Comments (1)
To make ForneyLab.jl
generate correct init()
, we need to provide optional parameters that specify the dimension of Nonlinear node's inputs.
The model specification should be changed as follows:
@RV θ ~ GaussianMeanPrecision(placeholder(:m_θ, dims=(dimensionality,)), placeholder(:W_θ, dims=(dimensionality, dimensionality)))
f(w, x) = 1/(1+exp(-w'x))
for i in 1:T
@RV z[i] ~ GaussianMeanPrecision(inputs[i, :], 1e4*diageye(dimensionality))
@RV x[i] ~ Nonlinear{Sampling}(θ, z[i], g=f, in_variates=[Multivariate, Multivariate], out_variate=Univariate)
@RV y[i] ~ Bernoulli(x[i])
placeholder(y[i], :y, index=i)
end
Thanks to @ThijsvdLaar.
The rest of the issue will be addressed in future PR.
from forneylab.jl.
Related Issues (20)
- messagePassingAlgorithm not found HOT 1
- MethodError: no method matching length(::Variable) HOT 2
- KeyError: key :log_pdf not found HOT 1
- TagBot trigger issue HOT 3
- Question: Can ForneyLab do parameter learning and graph structure learning? HOT 3
- code generation for repetitive graph structure HOT 3
- Belief Propagation HOT 2
- Error: No applicable marginal update rule for GaussianMeanVariance node with inbound types: Message{GaussianWeightedMeanPrecision}, Message{GaussianMeanVariance}
- Multiplication of two categorical variables HOT 2
- Incorrect outbound message update rules for GaussianMeanVariance HOT 1
- Where are parameters stored in ForneyLab.Bernoulli? HOT 3
- ForneyLab.step! must be explicitly imported HOT 29
- Deterministic to non-deterministic HOT 2
- A Discrete Bayesian Graph HOT 16
- ForneyLab License clarification
- Cutoff and epsilon values
- The update rule for the output of Poisson node is incorrect.
- http://forneylab.org/ domain expired HOT 1
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