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

@@ -17,7 +17,7 @@ public interface LinearOpsTensorAlgebra<T, TensorType : TensorStructure<T>, Inde
     public fun TensorType.lu(): Pair<TensorType, IndexTensorType>
 
     //https://pytorch.org/docs/stable/generated/torch.lu_unpack.html
-    public fun luPivot(lu: TensorType, pivots: IntTensor): Triple<TensorType, TensorType, TensorType>
+    public fun luPivot(lu: TensorType, pivots: IndexTensorType): Triple<TensorType, TensorType, TensorType>
 
     //https://pytorch.org/docs/stable/linalg.html#torch.linalg.svd
     public fun TensorType.svd(): Triple<TensorType, TensorType, TensorType>

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

@@ -3,9 +3,8 @@ package space.kscience.kmath.tensors
 // https://proofwiki.org/wiki/Definition:Algebra_over_Ring
 public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
 
-
     //https://pytorch.org/docs/stable/generated/torch.full.html
-    public fun full(shape: IntArray, value: T): TensorType
+    public fun full(value: T, shape: IntArray): TensorType
 
     public fun ones(shape: IntArray): TensorType
     public fun zeros(shape: IntArray): TensorType

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

@@ -1,10 +1,91 @@
-package space.kscience.kmath.tensors
+package space.kscience.kmath.tensors.core
 
-import space.kscience.kmath.structures.*
 import kotlin.math.max
 
+public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
+
+    override fun DoubleTensor.plus(other: DoubleTensor): DoubleTensor {
+        val broadcast = broadcastTensors(this, other)
+        val newThis = broadcast[0]
+        val newOther = broadcast[1]
+        val resBuffer = DoubleArray(newThis.strides.linearSize) { i ->
+            newThis.buffer.array()[i] + newOther.buffer.array()[i]
+        }
+        return DoubleTensor(newThis.shape, resBuffer)
+    }
+
+    override fun DoubleTensor.plusAssign(other: DoubleTensor) {
+        val newOther = broadcastTo(other, this.shape)
+        for (i in 0 until this.strides.linearSize) {
+            this.buffer.array()[this.bufferStart + i] +=
+                newOther.buffer.array()[this.bufferStart + i]
+        }
+    }
+
+    override fun DoubleTensor.minus(other: DoubleTensor): DoubleTensor {
+        val broadcast = broadcastTensors(this, other)
+        val newThis = broadcast[0]
+        val newOther = broadcast[1]
+        val resBuffer = DoubleArray(newThis.strides.linearSize) { i ->
+            newThis.buffer.array()[i] - newOther.buffer.array()[i]
+        }
+        return DoubleTensor(newThis.shape, resBuffer)
+    }
+
+    override fun DoubleTensor.minusAssign(other: DoubleTensor) {
+        val newOther = broadcastTo(other, this.shape)
+        for (i in 0 until this.strides.linearSize) {
+            this.buffer.array()[this.bufferStart + i] -=
+                newOther.buffer.array()[this.bufferStart + i]
+        }
+    }
+
+    override fun DoubleTensor.times(other: DoubleTensor): DoubleTensor {
+        val broadcast = broadcastTensors(this, other)
+        val newThis = broadcast[0]
+        val newOther = broadcast[1]
+        val resBuffer = DoubleArray(newThis.strides.linearSize) { i ->
+            newThis.buffer.array()[newOther.bufferStart + i] *
+                    newOther.buffer.array()[newOther.bufferStart + i]
+        }
+        return DoubleTensor(newThis.shape, resBuffer)
+    }
+
+    override fun DoubleTensor.timesAssign(other: DoubleTensor) {
+        val newOther = broadcastTo(other, this.shape)
+        for (i in 0 until this.strides.linearSize) {
+            this.buffer.array()[this.bufferStart + i] *=
+                newOther.buffer.array()[this.bufferStart + i]
+        }
+    }
+
+    override fun DoubleTensor.div(other: DoubleTensor): DoubleTensor {
+        val broadcast = broadcastTensors(this, other)
+        val newThis = broadcast[0]
+        val newOther = broadcast[1]
+        val resBuffer = DoubleArray(newThis.strides.linearSize) { i ->
+            newThis.buffer.array()[newOther.bufferStart + i] /
+                    newOther.buffer.array()[newOther.bufferStart + i]
+        }
+        return DoubleTensor(newThis.shape, resBuffer)
+    }
+
+    override fun DoubleTensor.divAssign(other: DoubleTensor) {
+        val newOther = broadcastTo(other, this.shape)
+        for (i in 0 until this.strides.linearSize) {
+            this.buffer.array()[this.bufferStart + i] /=
+                newOther.buffer.array()[this.bufferStart + i]
+        }
+    }
+
+}
+
+public inline fun <R> BroadcastDoubleTensorAlgebra(block: BroadcastDoubleTensorAlgebra.() -> R): R =
+    BroadcastDoubleTensorAlgebra().block()
+
 
 internal inline fun broadcastShapes(vararg shapes: IntArray): IntArray {
+    println(shapes)
     var totalDim = 0
     for (shape in shapes) {
         totalDim = max(totalDim, shape.size)
@@ -100,67 +181,3 @@ internal inline fun broadcastTensors(vararg tensors: DoubleTensor): List<DoubleT
 
     return res
 }
-
-internal inline fun <T, TensorType : TensorStructure<T>,
-        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
-        TorchTensorAlgebraType.checkDot(a: TensorType, b: TensorType): Unit {
-    val sa = a.shape
-    val sb = b.shape
-    val na = sa.size
-    val nb = sb.size
-    var status: Boolean
-    if (nb == 1) {
-        status = sa.last() == sb[0]
-    } else {
-        status = sa.last() == sb[nb - 2]
-        if ((na > 2) and (nb > 2)) {
-            status = status and
-                    (sa.take(nb - 2).toIntArray() contentEquals sb.take(nb - 2).toIntArray())
-        }
-    }
-    check(status) { "Incompatible shapes $sa and $sb for dot product" }
-}
-
-internal inline fun <T, TensorType : TensorStructure<T>,
-        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
-        TorchTensorAlgebraType.checkTranspose(dim: Int, i: Int, j: Int): Unit =
-    check((i < dim) and (j < dim)) {
-        "Cannot transpose $i to $j for a tensor of dim $dim"
-    }
-
-internal inline fun <T, TensorType : TensorStructure<T>,
-        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
-        TorchTensorAlgebraType.checkView(a: TensorType, shape: IntArray): Unit =
-    check(a.shape.reduce(Int::times) == shape.reduce(Int::times))
-
-/**
- * Returns a reference to [IntArray] containing all of the elements of this [Buffer].
- */
-internal fun Buffer<Int>.array(): IntArray = when(this) {
-    is IntBuffer -> array
-    else -> throw RuntimeException("Failed to cast Buffer to IntArray")
-}
-
-/**
- * Returns a reference to [LongArray] containing all of the elements of this [Buffer].
- */
-internal fun Buffer<Long>.array(): LongArray = when(this) {
-    is LongBuffer -> array
-    else -> throw RuntimeException("Failed to cast Buffer to LongArray")
-}
-
-/**
- * Returns a reference to [FloatArray] containing all of the elements of this [Buffer].
- */
-internal fun Buffer<Float>.array(): FloatArray = when(this) {
-    is FloatBuffer -> array
-    else -> throw RuntimeException("Failed to cast Buffer to FloatArray")
-}
-
-/**
- * Returns a reference to [DoubleArray] containing all of the elements of this [Buffer].
- */
-internal fun Buffer<Double>.array(): DoubleArray = when(this) {
-    is RealBuffer -> array
-    else -> throw RuntimeException("Failed to cast Buffer to DoubleArray")
-}

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

@@ -1,8 +1,10 @@
-package space.kscience.kmath.tensors
+package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.linear.Matrix
 import space.kscience.kmath.nd.*
 import space.kscience.kmath.structures.*
+import space.kscience.kmath.tensors.TensorStrides
+import space.kscience.kmath.tensors.TensorStructure
 
 
 public open class BufferedTensor<T>(
@@ -66,25 +68,25 @@ public open class BufferedTensor<T>(
 }
 
 
-public class IntTensor(
+public class IntTensor internal constructor(
     shape: IntArray,
     buffer: IntArray,
     offset: Int = 0
 ) : BufferedTensor<Int>(shape, IntBuffer(buffer), offset)
 
-public class LongTensor(
+public class LongTensor internal constructor(
     shape: IntArray,
     buffer: LongArray,
     offset: Int = 0
 ) : BufferedTensor<Long>(shape, LongBuffer(buffer), offset)
 
-public class FloatTensor(
+public class FloatTensor internal constructor(
     shape: IntArray,
     buffer: FloatArray,
     offset: Int = 0
 ) : BufferedTensor<Float>(shape, FloatBuffer(buffer), offset)
 
-public class DoubleTensor(
+public class DoubleTensor internal constructor(
     shape: IntArray,
     buffer: DoubleArray,
     offset: Int = 0

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

@@ -1,4 +1,6 @@
-package space.kscience.kmath.tensors
+package space.kscience.kmath.tensors.core
+
+import space.kscience.kmath.tensors.AnalyticTensorAlgebra
 
 public class DoubleAnalyticTensorAlgebra:
     AnalyticTensorAlgebra<Double, DoubleTensor>,

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

@@ -1,4 +1,6 @@
-package space.kscience.kmath.tensors
+package space.kscience.kmath.tensors.core
+
+import space.kscience.kmath.tensors.LinearOpsTensorAlgebra
 
 public class DoubleLinearOpsTensorAlgebra :
     LinearOpsTensorAlgebra<Double, DoubleTensor, IntTensor>,

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

@@ -1,4 +1,6 @@
-package space.kscience.kmath.tensors
+package space.kscience.kmath.tensors.core
+
+import space.kscience.kmath.tensors.OrderedTensorAlgebra
 
 public open class DoubleOrderedTensorAlgebra:
     OrderedTensorAlgebra<Double, DoubleTensor>,

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

@@ -1,8 +1,10 @@
-package space.kscience.kmath.tensors
+package space.kscience.kmath.tensors.core
+
+import space.kscience.kmath.tensors.ReduceOpsTensorAlgebra
 
 public class DoubleReduceOpsTensorAlgebra:
     DoubleTensorAlgebra(),
-    ReduceOpsTensorAlgebra<Double, DoubleTensor>  {
+    ReduceOpsTensorAlgebra<Double, DoubleTensor> {
 
     override fun DoubleTensor.value(): Double {
         check(this.shape contentEquals intArrayOf(1)) {

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

@@ -1,8 +1,18 @@
-package space.kscience.kmath.tensors
+package space.kscience.kmath.tensors.core
+
+import space.kscience.kmath.tensors.TensorPartialDivisionAlgebra
 
 
 public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, DoubleTensor> {
 
+    public fun fromArray(shape: IntArray, buffer: DoubleArray): DoubleTensor {
+        checkEmptyShape(shape)
+        checkEmptyDoubleBuffer(buffer)
+        checkBufferShapeConsistency(shape, buffer)
+        return DoubleTensor(shape, buffer, 0)
+    }
+
+
     override operator fun DoubleTensor.get(i: Int): DoubleTensor {
         val lastShape = this.shape.drop(1).toIntArray()
         val newShape = if (lastShape.isNotEmpty()) lastShape else intArrayOf(1)
@@ -10,6 +20,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         return DoubleTensor(newShape, this.buffer.array(), newStart)
     }
 
+<<<<<<< HEAD:kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
     override fun full(shape: IntArray, value: Double): DoubleTensor {
         val buffer = DoubleArray(TensorStrides(shape).linearSize) { value }
         return DoubleTensor(shape, buffer)
@@ -19,14 +30,31 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
 
     override fun ones(shape: IntArray): DoubleTensor = full(shape, 1.0)
 
+=======
+    override fun full(value: Double, shape: IntArray): DoubleTensor {
+        checkEmptyShape(shape)
+        val buffer = DoubleArray(shape.reduce(Int::times)) { value }
+        return DoubleTensor(shape, buffer)
+    }
+
+>>>>>>> ups/feature/tensor-algebra:kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
     override fun DoubleTensor.fullLike(value: Double): DoubleTensor {
         val shape = this.shape
         val buffer = DoubleArray(this.strides.linearSize) { value }
         return DoubleTensor(shape, buffer)
     }
 
+<<<<<<< HEAD:kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/DoubleTensorAlgebra.kt
     override fun DoubleTensor.zeroesLike(): DoubleTensor = this.fullLike(0.0)
 
+=======
+    override fun zeros(shape: IntArray): DoubleTensor = full(0.0, shape)
+
+    override fun DoubleTensor.zeroesLike(): DoubleTensor = this.fullLike(0.0)
+
+    override fun ones(shape: IntArray): DoubleTensor = full(1.0, shape)
+
+>>>>>>> ups/feature/tensor-algebra:kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
     override fun DoubleTensor.onesLike(): DoubleTensor = this.fullLike(1.0)
 
     override fun eye(n: Int): DoubleTensor {
@@ -54,13 +82,11 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     override fun DoubleTensor.plus(value: Double): DoubleTensor = value + this
 
     override fun DoubleTensor.plus(other: DoubleTensor): DoubleTensor {
-        val broadcast = broadcastTensors(this, other)
+        checkShapesCompatible(this, other)
-        val newThis = broadcast[0]
+        val resBuffer = DoubleArray(this.strides.linearSize) { i ->
-        val newOther = broadcast[1]
+            this.buffer.array()[i] + other.buffer.array()[i]
-        val resBuffer = DoubleArray(newThis.strides.linearSize) { i ->
-            newThis.buffer.array()[i] + newOther.buffer.array()[i]
         }
-        return DoubleTensor(newThis.shape, resBuffer)
+        return DoubleTensor(this.shape, resBuffer)
     }
 
     override fun DoubleTensor.plusAssign(value: Double) {
@@ -70,10 +96,10 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     }
 
     override fun DoubleTensor.plusAssign(other: DoubleTensor) {
-        val newOther = broadcastTo(other, this.shape)
+        checkShapesCompatible(this, other)
         for (i in 0 until this.strides.linearSize) {
             this.buffer.array()[this.bufferStart + i] +=
-                newOther.buffer.array()[this.bufferStart + i]
+                other.buffer.array()[this.bufferStart + i]
         }
     }
 
@@ -92,13 +118,11 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     }
 
     override fun DoubleTensor.minus(other: DoubleTensor): DoubleTensor {
-        val broadcast = broadcastTensors(this, other)
+        checkShapesCompatible(this, other)
-        val newThis = broadcast[0]
+        val resBuffer = DoubleArray(this.strides.linearSize) { i ->
-        val newOther = broadcast[1]
+            this.buffer.array()[i] - other.buffer.array()[i]
-        val resBuffer = DoubleArray(newThis.strides.linearSize) { i ->
-            newThis.buffer.array()[i] - newOther.buffer.array()[i]
         }
-        return DoubleTensor(newThis.shape, resBuffer)
+        return DoubleTensor(this.shape, resBuffer)
     }
 
     override fun DoubleTensor.minusAssign(value: Double) {
@@ -108,10 +132,10 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     }
 
     override fun DoubleTensor.minusAssign(other: DoubleTensor) {
-        val newOther = broadcastTo(other, this.shape)
+        checkShapesCompatible(this, other)
         for (i in 0 until this.strides.linearSize) {
             this.buffer.array()[this.bufferStart + i] -=
-                newOther.buffer.array()[this.bufferStart + i]
+                other.buffer.array()[this.bufferStart + i]
         }
     }
 
@@ -125,15 +149,12 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     override fun DoubleTensor.times(value: Double): DoubleTensor = value * this
 
     override fun DoubleTensor.times(other: DoubleTensor): DoubleTensor {
-        val broadcast = broadcastTensors(this, other)
+        checkShapesCompatible(this, other)
-        val newThis = broadcast[0]
+        val resBuffer = DoubleArray(this.strides.linearSize) { i ->
-        val newOther = broadcast[1]
+            this.buffer.array()[other.bufferStart + i] *
-
+                    other.buffer.array()[other.bufferStart + i]
-        val resBuffer = DoubleArray(newThis.strides.linearSize) { i ->
-            newThis.buffer.array()[newOther.bufferStart + i] *
-                    newOther.buffer.array()[newOther.bufferStart + i]
         }
-        return DoubleTensor(newThis.shape, resBuffer)
+        return DoubleTensor(this.shape, resBuffer)
     }
 
     override fun DoubleTensor.timesAssign(value: Double) {
@@ -143,10 +164,40 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     }
 
     override fun DoubleTensor.timesAssign(other: DoubleTensor) {
-        val newOther = broadcastTo(other, this.shape)
+        checkShapesCompatible(this, other)
         for (i in 0 until this.strides.linearSize) {
             this.buffer.array()[this.bufferStart + i] *=
-                newOther.buffer.array()[this.bufferStart + i]
+                other.buffer.array()[this.bufferStart + i]
+        }
+    }
+
+    override fun DoubleTensor.div(value: Double): DoubleTensor {
+        val resBuffer = DoubleArray(this.strides.linearSize) { i ->
+            this.buffer.array()[this.bufferStart + i] / value
+        }
+        return DoubleTensor(this.shape, resBuffer)
+    }
+
+    override fun DoubleTensor.div(other: DoubleTensor): DoubleTensor {
+        checkShapesCompatible(this, other)
+        val resBuffer = DoubleArray(this.strides.linearSize) { i ->
+            this.buffer.array()[other.bufferStart + i] /
+                    other.buffer.array()[other.bufferStart + i]
+        }
+        return DoubleTensor(this.shape, resBuffer)
+    }
+
+    override fun DoubleTensor.divAssign(value: Double) {
+        for (i in 0 until this.strides.linearSize) {
+            this.buffer.array()[this.bufferStart + i] /= value
+        }
+    }
+
+    override fun DoubleTensor.divAssign(other: DoubleTensor) {
+        checkShapesCompatible(this, other)
+        for (i in 0 until this.strides.linearSize) {
+            this.buffer.array()[this.bufferStart + i] /=
+                other.buffer.array()[this.bufferStart + i]
         }
     }
 
@@ -221,27 +272,10 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         TODO("Not yet implemented")
     }
 
-
-    override fun DoubleTensor.div(value: Double): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.div(other: DoubleTensor): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
     override fun DoubleTensor.flatten(startDim: Int, endDim: Int): DoubleTensor {
         TODO("Not yet implemented")
     }
 
-    override fun DoubleTensor.divAssign(value: Double) {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.divAssign(other: DoubleTensor) {
-        TODO("Not yet implemented")
-    }
-
     override fun DoubleTensor.mean(dim: Int, keepDim: Boolean): DoubleTensor {
         TODO("Not yet implemented")
     }
@@ -264,5 +298,6 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
 
 }
 
+
 public inline fun <R> DoubleTensorAlgebra(block: DoubleTensorAlgebra.() -> R): R =
     DoubleTensorAlgebra().block()

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

@@ -0,0 +1,67 @@
+package space.kscience.kmath.tensors.core
+
+import space.kscience.kmath.tensors.TensorAlgebra
+import space.kscience.kmath.tensors.TensorStructure
+
+
+internal inline fun <T, TensorType : TensorStructure<T>,
+        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
+        TorchTensorAlgebraType.checkEmptyShape(shape: IntArray): Unit =
+    check(shape.isNotEmpty()) {
+        "Illegal empty shape provided"
+    }
+
+internal inline fun < TensorType : TensorStructure<Double>,
+        TorchTensorAlgebraType : TensorAlgebra<Double, TensorType>>
+        TorchTensorAlgebraType.checkEmptyDoubleBuffer(buffer: DoubleArray): Unit =
+    check(buffer.isNotEmpty()) {
+        "Illegal empty buffer provided"
+    }
+
+internal inline fun < TensorType : TensorStructure<Double>,
+        TorchTensorAlgebraType : TensorAlgebra<Double, TensorType>>
+        TorchTensorAlgebraType.checkBufferShapeConsistency(shape: IntArray, buffer: DoubleArray): Unit =
+    check(buffer.size == shape.reduce(Int::times)) {
+        "Inconsistent shape ${shape.toList()} for buffer of size ${buffer.size} provided"
+    }
+
+
+internal inline fun <T, TensorType : TensorStructure<T>,
+        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
+        TorchTensorAlgebraType.checkShapesCompatible(a: TensorType, b: TensorType): Unit =
+    check(a.shape contentEquals b.shape) {
+        "Incompatible shapes ${a.shape.toList()} and ${b.shape.toList()} "
+    }
+
+
+internal inline fun <T, TensorType : TensorStructure<T>,
+        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
+        TorchTensorAlgebraType.checkDot(a: TensorType, b: TensorType): Unit {
+    val sa = a.shape
+    val sb = b.shape
+    val na = sa.size
+    val nb = sb.size
+    var status: Boolean
+    if (nb == 1) {
+        status = sa.last() == sb[0]
+    } else {
+        status = sa.last() == sb[nb - 2]
+        if ((na > 2) and (nb > 2)) {
+            status = status and
+                    (sa.take(nb - 2).toIntArray() contentEquals sb.take(nb - 2).toIntArray())
+        }
+    }
+    check(status) { "Incompatible shapes ${sa.toList()} and ${sb.toList()} provided for dot product" }
+}
+
+internal inline fun <T, TensorType : TensorStructure<T>,
+        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
+        TorchTensorAlgebraType.checkTranspose(dim: Int, i: Int, j: Int): Unit =
+    check((i < dim) and (j < dim)) {
+        "Cannot transpose $i to $j for a tensor of dim $dim"
+    }
+
+internal inline fun <T, TensorType : TensorStructure<T>,
+        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
+        TorchTensorAlgebraType.checkView(a: TensorType, shape: IntArray): Unit =
+    check(a.shape.reduce(Int::times) == shape.reduce(Int::times))

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

@@ -0,0 +1,36 @@
+package space.kscience.kmath.tensors.core
+
+import space.kscience.kmath.structures.*
+
+
+/**
+ * Returns a reference to [IntArray] containing all of the elements of this [Buffer].
+ */
+internal fun Buffer<Int>.array(): IntArray = when (this) {
+    is IntBuffer -> array
+    else -> throw RuntimeException("Failed to cast Buffer to IntArray")
+}
+
+/**
+ * Returns a reference to [LongArray] containing all of the elements of this [Buffer].
+ */
+internal fun Buffer<Long>.array(): LongArray = when (this) {
+    is LongBuffer -> array
+    else -> throw RuntimeException("Failed to cast Buffer to LongArray")
+}
+
+/**
+ * Returns a reference to [FloatArray] containing all of the elements of this [Buffer].
+ */
+internal fun Buffer<Float>.array(): FloatArray = when (this) {
+    is FloatBuffer -> array
+    else -> throw RuntimeException("Failed to cast Buffer to FloatArray")
+}
+
+/**
+ * Returns a reference to [DoubleArray] containing all of the elements of this [Buffer].
+ */
+internal fun Buffer<Double>.array(): DoubleArray = when (this) {
+    is RealBuffer -> array
+    else -> throw RuntimeException("Failed to cast Buffer to DoubleArray")
+}

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

@@ -1,105 +0,0 @@
-package space.kscience.kmath.tensors
-
-
-import kotlin.test.Test
-import kotlin.test.assertTrue
-
-class TestDoubleTensorAlgebra {
-
-    @Test
-    fun doublePlus() = DoubleTensorAlgebra {
-        val tensor = DoubleTensor(intArrayOf(2), doubleArrayOf(1.0, 2.0))
-        val res = 10.0 + tensor
-        assertTrue(res.buffer.array() contentEquals doubleArrayOf(11.0,12.0))
-    }
-
-    @Test
-    fun transpose1x1() = DoubleTensorAlgebra {
-        val tensor = DoubleTensor(intArrayOf(1), doubleArrayOf(0.0))
-        val res = tensor.transpose(0, 0)
-
-        assertTrue(res.buffer.array() contentEquals doubleArrayOf(0.0))
-        assertTrue(res.shape contentEquals intArrayOf(1))
-    }
-
-    @Test
-    fun transpose3x2() = DoubleTensorAlgebra {
-        val tensor = DoubleTensor(intArrayOf(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
-        val res = tensor.transpose(1, 0)
-
-        assertTrue(res.buffer.array() contentEquals doubleArrayOf(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
-        assertTrue(res.shape contentEquals intArrayOf(2, 3))
-    }
-
-    @Test
-    fun transpose1x2x3() = DoubleTensorAlgebra {
-        val tensor = DoubleTensor(intArrayOf(1, 2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
-        val res01 = tensor.transpose(0, 1)
-        val res02 = tensor.transpose(0, 2)
-        val res12 = tensor.transpose(1, 2)
-
-        assertTrue(res01.shape contentEquals intArrayOf(2, 1, 3))
-        assertTrue(res02.shape contentEquals intArrayOf(3, 2, 1))
-        assertTrue(res12.shape contentEquals intArrayOf(1, 3, 2))
-
-        assertTrue(res01.buffer.array() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
-        assertTrue(res02.buffer.array() contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
-        assertTrue(res12.buffer.array() contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
-    }
-
-    @Test
-    fun broadcastShapes() = DoubleTensorAlgebra {
-        assertTrue(broadcastShapes(
-            intArrayOf(2, 3), intArrayOf(1, 3), intArrayOf(1, 1, 1)
-        ) contentEquals intArrayOf(1, 2, 3))
-
-        assertTrue(broadcastShapes(
-            intArrayOf(6, 7), intArrayOf(5, 6, 1), intArrayOf(7,), intArrayOf(5, 1, 7)
-        ) contentEquals intArrayOf(5, 6, 7))
-    }
-
-    @Test
-    fun broadcastTo() = DoubleTensorAlgebra {
-        val tensor1 = DoubleTensor(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
-        val tensor2 = DoubleTensor(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
-
-        val res = broadcastTo(tensor2, tensor1.shape)
-        assertTrue(res.shape contentEquals intArrayOf(2, 3))
-        assertTrue(res.buffer.array() contentEquals doubleArrayOf(10.0, 20.0, 30.0, 10.0, 20.0, 30.0))
-    }
-
-    @Test
-    fun broadcastTensors() = DoubleTensorAlgebra {
-        val tensor1 = DoubleTensor(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
-        val tensor2 = DoubleTensor(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
-        val tensor3 = DoubleTensor(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
-
-        val res = broadcastTensors(tensor1, tensor2, tensor3)
-
-        assertTrue(res[0].shape contentEquals intArrayOf(1, 2, 3))
-        assertTrue(res[1].shape contentEquals intArrayOf(1, 2, 3))
-        assertTrue(res[2].shape contentEquals intArrayOf(1, 2, 3))
-
-        assertTrue(res[0].buffer.array() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
-        assertTrue(res[1].buffer.array() contentEquals doubleArrayOf(10.0, 20.0, 30.0, 10.0, 20.0, 30.0))
-        assertTrue(res[2].buffer.array() contentEquals doubleArrayOf(500.0, 500.0, 500.0, 500.0, 500.0, 500.0))
-    }
-
-    @Test
-    fun minusTensor() = DoubleTensorAlgebra {
-        val tensor1 = DoubleTensor(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
-        val tensor2 = DoubleTensor(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
-        val tensor3 = DoubleTensor(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
-
-        assertTrue((tensor2 - tensor1).shape contentEquals intArrayOf(2, 3))
-        assertTrue((tensor2 - tensor1).buffer.array() contentEquals doubleArrayOf(9.0, 18.0, 27.0, 6.0, 15.0, 24.0))
-
-        assertTrue((tensor3 - tensor1).shape contentEquals intArrayOf(1, 2, 3))
-        assertTrue((tensor3 - tensor1).buffer.array()
-                contentEquals doubleArrayOf(499.0, 498.0, 497.0, 496.0, 495.0, 494.0))
-
-        assertTrue((tensor3 - tensor2).shape contentEquals intArrayOf(1, 1, 3))
-        assertTrue((tensor3 - tensor2).buffer.array() contentEquals doubleArrayOf(490.0, 480.0, 470.0))
-    }
-
-}

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

@@ -0,0 +1,73 @@
+package space.kscience.kmath.tensors.core
+
+import kotlin.test.Test
+import kotlin.test.assertTrue
+
+class TestBroadcasting {
+
+    @Test
+    fun broadcastShapes() = DoubleTensorAlgebra {
+        assertTrue(
+            broadcastShapes(
+                intArrayOf(2, 3), intArrayOf(1, 3), intArrayOf(1, 1, 1)
+            ) contentEquals intArrayOf(1, 2, 3)
+        )
+
+        assertTrue(
+            broadcastShapes(
+                intArrayOf(6, 7), intArrayOf(5, 6, 1), intArrayOf(7), intArrayOf(5, 1, 7)
+            ) contentEquals intArrayOf(5, 6, 7)
+        )
+    }
+
+    @Test
+    fun broadcastTo() = DoubleTensorAlgebra {
+        val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
+
+        val res = broadcastTo(tensor2, tensor1.shape)
+        assertTrue(res.shape contentEquals intArrayOf(2, 3))
+        assertTrue(res.buffer.array() contentEquals doubleArrayOf(10.0, 20.0, 30.0, 10.0, 20.0, 30.0))
+    }
+
+    @Test
+    fun broadcastTensors() = DoubleTensorAlgebra {
+        val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
+        val tensor3 = fromArray(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
+
+        val res = broadcastTensors(tensor1, tensor2, tensor3)
+
+        assertTrue(res[0].shape contentEquals intArrayOf(1, 2, 3))
+        assertTrue(res[1].shape contentEquals intArrayOf(1, 2, 3))
+        assertTrue(res[2].shape contentEquals intArrayOf(1, 2, 3))
+
+        assertTrue(res[0].buffer.array() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        assertTrue(res[1].buffer.array() contentEquals doubleArrayOf(10.0, 20.0, 30.0, 10.0, 20.0, 30.0))
+        assertTrue(res[2].buffer.array() contentEquals doubleArrayOf(500.0, 500.0, 500.0, 500.0, 500.0, 500.0))
+    }
+
+    @Test
+    fun minusTensor() = BroadcastDoubleTensorAlgebra {
+        val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        val tensor2 = fromArray(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
+        val tensor3 = fromArray(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
+
+        val tensor21 = tensor2 - tensor1
+        val tensor31 = tensor3 - tensor1
+        val tensor32 = tensor3 - tensor2
+
+        assertTrue(tensor21.shape contentEquals intArrayOf(2, 3))
+        assertTrue(tensor21.buffer.array() contentEquals doubleArrayOf(9.0, 18.0, 27.0, 6.0, 15.0, 24.0))
+
+        assertTrue(tensor31.shape contentEquals intArrayOf(1, 2, 3))
+        assertTrue(
+            tensor31.buffer.array()
+                    contentEquals doubleArrayOf(499.0, 498.0, 497.0, 496.0, 495.0, 494.0)
+        )
+
+        assertTrue(tensor32.shape contentEquals intArrayOf(1, 1, 3))
+        assertTrue(tensor32.buffer.array() contentEquals doubleArrayOf(490.0, 480.0, 470.0))
+    }
+
+}

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

@@ -1,4 +1,4 @@
-package space.kscience.kmath.tensors
+package space.kscience.kmath.tensors.core
 
 
 import space.kscience.kmath.nd.as1D
@@ -13,20 +13,20 @@ class TestDoubleTensor {
     @Test
     fun valueTest() = DoubleReduceOpsTensorAlgebra {
         val value = 12.5
-        val tensor = DoubleTensor(intArrayOf(1), doubleArrayOf(value))
+        val tensor = fromArray(intArrayOf(1), doubleArrayOf(value))
         assertEquals(tensor.value(), value)
     }
 
     @Test
-    fun stridesTest(){
+    fun stridesTest() = DoubleTensorAlgebra {
-        val tensor = DoubleTensor(intArrayOf(2,2), doubleArrayOf(3.5,5.8,58.4,2.4))
+        val tensor = fromArray(intArrayOf(2,2), doubleArrayOf(3.5,5.8,58.4,2.4))
         assertEquals(tensor[intArrayOf(0,1)], 5.8)
         assertTrue(tensor.elements().map{ it.second }.toList().toDoubleArray() contentEquals tensor.buffer.toDoubleArray())
     }
 
     @Test
     fun getTest() = DoubleTensorAlgebra {
-        val tensor = DoubleTensor(intArrayOf(1,2,2), doubleArrayOf(3.5,5.8,58.4,2.4))
+        val tensor = fromArray(intArrayOf(1,2,2), doubleArrayOf(3.5,5.8,58.4,2.4))
         val matrix = tensor[0].as2D()
         assertEquals(matrix[0,1], 5.8)
 

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

@@ -0,0 +1,82 @@
+package space.kscience.kmath.tensors.core
+
+
+import kotlin.test.Test
+import kotlin.test.assertTrue
+
+class TestDoubleTensorAlgebra {
+
+    @Test
+    fun doublePlus() = DoubleTensorAlgebra {
+        val tensor = fromArray(intArrayOf(2), doubleArrayOf(1.0, 2.0))
+        val res = 10.0 + tensor
+        assertTrue(res.buffer.array() contentEquals doubleArrayOf(11.0, 12.0))
+    }
+
+    @Test
+    fun transpose1x1() = DoubleTensorAlgebra {
+        val tensor = fromArray(intArrayOf(1), doubleArrayOf(0.0))
+        val res = tensor.transpose(0, 0)
+
+        assertTrue(res.buffer.array() contentEquals doubleArrayOf(0.0))
+        assertTrue(res.shape contentEquals intArrayOf(1))
+    }
+
+    @Test
+    fun transpose3x2() = DoubleTensorAlgebra {
+        val tensor = fromArray(intArrayOf(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        val res = tensor.transpose(1, 0)
+
+        assertTrue(res.buffer.array() contentEquals doubleArrayOf(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
+        assertTrue(res.shape contentEquals intArrayOf(2, 3))
+    }
+
+    @Test
+    fun transpose1x2x3() = DoubleTensorAlgebra {
+        val tensor = fromArray(intArrayOf(1, 2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        val res01 = tensor.transpose(0, 1)
+        val res02 = tensor.transpose(0, 2)
+        val res12 = tensor.transpose(1, 2)
+
+        assertTrue(res01.shape contentEquals intArrayOf(2, 1, 3))
+        assertTrue(res02.shape contentEquals intArrayOf(3, 2, 1))
+        assertTrue(res12.shape contentEquals intArrayOf(1, 3, 2))
+
+        assertTrue(res01.buffer.array() contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        assertTrue(res02.buffer.array() contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
+        assertTrue(res12.buffer.array() contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
+    }
+
+    @Test
+    fun linearStructure() = DoubleTensorAlgebra {
+        val shape = intArrayOf(3)
+        val tensorA = full(value = -4.5, shape = shape)
+        val tensorB = full(value = 10.9, shape = shape)
+        val tensorC = full(value = 789.3, shape = shape)
+        val tensorD = full(value = -72.9, shape = shape)
+        val tensorE = full(value = 553.1, shape = shape)
+        val result = 15.8 * tensorA - 1.5 * tensorB * (-tensorD) + 0.02 * tensorC / tensorE - 39.4
+
+        val expected = fromArray(
+            shape,
+            (1..3).map {
+                15.8 * (-4.5) - 1.5 * 10.9 * 72.9 + 0.02 * 789.3 / 553.1 - 39.4
+            }.toDoubleArray()
+        )
+
+        val assignResult = zeros(shape)
+        tensorA *= 15.8
+        tensorB *= 1.5
+        tensorB *= -tensorD
+        tensorC *= 0.02
+        tensorC /= tensorE
+        assignResult += tensorA
+        assignResult -= tensorB
+        assignResult += tensorC
+        assignResult += -39.4
+
+        assertTrue(expected.buffer.array() contentEquals result.buffer.array())
+        assertTrue(expected.buffer.array() contentEquals assignResult.buffer.array())
+    }
+
+}