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pre-0.0.3 #46

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altavir merged 75 commits from dev into master 2019-02-20 13:05:39 +03:00
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6
benchmarks/src/main/kotlin/scientifik/kmath/structures/NDFieldBenchmark.kt
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@ -25,7 +25,7 @@ fun main(args: Array<String>) {
bufferedField.run {
var res: NDBuffer<Double> = one
repeat(n) {
res += 1.0
res += one
}
}
}
@ -34,7 +34,7 @@ fun main(args: Array<String>) {
val elementTime = measureTimeMillis {
var res = bufferedField.produce { one }
var res = bufferedField.run{one.toElement()}
repeat(n) {
res += 1.0
}
@ -77,7 +77,7 @@ fun main(args: Array<String>) {
genericField.run {
var res: NDBuffer<Double> = one
repeat(n) {
res += 1.0
res += one
}
}
}

1
benchmarks/src/main/kotlin/scientifik/kmath/structures/StructureWriteBenchmark.kt
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@ -1,5 +1,6 @@
package scientifik.kmath.structures
import scientifik.kmath.structures.Buffer.Companion.DoubleBufferFactory
import kotlin.system.measureTimeMillis

70
doc/algebra.md Normal file
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@ -0,0 +1,70 @@
# Algebra and algebra elements
The mathematical operations in `kmath` are generally separated from mathematical objects.
This means that in order to perform an operation, say `+`, one needs two objects of a type `T` and
and algebra context which defines appropriate operation, say `Space<T>`. Next one needs to run actual operation
in the context:
```kotlin
val a: T
val b: T
val space: Space<T>
val c = space.run{a + b}
```
From the first glance, this distinction seems to be a needless complication, but in fact one needs
to remember that in mathematics, one could define different operations on the same objects. For example,
one could use different types of geometry for vectors.
## Algebra hierarchy
Mathematical contexts have the following hierarchy:
**Space** <- **Ring** <- **Field**
All classes follow abstract mathematical constructs.
[Space](http://mathworld.wolfram.com/Space.html) defines `zero` element, addition operation and multiplication by constant,
[Ring](http://mathworld.wolfram.com/Ring.html) adds multiplication and unit `one` element,
[Field](http://mathworld.wolfram.com/Field.html) adds division operation.
Typical case of `Field` is the `RealField` which works on doubles. And typical case of `Space` is a `VectorSpace`.
In some cases algebra context could hold additional operation like `exp` or `sin`, in this case it inherits appropriate
interface. Also a context could have an operation which produces an element outside of its context. For example
`Matrix` `dot` operation produces a matrix with new dimensions which could not be compatible with initial matrix in
terms of linear operations.
## Algebra element
In order to achieve more familiar behavior (where you apply operations directly to mathematica objects), without involving contexts
`kmath` introduces special type objects called `MathElement`. A `MathElement` is basically some object coupled to
a mathematical context. For example `Complex` is the pair of real numbers representing real and imaginary parts,
but it also holds reference to the `ComplexField` singleton which allows to perform direct operations on `Complex`
numbers without explicit involving the context like:
```kotlin
val c1 = Complex(1.0, 1.0)
val c2 = Complex(1.0, -1.0)
val c3 = c1 + c2 + 3.0.toComplex()
//or with field notation:
val c4 = ComplexField.run{c1 + i - 2.0}
```
Both notations have their pros and cons.
The hierarchy for algebra elements follows the hierarchy for the corresponding algebra.
**MathElement** <- **SpaceElement** <- **RingElement** <- **FieldElement**
**MathElement** is the generic common ancestor of the class with context.
One important distinction between algebra elements and algebra contexts is that algebra element has three type parameters:
1. The type of elements, field operates on.
2. The self-type of the element returned from operation (must be algebra element).
3. The type of the algebra over first type-parameter.
The middle type is needed in case algebra members do not store context. For example, it is not possible to add
a context to regular `Double`. The element performs automatic conversions from context types and back.
One should used context operations in all important places. The performance of element operations is not guaranteed.

13
doc/nd-performance.md Normal file
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@ -0,0 +1,13 @@
# Performance for n-dimensional structures operations
One of the most sought after features of mathematical libraries is the high-performance operations on n-dimensional
structures. In `kmath` performance depends on which particular context was used for operation.
Let us consider following contexts:
```kotlin
// automatically build context
val bufferedField = NDField.auto(intArrayOf(dim, dim), RealField)
val specializedField = NDField.real(intArrayOf(dim, dim))
val genericField = NDField.buffered(intArrayOf(dim, dim), RealField)
val lazyNDField = NDField.lazy(intArrayOf(dim, dim), RealField)
```

2
doc/operations.md
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@ -20,7 +20,7 @@ val c3 = ComplexField.run{ c1 - i*2.0}
```
**Note**: In theory it is possible to add behaviors directly to the context, but currently kotlin syntax does not support
that. Watch [KT-10468](https://youtrack.jetbrains.com/issue/KT-10468) for updates.
that. Watch [KT-10468](https://youtrack.jetbrains.com/issue/KT-10468) and [KEEP-176](https://github.com/Kotlin/KEEP/pull/176) for updates.
## Nested fields

8
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/LUDecomposition.kt
View File

@ -2,7 +2,11 @@ package scientifik.kmath.linear
import scientifik.kmath.operations.Field
import scientifik.kmath.operations.RealField
import scientifik.kmath.structures.*
import scientifik.kmath.structures.MutableBuffer.Companion.boxing
import scientifik.kmath.structures.MutableNDStructure
import scientifik.kmath.structures.NDStructure
import scientifik.kmath.structures.get
import scientifik.kmath.structures.mutableNdStructure
import kotlin.math.absoluteValue
/**
@ -106,7 +110,7 @@ abstract class LUDecomposition<T : Comparable<T>, F : Field<T>>(val matrix: Matr
//TODO fix performance
val lu: MutableNDStructure<T> = mutableNdStructure(
intArrayOf(matrix.numRows, matrix.numCols),
::boxingMutableBuffer
::boxing
) { index: IntArray -> matrix[index[0], index[1]] }

5
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/LinearAlgrebra.kt
View File

@ -4,8 +4,9 @@ import scientifik.kmath.operations.Field
import scientifik.kmath.operations.Norm
import scientifik.kmath.operations.RealField
import scientifik.kmath.operations.Ring
import scientifik.kmath.structures.Buffer.Companion.boxing
import scientifik.kmath.structures.asSequence
import scientifik.kmath.structures.boxingBuffer
/**
@ -52,7 +53,7 @@ fun <T : Any, R : Ring<T>> Vector<T, R>.toMatrix(): Matrix<T, R> {
// matrix(size, 1, context.field) { i, j -> get(i) }
// }
//return Matrix.of(size, 1, context.space) { i, _ -> get(i) }
return StructureMatrixSpace(size, 1, context.space, ::boxingBuffer).produce { i, _ -> get(i) }
return StructureMatrixSpace(size, 1, context.space, ::boxing).produce { i, _ -> get(i) }
}
object VectorL2Norm : Norm<Vector<out Number, *>, Double> {

7
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/Matrix.kt
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@ -5,6 +5,9 @@ import scientifik.kmath.operations.Ring
import scientifik.kmath.operations.Space
import scientifik.kmath.operations.SpaceElement
import scientifik.kmath.structures.*
import scientifik.kmath.structures.Buffer.Companion.DoubleBufferFactory
import scientifik.kmath.structures.Buffer.Companion.auto
import scientifik.kmath.structures.Buffer.Companion.boxing
interface MatrixSpace<T : Any, R : Ring<T>> : Space<Matrix<T, R>> {
@ -52,14 +55,14 @@ interface MatrixSpace<T : Any, R : Ring<T>> : Space<Matrix<T, R>> {
rows: Int,
columns: Int,
ring: R,
bufferFactory: BufferFactory<T> = ::boxingBuffer
bufferFactory: BufferFactory<T> = ::boxing
): MatrixSpace<T, R> = StructureMatrixSpace(rows, columns, ring, bufferFactory)
/**
* Automatic buffered matrix, unboxed if it is possible
*/
inline fun <reified T : Any, R : Ring<T>> smart(rows: Int, columns: Int, ring: R): MatrixSpace<T, R> =
buffered(rows, columns, ring, ::autoBuffer)
buffered(rows, columns, ring, ::auto)
}
}

10
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/Vector.kt
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@ -3,7 +3,9 @@ package scientifik.kmath.linear
import scientifik.kmath.operations.RealField
import scientifik.kmath.operations.Space
import scientifik.kmath.operations.SpaceElement
import scientifik.kmath.structures.*
import scientifik.kmath.structures.Buffer
import scientifik.kmath.structures.BufferFactory
import scientifik.kmath.structures.asSequence
typealias Point<T> = Buffer<T>
@ -40,7 +42,7 @@ interface VectorSpace<T : Any, S : Space<T>> : Space<Point<T>> {
* Non-boxing double vector space
*/
fun real(size: Int): BufferVectorSpace<Double, RealField> {
return realSpaceCache.getOrPut(size) { BufferVectorSpace(size, RealField, DoubleBufferFactory) }
return realSpaceCache.getOrPut(size) { BufferVectorSpace(size, RealField, Buffer.DoubleBufferFactory) }
}
/**
@ -49,14 +51,14 @@ interface VectorSpace<T : Any, S : Space<T>> : Space<Point<T>> {
fun <T : Any, S : Space<T>> buffered(
size: Int,
space: S,
bufferFactory: BufferFactory<T> = ::boxingBuffer
bufferFactory: BufferFactory<T> = Buffer.Companion::boxing
): VectorSpace<T, S> = BufferVectorSpace(size, space, bufferFactory)
/**
* Automatic buffered vector, unboxed if it is possible
*/
inline fun <reified T : Any, S : Space<T>> smart(size: Int, space: S): VectorSpace<T, S> =
buffered(size, space, ::autoBuffer)
buffered(size, space, Buffer.Companion::auto)
}
}

10
kmath-core/src/commonMain/kotlin/scientifik/kmath/operations/Algebra.kt
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@ -68,11 +68,11 @@ interface Ring<T> : Space<T> {
operator fun T.times(b: T): T = multiply(this, b)
// operator fun T.plus(b: Number) = this.plus(b * one)
// operator fun Number.plus(b: T) = b + this
//
// operator fun T.minus(b: Number) = this.minus(b * one)
// operator fun Number.minus(b: T) = -b + this
operator fun T.plus(b: Number) = this.plus(b * one)
operator fun Number.plus(b: T) = b + this
operator fun T.minus(b: Number) = this.minus(b * one)
operator fun Number.minus(b: T) = -b + this
}
abstract class AbstractRing<T : Any> : AbstractSpace<T>(), Ring<T> {

49
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/BufferNDField.kt
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@ -22,6 +22,9 @@ abstract class StridedNDField<T, F : Field<T>>(shape: IntArray, elementField: F)
produce { index -> get(index) }
}
}
fun NDBuffer<T>.toElement(): StridedNDElement<T, F> =
StridedNDElement(this@StridedNDField, buffer)
}
@ -40,14 +43,14 @@ class BufferNDField<T, F : Field<T>>(
override val zero by lazy { produce { zero } }
override val one by lazy { produce { one } }
override fun produce(initializer: F.(IntArray) -> T): BufferNDElement<T, F> =
BufferNDElement(
override fun produce(initializer: F.(IntArray) -> T): StridedNDElement<T, F> =
StridedNDElement(
this,
buildBuffer(strides.linearSize) { offset -> elementField.initializer(strides.index(offset)) })
override fun map(arg: NDBuffer<T>, transform: F.(T) -> T): BufferNDElement<T, F> {
override fun map(arg: NDBuffer<T>, transform: F.(T) -> T): StridedNDElement<T, F> {
check(arg)
return BufferNDElement(
return StridedNDElement(
this,
buildBuffer(arg.strides.linearSize) { offset -> elementField.transform(arg.buffer[offset]) })
}
@ -55,9 +58,9 @@ class BufferNDField<T, F : Field<T>>(
override fun mapIndexed(
arg: NDBuffer<T>,
transform: F.(index: IntArray, T) -> T
): BufferNDElement<T, F> {
): StridedNDElement<T, F> {
check(arg)
return BufferNDElement(
return StridedNDElement(
this,
buildBuffer(arg.strides.linearSize) { offset ->
elementField.transform(
@ -71,17 +74,17 @@ class BufferNDField<T, F : Field<T>>(
a: NDBuffer<T>,
b: NDBuffer<T>,
transform: F.(T, T) -> T
): BufferNDElement<T, F> {
): StridedNDElement<T, F> {
check(a, b)
return BufferNDElement(
return StridedNDElement(
this,
buildBuffer(strides.linearSize) { offset -> elementField.transform(a.buffer[offset], b.buffer[offset]) })
}
}
class BufferNDElement<T, F : Field<T>>(override val context: StridedNDField<T, F>, override val buffer: Buffer<T>) :
class StridedNDElement<T, F : Field<T>>(override val context: StridedNDField<T, F>, override val buffer: Buffer<T>) :
NDBuffer<T>,
FieldElement<NDBuffer<T>, BufferNDElement<T, F>, StridedNDField<T, F>>,
FieldElement<NDBuffer<T>, StridedNDElement<T, F>, StridedNDField<T, F>>,
NDElement<T, F> {
override val elementField: F
@ -90,8 +93,8 @@ class BufferNDElement<T, F : Field<T>>(override val context: StridedNDField<T, F
override fun unwrap(): NDBuffer<T> =
this
override fun NDBuffer<T>.wrap(): BufferNDElement<T, F> =
BufferNDElement(context, this.buffer)
override fun NDBuffer<T>.wrap(): StridedNDElement<T, F> =
StridedNDElement(context, this.buffer)
override val strides
get() = context.strides
@ -106,42 +109,42 @@ class BufferNDElement<T, F : Field<T>>(override val context: StridedNDField<T, F
strides.indices().map { it to get(it) }
override fun map(action: F.(T) -> T) =
context.run { map(this@BufferNDElement, action) }.wrap()
context.run { map(this@StridedNDElement, action) }.wrap()
override fun mapIndexed(transform: F.(index: IntArray, T) -> T) =
context.run { mapIndexed(this@BufferNDElement, transform) }.wrap()
context.run { mapIndexed(this@StridedNDElement, transform) }.wrap()
}
/**
* Element by element application of any operation on elements to the whole array. Just like in numpy
*/
operator fun <T : Any, F : Field<T>> Function1<T, T>.invoke(ndElement: BufferNDElement<T, F>) =
operator fun <T : Any, F : Field<T>> Function1<T, T>.invoke(ndElement: StridedNDElement<T, F>) =
ndElement.context.run { ndElement.map { invoke(it) } }
/* plus and minus */
/**
* Summation operation for [BufferNDElement] and single element
* Summation operation for [StridedNDElement] and single element
*/
operator fun <T : Any, F : Field<T>> BufferNDElement<T, F>.plus(arg: T) =
operator fun <T : Any, F : Field<T>> StridedNDElement<T, F>.plus(arg: T) =
context.run { map { it + arg } }
/**
* Subtraction operation between [BufferNDElement] and single element
* Subtraction operation between [StridedNDElement] and single element
*/
operator fun <T : Any, F : Field<T>> BufferNDElement<T, F>.minus(arg: T) =
operator fun <T : Any, F : Field<T>> StridedNDElement<T, F>.minus(arg: T) =
context.run { map { it - arg } }
/* prod and div */
/**
* Product operation for [BufferNDElement] and single element
* Product operation for [StridedNDElement] and single element
*/
operator fun <T : Any, F : Field<T>> BufferNDElement<T, F>.times(arg: T) =
operator fun <T : Any, F : Field<T>> StridedNDElement<T, F>.times(arg: T) =
context.run { map { it * arg } }
/**
* Division operation between [BufferNDElement] and single element
* Division operation between [StridedNDElement] and single element
*/
operator fun <T : Any, F : Field<T>> BufferNDElement<T, F>.div(arg: T) =
operator fun <T : Any, F : Field<T>> StridedNDElement<T, F>.div(arg: T) =
context.run { map { it / arg } }

95
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/Buffers.kt
View File

@ -1,6 +1,10 @@
package scientifik.kmath.structures
typealias BufferFactory<T> = (Int, (Int) -> T) -> Buffer<T>
typealias MutableBufferFactory<T> = (Int, (Int) -> T) -> MutableBuffer<T>
/**
* A generic random access structure for both primitives and objects
*/
@ -14,6 +18,31 @@ interface Buffer<T> {
fun contentEquals(other: Buffer<*>): Boolean =
asSequence().mapIndexed { index, value -> value == other[index] }.all { it }
companion object {
/**
* Create a boxing buffer of given type
*/
inline fun <T> boxing(size: Int, initializer: (Int) -> T): Buffer<T> = ListBuffer(List(size, initializer))
/**
* Create most appropriate immutable buffer for given type avoiding boxing wherever possible
*/
@Suppress("UNCHECKED_CAST")
inline fun <reified T : Any> auto(size: Int, initializer: (Int) -> T): Buffer<T> {
return when (T::class) {
Double::class -> DoubleBuffer(DoubleArray(size) { initializer(it) as Double }) as Buffer<T>
Int::class -> IntBuffer(IntArray(size) { initializer(it) as Int }) as Buffer<T>
Long::class -> LongBuffer(LongArray(size) { initializer(it) as Long }) as Buffer<T>
else -> boxing(size, initializer)
}
}
val DoubleBufferFactory: BufferFactory<Double> = { size, initializer -> DoubleBuffer(DoubleArray(size, initializer)) }
val IntBufferFactory: BufferFactory<Int> = { size, initializer -> IntBuffer(IntArray(size, initializer)) }
val LongBufferFactory: BufferFactory<Long> = { size, initializer -> LongBuffer(LongArray(size, initializer)) }
}
}
fun <T> Buffer<T>.asSequence(): Sequence<T> = iterator().asSequence()
@ -27,6 +56,27 @@ interface MutableBuffer<T> : Buffer<T> {
* A shallow copy of the buffer
*/
fun copy(): MutableBuffer<T>
companion object {
/**
* Create a boxing mutable buffer of given type
*/
inline fun <T : Any> boxing(size: Int, initializer: (Int) -> T): MutableBuffer<T> =
MutableListBuffer(MutableList(size, initializer))
/**
* Create most appropriate mutable buffer for given type avoiding boxing wherever possible
*/
@Suppress("UNCHECKED_CAST")
inline fun <reified T : Any> auto(size: Int, initializer: (Int) -> T): MutableBuffer<T> {
return when (T::class) {
Double::class -> DoubleBuffer(DoubleArray(size) { initializer(it) as Double }) as MutableBuffer<T>
Int::class -> IntBuffer(IntArray(size) { initializer(it) as Int }) as MutableBuffer<T>
Long::class -> LongBuffer(LongArray(size) { initializer(it) as Long }) as MutableBuffer<T>
else -> boxing(size, initializer)
}
}
}
}
@ -130,48 +180,3 @@ fun <T> Buffer<T>.asReadOnly(): Buffer<T> = if (this is MutableBuffer) {
} else {
this
}
/**
* Create a boxing buffer of given type
*/
inline fun <T> boxingBuffer(size: Int, initializer: (Int) -> T): Buffer<T> = ListBuffer(List(size, initializer))
/**
* Create most appropriate immutable buffer for given type avoiding boxing wherever possible
*/
@Suppress("UNCHECKED_CAST")
inline fun <reified T : Any> autoBuffer(size: Int, initializer: (Int) -> T): Buffer<T> {
return when (T::class) {
Double::class -> DoubleBuffer(DoubleArray(size) { initializer(it) as Double }) as Buffer<T>
Int::class -> IntBuffer(IntArray(size) { initializer(it) as Int }) as Buffer<T>
Long::class -> LongBuffer(LongArray(size) { initializer(it) as Long }) as Buffer<T>
else -> boxingBuffer(size, initializer)
}
}
/**
* Create a boxing mutable buffer of given type
*/
inline fun <T : Any> boxingMutableBuffer(size: Int, initializer: (Int) -> T): MutableBuffer<T> =
MutableListBuffer(MutableList(size, initializer))
/**
* Create most appropriate mutable buffer for given type avoiding boxing wherever possible
*/
@Suppress("UNCHECKED_CAST")
inline fun <reified T : Any> autoMutableBuffer(size: Int, initializer: (Int) -> T): MutableBuffer<T> {
return when (T::class) {
Double::class -> DoubleBuffer(DoubleArray(size) { initializer(it) as Double }) as MutableBuffer<T>
Int::class -> IntBuffer(IntArray(size) { initializer(it) as Int }) as MutableBuffer<T>
Long::class -> LongBuffer(LongArray(size) { initializer(it) as Long }) as MutableBuffer<T>
else -> boxingMutableBuffer(size, initializer)
}
}
typealias BufferFactory<T> = (Int, (Int) -> T) -> Buffer<T>
typealias MutableBufferFactory<T> = (Int, (Int) -> T) -> MutableBuffer<T>
val DoubleBufferFactory: BufferFactory<Double> = { size, initializer -> DoubleBuffer(DoubleArray(size, initializer)) }
val IntBufferFactory: BufferFactory<Int> = { size, initializer -> IntBuffer(IntArray(size, initializer)) }
val LongBufferFactory: BufferFactory<Long> = { size, initializer -> LongBuffer(LongArray(size, initializer)) }

13
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/NDElement.kt
View File

@ -10,10 +10,8 @@ interface NDElement<T, F : Field<T>> : NDStructure<T> {
fun mapIndexed(transform: F.(index: IntArray, T) -> T): NDElement<T, F>
fun map(action: F.(T) -> T) = mapIndexed { _, value -> action(value) }
}
object NDElements {
companion object {
/**
* Create a optimized NDArray of doubles
*/
@ -39,8 +37,8 @@ object NDElements {
shape: IntArray,
field: F,
initializer: F.(IntArray) -> T
): BufferNDElement<T, F> {
val ndField = BufferNDField(shape, field, ::boxingBuffer)
): StridedNDElement<T, F> {
val ndField = BufferNDField(shape, field, Buffer.Companion::boxing)
return ndField.produce(initializer)
}
@ -48,9 +46,10 @@ object NDElements {
shape: IntArray,
field: F,
noinline initializer: F.(IntArray) -> T
): BufferNDElement<T, F> {
): StridedNDElement<T, F> {
val ndField = NDField.auto(shape, field)
return ndField.produce(initializer)
return StridedNDElement(ndField, ndField.produce(initializer).buffer)
}
}
}

12
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/NDField.kt
View File

@ -2,6 +2,7 @@ package scientifik.kmath.structures
import scientifik.kmath.operations.AbstractField
import scientifik.kmath.operations.Field
import scientifik.kmath.structures.Buffer.Companion.boxing
/**
* An exception is thrown when the expected ans actual shape of NDArray differs
@ -36,13 +37,18 @@ interface NDField<T, F : Field<T>, N : NDStructure<T>> : Field<N> {
* Create a nd-field with boxing generic buffer
*/
fun <T : Any, F : Field<T>> buffered(shape: IntArray, field: F) =
BufferNDField(shape, field, ::boxingBuffer)
BufferNDField(shape, field, Buffer.Companion::boxing)
/**
* Create a most suitable implementation for nd-field using reified class.
*/
inline fun <reified T : Any, F : Field<T>> auto(shape: IntArray, field: F) =
BufferNDField(shape, field, ::autoBuffer)
inline fun <reified T : Any, F : Field<T>> auto(shape: IntArray, field: F): StridedNDField<T, F> =
if (T::class == Double::class) {
@Suppress("UNCHECKED_CAST")
real(shape) as StridedNDField<T, F>
} else {
BufferNDField(shape, field, Buffer.Companion::auto)
}
}
}

14
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/NDStructure.kt
View File

@ -163,7 +163,7 @@ data class BufferNDStructure<T>(
* Transform structure to a new structure using provided [BufferFactory] and optimizing if argument is [BufferNDStructure]
*/
inline fun <T, reified R : Any> NDStructure<T>.mapToBuffer(
factory: BufferFactory<R> = ::autoBuffer,
factory: BufferFactory<R> = Buffer.Companion::auto,
crossinline transform: (T) -> R
): BufferNDStructure<R> {
return if (this is BufferNDStructure<T>) {
@ -179,16 +179,16 @@ inline fun <T, reified R : Any> NDStructure<T>.mapToBuffer(
*
* Strides should be reused if possible
*/
fun <T> ndStructure(strides: Strides, bufferFactory: BufferFactory<T> = ::boxingBuffer, initializer: (IntArray) -> T) =
fun <T> ndStructure(strides: Strides, bufferFactory: BufferFactory<T> = Buffer.Companion::boxing, initializer: (IntArray) -> T) =
BufferNDStructure(strides, bufferFactory(strides.linearSize) { i -> initializer(strides.index(i)) })
/**
* Inline create NDStructure with non-boxing buffer implementation if it is possible
*/
inline fun <reified T : Any> inlineNDStructure(strides: Strides, crossinline initializer: (IntArray) -> T) =
BufferNDStructure(strides, autoBuffer(strides.linearSize) { i -> initializer(strides.index(i)) })
BufferNDStructure(strides, Buffer.Companion.auto(strides.linearSize) { i -> initializer(strides.index(i)) })
fun <T> ndStructure(shape: IntArray, bufferFactory: BufferFactory<T> = ::boxingBuffer, initializer: (IntArray) -> T) =
fun <T> ndStructure(shape: IntArray, bufferFactory: BufferFactory<T> = Buffer.Companion::boxing, initializer: (IntArray) -> T) =
ndStructure(DefaultStrides(shape), bufferFactory, initializer)
inline fun <reified T : Any> inlineNdStructure(shape: IntArray, crossinline initializer: (IntArray) -> T) =
@ -216,17 +216,17 @@ class MutableBufferNDStructure<T>(
*/
fun <T : Any> mutableNdStructure(
strides: Strides,
bufferFactory: MutableBufferFactory<T> = ::boxingMutableBuffer,
bufferFactory: MutableBufferFactory<T> = MutableBuffer.Companion::boxing,
initializer: (IntArray) -> T
) =
MutableBufferNDStructure(strides, bufferFactory(strides.linearSize) { i -> initializer(strides.index(i)) })
inline fun <reified T : Any> inlineMutableNdStructure(strides: Strides, crossinline initializer: (IntArray) -> T) =
MutableBufferNDStructure(strides, autoMutableBuffer(strides.linearSize) { i -> initializer(strides.index(i)) })
MutableBufferNDStructure(strides, MutableBuffer.auto(strides.linearSize) { i -> initializer(strides.index(i)) })
fun <T : Any> mutableNdStructure(
shape: IntArray,
bufferFactory: MutableBufferFactory<T> = ::boxingMutableBuffer,
bufferFactory: MutableBufferFactory<T> = MutableBuffer.Companion::boxing,
initializer: (IntArray) -> T
) =
mutableNdStructure(DefaultStrides(shape), bufferFactory, initializer)

20
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/RealNDField.kt
View File

@ -2,7 +2,7 @@ package scientifik.kmath.structures
import scientifik.kmath.operations.RealField
typealias RealNDElement = BufferNDElement<Double, RealField>
typealias RealNDElement = StridedNDElement<Double, RealField>
class RealNDField(shape: IntArray) :
StridedNDField<Double, RealField>(shape, RealField),
@ -21,20 +21,20 @@ class RealNDField(shape: IntArray) :
): RealNDElement {
check(arg)
val array = buildBuffer(arg.strides.linearSize) { offset -> RealField.transform(arg.buffer[offset]) }
return BufferNDElement(this, array)
return StridedNDElement(this, array)
}
override fun produce(initializer: RealField.(IntArray) -> Double): RealNDElement {
val array = buildBuffer(strides.linearSize) { offset -> elementField.initializer(strides.index(offset)) }
return BufferNDElement(this, array)
return StridedNDElement(this, array)
}
override fun mapIndexed(
arg: NDBuffer<Double>,
transform: RealField.(index: IntArray, Double) -> Double
): BufferNDElement<Double, RealField> {
): StridedNDElement<Double, RealField> {
check(arg)
return BufferNDElement(
return StridedNDElement(
this,
buildBuffer(arg.strides.linearSize) { offset ->
elementField.transform(
@ -48,9 +48,9 @@ class RealNDField(shape: IntArray) :
a: NDBuffer<Double>,
b: NDBuffer<Double>,
transform: RealField.(Double, Double) -> Double
): BufferNDElement<Double, RealField> {
): StridedNDElement<Double, RealField> {
check(a, b)
return BufferNDElement(
return StridedNDElement(
this,
buildBuffer(strides.linearSize) { offset -> elementField.transform(a.buffer[offset], b.buffer[offset]) })
}
@ -80,7 +80,7 @@ class RealNDField(shape: IntArray) :
*/
inline fun StridedNDField<Double, RealField>.produceInline(crossinline initializer: RealField.(Int) -> Double): RealNDElement {
val array = DoubleArray(strides.linearSize) { offset -> elementField.initializer(offset) }
return BufferNDElement(this, DoubleBuffer(array))
return StridedNDElement(this, DoubleBuffer(array))
}
/**
@ -93,13 +93,13 @@ operator fun Function1<Double, Double>.invoke(ndElement: RealNDElement) =
/* plus and minus */
/**
* Summation operation for [BufferNDElement] and single element
* Summation operation for [StridedNDElement] and single element
*/
operator fun RealNDElement.plus(arg: Double) =
context.produceInline { i -> buffer[i] + arg }
/**
* Subtraction operation between [BufferNDElement] and single element
* Subtraction operation between [StridedNDElement] and single element
*/
operator fun RealNDElement.minus(arg: Double) =
context.produceInline { i -> buffer[i] - arg }

4
kmath-core/src/commonTest/kotlin/scientifik/kmath/expressions/ExpressionFieldTest.kt
View File

@ -12,7 +12,7 @@ class ExpressionFieldTest {
val context = ExpressionField(RealField)
val expression = with(context) {
val x = variable("x", 2.0)
x * x + 2 * x + 1.0
x * x + 2 * x + one
}
assertEquals(expression("x" to 1.0), 4.0)
assertEquals(expression(), 9.0)
@ -44,7 +44,7 @@ class ExpressionFieldTest {
fun valueExpression() {
val expressionBuilder: ExpressionField<Double>.() -> Expression<Double> = {
val x = variable("x")
x * x + 2 * x + 1.0
x * x + 2 * x + one
}
val expression = ExpressionField(RealField).expressionBuilder()

2
kmath-core/src/commonTest/kotlin/scientifik/kmath/structures/NDFieldTest.kt
View File

@ -7,7 +7,7 @@ import kotlin.test.assertEquals
class NDFieldTest {
@Test
fun testStrides() {
val ndArray = NDElements.real(intArrayOf(10, 10)) { (it[0] + it[1]).toDouble() }
val ndArray = NDElement.real(intArrayOf(10, 10)) { (it[0] + it[1]).toDouble() }
assertEquals(ndArray[5, 5], 10.0)
}
}

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

@ -1,7 +1,7 @@
package scientifik.kmath.structures
import scientifik.kmath.operations.Norm
import scientifik.kmath.structures.NDElements.real2D
import scientifik.kmath.structures.NDElement.Companion.real2D
import kotlin.math.abs
import kotlin.math.pow
import kotlin.test.Test

1
kmath-core/src/jvmMain/kotlin/scientifik/kmath/structures/ComplexBufferSpec.kt
View File

@ -23,3 +23,4 @@ object ComplexBufferSpec : FixedSizeBufferSpec<Complex> {
*/
fun Complex.Companion.createBuffer(size: Int) = ObjectBuffer.create(ComplexBufferSpec, size)