forked from kscience/kmath
Some refactoring, add test file
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@ -9,109 +9,138 @@ import scientifik.kmath.structures.Matrix
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import scientifik.kmath.structures.asSequence
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import kotlin.math.pow
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// Initial implementation of these functions is taken from:
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// https://github.com/thomasnield/numky/blob/master/src/main/kotlin/org/nield/numky/linear/DoubleOperators.kt
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/*
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* Functions for convenient "numpy-like" operations with Double matrices.
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*
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* Initial implementation of these functions is taken from:
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* https://github.com/thomasnield/numky/blob/master/src/main/kotlin/org/nield/numky/linear/DoubleOperators.kt
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*
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*/
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fun realMatrix(rowNum: Int, colNum: Int, initializer: (i: Int, j: Int) -> Double) = MatrixContext.real.produce(rowNum, colNum, initializer)
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/*
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* Functions that help create a real (Double) matrix
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*/
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fun realMatrix(rowNum: Int, colNum: Int, initializer: (i: Int, j: Int) -> Double) =
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MatrixContext.real.produce(rowNum, colNum, initializer)
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fun Sequence<DoubleArray>.toMatrix() = toList().let {
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MatrixContext.real.produce(it.size, it[0].size) { row, col -> it[row][col] }
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}
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operator fun Matrix<Double>.times(double: Double) = MatrixContext.real.produce(rowNum, colNum) { row, col ->
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this@times[row, col] * double
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fun Matrix<Double>.repeatStackVertical(n: Int) = VirtualMatrix(rowNum*n, colNum) {
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row, col -> get(if (row == 0) 0 else row % rowNum, col)
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}
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fun Matrix<Double>.square() = MatrixContext.real.produce(rowNum, colNum) { row, col ->
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this@square[row,col].pow(2)
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/*
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* Operations for matrix and real number
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*/
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operator fun Matrix<Double>.times(double: Double) = MatrixContext.real.produce(rowNum, colNum) {
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row, col -> this[row, col] * double
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}
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operator fun Matrix<Double>.plus(double: Double) = MatrixContext.real.produce(rowNum, colNum) { row, col ->
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this@plus[row,col] + double
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operator fun Matrix<Double>.plus(double: Double) = MatrixContext.real.produce(rowNum, colNum) {
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row, col -> this[row, col] + double
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}
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operator fun Matrix<Double>.minus(double: Double) = MatrixContext.real.produce(rowNum, colNum) { row, col ->
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this@minus[row,col] - double
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operator fun Matrix<Double>.minus(double: Double) = MatrixContext.real.produce(rowNum, colNum) {
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row, col -> this[row, col] - double
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}
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operator fun Matrix<Double>.div(double: Double) = MatrixContext.real.produce(rowNum, colNum) { row, col ->
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this@div[row,col] / double
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operator fun Matrix<Double>.div(double: Double) = MatrixContext.real.produce(rowNum, colNum) {
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row, col -> this[row, col] / double
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}
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operator fun Double.times(matrix: Matrix<Double>) = MatrixContext.real.produce(matrix.rowNum, matrix.colNum) { row, col ->
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matrix[row,col] * this
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operator fun Double.times(matrix: Matrix<Double>) = MatrixContext.real.produce(matrix.rowNum, matrix.colNum) {
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row, col -> this * matrix[row, col]
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}
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operator fun Double.plus(matrix: Matrix<Double>) = MatrixContext.real.produce(matrix.rowNum, matrix.colNum) { row, col ->
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matrix[row,col] + this
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operator fun Double.plus(matrix: Matrix<Double>) = MatrixContext.real.produce(matrix.rowNum, matrix.colNum) {
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row, col -> this * matrix[row, col]
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}
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operator fun Double.minus(matrix: Matrix<Double>) = MatrixContext.real.produce(matrix.rowNum, matrix.colNum) { row, col ->
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matrix[row,col] - this
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operator fun Double.minus(matrix: Matrix<Double>) = MatrixContext.real.produce(matrix.rowNum, matrix.colNum) {
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row, col -> this - matrix[row, col]
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}
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operator fun Double.div(matrix: Matrix<Double>) = MatrixContext.real.produce(matrix.rowNum, matrix.colNum) { row, col ->
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matrix[row,col] / this
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// TODO: does this operation make sense? Should it be 'this/matrix[row, col]'?
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//operator fun Double.div(matrix: Matrix<Double>) = MatrixContext.real.produce(matrix.rowNum, matrix.colNum) {
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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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fun Matrix<Double>.square() = MatrixContext.real.produce(rowNum, colNum) {
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row, col -> this[row, col].pow(2)
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}
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operator fun Matrix<Double>.times(other: Matrix<Double>) = MatrixContext.real.produce(rowNum, colNum) { row, col ->
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this@times[row,col] * other[row,col]
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fun Matrix<Double>.pow(n: Int) = MatrixContext.real.produce(rowNum, colNum) {
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i, j -> this[i, j].pow(n)
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}
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operator fun Matrix<Double>.minus(other: Matrix<Double>) = MatrixContext.real.produce(rowNum, colNum) { row, col ->
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this@minus[row,col] - other[row,col]
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/*
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* Operations on two matrices (per-element!)
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*/
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operator fun Matrix<Double>.times(other: Matrix<Double>) = MatrixContext.real.produce(rowNum, colNum) {
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row, col -> this[row, col] * other[row, col]
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}
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operator fun Matrix<Double>.plus(other: Matrix<Double>) = MatrixContext.real.add(this, other)
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fun Matrix<Double>.repeatStackVertical(n: Int) = VirtualMatrix(rowNum*n, colNum) { row, col ->
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get(if (row == 0) 0 else row % rowNum, col)
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operator fun Matrix<Double>.minus(other: Matrix<Double>) = MatrixContext.real.produce(rowNum, colNum) {
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row, col -> this[row,col] - other[row,col]
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}
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/*
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* Operations on columns
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*/
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inline fun Matrix<Double>.appendColumn(crossinline mapper: (Buffer<Double>) -> Double) =
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MatrixContext.real.produce(rowNum,colNum+1) { row,col ->
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MatrixContext.real.produce(rowNum,colNum+1) {
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row, col ->
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if (col < colNum)
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this[row, col]
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else
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mapper(rows[row])
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}
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fun Matrix<Double>.extractColumn(columnIndex: Int) = extractColumns(columnIndex..columnIndex)
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fun Matrix<Double>.extractColumns(columnRange: IntRange) = MatrixContext.real.produce(rowNum, columnRange.count()) { row, col ->
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this@extractColumns[row, columnRange.start + col]
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fun Matrix<Double>.extractColumns(columnRange: IntRange) = MatrixContext.real.produce(rowNum, columnRange.count()) {
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row, col -> this[row, columnRange.first + col]
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}
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fun Matrix<Double>.sumByColumn() = MatrixContext.real.produce(1, colNum) { i, j ->
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fun Matrix<Double>.extractColumn(columnIndex: Int) = extractColumns(columnIndex..columnIndex)
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fun Matrix<Double>.sumByColumn() = MatrixContext.real.produce(1, colNum) { _, j ->
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val column = columns[j]
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with(elementContext) {
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sum(column.asSequence())
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}
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}
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fun Matrix<Double>.minByColumn() = MatrixContext.real.produce(1, colNum) { i, j ->
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val column = columns[j]
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column.asSequence().min()?:throw Exception("Cannot produce min on empty column")
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fun Matrix<Double>.minByColumn() = MatrixContext.real.produce(1, colNum) {
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_, j -> columns[j].asSequence().min() ?: throw Exception("Cannot produce min on empty column")
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}
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fun Matrix<Double>.maxByColumn() = MatrixContext.real.produce(1, colNum) { i, j ->
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val column = columns[j]
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column.asSequence().max()?:throw Exception("Cannot produce min on empty column")
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fun Matrix<Double>.maxByColumn() = MatrixContext.real.produce(1, colNum) {
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_, j -> columns[j].asSequence().max() ?: throw Exception("Cannot produce min on empty column")
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}
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fun Matrix<Double>.averageByColumn() = MatrixContext.real.produce(1, colNum) { i, j ->
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val column = columns[j]
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column.asSequence().average()
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fun Matrix<Double>.averageByColumn() = MatrixContext.real.produce(1, colNum) {
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_, j -> columns[j].asSequence().average()
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}
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fun Matrix<Double>.sum() = this.elements().map { (_,value) -> value }.sum()
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/*
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* Operations processing all elements
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*/
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fun Matrix<Double>.min() = this.elements().map { (_,value) -> value }.min()
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fun Matrix<Double>.sum() = elements().map { (_, value) -> value }.sum()
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fun Matrix<Double>.max() = this.elements().map { (_,value) -> value }.max()
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fun Matrix<Double>.min() = elements().map { (_, value) -> value }.min()
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fun Matrix<Double>.average() = this.elements().map { (_,value) -> value }.average()
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fun Matrix<Double>.max() = elements().map { (_, value) -> value }.max()
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fun Matrix<Double>.pow(n: Int) = MatrixContext.real.produce(rowNum, colNum) { i, j ->
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this@pow[i,j].pow(n)
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}
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fun Matrix<Double>.average() = elements().map { (_, value) -> value }.average()
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@ -0,0 +1,16 @@
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package scientific.kmath.real
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import scientifik.kmath.real.average
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import scientifik.kmath.real.realMatrix
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import scientifik.kmath.real.sum
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import kotlin.test.Test
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import kotlin.test.assertEquals
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class RealMatrixTest {
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@Test
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fun testSum() {
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val m = realMatrix(10, 10) { i, j -> (i + j).toDouble() }
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assertEquals(m.sum(), 900.0)
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assertEquals(m.average(), 9.0)
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}
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}
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0
kmath-for-real/src/jvmMain/kotlin/.gitkeep
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0
kmath-for-real/src/jvmMain/kotlin/.gitkeep
Normal file
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