forked from kscience/kmath
Add tests
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@ -36,7 +36,7 @@ public class EjmlMatrix(public val origin: SimpleMatrix, features: Set<MatrixFea
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Triple(
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EjmlMatrix(SimpleMatrix(ludecompositionF64.getRowPivot(null))),
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EjmlMatrix(SimpleMatrix(ludecompositionF64.getLower(null))),
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EjmlMatrix(SimpleMatrix(ludecompositionF64.getUpper(null)))
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EjmlMatrix(SimpleMatrix(ludecompositionF64.getUpper(null))),
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)
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}
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@ -51,7 +51,7 @@ public class EjmlMatrix(public val origin: SimpleMatrix, features: Set<MatrixFea
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}
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) union features.orEmpty()
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public override fun suggestFeature(vararg features: MatrixFeature): FeaturedMatrix<Double> =
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public override fun suggestFeature(vararg features: MatrixFeature): EjmlMatrix =
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EjmlMatrix(origin, this.features + features)
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public override operator fun get(i: Int, j: Int): Double = origin[i, j]
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@ -0,0 +1,75 @@
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package kscience.kmath.ejml
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import kscience.kmath.linear.DeterminantFeature
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import kscience.kmath.linear.LUPDecompositionFeature
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import kscience.kmath.linear.MatrixFeature
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import kscience.kmath.linear.getFeature
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import org.ejml.dense.row.factory.DecompositionFactory_DDRM
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import org.ejml.simple.SimpleMatrix
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import kotlin.random.Random
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import kotlin.random.asJavaRandom
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import kotlin.test.*
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internal class EjmlMatrixTest {
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private val random = Random(0)
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private val randomMatrix: SimpleMatrix
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get() {
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val s = random.nextInt(2, 100)
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return SimpleMatrix.random_DDRM(s, s, 0.0, 10.0, random.asJavaRandom())
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}
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@Test
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fun rowNum() {
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val m = randomMatrix
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assertEquals(m.numRows(), EjmlMatrix(m).rowNum)
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}
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@Test
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fun colNum() {
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val m = randomMatrix
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assertEquals(m.numCols(), EjmlMatrix(m).rowNum)
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}
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@Test
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fun shape() {
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val m = randomMatrix
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val w = EjmlMatrix(m)
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assertEquals(listOf(m.numRows(), m.numCols()), w.shape.toList())
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}
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@Test
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fun features() {
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val m = randomMatrix
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val w = EjmlMatrix(m)
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val det = w.getFeature<DeterminantFeature<Double>>() ?: fail()
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assertEquals(m.determinant(), det.determinant)
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val lup = w.getFeature<LUPDecompositionFeature<Double>>() ?: fail()
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val ludecompositionF64 = DecompositionFactory_DDRM.lu(m.numRows(), m.numCols())
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.also { it.decompose(m.ddrm.copy()) }
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assertEquals(EjmlMatrix(SimpleMatrix(ludecompositionF64.getLower(null))), lup.l)
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assertEquals(EjmlMatrix(SimpleMatrix(ludecompositionF64.getUpper(null))), lup.u)
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assertEquals(EjmlMatrix(SimpleMatrix(ludecompositionF64.getRowPivot(null))), lup.p)
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}
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private object SomeFeature : MatrixFeature {}
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@Test
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fun suggestFeature() {
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assertNotNull(EjmlMatrix(randomMatrix).suggestFeature(SomeFeature).getFeature<SomeFeature>())
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}
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@Test
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fun get() {
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val m = randomMatrix
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assertEquals(m[0, 0], EjmlMatrix(m)[0, 0])
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}
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@Test
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fun origin() {
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val m = randomMatrix
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assertSame(m, EjmlMatrix(m).origin)
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}
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}
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@ -0,0 +1,47 @@
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package kscience.kmath.ejml
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import org.ejml.simple.SimpleMatrix
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import kotlin.random.Random
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import kotlin.random.asJavaRandom
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import kotlin.test.Test
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import kotlin.test.assertEquals
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import kotlin.test.assertSame
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internal class EjmlVectorTest {
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private val random = Random(0)
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private val randomMatrix: SimpleMatrix
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get() = SimpleMatrix.random_DDRM(random.nextInt(2, 100), 1, 0.0, 10.0, random.asJavaRandom())
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@Test
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fun size() {
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val m = randomMatrix
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val w = EjmlVector(m)
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assertEquals(m.numRows(), w.size)
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}
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@Test
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fun get() {
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val m = randomMatrix
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val w = EjmlVector(m)
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assertEquals(m[0, 0], w[0])
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}
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@Test
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fun iterator() {
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val m = randomMatrix
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val w = EjmlVector(m)
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assertEquals(
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m.iterator(true, 0, 0, m.numRows() - 1, 0).asSequence().toList(),
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w.iterator().asSequence().toList()
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)
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}
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@Test
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fun origin() {
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val m = randomMatrix
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val w = EjmlVector(m)
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assertSame(m, w.origin)
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}
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}
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