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

@@ -1,7 +1,7 @@
 package space.kscience.kmath.tensors
 
 
-public interface LinearOpsTensorAlgebra<T, TensorType : TensorStructure<T>, IndexTensorType: TensorStructure<Int>> :
+public interface LinearOpsTensorAlgebra<T, TensorType : TensorStructure<T>, IndexTensorType : TensorStructure<Int>> :
     TensorPartialDivisionAlgebra<T, TensorType> {
 
     //https://pytorch.org/docs/stable/linalg.html#torch.linalg.det
@@ -26,6 +26,6 @@ public interface LinearOpsTensorAlgebra<T, TensorType : TensorStructure<T>, Inde
     public fun TensorType.svd(): Triple<TensorType, TensorType, TensorType>
 
     //https://pytorch.org/docs/stable/generated/torch.symeig.html
-    public fun TensorType.symEig(eigenvectors: Boolean = true): Pair<TensorType, TensorType>
+    public fun TensorType.symEig(): Pair<TensorType, TensorType>
 
 }

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

@@ -94,8 +94,10 @@ public class DoubleLinearOpsTensorAlgebra :
         return qTensor to rTensor
     }
 
+    override fun DoubleTensor.svd(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> =
+        svd(epsilon = 1e-10)
 
-    override fun DoubleTensor.svd(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
+    public fun DoubleTensor.svd(epsilon: Double): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
         val size = this.linearStructure.dim
         val commonShape = this.shape.sliceArray(0 until size - 2)
         val (n, m) = this.shape.sliceArray(size - 2 until size)
@@ -110,17 +112,20 @@ public class DoubleLinearOpsTensorAlgebra :
                 matrix.shape,
                 matrix.buffer.array().slice(matrix.bufferStart until matrix.bufferStart + size).toDoubleArray()
             )
-            svdHelper(curMatrix, USV, m, n)
+            svdHelper(curMatrix, USV, m, n, epsilon)
         }
         return Triple(resU.transpose(), resS, resV.transpose())
     }
 
+    override fun DoubleTensor.symEig(): Pair<DoubleTensor, DoubleTensor> =
+        symEig(epsilon = 1e-15)
+
     //http://hua-zhou.github.io/teaching/biostatm280-2017spring/slides/16-eigsvd/eigsvd.html
-    override fun DoubleTensor.symEig(eigenvectors: Boolean): Pair<DoubleTensor, DoubleTensor> {
+    public fun DoubleTensor.symEig(epsilon: Double): Pair<DoubleTensor, DoubleTensor> {
-        checkSymmetric(this)
+        checkSymmetric(this, epsilon)
-        val (u, s, v) = this.svd()
+        val (u, s, v) = this.svd(epsilon)
         val shp = s.shape + intArrayOf(1)
-        val utv = (u.transpose() dot v).map { if (abs(it) < 0.99) 0.0 else sign(it) }
+        val utv = (u.transpose() dot v).map { if (abs(it) < 0.9) 0.0 else sign(it) }
         val eig = (utv dot s.view(shp)).view(s.shape)
         return Pair(eig, v)
     }

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

@@ -58,7 +58,7 @@ internal inline fun <T, TensorType : TensorStructure<T>,
     }
 }
 
-internal inline fun DoubleTensorAlgebra.checkSymmetric(tensor: DoubleTensor): Unit =
+internal inline fun DoubleTensorAlgebra.checkSymmetric(tensor: DoubleTensor, epsilon: Double = 1e-6): Unit =
-    check(tensor.eq(tensor.transpose())){
+    check(tensor.eq(tensor.transpose(), epsilon)) {
-        "Tensor is not symmetric about the last 2 dimensions"
+        "Tensor is not symmetric about the last 2 dimensions at precision $epsilon"
     }

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

@@ -246,7 +246,7 @@ internal inline fun DoubleLinearOpsTensorAlgebra.svd1d(a: DoubleTensor, epsilon:
 internal inline fun DoubleLinearOpsTensorAlgebra.svdHelper(
     matrix: DoubleTensor,
     USV: Pair<BufferedTensor<Double>, Pair<BufferedTensor<Double>, BufferedTensor<Double>>>,
-    m: Int, n: Int
+    m: Int, n: Int, epsilon: Double
 ): Unit {
     val res = ArrayList<Triple<Double, DoubleTensor, DoubleTensor>>(0)
     val (matrixU, SV) = USV
@@ -267,12 +267,12 @@ internal inline fun DoubleLinearOpsTensorAlgebra.svdHelper(
         var u: DoubleTensor
         var norm: Double
         if (n > m) {
-            v = svd1d(a)
+            v = svd1d(a, epsilon)
             u = matrix.dot(v)
             norm = DoubleAnalyticTensorAlgebra { (u dot u).sqrt().value() }
             u = u.times(1.0 / norm)
         } else {
-            u = svd1d(a)
+            u = svd1d(a, epsilon)
             v = matrix.transpose(0, 1).dot(u)
             norm = DoubleAnalyticTensorAlgebra { (v dot v).sqrt().value() }
             v = v.times(1.0 / norm)

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

@@ -143,7 +143,7 @@ class TestDoubleLinearOpsTensorAlgebra {
         val tensorSigma = tensor + tensor.transpose()
         val (tensorS, tensorV) = tensorSigma.symEig()
         val tensorSigmaCalc = tensorV dot (diagonalEmbedding(tensorS) dot tensorV.transpose())
-        assertTrue(tensorSigma.eq(tensorSigmaCalc, 0.01))
+        assertTrue(tensorSigma.eq(tensorSigmaCalc))
     }