testing algebra

kmath-noa/src/test/kotlin/space/kscience/kmath/noa/TestAlgebra.kt

@@ -0,0 +1,85 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.noa
+
+import kotlin.test.Test
+import kotlin.test.assertTrue
+
+
+internal fun NoaDouble.testingLinearStructure(device: Device = Device.CPU): Unit {
+
+    val shape = intArrayOf(3)
+    val tensorA = full(value = -4.5, shape = shape, device = device)
+    val tensorB = full(value = 10.9, shape = shape, device = device)
+    val tensorC = full(value = 789.3, shape = shape, device = device)
+    val tensorD = full(value = -72.9, shape = shape, device = device)
+    val tensorE = full(value = 553.1, shape = shape, device = device)
+    val result = 15.8 * tensorA - 1.5 * tensorB * (-tensorD) + 0.02 * tensorC / tensorE - 39.4
+    val expected = copyFromArray(
+        array = (1..3).map {
+            15.8 * (-4.5) - 1.5 * 10.9 * 72.9 + 0.02 * 789.3 / 553.1 - 39.4
+        }.toDoubleArray(),
+        shape = shape,
+        device = device
+    )
+
+    val assignResult = full(value = 0.0, shape = shape, device = device)
+    tensorA *= 15.8
+    tensorB *= 1.5
+    tensorB *= -tensorD
+    tensorC *= 0.02
+    tensorC /= tensorE
+    assignResult += tensorA
+    assignResult -= tensorB
+    assignResult += tensorC
+    assignResult += -39.4
+
+    val error = (expected - result).abs().sum() +
+            (expected - assignResult).abs().sum()
+    assertTrue(error < TOLERANCE)
+
+}
+
+internal fun NoaDouble.testingBatchedSVD(device: Device = Device.CPU): Unit {
+    val tensor = randNormal(shape = intArrayOf(7, 5, 3), device = device)
+    val (tensorU, tensorS, tensorV) = tensor.svd()
+    val error = tensor - (tensorU dot (diagonalEmbedding(tensorS) dot tensorV.transpose(-2, -1)))
+    assertTrue(error.abs().sum() < TOLERANCE)
+}
+
+internal fun NoaDouble.testingBatchedSymEig(device: Device = Device.CPU): Unit {
+    val tensor = randNormal(shape = intArrayOf(5, 5), device = device)
+    val tensorSigma = tensor + tensor.transpose(-2, -1)
+    val (tensorS, tensorV) = tensorSigma.symEig()
+    val error = tensorSigma - (tensorV dot (diagonalEmbedding(tensorS) dot tensorV.transpose(-2, -1)))
+    assertTrue(error.abs().sum() < TOLERANCE)
+}
+
+
+class TestAlgebra {
+
+    @Test
+    fun testLinearStructure() = NoaDouble {
+        withCuda { device ->
+            testingLinearStructure(device)
+        }
+    }!!
+
+    @Test
+    fun testBatchedSVD() = NoaDouble {
+        withCuda { device ->
+            testingBatchedSVD(device)
+        }
+    }!!
+
+    @Test
+    fun testBatchedSymEig() = NoaDouble {
+        withCuda { device ->
+            testingBatchedSymEig(device)
+        }
+    }!!
+
+}