javaslam.filter
Interface Filter

All Known Implementing Classes:

InformationFilter, JTFilter, KalmanFilter

public interface Filter

A linear-Gaussian filter.

Method Summary

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.

Method Detail

marginalizeOut

public void marginalizeOut(Set mvars)

Marginalizes a set of variables out of the belief state.

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.

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.

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.

Parameters:

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

Returns:

a filtered marginal potential over vars

getMarginals

public Map getMarginals(Collection vars)

Extracts a set of unary marginals.

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.