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/*
* 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.sql.catalyst.expressions.aggregate
import org.apache.spark.sql.catalyst.dsl.expressions._
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.types._
/**
* Compute the covariance between two expressions.
* When applied on empty data (i.e., count is zero), it returns NULL.
*/
abstract class Covariance(x: Expression, y: Expression) extends DeclarativeAggregate {
override def children: Seq[Expression] = Seq(x, y)
override def nullable: Boolean = true
override def dataType: DataType = DoubleType
override def inputTypes: Seq[AbstractDataType] = Seq(DoubleType, DoubleType)
protected val n = AttributeReference("n", DoubleType, nullable = false)()
protected val xAvg = AttributeReference("xAvg", DoubleType, nullable = false)()
protected val yAvg = AttributeReference("yAvg", DoubleType, nullable = false)()
protected val ck = AttributeReference("ck", DoubleType, nullable = false)()
override val aggBufferAttributes: Seq[AttributeReference] = Seq(n, xAvg, yAvg, ck)
override val initialValues: Seq[Expression] = Array.fill(4)(Literal(0.0))
override lazy val updateExpressions: Seq[Expression] = {
val newN = n + Literal(1.0)
val dx = x - xAvg
val dy = y - yAvg
val dyN = dy / newN
val newXAvg = xAvg + dx / newN
val newYAvg = yAvg + dyN
val newCk = ck + dx * (y - newYAvg)
val isNull = IsNull(x) || IsNull(y)
Seq(
If(isNull, n, newN),
If(isNull, xAvg, newXAvg),
If(isNull, yAvg, newYAvg),
If(isNull, ck, newCk)
)
}
override val mergeExpressions: Seq[Expression] = {
val n1 = n.left
val n2 = n.right
val newN = n1 + n2
val dx = xAvg.right - xAvg.left
val dxN = If(newN === Literal(0.0), Literal(0.0), dx / newN)
val dy = yAvg.right - yAvg.left
val dyN = If(newN === Literal(0.0), Literal(0.0), dy / newN)
val newXAvg = xAvg.left + dxN * n2
val newYAvg = yAvg.left + dyN * n2
val newCk = ck.left + ck.right + dx * dyN * n1 * n2
Seq(newN, newXAvg, newYAvg, newCk)
}
}
case class CovPopulation(left: Expression, right: Expression) extends Covariance(left, right) {
override val evaluateExpression: Expression = {
If(n === Literal(0.0), Literal.create(null, DoubleType),
ck / n)
}
override def prettyName: String = "covar_pop"
}
case class CovSample(left: Expression, right: Expression) extends Covariance(left, right) {
override val evaluateExpression: Expression = {
If(n === Literal(0.0), Literal.create(null, DoubleType),
If(n === Literal(1.0), Literal(Double.NaN),
ck / (n - Literal(1.0))))
}
override def prettyName: String = "covar_samp"
}
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