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Basic matrix inversion

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This commit is contained in:
Alexander Nozik 2018-10-11 16:23:33 +03:00
parent a9c084773b
commit 0cde0cfea5
7 changed files with 175 additions and 174 deletions
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3
kmath-commons/src/main/kotlin/scientifik/kmath/commons/CommonsMatrix.kt
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@ -1,3 +0,0 @@
package scientifik.kmath.commons
//val solver: DecompositionSolver

69
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/LUDecomposition.kt
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@ -1,44 +1,12 @@
package scientifik.kmath.linear
import scientifik.kmath.operations.Field
import scientifik.kmath.structures.MutableNDArray
import scientifik.kmath.structures.NDArray
import scientifik.kmath.structures.NDArrays
import kotlin.math.absoluteValue
/**
* Calculates the LUP-decomposition of a square matrix.
*
* The LUP-decomposition of a matrix A consists of three matrices L, U and
* P that satisfy: PA = LU. L is lower triangular (with unit
* diagonal terms), U is upper triangular and P is a permutation matrix. All
* matrices are mm.
*
* As shown by the presence of the P matrix, this decomposition is
* implemented using partial pivoting.
*
* This class is based on the class with similar name from the
* [JAMA](http://math.nist.gov/javanumerics/jama/) library.
*
* * a [getP][.getP] method has been added,
* * the `det` method has been renamed as [ getDeterminant][.getDeterminant],
* * the `getDoublePivot` method has been removed (but the int based
* [getPivot][.getPivot] method has been kept),
* * the `solve` and `isNonSingular` methods have been replaced
* by a [getSolver][.getSolver] method and the equivalent methods
* provided by the returned [DecompositionSolver].
*
*
* @see [MathWorld](http://mathworld.wolfram.com/LUDecomposition.html)
*
* @see [Wikipedia](http://en.wikipedia.org/wiki/LU_decomposition)
*
* @since 2.0 (changed to concrete class in 3.0)
*
* @param matrix The matrix to decompose.
* @param singularityThreshold threshold (based on partial row norm)
* under which a matrix is considered singular
* @throws NonSquareMatrixException if matrix is not square
* Implementation copier from Apache common-maths
*/
abstract class LUDecomposition<T : Comparable<T>>(val matrix: Matrix<T>) {
@ -51,7 +19,7 @@ abstract class LUDecomposition<T : Comparable<T>>(val matrix: Matrix<T>) {
private var even: Boolean = false
init {
val pair = matrix.context.field.calculateLU()
val pair = calculateLU()
lu = pair.first
pivot = pair.second
}
@ -134,18 +102,20 @@ abstract class LUDecomposition<T : Comparable<T>>(val matrix: Matrix<T>) {
abstract fun isSingular(value: T): Boolean
private fun Field<T>.calculateLU(): Pair<NDArray<T>, IntArray> {
private fun abs(value: T) = if (value > matrix.context.field.zero) value else with(matrix.context.field) { -value }
private fun calculateLU(): Pair<NDArray<T>, IntArray> {
if (matrix.rows != matrix.columns) {
error("LU decomposition supports only square matrices")
}
fun T.abs() = if (this > zero) this else -this
val m = matrix.columns
val pivot = IntArray(matrix.rows)
//TODO fix performance
val lu: MutableNDArray<T> = NDArrays.createMutable(matrix.context.field, listOf(matrix.rows, matrix.columns)) { index -> matrix[index[0], index[1]] }
with(matrix.context.field) {
// Initialize permutation array and parity
for (row in 0 until m) {
pivot[row] = row
@ -173,11 +143,11 @@ abstract class LUDecomposition<T : Comparable<T>>(val matrix: Matrix<T>) {
//luRow[col] = sum
lu[row, col] = sum
sum.abs()
abs(sum)
} ?: col
// Singularity check
if (isSingular(lu[max, col].abs())) {
if (isSingular(lu[max, col])) {
error("Singular matrix")
}
@ -199,7 +169,9 @@ abstract class LUDecomposition<T : Comparable<T>>(val matrix: Matrix<T>) {
// Divide the lower elements by the "winning" diagonal elt.
val luDiag = lu[col, col]
for (row in col + 1 until m) {
lu[row, col] /= luDiag
lu[row, col] = lu[row, col] / luDiag
// lu[row, col] /= luDiag
}
}
}
return Pair(lu, pivot)
@ -214,21 +186,22 @@ abstract class LUDecomposition<T : Comparable<T>>(val matrix: Matrix<T>) {
return pivot.copyOf()
}
}
class RealLUDecomposition(matrix: Matrix<Double>, private val singularityThreshold: Double = DEFAULT_TOO_SMALL) : LUDecomposition<Double>(matrix) {
override fun isSingular(value: Double): Boolean {
return value.absoluteValue < singularityThreshold
}
companion object {
/** Default bound to determine effective singularity in LU decomposition. */
private const val DEFAULT_TOO_SMALL = 1e-11
}
}
class RealLUDecomposition(matrix: Matrix<Double>, private val singularityThreshold: Double = 1e-11) : LUDecomposition<Double>(matrix) {
override fun isSingular(value: Double): Boolean {
return value < singularityThreshold
}
}
/** Specialized solver. */
class RealLUSolver : LinearSolver<Double> {
object RealLUSolver : LinearSolver<Double> {
//
// /** {@inheritDoc} */

146
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/LinearAlgrebra.kt
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@ -14,31 +14,31 @@ import scientifik.kmath.structures.realNDFieldFactory
* @param T type of individual element of the vector or matrix
* @param V the type of vector space element
*/
abstract class LinearSpace<T : Any, V : Matrix<T>>(val rows: Int, val columns: Int, val field: Field<T>) : Space<V> {
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): V
abstract fun produce(initializer: (Int, Int) -> T): Matrix<T>
/**
* Produce new linear space with given dimensions. The space produced could be raised from cache since [LinearSpace] does not have mutable elements
* 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): LinearSpace<T, V>
abstract fun produceSpace(rows: Int, columns: Int): MatrixSpace<T>
override val zero: V by lazy {
override val zero: Matrix<T> by lazy {
produce { _, _ -> field.zero }
}
val one: V by lazy {
produce { i, j -> if (i == j) field.one else field.zero }
}
// val one: Matrix<T> by lazy {
// produce { i, j -> if (i == j) field.one else field.zero }
// }
override fun add(a: V, b: V): V {
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: V, k: Double): V {
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 } }
}
@ -46,29 +46,23 @@ abstract class LinearSpace<T : Any, V : Matrix<T>>(val rows: Int, val columns: I
/**
* Dot product. Throws exception on dimension mismatch
*/
fun multiply(a: V, b: V): V {
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..a.columns).asSequence().map { k -> field.multiply(a[i, k], b[k, j]) }.reduce { first, second -> field.add(first, second) }
(0 until a.columns).asSequence().map { k -> field.multiply(a[i, k], b[k, j]) }.reduce { first, second -> field.add(first, second) }
}
}
}
infix fun V.dot(b: V): V = multiply(this, b)
}
/**
* A specialized [LinearSpace] which works with vectors
*/
abstract class VectorSpace<T : Any, V : Vector<T>>(size: Int, field: Field<T>) : LinearSpace<T, V>(size, 1, field)
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> {
val context: LinearSpace<T, out Matrix<T>>
interface Matrix<T : Any> : SpaceElement<Matrix<T>, MatrixSpace<T>> {
/**
* Number of rows
*/
@ -83,34 +77,58 @@ interface Matrix<T : Any> {
*/
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: LinearSpace<T, out Matrix<T>> = this@Matrix.context
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 {
fun <T : Any> one(rows: Int, columns: Int, field: Field<T>): Matrix<T> {
return matrix(rows, columns, field) { i, j -> if (i == j) field.one else field.zero }
}
}
}
interface Vector<T : Any> : Matrix<T> {
override val context: VectorSpace<T, Vector<T>>
override val columns: Int
get() = 1
operator fun get(i: Int) = get(i, 0)
/**
* A linear space for vectors
*/
abstract class VectorSpace<T : Any>(val size: Int, val field: Field<T>) : Space<Vector<T>> {
abstract fun produce(initializer: (Int) -> T): Vector<T>
override val zero: Vector<T> by lazy { produce { field.zero } }
override fun add(a: Vector<T>, b: Vector<T>): Vector<T> = produce { with(field) { a[it] + b[it] } }
override fun multiply(a: Vector<T>, k: Double): Vector<T> = produce { with(field) { a[it] * k } }
}
interface Vector<T : Any> : SpaceElement<Vector<T>, VectorSpace<T>> {
val size: Int
get() = context.size
operator fun get(i: Int): T
}
/**
* NDArray-based implementation of vector space. By default uses slow [SimpleNDField], but could be overridden with custom [NDField] factory.
*/
class ArraySpace<T : Any>(
class ArrayMatrixSpace<T : Any>(
rows: Int,
columns: Int,
field: Field<T>,
val ndFactory: NDFieldFactory<T> = createFactory(field)
) : LinearSpace<T, Matrix<T>>(rows, columns, field) {
) : MatrixSpace<T>(rows, columns, field) {
val ndField by lazy {
ndFactory(listOf(rows, columns))
@ -118,8 +136,8 @@ class ArraySpace<T : Any>(
override fun produce(initializer: (Int, Int) -> T): Matrix<T> = ArrayMatrix(this, initializer)
override fun produceSpace(rows: Int, columns: Int): ArraySpace<T> {
return ArraySpace(rows, columns, field, ndFactory)
override fun produceSpace(rows: Int, columns: Int): ArrayMatrixSpace<T> {
return ArrayMatrixSpace(rows, columns, field, ndFactory)
}
}
@ -127,26 +145,20 @@ class ArrayVectorSpace<T : Any>(
size: Int,
field: Field<T>,
val ndFactory: NDFieldFactory<T> = createFactory(field)
) : VectorSpace<T, Vector<T>>(size, field) {
) : VectorSpace<T>(size, field) {
val ndField by lazy {
ndFactory(listOf(size))
}
override fun produce(initializer: (Int, Int) -> T): Vector<T> = produceVector { i -> initializer(i, 0) }
fun produceVector(initializer: (Int) -> T): Vector<T> = ArrayVector(this, initializer)
override fun produceSpace(rows: Int, columns: Int): LinearSpace<T, Vector<T>> {
TODO("not implemented") //To change body of created functions use File | Settings | File Templates.
}
override fun produce(initializer: (Int) -> T): Vector<T> = ArrayVector(this, initializer)
}
/**
* Member of [ArraySpace] which wraps 2-D array
* Member of [ArrayMatrixSpace] which wraps 2-D array
*/
class ArrayMatrix<T : Any> internal constructor(override val context: ArraySpace<T>, val array: NDArray<T>) : Matrix<T>, SpaceElement<Matrix<T>, ArraySpace<T>> {
class ArrayMatrix<T : Any> internal constructor(override val context: ArrayMatrixSpace<T>, val array: NDArray<T>) : Matrix<T> {
constructor(context: ArraySpace<T>, initializer: (Int, Int) -> T) : this(context, context.ndField.produce { list -> initializer(list[0], list[1]) })
constructor(context: ArrayMatrixSpace<T>, initializer: (Int, Int) -> T) : this(context, context.ndField.produce { list -> initializer(list[0], list[1]) })
override val rows: Int get() = context.rows
@ -160,32 +172,21 @@ class ArrayMatrix<T : Any> internal constructor(override val context: ArraySpace
}
class ArrayVector<T : Any> internal constructor(override val context: ArrayVectorSpace<T>, val array: NDArray<T>) : Vector<T>, SpaceElement<Vector<T>, ArrayVectorSpace<T>> {
class ArrayVector<T : Any> internal constructor(override val context: ArrayVectorSpace<T>, val array: NDArray<T>) : Vector<T> {
constructor(context: ArrayVectorSpace<T>, initializer: (Int) -> T) : this(context, context.ndField.produce { list -> initializer(list[0]) })
init {
if (context.columns != 1) {
error("Vector must have single column")
}
if (context.rows != array.shape[0]) {
if (context.size != array.shape[0]) {
error("Array dimension mismatch")
}
}
//private val array = context.ndField.produce { list -> initializer(list[0]) }
override val rows: Int get() = context.rows
override val columns: Int = 1
override fun get(i: Int, j: Int): T {
override fun get(i: Int): T {
return array[i]
}
override val self: ArrayVector<T> get() = this
}
/**
@ -193,8 +194,8 @@ class ArrayVector<T : Any> internal constructor(override val context: ArrayVecto
*/
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 as Matrix<T>).toVector()
fun inverse(a: Matrix<T>): Matrix<T> = solve(a, a.context.one)
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.one(a.rows, a.columns, a.context.field))
}
/**
@ -220,13 +221,13 @@ fun DoubleArray.asVector() = realVector(this.size) { this[it] }
* Create [ArrayMatrix] with custom field
*/
fun <T : Any> matrix(rows: Int, columns: Int, field: Field<T>, initializer: (Int, Int) -> T) =
ArrayMatrix(ArraySpace(rows, columns, field), initializer)
ArrayMatrix(ArrayMatrixSpace(rows, columns, field), initializer)
/**
* Create [ArrayMatrix] of doubles.
*/
fun realMatrix(rows: Int, columns: Int, initializer: (Int, Int) -> Double) =
ArrayMatrix(ArraySpace(rows, columns, DoubleField, realNDFieldFactory), initializer)
ArrayMatrix(ArrayMatrixSpace(rows, columns, DoubleField, realNDFieldFactory), initializer)
/**
@ -234,17 +235,28 @@ fun realMatrix(rows: Int, columns: Int, initializer: (Int, Int) -> Double) =
*/
fun <T : Any> Matrix<T>.toVector(): Vector<T> {
return when {
this is Vector -> return this
this.columns == 1 -> {
if (this is ArrayMatrix) {
//Reuse existing underlying array
ArrayVector(ArrayVectorSpace(rows, context.field, context.ndFactory), array)
} else {
//Generic vector
// if (this is ArrayMatrix) {
// //Reuse existing underlying array
// ArrayVector(ArrayVectorSpace(rows, context.field, context.ndFactory), array)
// } else {
// //Generic vector
// vector(rows, context.field) { get(it, 0) }
// }
vector(rows, context.field) { get(it, 0) }
}
}
else -> error("Can't convert matrix with more than one column to vector")
}
}
fun <T : Any> Vector<T>.toMatrix(): Matrix<T> {
// return if (this is ArrayVector) {
// //Reuse existing underlying array
// ArrayMatrix(ArrayMatrixSpace(size, 1, context.field, context.ndFactory), array)
// } else {
// //Generic vector
// matrix(size, 1, context.field) { i, j -> get(i) }
// }
return matrix(size, 1, context.field) { i, j -> get(i) }
}

11
kmath-core/src/commonMain/kotlin/scientifik/kmath/operations/Algebra.kt
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@ -7,6 +7,11 @@ package scientifik.kmath.operations
* @param S the type of mathematical context for this element
*/
interface MathElement<T, S> {
/**
* Self value. Needed for static type checking.
*/
val self: T
/**
* The context this element belongs to
*/
@ -54,12 +59,6 @@ interface Space<T> {
* @param S the type of space
*/
interface SpaceElement<T, S : Space<T>> : MathElement<T, S> {
/**
* Self value. Needed for static type checking. Needed to avoid type erasure on JVM.
*/
val self: T
operator fun plus(b: T): T = context.add(self, b)
operator fun minus(b: T): T = context.add(self, context.multiply(b, -1.0))
operator fun times(k: Number): T = context.multiply(self, k.toDouble())

21
kmath-core/src/commonTest/kotlin/scientifik/kmath/structures/ArrayMatrixTest.kt → kmath-core/src/commonTest/kotlin/scientifik/kmath/linear/ArrayMatrixTest.kt
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@ -1,6 +1,5 @@
package scientifik.kmath.structures
package scientifik.kmath.linear
import scientifik.kmath.linear.realVector
import kotlin.test.Test
import kotlin.test.assertEquals
@ -11,17 +10,25 @@ class ArrayMatrixTest {
val vector1 = realVector(5) { it.toDouble() }
val vector2 = realVector(5) { 5 - it.toDouble() }
val sum = vector1 + vector2
assertEquals(5.0, sum[2, 0])
assertEquals(5.0, sum[2])
}
@Test
fun testVectorToMatrix() {
val vector = realVector(5) { it.toDouble() }
val matrix = vector.toMatrix()
assertEquals(4.0, matrix[4, 0])
}
@Test
fun testDot() {
val vector1 = realVector(5) { it.toDouble() }
val vector2 = realVector(5) { 5 - it.toDouble() }
val product = with(vector1.context) {
vector1 dot (vector2.transpose())
}
val product = vector1.toMatrix() dot (vector2.toMatrix().transpose())
assertEquals(10.0, product[1, 0])
assertEquals(5.0, product[1, 0])
assertEquals(6.0, product[2, 2])
}
}

14
kmath-core/src/commonTest/kotlin/scientifik/kmath/linear/RealLUSolverTest.kt Normal file
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@ -0,0 +1,14 @@
package scientifik.kmath.linear
import scientifik.kmath.operations.DoubleField
import kotlin.test.Test
import kotlin.test.assertEquals
class RealLUSolverTest {
@Test
fun testInvert() {
val matrix = Matrix.one(2, 2, DoubleField)
val inverted = RealLUSolver.inverse(matrix)
assertEquals(1.0, inverted[0, 0])
}
}

1
settings.gradle
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@ -10,5 +10,4 @@ enableFeaturePreview('GRADLE_METADATA')
rootProject.name = 'kmath'
include ':kmath-core'
include ':kmath-commons'