Merge 0a44df7 into e0a3875

.gitignore

@@ -1,2 +1,3 @@
 output
 test/perf/*.jld
+.*

Manifest.toml

@@ -0,0 +1,259 @@
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REQUIRE

@@ -3,4 +3,5 @@ Polynomials
 Distributions
 StaticArrays
 RecipesBase
-Colors
+Colors
+Trajectories

docs/src/library.md

@@ -55,6 +55,7 @@ Bridge.NoDrift
 Bridge.R3!
 Bridge.σ!
 Bridge.b!
+Bridge.kernelr3!
 ```
 
 ## Levy processes

landmarks/back/adapt_solveAndll.jl

@@ -0,0 +1,53 @@
+using ForwardDiff
+
+# convert dual to float, while retaining float if type is float
+deepvalue(x::Float64) = x
+deepvalue(x::ForwardDiff.Dual) = ForwardDiff.value(x)
+deepvalue(x) = deepvalue.(x)
+
+# here is a slight adaptation of Marcin's function solveAndll!
+# should be able to get gradient information for starting point; new guided proposal is written into X
+function solveAndll!(::EulerMaruyama, x0::T, W::SamplePath, X, P::GuidPropBridge, θ) where T
+    N = length(W)
+    N != length(X) && error("Y and W differ in length.")
+
+    tt =  Y.tt
+    X.yy[1] .= deepvalue(x0)
+    y = copy(x0)
+    ll::T  = 0.
+    ww = W.yy
+
+    ll::T = 0.0
+    for i in 1:N-1
+        yy[.., i] = y
+        dWt = ww[.., i+1]-ww[.., i]
+        s = tt[i]
+        dt = tt[i+1]-tt[i]
+        b_prop = _b((i,s), y, P, θ)
+        y = y + b_prop*dt + _scale(dWt, σ(s, y, P, θ))
+
+        b_trgt = _b((i,s), y, target(P), θ)
+        b_aux = _b((i,s), y, auxiliary(P), θ)
+        rₜₓ = r((i,s), y, P, θ)
+        ll += dot(b_trgt-b_aux, rₜₓ) * dt
+
+        if !constdiff(P)
+            Hₜₓ = H((i,s), y, P, θ)
+            aₜₓ = a((i,s), y, target(P), θ)
+            ãₜ = ã((i,s), y, P, θ)
+            ll -= 0.5*sum( (aₜₓ - ãₜ).*Hₜₓ ) * dt
+            ll += 0.5*( rₜₓ'*(aₜₓ - ãₜ)*rₜₓ ) * dt
+        end
+        X.yy[i+1] .= deepvalue(y)
+    end
+    copyto!(X.yy[end], Bridge.endpoint(X.yy[end],P))
+    ll # here the log of rhotilde(0,x0) should be added to ll, Marcin probabably wrote a function for that (please check it is Dual-number 'proof')
+end
+
+
+
+# apply as follows:
+slogρ(W, X, P, θ) = (x0) -> solveAndll!(LeftRule(), 0x, W, X, P, θ)
+∇x0 = copy(x0)
+# following returns the gradient, for starting point x and writes it into ∇x
+ForwardDiff.gradient!(∇x0, slogρ(W, X, P, θ),x0)

landmarks/back/autodiff.jl

@@ -0,0 +1,49 @@
+
+using DualNumbers
+
+# small test to understand
+ff(x) = x^3
+ff(Dual(2,1))
+
+# Redefine
+# const Point = SArray{Tuple{d},Float64,1,d}       # point in R2
+# const Unc = SArray{Tuple{d,d},Float64,d,d*d}     # Matrix presenting uncertainty
+# to
+
+const Point{T} = SArray{Tuple{d},T,1,d}
+const Unc{T} = SArray{Tuple{d,d},T,d,d*d}
+# > Point{Dual{Float64}}(rand(3))
+
+# matrix multiplication of mat of Uncs
+function Base.:*(A::Array{Unc{T},2},B::Array{Unc{T},2}) where T
+    C = zeros(Unc{T},size(A,1), size(B,2))
+    for i in 1:size(A,1)
+        for j in 1:size(B,2)
+            for k in 1:size(A,2)
+               C[i,j] += A[i,k] * B[k,j]
+            end
+        end
+    end
+    C
+end
+
+function Base.:*(A::Array{Unc,2},x::State)
+    vecofpoints2state(A*vec(x))
+end
+
+
+# DON'T NEED THIS PROBABLY
+#
+# function ll(x0,XX,Q)
+#     XX.yy[1] = deepvec2state(x0)
+#     llikelihood(LeftRule(), XX, Q; skip = 0)
+# end
+# y0 = deepvec(x0)
+# ll(y0,XX,Q)
+#
+# F(XX,Q) = (x) -> ll(x,  XX,Q)
+# F(XX,Q)(y0)
+# using ForwardDiff
+#
+# g = x0 -> ForwardDiff.gradient(F(XX,Q), x0) # g = ∇f
+# g(deepvec(x0))