reconstructTensorBF returns the reconstruction of the data based on
posterior samples of a given run. The function reconstructs the tensor for
each posterior sample and then computes the expected value.
The reconstruction is returned in the un-normalized space if
contains appropriate preprocessing information.
The model object from function
The reconstructed data, a tensor of the size equivalent to the data on which the model was run.
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#Data generation K <- 3 X <- matrix(rnorm(20*K),20,K) W <- matrix(rnorm(30*K),30,K) U <- matrix(rnorm(3*K),3,K) Y = 0 for(k in 1:K) Y <- Y + outer(outer(X[,k],W[,k]),U[,k]) Y <- Y + array(rnorm(20*30*3,0,0.25),dim=c(20,30,3)) #Run the method with default options and reconstruct the model's representation of the tensor ## Not run: res <- tensorBF(Y) ## Not run: recon = reconstructTensorBF(res) ## Not run: inds = sample(prod(dim(Y)),100) ## Not run: plot(Y[inds],recon[inds],xlab="obs",ylab="recon",main=round(cor(Y[inds],recon[inds]),2))
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