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

@@ -45,7 +45,7 @@ public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
         val newThis = broadcast[0]
         val newOther = broadcast[1]
         val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
-            newThis.buffer.array()[newOther.bufferStart + i] *
+            newThis.buffer.array()[newThis.bufferStart + i] *
                     newOther.buffer.array()[newOther.bufferStart + i]
         }
         return DoubleTensor(newThis.shape, resBuffer)
@@ -182,17 +182,13 @@ internal inline fun broadcastTensors(vararg tensors: DoubleTensor): List<DoubleT
 }
 
 internal inline fun broadcastOuterTensors(vararg tensors: DoubleTensor): List<DoubleTensor> {
-    var onlyTwoDims = true
+    val onlyTwoDims = tensors.asSequence().onEach {
-    for (tensor in tensors) {
+        require(it.shape.size >= 2) {
-        if (tensor.shape.size < 2) {
             throw RuntimeException("Tensors must have at least 2 dimensions")
         }
-        if (tensor.shape.size != 2) {
+    }.any { it.shape.size != 2 }
-            onlyTwoDims = false
-        }
-    }
 
-    if (onlyTwoDims) {
+    if (!onlyTwoDims) {
         return tensors.asList()
     }
 
@@ -233,7 +229,7 @@ internal inline fun broadcastOuterTensors(vararg tensors: DoubleTensor): List<Do
                     newTensor.buffer.array()[newTensor.bufferStart + curLinearIndex]
             }
         }
-        res.add(resTensor)
+        res += resTensor
     }
 
     return res

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

@@ -34,7 +34,8 @@ public open class BufferedTensor<T>(
 
     public fun vectorSequence(): Sequence<MutableStructure1D<T>> = sequence {
         check(shape.size >= 1) {"todo"}
-        val vectorOffset = linearStructure.strides[0]
+        val n = shape.size
+        val vectorOffset = shape[n - 1]
         val vectorShape = intArrayOf(shape.last())
         for (offset in 0 until numel step vectorOffset) {
             val vector = BufferedTensor<T>(vectorShape, buffer, offset).as1D()
@@ -44,8 +45,9 @@ public open class BufferedTensor<T>(
 
     public fun matrixSequence(): Sequence<MutableStructure2D<T>> = sequence {
         check(shape.size >= 2) {"todo"}
-        val matrixOffset = linearStructure.strides[1]
+        val n = shape.size
-        val matrixShape = intArrayOf(shape[shape.size - 2], shape.last()) //todo better way?
+        val matrixOffset = shape[n - 1] * shape[n - 2]
+        val matrixShape = intArrayOf(shape[n - 2], shape[n - 1]) //todo better way?
         for (offset in 0 until numel step matrixOffset) {
             val matrix = BufferedTensor<T>(matrixShape, buffer, offset).as2D()
             yield(matrix)

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

@@ -8,7 +8,7 @@ public class DoubleLinearOpsTensorAlgebra :
     DoubleTensorAlgebra() {
 
     override fun DoubleTensor.inv(): DoubleTensor {
-        TODO("Not yet implemented")
+        TODO("ANDREI")
     }
 
     override fun DoubleTensor.lu(tol: Double): Pair<DoubleTensor, IntTensor> {
@@ -135,16 +135,16 @@ public class DoubleLinearOpsTensorAlgebra :
     }
 
     override fun DoubleTensor.qr(): DoubleTensor {
-        TODO("Not yet implemented")
+        TODO("ANDREI")
     }
 
 
     override fun DoubleTensor.svd(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
-        TODO("Not yet implemented")
+        TODO("ALYA")
     }
 
     override fun DoubleTensor.symEig(eigenvectors: Boolean): Pair<DoubleTensor, DoubleTensor> {
-        TODO("Not yet implemented")
+        TODO("ANDREI")
     }
 
 }

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

@@ -241,37 +241,6 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         TODO("Not yet implemented")
     }
 
-    private fun DoubleTensor.dotTwoDimensionalTensors(other: DoubleTensor): DoubleTensor {
-        if (this.shape.size > 2 || other.shape.size > 2) {
-            throw RuntimeException("Both tensors must have a maximum of 2 dimensions")
-        }
-
-        if (this.shape[1] != other.shape[0]) {
-            throw RuntimeException("Tensors dot operation dimension mismatch: " +
-                    "(${this.shape[0]}, ${this.shape[1]}) x (${other.shape[0]}, ${other.shape[1]})")
-        }
-
-        val l = this.shape[0]
-        val m = this.shape[1]
-        val n = other.shape[1]
-
-        val res = DoubleTensor(intArrayOf(l, n), DoubleArray(l * n))
-
-        for (i in 0 until l) {
-            for (j in 0 until n) {
-                var curr = 0.0
-                for (k in 0 until m) {
-                    val ik = this.linearStructure.offset(intArrayOf(i, k))
-                    val kj = other.linearStructure.offset(intArrayOf(k, j))
-                    curr += this.buffer.array()[ik] * other.buffer.array()[kj]
-                }
-                val linearIndex = res.linearStructure.offset(intArrayOf(i, j))
-                res.buffer.array()[linearIndex] = curr
-            }
-        }
-        return res
-    }
-
     override fun DoubleTensor.dot(other: DoubleTensor): DoubleTensor {
         if (this.shape.size == 1 && other.shape.size == 1) {
             return DoubleTensor(intArrayOf(1), doubleArrayOf(this.times(other).buffer.array().sum()))
@@ -279,10 +248,15 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
 
         var newThis = this.copy()
         var newOther = other.copy()
+
+        var penultimateDim = false
+        var lastDim = false
         if (this.shape.size == 1) {
+            penultimateDim = true
             newThis = this.view(intArrayOf(1) + this.shape)
         }
         if (other.shape.size == 1) {
+            lastDim = true
             newOther = other.view(other.shape + intArrayOf(1) )
         }
 
@@ -299,13 +273,12 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         }
         val m = m1
 
-        var resShape = newThis.shape.sliceArray(0..(newThis.shape.size - 2)) + intArrayOf(newOther.shape.last())
+        val resShape = newThis.shape.sliceArray(0..(newThis.shape.size - 2)) + intArrayOf(newOther.shape.last())
         val resSize = resShape.reduce { acc, i -> acc * i }
         val resTensor = DoubleTensor(resShape, DoubleArray(resSize))
 
         for ((res, ab) in resTensor.matrixSequence().zip(newThis.matrixSequence().zip(newOther.matrixSequence()))) {
-            val a = ab.first
+            val (a, b) = ab
-            val b = ab.second
 
             for (i in 0 until l) {
                 for (j in 0 until n) {
@@ -318,6 +291,13 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
             }
         }
 
+        if (penultimateDim) {
+            return resTensor.view(resTensor.shape.dropLast(2).toIntArray() +
+                    intArrayOf(resTensor.shape.last()))
+        }
+        if (lastDim) {
+            return resTensor.view(resTensor.shape.dropLast(1).toIntArray())
+        }
         return resTensor
     }
 
@@ -338,7 +318,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     }
 
     override fun DoubleTensor.det(): DoubleTensor {
-        TODO("Not yet implemented")
+        TODO("ANDREI")
     }
 
     override fun DoubleTensor.square(): DoubleTensor {

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

@@ -82,15 +82,37 @@ class TestDoubleTensorAlgebra {
     @Test
     fun dot() = DoubleTensorAlgebra {
         val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        val tensor11 = fromArray(intArrayOf(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = fromArray(intArrayOf(3), doubleArrayOf(10.0, 20.0, 30.0))
+        val tensor3 = fromArray(intArrayOf(1, 1, 3), doubleArrayOf(-1.0, -2.0, -3.0))
+
         val res12 = tensor1.dot(tensor2)
-
         assertTrue(res12.buffer.array() contentEquals doubleArrayOf(140.0, 320.0))
-        assertTrue(res12.shape contentEquals intArrayOf(2, 1))
+        assertTrue(res12.shape contentEquals intArrayOf(2))
 
-        val tensor4 = fromArray(intArrayOf(10, 3, 4), DoubleArray(10 * 3 * 4) {0.0})
+        val res32 = tensor3.dot(tensor2)
-        val tensor5 = fromArray(intArrayOf(10, 4, 5), DoubleArray(10 * 4 * 5) {0.0})
+        assertTrue(res32.buffer.array() contentEquals doubleArrayOf(-140.0))
+        assertTrue(res32.shape contentEquals intArrayOf(1, 1))
+
+        val res22 = tensor2.dot(tensor2)
+        assertTrue(res22.buffer.array() contentEquals doubleArrayOf(1400.0))
+        assertTrue(res22.shape contentEquals intArrayOf(1))
+
+        val res11 = tensor1.dot(tensor11)
+        assertTrue(res11.buffer.array() contentEquals doubleArrayOf(22.0, 28.0, 49.0, 64.0))
+        assertTrue(res11.shape contentEquals intArrayOf(2, 2))
+
+        var tensor4 = fromArray(intArrayOf(10, 3, 4), DoubleArray(10 * 3 * 4) {0.0})
+        var tensor5 = fromArray(intArrayOf(10, 4, 5), DoubleArray(10 * 4 * 5) {0.0})
         assertTrue(tensor4.dot(tensor5).shape contentEquals intArrayOf(10, 3, 5))
+
+        tensor4 = fromArray(intArrayOf(10, 3, 4), DoubleArray(10 * 3 * 4) {0.0})
+        tensor5 = fromArray(intArrayOf(4, 5), DoubleArray(4 * 5) {0.0})
+        assertTrue(tensor4.dot(tensor5).shape contentEquals intArrayOf(10, 3, 5))
+
+        tensor4 = fromArray(intArrayOf(4, 2, 1, 3, 8, 1), DoubleArray(4 * 2 * 1 * 3 * 8 * 1) {0.0})
+        tensor5 = fromArray(intArrayOf(5, 1, 2, 8, 3, 1, 5), DoubleArray(5 * 1 * 2 * 8 * 3 * 1 * 5) {0.0})
+        assertTrue(tensor4.dot(tensor5).shape contentEquals intArrayOf(5, 4, 2, 8, 3, 8, 5))
     }
 
     @Test