Non-functional changes only

1 files changed, 12 insertions, 0 deletions

diff --git a/container-search/src/main/java/com/yahoo/search/dispatch/TopKEstimator.java b/container-search/src/main/java/com/yahoo/search/dispatch/TopKEstimator.java
index aef1ef2f498..315dfdd4320 100644
--- a/container-search/src/main/java/com/yahoo/search/dispatch/TopKEstimator.java
+++ b/container-search/src/main/java/com/yahoo/search/dispatch/TopKEstimator.java
@@ -6,9 +6,11 @@ import org.apache.commons.math3.distribution.TDistribution;
 /**
  * Use StudentT distribution and estimate how many hits you need from each partition
  * to to get the globally top-k documents with the desired probability
+ *
  * @author baldersheim
  */
 public class TopKEstimator {
+
     private final TDistribution studentT;
     private final double defaultP;
     private final boolean estimate;
@@ -19,9 +21,11 @@ public class TopKEstimator {
     private static boolean needEstimate(double p) {
         return (0.0 < p) && (p < 1.0);
     }
+
     TopKEstimator(double freedom, double defaultProbability) {
         this(freedom, defaultProbability, 0.0);
     }
+
     public TopKEstimator(double freedom, double defaultProbability, double skewFactor) {
         this.studentT = new TDistribution(null, freedom);
         defaultP = defaultProbability;
@@ -32,36 +36,44 @@ public class TopKEstimator {
             defaultCumulativeProbability[i] = computeCumulativeProbability(i+MIN_N, defaultP);
         }
     }
+
     private double inverseCumulativeProbability(int n, double p) {
         if (p == defaultP && (n >= MIN_N) && (n < defaultCumulativeProbability.length + MIN_N)) {
             return defaultCumulativeProbability[n - MIN_N];
         }
         return computeCumulativeProbability(n, p);
     }
+
     private double computeCumulativeProbability(int n, double p) {
         double p_inverse = 1 - (1 - p)/computeN(n);
         return studentT.inverseCumulativeProbability(p_inverse);
     }
+
     private double computeN(double n) {
         double p_max = (1 + skewFactor)/n;
         return Math.max(1, 1/p_max);
     }
+
     double estimateExactK(double k, int n_i, double p) {
         double n = computeN(n_i);
         double variance = k * 1/n * (1 - 1/n);
         return k/n + inverseCumulativeProbability(n_i, p) * Math.sqrt(variance);
     }
+
     double estimateExactK(double k, int n) {
         return estimateExactK(k, n, defaultP);
     }
+
     public int estimateK(int k, int n) {
         return (estimate && (n >= MIN_N))
                 ? Math.min(k, (int)Math.ceil(estimateExactK(k, n, defaultP)))
                 : k;
     }
+
     public int estimateK(int k, int n, double p) {
         return (needEstimate(p) && (n >= MIN_N))
                 ? Math.min(k, (int)Math.ceil(estimateExactK(k, n, p)))
                 : k;
     }
 }
+