当前位置: 首页>>代码示例>>Java>>正文

Java ValueDataUtil.divide方法代码示例

本文整理汇总了Java中org.pentaho.di.core.row.ValueDataUtil.divide方法的典型用法代码示例。如果您正苦于以下问题：Java ValueDataUtil.divide方法的具体用法？Java ValueDataUtil.divide怎么用？Java ValueDataUtil.divide使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类org.pentaho.di.core.row.ValueDataUtil的用法示例。


在下文中一共展示了ValueDataUtil.divide方法的8个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。

示例1: getAggregateResult

import org.pentaho.di.core.row.ValueDataUtil; //导入方法依赖的package包/类
private Object[] getAggregateResult() throws KettleValueException
{
    Object[] result = new Object[data.subjectnrs.length];

    if (data.subjectnrs!=null)
    {
        for (int i=0;i<data.subjectnrs.length;i++)
        {
            Object ag = data.agg[i];
            switch(meta.getAggregateType()[i])
            {
                case GroupByMeta.TYPE_GROUP_SUM            : break; 
                case GroupByMeta.TYPE_GROUP_AVERAGE        :
                		ag=ValueDataUtil.divide(data.aggMeta.getValueMeta(i), ag, 
                				new ValueMeta("c",ValueMetaInterface.TYPE_INTEGER), new Long(data.counts[i])); 
                		break;  //$NON-NLS-1$
                case GroupByMeta.TYPE_GROUP_COUNT_ALL      : ag=new Long(data.counts[i]); break;
                case GroupByMeta.TYPE_GROUP_COUNT_DISTINCT : break;
                case GroupByMeta.TYPE_GROUP_MIN            : break; 
                case GroupByMeta.TYPE_GROUP_MAX            : break; 
                case GroupByMeta.TYPE_GROUP_STANDARD_DEVIATION :
                	double sum = (Double)ag / data.counts[i];
                	ag = Double.valueOf( Math.sqrt( sum ) );
                	break;
                case GroupByMeta.TYPE_GROUP_CONCAT_COMMA:;
                case GroupByMeta.TYPE_GROUP_CONCAT_STRING: 
                    ag = ((StringBuilder) ag).toString();
                    break;
                default: break;
            }
            result[i]=ag;
        }
    }

    return result;

}
开发者ID:yintaoxue，项目名称:read-open-source-code，代码行数:38，代码来源:GroupBy.java

示例2: getAggregateResult

import org.pentaho.di.core.row.ValueDataUtil; //导入方法依赖的package包/类
private Object[] getAggregateResult(Aggregate aggregate) throws KettleValueException
{
    Object[] result = new Object[data.subjectnrs.length];

    if (data.subjectnrs!=null)
    {
        for (int i=0;i<data.subjectnrs.length;i++)
        {
            Object ag = aggregate.agg[i];
            switch(meta.getAggregateType()[i])
            {
                case MemoryGroupByMeta.TYPE_GROUP_SUM            : break; 
                case MemoryGroupByMeta.TYPE_GROUP_AVERAGE        :
                		ag=ValueDataUtil.divide(data.aggMeta.getValueMeta(i), ag, 
                				new ValueMeta("c",ValueMetaInterface.TYPE_INTEGER), new Long(aggregate.counts[i])); 
                		break;  //$NON-NLS-1$
                case MemoryGroupByMeta.TYPE_GROUP_COUNT_ALL      : ag=new Long(aggregate.counts[i]); break;
                case MemoryGroupByMeta.TYPE_GROUP_COUNT_DISTINCT : break;
                case MemoryGroupByMeta.TYPE_GROUP_MIN            : break; 
                case MemoryGroupByMeta.TYPE_GROUP_MAX            : break; 
                case MemoryGroupByMeta.TYPE_GROUP_STANDARD_DEVIATION :
                	double sum = (Double)ag / aggregate.counts[i];
                	ag = Double.valueOf( Math.sqrt( sum ) );
                	break;
                case GroupByMeta.TYPE_GROUP_CONCAT_COMMA:;
                case GroupByMeta.TYPE_GROUP_CONCAT_STRING: 
                  ag = ((StringBuilder) ag).toString();
                  break;
                default: break;
            }
            result[i]=ag;
        }
    }

    return result;

}
开发者ID:yintaoxue，项目名称:read-open-source-code，代码行数:38，代码来源:MemoryGroupBy.java

示例3: getAggregateResult

import org.pentaho.di.core.row.ValueDataUtil; //导入方法依赖的package包/类
private Object[] getAggregateResult() throws KettleValueException
{
    Object[] result = new Object[data.subjectnrs.length];

    if (data.subjectnrs!=null)
    {
        for (int i=0;i<data.subjectnrs.length;i++)
        {
            Object ag = data.agg[i];
            switch(meta.getAggregateType()[i])
            {
                case GroupByMeta.TYPE_GROUP_SUM            : break; 
                case GroupByMeta.TYPE_GROUP_AVERAGE        :
                		ag=ValueDataUtil.divide(data.aggMeta.getValueMeta(i), ag, 
                				new ValueMeta("c",ValueMetaInterface.TYPE_INTEGER), new Long(data.counts[i])); 
                		break;  //$NON-NLS-1$
                case GroupByMeta.TYPE_GROUP_COUNT_ANY      : 
                case GroupByMeta.TYPE_GROUP_COUNT_ALL      : ag=new Long(data.counts[i]); break;
                case GroupByMeta.TYPE_GROUP_COUNT_DISTINCT : break;
                case GroupByMeta.TYPE_GROUP_MIN            : break; 
                case GroupByMeta.TYPE_GROUP_MAX            : break; 
                case GroupByMeta.TYPE_GROUP_STANDARD_DEVIATION :
                	double sum = (Double)ag / data.counts[i];
                	ag = Double.valueOf( Math.sqrt( sum ) );
                	break;
                case GroupByMeta.TYPE_GROUP_CONCAT_COMMA:;
                case GroupByMeta.TYPE_GROUP_CONCAT_STRING: 
                    ag = ((StringBuilder) ag).toString();
                    break;
                default: break;
            }
            result[i]=ag;
        }
    }

    return result;

}
开发者ID:jjeb，项目名称:kettle-trunk，代码行数:39，代码来源:GroupBy.java

示例4: getAggregateResult

import org.pentaho.di.core.row.ValueDataUtil; //导入方法依赖的package包/类
private Object[] getAggregateResult(Aggregate aggregate) throws KettleValueException
{
    Object[] result = new Object[data.subjectnrs.length];

    if (data.subjectnrs!=null)
    {
        for (int i=0;i<data.subjectnrs.length;i++)
        {
            Object ag = aggregate.agg[i];
            switch(meta.getAggregateType()[i])
            {
                case MemoryGroupByMeta.TYPE_GROUP_SUM            : break; 
                case MemoryGroupByMeta.TYPE_GROUP_AVERAGE        :
                		ag=ValueDataUtil.divide(data.aggMeta.getValueMeta(i), ag, 
                				new ValueMeta("c",ValueMetaInterface.TYPE_INTEGER), new Long(aggregate.counts[i])); 
                		break;  //$NON-NLS-1$
                case MemoryGroupByMeta.TYPE_GROUP_COUNT_ANY      : 
                case MemoryGroupByMeta.TYPE_GROUP_COUNT_ALL      : ag=new Long(aggregate.counts[i]); break;
                case MemoryGroupByMeta.TYPE_GROUP_COUNT_DISTINCT : break;
                case MemoryGroupByMeta.TYPE_GROUP_MIN            : break; 
                case MemoryGroupByMeta.TYPE_GROUP_MAX            : break; 
                case MemoryGroupByMeta.TYPE_GROUP_STANDARD_DEVIATION :
                	double sum = (Double)ag / aggregate.counts[i];
                	ag = Double.valueOf( Math.sqrt( sum ) );
                	break;
                case MemoryGroupByMeta.TYPE_GROUP_CONCAT_COMMA:;
                case MemoryGroupByMeta.TYPE_GROUP_CONCAT_STRING: 
                    ag = ((StringBuilder) ag).toString();
                    break;
                default: break;
            }
            result[i]=ag;
        }
    }

    return result;

}
开发者ID:jjeb，项目名称:kettle-trunk，代码行数:39，代码来源:MemoryGroupBy.java

示例5: addCumulativeAverages

import org.pentaho.di.core.row.ValueDataUtil; //导入方法依赖的package包/类
private void addCumulativeAverages(Object[] row) throws KettleValueException {

		// We need to adjust this row with cumulative sums
        //
        for (int i=0;i<data.cumulativeAvgSourceIndexes.size();i++)
        {
        	int sourceIndex = data.cumulativeAvgSourceIndexes.get(i);
        	Object previousTarget = data.previousAvgSum[i];
        	Object sourceValue = row[sourceIndex];
        	
        	int targetIndex = data.cumulativeAvgTargetIndexes.get(i);
        	
        	ValueMetaInterface sourceMeta = data.inputRowMeta.getValueMeta(sourceIndex);
    		ValueMetaInterface targetMeta = data.outputRowMeta.getValueMeta(targetIndex);

    		// If the first values where null, or this is the first time around, just take the source value...
    		//
    		Object sum = null;
    		
        	if (targetMeta.isNull(previousTarget))
        	{
        		sum=sourceMeta.convertToNormalStorageType(sourceValue);
        	}
        	else
        	{
        		// If the source value is null, just take the previous target value
        		//
        		if (sourceMeta.isNull(sourceValue))
        		{
        			sum = previousTarget;
        		}
        		else
        		{
        			if (sourceMeta.isInteger())
        			{
        				sum = ValueDataUtil.plus(data.valueMetaInteger, data.previousAvgSum[i], sourceMeta, row[sourceIndex]);
        			}
        			else
        			{
        				sum = ValueDataUtil.plus(targetMeta, data.previousAvgSum[i], sourceMeta, row[sourceIndex]);
        			}
        		}
        	}
        	data.previousAvgSum[i] = sum;

        	if (!sourceMeta.isNull(sourceValue)) data.previousAvgCount[i]++;
        	
        	if (sourceMeta.isInteger()) {
        		// Change to number as the exception
        		//
        		if (sum==null)
        		{
        			row[targetIndex] = null;
        		}
        		else 
        		{
        			row[targetIndex] = new Double( ((Long)sum).doubleValue() / data.previousAvgCount[i] );
        		}
        	}
        	else
        	{
        		row[targetIndex] = ValueDataUtil.divide(targetMeta, sum, data.valueMetaInteger, data.previousAvgCount[i]);
        	}
        }

	}
开发者ID:icholy，项目名称:geokettle-2.0，代码行数:67，代码来源:GroupBy.java

示例6: addCumulativeAverages

import org.pentaho.di.core.row.ValueDataUtil; //导入方法依赖的package包/类
private void addCumulativeAverages( Object[] row ) throws KettleValueException {

    // We need to adjust this row with cumulative sums
    //
    for ( int i = 0; i < data.cumulativeAvgSourceIndexes.size(); i++ ) {
      int sourceIndex = data.cumulativeAvgSourceIndexes.get( i );
      Object previousTarget = data.previousAvgSum[ i ];
      Object sourceValue = row[ sourceIndex ];

      int targetIndex = data.cumulativeAvgTargetIndexes.get( i );

      ValueMetaInterface sourceMeta = data.inputRowMeta.getValueMeta( sourceIndex );
      ValueMetaInterface targetMeta = data.outputRowMeta.getValueMeta( targetIndex );

      // If the first values where null, or this is the first time around, just take the source value...
      //
      Object sum = null;

      if ( targetMeta.isNull( previousTarget ) ) {
        sum = sourceMeta.convertToNormalStorageType( sourceValue );
      } else {
        // If the source value is null, just take the previous target value
        //
        if ( sourceMeta.isNull( sourceValue ) ) {
          sum = previousTarget;
        } else {
          if ( sourceMeta.isInteger() ) {
            sum = ValueDataUtil.plus( data.valueMetaInteger, data.previousAvgSum[ i ], sourceMeta, row[ sourceIndex ] );
          } else {
            sum = ValueDataUtil.plus( targetMeta, data.previousAvgSum[ i ], sourceMeta, row[ sourceIndex ] );
          }
        }
      }
      data.previousAvgSum[ i ] = sum;

      if ( !sourceMeta.isNull( sourceValue ) ) {
        data.previousAvgCount[ i ]++;
      }

      if ( sourceMeta.isInteger() ) {
        // Change to number as the exception
        //
        if ( sum == null ) {
          row[ targetIndex ] = null;
        } else {
          row[ targetIndex ] = new Double( ( (Long) sum ).doubleValue() / data.previousAvgCount[ i ] );
        }
      } else {
        row[ targetIndex ] = ValueDataUtil.divide( targetMeta, sum, data.valueMetaInteger, data.previousAvgCount[ i ] );
      }
    }

  }
开发者ID:pentaho，项目名称:pentaho-kettle，代码行数:54，代码来源:GroupBy.java

示例7: getAggregateResult

import org.pentaho.di.core.row.ValueDataUtil; //导入方法依赖的package包/类
/**
 * Used for junits in GroupByAggregationNullsTest
 *
 * @return
 * @throws KettleValueException
 */
Object[] getAggregateResult() throws KettleValueException {

  if ( data.subjectnrs == null ) {
    return new Object[ 0 ];
  }

  Object[] result = new Object[ data.subjectnrs.length ];

  for ( int i = 0; i < data.subjectnrs.length; i++ ) {
    Object ag = data.agg[ i ];
    switch ( meta.getAggregateType()[ i ] ) {
      case GroupByMeta.TYPE_GROUP_SUM:
        break;
      case GroupByMeta.TYPE_GROUP_AVERAGE:
        ag =
            ValueDataUtil.divide( data.aggMeta.getValueMeta( i ), ag,
                new ValueMetaInteger( "c" ), new Long( data.counts[ i ] ) );
        break;
      case GroupByMeta.TYPE_GROUP_MEDIAN:
      case GroupByMeta.TYPE_GROUP_PERCENTILE:
        double percentile = 50.0;
        if ( meta.getAggregateType()[ i ] == GroupByMeta.TYPE_GROUP_PERCENTILE ) {
          percentile = Double.parseDouble( meta.getValueField()[ i ] );
        }
        @SuppressWarnings( "unchecked" )
        List<Double> valuesList = (List<Double>) data.agg[ i ];
        double[] values = new double[ valuesList.size() ];
        for ( int v = 0; v < values.length; v++ ) {
          values[ v ] = valuesList.get( v );
        }
        ag = new Percentile().evaluate( values, percentile );
        break;
      case GroupByMeta.TYPE_GROUP_COUNT_ANY:
      case GroupByMeta.TYPE_GROUP_COUNT_ALL:
        ag = new Long( data.counts[ i ] );
        break;
      case GroupByMeta.TYPE_GROUP_COUNT_DISTINCT:
        break;
      case GroupByMeta.TYPE_GROUP_MIN:
        break;
      case GroupByMeta.TYPE_GROUP_MAX:
        break;
      case GroupByMeta.TYPE_GROUP_STANDARD_DEVIATION:
        if ( ag == null ) {
          // PMD-1037 - when all input data is null ag is null, npe on access ag
          break;
        }
        double sum = (Double) ag / data.counts[ i ];
        ag = Double.valueOf( Math.sqrt( sum ) );
        break;
      case GroupByMeta.TYPE_GROUP_CONCAT_COMMA:
      case GroupByMeta.TYPE_GROUP_CONCAT_STRING:
        ag = ( (StringBuilder) ag ).toString();
        break;
      default:
        break;
    }
    if ( ag == null && allNullsAreZero ) {
      // PDI-10250, 6960 seems all rows for min function was nulls...
      // get output subject meta based on original subject meta calculation
      ValueMetaInterface vm = data.aggMeta.getValueMeta( i );
      ag = ValueDataUtil.getZeroForValueMetaType( vm );
    }
    result[ i ] = ag;
  }

  return result;

}
开发者ID:pentaho，项目名称:pentaho-kettle，代码行数:76，代码来源:GroupBy.java

示例8: getAggregateResult

import org.pentaho.di.core.row.ValueDataUtil; //导入方法依赖的package包/类
/**
 * Used for junits in MemoryGroupByAggregationNullsTest
 *
 * @param aggregate
 * @return
 * @throws KettleValueException
 */
Object[] getAggregateResult( Aggregate aggregate ) throws KettleValueException {
  Object[] result = new Object[data.subjectnrs.length];

  if ( data.subjectnrs != null ) {
    for ( int i = 0; i < data.subjectnrs.length; i++ ) {
      Object ag = aggregate.agg[i];
      switch ( meta.getAggregateType()[i] ) {
        case MemoryGroupByMeta.TYPE_GROUP_SUM:
          break;
        case MemoryGroupByMeta.TYPE_GROUP_AVERAGE:
          ag = ValueDataUtil.divide(
            data.aggMeta.getValueMeta( i ), ag,
            new ValueMetaInteger( "c" ), aggregate.counts[i]
          );
          break;
        case MemoryGroupByMeta.TYPE_GROUP_MEDIAN:
        case MemoryGroupByMeta.TYPE_GROUP_PERCENTILE:
          double percentile = 50.0;
          if ( meta.getAggregateType()[i] == MemoryGroupByMeta.TYPE_GROUP_PERCENTILE ) {
            percentile = Double.parseDouble( meta.getValueField()[i] );
          }
          @SuppressWarnings( "unchecked" )
          List<Double> valuesList = (List<Double>) aggregate.agg[i];
          double[] values = new double[valuesList.size()];
          for ( int v = 0; v < values.length; v++ ) {
            values[v] = valuesList.get( v );
          }
          ag = new Percentile().evaluate( values, percentile );
          break;
        case MemoryGroupByMeta.TYPE_GROUP_COUNT_ANY:
        case MemoryGroupByMeta.TYPE_GROUP_COUNT_ALL:
        case MemoryGroupByMeta.TYPE_GROUP_COUNT_DISTINCT:
          ag = aggregate.counts[i];
          break;
        case MemoryGroupByMeta.TYPE_GROUP_MIN:
          break;
        case MemoryGroupByMeta.TYPE_GROUP_MAX:
          break;
        case MemoryGroupByMeta.TYPE_GROUP_STANDARD_DEVIATION:
          double sum = (Double) ag / aggregate.counts[i];
          ag = Double.valueOf( Math.sqrt( sum ) );
          break;
        case MemoryGroupByMeta.TYPE_GROUP_CONCAT_COMMA:
        case MemoryGroupByMeta.TYPE_GROUP_CONCAT_STRING:
          ag = ( (StringBuilder) ag ).toString();
          break;
        default:
          break;
      }
      if ( ag == null && allNullsAreZero ) {
        // PDI-11530 seems all rows for min function was nulls...
        ValueMetaInterface vm = data.aggMeta.getValueMeta( i );
        ag = ValueDataUtil.getZeroForValueMetaType( vm );
      }
      result[i] = ag;
    }
  }

  return result;

}
开发者ID:pentaho，项目名称:pentaho-kettle，代码行数:69，代码来源:MemoryGroupBy.java


注：本文中的org.pentaho.di.core.row.ValueDataUtil.divide方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。