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

@@ -155,7 +155,7 @@ public object DoubleLinearOpsTensorAlgebra :
      * @return triple `(U, S, V)`.
      */
     public fun Tensor<Double>.svd(epsilon: Double): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
-        val size = tensor.linearStructure.dim
+        val size = tensor.dimension
         val commonShape = tensor.shape.sliceArray(0 until size - 2)
         val (n, m) = tensor.shape.sliceArray(size - 2 until size)
         val uTensor = zeros(commonShape + intArrayOf(min(n, m), n))

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

@@ -39,39 +39,20 @@ internal fun indexFromOffset(offset: Int, strides: IntArray, nDim: Int): IntArra
     return res
 }
 
-internal fun stepIndex(index: IntArray, shape: IntArray, nDim: Int): IntArray {
-    val res = index.copyOf()
-    var current = nDim - 1
-    var carry = 0
-
-    do {
-        res[current]++
-        if (res[current] >= shape[current]) {
-            carry = 1
-            res[current] = 0
-        }
-        current--
-    } while (carry != 0 && current >= 0)
-
-    return res
-}
-
-
-public class TensorLinearStructure(override val shape: IntArray) : Strides
-{
+/**
+ * This [Strides] implementation follows the last dimension first convention
+ * For more information: https://numpy.org/doc/stable/reference/generated/numpy.ndarray.strides.html
+ *
+ * @param shape the shape of the tensor.
+ */
+public class TensorLinearStructure(override val shape: IntArray) : Strides {
     override val strides: IntArray
         get() = stridesFromShape(shape)
 
     override fun index(offset: Int): IntArray =
         indexFromOffset(offset, strides, shape.size)
 
-    public fun stepIndex(index: IntArray): IntArray =
-        stepIndex(index, shape, shape.size)
-
     override val linearSize: Int
         get() = shape.reduce(Int::times)
 
-    public val dim: Int
-        get() = shape.size
-
 }