Casting receiver
This commit is contained in:
Roland Grinis
parent
76b5cd0de5
commit
cc11df6174
@ -1,58 +1,58 @@
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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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public interface AnalyticTensorAlgebra<T> :
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TensorPartialDivisionAlgebra<T> {
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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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public fun TensorStructure<T>.exp(): TensorStructure<T>
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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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public fun TensorStructure<T>.log(): TensorStructure<T>
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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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public fun TensorStructure<T>.sqrt(): TensorStructure<T>
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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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public fun TensorStructure<T>.cos(): TensorStructure<T>
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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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public fun TensorStructure<T>.acos(): TensorStructure<T>
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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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public fun TensorStructure<T>.cosh(): TensorStructure<T>
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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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public fun TensorStructure<T>.acosh(): TensorStructure<T>
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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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public fun TensorStructure<T>.sin(): TensorStructure<T>
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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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public fun TensorStructure<T>.asin(): TensorStructure<T>
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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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public fun TensorStructure<T>.sinh(): TensorStructure<T>
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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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public fun TensorStructure<T>.asinh(): TensorStructure<T>
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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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public fun TensorStructure<T>.tan(): TensorStructure<T>
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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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public fun TensorStructure<T>.atan(): TensorStructure<T>
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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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public fun TensorStructure<T>.tanh(): TensorStructure<T>
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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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public fun TensorStructure<T>.atanh(): TensorStructure<T>
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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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public fun TensorStructure<T>.ceil(): TensorStructure<T>
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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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public fun TensorStructure<T>.floor(): TensorStructure<T>
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}
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@ -1,31 +1,32 @@
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package space.kscience.kmath.tensors
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public interface LinearOpsTensorAlgebra<T, TensorType : TensorStructure<T>, IndexTensorType : TensorStructure<Int>> :
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TensorPartialDivisionAlgebra<T, TensorType> {
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public interface LinearOpsTensorAlgebra<T> :
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TensorPartialDivisionAlgebra<T> {
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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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public fun TensorStructure<T>.det(): TensorStructure<T>
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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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public fun TensorStructure<T>.inv(): TensorStructure<T>
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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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public fun TensorStructure<T>.cholesky(): TensorStructure<T>
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//https://pytorch.org/docs/stable/linalg.html#torch.linalg.qr
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public fun TensorType.qr(): Pair<TensorType, TensorType>
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public fun TensorStructure<T>.qr(): Pair<TensorStructure<T>, TensorStructure<T>>
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//https://pytorch.org/docs/stable/generated/torch.lu.html
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public fun TensorType.lu(): Pair<TensorType, IndexTensorType>
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public fun TensorStructure<T>.lu(): Pair<TensorStructure<T>, TensorStructure<Int>>
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//https://pytorch.org/docs/stable/generated/torch.lu_unpack.html
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public fun luPivot(luTensor: TensorType, pivotsTensor: IndexTensorType): Triple<TensorType, TensorType, TensorType>
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public fun luPivot(luTensor: TensorStructure<T>, pivotsTensor: TensorStructure<Int>):
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Triple<TensorStructure<T>, TensorStructure<T>, TensorStructure<T>>
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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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public fun TensorStructure<T>.svd(): Triple<TensorStructure<T>, TensorStructure<T>, TensorStructure<T>>
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//https://pytorch.org/docs/stable/generated/torch.symeig.html
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public fun TensorType.symEig(): Pair<TensorType, TensorType>
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public fun TensorStructure<T>.symEig(): Pair<TensorStructure<T>, TensorStructure<T>>
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}
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@ -1,46 +1,46 @@
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package space.kscience.kmath.tensors
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// https://proofwiki.org/wiki/Definition:Algebra_over_Ring
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public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
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public interface TensorAlgebra<T> {
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public fun TensorType.value(): T
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public fun TensorStructure<T>.value(): T
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public operator fun T.plus(other: TensorType): TensorType
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public operator fun TensorType.plus(value: T): TensorType
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public operator fun TensorType.plus(other: TensorType): TensorType
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public operator fun TensorType.plusAssign(value: T): Unit
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public operator fun TensorType.plusAssign(other: TensorType): Unit
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public operator fun T.plus(other: TensorStructure<T>): TensorStructure<T>
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public operator fun TensorStructure<T>.plus(value: T): TensorStructure<T>
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public operator fun TensorStructure<T>.plus(other: TensorStructure<T>): TensorStructure<T>
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public operator fun TensorStructure<T>.plusAssign(value: T): Unit
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public operator fun TensorStructure<T>.plusAssign(other: TensorStructure<T>): Unit
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public operator fun T.minus(other: TensorType): TensorType
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public operator fun TensorType.minus(value: T): TensorType
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public operator fun TensorType.minus(other: TensorType): TensorType
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public operator fun TensorType.minusAssign(value: T): Unit
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public operator fun TensorType.minusAssign(other: TensorType): Unit
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public operator fun T.minus(other: TensorStructure<T>): TensorStructure<T>
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public operator fun TensorStructure<T>.minus(value: T): TensorStructure<T>
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public operator fun TensorStructure<T>.minus(other: TensorStructure<T>): TensorStructure<T>
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public operator fun TensorStructure<T>.minusAssign(value: T): Unit
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public operator fun TensorStructure<T>.minusAssign(other: TensorStructure<T>): Unit
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public operator fun T.times(other: TensorType): TensorType
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public operator fun TensorType.times(value: T): TensorType
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public operator fun TensorType.times(other: TensorType): TensorType
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public operator fun TensorType.timesAssign(value: T): Unit
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public operator fun TensorType.timesAssign(other: TensorType): Unit
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public operator fun TensorType.unaryMinus(): TensorType
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public operator fun T.times(other: TensorStructure<T>): TensorStructure<T>
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public operator fun TensorStructure<T>.times(value: T): TensorStructure<T>
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public operator fun TensorStructure<T>.times(other: TensorStructure<T>): TensorStructure<T>
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public operator fun TensorStructure<T>.timesAssign(value: T): Unit
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public operator fun TensorStructure<T>.timesAssign(other: TensorStructure<T>): Unit
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public operator fun TensorStructure<T>.unaryMinus(): TensorStructure<T>
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//https://pytorch.org/cppdocs/notes/tensor_indexing.html
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public operator fun TensorType.get(i: Int): TensorType
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public operator fun TensorStructure<T>.get(i: Int): TensorStructure<T>
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//https://pytorch.org/docs/stable/generated/torch.transpose.html
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public fun TensorType.transpose(i: Int = -2, j: Int = -1): TensorType
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public fun TensorStructure<T>.transpose(i: Int = -2, j: Int = -1): TensorStructure<T>
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//https://pytorch.org/docs/stable/tensor_view.html
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public fun TensorType.view(shape: IntArray): TensorType
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public fun TensorType.viewAs(other: TensorType): TensorType
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public fun TensorStructure<T>.view(shape: IntArray): TensorStructure<T>
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public fun TensorStructure<T>.viewAs(other: TensorStructure<T>): TensorStructure<T>
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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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public infix fun TensorStructure<T>.dot(other: TensorStructure<T>): TensorStructure<T>
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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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diagonalEntries: TensorStructure<T>,
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offset: Int = 0, dim1: Int = -2, dim2: Int = -1
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): TensorType
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): TensorStructure<T>
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}
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package space.kscience.kmath.tensors
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// https://proofwiki.org/wiki/Definition:Division_Algebra
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public interface TensorPartialDivisionAlgebra<T, TensorType : TensorStructure<T>> :
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TensorAlgebra<T, TensorType> {
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public operator fun TensorType.div(value: T): TensorType
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public operator fun TensorType.div(other: TensorType): TensorType
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public operator fun TensorType.divAssign(value: T)
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public operator fun TensorType.divAssign(other: TensorType)
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public interface TensorPartialDivisionAlgebra<T> :
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TensorAlgebra<T> {
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public operator fun TensorStructure<T>.div(value: T): TensorStructure<T>
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public operator fun TensorStructure<T>.div(other: TensorStructure<T>): TensorStructure<T>
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public operator fun TensorStructure<T>.divAssign(value: T)
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public operator fun TensorStructure<T>.divAssign(other: TensorStructure<T>)
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}
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@ -1,11 +1,12 @@
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package space.kscience.kmath.tensors.core
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import space.kscience.kmath.tensors.TensorStructure
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import kotlin.math.max
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public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
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override fun DoubleTensor.plus(other: DoubleTensor): DoubleTensor {
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val broadcast = broadcastTensors(this, other)
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override fun TensorStructure<Double>.plus(other: TensorStructure<Double>): DoubleTensor {
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val broadcast = broadcastTensors(tensor, other.tensor)
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val newThis = broadcast[0]
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val newOther = broadcast[1]
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val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
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@ -14,16 +15,16 @@ public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
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return DoubleTensor(newThis.shape, resBuffer)
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}
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override fun DoubleTensor.plusAssign(other: DoubleTensor) {
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val newOther = broadcastTo(other, this.shape)
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for (i in 0 until this.linearStructure.size) {
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this.buffer.array()[this.bufferStart + i] +=
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newOther.buffer.array()[this.bufferStart + i]
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override fun TensorStructure<Double>.plusAssign(other: TensorStructure<Double>) {
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val newOther = broadcastTo(other.tensor, tensor.shape)
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for (i in 0 until tensor.linearStructure.size) {
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tensor.buffer.array()[tensor.bufferStart + i] +=
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newOther.buffer.array()[tensor.bufferStart + i]
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}
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}
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override fun DoubleTensor.minus(other: DoubleTensor): DoubleTensor {
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val broadcast = broadcastTensors(this, other)
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override fun TensorStructure<Double>.minus(other: TensorStructure<Double>): DoubleTensor {
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val broadcast = broadcastTensors(tensor, other.tensor)
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val newThis = broadcast[0]
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val newOther = broadcast[1]
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val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
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@ -32,16 +33,16 @@ public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
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return DoubleTensor(newThis.shape, resBuffer)
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}
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override fun DoubleTensor.minusAssign(other: DoubleTensor) {
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val newOther = broadcastTo(other, this.shape)
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for (i in 0 until this.linearStructure.size) {
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this.buffer.array()[this.bufferStart + i] -=
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newOther.buffer.array()[this.bufferStart + i]
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override fun TensorStructure<Double>.minusAssign(other: TensorStructure<Double>) {
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val newOther = broadcastTo(other.tensor, tensor.shape)
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for (i in 0 until tensor.linearStructure.size) {
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tensor.buffer.array()[tensor.bufferStart + i] -=
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newOther.buffer.array()[tensor.bufferStart + i]
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}
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}
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override fun DoubleTensor.times(other: DoubleTensor): DoubleTensor {
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val broadcast = broadcastTensors(this, other)
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override fun TensorStructure<Double>.times(other: TensorStructure<Double>): DoubleTensor {
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val broadcast = broadcastTensors(tensor, other.tensor)
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val newThis = broadcast[0]
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val newOther = broadcast[1]
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val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
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@ -51,16 +52,16 @@ public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
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return DoubleTensor(newThis.shape, resBuffer)
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}
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override fun DoubleTensor.timesAssign(other: DoubleTensor) {
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val newOther = broadcastTo(other, this.shape)
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for (i in 0 until this.linearStructure.size) {
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this.buffer.array()[this.bufferStart + i] *=
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newOther.buffer.array()[this.bufferStart + i]
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override fun TensorStructure<Double>.timesAssign(other: TensorStructure<Double>) {
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val newOther = broadcastTo(other.tensor, tensor.shape)
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for (i in 0 until tensor.linearStructure.size) {
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tensor.buffer.array()[tensor.bufferStart + i] *=
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newOther.buffer.array()[tensor.bufferStart + i]
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}
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}
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override fun DoubleTensor.div(other: DoubleTensor): DoubleTensor {
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val broadcast = broadcastTensors(this, other)
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override fun TensorStructure<Double>.div(other: TensorStructure<Double>): DoubleTensor {
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val broadcast = broadcastTensors(tensor, other.tensor)
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val newThis = broadcast[0]
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val newOther = broadcast[1]
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val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
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@ -70,11 +71,11 @@ public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
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return DoubleTensor(newThis.shape, resBuffer)
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}
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override fun DoubleTensor.divAssign(other: DoubleTensor) {
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val newOther = broadcastTo(other, this.shape)
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for (i in 0 until this.linearStructure.size) {
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this.buffer.array()[this.bufferStart + i] /=
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newOther.buffer.array()[this.bufferStart + i]
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override fun TensorStructure<Double>.divAssign(other: TensorStructure<Double>) {
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val newOther = broadcastTo(other.tensor, tensor.shape)
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for (i in 0 until tensor.linearStructure.size) {
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tensor.buffer.array()[tensor.bufferStart + i] /=
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newOther.buffer.array()[tensor.bufferStart + i]
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}
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}
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@ -6,13 +6,13 @@ import space.kscience.kmath.tensors.TensorStructure
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public open class BufferedTensor<T>(
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override val shape: IntArray,
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public val buffer: MutableBuffer<T>,
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internal val buffer: MutableBuffer<T>,
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internal val bufferStart: Int
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) : TensorStructure<T> {
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public val linearStructure: TensorLinearStructure
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get() = TensorLinearStructure(shape)
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public val numel: Int
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public val numElements: Int
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get() = linearStructure.size
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override fun get(index: IntArray): T = buffer[bufferStart + linearStructure.offset(index)]
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@ -41,26 +41,6 @@ public class IntTensor internal constructor(
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this(bufferedTensor.shape, bufferedTensor.buffer.array(), bufferedTensor.bufferStart)
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}
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public class LongTensor internal constructor(
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shape: IntArray,
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buffer: LongArray,
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offset: Int = 0
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) : BufferedTensor<Long>(shape, LongBuffer(buffer), offset)
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{
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internal constructor(bufferedTensor: BufferedTensor<Long>):
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this(bufferedTensor.shape, bufferedTensor.buffer.array(), bufferedTensor.bufferStart)
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}
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public class FloatTensor internal constructor(
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shape: IntArray,
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buffer: FloatArray,
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offset: Int = 0
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) : BufferedTensor<Float>(shape, FloatBuffer(buffer), offset)
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{
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internal constructor(bufferedTensor: BufferedTensor<Float>):
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this(bufferedTensor.shape, bufferedTensor.buffer.array(), bufferedTensor.bufferStart)
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}
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public class DoubleTensor internal constructor(
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shape: IntArray,
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buffer: DoubleArray,
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@ -74,7 +54,23 @@ public class DoubleTensor internal constructor(
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}
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internal fun BufferedTensor<Int>.asTensor(): IntTensor = IntTensor(this)
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internal fun BufferedTensor<Long>.asTensor(): LongTensor = LongTensor(this)
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internal fun BufferedTensor<Float>.asTensor(): FloatTensor = FloatTensor(this)
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internal fun BufferedTensor<Double>.asTensor(): DoubleTensor = DoubleTensor(this)
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internal inline fun BufferedTensor<Int>.asTensor(): IntTensor = IntTensor(this)
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internal inline fun BufferedTensor<Double>.asTensor(): DoubleTensor = DoubleTensor(this)
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internal inline fun <T> TensorStructure<T>.toBufferedTensor(): BufferedTensor<T> = when (this) {
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is BufferedTensor<T> -> this
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else -> BufferedTensor(this.shape, this.elements().map{ it.second }.toMutableList().asMutableBuffer(), 0)
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}
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internal val TensorStructure<Double>.tensor: DoubleTensor
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get() = when (this) {
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is DoubleTensor -> this
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else -> this.toBufferedTensor().asTensor()
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}
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internal val TensorStructure<Int>.tensor: IntTensor
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get() = when (this) {
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is IntTensor -> this
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else -> this.toBufferedTensor().asTensor()
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}
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@ -1,45 +1,46 @@
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package space.kscience.kmath.tensors.core
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import space.kscience.kmath.tensors.AnalyticTensorAlgebra
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import space.kscience.kmath.tensors.TensorStructure
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import kotlin.math.*
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public class DoubleAnalyticTensorAlgebra:
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AnalyticTensorAlgebra<Double, DoubleTensor>,
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AnalyticTensorAlgebra<Double>,
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DoubleTensorAlgebra()
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{
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override fun DoubleTensor.exp(): DoubleTensor = this.map(::exp)
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override fun TensorStructure<Double>.exp(): DoubleTensor = tensor.map(::exp)
|
||||
|
||||
override fun DoubleTensor.log(): DoubleTensor = this.map(::ln)
|
||||
override fun TensorStructure<Double>.log(): DoubleTensor = tensor.map(::ln)
|
||||
|
||||
override fun DoubleTensor.sqrt(): DoubleTensor = this.map(::sqrt)
|
||||
override fun TensorStructure<Double>.sqrt(): DoubleTensor = tensor.map(::sqrt)
|
||||
|
||||
override fun DoubleTensor.cos(): DoubleTensor = this.map(::cos)
|
||||
override fun TensorStructure<Double>.cos(): DoubleTensor = tensor.map(::cos)
|
||||
|
||||
override fun DoubleTensor.acos(): DoubleTensor = this.map(::acos)
|
||||
override fun TensorStructure<Double>.acos(): DoubleTensor = tensor.map(::acos)
|
||||
|
||||
override fun DoubleTensor.cosh(): DoubleTensor = this.map(::cosh)
|
||||
override fun TensorStructure<Double>.cosh(): DoubleTensor = tensor.map(::cosh)
|
||||
|
||||
override fun DoubleTensor.acosh(): DoubleTensor = this.map(::acosh)
|
||||
override fun TensorStructure<Double>.acosh(): DoubleTensor = tensor.map(::acosh)
|
||||
|
||||
override fun DoubleTensor.sin(): DoubleTensor = this.map(::sin)
|
||||
override fun TensorStructure<Double>.sin(): DoubleTensor = tensor.map(::sin)
|
||||
|
||||
override fun DoubleTensor.asin(): DoubleTensor = this.map(::asin)
|
||||
override fun TensorStructure<Double>.asin(): DoubleTensor = tensor.map(::asin)
|
||||
|
||||
override fun DoubleTensor.sinh(): DoubleTensor = this.map(::sinh)
|
||||
override fun TensorStructure<Double>.sinh(): DoubleTensor = tensor.map(::sinh)
|
||||
|
||||
override fun DoubleTensor.asinh(): DoubleTensor = this.map(::asinh)
|
||||
override fun TensorStructure<Double>.asinh(): DoubleTensor = tensor.map(::asinh)
|
||||
|
||||
override fun DoubleTensor.tan(): DoubleTensor = this.map(::tan)
|
||||
override fun TensorStructure<Double>.tan(): DoubleTensor = tensor.map(::tan)
|
||||
|
||||
override fun DoubleTensor.atan(): DoubleTensor = this.map(::atan)
|
||||
override fun TensorStructure<Double>.atan(): DoubleTensor = tensor.map(::atan)
|
||||
|
||||
override fun DoubleTensor.tanh(): DoubleTensor = this.map(::tanh)
|
||||
override fun TensorStructure<Double>.tanh(): DoubleTensor = tensor.map(::tanh)
|
||||
|
||||
override fun DoubleTensor.atanh(): DoubleTensor = this.map(::atanh)
|
||||
override fun TensorStructure<Double>.atanh(): DoubleTensor = tensor.map(::atanh)
|
||||
|
||||
override fun DoubleTensor.ceil(): DoubleTensor = this.map(::ceil)
|
||||
override fun TensorStructure<Double>.ceil(): DoubleTensor = tensor.map(::ceil)
|
||||
|
||||
override fun DoubleTensor.floor(): DoubleTensor = this.map(::floor)
|
||||
override fun TensorStructure<Double>.floor(): DoubleTensor = tensor.map(::floor)
|
||||
|
||||
}
|
||||
|
||||
|
||||
@ -3,44 +3,45 @@ package space.kscience.kmath.tensors.core
|
||||
import space.kscience.kmath.tensors.LinearOpsTensorAlgebra
|
||||
import space.kscience.kmath.nd.as1D
|
||||
import space.kscience.kmath.nd.as2D
|
||||
import space.kscience.kmath.tensors.TensorStructure
|
||||
import kotlin.math.min
|
||||
|
||||
|
||||
public class DoubleLinearOpsTensorAlgebra :
|
||||
LinearOpsTensorAlgebra<Double, DoubleTensor, IntTensor>,
|
||||
LinearOpsTensorAlgebra<Double>,
|
||||
DoubleTensorAlgebra() {
|
||||
|
||||
override fun DoubleTensor.inv(): DoubleTensor = invLU(1e-9)
|
||||
override fun TensorStructure<Double>.inv(): DoubleTensor = invLU(1e-9)
|
||||
|
||||
override fun DoubleTensor.det(): DoubleTensor = detLU(1e-9)
|
||||
override fun TensorStructure<Double>.det(): DoubleTensor = detLU(1e-9)
|
||||
|
||||
public fun DoubleTensor.lu(epsilon: Double): Pair<DoubleTensor, IntTensor> =
|
||||
computeLU(this, epsilon) ?:
|
||||
public fun TensorStructure<Double>.lu(epsilon: Double): Pair<DoubleTensor, IntTensor> =
|
||||
computeLU(tensor, epsilon) ?:
|
||||
throw RuntimeException("Tensor contains matrices which are singular at precision $epsilon")
|
||||
|
||||
override fun DoubleTensor.lu(): Pair<DoubleTensor, IntTensor> = lu(1e-9)
|
||||
override fun TensorStructure<Double>.lu(): Pair<DoubleTensor, IntTensor> = lu(1e-9)
|
||||
|
||||
override fun luPivot(
|
||||
luTensor: DoubleTensor,
|
||||
pivotsTensor: IntTensor
|
||||
luTensor: TensorStructure<Double>,
|
||||
pivotsTensor: TensorStructure<Int>
|
||||
): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
|
||||
//todo checks
|
||||
checkSquareMatrix(luTensor.shape)
|
||||
check(
|
||||
luTensor.shape.dropLast(2).toIntArray() contentEquals pivotsTensor.shape.dropLast(1).toIntArray() ||
|
||||
luTensor.shape.last() == pivotsTensor.shape.last() - 1
|
||||
) { "Bed shapes ((" } //todo rewrite
|
||||
) { "Bad shapes ((" } //todo rewrite
|
||||
|
||||
val n = luTensor.shape.last()
|
||||
val pTensor = luTensor.zeroesLike()
|
||||
for ((p, pivot) in pTensor.matrixSequence().zip(pivotsTensor.vectorSequence()))
|
||||
for ((p, pivot) in pTensor.matrixSequence().zip(pivotsTensor.tensor.vectorSequence()))
|
||||
pivInit(p.as2D(), pivot.as1D(), n)
|
||||
|
||||
val lTensor = luTensor.zeroesLike()
|
||||
val uTensor = luTensor.zeroesLike()
|
||||
|
||||
for ((pairLU, lu) in lTensor.matrixSequence().zip(uTensor.matrixSequence())
|
||||
.zip(luTensor.matrixSequence())) {
|
||||
.zip(luTensor.tensor.matrixSequence())) {
|
||||
val (l, u) = pairLU
|
||||
luPivotHelper(l.as2D(), u.as2D(), lu.as2D(), n)
|
||||
}
|
||||
@ -49,26 +50,26 @@ public class DoubleLinearOpsTensorAlgebra :
|
||||
|
||||
}
|
||||
|
||||
public fun DoubleTensor.cholesky(epsilon: Double): DoubleTensor {
|
||||
public fun TensorStructure<Double>.cholesky(epsilon: Double): DoubleTensor {
|
||||
checkSquareMatrix(shape)
|
||||
checkPositiveDefinite(this, epsilon)
|
||||
checkPositiveDefinite(tensor, epsilon)
|
||||
|
||||
val n = shape.last()
|
||||
val lTensor = zeroesLike()
|
||||
|
||||
for ((a, l) in this.matrixSequence().zip(lTensor.matrixSequence()))
|
||||
for ((a, l) in tensor.matrixSequence().zip(lTensor.matrixSequence()))
|
||||
for (i in 0 until n) choleskyHelper(a.as2D(), l.as2D(), n)
|
||||
|
||||
return lTensor
|
||||
}
|
||||
|
||||
override fun DoubleTensor.cholesky(): DoubleTensor = cholesky(1e-6)
|
||||
override fun TensorStructure<Double>.cholesky(): DoubleTensor = cholesky(1e-6)
|
||||
|
||||
override fun DoubleTensor.qr(): Pair<DoubleTensor, DoubleTensor> {
|
||||
override fun TensorStructure<Double>.qr(): Pair<DoubleTensor, DoubleTensor> {
|
||||
checkSquareMatrix(shape)
|
||||
val qTensor = zeroesLike()
|
||||
val rTensor = zeroesLike()
|
||||
val seq = matrixSequence().zip((qTensor.matrixSequence().zip(rTensor.matrixSequence())))
|
||||
val seq = tensor.matrixSequence().zip((qTensor.matrixSequence().zip(rTensor.matrixSequence())))
|
||||
for ((matrix, qr) in seq) {
|
||||
val (q, r) = qr
|
||||
qrHelper(matrix.asTensor(), q.asTensor(), r.as2D())
|
||||
@ -76,18 +77,18 @@ public class DoubleLinearOpsTensorAlgebra :
|
||||
return qTensor to rTensor
|
||||
}
|
||||
|
||||
override fun DoubleTensor.svd(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> =
|
||||
override fun TensorStructure<Double>.svd(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> =
|
||||
svd(epsilon = 1e-10)
|
||||
|
||||
public fun DoubleTensor.svd(epsilon: Double): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
|
||||
val size = this.linearStructure.dim
|
||||
val commonShape = this.shape.sliceArray(0 until size - 2)
|
||||
val (n, m) = this.shape.sliceArray(size - 2 until size)
|
||||
public fun TensorStructure<Double>.svd(epsilon: Double): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
|
||||
val size = tensor.linearStructure.dim
|
||||
val commonShape = tensor.shape.sliceArray(0 until size - 2)
|
||||
val (n, m) = tensor.shape.sliceArray(size - 2 until size)
|
||||
val resU = zeros(commonShape + intArrayOf(min(n, m), n))
|
||||
val resS = zeros(commonShape + intArrayOf(min(n, m)))
|
||||
val resV = zeros(commonShape + intArrayOf(min(n, m), m))
|
||||
|
||||
for ((matrix, USV) in this.matrixSequence()
|
||||
for ((matrix, USV) in tensor.matrixSequence()
|
||||
.zip(resU.matrixSequence().zip(resS.vectorSequence().zip(resV.matrixSequence())))) {
|
||||
val size = matrix.shape.reduce { acc, i -> acc * i }
|
||||
val curMatrix = DoubleTensor(
|
||||
@ -99,13 +100,13 @@ public class DoubleLinearOpsTensorAlgebra :
|
||||
return Triple(resU.transpose(), resS, resV.transpose())
|
||||
}
|
||||
|
||||
override fun DoubleTensor.symEig(): Pair<DoubleTensor, DoubleTensor> =
|
||||
override fun TensorStructure<Double>.symEig(): Pair<DoubleTensor, DoubleTensor> =
|
||||
symEig(epsilon = 1e-15)
|
||||
|
||||
//http://hua-zhou.github.io/teaching/biostatm280-2017spring/slides/16-eigsvd/eigsvd.html
|
||||
public fun DoubleTensor.symEig(epsilon: Double): Pair<DoubleTensor, DoubleTensor> {
|
||||
checkSymmetric(this, epsilon)
|
||||
val (u, s, v) = this.svd(epsilon)
|
||||
public fun TensorStructure<Double>.symEig(epsilon: Double): Pair<DoubleTensor, DoubleTensor> {
|
||||
checkSymmetric(tensor, epsilon)
|
||||
val (u, s, v) = tensor.svd(epsilon)
|
||||
val shp = s.shape + intArrayOf(1)
|
||||
val utv = u.transpose() dot v
|
||||
val n = s.shape.last()
|
||||
@ -116,11 +117,11 @@ public class DoubleLinearOpsTensorAlgebra :
|
||||
return Pair(eig, v)
|
||||
}
|
||||
|
||||
public fun DoubleTensor.detLU(epsilon: Double = 1e-9): DoubleTensor {
|
||||
public fun TensorStructure<Double>.detLU(epsilon: Double = 1e-9): DoubleTensor {
|
||||
|
||||
checkSquareMatrix(this.shape)
|
||||
val luTensor = this.copy()
|
||||
val pivotsTensor = this.setUpPivots()
|
||||
checkSquareMatrix(tensor.shape)
|
||||
val luTensor = tensor.copy()
|
||||
val pivotsTensor = tensor.setUpPivots()
|
||||
|
||||
val n = shape.size
|
||||
|
||||
@ -141,7 +142,7 @@ public class DoubleLinearOpsTensorAlgebra :
|
||||
return detTensor
|
||||
}
|
||||
|
||||
public fun DoubleTensor.invLU(epsilon: Double = 1e-9): DoubleTensor {
|
||||
public fun TensorStructure<Double>.invLU(epsilon: Double = 1e-9): DoubleTensor {
|
||||
val (luTensor, pivotsTensor) = lu(epsilon)
|
||||
val invTensor = luTensor.zeroesLike()
|
||||
|
||||
|
||||
@ -1,16 +1,18 @@
|
||||
package space.kscience.kmath.tensors.core
|
||||
|
||||
import space.kscience.kmath.tensors.TensorPartialDivisionAlgebra
|
||||
import space.kscience.kmath.nd.as2D
|
||||
import space.kscience.kmath.tensors.TensorPartialDivisionAlgebra
|
||||
import space.kscience.kmath.tensors.TensorStructure
|
||||
import kotlin.math.abs
|
||||
|
||||
public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, DoubleTensor> {
|
||||
public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
|
||||
|
||||
override fun DoubleTensor.value(): Double {
|
||||
check(this.shape contentEquals intArrayOf(1)) {
|
||||
|
||||
override fun TensorStructure<Double>.value(): Double {
|
||||
check(tensor.shape contentEquals intArrayOf(1)) {
|
||||
"Inconsistent value for tensor of shape ${shape.toList()}"
|
||||
}
|
||||
return this.buffer.array()[this.bufferStart]
|
||||
return tensor.buffer.array()[tensor.bufferStart]
|
||||
}
|
||||
|
||||
public fun fromArray(shape: IntArray, buffer: DoubleArray): DoubleTensor {
|
||||
@ -20,11 +22,11 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
|
||||
return DoubleTensor(shape, buffer, 0)
|
||||
}
|
||||
|
||||
override operator fun DoubleTensor.get(i: Int): DoubleTensor {
|
||||
val lastShape = this.shape.drop(1).toIntArray()
|
||||
override operator fun TensorStructure<Double>.get(i: Int): DoubleTensor {
|
||||
val lastShape = tensor.shape.drop(1).toIntArray()
|
||||
val newShape = if (lastShape.isNotEmpty()) lastShape else intArrayOf(1)
|
||||
val newStart = newShape.reduce(Int::times) * i + this.bufferStart
|
||||
return DoubleTensor(newShape, this.buffer.array(), newStart)
|
||||
val newStart = newShape.reduce(Int::times) * i + tensor.bufferStart
|
||||
return DoubleTensor(newShape, tensor.buffer.array(), newStart)
|
||||
}
|
||||
|
||||
public fun full(value: Double, shape: IntArray): DoubleTensor {
|
||||
@ -33,19 +35,19 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
|
||||
return DoubleTensor(shape, buffer)
|
||||
}
|
||||
|
||||
public fun DoubleTensor.fullLike(value: Double): DoubleTensor {
|
||||
val shape = this.shape
|
||||
val buffer = DoubleArray(this.linearStructure.size) { value }
|
||||
public fun TensorStructure<Double>.fullLike(value: Double): DoubleTensor {
|
||||
val shape = tensor.shape
|
||||
val buffer = DoubleArray(tensor.numElements) { value }
|
||||
return DoubleTensor(shape, buffer)
|
||||
}
|
||||
|
||||
public fun zeros(shape: IntArray): DoubleTensor = full(0.0, shape)
|
||||
|
||||
public fun DoubleTensor.zeroesLike(): DoubleTensor = this.fullLike(0.0)
|
||||
public fun TensorStructure<Double>.zeroesLike(): DoubleTensor = tensor.fullLike(0.0)
|
||||
|
||||
public fun ones(shape: IntArray): DoubleTensor = full(1.0, shape)
|
||||
|
||||
public fun DoubleTensor.onesLike(): DoubleTensor = this.fullLike(1.0)
|
||||
public fun TensorStructure<Double>.onesLike(): DoubleTensor = tensor.fullLike(1.0)
|
||||
|
||||
public fun eye(n: Int): DoubleTensor {
|
||||
val shape = intArrayOf(n, n)
|
||||
@ -57,200 +59,200 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
|
||||
return res
|
||||
}
|
||||
|
||||
public fun DoubleTensor.copy(): DoubleTensor {
|
||||
return DoubleTensor(this.shape, this.buffer.array().copyOf(), this.bufferStart)
|
||||
public fun TensorStructure<Double>.copy(): DoubleTensor {
|
||||
return DoubleTensor(tensor.shape, tensor.buffer.array().copyOf(), tensor.bufferStart)
|
||||
}
|
||||
|
||||
override fun Double.plus(other: DoubleTensor): DoubleTensor {
|
||||
val resBuffer = DoubleArray(other.linearStructure.size) { i ->
|
||||
other.buffer.array()[other.bufferStart + i] + this
|
||||
override fun Double.plus(other: TensorStructure<Double>): DoubleTensor {
|
||||
val resBuffer = DoubleArray(other.tensor.numElements) { i ->
|
||||
other.tensor.buffer.array()[other.tensor.bufferStart + i] + this
|
||||
}
|
||||
return DoubleTensor(other.shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun DoubleTensor.plus(value: Double): DoubleTensor = value + this
|
||||
override fun TensorStructure<Double>.plus(value: Double): DoubleTensor = value + tensor
|
||||
|
||||
override fun DoubleTensor.plus(other: DoubleTensor): DoubleTensor {
|
||||
checkShapesCompatible(this, other)
|
||||
val resBuffer = DoubleArray(this.linearStructure.size) { i ->
|
||||
this.buffer.array()[i] + other.buffer.array()[i]
|
||||
override fun TensorStructure<Double>.plus(other: TensorStructure<Double>): DoubleTensor {
|
||||
checkShapesCompatible(tensor, other.tensor)
|
||||
val resBuffer = DoubleArray(tensor.numElements) { i ->
|
||||
tensor.buffer.array()[i] + other.tensor.buffer.array()[i]
|
||||
}
|
||||
return DoubleTensor(this.shape, resBuffer)
|
||||
return DoubleTensor(tensor.shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun DoubleTensor.plusAssign(value: Double) {
|
||||
for (i in 0 until this.linearStructure.size) {
|
||||
this.buffer.array()[this.bufferStart + i] += value
|
||||
override fun TensorStructure<Double>.plusAssign(value: Double) {
|
||||
for (i in 0 until tensor.numElements) {
|
||||
tensor.buffer.array()[tensor.bufferStart + i] += value
|
||||
}
|
||||
}
|
||||
|
||||
override fun DoubleTensor.plusAssign(other: DoubleTensor) {
|
||||
checkShapesCompatible(this, other)
|
||||
for (i in 0 until this.linearStructure.size) {
|
||||
this.buffer.array()[this.bufferStart + i] +=
|
||||
other.buffer.array()[this.bufferStart + i]
|
||||
override fun TensorStructure<Double>.plusAssign(other: TensorStructure<Double>) {
|
||||
checkShapesCompatible(tensor, other.tensor)
|
||||
for (i in 0 until tensor.numElements) {
|
||||
tensor.buffer.array()[tensor.bufferStart + i] +=
|
||||
other.tensor.buffer.array()[tensor.bufferStart + i]
|
||||
}
|
||||
}
|
||||
|
||||
override fun Double.minus(other: DoubleTensor): DoubleTensor {
|
||||
val resBuffer = DoubleArray(other.linearStructure.size) { i ->
|
||||
this - other.buffer.array()[other.bufferStart + i]
|
||||
override fun Double.minus(other: TensorStructure<Double>): DoubleTensor {
|
||||
val resBuffer = DoubleArray(other.tensor.numElements) { i ->
|
||||
this - other.tensor.buffer.array()[other.tensor.bufferStart + i]
|
||||
}
|
||||
return DoubleTensor(other.shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun DoubleTensor.minus(value: Double): DoubleTensor {
|
||||
val resBuffer = DoubleArray(this.linearStructure.size) { i ->
|
||||
this.buffer.array()[this.bufferStart + i] - value
|
||||
override fun TensorStructure<Double>.minus(value: Double): DoubleTensor {
|
||||
val resBuffer = DoubleArray(tensor.numElements) { i ->
|
||||
tensor.buffer.array()[tensor.bufferStart + i] - value
|
||||
}
|
||||
return DoubleTensor(this.shape, resBuffer)
|
||||
return DoubleTensor(tensor.shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun DoubleTensor.minus(other: DoubleTensor): DoubleTensor {
|
||||
checkShapesCompatible(this, other)
|
||||
val resBuffer = DoubleArray(this.linearStructure.size) { i ->
|
||||
this.buffer.array()[i] - other.buffer.array()[i]
|
||||
override fun TensorStructure<Double>.minus(other: TensorStructure<Double>): DoubleTensor {
|
||||
checkShapesCompatible(tensor, other)
|
||||
val resBuffer = DoubleArray(tensor.numElements) { i ->
|
||||
tensor.buffer.array()[i] - other.tensor.buffer.array()[i]
|
||||
}
|
||||
return DoubleTensor(this.shape, resBuffer)
|
||||
return DoubleTensor(tensor.shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun DoubleTensor.minusAssign(value: Double) {
|
||||
for (i in 0 until this.linearStructure.size) {
|
||||
this.buffer.array()[this.bufferStart + i] -= value
|
||||
override fun TensorStructure<Double>.minusAssign(value: Double) {
|
||||
for (i in 0 until tensor.numElements) {
|
||||
tensor.buffer.array()[tensor.bufferStart + i] -= value
|
||||
}
|
||||
}
|
||||
|
||||
override fun DoubleTensor.minusAssign(other: DoubleTensor) {
|
||||
checkShapesCompatible(this, other)
|
||||
for (i in 0 until this.linearStructure.size) {
|
||||
this.buffer.array()[this.bufferStart + i] -=
|
||||
other.buffer.array()[this.bufferStart + i]
|
||||
override fun TensorStructure<Double>.minusAssign(other: TensorStructure<Double>) {
|
||||
checkShapesCompatible(tensor, other)
|
||||
for (i in 0 until tensor.numElements) {
|
||||
tensor.buffer.array()[tensor.bufferStart + i] -=
|
||||
other.tensor.buffer.array()[tensor.bufferStart + i]
|
||||
}
|
||||
}
|
||||
|
||||
override fun Double.times(other: DoubleTensor): DoubleTensor {
|
||||
val resBuffer = DoubleArray(other.linearStructure.size) { i ->
|
||||
other.buffer.array()[other.bufferStart + i] * this
|
||||
override fun Double.times(other: TensorStructure<Double>): DoubleTensor {
|
||||
val resBuffer = DoubleArray(other.tensor.numElements) { i ->
|
||||
other.tensor.buffer.array()[other.tensor.bufferStart + i] * this
|
||||
}
|
||||
return DoubleTensor(other.shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun DoubleTensor.times(value: Double): DoubleTensor = value * this
|
||||
override fun TensorStructure<Double>.times(value: Double): DoubleTensor = value * tensor
|
||||
|
||||
override fun DoubleTensor.times(other: DoubleTensor): DoubleTensor {
|
||||
checkShapesCompatible(this, other)
|
||||
val resBuffer = DoubleArray(this.linearStructure.size) { i ->
|
||||
this.buffer.array()[this.bufferStart + i] *
|
||||
other.buffer.array()[other.bufferStart + i]
|
||||
override fun TensorStructure<Double>.times(other: TensorStructure<Double>): DoubleTensor {
|
||||
checkShapesCompatible(tensor, other)
|
||||
val resBuffer = DoubleArray(tensor.numElements) { i ->
|
||||
tensor.buffer.array()[tensor.bufferStart + i] *
|
||||
other.tensor.buffer.array()[other.tensor.bufferStart + i]
|
||||
}
|
||||
return DoubleTensor(this.shape, resBuffer)
|
||||
return DoubleTensor(tensor.shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun DoubleTensor.timesAssign(value: Double) {
|
||||
for (i in 0 until this.linearStructure.size) {
|
||||
this.buffer.array()[this.bufferStart + i] *= value
|
||||
override fun TensorStructure<Double>.timesAssign(value: Double) {
|
||||
for (i in 0 until tensor.numElements) {
|
||||
tensor.buffer.array()[tensor.bufferStart + i] *= value
|
||||
}
|
||||
}
|
||||
|
||||
override fun DoubleTensor.timesAssign(other: DoubleTensor) {
|
||||
checkShapesCompatible(this, other)
|
||||
for (i in 0 until this.linearStructure.size) {
|
||||
this.buffer.array()[this.bufferStart + i] *=
|
||||
other.buffer.array()[this.bufferStart + i]
|
||||
override fun TensorStructure<Double>.timesAssign(other: TensorStructure<Double>) {
|
||||
checkShapesCompatible(tensor, other)
|
||||
for (i in 0 until tensor.numElements) {
|
||||
tensor.buffer.array()[tensor.bufferStart + i] *=
|
||||
other.tensor.buffer.array()[tensor.bufferStart + i]
|
||||
}
|
||||
}
|
||||
|
||||
override fun DoubleTensor.div(value: Double): DoubleTensor {
|
||||
val resBuffer = DoubleArray(this.linearStructure.size) { i ->
|
||||
this.buffer.array()[this.bufferStart + i] / value
|
||||
override fun TensorStructure<Double>.div(value: Double): DoubleTensor {
|
||||
val resBuffer = DoubleArray(tensor.numElements) { i ->
|
||||
tensor.buffer.array()[tensor.bufferStart + i] / value
|
||||
}
|
||||
return DoubleTensor(this.shape, resBuffer)
|
||||
return DoubleTensor(tensor.shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun DoubleTensor.div(other: DoubleTensor): DoubleTensor {
|
||||
checkShapesCompatible(this, other)
|
||||
val resBuffer = DoubleArray(this.linearStructure.size) { i ->
|
||||
this.buffer.array()[other.bufferStart + i] /
|
||||
other.buffer.array()[other.bufferStart + i]
|
||||
override fun TensorStructure<Double>.div(other: TensorStructure<Double>): DoubleTensor {
|
||||
checkShapesCompatible(tensor, other)
|
||||
val resBuffer = DoubleArray(tensor.numElements) { i ->
|
||||
tensor.buffer.array()[other.tensor.bufferStart + i] /
|
||||
other.tensor.buffer.array()[other.tensor.bufferStart + i]
|
||||
}
|
||||
return DoubleTensor(this.shape, resBuffer)
|
||||
return DoubleTensor(tensor.shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun DoubleTensor.divAssign(value: Double) {
|
||||
for (i in 0 until this.linearStructure.size) {
|
||||
this.buffer.array()[this.bufferStart + i] /= value
|
||||
override fun TensorStructure<Double>.divAssign(value: Double) {
|
||||
for (i in 0 until tensor.numElements) {
|
||||
tensor.buffer.array()[tensor.bufferStart + i] /= value
|
||||
}
|
||||
}
|
||||
|
||||
override fun DoubleTensor.divAssign(other: DoubleTensor) {
|
||||
checkShapesCompatible(this, other)
|
||||
for (i in 0 until this.linearStructure.size) {
|
||||
this.buffer.array()[this.bufferStart + i] /=
|
||||
other.buffer.array()[this.bufferStart + i]
|
||||
override fun TensorStructure<Double>.divAssign(other: TensorStructure<Double>) {
|
||||
checkShapesCompatible(tensor, other)
|
||||
for (i in 0 until tensor.numElements) {
|
||||
tensor.buffer.array()[tensor.bufferStart + i] /=
|
||||
other.tensor.buffer.array()[tensor.bufferStart + i]
|
||||
}
|
||||
}
|
||||
|
||||
override fun DoubleTensor.unaryMinus(): DoubleTensor {
|
||||
val resBuffer = DoubleArray(this.linearStructure.size) { i ->
|
||||
this.buffer.array()[this.bufferStart + i].unaryMinus()
|
||||
override fun TensorStructure<Double>.unaryMinus(): DoubleTensor {
|
||||
val resBuffer = DoubleArray(tensor.numElements) { i ->
|
||||
tensor.buffer.array()[tensor.bufferStart + i].unaryMinus()
|
||||
}
|
||||
return DoubleTensor(this.shape, resBuffer)
|
||||
return DoubleTensor(tensor.shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun DoubleTensor.transpose(i: Int, j: Int): DoubleTensor {
|
||||
val ii = minusIndex(i)
|
||||
val jj = minusIndex(j)
|
||||
checkTranspose(this.dimension, ii, jj)
|
||||
val n = this.linearStructure.size
|
||||
override fun TensorStructure<Double>.transpose(i: Int, j: Int): DoubleTensor {
|
||||
val ii = tensor.minusIndex(i)
|
||||
val jj = tensor.minusIndex(j)
|
||||
checkTranspose(tensor.dimension, ii, jj)
|
||||
val n = tensor.numElements
|
||||
val resBuffer = DoubleArray(n)
|
||||
|
||||
val resShape = this.shape.copyOf()
|
||||
val resShape = tensor.shape.copyOf()
|
||||
resShape[ii] = resShape[jj].also { resShape[jj] = resShape[ii] }
|
||||
|
||||
val resTensor = DoubleTensor(resShape, resBuffer)
|
||||
|
||||
for (offset in 0 until n) {
|
||||
val oldMultiIndex = this.linearStructure.index(offset)
|
||||
val oldMultiIndex = tensor.linearStructure.index(offset)
|
||||
val newMultiIndex = oldMultiIndex.copyOf()
|
||||
newMultiIndex[ii] = newMultiIndex[jj].also { newMultiIndex[jj] = newMultiIndex[ii] }
|
||||
|
||||
val linearIndex = resTensor.linearStructure.offset(newMultiIndex)
|
||||
resTensor.buffer.array()[linearIndex] =
|
||||
this.buffer.array()[this.bufferStart + offset]
|
||||
tensor.buffer.array()[tensor.bufferStart + offset]
|
||||
}
|
||||
return resTensor
|
||||
}
|
||||
|
||||
|
||||
override fun DoubleTensor.view(shape: IntArray): DoubleTensor {
|
||||
checkView(this, shape)
|
||||
return DoubleTensor(shape, this.buffer.array(), this.bufferStart)
|
||||
override fun TensorStructure<Double>.view(shape: IntArray): DoubleTensor {
|
||||
checkView(tensor, shape)
|
||||
return DoubleTensor(shape, tensor.buffer.array(), tensor.bufferStart)
|
||||
}
|
||||
|
||||
override fun DoubleTensor.viewAs(other: DoubleTensor): DoubleTensor {
|
||||
return this.view(other.shape)
|
||||
override fun TensorStructure<Double>.viewAs(other: TensorStructure<Double>): DoubleTensor {
|
||||
return tensor.view(other.shape)
|
||||
}
|
||||
|
||||
override infix fun DoubleTensor.dot(other: DoubleTensor): DoubleTensor {
|
||||
if (this.shape.size == 1 && other.shape.size == 1) {
|
||||
return DoubleTensor(intArrayOf(1), doubleArrayOf(this.times(other).buffer.array().sum()))
|
||||
override infix fun TensorStructure<Double>.dot(other: TensorStructure<Double>): DoubleTensor {
|
||||
if (tensor.shape.size == 1 && other.shape.size == 1) {
|
||||
return DoubleTensor(intArrayOf(1), doubleArrayOf(tensor.times(other).tensor.buffer.array().sum()))
|
||||
}
|
||||
|
||||
var newThis = this.copy()
|
||||
var newThis = tensor.copy()
|
||||
var newOther = other.copy()
|
||||
|
||||
var penultimateDim = false
|
||||
var lastDim = false
|
||||
if (this.shape.size == 1) {
|
||||
if (tensor.shape.size == 1) {
|
||||
penultimateDim = true
|
||||
newThis = this.view(intArrayOf(1) + this.shape)
|
||||
newThis = tensor.view(intArrayOf(1) + tensor.shape)
|
||||
}
|
||||
if (other.shape.size == 1) {
|
||||
lastDim = true
|
||||
newOther = other.view(other.shape + intArrayOf(1))
|
||||
newOther = other.tensor.view(other.shape + intArrayOf(1))
|
||||
}
|
||||
|
||||
val broadcastTensors = broadcastOuterTensors(newThis, newOther)
|
||||
val broadcastTensors = broadcastOuterTensors(newThis.tensor, newOther.tensor)
|
||||
newThis = broadcastTensors[0]
|
||||
newOther = broadcastTensors[1]
|
||||
|
||||
@ -284,10 +286,12 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
|
||||
return resTensor
|
||||
}
|
||||
|
||||
override fun diagonalEmbedding(diagonalEntries: DoubleTensor, offset: Int, dim1: Int, dim2: Int): DoubleTensor {
|
||||
val d1 = minusIndexFrom(diagonalEntries.linearStructure.dim + 1, dim1)
|
||||
val d2 = minusIndexFrom(diagonalEntries.linearStructure.dim + 1, dim2)
|
||||
override fun diagonalEmbedding(diagonalEntries: TensorStructure<Double>, offset: Int, dim1: Int, dim2: Int):
|
||||
DoubleTensor {
|
||||
val n = diagonalEntries.shape.size
|
||||
val d1 = minusIndexFrom(n + 1, dim1)
|
||||
val d2 = minusIndexFrom(n + 1, dim2)
|
||||
|
||||
if (d1 == d2) {
|
||||
throw RuntimeException("Diagonal dimensions cannot be identical $d1, $d2")
|
||||
}
|
||||
@ -300,7 +304,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
|
||||
var realOffset = offset
|
||||
if (lessDim > greaterDim) {
|
||||
realOffset *= -1
|
||||
lessDim = greaterDim.also {greaterDim = lessDim}
|
||||
lessDim = greaterDim.also { greaterDim = lessDim }
|
||||
}
|
||||
|
||||
val resShape = diagonalEntries.shape.slice(0 until lessDim).toIntArray() +
|
||||
@ -310,13 +314,13 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
|
||||
diagonalEntries.shape.slice(greaterDim - 1 until n - 1).toIntArray()
|
||||
val resTensor = zeros(resShape)
|
||||
|
||||
for (i in 0 until diagonalEntries.linearStructure.size) {
|
||||
val multiIndex = diagonalEntries.linearStructure.index(i)
|
||||
for (i in 0 until diagonalEntries.tensor.numElements) {
|
||||
val multiIndex = diagonalEntries.tensor.linearStructure.index(i)
|
||||
|
||||
var offset1 = 0
|
||||
var offset2 = abs(realOffset)
|
||||
if (realOffset < 0) {
|
||||
offset1 = offset2.also {offset2 = offset1}
|
||||
offset1 = offset2.also { offset2 = offset1 }
|
||||
}
|
||||
val diagonalMultiIndex = multiIndex.slice(0 until lessDim).toIntArray() +
|
||||
intArrayOf(multiIndex[n - 1] + offset1) +
|
||||
@ -327,32 +331,35 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
|
||||
resTensor[diagonalMultiIndex] = diagonalEntries[multiIndex]
|
||||
}
|
||||
|
||||
return resTensor
|
||||
return resTensor.tensor
|
||||
}
|
||||
|
||||
|
||||
public fun DoubleTensor.map(transform: (Double) -> Double): DoubleTensor {
|
||||
public fun TensorStructure<Double>.map(transform: (Double) -> Double): DoubleTensor {
|
||||
return DoubleTensor(
|
||||
this.shape,
|
||||
this.buffer.array().map { transform(it) }.toDoubleArray(),
|
||||
this.bufferStart
|
||||
tensor.shape,
|
||||
tensor.buffer.array().map { transform(it) }.toDoubleArray(),
|
||||
tensor.bufferStart
|
||||
)
|
||||
}
|
||||
|
||||
public fun DoubleTensor.eq(other: DoubleTensor, delta: Double): Boolean {
|
||||
return this.eq(other) { x, y -> abs(x - y) < delta }
|
||||
public fun TensorStructure<Double>.eq(other: TensorStructure<Double>, delta: Double): Boolean {
|
||||
return tensor.eq(other) { x, y -> abs(x - y) < delta }
|
||||
}
|
||||
|
||||
public fun DoubleTensor.eq(other: DoubleTensor): Boolean = this.eq(other, 1e-5)
|
||||
public fun TensorStructure<Double>.eq(other: TensorStructure<Double>): Boolean = tensor.eq(other, 1e-5)
|
||||
|
||||
private fun DoubleTensor.eq(other: DoubleTensor, eqFunction: (Double, Double) -> Boolean): Boolean {
|
||||
checkShapesCompatible(this, other)
|
||||
val n = this.linearStructure.size
|
||||
if (n != other.linearStructure.size) {
|
||||
private fun TensorStructure<Double>.eq(
|
||||
other: TensorStructure<Double>,
|
||||
eqFunction: (Double, Double) -> Boolean
|
||||
): Boolean {
|
||||
checkShapesCompatible(tensor, other)
|
||||
val n = tensor.numElements
|
||||
if (n != other.tensor.numElements) {
|
||||
return false
|
||||
}
|
||||
for (i in 0 until n) {
|
||||
if (!eqFunction(this.buffer[this.bufferStart + i], other.buffer[other.bufferStart + i])) {
|
||||
if (!eqFunction(tensor.buffer[tensor.bufferStart + i], other.tensor.buffer[other.tensor.bufferStart + i])) {
|
||||
return false
|
||||
}
|
||||
}
|
||||
@ -362,8 +369,8 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
|
||||
public fun randNormal(shape: IntArray, seed: Long = 0): DoubleTensor =
|
||||
DoubleTensor(shape, getRandomNormals(shape.reduce(Int::times), seed))
|
||||
|
||||
public fun DoubleTensor.randNormalLike(seed: Long = 0): DoubleTensor =
|
||||
DoubleTensor(this.shape, getRandomNormals(this.shape.reduce(Int::times), seed))
|
||||
public fun TensorStructure<Double>.randNormalLike(seed: Long = 0): DoubleTensor =
|
||||
DoubleTensor(tensor.shape, getRandomNormals(tensor.shape.reduce(Int::times), seed))
|
||||
|
||||
}
|
||||
|
||||
|
||||
@ -1,54 +1,40 @@
|
||||
package space.kscience.kmath.tensors.core
|
||||
|
||||
import space.kscience.kmath.tensors.TensorAlgebra
|
||||
import space.kscience.kmath.tensors.TensorStructure
|
||||
|
||||
|
||||
internal inline fun <T, TensorType : TensorStructure<T>,
|
||||
TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
|
||||
TorchTensorAlgebraType.checkEmptyShape(shape: IntArray): Unit =
|
||||
internal inline fun checkEmptyShape(shape: IntArray): Unit =
|
||||
check(shape.isNotEmpty()) {
|
||||
"Illegal empty shape provided"
|
||||
}
|
||||
|
||||
internal inline fun <TensorType : TensorStructure<Double>,
|
||||
TorchTensorAlgebraType : TensorAlgebra<Double, TensorType>>
|
||||
TorchTensorAlgebraType.checkEmptyDoubleBuffer(buffer: DoubleArray): Unit =
|
||||
internal inline fun checkEmptyDoubleBuffer(buffer: DoubleArray): Unit =
|
||||
check(buffer.isNotEmpty()) {
|
||||
"Illegal empty buffer provided"
|
||||
}
|
||||
|
||||
internal inline fun <TensorType : TensorStructure<Double>,
|
||||
TorchTensorAlgebraType : TensorAlgebra<Double, TensorType>>
|
||||
TorchTensorAlgebraType.checkBufferShapeConsistency(shape: IntArray, buffer: DoubleArray): Unit =
|
||||
internal inline fun checkBufferShapeConsistency(shape: IntArray, buffer: DoubleArray): Unit =
|
||||
check(buffer.size == shape.reduce(Int::times)) {
|
||||
"Inconsistent shape ${shape.toList()} for buffer of size ${buffer.size} provided"
|
||||
}
|
||||
|
||||
|
||||
internal inline fun <T, TensorType : TensorStructure<T>,
|
||||
TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
|
||||
TorchTensorAlgebraType.checkShapesCompatible(a: TensorType, b: TensorType): Unit =
|
||||
internal inline fun <T> checkShapesCompatible(a: TensorStructure<T>, b: TensorStructure<T>): Unit =
|
||||
check(a.shape contentEquals b.shape) {
|
||||
"Incompatible shapes ${a.shape.toList()} and ${b.shape.toList()} "
|
||||
}
|
||||
|
||||
|
||||
internal inline fun <T, TensorType : TensorStructure<T>,
|
||||
TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
|
||||
TorchTensorAlgebraType.checkTranspose(dim: Int, i: Int, j: Int): Unit =
|
||||
internal inline fun checkTranspose(dim: Int, i: Int, j: Int): Unit =
|
||||
check((i < dim) and (j < dim)) {
|
||||
"Cannot transpose $i to $j for a tensor of dim $dim"
|
||||
}
|
||||
|
||||
internal inline fun <T, TensorType : TensorStructure<T>,
|
||||
TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
|
||||
TorchTensorAlgebraType.checkView(a: TensorType, shape: IntArray): Unit =
|
||||
internal inline fun <T> checkView(a: TensorStructure<T>, shape: IntArray): Unit =
|
||||
check(a.shape.reduce(Int::times) == shape.reduce(Int::times))
|
||||
|
||||
internal inline fun <T, TensorType : TensorStructure<T>,
|
||||
TorchTensorAlgebraType : TensorAlgebra<T, TensorType>>
|
||||
TorchTensorAlgebraType.checkSquareMatrix(shape: IntArray): Unit {
|
||||
|
||||
internal inline fun checkSquareMatrix(shape: IntArray): Unit {
|
||||
val n = shape.size
|
||||
check(n >= 2) {
|
||||
"Expected tensor with 2 or more dimensions, got size $n instead"
|
||||
@ -58,16 +44,19 @@ internal inline fun <T, TensorType : TensorStructure<T>,
|
||||
}
|
||||
}
|
||||
|
||||
internal inline fun DoubleTensorAlgebra.checkSymmetric(tensor: DoubleTensor, epsilon: Double = 1e-6): Unit =
|
||||
internal inline fun DoubleTensorAlgebra.checkSymmetric(
|
||||
tensor: TensorStructure<Double>, epsilon: Double = 1e-6
|
||||
): Unit =
|
||||
check(tensor.eq(tensor.transpose(), epsilon)) {
|
||||
"Tensor is not symmetric about the last 2 dimensions at precision $epsilon"
|
||||
}
|
||||
|
||||
internal inline fun DoubleLinearOpsTensorAlgebra.checkPositiveDefinite(
|
||||
tensor: DoubleTensor, epsilon: Double = 1e-6): Unit {
|
||||
tensor: DoubleTensor, epsilon: Double = 1e-6
|
||||
): Unit {
|
||||
checkSymmetric(tensor, epsilon)
|
||||
for( mat in tensor.matrixSequence())
|
||||
check(mat.asTensor().detLU().value() > 0.0){
|
||||
for (mat in tensor.matrixSequence())
|
||||
check(mat.asTensor().detLU().value() > 0.0) {
|
||||
"Tensor contains matrices which are not positive definite ${mat.asTensor().detLU().value()}"
|
||||
}
|
||||
}
|
||||
@ -14,7 +14,7 @@ internal inline fun <T> BufferedTensor<T>.vectorSequence(): Sequence<BufferedTen
|
||||
val n = shape.size
|
||||
val vectorOffset = shape[n - 1]
|
||||
val vectorShape = intArrayOf(shape.last())
|
||||
for (offset in 0 until numel step vectorOffset) {
|
||||
for (offset in 0 until numElements step vectorOffset) {
|
||||
val vector = BufferedTensor(vectorShape, buffer, offset)
|
||||
yield(vector)
|
||||
}
|
||||
@ -25,7 +25,7 @@ internal inline fun <T> BufferedTensor<T>.matrixSequence(): Sequence<BufferedTen
|
||||
val n = shape.size
|
||||
val matrixOffset = shape[n - 1] * shape[n - 2]
|
||||
val matrixShape = intArrayOf(shape[n - 2], shape[n - 1])
|
||||
for (offset in 0 until numel step matrixOffset) {
|
||||
for (offset in 0 until numElements step matrixOffset) {
|
||||
val matrix = BufferedTensor(matrixShape, buffer, offset)
|
||||
yield(matrix)
|
||||
}
|
||||
|
||||
@ -58,7 +58,7 @@ internal inline fun minusIndexFrom(n: Int, i: Int) : Int = if (i >= 0) i else {
|
||||
ii
|
||||
}
|
||||
|
||||
internal inline fun <T> BufferedTensor<T>.minusIndex(i: Int): Int = minusIndexFrom(this.linearStructure.dim, i)
|
||||
internal inline fun <T> BufferedTensor<T>.minusIndex(i: Int): Int = minusIndexFrom(this.dimension, i)
|
||||
|
||||
internal inline fun format(value: Double, digits: Int = 4): String {
|
||||
val ten = 10.0
|
||||
@ -111,7 +111,7 @@ internal inline fun DoubleTensor.toPrettyString(): String = buildString {
|
||||
}
|
||||
offset += vectorSize
|
||||
// todo refactor
|
||||
if (this@toPrettyString.numel == offset) {
|
||||
if (this@toPrettyString.numElements == offset) {
|
||||
break
|
||||
}
|
||||
append(",\n")
|
||||
|
||||
Loading…
Reference in New Issue
Block a user