# GraphPPL RxInfer Model

**URL:** <https://discourse.julialang.org/t/graphppl-rxinfer-model/114255>\
**Category:** Machine Learning\
**Tags:** rxinfer\
**Created:** [May 14, 2024, 4:01pm UTC](https://discourse.julialang.org/t/graphppl-rxinfer-model/114255 "2024-05-14T16:01:06Z")\
**Posts on this page:** 4\
**Page:** 1

<div class="post-metadata">

**Author:** ![ohmsweetohm1](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/ohmsweetohm1/32/49126_2.png) [@ohmsweetohm1](https://discourse.julialang.org/u/ohmsweetohm1)\
**Post date:** [May 14, 2024, 4:01pm UTC](https://discourse.julialang.org/t/graphppl-rxinfer-model/114255/1 "2024-05-14T16:01:06Z")

</div>

I have tried to extend the example from [here](https://reactivebayes.github.io/RxInfer.jl/stable/examples/basic_examples/Kalman%20filtering%20and%20smoothing/#Gaussian-Linear-Dynamical-System) to include an unknown parameter `theta`:

```julia
@model function rotate_ssm(y, x0, Q, P)
    x_prior ~ MvNormalMeanCovariance(mean(x0), cov(x0))
    x_prev = x_prior

    theta ~ Normal(1,1)
    A = [cos(theta) -sin(theta); sin(theta) cos(theta)]
    B = diageye(2)
    
    for i in 1:length(y)
        x[i] ~ MvNormalMeanCovariance(A * x_prev, Q)
        y[i] ~ MvNormalMeanCovariance(B * x[i], P)
        x_prev = x[i]
    end
end

```

however I get:

```julia
ERROR: MethodError: no method matching cos(::GraphPPL.NodeLabel)

Closest candidates are:
  cos(::Irrational{:π})
   @ Base mathconstants.jl:127
  cos(::IrrationalConstants.Halfπ)
   @ IrrationalConstants C:\Users\KaisermayerV\.julia\packages\IrrationalConstants\vp5v4\src\trigonometric.jl:16
  cos(::BigFloat)
   @ Base mpfr.jl:801
  ...

Stacktrace:
  [1] macro expansion
    @ dev\dec_rxinfer.jl:51 [inlined]
  [2] add_terminated_submodel!( __model__ ::GraphPPL.Model{…}, __context__ ::GraphPPL.Context, __options__ ::GraphPPL.NodeCreationOptions{…}, ::typeof(rotate_ssm), __interfaces__ ::@NamedTuple{…}, ::Static.StaticInt{…})      
    @ Main .julia\packages\GraphPPL\Z49xA\src\model_macro.jl:737
  [3] add_terminated_submodel!
    @ .julia\packages\GraphPPL\Z49xA\src\graph_engine.jl:1892 [inlined]
  [4] add_terminated_submodel!
    @ .julia\packages\GraphPPL\Z49xA\src\graph_engine.jl:1888 [inlined]
  [5] add_toplevel_model!
    @ .julia\packages\GraphPPL\Z49xA\src\graph_engine.jl:1908 [inlined]
  [6] create_model(callback::RxInfer.var"#24#26"{…}, generator::GraphPPL.ModelGenerator{…})
    @ GraphPPL .julia\packages\GraphPPL\Z49xA\src\model_generator.jl:97
  [7] __infer_create_factor_graph_model
    @ .julia\packages\RxInfer\wbFg1\src\model\model.jl:122 [inlined]
  [8] create_model(generator::RxInfer.ConditionedModelGenerator{GraphPPL.ModelGenerator{…}, @NamedTuple{…}})
    @ RxInfer .julia\packages\RxInfer\wbFg1\src\model\model.jl:110
  [9] batch_inference(; model::GraphPPL.ModelGenerator{…}, data::@NamedTuple{…}, initialization::Nothing, constraints::Nothing, meta::GraphPPL.MetaSpecification, options::@NamedTuple{…}, returnvars::Nothing, predictvars::Nothing, iterations::Nothing, free_energy::Bool, free_energy_diagnostics::Tuple{…}, showprogress::Bool, callbacks::Nothing, addons::Nothing, postprocess::DefaultPostprocess, warn::Bool, catch_exception::Bool)
    @ RxInfer .julia\packages\RxInfer\wbFg1\src\inference\batch.jl:199
 [10] batch_inference
    @ .julia\packages\RxInfer\wbFg1\src\inference\batch.jl:94 [inlined]
 [11] #infer#242
    @ .julia\packages\RxInfer\wbFg1\src\inference\inference.jl:306 [inlined]
 [12] top-level scope
    @ dev\dec_rxinfer.jl:71
Some type information was truncated. Use `show(err)` to see complete types.

```

---

<div class="post-metadata">

**Author:** ![ohmsweetohm1](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/ohmsweetohm1/32/49126_2.png) [@ohmsweetohm1](https://discourse.julialang.org/u/ohmsweetohm1)\
**Post date:** [May 14, 2024, 4:31pm UTC](https://discourse.julialang.org/t/graphppl-rxinfer-model/114255/2 "2024-05-14T16:31:39Z")

</div>

My current model

```julia
@model function rotate_ssm(y, x0, Q, P)
    x_prior ~ MvNormalMeanCovariance(mean(x0), cov(x0))
    x_prev = x_prior

    Afunc = (theta::Real) -> [cos(theta) -sin(theta); sin(theta) cos(theta)]

    theta ~ Normal(mean=0.0,variance=1.0)
    A := Afunc(theta) where { meta=DeltaMeta(method=Unscented()) }
    B = diageye(2)

    for i in 1:length(y)
        x[i] ~ MvNormalMeanCovariance(A * x_prev, Q)
        y[i] ~ MvNormalMeanCovariance(B * x[i], P)
        x_prev = x[i]
    end
end

x0 = MvNormalMeanCovariance(zeros(2), 100.0 * diageye(2))

init = @initialization begin
    μ(theta) = Normal(0, 1)
end

result = infer(
    model = rotate_ssm(x0=x0, Q=Q, P=P), 
    data = (y = y,),
    free_energy = true,
    options = (limit_stack_depth = 100, ),
    initialization = init,
    iterations = 20,
)

```

```julia
ERROR: Variables [A, x_prior, theta, x] have not been updated after an update event. 
Therefore, make sure to initialize all required marginals and messages. See `initialization` keyword argument for the inference function.
See the official documentation for detailed information regarding the initialization.

```

---

<div class="post-metadata">

**Author:** ![albertpod](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/albertpod/32/31336_2.png) [@albertpod](https://discourse.julialang.org/u/albertpod)\
**Post date:** [June 26, 2024, 12:09pm UTC](https://discourse.julialang.org/t/graphppl-rxinfer-model/114255/3 "2024-06-26T12:09:20Z")

</div>

Hi @ohmsweetohm1! I suppose your question was somewhat resolved here:  
[State observer · ReactiveBayes · Discussion #309 · GitHub](https://github.com/orgs/ReactiveBayes/discussions/309)?

Cheers,

---

<div class="post-metadata">

**Author:** ![ohmsweetohm1](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/ohmsweetohm1/32/49126_2.png) [@ohmsweetohm1](https://discourse.julialang.org/u/ohmsweetohm1)\
**Post date:** [June 26, 2024, 4:44pm UTC](https://discourse.julialang.org/t/graphppl-rxinfer-model/114255/4 "2024-06-26T16:44:04Z")

</div>

Yes, certainly. Thanks for the help and patience.