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For-real refactoring and test fix.

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Never call equals on buffer
This commit is contained in:
Alexander Nozik 2020-05-07 09:54:46 +03:00
parent 7e31a98dc5
commit e5ffb22126
13 changed files with 331 additions and 255 deletions
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2
build.gradle.kts
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@ -2,7 +2,7 @@ plugins {
id("scientifik.publish") apply false
}
val kmathVersion by extra("0.1.4-dev-5")
val kmathVersion by extra("0.1.4-dev-6")
val bintrayRepo by extra("scientifik")
val githubProject by extra("kmath")

13
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/BufferMatrix.kt
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@ -27,7 +27,7 @@ class BufferMatrixContext<T : Any, R : Ring<T>>(
@Suppress("OVERRIDE_BY_INLINE")
object RealMatrixContext : GenericMatrixContext<Double, RealField> {
override val elementContext = RealField
override val elementContext get() = RealField
override inline fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> Double): Matrix<Double> {
val buffer = DoubleBuffer(rows * columns) { offset -> initializer(offset / columns, offset % columns) }
@ -101,8 +101,15 @@ infix fun BufferMatrix<Double>.dot(other: BufferMatrix<Double>): BufferMatrix<Do
val array = DoubleArray(this.rowNum * other.colNum)
val a = this.buffer.array
val b = other.buffer.array
//convert to array to insure there is not memory indirection
fun Buffer<out Double>.unsafeArray(): DoubleArray = if (this is DoubleBuffer) {
array
} else {
DoubleArray(size) { get(it) }
}
val a = this.buffer.unsafeArray()
val b = other.buffer.unsafeArray()
for (i in (0 until rowNum)) {
for (j in (0 until other.colNum)) {

6
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/LinearAlgebra.kt
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@ -1,12 +1,8 @@
package scientifik.kmath.linear
import scientifik.kmath.operations.Field
import scientifik.kmath.operations.Norm
import scientifik.kmath.operations.RealField
import scientifik.kmath.structures.Buffer
import scientifik.kmath.structures.Matrix
import scientifik.kmath.structures.VirtualBuffer
import scientifik.kmath.structures.asSequence
typealias Point<T> = Buffer<T>
@ -19,8 +15,6 @@ interface LinearSolver<T : Any> {
fun inverse(a: Matrix<T>): Matrix<T>
}
typealias RealMatrix = Matrix<Double>
/**
* Convert matrix to vector if it is possible
*/

38
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/MatrixBuilder.kt
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@ -1,14 +1,46 @@
package scientifik.kmath.linear
import scientifik.kmath.structures.Buffer
import scientifik.kmath.structures.BufferFactory
import scientifik.kmath.structures.Structure2D
import scientifik.kmath.structures.asBuffer
class MatrixBuilder<T : Any>(val rows: Int, val columns: Int) {
operator fun invoke(vararg elements: T): FeaturedMatrix<T> {
class MatrixBuilder(val rows: Int, val columns: Int) {
operator fun <T : Any> invoke(vararg elements: T): FeaturedMatrix<T> {
if (rows * columns != elements.size) error("The number of elements ${elements.size} is not equal $rows * $columns")
val buffer = elements.asBuffer()
return BufferMatrix(rows, columns, buffer)
}
//TODO add specific matrix builder functions like diagonal, etc
}
fun <T : Any> Structure2D.Companion.build(rows: Int, columns: Int): MatrixBuilder<T> = MatrixBuilder(rows, columns)
fun Structure2D.Companion.build(rows: Int, columns: Int): MatrixBuilder = MatrixBuilder(rows, columns)
fun <T : Any> Structure2D.Companion.row(vararg values: T): FeaturedMatrix<T> {
val buffer = values.asBuffer()
return BufferMatrix(1, values.size, buffer)
}
inline fun <reified T : Any> Structure2D.Companion.row(
size: Int,
factory: BufferFactory<T> = Buffer.Companion::auto,
noinline builder: (Int) -> T
): FeaturedMatrix<T> {
val buffer = factory(size, builder)
return BufferMatrix(1, size, buffer)
}
fun <T : Any> Structure2D.Companion.column(vararg values: T): FeaturedMatrix<T> {
val buffer = values.asBuffer()
return BufferMatrix(values.size, 1, buffer)
}
inline fun <reified T : Any> Structure2D.Companion.column(
size: Int,
factory: BufferFactory<T> = Buffer.Companion::auto,
noinline builder: (Int) -> T
): FeaturedMatrix<T> {
val buffer = factory(size, builder)
return BufferMatrix(size, 1, buffer)
}

31
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/Buffers.kt
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@ -161,36 +161,7 @@ class ArrayBuffer<T>(private val array: Array<T>) : MutableBuffer<T> {
override fun copy(): MutableBuffer<T> = ArrayBuffer(array.copyOf())
}
fun <T> Array<T>.asBuffer() = ArrayBuffer(this)
inline class DoubleBuffer(val array: DoubleArray) : MutableBuffer<Double> {
override val size: Int get() = array.size
override fun get(index: Int): Double = array[index]
override fun set(index: Int, value: Double) {
array[index] = value
}
override fun iterator() = array.iterator()
override fun copy(): MutableBuffer<Double> = DoubleBuffer(array.copyOf())
}
@Suppress("FunctionName")
inline fun DoubleBuffer(size: Int, init: (Int) -> Double) = DoubleBuffer(DoubleArray(size) { init(it) })
/**
* Transform buffer of doubles into array for high performance operations
*/
val Buffer<out Double>.array: DoubleArray
get() = if (this is DoubleBuffer) {
array
} else {
DoubleArray(size) { get(it) }
}
fun DoubleArray.asBuffer() = DoubleBuffer(this)
fun <T> Array<T>.asBuffer(): ArrayBuffer<T> = ArrayBuffer(this)
inline class ShortBuffer(val array: ShortArray) : MutableBuffer<Short> {
override val size: Int get() = array.size

34
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/DoubleBuffer.kt Normal file
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@ -0,0 +1,34 @@
package scientifik.kmath.structures
inline class DoubleBuffer(val array: DoubleArray) : MutableBuffer<Double> {
override val size: Int get() = array.size
override fun get(index: Int): Double = array[index]
override fun set(index: Int, value: Double) {
array[index] = value
}
override fun iterator() = array.iterator()
override fun copy(): MutableBuffer<Double> =
DoubleBuffer(array.copyOf())
}
@Suppress("FunctionName")
inline fun DoubleBuffer(size: Int, init: (Int) -> Double): DoubleBuffer = DoubleBuffer(DoubleArray(size) { init(it) })
@Suppress("FunctionName")
fun DoubleBuffer(vararg doubles: Double): DoubleBuffer = DoubleBuffer(doubles)
/**
* Transform buffer of doubles into array for high performance operations
*/
val MutableBuffer<out Double>.array: DoubleArray
get() = if (this is DoubleBuffer) {
array
} else {
DoubleArray(size) { get(it) }
}
fun DoubleArray.asBuffer() = DoubleBuffer(this)

6
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/NDStructure.kt
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@ -23,7 +23,11 @@ interface NDStructure<T> {
if (st1 === st2) return true
// fast comparison of buffers if possible
if(st1 is NDBuffer && st2 is NDBuffer && st1.strides == st2.strides){
if (
st1 is NDBuffer &&
st2 is NDBuffer &&
st1.strides == st2.strides
) {
return st1.buffer.contentEquals(st2.buffer)
}

2
kmath-core/src/commonTest/kotlin/scientifik/kmath/linear/MatrixTest.kt
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@ -17,7 +17,7 @@ class MatrixTest {
@Test
fun testBuilder() {
val matrix = Matrix.build<Double>(2, 3)(
val matrix = Matrix.build(2, 3)(
1.0, 0.0, 0.0,
0.0, 1.0, 2.0
)

146
kmath-for-real/src/commonMain/kotlin/scientifik/kmath/real/DoubleMatrixOperations.kt
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@ -1,146 +0,0 @@
package scientifik.kmath.real
import scientifik.kmath.linear.MatrixContext
import scientifik.kmath.linear.RealMatrixContext.elementContext
import scientifik.kmath.linear.VirtualMatrix
import scientifik.kmath.operations.sum
import scientifik.kmath.structures.Buffer
import scientifik.kmath.structures.Matrix
import scientifik.kmath.structures.asSequence
import kotlin.math.pow
/*
* Functions for convenient "numpy-like" operations with Double matrices.
*
* Initial implementation of these functions is taken from:
* https://github.com/thomasnield/numky/blob/master/src/main/kotlin/org/nield/numky/linear/DoubleOperators.kt
*
*/
/*
* Functions that help create a real (Double) matrix
*/
fun realMatrix(rowNum: Int, colNum: Int, initializer: (i: Int, j: Int) -> Double) =
MatrixContext.real.produce(rowNum, colNum, initializer)
fun Sequence<DoubleArray>.toMatrix() = toList().let {
MatrixContext.real.produce(it.size, it[0].size) { row, col -> it[row][col] }
}
fun Matrix<Double>.repeatStackVertical(n: Int) = VirtualMatrix(rowNum*n, colNum) {
row, col -> get(if (row == 0) 0 else row % rowNum, col)
}
/*
* Operations for matrix and real number
*/
operator fun Matrix<Double>.times(double: Double) = MatrixContext.real.produce(rowNum, colNum) {
row, col -> this[row, col] * double
}
operator fun Matrix<Double>.plus(double: Double) = MatrixContext.real.produce(rowNum, colNum) {
row, col -> this[row, col] + double
}
operator fun Matrix<Double>.minus(double: Double) = MatrixContext.real.produce(rowNum, colNum) {
row, col -> this[row, col] - double
}
operator fun Matrix<Double>.div(double: Double) = MatrixContext.real.produce(rowNum, colNum) {
row, col -> this[row, col] / double
}
operator fun Double.times(matrix: Matrix<Double>) = MatrixContext.real.produce(matrix.rowNum, matrix.colNum) {
row, col -> this * matrix[row, col]
}
operator fun Double.plus(matrix: Matrix<Double>) = MatrixContext.real.produce(matrix.rowNum, matrix.colNum) {
row, col -> this + matrix[row, col]
}
operator fun Double.minus(matrix: Matrix<Double>) = MatrixContext.real.produce(matrix.rowNum, matrix.colNum) {
row, col -> this - matrix[row, col]
}
// TODO: does this operation make sense? Should it be 'this/matrix[row, col]'?
//operator fun Double.div(matrix: Matrix<Double>) = MatrixContext.real.produce(matrix.rowNum, matrix.colNum) {
// row, col -> matrix[row, col] / this
//}
/*
* Per-element (!) square and power operations
*/
fun Matrix<Double>.square() = MatrixContext.real.produce(rowNum, colNum) {
row, col -> this[row, col].pow(2)
}
fun Matrix<Double>.pow(n: Int) = MatrixContext.real.produce(rowNum, colNum) {
i, j -> this[i, j].pow(n)
}
/*
* Operations on two matrices (per-element!)
*/
operator fun Matrix<Double>.times(other: Matrix<Double>) = MatrixContext.real.produce(rowNum, colNum) {
row, col -> this[row, col] * other[row, col]
}
operator fun Matrix<Double>.plus(other: Matrix<Double>) = MatrixContext.real.add(this, other)
operator fun Matrix<Double>.minus(other: Matrix<Double>) = MatrixContext.real.produce(rowNum, colNum) {
row, col -> this[row,col] - other[row,col]
}
/*
* Operations on columns
*/
inline fun Matrix<Double>.appendColumn(crossinline mapper: (Buffer<Double>) -> Double) =
MatrixContext.real.produce(rowNum,colNum+1) {
row, col ->
if (col < colNum)
this[row, col]
else
mapper(rows[row])
}
fun Matrix<Double>.extractColumns(columnRange: IntRange) = MatrixContext.real.produce(rowNum, columnRange.count()) {
row, col -> this[row, columnRange.first + col]
}
fun Matrix<Double>.extractColumn(columnIndex: Int) = extractColumns(columnIndex..columnIndex)
fun Matrix<Double>.sumByColumn() = MatrixContext.real.produce(1, colNum) { _, j ->
val column = columns[j]
with(elementContext) {
sum(column.asSequence())
}
}
fun Matrix<Double>.minByColumn() = MatrixContext.real.produce(1, colNum) {
_, j -> columns[j].asSequence().min() ?: throw Exception("Cannot produce min on empty column")
}
fun Matrix<Double>.maxByColumn() = MatrixContext.real.produce(1, colNum) {
_, j -> columns[j].asSequence().max() ?: throw Exception("Cannot produce min on empty column")
}
fun Matrix<Double>.averageByColumn() = MatrixContext.real.produce(1, colNum) {
_, j -> columns[j].asSequence().average()
}
/*
* Operations processing all elements
*/
fun Matrix<Double>.sum() = elements().map { (_, value) -> value }.sum()
fun Matrix<Double>.min() = elements().map { (_, value) -> value }.min()
fun Matrix<Double>.max() = elements().map { (_, value) -> value }.max()
fun Matrix<Double>.average() = elements().map { (_, value) -> value }.average()

8
kmath-for-real/src/commonMain/kotlin/scientifik/kmath/real/realBuffer.kt Normal file
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@ -0,0 +1,8 @@
package scientifik.kmath.real
import scientifik.kmath.structures.DoubleBuffer
/**
* C
*/
fun DoubleBuffer.contentEquals(vararg doubles: Double) = array.contentEquals(doubles)

161
kmath-for-real/src/commonMain/kotlin/scientifik/kmath/real/realMatrix.kt Normal file
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@ -0,0 +1,161 @@
package scientifik.kmath.real
import scientifik.kmath.linear.MatrixContext
import scientifik.kmath.linear.RealMatrixContext.elementContext
import scientifik.kmath.linear.VirtualMatrix
import scientifik.kmath.operations.sum
import scientifik.kmath.structures.Buffer
import scientifik.kmath.structures.DoubleBuffer
import scientifik.kmath.structures.Matrix
import scientifik.kmath.structures.asIterable
import kotlin.math.pow
/*
* Functions for convenient "numpy-like" operations with Double matrices.
*
* Initial implementation of these functions is taken from:
* https://github.com/thomasnield/numky/blob/master/src/main/kotlin/org/nield/numky/linear/DoubleOperators.kt
*
*/
/*
* Functions that help create a real (Double) matrix
*/
typealias RealMatrix = Matrix<Double>
fun realMatrix(rowNum: Int, colNum: Int, initializer: (i: Int, j: Int) -> Double): RealMatrix =
MatrixContext.real.produce(rowNum, colNum, initializer)
fun Sequence<DoubleArray>.toMatrix(): RealMatrix = toList().let {
MatrixContext.real.produce(it.size, it[0].size) { row, col -> it[row][col] }
}
fun Matrix<Double>.repeatStackVertical(n: Int): RealMatrix =
VirtualMatrix(rowNum * n, colNum) { row, col ->
get(if (row == 0) 0 else row % rowNum, col)
}
/*
* Operations for matrix and real number
*/
operator fun Matrix<Double>.times(double: Double): RealMatrix =
MatrixContext.real.produce(rowNum, colNum) { row, col ->
this[row, col] * double
}
operator fun Matrix<Double>.plus(double: Double): RealMatrix =
MatrixContext.real.produce(rowNum, colNum) { row, col ->
this[row, col] + double
}
operator fun Matrix<Double>.minus(double: Double): RealMatrix =
MatrixContext.real.produce(rowNum, colNum) { row, col ->
this[row, col] - double
}
operator fun Matrix<Double>.div(double: Double): RealMatrix =
MatrixContext.real.produce(rowNum, colNum) { row, col ->
this[row, col] / double
}
operator fun Double.times(matrix: Matrix<Double>): RealMatrix =
MatrixContext.real.produce(matrix.rowNum, matrix.colNum) { row, col ->
this * matrix[row, col]
}
operator fun Double.plus(matrix: Matrix<Double>): RealMatrix =
MatrixContext.real.produce(matrix.rowNum, matrix.colNum) { row, col ->
this + matrix[row, col]
}
operator fun Double.minus(matrix: Matrix<Double>): RealMatrix =
MatrixContext.real.produce(matrix.rowNum, matrix.colNum) { row, col ->
this - matrix[row, col]
}
// TODO: does this operation make sense? Should it be 'this/matrix[row, col]'?
//operator fun Double.div(matrix: Matrix<Double>) = MatrixContext.real.produce(matrix.rowNum, matrix.colNum) {
// row, col -> matrix[row, col] / this
//}
/*
* Per-element (!) square and power operations
*/
fun Matrix<Double>.square(): RealMatrix = MatrixContext.real.produce(rowNum, colNum) { row, col ->
this[row, col].pow(2)
}
fun Matrix<Double>.pow(n: Int): RealMatrix = MatrixContext.real.produce(rowNum, colNum) { i, j ->
this[i, j].pow(n)
}
/*
* Operations on two matrices (per-element!)
*/
operator fun Matrix<Double>.times(other: Matrix<Double>): RealMatrix =
MatrixContext.real.produce(rowNum, colNum) { row, col ->
this[row, col] * other[row, col]
}
operator fun Matrix<Double>.plus(other: Matrix<Double>): RealMatrix =
MatrixContext.real.add(this, other)
operator fun Matrix<Double>.minus(other: Matrix<Double>): RealMatrix =
MatrixContext.real.produce(rowNum, colNum) { row, col ->
this[row, col] - other[row, col]
}
/*
* Operations on columns
*/
inline fun Matrix<Double>.appendColumn(crossinline mapper: (Buffer<Double>) -> Double) =
MatrixContext.real.produce(rowNum, colNum + 1) { row, col ->
if (col < colNum)
this[row, col]
else
mapper(rows[row])
}
fun Matrix<Double>.extractColumns(columnRange: IntRange): RealMatrix =
MatrixContext.real.produce(rowNum, columnRange.count()) { row, col ->
this[row, columnRange.first + col]
}
fun Matrix<Double>.extractColumn(columnIndex: Int): RealMatrix =
extractColumns(columnIndex..columnIndex)
fun Matrix<Double>.sumByColumn(): DoubleBuffer = DoubleBuffer(colNum) { j ->
val column = columns[j]
with(elementContext) {
sum(column.asIterable())
}
}
fun Matrix<Double>.minByColumn(): DoubleBuffer = DoubleBuffer(colNum) { j ->
columns[j].asIterable().min() ?: throw Exception("Cannot produce min on empty column")
}
fun Matrix<Double>.maxByColumn(): DoubleBuffer = DoubleBuffer(colNum) { j ->
columns[j].asIterable().max() ?: throw Exception("Cannot produce min on empty column")
}
fun Matrix<Double>.averageByColumn(): DoubleBuffer = DoubleBuffer(colNum) { j ->
columns[j].asIterable().average()
}
/*
* Operations processing all elements
*/
fun Matrix<Double>.sum() = elements().map { (_, value) -> value }.sum()
fun Matrix<Double>.min() = elements().map { (_, value) -> value }.min()
fun Matrix<Double>.max() = elements().map { (_, value) -> value }.max()
fun Matrix<Double>.average() = elements().map { (_, value) -> value }.average()

55
kmath-for-real/src/commonTest/kotlin/scientific.kmath.real/RealMatrixTest.kt
View File

@ -1,11 +1,12 @@
package scientific.kmath.real
import scientifik.kmath.real.*
import scientifik.kmath.linear.VirtualMatrix
import scientifik.kmath.linear.build
import scientifik.kmath.real.*
import scientifik.kmath.structures.Matrix
import kotlin.test.Test
import kotlin.test.assertEquals
import kotlin.test.assertTrue
class RealMatrixTest {
@Test
@ -28,11 +29,11 @@ class RealMatrixTest {
@Test
fun testRepeatStackVertical() {
val matrix1 = Matrix.build<Double>(2, 3)(
val matrix1 = Matrix.build(2, 3)(
1.0, 0.0, 0.0,
0.0, 1.0, 2.0
)
val matrix2 = Matrix.build<Double>(6, 3)(
val matrix2 = Matrix.build(6, 3)(
1.0, 0.0, 0.0,
0.0, 1.0, 2.0,
1.0, 0.0, 0.0,
@ -45,12 +46,12 @@ class RealMatrixTest {
@Test
fun testMatrixAndDouble() {
val matrix1 = Matrix.build<Double>(2, 3)(
val matrix1 = Matrix.build(2, 3)(
1.0, 0.0, 3.0,
4.0, 6.0, 2.0
)
val matrix2 = (matrix1 * 2.5 + 1.0 - 2.0) / 2.0
val expectedResult = Matrix.build<Double>(2, 3)(
val expectedResult = Matrix.build(2, 3)(
0.75, -0.5, 3.25,
4.5, 7.0, 2.0
)
@ -59,13 +60,13 @@ class RealMatrixTest {
@Test
fun testDoubleAndMatrix() {
val matrix1 = Matrix.build<Double>(2, 3)(
val matrix1 = Matrix.build(2, 3)(
1.0, 0.0, 3.0,
4.0, 6.0, 2.0
)
val matrix2 = 20.0 - (10.0 + (5.0 * matrix1))
//val matrix2 = 10.0 + (5.0 * matrix1)
val expectedResult = Matrix.build<Double>(2, 3)(
val expectedResult = Matrix.build(2, 3)(
5.0, 10.0, -5.0,
-10.0, -20.0, 0.0
)
@ -74,15 +75,15 @@ class RealMatrixTest {
@Test
fun testSquareAndPower() {
val matrix1 = Matrix.build<Double>(2, 3)(
val matrix1 = Matrix.build(2, 3)(
-1.0, 0.0, 3.0,
4.0, -6.0, -2.0
)
val matrix2 = Matrix.build<Double>(2, 3)(
val matrix2 = Matrix.build(2, 3)(
1.0, 0.0, 9.0,
16.0, 36.0, 4.0
)
val matrix3 = Matrix.build<Double>(2, 3)(
val matrix3 = Matrix.build(2, 3)(
-1.0, 0.0, 27.0,
64.0, -216.0, -8.0
)
@ -92,16 +93,16 @@ class RealMatrixTest {
@Test
fun testTwoMatrixOperations() {
val matrix1 = Matrix.build<Double>(2, 3)(
val matrix1 = Matrix.build(2, 3)(
-1.0, 0.0, 3.0,
4.0, -6.0, 7.0
)
val matrix2 = Matrix.build<Double>(2, 3)(
val matrix2 = Matrix.build(2, 3)(
1.0, 0.0, 3.0,
4.0, 6.0, -2.0
)
val result = matrix1 * matrix2 + matrix1 - matrix2
val expectedResult = Matrix.build<Double>(2, 3)(
val expectedResult = Matrix.build(2, 3)(
-3.0, 0.0, 9.0,
16.0, -48.0, -5.0
)
@ -110,31 +111,41 @@ class RealMatrixTest {
@Test
fun testColumnOperations() {
val matrix1 = Matrix.build<Double>(2, 4)(
val matrix1 = Matrix.build(2, 4)(
-1.0, 0.0, 3.0, 15.0,
4.0, -6.0, 7.0, -11.0
)
val matrix2 = Matrix.build<Double>(2, 5)(
val matrix2 = Matrix.build(2, 5)(
-1.0, 0.0, 3.0, 15.0, -1.0,
4.0, -6.0, 7.0, -11.0, 4.0
)
val col1 = Matrix.build<Double>(2, 1)(0.0, -6.0)
val cols1to2 = Matrix.build<Double>(2, 2)(
val col1 = Matrix.build(2, 1)(0.0, -6.0)
val cols1to2 = Matrix.build(2, 2)(
0.0, 3.0,
-6.0, 7.0
)
assertEquals(matrix1.appendColumn { it[0] }, matrix2)
assertEquals(matrix1.extractColumn(1), col1)
assertEquals(matrix1.extractColumns(1..2), cols1to2)
assertEquals(matrix1.sumByColumn(), Matrix.build<Double>(1, 4)(3.0, -6.0, 10.0, 4.0))
assertEquals(matrix1.minByColumn(), Matrix.build<Double>(1, 4)(-1.0, -6.0, 3.0, -11.0))
assertEquals(matrix1.maxByColumn(), Matrix.build<Double>(1, 4)(4.0, 0.0, 7.0, 15.0))
assertEquals(matrix1.averageByColumn(), Matrix.build<Double>(1, 4)(1.5, -3.0, 5.0, 2.0))
//equals should never be called on buffers
assertTrue {
matrix1.sumByColumn().contentEquals(3.0, -6.0, 10.0, 4.0)
} //assertEquals(matrix1.sumByColumn(), DoubleBuffer(3.0, -6.0, 10.0, 4.0))
assertTrue {
matrix1.minByColumn().contentEquals(-1.0, -6.0, 3.0, -11.0)
} //assertEquals(matrix1.minByColumn(), DoubleBuffer(-1.0, -6.0, 3.0, -11.0))
assertTrue {
matrix1.maxByColumn().contentEquals(4.0, 0.0, 7.0, 15.0)
} //assertEquals(matrix1.maxByColumn(), DoubleBuffer(4.0, 0.0, 7.0, 15.0))
assertTrue {
matrix1.averageByColumn().contentEquals(1.5, -3.0, 5.0, 2.0)
} //assertEquals(matrix1.averageByColumn(), DoubleBuffer(1.5, -3.0, 5.0, 2.0))
}
@Test
fun testAllElementOperations() {
val matrix1 = Matrix.build<Double>(2, 4)(
val matrix1 = Matrix.build(2, 4)(
-1.0, 0.0, 3.0, 15.0,
4.0, -6.0, 7.0, -11.0
)