forked from kscience/kmath
add broadcast and functions plus and minus
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723e0e458e
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454d574ccd
@ -2,7 +2,9 @@ package space.kscience.kmath.tensors
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import space.kscience.kmath.nd.MutableNDBuffer
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import space.kscience.kmath.structures.RealBuffer
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import space.kscience.kmath.structures.ValueFlag
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import space.kscience.kmath.structures.array
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import kotlin.math.max
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public class RealTensor(
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@ -54,27 +56,85 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
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TODO("Not yet implemented")
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}
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override fun broadcastShapes(vararg shapes: IntArray): IntArray {
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var totalDim = 0
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for (shape in shapes) {
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totalDim = max(totalDim, shape.size)
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}
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val totalShape = IntArray(totalDim) {0}
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for (shape in shapes) {
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for (i in shape.indices) {
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val curDim = shape[i]
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val offset = totalDim - shape.size
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totalShape[i + offset] = max(totalShape[i + offset], curDim)
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}
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}
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for (shape in shapes) {
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for (i in shape.indices) {
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val curDim = shape[i]
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val offset = totalDim - shape.size
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if (curDim != 1 && totalShape[i + offset] != curDim) {
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throw IllegalArgumentException("Shapes are not compatible and cannot be broadcast")
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}
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}
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}
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return totalShape
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}
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override fun broadcastTensors(vararg tensors: RealTensor): List<RealTensor> {
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val totalShape = broadcastShapes(*(tensors.map { it.shape }).toTypedArray())
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val n = totalShape.reduce{ acc, i -> acc * i }
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val res = ArrayList<RealTensor>(0)
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for (tensor in tensors) {
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val resTensor = RealTensor(totalShape, DoubleArray(n))
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for (linearIndex in 0 until n) {
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val totalMultiIndex = resTensor.strides.index(linearIndex)
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val curMultiIndex = tensor.shape.copyOf()
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val offset = totalMultiIndex.size - curMultiIndex.size
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for (i in curMultiIndex.indices) {
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if (curMultiIndex[i] != 1) {
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curMultiIndex[i] = totalMultiIndex[i + offset]
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} else {
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curMultiIndex[i] = 0
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}
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}
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val curLinearIndex = tensor.strides.offset(curMultiIndex)
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resTensor.buffer.array[linearIndex] = tensor.buffer.array[curLinearIndex]
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}
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res.add(resTensor)
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}
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return res
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}
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override fun Double.plus(other: RealTensor): RealTensor {
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//todo should be change with broadcasting
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val resBuffer = DoubleArray(other.buffer.size) { i ->
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other.buffer.array[i] + this
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}
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return RealTensor(other.shape, resBuffer)
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}
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//todo should be change with broadcasting
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override fun RealTensor.plus(value: Double): RealTensor = value + this
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override fun RealTensor.plus(other: RealTensor): RealTensor {
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//todo should be change with broadcasting
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val resBuffer = DoubleArray(this.buffer.size) { i ->
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this.buffer.array[i] + other.buffer.array[i]
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val broadcast = broadcastTensors(this, other)
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val newThis = broadcast[0]
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val newOther = broadcast[1]
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val resBuffer = DoubleArray(newThis.buffer.size) { i ->
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newThis.buffer.array[i] + newOther.buffer.array[i]
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}
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return RealTensor(this.shape, resBuffer)
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return RealTensor(newThis.shape, resBuffer)
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}
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override fun RealTensor.plusAssign(value: Double) {
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//todo should be change with broadcasting
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for (i in this.buffer.array.indices) {
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this.buffer.array[i] += value
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}
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@ -88,19 +148,33 @@ public class RealTensorAlgebra : TensorPartialDivisionAlgebra<Double, RealTensor
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}
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override fun Double.minus(other: RealTensor): RealTensor {
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TODO("Alya")
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val resBuffer = DoubleArray(other.buffer.size) { i ->
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this - other.buffer.array[i]
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}
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return RealTensor(other.shape, resBuffer)
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}
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override fun RealTensor.minus(value: Double): RealTensor {
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TODO("Alya")
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val resBuffer = DoubleArray(this.buffer.size) { i ->
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this.buffer.array[i] - value
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}
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return RealTensor(this.shape, resBuffer)
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}
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override fun RealTensor.minus(other: RealTensor): RealTensor {
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TODO("Alya")
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val broadcast = broadcastTensors(this, other)
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val newThis = broadcast[0]
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val newOther = broadcast[1]
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val resBuffer = DoubleArray(newThis.buffer.size) { i ->
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newThis.buffer.array[i] - newOther.buffer.array[i]
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}
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return RealTensor(newThis.shape, resBuffer)
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}
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override fun RealTensor.minusAssign(value: Double) {
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TODO("Alya")
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for (i in this.buffer.array.indices) {
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this.buffer.array[i] -= value
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}
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}
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override fun RealTensor.minusAssign(other: RealTensor) {
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@ -13,6 +13,9 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
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public fun TensorType.copy(): TensorType
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public fun broadcastShapes(vararg shapes: IntArray): IntArray
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public fun broadcastTensors(vararg tensors: RealTensor): List<TensorType>
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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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@ -2,6 +2,8 @@ package space.kscience.kmath.tensors
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import space.kscience.kmath.structures.array
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import kotlin.test.Test
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import kotlin.test.assertFails
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import kotlin.test.assertFailsWith
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import kotlin.test.assertTrue
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class TestRealTensorAlgebra {
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@ -64,4 +66,49 @@ class TestRealTensorAlgebra {
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assertTrue(tensor.buffer.array contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
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assertTrue(tensor.shape contentEquals intArrayOf(3, 2))
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}
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@Test
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fun broadcastShapes() = RealTensorAlgebra {
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assertTrue(this.broadcastShapes(
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intArrayOf(2, 3), intArrayOf(1, 3), intArrayOf(1, 1, 1)
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) contentEquals intArrayOf(1, 2, 3))
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assertTrue(this.broadcastShapes(
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intArrayOf(6, 7), intArrayOf(5, 6, 1), intArrayOf(7,), intArrayOf(5, 1, 7)
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) contentEquals intArrayOf(5, 6, 7))
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}
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@Test
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fun broadcastTensors() = RealTensorAlgebra {
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val tensor1 = RealTensor(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
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val tensor2 = RealTensor(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
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val tensor3 = RealTensor(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
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val res = this.broadcastTensors(tensor1, tensor2, tensor3)
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assertTrue(res[0].shape contentEquals intArrayOf(1, 2, 3))
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assertTrue(res[1].shape contentEquals intArrayOf(1, 2, 3))
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assertTrue(res[2].shape contentEquals intArrayOf(1, 2, 3))
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assertTrue(res[0].buffer.array contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
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assertTrue(res[1].buffer.array contentEquals doubleArrayOf(10.0, 20.0, 30.0, 10.0, 20.0, 30.0))
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assertTrue(res[2].buffer.array contentEquals doubleArrayOf(500.0, 500.0, 500.0, 500.0, 500.0, 500.0))
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}
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@Test
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fun minusTensor() = RealTensorAlgebra {
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val tensor1 = RealTensor(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
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val tensor2 = RealTensor(intArrayOf(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
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val tensor3 = RealTensor(intArrayOf(1, 1, 1), doubleArrayOf(500.0))
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assertTrue((tensor2 - tensor1).shape contentEquals intArrayOf(2, 3))
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assertTrue((tensor2 - tensor1).buffer.array contentEquals doubleArrayOf(9.0, 18.0, 27.0, 6.0, 15.0, 24.0))
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assertTrue((tensor3 - tensor1).shape contentEquals intArrayOf(1, 2, 3))
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assertTrue((tensor3 - tensor1).buffer.array
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contentEquals doubleArrayOf(499.0, 498.0, 497.0, 496.0, 495.0, 494.0))
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assertTrue((tensor3 - tensor2).shape contentEquals intArrayOf(1, 1, 3))
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assertTrue((tensor3 - tensor2).buffer.array contentEquals doubleArrayOf(490.0, 480.0, 470.0))
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}
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}
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