Nothing
########### 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.Cproc <- function()
{
Cproc <- function(coefs,bvals,pars,more)
{
devals = as.matrix(bvals$bvals%*%coefs)
ddevals = as.matrix(bvals$dbvals%*%coefs)
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) )
}
dCproc.dc <- function(coefs,bvals,pars,more)
{
devals = as.matrix(bvals$bvals%*%coefs)
ddevals =as.matrix( bvals$dbvals%*%coefs)
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 + t(bvals$dbvals)%*%g2 )
return(g)
}
dCproc.dp <- function(coefs,bvals,pars,more)
{
devals = as.matrix(bvals$bvals%*%coefs)
ddevals = as.matrix(bvals$dbvals%*%coefs)
colnames(devals) = more$names
colnames(ddevals) = more$names
names(pars) = more$parnames
g = more$dfdp(ddevals,more$qpts,devals,pars,more$more)
g = apply(g,2,sum)
return(g)
}
d2Cproc.dc2 <- function(coefs,bvals,pars,more)
{
devals = as.matrix(bvals$bvals%*%coefs)
ddevals = as.matrix(bvals$dbvals%*%coefs)
colnames(devals) = more$names
colnames(ddevals) = more$names
names(pars) = more$parnames
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)))
# for(i in 1:dim(devals)[2]){
# for(j in 1:dim(devals)[2]){
# H[,,i,j] = t(bvals$bvals)%*%diag(H1[,i,j])%*%bvals$bvals +
# t(bvals$bvals)%*%diag(H2[,i,j])%*%bvals$dbvals +
# t(bvals$dbvals)%*%diag(H2[,j,i])%*%bvals$bvals +
# t(bvals$dbvals)%*%diag(H3[,i,j])%*%bvals$dbvals
# }
# }
# 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]){
H[[i]] = list(len=dim(devals))
for(j in 1:dim(devals)[2]){
H[[i]][[j]] = t(bvals$bvals)%*%diag(H1[,i,j])%*%bvals$bvals +
t(bvals$bvals)%*%diag(H2[,i,j])%*%bvals$dbvals +
t(bvals$dbvals)%*%diag(H2[,j,i])%*%bvals$bvals +
t(bvals$dbvals)%*%diag(H3[,i,j])%*%bvals$dbvals
}
}
H = blocks2mat(H)
return(H)
}
d2Cproc.dcdp <- function(coefs,bvals,pars,more)
{
devals = as.matrix(bvals$bvals%*%coefs)
ddevals = as.matrix(bvals$dbvals%*%coefs)
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)%*%H1[,,i] +
t(bvals$dbvals)%*%H2[,,i]))
}
return(H)
}
return(
list(
fn = Cproc,
dfdc = dCproc.dc,
dfdp = dCproc.dp,
d2fdc2 = d2Cproc.dc2,
d2fdcdp = d2Cproc.dcdp
)
)
}
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