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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 imp;
import blbutil.FloatList;
import ints.IntList;
/**
* <p>Class {@code StateProbsFactory} stores HMM state probabilities
* that that can be used to imputed impute missing HMM state probabilities
* using linear interpolation.
* </p>
* <p>Instances of class {@code StateProbsFactory} are not thread-safe.
* </p>
*
* @author Brian L. Browning {@code <browning@uw.edu>}
*/
public class StateProbsFactory {
private final int nTargMarkers;
private final int nTargMarkersM1;
private final IntList hapList;
private final FloatList probList;
private final FloatList probP1List;
private final int[][] haps;
private final float[][] probs;
private final float[][] probsP1;
/**
* Creates an {@code StateProbsFactory} instance from the specified data.
*
* @param nTargMarkers the number of target markers
* @throws IllegalArgumentException if {@code nTargMarkers <= 0}
*/
public StateProbsFactory(int nTargMarkers) {
if (nTargMarkers <= 0) {
throw new IllegalArgumentException(String.valueOf(nTargMarkers));
}
this.nTargMarkers = nTargMarkers;
this.nTargMarkersM1 = nTargMarkers - 1;
this.hapList = new IntList(50);
this.probList = new FloatList(50);
this.probP1List = new FloatList(50);
this.haps = new int[nTargMarkers][];
this.probs = new float[nTargMarkers][];
this.probsP1 = new float[nTargMarkers][];
}
/**
* Returns the number of target marker.
* @return the number of target marker
*/
public int nTargMarkers() {
return nTargMarkers;
}
/**
* Stores the reference haplotypes and state probabilities that
* will be used to imputed missing state probabilities using linear
* interpolation. If a HMM state probability at a marker or the
* following marker are greater than or equal to
* {@code Math.min(0.005f, 1.0f/nStates)}, then the reference haplotype,
* the state probability at the marker, and the state probability at
* the succeeding marker are stored.
* @param targHap a target data haplotype index
* @param nStates the number of HMM states at each marker
* @param hapIndices the reference haplotype indices corresponding to
* each state at each marker
* @param stateProbs the HMM state probabilities at each marker
* @throws IllegalArgumentException if
* {@code hapIndices.length != this.nTargMarkers()}
* @return the state probabilities
* @throws IllegalArgumentException if
* {@code stateProbs.length != this.nTargMarkers()}
* @throws IndexOutOfBoundsException if there exists {@code j} satisfying
* {@code (0 <= j && j <= this.nTargMarkers())} such that
* {@code (hapIndices[j].length < nStates || stateProbs[j].length < nStates)}
* @throws NullPointerException if
* {@code hapIndices == null || stateProbs == null}
* @throws NullPointerException if there exists {@code j} satisfying
* {@code (0 <= j && j <= this.nTargMarkers())} such that
* {@code (hapIndices[j] == null || stateProbs[j] == null)}
*/
public StateProbs stateProbs(int targHap, int nStates, int[][] hapIndices,
float[][] stateProbs) {
if (hapIndices.length != nTargMarkers) {
throw new IllegalArgumentException(String.valueOf(hapIndices.length));
}
if (stateProbs.length != nTargMarkers) {
throw new IllegalArgumentException(String.valueOf(stateProbs.length));
}
float threshold = threshold(nStates);
for (int m=0; m<nTargMarkers; ++m) {
hapList.clear();
probList.clear();
probP1List.clear();
int mP1 = m<nTargMarkersM1 ? m+1 : m;
for (int j=0; j<nStates; ++j) {
if (stateProbs[m][j] > threshold || stateProbs[mP1][j] > threshold) {
hapList.add(hapIndices[m][j]);
probList.add(stateProbs[m][j]);
probP1List.add(stateProbs[mP1][j]);
}
}
haps[m] = hapList.toArray();
probs[m] = probList.toArray();
probsP1[m] = probP1List.toArray();
}
return new BasicStateProbs(targHap, haps, probs, probsP1);
}
private float threshold(int nStates) {
return Math.min(0.005f, 0.9999f/nStates);
}
private static class BasicStateProbs implements StateProbs {
private final int targHap;
private final int[][] haps;
private final float[][] probs;
private final float[][] probsP1;
public BasicStateProbs(int targHap, int[][] haps, float[][] probs,
float[][] probsP1) {
this.targHap = targHap;
this.haps = haps.clone();
this.probs = probs.clone();
this.probsP1 = probsP1.clone();
}
@Override
public int nTargMarkers() {
return haps.length;
}
@Override
public int nStates(int marker) {
return haps[marker].length;
}
@Override
public int targHap() {
return targHap;
}
@Override
public int refHap(int marker, int index) {
return haps[marker][index];
}
@Override
public float probs(int marker, int index) {
return probs[marker][index];
}
@Override
public float probsP1(int marker, int index) {
return probsP1[marker][index];
}
}
}
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