Extending from Algebra

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt

@@ -5,13 +5,15 @@
 
 package space.kscience.kmath.tensors.api
 
+import space.kscience.kmath.operations.Algebra
+
 /**
  * Algebra over a ring on [TensorStructure].
  * For more information: https://proofwiki.org/wiki/Definition:Algebra_over_Ring
  *
  * @param T the type of items in the tensors.
  */
-public interface TensorAlgebra<T> {
+public interface TensorAlgebra<T>: Algebra<TensorStructure<T>> {
 
     /**
      * Returns a single tensor value of unit dimension. The tensor shape must be equal to [1].

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

@@ -14,7 +14,7 @@ import space.kscience.kmath.tensors.core.broadcastTo
  * Basic linear algebra operations implemented with broadcasting.
  * For more information: https://pytorch.org/docs/stable/notes/broadcasting.html
  */
-public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
+public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
 
     override fun TensorStructure<Double>.plus(other: TensorStructure<Double>): DoubleTensor {
         val broadcast = broadcastTensors(tensor, other.tensor)
@@ -89,5 +89,4 @@ public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
                 newOther.buffer.array()[tensor.bufferStart + i]
         }
     }
-
 }

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

@@ -11,10 +11,9 @@ import space.kscience.kmath.tensors.core.DoubleTensor
 import space.kscience.kmath.tensors.core.tensor
 import kotlin.math.*
 
-public class DoubleAnalyticTensorAlgebra:
+public object DoubleAnalyticTensorAlgebra :
     AnalyticTensorAlgebra<Double>,
-        DoubleTensorAlgebra()
+    DoubleTensorAlgebra() {
-{
     override fun TensorStructure<Double>.exp(): DoubleTensor = tensor.map(::exp)
 
     override fun TensorStructure<Double>.log(): DoubleTensor = tensor.map(::ln)
@@ -49,7 +48,4 @@ public class DoubleAnalyticTensorAlgebra:
 
     override fun TensorStructure<Double>.floor(): DoubleTensor = tensor.map(::floor)
 
-}
+}
-
-public inline fun <R> DoubleAnalyticTensorAlgebra(block: DoubleAnalyticTensorAlgebra.() -> R): R =
-    DoubleAnalyticTensorAlgebra().block()

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

@@ -21,7 +21,7 @@ import space.kscience.kmath.tensors.core.pivInit
 import kotlin.math.min
 
 
-public class DoubleLinearOpsTensorAlgebra :
+public object DoubleLinearOpsTensorAlgebra :
     LinearOpsTensorAlgebra<Double>,
     DoubleTensorAlgebra() {
 
@@ -30,8 +30,8 @@ public class DoubleLinearOpsTensorAlgebra :
     override fun TensorStructure<Double>.det(): DoubleTensor = detLU(1e-9)
 
     public fun TensorStructure<Double>.luFactor(epsilon: Double): Pair<DoubleTensor, IntTensor> =
-        computeLU(tensor, epsilon) ?:
+        computeLU(tensor, epsilon)
-        throw RuntimeException("Tensor contains matrices which are singular at precision $epsilon")
+            ?: throw RuntimeException("Tensor contains matrices which are singular at precision $epsilon")
 
     public fun TensorStructure<Double>.luFactor(): Pair<DoubleTensor, IntTensor> = luFactor(1e-9)
 
@@ -175,8 +175,4 @@ public class DoubleLinearOpsTensorAlgebra :
 
     override fun TensorStructure<Double>.lu(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> = lu(1e-9)
 
-
+}
-}
-
-public inline fun <R> DoubleLinearOpsTensorAlgebra(block: DoubleLinearOpsTensorAlgebra.() -> R): R =
-    DoubleLinearOpsTensorAlgebra().block()

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

@@ -23,6 +23,7 @@ import kotlin.math.abs
 
 public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
 
+    public companion object : DoubleTensorAlgebra()
 
     override fun TensorStructure<Double>.value(): Double {
         check(tensor.shape contentEquals intArrayOf(1)) {
@@ -395,7 +396,3 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         DoubleTensor(tensor.shape, getRandomNormals(tensor.shape.reduce(Int::times), seed))
 
 }
-
-
-public inline fun <R> DoubleTensorAlgebra(block: DoubleTensorAlgebra.() -> R): R =
-    DoubleTensorAlgebra().block()

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

@@ -1,11 +1,7 @@
 package space.kscience.kmath.tensors.core
 
-import space.kscience.kmath.tensors.core.algebras.BroadcastDoubleTensorAlgebra
 import kotlin.math.max
 
-public inline fun <R> BroadcastDoubleTensorAlgebra(block: BroadcastDoubleTensorAlgebra.() -> R): R =
-    BroadcastDoubleTensorAlgebra().block()
-
 internal inline fun multiIndexBroadCasting(tensor: DoubleTensor, resTensor: DoubleTensor, linearSize: Int) {
     for (linearIndex in 0 until linearSize) {
         val totalMultiIndex = resTensor.linearStructure.index(linearIndex)

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

@@ -4,6 +4,7 @@ import space.kscience.kmath.nd.MutableStructure1D
 import space.kscience.kmath.nd.MutableStructure2D
 import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.nd.as2D
+import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra
 import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
 import kotlin.math.abs
@@ -251,7 +252,7 @@ internal inline fun DoubleLinearOpsTensorAlgebra.qrHelper(
                 }
             }
         }
-        r[j, j] = DoubleAnalyticTensorAlgebra { (v dot v).sqrt().value() }
+        r[j, j] = DoubleAnalyticTensorAlgebra.invoke { (v dot v).sqrt().value() }
         for (i in 0 until n) {
             qM[i, j] = vv[i] / r[j, j]
         }

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

@@ -1,13 +1,15 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.operations.invoke
+import space.kscience.kmath.tensors.core.algebras.BroadcastDoubleTensorAlgebra
 import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 import kotlin.test.Test
 import kotlin.test.assertTrue
 
-class TestBroadcasting {
+internal class TestBroadcasting {
 
     @Test
-    fun broadcastShapes() = DoubleTensorAlgebra {
+    fun broadcastShapes() = DoubleTensorAlgebra.invoke {
         assertTrue(
             broadcastShapes(
                 intArrayOf(2, 3), intArrayOf(1, 3), intArrayOf(1, 1, 1)
@@ -22,7 +24,7 @@ class TestBroadcasting {
     }
 
     @Test
-    fun broadcastTo() = DoubleTensorAlgebra {
+    fun broadcastTo() = DoubleTensorAlgebra.invoke {
         val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
 
@@ -32,7 +34,7 @@ class TestBroadcasting {
     }
 
     @Test
-    fun broadcastTensors() = DoubleTensorAlgebra {
+    fun broadcastTensors() = DoubleTensorAlgebra.invoke {
         val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
         val tensor3 = fromArray(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
@@ -49,7 +51,7 @@ class TestBroadcasting {
     }
 
     @Test
-    fun broadcastOuterTensors() = DoubleTensorAlgebra {
+    fun broadcastOuterTensors() = DoubleTensorAlgebra.invoke {
         val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
         val tensor3 = fromArray(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
@@ -66,7 +68,7 @@ class TestBroadcasting {
     }
 
     @Test
-    fun broadcastOuterTensorsShapes() = DoubleTensorAlgebra {
+    fun broadcastOuterTensorsShapes() = DoubleTensorAlgebra.invoke {
         val tensor1 = fromArray(intArrayOf(2, 1, 3, 2, 3), DoubleArray(2 * 1 * 3 * 2 * 3) {0.0})
         val tensor2 = fromArray(intArrayOf(4, 2, 5, 1, 3, 3), DoubleArray(4 * 2 * 5 * 1 * 3 * 3) {0.0})
         val tensor3 = fromArray(intArrayOf(1, 1), doubleArrayOf(500.0))
@@ -79,7 +81,7 @@ class TestBroadcasting {
     }
 
     @Test
-    fun minusTensor() = BroadcastDoubleTensorAlgebra {
+    fun minusTensor() = BroadcastDoubleTensorAlgebra.invoke {
         val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
         val tensor3 = fromArray(intArrayOf(1, 1, 1), doubleArrayOf(500.0))

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

@@ -1,12 +1,13 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra
 import kotlin.math.abs
 import kotlin.math.exp
 import kotlin.test.Test
 import kotlin.test.assertTrue
 
-class TestDoubleAnalyticTensorAlgebra {
+internal class TestDoubleAnalyticTensorAlgebra {
 
     val shape = intArrayOf(2, 1, 3, 2)
     val buffer = doubleArrayOf(27.1, 20.0, 19.84, 23.123, 0.0, 1.0, 3.23, 133.7, 25.3, 100.3, 11.0, 12.012)
@@ -26,7 +27,7 @@ class TestDoubleAnalyticTensorAlgebra {
     }
 
     @Test
-    fun testExp() = DoubleAnalyticTensorAlgebra {
+    fun testExp() = DoubleAnalyticTensorAlgebra.invoke {
         tensor.exp().let {
             assertTrue { shape contentEquals it.shape }
             assertTrue { buffer.fmap(::exp).epsEqual(it.buffer.array())}

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

@@ -1,15 +1,16 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
 import kotlin.math.abs
 import kotlin.test.Test
 import kotlin.test.assertEquals
 import kotlin.test.assertTrue
 
-class TestDoubleLinearOpsTensorAlgebra {
+internal class TestDoubleLinearOpsTensorAlgebra {
 
     @Test
-    fun testDetLU() = DoubleLinearOpsTensorAlgebra {
+    fun testDetLU() = DoubleLinearOpsTensorAlgebra.invoke {
         val tensor = fromArray(
             intArrayOf(2, 2, 2),
             doubleArrayOf(
@@ -34,7 +35,7 @@ class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testDet() = DoubleLinearOpsTensorAlgebra {
+    fun testDet() = DoubleLinearOpsTensorAlgebra.invoke {
         val expectedValue = 0.019827417
         val m = fromArray(
             intArrayOf(3, 3), doubleArrayOf(
@@ -48,7 +49,7 @@ class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testDetSingle() = DoubleLinearOpsTensorAlgebra {
+    fun testDetSingle() = DoubleLinearOpsTensorAlgebra.invoke {
         val expectedValue = 48.151623
         val m = fromArray(
             intArrayOf(1, 1), doubleArrayOf(
@@ -60,7 +61,7 @@ class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testInvLU() = DoubleLinearOpsTensorAlgebra {
+    fun testInvLU() = DoubleLinearOpsTensorAlgebra.invoke {
         val tensor = fromArray(
             intArrayOf(2, 2, 2),
             doubleArrayOf(
@@ -85,14 +86,14 @@ class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testScalarProduct() = DoubleLinearOpsTensorAlgebra {
+    fun testScalarProduct() = DoubleLinearOpsTensorAlgebra.invoke {
         val a = fromArray(intArrayOf(3), doubleArrayOf(1.8, 2.5, 6.8))
         val b = fromArray(intArrayOf(3), doubleArrayOf(5.5, 2.6, 6.4))
         assertEquals(a.dot(b).value(), 59.92)
     }
 
     @Test
-    fun testQR() = DoubleLinearOpsTensorAlgebra {
+    fun testQR() = DoubleLinearOpsTensorAlgebra.invoke {
         val shape = intArrayOf(2, 2, 2)
         val buffer = doubleArrayOf(
             1.0, 3.0,
@@ -110,11 +111,10 @@ class TestDoubleLinearOpsTensorAlgebra {
 
         assertTrue((q dot r).eq(tensor))
 
-        //todo check orthogonality/upper triang.
     }
 
     @Test
-    fun testLU() = DoubleLinearOpsTensorAlgebra {
+    fun testLU() = DoubleLinearOpsTensorAlgebra.invoke {
         val shape = intArrayOf(2, 2, 2)
         val buffer = doubleArrayOf(
             1.0, 3.0,
@@ -134,7 +134,7 @@ class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testCholesky() = DoubleLinearOpsTensorAlgebra {
+    fun testCholesky() = DoubleLinearOpsTensorAlgebra.invoke {
         val tensor = randNormal(intArrayOf(2, 5, 5), 0)
         val sigma = (tensor dot tensor.transpose()) + diagonalEmbedding(
             fromArray(intArrayOf(2, 5), DoubleArray(10) { 0.1 })
@@ -145,7 +145,7 @@ class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testSVD1D() = DoubleLinearOpsTensorAlgebra {
+    fun testSVD1D() = DoubleLinearOpsTensorAlgebra.invoke {
         val tensor2 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
 
         val res = svd1d(tensor2)
@@ -156,13 +156,13 @@ class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testSVD() = DoubleLinearOpsTensorAlgebra {
+    fun testSVD() = DoubleLinearOpsTensorAlgebra.invoke{
         testSVDFor(fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)))
         testSVDFor(fromArray(intArrayOf(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0)))
     }
 
     @Test
-    fun testBatchedSVD() = DoubleLinearOpsTensorAlgebra {
+    fun testBatchedSVD() = DoubleLinearOpsTensorAlgebra.invoke {
         val tensor = randNormal(intArrayOf(2, 5, 3), 0)
         val (tensorU, tensorS, tensorV) = tensor.svd()
         val tensorSVD = tensorU dot (diagonalEmbedding(tensorS) dot tensorV.transpose())
@@ -170,7 +170,7 @@ class TestDoubleLinearOpsTensorAlgebra {
     }
 
     @Test
-    fun testBatchedSymEig() = DoubleLinearOpsTensorAlgebra {
+    fun testBatchedSymEig() = DoubleLinearOpsTensorAlgebra.invoke {
         val tensor = randNormal(shape = intArrayOf(2, 3, 3), 0)
         val tensorSigma = tensor + tensor.transpose()
         val (tensorS, tensorV) = tensorSigma.symEig()

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

@@ -4,6 +4,7 @@ import space.kscience.kmath.nd.DefaultStrides
 import space.kscience.kmath.nd.MutableBufferND
 import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.nd.as2D
+import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.structures.DoubleBuffer
 import space.kscience.kmath.structures.asMutableBuffer
 import space.kscience.kmath.structures.toDoubleArray
@@ -12,17 +13,17 @@ import kotlin.test.Test
 import kotlin.test.assertEquals
 import kotlin.test.assertTrue
 
-class TestDoubleTensor {
+internal class TestDoubleTensor {
 
     @Test
-    fun valueTest() = DoubleTensorAlgebra {
+    fun valueTest() = DoubleTensorAlgebra.invoke {
         val value = 12.5
         val tensor = fromArray(intArrayOf(1), doubleArrayOf(value))
         assertEquals(tensor.value(), value)
     }
 
     @Test
-    fun stridesTest() = DoubleTensorAlgebra {
+    fun stridesTest() = DoubleTensorAlgebra.invoke {
         val tensor = fromArray(intArrayOf(2, 2), doubleArrayOf(3.5, 5.8, 58.4, 2.4))
         assertEquals(tensor[intArrayOf(0, 1)], 5.8)
         assertTrue(
@@ -31,7 +32,7 @@ class TestDoubleTensor {
     }
 
     @Test
-    fun getTest() = DoubleTensorAlgebra {
+    fun getTest() = DoubleTensorAlgebra.invoke {
         val tensor = fromArray(intArrayOf(1, 2, 2), doubleArrayOf(3.5, 5.8, 58.4, 2.4))
         val matrix = tensor[0].as2D()
         assertEquals(matrix[0, 1], 5.8)

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

@@ -1,29 +1,30 @@
 package space.kscience.kmath.tensors.core
 
 
+import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 import kotlin.test.Test
 import kotlin.test.assertFalse
 import kotlin.test.assertTrue
 
-class TestDoubleTensorAlgebra {
+internal class TestDoubleTensorAlgebra {
 
     @Test
-    fun doublePlus() = DoubleTensorAlgebra {
+    fun doublePlus() = DoubleTensorAlgebra.invoke {
         val tensor = fromArray(intArrayOf(2), doubleArrayOf(1.0, 2.0))
         val res = 10.0 + tensor
         assertTrue(res.buffer.array() contentEquals doubleArrayOf(11.0, 12.0))
     }
 
     @Test
-    fun doubleDiv() = DoubleTensorAlgebra {
+    fun doubleDiv() = DoubleTensorAlgebra.invoke {
         val tensor = fromArray(intArrayOf(2), doubleArrayOf(2.0, 4.0))
         val res = 2.0/tensor
         assertTrue(res.buffer.array() contentEquals doubleArrayOf(1.0, 0.5))
     }
 
     @Test
-    fun divDouble() = DoubleTensorAlgebra {
+    fun divDouble() = DoubleTensorAlgebra.invoke {
         val tensor = fromArray(intArrayOf(2), doubleArrayOf(10.0, 5.0))
         val res = tensor / 2.5
         assertTrue(res.buffer.array() contentEquals doubleArrayOf(4.0, 2.0))
@@ -39,7 +40,7 @@ class TestDoubleTensorAlgebra {
     }
 
     @Test
-    fun transpose3x2() = DoubleTensorAlgebra {
+    fun transpose3x2() = DoubleTensorAlgebra.invoke {
         val tensor = fromArray(intArrayOf(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val res = tensor.transpose(1, 0)
 
@@ -48,7 +49,7 @@ class TestDoubleTensorAlgebra {
     }
 
     @Test
-    fun transpose1x2x3() = DoubleTensorAlgebra {
+    fun transpose1x2x3() = DoubleTensorAlgebra.invoke {
         val tensor = fromArray(intArrayOf(1, 2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val res01 = tensor.transpose(0, 1)
         val res02 = tensor.transpose(-3, 2)
@@ -64,7 +65,7 @@ class TestDoubleTensorAlgebra {
     }
 
     @Test
-    fun linearStructure() = DoubleTensorAlgebra {
+    fun linearStructure() = DoubleTensorAlgebra.invoke {
         val shape = intArrayOf(3)
         val tensorA = full(value = -4.5, shape = shape)
         val tensorB = full(value = 10.9, shape = shape)
@@ -96,7 +97,7 @@ class TestDoubleTensorAlgebra {
     }
 
     @Test
-    fun dot() = DoubleTensorAlgebra {
+    fun dot() = DoubleTensorAlgebra.invoke {
         val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor11 = fromArray(intArrayOf(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = fromArray(intArrayOf(3), doubleArrayOf(10.0, 20.0, 30.0))
@@ -132,7 +133,7 @@ class TestDoubleTensorAlgebra {
     }
 
     @Test
-    fun diagonalEmbedding() = DoubleTensorAlgebra {
+    fun diagonalEmbedding() = DoubleTensorAlgebra.invoke {
         val tensor1 = fromArray(intArrayOf(3), doubleArrayOf(10.0, 20.0, 30.0))
         val tensor2 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor3 = zeros(intArrayOf(2, 3, 4, 5))
@@ -165,7 +166,7 @@ class TestDoubleTensorAlgebra {
     }
 
     @Test
-    fun testEq() = DoubleTensorAlgebra {
+    fun testEq() = DoubleTensorAlgebra.invoke {
         val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor3 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 5.0))