inst/MatlabCollocInfer/Rcode/exp.Cproc.R In CollocInfer: Collocation Inference for Dynamic Systems

```########### Likelihood for Rates of Change ################
#
# Here we note that the derivative is modeled as a respose
# dependent on the state, and represented as "y" in
# in calls to gradient evalutations
###########################################################

make.exp.Cproc <- function()
{

exp.Cproc <- function(coefs,bvals,pars,more)
{
devals = exp(as.matrix(bvals\$bvals%*%coefs))
ddevals = (as.matrix(bvals\$dbvals%*%coefs))*devals

colnames(devals) = more\$names
colnames(ddevals) = more\$names
names(pars) = more\$parnames

f = more\$fn(ddevals,more\$qpts,devals,pars,more\$more)

return( sum(f) )
}

exp.dCproc.dc <- function(coefs,bvals,pars,more)
{
devals = exp(as.matrix(bvals\$bvals%*%coefs))
ddevals = (as.matrix(bvals\$dbvals%*%coefs))*devals

colnames(devals) = more\$names
colnames(ddevals) = more\$names
names(pars) = more\$parnames

g1 = more\$dfdx(ddevals,more\$qpts,devals,pars,more\$more)
g2 = more\$dfdy(ddevals,more\$qpts,devals,pars,more\$more)

g = as.vector( t(bvals\$bvals)%*%(g1*devals) +
t(bvals\$dbvals)%*%(g2*devals) +
t(bvals\$bvals)%*%(g2*ddevals) )

return(g)
}

exp.dCproc.dp <- function(coefs,bvals,pars,more)
{
devals = exp(as.matrix(bvals\$bvals%*%coefs))
ddevals = (as.matrix(bvals\$dbvals%*%coefs))*devals

colnames(devals) = more\$names
colnames(ddevals) = more\$names
names(pars) = more\$parnames

g = more\$dfdp(ddevals,more\$qpts,devals,pars,more\$more)

g = apply(g,2,sum)

return(g)
}

exp.d2Cproc.dc2 <- function(coefs,bvals,pars,more)
{
devals = exp(as.matrix(bvals\$bvals%*%coefs))
ddevals = (as.matrix(bvals\$dbvals%*%coefs))*devals

colnames(devals) = more\$names
colnames(ddevals) = more\$names
names(pars) = more\$parnames

g1 = more\$dfdx(ddevals,more\$qpts,devals,pars,more\$more)
g2 = more\$dfdy(ddevals,more\$qpts,devals,pars,more\$more)

H1 = more\$d2fdx2(ddevals,more\$qpts,devals,pars,more\$more)
H2 = more\$d2fdxdy(ddevals,more\$qpts,devals,pars,more\$more)
H3 = more\$d2fdy2(ddevals,more\$qpts,devals,pars,more\$more)

# H = array(0,c(rep(dim(bvals\$bvals)[2],2),rep(dim(devals)[2],2)))
H = list(len=dim(bvals\$bvals)[2])

for(i in 1:dim(devals)[2]){
ibvals = diag(devals[,i])%*%bvals\$bvals
idbvals = diag(ddevals[,i])%*%bvals\$bvals+diag(devals[,i])%*%bvals\$dbvals
H[[i]] = list(len=dim(devals))
for(j in 1:dim(devals)[2]){
jbvals = diag(devals[,j])%*%bvals\$bvals
jdbvals = diag(ddevals[,j])%*%bvals\$bvals+diag(devals[,j])%*%bvals\$dbvals
H[[i]][[j]] = t(ibvals)%*%diag(H1[,i,j])%*%jbvals +
t(ibvals)%*%diag(H2[,i,j])%*%jdbvals +
t(idbvals)%*%diag(H2[,j,i])%*%jbvals +
t(idbvals)%*%diag(H3[,i,j])%*%jdbvals
}
H[[i]][[i]] = H[[i]][[i]] + t(ibvals)%*%diag(g1[,i])%*%bvals\$bvals +
t(idbvals)%*%diag(g2[,i])%*%bvals\$bvals +
t(ibvals)%*%diag(g2[,i])%*%bvals\$dbvals

#    t(bvals\$dbvals)%*%diag(devals[,i]*g2[,i])%*%bvals\$bvals +
#                t(bvals\$bvals)%*%diag(devals[,i]*g2[,i]*devals[,i])%*%bvals\$dbvals
#                t(bvals\$bvals)%*%diag(ddevals[,i]*g2[,i]*devals[,i]+devals[,i]*g1[,i]*devals[,i])%*%bvals\$bvals
}

H = blocks2mat(H)

return(H)
}

exp.d2Cproc.dcdp <- function(coefs,bvals,pars,more)
{
devals = exp(as.matrix(bvals\$bvals%*%coefs))
ddevals = (as.matrix(bvals\$dbvals%*%coefs))*devals

colnames(devals) = more\$names
colnames(ddevals) = more\$names
names(pars) = more\$parnames

H1 = more\$d2fdxdp(ddevals,more\$qpts,devals,pars,more\$more)
H2 = more\$d2fdydp(ddevals,more\$qpts,devals,pars,more\$more)

H = c()

for(i in 1:length(pars)){
H = cbind(H,as.vector(t(bvals\$bvals)%*%(devals*H1[,,i] + ddevals*H2[,,i]) +
t(bvals\$dbvals)%*%(devals*H2[,,i])))
}

return(H)
}

return(
list(
fn = exp.Cproc,
dfdc = exp.dCproc.dc,
dfdp = exp.dCproc.dp,
d2fdc2 = exp.d2Cproc.dc2,
d2fdcdp = exp.d2Cproc.dcdp
)
)
}
```

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CollocInfer documentation built on May 2, 2019, 4:03 a.m.