}

    /**
     * Initializes the rank fusion processor after construction.
     * Sets up the window size based on configuration and loads available searchers.
     * This method is called automatically after the bean is constructed.
     */
    @PostConstruct
    public void init() {
        final FessConfig fessConfig = ComponentUtil.getFessConfig();
        final int maxPageSize = fessConfig.getPagingSearchPageMaxSizeAsInteger();

     * Applies filters based on the pager criteria such as ID and session ID,
     * and orders results by creation time in descending order.
     *
     * @param cb the condition bean for building the database query
     * @param crawlingInfoPager the pager containing filter criteria
     */
    protected void setupListCondition(final CrawlingInfoCB cb, final CrawlingInfoPager crawlingInfoPager) {

import org.opensearch.search.SearchHits;
import org.opensearch.transport.client.Client;

import jakarta.annotation.Resource;

/**
 * @param <ENTITY> The type of entity.
 * @param <CB> The type of condition-bean.
 * @author ESFlute (using FreeGen)
 */
public abstract class EsAbstractBehavior<ENTITY extends Entity, CB extends ConditionBean> extends AbstractBehaviorWritable<ENTITY, CB> {

    @Resource
    private Client client;

import org.codehaus.plexus.component.repository.ComponentDescriptor;
import org.codehaus.plexus.configuration.PlexusConfiguration;
import org.codehaus.plexus.configuration.xml.XmlPlexusConfiguration;

/**
 * The bean containing the Mojo descriptor.<br>
 * For more information about the usage tag, have a look to:
 * <a href="https://maven.apache.org/developers/mojo-api-specification.html">

        for (int i = 1; i < values.length; i++) {
          mean = mean + (values[i] - mean) / (i + 1);
        }
        return mean;
      }
    };

    abstract double mean(double[] values);
  }

  static class MeanAndVariance {
    private final double mean;
    private final double variance;

    MeanAndVariance(double mean, double variance) {
      this.mean = mean;
      this.variance = variance;
    }

        for (int i = 1; i < values.length; i++) {
          mean = mean + (values[i] - mean) / (i + 1);
        }
        return mean;
      }
    };

    abstract double mean(double[] values);
  }

  static class MeanAndVariance {
    private final double mean;
    private final double variance;

    MeanAndVariance(double mean, double variance) {
      this.mean = mean;
      this.variance = variance;
    }

      } else if (values.hasAnyNegativeInfinity()) {
        assertWithMessage("mean of %s", values).that(mean).isNegativeInfinity();
      } else {
        assertWithMessage("mean of %s", values)
            .that(mean)
            .isWithin(ALLOWED_ERROR)
            .of(MANY_VALUES_MEAN);
      }
    }

        mean += (value - mean) / (index + 1);
      } else {
        mean = calculateNewMeanNonFinite(mean, value);
      }
    }
    return mean;
  }

  /**
   * Returns the <a href="http://en.wikipedia.org/wiki/Arithmetic_mean">arithmetic mean</a> of the
   * values. The count must be non-zero.
   *
   * <p>The definition of the mean is the same as {@link Stats#mean}.
   *

   */
  @Deprecated
  public static double mean(long... values) {
    checkArgument(values.length > 0, "Cannot take mean of 0 values");
    long count = 1;
    double mean = values[0];
    for (int index = 1; index < values.length; ++index) {
      count++;
      // Art of Computer Programming vol. 2, Knuth, 4.2.2, (15)
      mean += (values[index] - mean) / count;
    }
    return mean;
  }

  /**

      }
    } else {
      count++;
      if (isFinite(value) && isFinite(mean)) {
        // Art of Computer Programming vol. 2, Knuth, 4.2.2, (15) and (16)
        double delta = value - mean;
        mean += delta / count;
        sumOfSquaresOfDeltas += delta * (value - mean);
      } else {
        mean = calculateNewMeanNonFinite(mean, value);
        sumOfSquaresOfDeltas = NaN;
      }
      min = Math.min(min, value);