build.gradle.kts

@@ -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")

kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/BufferMatrix.kt

@@ -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
+    //convert to array to insure there is not memory indirection
-    val b = other.buffer.array
+    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)) {

kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/LinearAlgebra.kt

@@ -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
  */

kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/MatrixBuilder.kt

@@ -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) {
+class MatrixBuilder(val rows: Int, val columns: Int) {
-    operator fun invoke(vararg elements: T): FeaturedMatrix<T> {
+    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)
+}

kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/Buffers.kt

@@ -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)
+fun <T> Array<T>.asBuffer(): ArrayBuffer<T> = 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)
 
 inline class ShortBuffer(val array: ShortArray) : MutableBuffer<Short> {
     override val size: Int get() = array.size

kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/DoubleBuffer.kt

@@ -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)

kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/NDStructure.kt

@@ -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)
             }
 

kmath-core/src/commonTest/kotlin/scientifik/kmath/linear/MatrixTest.kt

@@ -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
         )

kmath-for-real/src/commonMain/kotlin/scientifik/kmath/real/DoubleMatrixOperations.kt

@@ -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()

kmath-for-real/src/commonMain/kotlin/scientifik/kmath/real/realBuffer.kt

@@ -0,0 +1,8 @@
+package scientifik.kmath.real
+
+import scientifik.kmath.structures.DoubleBuffer
+
+/**
+ * C
+ */
+fun DoubleBuffer.contentEquals(vararg doubles: Double) = array.contentEquals(doubles)

kmath-for-real/src/commonMain/kotlin/scientifik/kmath/real/realMatrix.kt

@@ -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()

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

@@ -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 col1 = Matrix.build(2, 1)(0.0, -6.0)
-        val cols1to2 = Matrix.build<Double>(2, 2)(
+        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))
+        //equals should never be called on buffers
-        assertEquals(matrix1.minByColumn(), Matrix.build<Double>(1, 4)(-1.0, -6.0, 3.0, -11.0))
+        assertTrue {
-        assertEquals(matrix1.maxByColumn(), Matrix.build<Double>(1, 4)(4.0, 0.0, 7.0, 15.0))
+            matrix1.sumByColumn().contentEquals(3.0, -6.0, 10.0, 4.0)
-        assertEquals(matrix1.averageByColumn(), Matrix.build<Double>(1, 4)(1.5, -3.0, 5.0, 2.0))
+        } //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
         )