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fix bugs in function dot, add tests

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This commit is contained in:
AlyaNovikova 2021-03-24 13:43:54 +03:00
commit c2f11fb6e1
5 changed files with 56 additions and 56 deletions
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16
kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt
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@ -45,7 +45,7 @@ public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
val newThis = broadcast[0]
val newOther = broadcast[1]
val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
newThis.buffer.array()[newOther.bufferStart + i] *
newThis.buffer.array()[newThis.bufferStart + i] *
newOther.buffer.array()[newOther.bufferStart + i]
}
return DoubleTensor(newThis.shape, resBuffer)
@ -182,17 +182,13 @@ internal inline fun broadcastTensors(vararg tensors: DoubleTensor): List<DoubleT
}
internal inline fun broadcastOuterTensors(vararg tensors: DoubleTensor): List<DoubleTensor> {
var onlyTwoDims = true
for (tensor in tensors) {
if (tensor.shape.size < 2) {
val onlyTwoDims = tensors.asSequence().onEach {
require(it.shape.size >= 2) {
throw RuntimeException("Tensors must have at least 2 dimensions")
}
if (tensor.shape.size != 2) {
onlyTwoDims = false
}
}
}.any { it.shape.size != 2 }
if (onlyTwoDims) {
if (!onlyTwoDims) {
return tensors.asList()
}
@ -233,7 +229,7 @@ internal inline fun broadcastOuterTensors(vararg tensors: DoubleTensor): List<Do
newTensor.buffer.array()[newTensor.bufferStart + curLinearIndex]
}
}
res.add(resTensor)
res += resTensor
}
return res

8
kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BufferedTensor.kt
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@ -34,7 +34,8 @@ public open class BufferedTensor<T>(
public fun vectorSequence(): Sequence<MutableStructure1D<T>> = sequence {
check(shape.size >= 1) {"todo"}
val vectorOffset = linearStructure.strides[0]
val n = shape.size
val vectorOffset = shape[n - 1]
val vectorShape = intArrayOf(shape.last())
for (offset in 0 until numel step vectorOffset) {
val vector = BufferedTensor<T>(vectorShape, buffer, offset).as1D()
@ -44,8 +45,9 @@ public open class BufferedTensor<T>(
public fun matrixSequence(): Sequence<MutableStructure2D<T>> = sequence {
check(shape.size >= 2) {"todo"}
val matrixOffset = linearStructure.strides[1]
val matrixShape = intArrayOf(shape[shape.size - 2], shape.last()) //todo better way?
val n = shape.size
val matrixOffset = shape[n - 1] * shape[n - 2]
val matrixShape = intArrayOf(shape[n - 2], shape[n - 1]) //todo better way?
for (offset in 0 until numel step matrixOffset) {
val matrix = BufferedTensor<T>(matrixShape, buffer, offset).as2D()
yield(matrix)

8
kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleLinearOpsTensorAlgebra.kt
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@ -8,7 +8,7 @@ public class DoubleLinearOpsTensorAlgebra :
DoubleTensorAlgebra() {
override fun DoubleTensor.inv(): DoubleTensor {
TODO("Not yet implemented")
TODO("ANDREI")
}
override fun DoubleTensor.lu(tol: Double): Pair<DoubleTensor, IntTensor> {
@ -135,16 +135,16 @@ public class DoubleLinearOpsTensorAlgebra :
}
override fun DoubleTensor.qr(): DoubleTensor {
TODO("Not yet implemented")
TODO("ANDREI")
}
override fun DoubleTensor.svd(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
TODO("Not yet implemented")
TODO("ALYA")
}
override fun DoubleTensor.symEig(eigenvectors: Boolean): Pair<DoubleTensor, DoubleTensor> {
TODO("Not yet implemented")
TODO("ANDREI")
}
}

50
kmath-core/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
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@ -241,37 +241,6 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
TODO("Not yet implemented")
}
private fun DoubleTensor.dotTwoDimensionalTensors(other: DoubleTensor): DoubleTensor {
if (this.shape.size > 2 || other.shape.size > 2) {
throw RuntimeException("Both tensors must have a maximum of 2 dimensions")
}
if (this.shape[1] != other.shape[0]) {
throw RuntimeException("Tensors dot operation dimension mismatch: " +
"(${this.shape[0]}, ${this.shape[1]}) x (${other.shape[0]}, ${other.shape[1]})")
}
val l = this.shape[0]
val m = this.shape[1]
val n = other.shape[1]
val res = DoubleTensor(intArrayOf(l, n), DoubleArray(l * n))
for (i in 0 until l) {
for (j in 0 until n) {
var curr = 0.0
for (k in 0 until m) {
val ik = this.linearStructure.offset(intArrayOf(i, k))
val kj = other.linearStructure.offset(intArrayOf(k, j))
curr += this.buffer.array()[ik] * other.buffer.array()[kj]
}
val linearIndex = res.linearStructure.offset(intArrayOf(i, j))
res.buffer.array()[linearIndex] = curr
}
}
return res
}
override 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()))
@ -279,10 +248,15 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
var newThis = this.copy()
var newOther = other.copy()
var penultimateDim = false
var lastDim = false
if (this.shape.size == 1) {
penultimateDim = true
newThis = this.view(intArrayOf(1) + this.shape)
}
if (other.shape.size == 1) {
lastDim = true
newOther = other.view(other.shape + intArrayOf(1) )
}
@ -299,13 +273,12 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
}
val m = m1
var resShape = newThis.shape.sliceArray(0..(newThis.shape.size - 2)) + intArrayOf(newOther.shape.last())
val resShape = newThis.shape.sliceArray(0..(newThis.shape.size - 2)) + intArrayOf(newOther.shape.last())
val resSize = resShape.reduce { acc, i -> acc * i }
val resTensor = DoubleTensor(resShape, DoubleArray(resSize))
for ((res, ab) in resTensor.matrixSequence().zip(newThis.matrixSequence().zip(newOther.matrixSequence()))) {
val a = ab.first
val b = ab.second
val (a, b) = ab
for (i in 0 until l) {
for (j in 0 until n) {
@ -318,6 +291,13 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
}
}
if (penultimateDim) {
return resTensor.view(resTensor.shape.dropLast(2).toIntArray() +
intArrayOf(resTensor.shape.last()))
}
if (lastDim) {
return resTensor.view(resTensor.shape.dropLast(1).toIntArray())
}
return resTensor
}
@ -338,7 +318,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
}
override fun DoubleTensor.det(): DoubleTensor {
TODO("Not yet implemented")
TODO("ANDREI")
}
override fun DoubleTensor.square(): DoubleTensor {

30
kmath-core/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
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@ -82,15 +82,37 @@ class TestDoubleTensorAlgebra {
@Test
fun dot() = DoubleTensorAlgebra {
val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
val tensor11 = fromArray(intArrayOf(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
val tensor2 = fromArray(intArrayOf(3), doubleArrayOf(10.0, 20.0, 30.0))
val tensor3 = fromArray(intArrayOf(1, 1, 3), doubleArrayOf(-1.0, -2.0, -3.0))
val res12 = tensor1.dot(tensor2)
assertTrue(res12.buffer.array() contentEquals doubleArrayOf(140.0, 320.0))
assertTrue(res12.shape contentEquals intArrayOf(2, 1))
assertTrue(res12.shape contentEquals intArrayOf(2))
val tensor4 = fromArray(intArrayOf(10, 3, 4), DoubleArray(10 * 3 * 4) {0.0})
val tensor5 = fromArray(intArrayOf(10, 4, 5), DoubleArray(10 * 4 * 5) {0.0})
val res32 = tensor3.dot(tensor2)
assertTrue(res32.buffer.array() contentEquals doubleArrayOf(-140.0))
assertTrue(res32.shape contentEquals intArrayOf(1, 1))
val res22 = tensor2.dot(tensor2)
assertTrue(res22.buffer.array() contentEquals doubleArrayOf(1400.0))
assertTrue(res22.shape contentEquals intArrayOf(1))
val res11 = tensor1.dot(tensor11)
assertTrue(res11.buffer.array() contentEquals doubleArrayOf(22.0, 28.0, 49.0, 64.0))
assertTrue(res11.shape contentEquals intArrayOf(2, 2))
var tensor4 = fromArray(intArrayOf(10, 3, 4), DoubleArray(10 * 3 * 4) {0.0})
var tensor5 = fromArray(intArrayOf(10, 4, 5), DoubleArray(10 * 4 * 5) {0.0})
assertTrue(tensor4.dot(tensor5).shape contentEquals intArrayOf(10, 3, 5))
tensor4 = fromArray(intArrayOf(10, 3, 4), DoubleArray(10 * 3 * 4) {0.0})
tensor5 = fromArray(intArrayOf(4, 5), DoubleArray(4 * 5) {0.0})
assertTrue(tensor4.dot(tensor5).shape contentEquals intArrayOf(10, 3, 5))
tensor4 = fromArray(intArrayOf(4, 2, 1, 3, 8, 1), DoubleArray(4 * 2 * 1 * 3 * 8 * 1) {0.0})
tensor5 = fromArray(intArrayOf(5, 1, 2, 8, 3, 1, 5), DoubleArray(5 * 1 * 2 * 8 * 3 * 1 * 5) {0.0})
assertTrue(tensor4.dot(tensor5).shape contentEquals intArrayOf(5, 4, 2, 8, 3, 8, 5))
}
@Test