javaslam.filter
Class JTFilter

java.lang.Object
  |
  +--javaslam.filter.JTFilter

All Implemented Interfaces:

Filter

public class JTFilter

extends Object

implements Filter

A junction tree filter.

This class records counts of all floating point operations using Flops.count(long) (except those used in the service of debugging and avoiding numerical errors).

Field Summary

protected JunctionTree

jt The junction tree used to represent the belief state.

Constructor Summary

JTFilter(JunctionTree jt)
Default constructor.

Method Summary

JunctionTree	getJunctionTree() Gets the junction tree representation of the belief state.
Gaussian	getMarginal(Set vars) Extracts the filtered marginal distribution.
Map	getMarginals(Collection vars) Extracts a set of unary marginals.
Set	getVariables() Gets an unmodifiable set of the Variables in the filtered belief state.
void	marginalizeOut(Set mvars) Marginalizes a set of variables out of the belief state.
void	measurement(ListSet vars, double[] y0, double[][] C, double[][] R, double[] y) Performs a linear-Gaussian measurement update.
void	time(ListSet vars, double[] x0, double[][] A, double[][] Q) Performs a linear-Gaussian time update.
String	toString() Returns a string representation of this filter.

Methods inherited from class java.lang.Object

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

Field Detail

jt

protected JunctionTree jt

The junction tree used to represent the belief state.

Constructor Detail

JTFilter

public JTFilter(JunctionTree jt)

Default constructor.

Method Detail

marginalizeOut

public void marginalizeOut(Set mvars)

Marginalizes a set of variables out of the belief state.

Specified by:

marginalizeOut in interface Filter

Parameters:

mvars - a set of Variables to marginalize out

measurement

public void measurement(ListSet vars,
                        double[] y0,
                        double[][] C,
                        double[][] R,
                        double[] y)

Performs a linear-Gaussian measurement update. The parameters vars, C and R define the measurement equation as follows:

y=y0 + Cx(vars)+w

where w is a white-noise variable with covariance R. Given the actual measurement y, this method updates the belief state.

Specified by:

measurement in interface Filter

Parameters:

vars - an ordered set of the variables with sum dimension n in the belief state that causally influenced this measurement; any variables in this list that are not currently in the belief state are added with uninformative priors.

y0 - a k-vector giving the constant term

C - a k by n observation matrix that defines the linear measurement model (and whose columns are ordered consistently with the order of vars)

R - a k by k symmetric positive definite matrix giving the covariance of the measurement white noise

y - the measurement k-vector

Throws:

IllegalArgumentException - if there are any dimension mismatches

time

public void time(ListSet vars,
                 double[] x0,
                 double[][] A,
                 double[][] Q)

Performs a linear-Gaussian time update. The parameters vars, A and Q define the state evolution equation as follows:

x_{t + 1}(vars)=x0 Ax_t(vars)+v

where v is a white-noise variable with covariance Q. All variables not in vars are assumed stationary.

Specified by:

time in interface Filter

Parameters:

vars - an ordered set of the variables with sum dimension n in the belief state that evolve over time

x0 - an n-vector giving the constant term

A - an n by n evolution matrix that defines the linear evolution model (and whose blocks are ordered consistently with the order of vars)

Q - an n by n symmetric positive definite matrix giving the covariance of the evolution white noise (and whose blocks are ordered consistently with the order of vars)

Throws:

IllegalArgumentException - if there are any dimension mismatches or vars contains variables that are not in the current belief state

getMarginal

public Gaussian getMarginal(Set vars)

Extracts the filtered marginal distribution. The correct filtered marginal is returned (up to approximations made by the underlying junction tree); also, a product-of-marginals approximation may be returned if it can be computed more efficiently than the full marginal.

Specified by:

getMarginal in interface Filter

Parameters:

vars - the set of Variables whose filtered marginal is to be computed

Returns:

a filtered marginal potential over vars

See Also:

JunctionTree.getMarginal(Set,boolean)

getMarginals

public Map getMarginals(Collection vars)

Extracts a set of unary marginals.

Specified by:

getMarginals in interface Filter

Parameters:

vars - a collection of Variables, or null to indicate all variables in the belief state

Returns:

a map whose keys are the (distinct) elements of vars and whose values are the corresponding marginals (in the moment parameterization)

getVariables

public Set getVariables()

Gets an unmodifiable set of the Variables in the filtered belief state. The iteration order of this set is the order in which the variables were added to the belief state.

Specified by:

getVariables in interface Filter

getJunctionTree

public JunctionTree getJunctionTree()

Gets the junction tree representation of the belief state.

toString

public String toString()

Returns a string representation of this filter.

Overrides:

toString in class Object