fixed bugs after v15 of ComponentArrays

Project.toml

@@ -1,7 +1,7 @@
 name = "PiecewiseInference"
 uuid = "27a201f4-b6a1-4745-b96e-0c27845dca54"
 authors = ["Victor <bvictor@ethz.ch>"]
-version = "0.9.7"
+version = "0.9.8"
 
 [deps]
 Bijectors = "76274a88-744f-5084-9051-94815aaf08c4"

src/inference.jl

@@ -91,13 +91,12 @@ through the optimizers `optimizers`. Returns a `InferenceResult`.
 - `optimizers` : array of optimizers, e.g. `[Adam(0.01)]`
 - `epochs` : A vector with number of epochs for each optimizer in `optimizers`.
 - `batchsizes`: An vector of batch sizes, which should match the length of
-  `optimizers`.
+  `optimizers`. If nothing is provided, all segments are used at once (full batch).
 - `verbose_loss` : Whether to display loss during training.
 - `info_per_its = 50`: The frequency at which to display the training
   information.
 - `plotting` :  Whether to plot the convergence loss during training.
-- `cb` :  A call back function. Must be of the form `cb(θs, p_trained, losses,
-  pred, ranges)`.
+- `cb` :  A call back function. Must be of the form `cb(p_trained, losses, pred, ranges)`.
 - `threshold` : The tolerance for stopping training.
 - `save_pred = true`: Whether to save the predictions.
 - `save_losses = true` : Whether to save the losses.

src/utils.jl

@@ -217,15 +217,15 @@ end
 
 function to_optim_space(p::ComponentArray, infprob::InferenceProblem)
     @unpack p0, p_bij = infprob
-    pairs = [reshape(p_bij[k](p[k]),:) for k in keys(p0)]
+    pairs = [reshape(p_bij[k](getproperty(p,k)),:) for k in keys(p0)]
     ax = getaxes(p0)
     return ComponentArray(vcat(pairs...), ax)
 end
 
 # TODO /!\ order is not guaranteed!
 function to_param_space(θ::ComponentArray, infprob::InferenceProblem)
     @unpack p0, p_bij = infprob
-    pairs = [reshape(inverse(p_bij[k])(θ[k]),:) for k in keys(p0)]
+    pairs = [reshape(inverse(p_bij[k])(getproperty(θ,k)),:) for k in keys(p0)]
     ax = getaxes(p0)
     return ComponentArray(vcat(pairs...), ax)
 end