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

@@ -23,8 +23,8 @@ public interface LinearOpsTensorAlgebra<T> :
 
     /**
      * Computes the multiplicative inverse matrix of a square matrix input, or of each square matrix in a batched input.
-     * Given a square matrix `a`, return the matrix `aInv` satisfying
-     * ``a.dot(aInv) = aInv.dot(a) = eye(a.shape[0])``.
+     * Given a square matrix `A`, return the matrix `AInv` satisfying
+     * `A dot AInv  = AInv dot A = eye(a.shape[0])`.
      * For more information: https://pytorch.org/docs/stable/linalg.html#torch.linalg.inv
      *
      * @return the multiplicative inverse of a matrix.
@@ -37,7 +37,7 @@ public interface LinearOpsTensorAlgebra<T> :
      * Computes the Cholesky decomposition of a Hermitian (or symmetric for real-valued matrices)
      * positive-definite matrix or the Cholesky decompositions for a batch of such matrices.
      * Each decomposition has the form:
-     * Given a tensor `input`, return the tensor `L` satisfying ``input = L * L.H``,
+     * Given a tensor `input`, return the tensor `L` satisfying `input = L dot L.H`,
      * where L is a lower-triangular matrix and L.H is the conjugate transpose of L,
      * which is just a transpose for the case of real-valued input matrices.
      * For more information: https://pytorch.org/docs/stable/linalg.html#torch.linalg.cholesky
@@ -50,7 +50,7 @@ public interface LinearOpsTensorAlgebra<T> :
      * QR decomposition.
      *
      * Computes the QR decomposition of a matrix or a batch of matrices, and returns a pair `(Q, R)` of tensors.
-     * Given a tensor `input`, return tensors (Q, R) satisfying ``input = Q * R``,
+     * Given a tensor `input`, return tensors (Q, R) satisfying ``input = Q dot R``,
      * with `Q` being an orthogonal matrix or batch of orthogonal matrices
      * and `R` being an upper triangular matrix or batch of upper triangular matrices.
      * For more information: https://pytorch.org/docs/stable/linalg.html#torch.linalg.qr
@@ -63,7 +63,7 @@ public interface LinearOpsTensorAlgebra<T> :
      * LUP decomposition
      *
      * Computes the LUP decomposition of a matrix or a batch of matrices.
-     * Given a tensor `input`, return tensors (P, L, U) satisfying ``P * input = L * U``,
+     * Given a tensor `input`, return tensors (P, L, U) satisfying `P dot input = L dot  U`,
      * with `P` being a permutation matrix or batch of matrices,
      * `L` being a lower triangular matrix or batch of matrices,
      * `U` being an upper triangular matrix or batch of matrices.
@@ -77,7 +77,8 @@ public interface LinearOpsTensorAlgebra<T> :
      *
      * Computes the singular value decomposition of either a matrix or batch of matrices `input`.
      * The singular value decomposition is represented as a triple `(U, S, V)`,
-     * such that ``input = U.dot(diagonalEmbedding(S).dot(V.T))``.
+     * such that `input = U dot diagonalEmbedding(S) dot  V.H`,
+     * where V.H is the conjugate transpose of V.
      * If input is a batch of tensors, then U, S, and Vh are also batched with the same batch dimensions as input.
      * For more information: https://pytorch.org/docs/stable/linalg.html#torch.linalg.svd
      *

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

@@ -288,7 +288,7 @@ public interface TensorAlgebra<T>: Algebra<Tensor<T>> {
     public fun Tensor<T>.min(dim: Int, keepDim: Boolean): Tensor<T>
 
     /**
-     * @return the maximum value of all elements in the input tensor.
+     * Returns the maximum value of all elements in the input tensor.
      */
     public fun Tensor<T>.max(): T
 

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

@@ -343,7 +343,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         val m2 = newOther.shape[newOther.shape.size - 2]
         val n = newOther.shape[newOther.shape.size - 1]
         check(m1 == m2) {
-            throw RuntimeException("Tensors dot operation dimension mismatch: ($l, $m1) x ($m2, $n)")
+            "Tensors dot operation dimension mismatch: ($l, $m1) x ($m2, $n)"
         }
 
         val resShape = newThis.shape.sliceArray(0..(newThis.shape.size - 2)) + intArrayOf(newOther.shape.last())
@@ -436,9 +436,8 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
      * @param epsilon permissible error when comparing two Double values.
      * @return true if two tensors have the same shape and elements, false otherwise.
      */
-    public fun Tensor<Double>.eq(other: Tensor<Double>, epsilon: Double): Boolean {
-        return tensor.eq(other) { x, y -> abs(x - y) < epsilon }
-    }
+    public fun Tensor<Double>.eq(other: Tensor<Double>, epsilon: Double): Boolean =
+        tensor.eq(other) { x, y -> abs(x - y) < epsilon }
 
     /**
      * Compares element-wise two tensors.
@@ -510,7 +509,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
     }
 
     /**
-     * Build tensor from rows of input tensor
+     * Builds tensor from rows of input tensor
      *
      * @param indices the [IntArray] of 1-dimensional indices
      * @return tensor with rows corresponding to rows by [indices]

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

@@ -23,7 +23,6 @@ internal fun stridesFromShape(shape: IntArray): IntArray {
         current--
     }
     return res
-
 }
 
 internal fun indexFromOffset(offset: Int, strides: IntArray, nDim: Int): IntArray {