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java.lang.Object | +--javaslam.filter.Linearization
A linear-Gaussian approximation to a nonlinear vector-valued function of Gaussian-distributed inputs.
The inputs to a linearization are:
g(x) = a + B x + wwhere a is a k-by-1 vector (the constant term), B is a k-by-n matrix (the linear coefficient) and w is an independent white noise vector (of dimension k) with covariance G. The outputs of the linearization are the parameters of the approximation g: a, B, and G.
Field Summary | |
protected Matrix |
a
The constant term in the affine-Gaussian approximation g(x) = a + B x + w |
protected Matrix |
B
The linear coefficient in the affine-Gaussian approximation g(x) = a + B x + w |
protected Matrix |
G
The covariance of the white noise variable w in the affine-Gaussian approximation g(x) = a + B x + w |
protected int |
k
The dimension of the output vector. |
protected int |
m
The sum dimension of the noise vector. |
protected int |
n
The sum dimension of the input vector. |
protected Gaussian |
q
A Gaussian distribution (in the moment parameterization) that approximates p(x, v, y). |
protected ListSet |
vSet
A list of random variables representing the noise inputs of the function. |
protected ListSet |
xSet
A list of random variables representing the inputs of the function. |
protected Variable |
y
A random variable representing the output of the function. |
protected ListSet |
ySet
A list set containing only y (for convenience). |
Constructor Summary | |
Linearization(NoisyVectorFunction f,
Gaussian px)
Constructor. |
Method Summary | |
Matrix |
getCoefficient()
Returns the linear coefficient B in the affine-Gaussian approximation g(x) = a + B x + w |
Matrix |
getCoefficient(Variable u)
Returns the submatrix of B in the affine-Gaussian approximation g(x) = a + B x + w that corresponds to the subvariable u of x. |
Matrix |
getConstantTerm()
Returns the constant term in the affine-Gaussian approximation g(x) = a + B x + w |
Gaussian |
getDistribution()
Returns a Gaussian approximation of the distribution over the function's inputs and output. |
Matrix |
getNoiseCovariance()
Returns the covariance matrix of the noise term w in the affine-Gaussian approximation g(x) = a + B x + w |
Variable |
getOutputVariable()
Returns the variable representing the output of the random function. |
Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Field Detail |
protected Variable y
protected int k
protected ListSet ySet
y
(for convenience).
protected ListSet xSet
protected int n
protected ListSet vSet
protected int m
protected Gaussian q
protected Matrix a
g(x) = a + B x + w
protected Matrix B
g(x) = a + B x + w
protected Matrix G
g(x) = a + B x + w
Constructor Detail |
public Linearization(NoisyVectorFunction f, Gaussian px)
f
- a vector function that takes a
px
- a Gaussian distribution (in the moment parameterization)
over the n-by-1 vector x (which can be
partitioned into several sub-variables)
IllegalArgumentException
- if the sum dimension of
px
and
pv
does not match the
input dimension of f
Method Detail |
public Gaussian getDistribution()
public Variable getOutputVariable()
public Matrix getConstantTerm()
g(x) = a + B x + w
public Matrix getCoefficient()
g(x) = a + B x + w
public Matrix getCoefficient(Variable u)
g(x) = a + B x + wthat corresponds to the subvariable
u
of x.
u
- a subvariable of x
v
public Matrix getNoiseCovariance()
g(x) = a + B x + w
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