forked from kscience/kmath
Inefficient implementations in RingWithNumbers
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@ -2,6 +2,7 @@ package space.kscience.kmath.torch
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import space.kscience.kmath.memory.DeferScope
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import space.kscience.kmath.memory.withDeferScope
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import space.kscience.kmath.tensors.*
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public sealed class TorchTensorAlgebraJVM<
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T,
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@ -59,6 +60,10 @@ public sealed class TorchTensorAlgebraJVM<
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JTorch.minusTensorAssign(this.tensorHandle, other.tensorHandle)
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}
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override fun add(a: TorchTensorType, b: TorchTensorType): TorchTensorType = a + b
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override fun multiply(a: TorchTensorType, b: TorchTensorType): TorchTensorType = a * b
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override operator fun TorchTensorType.unaryMinus(): TorchTensorType =
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wrap(JTorch.unaryMinus(this.tensorHandle))
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@ -230,6 +235,14 @@ public class TorchTensorRealAlgebra(scope: DeferScope) :
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override fun full(value: Double, shape: IntArray, device: Device): TorchTensorReal =
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wrap(JTorch.fullDouble(value, shape, device.toInt()))
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override fun multiply(a: TorchTensorReal, k: Number): TorchTensorReal = a * k.toDouble()
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override val zero: TorchTensorReal
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get() = full(0.0, IntArray(0), Device.CPU)
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override val one: TorchTensorReal
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get() = full(1.0, IntArray(0), Device.CPU)
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}
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public class TorchTensorFloatAlgebra(scope: DeferScope) :
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@ -281,6 +294,14 @@ public class TorchTensorFloatAlgebra(scope: DeferScope) :
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override fun full(value: Float, shape: IntArray, device: Device): TorchTensorFloat =
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wrap(JTorch.fullFloat(value, shape, device.toInt()))
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override fun multiply(a: TorchTensorFloat, k: Number): TorchTensorFloat = a * k.toFloat()
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override val zero: TorchTensorFloat
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get() = full(0f, IntArray(0), Device.CPU)
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override val one: TorchTensorFloat
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get() = full(1f, IntArray(0), Device.CPU)
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}
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public class TorchTensorLongAlgebra(scope: DeferScope) :
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@ -326,6 +347,14 @@ public class TorchTensorLongAlgebra(scope: DeferScope) :
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override fun full(value: Long, shape: IntArray, device: Device): TorchTensorLong =
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wrap(JTorch.fullLong(value, shape, device.toInt()))
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override fun multiply(a: TorchTensorLong, k: Number): TorchTensorLong = a * k.toLong()
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override val zero: TorchTensorLong
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get() = full(0, IntArray(0), Device.CPU)
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override val one: TorchTensorLong
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get() = full(1, IntArray(0), Device.CPU)
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}
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public class TorchTensorIntAlgebra(scope: DeferScope) :
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@ -371,6 +400,14 @@ public class TorchTensorIntAlgebra(scope: DeferScope) :
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override fun full(value: Int, shape: IntArray, device: Device): TorchTensorInt =
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wrap(JTorch.fullInt(value, shape, device.toInt()))
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override fun multiply(a: TorchTensorInt, k: Number): TorchTensorInt = a * k.toInt()
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override val zero: TorchTensorInt
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get() = full(0, IntArray(0), Device.CPU)
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override val one: TorchTensorInt
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get() = full(1, IntArray(0), Device.CPU)
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}
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public inline fun <R> TorchTensorRealAlgebra(block: TorchTensorRealAlgebra.() -> R): R =
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@ -259,7 +259,6 @@ public class TorchTensorRealAlgebra(scope: DeferScope) :
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override fun full(value: Double, shape: IntArray, device: Device): TorchTensorReal =
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wrap(full_double(value, shape.toCValues(), shape.size, device.toInt())!!)
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override fun multiply(a: TorchTensorReal, k: Number): TorchTensorReal = a * k.toDouble()
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override val zero: TorchTensorReal
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@ -335,7 +334,6 @@ public class TorchTensorFloatAlgebra(scope: DeferScope) :
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override val zero: TorchTensorFloat
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get() = full(0f, IntArray(0), Device.CPU)
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override val one: TorchTensorFloat
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get() = full(1f, IntArray(0), Device.CPU)
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@ -400,7 +398,6 @@ public class TorchTensorLongAlgebra(scope: DeferScope) :
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override val zero: TorchTensorLong
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get() = full(0, IntArray(0), Device.CPU)
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override val one: TorchTensorLong
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get() = full(1, IntArray(0), Device.CPU)
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}
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@ -459,13 +456,11 @@ public class TorchTensorIntAlgebra(scope: DeferScope) :
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override fun full(value: Int, shape: IntArray, device: Device): TorchTensorInt =
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wrap(full_int(value, shape.toCValues(), shape.size, device.toInt())!!)
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override fun multiply(a: TorchTensorInt, k: Number): TorchTensorInt = a * k.toInt()
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override val zero: TorchTensorInt
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get() = full(0, IntArray(0), Device.CPU)
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override val one: TorchTensorInt
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get() = full(1, IntArray(0), Device.CPU)
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}
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