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breeze.features.FeatureVector.scala Maven / Gradle / Ivy
package breeze.features
import breeze.generic.UFunc.InPlaceImpl3
import breeze.linalg._
import java.util
import breeze.linalg.operators._
import breeze.linalg.support.CanTranspose
import breeze.macros.expand
import breeze.math.Semiring
import spire.syntax.cfor._
/**
* Represents a feature vector of indicator (i.e. binary) features.
* Not a full vector. Only supports dot and addition with a real vector.
*
* TODO: possibly rename to IndicatorVector?
*
* @author dlwh
*/
class FeatureVector(val data: Array[Int]) extends NumericOps[FeatureVector] {
def repr: FeatureVector = this
/**
* same as data.length
* @return
*/
def activeLength = data.length
/**
* same as data(i)
* @param i
* @return
*/
def apply(i: Int) = data(i)
override def toString: String = data.mkString("FeatureVector(",", ", ")")
override def hashCode = util.Arrays.hashCode(data)
override def equals(p1: Any): Boolean = p1 match {
case fv: FeatureVector => util.Arrays.equals(fv.data, data)
case _ => false
}
}
object FeatureVector {
def apply(ints: Int*) = new FeatureVector(ints.toArray)
object Implicits {
implicit def fromArrayInt(arr: Array[Int]): FeatureVector = new FeatureVector(arr)
}
implicit object TransposeFV extends CanTranspose[FeatureVector, Transpose[FeatureVector]] {
override def apply(from: FeatureVector): Transpose[FeatureVector] = {
new Transpose(from)
}
}
@expand
@expand.valify
implicit def FVScaleAddIntoV[@expand.args(Int, Float, Double) T]:TernaryUpdateRegistry[Vector[T], T, FeatureVector, scaleAdd.type] = {
new TernaryUpdateRegistry[Vector[T], T, FeatureVector, scaleAdd.type] {
override def bindingMissing(y: Vector[T], a: T, x: FeatureVector) {
if (a != 0.0) {
var i = 0
while (i < x.activeLength) {
y(x(i)) += a
i += 1
}
}
}
}
}
@expand
@expand.valify
implicit def FVScaleAddIntoDV[@expand.args(Int, Float, Double) T]:scaleAdd.InPlaceImpl3[DenseVector[T], T, FeatureVector] = {
new scaleAdd.InPlaceImpl3[DenseVector[T], T, FeatureVector] {
def apply(y: DenseVector[T], a: T, x: FeatureVector) {
var i = 0
while(i < x.activeLength) {
y(x(i)) += a
i += 1
}
}
implicitly[TernaryUpdateRegistry[Vector[T], T, FeatureVector, scaleAdd.type]].register(this)
}
}
// specialzied doesn't work here, so we're expanding
@expand
@expand.valify
implicit def FVCanDaxpyIntoVB[@expand.args(Float, Double) T]: InPlaceImpl3[scaleAdd.type, VectorBuilder[T], T, FeatureVector] = {
new scaleAdd.InPlaceImpl3[VectorBuilder[T], T, FeatureVector] {
def apply(y: VectorBuilder[T], a: T, x: FeatureVector) {
if(a != 0.0) {
var i = 0
while(i < x.activeLength) {
y.add(x(i), a)
i += 1
}
}
}
}
}
implicit def FVCanDaxpyIntoVB_Generic[T]: InPlaceImpl3[scaleAdd.type, VectorBuilder[T], T, FeatureVector] = {
new scaleAdd.InPlaceImpl3[VectorBuilder[T], T, FeatureVector] {
def apply(y: VectorBuilder[T], a: T, x: FeatureVector) {
if(a != 0.0) {
var i = 0
while(i < x.activeLength) {
y.add(x(i), a)
i += 1
}
}
}
}
}
@expand
@expand.valify
implicit def FVScaleAddIntoSV[@expand.args(Int, Float, Double) T]:scaleAdd.InPlaceImpl3[SparseVector[T], T, FeatureVector] = {
new scaleAdd.InPlaceImpl3[SparseVector[T], T, FeatureVector] {
def apply(y: SparseVector[T], a: T, x: FeatureVector) {
if (a != 0.0) {
var i = 0
while (i < x.activeLength) {
y(x(i)) += a
i += 1
}
}
}
implicitly[TernaryUpdateRegistry[Vector[T], T, FeatureVector, scaleAdd.type]].register(this)
}
}
@expand
@expand.valify
implicit def DotProductFVV[@expand.args(Int, Float, Double) T]:BinaryRegistry[FeatureVector, Vector[T], OpMulInner.type, T] = {
new BinaryRegistry[FeatureVector, Vector[T], OpMulInner.type, T] {
override def bindingMissing(a: FeatureVector, b: Vector[T]): T = {
var score: T = 0
var i = 0
while (i < a.activeLength) {
score += b(a(i))
i += 1
}
score
}
}
}
@expand
@expand.valify
implicit def DotProductFVDV[@expand.args(Int, Float, Double) T]:OpMulInner.Impl2[FeatureVector, DenseVector[T], T] = {
new OpMulInner.Impl2[FeatureVector, DenseVector[T], T] {
def apply(a: FeatureVector, b: DenseVector[T]): T = {
var score: T = 0
var i = 0
while (i < a.activeLength) {
score += b(a(i))
i += 1
}
score
}
}
}
implicit def dotProductVxFVfromFVxV[V, T](implicit op: OpMulInner.Impl2[FeatureVector, V, T]): OpMulInner.Impl2[V, FeatureVector, T] = {
new OpMulInner.Impl2[V, FeatureVector, T] {
def apply(b: V, a: FeatureVector): T = op(a, b)
}
}
implicit def opAddIntoFromAxpy[V, T](implicit op: scaleAdd.InPlaceImpl3[V, T, FeatureVector], semiring: Semiring[T]):OpAdd.InPlaceImpl2[V, FeatureVector] = {
new OpAdd.InPlaceImpl2[V, FeatureVector] {
override def apply(v: V, v2: FeatureVector): Unit = {
op(v, semiring.one, v2)
}
}
}
@expand
@expand.valify
implicit def CanMulDMFV[@expand.args(Int, Double, Float) T]: OpMulMatrix.Impl2[DenseMatrix[T], FeatureVector, DenseVector[T]] = {
new OpMulMatrix.Impl2[DenseMatrix[T], FeatureVector, DenseVector[T]] {
def apply(a: DenseMatrix[T], b: FeatureVector): DenseVector[T] = {
val result = DenseVector.zeros[T](a.rows)
cforRange(0 until b.activeLength) { i =>
result += a(::, b(i))
}
result
}
}
}
@expand
@expand.valify
implicit def CanMulCSCFV[@expand.args(Int, Double, Float) T]: OpMulMatrix.Impl2[CSCMatrix[T], FeatureVector, SparseVector[T]] = {
new OpMulMatrix.Impl2[CSCMatrix[T], FeatureVector, SparseVector[T]] {
def apply(a: CSCMatrix[T], b: FeatureVector): SparseVector[T] = {
val result = new VectorBuilder[T](a.rows)
cforRange(0 until b.activeLength) { i =>
val column = b(i)
cforRange(a.colPtrs(column) until a.colPtrs(column + 1)) { off =>
result.add(a.rowIndices(off), a.data(off))
}
}
result.toSparseVector
}
}
}
implicit def mulMatrixTrans[M, MTrans, MulResult, MRTrans](implicit trans: CanTranspose[M, MTrans],
mul: OpMulMatrix.Impl2[MTrans, FeatureVector, MulResult],
mrTrans: CanTranspose[MulResult, MRTrans]): OpMulMatrix.Impl2[Transpose[FeatureVector], M, MRTrans] = {
new OpMulMatrix.Impl2[Transpose[FeatureVector], M, MRTrans] {
override def apply(v: Transpose[FeatureVector], v2: M): MRTrans = {
mrTrans(mul(trans(v2), v.inner))
}
}
}
}
