# Custom Prior Factor

Julia's type inference allows overloading of member functions outside a module. Therefore new factors can be defined at any time.

RequiredBrief description
MyFactor structPrior (<:AbstractPrior) factor definition
Optional methodsBrief description
getSample(cfo::CalcFactor{<:MyFactor})Get a sample from the measurement model

To better illustrate, in this example we will add new factors into the Main context after construction of the factor graph has already begun.

Tip

IIF is a convenient const alias of the module IncrementalInference, similarly AMP for ApproxManifoldProducts.

## Defining a New Prior (<:AbsoluteFactor)

Now lets define our own prior, MyPrior which allows for arbitrary distributions that inherit from <: IIF.SamplableBelief:

struct MyPrior{T <: SamplableBelief} <: IIF.AbstractPrior
Z::T
end

New priors must inheret from IIF.AbstractPrior, and usually takes a user input <:SamplableBelief as probabilistic model. <:AbstractPrior is a unary factor that introduces absolute information about only one variable.

## Specialized getSample (if .Z)

Caesar.jl uses a convention (non-binding) to simplify factor definitions in easier cases, but not restrict more complicated cases – a default getSample function already exists in IIF which assumes the field .Z <: SamplableBelief is used to generate the random sample values. So, the example above actually does not require the user to provide a specific getSample(cf::CalcFactor{<:MyPrior}) dispatch.

For the sake of the tutorial, let's write one anyway. Remember that we are now overriding the IIF API with a new dispatch, for that we need to import the function

import IncrementalInference: getSample

# adding our own specialized dispatch on getSample
IIF.getSample(cfo::CalcFactor{<:MyPrior}) = rand(cfo.factor.Z)

It is important to note that for <:AbstractPrior the getSample must return a point on the manifold, not a tangent vector or coordinate.

To recap, the getSample function for priors returns a measurement sample as points on the manifold.

This new prior can now readily be added to an ongoing factor graph:

# lets generate a random nonparametric belief

pts = [samplePoint(getManifold(ContinuousEuclid{1}), Normal(8.0,2.0)) for _=1:75]
someBelief = manikde!(pts, ContinuousEuclid{1})

# and build your new factor as an object
myprior = MyPrior(someBelief)

and add it to the existing factor graph from earlier, lets say:

addFactor!(fg, [:x1], myprior)

Thats it, this factor is now part of the graph. This should be a solvable graph:

solveGraph!(fg); # exact alias of solveTree!(fg)

Later we will see how to ensure these new factors can be properly serialized to work with features like saveDFG and loadDFG. See What is CalcFactor for more details.

See the next page on how to build your own Custom Relative Factor. Serialization of factors is also discussed in more detail at Standardized Factor Serialization.