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

README.md

@@ -53,9 +53,6 @@ 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.
-                           
-* **Koma wrapper** [Koma](https://github.com/kyonifer/koma) is a well established numerics library in Kotlin, specifically linear algebra.
-The plan is to have wrappers for koma implementations for compatibility with kmath API.
 
 ## Planned features
 

doc/features.md

@@ -12,6 +12,3 @@ api and multiple library back-ends.
 * [Expressions](./expressions.md)
 
 * Commons math integration
-
-* Koma integration
-

examples/build.gradle.kts

@@ -29,10 +29,9 @@ dependencies {
     implementation(project(":kmath-coroutines"))
     implementation(project(":kmath-commons"))
     implementation(project(":kmath-prob"))
-    implementation(project(":kmath-koma"))
     implementation(project(":kmath-viktor"))
     implementation(project(":kmath-dimensions"))
-    implementation("com.kyonifer:koma-core-ejml:0.12")
+    implementation(project(":kmath-ejml"))
     implementation("org.jetbrains.kotlinx:kotlinx-io-jvm:0.2.0-npm-dev-6")
     implementation("org.jetbrains.kotlinx:kotlinx.benchmark.runtime:0.2.0-dev-8")
     "benchmarksCompile"(sourceSets.main.get().output + sourceSets.main.get().compileClasspath) //sourceSets.main.output + sourceSets.main.runtimeClasspath

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

@@ -1,9 +1,10 @@
 package scientifik.kmath.linear
 
-import koma.matrix.ejml.EJMLMatrixFactory
 import scientifik.kmath.commons.linear.CMMatrixContext
 import scientifik.kmath.commons.linear.inverse
 import scientifik.kmath.commons.linear.toCM
+import scientifik.kmath.ejml.EjmlMatrixContext
+import scientifik.kmath.ejml.inverse
 import scientifik.kmath.operations.RealField
 import scientifik.kmath.operations.invoke
 import scientifik.kmath.structures.Matrix
@@ -23,8 +24,8 @@ fun main() {
     val n = 5000 // iterations
 
     MatrixContext.real {
-        repeat(50) { val res = inverse(matrix) }
+        repeat(50) { inverse(matrix) }
-        val inverseTime = measureTimeMillis { repeat(n) { val res = inverse(matrix) } }
+        val inverseTime = measureTimeMillis { repeat(n) { inverse(matrix) } }
         println("[kmath] Inversion of $n matrices $dim x $dim finished in $inverseTime millis")
     }
 
@@ -33,23 +34,19 @@ fun main() {
     val commonsTime = measureTimeMillis {
         CMMatrixContext {
             val cm = matrix.toCM()             //avoid overhead on conversion
-            repeat(n) { val res = inverse(cm) }
+            repeat(n) { inverse(cm) }
         }
     }
 
 
     println("[commons-math] Inversion of $n matrices $dim x $dim finished in $commonsTime millis")
 
-    //koma-ejml
+    val ejmlTime = measureTimeMillis {
-
+        (EjmlMatrixContext(RealField)) {
-    val komaTime = measureTimeMillis {
+            val km = matrix.toEjml()      //avoid overhead on conversion
-        (KomaMatrixContext(EJMLMatrixFactory(), RealField)) {
+            repeat(n) { inverse(km) }
-            val km = matrix.toKoma()      //avoid overhead on conversion
-            repeat(n) {
-                val res = inverse(km)
-            }
         }
     }
 
-    println("[koma-ejml] Inversion of $n matrices $dim x $dim finished in $komaTime millis")
+    println("[ejml] Inversion of $n matrices $dim x $dim finished in $ejmlTime millis")
-}
+}

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

@@ -1,8 +1,8 @@
 package scientifik.kmath.linear
 
-import koma.matrix.ejml.EJMLMatrixFactory
 import scientifik.kmath.commons.linear.CMMatrixContext
 import scientifik.kmath.commons.linear.toCM
+import scientifik.kmath.ejml.EjmlMatrixContext
 import scientifik.kmath.operations.RealField
 import scientifik.kmath.operations.invoke
 import scientifik.kmath.structures.Matrix
@@ -22,28 +22,17 @@ fun main() {
     CMMatrixContext {
         val cmMatrix1 = matrix1.toCM()
         val cmMatrix2 = matrix2.toCM()
-
+        val cmTime = measureTimeMillis { cmMatrix1 dot cmMatrix2 }
-        val cmTime = measureTimeMillis {
-            cmMatrix1 dot cmMatrix2
-        }
-
         println("CM implementation time: $cmTime")
     }
 
-    (KomaMatrixContext(EJMLMatrixFactory(), RealField)) {
+    (EjmlMatrixContext(RealField)) {
-        val komaMatrix1 = matrix1.toKoma()
+        val ejmlMatrix1 = matrix1.toEjml()
-        val komaMatrix2 = matrix2.toKoma()
+        val ejmlMatrix2 = matrix2.toEjml()
-
+        val ejmlTime = measureTimeMillis { ejmlMatrix1 dot ejmlMatrix2 }
-        val komaTime = measureTimeMillis {
+        println("EJML implementation time: $ejmlTime")
-            komaMatrix1 dot komaMatrix2
-        }
-
-        println("Koma-ejml implementation time: $komaTime")
-    }
-
-    val genericTime = measureTimeMillis {
-        val res = matrix1 dot matrix2
     }
 
+    val genericTime = measureTimeMillis { val res = matrix1 dot matrix2 }
     println("Generic implementation time: $genericTime")
-}
+}

kmath-ejml/build.gradle.kts

@@ -0,0 +1,6 @@
+plugins { id("scientifik.jvm") }
+
+dependencies {
+    implementation("org.ejml:ejml-simple:0.39")
+    implementation(project(":kmath-core"))
+}

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

@@ -0,0 +1,69 @@
+package scientifik.kmath.ejml
+
+import org.ejml.dense.row.factory.DecompositionFactory_DDRM
+import org.ejml.simple.SimpleMatrix
+import scientifik.kmath.linear.DeterminantFeature
+import scientifik.kmath.linear.FeaturedMatrix
+import scientifik.kmath.linear.LUPDecompositionFeature
+import scientifik.kmath.linear.MatrixFeature
+import scientifik.kmath.structures.NDStructure
+
+/**
+ * Represents featured matrix over EJML [SimpleMatrix].
+ *
+ * @property origin the underlying [SimpleMatrix].
+ */
+class EjmlMatrix(val origin: SimpleMatrix, features: Set<MatrixFeature>? = null) : FeaturedMatrix<Double> {
+    override val rowNum: Int
+        get() = origin.numRows()
+
+    override val colNum: Int
+        get() = origin.numCols()
+
+    override val shape: IntArray
+        get() = intArrayOf(origin.numRows(), origin.numCols())
+
+    override val features: Set<MatrixFeature> = features ?: hashSetOf(
+        object : DeterminantFeature<Double> {
+            override val determinant: Double
+                get() = origin.determinant()
+        },
+
+        object : LUPDecompositionFeature<Double> {
+            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
+        }
+    )
+
+    override fun suggestFeature(vararg features: MatrixFeature): FeaturedMatrix<Double> =
+        EjmlMatrix(origin, this.features + features)
+
+    override operator fun get(i: Int, j: Int): Double = origin[i, j]
+
+    override fun equals(other: Any?): Boolean {
+        return NDStructure.equals(this, other as? NDStructure<*> ?: return false)
+    }
+
+    override fun hashCode(): Int {
+        var result = origin.hashCode()
+        result = 31 * result + features.hashCode()
+        return result
+    }
+}

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

@@ -0,0 +1,75 @@
+package scientifik.kmath.ejml
+
+import org.ejml.simple.SimpleMatrix
+import scientifik.kmath.linear.MatrixContext
+import scientifik.kmath.linear.Point
+import scientifik.kmath.operations.Space
+import scientifik.kmath.operations.invoke
+import scientifik.kmath.structures.Matrix
+
+/**
+ * Represents context of basic operations operating with [EjmlMatrix].
+ */
+class EjmlMatrixContext(private val space: Space<Double>) : MatrixContext<Double> {
+    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) }
+            }
+        })
+
+    fun Matrix<Double>.toEjml(): EjmlMatrix =
+        if (this is EjmlMatrix) this else produce(rowNum, colNum) { i, j -> get(i, j) }
+
+    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 Matrix<Double>.dot(other: Matrix<Double>): EjmlMatrix =
+        EjmlMatrix(toEjml().origin.mult(other.toEjml().origin))
+
+    override fun Matrix<Double>.dot(vector: Point<Double>): EjmlVector =
+        EjmlVector(toEjml().origin.mult(vector.toEjml().origin))
+
+    override fun add(a: Matrix<Double>, b: Matrix<Double>): EjmlMatrix =
+        EjmlMatrix(a.toEjml().origin + b.toEjml().origin)
+
+    override operator fun Matrix<Double>.minus(b: Matrix<Double>): EjmlMatrix =
+        EjmlMatrix(toEjml().origin - b.toEjml().origin)
+
+    override fun multiply(a: Matrix<Double>, k: Number): Matrix<Double> =
+        produce(a.rowNum, a.colNum) { i, j -> space { a[i, j] * k } }
+
+    override operator fun Matrix<Double>.times(value: Double): EjmlMatrix = EjmlMatrix(toEjml().origin.scale(value))
+
+    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.
+ */
+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.
+ */
+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.
+ */
+fun EjmlMatrixContext.inverse(a: Matrix<Double>): EjmlMatrix = EjmlMatrix(a.toEjml().origin.invert())

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

@@ -0,0 +1,30 @@
+package scientifik.kmath.ejml
+
+import org.ejml.simple.SimpleMatrix
+import scientifik.kmath.linear.Point
+
+/**
+ * Represents point over EJML [SimpleMatrix].
+ *
+ * @property origin the underlying [SimpleMatrix].
+ */
+class EjmlVector internal constructor(val origin: SimpleMatrix) : Point<Double> {
+    override val size: Int get() = origin.numRows()
+
+    init {
+        require(origin.numCols() == 1) { error("Only single column matrices are allowed") }
+    }
+
+    override operator fun get(index: Int): Double = origin[index]
+
+    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()
+    }
+}

kmath-koma/build.gradle.kts

@@ -1,31 +0,0 @@
-plugins {
-    id("scientifik.mpp")
-}
-
-repositories {
-    maven("http://dl.bintray.com/kyonifer/maven")
-}
-
-kotlin.sourceSets {
-    commonMain {
-        dependencies {
-            api(project(":kmath-core"))
-            api("com.kyonifer:koma-core-api-common:0.12")
-        }
-    }
-    jvmMain {
-        dependencies {
-            api("com.kyonifer:koma-core-api-jvm:0.12")
-        }
-    }
-    jvmTest {
-        dependencies {
-            implementation("com.kyonifer:koma-core-ejml:0.12")
-        }
-    }
-    jsMain {
-        dependencies {
-            api("com.kyonifer:koma-core-api-js:0.12")
-        }
-    }
-}

kmath-koma/src/commonMain/kotlin/scientifik.kmath.linear/KomaMatrix.kt

@@ -1,110 +0,0 @@
-package scientifik.kmath.linear
-
-import koma.extensions.fill
-import koma.matrix.MatrixFactory
-import scientifik.kmath.operations.Space
-import scientifik.kmath.operations.invoke
-import scientifik.kmath.structures.Matrix
-import scientifik.kmath.structures.NDStructure
-
-class KomaMatrixContext<T : Any>(
-    private val factory: MatrixFactory<koma.matrix.Matrix<T>>,
-    private val space: Space<T>
-) : MatrixContext<T> {
-
-    override fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> T): KomaMatrix<T> =
-        KomaMatrix(factory.zeros(rows, columns).fill(initializer))
-
-    fun Matrix<T>.toKoma(): KomaMatrix<T> = if (this is KomaMatrix) {
-        this
-    } else {
-        produce(rowNum, colNum) { i, j -> get(i, j) }
-    }
-
-    fun Point<T>.toKoma(): KomaVector<T> = if (this is KomaVector) {
-        this
-    } else {
-        KomaVector(factory.zeros(size, 1).fill { i, _ -> get(i) })
-    }
-
-
-    override fun Matrix<T>.dot(other: Matrix<T>): KomaMatrix<T> =
-        KomaMatrix(toKoma().origin * other.toKoma().origin)
-
-    override fun Matrix<T>.dot(vector: Point<T>): KomaVector<T> =
-        KomaVector(toKoma().origin * vector.toKoma().origin)
-
-    override operator fun Matrix<T>.unaryMinus(): KomaMatrix<T> =
-        KomaMatrix(toKoma().origin.unaryMinus())
-
-    override fun add(a: Matrix<T>, b: Matrix<T>): KomaMatrix<T> =
-        KomaMatrix(a.toKoma().origin + b.toKoma().origin)
-
-    override operator fun Matrix<T>.minus(b: Matrix<T>): KomaMatrix<T> =
-        KomaMatrix(toKoma().origin - b.toKoma().origin)
-
-    override fun multiply(a: Matrix<T>, k: Number): Matrix<T> =
-        produce(a.rowNum, a.colNum) { i, j -> space { a[i, j] * k } }
-
-    override operator fun Matrix<T>.times(value: T): KomaMatrix<T> =
-        KomaMatrix(toKoma().origin * value)
-
-    companion object
-}
-
-fun <T : Any> KomaMatrixContext<T>.solve(a: Matrix<T>, b: Matrix<T>) =
-    KomaMatrix(a.toKoma().origin.solve(b.toKoma().origin))
-
-fun <T : Any> KomaMatrixContext<T>.solve(a: Matrix<T>, b: Point<T>) =
-    KomaVector(a.toKoma().origin.solve(b.toKoma().origin))
-
-fun <T : Any> KomaMatrixContext<T>.inverse(a: Matrix<T>) =
-    KomaMatrix(a.toKoma().origin.inv())
-
-class KomaMatrix<T : Any>(val origin: koma.matrix.Matrix<T>, features: Set<MatrixFeature>? = null) : FeaturedMatrix<T> {
-    override val rowNum: Int get() = origin.numRows()
-    override val colNum: Int get() = origin.numCols()
-
-    override val shape: IntArray get() = intArrayOf(origin.numRows(), origin.numCols())
-
-    override val features: Set<MatrixFeature> = features ?: hashSetOf(
-        object : DeterminantFeature<T> {
-            override val determinant: T get() = origin.det()
-        },
-
-        object : LUPDecompositionFeature<T> {
-            private val lup by lazy { origin.LU() }
-            override val l: FeaturedMatrix<T> get() = KomaMatrix(lup.second)
-            override val u: FeaturedMatrix<T> get() = KomaMatrix(lup.third)
-            override val p: FeaturedMatrix<T> get() = KomaMatrix(lup.first)
-        }
-    )
-
-    override fun suggestFeature(vararg features: MatrixFeature): FeaturedMatrix<T> =
-        KomaMatrix(this.origin, this.features + features)
-
-    override operator fun get(i: Int, j: Int): T = origin.getGeneric(i, j)
-
-    override fun equals(other: Any?): Boolean {
-        return NDStructure.equals(this, other as? NDStructure<*> ?: return false)
-    }
-
-    override fun hashCode(): Int {
-        var result = origin.hashCode()
-        result = 31 * result + features.hashCode()
-        return result
-    }
-
-
-}
-
-class KomaVector<T : Any> internal constructor(val origin: koma.matrix.Matrix<T>) : Point<T> {
-    override val size: Int get() = origin.numRows()
-
-    init {
-        require(origin.numCols() == 1) { error("Only single column matrices are allowed") }
-    }
-
-    override operator fun get(index: Int): T = origin.getGeneric(index)
-    override operator fun iterator(): Iterator<T> = origin.toIterable().iterator()
-}

settings.gradle.kts

@@ -40,12 +40,12 @@ include(
     ":kmath-histograms",
     ":kmath-commons",
     ":kmath-viktor",
-    ":kmath-koma",
     ":kmath-prob",
     ":kmath-io",
     ":kmath-dimensions",
     ":kmath-for-real",
     ":kmath-geometry",
     ":kmath-ast",
-    ":examples"
+    ":examples",
+    ":kmath-ejml"
 )