Nothing
CPP_smooth.volume.FEM.basis<-function(locations, observations, FEMbasis, covariates = NULL, ndim, mydim, BC = NULL, incidence_matrix = NULL, areal.data.avg = TRUE, search, bary.locations, optim, lambda = NULL, DOF.stochastic.realizations = 100, DOF.stochastic.seed = 0, DOF.matrix = NULL, GCV.inflation.factor = 1, lambda.optimization.tolerance = 0.05, inference.data.object)
{
FEMbasis$mesh$tetrahedrons = FEMbasis$mesh$tetrahedrons - 1
FEMbasis$mesh$faces = FEMbasis$mesh$faces - 1
FEMbasis$mesh$neighbors[FEMbasis$mesh$neighbors != -1] = FEMbasis$mesh$neighbors[FEMbasis$mesh$neighbors != -1] - 1
if(is.null(covariates))
{
covariates<-matrix(nrow = 0, ncol = 1)
}
if(is.null(DOF.matrix))
{
DOF.matrix<-matrix(nrow = 0, ncol = 1)
}
if(is.null(locations))
{
locations<-matrix(nrow = 0, ncol = ndim)
}
if(is.null(incidence_matrix))
{
incidence_matrix<-matrix(nrow = 0, ncol = 1)
}
if(is.null(BC$BC_indices))
{
BC$BC_indices<-vector(length=0)
}else
{
BC$BC_indices<-as.vector(BC$BC_indices)-1
}
if(is.null(BC$BC_values))
{
BC$BC_values<-vector(length=0)
}else
{
BC$BC_values<-as.vector(BC$BC_values)
}
if(is.null(lambda))
{
lambda<-vector(length=0)
}else
{
lambda<-as.vector(lambda)
}
## Extract the parameters for inference from inference.data.object to prepare them for c++ reading
test_Type<-as.vector(inference.data.object@test)
interval_Type<-as.vector(inference.data.object@interval)
implementation_Type<-as.vector(inference.data.object@type)
component_Type<-as.vector(inference.data.object@component)
exact_Inference<-inference.data.object@exact
locs_Inference<-as.matrix(inference.data.object@locations)
locs_index_Inference<-as.vector(inference.data.object@locations_indices - 1) #converting indices from R to c++ ones
locs_are_nodes_Inference <- inference.data.object@locations_are_nodes
coeff_Inference<-as.matrix(inference.data.object@coeff)
beta_0<-as.vector(inference.data.object@beta0)
f_0_eval<-as.vector(inference.data.object@f0_eval)
f_var_Inference<-inference.data.object@f_var
inference_Quantile<-as.vector(inference.data.object@quantile)
inference_Alpha<-as.vector(inference.data.object@alpha)
inference_N_Flip<-inference.data.object@n_flip
inference_Tol_Fspai<-inference.data.object@tol_fspai
inference_Defined<-inference.data.object@definition
## Set propr type for correct C++ reading
locations <- as.matrix(locations)
storage.mode(locations) <- "double"
data <- as.vector(observations)
storage.mode(data) <- "double"
storage.mode(FEMbasis$mesh$order) <- "integer"
storage.mode(FEMbasis$mesh$nodes) <- "double"
storage.mode(FEMbasis$mesh$faces) <- "integer"
storage.mode(FEMbasis$mesh$neighbors) <- "integer"
storage.mode(FEMbasis$mesh$tetrahedrons) <- "integer"
covariates <- as.matrix(covariates)
storage.mode(covariates) <- "double"
storage.mode(ndim) <- "integer"
storage.mode(mydim) <- "integer"
storage.mode(BC$BC_indices) <- "integer"
storage.mode(BC$BC_values) <-"double"
incidence_matrix <- as.matrix(incidence_matrix)
storage.mode(incidence_matrix) <- "integer"
areal.data.avg <- as.integer(areal.data.avg)
storage.mode(areal.data.avg) <-"integer"
storage.mode(search) <- "integer"
storage.mode(optim) <- "integer"
storage.mode(lambda) <- "double"
DOF.matrix <- as.matrix(DOF.matrix)
storage.mode(DOF.matrix) <- "double"
storage.mode(DOF.stochastic.realizations) <- "integer"
storage.mode(DOF.stochastic.seed) <- "integer"
storage.mode(GCV.inflation.factor) <- "double"
storage.mode(lambda.optimization.tolerance) <- "double"
## Set proper type for correct C++ reading for inference parameters
storage.mode(test_Type) <- "integer"
storage.mode(interval_Type) <- "integer"
storage.mode(implementation_Type) <- "integer"
storage.mode(component_Type) <- "integer"
storage.mode(exact_Inference) <- "integer"
storage.mode(locs_Inference) <- "double"
storage.mode(locs_index_Inference) <- "integer"
storage.mode(locs_are_nodes_Inference) <- "integer"
storage.mode(coeff_Inference) <- "double"
storage.mode(beta_0) <- "double"
storage.mode(f_0_eval) <- "double"
storage.mode(f_var_Inference) <- "integer"
storage.mode(inference_Quantile) <- "double"
storage.mode(inference_Alpha) <- "double"
storage.mode(inference_N_Flip) <- "integer"
storage.mode(inference_Tol_Fspai) <- "double"
storage.mode(inference_Defined) <- "integer"
## Call C++ function
bigsol <- .Call("regression_Laplace", locations, bary.locations, data, FEMbasis$mesh, FEMbasis$mesh$order, mydim, ndim, covariates,
BC$BC_indices, BC$BC_values, incidence_matrix, areal.data.avg, search,
optim, lambda, DOF.stochastic.realizations, DOF.stochastic.seed, DOF.matrix, GCV.inflation.factor, lambda.optimization.tolerance,
test_Type,interval_Type,implementation_Type,component_Type,exact_Inference,locs_Inference,locs_index_Inference,locs_are_nodes_Inference,coeff_Inference,beta_0,
f_0_eval,f_var_Inference,inference_Quantile,inference_Alpha,inference_N_Flip,inference_Tol_Fspai,inference_Defined,
PACKAGE = "fdaPDE")
return(bigsol)
}
CPP_smooth.volume.FEM.PDE.basis<-function(locations, observations, FEMbasis, covariates = NULL, PDE_parameters, ndim, mydim, BC = NULL, incidence_matrix = NULL, areal.data.avg = TRUE, search, bary.locations, optim, lambda = NULL, DOF.stochastic.realizations = 100, DOF.stochastic.seed = 0, DOF.matrix = NULL, GCV.inflation.factor = 1, lambda.optimization.tolerance = 0.05, inference.data.object)
{
# Indexes in C++ starts from 0, in R from 1, opportune transformation
FEMbasis$mesh$tetrahedrons = FEMbasis$mesh$tetrahedrons - 1
FEMbasis$mesh$faces = FEMbasis$mesh$faces - 1
FEMbasis$mesh$neighbors[FEMbasis$mesh$neighbors != -1] = FEMbasis$mesh$neighbors[FEMbasis$mesh$neighbors != -1] - 1
#
if(is.null(covariates))
{
covariates<-matrix(nrow = 0, ncol = 1)
}
if(is.null(DOF.matrix))
{
DOF.matrix<-matrix(nrow = 0, ncol = 1)
}
if(is.null(locations))
{
locations<-matrix(nrow = 0, ncol = ndim)
}
if(is.null(incidence_matrix))
{
incidence_matrix<-matrix(nrow = 0, ncol = 1)
}
if(is.null(BC$BC_indices))
{
BC$BC_indices<-vector(length=0)
}else
{
BC$BC_indices<-as.vector(BC$BC_indices)-1
}
if(is.null(BC$BC_values))
{
BC$BC_values<-vector(length=0)
}else
{
BC$BC_values<-as.vector(BC$BC_values)
}
## Extract the parameters for inference from inference.data.object to prepare them for c++ reading
test_Type<-as.vector(inference.data.object@test)
interval_Type<-as.vector(inference.data.object@interval)
implementation_Type<-as.vector(inference.data.object@type)
component_Type<-as.vector(inference.data.object@component)
exact_Inference<-inference.data.object@exact
locs_Inference<-as.matrix(inference.data.object@locations)
locs_index_Inference<-as.vector(inference.data.object@locations_indices - 1) #converting indices from R to c++ ones
locs_are_nodes_Inference <- inference.data.object@locations_are_nodes
coeff_Inference<-as.matrix(inference.data.object@coeff)
beta_0<-as.vector(inference.data.object@beta0)
f_0_eval<-as.vector(inference.data.object@f0_eval)
f_var_Inference<-inference.data.object@f_var
inference_Quantile<-as.vector(inference.data.object@quantile)
inference_Alpha<-as.vector(inference.data.object@alpha)
inference_N_Flip<-inference.data.object@n_flip
inference_Tol_Fspai<-inference.data.object@tol_fspai
inference_Defined<-inference.data.object@definition
## Set propr type for correct C++ reading
locations <- as.matrix(locations)
storage.mode(locations) <- "double"
data <- as.vector(observations)
storage.mode(data) <- "double"
storage.mode(FEMbasis$mesh$nodes) <- "double"
storage.mode(FEMbasis$mesh$tetrahedrons) <- "integer"
storage.mode(FEMbasis$mesh$faces) <- "integer"
storage.mode(FEMbasis$mesh$neighbors) <- "integer"
storage.mode(FEMbasis$order) <- "integer"
covariates <- as.matrix(covariates)
storage.mode(covariates) <- "double"
DOF.matrix <- as.matrix(DOF.matrix)
storage.mode(DOF.matrix) <- "double"
incidence_matrix <- as.matrix(incidence_matrix)
storage.mode(incidence_matrix) <- "integer"
storage.mode(ndim) <- "integer"
storage.mode(mydim) <- "integer"
storage.mode(lambda) <- "double"
storage.mode(BC$BC_indices) <- "integer"
storage.mode(BC$BC_values) <- "double"
storage.mode(PDE_parameters$K) <- "double"
storage.mode(PDE_parameters$b) <- "double"
storage.mode(PDE_parameters$c) <- "double"
areal.data.avg <- as.integer(areal.data.avg)
storage.mode(areal.data.avg) <-"integer"
storage.mode(search) <- "integer"
storage.mode(optim) <- "integer"
storage.mode(lambda) <- "double"
DOF.matrix <- as.matrix(DOF.matrix)
storage.mode(DOF.matrix) <- "double"
storage.mode(DOF.stochastic.realizations) <- "integer"
storage.mode(DOF.stochastic.seed) <- "integer"
storage.mode(GCV.inflation.factor) <- "double"
storage.mode(lambda.optimization.tolerance) <- "double"
## Set proper type for correct C++ reading for inference parameters
storage.mode(test_Type) <- "integer"
storage.mode(interval_Type) <- "integer"
storage.mode(implementation_Type) <- "integer"
storage.mode(component_Type) <- "integer"
storage.mode(exact_Inference) <- "integer"
storage.mode(locs_Inference) <- "double"
storage.mode(locs_index_Inference) <- "integer"
storage.mode(locs_are_nodes_Inference) <- "integer"
storage.mode(coeff_Inference) <- "double"
storage.mode(beta_0) <- "double"
storage.mode(f_0_eval) <- "double"
storage.mode(f_var_Inference) <- "integer"
storage.mode(inference_Quantile) <- "double"
storage.mode(inference_Alpha) <- "double"
storage.mode(inference_N_Flip) <- "integer"
storage.mode(inference_Tol_Fspai) <- "double"
storage.mode(inference_Defined) <- "integer"
## Call C++ function
bigsol <- .Call("regression_PDE", locations, bary.locations, data, FEMbasis$mesh, FEMbasis$order, mydim, ndim, PDE_parameters$K, PDE_parameters$b, PDE_parameters$c, covariates,
BC$BC_indices, BC$BC_values, incidence_matrix, areal.data.avg, search,
optim, lambda, DOF.stochastic.realizations, DOF.stochastic.seed, DOF.matrix, GCV.inflation.factor, lambda.optimization.tolerance,
test_Type,interval_Type,implementation_Type,component_Type,exact_Inference,locs_Inference,locs_index_Inference,locs_are_nodes_Inference,coeff_Inference,beta_0,
f_0_eval,f_var_Inference,inference_Quantile,inference_Alpha,inference_N_Flip,inference_Tol_Fspai,inference_Defined,
PACKAGE = "fdaPDE")
return(bigsol)
}
CPP_smooth.volume.FEM.PDE.sv.basis<-function(locations, observations, FEMbasis, covariates = NULL, PDE_parameters, ndim, mydim, BC = NULL, incidence_matrix = NULL, areal.data.avg = TRUE, search, bary.locations, optim, lambda = NULL, DOF.stochastic.realizations = 100, DOF.stochastic.seed = 0, DOF.matrix = NULL, GCV.inflation.factor = 1, lambda.optimization.tolerance = 0.05, inference.data.object)
{
# Indexes in C++ starts from 0, in R from 1, opportune transformation
FEMbasis$mesh$tetrahedrons = FEMbasis$mesh$tetrahedrons - 1
FEMbasis$mesh$faces = FEMbasis$mesh$faces - 1
FEMbasis$mesh$neighbors[FEMbasis$mesh$neighbors != -1] = FEMbasis$mesh$neighbors[FEMbasis$mesh$neighbors != -1] - 1
if(is.null(covariates))
{
covariates<-matrix(nrow = 0, ncol = 1)
}
if(is.null(DOF.matrix))
{
DOF.matrix<-matrix(nrow = 0, ncol = 1)
}
if(is.null(locations))
{
locations<-matrix(nrow = 0, ncol = ndim)
}
if(is.null(incidence_matrix))
{
incidence_matrix<-matrix(nrow = 0, ncol = 1)
}
if(is.null(BC$BC_indices))
{
BC$BC_indices<-vector(length=0)
}else
{
BC$BC_indices<-as.vector(BC$BC_indices)-1
}
if(is.null(BC$BC_values))
{
BC$BC_values<-vector(length=0)
}else
{
BC$BC_values<-as.vector(BC$BC_values)
}
PDE_param_eval = NULL
points_eval = matrix(CPP_get_evaluations_points(mesh = FEMbasis$mesh, order = FEMbasis$order),ncol = 3)
PDE_param_eval$K = (PDE_parameters$K)(points_eval)
PDE_param_eval$b = (PDE_parameters$b)(points_eval)
PDE_param_eval$c = (PDE_parameters$c)(points_eval)
PDE_param_eval$u = (PDE_parameters$u)(points_eval)
if(inference.data.object@definition==1 && mean(PDE_param_eval$u != rep(0, nrow(points_eval)))!=0){
warning("Inference for linear estimators is implemented only if reaction term is zero, \nInference Data are ignored")
inference.data.object=new("inferenceDataObject", test = as.integer(0), interval = as.integer(0), type = as.integer(0), component = as.integer(0), exact = as.integer(0), dim = as.integer(0), n_cov = as.integer(0),
locations = matrix(data=0, nrow = 1 ,ncol = 1), locations_indices = as.integer(0), locations_are_nodes = as.integer(0), coeff = matrix(data=0, nrow = 1 ,ncol = 1), beta0 = -1, f0 = function(){},
f0_eval = -1, f_var = as.integer(0), quantile = -1, n_flip = as.integer(1000), tol_fspai = -1, definition=as.integer(0))
}
## Extract the parameters for inference from inference.data.object to prepare them for c++ reading
test_Type<-as.vector(inference.data.object@test)
interval_Type<-as.vector(inference.data.object@interval)
implementation_Type<-as.vector(inference.data.object@type)
component_Type<-as.vector(inference.data.object@component)
exact_Inference<-inference.data.object@exact
locs_Inference<-as.matrix(inference.data.object@locations)
locs_index_Inference<-as.vector(inference.data.object@locations_indices - 1) #converting indices from R to c++ ones
locs_are_nodes_Inference <- inference.data.object@locations_are_nodes
coeff_Inference<-as.matrix(inference.data.object@coeff)
beta_0<-as.vector(inference.data.object@beta0)
f_0_eval<-as.vector(inference.data.object@f0_eval)
f_var_Inference<-inference.data.object@f_var
inference_Quantile<-as.vector(inference.data.object@quantile)
inference_Alpha<-as.vector(inference.data.object@alpha)
inference_N_Flip<-inference.data.object@n_flip
inference_Tol_Fspai<-inference.data.object@tol_fspai
inference_Defined<-inference.data.object@definition
## Set propr type for correct C++ reading
locations <- as.matrix(locations)
storage.mode(locations) <- "double"
data <- as.vector(observations)
storage.mode(data) <- "double"
storage.mode(FEMbasis$mesh$nodes) <- "double"
storage.mode(FEMbasis$mesh$tetrahedrons) <- "integer"
storage.mode(FEMbasis$mesh$faces) <- "integer"
storage.mode(FEMbasis$mesh$neighbors) <- "integer"
storage.mode(FEMbasis$order) <- "integer"
covariates <- as.matrix(covariates)
storage.mode(covariates) <- "double"
incidence_matrix <- as.matrix(incidence_matrix)
storage.mode(incidence_matrix) <- "integer"
storage.mode(ndim) <- "integer"
storage.mode(mydim) <- "integer"
storage.mode(lambda) <- "double"
storage.mode(BC$BC_indices) <- "integer"
storage.mode(BC$BC_values) <- "double"
storage.mode(PDE_param_eval$K) <- "double"
storage.mode(PDE_param_eval$b) <- "double"
storage.mode(PDE_param_eval$c) <- "double"
storage.mode(PDE_param_eval$u) <- "double"
areal.data.avg <- as.integer(areal.data.avg)
storage.mode(areal.data.avg) <-"integer"
storage.mode(search) <- "integer"
storage.mode(optim) <- "integer"
storage.mode(lambda) <- "double"
DOF.matrix <- as.matrix(DOF.matrix)
storage.mode(DOF.matrix) <- "double"
storage.mode(DOF.stochastic.realizations) <- "integer"
storage.mode(DOF.stochastic.seed) <- "integer"
storage.mode(GCV.inflation.factor) <- "double"
storage.mode(lambda.optimization.tolerance) <- "double"
## Set proper type for correct C++ reading for inference parameters
storage.mode(test_Type) <- "integer"
storage.mode(interval_Type) <- "integer"
storage.mode(implementation_Type) <- "integer"
storage.mode(component_Type) <- "integer"
storage.mode(exact_Inference) <- "integer"
storage.mode(locs_Inference) <- "double"
storage.mode(locs_index_Inference) <- "integer"
storage.mode(locs_are_nodes_Inference) <- "integer"
storage.mode(coeff_Inference) <- "double"
storage.mode(beta_0) <- "double"
storage.mode(f_0_eval) <- "double"
storage.mode(f_var_Inference) <- "integer"
storage.mode(inference_Quantile) <- "double"
storage.mode(inference_Alpha) <- "double"
storage.mode(inference_N_Flip) <- "integer"
storage.mode(inference_Tol_Fspai) <- "double"
storage.mode(inference_Defined) <- "integer"
## Call C++ function
bigsol <- .Call("regression_PDE_space_varying", locations, bary.locations, data, FEMbasis$mesh, FEMbasis$order, mydim, ndim, PDE_param_eval$K, PDE_param_eval$b, PDE_param_eval$c, PDE_param_eval$u, covariates,
BC$BC_indices, BC$BC_values, incidence_matrix, areal.data.avg, search,
optim, lambda, DOF.stochastic.realizations, DOF.stochastic.seed, DOF.matrix, GCV.inflation.factor, lambda.optimization.tolerance,
test_Type,interval_Type,implementation_Type,component_Type,exact_Inference,locs_Inference,locs_index_Inference,locs_are_nodes_Inference,coeff_Inference,beta_0,
f_0_eval,f_var_Inference,inference_Quantile,inference_Alpha,inference_N_Flip,inference_Tol_Fspai,inference_Defined,
PACKAGE = "fdaPDE")
return(bigsol)
}
CPP_eval.volume.FEM = function(FEM, locations, incidence_matrix, redundancy, ndim, mydim, search, bary.locations)
{
FEMbasis = FEM$FEMbasis
FEMbasis$mesh$tetrahedrons = FEMbasis$mesh$tetrahedrons - 1
FEMbasis$mesh$faces = FEMbasis$mesh$faces - 1
FEMbasis$mesh$neighbors[FEMbasis$mesh$neighbors != -1] = FEMbasis$mesh$neighbors[FEMbasis$mesh$neighbors != -1] - 1
# Imposing types, this is necessary for correct reading from C++
## Set proper type for correct C++ reading
locations <- as.matrix(locations)
storage.mode(locations) <- "double"
incidence_matrix <- as.matrix(incidence_matrix)
storage.mode(incidence_matrix) <- "integer"
storage.mode(FEMbasis$mesh$nodes) <- "double"
storage.mode(FEMbasis$mesh$tetrahedrons) <- "integer"
storage.mode(FEMbasis$mesh$faces) <- "integer"
storage.mode(FEMbasis$mesh$neighbors) <- "integer"
storage.mode(FEMbasis$order) <- "integer"
coeff <- as.matrix(FEM$coeff)
storage.mode(coeff) <- "double"
storage.mode(ndim) <- "integer"
storage.mode(mydim) <- "integer"
storage.mode(locations) <- "double"
storage.mode(redundancy) <- "integer"
storage.mode(search) <- "integer"
if(!is.null(bary.locations))
{
storage.mode(bary.locations$element_ids) <- "integer"
element_ids <- as.matrix(bary.locations$element_ids)
storage.mode(bary.locations$barycenters) <- "double"
barycenters <- as.matrix(bary.locations$barycenters)
}else{
bary.locations = list(locations=matrix(nrow=0,ncol=ndim), element_ids=matrix(nrow=0,ncol=1), barycenters=matrix(nrow=0,ncol=2))
storage.mode(bary.locations$locations) <- "double"
storage.mode(bary.locations$element_ids) <- "integer"
storage.mode(bary.locations$barycenters) <- "double"
}
#Calling the C++ function "eval_FEM_fd" in RPDE_interface.cpp
evalmat = matrix(0,max(nrow(locations),nrow(incidence_matrix)),ncol(coeff))
for (i in 1:ncol(coeff)){
evalmat[,i] <- .Call("eval_FEM_fd", FEMbasis$mesh, locations, incidence_matrix, as.matrix(coeff[,i]),
FEMbasis$order, redundancy, mydim, ndim, search, bary.locations, PACKAGE = "fdaPDE")
}
#Returning the evaluation matrix
evalmat
}
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