update

src/LTVModelsBase.jl

 module LTVModelsBase
 
 # Interface exports
-export AbstractModel, AbstractCost, ModelAndCost, cost,
-cost_final, dc,calculate_cost,calculate_final_cost,
-fit_model, predict, df,costfun, covariance, LTVStateSpaceModel,
-SimpleLTVModel, KalmanModel, GMMModel
+export AbstractModel, AbstractCost, ModelAndCost,f,
+dc,calculate_cost,calculate_final_cost,
+fit_model, predict, df,costfun, LTVStateSpaceModel,
+SimpleLTVModel, covariance
+
+export rms, sse, nrmse
 
 
 rms(x)      = sqrt(mean(x.^2))
@@ -46,28 +48,6 @@ end
 
 SimpleLTVModel(At,Bt,extend::Bool) = SimpleLTVModel{eltype(At)}(At,Bt,extend)
 
-mutable struct KalmanModel{T} <: LTVStateSpaceModel
-    At::Array{T,3}
-    Bt::Array{T,3}
-    Pt::Array{T,3}
-    extended::Bool
-    function KalmanModel{T}(At::Array{T,3},Bt::Array{T,3},Pt::Array{T,3},extend::Bool)
-        if extend
-            At = cat(3,At,At[:,:,end])
-            Bt = cat(3,Bt,Bt[:,:,end])
-            Pt = cat(3,Pt,Pt[:,:,end])
-        end
-        return new(At,Bt,Pt,extend)
-    end
-end
-
-KalmanModel(At,Bt,Pt,extend::Bool=false) = KalmanModel{eltype(At)}(At,Bt,Pt,extend)
-
-mutable struct GMMModel <: AbstractModel
-    M
-    dynamics
-    T
-end
 
 """
     model = fit_model(::Type{AbstractModel}, x,u)::AbstractModel
@@ -172,7 +152,7 @@ function f(modelcost::ModelAndCost, x, u, i)
     predict(modelcost.model, x, u, i)
 end
 
-function constfun(modelcost::ModelAndCost, x, u)
+function costfun(modelcost::ModelAndCost, x, u)
     calculate_cost(modelcost.cost, x, u)
 end
 
@@ -188,4 +168,14 @@ function df(modelcost::ModelAndCost, x, u)
 end
 
 
+
+function covariance(model::AbstractModel, x, u)
+    xhat = similar(x)
+    xhat[:,1] = x[:,1]
+    for i = 1:size(x,2)-1
+        xhat[:,i+1] = model.At[:,:,i]*x[:,i] + model.Bt[:,:,i]*u[:,i]
+    end
+    return cov(x-xhat, 2)
+end
+
 end # module