kmath-for-real refactoring
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@ -28,6 +28,8 @@
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- `kmath-prob` renamed to `kmath-stat`
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- Grid generators moved to `kmath-for-real`
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- Use `Point<Double>` instead of specialized type in `kmath-for-real`
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- Optimized dot product for buffer matrices moved to `kmath-for-real`
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- EjmlMatrix context is an object
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### Deprecated
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@ -0,0 +1,51 @@
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package kscience.kmath.structures
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import kotlinx.benchmark.Benchmark
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import kscience.kmath.commons.linear.CMMatrixContext
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import kscience.kmath.commons.linear.CMMatrixContext.dot
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import kscience.kmath.commons.linear.toCM
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import kscience.kmath.ejml.EjmlMatrixContext
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import kscience.kmath.ejml.toEjml
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import kscience.kmath.linear.real
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import kscience.kmath.operations.RealField
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import kscience.kmath.operations.invoke
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import org.openjdk.jmh.annotations.Scope
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import org.openjdk.jmh.annotations.State
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import kotlin.random.Random
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@State(Scope.Benchmark)
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class MultiplicationBenchmark {
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companion object {
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val random = Random(12224)
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val dim = 1000
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//creating invertible matrix
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val matrix1 = Matrix.real(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
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val matrix2 = Matrix.real(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
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val cmMatrix1 = matrix1.toCM()
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val cmMatrix2 = matrix2.toCM()
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val ejmlMatrix1 = matrix1.toEjml()
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val ejmlMatrix2 = matrix2.toEjml()
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}
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@Benchmark
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fun commonsMathMultiplication() {
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CMMatrixContext.invoke {
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cmMatrix1 dot cmMatrix2
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}
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}
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@Benchmark
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fun ejmlMultiplication() {
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EjmlMatrixContext.invoke {
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ejmlMatrix1 dot ejmlMatrix2
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}
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}
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@Benchmark
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fun bufferedMultiplication() {
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matrix1 dot matrix2
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}
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}
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@ -70,6 +70,7 @@ fun main() {
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//minimize the chi^2 in given starting point. Derivatives are not required, they are already included.
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val result: OptimizationResult<Double> = chi2.minimize(a to 1.5, b to 0.9, c to 1.0)
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//display a page with plot and numerical results
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val page = Plotly.page {
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plot {
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scatter {
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@ -1,10 +1,12 @@
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package kscience.kmath.linear
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import kscience.kmath.commons.linear.CMMatrixContext
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import kscience.kmath.commons.linear.CMMatrixContext.dot
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import kscience.kmath.commons.linear.inverse
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import kscience.kmath.commons.linear.toCM
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import kscience.kmath.ejml.EjmlMatrixContext
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import kscience.kmath.ejml.inverse
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import kscience.kmath.ejml.toEjml
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import kscience.kmath.operations.RealField
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import kscience.kmath.operations.invoke
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import kscience.kmath.structures.Matrix
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@ -40,7 +42,7 @@ fun main() {
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println("[commons-math] Inversion of $n matrices $dim x $dim finished in $commonsTime millis")
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val ejmlTime = measureTimeMillis {
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(EjmlMatrixContext(RealField)) {
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EjmlMatrixContext {
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val km = matrix.toEjml() //avoid overhead on conversion
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repeat(n) { inverse(km) }
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}
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@ -1,38 +0,0 @@
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package kscience.kmath.linear
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import kscience.kmath.commons.linear.CMMatrixContext
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import kscience.kmath.commons.linear.toCM
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import kscience.kmath.ejml.EjmlMatrixContext
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import kscience.kmath.operations.RealField
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import kscience.kmath.operations.invoke
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import kscience.kmath.structures.Matrix
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import kotlin.random.Random
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import kotlin.system.measureTimeMillis
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fun main() {
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val random = Random(12224)
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val dim = 1000
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//creating invertible matrix
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val matrix1 = Matrix.real(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
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val matrix2 = Matrix.real(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
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// //warmup
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// matrix1 dot matrix2
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CMMatrixContext {
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val cmMatrix1 = matrix1.toCM()
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val cmMatrix2 = matrix2.toCM()
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val cmTime = measureTimeMillis { cmMatrix1 dot cmMatrix2 }
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println("CM implementation time: $cmTime")
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}
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(EjmlMatrixContext(RealField)) {
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val ejmlMatrix1 = matrix1.toEjml()
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val ejmlMatrix2 = matrix2.toEjml()
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val ejmlTime = measureTimeMillis { ejmlMatrix1 dot ejmlMatrix2 }
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println("EJML implementation time: $ejmlTime")
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}
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val genericTime = measureTimeMillis { val res = matrix1 dot matrix2 }
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println("Generic implementation time: $genericTime")
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}
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@ -9,7 +9,7 @@ import kscience.kmath.structures.*
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*/
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public class BufferMatrixContext<T : Any, R : Ring<T>>(
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public override val elementContext: R,
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private val bufferFactory: BufferFactory<T>
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private val bufferFactory: BufferFactory<T>,
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) : GenericMatrixContext<T, R> {
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public override fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> T): BufferMatrix<T> {
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val buffer = bufferFactory(rows * columns) { offset -> initializer(offset / columns, offset % columns) }
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@ -29,8 +29,8 @@ public object RealMatrixContext : GenericMatrixContext<Double, RealField> {
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public override inline fun produce(
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rows: Int,
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columns: Int,
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initializer: (i: Int, j: Int) -> Double
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): Matrix<Double> {
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initializer: (i: Int, j: Int) -> Double,
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): BufferMatrix<Double> {
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val buffer = RealBuffer(rows * columns) { offset -> initializer(offset / columns, offset % columns) }
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return BufferMatrix(rows, columns, buffer)
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}
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@ -43,15 +43,15 @@ public class BufferMatrix<T : Any>(
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public override val rowNum: Int,
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public override val colNum: Int,
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public val buffer: Buffer<out T>,
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public override val features: Set<MatrixFeature> = emptySet()
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public override val features: Set<MatrixFeature> = emptySet(),
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) : FeaturedMatrix<T> {
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override val shape: IntArray
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get() = intArrayOf(rowNum, colNum)
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init {
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require(buffer.size == rowNum * colNum) { "Dimension mismatch for matrix structure" }
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}
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override val shape: IntArray get() = intArrayOf(rowNum, colNum)
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public override fun suggestFeature(vararg features: MatrixFeature): BufferMatrix<T> =
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BufferMatrix(rowNum, colNum, buffer, this.features + features)
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@ -86,28 +86,3 @@ public class BufferMatrix<T : Any>(
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else "Matrix(rowsNum = $rowNum, colNum = $colNum, features=$features)"
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}
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}
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/**
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* Optimized dot product for real matrices
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*/
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public infix fun BufferMatrix<Double>.dot(other: BufferMatrix<Double>): BufferMatrix<Double> {
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require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
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val array = DoubleArray(this.rowNum * other.colNum)
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//convert to array to insure there is not memory indirection
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fun Buffer<out Double>.unsafeArray() = if (this is RealBuffer)
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array
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else
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DoubleArray(size) { get(it) }
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val a = this.buffer.unsafeArray()
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val b = other.buffer.unsafeArray()
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for (i in (0 until rowNum))
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for (j in (0 until other.colNum))
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for (k in (0 until colNum))
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array[i * other.colNum + j] += a[i * colNum + k] * b[k * other.colNum + j]
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val buffer = RealBuffer(array)
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return BufferMatrix(rowNum, other.colNum, buffer)
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}
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@ -27,9 +27,8 @@ public interface FeaturedMatrix<T : Any> : Matrix<T> {
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public inline fun Structure2D.Companion.real(
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rows: Int,
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columns: Int,
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initializer: (Int, Int) -> Double
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): Matrix<Double> =
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MatrixContext.real.produce(rows, columns, initializer)
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initializer: (Int, Int) -> Double,
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): BufferMatrix<Double> = MatrixContext.real.produce(rows, columns, initializer)
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/**
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* Build a square matrix from given elements.
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@ -82,5 +81,3 @@ public fun <T : Any> Matrix<T>.transpose(): Matrix<T> {
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setOf(TransposedFeature(this))
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) { i, j -> get(j, i) }
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}
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public infix fun Matrix<Double>.dot(other: Matrix<Double>): Matrix<Double> = with(MatrixContext.real) { dot(other) }
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@ -1,5 +1,6 @@
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package kscience.kmath.linear
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import kscience.kmath.operations.invoke
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import kscience.kmath.structures.Matrix
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import kscience.kmath.structures.NDStructure
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import kscience.kmath.structures.as2D
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@ -38,7 +39,7 @@ class MatrixTest {
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infix fun Matrix<Double>.pow(power: Int): Matrix<Double> {
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var res = this
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repeat(power - 1) {
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res = res dot this
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res = RealMatrixContext.invoke { res dot this@pow }
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}
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return res
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}
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@ -7,17 +7,18 @@ import kscience.kmath.operations.Space
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import kscience.kmath.operations.invoke
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import kscience.kmath.structures.Matrix
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/**
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* Converts this matrix to EJML one.
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*/
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public fun Matrix<Double>.toEjml(): EjmlMatrix =
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if (this is EjmlMatrix) this else EjmlMatrixContext.produce(rowNum, colNum) { i, j -> get(i, j) }
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/**
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* Represents context of basic operations operating with [EjmlMatrix].
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*
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* @author Iaroslav Postovalov
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*/
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public class EjmlMatrixContext(private val space: Space<Double>) : MatrixContext<Double> {
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/**
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* Converts this matrix to EJML one.
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*/
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public fun Matrix<Double>.toEjml(): EjmlMatrix =
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if (this is EjmlMatrix) this else produce(rowNum, colNum) { i, j -> get(i, j) }
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public object EjmlMatrixContext : MatrixContext<Double> {
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/**
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* Converts this vector to EJML one.
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@ -47,11 +48,10 @@ public class EjmlMatrixContext(private val space: Space<Double>) : MatrixContext
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EjmlMatrix(toEjml().origin - b.toEjml().origin)
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public override fun multiply(a: Matrix<Double>, k: Number): EjmlMatrix =
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produce(a.rowNum, a.colNum) { i, j -> space { a[i, j] * k } }
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produce(a.rowNum, a.colNum) { i, j -> a[i, j] * k.toDouble() }
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public override operator fun Matrix<Double>.times(value: Double): EjmlMatrix = EjmlMatrix(toEjml().origin.scale(value))
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public companion object
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public override operator fun Matrix<Double>.times(value: Double): EjmlMatrix =
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EjmlMatrix(toEjml().origin.scale(value))
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}
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/**
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@ -6,10 +6,7 @@ import kscience.kmath.linear.VirtualMatrix
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import kscience.kmath.misc.UnstableKMathAPI
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import kscience.kmath.operations.invoke
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import kscience.kmath.operations.sum
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import kscience.kmath.structures.Buffer
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import kscience.kmath.structures.Matrix
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import kscience.kmath.structures.RealBuffer
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import kscience.kmath.structures.asIterable
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import kscience.kmath.structures.*
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import kotlin.math.pow
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/*
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@ -86,18 +83,6 @@ public operator fun Double.minus(matrix: RealMatrix): RealMatrix =
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// row, col -> matrix[row, col] / this
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//}
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/*
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* Per-element (!) square and power operations
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*/
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public fun RealMatrix.square(): RealMatrix = MatrixContext.real.produce(rowNum, colNum) { row, col ->
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this[row, col].pow(2)
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}
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public fun RealMatrix.pow(n: Int): RealMatrix = MatrixContext.real.produce(rowNum, colNum) { i, j ->
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this[i, j].pow(n)
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}
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/*
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* Operations on two matrices (per-element!)
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*/
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@ -157,3 +142,30 @@ public fun RealMatrix.sum(): Double = elements().map { (_, value) -> value }.sum
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public fun RealMatrix.min(): Double? = elements().map { (_, value) -> value }.minOrNull()
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public fun RealMatrix.max(): Double? = elements().map { (_, value) -> value }.maxOrNull()
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public fun RealMatrix.average(): Double = elements().map { (_, value) -> value }.average()
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public inline fun RealMatrix.map(transform: (Double) -> Double): RealMatrix =
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MatrixContext.real.produce(rowNum, colNum) { i, j ->
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transform(get(i, j))
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}
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//extended operations
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public fun RealMatrix.pow(p: Double): RealMatrix = map { it.pow(p) }
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public fun RealMatrix.pow(p: Int): RealMatrix = map { it.pow(p) }
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public fun exp(arg: RealMatrix): RealMatrix = arg.map { kotlin.math.exp(it) }
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public fun sqrt(arg: RealMatrix): RealMatrix = arg.map { kotlin.math.sqrt(it) }
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public fun RealMatrix.square(): RealMatrix = map { it.pow(2) }
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public fun sin(arg: RealMatrix): RealMatrix = arg.map { kotlin.math.sin(it) }
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public fun cos(arg: RealMatrix): RealMatrix = arg.map { kotlin.math.cos(it) }
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public fun tan(arg: RealMatrix): RealMatrix = arg.map { kotlin.math.tan(it) }
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public fun ln(arg: RealMatrix): RealMatrix = arg.map { kotlin.math.ln(it) }
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public fun log10(arg: RealMatrix): RealMatrix = arg.map { kotlin.math.log10(it) }
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@ -3,7 +3,6 @@ package kscience.kmath.real
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import kscience.kmath.linear.Point
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import kscience.kmath.operations.Norm
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import kscience.kmath.structures.Buffer
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import kscience.kmath.structures.RealBuffer
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import kscience.kmath.structures.asBuffer
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import kscience.kmath.structures.asIterable
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import kotlin.math.pow
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@ -11,17 +10,11 @@ import kotlin.math.sqrt
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public typealias RealVector = Point<Double>
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public inline fun RealVector(size: Int, init: (Int) -> Double): RealVector = RealBuffer(size, init)
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public fun RealVector(vararg doubles: Double): RealVector = RealBuffer(doubles)
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public fun DoubleArray.asVector(): RealVector = asBuffer()
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public fun List<Double>.asVector(): RealVector = asBuffer()
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public object VectorL2Norm : Norm<Point<out Number>, Double> {
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override fun norm(arg: Point<out Number>): Double = sqrt(arg.asIterable().sumByDouble(Number::toDouble))
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}
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public operator fun Buffer.Companion.invoke(vararg doubles: Double): RealVector = doubles.asBuffer()
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/**
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* Fill the vector of given [size] with given [value]
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@ -36,12 +29,6 @@ public inline fun RealVector.map(transform: (Double) -> Double): RealVector =
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public inline fun RealVector.mapIndexed(transform: (index: Int, value: Double) -> Double): RealVector =
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Buffer.real(size) { transform(it, get(it)) }
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public fun RealVector.pow(p: Double): RealVector = map { it.pow(p) }
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public fun RealVector.pow(p: Int): RealVector = map { it.pow(p) }
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public fun exp(vector: RealVector): RealVector = vector.map { kotlin.math.exp(it) }
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public operator fun RealVector.plus(other: RealVector): RealVector =
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mapIndexed { index, value -> value + other[index] }
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@ -71,3 +58,25 @@ public operator fun RealVector.div(other: RealVector): RealVector =
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public operator fun RealVector.div(number: Number): RealVector = map { it / number.toDouble() }
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public operator fun Number.div(vector: RealVector): RealVector = vector.map { toDouble() / it }
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//extended operations
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public fun RealVector.pow(p: Double): RealVector = map { it.pow(p) }
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public fun RealVector.pow(p: Int): RealVector = map { it.pow(p) }
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public fun exp(vector: RealVector): RealVector = vector.map { kotlin.math.exp(it) }
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public fun sqrt(vector: RealVector): RealVector = vector.map { kotlin.math.sqrt(it) }
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public fun RealVector.square(): RealVector = map { it.pow(2) }
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public fun sin(vector: RealVector): RealVector = vector.map { kotlin.math.sin(it) }
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public fun cos(vector: RealVector): RealVector = vector.map { kotlin.math.cos(it) }
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public fun tan(vector: RealVector): RealVector = vector.map { kotlin.math.tan(it) }
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public fun ln(vector: RealVector): RealVector = vector.map { kotlin.math.ln(it) }
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public fun log10(vector: RealVector): RealVector = vector.map { kotlin.math.log10(it) }
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@ -0,0 +1,31 @@
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package kscience.kmath.real
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import kscience.kmath.linear.BufferMatrix
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import kscience.kmath.structures.Buffer
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import kscience.kmath.structures.RealBuffer
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/**
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* Optimized dot product for real matrices
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*/
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public infix fun BufferMatrix<Double>.dot(other: BufferMatrix<Double>): BufferMatrix<Double> {
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require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
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val resultArray = DoubleArray(this.rowNum * other.colNum)
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//convert to array to insure there is no memory indirection
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fun Buffer<out Double>.unsafeArray() = if (this is RealBuffer)
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this.array
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else
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DoubleArray(size) { get(it) }
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val a = this.buffer.unsafeArray()
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val b = other.buffer.unsafeArray()
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for (i in (0 until rowNum))
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for (j in (0 until other.colNum))
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for (k in (0 until colNum))
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resultArray[i * other.colNum + j] += a[i * colNum + k] * b[k * other.colNum + j]
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val buffer = RealBuffer(resultArray)
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return BufferMatrix(rowNum, other.colNum, buffer)
|
||||
}
|
@ -1,34 +1,33 @@
|
||||
package kaceince.kmath.real
|
||||
|
||||
import kscience.kmath.linear.MatrixContext
|
||||
import kscience.kmath.linear.asMatrix
|
||||
import kscience.kmath.linear.transpose
|
||||
import kscience.kmath.linear.*
|
||||
import kscience.kmath.operations.invoke
|
||||
import kscience.kmath.real.RealVector
|
||||
import kscience.kmath.real.plus
|
||||
import kscience.kmath.structures.Buffer
|
||||
import kotlin.test.Test
|
||||
import kotlin.test.assertEquals
|
||||
|
||||
internal class RealVectorTest {
|
||||
@Test
|
||||
fun testSum() {
|
||||
val vector1 = RealVector(5) { it.toDouble() }
|
||||
val vector2 = RealVector(5) { 5 - it.toDouble() }
|
||||
val vector1 = Buffer.real(5) { it.toDouble() }
|
||||
val vector2 = Buffer.real(5) { 5 - it.toDouble() }
|
||||
val sum = vector1 + vector2
|
||||
assertEquals(5.0, sum[2])
|
||||
}
|
||||
|
||||
@Test
|
||||
fun testVectorToMatrix() {
|
||||
val vector = RealVector(5) { it.toDouble() }
|
||||
val vector = Buffer.real(5) { it.toDouble() }
|
||||
val matrix = vector.asMatrix()
|
||||
assertEquals(4.0, matrix[4, 0])
|
||||
}
|
||||
|
||||
@Test
|
||||
fun testDot() {
|
||||
val vector1 = RealVector(5) { it.toDouble() }
|
||||
val vector2 = RealVector(5) { 5 - it.toDouble() }
|
||||
val vector1 = Buffer.real(5) { it.toDouble() }
|
||||
val vector2 = Buffer.real(5) { 5 - it.toDouble() }
|
||||
val matrix1 = vector1.asMatrix()
|
||||
val matrix2 = vector2.asMatrix().transpose()
|
||||
val product = MatrixContext.real { matrix1 dot matrix2 }
|
||||
|
@ -3,7 +3,6 @@ package kscience.kmath.histogram
|
||||
import kscience.kmath.linear.Point
|
||||
import kscience.kmath.operations.SpaceOperations
|
||||
import kscience.kmath.operations.invoke
|
||||
import kscience.kmath.real.asVector
|
||||
import kscience.kmath.structures.*
|
||||
import kotlin.math.floor
|
||||
|
||||
@ -123,8 +122,8 @@ public class RealHistogram(
|
||||
*```
|
||||
*/
|
||||
public fun fromRanges(vararg ranges: ClosedFloatingPointRange<Double>): RealHistogram = RealHistogram(
|
||||
ranges.map(ClosedFloatingPointRange<Double>::start).asVector(),
|
||||
ranges.map(ClosedFloatingPointRange<Double>::endInclusive).asVector()
|
||||
ranges.map(ClosedFloatingPointRange<Double>::start).asBuffer(),
|
||||
ranges.map(ClosedFloatingPointRange<Double>::endInclusive).asBuffer()
|
||||
)
|
||||
|
||||
/**
|
||||
|
@ -4,6 +4,8 @@ import kscience.kmath.histogram.RealHistogram
|
||||
import kscience.kmath.histogram.fill
|
||||
import kscience.kmath.histogram.put
|
||||
import kscience.kmath.real.RealVector
|
||||
import kscience.kmath.real.invoke
|
||||
import kscience.kmath.structures.Buffer
|
||||
import kotlin.random.Random
|
||||
import kotlin.test.*
|
||||
|
||||
|
@ -1,8 +1,8 @@
|
||||
package kscience.kmath.histogram
|
||||
|
||||
import kscience.kmath.real.RealVector
|
||||
import kscience.kmath.real.asVector
|
||||
import kscience.kmath.structures.Buffer
|
||||
import kscience.kmath.structures.asBuffer
|
||||
import java.util.*
|
||||
import kotlin.math.floor
|
||||
|
||||
@ -16,7 +16,7 @@ public class UnivariateBin(
|
||||
//TODO add weighting
|
||||
public override val value: Number get() = counter.sum()
|
||||
|
||||
public override val center: RealVector get() = doubleArrayOf(position).asVector()
|
||||
public override val center: RealVector get() = doubleArrayOf(position).asBuffer()
|
||||
public override val dimension: Int get() = 1
|
||||
|
||||
public operator fun contains(value: Double): Boolean = value in (position - size / 2)..(position + size / 2)
|
||||
|
Loading…
Reference in New Issue
Block a user