imp full and remove copypaste in geners
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1fa0da2810
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5e94610e28
@ -31,28 +31,6 @@ public open class BufferedTensor<T>(
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override fun hashCode(): Int = 0
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override fun hashCode(): Int = 0
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// todo rename to vector
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public inline fun forEachVector(vectorAction : (MutableStructure1D<T>) -> Unit): Unit {
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check(shape.size >= 1) {"todo"}
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val vectorOffset = strides.strides[0]
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val vectorShape = intArrayOf(shape.last())
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for (offset in 0 until numel step vectorOffset) {
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val vector = BufferedTensor<T>(vectorShape, buffer, offset).as1D()
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vectorAction(vector)
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}
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}
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public inline fun forEachMatrix(matrixAction : (MutableStructure2D<T>) -> Unit): Unit {
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check(shape.size >= 2) {"todo"}
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val matrixOffset = strides.strides[1]
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val matrixShape = intArrayOf(shape[shape.size - 2], shape.last()) //todo better way?
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for (offset in 0 until numel step matrixOffset) {
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val matrix = BufferedTensor<T>(matrixShape, buffer, offset).as2D()
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matrixAction(matrix)
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}
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}
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// todo remove code copy-pasting
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public fun vectorSequence(): Sequence<MutableStructure1D<T>> = sequence {
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public fun vectorSequence(): Sequence<MutableStructure1D<T>> = sequence {
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check(shape.size >= 1) {"todo"}
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check(shape.size >= 1) {"todo"}
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val vectorOffset = strides.strides[0]
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val vectorOffset = strides.strides[0]
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@ -73,6 +51,18 @@ public open class BufferedTensor<T>(
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}
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}
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}
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}
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public inline fun forEachVector(vectorAction : (MutableStructure1D<T>) -> Unit): Unit {
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for (vector in vectorSequence()){
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vectorAction(vector)
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}
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}
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public inline fun forEachMatrix(matrixAction : (MutableStructure2D<T>) -> Unit): Unit {
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for (matrix in matrixSequence()){
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matrixAction(matrix)
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}
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}
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}
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}
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@ -10,23 +10,24 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
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return DoubleTensor(newShape, this.buffer.array(), newStart)
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return DoubleTensor(newShape, this.buffer.array(), newStart)
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}
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}
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override fun zeros(shape: IntArray): DoubleTensor {
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override fun full(shape: IntArray, value: Double): DoubleTensor {
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TODO("Not yet implemented")
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val buffer = DoubleArray(TensorStrides(shape).linearSize) { value }
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}
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override fun DoubleTensor.zeroesLike(): DoubleTensor {
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val shape = this.shape
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val buffer = DoubleArray(this.strides.linearSize) { 0.0 }
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return DoubleTensor(shape, buffer)
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return DoubleTensor(shape, buffer)
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}
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}
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override fun ones(shape: IntArray): DoubleTensor {
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override fun zeros(shape: IntArray): DoubleTensor = full(shape, 0.0)
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TODO("Not yet implemented")
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override fun ones(shape: IntArray): DoubleTensor = full(shape, 1.0)
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override fun DoubleTensor.fullLike(value: Double): DoubleTensor {
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val shape = this.shape
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val buffer = DoubleArray(this.strides.linearSize) { value }
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return DoubleTensor(shape, buffer)
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}
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}
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override fun DoubleTensor.onesLike(): DoubleTensor {
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override fun DoubleTensor.zeroesLike(): DoubleTensor = this.fullLike(0.0)
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TODO("Not yet implemented")
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}
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override fun DoubleTensor.onesLike(): DoubleTensor = this.fullLike(1.0)
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override fun eye(n: Int): DoubleTensor {
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override fun eye(n: Int): DoubleTensor {
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val shape = intArrayOf(n, n)
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val shape = intArrayOf(n, n)
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@ -192,15 +193,6 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
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TODO("Not yet implemented")
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TODO("Not yet implemented")
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}
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}
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override fun full(shape: IntArray, value: Double): DoubleTensor {
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TODO("Not yet implemented")
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}
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override fun DoubleTensor.fullLike(value: Double): DoubleTensor {
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TODO("Not yet implemented")
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}
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override fun DoubleTensor.sum(dim: Int, keepDim: Boolean): DoubleTensor {
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override fun DoubleTensor.sum(dim: Int, keepDim: Boolean): DoubleTensor {
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TODO("Not yet implemented")
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TODO("Not yet implemented")
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}
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}
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@ -4,18 +4,18 @@ package space.kscience.kmath.tensors
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public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
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public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
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public fun zeros(shape: IntArray): TensorType
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public fun TensorType.zeroesLike(): TensorType // mb it shouldn't be tensor but algebra method (like in numpy/torch) ?
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public fun ones(shape: IntArray): TensorType
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public fun TensorType.onesLike(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.full.html
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//https://pytorch.org/docs/stable/generated/torch.full.html
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public fun full(shape: IntArray, value: T): TensorType
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public fun full(shape: IntArray, value: T): TensorType
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public fun ones(shape: IntArray): TensorType
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public fun zeros(shape: IntArray): TensorType
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//https://pytorch.org/docs/stable/generated/torch.full_like.html#torch.full_like
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//https://pytorch.org/docs/stable/generated/torch.full_like.html#torch.full_like
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public fun TensorType.fullLike(value: T): TensorType
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public fun TensorType.fullLike(value: T): TensorType
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public fun TensorType.zeroesLike(): TensorType
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public fun TensorType.onesLike(): TensorType
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//https://pytorch.org/docs/stable/generated/torch.eye.html
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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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public fun eye(n: Int): TensorType
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