(Shape(1), doubleArrayOf(0.0))
val res = tensor.transposed(0, 0)
assertTrue(res.source contentEquals doubleArrayOf(0.0))
assertTrue(res.shape contentEquals intArrayOf(1))
assertTrue(res.asDoubleTensor().source contentEquals doubleArrayOf(0.0))
assertTrue(res.shape contentEquals Shape(1))
}
@Test
fun testTranspose3x2() = DoubleTensorAlgebra {
val tensor = fromArray(intArrayOf(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
val tensor = fromArray(Shape(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
val res = tensor.transposed(1, 0)
assertTrue(res.source contentEquals doubleArrayOf(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
assertTrue(res.shape contentEquals intArrayOf(2, 3))
assertTrue(res.asDoubleTensor().source contentEquals doubleArrayOf(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
assertTrue(res.shape contentEquals Shape(2, 3))
}
@Test
fun testTranspose1x2x3() = DoubleTensorAlgebra {
val tensor = fromArray(intArrayOf(1, 2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
val tensor = fromArray(Shape(1, 2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
val res01 = tensor.transposed(0, 1)
val res02 = tensor.transposed(-3, 2)
val res12 = tensor.transposed()
assertTrue(res01.shape contentEquals intArrayOf(2, 1, 3))
assertTrue(res02.shape contentEquals intArrayOf(3, 2, 1))
assertTrue(res12.shape contentEquals intArrayOf(1, 3, 2))
assertTrue(res01.shape contentEquals Shape(2, 1, 3))
assertTrue(res02.shape contentEquals Shape(3, 2, 1))
assertTrue(res12.shape contentEquals Shape(1, 3, 2))
assertTrue(res01.source contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
assertTrue(res02.source contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
assertTrue(res12.source contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
assertTrue(res01.asDoubleTensor().source contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
assertTrue(res02.asDoubleTensor().source contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
assertTrue(res12.asDoubleTensor().source contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
}
@Test
fun testLinearStructure() = DoubleTensorAlgebra {
val shape = intArrayOf(3)
val shape = Shape(3)
val tensorA = full(value = -4.5, shape = shape)
val tensorB = full(value = 10.9, shape = shape)
val tensorC = full(value = 789.3, shape = shape)
@ -105,28 +107,28 @@ internal class TestDoubleTensorAlgebra {
@Test
fun testDot() = DoubleTensorAlgebra {
val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
val tensor11 = fromArray(intArrayOf(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
val tensor2 = fromArray(intArrayOf(3), doubleArrayOf(10.0, 20.0, 30.0))
val tensor3 = fromArray(intArrayOf(1, 1, 3), doubleArrayOf(-1.0, -2.0, -3.0))
val tensor4 = fromArray(intArrayOf(2, 3, 3), (1..18).map { it.toDouble() }.toDoubleArray())
val tensor5 = fromArray(intArrayOf(2, 3, 3), (1..18).map { 1 + it.toDouble() }.toDoubleArray())
val tensor1 = fromArray(Shape(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
val tensor11 = fromArray(Shape(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
val tensor2 = fromArray(Shape(3), doubleArrayOf(10.0, 20.0, 30.0))
val tensor3 = fromArray(Shape(1, 1, 3), doubleArrayOf(-1.0, -2.0, -3.0))
val tensor4 = fromArray(Shape(2, 3, 3), (1..18).map { it.toDouble() }.toDoubleArray())
val tensor5 = fromArray(Shape(2, 3, 3), (1..18).map { 1 + it.toDouble() }.toDoubleArray())
val res12 = tensor1.dot(tensor2)
assertTrue(res12.source contentEquals doubleArrayOf(140.0, 320.0))
assertTrue(res12.shape contentEquals intArrayOf(2))
assertTrue(res12.shape contentEquals Shape(2))
val res32 = tensor3.matmul(tensor2)
assertTrue(res32.source contentEquals doubleArrayOf(-140.0))
assertTrue(res32.shape contentEquals intArrayOf(1, 1))
assertTrue(res32.shape contentEquals Shape(1, 1))
val res22 = tensor2.dot(tensor2)
assertTrue(res22.source contentEquals doubleArrayOf(1400.0))
assertTrue(res22.shape contentEquals intArrayOf(1))
assertTrue(res22.shape contentEquals Shape(1))
val res11 = tensor1.dot(tensor11)
assertTrue(res11.source contentEquals doubleArrayOf(22.0, 28.0, 49.0, 64.0))
assertTrue(res11.shape contentEquals intArrayOf(2, 2))
assertTrue(res11.shape contentEquals Shape(2, 2))
val res45 = tensor4.matmul(tensor5)
assertTrue(
@ -135,44 +137,44 @@ internal class TestDoubleTensorAlgebra {
468.0, 501.0, 534.0, 594.0, 636.0, 678.0, 720.0, 771.0, 822.0
)
)
assertTrue(res45.shape contentEquals intArrayOf(2, 3, 3))
assertTrue(res45.shape contentEquals Shape(2, 3, 3))
}
@Test
fun testDiagonalEmbedding() = DoubleTensorAlgebra {
val tensor1 = fromArray(intArrayOf(3), doubleArrayOf(10.0, 20.0, 30.0))
val tensor2 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
val tensor3 = zeros(intArrayOf(2, 3, 4, 5))
val tensor1 = fromArray(Shape(3), doubleArrayOf(10.0, 20.0, 30.0))
val tensor2 = fromArray(Shape(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
val tensor3 = zeros(Shape(2, 3, 4, 5))
assertTrue(
diagonalEmbedding(tensor3, 0, 3, 4).shape contentEquals
intArrayOf(2, 3, 4, 5, 5)
Shape(2, 3, 4, 5, 5)
)
assertTrue(
diagonalEmbedding(tensor3, 1, 3, 4).shape contentEquals
intArrayOf(2, 3, 4, 6, 6)
Shape(2, 3, 4, 6, 6)
)
assertTrue(
diagonalEmbedding(tensor3, 2, 0, 3).shape contentEquals
intArrayOf(7, 2, 3, 7, 4)
Shape(7, 2, 3, 7, 4)
)
val diagonal1 = diagonalEmbedding(tensor1, 0, 1, 0)
assertTrue(diagonal1.shape contentEquals intArrayOf(3, 3))
assertTrue(diagonal1.shape contentEquals Shape(3, 3))
assertTrue(
diagonal1.source contentEquals
doubleArrayOf(10.0, 0.0, 0.0, 0.0, 20.0, 0.0, 0.0, 0.0, 30.0)
)
val diagonal1Offset = diagonalEmbedding(tensor1, 1, 1, 0)
assertTrue(diagonal1Offset.shape contentEquals intArrayOf(4, 4))
assertTrue(diagonal1Offset.shape contentEquals Shape(4, 4))
assertTrue(
diagonal1Offset.source contentEquals
doubleArrayOf(0.0, 0.0, 0.0, 0.0, 10.0, 0.0, 0.0, 0.0, 0.0, 20.0, 0.0, 0.0, 0.0, 0.0, 30.0, 0.0)
)
val diagonal2 = diagonalEmbedding(tensor2, 1, 0, 2)
assertTrue(diagonal2.shape contentEquals intArrayOf(4, 2, 4))
assertTrue(diagonal2.shape contentEquals Shape(4, 2, 4))
assertTrue(
diagonal2.source contentEquals
doubleArrayOf(
@ -186,9 +188,9 @@ internal class TestDoubleTensorAlgebra {
@Test
fun testEq() = DoubleTensorAlgebra {
val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
val tensor2 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
val tensor3 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 5.0))
val tensor1 = fromArray(Shape(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
val tensor2 = fromArray(Shape(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
val tensor3 = fromArray(Shape(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 5.0))
assertTrue(tensor1 eq tensor1)
assertTrue(tensor1 eq tensor2)
@ -202,7 +204,7 @@ internal class TestDoubleTensorAlgebra {
val l = tensor.getTensor(0).map { it + 1.0 }
val r = tensor.getTensor(1).map { it - 1.0 }
val res = l + r
assertTrue { intArrayOf(5, 5) contentEquals res.shape }
assertTrue { Shape(5, 5) contentEquals res.shape }
assertEquals(2.0, res[4, 4])
}
}

31
kmath-viktor/src/main/kotlin/space/kscience/kmath/viktor/ViktorFieldOpsND.kt
View File

@ -9,6 +9,7 @@ package space.kscience.kmath.viktor
import org.jetbrains.bio.viktor.F64Array
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.misc.UnsafeKMathAPI
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.nd.*
import space.kscience.kmath.operations.DoubleField
@ -31,8 +32,9 @@ public open class ViktorFieldOpsND :
override val elementAlgebra: DoubleField get() = DoubleField
override fun structureND(shape: IntArray, initializer: DoubleField.(IntArray) -> Double): ViktorStructureND =
F64Array(*shape).apply {
@OptIn(UnsafeKMathAPI::class)
override fun structureND(shape: Shape, initializer: DoubleField.(IntArray) -> Double): ViktorStructureND =
F64Array(*shape.asArray()).apply {
ColumnStrides(shape).asSequence().forEach { index ->
set(value = DoubleField.initializer(index), indices = index)
}
@ -40,23 +42,26 @@ public open class ViktorFieldOpsND :
override fun StructureND<Double>.unaryMinus(): StructureND<Double> = -1 * this
@OptIn(UnsafeKMathAPI::class)
@PerformancePitfall
override fun StructureND<Double>.map(transform: DoubleField.(Double) -> Double): ViktorStructureND =
F64Array(*shape).apply {
ColumnStrides(shape).asSequence().forEach { index ->
F64Array(*shape.asArray()).apply {
ColumnStrides(Shape(shape)).asSequence().forEach { index ->
set(value = DoubleField.transform(this@map[index]), indices = index)
}
}.asStructure()
@OptIn(UnsafeKMathAPI::class)
@PerformancePitfall
override fun StructureND<Double>.mapIndexed(
transform: DoubleField.(index: IntArray, Double) -> Double,
): ViktorStructureND = F64Array(*shape).apply {
ColumnStrides(shape).asSequence().forEach { index ->
): ViktorStructureND = F64Array(*shape.asArray()).apply {
ColumnStrides(Shape(shape)).asSequence().forEach { index ->
set(value = DoubleField.transform(index, this@mapIndexed[index]), indices = index)
}
}.asStructure()
@OptIn(UnsafeKMathAPI::class)
@PerformancePitfall
override fun zip(
left: StructureND<Double>,
@ -64,7 +69,7 @@ public open class ViktorFieldOpsND :
transform: DoubleField.(Double, Double) -> Double,
): ViktorStructureND {
require(left.shape.contentEquals(right.shape))
return F64Array(*left.shape).apply {
return F64Array(*left.shape.asArray()).apply {
ColumnStrides(left.shape).asSequence().forEach { index ->
set(value = DoubleField.transform(left[index], right[index]), indices = index)
}
@ -119,13 +124,17 @@ public val DoubleField.viktorAlgebra: ViktorFieldOpsND get() = ViktorFieldOpsND
@OptIn(UnstableKMathAPI::class)
public open class ViktorFieldND(
override val shape: Shape,
private val shapeAsArray: IntArray,
) : ViktorFieldOpsND(), FieldND<Double, DoubleField>, NumbersAddOps<StructureND<Double>> {
override val zero: ViktorStructureND by lazy { F64Array.full(init = 0.0, shape = shape).asStructure() }
override val one: ViktorStructureND by lazy { F64Array.full(init = 1.0, shape = shape).asStructure() }
override val shape: Shape = Shape(shapeAsArray)
override val zero: ViktorStructureND by lazy { F64Array.full(init = 0.0, shape = shapeAsArray).asStructure() }
override val one: ViktorStructureND by lazy { F64Array.full(init = 1.0, shape = shapeAsArray).asStructure() }
override fun number(value: Number): ViktorStructureND =
F64Array.full(init = value.toDouble(), shape = shape).asStructure()
F64Array.full(init = value.toDouble(), shape = shapeAsArray).asStructure()
}
public fun DoubleField.viktorAlgebra(vararg shape: Int): ViktorFieldND = ViktorFieldND(shape)

5
kmath-viktor/src/main/kotlin/space/kscience/kmath/viktor/ViktorStructureND.kt
View File

@ -9,13 +9,16 @@ import org.jetbrains.bio.viktor.F64Array
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.nd.ColumnStrides
import space.kscience.kmath.nd.MutableStructureND
import space.kscience.kmath.nd.Shape
@Suppress("OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
public class ViktorStructureND(public val f64Buffer: F64Array) : MutableStructureND<Double> {
override val shape: IntArray get() = f64Buffer.shape
override val shape: Shape get() = Shape(f64Buffer.shape)
@OptIn(PerformancePitfall::class)
override inline fun get(index: IntArray): Double = f64Buffer.get(*index)
@OptIn(PerformancePitfall::class)
override inline fun set(index: IntArray, value: Double) {
f64Buffer.set(*index, value = value)
}