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

@@ -11,36 +11,32 @@ import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
 
 // Dataset normalization
 
-fun main() {
+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)
 
-    // work in context with broadcast methods
-    BroadcastDoubleTensorAlgebra {
-        // take dataset of 5-element vectors from normal distribution
-        val dataset = randomNormal(intArrayOf(100, 5)) * 1.5 // all elements from N(0, 1.5)
-
-        dataset += fromArray(
-            intArrayOf(5),
-            doubleArrayOf(0.0, 1.0, 1.5, 3.0, 5.0) // rows means
-        )
+    dataset += fromArray(
+        intArrayOf(5),
+        doubleArrayOf(0.0, 1.0, 1.5, 3.0, 5.0) // rows means
+    )
 
 
-        // find out mean and standard deviation of each column
-        val mean = dataset.mean(0, false)
-        val std = dataset.std(0, false)
+    // find out mean and standard deviation of each column
+    val mean = dataset.mean(0, false)
+    val std = dataset.std(0, false)
 
-        println("Mean:\n$mean")
-        println("Standard deviation:\n$std")
+    println("Mean:\n$mean")
+    println("Standard deviation:\n$std")
 
-        // also we can calculate other statistic as minimum and maximum of rows
-        println("Minimum:\n${dataset.min(0, false)}")
-        println("Maximum:\n${dataset.max(0, false)}")
+    // also we can calculate other statistic as minimum and maximum of rows
+    println("Minimum:\n${dataset.min(0, false)}")
+    println("Maximum:\n${dataset.max(0, false)}")
 
-        // now we can scale dataset with mean normalization
-        val datasetScaled = (dataset - mean) / std
+    // now we can scale dataset with mean normalization
+    val datasetScaled = (dataset - mean) / std
 
-        // find out mean and std of scaled dataset
+    // find out mean and std of scaled dataset
 
-        println("Mean of scaled:\n${datasetScaled.mean(0, false)}")
-        println("Mean of scaled:\n${datasetScaled.std(0, false)}")
-    }
+    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

@@ -6,92 +6,88 @@
 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 () {
+fun main() = BroadcastDoubleTensorAlgebra {// work in context with linear operations
 
-    // work in context with linear operations
-    BroadcastDoubleTensorAlgebra {
+    // set true value of x
+    val trueX = fromArray(
+        intArrayOf(4),
+        doubleArrayOf(-2.0, 1.5, 6.8, -2.4)
+    )
 
-        // set true value of x
-        val trueX = fromArray(
-            intArrayOf(4),
-            doubleArrayOf(-2.0, 1.5, 6.8, -2.4)
+    // and A matrix
+    val a = fromArray(
+        intArrayOf(4, 4),
+        doubleArrayOf(
+            0.5, 10.5, 4.5, 1.0,
+            8.5, 0.9, 12.8, 0.1,
+            5.56, 9.19, 7.62, 5.45,
+            1.0, 2.0, -3.0, -2.5
         )
+    )
 
-        // and A matrix
-        val a = fromArray(
-            intArrayOf(4, 4),
-            doubleArrayOf(
-                0.5, 10.5, 4.5, 1.0,
-                8.5, 0.9, 12.8, 0.1,
-                5.56, 9.19, 7.62, 5.45,
-                1.0, 2.0, -3.0, -2.5
-            )
-        )
+    // calculate y value
+    val b = a dot trueX
 
-        // calculate y value
-        val b = a dot trueX
+    // check out A and b
+    println("A:\n$a")
+    println("b:\n$b")
 
-        // check out A and b
-        println("A:\n$a")
-        println("b:\n$b")
+    // solve `Ax = b` system using LUP decomposition
 
-        // solve `Ax = b` system using LUP decomposition
+    // get P, L, U such that PA = LU
+    val (p, l, u) = a.lu()
 
-        // get P, L, U such that PA = LU
-        val (p, l, u) = a.lu()
+    // check that P is permutation matrix
+    println("P:\n$p")
+    // L is lower triangular matrix and U is upper triangular matrix
+    println("L:\n$l")
+    println("U:\n$u")
+    // and PA = LU
+    println("PA:\n${p dot a}")
+    println("LU:\n${l dot u}")
 
-        // check that P is permutation matrix
-        println("P:\n$p")
-        // L is lower triangular matrix and U is upper triangular matrix
-        println("L:\n$l")
-        println("U:\n$u")
-        // and PA = LU
-        println("PA:\n${p dot a}")
-        println("LU:\n${l dot u}")
-
-     /* Ax = b;
-        PAx = Pb;
-        LUx = Pb;
-        let y = Ux, then
-        Ly = Pb -- this system can be easily solved, since the matrix L is lower triangular;
-        Ux = y can be solved the same way, since the matrix L is upper triangular
-         */
+    /* Ax = b;
+       PAx = Pb;
+       LUx = Pb;
+       let y = Ux, then
+       Ly = Pb -- this system can be easily solved, since the matrix L is lower triangular;
+       Ux = y can be solved the same way, since the matrix L is upper triangular
+        */
 
 
 
-        // this function returns solution x of a system lx = b, l should be lower triangular
-        fun solveLT(l: DoubleTensor, b: DoubleTensor): DoubleTensor {
-            val n = l.shape[0]
-            val x = zeros(intArrayOf(n))
-            for (i in 0 until n){
-                x[intArrayOf(i)] = (b[intArrayOf(i)] -  l[i].dot(x).value()) / l[intArrayOf(i, i)]
-            }
-            return x
-        }
-
-        val y = solveLT(l, p dot b)
-
-        // solveLT(l, b) function can be easily adapted for upper triangular matrix by the permutation matrix revMat
-        // create it by placing ones on side diagonal
-        val revMat = u.zeroesLike()
-        val n = revMat.shape[0]
+    // this function returns solution x of a system lx = b, l should be lower triangular
+    fun solveLT(l: DoubleTensor, b: DoubleTensor): DoubleTensor {
+        val n = l.shape[0]
+        val x = zeros(intArrayOf(n))
         for (i in 0 until n) {
-            revMat[intArrayOf(i, n - 1 - i)] = 1.0
+            x[intArrayOf(i)] = (b[intArrayOf(i)] - l[i].dot(x).value()) / l[intArrayOf(i, i)]
         }
-
-        // solution of system ux = b, u should be upper triangular
-        fun solveUT(u: DoubleTensor, b: DoubleTensor): DoubleTensor = revMat dot solveLT(
-            revMat dot u dot revMat, revMat dot b
-        )
-
-        val x = solveUT(u, y)
-
-        println("True x:\n$trueX")
-        println("x founded with LU method:\n$x")
+        return x
     }
+
+    val y = solveLT(l, p dot b)
+
+    // solveLT(l, b) function can be easily adapted for upper triangular matrix by the permutation matrix revMat
+    // create it by placing ones on side diagonal
+    val revMat = u.zeroesLike()
+    val n = revMat.shape[0]
+    for (i in 0 until n) {
+        revMat[intArrayOf(i, n - 1 - i)] = 1.0
+    }
+
+    // solution of system ux = b, u should be upper triangular
+    fun solveUT(u: DoubleTensor, b: DoubleTensor): DoubleTensor = revMat dot solveLT(
+        revMat dot u dot revMat, revMat dot b
+    )
+
+    val x = solveUT(u, y)
+
+    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 {
@@ -116,7 +116,7 @@ class NeuralNetwork(private val layers: List<Layer>) {
             onesForAnswers[intArrayOf(index, label)] = 1.0
         }
 
-        val softmaxValue =  yPred.exp() / yPred.exp().sum(dim = 1, keepDim = true)
+        val softmaxValue = yPred.exp() / yPred.exp().sum(dim = 1, keepDim = true)
 
         (-onesForAnswers + softmaxValue) / (yPred.shape[0].toDouble())
     }
@@ -175,67 +175,65 @@ class NeuralNetwork(private val layers: List<Layer>) {
 
 
 @OptIn(ExperimentalStdlibApi::class)
-fun main() {
-    BroadcastDoubleTensorAlgebra {
-        val features = 5
-        val sampleSize = 250
-        val trainSize = 180
-        //val testSize = sampleSize - trainSize
+fun main() = BroadcastDoubleTensorAlgebra {
+    val features = 5
+    val sampleSize = 250
+    val trainSize = 180
+    //val testSize = sampleSize - trainSize
 
-        // take sample of features from normal distribution
-        val x = randomNormal(intArrayOf(sampleSize, features), seed) * 2.5
+    // take sample of features from normal distribution
+    val x = randomNormal(intArrayOf(sampleSize, features), seed) * 2.5
 
-        x += fromArray(
-            intArrayOf(5),
-            doubleArrayOf(0.0, -1.0, -2.5, -3.0, 5.5) // rows means
-        )
+    x += fromArray(
+        intArrayOf(5),
+        doubleArrayOf(0.0, -1.0, -2.5, -3.0, 5.5) // rows means
+    )
 
 
-        // define class like '1' if the sum of features > 0 and '0' otherwise
-        val y = fromArray(
-            intArrayOf(sampleSize, 1),
-            DoubleArray(sampleSize) { i ->
-                if (x[i].sum() > 0.0) {
-                    1.0
-                } else {
-                    0.0
-                }
+    // define class like '1' if the sum of features > 0 and '0' otherwise
+    val y = fromArray(
+        intArrayOf(sampleSize, 1),
+        DoubleArray(sampleSize) { i ->
+            if (x[i].sum() > 0.0) {
+                1.0
+            } else {
+                0.0
             }
-        )
-
-        // split train ans test
-        val trainIndices = (0 until trainSize).toList().toIntArray()
-        val testIndices = (trainSize until sampleSize).toList().toIntArray()
-
-        val xTrain = x.rowsByIndices(trainIndices)
-        val yTrain = y.rowsByIndices(trainIndices)
-
-        val xTest = x.rowsByIndices(testIndices)
-        val yTest = y.rowsByIndices(testIndices)
-
-        // build model
-        val layers = buildList {
-            add(Dense(features, 64))
-            add(ReLU())
-            add(Dense(64, 16))
-            add(ReLU())
-            add(Dense(16, 2))
-            add(Sigmoid())
         }
-        val model = NeuralNetwork(layers)
+    )
 
-        // fit it with train data
-        model.fit(xTrain, yTrain, batchSize = 20, epochs = 10)
+    // split train ans test
+    val trainIndices = (0 until trainSize).toList().toIntArray()
+    val testIndices = (trainSize until sampleSize).toList().toIntArray()
 
-        // make prediction
-        val prediction = model.predict(xTest)
+    val xTrain = x.rowsByIndices(trainIndices)
+    val yTrain = y.rowsByIndices(trainIndices)
 
-        // process raw prediction via argMax
-        val predictionLabels = prediction.argMax(1, true)
-
-        // find out accuracy
-        val acc = accuracy(yTest, predictionLabels)
-        println("Test accuracy:$acc")
+    val xTest = x.rowsByIndices(testIndices)
+    val yTest = y.rowsByIndices(testIndices)
 
+    // build model
+    val layers = buildList {
+        add(Dense(features, 64))
+        add(ReLU())
+        add(Dense(64, 16))
+        add(ReLU())
+        add(Dense(16, 2))
+        add(Sigmoid())
     }
+    val model = NeuralNetwork(layers)
+
+    // fit it with train data
+    model.fit(xTrain, yTrain, batchSize = 20, epochs = 10)
+
+    // make prediction
+    val prediction = model.predict(xTest)
+
+    // process raw prediction via argMax
+    val predictionLabels = prediction.argMax(1, true)
+
+    // find out accuracy
+    val acc = accuracy(yTest, predictionLabels)
+    println("Test accuracy:$acc")
+
 }

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

@@ -11,68 +11,64 @@ 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),
+        (0 until 10).toList().map { it.toDouble() }.toDoubleArray()
+    )
 
-        // assume x is range from 0 until 10
-        val x = fromArray(
-            intArrayOf(10),
-            (0 until 10).toList().map { it.toDouble() }.toDoubleArray()
-        )
+    // take y dependent on x with noise
+    val y = 2.0 * x + (3.0 + x.randomNormalLike(seed) * 1.5)
 
-        // take y dependent on x with noise
-        val y = 2.0 * x + (3.0 + x.randomNormalLike(seed) * 1.5)
+    println("x:\n$x")
+    println("y:\n$y")
 
-        println("x:\n$x")
-        println("y:\n$y")
+    // stack them into single dataset
+    val dataset = stack(listOf(x, y)).transpose()
 
-        // stack them into single dataset
-        val dataset = stack(listOf(x, y)).transpose()
+    // normalize both x and y
+    val xMean = x.mean()
+    val yMean = y.mean()
 
-        // normalize both x and y
-        val xMean = x.mean()
-        val yMean = y.mean()
+    val xStd = x.std()
+    val yStd = y.std()
 
-        val xStd = x.std()
-        val yStd = y.std()
+    val xScaled = (x - xMean) / xStd
+    val yScaled = (y - yMean) / yStd
 
-        val xScaled = (x - xMean) / xStd
-        val yScaled = (y - yMean) / yStd
+    // save means ans standard deviations for further recovery
+    val mean = fromArray(
+        intArrayOf(2),
+        doubleArrayOf(xMean, yMean)
+    )
+    println("Means:\n$mean")
 
-        // save means ans standard deviations for further recovery
-        val mean = fromArray(
-            intArrayOf(2),
-            doubleArrayOf(xMean, yMean)
-        )
-        println("Means:\n$mean")
+    val std = fromArray(
+        intArrayOf(2),
+        doubleArrayOf(xStd, yStd)
+    )
+    println("Standard deviations:\n$std")
 
-        val std = fromArray(
-            intArrayOf(2),
-            doubleArrayOf(xStd, yStd)
-        )
-        println("Standard deviations:\n$std")
+    // calculate the covariance matrix of scaled x and y
+    val covMatrix = cov(listOf(xScaled, yScaled))
+    println("Covariance matrix:\n$covMatrix")
 
-        // calculate the covariance matrix of scaled x and y
-        val covMatrix = cov(listOf(xScaled, yScaled))
-        println("Covariance matrix:\n$covMatrix")
+    // and find out eigenvector of it
+    val (_, evecs) = covMatrix.symEig()
+    val v = evecs[0]
+    println("Eigenvector:\n$v")
 
-        // and find out eigenvector of it
-        val (_, evecs) = covMatrix.symEig()
-        val v = evecs[0]
-        println("Eigenvector:\n$v")
+    // reduce dimension of dataset
+    val datasetReduced  = v dot stack(listOf(xScaled, yScaled))
+    println("Reduced data:\n$datasetReduced")
 
-        // reduce dimension of dataset
-        val datasetReduced  = v dot stack(listOf(xScaled, yScaled))
-        println("Reduced data:\n$datasetReduced")
-
-        // we can restore original data from reduced data.
-        // for example, find 7th element of dataset
-        val n = 7
-        val restored = (datasetReduced[n] dot v.view(intArrayOf(1, 2))) * std + mean
-        println("Original value:\n${dataset[n]}")
-        println("Restored value:\n$restored")
-    }
+    // we can restore original data from reduced data.
+    // for example, find 7th element of dataset
+    val n = 7
+    val restored = (datasetReduced[n] dot v.view(intArrayOf(1, 2))) * std + mean
+    println("Original value:\n${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 {