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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.partial
import cern.jet.stat.Probability
/**
* An ApproximateEvaluator for counts.
*
* TODO: There's currently a lot of shared code between this and GroupedCountEvaluator. It might
* be best to make this a special case of GroupedCountEvaluator with one group.
*/
private[spark] class CountEvaluator(totalOutputs: Int, confidence: Double)
extends ApproximateEvaluator[Long, BoundedDouble] {
var outputsMerged = 0
var sum: Long = 0
override def merge(outputId: Int, taskResult: Long) {
outputsMerged += 1
sum += taskResult
}
override def currentResult(): BoundedDouble = {
if (outputsMerged == totalOutputs) {
new BoundedDouble(sum, 1.0, sum, sum)
} else if (outputsMerged == 0) {
new BoundedDouble(0, 0.0, Double.NegativeInfinity, Double.PositiveInfinity)
} else {
val p = outputsMerged.toDouble / totalOutputs
val mean = (sum + 1 - p) / p
val variance = (sum + 1) * (1 - p) / (p * p)
val stdev = math.sqrt(variance)
val confFactor = Probability.normalInverse(1 - (1 - confidence) / 2)
val low = mean - confFactor * stdev
val high = mean + confFactor * stdev
new BoundedDouble(mean, confidence, low, high)
}
}
}
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