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Merge pull request #495 from gtakata/DEV3.2.1.1
Add DataReduction methods to reduce distribution
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53 changes: 53 additions & 0 deletions
53
Figaro/src/main/scala/com/cra/figaro/util/visualization/reduction/DataReduction.scala
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| package com.cra.figaro.util.visualization.reduction | ||
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| import scala.collection._ | ||
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| /** | ||
| * @author gtakata | ||
| */ | ||
| object DataReduction { | ||
| def binToDistribution(data: List[(Double, Double)]): List[(Double, Double)] = { | ||
| if (data.size > 50) { | ||
| var mean = 0.0 | ||
| var totalProb = 0.0 | ||
| var count = 0 | ||
| var min = Double.MaxValue | ||
| var max = Double.MinValue | ||
| var ss = 0.0 | ||
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| for ((prob, value) <- data) { | ||
| totalProb += prob | ||
| mean += prob * value | ||
| min = math.min(min, value) | ||
| max = math.max(max, value) | ||
| ss += prob * value * value | ||
| count += 1 | ||
| } | ||
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| val variance = ss - mean * mean | ||
| val sd = math.sqrt(variance) | ||
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| val distMax = mean + 3 * sd | ||
| val distMin = mean - 3 * sd | ||
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| val nInterval = math.min(count, 300) | ||
| val interval = (distMax - distMin) / nInterval | ||
| var dist = Array.fill[Double](nInterval)(0) | ||
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| for ((prob, value) <- data) { | ||
| val pos = math.max(math.min(math.floor((value - distMin) / interval).toInt, nInterval - 1), 0) | ||
| val posProb = dist(pos) | ||
| dist(pos) = posProb + prob | ||
| } | ||
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| val probDist = for (i <- 1 to nInterval) yield { | ||
| val value = distMin + i * interval | ||
| (dist(i - 1), value) | ||
| } | ||
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| probDist.toList | ||
| } else { | ||
| data | ||
| } | ||
| } | ||
| } |
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48 changes: 48 additions & 0 deletions
48
FigaroExamples/src/main/scala/com/cra/figaro/example/visualization/Regression.scala
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| /* | ||
| * Regression.scala | ||
| * A Bayesian network example with visualization | ||
| * | ||
| * Created By: Glenn Takata (gtakata@cra.com) | ||
| * Creation Date: Jul 7, 2015 | ||
| * | ||
| * Copyright 2015 Avrom J. Pfeffer and Charles River Analytics, Inc. | ||
| * See http://www.cra.com or email figaro@cra.com for information. | ||
| * | ||
| * See http://www.github.com/p2t2/figaro for a copy of the software license. | ||
| */ | ||
| package com.cra.figaro.example.visualization | ||
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| import com.cra.figaro.language.{Universe} | ||
| import com.cra.figaro.algorithm.sampling.{Importance} | ||
| import com.cra.figaro.library.atomic.continuous.{Normal, Uniform} | ||
| import com.cra.figaro.util.visualization.ResultsGUI | ||
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| /** | ||
| * @author Glenn Takata (gtakata@cra.com) | ||
| */ | ||
| object Regression { | ||
| Universe.createNew() | ||
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| private val mean = Uniform(0, 1) | ||
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| for (_ <- 0 until 100) { | ||
| val n = Normal(mean, 1.0) | ||
| n.addConstraint((m: Double) => m + 5) | ||
| } | ||
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| def main(args: Array[String]) { | ||
| val gui = ResultsGUI | ||
| gui.startup(args) | ||
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| val alg = Importance(1000, mean) | ||
| alg.start() | ||
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| val dist = alg.distribution(mean) | ||
| println("Probability of mean: " + dist.toList) | ||
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| gui.addResult("mean", alg.distribution(mean).toList) | ||
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| alg.kill | ||
| } | ||
| } |