gov.sandia.cognition.math.signals

Class AutoRegressiveMovingAverageFilter

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.evaluator.AbstractStatefulEvaluator<java.lang.Double,java.lang.Double,DefaultPair<FiniteCapacityBuffer<java.lang.Double>,FiniteCapacityBuffer<java.lang.Double>>>

gov.sandia.cognition.math.signals.AutoRegressiveMovingAverageFilter

All Implemented Interfaces:

Evaluator<java.lang.Double,java.lang.Double>, StatefulEvaluator<java.lang.Double,java.lang.Double,DefaultPair<FiniteCapacityBuffer<java.lang.Double>,FiniteCapacityBuffer<java.lang.Double>>>, Vectorizable, DiscreteTimeFilter<DefaultPair<FiniteCapacityBuffer<java.lang.Double>,FiniteCapacityBuffer<java.lang.Double>>>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

@PublicationReference(author="Wikipedia",
                      title="Infinite impulse response",
                      type=WebPage,
                      year=2009,
                      url="http://en.wikipedia.org/wiki/Infinite_impulse_response")
public class AutoRegressiveMovingAverageFilter
extends AbstractStatefulEvaluator<java.lang.Double,java.lang.Double,DefaultPair<FiniteCapacityBuffer<java.lang.Double>,FiniteCapacityBuffer<java.lang.Double>>>
implements DiscreteTimeFilter<DefaultPair<FiniteCapacityBuffer<java.lang.Double>,FiniteCapacityBuffer<java.lang.Double>>>

A type of filter using a moving-average calculation. That is, a finite window of inputs are scaled by a (possibly) unique coefficient and then summed together. In other words,
y(n) + a(0)y(n-1) + a(1)y(n-2) + ... + a(p)y(n-p+1)
= b(0)x(n) + b(1)x(n-1) + ... + b(m)x(n-m).
Note that this is slightly different than MATLAB/octave's implementation in that the first autoregressive coefficient (a(0)) scales y(n-1) and not every value.

Since:

3.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
AutoRegressiveMovingAverageFilter(double[] autoRegressiveCoefficients, double[] movingAverageCoefficients) Creates a new instance of AutoRegressiveMovingAverageFilter
AutoRegressiveMovingAverageFilter(int numAutoregressiveCoefficients, int numMovingAverageCoefficients) Creates a new instance of AutoRegressiveMovingAverageFilter
AutoRegressiveMovingAverageFilter(Vector autoRegressiveCoefficients, Vector movingAverageCoefficients) Creates a new instance of AutoRegressiveMovingAverageFilter

Method Summary

Modifier and Type	Method and Description
AutoRegressiveMovingAverageFilter	clone() This makes public the clone method on the Object class and removes the exception that it throws.
void	convertFromVector(Vector parameters) Converts the object from a Vector of parameters.
Vector	convertToVector() Converts the object to a vector.
DefaultPair<FiniteCapacityBuffer<java.lang.Double>,FiniteCapacityBuffer<java.lang.Double>>	createDefaultState() Creates a new default state object.
java.lang.Double	evaluate(java.lang.Double input) Evaluates the object using the given input and current state objects, returning the output.
Vector	getAutoRegressiveCoefficients() Getter for autoregressiveCoefficients
Vector	getMovingAverageCoefficients() Setter for movingAverageCoefficients
int	getNumAutoRegressiveCoefficients() Gets the number of autoregressive coefficients.
int	getNumMovingAverageCoefficients() Returns the number of coefficients in the moving-average filter.
void	setAutoregressiveCoefficients(Vector autoRegressiveCoefficients) Setter for autoregressiveCoefficients
void	setMovingAverageCoefficients(Vector movingAverageCoefficients) Setter for movingAverageCoefficients

Methods inherited from class gov.sandia.cognition.evaluator.AbstractStatefulEvaluator

evaluate, getState, resetState, setState

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface gov.sandia.cognition.evaluator.StatefulEvaluator

evaluate, getState, resetState, setState

Constructor Detail

AutoRegressiveMovingAverageFilter

public AutoRegressiveMovingAverageFilter(int numAutoregressiveCoefficients,
                                         int numMovingAverageCoefficients)

Creates a new instance of AutoRegressiveMovingAverageFilter

Parameters:

numAutoregressiveCoefficients - Number of autoregressive coefficients.

numMovingAverageCoefficients - Number of moving-average coefficients.

AutoRegressiveMovingAverageFilter

public AutoRegressiveMovingAverageFilter(double[] autoRegressiveCoefficients,
                                         double[] movingAverageCoefficients)

Creates a new instance of AutoRegressiveMovingAverageFilter

Parameters:

autoRegressiveCoefficients - Coefficients of the autoregressive filter. Element 0 is applied to the most-recent output, Element 1 is applied to the second-most-recent, and so forth. The dimensionality of the Vector is the order of the filter.

movingAverageCoefficients - Coefficients of the moving-average filter. Element 0 is applied to the most-recent input, Element 1 is applied to the second-most-recent, and so forth. The dimensionality of the Vector is the order of the filter.

AutoRegressiveMovingAverageFilter

public AutoRegressiveMovingAverageFilter(Vector autoRegressiveCoefficients,
                                         Vector movingAverageCoefficients)

Creates a new instance of AutoRegressiveMovingAverageFilter

Parameters:

autoRegressiveCoefficients - Coefficients of the autoregressive filter. Element 0 is applied to the most-recent output, Element 1 is applied to the second-most-recent, and so forth. The dimensionality of the Vector is the order of the filter.

movingAverageCoefficients - Coefficients of the moving-average filter. Element 0 is applied to the most-recent input, Element 1 is applied to the second-most-recent, and so forth. The dimensionality of the Vector is the order of the filter.

Method Detail

createDefaultState

public DefaultPair<FiniteCapacityBuffer<java.lang.Double>,FiniteCapacityBuffer<java.lang.Double>> createDefaultState()

Description copied from interface: StatefulEvaluator

Creates a new default state object.

Specified by:

createDefaultState in interface StatefulEvaluator<java.lang.Double,java.lang.Double,DefaultPair<FiniteCapacityBuffer<java.lang.Double>,FiniteCapacityBuffer<java.lang.Double>>>

Returns:

A new default state object.

evaluate

public java.lang.Double evaluate(java.lang.Double input)

Description copied from interface: StatefulEvaluator

Evaluates the object using the given input and current state objects, returning the output. The current state may be modified by side effect.

Specified by:

evaluate in interface Evaluator<java.lang.Double,java.lang.Double>

Specified by:

evaluate in interface StatefulEvaluator<java.lang.Double,java.lang.Double,DefaultPair<FiniteCapacityBuffer<java.lang.Double>,FiniteCapacityBuffer<java.lang.Double>>>

Parameters:

input - The input to evaluate.

Returns:

output that results from the evaluation, state is modified by side effect

clone

public AutoRegressiveMovingAverageFilter clone()

Description copied from class: AbstractCloneableSerializable

This makes public the clone method on the Object class and removes the exception that it throws. Its default behavior is to automatically create a clone of the exact type of object that the clone is called on and to copy all primitives but to keep all references, which means it is a shallow copy. Extensions of this class may want to override this method (but call super.clone() to implement a "smart copy". That is, to target the most common use case for creating a copy of the object. Because of the default behavior being a shallow copy, extending classes only need to handle fields that need to have a deeper copy (or those that need to be reset). Some of the methods in ObjectUtil may be helpful in implementing a custom clone method. Note: The contract of this method is that you must use super.clone() as the basis for your implementation.

Specified by:

clone in interface Vectorizable

Specified by:

clone in interface CloneableSerializable

Overrides:

clone in class AbstractStatefulEvaluator<java.lang.Double,java.lang.Double,DefaultPair<FiniteCapacityBuffer<java.lang.Double>,FiniteCapacityBuffer<java.lang.Double>>>

Returns:

A clone of this object.

convertToVector

public Vector convertToVector()

Description copied from interface: Vectorizable

Converts the object to a vector.

Specified by:

convertToVector in interface Vectorizable

Returns:

The Vector form of the object.

convertFromVector

public void convertFromVector(Vector parameters)

Description copied from interface: Vectorizable

Converts the object from a Vector of parameters.

Specified by:

convertFromVector in interface Vectorizable

Parameters:

parameters - The parameters to incorporate.

getNumMovingAverageCoefficients

public int getNumMovingAverageCoefficients()

Returns the number of coefficients in the moving-average filter.

Returns:

Number of coefficients in the moving-average filter.

getMovingAverageCoefficients

public Vector getMovingAverageCoefficients()

Setter for movingAverageCoefficients

Returns:

Coefficients of the moving-average filter. Element 0 is applied to the most-recent input, Element 1 is applied to the second-most-recent, and so forth. The dimensionality of the Vector is the order of the filter.

setMovingAverageCoefficients

public void setMovingAverageCoefficients(Vector movingAverageCoefficients)

Setter for movingAverageCoefficients

Parameters:

movingAverageCoefficients - Coefficients of the moving-average filter. Element 0 is applied to the most-recent input, Element 1 is applied to the second-most-recent, and so forth. The dimensionality of the Vector is the order of the filter.

getNumAutoRegressiveCoefficients

public int getNumAutoRegressiveCoefficients()

Gets the number of autoregressive coefficients.

Returns:

Number of autoregressive coefficients.

getAutoRegressiveCoefficients

public Vector getAutoRegressiveCoefficients()

Getter for autoregressiveCoefficients

Returns:

Coefficients of the autoregressive filter. Element 0 is applied to the most-recent output, Element 1 is applied to the second-most-recent, and so forth. The dimensionality of the Vector is the order of the filter.

setAutoregressiveCoefficients

public void setAutoregressiveCoefficients(Vector autoRegressiveCoefficients)

Setter for autoregressiveCoefficients

Parameters:

autoRegressiveCoefficients - Coefficients of the autoregressive filter. Element 0 is applied to the most-recent output, Element 1 is applied to the second-most-recent, and so forth. The dimensionality of the Vector is the order of the filter.