fix names

JuliaPOMDP · Aug 9, 2019 · 15b0084 · 15b0084
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diff --git a/docs/src/model_transformations.md b/docs/src/model_transformations.md
@@ -12,6 +12,9 @@ SparseTabularPOMDP
 transition_matrix
 reward_vector
 observation_matrix
+transition_matrices
+reward_matrix
+observation_matrices
 ```
 
 ## Fully Observable POMDP

diff --git a/src/POMDPModelTools.jl b/src/POMDPModelTools.jl
@@ -94,7 +94,10 @@ export
     SparseTabularPOMDP,
     transition_matrix,
     reward_vector,
-    observation_matrix
+    observation_matrix,
+    transition_matrices,
+    reward_matrix,
+    observation_matrices
 
 include("sparse_tabular.jl")
 

diff --git a/src/sparse_tabular.jl b/src/sparse_tabular.jl
@@ -273,11 +273,11 @@ It returns a sparse matrix containing the transition probabilities when taking a
 transition_matrix(p::SparseTabularProblem, a) = p.T[a]
 
 """     
-    transition_matrix(p::SparseTabularProblem)
+    transition_matrices(p::SparseTabularProblem)
 Accessor function for the transition model of a sparse tabular problem.
 It returns a list of sparse matrices for each action of the problem.
 """
-transition_matrix(p::SparseTabularProblem) = p.T
+transition_matrices(p::SparseTabularProblem) = p.T
 
 """
     reward_vector(p::SparseTabularProblem, a)
@@ -310,8 +310,8 @@ It returns a sparse matrix containing the observation probabilities when having
 observation_matrix(p::SparseTabularPOMDP, a::Int64) = p.O[a]
 
 """     
-    observation_matrix(p::SparseTabularPOMDP)
+    observation_matrices(p::SparseTabularPOMDP)
 Accessor function for the observation model of a sparse tabular POMDP.
 It returns a list of sparse matrices for each action of the problem.
 """
-observation_matrix(p::SparseTabularPOMDP) = p.O
+observation_matrices(p::SparseTabularPOMDP) = p.O
diff --git a/test/test_tabular.jl b/test/test_tabular.jl
@@ -91,8 +91,8 @@ spomdp3 = SparseTabularPOMDP(spomdp, transition = [sparse(1:n_states(mdp), 1:n_s
 spomdp3 = SparseTabularPOMDP(spomdp, discount = 0.5)
 @test spomdp3.discount != spomdp.discount
 @test size(observation_matrix(spomdp, 1)) == (n_states(spomdp), n_observations(spomdp))
-@test observation_matrix(spomdp) == spomdp2.O
-@test transition_matrix(spomdp) == spomdp2.T
+@test observation_matrices(spomdp) == spomdp2.O
+@test transition_matrices(spomdp) == spomdp2.T
 @test reward_matrix(spomdp) == spomdp2.R
 
 ## Tests