de.lmu.ifi.dbs.elki.data
Class SparseFloatVector

java.lang.Object
  de.lmu.ifi.dbs.elki.data.AbstractDatabaseObject
      de.lmu.ifi.dbs.elki.data.AbstractNumberVector<SparseFloatVector,Float>
          de.lmu.ifi.dbs.elki.data.SparseFloatVector

All Implemented Interfaces:

DatabaseObject, FeatureVector<SparseFloatVector,Float>, NumberVector<SparseFloatVector,Float>, SpatialComparable, SpatialObject

public class SparseFloatVector
extends AbstractNumberVector<SparseFloatVector,Float>

A SparseFloatVector is to store real values approximately as float values.

A SparseFloatVector only requires storage for those attribute values that are non-zero.

Author:

Arthur Zimek

Field Summary

private int	dimensionality The dimensionality of this feature vector.
private Integer	maximumIndex The maximal occurring index of any dimension.
private Map<Integer,Float>	values Mapping of indices and corresponding values.

Fields inherited from class de.lmu.ifi.dbs.elki.data.AbstractNumberVector

ATTRIBUTE_SEPARATOR

Constructor Summary

SparseFloatVector(float[] values)
Provides a SparseFloatVector consisting of double values according to the specified mapping of indices and values.

SparseFloatVector(Map<Integer,Float> values, int dimensionality)
Provides a SparseFloatVector consisting of double values according to the specified mapping of indices and values.

Method Summary

double	doubleValue(int dimension) Returns the value in the specified dimension as double.
Vector	getColumnVector() Returns a Vector representing in one column and getDimensionality() rows the values of this NumberVector of V.
int	getDimensionality() The dimensionality of the vector space where of this FeatureVector of V is an element.
List<Integer>	getIndicesOfNotNullValues() Provides a list containing the indices (dimensions) with a value other than zero.
Matrix	getRowVector() Returns a Matrix representing in one row and getDimensionality() columns the values of this NumberVector of V.
Float	getValue(int dimension) Returns the value in the specified dimension.
private double[]	getValues() Returns an array consisting of the values of this feature vector.
long	longValue(int dimension) Returns the value in the specified dimension as long.
SparseFloatVector	minus(SparseFloatVector fv) Returns a new NumberVector of V that is the sum of this NumberVector of V and the negativeVector() of given NumberVector of V.
SparseFloatVector	multiplicate(double k) Provides a new SparseFloatVector as result of the multiplication of this SparseFloatVector by the scalar k.
SparseFloatVector	negativeVector() Returns the additive inverse to this NumberVector of V.
SparseFloatVector	newInstance(double[] values) Returns a new NumberVector of N for the given values.
SparseFloatVector	newInstance(Float[] values) Returns a new FeatureVector of V for the given values.
SparseFloatVector	newInstance(List<Float> values) Returns a new FeatureVector of V for the given values.
SparseFloatVector	newInstance(Vector values) Returns a new NumberVector of N for the given values.
SparseFloatVector	nullVector() Provides a null vector of the same Vector Space as this NumberVector of V (that is, of the same dimensionality).
SparseFloatVector	plus(SparseFloatVector fv) Returns a new NumberVector of V that is the sum of this NumberVector of V and the given NumberVector of V.
SparseFloatVector	randomInstance(Float min, Float max, Random random) Returns a NumberVector of V with random values between min and max.
SparseFloatVector	randomInstance(Random random) Returns a NumberVector of V with uniformly distributed (0-1) random values.
SparseFloatVector	randomInstance(SparseFloatVector min, SparseFloatVector max, Random random) Returns a NumberVector of V with random values between min and max.
Float	scalarProduct(SparseFloatVector fv) Provides the scalar product (inner product) of this and the given SparseFloatVector.
void	setDimensionality(int dimensionality) Sets the dimensionality to the new value.
String	toCompleteString() Provides a String representation of this SparseFloatVector as suitable for FloatVectorLabelParser.
String	toString() Provides a String representation of this SparseFloatVector as suitable for SparseFloatVectorLabelParser.

Methods inherited from class de.lmu.ifi.dbs.elki.data.AbstractNumberVector

byteValue, equals, floatValue, getMax, getMin, intValue, shortValue

Methods inherited from class de.lmu.ifi.dbs.elki.data.AbstractDatabaseObject

getID, setID

Methods inherited from class java.lang.Object

clone, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface de.lmu.ifi.dbs.elki.data.DatabaseObject

getID, setID

Methods inherited from interface de.lmu.ifi.dbs.elki.index.tree.spatial.SpatialObject

getID

Field Detail

values

private Map<Integer,Float> values

Mapping of indices and corresponding values. Only non-zero values will to be stored.

maximumIndex

private Integer maximumIndex

The maximal occurring index of any dimension.

dimensionality

private int dimensionality

The dimensionality of this feature vector.

Constructor Detail

SparseFloatVector

public SparseFloatVector(Map<Integer,Float> values,
                         int dimensionality)
                  throws IllegalArgumentException

Provides a SparseFloatVector consisting of double values according to the specified mapping of indices and values.

Parameters:

values - the values to be set as values of the real vector

dimensionality - the dimensionality of this feature vector

Throws:

IllegalArgumentException - if the given dimensionality is too small to cover the given values (i.e., the maximum index of any value not zero is bigger than the given dimensionality)

SparseFloatVector

public SparseFloatVector(float[] values)
                  throws IllegalArgumentException

Provides a SparseFloatVector consisting of double values according to the specified mapping of indices and values.

Parameters:

values - the values to be set as values of the real vector

Throws:

IllegalArgumentException - if the given dimensionality is too small to cover the given values (i.e., the maximum index of any value not zero is bigger than the given dimensionality)

Method Detail

newInstance

public SparseFloatVector newInstance(Vector values)

Description copied from interface: NumberVector

Returns a new NumberVector of N for the given values.

Parameters:

values - the values of the NumberVector

Returns:

a new NumberVector of N for the given values

See Also:

NumberVector.newInstance(double[])

newInstance

public SparseFloatVector newInstance(double[] values)

Description copied from interface: NumberVector

Returns a new NumberVector of N for the given values.

Parameters:

values - the values of the NumberVector

Returns:

a new NumberVector of N for the given values

See Also:

NumberVector.newInstance(double[])

newInstance

public SparseFloatVector newInstance(List<Float> values)

Description copied from interface: FeatureVector

Returns a new FeatureVector of V for the given values.

Parameters:

values - the values of the featureVector

Returns:

a new FeatureVector of V for the given values

See Also:

FeatureVector.newInstance(java.util.List)

newInstance

public SparseFloatVector newInstance(Float[] values)

Description copied from interface: FeatureVector

Returns a new FeatureVector of V for the given values.

Parameters:

values - the values of the featureVector

Returns:

a new FeatureVector of V for the given values

See Also:

FeatureVector.newInstance(D[])

randomInstance

public SparseFloatVector randomInstance(Random random)

Description copied from interface: NumberVector

Returns a NumberVector of V with uniformly distributed (0-1) random values.

Parameters:

random - a Random instance

Returns:

a NumberVector of V with random values

See Also:

NumberVector.randomInstance(java.util.Random)

randomInstance

public SparseFloatVector randomInstance(Float min,
                                        Float max,
                                        Random random)

Description copied from interface: NumberVector

Returns a NumberVector of V with random values between min and max.

Parameters:

min - minimum of random value

max - maximum of random value

random - a random instance

Returns:

a NumberVector of V with random values between min and max

See Also:

NumberVector.randomInstance(java.lang.Number, java.lang.Number, java.util.Random)

randomInstance

public SparseFloatVector randomInstance(SparseFloatVector min,
                                        SparseFloatVector max,
                                        Random random)

Description copied from interface: NumberVector

Returns a NumberVector of V with random values between min and max.

Parameters:

min - minimum of random value for each axis

max - maximum of random value for each axis

random - a random instance

Returns:

a NumberVector of V with random values between min and max

See Also:

NumberVector.randomInstance(de.lmu.ifi.dbs.elki.data.NumberVector, de.lmu.ifi.dbs.elki.data.NumberVector, java.util.Random)

getDimensionality

public int getDimensionality()

Description copied from interface: FeatureVector

The dimensionality of the vector space where of this FeatureVector of V is an element.

Returns:

the number of dimensions of this FeatureVector of V

setDimensionality

public void setDimensionality(int dimensionality)
                       throws IllegalArgumentException

Sets the dimensionality to the new value.

Parameters:

dimensionality - the new dimensionality

Throws:

IllegalArgumentException - if the given dimensionality is too small to cover the given values (i.e., the maximum index of any value not zero is bigger than the given dimensionality)

getValue

public Float getValue(int dimension)

Description copied from interface: FeatureVector

Returns the value in the specified dimension.

Parameters:

dimension - the desired dimension, where 1 ≤ dimension ≤ this.getDimensionality()

Returns:

the value in the specified dimension

See Also:

FeatureVector.getValue(int)

doubleValue

public double doubleValue(int dimension)

Description copied from interface: NumberVector

Returns the value in the specified dimension as double. Note: this might seem redundant with respect to getValue(dim).doubleValue(), but usually this is much more efficient due to boxing/unboxing cost.

Parameters:

dimension - the desired dimension, where 1 ≤ dimension ≤ this.getDimensionality()

Returns:

the value in the specified dimension

See Also:

NumberVector.doubleValue(int)

longValue

public long longValue(int dimension)

Description copied from interface: NumberVector

Returns the value in the specified dimension as long. Note: this might seem redundant with respect to getValue(dim).longValue(), but usually this is much more efficient due to boxing/unboxing cost.

Parameters:

dimension - the desired dimension, where 1 ≤ dimension ≤ this.getDimensionality()

Returns:

the value in the specified dimension

See Also:

NumberVector.longValue(int)

getColumnVector

public Vector getColumnVector()

Description copied from interface: NumberVector

Returns a Vector representing in one column and getDimensionality() rows the values of this NumberVector of V.

Returns:

a Matrix representing in one column and getDimensionality() rows the values of this NumberVector of V

See Also:

NumberVector.getColumnVector()

getRowVector

public Matrix getRowVector()

Description copied from interface: NumberVector

Returns a Matrix representing in one row and getDimensionality() columns the values of this NumberVector of V.

Returns:

a Matrix representing in one row and getDimensionality() columns the values of this NumberVector of V

See Also:

NumberVector.getRowVector()

plus

public SparseFloatVector plus(SparseFloatVector fv)

Description copied from interface: NumberVector

Returns a new NumberVector of V that is the sum of this NumberVector of V and the given NumberVector of V.

Parameters:

fv - a NumberVector of V to be added to this NumberVector of V

Returns:

a new NumberVector of V that is the sum of this NumberVector of V and the given NumberVector of V

See Also:

NumberVector.plus(de.lmu.ifi.dbs.elki.data.NumberVector)

minus

public SparseFloatVector minus(SparseFloatVector fv)

Description copied from interface: NumberVector

Returns a new NumberVector of V that is the sum of this NumberVector of V and the negativeVector() of given NumberVector of V.

Parameters:

fv - a NumberVector of V to be subtracted to this NumberVector of V

Returns:

a new NumberVector of V that is the sum of this NumberVector of V and the negative of given NumberVector of V

See Also:

NumberVector.plus(de.lmu.ifi.dbs.elki.data.NumberVector)

nullVector

public SparseFloatVector nullVector()

Description copied from interface: NumberVector

Provides a null vector of the same Vector Space as this NumberVector of V (that is, of the same dimensionality).

Returns:

a null vector of the same Vector Space as this NumberVector of V (that is, of the same dimensionality)

See Also:

NumberVector.nullVector()

negativeVector

public SparseFloatVector negativeVector()

Description copied from interface: NumberVector

Returns the additive inverse to this NumberVector of V.

Returns:

the additive inverse to this NumberVector of V

See Also:

NumberVector.negativeVector()

multiplicate

public SparseFloatVector multiplicate(double k)

Provides a new SparseFloatVector as result of the multiplication of this SparseFloatVector by the scalar k.

Parameters:

k - a scalar to multiply this SparseFloatVector

Returns:

a new SparseFloatVector as result of the multiplication

toCompleteString

public String toCompleteString()

Provides a String representation of this SparseFloatVector as suitable for FloatVectorLabelParser. This includes zero valued attributes but no indices.

Example: a vector (0,1.2,1.3,0)^T would result in the String
0 1.2 1.3 0

Returns:

a String representation of this SparseFloatVector as a list of all values

toString

public String toString()

Provides a String representation of this SparseFloatVector as suitable for SparseFloatVectorLabelParser.

The returned String is a single line with entries separated by AbstractNumberVector.ATTRIBUTE_SEPARATOR. The first entry gives the number of values actually not zero. Following entries are pairs of Integer and Float where the Integer gives the index of the dimensionality and the Float gives the corresponding value.

Example: a vector (0,1.2,1.3,0)^T would result in the String
2 2 1.2 3 1.3

Specified by:

toString in interface FeatureVector<SparseFloatVector,Float>

Overrides:

toString in class Object

Returns:

a String representation of this SparseFloatVector

getValues

private double[] getValues()

Returns an array consisting of the values of this feature vector.

Returns:

an array consisting of the values of this feature vector

getIndicesOfNotNullValues

public List<Integer> getIndicesOfNotNullValues()

Provides a list containing the indices (dimensions) with a value other than zero. The indices of occurring dimensions are sorted in ascending order. The returned list is not backed by this SparseFloatVector, i.e., changes of this SparseFloatVector will not affect the list returned by thins method.

Returns:

a list containing the indices (dimensions) with a value other than zero

scalarProduct

public Float scalarProduct(SparseFloatVector fv)

Provides the scalar product (inner product) of this and the given SparseFloatVector.

Parameters:

fv - the SparseFloatVector to compute the scalar product for

Returns:

the scalar product (inner product) of this and the given SparseFloatVector