refactor

examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt

@@ -21,7 +21,7 @@ fun main() {
     // work in context with linear operations
     DoubleLinearOpsTensorAlgebra.invoke {
         // take coefficient vector from normal distribution
-        val alpha = randNormal(
+        val alpha = randomNormal(
             intArrayOf(5),
             randSeed
         ) + fromArray(
@@ -32,14 +32,14 @@ fun main() {
         println("Real alpha:\n$alpha")
 
         // also take sample of size 20 from normal distribution for x
-        val x = randNormal(
+        val x = randomNormal(
             intArrayOf(20, 5),
             randSeed
         )
 
         // calculate y and add gaussian noise (N(0, 0.05))
         val y = x dot alpha
-        y += y.randNormalLike(randSeed) * 0.05
+        y += y.randomNormalLike(randSeed) * 0.05
 
         // now restore the coefficient vector with OSL estimator with SVD
         val (u, singValues, v) = x.svd()

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt

@@ -31,7 +31,7 @@ public object DoubleLinearOpsTensorAlgebra :
 
     public fun TensorStructure<Double>.luFactor(epsilon: Double): Pair<DoubleTensor, IntTensor> =
         computeLU(tensor, epsilon)
-            ?: throw RuntimeException("Tensor contains matrices which are singular at precision $epsilon")
+            ?: throw IllegalArgumentException("Tensor contains matrices which are singular at precision $epsilon")
 
     public fun TensorStructure<Double>.luFactor(): Pair<DoubleTensor, IntTensor> = luFactor(1e-9)
 
@@ -47,8 +47,10 @@ public object DoubleLinearOpsTensorAlgebra :
 
         val n = luTensor.shape.last()
         val pTensor = luTensor.zeroesLike()
-        for ((p, pivot) in pTensor.matrixSequence().zip(pivotsTensor.tensor.vectorSequence()))
+        pTensor
-            pivInit(p.as2D(), pivot.as1D(), n)
+            .matrixSequence()
+            .zip(pivotsTensor.tensor.vectorSequence())
+            .forEach { (p, pivot) -> pivInit(p.as2D(), pivot.as1D(), n) }
 
         val lTensor = luTensor.zeroesLike()
         val uTensor = luTensor.zeroesLike()

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleTensorAlgebra.kt

@@ -284,7 +284,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         val m1 = newThis.shape[newThis.shape.size - 1]
         val m2 = newOther.shape[newOther.shape.size - 2]
         val n = newOther.shape[newOther.shape.size - 1]
-        if (m1 != m2) {
+        check (m1 == m2) {
             throw RuntimeException("Tensors dot operation dimension mismatch: ($l, $m1) x ($m2, $n)")
         }
 
@@ -315,11 +315,11 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         val d1 = minusIndexFrom(n + 1, dim1)
         val d2 = minusIndexFrom(n + 1, dim2)
 
-        if (d1 == d2) {
+        check(d1 != d2) {
-            throw RuntimeException("Diagonal dimensions cannot be identical $d1, $d2")
+            "Diagonal dimensions cannot be identical $d1, $d2"
         }
-        if (d1 > n || d2 > n) {
+        check(d1 <= n && d2 <= n) {
-            throw RuntimeException("Dimension out of range")
+            "Dimension out of range"
         }
 
         var lessDim = d1
@@ -366,8 +366,8 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         )
     }
 
-    public fun TensorStructure<Double>.eq(other: TensorStructure<Double>, delta: Double): Boolean {
+    public fun TensorStructure<Double>.eq(other: TensorStructure<Double>, epsilon: Double): Boolean {
-        return tensor.eq(other) { x, y -> abs(x - y) < delta }
+        return tensor.eq(other) { x, y -> abs(x - y) < epsilon }
     }
 
     public infix fun TensorStructure<Double>.eq(other: TensorStructure<Double>): Boolean = tensor.eq(other, 1e-5)
@@ -393,10 +393,10 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         return true
     }
 
-    public fun randNormal(shape: IntArray, seed: Long = 0): DoubleTensor =
+    public fun randomNormal(shape: IntArray, seed: Long = 0): DoubleTensor =
         DoubleTensor(shape, getRandomNormals(shape.reduce(Int::times), seed))
 
-    public fun TensorStructure<Double>.randNormalLike(seed: Long = 0): DoubleTensor =
+    public fun TensorStructure<Double>.randomNormalLike(seed: Long = 0): DoubleTensor =
         DoubleTensor(tensor.shape, getRandomNormals(tensor.shape.reduce(Int::times), seed))
 
     // stack tensors by axis 0

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/broadcastUtils.kt

@@ -42,8 +42,8 @@ internal fun broadcastShapes(vararg shapes: IntArray): IntArray {
         for (i in shape.indices) {
             val curDim = shape[i]
             val offset = totalDim - shape.size
-            if (curDim != 1 && totalShape[i + offset] != curDim) {
+            check(curDim == 1 || totalShape[i + offset] == curDim) {
-                throw RuntimeException("Shapes are not compatible and cannot be broadcast")
+                "Shapes are not compatible and cannot be broadcast"
             }
         }
     }
@@ -52,8 +52,8 @@ internal fun broadcastShapes(vararg shapes: IntArray): IntArray {
 }
 
 internal fun broadcastTo(tensor: DoubleTensor, newShape: IntArray): DoubleTensor {
-    if (tensor.shape.size > newShape.size) {
+    require(tensor.shape.size <= newShape.size) {
-        throw RuntimeException("Tensor is not compatible with the new shape")
+        "Tensor is not compatible with the new shape"
     }
 
     val n = newShape.reduce { acc, i -> acc * i }
@@ -62,8 +62,8 @@ internal fun broadcastTo(tensor: DoubleTensor, newShape: IntArray): DoubleTensor
     for (i in tensor.shape.indices) {
         val curDim = tensor.shape[i]
         val offset = newShape.size - tensor.shape.size
-        if (curDim != 1 && newShape[i + offset] != curDim) {
+        check(curDim == 1 || newShape[i + offset] == curDim) {
-            throw RuntimeException("Tensor is not compatible with the new shape and cannot be broadcast")
+            "Tensor is not compatible with the new shape and cannot be broadcast"
         }
     }
 
@@ -75,19 +75,17 @@ internal fun broadcastTensors(vararg tensors: DoubleTensor): List<DoubleTensor>
     val totalShape = broadcastShapes(*(tensors.map { it.shape }).toTypedArray())
     val n = totalShape.reduce { acc, i -> acc * i }
 
-    return  buildList {
+    return tensors.map { tensor ->
-        for (tensor in tensors) {
         val resTensor = DoubleTensor(totalShape, DoubleArray(n))
         multiIndexBroadCasting(tensor, resTensor, n)
-            add(resTensor)
+        resTensor
-        }
     }
 }
 
 internal fun broadcastOuterTensors(vararg tensors: DoubleTensor): List<DoubleTensor> {
     val onlyTwoDims = tensors.asSequence().onEach {
         require(it.shape.size >= 2) {
-            throw RuntimeException("Tensors must have at least 2 dimensions")
+            "Tensors must have at least 2 dimensions"
         }
     }.any { it.shape.size != 2 }
 

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt

@@ -69,8 +69,7 @@ internal fun DoubleTensor.toPrettyString(): String = buildString {
     val shape = this@toPrettyString.shape
     val linearStructure = this@toPrettyString.linearStructure
     val vectorSize = shape.last()
-    val initString = "DoubleTensor(\n"
+    append("DoubleTensor(\n")
-    append(initString)
     var charOffset = 3
     for (vector in vectorSequence()) {
         repeat(charOffset) { append(' ') }

kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt

@@ -135,7 +135,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
 
     @Test
     fun testCholesky() = DoubleLinearOpsTensorAlgebra.invoke {
-        val tensor = randNormal(intArrayOf(2, 5, 5), 0)
+        val tensor = randomNormal(intArrayOf(2, 5, 5), 0)
         val sigma = (tensor dot tensor.transpose()) + diagonalEmbedding(
             fromArray(intArrayOf(2, 5), DoubleArray(10) { 0.1 })
         )
@@ -163,7 +163,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
 
     @Test
     fun testBatchedSVD() = DoubleLinearOpsTensorAlgebra.invoke {
-        val tensor = randNormal(intArrayOf(2, 5, 3), 0)
+        val tensor = randomNormal(intArrayOf(2, 5, 3), 0)
         val (tensorU, tensorS, tensorV) = tensor.svd()
         val tensorSVD = tensorU dot (diagonalEmbedding(tensorS) dot tensorV.transpose())
         assertTrue(tensor.eq(tensorSVD))
@@ -171,7 +171,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
 
     @Test
     fun testBatchedSymEig() = DoubleLinearOpsTensorAlgebra.invoke {
-        val tensor = randNormal(shape = intArrayOf(2, 3, 3), 0)
+        val tensor = randomNormal(shape = intArrayOf(2, 3, 3), 0)
         val tensorSigma = tensor + tensor.transpose()
         val (tensorS, tensorV) = tensorSigma.symEig()
         val tensorSigmaCalc = tensorV dot (diagonalEmbedding(tensorS) dot tensorV.transpose())