rename bdot to matmul

CHANGELOG.md

@@ -9,7 +9,7 @@
 ### Changed
 - Kotlin 1.7.20
 - `LazyStructure` `deffered` -> `async` to comply with coroutines code style
-- Default `dot` operation in tensor algebra no longer support broadcasting. Instead `bdot` operation is added to `DoubleTensorAlgebra`.
+- Default `dot` operation in tensor algebra no longer support broadcasting. Instead `matmul` operation is added to `DoubleTensorAlgebra`.
 
 ### Deprecated
 

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

@@ -9,6 +9,7 @@
 package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.misc.PerformancePitfall
+import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.nd.*
 import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.structures.MutableBuffer
@@ -410,7 +411,8 @@ public open class DoubleTensorAlgebra :
      * @param other tensor to be multiplied.
      * @return a mathematical product of two tensors.
      */
-    public infix fun StructureND<Double>.bdot(other: StructureND<Double>): DoubleTensor {
+    @UnstableKMathAPI
+    public infix fun StructureND<Double>.matmul(other: StructureND<Double>): DoubleTensor {
         if (tensor.shape.size == 1 && other.shape.size == 1) {
             return DoubleTensor(intArrayOf(1), doubleArrayOf(tensor.times(other).tensor.mutableBuffer.array().sum()))
         }
@@ -460,7 +462,7 @@ public open class DoubleTensorAlgebra :
     }
 
     override fun StructureND<Double>.dot(other: StructureND<Double>): DoubleTensor {
-        return if (dimension in 0..2 && other.dimension in 0..2) bdot(other)
+        return if (dimension in 0..2 && other.dimension in 0..2) matmul(other)
         else error("Only vectors and matrices are allowed in non-broadcasting dot operation")
     }
 
@@ -945,7 +947,7 @@ public open class DoubleTensorAlgebra :
 
         val (u, s, v) = tensor.svd(epsilon)
         val shp = s.shape + intArrayOf(1)
-        val utv = u.transpose() bdot v
+        val utv = u.transpose() matmul v
         val n = s.shape.last()
         for (matrix in utv.matrixSequence()) {
             matrix.as2D().cleanSym(n)

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

@@ -115,7 +115,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
         assertTrue { q.shape contentEquals shape }
         assertTrue { r.shape contentEquals shape }
 
-        assertTrue((q bdot r).eq(tensor))
+        assertTrue((q matmul r).eq(tensor))
 
     }
 
@@ -136,17 +136,17 @@ internal class TestDoubleLinearOpsTensorAlgebra {
         assertTrue { l.shape contentEquals shape }
         assertTrue { u.shape contentEquals shape }
 
-        assertTrue((p bdot tensor).eq(l bdot u))
+        assertTrue((p matmul tensor).eq(l matmul u))
     }
 
     @Test
     fun testCholesky() = DoubleTensorAlgebra {
         val tensor = randomNormal(intArrayOf(2, 5, 5), 0)
-        val sigma = (tensor bdot tensor.transpose()) + diagonalEmbedding(
+        val sigma = (tensor matmul tensor.transpose()) + diagonalEmbedding(
             fromArray(intArrayOf(2, 5), DoubleArray(10) { 0.1 })
         )
         val low = sigma.cholesky()
-        val sigmChol = low bdot low.transpose()
+        val sigmChol = low matmul low.transpose()
         assertTrue(sigma.eq(sigmChol))
     }
 
@@ -171,7 +171,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
     fun testBatchedSVD() = DoubleTensorAlgebra {
         val tensor = randomNormal(intArrayOf(2, 5, 3), 0)
         val (tensorU, tensorS, tensorV) = tensor.svd()
-        val tensorSVD = tensorU bdot (diagonalEmbedding(tensorS) bdot tensorV.transpose())
+        val tensorSVD = tensorU matmul (diagonalEmbedding(tensorS) matmul tensorV.transpose())
         assertTrue(tensor.eq(tensorSVD))
     }
 
@@ -180,7 +180,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
         val tensor = randomNormal(shape = intArrayOf(2, 3, 3), 0)
         val tensorSigma = tensor + tensor.transpose()
         val (tensorS, tensorV) = tensorSigma.symEig()
-        val tensorSigmaCalc = tensorV bdot (diagonalEmbedding(tensorS) bdot tensorV.transpose())
+        val tensorSigmaCalc = tensorV matmul (diagonalEmbedding(tensorS) matmul tensorV.transpose())
         assertTrue(tensorSigma.eq(tensorSigmaCalc))
     }
 

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

@@ -114,7 +114,7 @@ internal class TestDoubleTensorAlgebra {
         assertTrue(res12.mutableBuffer.array() contentEquals doubleArrayOf(140.0, 320.0))
         assertTrue(res12.shape contentEquals intArrayOf(2))
 
-        val res32 = tensor3.bdot(tensor2)
+        val res32 = tensor3.matmul(tensor2)
         assertTrue(res32.mutableBuffer.array() contentEquals doubleArrayOf(-140.0))
         assertTrue(res32.shape contentEquals intArrayOf(1, 1))
 
@@ -126,7 +126,7 @@ internal class TestDoubleTensorAlgebra {
         assertTrue(res11.mutableBuffer.array() contentEquals doubleArrayOf(22.0, 28.0, 49.0, 64.0))
         assertTrue(res11.shape contentEquals intArrayOf(2, 2))
 
-        val res45 = tensor4.bdot(tensor5)
+        val res45 = tensor4.matmul(tensor5)
         assertTrue(res45.mutableBuffer.array() contentEquals doubleArrayOf(
             36.0, 42.0, 48.0, 81.0, 96.0, 111.0, 126.0, 150.0, 174.0,
             468.0, 501.0, 534.0, 594.0, 636.0, 678.0, 720.0, 771.0, 822.0