Update interpolation API to agree with other conventions.
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@ -15,8 +15,10 @@ import space.kscience.kmath.linear.invoke
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import space.kscience.kmath.linear.linearSpace
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import space.kscience.kmath.multik.multikAlgebra
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import space.kscience.kmath.operations.DoubleField
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import space.kscience.kmath.operations.invoke
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import space.kscience.kmath.structures.Buffer
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import space.kscience.kmath.tensorflow.produceWithTF
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import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
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import space.kscience.kmath.tensors.core.tensorAlgebra
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import kotlin.random.Random
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@ -90,4 +92,9 @@ internal class DotBenchmark {
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fun doubleDot(blackhole: Blackhole) = with(DoubleField.linearSpace) {
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blackhole.consume(matrix1 dot matrix2)
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}
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@Benchmark
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fun doubleTensorDot(blackhole: Blackhole) = DoubleTensorAlgebra.invoke {
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blackhole.consume(matrix1 dot matrix2)
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}
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}
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@ -5,8 +5,8 @@
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package space.kscience.kmath.functions
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import space.kscience.kmath.interpolation.SplineInterpolator
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import space.kscience.kmath.interpolation.interpolatePolynomials
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import space.kscience.kmath.interpolation.splineInterpolator
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import space.kscience.kmath.operations.DoubleField
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import space.kscience.kmath.real.map
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import space.kscience.kmath.real.step
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@ -28,7 +28,7 @@ fun main() {
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val xs = 0.0..100.0 step 0.5
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val ys = xs.map(function)
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val polynomial: PiecewisePolynomial<Double> = SplineInterpolator.double.interpolatePolynomials(xs, ys)
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val polynomial: PiecewisePolynomial<Double> = DoubleField.splineInterpolator.interpolatePolynomials(xs, ys)
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val polyFunction = polynomial.asFunction(DoubleField, 0.0)
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@ -28,6 +28,8 @@ public fun <T : Comparable<T>> PiecewisePolynomial<T>.integrate(algebra: Field<T
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/**
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* Compute definite integral of given [PiecewisePolynomial] piece by piece in a given [range]
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* Requires [UnivariateIntegrationNodes] or [IntegrationRange] and [IntegrandMaxCalls]
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*
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* TODO use context receiver for algebra
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*/
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@UnstableKMathAPI
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public fun <T : Comparable<T>> PiecewisePolynomial<T>.integrate(
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@ -98,6 +100,7 @@ public object DoubleSplineIntegrator : UnivariateIntegrator<Double> {
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}
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}
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@Suppress("unused")
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@UnstableKMathAPI
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public inline val DoubleField.splineIntegrator: UnivariateIntegrator<Double>
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get() = DoubleSplineIntegrator
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@ -9,6 +9,7 @@ package space.kscience.kmath.interpolation
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import space.kscience.kmath.data.XYColumnarData
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import space.kscience.kmath.functions.PiecewisePolynomial
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import space.kscience.kmath.functions.asFunction
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import space.kscience.kmath.functions.value
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import space.kscience.kmath.misc.UnstableKMathAPI
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import space.kscience.kmath.operations.Ring
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@ -59,3 +60,33 @@ public fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolatePolynomials(
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val pointSet = XYColumnarData.of(data.map { it.first }.asBuffer(), data.map { it.second }.asBuffer())
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return interpolatePolynomials(pointSet)
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}
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public fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolate(
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x: Buffer<T>,
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y: Buffer<T>,
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): (T) -> T? = interpolatePolynomials(x, y).asFunction(algebra)
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public fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolate(
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data: Map<T, T>,
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): (T) -> T? = interpolatePolynomials(data).asFunction(algebra)
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public fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolate(
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data: List<Pair<T, T>>,
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): (T) -> T? = interpolatePolynomials(data).asFunction(algebra)
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public fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolate(
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x: Buffer<T>,
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y: Buffer<T>,
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defaultValue: T,
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): (T) -> T = interpolatePolynomials(x, y).asFunction(algebra, defaultValue)
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public fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolate(
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data: Map<T, T>,
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defaultValue: T,
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): (T) -> T = interpolatePolynomials(data).asFunction(algebra, defaultValue)
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public fun <T : Comparable<T>> PolynomialInterpolator<T>.interpolate(
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data: List<Pair<T, T>>,
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defaultValue: T,
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): (T) -> T = interpolatePolynomials(data).asFunction(algebra, defaultValue)
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@ -22,6 +22,7 @@ internal fun <T : Comparable<T>> insureSorted(points: XYColumnarData<*, T, *>) {
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* Reference JVM implementation: https://github.com/apache/commons-math/blob/master/src/main/java/org/apache/commons/math4/analysis/interpolation/LinearInterpolator.java
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*/
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public class LinearInterpolator<T : Comparable<T>>(override val algebra: Field<T>) : PolynomialInterpolator<T> {
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@OptIn(UnstableKMathAPI::class)
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override fun interpolatePolynomials(points: XYColumnarData<T, T, T>): PiecewisePolynomial<T> = algebra {
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require(points.size > 0) { "Point array should not be empty" }
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@ -37,3 +38,6 @@ public class LinearInterpolator<T : Comparable<T>>(override val algebra: Field<T
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}
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}
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}
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public val <T : Comparable<T>> Field<T>.linearInterpolator: LinearInterpolator<T>
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get() = LinearInterpolator(this)
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@ -72,8 +72,12 @@ public class SplineInterpolator<T : Comparable<T>>(
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}
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}
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}
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}
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public companion object {
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public val double: SplineInterpolator<Double> = SplineInterpolator(DoubleField, ::DoubleBuffer)
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}
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}
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public fun <T : Comparable<T>> Field<T>.splineInterpolator(
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bufferFactory: MutableBufferFactory<T>,
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): SplineInterpolator<T> = SplineInterpolator(this, bufferFactory)
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public val DoubleField.splineInterpolator: SplineInterpolator<Double>
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get() = SplineInterpolator(this, ::DoubleBuffer)
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@ -5,8 +5,6 @@
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package space.kscience.kmath.interpolation
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import space.kscience.kmath.functions.PiecewisePolynomial
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import space.kscience.kmath.functions.asFunction
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import space.kscience.kmath.operations.DoubleField
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import kotlin.test.Test
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import kotlin.test.assertEquals
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@ -21,8 +19,8 @@ internal class LinearInterpolatorTest {
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3.0 to 4.0
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)
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val polynomial: PiecewisePolynomial<Double> = LinearInterpolator(DoubleField).interpolatePolynomials(data)
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val function = polynomial.asFunction(DoubleField)
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//val polynomial: PiecewisePolynomial<Double> = DoubleField.linearInterpolator.interpolatePolynomials(data)
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val function = DoubleField.linearInterpolator.interpolate(data)
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assertEquals(null, function(-1.0))
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assertEquals(0.5, function(0.5))
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assertEquals(2.0, function(1.5))
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@ -5,8 +5,6 @@
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package space.kscience.kmath.interpolation
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import space.kscience.kmath.functions.PiecewisePolynomial
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import space.kscience.kmath.functions.asFunction
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import space.kscience.kmath.operations.DoubleField
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import kotlin.math.PI
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import kotlin.math.sin
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@ -21,9 +19,10 @@ internal class SplineInterpolatorTest {
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x to sin(x)
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}
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val polynomial: PiecewisePolynomial<Double> = SplineInterpolator.double.interpolatePolynomials(data)
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//val polynomial: PiecewisePolynomial<Double> = DoubleField.splineInterpolator.interpolatePolynomials(data)
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val function = DoubleField.splineInterpolator.interpolate(data, Double.NaN)
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val function = polynomial.asFunction(DoubleField, Double.NaN)
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assertEquals(Double.NaN, function(-1.0))
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assertEquals(sin(0.5), function(0.5), 0.1)
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assertEquals(sin(1.5), function(1.5), 0.1)
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@ -6,13 +6,38 @@
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package space.kscience.kmath.multik
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import org.junit.jupiter.api.Test
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import space.kscience.kmath.nd.StructureND
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import space.kscience.kmath.nd.one
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import space.kscience.kmath.operations.DoubleField
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import space.kscience.kmath.operations.invoke
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import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
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import space.kscience.kmath.tensors.core.tensorAlgebra
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import kotlin.test.assertTrue
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internal class MultikNDTest {
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@Test
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fun basicAlgebra(): Unit = DoubleField.multikAlgebra{
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one(2,2) + 1.0
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}
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@Test
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fun dotResult(){
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val dim = 100
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val tensor1 = DoubleTensorAlgebra.randomNormal(shape = intArrayOf(dim, dim), 12224)
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val tensor2 = DoubleTensorAlgebra.randomNormal(shape = intArrayOf(dim, dim), 12225)
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val multikResult = with(DoubleField.multikAlgebra){
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tensor1 dot tensor2
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}
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val defaultResult = with(DoubleField.tensorAlgebra){
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tensor1 dot tensor2
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}
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assertTrue {
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StructureND.contentEquals(multikResult, defaultResult)
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}
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}
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}
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@ -6,7 +6,6 @@ import space.kscience.kmath.nd.structureND
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import space.kscience.kmath.operations.DoubleField
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import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
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import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.sum
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import kotlin.random.Random
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import kotlin.test.assertEquals
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class DoubleTensorFlowOps {
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@ -23,7 +22,6 @@ class DoubleTensorFlowOps {
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@Test
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fun dot(){
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val random = Random(12224)
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val dim = 1000
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val tensor1 = DoubleTensorAlgebra.randomNormal(shape = intArrayOf(dim, dim), 12224)
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