org.knime.base.node.mine.sota.distances

## Class Distances

• ```public final class Distances
extends Object```
Author:
Kilian Thiel, University of Konstanz
• ### Method Summary

All Methods
Modifier and Type Method and Description
`static double` ```getCorrelationDistance(DataRow row1, DataRow row2, double offset, boolean abs, boolean fuzzy)```
Returns the coefficient of correlation distance between the rows with a given offset.
`static double` ```getCorrelationDistance(DataRow row, SotaTreeCell cell, double offset, boolean abs, boolean fuzzy)```
Returns the coefficient of correlation distance between the cells values and the number cells of the given row with a given offset.
`static double` ```getCosinusDistance(DataRow row1, DataRow row2, double offset, boolean fuzzy)```
Computes the cosinus distance between the given two rows, with given offset.
`static double` ```getCosinusDistance(DataRow row, SotaTreeCell cell, double offset, boolean fuzzy)```
Returns the cosinus distance between the cells values and the number cells of the given row with a given offset.
`static double` ```getEuclideanDistance(DataRow row1, DataRow row2, boolean fuzzy)```
Calculates the euclidean distance between two `DataRow`s using the Minkowski distance with power 2.
`static double` ```getEuclideanDistance(DataRow row, SotaTreeCell cell, boolean fuzzy)```
Returns the euclidean distance between a given `DataRow` and `SotaTreeCell` using the Minkowski distance with power 2.
`static double` ```getManhattanDistance(DataRow row1, DataRow row2, boolean fuzzy)```
Calculates the manhattan distance between two `DataRow`s using the Minkowski distance with power 1.
`static double` ```getManhattanDistance(DataRow row, SotaTreeCell cell, boolean fuzzy)```
Returns the manhattan distance between a given `DataRow` and `SotaTreeCell` using the Minkowski distance with power 1.
`static double` ```getMean(DataRow row, boolean fuzzy)```
Returns the mean value of the given row.
`static double` `getMean(SotaTreeCell cell)`
Returns the mean value of the given cell.
`static double` ```getMinkowskiDistance(int power, DataRow row1, DataRow row2, boolean fuzzy)```
Calculates the Minkowski distance between two rows.
`static double` ```getMinkowskiDistance(int power, DataRow row, SotaTreeCell cell, boolean fuzzy)```
Calculates the Minkowski distance between a regular `DataRow` and a `SotaTreeCell`.
`static double` ```getStandardDeviation(DataRow row, boolean fuzzy)```
Returns the standard deviation of the given row.
`static double` `getStandardDeviation(SotaTreeCell cell)`
Returns the standard deviation of the given cell.
• ### Methods inherited from class java.lang.Object

`clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`
• ### Method Detail

• #### getMinkowskiDistance

```public static double getMinkowskiDistance(int power,
DataRow row1,
DataRow row2,
boolean fuzzy)```
Calculates the Minkowski distance between two rows. If fuzzy is set true only columns with cells containing numbers are used to compute the distance. The given power specifies the distance kind, i.e. if power is set to 2 the euclidean distance will be computed.
Parameters:
`power` - The power to use.
`row1` - The first row
`row2` - The second row
`fuzzy` - If true only fuzzy data is taken into account, if `false` only number data.
Returns:
Minkowski distance between the two rows.
• #### getMinkowskiDistance

```public static double getMinkowskiDistance(int power,
DataRow row,
SotaTreeCell cell,
boolean fuzzy)```
Calculates the Minkowski distance between a regular `DataRow` and a `SotaTreeCell`. If fuzzy is set true only columns with cells containing numbers are used to compute the distance. If the number of columns, which are used to compute the distance, contained in the given `DataRow` is different to the number of cells contained in the given `SotaTreeCell`, only the first n columns of the `DataRow` or n cells of the `SotaTreeCell` are used to compute the distance. The rest is simply ignored. The given power specifies the distance kind, i.e. if power is set to 2 the euclidean distance will be computed.
Parameters:
`power` - The power to use.
`row` - The row to compute the distance.
`cell` - The cell to compute the distance.
`fuzzy` - If true only fuzzy data is taken into account, if `false` only number data.
Returns:
Minkowski distance between the two rows.
• #### getEuclideanDistance

```public static double getEuclideanDistance(DataRow row,
SotaTreeCell cell,
boolean fuzzy)```
Returns the euclidean distance between a given `DataRow` and `SotaTreeCell` using the Minkowski distance with power 2.
Parameters:
`row` - row to compute the distance
`cell` - cell to compute the distance
`fuzzy` - if `true` only fuzzy data is respected, if `false` only number data
Returns:
the euclidian distance between given row and cell
`getMinkowskiDistance(int, DataRow, DataRow, boolean)`
• #### getEuclideanDistance

```public static double getEuclideanDistance(DataRow row1,
DataRow row2,
boolean fuzzy)```
Calculates the euclidean distance between two `DataRow`s using the Minkowski distance with power 2.
Parameters:
`row1` - the first row
`row2` - the second row
`fuzzy` - if `true` only fuzzy data is respected, if `false` only number data
Returns:
distance between the two rows
`getMinkowskiDistance(int, DataRow, DataRow, boolean)`
• #### getManhattanDistance

```public static double getManhattanDistance(DataRow row,
SotaTreeCell cell,
boolean fuzzy)```
Returns the manhattan distance between a given `DataRow` and `SotaTreeCell` using the Minkowski distance with power 1.
Parameters:
`row` - row to compute the distance
`cell` - cell to compute the distance
`fuzzy` - if `true` only fuzzy data is respected, if `false` only number data
Returns:
the euclidian distance between given row and cell
`getMinkowskiDistance(int, DataRow, DataRow, boolean)`
• #### getManhattanDistance

```public static double getManhattanDistance(DataRow row1,
DataRow row2,
boolean fuzzy)```
Calculates the manhattan distance between two `DataRow`s using the Minkowski distance with power 1.
Parameters:
`row1` - the first row
`row2` - the second row
`fuzzy` - if `true` only fuzzy data is respected, if `false` only number data
Returns:
distance between the two rows
`getMinkowskiDistance(int, DataRow, DataRow, boolean)`
• #### getCosinusDistance

```public static double getCosinusDistance(DataRow row1,
DataRow row2,
double offset,
boolean fuzzy)```
Computes the cosinus distance between the given two rows, with given offset.
Parameters:
`row1` - first row to compute the cosinus distance of
`row2` - second row to compute the cosinus distance of
`offset` - offset to substract cosinus distance from
`fuzzy` - if `true` only fuzzy data is respected, if `false` only number data
Returns:
the cosinus distance between the given two rows
• #### getCosinusDistance

```public static double getCosinusDistance(DataRow row,
SotaTreeCell cell,
double offset,
boolean fuzzy)```
Returns the cosinus distance between the cells values and the number cells of the given row with a given offset.
Parameters:
`row` - row to compute the cosinus distance of
`cell` - cell to compute the cosinus distance of
`offset` - offset to substract cosinus distance from
`fuzzy` - if `true` only fuzzy data is respected, if `false` only number data
Returns:
the cosinus distance between given row and cell
• #### getCorrelationDistance

```public static double getCorrelationDistance(DataRow row,
SotaTreeCell cell,
double offset,
boolean abs,
boolean fuzzy)```
Returns the coefficient of correlation distance between the cells values and the number cells of the given row with a given offset.
Parameters:
`row` - row to compute the coefficient of correlation
`cell` - cell to compute the coefficient of correlation
`offset` - offset to substract coefficient of correlation from
`abs` - flags if correlations distance should be used absolute
`fuzzy` - if `true` only fuzzy data is respected, if `false` only number data
Returns:
the coefficient of correlation between given row and cel
• #### getCorrelationDistance

```public static double getCorrelationDistance(DataRow row1,
DataRow row2,
double offset,
boolean abs,
boolean fuzzy)```
Returns the coefficient of correlation distance between the rows with a given offset.
Parameters:
`row1` - first row to compute the coefficient of correlation
`row2` - second rell to compute the coefficient of correlation
`offset` - offset to substract coefficient of correlation from
`abs` - flags if correlations distance should be used absolute
`fuzzy` - if `true` only fuzzy data is respected, if `false` only number data
Returns:
the coefficient of correlation between given rows
• #### getStandardDeviation

```public static double getStandardDeviation(DataRow row,
boolean fuzzy)```
Returns the standard deviation of the given row.
Parameters:
`row` - the row to compute the standard deviation of.
`fuzzy` - if `true` only fuzzy data is respected, if `false` only number data
Returns:
the standard deviation of the given row
• #### getStandardDeviation

`public static double getStandardDeviation(SotaTreeCell cell)`
Returns the standard deviation of the given cell.
Parameters:
`cell` - the SotaTreeCell to compute the standard deviation of
Returns:
the standard deviation of the given cell
• #### getMean

```public static double getMean(DataRow row,
boolean fuzzy)```
Returns the mean value of the given row.
Parameters:
`row` - row to get the mean value of
`fuzzy` - if `true` only fuzzy data is respected, if `false` only number data
Returns:
the mean value of the given row
• #### getMean

`public static double getMean(SotaTreeCell cell)`
Returns the mean value of the given cell.
Parameters:
`cell` - SotaTreeCell to get the mean value of
Returns:
the mean value of the given cell