Document imputation and sampling

README.md

@@ -386,6 +386,15 @@ The default parameters are: `ProxGradParams(stepsize=1.0;max_iter=100,inner_iter
 `ch.objective` stores the objective values, and `ch.times` captures the times these objective values were achieved.
 Try plotting this to see if you just need to increase `max_iter` to converge to a better model.
 
+# Imputation
+
+After fitting a GLRM, you can use it to impute values of `A` in
+four different ways:
+* `impute(glrm)` gives the maximum likelihood estimates for each entry
+* `impute_missing(glrm)` imputes missing entries and leaves observed entries unchanged
+* `sample(glrm)` gives a draw from the posterior distribution, conditioned on the fit values of `X` and `Y`, for each entry
+* `sample_missing(glrm)` samples missing entries and leaves observed entries unchanged
+
 # Cross validation
 
 A number of useful functions are available to help you check whether a given low rank model overfits to the test data set.

src/evaluate_fit.jl

@@ -1,4 +1,4 @@
-export objective, error_metric, impute
+export objective, error_metric, impute, impute_missing
 
 ### OBJECTIVE FUNCTION EVALUATION FOR MPCA
 function objective(glrm::GLRM, X::Array{Float64,2}, Y::Array{Float64,2},
@@ -158,3 +158,12 @@ error_metric(glrm::AbstractGLRM; kwargs...) = error_metric(glrm, Domain[l.domain
 
 # Use impute and errors over GLRMS
 impute(glrm::AbstractGLRM) = impute(glrm.losses, glrm.X'*glrm.Y)
+function impute_missing(glrm::AbstractGLRM)
+  Ahat = impute(glrm)
+  for j in 1:size(glrm.A,2)
+    for i in glrm.observed_examples[j]
+      Ahat[i,j] = glrm.A[i,j]
+    end
+  end
+  return Ahat
+end