com.actelion.research.calc.statistics.median.MedianStatisticFunctions Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of openchemlib Show documentation
Show all versions of openchemlib Show documentation
Open Source Chemistry Library
/*
* Copyright (c) 1997 - 2018
* Idorsia Pharmaceuticals Ltd.
* Hegenheimermattweg 91
* CH-4123 Allschwil, Switzerland
*
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
* 3. Neither the name of the the copyright holder nor the
* names of its contributors may be used to endorse or promote products
* derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
* ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
* @author Modest v. Korff
*/
package com.actelion.research.calc.statistics.median;
import com.actelion.research.util.ArrayUtils;
import java.util.ArrayList;
import java.util.Collection;
import java.util.Collections;
import java.util.List;
/**
* MedianStatisticFunctions
* Aug 5, 2011 MvK: Start implementation
*/
public class MedianStatisticFunctions {
/**
*
* @param liScore list has to be sorted in ascending order.
* @param fraction 0.25 lower quartile, 0,5 median and 0.75 upper quartile.
* @return
*/
public static double getPercentileFromSorted(List liScore, double fraction) {
if(liScore.size()==1){
return liScore.get(0);
}
double percentile=0;
int len = liScore.size();
if(((int)(len*fraction))==(len*fraction)) {
int index1 = (int)(len*fraction)-1;
int index2 = index1+1;
if(index1<0){
throw new RuntimeException("Fraction to small.");
}
percentile = (liScore.get(index1)+liScore.get(index2))/2.0;
} else {
int index1 = (int)(len*fraction);
percentile = liScore.get(index1);
}
return percentile;
}
public static double getPercentileFromSorted(double [] arr, double fraction) {
return getPercentileFromSorted(arr, fraction, 0, arr.length);
}
public static double getPercentileFromSorted(float [] arr, double fraction) {
return getPercentileFromSorted(arr, fraction, 0, arr.length);
}
public static double getPercentileFromSorted(double [] arr, double fraction, int indexStart, int length) {
if(arr.length==1){
return arr[0];
}
double percentile=0;
if(((int)(length*fraction))==(length*fraction)) {
int index1 = (int)(length*fraction)-1 + indexStart;
int index2 = index1+1 + indexStart;
if(index1<0){
throw new RuntimeException("Fraction to small.");
}
percentile = (arr[index1]+arr[index2])/2.0;
} else {
int index1 = (int)(length*fraction) + indexStart;
percentile = arr[index1];
}
return percentile;
}
public static double getPercentileFromSorted(float [] arr, double fraction, int indexStart, int length) {
if(arr.length==1){
return arr[0];
}
double percentile=0;
if(((int)(length*fraction))==(length*fraction)) {
int index1 = (int)(length*fraction)-1 + indexStart;
int index2 = index1+1 + indexStart;
if(index1<0){
throw new RuntimeException("Fraction to small.");
}
percentile = (arr[index1]+arr[index2])/2.0;
} else {
int index1 = (int)(length*fraction) + indexStart;
percentile = arr[index1];
}
return percentile;
}
public static double getPercentileFromSortedInt(List liScore, double fraction) {
if(liScore.size()==1){
return liScore.get(0);
}
double percentile=0;
int len = liScore.size();
if(((int)(len*fraction))==(len*fraction)) {
int index1 = (int)((len*fraction)+0.5)-1;
int index2 = index1+1;
if(index1<0){
index1=0;
}
percentile = (liScore.get(index1)+liScore.get(index2))/2.0;
} else {
int index1 = (int)(len*fraction);
percentile = liScore.get(index1);
}
return percentile;
}
public static double getPercentileFromSortedLong(List liScore, double fraction) {
if(liScore.size()==1){
return liScore.get(0);
}
long percentile=0;
int len = liScore.size();
if(((int)(len*fraction))==(len*fraction)) {
int index1 = (int)((len*fraction)+0.5)-1;
int index2 = index1+1;
if(index1<0){
index1=0;
}
percentile = (long)((liScore.get(index1)+liScore.get(index2))/2.0);
} else {
int index1 = (int)(len*fraction);
percentile = liScore.get(index1);
}
return percentile;
}
/**
*
* @param liScore the list is sorted in the method.
* @return
*/
public static ModelMedianInteger getMedianForInteger(List liScore) {
Collections.sort(liScore);
ModelMedianInteger modelMedian = new ModelMedianInteger();
modelMedian.lowerQuartile = (int)(MedianStatisticFunctions.getPercentileFromSortedInt(liScore, 0.25) + 0.5);
modelMedian.median = (int)(MedianStatisticFunctions.getPercentileFromSortedInt(liScore, 0.5) + 0.5);
modelMedian.upperQuartile = (int)(MedianStatisticFunctions.getPercentileFromSortedInt(liScore, 0.75) + 0.5);
modelMedian.size = liScore.size();
return modelMedian;
}
public static ModelMedianInteger getMedianForInteger(int [] a) {
return getMedianForInteger(ArrayUtils.toList(a));
}
public static ModelMedianLong getMedianForLong(List liScore) {
Collections.sort(liScore);
ModelMedianLong modelMedian = new ModelMedianLong();
modelMedian.lowerQuartile = (long)(MedianStatisticFunctions.getPercentileFromSortedLong(liScore, 0.25) + 0.5);
modelMedian.median = (long)(MedianStatisticFunctions.getPercentileFromSortedLong(liScore, 0.5) + 0.5);
modelMedian.upperQuartile = (long)(MedianStatisticFunctions.getPercentileFromSortedLong(liScore, 0.75) + 0.5);
modelMedian.size = liScore.size();
return modelMedian;
}
/**
*
* @param
* @return
*/
public static ModelMedianDouble getMedianForDouble(Collection liScoreRaw) {
if(liScoreRaw.size()<1)
return null;
List liScore = new ArrayList<>(liScoreRaw);
Collections.sort(liScore);
ModelMedianDouble modelMedian = new ModelMedianDouble();
modelMedian.lowerQuartile = MedianStatisticFunctions.getPercentileFromSorted(liScore, 0.25);
modelMedian.median = MedianStatisticFunctions.getPercentileFromSorted(liScore, 0.5);
modelMedian.upperQuartile = MedianStatisticFunctions.getPercentileFromSorted(liScore, 0.75);
modelMedian.size = liScore.size();
return modelMedian;
}
}