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89
...fe/src/main/spark_3.4_3.5/scala/org.apache.spark/sql/catalyst/expressions/RandGamma.scala
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| package org.apache.spark.sql.catalyst.expressions | ||
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| import org.apache.commons.math3.distribution.GammaDistribution | ||
| import org.apache.spark.sql.catalyst.analysis.UnresolvedSeed | ||
| import org.apache.spark.sql.catalyst.InternalRow | ||
| import org.apache.spark.sql.catalyst.expressions.codegen.FalseLiteral | ||
| import org.apache.spark.sql.catalyst.expressions.codegen.Block.BlockHelper | ||
| import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenerator, CodegenContext, ExprCode} | ||
| import org.apache.spark.sql.types._ | ||
| import org.apache.spark.util.random.XORShiftRandomAdapted | ||
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| case class RandGamma(child: Expression, shape: Expression, scale: Expression, hideSeed: Boolean = false) | ||
| extends TernaryExpression | ||
| with ExpectsInputTypes | ||
| with Nondeterministic | ||
| with ExpressionWithRandomSeed { | ||
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| def seedExpression: Expression = child | ||
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| @transient protected lazy val seed: Long = seedExpression match { | ||
| case e if e.dataType == IntegerType => e.eval().asInstanceOf[Int] | ||
| case e if e.dataType == LongType => e.eval().asInstanceOf[Long] | ||
| } | ||
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| @transient protected lazy val shapeVal: Double = shape.dataType match { | ||
| case IntegerType => shape.eval().asInstanceOf[Int] | ||
| case LongType => shape.eval().asInstanceOf[Long] | ||
| case FloatType | DoubleType => shape.eval().asInstanceOf[Double] | ||
| } | ||
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| @transient protected lazy val scaleVal: Double = scale.dataType match { | ||
| case IntegerType => scale.eval().asInstanceOf[Int] | ||
| case LongType => scale.eval().asInstanceOf[Long] | ||
| case FloatType | DoubleType => scale.eval().asInstanceOf[Double] | ||
| } | ||
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| @transient private var distribution: GammaDistribution = _ | ||
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| override protected def initializeInternal(partitionIndex: Int): Unit = { | ||
| distribution = new GammaDistribution(new XORShiftRandomAdapted(seed + partitionIndex), shapeVal, scaleVal) | ||
| } | ||
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| def this() = this(UnresolvedSeed, Literal(1.0, DoubleType), Literal(1.0, DoubleType), true) | ||
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| def this(child: Expression, shape: Expression, scale: Expression) = this(child, shape, scale, false) | ||
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| def withNewSeed(seed: Long): RandGamma = RandGamma(Literal(seed, LongType), shape, scale, hideSeed) | ||
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| protected def evalInternal(input: InternalRow): Double = distribution.sample() | ||
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| def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = { | ||
| val distributionClassName = classOf[GammaDistribution].getName | ||
| val rngClassName = classOf[XORShiftRandomAdapted].getName | ||
| val disTerm = ctx.addMutableState(distributionClassName, "distribution") | ||
| ctx.addPartitionInitializationStatement( | ||
| s"$disTerm = new $distributionClassName(new $rngClassName(${seed}L + partitionIndex), $shapeVal, $scaleVal);" | ||
| ) | ||
| ev.copy(code = code""" | ||
| final ${CodeGenerator.javaType(dataType)} ${ev.value} = $disTerm.sample();""", isNull = FalseLiteral) | ||
| } | ||
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| def freshCopy(): RandGamma = RandGamma(child, shape, scale, hideSeed) | ||
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| override def flatArguments: Iterator[Any] = Iterator(child, shape, scale) | ||
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| override def prettyName: String = "rand_gamma" | ||
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| override def sql: String = s"rand_gamma(${if (hideSeed) "" else s"${child.sql}, ${shape.sql}, ${scale.sql}"})" | ||
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| override def stateful: Boolean = true | ||
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| def inputTypes: Seq[AbstractDataType] = Seq(LongType, DoubleType, DoubleType) | ||
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| def dataType: DataType = DoubleType | ||
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| def first: Expression = child | ||
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| def second: Expression = shape | ||
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| def third: Expression = scale | ||
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| protected def withNewChildrenInternal(newFirst: Expression, newSecond: Expression, newThird: Expression): Expression = | ||
| copy(child = newFirst, shape = newSecond, scale = newThird) | ||
| } | ||
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| object RandGamma { | ||
| def apply(seed: Long, shape: Double, scale: Double): RandGamma = | ||
| RandGamma(Literal(seed, LongType), Literal(shape, DoubleType), Literal(scale, DoubleType)) | ||
| } |