org.apache.mahout.common.distance
Class CosineDistanceMeasure
java.lang.Object
org.apache.mahout.common.distance.CosineDistanceMeasure
- All Implemented Interfaces:
- DistanceMeasure, Parametered
public class CosineDistanceMeasure
- extends Object
- implements DistanceMeasure
This class implements a cosine distance metric by dividing the dot product of two vectors by the product of their
lengths. That gives the cosine of the angle between the two vectors. To convert this to a usable distance,
1-cos(angle) is what is actually returned.
Fields inherited from interface org.apache.mahout.common.parameters.Parametered |
log |
Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
CosineDistanceMeasure
public CosineDistanceMeasure()
configure
public void configure(org.apache.hadoop.conf.Configuration job)
- Specified by:
configure
in interface Parametered
getParameters
public Collection<Parameter<?>> getParameters()
- Specified by:
getParameters
in interface Parametered
createParameters
public void createParameters(String prefix,
org.apache.hadoop.conf.Configuration jobConf)
- Description copied from interface:
Parametered
- EXPERT: consumers should never have to call this method. It would be friendly visible to
Parametered.ParameteredGeneralizations
if java supported it. Calling this method should create a new list of
parameters and is called
- Specified by:
createParameters
in interface Parametered
- Parameters:
prefix
- ends with a dot if not empty.jobConf
- configuration used for retrieving values- See Also:
invoking method
,
invoking method
distance
public static double distance(double[] p1,
double[] p2)
distance
public double distance(Vector v1,
Vector v2)
- Description copied from interface:
DistanceMeasure
- Returns the distance metric applied to the arguments
- Specified by:
distance
in interface DistanceMeasure
- Parameters:
v1
- a Vector defining a multidimensional point in some feature spacev2
- a Vector defining a multidimensional point in some feature space
- Returns:
- a scalar doubles of the distance
distance
public double distance(double centroidLengthSquare,
Vector centroid,
Vector v)
- Description copied from interface:
DistanceMeasure
- Optimized version of distance metric for sparse vectors. This distance computation requires operations
proportional to the number of non-zero elements in the vector instead of the cardinality of the vector.
- Specified by:
distance
in interface DistanceMeasure
- Parameters:
centroidLengthSquare
- Square of the length of centroidcentroid
- Centroid vector
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