Merge pull request #136 from mipt-npm/ejml

Drop koma module, implement kmath-ejml module copying it, but for EJML SimpleMatrix

CHANGELOG.md

@@ -7,6 +7,7 @@
 - Better trigonometric and hyperbolic functions for `AutoDiffField` (https://github.com/mipt-npm/kmath/pull/140).
 - Automatic README generation for features (#139)
 - Native support for `memory`, `core` and `dimensions`
+- `kmath-ejml` to supply EJML SimpleMatrix wrapper.
 
 ### Changed
 - Package changed from `scientifik` to `kscience.kmath`.

README.md

@@ -53,7 +53,9 @@ can be used for a wide variety of purposes from high performance calculations to
 * **Commons-math wrapper** It is planned to gradually wrap most parts of [Apache commons-math](http://commons.apache.org/proper/commons-math/)
                            library in Kotlin code and maybe rewrite some parts to better suit the Kotlin programming paradigm, however there is no fixed roadmap for that. Feel free
                            to submit a feature request if you want something to be done first.
-                           
+
+* **EJML wrapper** Provides EJML `SimpleMatrix` wrapper consistent with the core matrix structures.
+
 ## Planned features
 
 * **Messaging** A mathematical notation to support multi-language and multi-node communication for mathematical tasks.

examples/build.gradle.kts

@@ -26,6 +26,7 @@ dependencies {
     implementation(project(":kmath-prob"))
     implementation(project(":kmath-viktor"))
     implementation(project(":kmath-dimensions"))
+    implementation(project(":kmath-ejml"))
     implementation("org.jetbrains.kotlinx:kotlinx-io:0.2.0-npm-dev-11")
     implementation("org.jetbrains.kotlinx:kotlinx.benchmark.runtime:0.2.0-dev-20")
     implementation("org.slf4j:slf4j-simple:1.7.30")

examples/src/main/kotlin/kscience/kmath/linear/LinearAlgebraBenchmark.kt

@@ -0,0 +1,50 @@
+package kscience.kmath.linear
+
+import kscience.kmath.commons.linear.CMMatrixContext
+import kscience.kmath.commons.linear.inverse
+import kscience.kmath.commons.linear.toCM
+import kscience.kmath.ejml.EjmlMatrixContext
+import kscience.kmath.ejml.inverse
+import kscience.kmath.operations.RealField
+import kscience.kmath.operations.invoke
+import kscience.kmath.structures.Matrix
+import kotlin.random.Random
+import kotlin.system.measureTimeMillis
+
+fun main() {
+    val random = Random(1224)
+    val dim = 100
+    //creating invertible matrix
+    val u = Matrix.real(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
+    val l = Matrix.real(dim, dim) { i, j -> if (i >= j) random.nextDouble() else 0.0 }
+    val matrix = l dot u
+
+    val n = 5000 // iterations
+
+    MatrixContext.real {
+        repeat(50) { inverse(matrix) }
+        val inverseTime = measureTimeMillis { repeat(n) { inverse(matrix) } }
+        println("[kmath] Inversion of $n matrices $dim x $dim finished in $inverseTime millis")
+    }
+
+    //commons-math
+
+    val commonsTime = measureTimeMillis {
+        CMMatrixContext {
+            val cm = matrix.toCM()             //avoid overhead on conversion
+            repeat(n) { inverse(cm) }
+        }
+    }
+
+
+    println("[commons-math] Inversion of $n matrices $dim x $dim finished in $commonsTime millis")
+
+    val ejmlTime = measureTimeMillis {
+        (EjmlMatrixContext(RealField)) {
+            val km = matrix.toEjml()      //avoid overhead on conversion
+            repeat(n) { inverse(km) }
+        }
+    }
+
+    println("[ejml] Inversion of $n matrices $dim x $dim finished in $ejmlTime millis")
+}

examples/src/main/kotlin/kscience/kmath/linear/MultiplicationBenchmark.kt

@@ -0,0 +1,38 @@
+package kscience.kmath.linear
+
+import kscience.kmath.commons.linear.CMMatrixContext
+import kscience.kmath.commons.linear.toCM
+import kscience.kmath.ejml.EjmlMatrixContext
+import kscience.kmath.operations.RealField
+import kscience.kmath.operations.invoke
+import kscience.kmath.structures.Matrix
+import kotlin.random.Random
+import kotlin.system.measureTimeMillis
+
+fun main() {
+    val random = Random(12224)
+    val dim = 1000
+    //creating invertible matrix
+    val matrix1 = Matrix.real(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
+    val matrix2 = Matrix.real(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
+
+//    //warmup
+//    matrix1 dot matrix2
+
+    CMMatrixContext {
+        val cmMatrix1 = matrix1.toCM()
+        val cmMatrix2 = matrix2.toCM()
+        val cmTime = measureTimeMillis { cmMatrix1 dot cmMatrix2 }
+        println("CM implementation time: $cmTime")
+    }
+
+    (EjmlMatrixContext(RealField)) {
+        val ejmlMatrix1 = matrix1.toEjml()
+        val ejmlMatrix2 = matrix2.toEjml()
+        val ejmlTime = measureTimeMillis { ejmlMatrix1 dot ejmlMatrix2 }
+        println("EJML implementation time: $ejmlTime")
+    }
+
+    val genericTime = measureTimeMillis { val res = matrix1 dot matrix2 }
+    println("Generic implementation time: $genericTime")
+}

kmath-core/src/commonMain/kotlin/kscience/kmath/linear/MatrixContext.kt

@@ -18,20 +18,52 @@ public interface MatrixContext<T : Any> : SpaceOperations<Matrix<T>> {
      */
     public fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> T): Matrix<T>
 
+    public override fun binaryOperation(operation: String, left: Matrix<T>, right: Matrix<T>): Matrix<T> = when (operation) {
+        "dot" -> left dot right
+        else -> super.binaryOperation(operation, left, right)
+    }
+
+    /**
+     * Computes the dot product of this matrix and another one.
+     *
+     * @receiver the multiplicand.
+     * @param other the multiplier.
+     * @return the dot product.
+     */
     public infix fun Matrix<T>.dot(other: Matrix<T>): Matrix<T>
 
+    /**
+     * Computes the dot product of this matrix and a vector.
+     *
+     * @receiver the multiplicand.
+     * @param vector the multiplier.
+     * @return the dot product.
+     */
     public infix fun Matrix<T>.dot(vector: Point<T>): Point<T>
 
+    /**
+     * Multiplies a matrix by its element.
+     *
+     * @receiver the multiplicand.
+     * @param value the multiplier.
+     * @receiver the product.
+     */
     public operator fun Matrix<T>.times(value: T): Matrix<T>
 
+    /**
+     * Multiplies an element by a matrix of it.
+     *
+     * @receiver the multiplicand.
+     * @param value the multiplier.
+     * @receiver the product.
+     */
     public operator fun T.times(m: Matrix<T>): Matrix<T> = m * this
 
     public companion object {
         /**
          * Non-boxing double matrix
          */
-        public val real: RealMatrixContext
-            get() = RealMatrixContext
+        public val real: RealMatrixContext = RealMatrixContext
 
         /**
          * A structured matrix with custom buffer
@@ -60,7 +92,7 @@ public interface GenericMatrixContext<T : Any, R : Ring<T>> : MatrixContext<T> {
      */
     public fun point(size: Int, initializer: (Int) -> T): Point<T>
 
-    override infix fun Matrix<T>.dot(other: Matrix<T>): Matrix<T> {
+    public override infix fun Matrix<T>.dot(other: Matrix<T>): Matrix<T> {
         //TODO add typed error
         require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
 
@@ -71,7 +103,7 @@ public interface GenericMatrixContext<T : Any, R : Ring<T>> : MatrixContext<T> {
         }
     }
 
-    override infix fun Matrix<T>.dot(vector: Point<T>): Point<T> {
+    public override infix fun Matrix<T>.dot(vector: Point<T>): Point<T> {
         //TODO add typed error
         require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" }
 
@@ -81,10 +113,10 @@ public interface GenericMatrixContext<T : Any, R : Ring<T>> : MatrixContext<T> {
         }
     }
 
-    override operator fun Matrix<T>.unaryMinus(): Matrix<T> =
+    public override operator fun Matrix<T>.unaryMinus(): Matrix<T> =
         produce(rowNum, colNum) { i, j -> elementContext { -get(i, j) } }
 
-    override fun add(a: Matrix<T>, b: Matrix<T>): Matrix<T> {
+    public override fun add(a: Matrix<T>, b: Matrix<T>): Matrix<T> {
         require(a.rowNum == b.rowNum && a.colNum == b.colNum) {
             "Matrix operation dimension mismatch. [${a.rowNum},${a.colNum}] + [${b.rowNum},${b.colNum}]"
         }
@@ -92,7 +124,7 @@ public interface GenericMatrixContext<T : Any, R : Ring<T>> : MatrixContext<T> {
         return produce(a.rowNum, a.colNum) { i, j -> elementContext { a[i, j] + b[i, j] } }
     }
 
-    override operator fun Matrix<T>.minus(b: Matrix<T>): Matrix<T> {
+    public override operator fun Matrix<T>.minus(b: Matrix<T>): Matrix<T> {
         require(rowNum == b.rowNum && colNum == b.colNum) {
             "Matrix operation dimension mismatch. [$rowNum,$colNum] - [${b.rowNum},${b.colNum}]"
         }
@@ -100,11 +132,11 @@ public interface GenericMatrixContext<T : Any, R : Ring<T>> : MatrixContext<T> {
         return produce(rowNum, colNum) { i, j -> elementContext { get(i, j) + b[i, j] } }
     }
 
-    override fun multiply(a: Matrix<T>, k: Number): Matrix<T> =
+    public override fun multiply(a: Matrix<T>, k: Number): Matrix<T> =
         produce(a.rowNum, a.colNum) { i, j -> elementContext { a[i, j] * k } }
 
     public operator fun Number.times(matrix: FeaturedMatrix<T>): Matrix<T> = matrix * this
 
-    override operator fun Matrix<T>.times(value: T): Matrix<T> =
+    public override operator fun Matrix<T>.times(value: T): Matrix<T> =
         produce(rowNum, colNum) { i, j -> elementContext { get(i, j) * value } }
 }

kmath-ejml/build.gradle.kts

@@ -0,0 +1,8 @@
+plugins {
+    id("ru.mipt.npm.jvm")
+}
+
+dependencies {
+    implementation("org.ejml:ejml-simple:0.39")
+    implementation(project(":kmath-core"))
+}

kmath-ejml/src/main/kotlin/kscience/kmath/ejml/EjmlMatrix.kt

@@ -0,0 +1,71 @@
+package kscience.kmath.ejml
+
+import org.ejml.dense.row.factory.DecompositionFactory_DDRM
+import org.ejml.simple.SimpleMatrix
+import kscience.kmath.linear.DeterminantFeature
+import kscience.kmath.linear.FeaturedMatrix
+import kscience.kmath.linear.LUPDecompositionFeature
+import kscience.kmath.linear.MatrixFeature
+import kscience.kmath.structures.NDStructure
+
+/**
+ * Represents featured matrix over EJML [SimpleMatrix].
+ *
+ * @property origin the underlying [SimpleMatrix].
+ * @author Iaroslav Postovalov
+ */
+public class EjmlMatrix(public val origin: SimpleMatrix, features: Set<MatrixFeature>? = null) : FeaturedMatrix<Double> {
+    public override val rowNum: Int
+        get() = origin.numRows()
+
+    public override val colNum: Int
+        get() = origin.numCols()
+
+    public override val shape: IntArray
+        get() = intArrayOf(origin.numRows(), origin.numCols())
+
+    public override val features: Set<MatrixFeature> = setOf(
+        object : LUPDecompositionFeature<Double>, DeterminantFeature<Double> {
+            override val determinant: Double
+                get() = origin.determinant()
+
+            private val lup by lazy {
+                val ludecompositionF64 = DecompositionFactory_DDRM.lu(origin.numRows(), origin.numCols())
+                    .also { it.decompose(origin.ddrm.copy()) }
+
+                Triple(
+                    EjmlMatrix(SimpleMatrix(ludecompositionF64.getRowPivot(null))),
+                    EjmlMatrix(SimpleMatrix(ludecompositionF64.getLower(null))),
+                    EjmlMatrix(SimpleMatrix(ludecompositionF64.getUpper(null))),
+                )
+            }
+
+            override val l: FeaturedMatrix<Double>
+                get() = lup.second
+
+            override val u: FeaturedMatrix<Double>
+                get() = lup.third
+
+            override val p: FeaturedMatrix<Double>
+                get() = lup.first
+        }
+    ) union features.orEmpty()
+
+    public override fun suggestFeature(vararg features: MatrixFeature): EjmlMatrix =
+        EjmlMatrix(origin, this.features + features)
+
+    public override operator fun get(i: Int, j: Int): Double = origin[i, j]
+
+    public override fun equals(other: Any?): Boolean {
+        if (other is EjmlMatrix) return origin.isIdentical(other.origin, 0.0)
+        return NDStructure.equals(this, other as? NDStructure<*> ?: return false)
+    }
+
+    public override fun hashCode(): Int {
+        var result = origin.hashCode()
+        result = 31 * result + features.hashCode()
+        return result
+    }
+
+    public override fun toString(): String = "EjmlMatrix(origin=$origin, features=$features)"
+}

kmath-ejml/src/main/kotlin/kscience/kmath/ejml/EjmlMatrixContext.kt

@@ -0,0 +1,86 @@
+package kscience.kmath.ejml
+
+import org.ejml.simple.SimpleMatrix
+import kscience.kmath.linear.MatrixContext
+import kscience.kmath.linear.Point
+import kscience.kmath.operations.Space
+import kscience.kmath.operations.invoke
+import kscience.kmath.structures.Matrix
+
+/**
+ * Represents context of basic operations operating with [EjmlMatrix].
+ *
+ * @author Iaroslav Postovalov
+ */
+public class EjmlMatrixContext(private val space: Space<Double>) : MatrixContext<Double> {
+    /**
+     * Converts this matrix to EJML one.
+     */
+    public fun Matrix<Double>.toEjml(): EjmlMatrix =
+        if (this is EjmlMatrix) this else produce(rowNum, colNum) { i, j -> get(i, j) }
+
+    /**
+     * Converts this vector to EJML one.
+     */
+    public fun Point<Double>.toEjml(): EjmlVector =
+        if (this is EjmlVector) this else EjmlVector(SimpleMatrix(size, 1).also {
+            (0 until it.numRows()).forEach { row -> it[row, 0] = get(row) }
+        })
+
+    override fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> Double): EjmlMatrix =
+        EjmlMatrix(SimpleMatrix(rows, columns).also {
+            (0 until it.numRows()).forEach { row ->
+                (0 until it.numCols()).forEach { col -> it[row, col] = initializer(row, col) }
+            }
+        })
+
+    public override fun Matrix<Double>.dot(other: Matrix<Double>): EjmlMatrix =
+        EjmlMatrix(toEjml().origin.mult(other.toEjml().origin))
+
+    public override fun Matrix<Double>.dot(vector: Point<Double>): EjmlVector =
+        EjmlVector(toEjml().origin.mult(vector.toEjml().origin))
+
+    public override fun add(a: Matrix<Double>, b: Matrix<Double>): EjmlMatrix =
+        EjmlMatrix(a.toEjml().origin + b.toEjml().origin)
+
+    public override operator fun Matrix<Double>.minus(b: Matrix<Double>): EjmlMatrix =
+        EjmlMatrix(toEjml().origin - b.toEjml().origin)
+
+    public override fun multiply(a: Matrix<Double>, k: Number): EjmlMatrix =
+        produce(a.rowNum, a.colNum) { i, j -> space { a[i, j] * k } }
+
+    public override operator fun Matrix<Double>.times(value: Double): EjmlMatrix = EjmlMatrix(toEjml().origin.scale(value))
+
+    public companion object
+}
+
+/**
+ * Solves for X in the following equation: x = a^-1*b, where 'a' is base matrix and 'b' is an n by p matrix.
+ *
+ * @param a the base matrix.
+ * @param b n by p matrix.
+ * @return the solution for 'x' that is n by p.
+ * @author Iaroslav Postovalov
+ */
+public fun EjmlMatrixContext.solve(a: Matrix<Double>, b: Matrix<Double>): EjmlMatrix =
+    EjmlMatrix(a.toEjml().origin.solve(b.toEjml().origin))
+
+/**
+ * Solves for X in the following equation: x = a^(-1)*b, where 'a' is base matrix and 'b' is an n by p matrix.
+ *
+ * @param a the base matrix.
+ * @param b n by p vector.
+ * @return the solution for 'x' that is n by p.
+ * @author Iaroslav Postovalov
+ */
+public fun EjmlMatrixContext.solve(a: Matrix<Double>, b: Point<Double>): EjmlVector =
+    EjmlVector(a.toEjml().origin.solve(b.toEjml().origin))
+
+/**
+ * Returns the inverse of given matrix: b = a^(-1).
+ *
+ * @param a the matrix.
+ * @return the inverse of this matrix.
+ * @author Iaroslav Postovalov
+ */
+public fun EjmlMatrixContext.inverse(a: Matrix<Double>): EjmlMatrix = EjmlMatrix(a.toEjml().origin.invert())

kmath-ejml/src/main/kotlin/kscience/kmath/ejml/EjmlVector.kt

@@ -0,0 +1,40 @@
+package kscience.kmath.ejml
+
+import org.ejml.simple.SimpleMatrix
+import kscience.kmath.linear.Point
+import kscience.kmath.structures.Buffer
+
+/**
+ * Represents point over EJML [SimpleMatrix].
+ *
+ * @property origin the underlying [SimpleMatrix].
+ * @author Iaroslav Postovalov
+ */
+public class EjmlVector internal constructor(public val origin: SimpleMatrix) : Point<Double> {
+    public override val size: Int
+        get() = origin.numRows()
+
+    init {
+        require(origin.numCols() == 1) { "Only single column matrices are allowed" }
+    }
+
+    public override operator fun get(index: Int): Double = origin[index]
+
+    public override operator fun iterator(): Iterator<Double> = object : Iterator<Double> {
+        private var cursor: Int = 0
+
+        override fun next(): Double {
+            cursor += 1
+            return origin[cursor - 1]
+        }
+
+        override fun hasNext(): Boolean = cursor < origin.numCols() * origin.numRows()
+    }
+
+    public override fun contentEquals(other: Buffer<*>): Boolean {
+        if (other is EjmlVector) return origin.isIdentical(other.origin, 0.0)
+        return super.contentEquals(other)
+    }
+
+    public override fun toString(): String = "EjmlVector(origin=$origin)"
+}

kmath-ejml/src/test/kotlin/kscience/kmath/ejml/EjmlMatrixTest.kt

@@ -0,0 +1,75 @@
+package kscience.kmath.ejml
+
+import kscience.kmath.linear.DeterminantFeature
+import kscience.kmath.linear.LUPDecompositionFeature
+import kscience.kmath.linear.MatrixFeature
+import kscience.kmath.linear.getFeature
+import org.ejml.dense.row.factory.DecompositionFactory_DDRM
+import org.ejml.simple.SimpleMatrix
+import kotlin.random.Random
+import kotlin.random.asJavaRandom
+import kotlin.test.*
+
+internal class EjmlMatrixTest {
+    private val random = Random(0)
+
+    private val randomMatrix: SimpleMatrix
+        get() {
+            val s = random.nextInt(2, 100)
+            return SimpleMatrix.random_DDRM(s, s, 0.0, 10.0, random.asJavaRandom())
+        }
+
+    @Test
+    fun rowNum() {
+        val m = randomMatrix
+        assertEquals(m.numRows(), EjmlMatrix(m).rowNum)
+    }
+
+    @Test
+    fun colNum() {
+        val m = randomMatrix
+        assertEquals(m.numCols(), EjmlMatrix(m).rowNum)
+    }
+
+    @Test
+    fun shape() {
+        val m = randomMatrix
+        val w = EjmlMatrix(m)
+        assertEquals(listOf(m.numRows(), m.numCols()), w.shape.toList())
+    }
+
+    @Test
+    fun features() {
+        val m = randomMatrix
+        val w = EjmlMatrix(m)
+        val det = w.getFeature<DeterminantFeature<Double>>() ?: fail()
+        assertEquals(m.determinant(), det.determinant)
+        val lup = w.getFeature<LUPDecompositionFeature<Double>>() ?: fail()
+
+        val ludecompositionF64 = DecompositionFactory_DDRM.lu(m.numRows(), m.numCols())
+            .also { it.decompose(m.ddrm.copy()) }
+
+        assertEquals(EjmlMatrix(SimpleMatrix(ludecompositionF64.getLower(null))), lup.l)
+        assertEquals(EjmlMatrix(SimpleMatrix(ludecompositionF64.getUpper(null))), lup.u)
+        assertEquals(EjmlMatrix(SimpleMatrix(ludecompositionF64.getRowPivot(null))), lup.p)
+    }
+
+    private object SomeFeature : MatrixFeature {}
+
+    @Test
+    fun suggestFeature() {
+        assertNotNull(EjmlMatrix(randomMatrix).suggestFeature(SomeFeature).getFeature<SomeFeature>())
+    }
+
+    @Test
+    fun get() {
+        val m = randomMatrix
+        assertEquals(m[0, 0], EjmlMatrix(m)[0, 0])
+    }
+
+    @Test
+    fun origin() {
+        val m = randomMatrix
+        assertSame(m, EjmlMatrix(m).origin)
+    }
+}

kmath-ejml/src/test/kotlin/kscience/kmath/ejml/EjmlVectorTest.kt

@@ -0,0 +1,47 @@
+package kscience.kmath.ejml
+
+import org.ejml.simple.SimpleMatrix
+import kotlin.random.Random
+import kotlin.random.asJavaRandom
+import kotlin.test.Test
+import kotlin.test.assertEquals
+import kotlin.test.assertSame
+
+internal class EjmlVectorTest {
+    private val random = Random(0)
+
+    private val randomMatrix: SimpleMatrix
+        get() = SimpleMatrix.random_DDRM(random.nextInt(2, 100), 1, 0.0, 10.0, random.asJavaRandom())
+
+    @Test
+    fun size() {
+        val m = randomMatrix
+        val w = EjmlVector(m)
+        assertEquals(m.numRows(), w.size)
+    }
+
+    @Test
+    fun get() {
+        val m = randomMatrix
+        val w = EjmlVector(m)
+        assertEquals(m[0, 0], w[0])
+    }
+
+    @Test
+    fun iterator() {
+        val m = randomMatrix
+        val w = EjmlVector(m)
+
+        assertEquals(
+            m.iterator(true, 0, 0, m.numRows() - 1, 0).asSequence().toList(),
+            w.iterator().asSequence().toList()
+        )
+    }
+
+    @Test
+    fun origin() {
+        val m = randomMatrix
+        val w = EjmlVector(m)
+        assertSame(m, w.origin)
+    }
+}

settings.gradle.kts

@@ -39,5 +39,6 @@ include(
     ":kmath-for-real",
     ":kmath-geometry",
     ":kmath-ast",
-    ":examples"
+    ":examples",
+    ":kmath-ejml"
 )