org.apache.spark.linalg.Vectors.scala Maven / Gradle / Ivy
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.linalg
import java.lang.{Double => JavaDouble, Integer => JavaInteger, Iterable => JavaIterable, Long => JavaLong}
import java.util
import breeze.linalg.{DenseVector => BDV, SparseVector => BSV, Vector => BV}
import org.apache.spark.SparkException
import scala.collection.JavaConverters._
import scala.reflect.ClassTag
import scala.{specialized => spec}
import scala.language.implicitConversions
/**
* Represents a numeric vector, whose index type is Int and value type is Double.
*
* @note Users should not implement this interface.
*/
sealed trait Vector extends Serializable {
def size: Long
def toArray: Array[Double]
override def equals(other: Any): Boolean = {
other match {
case v2: Vector =>
if (this.size != v2.size) return false
(this, v2) match {
case (s1: IntSparseVector, s2: IntSparseVector) =>
Vectors.equals(s1.indices, s1.values, s2.indices, s2.values)
case (s1: IntSparseVector, d1: DenseVector) =>
Vectors.equals(s1.indices, s1.values, 0 until d1.size.toInt, d1.values)
case (d1: DenseVector, s1: IntSparseVector) =>
Vectors.equals(0 until d1.size.toInt, d1.values, s1.indices, s1.values)
case (_, _) => util.Arrays.equals(this.toArray, v2.toArray)
}
case _ => false
}
}
override def hashCode(): Int = {
// This is a reference implementation. It calls return in foreachActive, which is slow.
// Subclasses should override it with optimized implementation.
var result: Int = 31 + size.toInt
var nnz = 0
this.foreachActive { (index, value) =>
if (nnz < Vectors.MAX_HASH_NNZ) {
// ignore explicit 0 for comparison between sparse and dense
if (value != 0) {
result = 31 * result + index.toInt
val bits = java.lang.Double.doubleToLongBits(value)
result = 31 * result + (bits ^ (bits >>> 32)).toInt
nnz += 1
}
} else {
return result
}
}
result
}
def asBreeze: BV[Double]
def apply(i: Long): Double
def copy: Vector = {
throw new NotImplementedError(s"copy is not implemented for ${this.getClass}.")
}
def foreachActive(f: (Long, Double) => Unit): Unit
def numActives: Long
def numNonzeros: Long
def toSparse: SparseVector = toSparseWithSize(numNonzeros)
private[linalg] def toSparseWithSize(nnz: Long): SparseVector
def toDense: DenseVector
def compressed: Vector = {
val nnz = numNonzeros
// A dense vector needs 8 * size + 8 bytes, while a sparse vector needs 12 * nnz + 20 bytes.
if (1.5 * (nnz + 1.0) < size) {
toSparseWithSize(nnz)
} else {
toDense
}
}
def argmax: Long
}
/**
* Factory methods for [[Vector]].
* We don't use the name `Vector` because Scala imports
* `scala.collection.immutable.Vector` by default.
*/
object Vectors {
/**
* Creates a dense vector from its values.
*/
// @varargs
def dense(firstValue: Double, otherValues: Double*): Vector =
new DenseVector((firstValue +: otherValues).toArray)
// A dummy implicit is used to avoid signature collision with the one generated by @varargs.
/**
* Creates a dense vector from a double array.
*/
def dense(values: Array[Double]): Vector = new DenseVector(values)
/**
* Creates a sparse vector providing its index array and value array.
*
* @param size vector size.
* @param indices index array, must be strictly increasing.
* @param values value array, must have the same length as indices.
*/
def sparse[@spec(Int, Long) K: ClassTag](size: Long, indices: Array[K], values: Array[Double]): Vector =
implicitly[ClassTag[K]].runtimeClass match {
case intType if classOf[Int] == intType =>
new IntSparseVector(size, indices.asInstanceOf[Array[Int]], values)
case longType if classOf[Long] == longType =>
new LongSparseVector(size, indices.asInstanceOf[Array[Long]], values)
}
/**
* Creates a sparse vector using unordered (index, value) pairs.
*
* @param size vector size.
* @param elements vector elements in (index, value) pairs.
*/
def sparse[@spec(Int, Long) K <% Ordered[K] : ClassTag](size: Long, elements: Seq[(K, Double)]): Vector = {
val (indices, values) = elements.sortBy(_._1).unzip
implicitly[ClassTag[K]].runtimeClass match {
case intType if classOf[Int] == intType =>
new IntSparseVector(size, indices.toArray.asInstanceOf[Array[Int]], values.toArray)
case longType if classOf[Long] == longType =>
new LongSparseVector(size, indices.toArray.asInstanceOf[Array[Long]], values.toArray)
}
}
/**
* Creates a sparse vector using unordered (index, value) pairs in a Java friendly way.
*
* @param size vector size.
* @param elements vector elements in (index, value) pairs.
*/
def sparse[K: ClassTag](size: Long, elements: JavaIterable[(K, JavaDouble)]): Vector = {
implicitly[ClassTag[K]].runtimeClass match {
case intType if classOf[JavaInteger] == intType =>
sparse(size, elements.asScala.map { case (i: JavaInteger, x) => (i.intValue(), x.doubleValue()) }.toSeq)
case longType if classOf[JavaLong] == longType =>
sparse(size, elements.asScala.map { case (i: JavaLong, x) => (i.longValue(), x.doubleValue()) }.toSeq)
}
}
/**
* Creates a vector of all zeros.
*
* @param size vector size
* @return a zero vector
*/
def zeros(size: Int): Vector = {
new DenseVector(new Array[Double](size))
}
def parseNumeric(any: Any): Vector = {
any match {
case values: Array[Double] =>
Vectors.dense(values)
case Seq(size: Int, indices: Array[Int], values: Array[Double]) =>
Vectors.sparse(size, indices, values)
case Seq(size: Long, indices: Array[Long], values: Array[Double]) =>
Vectors.sparse(size, indices, values)
case Seq(size: Double, indices: Array[Double], values: Array[Double]) if size < Int.MaxValue =>
Vectors.sparse(size.toInt, indices.map(_.toInt), values)
case Seq(size: Double, indices: Array[Double], values: Array[Double]) if size > Int.MaxValue =>
Vectors.sparse(size.toLong, indices.map(_.toLong), values)
case other =>
throw new SparkException(s"Cannot parse $other.")
}
}
def parse(s: String): Vector = {
parseNumeric(NumericParser.parse(s))
}
/**
* Creates a vector instance from a breeze vector.
*/
def fromBreeze(breezeVector: BV[Double]): Vector = {
breezeVector match {
case v: BDV[Double] =>
if (v.offset == 0 && v.stride == 1 && v.length == v.data.length) {
new DenseVector(v.data)
} else {
new DenseVector(v.toArray) // Can't use underlying array directly, so make a new one
}
case v: BSV[Double] =>
if (v.index.length == v.used) {
new IntSparseVector(v.length, v.index, v.data)
} else {
new IntSparseVector(v.length, v.index.slice(0, v.used), v.data.slice(0, v.used))
}
case v: BV[_] =>
sys.error("Unsupported Breeze vector type: " + v.getClass.getName)
}
}
/**
* Returns the p-norm of this vector.
*
* @param vector input vector.
* @param p norm.
* @return norm in L^p^ space.
*/
def norm(vector: Vector, p: Double): Double = {
require(p >= 1.0, "To compute the p-norm of the vector, we require that you specify a p>=1. " +
s"You specified p=$p.")
val values = vector match {
case DenseVector(vs) => vs
case IntSparseVector(n, ids, vs) => vs
case LongSparseVector(n, ids, vs) => vs
case v => throw new IllegalArgumentException("Do not support vector type " + v.getClass)
}
val size = values.length
if (p == 1) {
var sum = 0.0
var i = 0
while (i < size) {
sum += math.abs(values(i))
i += 1
}
sum
} else if (p == 2) {
var sum = 0.0
var i = 0
while (i < size) {
sum += values(i) * values(i)
i += 1
}
math.sqrt(sum)
} else if (p == Double.PositiveInfinity) {
var max = 0.0
var i = 0
while (i < size) {
val value = math.abs(values(i))
if (value > max) max = value
i += 1
}
max
} else {
var sum = 0.0
var i = 0
while (i < size) {
sum += math.pow(math.abs(values(i)), p)
i += 1
}
math.pow(sum, 1.0 / p)
}
}
/**
* Returns the squared distance between two Vectors.
*
* @param v1 first Vector.
* @param v2 second Vector.
* @return squared distance between two Vectors.
*/
def sqdist(v1: Vector, v2: Vector): Double = {
require(v1.size == v2.size, s"Vector dimensions do not match: Dim(v1)=${v1.size} and Dim(v2)" +
s"=${v2.size}.")
var squaredDistance = 0.0
(v1, v2) match {
case (v1: IntSparseVector, v2: IntSparseVector) =>
squaredDistance = sqdist(v1.indices, v1.values, v2.indices, v2.values)
case (v1: IntSparseVector, v2: DenseVector) =>
squaredDistance = sqdist(v1.indices, v1.values, v2.values)
case (v1: DenseVector, v2: IntSparseVector) =>
squaredDistance = sqdist(v2, v1)
case (DenseVector(vv1), DenseVector(vv2)) =>
var kv = 0
val sz = vv1.length
while (kv < sz) {
val score = vv1(kv) - vv2(kv)
squaredDistance += score * score
kv += 1
}
case (v1: LongSparseVector, DenseVector(vv2)) =>
squaredDistance = sqdist(v1.indices, v1.values, vv2)
case (v1: DenseVector, v2: LongSparseVector) =>
squaredDistance = sqdist(v2, v1)
case (v1: LongSparseVector, v2: IntSparseVector) =>
squaredDistance = sqdist(v1.indices, v1.values, v2.indices, v2.values)
case (v1: IntSparseVector, v2: LongSparseVector) =>
squaredDistance = sqdist(v2, v1)
case (v1: LongSparseVector, v2: LongSparseVector) =>
squaredDistance = sqdist(v1.indices, v1.values, v2.indices, v2.values)
case _ =>
throw new IllegalArgumentException("Do not support vector type " + v1.getClass +
" and " + v2.getClass)
}
squaredDistance
}
/**
* Returns the squared distance between DenseVector and SparseVector.
*/
def sqdist[K](v1Indices: Array[K], v1Values: Array[Double], v2Values: Array[Double]): Double = {
var kv1 = 0
var kv2 = 0
var squaredDistance = 0.0
val nnzv1 = v1Indices.length
val nnzv2 = v2Values.length
var iv1 = if (nnzv1 > 0) v1Indices(kv1) else -1
while (kv2 < nnzv2) {
var score = 0.0
if (kv2 != iv1) {
score = v2Values(kv2)
} else {
score = v1Values(kv1) - v2Values(kv2)
if (kv1 < nnzv1 - 1) {
kv1 += 1
iv1 = v1Indices(kv1)
}
}
squaredDistance += score * score
kv2 += 1
}
squaredDistance
}
def sqdist[K1 <% Ordered[K1], K2 <% Ordered[K2]](v1Indices: Array[K1], v1Values: Array[Double],
v2Indices: Array[K2], v2Values: Array[Double]): Double = {
val nnzv1 = v1Indices.length
val nnzv2 = v2Indices.length
implicit def K2toK1(value: K2): K1 = value.asInstanceOf[K1]
var kv1 = 0
var kv2 = 0
var squaredDistance = 0.0
while (kv1 < nnzv1 || kv2 < nnzv2) {
var score = 0.0
if (kv2 >= nnzv2 || (kv1 < nnzv1 && v1Indices(kv1) < v2Indices(kv2))) {
score = v1Values(kv1)
kv1 += 1
} else if (kv1 >= nnzv1 || (kv2 < nnzv2 && v1Indices(kv1) > v2Indices(kv2))) {
score = v2Values(kv2)
kv2 += 1
} else {
score = v1Values(kv1) - v2Values(kv2)
kv1 += 1
kv2 += 1
}
squaredDistance += score * score
}
squaredDistance
}
/**
* Check equality between sparse/dense vectors
*/
def equals[K1, K2](
v1Indices: IndexedSeq[K1],
v1Values: Array[Double],
v2Indices: IndexedSeq[K2],
v2Values: Array[Double]): Boolean = {
val v1Size = v1Values.length
val v2Size = v2Values.length
var k1 = 0
var k2 = 0
var allEqual = true
while (allEqual) {
while (k1 < v1Size && v1Values(k1) == 0) k1 += 1
while (k2 < v2Size && v2Values(k2) == 0) k2 += 1
if (k1 >= v1Size || k2 >= v2Size) {
return k1 >= v1Size && k2 >= v2Size // check end alignment
}
allEqual = v1Indices(k1) == v2Indices(k2) && v1Values(k1) == v2Values(k2)
k1 += 1
k2 += 1
}
allEqual
}
/** Max number of nonzero entries used in computing hash code. */
private[linalg] val MAX_HASH_NNZ = 128
}
/**
* A dense vector represented by a value array.
*/
class DenseVector(val values: Array[Double]) extends Vector with Serializable {
override def size: Long = values.length
override def toString: String = values.mkString("[", ",", "]")
override def toArray: Array[Double] = values
override def asBreeze: BV[Double] = new BDV[Double](values)
override def apply(i: Long): Double = {
assert(i >= 0 && i < values.length)
values(i.toInt)
}
override def copy: DenseVector = {
new DenseVector(values.clone())
}
override def foreachActive(f: (Long, Double) => Unit): Unit = {
var i = 0
val localValuesSize = values.length
val localValues = values
while (i < localValuesSize) {
f(i, localValues(i))
i += 1
}
}
override def equals(other: Any): Boolean = super.equals(other)
override def hashCode(): Int = {
var result: Int = 31 + size.toInt
var i = 0
val end = values.length
var nnz = 0
while (i < end && nnz < Vectors.MAX_HASH_NNZ) {
val v = values(i)
if (v != 0.0) {
result = 31 * result + i
val bits = java.lang.Double.doubleToLongBits(values(i))
result = 31 * result + (bits ^ (bits >>> 32)).toInt
nnz += 1
}
i += 1
}
result
}
override def numActives: Long = size
override def numNonzeros: Long = {
// same as values.count(_ != 0.0) but faster
var nnz = 0
values.foreach { v =>
if (v != 0.0) {
nnz += 1
}
}
nnz
}
private[linalg] override def toSparseWithSize(nnz: Long): IntSparseVector = {
assert(nnz < Int.MaxValue)
val ii = new Array[Int](nnz.toInt)
val vv = new Array[Double](nnz.toInt)
var k = 0
foreachActive { (i, v) =>
if (v != 0) {
ii(k) = i.toInt
vv(k) = v
k += 1
}
}
new IntSparseVector(size, ii, vv)
}
override def toDense: DenseVector = new DenseVector(toArray)
override def argmax: Long = {
if (size == 0) {
-1
} else {
var maxIdx = 0
var maxValue = values(0)
var i = 1
while (i < size) {
if (values(i) > maxValue) {
maxIdx = i
maxValue = values(i)
}
i += 1
}
maxIdx
}
}
}
object DenseVector {
/** Extracts the value array from a dense vector. */
def unapply(dv: DenseVector): Option[Array[Double]] = Some(dv.values)
}
trait SparseVector extends Vector
/**
* A sparse vector represented by an index array and a value array.
*
* @param size size of the vector.
* @param indices index array, assume to be strictly increasing.
* @param values value array, must have the same length as the index array.
*/
class IntSparseVector(
override val size: Long,
val indices: Array[Int],
val values: Array[Double]) extends SparseVector with Serializable {
// validate the data
{
require(size >= 0, "The size of the requested sparse vector must be no less than 0.")
require(indices.length == values.length, "Sparse vectors require that the dimension of the" +
s" indices match the dimension of the values. You provided ${indices.length} indices and " +
s" ${values.length} values.")
require(indices.length <= size, s"You provided ${indices.length} indices and values, " +
s"which exceeds the specified vector size ${size}.")
if (indices.nonEmpty) {
require(indices(0) >= 0, s"Found negative index: ${indices(0)}.")
}
var prev = -1
indices.foreach { i =>
require(prev < i, s"Index $i follows $prev and is not strictly increasing")
prev = i
}
require(prev < size, s"Index $prev out of bounds for vector of size $size")
}
override def toString: String =
s"($size,${indices.mkString("[", ",", "]")},${values.mkString("[", ",", "]")})"
override def toArray: Array[Double] = {
val data = new Array[Double](size.toInt)
var i = 0
val nnz = indices.length
while (i < nnz) {
data(indices(i)) = values(i)
i += 1
}
data
}
override def copy: IntSparseVector = {
new IntSparseVector(size, indices.clone(), values.clone())
}
override def asBreeze: BV[Double] = new BSV[Double](indices, values, size.toInt)
override def foreachActive(f: (Long, Double) => Unit): Unit = {
var i = 0
val localValuesSize = values.length
val localIndices = indices
val localValues = values
while (i < localValuesSize) {
f(localIndices(i), localValues(i))
i += 1
}
}
override def equals(other: Any): Boolean = super.equals(other)
override def hashCode(): Int = {
var result: Int = 31 + size.toInt
val end = values.length
var k = 0
var nnz = 0
while (k < end && nnz < Vectors.MAX_HASH_NNZ) {
val v = values(k)
if (v != 0.0) {
val i = indices(k)
result = 31 * result + i
val bits = java.lang.Double.doubleToLongBits(v)
result = 31 * result + (bits ^ (bits >>> 32)).toInt
nnz += 1
}
k += 1
}
result
}
override def numActives: Long = values.length
override def numNonzeros: Long = {
var nnz = 0
values.foreach { v =>
if (v != 0.0) {
nnz += 1
}
}
nnz
}
private[linalg] override def toSparseWithSize(nnz: Long): IntSparseVector = {
assert(nnz < Int.MaxValue)
if (nnz == numActives) {
this
} else {
val ii = new Array[Int](nnz.toInt)
val vv = new Array[Double](nnz.toInt)
var k = 0
foreachActive { (i, v) =>
if (v != 0.0) {
ii(k) = i.toInt
vv(k) = v
k += 1
}
}
new IntSparseVector(size, ii, vv)
}
}
override def argmax: Long = {
if (size == 0) {
-1
} else if (numActives == 0) {
0
} else {
// Find the max active entry.
var maxIdx = indices(0)
var maxValue = values(0)
var maxJ = 0
var j = 1
val na = numActives
while (j < na) {
val v = values(j)
if (v > maxValue) {
maxValue = v
maxIdx = indices(j)
maxJ = j
}
j += 1
}
// If the max active entry is nonpositive and there exists inactive ones, find the first zero.
if (maxValue <= 0.0 && na < size) {
if (maxValue == 0.0) {
// If there exists an inactive entry before maxIdx, find it and return its index.
if (maxJ < maxIdx) {
var k = 0
while (k < maxJ && indices(k) == k) {
k += 1
}
maxIdx = k
}
} else {
// If the max active value is negative, find and return the first inactive index.
var k = 0
while (k < na && indices(k) == k) {
k += 1
}
maxIdx = k
}
}
maxIdx
}
}
/**
* Create a slice of this vector based on the given indices.
*
* @param selectedIndices Unsorted list of indices into the vector.
* This does NOT do bound checking.
* @return New SparseVector with values in the order specified by the given indices.
*
* NOTE: The API needs to be discussed before making this public.
* Also, if we have a version assuming indices are sorted, we should optimize it.
*/
def slice(selectedIndices: Array[Int]): IntSparseVector = {
var currentIdx = 0
val (sliceInds, sliceVals) = selectedIndices.flatMap { origIdx =>
val iIdx = java.util.Arrays.binarySearch(this.indices, origIdx)
val i_v = if (iIdx >= 0) {
Iterator((currentIdx, this.values(iIdx)))
} else {
Iterator()
}
currentIdx += 1
i_v
}.unzip
new IntSparseVector(selectedIndices.length, sliceInds, sliceVals)
}
/**
* Gets the value of the ith element.
*
* @param i index
*/
override def apply(i: Long): Double = {
val iIdx = java.util.Arrays.binarySearch(this.indices, i.toInt)
if (iIdx >= 0) {
this.values(iIdx)
} else {
Double.NaN
}
}
/**
* Converts this vector to a dense vector.
*/
override def toDense: DenseVector = {
assert(size < Int.MaxValue)
val denseValues = new Array[Double](size.toInt)
indices.zip(values).foreach { case (idx, value) =>
denseValues(idx) = value
}
new DenseVector(denseValues)
}
}
object IntSparseVector {
def unapply(sv: IntSparseVector): Option[(Long, Array[Int], Array[Double])] =
Some((sv.size, sv.indices, sv.values))
}
/**
* A sparse vector represented by an index array and a value array.
*
* @param size size of the vector.
* @param indices index array, assume to be strictly increasing.
* @param values value array, must have the same length as the index array.
*/
class LongSparseVector(
override val size: Long,
val indices: Array[Long],
val values: Array[Double]) extends SparseVector with Serializable {
// validate the data
{
require(size >= 0, "The size of the requested sparse vector must be no less than 0.")
require(indices.length == values.length, "Sparse vectors require that the dimension of the" +
s" indices match the dimension of the values. You provided ${indices.length} indices and " +
s" ${values.length} values.")
require(indices.length <= size, s"You provided ${indices.length} indices and values, " +
s"which exceeds the specified vector size ${size}.")
if (indices.nonEmpty) {
require(indices(0) >= 0, s"Found negative index: ${indices(0)}.")
}
var prev = -1L
indices.foreach { i =>
require(prev < i, s"Index $i follows $prev and is not strictly increasing")
prev = i
}
require(prev < size, s"Index $prev out of bounds for vector of size $size")
}
override def toString: String =
s"($size,${indices.mkString("[", ",", "]")},${values.mkString("[", ",", "]")})"
override def toArray: Array[Double] = {
if (size < Int.MaxValue) {
val data = new Array[Double](size.toInt)
var i = 0
val nnz = indices.length
while (i < nnz) {
data(indices(i).toInt) = values(i)
i += 1
}
data
} else {
throw new Exception("The LongSparseVector is too large to convert to Array")
}
}
override def copy: LongSparseVector = {
new LongSparseVector(size, indices.clone(), values.clone())
}
override def asBreeze: BV[Double] = {
if (size < Int.MaxValue) {
new BSV[Double](indices.map(_.toInt), values, size.toInt)
} else {
throw new Exception("The LongSparseVector is too large to convert to Breeze Vector")
}
}
override def foreachActive(f: (Long, Double) => Unit): Unit = {
var i = 0
val localValuesSize = values.length
val localIndices = indices
val localValues = values
while (i < localValuesSize) {
f(localIndices(i), localValues(i))
i += 1
}
}
override def equals(other: Any): Boolean = super.equals(other)
override def hashCode(): Int = {
var result: Int = 31 + size.toInt
val end = values.length
var k = 0
var nnz = 0
while (k < end && nnz < Vectors.MAX_HASH_NNZ) {
val v = values(k)
if (v != 0.0) {
val i = indices(k).toInt
result = 31 * result + i
val bits = java.lang.Double.doubleToLongBits(v)
result = 31 * result + (bits ^ (bits >>> 32)).toInt
nnz += 1
}
k += 1
}
result
}
override def numActives: Long = values.length
override def numNonzeros: Long = {
var nnz = 0
values.foreach { v =>
if (v != 0.0) {
nnz += 1
}
}
nnz
}
private[linalg] override def toSparseWithSize(nnz: Long): LongSparseVector = {
assert(nnz < Int.MaxValue)
if (nnz == numActives) {
this
} else {
val ii = new Array[Long](nnz.toInt)
val vv = new Array[Double](nnz.toInt)
var k = 0
foreachActive { (i, v) =>
if (v != 0.0) {
ii(k) = i
vv(k) = v
k += 1
}
}
new LongSparseVector(size, ii, vv)
}
}
override def argmax: Long = {
if (size == 0) {
-1
} else if (numActives == 0) {
0
} else {
// Find the max active entry.
var maxIdx = indices(0)
var maxValue = values(0)
var maxJ = 0
var j = 1
val na = numActives
while (j < na) {
val v = values(j)
if (v > maxValue) {
maxValue = v
maxIdx = indices(j)
maxJ = j
}
j += 1
}
// If the max active entry is nonpositive and there exists inactive ones, find the first zero.
if (maxValue <= 0.0 && na < size) {
if (maxValue == 0.0) {
// If there exists an inactive entry before maxIdx, find it and return its index.
if (maxJ < maxIdx) {
var k = 0
while (k < maxJ && indices(k) == k) {
k += 1
}
maxIdx = k
}
} else {
// If the max active value is negative, find and return the first inactive index.
var k = 0
while (k < na && indices(k) == k) {
k += 1
}
maxIdx = k
}
}
maxIdx
}
}
/**
* Create a slice of this vector based on the given indices.
*
* @param selectedIndices Unsorted list of indices into the vector.
* This does NOT do bound checking.
* @return New SparseVector with values in the order specified by the given indices.
*
* NOTE: The API needs to be discussed before making this public.
* Also, if we have a version assuming indices are sorted, we should optimize it.
*/
def slice(selectedIndices: Array[Long]): LongSparseVector = {
var currentIdx = 0L
val (sliceInds, sliceVals) = selectedIndices.flatMap { origIdx =>
val iIdx = java.util.Arrays.binarySearch(this.indices, origIdx)
val i_v = if (iIdx >= 0) {
Iterator((currentIdx, this.values(iIdx)))
} else {
Iterator()
}
currentIdx += 1
i_v
}.unzip
new LongSparseVector(selectedIndices.length, sliceInds, sliceVals)
}
override def apply(i: Long): Double = {
val iIdx = java.util.Arrays.binarySearch(this.indices, i.toInt)
if (iIdx >= 0) {
this.values(iIdx)
} else {
Double.NaN
}
}
override def toDense: DenseVector = {
if (size < Int.MaxValue) {
new DenseVector(toArray)
} else {
throw new Exception("The LongSparseVector is too large to convert to Array")
}
}
}
object LongSparseVector {
def unapply(sv: LongSparseVector): Option[(Long, Array[Long], Array[Double])] =
Some((sv.size, sv.indices, sv.values))
}© 2015 - 2025 Weber Informatics LLC | Privacy Policy