build.gradle.kts

@@ -15,7 +15,7 @@ allprojects {
     }
 
     group = "space.kscience"
-    version = "0.3.0-dev-8"
+    version = "0.3.0-dev-9"
 }
 
 subprojects {

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

@@ -11,10 +11,7 @@ import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
 
 // Dataset normalization
 
-fun main() {
-
-    // work in context with broadcast methods
-    BroadcastDoubleTensorAlgebra {
+fun main() = BroadcastDoubleTensorAlgebra {  // work in context with broadcast methods
     // take dataset of 5-element vectors from normal distribution
     val dataset = randomNormal(intArrayOf(100, 5)) * 1.5 // all elements from N(0, 1.5)
 
@@ -42,5 +39,4 @@ fun main() {
 
     println("Mean of scaled:\n${datasetScaled.mean(0, false)}")
     println("Mean of scaled:\n${datasetScaled.std(0, false)}")
-    }
 }

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

@@ -6,15 +6,12 @@
 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
 
 // solving linear system with LUP decomposition
 
-fun main () {
-
-    // work in context with linear operations
-    BroadcastDoubleTensorAlgebra {
+fun main() = BroadcastDoubleTensorAlgebra {// work in context with linear operations
 
     // set true value of x
     val trueX = fromArray(
@@ -68,7 +65,7 @@ fun main () {
     fun solveLT(l: DoubleTensor, b: DoubleTensor): DoubleTensor {
         val n = l.shape[0]
         val x = zeros(intArrayOf(n))
-            for (i in 0 until n){
+        for (i in 0 until n) {
             x[intArrayOf(i)] = (b[intArrayOf(i)] - l[i].dot(x).value()) / l[intArrayOf(i, i)]
         }
         return x
@@ -93,5 +90,4 @@ fun main () {
 
     println("True x:\n$trueX")
     println("x founded with LU method:\n$x")
-    }
 }

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

@@ -25,7 +25,7 @@ interface Layer {
 // activation layer
 open class Activation(
     val activation: (DoubleTensor) -> DoubleTensor,
-    val activationDer: (DoubleTensor) -> DoubleTensor
+    val activationDer: (DoubleTensor) -> DoubleTensor,
 ) : Layer {
     override fun forward(input: DoubleTensor): DoubleTensor {
         return activation(input)
@@ -62,7 +62,7 @@ class Sigmoid : Activation(::sigmoid, ::sigmoidDer)
 class Dense(
     private val inputUnits: Int,
     private val outputUnits: Int,
-    private val learningRate: Double = 0.1
+    private val learningRate: Double = 0.1,
 ) : Layer {
 
     private val weights: DoubleTensor = DoubleTensorAlgebra {
@@ -74,8 +74,8 @@ class Dense(
 
     private val bias: DoubleTensor = DoubleTensorAlgebra { zeros(intArrayOf(outputUnits)) }
 
-    override fun forward(input: DoubleTensor): DoubleTensor {
-        return BroadcastDoubleTensorAlgebra { (input dot weights) + bias }
+    override fun forward(input: DoubleTensor): DoubleTensor = BroadcastDoubleTensorAlgebra {
+        (input dot weights) + bias
     }
 
     override fun backward(input: DoubleTensor, outputError: DoubleTensor): DoubleTensor = DoubleTensorAlgebra {
@@ -175,8 +175,7 @@ class NeuralNetwork(private val layers: List<Layer>) {
 
 
 @OptIn(ExperimentalStdlibApi::class)
-fun main() {
-    BroadcastDoubleTensorAlgebra {
+fun main() = BroadcastDoubleTensorAlgebra {
     val features = 5
     val sampleSize = 250
     val trainSize = 180
@@ -237,5 +236,4 @@ fun main() {
     val acc = accuracy(yTest, predictionLabels)
     println("Test accuracy:$acc")
 
-    }
 }

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

@@ -11,12 +11,9 @@ import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
 
 // simple PCA
 
-fun main(){
+fun main() = BroadcastDoubleTensorAlgebra {  // work in context with broadcast methods
     val seed = 100500L
 
-    // work in context with broadcast methods
-    BroadcastDoubleTensorAlgebra {
-
     // assume x is range from 0 until 10
     val x = fromArray(
         intArrayOf(10),
@@ -74,5 +71,4 @@ fun main(){
     val restored = (datasetReduced[n] dot v.view(intArrayOf(1, 2))) * std + mean
     println("Original value:\n${dataset[n]}")
     println("Restored value:\n$restored")
-    }
 }

kmath-core/src/commonMain/kotlin/space/kscience/kmath/data/ColumnarData.kt

@@ -19,6 +19,9 @@ import space.kscience.kmath.structures.Buffer
 public interface ColumnarData<out T> {
     public val size: Int
 
+    /**
+     * Provide a column by symbol or null if column with given symbol is not defined
+     */
     public operator fun get(symbol: Symbol): Buffer<T>?
 }
 

settings.gradle.kts

@@ -5,7 +5,7 @@ pluginManagement {
         maven("https://repo.kotlin.link")
     }
 
-    val toolsVersion = "0.9.6"
+    val toolsVersion = "0.9.7"
     val kotlinVersion = "1.5.0"
 
     plugins {