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dev #24

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altavir merged 14 commits from dev into master 2018-12-11 17:42:42 +03:00
Conversation 0 Commits 14 Files Changed 40 +306 -294
9 changed files with 306 additions and 294 deletions
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3
kmath-core/src/commonMain/kotlin/scientifik/kmath/histogram/Histogram.kt
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@ -2,6 +2,7 @@ package scientifik.kmath.histogram
import scientifik.kmath.structures.ArrayBuffer
import scientifik.kmath.structures.Buffer
import scientifik.kmath.structures.DoubleBuffer
typealias Point<T> = Buffer<T>
@ -49,6 +50,8 @@ interface MutableHistogram<T: Any, out B : Bin<T>>: Histogram<T,B>{
fun <T: Any> MutableHistogram<T,*>.put(vararg point: T) = put(ArrayBuffer(point))
fun MutableHistogram<Double,*>.put(vararg point: Number) = put(DoubleBuffer(point.map { it.toDouble() }.toDoubleArray()))
fun <T: Any> MutableHistogram<T,*>.fill(sequence: Iterable<Point<T>>) = sequence.forEach { put(it) }
/**

6
kmath-core/src/commonMain/kotlin/scientifik/kmath/histogram/PhantomHistogram.kt
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@ -4,11 +4,11 @@ import scientifik.kmath.linear.Vector
import scientifik.kmath.operations.Space
import scientifik.kmath.structures.NDStructure
data class BinTemplate<T : Comparable<T>>(val center: Vector<T>, val sizes: Vector<T>) {
data class BinTemplate<T : Comparable<T>>(val center: Vector<T, *>, val sizes: Vector<T, *>) {
fun contains(vector: Point<out T>): Boolean {
if (vector.size != center.size) error("Dimension mismatch for input vector. Expected ${center.size}, but found ${vector.size}")
val upper = center + sizes/2.0
val lower = center - sizes/2.0
val upper = center + sizes / 2.0
val lower = center - sizes / 2.0
return vector.asSequence().mapIndexed { i, value ->
value in lower[i]..upper[i]
}.all { it }

18
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/LUDecomposition.kt
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@ -1,5 +1,7 @@
package scientifik.kmath.linear
import scientifik.kmath.operations.DoubleField
import scientifik.kmath.operations.Field
import scientifik.kmath.structures.MutableNDStructure
import scientifik.kmath.structures.NDStructure
import scientifik.kmath.structures.genericNdStructure
@ -9,7 +11,7 @@ import kotlin.math.absoluteValue
/**
* Implementation based on Apache common-maths LU-decomposition
*/
abstract class LUDecomposition<T : Comparable<T>>(val matrix: Matrix<T>) {
abstract class LUDecomposition<T : Comparable<T>, F : Field<T>>(val matrix: Matrix<T, F>) {
private val field get() = matrix.context.field
/** Entries of LU decomposition. */
@ -31,7 +33,7 @@ abstract class LUDecomposition<T : Comparable<T>>(val matrix: Matrix<T>) {
* L is a lower-triangular matrix
* @return the L matrix (or null if decomposed matrix is singular)
*/
val l: Matrix<out T> by lazy {
val l: Matrix<out T, F> by lazy {
matrix.context.produce { i, j ->
when {
j < i -> lu[i, j]
@ -48,7 +50,7 @@ abstract class LUDecomposition<T : Comparable<T>>(val matrix: Matrix<T>) {
* U is an upper-triangular matrix
* @return the U matrix (or null if decomposed matrix is singular)
*/
val u: Matrix<out T> by lazy {
val u: Matrix<out T, F> by lazy {
matrix.context.produce { i, j ->
if (j >= i) lu[i, j] else field.zero
}
@ -64,7 +66,7 @@ abstract class LUDecomposition<T : Comparable<T>>(val matrix: Matrix<T>) {
* @return the P rows permutation matrix (or null if decomposed matrix is singular)
* @see .getPivot
*/
val p: Matrix<out T> by lazy {
val p: Matrix<out T, F> by lazy {
matrix.context.produce { i, j ->
//TODO ineffective. Need sparse matrix for that
if (j == pivot[i]) field.one else field.zero
@ -181,7 +183,7 @@ abstract class LUDecomposition<T : Comparable<T>>(val matrix: Matrix<T>) {
}
class RealLUDecomposition(matrix: Matrix<Double>, private val singularityThreshold: Double = DEFAULT_TOO_SMALL) : LUDecomposition<Double>(matrix) {
class RealLUDecomposition(matrix: RealMatrix, private val singularityThreshold: Double = DEFAULT_TOO_SMALL) : LUDecomposition<Double, DoubleField>(matrix) {
override fun isSingular(value: Double): Boolean {
return value.absoluteValue < singularityThreshold
}
@ -194,12 +196,12 @@ class RealLUDecomposition(matrix: Matrix<Double>, private val singularityThresho
/** Specialized solver. */
object RealLUSolver : LinearSolver<Double> {
object RealLUSolver : LinearSolver<Double, DoubleField> {
fun decompose(mat: Matrix<Double>, threshold: Double = 1e-11): RealLUDecomposition = RealLUDecomposition(mat, threshold)
fun decompose(mat: Matrix<Double, DoubleField>, threshold: Double = 1e-11): RealLUDecomposition = RealLUDecomposition(mat, threshold)
override fun solve(a: Matrix<Double>, b: Matrix<Double>): Matrix<Double> {
override fun solve(a: RealMatrix, b: RealMatrix): RealMatrix {
val decomposition = decompose(a, a.context.field.zero)
if (b.rows != a.rows) {

194
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/LinearAlgrebra.kt
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@ -1,184 +1,17 @@
package scientifik.kmath.linear
import scientifik.kmath.operations.*
import scientifik.kmath.structures.ExtendedNDField
import scientifik.kmath.structures.GenericNDField
import scientifik.kmath.structures.NDField
/**
* The space for linear elements. Supports scalar product alongside with standard linear operations.
* @param T type of individual element of the vector or matrix
* @param V the type of vector space element
*/
abstract class MatrixSpace<T : Any>(val rows: Int, val columns: Int, val field: Field<T>) : Space<Matrix<T>> {
/**
* Produce the element of this space
*/
abstract fun produce(initializer: (Int, Int) -> T): Matrix<T>
/**
* Produce new matrix space with given dimensions. The space produced could be raised from cache since [MatrixSpace] does not have mutable elements
*/
abstract fun produceSpace(rows: Int, columns: Int): MatrixSpace<T>
override val zero: Matrix<T> by lazy {
produce { _, _ -> field.zero }
}
// val one: Matrix<T> by lazy {
// produce { i, j -> if (i == j) field.one else field.zero }
// }
override fun add(a: Matrix<T>, b: Matrix<T>): Matrix<T> {
return produce { i, j -> with(field) { a[i, j] + b[i, j] } }
}
override fun multiply(a: Matrix<T>, k: Double): Matrix<T> {
//TODO it is possible to implement scalable linear elements which normed values and adjustable scale to save memory and processing poser
return produce { i, j -> with(field) { a[i, j] * k } }
}
/**
* Dot product. Throws exception on dimension mismatch
*/
fun multiply(a: Matrix<T>, b: Matrix<T>): Matrix<T> {
if (a.rows != b.columns) {
//TODO replace by specific exception
error("Dimension mismatch in linear structure dot product: [${a.rows},${a.columns}]*[${b.rows},${b.columns}]")
}
return produceSpace(a.rows, b.columns).produce { i, j ->
(0 until a.columns).asSequence().map { k -> field.multiply(a[i, k], b[k, j]) }.reduce { first, second -> field.add(first, second) }
}
}
override fun equals(other: Any?): Boolean {
if (this === other) return true
if (other !is MatrixSpace<*>) return false
if (rows != other.rows) return false
if (columns != other.columns) return false
if (field != other.field) return false
return true
}
override fun hashCode(): Int {
var result = rows
result = 31 * result + columns
result = 31 * result + field.hashCode()
return result
}
}
infix fun <T : Any> Matrix<T>.dot(b: Matrix<T>): Matrix<T> = this.context.multiply(this, b)
/**
* A matrix-like structure
*/
interface Matrix<T : Any> : SpaceElement<Matrix<T>, MatrixSpace<T>> {
/**
* Number of rows
*/
val rows: Int
/**
* Number of columns
*/
val columns: Int
/**
* Get element in row [i] and column [j]. Throws error in case of call ounside structure dimensions
*/
operator fun get(i: Int, j: Int): T
override val self: Matrix<T>
get() = this
fun transpose(): Matrix<T> {
return object : Matrix<T> {
override val context: MatrixSpace<T> = this@Matrix.context
override val rows: Int = this@Matrix.columns
override val columns: Int = this@Matrix.rows
override fun get(i: Int, j: Int): T = this@Matrix[j, i]
}
}
companion object {
/**
* Create [ArrayMatrix] with custom field
*/
fun <T : Any> of(rows: Int, columns: Int, field: Field<T>, initializer: (Int, Int) -> T) =
ArrayMatrix(ArrayMatrixSpace(rows, columns, field), initializer)
/**
* Create [ArrayMatrix] of doubles. The implementation in general should be faster than generic one due to boxing.
*/
fun ofReal(rows: Int, columns: Int, initializer: (Int, Int) -> Double) =
ArrayMatrix(ArrayMatrixSpace(rows, columns, DoubleField, realNDFieldFactory), initializer)
/**
* Create a diagonal value matrix. By default value equals [Field.one].
*/
fun <T : Any> diagonal(rows: Int, columns: Int, field: Field<T>, values: (Int) -> T = { field.one }): Matrix<T> {
return of(rows, columns, field) { i, j -> if (i == j) values(i) else field.zero }
}
/**
* Equality check on two generic matrices
*/
fun equals(mat1: Matrix<*>, mat2: Matrix<*>): Boolean {
if (mat1 === mat2) return true
if (mat1.context != mat2.context) return false
for (i in 0 until mat1.rows) {
for (j in 0 until mat2.columns) {
if (mat1[i, j] != mat2[i, j]) return false
}
}
return true
}
}
}
typealias NDFieldFactory<T> = (IntArray) -> NDField<T>
internal fun <T : Any> genericNDFieldFactory(field: Field<T>): NDFieldFactory<T> = { index -> GenericNDField(index, field) }
internal val realNDFieldFactory: NDFieldFactory<Double> = { index -> ExtendedNDField(index, DoubleField) }
/**
* NDArray-based implementation of vector space. By default uses slow [GenericNDField], but could be overridden with custom [NDField] factory.
*/
class ArrayMatrixSpace<T : Any>(
rows: Int,
columns: Int,
field: Field<T>,
val ndFactory: NDFieldFactory<T> = genericNDFieldFactory(field)
) : MatrixSpace<T>(rows, columns, field) {
val ndField by lazy {
ndFactory(intArrayOf(rows, columns))
}
override fun produce(initializer: (Int, Int) -> T): Matrix<T> = ArrayMatrix(this, initializer)
override fun produceSpace(rows: Int, columns: Int): ArrayMatrixSpace<T> {
return ArrayMatrixSpace(rows, columns, field, ndFactory)
}
}
import scientifik.kmath.operations.DoubleField
import scientifik.kmath.operations.Field
import scientifik.kmath.operations.Norm
/**
* A group of methods to resolve equation A dot X = B, where A and B are matrices or vectors
*/
interface LinearSolver<T : Any> {
fun solve(a: Matrix<T>, b: Matrix<T>): Matrix<T>
fun solve(a: Matrix<T>, b: Vector<T>): Vector<T> = solve(a, b.toMatrix()).toVector()
fun inverse(a: Matrix<T>): Matrix<T> = solve(a, Matrix.diagonal(a.rows, a.columns, a.context.field))
interface LinearSolver<T : Any, F : Field<T>> {
fun solve(a: Matrix<T, F>, b: Matrix<T, F>): Matrix<T, F>
fun solve(a: Matrix<T, F>, b: Vector<T, F>): Vector<T, F> = solve(a, b.toMatrix()).toVector()
fun inverse(a: Matrix<T, F>): Matrix<T, F> = solve(a, Matrix.diagonal(a.rows, a.columns, a.context.field))
}
/**
@ -192,7 +25,7 @@ fun List<Double>.toVector() = Vector.ofReal(this.size) { this[it] }
/**
* Convert matrix to vector if it is possible
*/
fun <T : Any> Matrix<T>.toVector(): Vector<T> {
fun <T : Any, F : Field<T>> Matrix<T, F>.toVector(): Vector<T, F> {
return when {
this.columns == 1 -> {
// if (this is ArrayMatrix) {
@ -208,7 +41,7 @@ fun <T : Any> Matrix<T>.toVector(): Vector<T> {
}
}
fun <T : Any> Vector<T>.toMatrix(): Matrix<T> {
fun <T : Any, F : Field<T>> Vector<T, F>.toMatrix(): Matrix<T, F> {
// return if (this is ArrayVector) {
// //Reuse existing underlying array
// ArrayMatrix(ArrayMatrixSpace(size, 1, context.field, context.ndFactory), array)
@ -216,11 +49,14 @@ fun <T : Any> Vector<T>.toMatrix(): Matrix<T> {
// //Generic vector
// matrix(size, 1, context.field) { i, j -> get(i) }
// }
return Matrix.of(size, 1, context.field) { i, _ -> get(i) }
return Matrix.of(size, 1, context.space) { i, _ -> get(i) }
}
object VectorL2Norm: Norm<Vector<out Number>, Double> {
override fun norm(arg: Vector<out Number>): Double {
object VectorL2Norm : Norm<Vector<out Number, *>, Double> {
override fun norm(arg: Vector<out Number, *>): Double {
return kotlin.math.sqrt(arg.sumByDouble { it.toDouble() })
}
}
typealias RealVector = Vector<Double, DoubleField>
typealias RealMatrix = Matrix<Double, DoubleField>

171
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/Matrix.kt Normal file
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@ -0,0 +1,171 @@
package scientifik.kmath.linear
import scientifik.kmath.operations.*
import scientifik.kmath.structures.ExtendedNDField
import scientifik.kmath.structures.GenericNDField
import scientifik.kmath.structures.NDField
/**
* The space for linear elements. Supports scalar product alongside with standard linear operations.
* @param T type of individual element of the vector or matrix
* @param V the type of vector space element
*/
abstract class MatrixSpace<T : Any, F : Ring<T>>(val rows: Int, val columns: Int, val field: F) : Space<Matrix<T, F>> {
/**
* Produce the element of this space
*/
abstract fun produce(initializer: (Int, Int) -> T): Matrix<T, F>
/**
* Produce new matrix space with given dimensions. The space produced could be raised from cache since [MatrixSpace] does not have mutable elements
*/
abstract fun produceSpace(rows: Int, columns: Int): MatrixSpace<T, F>
override val zero: Matrix<T, F> by lazy {
produce { _, _ -> field.zero }
}
// val one: Matrix<T> by lazy {
// produce { i, j -> if (i == j) field.one else field.zero }
// }
override fun add(a: Matrix<T, F>, b: Matrix<T, F>): Matrix<T, F> {
return produce { i, j -> with(field) { a[i, j] + b[i, j] } }
}
override fun multiply(a: Matrix<T, F>, k: Double): Matrix<T, F> {
//TODO it is possible to implement scalable linear elements which normed values and adjustable scale to save memory and processing poser
return produce { i, j -> with(field) { a[i, j] * k } }
}
/**
* Dot product. Throws exception on dimension mismatch
*/
fun multiply(a: Matrix<T, F>, b: Matrix<T, F>): Matrix<T, F> {
if (a.rows != b.columns) {
//TODO replace by specific exception
error("Dimension mismatch in linear structure dot product: [${a.rows},${a.columns}]*[${b.rows},${b.columns}]")
}
return produceSpace(a.rows, b.columns).produce { i, j ->
(0 until a.columns).asSequence().map { k -> field.multiply(a[i, k], b[k, j]) }.reduce { first, second -> field.add(first, second) }
}
}
override fun equals(other: Any?): Boolean {
if (this === other) return true
if (other !is MatrixSpace<*,*>) return false
if (rows != other.rows) return false
if (columns != other.columns) return false
if (field != other.field) return false
return true
}
override fun hashCode(): Int {
var result = rows
result = 31 * result + columns
result = 31 * result + field.hashCode()
return result
}
}
infix fun <T : Any, F : Field<T>> Matrix<T, F>.dot(b: Matrix<T, F>): Matrix<T, F> = this.context.multiply(this, b)
/**
* A matrix-like structure
*/
interface Matrix<T : Any, F: Ring<T>> : SpaceElement<Matrix<T, F>, MatrixSpace<T, F>> {
/**
* Number of rows
*/
val rows: Int
/**
* Number of columns
*/
val columns: Int
/**
* Get element in row [i] and column [j]. Throws error in case of call ounside structure dimensions
*/
operator fun get(i: Int, j: Int): T
override val self: Matrix<T, F>
get() = this
fun transpose(): Matrix<T, F> {
return object : Matrix<T, F> {
override val context: MatrixSpace<T, F> = this@Matrix.context
override val rows: Int = this@Matrix.columns
override val columns: Int = this@Matrix.rows
override fun get(i: Int, j: Int): T = this@Matrix[j, i]
}
}
companion object {
/**
* Create [ArrayMatrix] with custom field
*/
fun <T : Any, F: Field<T>> of(rows: Int, columns: Int, field: F, initializer: (Int, Int) -> T) =
ArrayMatrix(ArrayMatrixSpace(rows, columns, field), initializer)
/**
* Create [ArrayMatrix] of doubles. The implementation in general should be faster than generic one due to boxing.
*/
fun ofReal(rows: Int, columns: Int, initializer: (Int, Int) -> Double) =
ArrayMatrix(ArrayMatrixSpace(rows, columns, DoubleField, realNDFieldFactory), initializer)
/**
* Create a diagonal value matrix. By default value equals [Field.one].
*/
fun <T : Any, F: Field<T>> diagonal(rows: Int, columns: Int, field: F, values: (Int) -> T = { field.one }): Matrix<T, F> {
return of(rows, columns, field) { i, j -> if (i == j) values(i) else field.zero }
}
/**
* Equality check on two generic matrices
*/
fun equals(mat1: Matrix<*, *>, mat2: Matrix<*, *>): Boolean {
if (mat1 === mat2) return true
if (mat1.context != mat2.context) return false
for (i in 0 until mat1.rows) {
for (j in 0 until mat2.columns) {
if (mat1[i, j] != mat2[i, j]) return false
}
}
return true
}
}
}
typealias NDFieldFactory<T, F> = (IntArray) -> NDField<T, F>
internal fun <T : Any, F : Field<T>> genericNDFieldFactory(field: F): NDFieldFactory<T, F> = { index -> GenericNDField(index, field) }
internal val realNDFieldFactory: NDFieldFactory<Double, DoubleField> = { index -> ExtendedNDField(index, DoubleField) }
/**
* NDArray-based implementation of vector space. By default uses slow [GenericNDField], but could be overridden with custom [NDField] factory.
*/
class ArrayMatrixSpace<T : Any, F : Field<T>>(
rows: Int,
columns: Int,
field: F,
val ndFactory: NDFieldFactory<T, F> = genericNDFieldFactory(field)
) : MatrixSpace<T, F>(rows, columns, field) {
val ndField by lazy {
ndFactory(intArrayOf(rows, columns))
}
override fun produce(initializer: (Int, Int) -> T): Matrix<T, F> = ArrayMatrix(this, initializer)
override fun produceSpace(rows: Int, columns: Int): ArrayMatrixSpace<T, F> {
return ArrayMatrixSpace(rows, columns, field, ndFactory)
}
}

49
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/Vector.kt
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@ -12,34 +12,34 @@ import scientifik.kmath.structures.get
* A linear space for vectors.
* Could be used on any point-like structure
*/
abstract class VectorSpace<T : Any>(val size: Int, val field: Field<T>) : Space<Point<T>> {
abstract class VectorSpace<T : Any, S : Space<T>>(val size: Int, val space: S) : Space<Point<T>> {
abstract fun produce(initializer: (Int) -> T): Vector<T>
abstract fun produce(initializer: (Int) -> T): Vector<T, S>
override val zero: Vector<T> by lazy { produce { field.zero } }
override val zero: Vector<T, S> by lazy { produce { space.zero } }
override fun add(a: Point<T>, b: Point<T>): Vector<T> = produce { with(field) { a[it] + b[it] } }
override fun add(a: Point<T>, b: Point<T>): Vector<T, S> = produce { with(space) { a[it] + b[it] } }
override fun multiply(a: Point<T>, k: Double): Vector<T> = produce { with(field) { a[it] * k } }
override fun multiply(a: Point<T>, k: Double): Vector<T, S> = produce { with(space) { a[it] * k } }
}
/**
* A point coupled to the linear space
*/
interface Vector<T : Any> : SpaceElement<Point<T>, VectorSpace<T>>, Point<T>, Iterable<T> {
interface Vector<T : Any, S : Space<T>> : SpaceElement<Point<T>, VectorSpace<T, S>>, Point<T>, Iterable<T> {
override val size: Int get() = context.size
override operator fun plus(b: Point<T>): Vector<T> = context.add(self, b)
override operator fun minus(b: Point<T>): Vector<T> = context.add(self, context.multiply(b, -1.0))
override operator fun times(k: Number): Vector<T> = context.multiply(self, k.toDouble())
override operator fun div(k: Number): Vector<T> = context.multiply(self, 1.0 / k.toDouble())
override operator fun plus(b: Point<T>): Vector<T, S> = context.add(self, b)
override operator fun minus(b: Point<T>): Vector<T, S> = context.add(self, context.multiply(b, -1.0))
override operator fun times(k: Number): Vector<T, S> = context.multiply(self, k.toDouble())
override operator fun div(k: Number): Vector<T, S> = context.multiply(self, 1.0 / k.toDouble())
companion object {
/**
* Create vector with custom field
*/
fun <T : Any> of(size: Int, field: Field<T>, initializer: (Int) -> T) =
fun <T : Any, F: Field<T>> of(size: Int, field: F, initializer: (Int) -> T) =
ArrayVector(ArrayVectorSpace(size, field), initializer)
/**
@ -50,7 +50,7 @@ interface Vector<T : Any> : SpaceElement<Point<T>, VectorSpace<T>>, Point<T>, It
fun ofReal(vararg point: Double) = point.toVector()
fun equals(v1: Vector<*>, v2: Vector<*>): Boolean {
fun equals(v1: Vector<*,*>, v2: Vector<*,*>): Boolean {
if (v1 === v2) return true
if (v1.context != v2.context) return false
for (i in 0 until v2.size) {
@ -61,24 +61,24 @@ interface Vector<T : Any> : SpaceElement<Point<T>, VectorSpace<T>>, Point<T>, It
}
}
class ArrayVectorSpace<T : Any>(
class ArrayVectorSpace<T : Any, F : Field<T>>(
size: Int,
field: Field<T>,
val ndFactory: NDFieldFactory<T> = genericNDFieldFactory(field)
) : VectorSpace<T>(size, field) {
field: F,
val ndFactory: NDFieldFactory<T, F> = genericNDFieldFactory(field)
) : VectorSpace<T, F>(size, field) {
val ndField by lazy {
ndFactory(intArrayOf(size))
}
override fun produce(initializer: (Int) -> T): Vector<T> = ArrayVector(this, initializer)
override fun produce(initializer: (Int) -> T): Vector<T, F> = ArrayVector(this, initializer)
}
/**
* Member of [ArrayMatrixSpace] which wraps 2-D array
*/
class ArrayMatrix<T : Any> internal constructor(override val context: ArrayMatrixSpace<T>, val element: NDElement<T>) : Matrix<T> {
class ArrayMatrix<T : Any, F : Field<T>> internal constructor(override val context: ArrayMatrixSpace<T, F>, val element: NDElement<T, F>) : Matrix<T, F> {
constructor(context: ArrayMatrixSpace<T>, initializer: (Int, Int) -> T) : this(context, context.ndField.produce { list -> initializer(list[0], list[1]) })
constructor(context: ArrayMatrixSpace<T, F>, initializer: (Int, Int) -> T) : this(context, context.ndField.produce { list -> initializer(list[0], list[1]) })
override val rows: Int get() = context.rows
@ -88,13 +88,13 @@ class ArrayMatrix<T : Any> internal constructor(override val context: ArrayMatri
return element[i, j]
}
override val self: ArrayMatrix<T> get() = this
override val self: ArrayMatrix<T, F> get() = this
}
class ArrayVector<T : Any> internal constructor(override val context: VectorSpace<T>, val element: NDElement<T>) : Vector<T> {
class ArrayVector<T : Any, F : Field<T>> internal constructor(override val context: VectorSpace<T, F>, val element: NDElement<T, F>) : Vector<T, F> {
constructor(context: ArrayVectorSpace<T>, initializer: (Int) -> T) : this(context, context.ndField.produce { list -> initializer(list[0]) })
constructor(context: ArrayVectorSpace<T, F>, initializer: (Int) -> T) : this(context, context.ndField.produce { list -> initializer(list[0]) })
init {
if (context.size != element.shape[0]) {
@ -106,13 +106,12 @@ class ArrayVector<T : Any> internal constructor(override val context: VectorSpac
return element[index]
}
override val self: ArrayVector<T> get() = this
override val self: ArrayVector<T, F> get() = this
override fun iterator(): Iterator<T> = (0 until size).map { element[it] }.iterator()
override fun copy(): ArrayVector<T> = ArrayVector(context, element)
override fun copy(): ArrayVector<T, F> = ArrayVector(context, element)
override fun toString(): String = this.joinToString(prefix = "[", postfix = "]", separator = ", ") { it.toString() }
}
typealias RealVector = Vector<Double>

32
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/ExtendedNDField.kt
View File

@ -9,33 +9,33 @@ import scientifik.kmath.operations.TrigonometricOperations
/**
* NDField that supports [ExtendedField] operations on its elements
*/
class ExtendedNDField<N: Any>(shape: IntArray, override val field: ExtendedField<N>) : NDField<N>(shape, field),
TrigonometricOperations<NDElement<N>>,
PowerOperations<NDElement<N>>,
ExponentialOperations<NDElement<N>> {
class ExtendedNDField<N : Any, F : ExtendedField<N>>(shape: IntArray, field: F) : NDField<N, F>(shape, field),
TrigonometricOperations<NDElement<N, F>>,
PowerOperations<NDElement<N, F>>,
ExponentialOperations<NDElement<N, F>> {
override fun produceStructure(initializer: (IntArray) -> N): NDStructure<N> {
return genericNdStructure(shape, initializer)
override fun produceStructure(initializer: F.(IntArray) -> N): NDStructure<N> {
return genericNdStructure(shape) { field.initializer(it) }
}
override fun power(arg: NDElement<N>, pow: Double): NDElement<N> {
return arg.transform { d -> with(field){power(d,pow)} }
override fun power(arg: NDElement<N, F>, pow: Double): NDElement<N, F> {
return arg.transform { d -> with(field) { power(d, pow) } }
}
override fun exp(arg: NDElement<N>): NDElement<N> {
return arg.transform { d -> with(field){exp(d)} }
override fun exp(arg: NDElement<N, F>): NDElement<N, F> {
return arg.transform { d -> with(field) { exp(d) } }
}
override fun ln(arg: NDElement<N>): NDElement<N> {
return arg.transform { d -> with(field){ln(d)} }
override fun ln(arg: NDElement<N, F>): NDElement<N, F> {
return arg.transform { d -> with(field) { ln(d) } }
}
override fun sin(arg: NDElement<N>): NDElement<N> {
return arg.transform { d -> with(field){sin(d)} }
override fun sin(arg: NDElement<N, F>): NDElement<N, F> {
return arg.transform { d -> with(field) { sin(d) } }
}
override fun cos(arg: NDElement<N>): NDElement<N> {
return arg.transform { d -> with(field){cos(d)} }
override fun cos(arg: NDElement<N, F>): NDElement<N, F> {
return arg.transform { d -> with(field) { cos(d) } }
}
}

113
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/NDField.kt
View File

@ -15,24 +15,24 @@ class ShapeMismatchException(val expected: IntArray, val actual: IntArray) : Run
* @param field - operations field defined on individual array element
* @param T the type of the element contained in NDArray
*/
abstract class NDField<T>(val shape: IntArray, open val field: Field<T>) : Field<NDElement<T>> {
abstract class NDField<T, F : Field<T>>(val shape: IntArray, val field: F) : Field<NDElement<T, F>> {
abstract fun produceStructure(initializer: (IntArray) -> T): NDStructure<T>
abstract fun produceStructure(initializer: F.(IntArray) -> T): NDStructure<T>
/**
* Create new instance of NDArray using field shape and given initializer
* The producer takes list of indices as argument and returns contained value
*/
fun produce(initializer: (IntArray) -> T): NDElement<T> = NDElement(this, produceStructure(initializer))
fun produce(initializer: F.(IntArray) -> T): NDElement<T, F> = NDElement(this, produceStructure(initializer))
override val zero: NDElement<T> by lazy {
produce { this.field.zero }
override val zero: NDElement<T, F> by lazy {
produce { zero }
}
/**
* Check the shape of given NDArray and throw exception if it does not coincide with shape of the field
*/
private fun checkShape(vararg elements: NDElement<T>) {
private fun checkShape(vararg elements: NDElement<T, F>) {
elements.forEach {
if (!shape.contentEquals(it.shape)) {
throw ShapeMismatchException(shape, it.shape)
@ -43,7 +43,7 @@ abstract class NDField<T>(val shape: IntArray, open val field: Field<T>) : Field
/**
* Element-by-element addition
*/
override fun add(a: NDElement<T>, b: NDElement<T>): NDElement<T> {
override fun add(a: NDElement<T, F>, b: NDElement<T, F>): NDElement<T, F> {
checkShape(a, b)
return produce { with(field) { a[it] + b[it] } }
}
@ -51,18 +51,18 @@ abstract class NDField<T>(val shape: IntArray, open val field: Field<T>) : Field
/**
* Multiply all elements by cinstant
*/
override fun multiply(a: NDElement<T>, k: Double): NDElement<T> {
override fun multiply(a: NDElement<T, F>, k: Double): NDElement<T, F> {
checkShape(a)
return produce { with(field) { a[it] * k } }
}
override val one: NDElement<T>
get() = produce { this.field.one }
override val one: NDElement<T, F>
get() = produce { one }
/**
* Element-by-element multiplication
*/
override fun multiply(a: NDElement<T>, b: NDElement<T>): NDElement<T> {
override fun multiply(a: NDElement<T, F>, b: NDElement<T, F>): NDElement<T, F> {
checkShape(a)
return produce { with(field) { a[it] * b[it] } }
}
@ -70,65 +70,65 @@ abstract class NDField<T>(val shape: IntArray, open val field: Field<T>) : Field
/**
* Element-by-element division
*/
override fun divide(a: NDElement<T>, b: NDElement<T>): NDElement<T> {
override fun divide(a: NDElement<T, F>, b: NDElement<T, F>): NDElement<T, F> {
checkShape(a)
return produce { with(field) { a[it] / b[it] } }
}
/**
* Reverse sum operation
*/
operator fun <T> T.plus(arg: NDElement<T>): NDElement<T> = arg + this
/**
* Reverse minus operation
*/
operator fun <T> T.minus(arg: NDElement<T>): NDElement<T> = arg.transform { _, value ->
with(arg.context.field) {
this@minus - value
}
}
/**
* Reverse product operation
*/
operator fun <T> T.times(arg: NDElement<T>): NDElement<T> = arg * this
/**
* Reverse division operation
*/
operator fun <T> T.div(arg: NDElement<T>): NDElement<T> = arg.transform { _, value ->
with(arg.context.field) {
this@div / value
}
}
// /**
// * Reverse sum operation
// */
// operator fun T.plus(arg: NDElement<T, F>): NDElement<T, F> = arg + this
//
// /**
// * Reverse minus operation
// */
// operator fun T.minus(arg: NDElement<T, F>): NDElement<T, F> = arg.transform { _, value ->
// with(arg.context.field) {
// this@minus - value
// }
// }
//
// /**
// * Reverse product operation
// */
// operator fun T.times(arg: NDElement<T, F>): NDElement<T, F> = arg * this
//
// /**
// * Reverse division operation
// */
// operator fun T.div(arg: NDElement<T, F>): NDElement<T, F> = arg.transform { _, value ->
// with(arg.context.field) {
// this@div / value
// }
// }
}
/**
* Immutable [NDStructure] coupled to the context. Emulates Python ndarray
*/
class NDElement<T>(override val context: NDField<T>, private val structure: NDStructure<T>) : FieldElement<NDElement<T>, NDField<T>>, NDStructure<T> by structure {
class NDElement<T, F : Field<T>>(override val context: NDField<T, F>, private val structure: NDStructure<T>) : FieldElement<NDElement<T, F>, NDField<T, F>>, NDStructure<T> by structure {
//TODO ensure structure is immutable
override val self: NDElement<T>
override val self: NDElement<T, F>
get() = this
inline fun transform(crossinline action: (IntArray, T) -> T): NDElement<T> = context.produce { action(it, get(*it)) }
inline fun transform(crossinline action: (T) -> T): NDElement<T> = context.produce { action(get(*it)) }
inline fun transform(crossinline action: (IntArray, T) -> T): NDElement<T, F> = context.produce { action(it, get(*it)) }
inline fun transform(crossinline action: (T) -> T): NDElement<T, F> = context.produce { action(get(*it)) }
}
/**
* Element by element application of any operation on elements to the whole array. Just like in numpy
*/
operator fun <T> Function1<T, T>.invoke(ndElement: NDElement<T>): NDElement<T> = ndElement.transform { _, value -> this(value) }
operator fun <T, F : Field<T>> Function1<T, T>.invoke(ndElement: NDElement<T, F>): NDElement<T, F> = ndElement.transform { _, value -> this(value) }
/* plus and minus */
/**
* Summation operation for [NDElement] and single element
*/
operator fun <T> NDElement<T>.plus(arg: T): NDElement<T> = transform { _, value ->
operator fun <T, F : Field<T>> NDElement<T, F>.plus(arg: T): NDElement<T, F> = transform { _, value ->
with(context.field) {
arg + value
}
@ -137,7 +137,7 @@ operator fun <T> NDElement<T>.plus(arg: T): NDElement<T> = transform { _, value
/**
* Subtraction operation between [NDElement] and single element
*/
operator fun <T> NDElement<T>.minus(arg: T): NDElement<T> = transform { _, value ->
operator fun <T, F : Field<T>> NDElement<T, F>.minus(arg: T): NDElement<T, F> = transform { _, value ->
with(context.field) {
arg - value
}
@ -148,7 +148,7 @@ operator fun <T> NDElement<T>.minus(arg: T): NDElement<T> = transform { _, value
/**
* Product operation for [NDElement] and single element
*/
operator fun <T> NDElement<T>.times(arg: T): NDElement<T> = transform { _, value ->
operator fun <T, F : Field<T>> NDElement<T, F>.times(arg: T): NDElement<T, F> = transform { _, value ->
with(context.field) {
arg * value
}
@ -157,14 +157,14 @@ operator fun <T> NDElement<T>.times(arg: T): NDElement<T> = transform { _, value
/**
* Division operation between [NDElement] and single element
*/
operator fun <T> NDElement<T>.div(arg: T): NDElement<T> = transform { _, value ->
operator fun <T, F : Field<T>> NDElement<T, F>.div(arg: T): NDElement<T, F> = transform { _, value ->
with(context.field) {
arg / value
}
}
class GenericNDField<T : Any>(shape: IntArray, field: Field<T>) : NDField<T>(shape, field) {
override fun produceStructure(initializer: (IntArray) -> T): NDStructure<T> = genericNdStructure(shape, initializer)
class GenericNDField<T : Any, F : Field<T>>(shape: IntArray, field: F) : NDField<T, F>(shape, field) {
override fun produceStructure(initializer: F.(IntArray) -> T): NDStructure<T> = genericNdStructure(shape) { field.initializer(it) }
}
//typealias NDFieldFactory<T> = (IntArray)->NDField<T>
@ -173,23 +173,24 @@ object NDArrays {
/**
* Create a platform-optimized NDArray of doubles
*/
fun realNDArray(shape: IntArray, initializer: (IntArray) -> Double = { 0.0 }): NDElement<Double> {
fun realNDArray(shape: IntArray, initializer: DoubleField.(IntArray) -> Double = { 0.0 }): NDElement<Double, DoubleField> {
return ExtendedNDField(shape, DoubleField).produce(initializer)
}
fun real1DArray(dim: Int, initializer: (Int) -> Double = { _ -> 0.0 }): NDElement<Double> {
fun real1DArray(dim: Int, initializer: (Int) -> Double = { _ -> 0.0 }): NDElement<Double, DoubleField> {
return realNDArray(intArrayOf(dim)) { initializer(it[0]) }
}
fun real2DArray(dim1: Int, dim2: Int, initializer: (Int, Int) -> Double = { _, _ -> 0.0 }): NDElement<Double> {
fun real2DArray(dim1: Int, dim2: Int, initializer: (Int, Int) -> Double = { _, _ -> 0.0 }): NDElement<Double, DoubleField> {
return realNDArray(intArrayOf(dim1, dim2)) { initializer(it[0], it[1]) }
}
fun real3DArray(dim1: Int, dim2: Int, dim3: Int, initializer: (Int, Int, Int) -> Double = { _, _, _ -> 0.0 }): NDElement<Double> {
fun real3DArray(dim1: Int, dim2: Int, dim3: Int, initializer: (Int, Int, Int) -> Double = { _, _, _ -> 0.0 }): NDElement<Double, DoubleField> {
return realNDArray(intArrayOf(dim1, dim2, dim3)) { initializer(it[0], it[1], it[2]) }
}
inline fun produceReal(shape: IntArray, block: ExtendedNDField<Double>.() -> NDElement<Double>) = ExtendedNDField(shape, DoubleField).run(block)
inline fun produceReal(shape: IntArray, block: ExtendedNDField<Double, DoubleField>.() -> NDElement<Double, DoubleField>) =
ExtendedNDField(shape, DoubleField).run(block)
// /**
// * Simple boxing NDField
@ -199,7 +200,7 @@ object NDArrays {
/**
* Simple boxing NDArray
*/
fun <T : Any> create(field: Field<T>, shape: IntArray, initializer: (IntArray) -> T): NDElement<T> {
fun <T : Any, F : Field<T>> create(field: F, shape: IntArray, initializer: (IntArray) -> T): NDElement<T, F> {
return GenericNDField(shape, field).produce { initializer(it) }
}
}

14
kmath-core/src/commonTest/kotlin/scientifik/kmath/structures/NumberNDFieldTest.kt
View File

@ -32,7 +32,7 @@ class NumberNDFieldTest {
for (i in 0..2) {
for (j in 0..2) {
val expected = (i * 10 + j).toDouble()
assertEquals(expected, array[i, j],"Error at index [$i, $j]")
assertEquals(expected, array[i, j], "Error at index [$i, $j]")
}
}
}
@ -41,25 +41,25 @@ class NumberNDFieldTest {
fun testExternalFunction() {
val function: (Double) -> Double = { x -> x.pow(2) + 2 * x + 1 }
val result = function(array1) + 1.0
assertEquals(10.0, result[1,1])
assertEquals(10.0, result[1, 1])
}
@Test
fun testLibraryFunction() {
val abs: (Double) -> Double = ::abs
val result = abs(array2)
assertEquals(2.0, result[0,2])
assertEquals(2.0, result[0, 2])
}
object L2Norm: Norm<NDElement<out Number>, Double> {
override fun norm(arg: NDElement<out Number>): Double {
object L2Norm : Norm<NDElement<out Number, *>, Double> {
override fun norm(arg: NDElement<out Number, *>): Double {
return kotlin.math.sqrt(arg.sumByDouble { it.second.toDouble() })
}
}
@Test
fun testInternalContext(){
produceReal(array1.shape){
fun testInternalContext() {
produceReal(array1.shape) {
with(L2Norm) {
1 + norm(array1) + exp(array2)
}