OrderedTensorAlgebra

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

@@ -8,6 +8,12 @@ public interface ComplexTensorAlgebra<T,
     //https://pytorch.org/docs/stable/generated/torch.view_as_complex.html
     public fun RealTensorType.viewAsComplex(): ComplexTensorType
 
+    // Embed a real tensor as real + i * imaginary
+    public fun RealTensorType.cartesianEmbedding(imaginary: RealTensorType): ComplexTensorType
+
+    // Embed a real tensor as real * exp(i * angle)
+    public fun RealTensorType.polarEmbedding(angle: RealTensorType): ComplexTensorType
+
     //https://pytorch.org/docs/stable/generated/torch.angle.html
     public fun ComplexTensorType.angle(): RealTensorType
 

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

@@ -1,8 +1,8 @@
 package space.kscience.kmath.tensors
 
-public class RealAnalyticTensorAlgebra:
+public class DoubleAnalyticTensorAlgebra:
     AnalyticTensorAlgebra<Double, DoubleTensor>,
-    RealTensorAlgebra()
+    DoubleTensorAlgebra()
 {
     override fun DoubleTensor.exp(): DoubleTensor {
         TODO("Not yet implemented")
@@ -146,5 +146,5 @@ public class RealAnalyticTensorAlgebra:
 
 }
 
-public inline fun <R> RealAnalyticTensorAlgebra(block: RealAnalyticTensorAlgebra.() -> R): R =
+public inline fun <R> DoubleAnalyticTensorAlgebra(block: DoubleAnalyticTensorAlgebra.() -> R): R =
-    RealAnalyticTensorAlgebra().block()
+    DoubleAnalyticTensorAlgebra().block()

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

@@ -2,27 +2,12 @@ package space.kscience.kmath.tensors
 
 public class DoubleLinearOpsTensorAlgebra :
     LinearOpsTensorAlgebra<Double, DoubleTensor>,
-    RealTensorAlgebra() {
+    DoubleTensorAlgebra() {
-    override fun eye(n: Int): DoubleTensor {
+
-        val shape = intArrayOf(n, n)
+    override fun DoubleTensor.inv(): DoubleTensor {
-        val buffer = DoubleArray(n * n) { 0.0 }
+        TODO("Not yet implemented")
-        val res = DoubleTensor(shape, buffer)
-        for (i in 0 until n) {
-            res[intArrayOf(i, i)] = 1.0
-        }
-        return res
     }
 
-
-    override fun DoubleTensor.dot(other: DoubleTensor): DoubleTensor {
-        TODO("Alya")
-    }
-
-    override fun diagonalEmbedding(diagonalEntries: DoubleTensor, offset: Int, dim1: Int, dim2: Int): DoubleTensor {
-        TODO("Alya")
-    }
-
-
     override fun DoubleTensor.lu(): Pair<DoubleTensor, IntTensor> {
         // todo checks
         val luTensor = this.copy()
@@ -115,15 +100,6 @@ public class DoubleLinearOpsTensorAlgebra :
         return Triple(p, l, u)
     }
 
-    override fun DoubleTensor.det(): DoubleTensor {
-
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.inv(): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
     override fun DoubleTensor.cholesky(): DoubleTensor {
         TODO("Not yet implemented")
     }

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

@@ -0,0 +1,41 @@
+package space.kscience.kmath.tensors
+
+public class DoubleOrderedTensorAlgebra:
+    OrderedTensorAlgebra<Double, DoubleTensor>,
+    DoubleTensorAlgebra()
+{
+    override fun DoubleTensor.max(dim: Int, keepDim: Boolean): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.cummax(dim: Int): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.min(dim: Int, keepDim: Boolean): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.cummin(dim: Int): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.median(dim: Int, keepDim: Boolean): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
+    override fun maximum(lhs: DoubleTensor, rhs: DoubleTensor) {
+        TODO("Not yet implemented")
+    }
+
+    override fun minimum(lhs: DoubleTensor, rhs: DoubleTensor) {
+        TODO("Not yet implemented")
+    }
+
+    override fun DoubleTensor.sort(dim: Int, keepDim: Boolean, descending: Boolean): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+}
+
+public inline fun <R> DoubleOrderedTensorAlgebra(block: DoubleOrderedTensorAlgebra.() -> R): R =
+    DoubleOrderedTensorAlgebra().block()

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

@@ -1,7 +1,7 @@
 package space.kscience.kmath.tensors
 
 
-public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, DoubleTensor> {
+public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, DoubleTensor> {
 
     override fun DoubleTensor.value(): Double {
         check(this.shape contentEquals intArrayOf(1)) {
@@ -27,6 +27,15 @@ public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, Doubl
     override fun DoubleTensor.onesLike(): DoubleTensor {
         TODO("Not yet implemented")
     }
+    override fun eye(n: Int): DoubleTensor {
+        val shape = intArrayOf(n, n)
+        val buffer = DoubleArray(n * n) { 0.0 }
+        val res = DoubleTensor(shape, buffer)
+        for (i in 0 until n) {
+            res[intArrayOf(i, i)] = 1.0
+        }
+        return res
+    }
 
     override fun DoubleTensor.copy(): DoubleTensor {
         // should be rework as soon as copy() method for NDBuffer will be available
@@ -199,36 +208,12 @@ public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, Doubl
         TODO("Not yet implemented")
     }
 
-    override fun DoubleTensor.max(dim: Int, keepDim: Boolean): DoubleTensor {
+    override fun DoubleTensor.dot(other: DoubleTensor): DoubleTensor {
-        TODO("Not yet implemented")
+        TODO("Alya")
     }
 
-    override fun DoubleTensor.cummax(dim: Int): DoubleTensor {
+    override fun diagonalEmbedding(diagonalEntries: DoubleTensor, offset: Int, dim1: Int, dim2: Int): DoubleTensor {
-        TODO("Not yet implemented")
+        TODO("Alya")
-    }
-
-    override fun DoubleTensor.min(dim: Int, keepDim: Boolean): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.cummin(dim: Int): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.median(dim: Int, keepDim: Boolean): DoubleTensor {
-        TODO("Not yet implemented")
-    }
-
-    override fun maximum(lhs: DoubleTensor, rhs: DoubleTensor) {
-        TODO("Not yet implemented")
-    }
-
-    override fun minimum(lhs: DoubleTensor, rhs: DoubleTensor) {
-        TODO("Not yet implemented")
-    }
-
-    override fun DoubleTensor.sort(dim: Int, keepDim: Boolean, descending: Boolean): DoubleTensor {
-        TODO("Not yet implemented")
     }
 
     override fun cat(tensors: List<DoubleTensor>, dim: Int): DoubleTensor {
@@ -276,7 +261,11 @@ public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, Doubl
         TODO("Not yet implemented")
     }
 
+    override fun DoubleTensor.det(): DoubleTensor {
+        TODO("Not yet implemented")
+    }
+
 }
 
-public inline fun <R> RealTensorAlgebra(block: RealTensorAlgebra.() -> R): R =
+public inline fun <R> DoubleTensorAlgebra(block: DoubleTensorAlgebra.() -> R): R =
-    RealTensorAlgebra().block()
+    DoubleTensorAlgebra().block()

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

@@ -4,21 +4,6 @@ package space.kscience.kmath.tensors
 public interface LinearOpsTensorAlgebra<T, TensorType : TensorStructure<T>> :
     TensorPartialDivisionAlgebra<T, TensorType> {
 
-    //https://pytorch.org/docs/stable/generated/torch.eye.html
-    public fun eye(n: Int): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.matmul.html
-    public infix fun TensorType.dot(other: TensorType): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.diag_embed.html
-    public fun diagonalEmbedding(
-        diagonalEntries: TensorType,
-        offset: Int = 0, dim1: Int = -2, dim2: Int = -1
-    ): TensorType
-
-    //https://pytorch.org/docs/stable/linalg.html#torch.linalg.det
-    public fun TensorType.det(): TensorType
-
     //https://pytorch.org/docs/stable/linalg.html#torch.linalg.inv
     public fun TensorType.inv(): TensorType
 

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

@@ -0,0 +1,29 @@
+package space.kscience.kmath.tensors
+
+public interface OrderedTensorAlgebra<T, TensorType : TensorStructure<T>> :
+    TensorAlgebra<T, TensorType> {
+
+    //https://pytorch.org/docs/stable/generated/torch.max.html#torch.max
+    public fun TensorType.max(dim: Int, keepDim: Boolean): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.cummax.html#torch.cummax
+    public fun TensorType.cummax(dim: Int): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.min.html#torch.min
+    public fun TensorType.min(dim: Int, keepDim: Boolean): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.cummin.html#torch.cummin
+    public fun TensorType.cummin(dim: Int): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.median.html#torch.median
+    public fun TensorType.median(dim: Int, keepDim: Boolean): TensorType
+
+    //https://pytorch.org/docs/stable/generated/torch.maximum.html#torch.maximum
+    public fun maximum(lhs: TensorType, rhs: TensorType)
+
+    //https://pytorch.org/docs/stable/generated/torch.minimum.html#torch.minimum
+    public fun minimum(lhs: TensorType, rhs: TensorType)
+
+    //https://pytorch.org/docs/stable/generated/torch.sort.html#torch.sort
+    public fun TensorType.sort(dim: Int, keepDim: Boolean, descending: Boolean): TensorType
+}

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

@@ -17,6 +17,9 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
     //https://pytorch.org/docs/stable/generated/torch.full_like.html#torch.full_like
     public fun TensorType.fullLike(value: T): TensorType
 
+    //https://pytorch.org/docs/stable/generated/torch.eye.html
+    public fun eye(n: Int): TensorType
+
     public fun TensorType.copy(): TensorType
 
     public operator fun T.plus(other: TensorType): TensorType
@@ -46,6 +49,9 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
     public fun TensorType.view(shape: IntArray): TensorType
     public fun TensorType.viewAs(other: TensorType): TensorType
 
+    //https://pytorch.org/docs/stable/linalg.html#torch.linalg.det
+    public fun TensorType.det(): TensorType
+
     //https://pytorch.org/docs/stable/generated/torch.abs.html
     public fun TensorType.abs(): TensorType
 
@@ -61,29 +67,14 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
     //https://pytorch.org/docs/stable/generated/torch.cumprod.html#torch.cumprod
     public fun TensorType.cumprod(dim: Int): TensorType
 
-    //https://pytorch.org/docs/stable/generated/torch.max.html#torch.max
+    //https://pytorch.org/docs/stable/generated/torch.matmul.html
-    public fun TensorType.max(dim: Int, keepDim: Boolean): TensorType
+    public infix fun TensorType.dot(other: TensorType): TensorType
 
-    //https://pytorch.org/docs/stable/generated/torch.cummax.html#torch.cummax
+    //https://pytorch.org/docs/stable/generated/torch.diag_embed.html
-    public fun TensorType.cummax(dim: Int): TensorType
+    public fun diagonalEmbedding(
-
+        diagonalEntries: TensorType,
-    //https://pytorch.org/docs/stable/generated/torch.min.html#torch.min
+        offset: Int = 0, dim1: Int = -2, dim2: Int = -1
-    public fun TensorType.min(dim: Int, keepDim: Boolean): TensorType
+    ): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.cummin.html#torch.cummin
-    public fun TensorType.cummin(dim: Int): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.median.html#torch.median
-    public fun TensorType.median(dim: Int, keepDim: Boolean): TensorType
-
-    //https://pytorch.org/docs/stable/generated/torch.maximum.html#torch.maximum
-    public fun maximum(lhs: TensorType, rhs: TensorType)
-
-    //https://pytorch.org/docs/stable/generated/torch.minimum.html#torch.minimum
-    public fun minimum(lhs: TensorType, rhs: TensorType)
-
-    //https://pytorch.org/docs/stable/generated/torch.sort.html#torch.sort
-    public fun TensorType.sort(dim: Int, keepDim: Boolean, descending: Boolean): TensorType
 
     //https://pytorch.org/docs/stable/generated/torch.cat.html#torch.cat
     public fun cat(tensors: List<TensorType>, dim: Int): TensorType

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

@@ -9,7 +9,7 @@ import kotlin.test.assertTrue
 class TestRealTensor {
 
     @Test
-    fun valueTest() = RealTensorAlgebra {
+    fun valueTest() = DoubleTensorAlgebra {
         val value = 12.5
         val tensor = DoubleTensor(intArrayOf(1), doubleArrayOf(value))
         assertEquals(tensor.value(), value)

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

@@ -7,14 +7,14 @@ import kotlin.test.assertTrue
 class TestRealTensorAlgebra {
 
     @Test
-    fun doublePlus() = RealTensorAlgebra {
+    fun doublePlus() = DoubleTensorAlgebra {
         val tensor = DoubleTensor(intArrayOf(2), doubleArrayOf(1.0, 2.0))
         val res = 10.0 + tensor
         assertTrue(res.buffer.unsafeToDoubleArray() contentEquals doubleArrayOf(11.0,12.0))
     }
 
     @Test
-    fun transpose1x1() = RealTensorAlgebra {
+    fun transpose1x1() = DoubleTensorAlgebra {
         val tensor = DoubleTensor(intArrayOf(1), doubleArrayOf(0.0))
         val res = tensor.transpose(0, 0)
 
@@ -23,7 +23,7 @@ class TestRealTensorAlgebra {
     }
 
     @Test
-    fun transpose3x2() = RealTensorAlgebra {
+    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)
 
@@ -32,7 +32,7 @@ class TestRealTensorAlgebra {
     }
 
     @Test
-    fun transpose1x2x3() = RealTensorAlgebra {
+    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)
@@ -48,7 +48,7 @@ class TestRealTensorAlgebra {
     }
 
     @Test
-    fun broadcastShapes() = RealTensorAlgebra {
+    fun broadcastShapes() = DoubleTensorAlgebra {
         assertTrue(broadcastShapes(
             intArrayOf(2, 3), intArrayOf(1, 3), intArrayOf(1, 1, 1)
         ) contentEquals intArrayOf(1, 2, 3))
@@ -59,7 +59,7 @@ class TestRealTensorAlgebra {
     }
 
     @Test
-    fun broadcastTensors() = RealTensorAlgebra {
+    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))
@@ -76,7 +76,7 @@ class TestRealTensorAlgebra {
     }
 
     @Test
-    fun minusTensor() = RealTensorAlgebra {
+    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))