CHANGELOG.md

@@ -35,6 +35,7 @@
 - `contentEquals` from Buffer. It moved to the companion.
 - MSTExpression
 - Expression algebra builders
+- Comples and Quaternion no longer are elements.
 
 ### Fixed
 - Ring inherits RingOperations, not GroupOperations

build.gradle.kts

@@ -15,7 +15,7 @@ allprojects {
     }
 
     group = "space.kscience"
-    version = "0.3.0-dev-7"
+    version = "0.3.0-dev-8"
 }
 
 subprojects {

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

@@ -6,8 +6,8 @@
 package space.kscience.kmath.tensors
 
 import space.kscience.kmath.operations.invoke
-import space.kscience.kmath.tensors.core.DoubleTensor
 import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.DoubleTensor
 import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
 import space.kscience.kmath.tensors.core.toDoubleArray
 import kotlin.math.sqrt
@@ -106,6 +106,7 @@ fun accuracy(yPred: DoubleTensor, yTrue: DoubleTensor): Double {
 }
 
 // neural network class
+@OptIn(ExperimentalStdlibApi::class)
 class NeuralNetwork(private val layers: List<Layer>) {
     private fun softMaxLoss(yPred: DoubleTensor, yTrue: DoubleTensor): DoubleTensor = BroadcastDoubleTensorAlgebra {
 
@@ -120,7 +121,7 @@ class NeuralNetwork(private val layers: List<Layer>) {
         (-onesForAnswers + softmaxValue) / (yPred.shape[0].toDouble())
     }
 
-    @OptIn(ExperimentalStdlibApi::class)
+
     private fun forward(x: DoubleTensor): List<DoubleTensor> {
         var input = x
 
@@ -133,7 +134,6 @@ class NeuralNetwork(private val layers: List<Layer>) {
         }
     }
 
-    @OptIn(ExperimentalStdlibApi::class)
     private fun train(xTrain: DoubleTensor, yTrain: DoubleTensor) {
         val layerInputs = buildList {
             add(xTrain)
@@ -180,7 +180,7 @@ fun main() {
         val features = 5
         val sampleSize = 250
         val trainSize = 180
-        val testSize = sampleSize - trainSize
+        //val testSize = sampleSize - trainSize
 
         // take sample of features from normal distribution
         val x = randomNormal(intArrayOf(sampleSize, features), seed) * 2.5

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

@@ -48,7 +48,7 @@ fun main() {
 
 
         // inverse Sigma matrix can be restored from singular values with diagonalEmbedding function
-        val sigma = diagonalEmbedding(singValues.map{ x -> if (abs(x) < 1e-3) 0.0 else 1.0/x })
+        val sigma = diagonalEmbedding(singValues.map{ if (abs(it) < 1e-3) 0.0 else 1.0/it })
 
         val alphaOLS = v dot sigma dot u.transpose() dot y
         println("Estimated alpha:\n" +

kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Complex.kt

@@ -9,7 +9,10 @@ import space.kscience.kmath.memory.MemoryReader
 import space.kscience.kmath.memory.MemorySpec
 import space.kscience.kmath.memory.MemoryWriter
 import space.kscience.kmath.misc.UnstableKMathAPI
-import space.kscience.kmath.operations.*
+import space.kscience.kmath.operations.ExtendedField
+import space.kscience.kmath.operations.Norm
+import space.kscience.kmath.operations.NumbersAddOperations
+import space.kscience.kmath.operations.ScaleOperations
 import space.kscience.kmath.structures.Buffer
 import space.kscience.kmath.structures.MemoryBuffer
 import space.kscience.kmath.structures.MutableBuffer
@@ -180,12 +183,10 @@ public object ComplexField : ExtendedField<Complex>, Norm<Complex, Complex>, Num
  * @property im The imaginary part.
  */
 @OptIn(UnstableKMathAPI::class)
-public data class Complex(val re: Double, val im: Double) : FieldElement<Complex, ComplexField> {
+public data class Complex(val re: Double, val im: Double) {
     public constructor(re: Number, im: Number) : this(re.toDouble(), im.toDouble())
     public constructor(re: Number) : this(re.toDouble(), 0.0)
 
-    public override val context: ComplexField get() = ComplexField
-
     public override fun toString(): String = "($re + i*$im)"
 
     public companion object : MemorySpec<Complex> {

kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Quaternion.kt

@@ -27,8 +27,10 @@ public val Quaternion.conjugate: Quaternion
  */
 public val Quaternion.reciprocal: Quaternion
     get() {
-        val n = QuaternionField { norm(this@reciprocal) }
-        return conjugate / (n * n)
+        QuaternionField {
+            val n = norm(this@reciprocal)
+            return conjugate / (n * n)
+        }
     }
 
 /**
@@ -198,7 +200,7 @@ public object QuaternionField : Field<Quaternion>, Norm<Quaternion, Quaternion>,
 @OptIn(UnstableKMathAPI::class)
 public data class Quaternion(
     val w: Double, val x: Double, val y: Double, val z: Double,
-) : FieldElement<Quaternion, QuaternionField> {
+) {
     public constructor(w: Number, x: Number, y: Number, z: Number) : this(
         w.toDouble(),
         x.toDouble(),
@@ -219,8 +221,6 @@ public data class Quaternion(
         require(!z.isNaN()) { "x-component of quaternion is not-a-number" }
     }
 
-    public override val context: QuaternionField get() = QuaternionField
-
     /**
      * Returns a string representation of this quaternion.
      */

kmath-stat/src/jvmTest/kotlin/space/kscience/kmath/stat/CommonsDistributionsTest.kt

@@ -5,15 +5,14 @@
 
 package space.kscience.kmath.stat
 
-import kotlinx.coroutines.GlobalScope
-import kotlinx.coroutines.launch
+import kotlinx.coroutines.runBlocking
 import org.junit.jupiter.api.Assertions
 import org.junit.jupiter.api.Test
 import space.kscience.kmath.samplers.GaussianSampler
 
 internal class CommonsDistributionsTest {
     @Test
-    fun testNormalDistributionSuspend() = GlobalScope.launch {
+    fun testNormalDistributionSuspend() = runBlocking {
         val distribution = GaussianSampler(7.0, 2.0)
         val generator = RandomGenerator.default(1)
         val sample = distribution.sample(generator).nextBuffer(1000)