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Class JSci.maths.wavelet.SparseDiscreteFunction 

java.lang.Object
   |
   +----JSci.maths.wavelet.MultiscaleFunction
           |
           +----JSci.maths.wavelet.DiscreteFunction
                   |
                   +----JSci.maths.wavelet.SparseDiscreteFunction
public class SparseDiscreteFunction
extends DiscreteFunction
implements Cloneable

Variable Index

o Data

Constructor Index

o SparseDiscreteFunction(double[])

Method Index

o clone()
Return a copy of this object
o dimension()
Tells you how many samples you'll get from this function
o dimension(int)
Tells you how many samples you'll get from this function (will not depend on the parameter)
o equals(Object)
Check if another object is equal to this DiscreteFunction object
o evaluate()
Return as an array the sampled values of the function
o evaluate(int)
Return as an array the sampled values of the function
o getFilterType()
This method is used to compute how the number of scaling functions changes from on scale to the other.
o mass(double, double, int)
Compute the mass (integral)
o norm()
Compute the L2 norm of the signal
o norm(int)
Compute the L2 norm of the function The parameter doesn't do anything.
o normalize()
Makes the L2norm of the internal array=1.
o setData(double[])
o toString()
Return a String representation of the object

Variables

o Data 
 public DoubleSparseVector Data

Constructors

o SparseDiscreteFunction 
 public SparseDiscreteFunction(double v[])

Methods

o toString 
 public String toString()
Return a String representation of the object

Overrides:
toString in class DiscreteFunction
o normalize 
 public void normalize()
Makes the L2norm of the internal array=1.

Overrides:
normalize in class DiscreteFunction
o setData 
 public void setData(double v[])
Overrides:
setData in class DiscreteFunction
o evaluate 
 public double[] evaluate()
Return as an array the sampled values of the function

Overrides:
evaluate in class DiscreteFunction
o equals 
 public boolean equals(Object a)
Check if another object is equal to this DiscreteFunction object

Overrides:
equals in class DiscreteFunction
o evaluate 
 public double[] evaluate(int j1)
Return as an array the sampled values of the function

Parameters:
j - number of iterations (doesn't do anything)
Overrides:
evaluate in class DiscreteFunction
o mass 
 public double mass(double a,
                    double b,
                    int jfin)
Compute the mass (integral)

Parameters:
a - left boundary of the interval
b - right boundary of the interval
jfin - number of iterations to consider (precision)
Overrides:
mass in class DiscreteFunction
o norm 
 public double norm()
Compute the L2 norm of the signal

Overrides:
norm in class DiscreteFunction
o norm 
 public double norm(int j)
Compute the L2 norm of the function The parameter doesn't do anything.

Parameters:
j - number of iterations
Overrides:
norm in class DiscreteFunction
o clone 
 public Object clone()
Return a copy of this object

Overrides:
clone in class DiscreteFunction
o dimension 
 public int dimension(int jfin)
Tells you how many samples you'll get from this function (will not depend on the parameter)

Overrides:
dimension in class DiscreteFunction
o dimension 
 public int dimension()
Tells you how many samples you'll get from this function

Overrides:
dimension in class DiscreteFunction
o getFilterType 
 public int getFilterType()
This method is used to compute how the number of scaling functions changes from on scale to the other. Basically, if you have k scaling function and a Filter of type t, you'll have 2*k+t scaling functions at the next scale (dyadic case). Notice that this method assumes that one is working with the dyadic grid while the method "previousDimension" define in the interface "Filter" doesn't.

Overrides:
getFilterType in class DiscreteFunction 
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