◆ aggregateStatisticsByRange() [1/4]
| AggregateStatisticsByRangeResponse gpudb::GPUdb::aggregateStatisticsByRange | ( | const AggregateStatisticsByRangeRequest & | request_ | ) | const |
Divides the given set into bins and calculates statistics of the values of a value-column in each bin.
The bins are based on the values of a given binning-column. The statistics that may be requested are mean, stdv (standard deviation), variance, skew, kurtosis, sum, min, max, first, last and weighted average. In addition to the requested statistics the count of total samples in each bin is returned. This counts vector is just the histogram of the column used to divide the set members into bins. The weighted average statistic requires a weight column to be specified in weight_column_name. The weighted average is then defined as the sum of the products of the value column times the weight column divided by the sum of the weight column.
There are two methods for binning the set members. In the first, which can be used for numeric valued binning-columns, a min, max and interval are specified. The number of bins, nbins, is the integer upper bound of (max-min)/interval. Values that fall in the range [min+n*interval,min+(n+1)*interval) are placed in the nth bin where n ranges from 0..nbin-2. The final bin is [min+(nbin-1)*interval,max]. In the second method, bin_values specifies a list of binning column values. Binning-columns whose value matches the nth member of the bin_values list are placed in the nth bin. When a list is provided, the binning-column must be of type string or int.
NOTE: The Kinetica instance being accessed must be running a CUDA (GPU-based) build to service this request.
| [in] | request_ | Request object containing the parameters for the operation. |
◆ aggregateStatisticsByRange() [2/4]
| AggregateStatisticsByRangeResponse & gpudb::GPUdb::aggregateStatisticsByRange | ( | const AggregateStatisticsByRangeRequest & | request_, |
| AggregateStatisticsByRangeResponse & | response_ ) const |
Divides the given set into bins and calculates statistics of the values of a value-column in each bin.
The bins are based on the values of a given binning-column. The statistics that may be requested are mean, stdv (standard deviation), variance, skew, kurtosis, sum, min, max, first, last and weighted average. In addition to the requested statistics the count of total samples in each bin is returned. This counts vector is just the histogram of the column used to divide the set members into bins. The weighted average statistic requires a weight column to be specified in weight_column_name. The weighted average is then defined as the sum of the products of the value column times the weight column divided by the sum of the weight column.
There are two methods for binning the set members. In the first, which can be used for numeric valued binning-columns, a min, max and interval are specified. The number of bins, nbins, is the integer upper bound of (max-min)/interval. Values that fall in the range [min+n*interval,min+(n+1)*interval) are placed in the nth bin where n ranges from 0..nbin-2. The final bin is [min+(nbin-1)*interval,max]. In the second method, bin_values specifies a list of binning column values. Binning-columns whose value matches the nth member of the bin_values list are placed in the nth bin. When a list is provided, the binning-column must be of type string or int.
NOTE: The Kinetica instance being accessed must be running a CUDA (GPU-based) build to service this request.
◆ aggregateStatisticsByRange() [3/4]
| AggregateStatisticsByRangeResponse gpudb::GPUdb::aggregateStatisticsByRange | ( | const std::string & | tableName, |
| const std::string & | selectExpression, | ||
| const std::string & | columnName, | ||
| const std::string & | valueColumnName, | ||
| const std::string & | stats, | ||
| const double | start, | ||
| const double | end, | ||
| const double | interval, | ||
| const std::map< std::string, std::string > & | options ) const |
Divides the given set into bins and calculates statistics of the values of a value-column in each bin.
The bins are based on the values of a given binning-column. The statistics that may be requested are mean, stdv (standard deviation), variance, skew, kurtosis, sum, min, max, first, last and weighted average. In addition to the requested statistics the count of total samples in each bin is returned. This counts vector is just the histogram of the column used to divide the set members into bins. The weighted average statistic requires a weight column to be specified in weight_column_name. The weighted average is then defined as the sum of the products of the value column times the weight column divided by the sum of the weight column.
There are two methods for binning the set members. In the first, which can be used for numeric valued binning-columns, a min, max and interval are specified. The number of bins, nbins, is the integer upper bound of (max-min)/interval. Values that fall in the range [min+n*interval,min+(n+1)*interval) are placed in the nth bin where n ranges from 0..nbin-2. The final bin is [min+(nbin-1)*interval,max]. In the second method, bin_values specifies a list of binning column values. Binning-columns whose value matches the nth member of the bin_values list are placed in the nth bin. When a list is provided, the binning-column must be of type string or int.
NOTE: The Kinetica instance being accessed must be running a CUDA (GPU-based) build to service this request.
| [in] | tableName | Name of the table on which the ranged-statistics operation will be performed, in [schema_name.]table_name format, using standard name resolution rules. |
| [in] | selectExpression | For a non-empty expression statistics are calculated for those records for which the expression is true. The default value is ”. |
| [in] | columnName | Name of the binning-column used to divide the set samples into bins. |
| [in] | valueColumnName | Name of the value-column for which statistics are to be computed. |
| [in] | stats | A string of comma separated list of the statistics to calculate, e.g. ‘sum,mean’. Available statistics: mean, stdv (standard deviation), variance, skew, kurtosis, sum. |
| [in] | start | The lower bound of the binning-column. |
| [in] | end | The upper bound of the binning-column. |
| [in] | interval | The interval of a bin. Set members fall into bin i if the binning-column falls in the range [start+interval*i, start+interval*(i+1)). |
| [in] | options | Optional parameters.
|
◆ aggregateStatisticsByRange() [4/4]
| AggregateStatisticsByRangeResponse & gpudb::GPUdb::aggregateStatisticsByRange | ( | const std::string & | tableName, |
| const std::string & | selectExpression, | ||
| const std::string & | columnName, | ||
| const std::string & | valueColumnName, | ||
| const std::string & | stats, | ||
| const double | start, | ||
| const double | end, | ||
| const double | interval, | ||
| const std::map< std::string, std::string > & | options, | ||
| AggregateStatisticsByRangeResponse & | response_ ) const |
Divides the given set into bins and calculates statistics of the values of a value-column in each bin.
The bins are based on the values of a given binning-column. The statistics that may be requested are mean, stdv (standard deviation), variance, skew, kurtosis, sum, min, max, first, last and weighted average. In addition to the requested statistics the count of total samples in each bin is returned. This counts vector is just the histogram of the column used to divide the set members into bins. The weighted average statistic requires a weight column to be specified in weight_column_name. The weighted average is then defined as the sum of the products of the value column times the weight column divided by the sum of the weight column.
There are two methods for binning the set members. In the first, which can be used for numeric valued binning-columns, a min, max and interval are specified. The number of bins, nbins, is the integer upper bound of (max-min)/interval. Values that fall in the range [min+n*interval,min+(n+1)*interval) are placed in the nth bin where n ranges from 0..nbin-2. The final bin is [min+(nbin-1)*interval,max]. In the second method, bin_values specifies a list of binning column values. Binning-columns whose value matches the nth member of the bin_values list are placed in the nth bin. When a list is provided, the binning-column must be of type string or int.
NOTE: The Kinetica instance being accessed must be running a CUDA (GPU-based) build to service this request.
| [in] | tableName | Name of the table on which the ranged-statistics operation will be performed, in [schema_name.]table_name format, using standard name resolution rules. |
| [in] | selectExpression | For a non-empty expression statistics are calculated for those records for which the expression is true. The default value is ”. |
| [in] | columnName | Name of the binning-column used to divide the set samples into bins. |
| [in] | valueColumnName | Name of the value-column for which statistics are to be computed. |
| [in] | stats | A string of comma separated list of the statistics to calculate, e.g. ‘sum,mean’. Available statistics: mean, stdv (standard deviation), variance, skew, kurtosis, sum. |
| [in] | start | The lower bound of the binning-column. |
| [in] | end | The upper bound of the binning-column. |
| [in] | interval | The interval of a bin. Set members fall into bin i if the binning-column falls in the range [start+interval*i, start+interval*(i+1)). |
| [in] | options | Optional parameters.
|
| [out] | response_ | Response object containing the results of the operation. |