private void cookey(final int[] raw, final int KnL[]) {
        int raw0, raw1;
        int rawi, KnLi;
        int i;

        for (i = 0, rawi = 0, KnLi = 0; i < 16; ++i) {
            raw0 = raw[rawi];
            rawi++;
            raw1 = raw[rawi++];
            KnL[KnLi] = (raw0 & 0x00fc0000) << 6;
            KnL[KnLi] |= (raw0 & 0x00000fc0) << 10;
            KnL[KnLi] |= (raw1 & 0x00fc0000) >>> 10;

 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,
 * either express or implied. See the License for the specific language
 * governing permissions and limitations under the License.
 */
package org.codelibs.fess.rank.fusion;

import java.util.HashMap;
import java.util.Map;

import org.apache.logging.log4j.LogManager;
import org.apache.logging.log4j.Logger;
import org.codelibs.core.collection.ArrayUtil;

searchSourceJSON":"{\"query\":{\"query\":\"\",\"language\":\"kuery\"},\"filter\":[],\"indexRefName\":\"kibanaSavedObjectMeta.searchSourceJSON.index\"}"},"title":"search-term-rank","uiStateJSON":"{\"vis\":{\"params\":{\"sort\":{\"columnIndex\":null,\"direction\":null}}}}","version":1,"visState":"{\"title\":\"search-term-rank\",\"type\":\"table\",\"params\":{\"perPage\":10,\"showPartialRows\":false,\"showMetricsAtAllLevels\":false,\"sort\":{\"columnIndex\":null,\"direction\":null},\"showTotal\":fa...

    Random rand = new Random(0);
    int keyBits = 32;
    int hashBits = function.bits();
    for (int i = 0; i < keyBits; i++) {
      int[] same = new int[hashBits];
      int[] diff = new int[hashBits];
      // go through trials to compute probability
      for (int j = 0; j < trials; j++) {
        int key1 = rand.nextInt();
        // flip input bit for key2

    Random rand = new Random(0);
    int keyBits = 32;
    int hashBits = function.bits();
    for (int i = 0; i < keyBits; i++) {
      int[] same = new int[hashBits];
      int[] diff = new int[hashBits];
      // go through trials to compute probability
      for (int j = 0; j < trials; j++) {
        int key1 = rand.nextInt();
        // flip input bit for key2

 * governing permissions and limitations under the License.
 */
package org.codelibs.fess.rank.fusion;

import java.util.ArrayList;
import java.util.List;
import java.util.Map;

import org.apache.lucene.search.TotalHits.Relation;
import org.codelibs.fess.util.FacetResponse;

/**
 * Represents the result of a search operation in the rank fusion system.
 *
 * This class encapsulates all the information returned from a search query,

  abstract Map<Integer, Double> multipleQuantiles(
      Collection<Integer> indexes, int scale, double[] dataset);

  static double getMinValue(double[] array, int from) {
    // This is basically a copy of com.google.math.Rank#getMinValue, with a small change in the
    // method signature: we always search to the end of the array.
    int min = from;
    for (int i = from + 1; i < array.length; i++) {
      if (array[min] > array[i]) {

import org.codelibs.fess.util.ComponentUtil;
import org.opensearch.search.rescore.QueryRescorerBuilder;
import org.opensearch.search.rescore.RescorerBuilder;

/**
 * Learning to Rank query rescorer implementation.
 */
public class LtrQueryRescorer implements QueryRescorer {

    /**
     * Default constructor.
     */
    public LtrQueryRescorer() {
        // Default constructor
    }

import org.opensearch.index.query.QueryBuilder;
import org.opensearch.index.query.QueryShardContext;

/**
 * A query builder for a stored LTR (Learning to Rank) query.
 * This builder constructs a query that uses a pre-trained LTR model
 * to re-rank search results based on a given set of features.
 */
public class StoredLtrQueryBuilder extends AbstractQueryBuilder<StoredLtrQueryBuilder> implements NamedWriteable {

    final T value;
    final int rank;

    Composite(T value, int rank) {
      this.value = value;
      this.rank = rank;
    }

    // natural order is by rank only; the test will compound() this with the
    // order of 't'.
    @Override
    public int compareTo(Composite<T> that) {
      return Integer.compare(rank, that.rank);
    }