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

@@ -49,6 +49,7 @@ public class DoubleTensor internal constructor(
 
 internal fun BufferedTensor<Int>.asTensor(): IntTensor =
     IntTensor(this.shape, this.mutableBuffer.array(), this.bufferStart)
+
 internal fun BufferedTensor<Double>.asTensor(): DoubleTensor =
     DoubleTensor(this.shape, this.mutableBuffer.array(), this.bufferStart)
 
@@ -78,4 +79,39 @@ internal val TensorStructure<Int>.tensor: IntTensor
     }
 
 public fun TensorStructure<Double>.toDoubleTensor(): DoubleTensor = this.tensor
-public fun TensorStructure<Int>.toIntTensor(): IntTensor = this.tensor
+public fun TensorStructure<Int>.toIntTensor(): IntTensor = this.tensor
+
+public fun Array<DoubleArray>.toDoubleTensor(): DoubleTensor {
+    val n = size
+    check(n > 0) { "An empty array cannot be casted to tensor" }
+    val m = first().size
+    check(m > 0) { "Inner arrays must have at least 1 argument" }
+    check(all { size == m }) { "Inner arrays must be the same size" }
+
+    val shape = intArrayOf(n, m)
+    val buffer = this.flatMap { arr -> arr.map { it } }.toDoubleArray()
+
+    return DoubleTensor(shape, buffer, 0)
+}
+
+
+public fun Array<IntArray>.toIntTensor(): IntTensor {
+    val n = size
+    check(n > 0) { "An empty array cannot be casted to tensor" }
+    val m = first().size
+    check(m > 0) { "Inner arrays must have at least 1 argument" }
+    check(all { size == m }) { "Inner arrays must be the same size" }
+
+    val shape = intArrayOf(n, m)
+    val buffer = this.flatMap { arr -> arr.map { it } }.toIntArray()
+
+    return IntTensor(shape, buffer, 0)
+}
+
+public fun DoubleTensor.toDoubleArray(): DoubleArray {
+    return tensor.mutableBuffer.array().drop(bufferStart).take(numElements).toDoubleArray()
+}
+
+public fun IntTensor.toIntArray(): IntArray {
+    return tensor.mutableBuffer.array().drop(bufferStart).take(numElements).toIntArray()
+}

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

@@ -382,7 +382,11 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
             return false
         }
         for (i in 0 until n) {
-            if (!eqFunction(tensor.mutableBuffer[tensor.bufferStart + i], other.tensor.mutableBuffer[other.tensor.bufferStart + i])) {
+            if (!eqFunction(
+                    tensor.mutableBuffer[tensor.bufferStart + i],
+                    other.tensor.mutableBuffer[other.tensor.bufferStart + i]
+                )
+            ) {
                 return false
             }
         }
@@ -395,4 +399,20 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
     public fun TensorStructure<Double>.randNormalLike(seed: Long = 0): DoubleTensor =
         DoubleTensor(tensor.shape, getRandomNormals(tensor.shape.reduce(Int::times), seed))
 
+    // stack tensors by axis 0
+    public fun stack(tensors: List<DoubleTensor>): DoubleTensor {
+        val shape = tensors.firstOrNull()?.shape
+        check(shape != null) { "Collection must have at least 1 element" }
+        check(tensors.all { it.shape contentEquals shape }) {"Stacking tensors must have same shapes"}
+        val resShape = intArrayOf(tensors.size) + shape
+        val resBuffer = tensors.flatMap {
+            it.tensor.mutableBuffer.array().drop(it.bufferStart).take(it.numElements)
+        }.toDoubleArray()
+        return DoubleTensor(resShape, resBuffer, 0)
+    }
+
+    // build tensor from this rows by given indices
+    public fun TensorStructure<Double>.rowsByIndices(indices: IntArray): DoubleTensor {
+        return stack(indices.map { this[it] })
+    }
 }

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

@@ -18,7 +18,7 @@ internal fun <T> BufferedTensor<T>.vectorSequence(): Sequence<BufferedTensor<T>>
     val vectorOffset = shape[n - 1]
     val vectorShape = intArrayOf(shape.last())
     for (offset in 0 until numElements step vectorOffset) {
-        val vector = BufferedTensor(vectorShape, mutableBuffer, offset)
+        val vector = BufferedTensor(vectorShape, mutableBuffer, bufferStart + offset)
         yield(vector)
     }
 }
@@ -29,7 +29,7 @@ internal fun <T> BufferedTensor<T>.matrixSequence(): Sequence<BufferedTensor<T>>
     val matrixOffset = shape[n - 1] * shape[n - 2]
     val matrixShape = intArrayOf(shape[n - 2], shape[n - 1])
     for (offset in 0 until numElements step matrixOffset) {
-        val matrix = BufferedTensor(matrixShape, mutableBuffer, offset)
+        val matrix = BufferedTensor(matrixShape, mutableBuffer, bufferStart + offset)
         yield(matrix)
     }
 }