# Bayes\_rule\_discrete.ipynb demo in ForneyLab

**URL:** <https://discourse.julialang.org/t/bayes-rule-discrete-ipynb-demo-in-forneylab/79758>\
**Category:** General Usage\
**Created:** [April 20, 2022, 9:45pm UTC](https://discourse.julialang.org/t/bayes-rule-discrete-ipynb-demo-in-forneylab/79758 "2022-04-20T21:45:54Z")\
**Posts on this page:** 4\
**Page:** 1

<div class="post-metadata">

**Author:** ![erlebach](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/erlebach/32/12973_2.png) [@erlebach](https://discourse.julialang.org/u/erlebach)\
**Post date:** [April 20, 2022, 9:45pm UTC](https://discourse.julialang.org/t/bayes-rule-discrete-ipynb-demo-in-forneylab/79758/1 "2022-04-20T21:45:54Z")

</div>

Hi,

I am relatively new to ForneyLab and am trying it again after a year-long break using Julia 1.7 and ForneyLab 0.11.4 . While most of the demos provided run fine, the simplest demo of all, `Bayes-rule_discrete.ipynb` generates an error when run. More specifically, when executing

```julia
using ForneyLab

# Build the generative model
g = FactorGraph()

b = [0.7, 0.3] # Prior probability vector
A = [0.2 0.9; 0.8 0.1] # Left-stochastic matrix for conditional probability

@RV m ~ Categorical(b) # Prior
@RV w ~ Transition(m, A) # Observation model

placeholder(w, :w, dims=(2,)); # Placeholder for observation

algo = messagePassingAlgorithm(m) # Build the algorithm code
source_code = algorithmSourceCode(algo)
eval(Meta.parse(source_code)) # Parse and load the algorithm in scope

println(source_code)

```

the following error is generated on `eval(Meta.parse(source_code)`:

```julia
error in method definition: function ForneyLab.step! must be explicitly imported to be extended

Stacktrace:
 [1] top-level scope
   @ none:0
 [2] top-level scope
   @ none:22
 [3] eval
   @ ./boot.jl:373 [inlined]
 [4] eval(x::Expr)
   @ Base.MainInclude ./client.jl:453
 [5] top-level scope
   @ In[35]:3
 [6] eval
   @ ./boot.jl:373 [inlined]
 [7] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String)
   @ Base ./loading.jl:1196

```

I searched the internet and the issues reported on the ForneyLab github, but did not find anything. Here is the source code generated via `println(source_code)`:

```julia
begin

# You have created an algorithm that requires updates for (a) clamped parameter(s).
# This algorithm requires the definition of a custom `optimize!` function that updates the parameter value(s)
# by altering the `data` dictionary in-place. The custom `optimize!` function may be based on the mockup below:

# function optimize!(data::Dict, marginals::Dict=Dict(), messages::Vector{Message}=init())
# ...
# return data
# end

function init()

messages = Array{Message}(undef, 3)

messages[2] = Message(vague(Categorical, (2,)))

return messages

end

function step!(data::Dict, marginals::Dict=Dict(), messages::Vector{Message}=Array{Message}(undef, 3))

messages[1] = ruleSPCategoricalOutNP(nothing, Message(Multivariate, PointMass, m=[0.7, 0.3]))
messages[2] = ruleSPTransitionOutNCP(nothing, messages[1], Message(MatrixVariate, PointMass, m=[0.2 0.9; 0.8 0.1]))
messages[3] = ruleSPTransitionIn1PNP(Message(Multivariate, PointMass, m=data[:w]), nothing, Message(MatrixVariate, PointMass, m=[0.2 0.9; 0.8 0.1]))

marginals[:m] = messages[1].dist * messages[3].dist

return marginals

end

end # block

```

I assume I am doing something incorrectly. Note however, that most of all the demos use continuous distributions as opposed to this one. Any insight would be greatly appreciated!

```
Gordon

```

---

<div class="post-metadata">

**Author:** ![goerch](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/goerch/32/29122_2.png) [@goerch](https://discourse.julialang.org/u/goerch)\
**Post date:** [April 20, 2022, 9:58pm UTC](https://discourse.julialang.org/t/bayes-rule-discrete-ipynb-demo-in-forneylab/79758/2 "2022-04-20T21:58:53Z")

</div>

Here is what I get on Windows with Julia 1.7.2

```julia
begin

function step!(data::Dict, marginals::Dict=Dict(), messages::Vector{Message}=Array{Message}(undef, 2))

messages[1] = ruleSPCategoricalOutNP(nothing, Message(Multivariate, PointMass, m=[0.7, 0.3]))
messages[2] = ruleSPTransitionIn1PNP(Message(Multivariate, PointMass, m=data[:w]), nothing, Message(MatrixVariate, PointMass, m=[0.2 0.9; 0.8 0.1]))

marginals[:m] = messages[1].dist * messages[2].dist

return marginals

end

end # block

```

So I’d assume an ‘environmental’ problem on your side(which means either dependent on Julia or package version)?

---

<div class="post-metadata">

**Author:** ![erlebach](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/erlebach/32/12973_2.png) [@erlebach](https://discourse.julialang.org/u/erlebach)\
**Post date:** [April 21, 2022, 1:10am UTC](https://discourse.julialang.org/t/bayes-rule-discrete-ipynb-demo-in-forneylab/79758/3 "2022-04-21T01:10:25Z")

</div>

@goerch : I agree with you. I am using Julia 1.7.2 on a Macbook M1 Air. That might be the reason for the error. The error occurred in `eval()`. Here is the status of my packages:

```julia
      Status `~/.julia/environments/v1.7/Project.toml`
  [c52e3926] Atom v0.12.36
  [31c24e10] Distributions v0.25.53
  [587475ba] Flux v0.13.0
  [9fc3f58a] ForneyLab v0.11.4
  [f526b714] GraphViz v0.2.0
  [7073ff75] IJulia v1.23.3
  [e5e0dc1b] Juno v0.8.4
  [b964fa9f] LaTeXStrings v1.3.0
  [91a5bcdd] Plots v1.27.5
  [d330b81b] PyPlot v2.10.0
  [276daf66] SpecialFunctions v2.1.4

```

I also executed the example in “Getting Started” in the documentation, which is discreate, and it ran perfectly fine. Not only that, but the source code generated was identical, except in the number of edges in the graph. Here is the code that worked:

```julia
using ForneyLab

 = 25 # number of coin tosses
p = 0.75 # p parameter of the Bernoulli distribution
sbernoulli(n, p) = [(rand() < p) ? 1 : 0 for _ = 1:n] # define Bernoulli sampler
dataset = sbernoulli(N, p); # run N Bernoulli trials
print("dataset = ") ; show(dataset)

g = FactorGraph() # create a factor graph
a = placeholder(:a) # define hyperparameter a as placeholder
b = placeholder(:b) # define hyperparameter b as placeholder
@RV θ ~ Beta(a, b) # prior
@RV y ~ Bernoulli(θ) # likelihood
placeholder(y, :y) # define y as a placeholder for data
draw(g)  

# Generate a message passging sum-product algorithm that infers theta
algo = messagePassingAlgorithm(θ) # derive a sum-product algorithm to infer θ
algo_code = algorithmSourceCode(algo) # convert the algorithm to Julia code
algo_expr = Meta.parse(algo_code) # parse the algorithm into a Julia expression
eval(algo_expr) 

```

---

<div class="post-metadata">

**Author:** ![erlebach](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/erlebach/32/12973_2.png) [@erlebach](https://discourse.julialang.org/u/erlebach)\
**Post date:** [April 21, 2022, 9:23pm UTC](https://discourse.julialang.org/t/bayes-rule-discrete-ipynb-demo-in-forneylab/79758/4 "2022-04-21T21:23:17Z")

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I am answering my own question. I rebooted my mac and everything works. Some strange configuration must have occurred during my work, messing things up.