1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
|
/*
* 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 spark.partial
import cern.jet.stat.Probability
import spark.util.StatCounter
/**
* An ApproximateEvaluator for means.
*/
private[spark] class MeanEvaluator(totalOutputs: Int, confidence: Double)
extends ApproximateEvaluator[StatCounter, BoundedDouble] {
var outputsMerged = 0
var counter = new StatCounter
override def merge(outputId: Int, taskResult: StatCounter) {
outputsMerged += 1
counter.merge(taskResult)
}
override def currentResult(): BoundedDouble = {
if (outputsMerged == totalOutputs) {
new BoundedDouble(counter.mean, 1.0, counter.mean, counter.mean)
} else if (outputsMerged == 0) {
new BoundedDouble(0, 0.0, Double.NegativeInfinity, Double.PositiveInfinity)
} else {
val mean = counter.mean
val stdev = math.sqrt(counter.sampleVariance / counter.count)
val confFactor = {
if (counter.count > 100) {
Probability.normalInverse(1 - (1 - confidence) / 2)
} else {
Probability.studentTInverse(1 - confidence, (counter.count - 1).toInt)
}
}
val low = mean - confFactor * stdev
val high = mean + confFactor * stdev
new BoundedDouble(mean, confidence, low, high)
}
}
}
|
