forked from kscience/kmath
Adding the interfaces for AnalyticTensorAlgebra and LinearOpsTensorAlgebra
This commit is contained in:
Roland Grinis
parent
384415dc98
commit
c02f71263d
@ -0,0 +1,112 @@
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package space.kscience.kmath.tensors
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public interface AnalyticTensorAlgebra<T, TensorType : TensorStructure<T>> :
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TensorPartialDivisionAlgebra<T, TensorType> {
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//https://pytorch.org/docs/stable/generated/torch.exp.html
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public fun TensorType.exp(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.log.html
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public fun TensorType.log(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.sqrt.html
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public fun TensorType.sqrt(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.square.html
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public fun TensorType.square(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.acos.html#torch.cos
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public fun TensorType.cos(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.acos.html#torch.acos
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public fun TensorType.acos(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.acosh.html#torch.cosh
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public fun TensorType.cosh(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.acosh.html#torch.acosh
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public fun TensorType.acosh(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.asin.html#torch.sin
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public fun TensorType.sin(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.asin.html#torch.asin
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public fun TensorType.asin(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.asin.html#torch.sinh
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public fun TensorType.sinh(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.asin.html#torch.asinh
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public fun TensorType.asinh(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.atan.html#torch.tan
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public fun TensorType.tan(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.atan.html#torch.atan
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public fun TensorType.atan(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.atanh.html#torch.tanh
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public fun TensorType.tanh(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.atanh.html#torch.atanh
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public fun TensorType.atanh(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.ceil.html#torch.ceil
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public fun TensorType.ceil(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.floor.html#torch.floor
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public fun TensorType.floor(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.clamp.html#torch.clamp
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public fun TensorType.clamp(min: T, max: T): TensorType
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//https://pytorch.org/docs/stable/generated/torch.erf.html#torch.erf
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public fun TensorType.erf(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.erfinv.html#torch.erfinv
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public fun TensorType.erfinv(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.erfc.html#torch.erfc
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public fun TensorType.erfc(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.lerp.html#torch.lerp
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public fun TensorType.lerp(end: TensorType, weight: TensorType): TensorType
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//https://pytorch.org/docs/stable/generated/torch.lgamma.html#torch.lgamma
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public fun TensorType.lgamma(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.logit.html#torch.logit
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public fun TensorType.logit(eps: T): TensorType
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//https://pytorch.org/docs/stable/generated/torch.igamma.html#torch.igamma
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public fun TensorType.igamma(other: TensorType): TensorType
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//https://pytorch.org/docs/stable/generated/torch.igammac.html#torch.igammac
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public fun TensorType.igammac(other: TensorType): TensorType
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//https://pytorch.org/docs/stable/generated/torch.mvlgamma.html#torch.mvlgamma
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public fun TensorType.mvlgamma(dimensions: Int): TensorType
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//https://pytorch.org/docs/stable/generated/torch.polygamma.html#torch.polygamma
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public fun TensorType.polygamma(order: Int): TensorType
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//https://pytorch.org/docs/stable/generated/torch.pow.html#torch.pow
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public fun TensorType.pow(exponent: T): TensorType
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//https://pytorch.org/docs/stable/generated/torch.round.html#torch.round
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public fun TensorType.round(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.sigmoid.html#torch.sigmoid
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public fun TensorType.sigmoid(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.sinc.html#torch.sinc
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public fun TensorType.sinc(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.heaviside.html#torch.heaviside
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public fun TensorType.heaviside(values: TensorType): TensorType
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//https://pytorch.org/docs/stable/generated/torch.trapz.html#torch.trapz
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public fun TensorType.trapz(xValues: TensorType, dim: Int): TensorType
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}
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@ -0,0 +1,43 @@
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package space.kscience.kmath.tensors
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public interface LinearOpsTensorAlgebra<T, TensorType : TensorStructure<T>> :
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TensorPartialDivisionAlgebra<T, TensorType> {
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//https://pytorch.org/docs/stable/generated/torch.eye.html
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public fun eye(n: Int): TensorType
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//https://pytorch.org/docs/stable/generated/torch.matmul.html
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public infix fun TensorType.dot(other: TensorType): TensorType
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//https://pytorch.org/docs/stable/generated/torch.diag_embed.html
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public fun diagonalEmbedding(
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diagonalEntries: TensorType,
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offset: Int = 0, dim1: Int = -2, dim2: Int = -1
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): TensorType
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//https://pytorch.org/docs/stable/linalg.html#torch.linalg.det
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public fun TensorType.det(): TensorType
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//https://pytorch.org/docs/stable/linalg.html#torch.linalg.inv
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public fun TensorType.inv(): TensorType
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//https://pytorch.org/docs/stable/linalg.html#torch.linalg.cholesky
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public fun TensorType.cholesky(): TensorType
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//https://pytorch.org/docs/stable/linalg.html#torch.linalg.qr
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public fun TensorType.qr(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.lu.html
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public fun TensorType.lu(): Pair<TensorType, IntTensor>
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//https://pytorch.org/docs/stable/generated/torch.lu_unpack.html
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public fun luPivot(aLU: TensorType, pivots: IntTensor): Triple<TensorType, TensorType, TensorType>
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//https://pytorch.org/docs/stable/linalg.html#torch.linalg.svd
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public fun TensorType.svd(): Triple<TensorType, TensorType, TensorType>
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//https://pytorch.org/docs/stable/generated/torch.symeig.html
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public fun TensorType.symEig(eigenvectors: Boolean = true): Pair<TensorType, TensorType>
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}
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@ -0,0 +1,148 @@
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package space.kscience.kmath.tensors
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public class RealAnalyticTensorAlgebra:
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AnalyticTensorAlgebra<Double, RealTensor>,
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RealTensorAlgebra()
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{
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override fun RealTensor.exp(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.log(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.sqrt(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.square(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.cos(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.acos(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.cosh(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.acosh(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.sin(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.asin(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.sinh(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.asinh(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.tan(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.atan(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.tanh(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.atanh(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.ceil(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.floor(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.clamp(min: Double, max: Double): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.erf(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.erfinv(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.erfc(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.lerp(end: RealTensor, weight: RealTensor): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.lgamma(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.logit(eps: Double): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.igamma(other: RealTensor): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.igammac(other: RealTensor): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.mvlgamma(dimensions: Int): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.polygamma(order: Int): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.pow(exponent: Double): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.round(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.sigmoid(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.sinc(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.heaviside(values: RealTensor): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.trapz(xValues: RealTensor, dim: Int): RealTensor {
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TODO("Not yet implemented")
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}
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}
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@ -0,0 +1,133 @@
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package space.kscience.kmath.tensors
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import space.kscience.kmath.structures.array
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public class RealLinearOpsTensorAlgebra :
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LinearOpsTensorAlgebra<Double, RealTensor>,
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RealTensorAlgebra()
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{
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override fun eye(n: Int): RealTensor {
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val shape = intArrayOf(n, n)
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val buffer = DoubleArray(n * n) { 0.0 }
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val res = RealTensor(shape, buffer)
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for (i in 0 until n) {
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res[intArrayOf(i, i)] = 1.0
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}
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return res
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}
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override fun RealTensor.dot(other: RealTensor): RealTensor {
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TODO("Alya")
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}
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override fun diagonalEmbedding(diagonalEntries: RealTensor, offset: Int, dim1: Int, dim2: Int): RealTensor {
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TODO("Alya")
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}
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override fun RealTensor.lu(): Pair<RealTensor, IntTensor> {
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// todo checks
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val lu = this.copy()
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val m = this.shape[0]
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val pivot = IntArray(m)
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// Initialize permutation array and parity
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for (row in 0 until m) pivot[row] = row
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var even = true
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for (i in 0 until m) {
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var maxA = -1.0
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var iMax = i
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for (k in i until m) {
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val absA = kotlin.math.abs(lu[k, i])
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if (absA > maxA) {
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maxA = absA
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iMax = k
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}
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}
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//todo check singularity
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if (iMax != i) {
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val j = pivot[i]
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pivot[i] = pivot[iMax]
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pivot[iMax] = j
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even != even
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for (k in 0 until m) {
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val tmp = lu[i, k]
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lu[i, k] = lu[iMax, k]
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lu[iMax, k] = tmp
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}
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}
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for (j in i + 1 until m) {
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lu[j, i] /= lu[i, i]
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for (k in i + 1 until m) {
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lu[j, k] -= lu[j, i] * lu[i, k]
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}
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}
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}
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return Pair(lu, IntTensor(intArrayOf(m), pivot))
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}
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override fun luPivot(lu: RealTensor, pivots: IntTensor): Triple<RealTensor, RealTensor, RealTensor> {
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// todo checks
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val n = lu.shape[0]
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val p = lu.zeroesLike()
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pivots.buffer.array.forEachIndexed { i, pivot ->
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p[i, pivot] = 1.0
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}
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val l = lu.zeroesLike()
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val u = lu.zeroesLike()
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for (i in 0 until n) {
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for (j in 0 until n) {
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if (i == j) {
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l[i, j] = 1.0
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}
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if (j < i) {
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l[i, j] = lu[i, j]
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}
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if (j >= i) {
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u[i, j] = lu[i, j]
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}
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}
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}
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return Triple(p, l, u)
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}
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override fun RealTensor.det(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.inv(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.cholesky(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.qr(): RealTensor {
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TODO("Not yet implemented")
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}
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override fun RealTensor.svd(): Triple<RealTensor, RealTensor, RealTensor> {
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TODO("Not yet implemented")
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}
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override fun RealTensor.symEig(eigenvectors: Boolean): Pair<RealTensor, RealTensor> {
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TODO("Not yet implemented")
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}
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}
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public inline fun <R> RealLinearOpsTensorAlgebra(block: RealTensorAlgebra.() -> R): R =
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RealTensorAlgebra().block()
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@ -1,12 +1,9 @@
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package space.kscience.kmath.tensors
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import space.kscience.kmath.structures.RealBuffer
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import space.kscience.kmath.structures.array
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import kotlin.math.abs
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import kotlin.math.max
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public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor> {
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public open class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor> {
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override fun RealTensor.value(): Double {
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check(this.shape contentEquals intArrayOf(1)) {
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@ -15,24 +12,13 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
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return this.buffer.array[0]
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}
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override fun eye(n: Int): RealTensor {
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val shape = intArrayOf(n, n)
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val buffer = DoubleArray(n * n) { 0.0 }
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val res = RealTensor(shape, buffer)
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for (i in 0 until n) {
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res[intArrayOf(i, i)] = 1.0
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}
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return res
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}
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override fun zeros(shape: IntArray): RealTensor {
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TODO("Not yet implemented")
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}
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override fun zeroesLike(other: RealTensor): RealTensor {
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// TODO refactor very bad!!!
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val shape = other.shape
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val buffer = DoubleArray(other.buffer.size) { 0.0 }
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override fun RealTensor.zeroesLike(): RealTensor {
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val shape = this.shape
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val buffer = DoubleArray(this.buffer.size) { 0.0 }
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return RealTensor(shape, buffer)
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}
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@ -40,7 +26,7 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
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TODO("Not yet implemented")
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}
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override fun onesLike(shape: IntArray): RealTensor {
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override fun RealTensor.onesLike(): RealTensor {
|
||||
TODO("Not yet implemented")
|
||||
}
|
||||
|
||||
@ -156,14 +142,6 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
|
||||
return RealTensor(this.shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun RealTensor.dot(other: RealTensor): RealTensor {
|
||||
TODO("Alya")
|
||||
}
|
||||
|
||||
override fun diagonalEmbedding(diagonalEntries: RealTensor, offset: Int, dim1: Int, dim2: Int): RealTensor {
|
||||
TODO("Alya")
|
||||
}
|
||||
|
||||
override fun RealTensor.transpose(i: Int, j: Int): RealTensor {
|
||||
checkTranspose(this.dimension, i, j)
|
||||
val n = this.buffer.size
|
||||
@ -198,9 +176,64 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
|
||||
TODO("Not yet implemented")
|
||||
}
|
||||
|
||||
override fun full(shape: IntArray, value: Double): RealTensor {
|
||||
TODO("Not yet implemented")
|
||||
}
|
||||
|
||||
override fun RealTensor.fullLike(value: Double): RealTensor {
|
||||
TODO("Not yet implemented")
|
||||
}
|
||||
|
||||
|
||||
override fun RealTensor.sum(): RealTensor {
|
||||
override fun RealTensor.sum(dim: Int, keepDim: Boolean): RealTensor {
|
||||
TODO("Not yet implemented")
|
||||
}
|
||||
|
||||
override fun RealTensor.cumsum(dim: Int): RealTensor {
|
||||
TODO("Not yet implemented")
|
||||
}
|
||||
|
||||
override fun RealTensor.prod(dim: Int, keepDim: Boolean): RealTensor {
|
||||
TODO("Not yet implemented")
|
||||
}
|
||||
|
||||
override fun RealTensor.cumprod(dim: Int): RealTensor {
|
||||
TODO("Not yet implemented")
|
||||
}
|
||||
|
||||
override fun RealTensor.max(dim: Int, keepDim: Boolean): RealTensor {
|
||||
TODO("Not yet implemented")
|
||||
}
|
||||
|
||||
override fun RealTensor.cummax(dim: Int): RealTensor {
|
||||
TODO("Not yet implemented")
|
||||
}
|
||||
|
||||
override fun RealTensor.min(dim: Int, keepDim: Boolean): RealTensor {
|
||||
TODO("Not yet implemented")
|
||||
}
|
||||
|
||||
override fun RealTensor.cummin(dim: Int): RealTensor {
|
||||
TODO("Not yet implemented")
|
||||
}
|
||||
|
||||
override fun RealTensor.median(dim: Int, keepDim: Boolean): RealTensor {
|
||||
TODO("Not yet implemented")
|
||||
}
|
||||
|
||||
override fun maximum(lhs: RealTensor, rhs: RealTensor) {
|
||||
TODO("Not yet implemented")
|
||||
}
|
||||
|
||||
override fun minimum(lhs: RealTensor, rhs: RealTensor) {
|
||||
TODO("Not yet implemented")
|
||||
}
|
||||
|
||||
override fun RealTensor.sort(dim: Int, keepDim: Boolean, descending: Boolean): RealTensor {
|
||||
TODO("Not yet implemented")
|
||||
}
|
||||
|
||||
override fun cat(tensors: List<RealTensor>, dim: Int): RealTensor {
|
||||
TODO("Not yet implemented")
|
||||
}
|
||||
|
||||
@ -213,6 +246,10 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
|
||||
TODO("Not yet implemented")
|
||||
}
|
||||
|
||||
override fun RealTensor.flatten(startDim: Int, endDim: Int): RealTensor {
|
||||
TODO("Not yet implemented")
|
||||
}
|
||||
|
||||
override fun RealTensor.divAssign(value: Double) {
|
||||
TODO("Not yet implemented")
|
||||
}
|
||||
@ -221,99 +258,23 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
|
||||
TODO("Not yet implemented")
|
||||
}
|
||||
|
||||
override fun RealTensor.exp(): RealTensor {
|
||||
override fun RealTensor.mean(dim: Int, keepDim: Boolean): RealTensor {
|
||||
TODO("Not yet implemented")
|
||||
}
|
||||
|
||||
override fun RealTensor.log(): RealTensor {
|
||||
override fun RealTensor.quantile(q: Double, dim: Int, keepDim: Boolean): RealTensor {
|
||||
TODO("Not yet implemented")
|
||||
}
|
||||
|
||||
|
||||
override fun RealTensor.lu(): Pair<RealTensor, IntTensor> {
|
||||
// todo checks
|
||||
val lu = this.copy()
|
||||
val m = this.shape[0]
|
||||
val pivot = IntArray(m)
|
||||
|
||||
|
||||
// Initialize permutation array and parity
|
||||
for (row in 0 until m) pivot[row] = row
|
||||
var even = true
|
||||
|
||||
for (i in 0 until m) {
|
||||
var maxA = -1.0
|
||||
var iMax = i
|
||||
|
||||
for (k in i until m) {
|
||||
val absA = abs(lu[k, i])
|
||||
if (absA > maxA) {
|
||||
maxA = absA
|
||||
iMax = k
|
||||
}
|
||||
}
|
||||
|
||||
//todo check singularity
|
||||
|
||||
if (iMax != i) {
|
||||
|
||||
val j = pivot[i]
|
||||
pivot[i] = pivot[iMax]
|
||||
pivot[iMax] = j
|
||||
even != even
|
||||
|
||||
for (k in 0 until m) {
|
||||
val tmp = lu[i, k]
|
||||
lu[i, k] = lu[iMax, k]
|
||||
lu[iMax, k] = tmp
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
for (j in i + 1 until m) {
|
||||
lu[j, i] /= lu[i, i]
|
||||
for (k in i + 1 until m) {
|
||||
lu[j, k] -= lu[j, i] * lu[i, k]
|
||||
}
|
||||
}
|
||||
}
|
||||
return Pair(lu, IntTensor(intArrayOf(m), pivot))
|
||||
}
|
||||
|
||||
override fun luUnpack(lu: RealTensor, pivots: IntTensor): Triple<RealTensor, RealTensor, RealTensor> {
|
||||
// todo checks
|
||||
val n = lu.shape[0]
|
||||
val p = zeroesLike(lu)
|
||||
pivots.buffer.array.forEachIndexed { i, pivot ->
|
||||
p[i, pivot] = 1.0
|
||||
}
|
||||
val l = zeroesLike(lu)
|
||||
val u = zeroesLike(lu)
|
||||
|
||||
for (i in 0 until n){
|
||||
for (j in 0 until n){
|
||||
if (i == j) {
|
||||
l[i, j] = 1.0
|
||||
}
|
||||
if (j < i) {
|
||||
l[i, j] = lu[i, j]
|
||||
}
|
||||
if (j >= i) {
|
||||
u[i, j] = lu[i, j]
|
||||
}
|
||||
}
|
||||
}
|
||||
return Triple(p, l, u)
|
||||
}
|
||||
|
||||
override fun RealTensor.svd(): Triple<RealTensor, RealTensor, RealTensor> {
|
||||
/**
|
||||
* Main first task for @AlyaNovikova
|
||||
*/
|
||||
override fun RealTensor.std(dim: Int, unbiased: Boolean, keepDim: Boolean): RealTensor {
|
||||
TODO("Not yet implemented")
|
||||
}
|
||||
|
||||
override fun RealTensor.symEig(eigenvectors: Boolean): Pair<RealTensor, RealTensor> {
|
||||
override fun RealTensor.variance(dim: Int, unbiased: Boolean, keepDim: Boolean): RealTensor {
|
||||
TODO("Not yet implemented")
|
||||
}
|
||||
|
||||
override fun RealTensor.histc(bins: Int, min: Double, max: Double): RealTensor {
|
||||
TODO("Not yet implemented")
|
||||
}
|
||||
|
||||
|
||||
@ -5,11 +5,17 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
|
||||
|
||||
public fun TensorType.value(): T
|
||||
|
||||
public fun eye(n: Int): TensorType
|
||||
public fun zeros(shape: IntArray): TensorType
|
||||
public fun zeroesLike(other: TensorType): TensorType
|
||||
public fun TensorType.zeroesLike(): TensorType
|
||||
public fun ones(shape: IntArray): TensorType
|
||||
public fun onesLike(shape: IntArray): TensorType
|
||||
public fun TensorType.onesLike(): TensorType
|
||||
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.full.html
|
||||
public fun full(shape: IntArray, value: T): TensorType
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.full_like.html#torch.full_like
|
||||
public fun TensorType.fullLike(value: T): TensorType
|
||||
|
||||
public fun TensorType.copy(): TensorType
|
||||
|
||||
@ -33,15 +39,6 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
|
||||
public operator fun TensorType.unaryMinus(): TensorType
|
||||
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.matmul.html
|
||||
public infix fun TensorType.dot(other: TensorType): TensorType
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.diag_embed.html
|
||||
public fun diagonalEmbedding(
|
||||
diagonalEntries: TensorType,
|
||||
offset: Int = 0, dim1: Int = -2, dim2: Int = -1
|
||||
): TensorType
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.transpose.html
|
||||
public fun TensorType.transpose(i: Int, j: Int): TensorType
|
||||
|
||||
@ -53,35 +50,45 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
|
||||
public fun TensorType.abs(): TensorType
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.sum.html
|
||||
public fun TensorType.sum(): TensorType
|
||||
public fun TensorType.sum(dim: Int, keepDim: Boolean): TensorType
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.cumsum.html#torch.cumsum
|
||||
public fun TensorType.cumsum(dim: Int): TensorType
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.prod.html#torch.prod
|
||||
public fun TensorType.prod(dim: Int, keepDim: Boolean): TensorType
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.cumprod.html#torch.cumprod
|
||||
public fun TensorType.cumprod(dim: Int): TensorType
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.max.html#torch.max
|
||||
public fun TensorType.max(dim: Int, keepDim: Boolean): TensorType
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.cummax.html#torch.cummax
|
||||
public fun TensorType.cummax(dim: Int): TensorType
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.min.html#torch.min
|
||||
public fun TensorType.min(dim: Int, keepDim: Boolean): TensorType
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.cummin.html#torch.cummin
|
||||
public fun TensorType.cummin(dim: Int): TensorType
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.median.html#torch.median
|
||||
public fun TensorType.median(dim: Int, keepDim: Boolean): TensorType
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.maximum.html#torch.maximum
|
||||
public fun maximum(lhs: TensorType, rhs: TensorType)
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.minimum.html#torch.minimum
|
||||
public fun minimum(lhs: TensorType, rhs: TensorType)
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.sort.html#torch.sort
|
||||
public fun TensorType.sort(dim: Int, keepDim: Boolean, descending: Boolean): TensorType
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.cat.html#torch.cat
|
||||
public fun cat(tensors: List<TensorType>, dim: Int): TensorType
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.flatten.html#torch.flatten
|
||||
public fun TensorType.flatten(startDim: Int, endDim: Int): TensorType
|
||||
|
||||
}
|
||||
|
||||
// https://proofwiki.org/wiki/Definition:Division_Algebra
|
||||
public interface TensorPartialDivisionAlgebra<T, TensorType : TensorStructure<T>> :
|
||||
TensorAlgebra<T, TensorType> {
|
||||
|
||||
public operator fun TensorType.div(value: T): TensorType
|
||||
public operator fun TensorType.div(other: TensorType): TensorType
|
||||
public operator fun TensorType.divAssign(value: T)
|
||||
public operator fun TensorType.divAssign(other: TensorType)
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.exp.html
|
||||
public fun TensorType.exp(): TensorType
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.log.html
|
||||
public fun TensorType.log(): TensorType
|
||||
|
||||
// todo change type of pivots
|
||||
//https://pytorch.org/docs/stable/generated/torch.lu.html
|
||||
public fun TensorType.lu(): Pair<TensorType, IntTensor>
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.lu_unpack.html
|
||||
public fun luUnpack(A_LU: TensorType, pivots: IntTensor): Triple<TensorType, TensorType, TensorType>
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.svd.html
|
||||
public fun TensorType.svd(): Triple<TensorType, TensorType, TensorType>
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.symeig.html
|
||||
public fun TensorType.symEig(eigenvectors: Boolean = true): Pair<TensorType, TensorType>
|
||||
|
||||
}
|
||||
@ -0,0 +1,27 @@
|
||||
package space.kscience.kmath.tensors
|
||||
|
||||
// https://proofwiki.org/wiki/Definition:Division_Algebra
|
||||
public interface TensorPartialDivisionAlgebra<T, TensorType : TensorStructure<T>> :
|
||||
TensorAlgebra<T, TensorType> {
|
||||
|
||||
public operator fun TensorType.div(value: T): TensorType
|
||||
public operator fun TensorType.div(other: TensorType): TensorType
|
||||
public operator fun TensorType.divAssign(value: T)
|
||||
public operator fun TensorType.divAssign(other: TensorType)
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.mean.html#torch.mean
|
||||
public fun TensorType.mean(dim: Int, keepDim: Boolean): TensorType
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.quantile.html#torch.quantile
|
||||
public fun TensorType.quantile(q: T, dim: Int, keepDim: Boolean): TensorType
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.std.html#torch.std
|
||||
public fun TensorType.std(dim: Int, unbiased: Boolean, keepDim: Boolean): TensorType
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.var.html#torch.var
|
||||
public fun TensorType.variance(dim: Int, unbiased: Boolean, keepDim: Boolean): TensorType
|
||||
|
||||
//https://pytorch.org/docs/stable/generated/torch.histc.html#torch.histc
|
||||
public fun TensorType.histc(bins: Int, min: T, max: T): TensorType
|
||||
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user