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Fixed some performance issues with histogram

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This commit is contained in:
Alexander Nozik 2018-11-23 21:44:42 +03:00
parent 5e3e0eb09d
commit 15cc4a22e2
5 changed files with 74 additions and 47 deletions
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56
kmath-core/src/commonMain/kotlin/scientifik/kmath/histogram/FastHistogram.kt
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@ -1,24 +1,31 @@
package scientifik.kmath.histogram
import scientifik.kmath.linear.RealVector
import scientifik.kmath.linear.toVector
import scientifik.kmath.structures.Buffer
import scientifik.kmath.structures.ListBuffer
import scientifik.kmath.structures.NDStructure
import scientifik.kmath.structures.ndStructure
import kotlin.math.floor
class MultivariateBin(override val center: RealVector, val sizes: RealVector, val counter: LongCounter = LongCounter()) : Bin<Double> {
typealias RealPoint = Point<Double>
private operator fun RealPoint.minus(other: RealPoint) = ListBuffer((0 until size).map { get(it) - other[it] })
private inline fun <T> Buffer<out Double>.mapIndexed(crossinline mapper: (Int, Double) -> T): Sequence<T> = (0 until size).asSequence().map { mapper(it, get(it)) }
class MultivariateBin(override val center: RealPoint, val sizes: RealPoint, var counter: Long = 0) : Bin<Double> {
init {
if (center.size != sizes.size) error("Dimension mismatch in bin creation. Expected ${center.size}, but found ${sizes.size}")
}
override fun contains(vector: Buffer<out Double>): Boolean {
if (vector.size != center.size) error("Dimension mismatch for input vector. Expected ${center.size}, but found ${vector.size}")
return vector.asSequence().mapIndexed { i, value -> value in (center[i] - sizes[i] / 2)..(center[i] + sizes[i] / 2) }.all { it }
return vector.mapIndexed { i, value -> value in (center[i] - sizes[i] / 2)..(center[i] + sizes[i] / 2) }.all { it }
}
override val value get() = counter.sum()
internal operator fun inc() = this.also { counter.increment() }
override val value get() = counter
internal operator fun inc() = this.also { counter++ }
override val dimension: Int get() = center.size
}
@ -28,8 +35,8 @@ class MultivariateBin(override val center: RealVector, val sizes: RealVector, va
* The histogram is optimized for speed, but have large size in memory
*/
class FastHistogram(
private val lower: RealVector,
private val upper: RealVector,
private val lower: RealPoint,
private val upper: RealPoint,
private val binNums: IntArray = IntArray(lower.size) { 20 }
) : MutableHistogram<Double, MultivariateBin> {
@ -37,25 +44,27 @@ class FastHistogram(
// argument checks
if (lower.size != upper.size) error("Dimension mismatch in histogram lower and upper limits.")
if (lower.size != binNums.size) error("Dimension mismatch in bin count.")
if ((upper - lower).any { it <= 0 }) error("Range for one of axis is not strictly positive")
if ((upper - lower).asSequence().any { it <= 0 }) error("Range for one of axis is not strictly positive")
}
override val dimension: Int get() = lower.size
//TODO optimize binSize performance if needed
private val binSize = (upper - lower).mapIndexed { index, value -> value / binNums[index] }.toVector()
private val binSize: RealPoint = ListBuffer((upper - lower).mapIndexed { index, value -> value / binNums[index] }.toList())
private val bins: NDStructure<MultivariateBin> by lazy {
val actualSizes = IntArray(binNums.size) { binNums[it] + 2 }
ndStructure(actualSizes) { indexArray ->
val center = indexArray.mapIndexed { axis, index ->
when (index) {
0 -> Double.NEGATIVE_INFINITY
actualSizes[axis] - 1 -> Double.POSITIVE_INFINITY
else -> lower[axis] + (index.toDouble() - 0.5) * binSize[axis]
}
}.toVector()
val center = ListBuffer(
indexArray.mapIndexed { axis, index ->
when (index) {
0 -> Double.NEGATIVE_INFINITY
actualSizes[axis] - 1 -> Double.POSITIVE_INFINITY
else -> lower[axis] + (index.toDouble() - 0.5) * binSize[axis]
}
}
)
MultivariateBin(center, binSize)
}
}
@ -91,13 +100,14 @@ class FastHistogram(
return ndStructure(this.bins.shape) { bins[it].value }
}
/**
* Create a phantom lightweight immutable copy of this histogram
*/
fun asPhantom(): PhantomHistogram<Double> {
val binTemplates = bins.associate { (index, bin) -> BinTemplate<Double>(bin.center, bin.sizes) to index }
return PhantomHistogram(binTemplates, asND())
}
// /**
// * Create a phantom lightweight immutable copy of this histogram
// */
// fun asPhantom(): PhantomHistogram<Double> {
// val center =
// val binTemplates = bins.associate { (index, bin) -> BinTemplate<Double>(bin.center, bin.sizes) to index }
// return PhantomHistogram(binTemplates, asND())
// }
companion object {

22
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/Matrix.kt
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@ -1,9 +1,7 @@
package scientifik.kmath.linear
import scientifik.kmath.operations.*
import scientifik.kmath.structures.ExtendedNDField
import scientifik.kmath.structures.GenericNDField
import scientifik.kmath.structures.NDField
import scientifik.kmath.structures.*
/**
* The space for linear elements. Supports scalar product alongside with standard linear operations.
@ -169,3 +167,21 @@ class ArrayMatrixSpace<T : Any, F : Field<T>>(
return ArrayMatrixSpace(rows, columns, field, ndFactory)
}
}
/**
* Member of [ArrayMatrixSpace] which wraps 2-D array
*/
class ArrayMatrix<T : Any, F : Field<T>> internal constructor(override val context: ArrayMatrixSpace<T, F>, val element: NDElement<T, F>) : Matrix<T, F> {
constructor(context: ArrayMatrixSpace<T, F>, initializer: (Int, Int) -> T) : this(context, context.ndField.produce { list -> initializer(list[0], list[1]) })
override val rows: Int get() = context.rows
override val columns: Int get() = context.columns
override fun get(i: Int, j: Int): T {
return element[i, j]
}
override val self: ArrayMatrix<T, F> get() = this
}

16
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/Vector.kt
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@ -73,23 +73,7 @@ class ArrayVectorSpace<T : Any, F : Field<T>>(
override fun produce(initializer: (Int) -> T): Vector<T, F> = ArrayVector(this, initializer)
}
/**
* Member of [ArrayMatrixSpace] which wraps 2-D array
*/
class ArrayMatrix<T : Any, F : Field<T>> internal constructor(override val context: ArrayMatrixSpace<T, F>, val element: NDElement<T, F>) : Matrix<T, F> {
constructor(context: ArrayMatrixSpace<T, F>, initializer: (Int, Int) -> T) : this(context, context.ndField.produce { list -> initializer(list[0], list[1]) })
override val rows: Int get() = context.rows
override val columns: Int get() = context.columns
override fun get(i: Int, j: Int): T {
return element[i, j]
}
override val self: ArrayMatrix<T, F> get() = this
}
class ArrayVector<T : Any, F : Field<T>> internal constructor(override val context: VectorSpace<T, F>, val element: NDElement<T, F>) : Vector<T, F> {

23
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/Buffers.kt
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@ -4,12 +4,16 @@ package scientifik.kmath.structures
/**
* A generic random access structure for both primitives and objects
*/
interface Buffer<T> : Iterable<T> {
interface Buffer<T> {
val size: Int
operator fun get(index: Int): T
operator fun iterator(): Iterator<T>
fun asSequence(): Sequence<T> = iterator().asSequence()
/**
* A shallow copy of the buffer
*/
@ -25,7 +29,20 @@ interface MutableBuffer<T> : Buffer<T> {
override fun copy(): MutableBuffer<T>
}
inline class ListBuffer<T>(private val list: MutableList<T>) : MutableBuffer<T> {
inline class ListBuffer<T>(private val list: List<T>) : Buffer<T> {
override val size: Int
get() = list.size
override fun get(index: Int): T = list[index]
override fun iterator(): Iterator<T> = list.iterator()
override fun copy(): ListBuffer<T> = ListBuffer(ArrayList(list))
}
inline class MutableListBuffer<T>(private val list: MutableList<T>) : MutableBuffer<T> {
override val size: Int
get() = list.size
@ -38,7 +55,7 @@ inline class ListBuffer<T>(private val list: MutableList<T>) : MutableBuffer<T>
override fun iterator(): Iterator<T> = list.iterator()
override fun copy(): MutableBuffer<T> = ListBuffer(ArrayList(list))
override fun copy(): MutableBuffer<T> = MutableListBuffer(ArrayList(list))
}
class ArrayBuffer<T>(private val array: Array<T>) : MutableBuffer<T> {

4
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/NDStructure.kt
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@ -76,7 +76,7 @@ class DefaultStrides(override val shape: IntArray) : Strides {
override fun offset(index: IntArray): Int {
return index.mapIndexed { i, value ->
if (value < 0 || value >= shape[i]) {
throw RuntimeException("Index $value out of shape bounds: (0,${shape[i]})")
throw RuntimeException("Index $value out of shape bounds: (0,${this.shape[i]})")
}
value * strides[i]
}.sum()
@ -169,6 +169,6 @@ fun <T> genericNdStructure(shape: IntArray, initializer: (IntArray) -> T): Mutab
yield(initializer(it))
}
}
val buffer = ListBuffer<T>(sequence.toMutableList())
val buffer = MutableListBuffer<T>(sequence.toMutableList())
return MutableBufferNDStructure(strides, buffer)
}