More quick fixes. Tests pass except for docs

Docs need to be updated. Better delineation between a table and a dataframe
Sampling works, inference hasent been touched

src/DiscreteBayesNet/tables.jl

@@ -18,7 +18,13 @@ for f in [:names, :unique, :size, :eltype, :setindex!, :getindex]
  @eval (Base.$f)(t::Table, x...) = $f(t.potential, x...)
 end
 
-nrow(t::Table) = nrow(t.potential)
+for s in [:(==), :(!=)]
+ @eval (Base.$s)(t::Table, f::DataFrame) = $s(t.potential, f)
+ @eval (Base.$s)(f::DataFrame, t::Table) = $s(f, t.potential)
+ @eval (Base.$s)(t1::Table, t2::Table) = $s(t1.potential, t2.potential)
+end
+
+DataFrames.nrow(t::Table) = nrow(t.potential)
 
 """
 Table multiplication

test/test_discrete_bayes_nets.jl

@@ -10,10 +10,10 @@ let
  push!(bn, DiscreteCPD(:b, [:a], [2], [Categorical([0.5,0.5]),Categorical([0.2,0.8])]))
 
  T = table(bn, :a)
- @test T == DataFrame(a=[1,2], p=[0.4,0.6]) |> Table
+ @test T == DataFrame(a=[1,2], p=[0.4,0.6])
 
  T = table(bn, :b)
- @test T == DataFrame(a=[1,2,1,2], b=[1,1,2,2], p=[0.5,0.2,0.5,0.8]) |> Table
+ @test T == DataFrame(a=[1,2,1,2], b=[1,1,2,2], p=[0.5,0.2,0.5,0.8])
 
  data = DataFrame(a=[1,1,1,1,2,2,2,2],
  b=[1,2,1,2,1,1,1,2])

test/test_sampling.jl

@@ -1,26 +1,28 @@
 let
- # A → C ← B
- bn = BayesNet()
- push!(bn, StaticCPD(:a, Categorical([1.0,0.0])))
- push!(bn, StaticCPD(:b, Categorical([0.0,1.0])))
- push!(bn, CategoricalCPD{Bernoulli}(:c, [:a, :b], [2,2], [Bernoulli(0.1), Bernoulli(0.2), Bernoulli(1.0), Bernoulli(0.4)]))
+ # A → C ← B
+ bn = BayesNet()
+ push!(bn, StaticCPD(:a, Categorical([1.0,0.0])))
+ push!(bn, StaticCPD(:b, Categorical([0.0,1.0])))
+ push!(bn, CategoricalCPD{Bernoulli}(:c, [:a, :b], [2,2], 
+ [Bernoulli(0.1), Bernoulli(0.2), Bernoulli(1.0), Bernoulli(0.4)]))
 
- @test rand(bn) == Dict(:a=>1, :b=>2, :c=>1)
+ @test rand(bn) == Dict(:a=>1, :b=>2, :c=>1)
 
- t1 = rand(bn, 5)
- @test size(t1) == (5,3)
- @test t1[:a] == [1,1,1,1,1]
- @test t1[:b] == [2,2,2,2,2]
+ t1 = rand(bn, 5)
+ @test size(t1) == (5,3)
+ @test t1[:a] == [1,1,1,1,1]
+ @test t1[:b] == [2,2,2,2,2]
 
- t2 = rand(bn, 5, Assignment(:c=>1))
- @test size(t1) == (5,3)
+ t2 = rand(bn, 5, Assignment(:c=>1))
+ @test size(t1) == (5,3)
 
- t3 = rand(bn, 5, :c=>1, :b=>2)
- @test size(t1) == (5,3)
+ t3 = rand(bn, 5, :c=>1, :b=>2)
+ @test size(t1) == (5,3)
 
- t4 = rand(bn, LikelihoodWeightedSampler(bn, 10, :c=>1), 5)
+ t4 = rand(bn, LikelihoodWeightedSampler(:c=>1), 5)
+ # is there a test here?
 
- t5 = rand(bn, GibbsSampler(Assignment(:c=>1), burn_in=5), 5)
- @test t5[:a] == [1,1,1,1,1]
- @test t5[:b] == [2,2,2,2,2]
+ t5 = rand(bn, GibbsSampler(Assignment(:c=>1), burn_in=5), 5)
+ @test t5[:a] == [1,1,1,1,1]
+ @test t5[:b] == [2,2,2,2,2]
 end

test/test_tables.jl

@@ -44,9 +44,9 @@ end
 
 let
  # estimation
- df = estimate(Table(DataFrame(
+ df = estimate(DataFrame(
  A = [false, false, true, true, true]
- )))
+ ))
  @test elementwise_isapprox(df[:p], [2/5, 3/5])
 
  # TODO: properly test this