Minor bugfixes

src/rbfmodels/trainRBF_ARX.jl

@@ -25,7 +25,7 @@ The number of centers is equal to `nc` if Kmeans is used to get initial centers,
 `n_state` is equal to the state dimension, possibly reduced to `inputpca` if so desired.\n
 The number of nonlinear parameters is `n_centers × n_state`\n
 The number of linear parameters is `outputnet ? n_state × (n_centers+1) × (na)+1) : (na)×(n_centers+1)+1)`"""
-function trainRBF_ARX(y, A, state, nc; liniters=3,nonliniters=50, normalized=false, initialcenters="equidistant", inputpca=false, outputnet = true, cuckoosearch = false, cuckooiter=100)
+function trainRBF_ARX(y, A, state, nc; liniters=3,nonliniters=50, normalized=false, initialcenters="equidistant", inputpca=false, outputnet = true, cuckoosearch = false, cuckooiter=100, timeout=60)
     n_points = length(y)
     na = isa(A,Matrix) ? size(A,2) : 1
 
@@ -93,7 +93,7 @@ function trainRBF_ARX(y, A, state, nc; liniters=3,nonliniters=50, normalized=fal
                                             tolG = 1e-7,
                                             tolX = 1e-10,
                                             show_trace=true,
-                                            timeout = 60,
+                                            timeout = timeout,
                                             n_state = n_state)
             X0 = deepcopy(res.minimum)
             DEBUG && assert(X0 == res.minimum)
@@ -122,7 +122,7 @@ function trainRBF_ARX(y, A, state, nc; liniters=3,nonliniters=50, normalized=fal
                                                   pa=0.25,
                                                   Tol=1.0e-5,
                                                   max_iter = i < liniters-1 ? cuckooiter : 2cuckooiter,
-                                                  timeout = 120)
+                                                  timeout = 2timeout)
             debug("cuckoo_search done")
             X0 = deepcopy(bestnest)
             @ddshow rms(f(X0))
@@ -155,7 +155,7 @@ end
 
 
 
-function trainRBF(y, state, nc; liniters=3,nonliniters=50, normalized=false, initialcenters="equidistant", inputpca=false, cuckoosearch = false, cuckooiter=100)
+function trainRBF(y, state, nc; liniters=3,nonliniters=50, normalized=false, initialcenters="equidistant", inputpca=false, cuckoosearch = false, cuckooiter=100, timeout=60)
     n_points = length(y)
     function predictionerror(z)
         znl = RbfNonlinearParameters(z,n_state,n_centers)
@@ -195,7 +195,7 @@ function trainRBF(y, state, nc; liniters=3,nonliniters=50, normalized=false, ini
 
     @ddshow sum(!isfinite(Ψ))
     w = fitlinear(Ψ,y); debug("fitlinear")
-    newplot(w,"o"); title("Linear parameters")
+#     newplot(w,"o"); title("Linear parameters")
     @ddshow sum(!isfinite(Zl))
     prediction = Ψ*w
     error = y - prediction
@@ -221,7 +221,7 @@ function trainRBF(y, state, nc; liniters=3,nonliniters=50, normalized=false, ini
                                             tolG = 1e-7,
                                             tolX = 1e-10,
                                             show_trace=true,
-                                            timeout = 60,
+                                            timeout = timeout,
                                             n_state = n_state)
             X0 = deepcopy(res.minimum)
             DEBUG && assert(X0 == res.minimum)
@@ -248,7 +248,7 @@ function trainRBF(y, state, nc; liniters=3,nonliniters=50, normalized=false, ini
                                                   pa=0.25,
                                                   Tol=1.0e-5,
                                                   max_iter = i < liniters-1 ? cuckooiter : 2cuckooiter,
-                                                  timeout = 120)
+                                                  timeout = 2timeout)
             debug("cuckoo_search done")
             X0 = deepcopy(bestnest)
             @ddshow rms(f(X0))
@@ -320,7 +320,7 @@ function getcentersEq(state::VecOrMat, nc::Integer)
     end
     # add bandwidth parameters γ, give all centers the same bandwidth with Δc as a (hopefully) good initial guess
     #     display(Z)
-    Z[:,n_state+1:end] = ones(1*repmat(4./(Δc.^2)',nc,1)) # Spread the initial guess to all centers
+    Z[:,n_state+1:end] = 1*repmat(4./(Δc.^2)',nc,1) # Spread the initial guess to all centers
     assert(all(Z[:,n_state+1:end].> 0))
     debug("Z done")
     n_centers::Int64 = nc^n_state # new number of centers wich considers gridding of 1D centers