Multik integration finished (for now)
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726864ed0e
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0e1e97a3ff
@ -17,12 +17,15 @@ import kotlin.math.*
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/**
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* [ExtendedFieldOps] over [DoubleBuffer].
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*/
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public abstract class DoubleBufferOps :
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BufferAlgebra<Double, DoubleField>, ExtendedFieldOps<Buffer<Double>>, Norm<Buffer<Double>, Double> {
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public abstract class DoubleBufferOps : BufferAlgebra<Double, DoubleField>, ExtendedFieldOps<Buffer<Double>>,
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Norm<Buffer<Double>, Double> {
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override val elementAlgebra: DoubleField get() = DoubleField
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override val bufferFactory: BufferFactory<Double> get() = ::DoubleBuffer
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override fun Buffer<Double>.map(block: DoubleField.(Double) -> Double): DoubleBuffer =
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mapInline { DoubleField.block(it) }
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@UnstableKMathAPI
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override fun unaryOperationFunction(operation: String): (arg: Buffer<Double>) -> Buffer<Double> =
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super<ExtendedFieldOps>.unaryOperationFunction(operation)
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@ -87,8 +90,7 @@ public abstract class DoubleBufferOps :
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val aArray = left.array
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val bArray = right.array
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DoubleBuffer(DoubleArray(left.size) { aArray[it] * bArray[it] })
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} else
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DoubleBuffer(DoubleArray(left.size) { left[it] * right[it] })
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} else DoubleBuffer(DoubleArray(left.size) { left[it] * right[it] })
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}
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override fun divide(left: Buffer<Double>, right: Buffer<Double>): DoubleBuffer {
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@ -1,134 +1,47 @@
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package space.kscience.kmath.multik
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import org.jetbrains.kotlinx.multik.ndarray.data.DN
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import org.jetbrains.kotlinx.multik.ndarray.data.DataType
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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.api.AnalyticTensorAlgebra
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import space.kscience.kmath.tensors.api.LinearOpsTensorAlgebra
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import space.kscience.kmath.tensors.api.Tensor
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import space.kscience.kmath.operations.ExponentialOperations
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import space.kscience.kmath.operations.TrigonometricOperations
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public object MultikDoubleAlgebra : MultikDivisionTensorAlgebra<Double, DoubleField>(),
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AnalyticTensorAlgebra<Double, DoubleField>, LinearOpsTensorAlgebra<Double, DoubleField> {
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TrigonometricOperations<StructureND<Double>>, ExponentialOperations<StructureND<Double>> {
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override val elementAlgebra: DoubleField get() = DoubleField
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override val type: DataType get() = DataType.DoubleDataType
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override fun StructureND<Double>.mean(): Double = multikStat.mean(asMultik().array)
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override fun sin(arg: StructureND<Double>): MultikTensor<Double> = multikMath.mathEx.sin(arg.asMultik().array).wrap()
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override fun StructureND<Double>.mean(dim: Int, keepDim: Boolean): Tensor<Double> =
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multikStat.mean<Double,DN, DN>(asMultik().array, dim).wrap()
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override fun cos(arg: StructureND<Double>): MultikTensor<Double> = multikMath.mathEx.cos(arg.asMultik().array).wrap()
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override fun StructureND<Double>.std(): Double {
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TODO("Not yet implemented")
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override fun tan(arg: StructureND<Double>): MultikTensor<Double> = sin(arg) / cos(arg)
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override fun asin(arg: StructureND<Double>): MultikTensor<Double> = arg.map { asin(it) }
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override fun acos(arg: StructureND<Double>): MultikTensor<Double> = arg.map { acos(it) }
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override fun atan(arg: StructureND<Double>): MultikTensor<Double> = arg.map { atan(it) }
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override fun exp(arg: StructureND<Double>): MultikTensor<Double> = multikMath.mathEx.exp(arg.asMultik().array).wrap()
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override fun ln(arg: StructureND<Double>): MultikTensor<Double> = multikMath.mathEx.log(arg.asMultik().array).wrap()
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override fun sinh(arg: StructureND<Double>): MultikTensor<Double> = (exp(arg) - exp(-arg)) / 2.0
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override fun cosh(arg: StructureND<Double>): MultikTensor<Double> = (exp(arg) + exp(-arg)) / 2.0
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override fun tanh(arg: StructureND<Double>): MultikTensor<Double> {
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val expPlus = exp(arg)
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val expMinus = exp(-arg)
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return (expPlus - expMinus) / (expPlus + expMinus)
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}
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override fun StructureND<Double>.std(dim: Int, keepDim: Boolean): Tensor<Double> {
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TODO("Not yet implemented")
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}
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override fun asinh(arg: StructureND<Double>): MultikTensor<Double> = arg.map { asinh(it) }
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override fun StructureND<Double>.variance(): Double {
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TODO("Not yet implemented")
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}
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override fun acosh(arg: StructureND<Double>): MultikTensor<Double> = arg.map { acosh(it) }
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override fun StructureND<Double>.variance(dim: Int, keepDim: Boolean): Tensor<Double> {
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TODO("Not yet implemented")
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}
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override fun StructureND<Double>.exp(): Tensor<Double> {
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TODO("Not yet implemented")
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}
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override fun StructureND<Double>.ln(): Tensor<Double> {
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TODO("Not yet implemented")
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}
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override fun StructureND<Double>.sqrt(): Tensor<Double> {
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TODO("Not yet implemented")
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}
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override fun StructureND<Double>.cos(): Tensor<Double> {
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TODO("Not yet implemented")
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}
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override fun StructureND<Double>.acos(): Tensor<Double> {
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TODO("Not yet implemented")
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}
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override fun StructureND<Double>.cosh(): Tensor<Double> {
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TODO("Not yet implemented")
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}
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override fun StructureND<Double>.acosh(): Tensor<Double> {
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TODO("Not yet implemented")
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}
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override fun StructureND<Double>.sin(): Tensor<Double> {
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TODO("Not yet implemented")
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}
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override fun StructureND<Double>.asin(): Tensor<Double> {
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TODO("Not yet implemented")
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}
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override fun StructureND<Double>.sinh(): Tensor<Double> {
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TODO("Not yet implemented")
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}
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override fun StructureND<Double>.asinh(): Tensor<Double> {
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TODO("Not yet implemented")
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}
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override fun StructureND<Double>.tan(): Tensor<Double> {
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TODO("Not yet implemented")
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}
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override fun StructureND<Double>.atan(): Tensor<Double> {
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TODO("Not yet implemented")
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}
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override fun StructureND<Double>.tanh(): Tensor<Double> {
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TODO("Not yet implemented")
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}
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override fun StructureND<Double>.atanh(): Tensor<Double> {
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TODO("Not yet implemented")
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}
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override fun StructureND<Double>.ceil(): Tensor<Double> {
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TODO("Not yet implemented")
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}
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override fun StructureND<Double>.floor(): Tensor<Double> {
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TODO("Not yet implemented")
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}
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override fun StructureND<Double>.det(): Tensor<Double> {
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TODO("Not yet implemented")
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}
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override fun StructureND<Double>.inv(): Tensor<Double> {
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TODO("Not yet implemented")
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}
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override fun StructureND<Double>.cholesky(): Tensor<Double> {
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TODO("Not yet implemented")
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}
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override fun StructureND<Double>.qr(): Pair<Tensor<Double>, Tensor<Double>> {
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TODO("Not yet implemented")
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}
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override fun StructureND<Double>.lu(): Triple<Tensor<Double>, Tensor<Double>, Tensor<Double>> {
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TODO("Not yet implemented")
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}
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override fun StructureND<Double>.svd(): Triple<Tensor<Double>, Tensor<Double>, Tensor<Double>> {
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TODO("Not yet implemented")
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}
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override fun StructureND<Double>.symEig(): Pair<Tensor<Double>, Tensor<Double>> {
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TODO("Not yet implemented")
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}
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override fun atanh(arg: StructureND<Double>): MultikTensor<Double> = arg.map { atanh(it) }
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}
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public val Double.Companion.multikAlgebra: MultikTensorAlgebra<Double, DoubleField> get() = MultikDoubleAlgebra
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@ -75,7 +75,7 @@ public open class DoubleTensorAlgebra :
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override fun zip(
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left: StructureND<Double>,
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right: StructureND<Double>,
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transform: DoubleField.(Double, Double) -> Double
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transform: DoubleField.(Double, Double) -> Double,
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): DoubleTensor {
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require(left.shape.contentEquals(right.shape)) {
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"The shapes in zip are not equal: left - ${left.shape}, right - ${right.shape}"
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@ -422,14 +422,11 @@ public open class DoubleTensorAlgebra :
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for ((res, ab) in resTensor.matrixSequence().zip(newThis.matrixSequence().zip(newOther.matrixSequence()))) {
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val (a, b) = ab
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dotHelper(a.as2D(), b.as2D(), res.as2D(), l, m1, n)
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dotTo(a.as2D(), b.as2D(), res.as2D(), l, m1, n)
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}
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if (penultimateDim) {
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return resTensor.view(
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resTensor.shape.dropLast(2).toIntArray() +
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intArrayOf(resTensor.shape.last())
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)
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return resTensor.view(resTensor.shape.dropLast(2).toIntArray() + intArrayOf(resTensor.shape.last()))
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}
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if (lastDim) {
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return resTensor.view(resTensor.shape.dropLast(1).toIntArray())
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@ -441,7 +438,7 @@ public open class DoubleTensorAlgebra :
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diagonalEntries: Tensor<Double>,
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offset: Int,
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dim1: Int,
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dim2: Int
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dim2: Int,
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): DoubleTensor {
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val n = diagonalEntries.shape.size
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val d1 = minusIndexFrom(n + 1, dim1)
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@ -577,13 +574,13 @@ public open class DoubleTensorAlgebra :
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*/
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public fun Tensor<Double>.rowsByIndices(indices: IntArray): DoubleTensor = stack(indices.map { this[it] })
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internal inline fun StructureND<Double>.fold(foldFunction: (DoubleArray) -> Double): Double =
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private inline fun StructureND<Double>.fold(foldFunction: (DoubleArray) -> Double): Double =
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foldFunction(tensor.copyArray())
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internal inline fun <reified R: Any> StructureND<Double>.foldDim(
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foldFunction: (DoubleArray) -> R,
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private inline fun <reified R : Any> StructureND<Double>.foldDim(
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dim: Int,
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keepDim: Boolean,
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foldFunction: (DoubleArray) -> R,
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): BufferedTensor<R> {
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check(dim < dimension) { "Dimension $dim out of range $dimension" }
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val resShape = if (keepDim) {
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@ -608,66 +605,59 @@ public open class DoubleTensorAlgebra :
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override fun StructureND<Double>.sum(): Double = tensor.fold { it.sum() }
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override fun StructureND<Double>.sum(dim: Int, keepDim: Boolean): DoubleTensor =
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foldDim({ x -> x.sum() }, dim, keepDim).toDoubleTensor()
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foldDim(dim, keepDim) { x -> x.sum() }.toDoubleTensor()
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override fun StructureND<Double>.min(): Double = this.fold { it.minOrNull()!! }
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override fun StructureND<Double>.min(dim: Int, keepDim: Boolean): DoubleTensor =
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foldDim({ x -> x.minOrNull()!! }, dim, keepDim).toDoubleTensor()
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foldDim(dim, keepDim) { x -> x.minOrNull()!! }.toDoubleTensor()
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override fun StructureND<Double>.max(): Double = this.fold { it.maxOrNull()!! }
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override fun StructureND<Double>.max(dim: Int, keepDim: Boolean): DoubleTensor =
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foldDim({ x -> x.maxOrNull()!! }, dim, keepDim).toDoubleTensor()
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foldDim(dim, keepDim) { x -> x.maxOrNull()!! }.toDoubleTensor()
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override fun StructureND<Double>.argMax(dim: Int, keepDim: Boolean): IntTensor =
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foldDim({ x ->
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foldDim(dim, keepDim) { x ->
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x.withIndex().maxByOrNull { it.value }?.index!!
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}, dim, keepDim).toIntTensor()
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}.toIntTensor()
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override fun StructureND<Double>.mean(): Double = this.fold { it.sum() / tensor.numElements }
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override fun StructureND<Double>.mean(dim: Int, keepDim: Boolean): DoubleTensor =
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foldDim(
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{ arr ->
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override fun StructureND<Double>.mean(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(dim, keepDim) { arr ->
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check(dim < dimension) { "Dimension $dim out of range $dimension" }
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arr.sum() / shape[dim]
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},
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dim,
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keepDim
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).toDoubleTensor()
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}.toDoubleTensor()
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override fun StructureND<Double>.std(): Double = this.fold { arr ->
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override fun StructureND<Double>.std(): Double = fold { arr ->
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val mean = arr.sum() / tensor.numElements
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sqrt(arr.sumOf { (it - mean) * (it - mean) } / (tensor.numElements - 1))
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}
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override fun StructureND<Double>.std(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(
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{ arr ->
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dim,
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keepDim
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) { arr ->
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check(dim < dimension) { "Dimension $dim out of range $dimension" }
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val mean = arr.sum() / shape[dim]
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sqrt(arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1))
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},
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dim,
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keepDim
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).toDoubleTensor()
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}.toDoubleTensor()
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override fun StructureND<Double>.variance(): Double = this.fold { arr ->
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override fun StructureND<Double>.variance(): Double = fold { arr ->
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val mean = arr.sum() / tensor.numElements
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arr.sumOf { (it - mean) * (it - mean) } / (tensor.numElements - 1)
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}
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override fun StructureND<Double>.variance(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(
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{ arr ->
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dim,
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keepDim
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) { arr ->
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check(dim < dimension) { "Dimension $dim out of range $dimension" }
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val mean = arr.sum() / shape[dim]
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arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1)
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},
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dim,
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keepDim
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).toDoubleTensor()
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}.toDoubleTensor()
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private fun cov(x: DoubleTensor, y: DoubleTensor): Double {
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val n = x.shape[0]
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@ -699,19 +689,14 @@ public open class DoubleTensorAlgebra :
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return resTensor
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}
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@OptIn(PerformancePitfall::class)
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override fun StructureND<Double>.exp(): DoubleTensor = tensor.map { exp(it) }
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@OptIn(PerformancePitfall::class)
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override fun StructureND<Double>.ln(): DoubleTensor = tensor.map { ln(it) }
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@OptIn(PerformancePitfall::class)
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override fun StructureND<Double>.sqrt(): DoubleTensor = tensor.map { sqrt(it) }
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@OptIn(PerformancePitfall::class)
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override fun StructureND<Double>.cos(): DoubleTensor = tensor.map { cos(it) }
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@OptIn(PerformancePitfall::class)
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override fun StructureND<Double>.acos(): DoubleTensor = tensor.map { acos(it) }
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override fun StructureND<Double>.cosh(): DoubleTensor = tensor.map { cosh(it) }
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@ -53,7 +53,7 @@ internal val <T> BufferedTensor<T>.matrices: VirtualBuffer<BufferedTensor<T>>
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internal fun <T> BufferedTensor<T>.matrixSequence(): Sequence<BufferedTensor<T>> = matrices.asSequence()
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internal fun dotHelper(
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internal fun dotTo(
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a: MutableStructure2D<Double>,
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b: MutableStructure2D<Double>,
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res: MutableStructure2D<Double>,
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