Updates to particle filter implementations

src/particle_filters/particle_filter_test.jl

@@ -28,7 +28,7 @@ f(xn,x,t::Int64) = begin
     end
 end
 
-function f_sample(x::Vector, t::Int64)
+function f_sample(x, t::Int64)
     c = 8*cos(1.2*(t-1))
     @inbounds for i = 1:length(x)
         x[i] =  0.5*x[i] + 25*x[i]./(1+x[i]^2) + c + σw*randn()
@@ -45,19 +45,19 @@ function f_density(xb, xp, t)
 end
 
 
-const den = 0.5/σv^2
-function g_density(y::Float64, x::Vector{Float64}, w::Vector{Float64})
+const den = -0.5/σv^2
+function g_density(y::Float64, x, w)
     @inbounds for i = 1:length(w)
-        w[i] -= (den * (y-0.05x[i]^2)^2 - s2piσv)
+        w[i] += (den * (y-0.05x[i]^2)^2 - s2piσv)
     end
 end
 
 
 ## Parameter estimation
-function f_theta_sample(x::Vector, t, theta)
+function f_theta_sample(x,x1, t, theta)
     c = theta[3]*cos(1.2*(t-1))
     @inbounds for i = 1:length(x)
-        x[i] =  theta[1]*x[i] + theta[2]*x[i]./(1+x[i]^2) + c + σw*randn()
+        x[i] =  theta[1]*x1[i] + theta[2]*x1[i]./(1+x1[i]^2) + c + σw*randn()
     end
     return x
 end
@@ -72,10 +72,10 @@ function f_density_theta(xb, xp, t, theta)
 end
 
 
-function g_density_theta(y::Float64,x::Vector{Float64},w, theta)
+function g_density_theta(y::Float64,x, w, theta)
     #     w = Array(Float64,size(x))
     @inbounds for i = 1:length(w)
-        w[i] = -(den * (y-0.05x[i]^2)^2)
+        w[i] = den * (y-0.05x[i]^2)^2 - s2piσv
     end
     return w
 end
@@ -133,14 +133,14 @@ function test_pf()
     return RMSE, RMSE_FFBSi
 end
 
-using Winston
+
 function test_PMMH()
     particle_count = [100]
     time_steps = [200]
-    R = 5_000
+    R = 30_000
     for (Ti,T) in enumerate(time_steps)
         for (Ni, N) in enumerate(particle_count)
-            montecarlo_runs = 4# maximum(particle_count)*maximum(time_steps) / T / N*10
+            montecarlo_runs = 8# maximum(particle_count)*maximum(time_steps) / T / N*10
             x = Array(Float64,T)
             y = Array(Float64,T)
             theta_PMMH = SharedArray(Float64,(size(theta0,1),R,montecarlo_runs))
@@ -152,13 +152,18 @@ function test_PMMH()
                     y[t+1] = 0.05x[t+1]^2  + σv*randn()
                 end # t
                 theta0i = draw_theta0()
-                theta_PMMH[:,:,mc_iter] = pf_PMMH(y, N, R, pf_bootstrap_nn, f_theta_sample, draw_theta_random, f_density_theta, g_density_theta, σw0, theta0i)
+                theta_PMMH[:,:,mc_iter] = pf_PMMH(y, N, R, pf_bootstrap_nn, f_theta_sample, draw_theta_random, f_density_theta, g_density_theta, theta0i)
             end # MC
             newplot(theta_PMMH[:,:,1]'); hold(true)
             for mc_iter = 2:montecarlo_runs
                 plot(theta_PMMH[:,:,mc_iter]')
             end
             hold(false); title("PMMH")
+            th = Array(theta_PMMH)
+#             pp = Winston.figure(); Winston.plothist(th[1,:,:][:],500); Winston.hold(true)
+#             Winston.plothist(th[2,:,:][:],500)
+#             Winston.plothist(th[3,:,:][:],500)
+#             Winston.hold(false); Winston.display(pp)
             @show N
         end # N
         @show T

src/particle_filters/pf_PMMH.jl

@@ -12,7 +12,7 @@ function pf_PMMH(y, N, R, pf, f_theta, draw_theta, f_density_theta, g_density_th
     theta[:,1] = theta0
 
     g_density(y_t, x_t,w)  = g_density_theta(y_t, x_t,w, theta0)
-    f(x,t) = f_theta(x,t,theta0)
+    f(x,x1,t) = f_theta(x,x1,t,theta0)
     pf(xp, w, wnn, y, N, g_density, f)
 
     Z[1] = Zf(wnn)
@@ -24,7 +24,7 @@ function pf_PMMH(y, N, R, pf, f_theta, draw_theta, f_density_theta, g_density_th
     breakTime = floor(Int64, R / breakPoints)
     particleIncrease = 10*N
     thetaP = draw_theta(theta0)
-    f(x,t) = f_theta(x,t,thetaP)
+    f(x,x1,t) = f_theta(x,x1,t,thetaP)
     g_density(y_t, x_t,w)  = g_density_theta(y_t, x_t,w, thetaP)
 
     for r = 2:R

src/particle_filters/pf_bootstrap.jl

@@ -7,7 +7,7 @@ function pf_bootstrap!(xp,w, y, N, g_density, f)
     wT = slice(w,:,1)
     xT = slice(xp,:,1)
     fill!(wT,-log(N))
-    g(y[1],xT, wT)
+    g_density(y[1],xT, wT)
     wT -= logsumexp(wT)
     j = Array(Int64,N)
     #     tw = Float64(N)
@@ -27,7 +27,7 @@ function pf_bootstrap!(xp,w, y, N, g_density, f)
         end
 
         # Time update
-        g(y[t],xT, wT)
+        g_density(y[t],xT, wT)
 
         # Normalize weights
         wT -= logsumexp(wT)
@@ -45,7 +45,7 @@ function pf_bootstrap(y, N, g_density, f)
     wT = slice(w,:,1)
     xT = slice(xp,:,1)
     fill!(wT,-log(N))
-    g(y[1],xT, wT)
+    g_density(y[1],xT, wT)
     wT -= logsumexp(wT)
     j = Array(Int64,N)
     #     tw = Float64(N)
@@ -65,7 +65,7 @@ function pf_bootstrap(y, N, g_density, f)
         end
 
         # Time update
-        g(y[t],xT, wT)
+        g_density(y[t],xT, wT)
 
         # Normalize weights
         wT -= logsumexp(wT)
@@ -84,8 +84,8 @@ function pf_bootstrap_nn(xp, w, wnn, y, N, g_density, f)
     wT = slice(w,:,1)
     xT = slice(xp,:,1)
     fill!(wT,-log(N))
-    g(y[1],xT, wT)
-    wnn[:,t] = wT
+    g_density(y[1],xT, wT)
+    wnn[:,1] = wT
     wT -= logsumexp(wT)
     j = Array(Int64,N)
     #     tw = Float64(N)
@@ -178,7 +178,7 @@ function pf_CSMC!(xp,w, y, N, g_density, f, xc )
     wT = slice(w,:,1)
     xT = slice(xp,:,1)
     fill!(wT,-log(N))
-    g(y[1],xT, wT)
+    g_density(y[1],xT, wT)
     wT -= logsumexp(wT)
     j = Array(Int64,N)
     #     tw = Float64(N)
@@ -198,7 +198,7 @@ function pf_CSMC!(xp,w, y, N, g_density, f, xc )
         end
         xp[N,t] = xc[t]
         # Time update
-        g(y[t],xT, wT)
+        g_density(y[t],xT, wT)
 
         # Normalize weights
         wT -= logsumexp(wT)