removing obsolete code

Modified-by: Michel Juillard <michel.juillard@mjui.fr>

src/KalmanFilterTools.jl

@@ -822,78 +822,6 @@ function diffuse_kalman_likelihood(Y::Matrix{U},
     LIK
 end
 
-#=
-function univariate_step(t, Y, Z, H, T, QQ, a, Pinf, Pstar, diffuse_kalman_tol, kalman_tol, ws)
-    llik = 0
-    for i=1:size(Y, 1)
-        Zi = view(Z, i, :)
-        prediction_error = get_prediction_error(Y, Z, a, i, t)
-        Fstar = get_Fstar!(Zi, Pstar, H[i], ws.uKstar)
-        Finf = get_Finf!(Zi, Pstar, ws.uKstar)
-        # Conduct check of rank
-        # Pinf and Finf are always scaled such that their norm=1: Fstar/Pstar, instead,
-        # depends on the actual values of std errors in the model and can be badly scaled.
-        # experience is that diffuse_kalman_tol has to be bigger than kalman_tol, to ensure
-        # exiting the diffuse filter properly, avoiding tests that provide false non-zero rank for Pinf.
-        # Also the test for singularity is better set coarser for Finf than for Fstar for the same reason
-        if Finf > diffuse_kalman_tol                 # F_{\infty,t,i} = 0, use upper part of bracket on p. 175 DK (2012) for w_{t,i}
-            ws.Kinf_Finf .= ws.uKinf./Finf
-            a .+= prediction_error.*ws.Kinf_Finf
-            # Pstar     = Pstar + Kinf*(Kinf_Finf'*(Fstar/Finf)) - Kstar*Kinf_Finf' - Kinf_Finf*Kstar'
-            ger!( Fstar/Finf, ws.uKinf, ws.Kinf_Finf, Pstar) 
-            ger!( -1.0, ws.uKstar, ws.Kinf_Finf, Pstar) 
-            ger!( -1.0, ws.Kinf_Finf, ws.uKstar, Pstar) 
-            # Pinf      = Pinf - Kinf*Kinf_Finf'
-            ger!(-1.0, ws.uKinf, ws.Kinf_Finf, Pinf) 
-            llik += log(Finf)
-        elseif Fstar > kalman_tol
-            llik += log(Fstar) + prediction_error*prediction_error/Fstar
-            a .+= ws.uKstar.*(prediction_error/Fstar)
-            ger!(-1/Fstar, ws.uKstar, ws.uKstar, Pstar)
-        else
-            # do nothing as a_{t,i+1}=a_{t,i} and P_{t,i+1}=P_{t,i}, see
-            # p. 157, DK (2012)
-        end
-    end
-    return llik
-end
-
-function univariate_step(t, Y, Z, H, T, QQ, a, Pinf, Pstar, diffuse_kalman_tol, kalman_tol, pattern, ws)
-    llik = 0
-    for i=1:size(pattern, 1)
-        Zi = view(Z, pattern[i], :)
-        prediction_error = get_prediction_error(Y, Z, a, pattern[i], t)
-        Fstar = get_Fstar!(Zi, Pstar, H[i], ws.uKstar)
-        Finf = get_Finf!(Zi, Pstar, ws.uKstar)
-        # Conduct check of rank
-        # Pinf and Finf are always scaled such that their norm=1: Fstar/Pstar, instead,
-        # depends on the actual values of std errors in the model and can be badly scaled.
-        # experience is that diffuse_kalman_tol has to be bigger than kalman_tol, to ensure
-        # exiting the diffuse filter properly, avoiding tests that provide false non-zero rank for Pinf.
-        # Also the test for singularity is better set coarser for Finf than for Fstar for the same reason
-        if Finf > diffuse_kalman_tol                 # F_{\infty,t,i} = 0, use upper part of bracket on p. 175 DK (2012) for w_{t,i}
-            ws.Kinf_Finf .= ws.uKinf./Finf
-            a .+= prediction_error.*ws.Kinf_Finf
-            # Pstar     = Pstar + Kinf*(Kinf_Finf'*(Fstar/Finf)) - Kstar*Kinf_Finf' - Kinf_Finf*Kstar'
-            ger!( Fstar/Finf, ws.uKinf, ws.Kinf_Finf, Pstar) 
-            ger!( -1.0, ws.uKstar, ws.Kinf_Finf, Pstar) 
-            ger!( -1.0, ws.Kinf_Finf, ws.uKstar, Pstar) 
-            # Pinf      = Pinf - Kinf*Kinf_Finf'
-            ger!(-1.0, ws.uKinf, ws.Kinf_Finf, Pinf) 
-            llik += log(Finf)
-        elseif Fstar > kalman_tol
-            llik += log(Fstar) + prediction_error*prediction_error/Fstar
-            a .+= ws.uKstar.*(prediction_error/Fstar)
-            ger!(-1/Fstar, ws.uKstar, ws.uKstar, Pstar)
-        else
-            # do nothing as a_{t,i+1}=a_{t,i} and P_{t,i+1}=P_{t,i}, see
-            # p. 157, DK (2012)
-        end
-    end
-    return llik
-end
-=#
-
 # Filters
 
 function kalman_filter!(Y::AbstractArray{U},