Some refactoring, add test file

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

@@ -9,109 +9,138 @@ import scientifik.kmath.structures.Matrix
 import scientifik.kmath.structures.asSequence
 import kotlin.math.pow
 
-// 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 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
+ *
+ */
 
-fun realMatrix(rowNum: Int, colNum: Int, initializer: (i: Int, j: Int) -> Double) = MatrixContext.real.produce(rowNum, colNum, initializer)
+/*
+ *  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] }
+    MatrixContext.real.produce(it.size, it[0].size) { row, col -> it[row][col] }
 }
 
-operator fun Matrix<Double>.times(double: Double) = MatrixContext.real.produce(rowNum, colNum) { row, col ->
-    this@times[row, col] * double
+fun Matrix<Double>.repeatStackVertical(n: Int) = VirtualMatrix(rowNum*n, colNum) {
+    row, col -> get(if (row == 0) 0 else row % rowNum, col)
 }
 
-fun Matrix<Double>.square() =  MatrixContext.real.produce(rowNum, colNum) { row, col ->
-    this@square[row,col].pow(2)
+/*
+ *  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@plus[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@minus[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@div[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 ->
-    matrix[row,col] * this
+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 ->
-    matrix[row,col] + this
+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 ->
-    matrix[row,col] - this
+operator fun Double.minus(matrix: Matrix<Double>) = MatrixContext.real.produce(matrix.rowNum, matrix.colNum) {
+    row, col -> this - matrix[row, col]
 }
 
-operator fun Double.div(matrix: Matrix<Double>) = MatrixContext.real.produce(matrix.rowNum, matrix.colNum) { row, col ->
-    matrix[row,col] / this
+// 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)
 }
 
-operator fun Matrix<Double>.times(other: Matrix<Double>) = MatrixContext.real.produce(rowNum, colNum) { row, col ->
-    this@times[row,col] * other[row,col]
+fun Matrix<Double>.pow(n: Int) = MatrixContext.real.produce(rowNum, colNum) {
+    i, j -> this[i, j].pow(n)
 }
 
-operator fun Matrix<Double>.minus(other: Matrix<Double>) = MatrixContext.real.produce(rowNum, colNum) { row, col ->
-    this@minus[row,col] - other[row,col]
+/*
+ * 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>.plus(other: Matrix<Double>) = MatrixContext.real.add(this, other)
 
-fun Matrix<Double>.repeatStackVertical(n: Int) = VirtualMatrix(rowNum*n, colNum) { row, col ->
-    get(if (row == 0) 0 else row % rowNum, col)
+operator fun Matrix<Double>.minus(other: Matrix<Double>) = MatrixContext.real.produce(rowNum, colNum) {
+    row, col -> this[row,col] - other[row,col]
 }
 
-inline fun Matrix<Double>.appendColumn(crossinline  mapper: (Buffer<Double>) -> Double) =
-        MatrixContext.real.produce(rowNum,colNum+1) { 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]
+                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>.extractColumns(columnRange: IntRange) = MatrixContext.real.produce(rowNum, columnRange.count()) { row, col ->
-    this@extractColumns[row, columnRange.start + col]
-}
-
-fun Matrix<Double>.sumByColumn() = MatrixContext.real.produce(1, colNum) { i, j ->
+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) { i, j ->
-    val column = columns[j]
-    column.asSequence().min()?:throw Exception("Cannot produce min on empty column")
+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) { i, j ->
-    val column = columns[j]
-    column.asSequence().max()?: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) { i, j ->
-    val column = columns[j]
-    column.asSequence().average()
+fun Matrix<Double>.averageByColumn() = MatrixContext.real.produce(1, colNum) {
+    _, j -> columns[j].asSequence().average()
 }
 
-fun Matrix<Double>.sum() = this.elements().map { (_,value) -> value  }.sum()
+/*
+ * Operations processing all elements
+ */
 
-fun Matrix<Double>.min() = this.elements().map { (_,value) -> value  }.min()
+fun Matrix<Double>.sum() = elements().map { (_, value) -> value }.sum()
 
-fun Matrix<Double>.max() = this.elements().map { (_,value) -> value  }.max()
+fun Matrix<Double>.min() = elements().map { (_, value) -> value }.min()
 
-fun Matrix<Double>.average() = this.elements().map { (_,value) -> value  }.average()
+fun Matrix<Double>.max() = elements().map { (_, value) -> value }.max()
 
-fun Matrix<Double>.pow(n: Int) = MatrixContext.real.produce(rowNum, colNum) { i, j ->
-    this@pow[i,j].pow(n)
-}
+fun Matrix<Double>.average() = elements().map { (_, value) -> value }.average()

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

@@ -0,0 +1,16 @@
+package scientific.kmath.real
+
+import scientifik.kmath.real.average
+import scientifik.kmath.real.realMatrix
+import scientifik.kmath.real.sum
+import kotlin.test.Test
+import kotlin.test.assertEquals
+
+class RealMatrixTest {
+    @Test
+    fun testSum() {
+        val m = realMatrix(10, 10) { i, j -> (i + j).toDouble() }
+        assertEquals(m.sum(), 900.0)
+        assertEquals(m.average(), 9.0)
+    }
+}