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

@@ -40,7 +40,7 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
     //https://pytorch.org/docs/stable/generated/torch.diag_embed.html
     public fun diagonalEmbedding(
         diagonalEntries: TensorType,
-        offset: Int = 0, dim1: Int = -2, dim2: Int = -1
+        offset: Int = 0, dim1: Int = 0, dim2: Int = 1
     ): TensorType
 
 }

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

@@ -362,6 +362,12 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         return true
     }
 
+    public fun randNormal(shape: IntArray, seed: Long = 0): DoubleTensor =
+        DoubleTensor(shape, getRandomNormals(shape.reduce(Int::times), seed))
+
+    public fun DoubleTensor.randNormalLike(seed: Long = 0): DoubleTensor =
+        DoubleTensor(this.shape, getRandomNormals(this.shape.reduce(Int::times), seed))
+
 }
 
 

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

@@ -1,7 +1,5 @@
 package space.kscience.kmath.tensors.core
 
-import space.kscience.kmath.stat.RandomGenerator
-import space.kscience.kmath.stat.samplers.BoxMullerNormalizedGaussianSampler
 import space.kscience.kmath.structures.*
 import kotlin.random.Random
 import kotlin.math.*

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

@@ -7,21 +7,6 @@ import kotlin.test.assertTrue
 
 class TestDoubleLinearOpsTensorAlgebra {
 
-    private val eps = 1e-5
-
-    private fun Double.epsEqual(other: Double): Boolean {
-        return abs(this - other) < eps
-    }
-
-    fun DoubleArray.epsEqual(other: DoubleArray, eps: Double = 1e-5): Boolean {
-        for ((elem1, elem2) in this.asSequence().zip(other.asSequence())) {
-            if (abs(elem1 - elem2) > eps) {
-                return false
-            }
-        }
-        return true
-    }
-
     @Test
     fun testDetLU() = DoubleLinearOpsTensorAlgebra {
         val tensor = fromArray(
@@ -136,8 +121,35 @@ class TestDoubleLinearOpsTensorAlgebra {
 
     @Test
     fun testSVD() = DoubleLinearOpsTensorAlgebra {
-        val epsilon = 1e-10
+        testSVDFor(fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)))
-        fun test_tensor(tensor: DoubleTensor) {
+        testSVDFor(fromArray(intArrayOf(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0)))
+    }
+
+    @Test
+    fun testBatchedSVD() = DoubleLinearOpsTensorAlgebra {
+        val tensor = randNormal(intArrayOf(7, 5, 3), 0)
+        val (tensorU, tensorS, tensorV) = tensor.svd()
+        val tensorSVD = tensorU dot (diagonalEmbedding(tensorS,0,1,2) dot tensorV)
+        println(tensor.eq(tensorSVD))
+    }
+
+
+}
+
+private inline fun Double.epsEqual(other: Double, eps: Double = 1e-5): Boolean {
+    return abs(this - other) < eps
+}
+
+private inline fun DoubleArray.epsEqual(other: DoubleArray, eps: Double = 1e-5): Boolean {
+    for ((elem1, elem2) in this.asSequence().zip(other.asSequence())) {
+        if (abs(elem1 - elem2) > eps) {
+            return false
+        }
+    }
+    return true
+}
+
+private inline fun DoubleLinearOpsTensorAlgebra.testSVDFor(tensor: DoubleTensor, epsilon: Double = 1e-10): Unit {
     val svd = tensor.svd()
 
     val tensorSVD = svd.first
@@ -150,8 +162,5 @@ class TestDoubleLinearOpsTensorAlgebra {
         assertTrue { abs(x1 - x2) < epsilon }
     }
 }
-        test_tensor(fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)))
-        test_tensor(fromArray(intArrayOf(2, 2), doubleArrayOf(-1.0, 0.0, 239.0, 238.0)))
 
-    }
+
-}