map and analytic funcions

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

@@ -14,10 +14,10 @@ public interface LinearOpsTensorAlgebra<T, TensorType : TensorStructure<T>, Inde
     public fun TensorType.qr(): TensorType
 
     //https://pytorch.org/docs/stable/generated/torch.lu.html
-    public fun TensorType.lu(): Pair<TensorType, IndexTensorType>
+    public fun TensorType.lu(tol: T): Pair<TensorType, IndexTensorType>
 
     //https://pytorch.org/docs/stable/generated/torch.lu_unpack.html
-    public fun luPivot(lu: TensorType, pivots: IndexTensorType): Triple<TensorType, TensorType, TensorType>
+    public fun luPivot(luTensor: TensorType, pivotsTensor: 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

@@ -3,6 +3,11 @@ package space.kscience.kmath.tensors
 // https://proofwiki.org/wiki/Definition:Algebra_over_Ring
 public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
 
+    public fun TensorType.map(transform: (T) -> T): TensorType
+
+    public fun TensorType.eq(other: TensorType, eqFunction: (T, T) -> Boolean): Boolean
+    public fun TensorType.contentEquals(other: TensorType, eqFunction: (T, T) -> Boolean): Boolean
+
     //https://pytorch.org/docs/stable/generated/torch.full.html
     public fun full(value: T, shape: IntArray): TensorType
 

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

@@ -5,6 +5,7 @@ import space.kscience.kmath.nd.*
 import space.kscience.kmath.structures.*
 import space.kscience.kmath.tensors.TensorStrides
 import space.kscience.kmath.tensors.TensorStructure
+import kotlin.math.atanh
 
 
 public open class BufferedTensor<T>(

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

@@ -7,77 +7,40 @@ public class DoubleAnalyticTensorAlgebra:
     AnalyticTensorAlgebra<Double, DoubleTensor>,
     DoubleOrderedTensorAlgebra()
 {
-    override fun DoubleTensor.exp(): DoubleTensor {
+    override fun DoubleTensor.exp(): DoubleTensor = this.map(::exp)
-        TODO("Not yet implemented")
-    }
 
-    override fun DoubleTensor.log(): DoubleTensor {
+    // todo log with other base????
-        TODO("Not yet implemented")
+    override fun DoubleTensor.log(): DoubleTensor = this.map(::ln)
-    }
 
-    override fun DoubleTensor.sqrt(): DoubleTensor {
+    override fun DoubleTensor.sqrt(): DoubleTensor = this.map(::sqrt)
-        TODO("Not yet implemented")
-    }
 
-    override fun DoubleTensor.cos(): DoubleTensor {
+    override fun DoubleTensor.cos(): DoubleTensor = this.map(::cos)
-        TODO("Not yet implemented")
-    }
 
-    override fun DoubleTensor.acos(): DoubleTensor {
+    override fun DoubleTensor.acos(): DoubleTensor = this.map(::acos)
-        TODO("Not yet implemented")
-    }
 
-    override fun DoubleTensor.cosh(): DoubleTensor {
+    override fun DoubleTensor.cosh(): DoubleTensor = this.map(::cosh)
-        TODO("Not yet implemented")
-    }
 
-    override fun DoubleTensor.acosh(): DoubleTensor {
+    override fun DoubleTensor.acosh(): DoubleTensor = this.map(::acosh)
-        TODO("Not yet implemented")
-    }
 
-    override fun DoubleTensor.sin(): DoubleTensor {
+    override fun DoubleTensor.sin(): DoubleTensor = this.map(::sin)
-        TODO("Not yet implemented")
-    }
 
-    override fun DoubleTensor.asin(): DoubleTensor {
+    override fun DoubleTensor.asin(): DoubleTensor = this.map(::asin)
-        TODO("Not yet implemented")
-    }
 
-    override fun DoubleTensor.sinh(): DoubleTensor {
+    override fun DoubleTensor.sinh(): DoubleTensor = this.map(::sinh)
-        TODO("Not yet implemented")
-    }
 
-    override fun DoubleTensor.asinh(): DoubleTensor {
+    override fun DoubleTensor.asinh(): DoubleTensor = this.map(::asinh)
-        TODO("Not yet implemented")
-    }
 
-    override fun DoubleTensor.tan(): DoubleTensor {
+    override fun DoubleTensor.tan(): DoubleTensor = this.map(::tan)
-        TODO("Not yet implemented")
-    }
 
-    override fun DoubleTensor.atan(): DoubleTensor {
+    override fun DoubleTensor.atan(): DoubleTensor = this.map(::atan)
-        TODO("Not yet implemented")
-    }
 
-    override fun DoubleTensor.tanh(): DoubleTensor {
+    override fun DoubleTensor.tanh(): DoubleTensor = this.map(::tanh)
-        TODO("Not yet implemented")
-    }
 
-    override fun DoubleTensor.atanh(): DoubleTensor {
+    override fun DoubleTensor.atanh(): DoubleTensor = this.map(::atanh)
-        return DoubleTensor(
-            this.shape,
-            this.buffer.array().map(::atanh).toDoubleArray(),
-            this.bufferStart
-        )
-    }
 
-    override fun DoubleTensor.ceil(): DoubleTensor {
+    override fun DoubleTensor.ceil(): DoubleTensor = this.map(::ceil)
-        TODO("Not yet implemented")
-    }
 
-    override fun DoubleTensor.floor(): DoubleTensor {
+    override fun DoubleTensor.floor(): DoubleTensor = this.map(::floor)
-        TODO("Not yet implemented")
-    }
 
     override fun DoubleTensor.clamp(min: Double, max: Double): DoubleTensor {
         TODO("Not yet implemented")

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

@@ -1,6 +1,7 @@
 package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.tensors.LinearOpsTensorAlgebra
+import kotlin.math.sqrt
 
 public class DoubleLinearOpsTensorAlgebra :
     LinearOpsTensorAlgebra<Double, DoubleTensor, IntTensor>,
@@ -10,47 +11,47 @@ public class DoubleLinearOpsTensorAlgebra :
         TODO("Not yet implemented")
     }
 
-    override fun DoubleTensor.lu(): Pair<DoubleTensor, IntTensor> {
+    override fun DoubleTensor.lu(tol: Double): Pair<DoubleTensor, IntTensor> {
 
-        // todo checks
+        checkSquareMatrix(shape)
 
-        val luTensor = this.copy()
+        val luTensor = copy()
 
-        val n = this.shape.size
+        val n = shape.size
-        val m = this.shape.last()
+        val m = shape.last()
-        val pivotsShape = IntArray(n - 1) { i -> this.shape[i] }
+        val pivotsShape = IntArray(n - 1) { i -> shape[i] }
         val pivotsTensor = IntTensor(
             pivotsShape,
-            IntArray(pivotsShape.reduce(Int::times)) { 0 } //todo default???
+            IntArray(pivotsShape.reduce(Int::times)) { 0 }
         )
 
         for ((lu, pivots) in luTensor.matrixSequence().zip(pivotsTensor.vectorSequence())){
             for (row in 0 until m) pivots[row] = row
 
             for (i in 0 until m) {
-                var maxA = -1.0
+                var maxVal = -1.0
-                var iMax = i
+                var maxInd = i
 
                 for (k in i until m) {
                     val absA = kotlin.math.abs(lu[k, i])
-                    if (absA > maxA) {
+                    if (absA > maxVal) {
-                        maxA = absA
+                        maxVal = absA
-                        iMax = k
+                        maxInd = k
                     }
                 }
 
                 //todo check singularity
 
-                if (iMax != i) {
+                if (maxInd != i) {
 
                     val j = pivots[i]
-                    pivots[i] = pivots[iMax]
+                    pivots[i] = pivots[maxInd]
-                    pivots[iMax] = j
+                    pivots[maxInd] = j
 
                     for (k in 0 until m) {
                         val tmp = lu[i, k]
-                        lu[i, k] = lu[iMax, k]
+                        lu[i, k] = lu[maxInd, k]
-                        lu[iMax, k] = tmp
+                        lu[maxInd, k] = tmp
                     }
 
                 }
@@ -71,6 +72,9 @@ public class DoubleLinearOpsTensorAlgebra :
 
     override fun luPivot(luTensor: DoubleTensor, pivotsTensor: IntTensor): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
         //todo checks
+        checkSquareMatrix(luTensor.shape)
+        check(luTensor.shape.dropLast(1).toIntArray() contentEquals pivotsTensor.shape) { "Bed shapes (("} //todo rewrite
+
         val n = luTensor.shape.last()
         val pTensor = luTensor.zeroesLike()
         for ((p, pivot) in pTensor.matrixSequence().zip(pivotsTensor.vectorSequence())){
@@ -104,7 +108,30 @@ public class DoubleLinearOpsTensorAlgebra :
     }
 
     override fun DoubleTensor.cholesky(): DoubleTensor {
-        TODO("Not yet implemented")
+        // todo checks
+        checkSquareMatrix(shape)
+
+        val n = shape.last()
+        val lTensor = zeroesLike()
+
+        for ((a, l) in this.matrixSequence().zip(lTensor.matrixSequence())) {
+            for (i in 0 until n) {
+                for (j in 0 until i) {
+                    var h = a[i, j]
+                    for (k in 0 until j) {
+                        h -= l[i, k] * l[j, k]
+                    }
+                    l[i, j] = h / l[j, j]
+                }
+                var h = a[i, i]
+                for (j in 0 until i) {
+                    h -= l[i, j] * l[i, j]
+                }
+                l[i, i] = sqrt(h)
+            }
+        }
+
+        return lTensor
     }
 
     override fun DoubleTensor.qr(): DoubleTensor {

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

@@ -1,7 +1,7 @@
 package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.tensors.TensorPartialDivisionAlgebra
-
+import kotlin.math.abs
 
 public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, DoubleTensor> {
 
@@ -277,6 +277,43 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         TODO("Not yet implemented")
     }
 
+    override fun DoubleTensor.map(transform: (Double) -> Double): DoubleTensor {
+        return DoubleTensor(
+            this.shape,
+            this.buffer.array().map { transform(it) }.toDoubleArray(),
+            this.bufferStart
+        )
+    }
+
+    public fun DoubleTensor.contentEquals(other: DoubleTensor, delta: Double = 1e-5): Boolean {
+        return this.contentEquals(other) { x, y -> abs(x - y) < delta }
+    }
+
+    public fun DoubleTensor.eq(other: DoubleTensor, delta: Double = 1e-5): Boolean {
+        return this.eq(other) { x, y -> abs(x - y) < delta }
+    }
+
+    override fun DoubleTensor.contentEquals(other: DoubleTensor, eqFunction: (Double, Double) -> Boolean): Boolean {
+        if (!(this.shape contentEquals other.shape)){
+            return false
+        }
+        return this.eq(other, eqFunction)
+    }
+
+    override fun DoubleTensor.eq(other: DoubleTensor, eqFunction: (Double, Double) -> Boolean): Boolean {
+        // todo broadcasting checking
+        val n = this.strides.linearSize
+        if (n != other.strides.linearSize){
+            return false
+        }
+        for (i in 0 until n){
+            if (!eqFunction(this.buffer[this.bufferStart + i], other.buffer[other.bufferStart + i])) {
+                return false
+            }
+        }
+        return true
+    }
+
 }
 
 

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

@@ -65,3 +65,15 @@ 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))
+
+internal inline fun <T, TensorType : TensorStructure<T>,
+        TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
+        TorchTensorAlgebraType.checkSquareMatrix(shape: IntArray): Unit {
+    val n = shape.size
+    check(n >= 2) {
+        "Expected tensor with 2 or more dimensions, got size $n instead"
+    }
+    check(shape[n - 1] == shape[n - 2]) {
+        "Tensor must be batches of square matrices, but they are ${shape[n - 1]} by ${shape[n - 1]} matrices"
+    }
+}