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/*
* Copyright (C) 2014-2021 Brian L. Browning
*
* This file is part of Beagle
*
* Beagle is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* Beagle is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
package phase;
import java.util.concurrent.ConcurrentLinkedQueue;
/**
* <p>Class {@code ParamEstimates} estimates the allele mismatch probability
* and the recombination intensity for a haploid Li and Stephens hidden
* Markov model.</p>
*
* <p>Instances of class {@code ParamEstimates} are thread-safe.</p>
*
* @author Brian L. Browning {@code <browning@uw.edu>}
*/
public class ParamEstimates {
private final ConcurrentLinkedQueue<RecombData> switchData;
private final ConcurrentLinkedQueue<MismatchData> mismatchData;
/**
* Constructs a new {@code ParamEstimates} instance for the specified
* data.
*/
public ParamEstimates() {
this.switchData = new ConcurrentLinkedQueue<>();
this.mismatchData = new ConcurrentLinkedQueue<>();
}
/**
* Records the specified allele mismatch data if {@code markerCnt} and
* {@code pMismatchSum} are finite positive values.
* @param markerCnt the number of markers with mismatch data
* @param pMismatchSum the sum of estimated allele mismatch probabilities
*/
public void addMismatchData(int markerCnt, double pMismatchSum) {
if (markerCnt>0 && pMismatchSum>0 && Double.isFinite(pMismatchSum)) {
mismatchData.add(new MismatchData(markerCnt, pMismatchSum));
}
}
/**
* Records the specified genetic distance and switch probability
* if {@code genDistances} and {@code switchProbs} are finite positive
* values.
* @param genDistances the list of genetic distance
* @param switchProbs the list of haplotype switch probabilities
*/
public void addSwitchData(double genDistances, double switchProbs) {
if (genDistances>0 && switchProbs>0
&& Double.isFinite(genDistances) && Double.isFinite(switchProbs)) {
switchData.add(new RecombData(genDistances, switchProbs));
}
}
/**
* Returns the estimated allele mismatch rate. Returns {@code Float.NaN}
* if there is no data to estimate the allele mismatch rate. The returned
* value is not an atomic snapshot. Invocation in the absence of concurrent
* update will return an accurate result, but concurrent updates that
* occur white the sum is being calculated might not be incorporated
* in the returned result.
* @return the estimated allele mismatch rate
*/
public float pMismatch() {
MismatchData[] mda = mismatchData.stream()
.sorted() // ensures sum of values is repeatable
.toArray(MismatchData[]::new);
long sumMarkers = 0;
double sumPMismatch = 0d;
for (MismatchData md : mda) {
sumMarkers += md.markerCnt;
sumPMismatch += md.pMismatchSum;
}
return sumMarkers==0 ? Float.NaN : (float) (sumPMismatch/sumMarkers);
}
/**
* Returns the estimated recombination intensities. Returns {@code Float.NaN}
* if there is no data from which to estimate recombination intensities.
* The returned value is NOT an atomic snapshot. An accurate result is
* guaranteed only if no concurrent updates occur during method
* invocation.
* @return the estimated recombination intensities
*/
public float recombIntensity() {
RecombData[] rda = switchData.stream()
.sorted() // ensures sum of values is repeatable
.toArray(RecombData[]::new);
double sumSwitches = 0d;
double sumDistances = 0d;
for (RecombData rd : rda) {
sumSwitches += rd.switchProb;
sumDistances += rd.genDistance;
}
return sumDistances==0d ? Float.NaN : (float) (sumSwitches/sumDistances);
}
/**
* Clears all data that has been added via the
* {@code this.addMismatchData} and {@code this.addSwitchData()}
* methods.
*/
public void clear() {
switchData.clear();
mismatchData.clear();
}
private static class RecombData implements Comparable<RecombData> {
private final double genDistance;
private final double switchProb;
public RecombData(double genDistance, double switchProb) {
this.genDistance = genDistance;
this.switchProb = switchProb;
}
@Override
public int compareTo(RecombData o) {
int val = Double.compare(this.genDistance, o.genDistance);
return val!=0 ? val : Double.compare(this.switchProb, o.switchProb);
}
}
private static class MismatchData implements Comparable<MismatchData> {
private final int markerCnt;
private final double pMismatchSum;
public MismatchData(int markerCnt, double pMismatchSum) {
this.markerCnt = markerCnt;
this.pMismatchSum = pMismatchSum;
}
@Override
public int compareTo(MismatchData o) {
int val = Double.compare(this.pMismatchSum, o.pMismatchSum);
return val!=0 ? val : Integer.compare(this.markerCnt, o.markerCnt);
}
}
}
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