/
NextGenerationInfinity1.jl
132 lines (109 loc) · 3.4 KB
/
NextGenerationInfinity1.jl
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using JLD
include("Transformation.jl")
include("AbstractSystem.jl")
include("Tree.jl")
include("Evaluation.jl")
include("BackPropogation.jl")
include("Facility.jl")
import Base.push!
type Axiom
tree1 :: Tree
tree2 :: Tree
index :: Dict
end
function Base.show(io :: IO, m :: Axiom)
print(io, "Axiom[")
print(io, m.tree1)
print(io, ", ")
print(io, m.tree2)
print(io, "]")
end
function beautify(m :: Axiom)
string("Axiom:\n", beautify(m.tree1, 1), "\n", beautify(m.tree2, 1), "\n")
end
Axiom(tree1 :: Tree, tree2 :: Tree) = Axiom(tree1, tree2, add!(index(tree1), index(tree2)))
Axiom(skeleton1, skeleton2) = Axiom(toTree(skeleton1), toTree(skeleton2))
function push!(index :: Dict, ops :: Array)
n = length(ops)
for i in 1:n
for t in index[i]
t.op = ops[i]
end
end
end
function push!(index :: Dict, ops :: Dict)
for key in keys(ops)
if haskey(index, key)
ts = index[key]
for t in ts
t.op = ops[key]
end
end
end
end
function push!(axiom :: Axiom, ops)
push!(axiom.index, variables)
end
function init_axiom!(axiom :: Axiom, variables)
push!(axiom.index, variables)
init_tree!(axiom.tree1)
init_tree!(axiom.tree2)
end
init_axioms! = distribute(init_axiom!)
## add some loss function to deal with degenerating problem?
function loss(a, b)
b * (1. - a * b)
end
function train_axiom!(axiom :: Axiom, variables, n = 1, randomize = identity)
push!(axiom.index, variables)
d1 :: Array{Float64, 1} = axiom.tree1.value[:d]
d2 :: Array{Float64, 1} = axiom.tree2.value[:d]
v1 :: Array{Float64, 1} = axiom.tree1.value[:value]
v2 :: Array{Float64, 1} = axiom.tree2.value[:value]
for i in 1:n
randomize(variables)
push!(axiom.index, variables)
eval_tree!(axiom.tree2)
eval_tree!(axiom.tree1)
for j in 1:length(d1)
d1[j] = loss(v1[j], v2[j])
d2[j] = loss(v2[j], v1[j])
end
bp_tree!(axiom.tree1)
bp_tree!(axiom.tree2)
end
end
train_axioms! = distribute(train_axiom!)
## to prevent degeneration problem, we use anti-traing to deal with the problem.
function anti_train_axiom!(axiom :: Axiom, n = 1, randomize = identity)
d1 :: Array{Float64, 1} = axiom.tree1.value[:d]
d2 :: Array{Float64, 1} = axiom.tree2.value[:d]
v1 :: Array{Float64, 1} = axiom.tree1.value[:value]
v2 :: Array{Float64, 1} = axiom.tree2.value[:value]
for i in 1:n
randomize(axiom.tree1)
randomize(axiom.tree2)
eval_tree!(axiom.tree2)
eval_tree!(axiom.tree1)
for j in 1:length(d1)
d1[j] = - loss(v1[j], v2[j])
d2[j] = - loss(v2[j], v1[j])
end
bp_tree!(axiom.tree1)
bp_tree!(axiom.tree2)
end
end
anti_train_axioms! = distribute(anti_train_axiom!)
include("DataStructs.jl")
@load "infinity.jld"
## The Repl facility, note that for the program structure parsing, we rely on the julia parser;
## and we also need to read the variable name from string.
include("Repl.jl")
r = REPL(Env.empty, Seq.empty)
a1 = repl(r, "function(x) x end").value;
a2 = repl(r, "var y = function(x) x end").value;
a3 = repl(r, "y").value;
a4 = repl(r, "function(x) x(x) end").value;
a5 = repl(r, "begin var y = function(x) x end; y end").value;
as = [a1, a2, a3, a4, a5];
pair_computation(function(x, y) sum(abs(as[x] - as[y])) end, 1:5)