<NME> IsotonicRegression.java
<BEF> package edu.neu.ccs.pyramid.regression;

import edu.neu.ccs.pyramid.util.Pair;

import java.util.Arrays;
import java.util.Comparator;
import java.util.List;

import java.util.ArrayList;
import java.util.Arrays;
import java.util.Comparator;
import java.util.List;
 * http://stat.wikia.com/wiki/Isotonic_regression
 */

public class IsotonicRegression {
    //sorted locations
    private double[] locations;
    private double[] values;

public class IsotonicRegression implements Regressor {
    private static final long serialVersionUID = 1L;
    //sorted locations
    private double[] locations;
    private double[] values;

    /**
     * @param locations unsorted
     * @param numbers
     */
//    public IsotonicRegression(double[] locations, double[] numbers) {
//        this(locations,numbers,true);
//    }


    public IsotonicRegression(double[] locations, double[] numbers, boolean interpolate) {
        List<Pair<Double, Double>> sorted = IntStream.range(0, locations.length).mapToObj(i -> new Pair<>(locations[i], numbers[i]))
                .sorted(Comparator.comparing(pair -> pair.getFirst())).collect(Collectors.toList());
        double[] sortedLocations = sorted.stream().mapToDouble(p -> p.getFirst()).toArray();
        double[] sortedNumbers = sorted.stream().mapToDouble(p -> p.getSecond()).toArray();
        double[] weights = new double[numbers.length];
        Arrays.fill(weights, 1.0);
        this.locations = sortedLocations;
        this.values = fit(sortedNumbers, weights);
        if (interpolate){
            shrink();
        }

    }

//    /**
//     * @param locations unsorted
//     * @param numbers
//     */
//    public IsotonicRegression(double[] locations, double[] numbers, double[] weights) {
//        this(locations, numbers, weights, true);
//    }



    public IsotonicRegression(double[] locations, double[] numbers, double[] weights, boolean interpolate) {
        List<Element> sorted = IntStream.range(0, locations.length).mapToObj(i -> new Element(locations[i], numbers[i], weights[i]))
                .sorted(Comparator.comparing(element -> element.location)).collect(Collectors.toList());
        double[] sortedLocations = sorted.stream().mapToDouble(p -> p.location).toArray();
        double[] sortedNumbers = sorted.stream().mapToDouble(p -> p.number).toArray();
        double[] sortedWeights = sorted.stream().mapToDouble(p -> p.weight).toArray();
        this.locations = sortedLocations;
        this.values = fit(sortedNumbers, sortedWeights);
        if (interpolate){
            shrink();
        }

    }

    public IsotonicRegression(WeightedInput weightedInput, boolean interpolate) {
        this(weightedInput.locationNonEmpty, weightedInput.accsNonempty, weightedInput.countsNonEmpty, interpolate);
    }


    public IsotonicRegression(Stream<Pair<Double, Double>> stream, boolean interpolate) {
        this(new WeightedInput(stream), interpolate);
    }


    public static IsotonicRegression train(Stream<Pair<Vector, Integer>> stream, boolean interpolate) {
        Stream singleStream = stream.map(s -> new Pair<>(s.getFirst().get(0), s.getSecond()));
        IsotonicRegression isotonicRegression = new IsotonicRegression(singleStream, interpolate);
        return isotonicRegression;
    }


    public double[] getLocations() {
        return locations;
    }

    public double[] getValues() {
        return values;
    }

    public double predict(double location) {
        if (location <= locations[0]) {
            return values[0];
        }

        if (location >= locations[locations.length - 1]) {
            return values[values.length - 1];
        }

        //use binary search
        int l = locations.length;
        int start = 0;
        int end = l - 1;

        while (end - start > 1) {
            int middle = (start + end) / 2;
            if (locations[middle] == location) {
                return values[middle];
            } else if (locations[middle] > location) {
                end = middle;
            } else {
                start = middle;
            }
        }

        if (start == end) {
            return values[start];
        } else {
            return (location - locations[start]) * (values[end] - values[start]) / (locations[end] - locations[start]) + values[start];
        }

    }








    /**
     *
     * @param numbers sorted by locations
     */
    public IsotonicRegression(double[] numbers) {
        double[] weights = new double[numbers.length];
        Arrays.fill(weights, 1.0);
        this.values = fit(numbers, weights);
    }

    private double[] fit1Based(double[] a, double[] w){
        double[] aprime = new double[a.length];
        double[] wprime = new double[w.length];
        int[] s = new int[a.length];
        aprime[1]= a[1];
        wprime[1] = w[1];
        int j=1;
        s[0]=0;
        s[1]=1;
        for (int i=2;i<a.length;i++){
            j += 1;
            aprime[j] = a[i];
            wprime[j] = w[i];
            while (j>1 && aprime[j]<aprime[j-1]){
                aprime[j-1] = (wprime[j]*aprime[j]+wprime[j-1]*aprime[j-1])/(wprime[j]+wprime[j-1]);
                wprime[j-1] = wprime[j]+wprime[j-1];
                j -=1;
            }
            s[j] = i;
        }
        double[] parameters = new double[a.length];
        for (int k=1;k<=j;k++){
            for (int l=s[k-1]+1;l<=s[k];l++){
                parameters[l] = aprime[k];
            }
        }
        return parameters;
    }

    private double[] fit(double[] a, double[] w){
        double[] a1Based = new double[a.length+1];
        double[] w1Based = new double[w.length+1];
        for (int i=0;i<a.length;i++){
            a1Based[i+1]=a[i];
        }
        for (int i=0;i<w.length;i++){
            w1Based[i+1]=w[i];
        }
        double[] p =  fit1Based(a1Based, w1Based);
        return Arrays.copyOfRange(p,1,p.length);

    }

    private void shrink(){
        List<Double> uniqueLocations = new ArrayList<>();
        List<Double> uniqueValues = new ArrayList<>();
        double last = Double.NEGATIVE_INFINITY;
        for(int i = 0; i < locations.length; i++){
            if (values[i]!=last){
                uniqueLocations.add(locations[i]);
                uniqueValues.add(values[i]);
                last = values[i];
            }
        }

        this.locations = uniqueLocations.stream().mapToDouble(a->a).toArray();
        this.values = uniqueValues.stream().mapToDouble(a->a).toArray();
    }

    @Override
    public String toString() {
        final StringBuilder sb = new StringBuilder("IsotonicRegression{");
        for (int i=0;i<locations.length;i++){
            sb.append(locations[i]).append(":").append(values[i]).append(", ");
        }
        sb.append('}');
        return sb.toString();
    }

    @Override
    public double predict(Vector vector) {
        return predict(vector.get(0));
    }

    @Override
    public FeatureList getFeatureList() {
        return null;
    }

    private static class Element{
        Element(double location, double number, double weight) {
            this.location = location;
            this.number = number;
            this.weight = weight;
        }

        private double location;
        private double number;
        private double weight;
    }



    public static class WeightedInput{
        private double[] locationNonEmpty;
        private double[] accsNonempty;
        private double[] countsNonEmpty;

        public WeightedInput(Stream<Pair<Double,Double>> stream) {
            //todo deal with the stream better
            List<Pair<Double,Double>> streamCopy = stream.collect(Collectors.toList());
            double min = streamCopy.stream().mapToDouble(Pair::getFirst).min().getAsDouble();
            double max = streamCopy.stream().mapToDouble(Pair::getFirst).max().getAsDouble();
            final int numBuckets = 10000;
            double bucketLength = (max-min)/numBuckets;
            double[] locations = new double[numBuckets];
            for (int i=0;i<numBuckets;i++){
                locations[i]= min+i*bucketLength + 0.5*bucketLength;
            }


            BucketInfo total = BucketInfo.aggregate(streamCopy.parallelStream(), numBuckets, min, max);

            double[] counts = total.counts;
            double[] sums = total.sumLabels;


            this.countsNonEmpty = IntStream.range(0,counts.length).filter(i->counts[i]!=0)
                    .mapToDouble(i->counts[i]).toArray();
            this.locationNonEmpty = IntStream.range(0,counts.length).filter(i->counts[i]!=0)
                    .mapToDouble(i->locations[i]).toArray();
            double[] sumNonempty = IntStream.range(0,counts.length).filter(i->counts[i]!=0)
                    .mapToDouble(i->sums[i]).toArray();
            this. accsNonempty = IntStream.range(0,countsNonEmpty.length)
                    .mapToDouble(i->sumNonempty[i]/countsNonEmpty[i]).toArray();
        }


        @Override
        public String toString() {
            final StringBuilder sb = new StringBuilder("WeightedInput{");
            sb.append("locationNonEmpty=").append(Arrays.toString(locationNonEmpty));
            sb.append(", accsNonempty=").append(Arrays.toString(accsNonempty));
            sb.append(", countsNonEmpty=").append(Arrays.toString(countsNonEmpty));
            sb.append('}');
            return sb.toString();
        }
    }
}

<MSG> integrate calibration into App3

<DFF> @@ -2,6 +2,7 @@ package edu.neu.ccs.pyramid.regression;
 
 import edu.neu.ccs.pyramid.util.Pair;
 
+import java.io.Serializable;
 import java.util.Arrays;
 import java.util.Comparator;
 import java.util.List;
@@ -13,7 +14,8 @@ import java.util.stream.IntStream;
  * http://stat.wikia.com/wiki/Isotonic_regression
  */
 
-public class IsotonicRegression {
+public class IsotonicRegression implements Serializable{
+    private static final long serialVersionUID = 1L;
     //sorted locations
     private double[] locations;
     private double[] values;

{'addition_count': 3, 'commit_subject': 'integrate calibration into App3', 'deletion_count': 1, 'file_extension': '.java', 'lang': 'java', 'license': 'apache-2.0', 'repo_name': 'cheng-li/pyramid'}