diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 00000000..2fc97ab2 Binary files /dev/null and b/.DS_Store differ diff --git a/.gitignore b/.gitignore index dd8feedf..c00e41b5 100644 --- a/.gitignore +++ b/.gitignore @@ -7,9 +7,13 @@ *.RData *.o *.so +*.Identifier test/fdapde_test test/.cache test/build/ test/CMakeFiles/ test/_deps/ test/libs/ +test/lib/ +./run_tests.sh + diff --git a/.gitmodules b/.gitmodules index ce546b35..b59521e3 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,4 +1,6 @@ [submodule "fdaPDE/core"] path = fdaPDE/core - url = https://github.com/fdaPDE/fdaPDE-core + url = https://github.com/RiccardoSena/fdaPDE-core.git branch = stable +[submodule "/home/jellyfish/fdaPDE-cpp/fdaPDE/core"] + url = https://github.com/RiccardoSena/fdaPDE-core.git diff --git a/README.md b/README.md index 57063011..0558ac5a 100644 --- a/README.md +++ b/README.md @@ -12,3 +12,33 @@ It is built on top of the [fdaPDE Core Library](https://github.com/fdaPDE/fdaPDE ## Documentation Documentation can be found on our [documentation site](https://fdapde.github.io/) + +## Installation +The source code of this project can be found at [https://github.com/RiccardoSena/fdaPDE-cpp.git](https://github.com/RiccardoSena/fdaPDE-cpp.git), which is a fork of the fdaPDE repository. The prerequisites to install and run test cases are: + +- A C++17 compliant compiler +- Make +- CMake +- The **Eigen** library (at least version 3.3) + +### Instructions to install the library: + +1. Clone the repository: + ```bash + git clone https://github.com/RiccardoSena/fdaPDE-cpp.git +2. Then, navigate into the develop branch and update the core submodule: + ```bash + cd fdaPDE-cpp + git submodule init + git submodule update +3. To run the test cases, use the following commands inside the fdaPDE-cpp directory: + ```bash + cd test + mkdir build + cd build + cmake .. + make + cd .. + ./run_tests.sh +It should be noted that in the test/main.cpp file, one can choose to run all tests about different models and methods. The ones that are compliant to our development are test/src/inference_test.cpp and test/src/inferencetime_test.cpp + diff --git a/fdaPDE/calibration/calibration_base.h b/fdaPDE/calibration/calibration_base.h index b56f4afd..889fc0cb 100644 --- a/fdaPDE/calibration/calibration_base.h +++ b/fdaPDE/calibration/calibration_base.h @@ -24,7 +24,7 @@ namespace calibration { template class ConfiguredCalibrator { private: - using CalibratorType = std::decay_t; + using CalibratorType = std::remove_const_t; using ArgsPackType = std::tuple...>; CalibratorType c_; ArgsPackType args_; // parameter pack forwarded to calibration strategy at fit time @@ -32,16 +32,15 @@ template class ConfiguredCalibrator { auto call_(ModelType_& model, [[maybe_unused]] std::index_sequence seq) { return c_.fit(model, std::get(args_)...); // forward stored parameters to calibrator } - // templated member detection trait for set_model() template struct callback_require_model : std::false_type { }; template struct callback_require_model< - T, M, std::void_t().template set_model(std::declval()))>> : std::true_type { }; + T, M, std::void_t().template set_model(std::declval()))> + > : std::true_type { }; public: - ConfiguredCalibrator() = default; - ConfiguredCalibrator(const CalibratorType& c, Args_&&... args) : - c_(c), args_(std::make_tuple(std::forward(args)...)) {}; + ConfiguredCalibrator(CalibratorType c, Args_&&... args) : + c_(c), args_(std::make_tuple(std::forward(args)...)) { } template DVector fit(ModelType_&& model) { // forward model instance to callbacks which require it auto set_model = [&model](auto&& arg) { @@ -56,18 +55,24 @@ template class ConfiguredCalibrator { }; template struct CalibratorBase { - template ConfiguredCalibrator operator()(Args&&... args) const { - return ConfiguredCalibrator(static_cast(*this), std::forward(args)...); + template + ConfiguredCalibrator, Args...> operator()(Args&&... args) & { + return ConfiguredCalibrator, Args...>( + static_cast>(*this), std::forward(args)...); + } + // rvalue ref-qualified overload pushes calibrator by copy + template ConfiguredCalibrator operator()(Args&&... args) && { + return {static_cast(*this), std::forward(args)...}; } }; // a type-erasure wrapper for a (configured) calibration algorithm for models of type ModelType template struct Calibrator__ { - template using fn_ptrs = fdapde::mem_fn_ptrs<&T::template fit, &T::optimum>; - decltype(auto) fit(ModelType_& model) { return fdapde::invoke, 0>(*this, model); } - decltype(auto) optimum(ModelType_& model) { return fdapde::invoke, 1>(*this, model); } + template using fn_ptrs = mem_fn_ptrs<&T::template fit, &T::optimum>; + decltype(auto) fit(ModelType_& model) { return invoke, 0>(*this, model); } + decltype(auto) optimum() { return invoke, 1>(*this); } }; -template using Calibrator = fdapde::erase>; +template using Calibrator = erase>; } // namespace calibration } // namespace fdapde diff --git a/fdaPDE/calibration/gcv.h b/fdaPDE/calibration/gcv.h index 0ba4634a..c88e155f 100644 --- a/fdaPDE/calibration/gcv.h +++ b/fdaPDE/calibration/gcv.h @@ -34,31 +34,33 @@ template class GCV : public CalibratorBase GCV(Optimizer_&& opt, EDFStrategy_&& edf) : opt_(opt) { + GCV() { if constexpr (!is_void::value) { - if constexpr (std::is_same_v) { - gcv_.resize(1); - } else { // space-time models - gcv_.resize(2); - } + constexpr int n_lambda = std::is_same_v ? 1 : 2; + gcv_.resize(n_lambda); } + } + template GCV(Optimizer_&& opt, EDFStrategy_&& edf) : GCV() { + opt_ = opt; gcv_.set_edf_strategy(edf); } // selects best smoothing parameter of regression model by minimization of GCV index template DVector fit(ModelType_& model, Args&&... args) { - fdapde_assert(gcv_.inner_size() != 0); + fdapde_assert(gcv_.inner_size() != 0 && bool(opt_) == true); gcv_.set_model(model); return opt_.optimize(gcv_, std::forward(args)...); } DVector optimum() { return opt_.optimum(); } // optimal \lambda found const std::vector& edfs() const { return gcv_.edfs(); } // equivalent degrees of freedom q + Tr[S] const std::vector& gcvs() const { return gcv_.gcvs(); } // computed values of GCV index + // setters void set_step(double step) { gcv_.set_step(step); } + template void set_optimizer(Optimizer_&& opt) { opt_ = opt; } template void set() { // set GCV's domain dimension fdapde_static_assert(is_void::value, THIS_METHOD_IS_FOR_VOID_REGULARIZATION_ONLY); gcv_.resize(models::n_smoothing_parameters::value); } + template void set_edf_strategy(EDFStrategy_&& edf) { gcv_.set_edf_strategy(edf); } }; // template argument deduction rule template GCV(Optimizer_&& opt, EDFStrategy_&& edf) -> GCV; diff --git a/fdaPDE/calibration/kfold_cv.h b/fdaPDE/calibration/kfold_cv.h index ff9ca513..b8bd23f4 100644 --- a/fdaPDE/calibration/kfold_cv.h +++ b/fdaPDE/calibration/kfold_cv.h @@ -71,24 +71,25 @@ class KCV : public CalibratorBase { // selects best smoothing parameter of model according to a K-fold cross validation strategy template - DVector fit(ModelType& model, const std::vector>& lambdas, ScoreType cv_score) { + DVector fit(ModelType& model, const DMatrix& lambdas, ScoreType cv_score) { + fdapde_assert(lambdas.cols() == 1 || lambdas.cols() == 2); // reserve space for CV scores - scores_.resize(K_, lambdas.size()); + scores_.resize(K_, lambdas.rows()); if (shuffle_) { // perform a first shuffling of the data if required model.set_data(model.data().shuffle(seed_)); } // cycle over all tuning parameters - for (std::size_t j = 0; j < lambdas.size(); ++j) { + for (int j = 0; j < lambdas.rows(); ++j) { for (int fold = 0; fold < K_; ++fold) { // fixed a tuning parameter, cycle over all data splits // compute train-test partition and evaluate CV score TrainTestPartition partition_mask = split(model.data(), fold); - scores_.coeffRef(fold, j) = cv_score(lambdas[j], partition_mask.first, partition_mask.second); + scores_.coeffRef(fold, j) = cv_score(lambdas.row(j), partition_mask.first, partition_mask.second); } } // reserve space for storing results - avg_scores_ = DVector::Zero(lambdas.size()); - std_scores_ = DVector::Zero(lambdas.size()); - for (std::size_t j = 0; j < lambdas.size(); ++j) { + avg_scores_ = DVector::Zero(lambdas.rows()); + std_scores_ = DVector::Zero(lambdas.rows()); + for (int j = 0; j < lambdas.rows(); ++j) { // record the average score and its standard deviation computed among the K_ runs double avg_score = 0; for (int i = 0; i < K_; ++i) avg_score += scores_(i, j); @@ -104,7 +105,7 @@ class KCV : public CalibratorBase { // store optimal lambda according to given metric F Eigen::Index opt_score; avg_scores_.minCoeff(&opt_score); - optimum_ = lambdas[opt_score]; + optimum_ = lambdas.row(opt_score); return optimum_; } diff --git a/fdaPDE/calibration/rmse.h b/fdaPDE/calibration/rmse.h index c4c2f25a..dba5bf6b 100644 --- a/fdaPDE/calibration/rmse.h +++ b/fdaPDE/calibration/rmse.h @@ -34,7 +34,7 @@ class RMSE { RegressionView model_; public: RMSE() = default; - template RMSE(const ModelType& model) : model_(model) {}; + template RMSE(const ModelType& model) : model_(model) {} template void set_model(const ModelType& model) { model_ = model; } double operator()( diff --git a/fdaPDE/core b/fdaPDE/core index f8132c8c..ec5a765f 160000 --- a/fdaPDE/core +++ b/fdaPDE/core @@ -1 +1 @@ -Subproject commit f8132c8c0fbdb219de25b0f0a9b93865322be526 +Subproject commit ec5a765f403b455f5eacd51a27739be64f6a3333 diff --git a/fdaPDE/models/density_estimation/density_estimation_base.h b/fdaPDE/models/density_estimation/density_estimation_base.h new file mode 100644 index 00000000..eec44c96 --- /dev/null +++ b/fdaPDE/models/density_estimation/density_estimation_base.h @@ -0,0 +1,222 @@ +// This file is part of fdaPDE, a C++ library for physics-informed +// spatial and functional data analysis. +// +// This program is free software: you can redistribute it and/or modify +// it under the terms of the GNU General Public License as published by +// the Free Software Foundation, either version 3 of the License, or +// (at your option) any later version. +// +// This program is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +// GNU General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with this program. If not, see . + +#ifndef __DENSITY_ESTIMATION_BASE_H__ +#define __DENSITY_ESTIMATION_BASE_H__ + +#include +#include "../model_macros.h" +#include "../model_traits.h" +#include "../space_only_base.h" +#include "../space_time_separable_base.h" +#include "../sampling_design.h" + +namespace fdapde { +namespace models { + +#define SPACE_LOCS "SPACE_LOCS" +#define TIME_LOCS "TIME_LOCS" + +namespace internal { +consteval int de_select_quadrature(int local_dim) { return local_dim == 1 ? 4 : local_dim == 2 ? 6 : 14; } +}; // namespace internal + +template +class DensityEstimationBase : + public select_regularization_base::type, + public SamplingBase { + protected: + std::function&)> int_exp_; // \int_{\mathcal{D}} exp(g(x)), x \in \mathcal{D} + std::function(const DVector&)> grad_int_exp_; // gradient of int_exp + DVector g_; // expansion coefficients vector of estimated density function + DMatrix PsiQuad_; // reference element basis evaluation at quadrature nodes + DVector w_; // quadrature weights + SpMatrix Upsilon_; // \Upsilon_(i,:) = \Phi(i,:) \kron S(p_i) \Psi + SpMatrix B_; // \Upsilon matrix corrected for masked observations + BinaryVector mask_; + + template + DMatrix eval_basis_at_quadrature_(BasisType_&& basis, IntegratorType_&& integrator) const { + using IntegratorType = std::decay_t; + DMatrix result(IntegratorType::num_nodes, basis.size()); + for (int i = 0; i < IntegratorType::num_nodes; ++i) { + for (int j = 0; j < basis.size(); ++j) { result(i, j) = basis[j](integrator.nodes[i]); } + } + return result; + } + public: + static constexpr int DomainDimension = fdapde::Dynamic; + using Base = typename select_regularization_base::type; + using Base::model; + using SamplingBase::Psi; // matrix of basis evaluations at data locations + using SamplingBase::n_spatial_locs; + + DensityEstimationBase() = default; + // space-only constructor + template + DensityEstimationBase(PDE_&& pde) + requires(is_space_only::value) + : Base(pde), SamplingBase(Sampling::pointwise) { + pde.init(); // early PDE initialization + constexpr int local_dim = std::decay_t::SpaceDomainType::local_dim; + constexpr int n_quadrature_nodes = internal::de_select_quadrature(local_dim); + // compute reference basis evaluation at quadrature nodes + core::IntegratorTable integrator {}; + PsiQuad_ = eval_basis_at_quadrature_(pde.reference_basis(), integrator); + w_ = Eigen::Map>(integrator.weights.data()); + // store functor for approximation of \int_{\mathcal{D}} \exp(g(p)). Computes + // \sum_{e \in mesh} {e.measure() * \sum_j {w_j * exp[\sum_i {g_i * \psi_i(q_j)}]}} + int_exp_ = [&](const DVector& g) { + double result = 0; + for (auto e = pde.domain().cells_begin(); e != pde.domain().cells_end(); ++e) { + result += w_.dot((PsiQuad_ * g(pde.dofs().row(e->id()))).array().exp().matrix()) * e->measure(); + } + return result; + }; + // store functor for computation of \nabla_g(\int_{\mathcal{D}} \exp(g(p))) + grad_int_exp_ = [&](const DVector& g) { + DVector grad = DVector::Zero(g.rows()); + for (auto e = pde.domain().cells_begin(); e != pde.domain().cells_end(); ++e) { + grad(pde.dofs().row(e->id())) += + PsiQuad_.transpose() * + (PsiQuad_ * g(pde.dofs().row(e->id()))).array().exp().cwiseProduct(w_.array()).matrix() * + e->measure(); + } + return grad; + }; + } + // space-time separable constructor + template + DensityEstimationBase(SpacePDE_&& s_pen, TimePDE_&& t_pen) + requires(is_space_time_separable::value) + : Base(s_pen, t_pen), SamplingBase(Sampling::pointwise) { + // early penalties initialization + s_pen.init(); + t_pen.init(); + constexpr int local_dim = std::decay_t::SpaceDomainType::local_dim; + constexpr int n_quadrature_s = internal::de_select_quadrature(local_dim); + constexpr int n_quadrature_t = 5; + // compute reference basis evaluation at quadrature nodes + core::IntegratorTable s_integrator {}; + core::IntegratorTable<1, n_quadrature_t, core::GaussLegendre> t_integrator {}; + DMatrix Psi = eval_basis_at_quadrature_(s_pen.reference_basis(), s_integrator); + DMatrix Phi = eval_basis_at_quadrature_(t_pen.reference_basis(), t_integrator); + PsiQuad_ = Kronecker(Phi, Psi); + w_ = Kronecker( + Eigen::Map>(t_integrator.weights.data()), + Eigen::Map>(s_integrator.weights.data())); + // store functor for approximation of double integral \int_T \int_{\mathcal{D}} \exp(g(p, t)) + int_exp_ = + [&, n = s_pen.reference_basis().size(), m = t_pen.reference_basis().size()](const DVector& g) { + double result = 0; + DVector active_dofs(n * m); + for (auto e = s_pen.domain().cells_begin(); e != s_pen.domain().cells_end(); ++e) { // space loop + for (auto i = t_pen.domain().cells_begin(); i != t_pen.domain().cells_end(); ++i) { // time loop + for (int j = 0; j < m; ++j) { // compute active dofs + active_dofs.middleRows(j * n, n) = + s_pen.dofs().row(e->id()).transpose().array() + j * Base::n_spatial_basis(); + } + result += w_.dot((PsiQuad_ * g(active_dofs)).array().exp().matrix()) * e->measure() * + (0.5 * i->measure()); + } + } + return result; + }; + // store functor for computation of \nabla_g(\int_T \int_{\mathcal{D}} \exp(g(p, t))) + grad_int_exp_ = + [&, n = s_pen.reference_basis().size(), m = t_pen.reference_basis().size()](const DVector& g) { + DVector grad = DVector::Zero(g.rows()); + DVector active_dofs(n * m); + for (auto e = s_pen.domain().cells_begin(); e != s_pen.domain().cells_end(); ++e) { // space loop + for (auto i = t_pen.domain().cells_begin(); i != t_pen.domain().cells_end(); ++i) { // time loop + for (int j = 0; j < m; ++j) { // compute active dofs + active_dofs.middleRows(j * n, n) = + s_pen.dofs().row(e->id()).transpose().array() + j * Base::n_spatial_basis(); + } + grad(active_dofs) += PsiQuad_.transpose() * + ((PsiQuad_ * g(active_dofs)).array().exp()).cwiseProduct(w_.array()).matrix() * + e->measure() * (0.5 * i->measure()); + } + } + return grad; + }; + } + + // setters + // redefine set_data() as it forwards data as locations in density estimation + void set_data(const BlockFrame& df, bool reindex = false) { + fdapde_assert( + df.cols() > 0 && + ((is_space_only::value && df.has_block(SPACE_LOCS)) || + (is_space_time_separable::value && df.has_block(SPACE_LOCS) && df.has_block(TIME_LOCS)))); + Base::set_data(df, reindex); + if (df.has_block(SPACE_LOCS)) SamplingBase::set_spatial_locations(df.get(SPACE_LOCS)); + if constexpr (is_space_time_separable::value) Base::set_temporal_locations(df.get(TIME_LOCS)); + } + void set_mask(const BinaryVector& mask) { + fdapde_assert(mask.size() == n_locs()); + if (mask.any()) { + model().runtime().set(runtime_status::require_psi_correction); + mask_ = mask; // mask[i] == true, removes the contribution of the i-th observation from fit + } + } + // getters + int n_obs() const { return Base::df_.rows() - mask_.count(); }; + int n_locs() const { return Base::df_.rows(); } + const SpMatrix& Psi() const { return is_space_only::value ? Psi(not_nan()) : Upsilon_; } + const SpMatrix& Upsilon() const { return !is_empty(B_) ? B_ : Psi(); } + const DMatrix& PsiQuad() const { return PsiQuad_; } + const DMatrix& w() const { return w_; } + double int_exp(const DVector& g) const { return int_exp_(g); } + double int_exp() const { return int_exp_(g_); } + DVector grad_int_exp(const DVector& g) const { return grad_int_exp_(g); } + DVector grad_int_exp() const { return grad_int_exp_(g_); } + const DVector& g() const { return g_; } // expansion coefficient vector of log density field + DVector f() const { return g_.array().exp(); } // expansion coefficient vector of density field + const BinaryVector& masked_obs() const { return mask_; } + + // initialization methods + void analyze_data() { + if constexpr (is_space_time_separable::value) { + if (is_empty(Upsilon_)) { + Upsilon_.resize(Base::n_temporal_locs(), Base::n_basis()); + std::vector> triplet_list; + // reserve with some bound + for (int i = 0; i < Base::n_temporal_locs(); ++i) { + // kronecker product between Phi i-th row and Psi i-th row + SpMatrix tmp = + Kronecker(SpMatrix(Base::Phi().row(i)), SpMatrix(Psi().row(i))); + for (int j = 0; j < tmp.outerSize(); ++j) + for (SpMatrix::InnerIterator it(tmp, j); it; ++it) { + triplet_list.emplace_back(i, it.col(), it.value()); + } + } + Upsilon_.setFromTriplets(triplet_list.begin(), triplet_list.end()); + Upsilon_.makeCompressed(); + } + } + return; + } + void tensorize_psi() { return; } // avoid tensorization for space-time problems + void correct_psi() { + if (masked_obs().any()) B_ = (~masked_obs().repeat(1, Base::n_basis())).select(Psi()); + } +}; + +} // namespace models +} // namespace fdapde + +#endif // __DENSITY_ESTIMATION_BASE_H__ diff --git a/fdaPDE/models/density_estimation/depde.h b/fdaPDE/models/density_estimation/depde.h new file mode 100644 index 00000000..6eb7e898 --- /dev/null +++ b/fdaPDE/models/density_estimation/depde.h @@ -0,0 +1,126 @@ +// This file is part of fdaPDE, a C++ library for physics-informed +// spatial and functional data analysis. +// +// This program is free software: you can redistribute it and/or modify +// it under the terms of the GNU General Public License as published by +// the Free Software Foundation, either version 3 of the License, or +// (at your option) any later version. +// +// This program is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +// GNU General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with this program. If not, see . + +#ifndef __DEPDE_H__ +#define __DEPDE_H__ + +#include +#include "../model_base.h" +#include "../model_macros.h" +#include "../sampling_design.h" +#include "density_estimation_base.h" + +namespace fdapde { +namespace models { + +template +class DEPDE : public DensityEstimationBase, RegularizationType_> { + private: + using Base = DensityEstimationBase, RegularizationType_>; + double tol_ = 1e-5; // tolerance on custom stopping criterion + DVector g_init_; // density initialization basis expansion coefficients + core::Optimizer> opt_; + public: + using RegularizationType = std::decay_t; + using This = DEPDE; + using Base::grad_int_exp; // \nabla_g (\int_{\Omega} \exp(g)) + using Base::int_exp; // \int_{\Omega} \exp(g) + using Base::n_locs; // overall number of data locations + using Base::n_obs; // number of observations (!= n_locs if masked) + using Base::P; // discretized penalty matrix R_1^\top * (R_0)^{-1} * R_1 + using Base::PsiQuad; // reference basis evaluation at quadrature nodes + using Base::Upsilon; // \Upsilon_(i,:) = is_space_only ? \Psi : \Phi(i,:) \kron S(p_i) \Psi + + // space-only constructor + template + DEPDE(PDE_&& pde) requires(is_space_only::value) : Base(pde) { } + // space-time separable constructor + template + DEPDE(SpacePDE_&& space_penalty, TimePDE_&& time_penalty) requires(is_space_time_separable::value) + : Base(space_penalty, time_penalty) { } + + // K-fold cross validation index + struct CVScore { + CVScore(DEPDE& model) : model_(model) { } + double operator()( + const DVector& lambda, [[maybe_unused]] const BinaryVector& train_mask, + const BinaryVector& test_mask) { + model_.set_lambda(lambda); + // fit model on train set + model_.set_mask(test_mask); // discard test set from training phase + model_.init(); + model_.solve(); + double test_err = 0; + for (int i = 0; i < test_mask.size(); ++i) { + if (test_mask[i]) { test_err += std::exp(model_.Psi().row(i) * model_.g()); } + } + return model_.int_exp(2. * model_.g()) - 2. / test_mask.count() * test_err; + } + private: + DEPDE& model_; + }; + + // evaluates penalized negative log-likelihood at point + // L(g) = - 1^\top*\Upsilon*g + \sum_{e \in mesh} w^\top*exp[\Psi_q*g_e] + \lambda_S*g^\top*P*g + double operator()(const DVector& g) { + return -(Upsilon() * g).sum() + n_obs() * int_exp(g) + g.dot(P() * g); + } + // log-likelihood gradient functor + // \nabla_g(L(g)) = -\Upsilon^\top*1 + n*\sum_{e \in mesh} w*exp[\Psi_q*g_e]*\Psi_q^\top + 2*P*g + std::function(const DVector&)> derive() { + return [this, dllik = DVector(-Upsilon().transpose() * DVector::Ones(n_locs()))]( + const DVector& g) { + return DVector(dllik + n_obs() * grad_int_exp(g) + 2 * P() * g); + }; + } + // optimization algorithm custom stopping criterion + template bool opt_stopping_criterion(OptimizerType& opt) { + // opt.x_old: density function before update step, opt.x_new: density function after update step + bool stop = true; + double llik_old = -(Upsilon() * opt.x_old).sum() + n_obs() * int_exp(opt.x_old); + double llik_new = -(Upsilon() * opt.x_new).sum() + n_obs() * int_exp(opt.x_new); + stop &= std::abs((llik_new - llik_old) / llik_old) < tol_; + if(!stop) return false; + double penD_old = Base::xtPDx(opt.x_old), penD_new = Base::xtPDx(opt.x_new); //opt.x_old.dot(Base::PD() * opt.x_old); + stop &= std::abs((penD_new - penD_old) / penD_old) < tol_; + if (!stop) return false; + double loss_old = llik_old + Base::lambda_D() * penD_old, loss_new = llik_new + Base::lambda_D() * penD_new; + // space-time case + if constexpr (is_space_time_separable::value) { + double penT_old = Base::xtPTx(opt.x_old), penT_new = Base::xtPTx(opt.x_new); + loss_old += Base::lambda_T() * penT_old; + loss_new += Base::lambda_T() * penT_new; + stop &= std::abs((penT_new - penT_old) / penT_old) < tol_; + } + stop &= std::abs((loss_new - loss_old) / loss_old) < tol_; + return stop; + } + // call optimization algorithm for log-likelihood minimization + void solve() { Base::g_ = opt_.optimize(*this, g_init_); } + void init_model() { return; } + // setters + void set_tolerance(double tol) { tol_ = tol; } + void set_g_init(const DVector& g_init) { g_init_ = g_init; } + void set_f_init(const DVector& f_init) { g_init_ = f_init.array().log(); } + template void set_optimizer(OptimizerType&& opt) { opt_ = opt; } + // getters + const DVector& g_init() const { return g_init_; } +}; + +} // namespace models +} // namespace fdapde + +#endif // __DEPDE_H__ diff --git a/fdaPDE/models/functional/fpca.h b/fdaPDE/models/functional/fpca.h index 822e5689..fad67ee1 100644 --- a/fdaPDE/models/functional/fpca.h +++ b/fdaPDE/models/functional/fpca.h @@ -47,7 +47,7 @@ class FPCA : public FunctionalBase, RegularizationType decltype(auto) scores() const { return invoke&, 2>(*this); } }; using SolverType = fdapde::erase; - SolverType solver_; + SolverType solver_; // RegularizedSVD solver public: using RegularizationType = std::decay_t; using This = FPCA; @@ -58,13 +58,13 @@ class FPCA : public FunctionalBase, RegularizationType // constructors FPCA() = default; // space-only constructor - FPCA(const Base::PDE& pde, Sampling s, SolverType solver) - requires(is_space_only::value) : Base(pde, s), solver_(solver) { } + FPCA(const Base::PDE& pde, Sampling s, SolverType solver) requires(is_space_only::value) : + Base(pde, s), solver_(solver) { } // space-time separable constructor FPCA(const Base::PDE& space_penalty, const Base::PDE& time_penalty, Sampling s, SolverType solver) - requires(is_space_time_separable::value) : Base(space_penalty, time_penalty, s), solver_(solver) { } + requires(is_space_time_separable::value) : Base(space_penalty, time_penalty, s), solver_(solver) { } - void init_model() { fdapde_assert(bool(solver_) == true); }; // check solver is assigned + void init_model() { fdapde_assert(bool(solver_) == true); }; void solve() { solver_.compute(X(), *this, n_pc_); } // getters const DMatrix& loadings() const { return solver_.loadings(); } diff --git a/fdaPDE/models/functional/fpls.h b/fdaPDE/models/functional/fpls.h index c4e9ba81..0032a882 100644 --- a/fdaPDE/models/functional/fpls.h +++ b/fdaPDE/models/functional/fpls.h @@ -49,11 +49,11 @@ class FPLS : public FunctionalBase, RegularizationType // constructors FPLS() = default; // space-only constructor - FPLS(const Base::PDE& pde, Sampling s, RegularizedSVD rsvd) - requires(is_space_only::value) : Base(pde, s), rsvd_(rsvd) { } + FPLS(const Base::PDE& pde, Sampling s, RegularizedSVD rsvd) requires(is_space_only::value) : + Base(pde, s), rsvd_(rsvd) { } // space-time separable constructor FPLS(const Base::PDE& space_penalty, const Base::PDE& time_penalty, Sampling s, RegularizedSVD rsvd) - requires(is_space_time_separable::value) : Base(space_penalty, time_penalty, s), rsvd_(rsvd) { } + requires(is_space_time_separable::value) : Base(space_penalty, time_penalty, s), rsvd_(rsvd) { } void init_model() { // initialize smoothing solver for regression step diff --git a/fdaPDE/models/functional/regularized_svd.h b/fdaPDE/models/functional/regularized_svd.h index a973d130..9f0cea2d 100644 --- a/fdaPDE/models/functional/regularized_svd.h +++ b/fdaPDE/models/functional/regularized_svd.h @@ -41,7 +41,7 @@ template <> class RegularizedSVD { private: Calibration calibration_; // PC function's smoothing parameter selection strategy int n_folds_ = 10; // for a kcv calibration strategy, the number of folds - std::vector> lambda_grid_; + DMatrix lambda_grid_; // power iteration parameters double tolerance_ = 1e-6; // relative tolerance between Jnew and Jold, used as stopping criterion int max_iter_ = 20; // maximum number of allowed iterations @@ -89,11 +89,11 @@ template <> class RegularizedSVD { auto cv_score = [&]( const DVector& lambda, const core::BinaryVector& train_set, const core::BinaryVector& test_set) -> double { - solver_.compute(train_set.blk_repeat(1, X_.cols()).select(X_), lambda, f0); // fit on train set + solver_.compute(train_set.repeat(1, X_.cols()).select(X_), lambda, f0); // fit on train set // reconstruction error on test set: \norm{X_test * (I - fn*fn^\top/J)}_F/n_test, with // J = \norm{f_n}_2^2 + f^\top*P(\lambda)*f (PS: the division of f^\top*P(\lambda)*f by // \norm{f}_{L^2} is necessary to obtain J as expected) - return (test_set.blk_repeat(1, X_.cols()).select(X_) * + return (test_set.repeat(1, X_.cols()).select(X_) * (DMatrix::Identity(X_.cols(), X_.cols()) - solver_.fn() * solver_.fn().transpose() / (solver_.fn().squaredNorm() + solver_.ftPf(lambda) / solver_.f_squaredNorm()))) @@ -163,13 +163,13 @@ template <> class RegularizedSVD { const DMatrix& loadings() const { return loadings_; } const DVector& loadings_norm() const { return loadings_norm_; } const std::vector>& selected_lambdas() const { return selected_lambdas_; } - const std::vector>& lambda_grid() const { return lambda_grid_; } + const DMatrix& lambda_grid() const { return lambda_grid_; } Calibration calibration() const { return calibration_; } // setters void set_tolerance(double tolerance) { tolerance_ = tolerance; } void set_max_iter(int max_iter) { max_iter_ = max_iter; } void set_seed(int seed) { seed_ = seed; } - RegularizedSVD& set_lambda(const std::vector>& lambda_grid) { + RegularizedSVD& set_lambda(const DMatrix& lambda_grid) { fdapde_assert(calibration_ != Calibration::off); lambda_grid_ = lambda_grid; return *this; diff --git a/fdaPDE/models/model_base.h b/fdaPDE/models/model_base.h index 88cd5ee3..de855bcc 100644 --- a/fdaPDE/models/model_base.h +++ b/fdaPDE/models/model_base.h @@ -19,6 +19,7 @@ #include using fdapde::core::BlockFrame; +using fdapde::core::is_empty; #include "model_macros.h" #include "model_traits.h" diff --git a/fdaPDE/models/model_type_erasure.h b/fdaPDE/models/model_type_erasure.h index 1b3cb7b1..b7dc5863 100644 --- a/fdaPDE/models/model_type_erasure.h +++ b/fdaPDE/models/model_type_erasure.h @@ -64,7 +64,7 @@ template struct StatisticalModel__ { }; template <> struct StatisticalModel__ { using RegularizationType = void; template using Base = ModelBase; - static constexpr int n_lambda = fdapde::Dynamic; + static constexpr int n_lambda = fdapde::core::Dynamic; // interface implementation template using fn_ptrs = fdapde::mem_fn_ptrs< @@ -74,7 +74,7 @@ template <> struct StatisticalModel__ { // interface implementation BASE_MODEL_ERASED_INTERFACE void set_lambda(const DVector& lambda) { invoke(*this, lambda); } - decltype(auto) lambda() const { return invoke, 13>(*this, fdapde::Dynamic); } + decltype(auto) lambda() const { return invoke, 13>(*this, fdapde::core::Dynamic); } }; // space-only statistical model interface diff --git a/fdaPDE/models/regression/esf.h b/fdaPDE/models/regression/esf.h new file mode 100644 index 00000000..0fcb34df --- /dev/null +++ b/fdaPDE/models/regression/esf.h @@ -0,0 +1,1123 @@ +// This file is part of fdaPDE, a C++ library for physics-informed +// spatial and functional data analysis. +// +// This program is free software: you can redistribute it and/or modify +// it under the terms of the GNU General Public License as published by +// the Free Software Foundation, either version 3 of the License, or +// (at your option) any later version. +// +// This program is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +// GNU General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with this program. If not, see . + +#ifndef __ESF_H__ +#define __ESF_H__ + +#include +#include + +#include "../model_macros.h" +#include "../model_traits.h" +#include "../model_base.h" +#include "srpde.h" +#include "strpde.h" +#include "exact_edf.h" +#include "stochastic_edf.h" +#include "inference_base.h" +#include "inference.h" + +#include + + +using fdapde::core::is_empty; + +namespace fdapde { +namespace models { + +template class ESF: public InferenceBase{ + + private: + struct ExactInverse{ + DMatrix compute(Model m){ + return inverse(m.E()); + } + }; + + struct NonExactInverse{ + SpMatrix compute(Model m){ + return Base::invE_approx(m); + } + }; + + int n_flip = 1000; //default value + int set_seed = 0; + DMatrix Lambda_ {}; + + bool is_speckman_aux_computed = false; + DVector Speckman_aux_ranges; + + DMatrix Qp_ {}; + int p_l_ = 0; // number of locations for inference on f + SpMatrix Psi_p_ {}; // Psi only in the locations for inference + DMatrix Qp_dec_ {}; // Decomposition of Qp matrix + DVector mesh_nodes_ {}; // if inference is performed on a subset of mesh nodes + + + public: + using Base = InferenceBase; + using Base::m_; + using Base::beta_; + using Base::f0_; + using Base::C_; + using Base::locations_f_; + using Base::alpha_f_; + using Base::beta0_; + using Solver = typename std::conditional::value, ExactInverse, NonExactInverse>::type; + Solver s_; + + using Base::V_; + + // constructors + ESF() = default; // deafult constructor + ESF(const Model& m): Base(m) {}; // constructor + + DVector p_value(CIType type) override{ + // extract matrix C (in the eigen-sign-flip case we cannot have linear combinations, but we can have at most one 1 for each column of C) + fdapde_assert(!is_empty(C_)); + + if(is_empty(beta0_)){ + Base::setBeta0(DVector::Zero(m_.beta().size())); + } + + int p = C_.rows(); + + DVector result; // declare the vector that will store the p-values + + // compute Lambda + if(is_empty(Lambda_)){ + V(); + } + + Eigen::SelfAdjointEigenSolver> solver(Lambda_); // eigenvectors and eigenvalues of Lambda + + DMatrix eigenvalues = solver.eigenvalues(); + DMatrix eigenvectors = solver.eigenvectors(); + // beta_hat + DVector beta_hat = m_.beta(); + DVector beta_hat_mod = beta_hat; + + if(type == simultaneous){ + // SIMULTANEOUS + for(int i = 0; i < p; ++i){ + for(int j = 0; j < C_.cols(); j++){ + if(C_(i,j) > 0){ + beta_hat_mod[j] = beta0_[j]; + } + } + } + + // partial residuals + DMatrix X = m_.X(); + DMatrix res_H0 = m_.y() - X * beta_hat_mod; + // W^t * V * D + DMatrix Xt = (C_ * X.transpose()) * eigenvectors * eigenvalues.asDiagonal(); + DVector Tilder = eigenvectors.transpose() * res_H0; + + // observed and sign-flipped statistic + DVector stat = Xt * Tilder; + DVector stat_flip = stat; + + // Random sign-flips + std::default_random_engine eng; + std::uniform_int_distribution distr(0, 1); + + // if we have a seed + if(set_seed != 0) { + eng.seed(set_seed); + } else { + std::random_device rd; + eng.seed(rd()); // random seed + } + + int up = 0; + int down = 0; + + DVector Tilder_perm = Tilder; + + for(int i = 0; i < n_flip; i++){ + for(int j = 0; j < Xt.cols(); ++j){ + int flip = 2 * distr(eng) - 1; + Tilder_perm(j) = Tilder(j) * flip; + } + stat_flip = Xt * Tilder_perm; // Flipped statistic + + if(is_Unilaterally_Greater(stat_flip, stat)){ + up = up + 1; + } + else{ + if(is_Unilaterally_Smaller(stat_flip, stat)){ + down = down + 1; + } + } + } + + double pval_up = static_cast(up) / n_flip; + double pval_down = static_cast(down) / n_flip; + + result.resize(p); + result(0) = 2 * std::min(pval_up, pval_down); + for(int k = 1; k < p; k++){ + result(k) = 0.0; + } + } + else{ + // ONE AT THE TIME + DMatrix res_H0(Lambda_.cols(), p); + DMatrix X = m_.X(); + for(int i = 0; i < p; ++i){ + + beta_hat_mod = beta_hat; + + for(int j = 0; j < C_.cols(); ++j){ + if(C_(i,j)>0){ + beta_hat_mod[j] = beta0_[j]; + } + } + // compute the partial residuals + res_H0.col(i) = m_.y() - X * beta_hat_mod; + } + // compute the vectors needed for the statistic + DMatrix Xt = (C_ * X.transpose()) * eigenvectors * eigenvalues.asDiagonal(); // W^t * V * D + DMatrix Tilder = eigenvectors.transpose() * res_H0; // V^t * partial_res_H0 + + // Observed statistic + DMatrix stat = Xt * Tilder; + DMatrix stat_flip = stat; + + // Random sign-flips + std::default_random_engine eng; + std::uniform_int_distribution distr(0, 1); + + // if we have a seed + if(set_seed != 0) { + eng.seed(set_seed); + } else { + std::random_device rd; + eng.seed(rd()); // random seed + } + DVector up = DVector::Zero(p); + DVector down = DVector::Zero(p); + + DMatrix Tilder_perm = Tilder; + + for(int i = 0; i < n_flip; ++i){ + for(int j = 0; j < Xt.cols(); ++j){ + int flip = 2 * distr(eng) - 1; + Tilder_perm.row(j) = Tilder.row(j) * flip; + } + stat_flip = Xt * Tilder_perm; // Flipped statistic + + for(int k = 0; k < p; ++k){ + if(stat_flip(k, k) > stat(k, k)){ + ++up(k); + }else{ + ++down(k); + } + } + } + + DVector pval_up = up.array() / static_cast(n_flip); + DVector pval_down = down.array() / static_cast(n_flip); + + result.resize(p); + result = 2 * min(pval_up, pval_down); + } + return result; + } + + + // CI for beta + DMatrix computeCI(CIType type) override{ + if(is_empty(Lambda_)){ + V(); // compute Lambda + } + DVector beta_hat = m_.beta(); // beta_hat + DVector beta_hat_mod = beta_hat; + + fdapde_assert(!is_empty(C_)); + int p = C_.rows(); + + DVector beta_in_test; + beta_in_test.resize(p); + for(int i=0; i0){ + beta_in_test[i]=j; + } + } + } + + Eigen::SelfAdjointEigenSolver> solver(Lambda_); // eigenvectors and eigenvalues of Lambda + DMatrix eigenvalues = solver.eigenvalues(); + DMatrix eigenvectors = solver.eigenvectors(); + + // intervals + DMatrix result; + result.resize(p, 2); + + // speckman's CI (initial guess for CI) + if(!is_speckman_aux_computed){ + Compute_speckman_aux(); + } + + // this vector will store the tolerance for each interval upper/lower limit + DVector ESF_bisection_tolerances = 0.1*Speckman_aux_ranges; // 0.1 of the speckman CI as maximum tolerance + + + // define storage structures for bisection algorithms + DVector UU; // Upper limit for Upper bound + UU.resize(p); + DVector UL; // Lower limit for Upper bound + UL.resize(p); + DVector LU; // Upper limit for Lower bound + LU.resize(p); + DVector LL; // Lower limit for Lower bound + LL.resize(p); + + + + // ESF initialization with Speckman intervals as initial guess + for(int i = 0; i < p; ++i){ + double half_range = Speckman_aux_ranges(i); + + // compute the limits of the interval + result(i,0) = beta_hat(beta_in_test[i]) - half_range; + LU(i)=result(i,0)+0.5*half_range; + LL(i)=result(i,0)-0.5*half_range; + result(i,1) = beta_hat(beta_in_test[i]) + half_range; + UU(i)=result(i,1) +0.5*half_range; + UL(i)=result(i,1) -0.5*half_range; + } + + + // define booleans used to understand which CI need to be computed forward on + std::vector converged_up(p,false); + std::vector converged_low(p,false); + bool all_betas_converged=false; + + // matrix that stores p_values of the bounds at actual step + DMatrix local_p_values; + local_p_values.resize(4,p); + + // compute the vectors needed for the statistic + DMatrix TildeX = (C_ * m_.X().transpose()) * eigenvectors * eigenvalues.asDiagonal(); // W^t * V * D + DMatrix Tilder_star = eigenvectors.transpose(); // V^t + // Select eigenvalues that will not be flipped basing on the estimated bias carried + DVector Tilder_hat = eigenvectors.transpose()* (m_.y() - (m_.X())* beta_hat); // This vector represents Tilder using only beta_hat, needed for bias estimation + DVector Partial_res_H0_CI; + Partial_res_H0_CI.resize(Lambda_.cols()); + + + // fill the p_values matrix + for (int i=0; i TildeX_loc = TildeX.row(beta_in_test[i]); + beta_hat_mod = beta_hat; + + // compute the partial residuals and p value + beta_hat_mod(beta_in_test[i])=UU(i); // beta_hat_mod(i) = beta_hat(i) if i not in test; beta_HP otherwise + Partial_res_H0_CI = m_.y() - (m_.X()) * (beta_hat_mod); // (y-W*beta_hat(non in test)-W*UU[i](in test)) + local_p_values(0,i)=compute_CI_aux_beta_pvalue(Partial_res_H0_CI, TildeX_loc, Tilder_star); + + // compute the partial residuals and p value + beta_hat_mod(beta_in_test[i])=UL(i); // beta_hat_mod(i) = beta_hat(i) if i not in test; beta_HP otherwise + Partial_res_H0_CI = m_.y() - (m_.X()) * (beta_hat_mod); // (y-W*beta_hat(non in test)-W*UL[i](in test)) + local_p_values(1,i)=compute_CI_aux_beta_pvalue(Partial_res_H0_CI, TildeX_loc, Tilder_star); + + // compute the partial residuals and p value + beta_hat_mod(beta_in_test[i])=LU(i); // beta_hat_mod(i) = beta_hat(i) if i not in test; beta_HP otherwise + Partial_res_H0_CI = m_.y() - (m_.X()) * (beta_hat_mod); // (y-W*beta_hat(non in test)-W*LU[i](in test)) + local_p_values(2,i)=compute_CI_aux_beta_pvalue(Partial_res_H0_CI, TildeX_loc, Tilder_star); + + // compute the partial residuals and p value + beta_hat_mod(beta_in_test[i])=LL(i); // beta_hat_mod(i) = beta_hat(i) if i not in test; beta_HP otherwise + Partial_res_H0_CI = m_.y() - (m_.X()) * (beta_hat_mod); // (y-W*beta_hat(non in test)-W*LL[i](in test)) + local_p_values(3,i)=compute_CI_aux_beta_pvalue(Partial_res_H0_CI, TildeX_loc, Tilder_star); + + } + + // extract the CI significance (1-confidence) + double alpha=0.05; + + if(type == one_at_the_time){ + alpha=0.5*alpha; + }else{ + alpha=0.5/p*alpha; + } + + int Max_Iter=50; + int Count_Iter=0; + while((!all_betas_converged) & (Count_Iter TildeX_loc= TildeX.row(beta_in_test[i]); + beta_hat_mod = beta_hat; + + if(!converged_up[i]){ + if(local_p_values(0,i)>alpha){ // Upper-Upper bound excessively tight + + UU(i)=UU(i)+0.5*(UU(i)-UL(i)); + + // compute the partial residuals + beta_hat_mod(beta_in_test[i])=UU(i); // beta_hat_mod(i) = beta_hat(i) if i not in test; beta_HP otherwise + Partial_res_H0_CI = m_.y() - (m_.X()) * (beta_hat_mod); // (z-W*beta_hat(non in test)-W*UU[i](in test)) + local_p_values(0,i)=compute_CI_aux_beta_pvalue(Partial_res_H0_CI, TildeX_loc, Tilder_star); + + }else{ + + if(local_p_values(1,i)alpha){ // Lower-Lower bound excessively tight + LL(i)=LL(i)-0.5*(LU(i)-LL(i)); + + // compute the partial residuals + beta_hat_mod(beta_in_test[i])=LL(i); // beta_hat_mod(i) = beta_hat(i) if i not in test; beta_HP otherwise + Partial_res_H0_CI = m_.y() - (m_.X()) * (beta_hat_mod);// (z-W*beta_hat(non in test)-W*LL[i](in test)) + local_p_values(3,i)=compute_CI_aux_beta_pvalue(Partial_res_H0_CI, TildeX_loc, Tilder_star); + + }else{//both the Upper bounds are well defined + + if(LU(i)-LL(i) X = m_.X(); + DMatrix X_t = m_.X().transpose(); + // Decomposition of [W^t * Lambda^2 * W] + Eigen::PartialPivLU> XLX_dec; + XLX_dec.compute((X_t)*(Lambda_*Lambda_)*(X)); + + // get the residuals + DVector eps_hat = (m_.y() - m_.fitted()); + // build squared residuals + DVector Res2=eps_hat.array()*eps_hat.array(); + + // resize the variance-covariance matrix + int q = C_.cols(); + DMatrix V; + V.resize(q,q); + DMatrix diag = Res2.asDiagonal(); + + V = (XLX_dec).solve((X_t)*(Lambda_*Lambda_)*Res2.asDiagonal()*(Lambda_*Lambda_)*(X)*(XLX_dec).solve(DMatrix::Identity(q,q))); // V = [(W*Lambda2*W)^-1 * Res2 * (W*Lambda2*W)^-1] + + // Extract the quantile needed for the computation of upper and lower bounds + double quant = normal_standard_quantile(1 - alpha_/2); + + // extract matrix C + int p = C_.rows(); + Speckman_aux_ranges.resize(p); + + // for each row of C matrix + for(int i=0; i col = C_.row(i); + // compute the standard deviation of the linear combination and half range of the interval + double sd_comb = std::sqrt(col.adjoint()*V*col); + double half_range=sd_comb*quant; + // save the half range + Speckman_aux_ranges(i)=half_range; + } + + this->is_speckman_aux_computed = true; + + return; + } + + + double compute_CI_aux_beta_pvalue(const DVector & partial_res_H0_CI, const DMatrix & TildeX, const DMatrix & Tilder_star) const { + // declare the vector that will store the p-values + double result; + + // compute the vectors needed for the statistic + DVector Tilder = Tilder_star * partial_res_H0_CI; + + // Initialize observed statistic and sign_flipped statistic + DMatrix stat_temp = TildeX*Tilder; + double stat=stat_temp(0); + double stat_flip=stat; + + // Random sign-flips + std::default_random_engine eng; + std::uniform_int_distribution distr(0, 1); + + //if we have a seed + if(set_seed != 0) { + eng.seed(set_seed); + } else { + std::random_device rd; + eng.seed(rd()); // random seed + } + double count_Up = 0; // Counter for the number of flipped statistics that are larger the observed statistic + double count_Down = 0; // Counter for the number of flipped statistics that are smaller the observed statistic + + DVector Tilder_perm=Tilder; + + // get the number of flips + int nflip=n_flip; + + for(int i=0;i stat_flip_temp = TildeX*Tilder_perm; + stat_flip= stat_flip_temp(0);// Flipped statistic + if(stat_flip > stat){ ++count_Up;}else{ + if(stat_flip < stat){ ++count_Down;} + } + } + + double pval_Up = count_Up/n_flip; + double pval_Down = count_Down/n_flip; + + result = std::min(pval_Up, pval_Down); // select the correct unilateral p_value + + return result; + + }; + + + void Psi_p(){ + // case in which the locations are extracted from the observed ones + if(is_empty(locations_f_) && is_empty(mesh_nodes_)){ + Psi_p_ = m_.Psi(); + if(p_l_ == 0) + p_l_ = Psi_p_.rows(); + } + else if(is_empty(mesh_nodes_)){ + int m = locations_f_.size(); + SpMatrix Psi = m_.Psi().transpose(); + Psi_p_.resize(m, Psi.rows()); + for(int j = 0; j < m; ++j) { + int row = locations_f_[j]; + for(SpMatrix::InnerIterator it(Psi, row); it; ++it) { + Psi_p_.insert(j, it.row()) = it.value(); + } + } + Psi_p_.makeCompressed(); + } + else { + // opposite dimensions of the Psi in the other cases + int m = mesh_nodes_.size(); + SpMatrix Psi = m_.PsiESF().transpose(); + SpMatrix temp_psip; + temp_psip.resize(m, Psi.rows()); + for(int j = 0; j < m; ++j) { + int row = mesh_nodes_[j]; + for(SpMatrix::InnerIterator it(Psi, row); it; ++it) { + temp_psip.insert(j, it.row()) = it.value(); + } + } + Psi_p_ = temp_psip; + } + Psi_p_.makeCompressed(); + if(p_l_ == 0) + p_l_ = Psi_p_.rows(); + } + + DVector yp(){ + if(is_empty(locations_f_) && is_empty(mesh_nodes_)) + return m_.y(); + + else if (!is_empty(locations_f_)){ + int m = locations_f_.size(); + DVector y = m_.y(); + DVector yp; + yp.resize(m); + for(int j = 0; j < m; ++j) { + int row = locations_f_[j]; + yp.row(j) = y.row(row); + } + return yp; + } + else{ + int m = mesh_nodes_.size(); + DVector y = m_.y(); + DVector yp; + yp.resize(m); + for(int j = 0; j < m; ++j) { + int row = mesh_nodes_[j]; + yp.row(j) = y.row(row); + } + return yp; + } + } + + DMatrix Xp(){ + // case in which the locations are extracted from the observed ones + if(is_empty(locations_f_) && is_empty(mesh_nodes_)){ + return m_.X(); + } + if(!m_.has_covariates()) + return m_.X(); + if(!is_empty(locations_f_)){ + int m = locations_f_.size(); + DMatrix X = m_.X(); + DMatrix Xp; + Xp.resize(m, X.cols()); + for(int j = 0; j < m; ++j) { + int row = locations_f_[j]; + Xp.row(j) = X.row(row); + } + return Xp; + } + if(!is_empty(mesh_nodes_)){ + int m = mesh_nodes_.size(); + DMatrix X = m_.X(); + DMatrix Xp; + Xp.resize(m, X.cols()); + for(int j = 0; j < m; ++j) { + int row = mesh_nodes_[j]; + Xp.row(j) = X.row(row); + } + return Xp; + } + return m_.X(); + } + + DMatrix Wp(){ + // set it as a identity matrix + if(is_empty(Psi_p_)){ + Psi_p(); + } + DMatrix id = DMatrix::Identity(p_l_, p_l_); + return id; + } + + // computes matrix Q = W(I - X*(X^\top*W*X)^{-1}*X^\top*W) + void Qp() { + if (!m_.has_covariates()){ + Qp_ = Wp() * DMatrix::Identity(p_l_, p_l_); + } + else{ + DMatrix Xp_ = Xp(); // X^\top*W, q x p_l_ + DMatrix XpTXp = Xp_.transpose() * Xp_; + Qp_ = DMatrix::Identity(p_l_, p_l_) - Xp_ * XpTXp.ldlt().solve(Xp_.transpose()); + } + + } + + void Qp_dec(){ + // compute the Q only on the locations in which you want inference + if(is_empty(Qp_)) + Qp(); + // Q * (y - epislon) = Q * r + // Eigen decomposition + Eigen::SelfAdjointEigenSolver> Q_eigen(Qp_); + // matrix V is the matrix p_l_ x (p_l_-q) of the nonzero eigenvectors of Q + DMatrix Q_eigenvectors = Q_eigen.eigenvectors(); + if(m_.has_covariates()) + Qp_dec_ = Q_eigenvectors.rightCols(p_l_ - m_.X().cols()); + else + Qp_dec_ = Q_eigenvectors; + } + + + + double f_p_value(){ + if(is_empty(Psi_p_)) + Psi_p(); + if(is_empty(f0_)){ + Base::setf0(DVector::Zero(Psi_p_.rows())); + } + if(is_empty(Qp_dec_)) + Qp_dec(); + + // test statistic when Pi = Id + DVector VQr = Qp_dec_.transpose() * Qp_ * (yp() - f0_); + + DVector Ti = Psi_p_.transpose() * Qp_dec_ * VQr; + + // save the rank of Ti + double Ti_rank = Ti.array().square().sum(); + + // random sign-flips + // Bernoulli dist (-1, 1) with p = 0.5 + std::default_random_engine eng; + std::uniform_int_distribution distr(0, 1); + + //if we have a set seed + if(set_seed != 0) { + eng.seed(set_seed); + } else { + std::random_device rd; + eng.seed(rd()); // random seed + } + int count = 0; + DVector tp_vqr = VQr; + DVector Tp = Ti; + + for(int i = 0; i < n_flip; ++i){ + for(int j = 0; j < VQr.size(); ++j){ + int flip = 2 * distr(eng) - 1; + tp_vqr(j) = VQr(j) * flip; + } + Tp = Psi_p_.transpose() * Qp_dec_ * tp_vqr; + // flipped statistics + double Tp_rank = Tp.array().square().sum(); + if(Tp_rank >= Ti_rank){ + ++count; + } + } + double p_value = count/static_cast(n_flip); + return p_value; + } + + + double sign_flip_p_value(){ + if(is_empty(Psi_p_)) + Psi_p(); + if(is_empty(f0_)){ + Base::setf0(DVector::Zero(Psi_p_.rows())); + } + if(is_empty(Qp_)){ + Qp(); + } + // test statistic when Pi = Id + DVector VQr = Qp_ * (yp() - f0_); + + DVector Ti = Psi_p_.transpose() * VQr; + + // save the rank of Ti + double Ti_rank = Ti.array().square().sum(); + + // random sign-flips + // Bernoulli dist (-1, 1) with p = 0.5 + std::default_random_engine eng; + std::uniform_int_distribution distr(0, 1); + + //if we have a set seed + if(set_seed != 0) { + eng.seed(set_seed); + } else { + std::random_device rd; + eng.seed(rd()); // random seed + } + int count = 0; + DVector tp_vqr = VQr; + DVector Tp = Ti; + + for(int i = 0; i < n_flip; ++i){ + for(int j = 0; j < VQr.size(); ++j){ + int flip = 2 * distr(eng) - 1; + tp_vqr(j) = VQr(j) * flip; + } + Tp = Psi_p_.transpose() * tp_vqr; + // flipped statistics + double Tp_rank = Tp.array().square().sum(); + + if(Tp_rank >= Ti_rank){ + ++count; + } + } + double p_value = static_cast(count)/static_cast(n_flip); + + return p_value; + } + + + double f_CI_p_val(const DVector res, const int curr_index){ + if(is_empty(Qp_dec_)) + Qp_dec(); + + DVector Vres = Qp_dec_.transpose() * res; + + DVector Ti = Psi_p_.transpose() * Qp_dec_ * Vres; + + // random sign-flips + // Bernoulli dist (-1, 1) with p = 0.5 + std::default_random_engine eng; + std::uniform_int_distribution distr(0, 1); + + //if we have a set seed + if(set_seed != 0) { + eng.seed(set_seed); + } else { + std::random_device rd; + eng.seed(rd()); // random seed + } + int up = 0; + int down = 0; + DVector Tp = Ti; + DVector perm_res = Vres; + + for(int i = 0; i < n_flip; ++i){ + for(int j = 0; j < Vres.size(); ++j){ + int flip = 2 * distr(eng) - 1; + perm_res(j) = Vres(j) * flip; + } + Tp = Psi_p_.transpose() * Qp_dec_ * perm_res; + + if(Tp(curr_index) > Ti(curr_index)){ + ++up; + } + else if (Tp(curr_index) < Ti(curr_index)){ + ++down; + } + } + + return std::min(up, down); + + } + + + + DVector Wald_range(){ + // create Wald object + Wald Wald_(m_); + + DMatrix id = DMatrix::Identity(m_.Psi().cols(), m_.Psi().cols()); + + DMatrix Wald_CI = Wald_.f_CI(); + int size = Wald_CI.rows(); + DVector ranges {}; + ranges.resize(size); + + for(int i = 0; i < size; ++i){ + ranges(i) = 0.5 * (Wald_CI(i, 1) - Wald_CI(i, 0)); + } + + return ranges; + } + + DMatrix f_CI(){ + // only if the locations are a subset of the mesh + // compute the Q only on the locations in which you want inference + if(is_empty(Qp_)) + Qp(); + + DVector fp = Psi_p_ * m_.f(); + int nloc = fp.size(); + + DMatrix CI; + CI.resize(nloc, 2); + + DVector Wald_ranges_ = Wald_range(); + + // bisection tolerance + DVector bis_tol = 0.2 * Wald_ranges_; + + DVector UU; // Upper limit for Upper bound + UU.resize(nloc); + DVector UL; // Lower limit for Upper bound + UL.resize(nloc); + DVector LU; // Upper limit for Lower bound + LU.resize(nloc); + DVector LL; // Lower limit for Lower bound + LL.resize(nloc); + + // initialize the intervals with a Wald estimate + for(int i = 0; i < nloc; ++i){ + double range = Wald_ranges_(i); + CI(i, 0) = fp(i) - range; + CI(i, 1) = fp(i) + range; + UU(i) = CI(i, 1) + 0.5 * range; + UL(i) = CI(i, 1) - 0.5 * range; + LU(i) = CI(i, 0) + 0.5 * range; + LL(i) = CI(i, 0) - 0.5 * range; + } + + // vector of booleans to check which inetrvals have converged + std::vector converged_up(nloc, false); + std::vector converged_low(nloc, false); + bool converged = false; + + // we need p-values ofr UU, UL, LU, LL + DMatrix p_values(4, nloc); + // need this to compute the p-values + DVector res(nloc); + + // for getting the current index + if(is_empty(locations_f_)){ + locations_f_ = DVector::LinSpaced(nloc, 0, nloc-1); + } + + for(int i = 0; i < nloc; ++i){ + DVector fp_UU = fp; + DVector fp_UL = fp; + DVector fp_LU = fp; + DVector fp_LL = fp; + + res = Qp_ * (yp() - fp_UU); + p_values(0,i) = f_CI_p_val(res, locations_f_[i]); + res = Qp_ * (yp() - fp_UL); + p_values(1,i) = f_CI_p_val(res, locations_f_[i]); + res = Qp_ * (yp() - fp_LU); + p_values(2,i) = f_CI_p_val(res, locations_f_[i]); + res = Qp_ * (yp() - fp_LL); + p_values(3,i) = f_CI_p_val(res, locations_f_[i]); + + } + // one at the time confidence intervals + int max_it = 5; + int count = 0; + double ppval = 0; + + while(!converged && count < max_it){ + + // compute p-values needed + for(int i = 0; i < nloc; ++i){ + DVector fp_UU = fp; + DVector fp_UL = fp; + DVector fp_LU = fp; + DVector fp_LL = fp; + + if(!converged_up[i]){ + // check upper upper + if(p_values(0, i) > 0.5 * alpha_f_){ + UU(i) = UU(i) + 0.5 * (UU(i) - UL(i)); + res = Qp_ * (yp() - fp_UU); + p_values(0,i) = f_CI_p_val(res, locations_f_[i]); + } + else if(p_values(1, i) < 0.5 * alpha_f_){ + UL(i) = fp(i) + 0.5 * (UL(i) - fp(i)); + res = Qp_ * (yp() - fp_UL); + p_values(1,i) = f_CI_p_val(res, locations_f_[i]); + } + else{ + if((UU(i) - UL(i)) < bis_tol(i)){ + converged_up[i] = true; + } + else{ + double proposal = 0.5 * (UU(i) + UL(i)); + DVector fpp = fp; + res = Qp_ * (yp() - fpp); + ppval = f_CI_p_val(res, locations_f_[i]); + if(ppval <= 0.5 * alpha_f_){ + UU(i) = proposal; + p_values(0, i) = ppval; + } + else{ + UL(i) = proposal; + p_values(1, i) = ppval; + } + } + } + } // end if + + if(!converged_low[i]){ + // check lower upper + if(p_values(2, i) < 0.5 * alpha_f_){ + LU(i) = fp(i) - 0.5 * (fp(i) - LU(i)); + res = Qp_ * (yp() - fp_LU); + p_values(2,i) = f_CI_p_val(res, locations_f_[i]); + } + else if(p_values(3, i) > 0.5 * alpha_f_){ + LL(i) = LL(i) + 0.5 * (LU(i) - LL(i)); + res = Qp_ * (yp() - fp_LL); + p_values(3,i) = f_CI_p_val(res, locations_f_[i]); + } + else{ + if((LU(i) - LL(i)) < bis_tol(i)){ + converged_low[i] = true; + } + else{ + double proposal = 0.5 * (LU(i) + LL(i)); + DVector fpp = fp; + res = Qp_ * (yp() - fpp); + ppval = f_CI_p_val(res, locations_f_[i]); + if(ppval <= 0.5 * alpha_f_){ + LL(i) = proposal; + p_values(3, i) = ppval; + } + else{ + LU(i) = proposal; + p_values(2, i) = ppval; + } + } + } + + } // end if + } // for end + + converged = true; + + for(int j = 0; j < nloc; ++j){ + if(!converged_up[j] || !converged_low[j]){ + converged = false; + } + } + + count++; + + } + if(!converged){ + std::cout << "Not all ESF CI converged" << std::endl; + } + + for(int k = 0; k < nloc; ++k){ + if(!converged_up[k] || !converged_low[k]){ + // set a unfeasible number if the interval has not converged + CI(k, 0) = 10e20; + CI(k, 1) = 10e20; + } + else{ + CI(k, 0) = 0.5 * (LU(k) + LL(k)); + CI(k, 1) = 0.5 * (UU(k) + UL(k)); + } + } + + return CI; + } + + void V() override{ + Lambda_ = DMatrix::Identity(m_.n_obs(), m_.n_obs()) - m_.Psi() * s_.compute(m_) * m_.PsiTD(); + } + + inline bool is_Unilaterally_Greater (DVector v, DVector u){ + int q = v.size(); + for (int i = 0; i < q; ++i){ + if(v(i) <= u(i)){ + return false; + } + } + return true; + } + + inline bool is_Unilaterally_Smaller (DVector v, DVector u){ + int q = v.size(); + for (int i = 0; i < q; ++i){ + if(v(i) >= u(i)){ + return false; + } + } + return true; + } + + inline DVector min(const DVector & v, const DVector & u){ + DVector result; + result.resize(v.size()); + for(int i = 0; i < v.size(); ++i){ + result(i) = std::min(v(i), u(i)); + } + return result; + } + + void setNflip(int m){ + n_flip = m; + } + + void setseed(int k){ + set_seed=k; + } + + void setMesh_loc(DVector m_nodes){ + mesh_nodes_ = m_nodes; + } + +}; + +} // namespace models +} // namespace fdapde + +#endif // __ESF_H__ diff --git a/fdaPDE/models/regression/gcv.h b/fdaPDE/models/regression/gcv.h index da66566a..26d6c038 100644 --- a/fdaPDE/models/regression/gcv.h +++ b/fdaPDE/models/regression/gcv.h @@ -39,27 +39,26 @@ class GCV { using This = GCV; using VectorType = DVector; using MatrixType = DMatrix; - // type-erased wrapper for Tr[S] computation strategies + // type-erased wrapper for Tr[S] computation strategy struct EDFStrategy__ { template using fn_ptrs = fdapde::mem_fn_ptrs<&M::compute, &M::set_model>; // forwardings decltype(auto) compute() { return fdapde::invoke(*this); } void set_model(const RegressionView& model) { fdapde::invoke(*this, model); } }; - using EDFStrategy = fdapde::erase; RegressionView model_; - EDFStrategy trS_; // strategy used to evaluate the trace of smoothing matrix S - std::vector edfs_; // equivalent degrees of freedom q + Tr[S] - std::vector gcvs_; // computed values of GCV index + erase trS_; // strategy used to evaluate the trace of smoothing matrix S + std::vector edfs_; // equivalent degrees of freedom q + Tr[S] + std::vector gcvs_; // computed values of GCV index // cache pairs (lambda, Tr[S]) for fast access if GCV is queried at an already computed point - std::map> cache_; + std::map> cache_; // analytical expression of gcv at \lambda // // edf = n - (q + Tr[S]) // GCV(\lambda) = n/(edf^2)*norm(y - \hat y)^2 - ScalarField gcv_; + ScalarField gcv_; double gcv_impl(const VectorType& lambda) { // fit the model given current lambda model_.set_lambda(lambda); @@ -78,7 +77,8 @@ class GCV { return gcv_value; } public: - static constexpr int DomainDimension = fdapde::Dynamic; + static constexpr int DomainDimension = fdapde::core::Dynamic; + using EDFStrategy = erase; // constructors template GCV(const ModelType_& model, EDFStrategy_&& trS) : model_(model), trS_(trS), gcv_(this, &This::gcv_impl) { @@ -95,7 +95,7 @@ class GCV { GCV& operator=(const GCV& other) { model_ = other.model_; trS_ = other.trS_; - gcv_ = ScalarField(this, &This::gcv_impl); + gcv_ = ScalarField(this, &This::gcv_impl); return *this; } diff --git a/fdaPDE/models/regression/inference.h b/fdaPDE/models/regression/inference.h new file mode 100644 index 00000000..5d34160c --- /dev/null +++ b/fdaPDE/models/regression/inference.h @@ -0,0 +1,131 @@ +// This file is part of fdaPDE, a C++ library for physics-informed +// spatial and functional data analysis. +// +// This program is free software: you can redistribute it and/or modify +// it under the terms of the GNU General Public License as published by +// the Free Software Foundation, either version 3 of the License, or +// (at your option) any later version. +// +// This program is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +// GNU General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with this program. If not, see . + +#ifndef __INFERENCE_H__ +#define __INFERENCE_H__ + +#include +#include +#include + + + +#include +#include + +namespace fdapde { +namespace models { + + // oggetti comuni a Wald e Speckman + struct exact {}; + struct nonexact {}; + enum CIType {bonferroni,simultaneous,one_at_the_time}; + + + // P-VALUES + + double gamma(double x) { + if (x <= 0) { // value not valid + return 0; + } + if (x == 1) { + return 1; // Caso base: gamma(1) = 1 + } + if (x > 1) { + + double logGamma = 0.5 * log(2 * M_PI * x) + (x - 0.5) * log(x) - x + 1.0 / (12 * x); + return exp(logGamma); + } else { + // Formula di riflessione di Euler + return M_PI / (sin(M_PI * x) * gamma(1 - x)); + } + } + + double integrand(double t, double a) { + return pow(t, a - 1) * exp(-t); + } + + double gamma_incompleta(double a, double x, int numIntervals = 1000) { + double sum = 0.0; + double intervalWidth = x / numIntervals; + + for (int i = 0; i < numIntervals; ++i) { + double left = i * intervalWidth; + double right = (i + 1) * intervalWidth; + sum += (integrand(right, a) + integrand(left, a)) / 2.0 * (right - left); + } + + return sum; + } + + double chi_squared_cdf(double chiSquaredStat, int degreesOfFreedom) { + + double pValue = gamma_incompleta(degreesOfFreedom/2.0,chiSquaredStat/2.0)/gamma(degreesOfFreedom / 2.0); + + return pValue; + } + + double gaussian_cdf(double x, double mean, double stddev) { + return 0.5 * (1 + std::erf((x - mean) / (stddev * std::sqrt(2)))); + } + + + double inverseChiSquaredCDF(double alpha, int degreesOfFreedom, double tolerance = 1e-6) { + double low = 0.0; + double high = 1000.0; + + while (high - low > tolerance) { + double mid = (low + high) / 2.0; + double pValue = chi_squared_cdf(mid, degreesOfFreedom); + + if (pValue < alpha) { + low = mid; + } else { + high = mid; + } + } + + return (low + high) / 2.0; + } + + double inverse_erf(double x) { + const double epsilon = 1e-10; + double y = 0.0; + double delta; + do { + delta = (std::erf(y) - x) / (2.0 / std::sqrt(M_PI) * std::exp(-y * y)); + y -= delta; + } while (std::fabs(delta) > epsilon); + return y; + } + + + double normal_standard_quantile(double percentile) { + return std::sqrt(2.0) * inverse_erf(2.0 * percentile - 1.0); + } + + + // function that returns the exact inverse of a matrix + DMatrix inverse(DMatrix M){ + Eigen::PartialPivLU> Mdec_ (M); + return Mdec_.solve(DMatrix::Identity(M.rows(), M.cols())); + } + + +} // closing models namespace +} // closing fdapde namespace + +#endif // __INFERENCE_H__ \ No newline at end of file diff --git a/fdaPDE/models/regression/inference_base.h b/fdaPDE/models/regression/inference_base.h new file mode 100644 index 00000000..30fdc34e --- /dev/null +++ b/fdaPDE/models/regression/inference_base.h @@ -0,0 +1,297 @@ +// This file is part of fdaPDE, a C++ library for physics-informed +// spatial and functional data analysis. +// +// This program is free software: you can redistribute it and/or modify +// it under the terms of the GNU General Public License as published by +// the Free Software Foundation, either version 3 of the License, or +// (at your option) any later version. +// +// This program is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +// GNU General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with this program. If not, see . + +#ifndef __INFERENCE_BASE_H__ +#define __INFERENCE_BASE_H__ + +#include +#include +using fdapde::core::FSPAI; +using fdapde::core::lump; +using fdapde::core::is_empty; +#include "../model_macros.h" +#include "../model_traits.h" +#include "../model_base.h" +#include "srpde.h" +#include "strpde.h" +#include "exact_edf.h" +#include "stochastic_edf.h" +#include "inference.h" + +#include +#include + +//serve per salvare le matrici +#include +#include + + + +namespace fdapde { +namespace models { + +template class InferenceBase{ + + protected: + + Model m_; + DMatrix V_ {}; + DMatrix C_ {}; // inference matrix C (p x q) matrix + DVector beta_ {}; // sol of srpde ( q x 1 ) matrix + DVector beta0_ {}; // inference hypothesis H0 (p x 1) matrix + DVector f0_ {}; // inference hypothesis H0 + double alpha_ = 0; // level of the confidence intervals for beta + double alpha_f_ = 0.05; // level of confidence intervals for f + DVector locations_f_ {}; // indexes of the subset of locations if locations are exctracted from existing ones + + + public: + + InferenceBase() = default; // deafult constructor + InferenceBase(const Model& m): m_(m) {}; // constructor + + virtual void beta() {}; + + virtual void V() = 0; + + virtual DMatrix computeCI(CIType type){ + + fdapde_assert(!is_empty(C_)); + + if(alpha_ == 0.0) { + setAlpha(0.05); // default value 5% + } + if(is_empty(V_)){ + V(); + } + if(is_empty(beta_)){ + beta(); + } + + int p = C_.rows(); + int size = std::min(C_.rows(), V_.rows()); + DVector diagon(size); + for (int i = 0; i < C_.rows(); ++i) { + DVector ci = C_.row(i); + diagon[i] = ci.transpose() * V_ * ci; + } + + DVector lowerBound(size); + DVector upperBound(size); + + if(type == simultaneous){ + // SIMULTANEOUS + double quantile = inverseChiSquaredCDF(1 - alpha_, p); + lowerBound = (C_ * beta_).array() - (quantile * diagon.array()).sqrt(); + upperBound = (C_ * beta_).array() + (quantile * diagon.array()).sqrt(); + } + else if (type == bonferroni){ + // BONFERRONI + double quantile = normal_standard_quantile(1 - alpha_/(2 * p)); + lowerBound = (C_ * beta_).array() - quantile * (diagon.array()).sqrt(); + upperBound = (C_ * beta_).array() + quantile * (diagon.array()).sqrt(); + } + else if (type == one_at_the_time){ + // ONE AT THE TIME + double quantile = normal_standard_quantile(1 - alpha_/2); + lowerBound = (C_ * beta_).array() - quantile * (diagon.array()).sqrt(); + upperBound = (C_ * beta_).array() + quantile * (diagon.array()).sqrt(); + } + else{ + // inserire errore: nome intervallo non valido + return DMatrix::Zero(1, 1); + } + + DMatrix CIMatrix(p, 2); //matrix of confidence intervals + CIMatrix.col(0) = lowerBound; + CIMatrix.col(1) = upperBound; + return CIMatrix; + } + + virtual DVector p_value(CIType type){ + + fdapde_assert(!is_empty(C_)); + if(is_empty(beta0_)){ + if(is_empty(beta_)){ + beta(); + } + setBeta0(DVector::Zero(beta_.size())); + } + if(is_empty(beta_)){ + beta(); + } + if(is_empty(V_)){ + V(); + } + int p = C_.rows(); + DVector statistics(p); + if(type == simultaneous){ + // SIMULTANEOUS + DVector diff = C_ * beta_ - beta0_; + DMatrix Sigma = C_ * V_ * C_.transpose(); + DMatrix Sigmadec_ = inverse(Sigma); + double stat = diff.adjoint() * Sigmadec_ * diff; + statistics.resize(p); + double pvalue = chi_squared_cdf(stat, p); + if(pvalue < 0){ + statistics(0) = 1; + } + if(pvalue > 1){ + statistics(0) = 0; + } + else{ + statistics(0) = 1 - pvalue; + } + for(int i = 1; i < C_.rows(); i++){ + statistics(i) = 10e20; + } + return statistics; + } + + else if (type == one_at_the_time){ + // ONE AT THE TIME + int p = C_.rows(); + statistics.resize(p); + for(int i = 0; i < p; i++){ + DVector col = C_.row(i); + double diff = col.adjoint() * beta_ - beta0_[i]; + double sigma = col.adjoint() * V_ *col; + double stat = diff/std::sqrt(sigma); + double pvalue = 2 * gaussian_cdf(-std::abs(stat), 0, 1); + if(pvalue < 0){ + statistics(i) = 0; + } + if(pvalue > 1){ + statistics(i) = 1; + } + else{ + statistics(i) = pvalue; + } + } + return statistics; + } + else{ + //inserire messaggio di errore + return DVector::Zero(1); + } + } + + // return the sparse approx of E^{-1} + static SpMatrix invE_approx(const Model& m){ + /* SpMatrix decR0_ = lump(m.R0()); + DiagMatrix invR0_(decR0_.rows()); + invR0_.setZero(); + for (int i = 0; i < decR0_.rows(); ++i) { + double diagElement = decR0_.diagonal()[i]; + invR0_.diagonal()[i] = 1.0 / diagElement; + }*/ + //DMatrix Et_ = m.PsiTD()* m.Psi()+ m.lambda_D() * m.R1().transpose() * invR0_ * m.R1(); + + //applico FSPAI su Atilde + int alpha = 10; // Numero di aggiornamenti del pattern di sparsità per ogni colonna di A (perform alpha steps of approximate inverse update along column k) + int beta = 10; // Numero di indici da aggiungere al pattern di sparsità di Lk per ogni passo di aggiornamento + double epsilon = 0.005; // Soglia di tolleranza per l'aggiornamento del pattern di sparsità (the best improvement is higher than accetable treshold) + //questi sono quelli trovati nella libreria vecchia + //std::string tol_Inverse = "0.005"; oppure 0.05 // Controls the quality of approximation, default 0.005 + //std::string max_Step_Col = "20"; oppure 10 // Max number of improvement steps per columns + // std::string max_New_Nz = "20"; oppure 10 // Max number of new nonzero candidates per step + //Et_ should be stored as a sparse matrix + + // questo serve se voglio invertire R0 cojn fspai + FSPAI fspai_R0(m.R0()); + fspai_R0.compute(m.R0(),alpha, beta, epsilon); + SpMatrix invR0_ = fspai_R0.inverse(); + + DMatrix Et_ = m.PsiTD()* m.Psi()+ m.lambda_D() * m.R1().transpose() * invR0_ * m.R1(); + + + + SpMatrix Et_sparse = Et_.sparseView(); + + //Eigen::saveMarket(Et_sparse, "Edainvertire.mtx"); + FSPAI fspai_E(Et_sparse); + fspai_E.compute(Et_sparse,alpha, beta, epsilon); + SpMatrix invE_ = fspai_E.inverse(); + //Eigen::saveMarket(invE_, "inversaE2.mtx"); + //SpMatrix precondE= fspai_E.getL(); + //Eigen::saveMarket(precondE, "precondE.mtx"); + + /* + //CALCOLO DELL'INVERSA PER CONFRONTI CON LIBRERIA ORIGINALE DI FSPAI + //SpMatrix Edainvertire; + //Eigen::loadMarket(Edainvertire, "Edainvertire.mtx"); + //std::cout<<"righe di Edainveritre: "< precond_nostro = fspai_Edainvertire.getL(); + Eigen::saveMarket(precond_nostro, "precond_nostro.mtx"); +*/ + + + + + //SpMatrix risultatoFSPAI; + //Eigen::loadMarket(risultatoFSPAI, "risultatoFSPAI.mtx"); + //std::cout<<"righe di fspai"<& C) { + C_ = C; + } + + // setter for alpha + void setAlpha(double alpha){ + fdapde_assert(0 <= alpha && alpha <= 1); // throw an exception if condition is not met + if( 0 <= alpha && alpha <= 1) { + alpha_ = alpha; + } + } + + // setter for alpha f + void setAlpha_f(double alpha){ + fdapde_assert(0 <= alpha&& alpha <= 1); // throw an exception if condition is not met + if( 0 <= alpha && alpha <= 1) { + alpha_f_ = alpha; + } + } + + // setter for beta0_ + void setBeta0(DVector beta0){ + beta0_ = beta0; + } + + // setter for f0_ + void setf0(DVector f0){ + f0_ = f0; + } + + void setLocationsF(DVector locs){ + locations_f_ = locs; + } + + +}; + +} // namespace models +} // namespace fdapde + +#endif // __INFERENCE_BASE_H__ \ No newline at end of file diff --git a/fdaPDE/models/regression/pesf.h b/fdaPDE/models/regression/pesf.h new file mode 100644 index 00000000..036316f0 --- /dev/null +++ b/fdaPDE/models/regression/pesf.h @@ -0,0 +1,672 @@ +// This file is part of fdaPDE, a C++ library for physics-informed +// spatial and functional data analysis. +// +// This program is free software: you can redistribute it and/or modify +// it under the terms of the GNU General Public License as published by +// the Free Software Foundation, either version 3 of the License, or +// (at your option) any later version. +// +// This program is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +// GNU General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with this program. If not, see . + +#ifndef __PESF_H__ +#define __PESF_H__ + +#include +#include + +#include "../model_macros.h" +#include "../model_traits.h" +#include "../model_base.h" +#include "srpde.h" +#include "strpde.h" +#include "exact_edf.h" +#include "stochastic_edf.h" +#include "inference_base.h" +#include "inference.h" + +#include + +namespace fdapde { +namespace models { + +template class PESF: public InferenceBase{ + + private: + struct ExactInverse{ + DMatrix compute(Model m){ + return inverse(m.E()); + } + }; + + struct NonExactInverse{ + SpMatrix compute(Model m){ + return Base::invE_approx(m); + } + }; + + int n_flip = 1000; //default value + int set_seed=0; + + DMatrix Lambda_ {}; + + // variabili aggiunte per confidence intervals + bool is_speckman_aux_computed = false; + DVector Speckman_aux_ranges; //Speckman auxiliary CI ranges needed for CI method initialization (for beta) + // vairabili aggiunte per inferenza su f + DMatrix Qp_ {}; + int p_l_ = 0; // number of locations for inference on f + SpMatrix Psi_p_ {}; // Psi only in the locations for inference + DMatrix Qp_dec_ {}; // Decomposition of Qp matrix + DVector mesh_nodes_ {}; // if inference is performed on a subset of mesh nodes + + + public: + using Base = InferenceBase; + using Base::m_; + using Base::beta_; + using Base::f0_; + using Base::C_; + using Base::locations_f_; + using Base::alpha_f_; + using Base::beta0_; + using Solver = typename std::conditional::value, ExactInverse, NonExactInverse>::type; + Solver s_; + using Base::V_; + + // constructors + PESF() = default; // deafult constructor + PESF(const Model& m): Base(m) {}; // constructor + + + + DVector p_value(CIType type){ + fdapde_assert(!is_empty(C_)); // throw an exception if condition is not met + + if(is_empty(beta0_)){ + Base::setBeta0(DVector::Zero(m_.beta().size())); + } + + int p = C_.rows(); + + DVector result; // declare the vector that will store the p-values + + // compute Lambda + if(is_empty(Lambda_)){ + V(); + } + + Eigen::SelfAdjointEigenSolver> solver(Lambda_); // eigenvectors and eigenvalues of Lambda + + DMatrix eigenvalues = solver.eigenvalues(); + DMatrix eigenvectors = solver.eigenvectors(); + + // beta_hat + DVector beta_hat = m_.beta(); + DVector beta_hat_mod = beta_hat; + + if(type == simultaneous){ + // SIMULTANEOUS + // betas under test + for(int i = 0; i < p; ++i){ + for(int j = 0; j < C_.cols(); j++){ + if(C_(i,j) > 0){ + beta_hat_mod[j] = beta0_[j]; + } + } + } + + // partial residuals + DMatrix res_H0 = m_.y() - m_.X() * beta_hat_mod; + // W^t * V * D + DMatrix Xt = (C_ * m_.X().transpose()) * eigenvectors * eigenvalues.asDiagonal(); + DVector Tilder = eigenvectors.transpose() * res_H0; + int n_obs=m_.n_obs(); + + DVector Tilder_hat= eigenvectors.transpose()*(m_.y() - m_.X()* beta_hat); + + // standard error estimated + DVector eps_hat = m_.y() - m_.fitted(); + double SS_res = eps_hat.squaredNorm(); + double Sigma_hat = std::sqrt(SS_res/(n_obs-1)); + + double threshold = 10 * Sigma_hat; // This threshold is used to determine how many components will not be flipped: we drop those that show large alpha_hat w.r.t. the expected standar error + int N_Eig_Out = 0; // It will store the number of biased components that will be kept fixed if enhanced-ESF is required + + // observed and sign-flipped statistic + DVector stat = Xt * Tilder; + DVector stat_flip = stat; + + // Random sign-flips + std::default_random_engine eng; + std::uniform_int_distribution distr(0, 1); + + //if we have a set seed + if(set_seed != 0) { + eng.seed(set_seed); + } else { + std::random_device rd; + eng.seed(rd()); // random seed + } + int up = 0; + int down = 0; + + DVector Tilder_perm = Tilder; + + for(int i = 0; i < n_flip; ++i){ + N_Eig_Out=0; + for(int j = 0; j < Xt.cols(); ++j){ + int flip; + if((N_Eig_Outthreshold)){ + flip=1; + } + else{ + flip=2 * distr(eng) - 1; + } + Tilder_perm.row(j) = Tilder.row(j) * flip; + } + stat_flip = Xt * Tilder_perm; + + if(is_Unilaterally_Greater(stat_flip, stat)){ + up = up + 1; + } + else{ + if(is_Unilaterally_Smaller(stat_flip, stat)){ + down = down + 1; + } + } + } + + double pval_up = static_cast(up) / n_flip; + double pval_down = static_cast(down) / n_flip; + + result.resize(p); + result(0) = 2 * std::min(pval_up, pval_down); + for(int k = 1; k < p; k++){ + result(k) = 0.0; + } + } + else{ + // ONE AT THE TIME + DMatrix res_H0(Lambda_.cols(), p); + for(int i = 0; i < p; ++i){ + // beta under test + beta_hat_mod = beta_hat; + + for(int j = 0; j < C_.cols(); ++j){ + if(C_(i,j)>0){ + beta_hat_mod[j] = beta0_[j]; + } + } + // partial residuals + res_H0.col(i) = m_.y() - m_.X()* beta_hat_mod; + } + // compute the vectors needed for the statistic + DMatrix Xt = (C_ * m_.X().transpose()) * eigenvectors * eigenvalues.asDiagonal(); // W^t * V * D + DMatrix Tilder = eigenvectors.transpose() * res_H0; // V^t * partial_res_H0 + + int n_obs=m_.n_obs(); + + DVector Tilder_hat= eigenvectors.transpose()*(m_.y() - m_.X()* beta_hat); + + // standard error estimated + DVector eps_hat = m_.y() - m_.fitted(); + double SS_res = eps_hat.squaredNorm(); + double Sigma_hat = std::sqrt(SS_res/(n_obs-1)); + + double threshold = 10 * Sigma_hat; // This threshold is used to determine how many components will not be flipped: we drop those that show large alpha_hat w.r.t. the expected standar error + int N_Eig_Out = 0; // It will store the number of biased components that will be kept fixed if enhanced-ESF is required + + + // Observed statistic + DMatrix stat = Xt * Tilder; + DMatrix stat_flip = stat; + + // Random sign-flips + std::default_random_engine eng; + std::uniform_int_distribution distr(0, 1); + + //if we have a set seed + if(set_seed != 0) { + eng.seed(set_seed); + } else { + std::random_device rd; + eng.seed(rd()); // random seed + } + DVector up = DVector::Zero(p); + DVector down = DVector::Zero(p); + + DMatrix Tilder_perm = Tilder; + + for(int i = 0; i < n_flip; ++i){ + N_Eig_Out=0; + for(int j = 0; j < Xt.cols(); ++j){ + int flip; + if((N_Eig_Outthreshold)){ + flip=1; + }else{ + flip=2 * distr(eng) - 1; + } + Tilder_perm.row(j) = Tilder.row(j) * flip; + } + stat_flip = Xt * Tilder_perm; + + for(int k = 0; k < p; ++k){ + if(stat_flip(k, k) > stat(k, k)){ + ++up(k); + }else{ + ++down(k); + } + } + } + + DVector pval_up = up.array() / static_cast(n_flip); + DVector pval_down = down.array() / static_cast(n_flip); + + result.resize(p); + result = 2 * min(pval_up, pval_down); + } + return result; + } + + DMatrix computeCI(CIType type){ + // compute Lambda + if(is_empty(Lambda_)){ + V(); + } + + // beta_hat + DVector beta_hat = m_.beta(); + DVector beta_hat_mod = beta_hat; + + fdapde_assert(!is_empty(C_)); // throw an exception if condition is not met + int p = C_.rows(); + + DVector beta_in_test; + beta_in_test.resize(p); + for(int i=0; i0){ + beta_in_test[i]=j; + } + } + } + + // compute eigenvectors and eigenvalues of Lambda + Eigen::SelfAdjointEigenSolver> solver(Lambda_); + + DMatrix eigenvalues = solver.eigenvalues(); + DMatrix eigenvectors = solver.eigenvectors(); + // intervals + DMatrix result; + result.resize(p, 2); + + // speckman's CI (initial guess for CI) + if(!is_speckman_aux_computed){ + Compute_speckman_aux(); + } + + // this vector will store the tolerance for each interval upper/lower limit + DVector ESF_bisection_tolerances = 0.1*Speckman_aux_ranges; // 0.1 of the speckman CI as maximum tolerance + + + // define storage structures for bisection algorithms + DVector UU; // Upper limit for Upper bound + UU.resize(p); + DVector UL; // Lower limit for Upper bound + UL.resize(p); + DVector LU; // Upper limit for Lower bound + LU.resize(p); + DVector LL; // Lower limit for Lower bound + LL.resize(p); + + // ESF initialization with Speckman intervals as initial guess + for(int i = 0; i < p; ++i){ + double half_range = Speckman_aux_ranges(i); + + // compute the limits of the interval + result(i,0) = beta_hat(beta_in_test[i]) - half_range; + LU(i)=result(i,0)+0.5*half_range; + LL(i)=result(i,0)-0.5*half_range; + result(i,1) = beta_hat(beta_in_test[i]) + half_range; + UU(i)=result(i,1) +0.5*half_range; + UL(i)=result(i,1) -0.5*half_range; + } + + // define booleans used to understand which CI need to be computed forward on + std::vector converged_up(p,false); + std::vector converged_low(p,false); + bool all_betas_converged=false; + + // matrix that stores p_values of the bounds at actual step + DMatrix local_p_values; + local_p_values.resize(4,p); + + // compute the vectors needed for the statistic + DMatrix TildeX = (C_ * m_.X().transpose()) * eigenvectors * eigenvalues.asDiagonal(); // W^t * V * D + DMatrix Tilder_star = eigenvectors.transpose(); + // Select eigenvalues that will not be flipped basing on the estimated bias carried + DVector Tilder_hat = eigenvectors.transpose()* (m_.y() - (m_.X())* beta_hat); // This vector represents Tilder using only beta_hat, needed for bias estimation + DVector Partial_res_H0_CI; + Partial_res_H0_CI.resize(Lambda_.cols()); + + // fill the p_values matrix + for (int i=0; i TildeX_loc = TildeX.row(beta_in_test[i]); + beta_hat_mod = beta_hat; + + // compute the partial residuals and p value + beta_hat_mod(beta_in_test[i])=UU(i); // beta_hat_mod(i) = beta_hat(i) if i not in test; beta_HP otherwise + Partial_res_H0_CI = m_.y() - (m_.X()) * (beta_hat_mod); // (y-W*beta_hat(non in test)-W*UU[i](in test)) + local_p_values(0,i)=compute_CI_aux_beta_pvalue(Partial_res_H0_CI, TildeX_loc, Tilder_star); + + // compute the partial residuals and p value + beta_hat_mod(beta_in_test[i])=UL(i); // beta_hat_mod(i) = beta_hat(i) if i not in test; beta_HP otherwise + Partial_res_H0_CI = m_.y() - (m_.X()) * (beta_hat_mod); // (y-W*beta_hat(non in test)-W*UL[i](in test)) + local_p_values(1,i)=compute_CI_aux_beta_pvalue(Partial_res_H0_CI, TildeX_loc, Tilder_star); + + // compute the partial residuals and p value + beta_hat_mod(beta_in_test[i])=LU(i); // beta_hat_mod(i) = beta_hat(i) if i not in test; beta_HP otherwise + Partial_res_H0_CI = m_.y() - (m_.X()) * (beta_hat_mod); // (y-W*beta_hat(non in test)-W*LU[i](in test)) + local_p_values(2,i)=compute_CI_aux_beta_pvalue(Partial_res_H0_CI, TildeX_loc, Tilder_star); + + // compute the partial residuals and p value + beta_hat_mod(beta_in_test[i])=LL(i); // beta_hat_mod(i) = beta_hat(i) if i not in test; beta_HP otherwise + Partial_res_H0_CI = m_.y() - (m_.X()) * (beta_hat_mod); // (y-W*beta_hat(non in test)-W*LL[i](in test)) + local_p_values(3,i)=compute_CI_aux_beta_pvalue(Partial_res_H0_CI, TildeX_loc, Tilder_star); + + } + + // extract the CI significance (1-confidence) + double alpha = 0.05; + + if(type == one_at_the_time){ + alpha = 0.5 * alpha; + }else{ + alpha = 0.5/p * alpha; + } + + int Max_Iter=50; + int Count_Iter=0; + while((!all_betas_converged) & (Count_Iter TildeX_loc= TildeX.row(beta_in_test[i]); + beta_hat_mod = beta_hat; + + if(!converged_up[i]){ + if(local_p_values(0,i)>alpha){ // Upper-Upper bound excessively tight + + UU(i)=UU(i)+0.5*(UU(i)-UL(i)); + + // compute the partial residuals + beta_hat_mod(beta_in_test[i])=UU(i); // beta_hat_mod(i) = beta_hat(i) if i not in test; beta_HP otherwise + Partial_res_H0_CI = m_.y() - (m_.X()) * (beta_hat_mod); // (z-W*beta_hat(non in test)-W*UU[i](in test)) + local_p_values(0,i)=compute_CI_aux_beta_pvalue(Partial_res_H0_CI, TildeX_loc, Tilder_star); + + }else{ + + if(local_p_values(1,i)alpha){ // Lower-Lower bound excessively tight + LL(i)=LL(i)-0.5*(LU(i)-LL(i)); + + // compute the partial residuals + beta_hat_mod(beta_in_test[i])=LL(i); // beta_hat_mod(i) = beta_hat(i) if i not in test; beta_HP otherwise + Partial_res_H0_CI = m_.y() - (m_.X()) * (beta_hat_mod);// (z-W*beta_hat(non in test)-W*LL[i](in test)) + local_p_values(3,i)=compute_CI_aux_beta_pvalue(Partial_res_H0_CI, TildeX_loc, Tilder_star); + + }else{//both the Upper bounds are well defined + + if(LU(i)-LL(i) X = m_.X(); + DMatrix X_t = m_.X().transpose(); + // Decomposition of [W^t * Lambda^2 * W] + Eigen::PartialPivLU> XLX_dec; + XLX_dec.compute((X_t)*(Lambda_*Lambda_)*(X)); + + // get the residuals + DVector eps_hat = (m_.y() - m_.fitted()); + // build squared residuals + DVector Res2=eps_hat.array()*eps_hat.array(); + + // resize the variance-covariance matrix + int q = C_.cols(); + DMatrix V; + V.resize(q,q); + DMatrix diag = Res2.asDiagonal(); + + V = (XLX_dec).solve((X_t)*(Lambda_*Lambda_)*Res2.asDiagonal()*(Lambda_*Lambda_)*(X)*(XLX_dec).solve(DMatrix::Identity(q,q))); // V = [(W*Lambda2*W)^-1 * Res2 * (W*Lambda2*W)^-1] + + // Extract the quantile needed for the computation of upper and lower bounds + double quant = normal_standard_quantile(1 - alpha_/2); + + // extract matrix C + int p = C_.rows(); + Speckman_aux_ranges.resize(p); + + // for each row of C matrix + for(int i=0; i col = C_.row(i); + // compute the standard deviation of the linear combination and half range of the interval + double sd_comb = std::sqrt(col.adjoint()*V*col); + double half_range=sd_comb*quant; + // save the half range + Speckman_aux_ranges(i)=half_range; + } + + this->is_speckman_aux_computed = true; + + return; + } + + double compute_CI_aux_beta_pvalue(const DVector & partial_res_H0_CI, const DMatrix & TildeX, const DMatrix & Tilder_star) const { + // declare the vector that will store the p-values + double result; + + // compute the vectors needed for the statistic + DVector Tilder = Tilder_star * partial_res_H0_CI; + + // Initialize observed statistic and sign_flipped statistic + DMatrix stat_temp = TildeX*Tilder; + double stat=stat_temp(0); + double stat_flip=stat; + + // Random sign-flips + std::default_random_engine eng; + std::uniform_int_distribution distr(0, 1); + + //if we have a set seed + if(set_seed != 0) { + eng.seed(set_seed); + } else { + std::random_device rd; + eng.seed(rd()); // random seed + } + + double count_Up = 0; // Counter for the number of flipped statistics that are larger the observed statistic + double count_Down = 0; // Counter for the number of flipped statistics that are smaller the observed statistic + + DVector Tilder_perm=Tilder; + + // get the number of flips + int nflip=n_flip; + + for(int i=0;i stat_flip_temp = TildeX*Tilder_perm; + stat_flip= stat_flip_temp(0);// Flipped statistic + if(stat_flip > stat){ ++count_Up;}else{ + if(stat_flip < stat){ ++count_Down;} + } + } + + double pval_Up = count_Up/n_flip; + double pval_Down = count_Down/n_flip; + + result = std::min(pval_Up, pval_Down); // select the correct unilateral p_value + + return result; + + } + + + + void V() override{ + Lambda_ = DMatrix::Identity(m_.n_obs(), m_.n_obs()) - m_.Psi() * s_.compute(m_) * m_.PsiTD(); + } + + inline bool is_Unilaterally_Greater (DVector v, DVector u){ + int q = v.size(); + for (int i = 0; i < q; ++i){ + if(v(i) <= u(i)){ + return false; + } + } + return true; + } + + inline bool is_Unilaterally_Smaller (DVector v, DVector u){ + int q = v.size(); + for (int i = 0; i < q; ++i){ + if(v(i) >= u(i)){ + return false; + } + } + return true; + }; + + inline DVector min(const DVector & v, const DVector & u){ + DVector result; + result.resize(v.size()); + for(int i = 0; i < v.size(); ++i){ + result(i) = std::min(v(i), u(i)); + } + return result; + } + + void setNflip(int m){ + n_flip = m; + }; + void setseed(int k){ + set_seed=k; + } + void setMesh_loc(DVector m_nodes){ + mesh_nodes_ = m_nodes; + } + +}; + +} // namespace models +} // namespace fdapde + +#endif // __PESF_H__ diff --git a/fdaPDE/models/regression/regression_base.h b/fdaPDE/models/regression/regression_base.h index 2aa6ed8d..2f07b006 100644 --- a/fdaPDE/models/regression/regression_base.h +++ b/fdaPDE/models/regression/regression_base.h @@ -44,9 +44,9 @@ class RegressionBase : DMatrix T_ {}; // T = \Psi^\top*Q*\Psi + P (required by GCV) Eigen::PartialPivLU> invXtWX_ {}; // factorization of the dense q x q matrix XtWX_. // missing data and masking logic - BinaryVector nan_mask_; // indicator function over missing observations - BinaryVector y_mask_; // discards i-th observation from the fitting if y_mask_[i] == true - int n_nan_ = 0; // number of missing entries in observation vector + BinaryVector nan_mask_; // indicator function over missing observations + BinaryVector y_mask_; // discards i-th observation from the fitting if y_mask_[i] == true + int n_nan_ = 0; // number of missing entries in observation vector SpMatrix B_; // matrix \Psi corrected for NaN and masked observations // matrices required for Woodbury decomposition @@ -91,20 +91,20 @@ class RegressionBase : const DVector& f() const { return f_; }; // estimate of spatial field const DVector& g() const { return g_; }; // PDE misfit const DVector& beta() const { return beta_; }; // estimate of regression coefficients - const BinaryVector& nan_mask() const { return nan_mask_; } - BinaryVector masked_obs() const { return y_mask_ | nan_mask_; } + const BinaryVector& nan_mask() const { return nan_mask_; } + BinaryVector masked_obs() const { return y_mask_ | nan_mask_; } int n_obs() const { return y().rows() - masked_obs().count(); } // number of (active) observations // getters to Woodbury decomposition matrices const DMatrix& U() const { return U_; } const DMatrix& V() const { return V_; } // access to NaN corrected \Psi and \Psi^\top*D matrices - const SpMatrix& Psi() const { return !is_empty(B_) ? B_ : Psi(not_nan()); } - auto PsiTD() const { return !is_empty(B_) ? B_.transpose() * D() : Psi(not_nan()).transpose() * D(); } + const SpMatrix& Psi() const { return !fdapde::core::is_empty(B_) ? B_ : Psi(not_nan()); } + auto PsiTD() const { return !fdapde::core::is_empty(B_) ? B_.transpose() * D() : Psi(not_nan()).transpose() * D(); } bool has_covariates() const { return q() != 0; } // true if the model has a parametric part bool has_weights() const { return df_.has_block(WEIGHTS_BLK); } // true if heteroskedastic observation are provided bool has_nan() const { return n_nan_ != 0; } // true if there are missing data // setters - void set_mask(const BinaryVector& mask) { + void set_mask(const BinaryVector& mask) { fdapde_assert(mask.size() == Base::n_locs()); if (mask.any()) { model().runtime().set(runtime_status::require_psi_correction); @@ -119,9 +119,29 @@ class RegressionBase : // compute W*x - W*X*z = W*x - (W*X*(X^\top*W*X)^{-1}*X^\top*W)*x = W(I - H)*x = Q*x return W_ * x - W_ * X() * z; } + + //PARTE AGGIUNTA + // + // computes matrix Q = W(I - X*(X^\top*W*X)^{-1}*X^\top*W) + DMatrix Q() const { + if (!has_covariates()) return W_; + DMatrix v = X().transpose() * W_; // X^\top*W + DMatrix z = invXtWX_.solve(v); // (X^\top*W*X)^{-1}*X^\top*W dovrebbe funzionare + // perchè unica richiesta di solve per PartialPivLU è che il numero di righe di XtWX e v sia uguale + // compute W - W*X*z = W - (W*X*(X^\top*W*X)^{-1}*X^\top*W) = W(I - H) = Q + return W_ * DMatrix::Identity(X().rows(), X().rows())- W_ * X() * z; + } + + DMatrix E() const { + return (PsiTD() * Psi() + P()); + } + + // + // FINE PARTE AGGIUNTA + // computes fitted values \hat y = \Psi*f_ + X*beta_ DMatrix fitted() const { - fdapde_assert(!is_empty(f_)); + fdapde_assert(!fdapde::core::is_empty(f_)); DMatrix hat_y = Psi(not_nan()) * f_; if (has_covariates()) hat_y += X() * beta_; return hat_y; @@ -136,7 +156,7 @@ class RegressionBase : } } double ftPf(const SVector& lambda) const { - if (is_empty(g_)) return f().dot(Base::P(lambda) * f()); // fallback to explicit f^\top*P*f + if (fdapde::core::is_empty(g_)) return f().dot(Base::P(lambda) * f()); // fallback to explicit f^\top*P*f return ftPf(lambda, f(), g()); } double ftPf() const { return ftPf(Base::lambda()); } @@ -159,7 +179,7 @@ class RegressionBase : if (has_weights() && df_.is_dirty(WEIGHTS_BLK)) { W_ = df_.template get(WEIGHTS_BLK).col(0).asDiagonal(); model().runtime().set(runtime_status::require_W_update); - } else if (is_empty(W_)) { + } else if (fdapde::core::is_empty(W_)) { // default to homoskedastic observations W_ = DVector::Ones(Base::n_locs()).asDiagonal(); } @@ -184,7 +204,7 @@ class RegressionBase : } // correct \Psi setting to zero rows corresponding to masked observations void correct_psi() { - if (masked_obs().any()) B_ = (~masked_obs().blk_repeat(1, n_basis())).select(Psi(not_nan())); + if (masked_obs().any()) B_ = (~masked_obs().repeat(1, n_basis())).select(Psi(not_nan())); } }; diff --git a/fdaPDE/models/regression/regression_type_erasure.h b/fdaPDE/models/regression/regression_type_erasure.h index 923d850a..975d5f5f 100644 --- a/fdaPDE/models/regression/regression_type_erasure.h +++ b/fdaPDE/models/regression/regression_type_erasure.h @@ -26,7 +26,7 @@ using fdapde::models::is_space_only; using fdapde::models::Sampling; using fdapde::models::SpaceOnly; using fdapde::core::BinaryVector; -using fdapde::Dynamic; +using fdapde::core::Dynamic; namespace fdapde { namespace models { @@ -47,7 +47,7 @@ struct RegressionModel__ { decltype(auto) U() const { return invoke& , 6>(*this); } decltype(auto) V() const { return invoke& , 7>(*this); } decltype(auto) invXtWX() const { return invoke>&, 8>(*this); } - decltype(auto) invA() const { return invoke>& , 9>(*this); } + decltype(auto) invA() const { return invoke>& , 9>(*this); } decltype(auto) q() const { return invoke(*this); } decltype(auto) n_obs() const { return invoke(*this); } decltype(auto) norm(const DMatrix& op1, const DMatrix& op2) const { diff --git a/fdaPDE/models/regression/speckman.h b/fdaPDE/models/regression/speckman.h new file mode 100644 index 00000000..dc3b0044 --- /dev/null +++ b/fdaPDE/models/regression/speckman.h @@ -0,0 +1,110 @@ +// This file is part of fdaPDE, a C++ library for physics-informed +// spatial and functional data analysis. +// +// This program is free software: you can redistribute it and/or modify +// it under the terms of the GNU General Public License as published by +// the Free Software Foundation, either version 3 of the License, or +// (at your option) any later version. +// +// This program is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +// GNU General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with this program. If not, see . + +#ifndef __SPECKMAN_H__ +#define __SPECKMAN_H__ + +#include +#include +using fdapde::core::FSPAI; +using fdapde::core::lump; +using fdapde::core::is_empty; + +#include "../model_macros.h" +#include "../model_traits.h" +#include "../model_base.h" +#include "srpde.h" +#include "strpde.h" +#include "exact_edf.h" +#include "stochastic_edf.h" +#include "inference_base.h" +#include "inference.h" + + +namespace fdapde { +namespace models { + +template class Speckman: public InferenceBase { + + private: + struct ExactInverse{ + DMatrix compute(Model m){ + return inverse(m.E()); + } + }; + + struct NonExactInverse{ + SpMatrix compute(Model m){ + SpMatrix invE_ = Base::invE_approx(m); + return Base::invE_approx(m); + } + }; + + DMatrix Lambda_ {}; + + public: + using Base = InferenceBase; + using Base::m_; + using Base::V_; + using Base::beta_; + using Solver = typename std::conditional::value, ExactInverse, NonExactInverse>::type; + Solver s_; + + + // constructors + Speckman() = default; // deafult constructor + Speckman(const Model& m): Base(m) {}; // constructor + + // return Lambda_^2 + DMatrix Lambda() { + int n = m_.n_obs(); + DMatrix pax = s_.compute(m_); + DMatrix Lambda = DMatrix::Identity(n, n) - m_.Psi() * pax * m_.PsiTD(); + DMatrix Lambda_squared = Lambda * Lambda; + return Lambda_squared; + } + + void beta() override{ + if(is_empty(Lambda_)){ + Lambda_ = Lambda(); + } + DMatrix W = m_.X(); + DMatrix invWtW = inverse(W.transpose() * Lambda_ * (W)); + beta_ = invWtW * W.transpose() * Lambda_ * (m_.y()); + } + + void V() override{ + if(is_empty(Lambda_)){ + Lambda_ = Lambda(); + } + DMatrix W = m_.X(); + DMatrix invWtW = inverse(W.transpose() * Lambda_ * (W)); + DVector eps_ = (m_.y() - m_.fitted()); + DVector Res2 = eps_.array() * eps_.array(); + // resize the variance-covariance matrix + V_.resize(m_.q(), m_.q()); + DMatrix W_t = W.transpose(); + DMatrix diag = Res2.asDiagonal(); + V_ = invWtW * (W_t) * Lambda_ * Res2.asDiagonal() * Lambda_ * (W) * invWtW; + } + +}; + + +} // namespace models +} // namespace fdapde + +#endif // __SPECKMAN_H__ \ No newline at end of file diff --git a/fdaPDE/models/regression/srpde.h b/fdaPDE/models/regression/srpde.h index 4f05c55f..7d95cb44 100644 --- a/fdaPDE/models/regression/srpde.h +++ b/fdaPDE/models/regression/srpde.h @@ -36,10 +36,11 @@ namespace models { class SRPDE : public RegressionBase { private: - typedef RegressionBase Base; + using Base = RegressionBase; SparseBlockMatrix A_ {}; // system matrix of non-parametric problem (2N x 2N matrix) - fdapde::SparseLU> invA_ {}; // factorization of matrix A + fdapde::core::SparseLU> invA_ {}; // factorization of matrix A DVector b_ {}; // right hand side of problem's linear system (1 x 2N vector) + SpMatrix PsiESF_ {}; public: IMPORT_REGRESSION_SYMBOLS using Base::lambda_D; // smoothing parameter in space @@ -52,6 +53,62 @@ class SRPDE : public RegressionBase { SRPDE() = default; SRPDE(const Base::PDE& pde, Sampling s) : Base(pde, s) {}; + + + // template + // SRPDE(PDETtype&& pde, Sampling s) : Base(pde, s) {}; + + + template + void init_psi_esf(PDEType&& pde) { + + // perchè psi cols e non psi rows?????? + PsiESF_.resize(Psi().cols(), Psi().cols()); + + std::vector> triplet_list; + + // not sure about Psi().rows(, should be the number of nodes + // take a look at linear_network.h + + // should be Psi().rows() + for(int i = 0; i < Psi().cols(); ++i) { + auto patch = pde.domain().node_patch(i); + std::set s; + s.insert(i); + // should be that the matrix cells stores in the rows the nodes composing each cells + for (auto e : patch) { + // e.nodes() dovrebbe dare i nodi che sono presenti nella cell della patch + for(int j : pde.domain().cells().row(e)) { + s.insert(j); + } + } + for(int j : s) { + triplet_list.emplace_back(i, j, 1.0); + } + } + + /* + for (const auto& triplet : triplet_list) { + std::cout << "Row: " << triplet.row() + << ", Col: " << triplet.col() + << ", Value: " << triplet.value() + << std::endl; + } + */ + + + PsiESF_.setFromTriplets(triplet_list.begin(), triplet_list.end()); + // std::cout << PsiESF_ << std::endl; + PsiESF_.makeCompressed(); + + // save PsiESF in private member + + } + + // getter for PsiESF (needed for inference) + const SpMatrix& PsiESF() const{return PsiESF_;} + + void init_model() { if (runtime().query(runtime_status::is_lambda_changed)) { // assemble system matrix for nonparameteric part @@ -102,7 +159,7 @@ class SRPDE : public RegressionBase { double norm(const DMatrix& op1, const DMatrix& op2) const { return (op1 - op2).squaredNorm(); } // getters const SparseBlockMatrix& A() const { return A_; } - const fdapde::SparseLU>& invA() const { return invA_; } + const fdapde::core::SparseLU>& invA() const { return invA_; } virtual ~SRPDE() = default; }; diff --git a/fdaPDE/models/regression/stochastic_edf.h b/fdaPDE/models/regression/stochastic_edf.h index dc7fc168..24d5c435 100644 --- a/fdaPDE/models/regression/stochastic_edf.h +++ b/fdaPDE/models/regression/stochastic_edf.h @@ -35,12 +35,12 @@ class StochasticEDF { DMatrix Us_; // sample from Rademacher distribution DMatrix Bs_; // \Psi^T*Q*Us_ DMatrix Y_; // Us_^T*\Psi - int seed_ = fdapde::random_seed; + int seed_ = fdapde::core::random_seed; bool init_ = false; public: // constructor StochasticEDF(int r, int seed) : - r_(r), seed_((seed == fdapde::random_seed) ? std::random_device()() : seed) { } + r_(r), seed_((seed == fdapde::core::random_seed) ? std::random_device()() : seed) { } StochasticEDF(int r) : StochasticEDF(r, std::random_device()()) { } StochasticEDF() : StochasticEDF(100) { } diff --git a/fdaPDE/models/regression/strpde.h b/fdaPDE/models/regression/strpde.h index d2ea42ee..ee27fc9a 100644 --- a/fdaPDE/models/regression/strpde.h +++ b/fdaPDE/models/regression/strpde.h @@ -48,7 +48,7 @@ class STRPDE : public RegressionBase, SpaceTimeSeparable> { private: SparseBlockMatrix A_ {}; // system matrix of non-parametric problem (2N x 2N matrix) - fdapde::SparseLU> invA_; // factorization of matrix A + fdapde::core::SparseLU> invA_; // factorization of matrix A DVector b_ {}; // right hand side of problem's linear system (1 x 2N vector) DMatrix T_; // T = \Psi^T*Q*\Psi + \lambda*R SpMatrix K_; // P1 \kron R0 @@ -70,7 +70,7 @@ class STRPDE : void init_model() { if (runtime().query(runtime_status::is_lambda_changed)) { // assemble system matrix for the nonparameteric part - if (is_empty(K_)) K_ = Kronecker(P1(), pde().mass()); + if (fdapde::core::is_empty(K_)) K_ = Kronecker(P1(), pde().mass()); A_ = SparseBlockMatrix( -PsiTD() * W() * Psi() - lambda_T() * K_, lambda_D() * R1().transpose(), lambda_D() * R1(), lambda_D() * R0() ); @@ -118,7 +118,7 @@ class STRPDE : double norm(const DMatrix& op1, const DMatrix& op2) const { return (op1 - op2).squaredNorm(); } // getters const SparseBlockMatrix& A() const { return A_; } - const fdapde::SparseLU>& invA() const { return invA_; } + const fdapde::core::SparseLU>& invA() const { return invA_; } }; // implementation of STRPDE for parabolic space-time regularization, monolithic approach @@ -127,7 +127,7 @@ class STRPDE : public RegressionBase, SpaceTimeParabolic> { private: SparseBlockMatrix A_ {}; // system matrix of non-parametric problem (2N x 2N matrix) - fdapde::SparseLU> invA_; // factorization of matrix A + fdapde::core::SparseLU> invA_; // factorization of matrix A DVector b_ {}; // right hand side of problem's linear system (1 x 2N vector) SpMatrix L_; // L \kron R0 public: @@ -146,7 +146,7 @@ class STRPDE : void init_model() { // update model object in case of **structural** changes in its definition // assemble system matrix for the nonparameteric part of the model - if (is_empty(L_)) L_ = Kronecker(L(), pde().mass()); + if (fdapde::core::is_empty(L_)) L_ = Kronecker(L(), pde().mass()); A_ = SparseBlockMatrix( -PsiTD() * W() * Psi(), lambda_D() * (R1() + lambda_T() * L_).transpose(), lambda_D() * (R1() + lambda_T() * L_), lambda_D() * R0()); @@ -192,7 +192,7 @@ class STRPDE : } // getters const SparseBlockMatrix& A() const { return A_; } - const fdapde::SparseLU>& invA() const { return invA_; } + const fdapde::core::SparseLU>& invA() const { return invA_; } double norm(const DMatrix& op1, const DMatrix& op2) const { // euclidian norm of op1 - op2 return (op1 - op2).squaredNorm(); // NB: to check, defined just for compiler } @@ -204,7 +204,7 @@ class STRPDE : public RegressionBase, SpaceTimeParabolic> { private: SparseBlockMatrix A_ {}; // system matrix of non-parametric problem (2N x 2N matrix) - fdapde::SparseLU> invA_; // factorization of matrix A + fdapde::core::SparseLU> invA_; // factorization of matrix A DVector b_ {}; // right hand side of problem's linear system (1 x 2N vector) // the functional minimized by the iterative scheme diff --git a/fdaPDE/models/regression/wald.h b/fdaPDE/models/regression/wald.h new file mode 100644 index 00000000..180b7de4 --- /dev/null +++ b/fdaPDE/models/regression/wald.h @@ -0,0 +1,245 @@ +// This file is part of fdaPDE, a C++ library for physics-informed +// spatial and functional data analysis. +// +// This program is free software: you can redistribute it and/or modify +// it under the terms of the GNU General Public License as published by +// the Free Software Foundation, either version 3 of the License, or +// (at your option) any later version. +// +// This program is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +// GNU General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with this program. If not, see . + +#ifndef __WALD_H__ +#define __WALD_H__ + +#include +#include +#include +using fdapde::core::FSPAI; +using fdapde::core::lump; +using fdapde::core::is_empty; + +#include "../model_macros.h" +#include "../sampling_design.h" +#include "../model_traits.h" +#include "../model_base.h" +#include "srpde.h" +#include "strpde.h" +#include "exact_edf.h" +#include "stochastic_edf.h" +#include "inference_base.h" +#include "inference.h" + +// per salvare la matrice con savemarket +#include + +namespace fdapde { +namespace models { + +template class Wald: public InferenceBase { + + private: + struct ExactInverse { + DMatrix compute(Model m){ + return inverse(m.T()); + } + }; + struct NonExactInverse { + DMatrix compute(Model m){ + SpMatrix invE_ = Base::invE_approx(m); + int nodes = m.Psi().cols(); + DMatrix Ut_ = m.U().topRows(nodes); + DMatrix Vt_ = m.V().leftCols(nodes); + DMatrix Ct_ = - inverse(m.X().transpose() * m.X()); + DMatrix invMt_ = invE_ - invE_ * Ut_ * inverse(Ct_ + Vt_ * invE_ * Ut_) * Vt_ * invE_; + return invMt_; + } + }; + + DMatrix Vf_ {}; // variance matrix of f + SpMatrix Psi_p_ {}; // Psi reductued only in the locations needed for inference + DVector fp_ {}; // f in the locations of inference + int p_l_; + int rank; // need to save this, since it's the degrees of freedom of the chi + DMatrix new_locations {}; // vector of new locations for inference in f (only Wald) + int loc_subset = 1; // =1 if the locations needed for inference are a subset of the locations + + public: + using Base = InferenceBase; + using Base::m_; + using Base::V_; + using Base::f0_; + using Base::locations_f_; + using Base::alpha_f_; + using Base::beta_; + using Base::invE_approx; + using Solver = typename std::conditional::value, ExactInverse, NonExactInverse>::type; + Solver s_; + + // constructors + Wald() = default; // deafult constructor + Wald(const Model& m): Base(m) {}; // constructor + + void beta() override{ + beta_ = m_.beta(); + } + double sigma_sq(double trace) { + double sigma_sq_ = 0; // sigma^2 + DMatrix epsilon = m_.y() - m_.fitted(); + sigma_sq_ = (1/(m_.n_obs() - m_.q() - trace)) * epsilon.squaredNorm(); + return sigma_sq_; + } + + void V() override{ + DMatrix X = m_.X(); + DMatrix invSigma_ = inverse(X.transpose() * X); + DMatrix S = m_.Psi() * s_.compute(m_) * m_.PsiTD() * m_.Q(); + double trace = S.trace(); + DMatrix left = invSigma_ * X.transpose(); + V_ = sigma_sq(trace) * (invSigma_ + left * S * S.transpose() * left.transpose()); + } + + void Psi_p(){ + // case in which the locations are extracted from the observed ones + if(is_empty(locations_f_) && is_empty(new_locations)){ + Psi_p_ = m_.Psi(); + } + else if (loc_subset == 1){ + int m = locations_f_.size(); + SpMatrix Psi = m_.Psi().transpose(); + Psi_p_.resize(m, Psi.rows()); + for(int j = 0; j < m; ++j) { + int row = locations_f_[j]; + for(SpMatrix::InnerIterator it(Psi, row); it; ++it) { + Psi_p_.insert(j, it.row()) = it.value(); + } + } + Psi_p_.makeCompressed(); + + } + else{ + auto basis_evaluation = m_.pde().eval_basis(core::eval::pointwise, new_locations); + Psi_p_ = basis_evaluation->Psi; + } + } + + // setter needed for ESF f CI + void set_Psi_p(SpMatrix Psi){ + Psi_p_ = Psi; + } + + void fp(){ + if(is_empty(Psi_p_)) + Psi_p(); + fp_ = Psi_p_ * m_.f(); + } + + void Vf(){ + // covariance matrice of f^ + // still difference in exact and non exact when computing S + DMatrix S_psiT = s_.compute(m_) * m_.Psi().transpose(); + // needed to compute the variance of the residuals + DMatrix S = m_.Psi() * s_.compute(m_) * m_.Psi().transpose() * m_.Q(); + double trace = S.trace(); + DMatrix Vff = sigma_sq(trace) * S_psiT * m_.Q() * S_psiT.transpose(); + // need to create a new Psi: matrix of basis evaluation in the set of observed locations + // belonging to the chosen portion Omega_p + if(is_empty(Psi_p_)) + Psi_p(); + Vf_ = Psi_p_ * Vff * Psi_p_.transpose(); + } + + DMatrix invVf(){ + if(is_empty(Vf_)) + Vf(); + // to retrieve eigenvalues and eigenvectors of the Vw matrix + Eigen::SelfAdjointEigenSolver> Vw_eigen(Vf_); + DVector eigenvalues = Vw_eigen.eigenvalues(); + // now we need to discard the one really close to 0 + double thresh = 0.0001; + int flag = 0; + int it = 0; + while(flag == 0 && it < eigenvalues.size()){ + //if(eigenvalues(it, it) > thresh) + if(eigenvalues(it) > thresh) + flag = 1; + ++it; + } + + // rank + rank = eigenvalues.size() - it + 1; + // consider only the significant eigenvalues and create the diagonal matrix + DVector imp_eigval = eigenvalues.tail(rank); + // consider only the significant eigenvectors + DMatrix imp_eigvec = Vw_eigen.eigenvectors().rightCols(rank); + // now we can compute the r-rank pseudoinverse + DVector temp = imp_eigval.array().inverse(); + DiagMatrix inv_imp_eigval = temp.asDiagonal(); + DMatrix invVf = imp_eigvec * inv_imp_eigval * imp_eigvec.transpose(); + + return invVf; + } + + double f_p_value(){ + if(is_empty(fp_)) + fp(); + if(is_empty(f0_)) + Base::setf0(DVector::Zero(fp_.size())); + // compute the test statistic + double stat = (fp_ - f0_).transpose() * invVf() * (fp_ - f0_); + // chi squared function needs to be fixed + //double p = chi_squared_cdf(stat, rank); + //if(p < 0){ + // pvalue = 1; + //} + //if(p > 1){ + // pvalue = 0; + //} + //else{ + // pvalue = 1 - p; + //} + return stat; + } + + DMatrix f_CI(){ + + if(is_empty(Vf_)) + Vf(); + + if(alpha_f_ == 0.) + Base::setAlpha_f(0.05); + + if(is_empty(fp_)) + fp(); + + int p = Vf_.rows(); + DVector diagon = Vf_.diagonal(); + DVector lowerBound(p); + DVector upperBound(p); + double quantile = normal_standard_quantile(1 - alpha_f_/2); + lowerBound = fp_.array() - quantile * (diagon.array()).sqrt(); + upperBound = fp_.array() + quantile * (diagon.array()).sqrt(); + + DMatrix CIMatrix(p, 2); + CIMatrix.col(0) = lowerBound; + CIMatrix.col(1) = upperBound; + return CIMatrix; + } + + void setNewLocations_f(DMatrix loc){ + loc_subset = 0; + new_locations = loc; + } + + +}; + +} // namespace models +} // namespace fdapde + +#endif // __WALD_H__ \ No newline at end of file diff --git a/fdaPDE/models/sampling_design.h b/fdaPDE/models/sampling_design.h index ecec2d25..ae900d5e 100644 --- a/fdaPDE/models/sampling_design.h +++ b/fdaPDE/models/sampling_design.h @@ -60,7 +60,7 @@ template class SamplingBase { void init_sampling(bool forced = false) { // compute once if not forced to recompute - if (!is_empty(Psi_) && forced == false) return; + if (!fdapde::core::is_empty(Psi_) && forced == false) return; switch (sampling_) { case Sampling::mesh_nodes: { // data sampled at mesh nodes: \Psi is the identity matrix @@ -68,7 +68,7 @@ template class SamplingBase { int n = model().n_spatial_basis(); int N = model().n_spatial_basis(); Psi_.resize(n, N); - std::vector> triplet_list; + std::vector> triplet_list; triplet_list.reserve(n); // if data locations are equal to mesh nodes then \Psi is the identity matrix. // \psi_i(p_i) = 1 and \psi_i(p_j) = 0 \forall i \neq j diff --git a/fdaPDE/models/space_only_base.h b/fdaPDE/models/space_only_base.h index e2a3d6d4..ec118e54 100644 --- a/fdaPDE/models/space_only_base.h +++ b/fdaPDE/models/space_only_base.h @@ -65,14 +65,16 @@ template class SpaceOnlyBase : public ModelBase { int n_basis() const { return pde_.n_dofs(); }; // number of basis functions int n_spatial_basis() const { return n_basis(); } // number of basis functions in space const PDE& pde() const { return pde_; } // regularizing term Lf - u - const fdapde::SparseLU>& invR0() const { // LU factorization of mass matrix R0 + const fdapde::core::SparseLU>& invR0() const { // LU factorization of mass matrix R0 if (!invR0_) { invR0_.compute(R0()); } return invR0_; } const SpMatrix& PD() const { // space-penalty component (R1^T*R0^{-1}*R1) - if (is_empty(P_)) { P_ = R1().transpose() * invR0().solve(R1()); } + if (fdapde::core::is_empty(P_)) { P_ = R1().transpose() * invR0().solve(R1()); } return P_; } + // evaluation of x^\top*(R1^\top*R0^{-1}*R1)*x + double xtPDx(const DVector& x) const { return x.dot(PD() * x); } // evaluation of penalty term \lambda*(R1^\top*R0^{-1}*R1) at \lambda auto P(const SVector& lambda) const { return lambda[0] * PD(); } auto P() const { return P(lambda()); } @@ -81,7 +83,7 @@ template class SpaceOnlyBase : public ModelBase { protected: PDE pde_ {}; // differential penalty in space Lf - u mutable SpMatrix P_; // discretization of penalty term: R1^T*R0^{-1}*R1 - mutable fdapde::SparseLU> invR0_; + mutable fdapde::core::SparseLU> invR0_; SpMatrix R0_lumped_; // lumped mass matrix, if mass_lumping == true, empty otherwise SVector lambda_ = SVector::Zero(); }; diff --git a/fdaPDE/models/space_time_parabolic_base.h b/fdaPDE/models/space_time_parabolic_base.h index 38c1ec95..1e78b3f9 100644 --- a/fdaPDE/models/space_time_parabolic_base.h +++ b/fdaPDE/models/space_time_parabolic_base.h @@ -54,7 +54,7 @@ class SpaceTimeParabolicBase : public SpaceTimeBase { Im_.setIdentity(); // assemble matrix associated with derivation in time L_ // [L_]_{ii} = 1/DeltaT for i \in {1 ... m} and [L_]_{i,i-1} = -1/DeltaT for i \in {1 ... m-1} - std::vector> triplet_list; + std::vector> triplet_list; triplet_list.reserve(2 * m_); // start assembly loop double invDeltaT = 1.0 / DeltaT_; @@ -82,6 +82,10 @@ class SpaceTimeParabolicBase : public SpaceTimeBase { const PDE& pde() const { return pde_; } // regularizing term df/dt + Lf - u const SpMatrix& R0() const { return R0_; } const SpMatrix& R1() const { return R1_; } + const fdapde::core::SparseLU>& invR0() const { // LU factorization of mass matrix R0 + if (!invR0_) { invR0_.compute(R0()); } + return invR0_; + } int n_basis() const { return pde_.n_dofs(); } // number of basis functions int n_spatial_basis() const { return pde_.n_dofs(); } const SpMatrix& L() const { return L_; } @@ -91,26 +95,25 @@ class SpaceTimeParabolicBase : public SpaceTimeBase { // computes and cache matrices (Im \kron R0)^{-1} and L \kron R0, returns the discretized penalty P = // \lambda_D*((Im \kron R1 + \lambda_T*(L \kron R0))^T*(I_m \kron R0)^{-1}*(Im \kron R1 + \lambda_T*(L \kron R0))) auto P() { - if (is_empty(pen_)) { // compute once and cache result - invR0_.compute(R0()); + if (fdapde::core::is_empty(pen_)) { // compute once and cache result penT_ = Kronecker(L_, pde_.mass()); } - return lambda_D() * (R1() + lambda_T() * penT_).transpose() * invR0_.solve(R1() + lambda_T() * penT_); + return lambda_D() * (R1() + lambda_T() * penT_).transpose() * invR0().solve(R1() + lambda_T() * penT_); } // destructor virtual ~SpaceTimeParabolicBase() = default; protected: PDE pde_ {}; // parabolic differential penalty df/dt + Lf - u // let m the number of time points - DMatrix s_; // N x 1 initial condition vector - DMatrix u_; // discretized forcing [1/DeltaT * (u_1 + R_0*s) \ldots u_n] - SpMatrix Im_; // m x m sparse identity matrix (assembled once and cached for reuse) - SpMatrix L_; // m x m matrix associated with the derivation in time - double DeltaT_; // time step (assumes equidistant points in time) - SpMatrix R0_; // Im \kron R0 (R0: spatial mass matrix) - SpMatrix R1_; // Im \kron R1 (R1: spatial penalty discretization) - SpMatrix penT_; // L_ \kron pde.R0 - fdapde::SparseLU> invR0_; // factorization of Im \kron R0 + DMatrix s_; // N x 1 initial condition vector + DMatrix u_; // discretized forcing [1/DeltaT * (u_1 + R_0*s) \ldots u_n] + SpMatrix Im_; // m x m sparse identity matrix (assembled once and cached for reuse) + SpMatrix L_; // m x m matrix associated with the derivation in time + double DeltaT_; // time step (assumes equidistant points in time) + SpMatrix R0_; // Im \kron R0 (R0: spatial mass matrix) + SpMatrix R1_; // Im \kron R1 (R1: spatial penalty discretization) + SpMatrix penT_; // L_ \kron pde.R0 + mutable fdapde::core::SparseLU> invR0_; // discretized penalty: (Im \kron R1 + L \kron R0)^T*(I_m \kron R0)^{-1}*(Im \kron R1 + L \kron R0) SpMatrix pen_; }; diff --git a/fdaPDE/models/space_time_separable_base.h b/fdaPDE/models/space_time_separable_base.h index 732e4cbd..d4bd0256 100644 --- a/fdaPDE/models/space_time_separable_base.h +++ b/fdaPDE/models/space_time_separable_base.h @@ -46,7 +46,7 @@ class SpaceTimeSeparableBase : public SpaceTimeBase { space_pde_.init(); // initialize penalty in space time_pde_.init(); // initialize penalty in time // compute \Phi matrix [\Phi]_{ij} = \phi_i(t_j) - DVector time_locs = is_empty(time_locs_) ? time_ : time_locs_; + DVector time_locs = fdapde::core::is_empty(time_locs_) ? time_ : time_locs_; Phi_ = time_pde_.eval_basis(core::eval::pointwise, time_locs)->Psi; // compute tensorized matrices R0_ = Kronecker(time_pde_.mass(), space_pde_.mass()); // P0 \kron R0 @@ -65,6 +65,10 @@ class SpaceTimeSeparableBase : public SpaceTimeBase { // getters const SpMatrix& R0() const { return R0_; } const SpMatrix& R1() const { return R1_; } + const fdapde::core::SparseLU>& invR0() const { // LU factorization of mass matrix R0 + if (!invR0_) { invR0_.compute(R0()); } + return invR0_; + } int n_basis() const { return space_pde_.n_dofs() * time_pde_.n_dofs(); } int n_spatial_basis() const { return space_pde_.n_dofs(); } // number of space basis functions int n_temporal_basis() const { return time_pde_.n_dofs(); } // number of time basis functions @@ -72,13 +76,13 @@ class SpaceTimeSeparableBase : public SpaceTimeBase { const SpMatrix& P0() const { return time_pde_.mass(); } const SpMatrix& P1() const { return time_pde_.stiff(); } const SpMatrix& Phi() const { return Phi_; } - inline int n_temporal_locs() const { return is_empty(time_locs_) ? time_.rows() : time_locs_.rows(); } - const DVector& time_locs() const { return is_empty(time_locs_) ? time_ : time_locs_; } + inline int n_temporal_locs() const { return fdapde::core::is_empty(time_locs_) ? time_.rows() : time_locs_.rows(); } + const DVector& time_locs() const { return fdapde::core::is_empty(time_locs_) ? time_ : time_locs_; } const PDE& time_pde() const { return time_pde_; } const PDE& pde() const { return space_pde_; } // return stacked version of discretized forcing field const DVector& u() { - if (is_empty(u_)) { // compute once and cache + if (fdapde::core::is_empty(u_)) { // compute once and cache int N = n_spatial_basis(); u_.resize(n_basis()); // in separable regularization PDE doesn't depend on time. stack forcing term m times @@ -87,17 +91,20 @@ class SpaceTimeSeparableBase : public SpaceTimeBase { return u_; } const SpMatrix& PT() const { // time-penalty component (P1 \kron R0) - if (is_empty(PT_)) { PT_ = Kronecker(P1(), space_pde_.mass()); } + if (fdapde::core::is_empty(PT_)) { PT_ = Kronecker(P1(), space_pde_.mass()); } return PT_; } const SpMatrix& PD() const { // space-penalty component (P0 \kron (R1^T*R0^{-1}*R1)) - if (is_empty(PD_)) { - fdapde::SparseLU> invR0_; - invR0_.compute(space_pde_.mass()); - PD_ = Kronecker(P0(), space_pde_.stiff().transpose() * invR0_.solve(space_pde_.stiff())); + if (fdapde::core::is_empty(PD_)) { + fdapde::core::SparseLU> invR0; // inverse of space-only mass + invR0.compute(space_pde_.mass()); + PD_ = Kronecker(P0(), space_pde_.stiff().transpose() * invR0.solve(space_pde_.stiff())); } return PD_; } + // evaluation of x^\top*PD*x and x^\top*PT*x + double xtPDx(const DVector& x) const { return x.dot(PD() * x); } + double xtPTx(const DVector& x) const { return x.dot(PT() * x); } // discretized penalty P = \lambda_D*(P0 \kron (R1^T*R0^{-1}*R1)) + \lambda_T*(P1 \kron R0) auto P(const SVector& lambda) const { return lambda[0] * PD() + lambda[1] * PT(); } auto P() const { return P(Base::lambda()); } @@ -114,6 +121,7 @@ class SpaceTimeSeparableBase : public SpaceTimeBase { DVector time_locs_; // time instants t_1, ..., t_m mutable SpMatrix PD_; // discretization of space regularization: (R1^T*R0^{-1}*R1) \kron P0 mutable SpMatrix PT_; // discretization of time regularization: (R0 \kron P1) + mutable fdapde::core::SparseLU> invR0_; }; } // namespace models diff --git a/test/.DS_Store b/test/.DS_Store new file mode 100644 index 00000000..50396093 Binary files /dev/null and b/test/.DS_Store differ diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt index 7a8413f7..75433146 100644 --- a/test/CMakeLists.txt +++ b/test/CMakeLists.txt @@ -4,7 +4,15 @@ project(TEST) set(CMAKE_CXX_STANDARD 20) set(CMAKE_EXPORT_COMPILE_COMMANDS ON) set(CMAKE_WARNINGS_FLAGS "-Wall -Wpedantic -Wextra -Werror") -set(CMAKE_CXX_FLAGS "-g -O2 -march=x86-64 -std=c++20 ${CMAKE_WARNINGS_FLAGS} -I${CMAKE_CURRENT_SOURCE_DIR}/../ -I${CMAKE_CURRENT_SOURCE_DIR}/../fdaPDE/core/") +set(CMAKE_CXX_FLAGS "-g -O2 -march=x86-64 -std=c++20 ${CMAKE_WARNINGS_FLAGS} -I${CMAKE_CURRENT_SOURCE_DIR}/../ -I${CMAKE_CURRENT_SOURCE_DIR}/../fdaPDE/core/ -fPIE") + +find_package(OpenMP REQUIRED) + +if (OpenMP_CXX_FOUND) + # Aggiungi le flag OpenMP ai compilatori C e C++ + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}") + set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}") +endif() find_package (Eigen3 3.4 REQUIRED NO_MODULE) @@ -28,6 +36,8 @@ enable_testing() add_executable(fdapde_test main.cpp) target_link_libraries (fdapde_test Eigen3::Eigen) target_link_libraries (fdapde_test gtest_main) +target_link_libraries(fdapde_test OpenMP::OpenMP_CXX) + add_test(NAME fdapde_test COMMAND $) diff --git a/test/data/.DS_Store b/test/data/.DS_Store new file mode 100644 index 00000000..813a75ff Binary files /dev/null and b/test/data/.DS_Store differ diff --git a/test/data/mesh/.DS_Store b/test/data/mesh/.DS_Store new file mode 100644 index 00000000..22053b83 Binary files /dev/null and b/test/data/mesh/.DS_Store differ diff --git a/test/data/mesh/c_shaped1.5D/boundary.csv b/test/data/mesh/c_shaped1.5D/boundary.csv new file mode 100644 index 00000000..4c1e13e7 --- /dev/null +++ b/test/data/mesh/c_shaped1.5D/boundary.csv @@ -0,0 +1,202 @@ +"","V1" +"1",0 +"2",1 +"3",0 +"4",1 +"5",0 +"6",0 +"7",0 +"8",0 +"9",0 +"10",0 +"11",0 +"12",0 +"13",0 +"14",0 +"15",0 +"16",0 +"17",0 +"18",0 +"19",0 +"20",0 +"21",0 +"22",0 +"23",0 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#include // regression test suite +/* #include "src/srpde_test.cpp" #include "src/strpde_test.cpp" #include "src/gsrpde_test.cpp" @@ -16,6 +17,13 @@ #include "src/fpca_test.cpp" #include "src/fpls_test.cpp" #include "src/centering_test.cpp" +*/ +#include "src/inference_test.cpp" +#include "src/inferencetime_test.cpp" +#include +#include + + int main(int argc, char **argv){ // start testing diff --git a/test/run_tests.sh b/test/run_tests.sh index 6b1dea13..f8cd38ef 100755 --- a/test/run_tests.sh +++ b/test/run_tests.sh @@ -16,6 +16,7 @@ help() exit 2 } + clean_build_dir() { if [ -d "$BUILD_DIR" ]; @@ -47,7 +48,7 @@ while :; do --) shift; break - ;; + ;; *) echo "Unexpected option: $1" help @@ -57,25 +58,37 @@ done ## set CMake compiler if [ "$COMPILER" = "gcc" ]; then - export CC=/usr/bin/gcc - export CXX=/usr/bin/g++ + # find GCC in the sytem + export CC=$(which gcc) + export CXX=$(which g++) elif [ "$COMPILER" = "clang" ]; then - export CC=/usr/bin/clang - export CXX=/usr/bin/clang++ + # find Clang in the system + export CC=$(which clang) + export CXX=$(which clang++) fi -# cd into build directory -if [ -d "$BUILD_DIR" ]; -then - clean_build_dir + +if [ -d "$BUILD_DIR" ]; then + # If the build directory exists, check if cmake has already been run + if [ ! -f "$BUILD_DIR/CMakeCache.txt" ]; then + echo "CMake not executed. Running configuration..." + clean_build_dir + cmake -Wno-dev ../CMakeLists.txt + fi cd build/ else mkdir build/ cd build/ + cmake -Wno-dev ../CMakeLists.txt +fi + + +# Check if the executable already exists, if so, do not run make +if [ ! -f "./fdapde_test" ]; then + echo "Compilation needed. Running make..." + make fi -cmake -Wno-dev ../CMakeLists.txt -make if [ "$MEMCHECK" = true ]; then valgrind --leak-check=full --track-origins=yes ./fdapde_test diff --git a/test/src/centering_test.cpp b/test/src/centering_test.cpp index 70c01c29..bf76c1c8 100644 --- a/test/src/centering_test.cpp +++ b/test/src/centering_test.cpp @@ -23,6 +23,7 @@ using fdapde::core::FEM; using fdapde::core::Grid; using fdapde::core::laplacian; using fdapde::core::PDE; +using fdapde::core::Triangulation; #include "../../fdaPDE/models/regression/srpde.h" #include "../../fdaPDE/models/regression/gcv.h" @@ -54,16 +55,16 @@ using fdapde::testing::read_csv; // GCV optimization: grid stochastic TEST(centering_test, srpde_gcv_stochastic_grid) { // define domain - MeshLoader domain("unit_square"); + MeshLoader> domain("unit_square"); // import data from files DMatrix X = read_csv("../data/models/centering/2D_test1/X.csv"); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> pde(domain.mesh, L, u); // perform centering - std::vector> lambda_grid; - for (double x = -6.0; x <= 0.0; x += 0.5) lambda_grid.push_back(SVector<1>(std::pow(10, x))); + DMatrix lambda_grid(12, 1); + for (int i = 0; i < 12; ++i) lambda_grid(i, 0) = std::pow(10, -6 + 0.5 * i); auto centered_data = center( X, SRPDE {pde, Sampling::mesh_nodes}, fdapde::calibration::GCV {Grid {}, StochasticEDF(100)}(lambda_grid)); diff --git a/test/src/density_estimation_test.cpp b/test/src/density_estimation_test.cpp new file mode 100644 index 00000000..904deddd --- /dev/null +++ b/test/src/density_estimation_test.cpp @@ -0,0 +1,168 @@ +// This file is part of fdaPDE, a C++ library for physics-informed +// spatial and functional data analysis. +// +// This program is free software: you can redistribute it and/or modify +// it under the terms of the GNU General Public License as published by +// the Free Software Foundation, either version 3 of the License, or +// (at your option) any later version. +// +// This program is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +// GNU General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with this program. If not, see . + +#include +#include // testing framework + +#include +using fdapde::core::FEM; +using fdapde::core::fem_order; +using fdapde::core::laplacian; +using fdapde::core::PDE; +using fdapde::core::Triangulation; +using fdapde::core::GradientDescent; +using fdapde::core::BacktrackingLineSearch; +using fdapde::core::WolfeLineSearch; +using fdapde::core::BFGS; + +#include "../../fdaPDE/models/density_estimation/depde.h" +#include "../../fdaPDE/models/sampling_design.h" +using fdapde::models::DEPDE; +using fdapde::models::Sampling; +#include "../../fdaPDE/calibration/kfold_cv.h" +using fdapde::calibration::KCV; + +#include "utils/constants.h" +#include "utils/mesh_loader.h" +#include "utils/utils.h" +using fdapde::testing::almost_equal; +using fdapde::testing::MeshLoader; +using fdapde::testing::read_csv; + +// test 1 +// domain: unit square [1,1] x [1,1] +// optimizer: BGFS, fixed step +// lambda: fixed +// order FE: 1 +TEST(depde_test, fixed_smoothing_bfgs_fixed_step) { + // define domain + MeshLoader> domain("square_density"); + // define regularizing PDE + auto L = -laplacian(); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); + PDE, decltype(L), DMatrix, FEM, fem_order<1>> problem(domain.mesh, L, u); + // define model + double lambda = 0.1; + DEPDE model(problem); + model.set_lambda_D(lambda); + model.set_tolerance(1e-15); // to let the optimization process stop for maximum number of iterations + // set model's data + BlockFrame df; + df.insert(SPACE_LOCS, read_csv("../data/models/depde/2D_test1/data.csv")); + model.set_data(df); + model.set_optimizer(BFGS {500, 1e-5, 1e-2}); + model.set_g_init(read_csv("../data/models/depde/2D_test1/f_init.csv").array().log()); + // solve density estimation problem + model.init(); + model.solve(); // this stops at maximum number of iterations (expected) + // test correctness + EXPECT_TRUE(almost_equal(model.g(), "../data/models/depde/2D_test1/g.mtx")); +} + +// test 2 +// domain: unit square [1,1] x [1,1] +// optimizer: gradient descent, backtracking adaptive step +// lambda: fixed +// order FE: 1 +TEST(depde_test, fixed_smoothing_gradient_descent_backtracking_step) { + // define domain + MeshLoader> domain("square_density"); + // define regularizing PDE + auto L = -laplacian(); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); + PDE, decltype(L), DMatrix, FEM, fem_order<1>> problem(domain.mesh, L, u); + // define model + double lambda = 0.1; + DEPDE model(problem); + model.set_lambda_D(lambda); + // set model's data + BlockFrame df; + df.insert(SPACE_LOCS, read_csv("../data/models/depde/2D_test2/data.csv")); + model.set_data(df); + model.set_optimizer(GradientDescent {1000, 1e-5, 1e-2}); + model.set_g_init(read_csv("../data/models/depde/2D_test2/f_init.csv").array().log()); + // solve density estimation problem + model.init(); + model.solve(); + // test correctness + EXPECT_TRUE(almost_equal(model.g(), "../data/models/depde/2D_test2/g.mtx")); +} + +// test 3 +// domain: unit square [1,1] x [1,1] +// optimizer: BFGS, wolfe adaptive step +// lambda: fixed +// order FE: 1 +TEST(depde_test, fixed_smoothing_bfgs_wolfe_step) { + // define domain + MeshLoader> domain("square_density"); + // define regularizing PDE + auto L = -laplacian(); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); + PDE, decltype(L), DMatrix, FEM, fem_order<1>> problem(domain.mesh, L, u); + // define model + double lambda = 0.1; + DEPDE model(problem); + model.set_lambda_D(lambda); + // set model's data + BlockFrame df; + df.insert(SPACE_LOCS, read_csv("../data/models/depde/2D_test3/data.csv")); + model.set_data(df); + model.set_optimizer(BFGS {1000, 1e-5, 1e-2}); + model.set_g_init(read_csv("../data/models/depde/2D_test3/f_init.csv").array().log()); + // solve density estimation problem + model.init(); + model.solve(); + // test correctness + EXPECT_TRUE(almost_equal(model.g(), "../data/models/depde/2D_test3/g.mtx")); +} + +// test 4 +// domain: unit square [1,1] x [1,1] +// optimizer: BFGS, backtracking adaptive step +// lambda: K-fold cross validation, 5 folds +// order FE: 1 +TEST(depde_test, kcv_smoothing_bfgs_backtracking_step) { + // define domain + MeshLoader> domain("square_density"); + // define regularizing PDE + auto L = -laplacian(); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); + PDE, decltype(L), DMatrix, FEM, fem_order<1>> problem(domain.mesh, L, u); + // define model + DVector lambda_vect; + lambda_vect.resize(4); + lambda_vect << 0.001, 0.01, 0.1, 1; + DEPDE model(problem); + // set model's data + BlockFrame df; + df.insert(SPACE_LOCS, read_csv("../data/models/depde/2D_test4/data.csv")); + model.set_data(df); + model.set_optimizer(BFGS {1000, 1e-5, 1e-2}); + DMatrix f_init = read_csv("../data/models/depde/2D_test4/f_init.csv"); + // declare cross validation engine + KCV kcv(5, 10); + DVector cv_error; + cv_error.resize(lambda_vect.size()); + + for (int i = 0; i < lambda_vect.rows(); ++i) { + model.set_g_init(f_init.col(i).array().log()); + kcv.fit(model, SVector<1>(lambda_vect[i]), DEPDE::CVScore(model)); + cv_error[i] = kcv.avg_scores()[0]; + } + // test correctness + EXPECT_TRUE(almost_equal(cv_error, "../data/models/depde/2D_test4/cv_error.mtx")); +} diff --git a/test/src/fpca_test.cpp b/test/src/fpca_test.cpp index 965391ff..a448243a 100644 --- a/test/src/fpca_test.cpp +++ b/test/src/fpca_test.cpp @@ -24,6 +24,7 @@ using fdapde::core::FEM; using fdapde::core::fem_order; using fdapde::core::laplacian; using fdapde::core::PDE; +using fdapde::core::Triangulation; #include "../../fdaPDE/models/functional/fpca.h" #include "../../fdaPDE/models/sampling_design.h" @@ -40,7 +41,7 @@ using fdapde::testing::almost_equal; using fdapde::testing::MeshLoader; using fdapde::testing::read_csv; using fdapde::testing::read_mtx; - +/* // test 1 // domain: unit square [1,1] x [1,1] // sampling: locations = nodes @@ -51,12 +52,12 @@ using fdapde::testing::read_mtx; // solver: sequential (power iteration) TEST(fpca_test, laplacian_samplingatnodes_sequential) { // define domain - MeshLoader domain("unit_square"); + MeshLoader> domain("unit_square"); // import data from files DMatrix y = read_csv("../data/models/fpca/2D_test1/y.csv"); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> pde(domain.mesh, L, u); // define model double lambda_D = 1e-2; @@ -84,12 +85,12 @@ TEST(fpca_test, laplacian_samplingatnodes_sequential) { // solver: monolithic (rsvd) TEST(fpca_test, laplacian_samplingatnodes_monolithic) { // define domain - MeshLoader domain("unit_square"); + MeshLoader> domain("unit_square"); // import data from files DMatrix y = read_csv("../data/models/fpca/2D_test1/y.csv"); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model double lambda_D = 1e-2; @@ -106,7 +107,7 @@ TEST(fpca_test, laplacian_samplingatnodes_monolithic) { EXPECT_TRUE(almost_equal(model.Psi() * model.loadings(), "../data/models/fpca/2D_test1/loadings_mon.mtx")); EXPECT_TRUE(almost_equal(model.scores(), "../data/models/fpca/2D_test1/scores_mon.mtx" )); } - +*/ // test 3 // domain: unit square [1,1] x [1,1] // sampling: locations != nodes @@ -117,17 +118,17 @@ TEST(fpca_test, laplacian_samplingatnodes_monolithic) { // solver: sequential (power iteration) + GCV \lambda selection TEST(fpca_test, laplacian_samplingatlocations_sequential_gcv) { // define domain - MeshLoader domain("unit_square"); + MeshLoader> domain("unit_square"); // import data from files DMatrix locs = read_csv("../data/models/fpca/2D_test2/locs.csv"); DMatrix y = read_csv("../data/models/fpca/2D_test2/y.csv"); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> pde(domain.mesh, L, u); // grid of smoothing parameters - std::vector> lambda_grid; - for (double x = -4; x <= -2; x += 0.1) { lambda_grid.push_back(SVector<1>(std::pow(10, x))); } + DMatrix lambda_grid(20, 1); + for (int i = 0; i < 20; ++i) lambda_grid(i, 0) = std::pow(10, -4 + 0.1 * i); // define model RegularizedSVD rsvd(Calibration::gcv); rsvd.set_lambda(lambda_grid); @@ -156,17 +157,17 @@ TEST(fpca_test, laplacian_samplingatlocations_sequential_gcv) { // solver: sequential (power iteration) + KCV \lambda selection TEST(fpca_test, laplacian_samplingatlocations_sequential_kcv) { // define domain - MeshLoader domain("unit_square"); + MeshLoader> domain("unit_square"); // import data from files DMatrix locs = read_csv("../data/models/fpca/2D_test3/locs.csv"); DMatrix y = read_csv("../data/models/fpca/2D_test3/y.csv"); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // grid of smoothing parameters - std::vector> lambda_grid; - for (double x = -4; x <= -2; x += 0.1) lambda_grid.push_back(SVector<1>(std::pow(10, x))); + DMatrix lambda_grid(20, 1); + for (int i = 0; i < 20; ++i) lambda_grid(i, 0) = std::pow(10, -4 + 0.1 * i); // define model RegularizedSVD rsvd(Calibration::kcv); rsvd.set_lambda(lambda_grid); @@ -195,18 +196,18 @@ TEST(fpca_test, laplacian_samplingatlocations_sequential_kcv) { // solver: sequential (power iteration) // TEST(fpca_test, laplacian_samplingatnodes_separable_sequential) { // // define time domain -// Mesh<1, 1> time_mesh(0, 1, 14); +// Triangulation<1, 1> time_mesh(0, 1, 14); // // define domain and regularizing PDE -// MeshLoader domain("unit_square15"); +// MeshLoader> domain("unit_square15"); // // import data from files // DMatrix y = read_csv("../data/models/fpca/2D_test5/y.csv"); // // define regularizing PDE in space // auto Ld = -laplacian(); -// DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3 * time_mesh.n_nodes(), 1); -// PDE, decltype(Ld), DMatrix, FEM, fem_order<1>> space_penalty(domain.mesh, Ld, u); +// DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3 * time_mesh.n_nodes(), 1); +// PDE, decltype(Ld), DMatrix, FEM, fem_order<1>> space_penalty(domain.mesh, Ld, u); // // define regularizing PDE in time // auto Lt = -bilaplacian(); -// PDE, decltype(Lt), DMatrix, SPLINE, spline_order<3>> time_penalty(time_mesh, Lt); +// PDE, decltype(Lt), DMatrix, SPLINE, spline_order<3>> time_penalty(time_mesh, Lt); // // define model // double lambda_D = std::pow(10, -3.6); // 1e-3.6 // double lambda_T = std::pow(10, -2.2); // 1e-2.2 @@ -235,12 +236,12 @@ TEST(fpca_test, laplacian_samplingatlocations_sequential_kcv) { // missing data: yes TEST(fpca_test, laplacian_samplingatnodes_nocalibration_missingdata) { // define domain - MeshLoader domain("unit_square_coarse"); + MeshLoader> domain("unit_square_coarse"); // import data from files DMatrix y = read_csv("../data/models/fpca/2D_test4/y.csv"); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model double lambda_D = 1e-2; diff --git a/test/src/fpls_test.cpp b/test/src/fpls_test.cpp index 3ca29b09..7368d2ef 100644 --- a/test/src/fpls_test.cpp +++ b/test/src/fpls_test.cpp @@ -25,6 +25,7 @@ using fdapde::core::fem_order; using fdapde::core::laplacian; using fdapde::core::PDE; using fdapde::core::Grid; +using fdapde::core::Triangulation; #include "../../fdaPDE/models/regression/srpde.h" using fdapde::models::SRPDE; @@ -59,13 +60,13 @@ using fdapde::testing::read_mtx; // solver: sequential (power iteration) without calibration TEST(fpls_test, laplacian_samplingatnodes_sequential_off) { // define domain - MeshLoader domain("unit_square"); + MeshLoader> domain("unit_square"); // import data from files DMatrix X = read_csv("../data/models/fpls/2D_test1/X.csv"); DMatrix Y = read_csv("../data/models/fpls/2D_test1/Y.csv"); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> pde(domain.mesh, L, u); // define model double lambda_D = 10.0; @@ -104,19 +105,19 @@ TEST(fpls_test, laplacian_samplingatnodes_sequential_off) { // solver: sequential (power iteration) with GCV calibration TEST(fpls_test, laplacian_samplingatnodes_sequential_gcv) { // define domain - MeshLoader domain("unit_square"); + MeshLoader> domain("unit_square"); // import data from files DMatrix X = read_csv("../data/models/fpls/2D_test2/X.csv"); DMatrix Y = read_csv("../data/models/fpls/2D_test2/Y.csv"); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> pde(domain.mesh, L, u); // define model std::size_t seed = 476813; // grid for smoothing parameter selection - std::vector> lambda_grid; - for (double x = -4; x <= 0; x += 1) lambda_grid.push_back(SVector<1>(std::pow(10, x))); + DMatrix lambda_grid(5, 1); + for (int i = 0; i < 5; ++i) lambda_grid(i, 0) = std::pow(10, -4 + 1.0 * i); RegularizedSVD rsvd {Calibration::gcv}; rsvd.set_tolerance(1e-2); rsvd.set_max_iter(20); diff --git a/test/src/gcv_qsrpde_test.cpp b/test/src/gcv_qsrpde_test.cpp index 21515036..9e29c8b1 100644 --- a/test/src/gcv_qsrpde_test.cpp +++ b/test/src/gcv_qsrpde_test.cpp @@ -23,7 +23,7 @@ using fdapde::core::fem_order; using fdapde::core::laplacian; using fdapde::core::diffusion; using fdapde::core::PDE; -using fdapde::core::Mesh; +using fdapde::core::Triangulation; using fdapde::core::bilaplacian; using fdapde::core::SPLINE; using fdapde::core::spline_order; @@ -38,6 +38,7 @@ using fdapde::models::ExactEDF; using fdapde::models::StochasticEDF; using fdapde::models::Sampling; using fdapde::models::SpaceTime; +using fdapde::models::SpaceTimeSeparable; #include "../../fdaPDE/calibration/gcv.h" #include "utils/constants.h" @@ -58,12 +59,12 @@ using fdapde::testing::read_csv; // GCV optimization: grid exact TEST(gcv_qsrpde_test, laplacian_nonparametric_samplingatnodes_spaceonly_gridexact) { // define domain - MeshLoader domain("unit_square_coarse"); + MeshLoader> domain("unit_square_coarse"); // import data from files DMatrix y = read_csv("../data/gcv/qsrpde/2D_test1/y.csv"); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model double alpha = 0.1; @@ -75,8 +76,8 @@ TEST(gcv_qsrpde_test, laplacian_nonparametric_samplingatnodes_spaceonly_gridexac model.init(); // define GCV function and grid of \lambda_D values auto GCV = model.gcv(); - std::vector> lambdas; - for (double x = -8.0; x <= -5.0; x += 0.25) lambdas.push_back(SVector<1>(std::pow(10, x))); + DMatrix lambdas(13, 1); + for (int i = 0; i < 13; ++i) { lambdas(i, 0) = std::pow(10, -8.0 + 0.25 * i); } // optimize GCV Grid opt; opt.optimize(GCV, lambdas); @@ -95,12 +96,12 @@ TEST(gcv_qsrpde_test, laplacian_nonparametric_samplingatnodes_spaceonly_gridexac // GCV optimization: grid stochastic TEST(gcv_qsrpde_test, laplacian_nonparametric_samplingatnodes_spaceonly_gridstochastic) { // define domain - MeshLoader domain("unit_square_coarse"); + MeshLoader> domain("unit_square_coarse"); // import data from files DMatrix y = read_csv("../data/gcv/qsrpde/2D_test2/y.csv"); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model double alpha = 0.1; @@ -113,8 +114,8 @@ TEST(gcv_qsrpde_test, laplacian_nonparametric_samplingatnodes_spaceonly_gridstoc // define GCV function and grid of \lambda_D values std::size_t seed = 438172; auto GCV = model.gcv(1000, seed); - std::vector> lambdas; - for (double x = -8.0; x <= -5.0; x += 0.25) lambdas.push_back(SVector<1>(std::pow(10, x))); + DMatrix lambdas(13, 1); + for (int i = 0; i < 13; ++i) { lambdas(i, 0) = std::pow(10, -8.0 + 0.25 * i); } // optimize GCV Grid opt; opt.optimize(GCV, lambdas); @@ -133,14 +134,14 @@ TEST(gcv_qsrpde_test, laplacian_nonparametric_samplingatnodes_spaceonly_gridstoc // GCV optimization: grid exact TEST(gcv_qsrpde_test, laplacian_semiparametric_samplingatlocations_gridexact) { // define domain - MeshLoader domain("c_shaped"); + MeshLoader> domain("c_shaped"); // import data from files DMatrix locs = read_csv("../data/gcv/qsrpde/2D_test3/locs.csv"); DMatrix y = read_csv("../data/gcv/qsrpde/2D_test3/y.csv"); DMatrix X = read_csv("../data/gcv/qsrpde/2D_test3/X.csv"); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model double alpha = 0.9; @@ -154,8 +155,8 @@ TEST(gcv_qsrpde_test, laplacian_semiparametric_samplingatlocations_gridexact) { model.init(); // define GCV function and grid of \lambda_D values auto GCV = model.gcv(); - std::vector> lambdas; - for (double x = -5.0; x <= -3.0; x += 0.25) lambdas.push_back(SVector<1>(std::pow(10, x))); + DMatrix lambdas(9, 1); + for (int i = 0; i < 9; ++i) { lambdas(i, 0) = std::pow(10, -5.0 + 0.25 * i); } // optimize GCV Grid opt; opt.optimize(GCV, lambdas); @@ -174,14 +175,14 @@ TEST(gcv_qsrpde_test, laplacian_semiparametric_samplingatlocations_gridexact) { // GCV optimization: grid stochastic TEST(gcv_qsrpde_test, laplacian_semiparametric_samplingatlocations_gridstochastic) { // define domain - MeshLoader domain("c_shaped"); + MeshLoader> domain("c_shaped"); // import data from files DMatrix locs = read_csv("../data/gcv/qsrpde/2D_test4/locs.csv"); DMatrix y = read_csv("../data/gcv/qsrpde/2D_test4/y.csv"); DMatrix X = read_csv("../data/gcv/qsrpde/2D_test4/X.csv"); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model double alpha = 0.9; @@ -196,8 +197,8 @@ TEST(gcv_qsrpde_test, laplacian_semiparametric_samplingatlocations_gridstochasti // define GCV function and grid of \lambda_D value std::size_t seed = 66546513; auto GCV = model.gcv(1000, seed); - std::vector> lambdas; - for (double x = -5.0; x <= -3.0; x += 0.25) lambdas.push_back(SVector<1>(std::pow(10, x))); + DMatrix lambdas(9, 1); + for (int i = 0; i < 9; ++i) { lambdas(i, 0) = std::pow(10, -5.0 + 0.25 * i); } // optimize GCV Grid opt; opt.optimize(GCV, lambdas); @@ -216,14 +217,14 @@ TEST(gcv_qsrpde_test, laplacian_semiparametric_samplingatlocations_gridstochasti // GCV optimization: grid exact TEST(gcv_qsrpde_test, costantcoefficientspde_nonparametric_samplingatnodes_gridexact) { // define domain - MeshLoader domain("unit_square_coarse"); + MeshLoader> domain("unit_square_coarse"); // import data from files DMatrix y = read_csv("../data/gcv/qsrpde/2D_test5/y.csv"); // define regularizing PDE SMatrix<2> K; K << 1, 0, 0, 4; auto L = -diffusion(K); // anisotropic diffusion - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model double alpha = 0.1; @@ -235,8 +236,8 @@ TEST(gcv_qsrpde_test, costantcoefficientspde_nonparametric_samplingatnodes_gride model.init(); // define GCV function and grid of \lambda_D values auto GCV = model.gcv(); - std::vector> lambdas; - for (double x = -7.0; x <= -5.0; x += 0.25) lambdas.push_back(SVector<1>(std::pow(10, x))); + DMatrix lambdas(9, 1); + for (int i = 0; i < 9; ++i) { lambdas(i, 0) = std::pow(10, -7.0 + 0.25 * i); } // optimize GCV Grid opt; opt.optimize(GCV, lambdas); // optimize gcv field @@ -255,14 +256,14 @@ TEST(gcv_qsrpde_test, costantcoefficientspde_nonparametric_samplingatnodes_gride // GCV optimization: grid stochastic TEST(gcv_qsrpde_test, costantcoefficientspde_nonparametric_samplingatnodes_gridstochastic) { // define domain - MeshLoader domain("unit_square_coarse"); + MeshLoader> domain("unit_square_coarse"); // import data from files DMatrix y = read_csv("../data/gcv/qsrpde/2D_test6/y.csv"); // define regularizing PDE SMatrix<2> K; K << 1, 0, 0, 4; auto L = -diffusion(K); // anisotropic diffusion - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model double alpha = 0.1; @@ -275,8 +276,8 @@ TEST(gcv_qsrpde_test, costantcoefficientspde_nonparametric_samplingatnodes_grids // define GCV function and grid of \lambda_D values std::size_t seed = 438172; auto GCV = model.gcv(1000, seed); - std::vector> lambdas; - for (double x = -7.0; x <= -5.0; x += 0.25) lambdas.push_back(SVector<1>(std::pow(10, x))); + DMatrix lambdas(9, 1); + for (int i = 0; i < 9; ++i) { lambdas(i, 0) = std::pow(10, -7.0 + 0.25 * i); } // optimize GCV Grid opt; opt.optimize(GCV, lambdas); // optimize gcv field @@ -295,14 +296,14 @@ TEST(gcv_qsrpde_test, costantcoefficientspde_nonparametric_samplingatnodes_grids // GCV optimization: grid exact TEST(gcv_qsrpde_test, laplacian_semiparametric_samplingareal_gridexact) { // define domain - MeshLoader domain("c_shaped_areal"); + MeshLoader> domain("c_shaped_areal"); // import data from files DMatrix y = read_csv("../data/gcv/qsrpde/2D_test7/y.csv"); DMatrix X = read_csv("../data/gcv/qsrpde/2D_test7/X.csv"); DMatrix subdomains = read_csv("../data/gcv/qsrpde/2D_test7/incidence_matrix.csv"); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model double alpha = 0.5; @@ -316,8 +317,8 @@ TEST(gcv_qsrpde_test, laplacian_semiparametric_samplingareal_gridexact) { model.init(); // define GCV function and grid of \lambda_D values auto GCV = model.gcv(); - std::vector> lambdas; - for (double x = -4.0; x <= -1.0; x += 0.25) lambdas.push_back(SVector<1>(std::pow(10, x))); + DMatrix lambdas(13, 1); + for (int i = 0; i < 13; ++i) { lambdas(i, 0) = std::pow(10, -4.0 + 0.25 * i); } // optimize GCV Grid opt; opt.optimize(GCV, lambdas); // optimize gcv field @@ -336,14 +337,14 @@ TEST(gcv_qsrpde_test, laplacian_semiparametric_samplingareal_gridexact) { // GCV optimization: grid stochastic TEST(gcv_qsrpde_test, laplacian_semiparametric_samplingareal_gridstochastic) { // define domain - MeshLoader domain("c_shaped_areal"); + MeshLoader> domain("c_shaped_areal"); // import data from files DMatrix y = read_csv("../data/gcv/qsrpde/2D_test8/y.csv"); DMatrix X = read_csv("../data/gcv/qsrpde/2D_test8/X.csv"); DMatrix subdomains = read_csv("../data/gcv/qsrpde/2D_test8/incidence_matrix.csv"); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model double alpha = 0.5; @@ -358,8 +359,8 @@ TEST(gcv_qsrpde_test, laplacian_semiparametric_samplingareal_gridstochastic) { // define GCV function and grid of \lambda_D values std::size_t seed = 438172; auto GCV = model.gcv(100, seed); - std::vector> lambdas; - for (double x = -4.0; x <= -1.0; x += 0.25) lambdas.push_back(SVector<1>(std::pow(10, x))); + DMatrix lambdas(13, 1); + for (int i = 0; i < 13; ++i) { lambdas(i, 0) = std::pow(10, -4.0 + 0.25 * i); } // optimize GCV Grid opt; opt.optimize(GCV, lambdas); // optimize gcv field @@ -381,19 +382,19 @@ TEST(gcv_qsrpde_test, laplacian_semiparametric_samplingareal_gridstochastic) { // time penalization: separable (mass penalization) TEST(gcv_qsrpde_test, laplacian_nonparametric_samplingatlocations_timelocations_separable_gridexact) { // define temporal and spatial domain - Mesh<1, 1> time_mesh(0, fdapde::testing::pi, 2); // interval [0, \pi] with 3 knots - MeshLoader domain("c_shaped_adj"); + Triangulation<1, 1> time_mesh(0, fdapde::testing::pi, 2); // interval [0, \pi] with 3 knots + MeshLoader> domain("c_shaped_adj"); // import data from files DMatrix space_locs = read_csv("../data/gcv/qsrpde/2D_test9/locs.csv"); DMatrix time_locs = read_csv("../data/gcv/qsrpde/2D_test9/time_locations.csv"); DMatrix y = read_csv("../data/gcv/qsrpde/2D_test9/y.csv"); // define regularizing PDE in space auto Ld = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3 * time_mesh.n_nodes(), 1); - PDE, decltype(Ld), DMatrix, FEM, fem_order<1>> space_penalty(domain.mesh, Ld, u); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3 * time_mesh.n_nodes(), 1); + PDE, decltype(Ld), DMatrix, FEM, fem_order<1>> space_penalty(domain.mesh, Ld, u); // define regularizing PDE in time auto Lt = -bilaplacian(); - PDE, decltype(Lt), DMatrix, SPLINE, spline_order<3>> time_penalty(time_mesh, Lt); + PDE, decltype(Lt), DMatrix, SPLINE, spline_order<3>> time_penalty(time_mesh, Lt); // define model double alpha = 0.5; QSRPDE model(space_penalty, time_penalty, Sampling::pointwise, alpha); @@ -406,14 +407,16 @@ TEST(gcv_qsrpde_test, laplacian_nonparametric_samplingatlocations_timelocations_ model.init(); // define GCV function and grid of \lambda_D values auto GCV = model.gcv(); - std::vector> lambda_grid; - for (double lambda_s = -4.0; lambda_s <= -2.0; lambda_s += 1.0) { - for (double lambda_t = -7.0; lambda_t <= -5.0; lambda_t += 1.0) - lambda_grid.push_back(SVector<2>(std::pow(10, lambda_s), std::pow(10, lambda_t))); + DMatrix lambdas(9, 2); + for (int i = 0; i < 3; ++i) { + for (int j = 0; j < 3; ++j) { + lambdas(i * 3 + j, 0) = std::pow(10, -4.0 + 1.0 * i); + lambdas(i * 3 + j, 1) = std::pow(10, -7.0 + 1.0 * j); + } } // optimize GCV Grid opt; - opt.optimize(GCV, lambda_grid); + opt.optimize(GCV, lambdas); // test correctness EXPECT_TRUE(almost_equal(GCV.edfs(), "../data/gcv/qsrpde/2D_test9/edfs.mtx")); EXPECT_TRUE(almost_equal(GCV.gcvs(), "../data/gcv/qsrpde/2D_test9/gcvs.mtx")); @@ -432,19 +435,19 @@ TEST(gcv_qsrpde_test, laplacian_nonparametric_samplingatlocations_timelocations_ // time penalization: separable (mass penalization) TEST(gcv_qsrpde_test, laplacian_nonparametric_samplingatlocations_timelocations_separable_gridstochastic) { // define temporal and spatial domain - Mesh<1, 1> time_mesh(0, fdapde::testing::pi, 2); // interval [0, \pi] with 3 knots - MeshLoader domain("c_shaped_adj"); + Triangulation<1, 1> time_mesh(0, fdapde::testing::pi, 2); // interval [0, \pi] with 3 knots + MeshLoader> domain("c_shaped_adj"); // import data from files DMatrix space_locs = read_csv("../data/gcv/qsrpde/2D_test10/locs.csv"); DMatrix time_locs = read_csv("../data/gcv/qsrpde/2D_test10/time_locations.csv"); DMatrix y = read_csv("../data/gcv/qsrpde/2D_test10/y.csv"); // define regularizing PDE in space auto Ld = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3 * time_mesh.n_nodes(), 1); - PDE, decltype(Ld), DMatrix, FEM, fem_order<1>> space_penalty(domain.mesh, Ld, u); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3 * time_mesh.n_nodes(), 1); + PDE, decltype(Ld), DMatrix, FEM, fem_order<1>> space_penalty(domain.mesh, Ld, u); // define regularizing PDE in time auto Lt = -bilaplacian(); - PDE, decltype(Lt), DMatrix, SPLINE, spline_order<3>> time_penalty(time_mesh, Lt); + PDE, decltype(Lt), DMatrix, SPLINE, spline_order<3>> time_penalty(time_mesh, Lt); // define model double alpha = 0.5; QSRPDE model(space_penalty, time_penalty, Sampling::pointwise, alpha); @@ -458,18 +461,20 @@ TEST(gcv_qsrpde_test, laplacian_nonparametric_samplingatlocations_timelocations_ // define GCV function and grid of \lambda_D values std::size_t seed = 66546513; auto GCV = model.gcv(100, seed); - std::vector> lambda_grid; - for (double lambda_s = -4.0; lambda_s <= -2.0; lambda_s += 1.0) { - for (double lambda_t = -7.0; lambda_t <= -5.0; lambda_t += 1.0) - lambda_grid.push_back(SVector<2>(std::pow(10, lambda_s), std::pow(10, lambda_t))); + DMatrix lambdas(9, 2); + for (int i = 0; i < 3; ++i) { + for (int j = 0; j < 3; ++j) { + lambdas(i * 3 + j, 0) = std::pow(10, -4.0 + 1.0 * i); + lambdas(i * 3 + j, 1) = std::pow(10, -7.0 + 1.0 * j); + } } // optimize GCV Grid opt; - opt.optimize(GCV, lambda_grid); + opt.optimize(GCV, lambdas); // test correctness EXPECT_TRUE(almost_equal(GCV.edfs(), "../data/gcv/qsrpde/2D_test10/edfs.mtx")); EXPECT_TRUE(almost_equal(GCV.gcvs(), "../data/gcv/qsrpde/2D_test10/gcvs.mtx")); // check consistency with GCV calibrator - auto GCV_ = fdapde::calibration::GCV {Grid {}, StochasticEDF(100, seed)}(lambda_grid); + auto GCV_ = fdapde::calibration::GCV {Grid {}, StochasticEDF(100, seed)}(lambdas); EXPECT_TRUE(GCV_.fit(model) == opt.optimum()); } diff --git a/test/src/gcv_srpde_newton_test.cpp b/test/src/gcv_srpde_newton_test.cpp index 9ba23c8d..74eb65b8 100644 --- a/test/src/gcv_srpde_newton_test.cpp +++ b/test/src/gcv_srpde_newton_test.cpp @@ -23,6 +23,7 @@ using fdapde::core::FEM; using fdapde::core::Newton; using fdapde::core::laplacian; using fdapde::core::PDE; +using fdapde::core::Triangulation; #include "../../fdaPDE/models/regression/srpde.h" #include "../../fdaPDE/models/regression/gcv.h" @@ -55,7 +56,7 @@ using fdapde::testing::read_csv; */ /*TEST(GCV_SRPDE, Test1_Laplacian_NonParametric_GeostatisticalAtNodes_NewtonExact) { // define domain and regularizing PDE - MeshLoader> domain("unit_square_coarse"); + MeshLoader<>> domain("unit_square_coarse"); auto L = Laplacian(); DMatrix u = DMatrix::Zero(domain.mesh.elements()*3, 1); PDE problem(domain.mesh, L, u); // definition of regularizing PDE @@ -112,12 +113,12 @@ using fdapde::testing::read_csv; // GCV optimization: newton finite differences, exact evaluation of Tr[S] TEST(gcv_srpde_newton_test, laplacian_nonparametric_samplingatnodes_newton_fd_exact) { // define domain - MeshLoader domain("unit_square_coarse"); + MeshLoader> domain("unit_square_coarse"); // import data from files DMatrix y = read_csv("../data/models/gcv/2D_test1/y.csv"); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model SRPDE model(problem, Sampling::mesh_nodes); @@ -153,12 +154,12 @@ TEST(gcv_srpde_newton_test, laplacian_nonparametric_samplingatnodes_newton_fd_ex // GCV optimization: newton finite differences, stochastic evaluation of Tr[S] TEST(gcv_srpde_newton_test, laplacian_nonparametric_samplingatnodes_newton_fd_stochastic) { // define domain - MeshLoader domain("unit_square_coarse"); + MeshLoader> domain("unit_square_coarse"); // import data from files DMatrix y = read_csv("../data/models/gcv/2D_test1/y.csv"); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model SRPDE model(problem, Sampling::mesh_nodes); @@ -173,7 +174,7 @@ TEST(gcv_srpde_newton_test, laplacian_nonparametric_samplingatnodes_newton_fd_st GCV.set_step(4e-08); // optimize GCV Newton opt(10, 0.05, 1); - DVector pt = SVector<1>(6.25e-06); + DMatrix pt = SVector<1>(6.25e-06); opt.optimize(GCV, pt); auto best_lambda = opt.optimum(); DVector expected_lambda = SVector<1>(0.0000075627208132); diff --git a/test/src/gcv_srpde_test.cpp b/test/src/gcv_srpde_test.cpp index ed55b8e3..92ea5833 100644 --- a/test/src/gcv_srpde_test.cpp +++ b/test/src/gcv_srpde_test.cpp @@ -27,6 +27,7 @@ using fdapde::core::laplacian; using fdapde::core::DiscretizedMatrixField; using fdapde::core::PDE; using fdapde::core::DiscretizedVectorField; +using fdapde::core::Triangulation; #include "../../fdaPDE/models/regression/srpde.h" #include "../../fdaPDE/models/regression/gcv.h" @@ -57,12 +58,12 @@ using fdapde::testing::read_csv; // GCV optimization: grid exact TEST(gcv_srpde_test, laplacian_nonparametric_samplingatnodes_spaceonly_gridexact) { // define domain - MeshLoader domain("unit_square_coarse"); + MeshLoader> domain("unit_square_coarse"); // import data from files DMatrix y = read_csv("../data/models/gcv/2D_test1/y.csv"); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model SRPDE model(problem, Sampling::mesh_nodes); @@ -73,8 +74,8 @@ TEST(gcv_srpde_test, laplacian_nonparametric_samplingatnodes_spaceonly_gridexact model.init(); // define GCV function and grid of \lambda_D values auto GCV = model.gcv(); - std::vector> lambdas; - for (double x = -6.0; x <= -3.0; x += 0.25) lambdas.push_back(SVector<1>(std::pow(10, x))); + DMatrix lambdas(13, 1); + for (int i = 0; i < 13; ++i) { lambdas(i, 0) = std::pow(10, -6.0 + 0.25 * i); } // optimize GCV Grid opt; opt.optimize(GCV, lambdas); @@ -93,12 +94,12 @@ TEST(gcv_srpde_test, laplacian_nonparametric_samplingatnodes_spaceonly_gridexact // GCV optimization: grid stochastic TEST(gcv_srpde_test, laplacian_nonparametric_samplingatnodes_spaceonly_gridstochastic) { // define domain - MeshLoader domain("unit_square_coarse"); + MeshLoader> domain("unit_square_coarse"); // import data from files DMatrix y = read_csv("../data/models/gcv/2D_test2/y.csv"); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model SRPDE model(problem, Sampling::mesh_nodes); @@ -110,8 +111,8 @@ TEST(gcv_srpde_test, laplacian_nonparametric_samplingatnodes_spaceonly_gridstoch // define GCV function and grid of \lambda_D values std::size_t seed = 476813; auto GCV = model.gcv(100, seed); - std::vector> lambdas; - for (double x = -6.0; x <= -3.0; x += 0.25) lambdas.push_back(SVector<1>(std::pow(10, x))); + DMatrix lambdas(13, 1); + for (int i = 0; i < 13; ++i) { lambdas(i, 0) = std::pow(10, -6.0 + 0.25 * i); } // optimize GCV Grid opt; opt.optimize(GCV, lambdas); @@ -133,14 +134,14 @@ TEST(gcv_srpde_test, laplacian_nonparametric_samplingatnodes_spaceonly_gridstoch // GCV optimization: grid exact TEST(gcv_srpde_test, laplacian_semiparametric_samplingatlocations_gridexact) { // define domain - MeshLoader domain("c_shaped"); + MeshLoader> domain("c_shaped"); // import data from files DMatrix locs = read_csv("../data/models/gcv/2D_test3/locs.csv"); DMatrix y = read_csv("../data/models/gcv/2D_test3/y.csv" ); DMatrix X = read_csv("../data/models/gcv/2D_test3/X.csv" ); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model SRPDE model(problem, Sampling::pointwise); @@ -153,14 +154,19 @@ TEST(gcv_srpde_test, laplacian_semiparametric_samplingatlocations_gridexact) { model.init(); // define GCV function and grid of \lambda_D values auto GCV = model.gcv(); - std::vector> lambdas; - for (double x = -3.0; x <= 3.0; x += 0.25) lambdas.push_back(SVector<1>(std::pow(10, x))); + DMatrix lambdas(25, 1); + for (int i = 0; i < 25; ++i) { lambdas(i, 0) = std::pow(10, -3.0 + 0.25 * i); } // optimize GCV Grid opt; opt.optimize(GCV, lambdas); // test correctness EXPECT_TRUE(almost_equal(GCV.edfs(), "../data/models/gcv/2D_test3/edfs.mtx")); EXPECT_TRUE(almost_equal(GCV.gcvs(), "../data/models/gcv/2D_test3/gcvs.mtx")); + // check consistency with GCV calibrator + fdapde::calibration::GCV GCV_(Grid {}, ExactEDF()); + GCV_(lambdas).fit(model); // should have a side-effect on GCV calibrator + EXPECT_TRUE(GCV_.optimum() == opt.optimum()); + } // test 4 @@ -173,14 +179,14 @@ TEST(gcv_srpde_test, laplacian_semiparametric_samplingatlocations_gridexact) { // GCV optimization: grid stochastic TEST(gcv_srpde_test, laplacian_semiparametric_samplingatlocations_gridstochastic) { // define domain - MeshLoader domain("c_shaped"); + MeshLoader> domain("c_shaped"); // import data from files DMatrix locs = read_csv("../data/models/gcv/2D_test4/locs.csv"); DMatrix y = read_csv("../data/models/gcv/2D_test4/y.csv" ); DMatrix X = read_csv("../data/models/gcv/2D_test4/X.csv" ); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model SRPDE model(problem, Sampling::pointwise); @@ -194,8 +200,8 @@ TEST(gcv_srpde_test, laplacian_semiparametric_samplingatlocations_gridstochastic // define GCV function and grid of \lambda_D values std::size_t seed = 66546513; auto GCV = model.gcv(100, seed); - std::vector> lambdas; - for (double x = -3.0; x <= 3.0; x += 0.25) lambdas.push_back(SVector<1>(std::pow(10, x))); + DMatrix lambdas(25, 1); + for (int i = 0; i < 25; ++i) { lambdas(i, 0) = std::pow(10, -3.0 + 0.25 * i); } // optimize GCV Grid opt; opt.optimize(GCV, lambdas); @@ -214,14 +220,14 @@ TEST(gcv_srpde_test, laplacian_semiparametric_samplingatlocations_gridstochastic // GCV optimization: grid exact TEST(gcv_srpde_test, costantcoefficientspde_nonparametric_samplingatnodes_gridexact) { // define domain - MeshLoader domain("unit_square_coarse"); + MeshLoader> domain("unit_square_coarse"); // import data from files DMatrix y = read_csv("../data/models/gcv/2D_test5/y.csv"); // define regularizing PDE SMatrix<2> K; K << 1, 0, 0, 4; auto L = -diffusion(K); // anisotropic diffusion - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model SRPDE model(problem, Sampling::mesh_nodes); @@ -232,8 +238,8 @@ TEST(gcv_srpde_test, costantcoefficientspde_nonparametric_samplingatnodes_gridex model.init(); // define GCV function and grid of \lambda_D values auto GCV = model.gcv(); - std::vector> lambdas; - for (double x = -6.0; x <= -3.0; x += 0.25) lambdas.push_back(SVector<1>(std::pow(10, x))); + DMatrix lambdas(13, 1); + for (int i = 0; i < 13; ++i) { lambdas(i, 0) = std::pow(10, -6.0 + 0.25 * i); } // optimize GCV Grid opt; opt.optimize(GCV, lambdas); // optimize gcv field @@ -252,14 +258,14 @@ TEST(gcv_srpde_test, costantcoefficientspde_nonparametric_samplingatnodes_gridex // GCV optimization: grid stochastic TEST(gcv_srpde_test, costantcoefficientspde_nonparametric_samplingatnodes_gridstochastic) { // define domain - MeshLoader domain("unit_square_coarse"); + MeshLoader> domain("unit_square_coarse"); // import data from files DMatrix y = read_csv("../data/models/gcv/2D_test5/y.csv"); // define regularizing PDE SMatrix<2> K; K << 1, 0, 0, 4; auto L = -diffusion(K); // anisotropic diffusion - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model SRPDE model(problem, Sampling::mesh_nodes); @@ -271,8 +277,8 @@ TEST(gcv_srpde_test, costantcoefficientspde_nonparametric_samplingatnodes_gridst // define GCV function and grid of \lambda_D values std::size_t seed = 4564168; auto GCV = model.gcv(100, seed); - std::vector> lambdas; - for (double x = -6.0; x <= -3.0; x += 0.25) lambdas.push_back(SVector<1>(std::pow(10, x))); + DMatrix lambdas(13, 1); + for (int i = 0; i < 13; ++i) { lambdas(i, 0) = std::pow(10, -6.0 + 0.25 * i); } // optimize GCV Grid opt; opt.optimize(GCV, lambdas); // optimize gcv field @@ -291,7 +297,7 @@ TEST(gcv_srpde_test, costantcoefficientspde_nonparametric_samplingatnodes_gridst // GCV optimization: grid exact TEST(gcv_srpde_test, noncostantcoefficientspde_nonparametric_samplingareal_gridexact) { // define domain and regularizing PDE - MeshLoader domain("quasi_circle"); + MeshLoader> domain("quasi_circle"); // import data from files DMatrix K_data = read_csv("../data/models/gcv/2D_test7/K.csv"); DMatrix b_data = read_csv("../data/models/gcv/2D_test7/b.csv"); @@ -313,8 +319,8 @@ TEST(gcv_srpde_test, noncostantcoefficientspde_nonparametric_samplingareal_gride model.init(); // define GCV function and grid of \lambda_D values auto GCV = model.gcv(); - std::vector> lambdas; - for (double x = -6.0; x <= -3.0; x += 0.25) lambdas.push_back(SVector<1>(std::pow(10, x))); + DMatrix lambdas(13, 1); + for (int i = 0; i < 13; ++i) { lambdas(i, 0) = std::pow(10, -6.0 + 0.25 * i); } // optimize GCV Grid opt; opt.optimize(GCV, lambdas); // optimize gcv field @@ -333,7 +339,7 @@ TEST(gcv_srpde_test, noncostantcoefficientspde_nonparametric_samplingareal_gride // GCV optimization: grid stochastic TEST(gcv_srpde_test, noncostantcoefficientspde_nonparametric_samplingareal_gridstochastic) { // define domain and regularizing PDE - MeshLoader domain("quasi_circle"); + MeshLoader> domain("quasi_circle"); // import data from files DMatrix K_data = read_csv("../data/models/gcv/2D_test8/K.csv"); DMatrix b_data = read_csv("../data/models/gcv/2D_test8/b.csv"); @@ -356,8 +362,8 @@ TEST(gcv_srpde_test, noncostantcoefficientspde_nonparametric_samplingareal_grids // define GCV function and grid of \lambda_D values std::size_t seed = 438172; auto GCV = model.gcv(100, seed); - std::vector> lambdas; - for (double x = -6.0; x <= -3.0; x += 0.25) lambdas.push_back(SVector<1>(std::pow(10, x))); + DMatrix lambdas(13, 1); + for (int i = 0; i < 13; ++i) { lambdas(i, 0) = std::pow(10, -6.0 + 0.25 * i); } // optimize GCV Grid opt; opt.optimize(GCV, lambdas); diff --git a/test/src/gsrpde_test.cpp b/test/src/gsrpde_test.cpp index 2635e7b8..b304bc67 100644 --- a/test/src/gsrpde_test.cpp +++ b/test/src/gsrpde_test.cpp @@ -23,6 +23,7 @@ using fdapde::core::fem_order; using fdapde::core::laplacian; using fdapde::core::dt; using fdapde::core::PDE; +using fdapde::core::Triangulation; #include "../../fdaPDE/models/regression/distributions.h" #include "../../fdaPDE/models/regression/gsrpde.h" @@ -55,13 +56,13 @@ using fdapde::testing::read_csv; // distribution: poisson TEST(gsrpde_test, laplacian_nonparametric_samplingatnodes_poisson) { // define domain - MeshLoader domain("unit_square_medium"); + MeshLoader> domain("unit_square_medium"); // import data from files DMatrix locs = read_csv("../data/models/gsrpde/2D_test1/locs.csv"); DMatrix y = read_csv("../data/models/gsrpde/2D_test1/y.csv" ); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model double lambda_D = 1e-3; @@ -89,13 +90,13 @@ TEST(gsrpde_test, laplacian_nonparametric_samplingatnodes_poisson) { // distribution: bernulli TEST(gsrpde_test, laplacian_nonparametric_samplingatlocations_bernulli) { // define domain - MeshLoader domain("unit_square_medium"); + MeshLoader> domain("unit_square_medium"); // import data from files DMatrix locs = read_csv("../data/models/gsrpde/2D_test2/locs.csv"); DMatrix y = read_csv("../data/models/gsrpde/2D_test2/y.csv" ); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model double lambda_D = 1e-3; @@ -123,13 +124,13 @@ TEST(gsrpde_test, laplacian_nonparametric_samplingatlocations_bernulli) { // distribution: exponential TEST(gsrpde_test, laplacian_nonparametric_samplingatlocations_exponential) { // define domain - MeshLoader domain("unit_square_medium"); + MeshLoader> domain("unit_square_medium"); // import data from files DMatrix locs = read_csv("../data/models/gsrpde/2D_test3/locs.csv"); DMatrix y = read_csv("../data/models/gsrpde/2D_test3/y.csv" ); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model double lambda_D = 1e-3; @@ -157,13 +158,13 @@ TEST(gsrpde_test, laplacian_nonparametric_samplingatlocations_exponential) { // distribution: gamma TEST(gsrpde_test, laplacian_nonparametric_samplingatlocations_gamma) { // define domain - MeshLoader domain("unit_square_medium"); + MeshLoader> domain("unit_square_medium"); // import data from files DMatrix locs = read_csv("../data/models/gsrpde/2D_test4/locs.csv"); DMatrix y = read_csv("../data/models/gsrpde/2D_test4/y.csv" ); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model double lambda_D = 1e-3; @@ -192,19 +193,19 @@ TEST(gsrpde_test, laplacian_nonparametric_samplingatlocations_gamma) { // distribution: gamma TEST(gsrpde_test, laplacian_semiparametric_samplingatlocations_separable_monolithic_gamma) { // define temporal and spatial domain - Mesh<1, 1> time_mesh(0, 1, 3); - MeshLoader domain("c_shaped"); + Triangulation<1, 1> time_mesh(0, 1, 3); + MeshLoader> domain("c_shaped"); // import data from files DMatrix locs = read_csv("../data/models/gsrpde/2D_test5/locs.csv"); DMatrix y = read_csv("../data/models/gsrpde/2D_test5/y.csv" ); DMatrix X = read_csv("../data/models/gsrpde/2D_test5/X.csv" ); // define regularizing PDE in space auto Ld = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); - PDE, decltype(Ld), DMatrix, FEM, fem_order<1>> space_penalty(domain.mesh, Ld, u); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); + PDE, decltype(Ld), DMatrix, FEM, fem_order<1>> space_penalty(domain.mesh, Ld, u); // define regularizing PDE in time auto Lt = -bilaplacian(); - PDE, decltype(Lt), DMatrix, SPLINE, spline_order<3>> time_penalty(time_mesh, Lt); + PDE, decltype(Lt), DMatrix, SPLINE, spline_order<3>> time_penalty(time_mesh, Lt); // define model double lambda_D = std::pow(0.1, 2.5); double lambda_T = std::pow(0.1, 2.5); @@ -240,7 +241,7 @@ TEST(gsrpde_test, laplacian_semiparametric_samplingatlocations_parabolic_monolit time_mesh.resize(3); for (std::size_t i = 0; i < 3; ++i) time_mesh[i] = (1. / 3) * i; // define spatial domain - MeshLoader domain("c_shaped"); + MeshLoader> domain("c_shaped"); // import data from files DMatrix locs = read_csv("../data/models/gsrpde/2D_test6/locs.csv"); DMatrix y = read_mtx("../data/models/gsrpde/2D_test6/y.mtx" ); @@ -248,7 +249,7 @@ TEST(gsrpde_test, laplacian_semiparametric_samplingatlocations_parabolic_monolit DMatrix IC = read_mtx("../data/models/gsrpde/2D_test6/IC.mtx" ); // define regularizing PDE auto L = dt() - laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, time_mesh.rows()); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, time_mesh.rows()); PDE, FEM, fem_order<1>> pde(domain.mesh, time_mesh, L, u); pde.set_initial_condition(IC); // define model diff --git a/test/src/inference_test.cpp b/test/src/inference_test.cpp new file mode 100644 index 00000000..f782ea0d --- /dev/null +++ b/test/src/inference_test.cpp @@ -0,0 +1,309 @@ +// This file is part of fdaPDE, a C++ library for physics-informed +// spatial and functional data analysis. +// +// This program is free software: you can redistribute it and/or modify +// it under the terms of the GNU General Public License as published by +// the Free Software Foundation, either version 3 of the License, or +// (at your option) any later version. +// +// This program is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +// GNU General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with this program. If not, see . + + + +#include +#include // testing framework +#include +#include + +using fdapde::core::advection; +using fdapde::core::diffusion; +using fdapde::core::FEM; +using fdapde::core::fem_order; +using fdapde::core::laplacian; +using fdapde::core::DiscretizedMatrixField; +using fdapde::core::PDE; +using fdapde::core::DiscretizedVectorField; +using fdapde::core::Triangulation; + +#include "../../fdaPDE/models/regression/srpde.h" +#include "../../fdaPDE/models/sampling_design.h" +using fdapde::models::SRPDE; +using fdapde::models::Sampling; + +#include "utils/constants.h" +#include "utils/mesh_loader.h" +#include "utils/utils.h" +using fdapde::testing::almost_equal; +using fdapde::testing::MeshLoader; +using fdapde::testing::read_csv; +using fdapde::testing::read_mtx; + +#include "../../fdaPDE/models/regression/wald.h" +#include "../../fdaPDE/models/regression/speckman.h" +#include "../../fdaPDE/models/regression/esf.h" +#include "../../fdaPDE/models/regression/pesf.h" + + + +// test +// domain: c-shaped +// sampling: locations != nodes +// penalization: simple laplacian +// covariates: yes +// BC: no +// order FE: 1 + +TEST(inference_test, exact27) { + // define domain + MeshLoader> domain("c_shaped"); + // import data from files + DMatrix locs = read_csv("../data/models/srpde/2D_test2/locs.csv"); + DMatrix y = read_csv("../data/models/srpde/2D_test2/y.csv"); + DMatrix X = read_csv("../data/models/srpde/2D_test2/X.csv"); + // define regularizing PDE + auto L = -laplacian(); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); + PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); + // define statistical model + double lambda = 0.2201047; + SRPDE model(problem, Sampling::pointwise); + model.set_lambda_D(lambda); + model.set_spatial_locations(locs); + // set model's data + BlockFrame df; + df.insert(OBSERVATIONS_BLK, y); + df.insert(DESIGN_MATRIX_BLK, X); + model.set_data(df); + // solve smoothing problem + model.init(); + model.solve(); + + fdapde::models::Wald inferenceWald(model); + fdapde::models::Speckman inferenceSpeck(model); + fdapde::models::ESF inferenceESF(model); + fdapde::models::PESF inferencePESF(model); + + int cols = model.beta().size(); + DMatrix C=DMatrix::Identity(cols, cols); + + inferenceWald.setC(C); + inferenceSpeck.setC(C); + inferenceESF.setC(C); + inferencePESF.setC(C); + + DVector beta0(2); + beta0(0)=2; + beta0(1)=-1; + inferenceWald.setBeta0(beta0); + inferenceSpeck.setBeta0(beta0); + inferenceESF.setBeta0(beta0); + inferencePESF.setBeta0(beta0); + + int n = 100000; + inferenceESF.setNflip(n); + inferenceESF.setseed(46); + inferencePESF.setNflip(n); + inferencePESF.setseed(46); + + inferenceESF.setNflip(10000); + DVector loc_indexes(6); + loc_indexes << 1, 5, 7, 8, 9, 10; + inferenceWald.setLocationsF(loc_indexes); + inferenceESF.setLocationsF(loc_indexes); + + DVector pvalueswald = inferenceWald.p_value(fdapde::models::simultaneous); + DVector pvaluesspeck = inferenceSpeck.p_value(fdapde::models::one_at_the_time); + DVector pvaluesesf = inferenceESF.p_value(fdapde::models::one_at_the_time); + //double waldstatisticf = inferenceWald.f_p_value(); + double pvalueesf_f = inferenceESF.f_p_value(); + + // test correctness Wald + EXPECT_TRUE(almost_equal(pvalueswald(0), 0.411991314607044 , 1e-7)); + + // test correctness Speckman + EXPECT_TRUE(almost_equal(pvaluesspeck(0), 0.0868023617435293, 1e-7)); + EXPECT_TRUE(almost_equal(pvaluesspeck(1), 0.4810795610695496, 1e-7)); + + // test correctness ESF + EXPECT_TRUE(almost_equal(pvaluesesf(0), 0.159 , 1e-7)); + EXPECT_TRUE(almost_equal(pvaluesesf(1), 0.9022 , 1e-7)); + + EXPECT_TRUE(almost_equal(pvalueesf_f, 0.6249 , 1e-7)); + +} + + + +TEST(inference_test, nonexact27) { + // define domain + MeshLoader> domain("c_shaped"); + // import data from files + DMatrix locs = read_csv("../data/models/srpde/2D_test2/locs.csv"); + DMatrix y = read_csv("../data/models/srpde/2D_test2/y.csv"); + DMatrix X = read_csv("../data/models/srpde/2D_test2/X.csv"); + // define regularizing PDE + auto L = -laplacian(); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); + PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); + // define statistical model + double lambda = 0.2201047; + SRPDE model(problem, Sampling::pointwise); + model.set_lambda_D(lambda); + model.set_spatial_locations(locs); + // set model's data + BlockFrame df; + df.insert(OBSERVATIONS_BLK, y); + df.insert(DESIGN_MATRIX_BLK, X); + model.set_data(df); + // solve smoothing problem + model.init(); + model.solve(); + + fdapde::models::Wald inferenceWald(model); + fdapde::models::Speckman inferenceSpeck(model); + + int cols = model.beta().size(); + DMatrix C=DMatrix::Identity(cols, cols); + + inferenceWald.setC(C); + inferenceSpeck.setC(C); + + DVector beta0(2); + beta0(0)=2; + beta0(1)=-1; + inferenceWald.setBeta0(beta0); + inferenceSpeck.setBeta0(beta0); + + DVector pvalueswald = inferenceWald.p_value(fdapde::models::simultaneous); + DVector pvaluesspeck = inferenceSpeck.p_value(fdapde::models::one_at_the_time); + + // test correctness Wald + EXPECT_TRUE(almost_equal(pvalueswald(0), 0.2368866 , 1e-6)); + + // test correctness Speckman + EXPECT_TRUE(almost_equal(pvaluesspeck(0), 0.0903161, 1e-6)); + EXPECT_TRUE(almost_equal(pvaluesspeck(1), 0.6466615, 1e-7)); +} + + + + + +TEST(inference_test, inference25D){ + MeshLoader> domain("horsehoe2.5D"); + // import data from files + DMatrix y = read_csv("../data/models/srpde/25D_test1/y.csv"); + DMatrix X = read_csv("../data/models/srpde/25D_test1/X.csv"); + // define regularizing PDE + auto L = -laplacian(); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); + PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); + double lambda = 0.1; + SRPDE model(problem, Sampling::mesh_nodes); + model.set_lambda_D(lambda); + // set model's data + BlockFrame df; + df.insert(OBSERVATIONS_BLK, y); + df.insert(DESIGN_MATRIX_BLK, X); + model.set_data(df); + // solve smoothing problem + model.init(); + model.solve(); + + fdapde::models::Wald inferenceWald(model); + fdapde::models::Speckman inferenceSpeck(model); + fdapde::models::ESF inferenceESF(model); + + DVector beta0(1); + int cols = model.beta().size(); + DMatrix C = DMatrix::Identity(cols, cols); + beta0(0) = 1; + inferenceWald.setBeta0(beta0); + inferenceSpeck.setBeta0(beta0); + inferenceESF.setBeta0(beta0); + + inferenceWald.setC(C); + inferenceSpeck.setC(C); + inferenceESF.setC(C); + + inferenceESF.setNflip(10000); + + DVector Wald_beta_p = inferenceWald.p_value(fdapde::models::one_at_the_time); + DMatrix Wald_beta_CI = inferenceWald.computeCI(fdapde::models::one_at_the_time); + DVector Speck_beta_p = inferenceSpeck.p_value(fdapde::models::one_at_the_time); + DMatrix Speck_beta_CI = inferenceSpeck.computeCI(fdapde::models::one_at_the_time); + + EXPECT_TRUE(almost_equal(Wald_beta_p(0), 0.01282658 , 1e-7)); + EXPECT_TRUE(almost_equal(Speck_beta_p(0), 0.02520083 , 1e-7)); + +} + + +TEST(inference_test, inference3D){ + + MeshLoader> domain("unit_sphere3D"); + DMatrix y = read_csv("../data/models/srpde/3D_test1/y.csv"); + DMatrix X = read_csv("../data/models/srpde/3D_test1/X.csv"); + // define regularizing PDE + auto L = -laplacian(); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 4, 1); + PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); + double lambda = 0.01; + SRPDE model(problem, Sampling::mesh_nodes); + model.set_lambda_D(lambda); + //model.set_spatial_locations(locs); + // set model's data + BlockFrame df; + df.insert(OBSERVATIONS_BLK, y); + df.insert(DESIGN_MATRIX_BLK, X); + model.set_data(df); + + // solve smoothing problem + model.init(); + model.init_psi_esf(problem); + model.solve(); + + fdapde::models::Wald inferenceWald(model); + fdapde::models::Speckman inferenceSpeck(model); + fdapde::models::ESF inferenceESF(model); + + DVector beta0(2); + int cols = model.beta().size(); + DMatrix C = DMatrix::Identity(cols, cols); + beta0(0) = 2; + beta0(1) = -1; + inferenceWald.setBeta0(beta0); + inferenceSpeck.setBeta0(beta0); + inferenceESF.setBeta0(beta0); + + inferenceWald.setC(C); + inferenceSpeck.setC(C); + inferenceESF.setC(C); + + inferenceESF.setNflip(10000); + inferenceESF.setseed(46); + + DVector locs_ind(10); + locs_ind << 1, 6, 8, 11, 16, 18, 20, 21, 23, 24; + inferenceWald.setLocationsF(locs_ind); + inferenceESF.setMesh_loc(locs_ind); + + DVector Wald_beta_p = inferenceWald.p_value(fdapde::models::simultaneous); + DVector Speck_beta_p = inferenceSpeck.p_value(fdapde::models::one_at_the_time); + + double pvalueesf_f = inferenceESF.f_p_value(); + double pvalueesf_sf = inferenceESF.sign_flip_p_value(); + + EXPECT_TRUE(almost_equal(Wald_beta_p(0), 0.9684002 , 1e-7)); + EXPECT_TRUE(almost_equal(Speck_beta_p(0), 0.6479218 , 1e-7)); + EXPECT_TRUE(almost_equal(Speck_beta_p(1), 0.4182482 , 1e-7)); + EXPECT_TRUE(almost_equal(pvalueesf_f, 0.3525 , 1e-7)); + EXPECT_TRUE(almost_equal(pvalueesf_sf, 0.5488 , 1e-7)); + +} \ No newline at end of file diff --git a/test/src/inferencetime_test.cpp b/test/src/inferencetime_test.cpp new file mode 100644 index 00000000..91ef52db --- /dev/null +++ b/test/src/inferencetime_test.cpp @@ -0,0 +1,187 @@ +#include +#include // testing framework + +#include +using fdapde::core::advection; +using fdapde::core::diffusion; +using fdapde::core::dt; +using fdapde::core::FEM; +using fdapde::core::SPLINE; +using fdapde::core::bilaplacian; +using fdapde::core::laplacian; +using fdapde::core::PDE; +using fdapde::core::Triangulation; +using fdapde::core::spline_order; + +#include "../../fdaPDE/models/regression/strpde.h" +#include "../../fdaPDE/models/sampling_design.h" +using fdapde::models::STRPDE; +using fdapde::models::SpaceTimeSeparable; +using fdapde::models::SpaceTimeParabolic; +using fdapde::models::Sampling; + +#include "utils/constants.h" +#include "utils/mesh_loader.h" +#include "utils/utils.h" +using fdapde::testing::almost_equal; +using fdapde::testing::MeshLoader; +using fdapde::testing::read_mtx; +using fdapde::testing::read_csv; + +#include "../../fdaPDE/models/regression/wald.h" +#include "../../fdaPDE/models/regression/speckman.h" +#include "../../fdaPDE/models/regression/esf.h" + +using fdapde::core::fem_order; +using fdapde::core::Newton; +using fdapde::core::laplacian; +using fdapde::core::PDE; +using fdapde::core::advection; +using fdapde::core::diffusion; +using fdapde::core::dt; +using fdapde::core::SPLINE; +using fdapde::core::bilaplacian; +using fdapde::core::spline_order; + +#include "../../fdaPDE/models/regression/srpde.h" +#include "../../fdaPDE/models/regression/gcv.h" +#include "../../fdaPDE/models/sampling_design.h" +#include "../../fdaPDE/models/regression/regression_type_erasure.h" +using fdapde::models::SRPDE; +using fdapde::models::ExactEDF; +using fdapde::models::GCV; +using fdapde::models::StochasticEDF; +using fdapde::models::Sampling; +using fdapde::models::RegressionView; + +#include "../../fdaPDE/calibration/gcv.h" + + +// test 2 +// domain: c-shaped +// sampling: locations != nodes +// penalization: simple laplacian +// covariates: yes +// BC: no +// order FE: 1 +// time penalization: separable (mass penalization) + + +TEST(inferencetime_test, Exact24) { + // define temporal and spatial domain + Triangulation<1, 1> time_mesh(0, fdapde::testing::pi, 4); + MeshLoader> domain("c_shaped"); + // import data from files + DMatrix locs = read_csv("../data/models/strpde/2D_test2/locs.csv"); + DMatrix y = read_csv("../data/models/strpde/2D_test2/y.csv"); + DMatrix X = read_csv("../data/models/strpde/2D_test2/X.csv"); + // define regularizing PDE in space + auto Ld = -laplacian(); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); + PDE, decltype(Ld), DMatrix, FEM, fem_order<1>> space_penalty(domain.mesh, Ld, u); + // define regularizing PDE in time + auto Lt = -bilaplacian(); + PDE, decltype(Lt), DMatrix, SPLINE, spline_order<3>> time_penalty(time_mesh, Lt); + // define model + double lambda_D = 0.01; + double lambda_T = 0.01; + STRPDE model(space_penalty, time_penalty, Sampling::pointwise); + model.set_lambda_D(lambda_D); + model.set_lambda_T(lambda_T); + model.set_spatial_locations(locs); + // set model's data + BlockFrame df; + df.stack(OBSERVATIONS_BLK, y); + df.stack(DESIGN_MATRIX_BLK, X); + model.set_data(df); + // solve smoothing problem + model.init(); + model.solve(); + + // test correctness WALD + fdapde::models::Wald, fdapde::models::exact> inferenceW(model); + fdapde::models::Speckman, fdapde::models::exact> inferenceS(model); + fdapde::models::ESF, fdapde::models::exact> inferenceESF(model); + int cols = model.beta().size(); + DMatrix C=DMatrix::Identity(cols, cols); + inferenceW.setC(C); + inferenceS.setC(C); + inferenceESF.setC(C); + DVector beta0(1); + beta0(0)=2; + inferenceW.setBeta0(beta0); + inferenceS.setBeta0(beta0); + inferenceESF.setBeta0(beta0); + inferenceESF.setNflip(10000); + + inferenceESF.setseed(46); + + EXPECT_TRUE(almost_equal(inferenceW.p_value(fdapde::models::one_at_the_time)(0), 0.7660934 , 1e-7)); + EXPECT_TRUE(almost_equal(inferenceS.p_value(fdapde::models::one_at_the_time)(0), 0.715712 , 1e-7)); + EXPECT_TRUE(almost_equal(inferenceESF.p_value(fdapde::models::one_at_the_time)(0), 0.8168 , 1e-7)); + +} + + + +TEST(inferencetime_test, spacetime25D) { + // define temporal and spatial domain + Triangulation<1, 1> time_mesh(0, 4, 4); + MeshLoader> domain("hub2.5D"); + // import data from files + DMatrix y = read_csv("../data/models/strpde/25D_test1/y.csv"); + DMatrix X = read_csv("../data/models/strpde/25D_test1/X.csv"); + // define regularizing PDE in space + auto Ld = -laplacian(); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); + PDE, FEM, fem_order<1>> space_penalty(domain.mesh, Ld, u); + // define regularizing PDE in time + auto Lt = -bilaplacian(); + PDE, SPLINE, spline_order<3>> time_penalty(time_mesh, Lt); + // define model + double lambda_D = 0.00001; + double lambda_T = 0.00001; + STRPDE model(space_penalty, time_penalty, Sampling::mesh_nodes); + model.set_lambda_D(lambda_D); + model.set_lambda_T(lambda_T); + // set model's data + BlockFrame df; + df.stack(OBSERVATIONS_BLK, y); + df.stack(DESIGN_MATRIX_BLK, X); + model.set_data(df); + // solve smoothing problem + model.init(); + model.solve(); + + // test correctness + fdapde::models::Wald, fdapde::models::exact> inferenceW(model); + fdapde::models::Speckman, fdapde::models::exact> inferenceS(model); + fdapde::models::ESF, fdapde::models::exact> inferenceESF(model); + + // set H0 + DVector beta0(1); + beta0 << 0.45; + inferenceW.setBeta0(beta0); + inferenceS.setBeta0(beta0); + inferenceESF.setBeta0(beta0); + + // set C + DMatrix C = DMatrix::Identity(1, 1); + inferenceW.setC(C); + inferenceS.setC(C); + inferenceESF.setC(C); + + // set N flips + inferenceESF.setNflip(10000); + inferenceESF.setseed(46); + + DVector waldpval = inferenceW.p_value(fdapde::models::one_at_the_time); + DVector speckpval = inferenceS.p_value(fdapde::models::one_at_the_time); + DVector esfpval = inferenceESF.p_value(fdapde::models::one_at_the_time); + + EXPECT_TRUE(almost_equal(waldpval(0), 9.184956e-08 , 1e-7)); + EXPECT_TRUE(almost_equal(speckpval(0), 0.9566908 , 1e-7)); + +} + + diff --git a/test/src/kcv_srpde_test.cpp b/test/src/kcv_srpde_test.cpp index def7241f..e67b4f33 100644 --- a/test/src/kcv_srpde_test.cpp +++ b/test/src/kcv_srpde_test.cpp @@ -22,6 +22,7 @@ using fdapde::core::fem_order; using fdapde::core::FEM; using fdapde::core::laplacian; using fdapde::core::PDE; +using fdapde::core::Triangulation; #include "../../fdaPDE/models/regression/srpde.h" #include "../../fdaPDE/models/regression/qsrpde.h" @@ -53,12 +54,12 @@ using fdapde::testing::read_csv; // GCV optimization: grid exact TEST(kcv_srpde_test, laplacian_nonparametric_samplingatnodes_spaceonly_rmse) { // define domain - MeshLoader domain("unit_square_coarse"); + MeshLoader> domain("unit_square_coarse"); // import data from files DMatrix y = read_csv("../data/models/gcv/2D_test1/y.csv"); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model SRPDE model(problem, Sampling::mesh_nodes); @@ -84,12 +85,12 @@ TEST(kcv_srpde_test, laplacian_nonparametric_samplingatnodes_spaceonly_rmse) { TEST(kcv_srpde_test, qsrpde_laplacian_nonparametric_samplingatnodes_spaceonly_rmse) { // define domain - MeshLoader domain("unit_square_coarse"); + MeshLoader> domain("unit_square_coarse"); // import data from files DMatrix y = read_csv("../data/models/qsrpde/2D_test1/y.csv"); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model double lambda = 1.778279 * std::pow(0.1, 4); diff --git a/test/src/qsrpde_test.cpp b/test/src/qsrpde_test.cpp index 1b096ff5..e4c209c8 100644 --- a/test/src/qsrpde_test.cpp +++ b/test/src/qsrpde_test.cpp @@ -23,7 +23,7 @@ using fdapde::core::fem_order; using fdapde::core::laplacian; using fdapde::core::diffusion; using fdapde::core::PDE; -using fdapde::core::Mesh; +using fdapde::core::Triangulation; using fdapde::core::bilaplacian; using fdapde::core::SPLINE; using fdapde::core::spline_order; @@ -52,12 +52,12 @@ using fdapde::testing::read_csv; // order FE: 1 TEST(qsrpde_test, laplacian_nonparametric_samplingatnodes) { // define domain - MeshLoader domain("unit_square_coarse"); + MeshLoader> domain("unit_square_coarse"); // import data from files DMatrix y = read_csv("../data/models/qsrpde/2D_test1/y.csv"); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model double lambda = 1.778279 * std::pow(0.1, 4); @@ -84,14 +84,14 @@ TEST(qsrpde_test, laplacian_nonparametric_samplingatnodes) { // order FE: 1 TEST(qsrpde_test, laplacian_semiparametric_samplingatlocations) { // define domain and regularizing PDE - MeshLoader domain("c_shaped"); + MeshLoader> domain("c_shaped"); // import data from files DMatrix locs = read_csv("../data/models/qsrpde/2D_test2/locs.csv"); DMatrix y = read_csv("../data/models/qsrpde/2D_test2/y.csv"); DMatrix X = read_csv("../data/models/qsrpde/2D_test2/X.csv"); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define statistical model double alpha = 0.9; @@ -121,14 +121,14 @@ TEST(qsrpde_test, laplacian_semiparametric_samplingatlocations) { // order FE: 1 TEST(qsrpde_test, costantcoefficientspde_nonparametric_samplingatnodes) { // define domain and regularizing PDE - MeshLoader domain("unit_square_coarse"); + MeshLoader> domain("unit_square_coarse"); // import data from files DMatrix y = read_csv("../data/models/qsrpde/2D_test3/y.csv"); // define regularizing PDE SMatrix<2> K; K << 1, 0, 0, 4; auto L = -diffusion(K); // anisotropic diffusion - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define statistical model double alpha = 0.1; @@ -155,14 +155,14 @@ TEST(qsrpde_test, costantcoefficientspde_nonparametric_samplingatnodes) { // order FE: 1 TEST(qsrpde_test, laplacian_semiparametric_samplingareal) { // define domain and regularizing PDE - MeshLoader domain("c_shaped_areal"); + MeshLoader> domain("c_shaped_areal"); // import data from files DMatrix y = read_csv("../data/models/qsrpde/2D_test4/y.csv"); DMatrix X = read_csv("../data/models/qsrpde/2D_test4/X.csv"); DMatrix subdomains = read_csv("../data/models/qsrpde/2D_test4/incidence_matrix.csv"); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define statistical model double alpha = 0.5; @@ -195,19 +195,19 @@ TEST(qsrpde_test, laplacian_semiparametric_samplingareal) { // time penalization: separable (mass penalization) TEST(qsrpde_test, laplacian_nonparametric_samplingatlocations_separable_monolithic) { // define temporal and spatial domain - Mesh<1, 1> time_mesh(0, fdapde::testing::pi, 6); // interval [0, \pi] with 7 knots - MeshLoader domain("c_shaped_adj"); + Triangulation<1, 1> time_mesh(0, fdapde::testing::pi, 6); // interval [0, \pi] with 7 knots + MeshLoader> domain("c_shaped_adj"); // import data from files DMatrix space_locs = read_csv("../data/models/qsrpde/2D_test5/locs.csv"); DMatrix time_locs = read_csv("../data/models/qsrpde/2D_test5/time_locations.csv"); DMatrix y = read_csv("../data/models/qsrpde/2D_test5/y.csv"); // define regularizing PDE in space auto Ld = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3 * time_mesh.n_nodes(), 1); - PDE, decltype(Ld), DMatrix, FEM, fem_order<1>> space_penalty(domain.mesh, Ld, u); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3 * time_mesh.n_nodes(), 1); + PDE, decltype(Ld), DMatrix, FEM, fem_order<1>> space_penalty(domain.mesh, Ld, u); // define regularizing PDE in time auto Lt = -bilaplacian(); - PDE, decltype(Lt), DMatrix, SPLINE, spline_order<3>> time_penalty(time_mesh, Lt); + PDE, decltype(Lt), DMatrix, SPLINE, spline_order<3>> time_penalty(time_mesh, Lt); // define model double alpha = 0.5; double lambda_D = 1e-3; diff --git a/test/src/srpde_test.cpp b/test/src/srpde_test.cpp index d43fc4a7..2f9a6002 100644 --- a/test/src/srpde_test.cpp +++ b/test/src/srpde_test.cpp @@ -26,6 +26,7 @@ using fdapde::core::laplacian; using fdapde::core::DiscretizedMatrixField; using fdapde::core::PDE; using fdapde::core::DiscretizedVectorField; +using fdapde::core::Triangulation; #include "../../fdaPDE/models/regression/srpde.h" #include "../../fdaPDE/models/sampling_design.h" @@ -48,12 +49,12 @@ using fdapde::testing::read_csv; // order FE: 1 TEST(srpde_test, laplacian_nonparametric_samplingatnodes) { // define domain - MeshLoader domain("unit_square"); + MeshLoader> domain("unit_square"); // import data from files DMatrix y = read_csv("../data/models/srpde/2D_test1/y.csv"); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model double lambda = 5.623413 * std::pow(0.1, 5); @@ -79,14 +80,14 @@ TEST(srpde_test, laplacian_nonparametric_samplingatnodes) { // order FE: 1 TEST(srpde_test, laplacian_semiparametric_samplingatlocations) { // define domain - MeshLoader domain("c_shaped"); + MeshLoader> domain("c_shaped"); // import data from files DMatrix locs = read_csv("../data/models/srpde/2D_test2/locs.csv"); DMatrix y = read_csv("../data/models/srpde/2D_test2/y.csv"); DMatrix X = read_csv("../data/models/srpde/2D_test2/X.csv"); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define statistical model double lambda = 0.2201047; @@ -115,14 +116,14 @@ TEST(srpde_test, laplacian_semiparametric_samplingatlocations) { // order FE: 1 TEST(srpde_test, costantcoefficientspde_nonparametric_samplingatnodes) { // define domain - MeshLoader domain("unit_square"); + MeshLoader> domain("unit_square"); // import data from files DMatrix y = read_csv("../data/models/srpde/2D_test3/y.csv"); // define regularizing PDE SMatrix<2> K; K << 1, 0, 0, 4; auto L = -diffusion(K); // anisotropic diffusion - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model double lambda = 10; @@ -148,7 +149,7 @@ TEST(srpde_test, costantcoefficientspde_nonparametric_samplingatnodes) { // order FE: 1 TEST(srpde_test, noncostantcoefficientspde_nonparametric_samplingareal) { // define domain - MeshLoader domain("quasi_circle"); + MeshLoader> domain("quasi_circle"); // import data from files DMatrix K_data = read_csv("../data/models/srpde/2D_test4/K.csv"); DMatrix b_data = read_csv("../data/models/srpde/2D_test4/b.csv"); @@ -185,12 +186,12 @@ TEST(srpde_test, noncostantcoefficientspde_nonparametric_samplingareal) { // order FE: 1 TEST(srpde_test, laplacian_nonparametric_samplingatnodes_surface) { // define domain - MeshLoader domain("c_shaped_surface"); + MeshLoader> domain("c_shaped_surface"); // import data from files DMatrix y = read_csv("../data/models/srpde/2D_test5/y.csv"); // define regularizing PDE auto L = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); PDE, FEM, fem_order<1>> problem(domain.mesh, L, u); // define model double lambda = 1e-2; diff --git a/test/src/strpde_test.cpp b/test/src/strpde_test.cpp index 16d333d1..9e82da55 100644 --- a/test/src/strpde_test.cpp +++ b/test/src/strpde_test.cpp @@ -26,7 +26,7 @@ using fdapde::core::SPLINE; using fdapde::core::bilaplacian; using fdapde::core::laplacian; using fdapde::core::PDE; -using fdapde::core::Mesh; +using fdapde::core::Triangulation; using fdapde::core::spline_order; #include "../../fdaPDE/models/regression/strpde.h" @@ -52,19 +52,19 @@ using fdapde::testing::read_csv; // BC: no // order FE: 1 // time penalization: separable (mass penalization) -TEST(strpde_test, laplacian_nonparametric_samplingatnodes_separable_monolithic) { +/*TEST(strpde_test, laplacian_nonparametric_samplingatnodes_separable_monolithic) { // define temporal and spatial domain - Mesh<1, 1> time_mesh(0, 2, 10); - MeshLoader domain("unit_square_coarse"); + Triangulation<1, 1> time_mesh(0, 2, 10); + MeshLoader> domain("unit_square_coarse"); // import data from files DMatrix y = read_csv("../data/models/strpde/2D_test1/y.csv"); // define regularizing PDE in space auto Ld = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3 * time_mesh.n_nodes(), 1); - PDE, decltype(Ld), DMatrix, FEM, fem_order<1>> space_penalty(domain.mesh, Ld, u); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3 * time_mesh.n_nodes(), 1); + PDE, decltype(Ld), DMatrix, FEM, fem_order<1>> space_penalty(domain.mesh, Ld, u); // define regularizing PDE in time auto Lt = -bilaplacian(); - PDE, decltype(Lt), DMatrix, SPLINE, spline_order<3>> time_penalty(time_mesh, Lt); + PDE, decltype(Lt), DMatrix, SPLINE, spline_order<3>> time_penalty(time_mesh, Lt); // define model double lambda_D = 0.01, lambda_T = 0.01; STRPDE model(space_penalty, time_penalty, Sampling::mesh_nodes); @@ -80,7 +80,7 @@ TEST(strpde_test, laplacian_nonparametric_samplingatnodes_separable_monolithic) // test correctness EXPECT_TRUE(almost_equal(model.f() , "../data/models/strpde/2D_test1/sol.mtx")); } - +*/ // test 2 // domain: c-shaped // sampling: locations != nodes @@ -91,19 +91,19 @@ TEST(strpde_test, laplacian_nonparametric_samplingatnodes_separable_monolithic) // time penalization: separable (mass penalization) TEST(strpde_test, laplacian_semiparametric_samplingatlocations_separable_monolithic) { // define temporal and spatial domain - Mesh<1, 1> time_mesh(0, fdapde::testing::pi, 4); - MeshLoader domain("c_shaped"); + Triangulation<1, 1> time_mesh(0, fdapde::testing::pi, 4); + MeshLoader> domain("c_shaped"); // import data from files DMatrix locs = read_csv("../data/models/strpde/2D_test2/locs.csv"); DMatrix y = read_csv("../data/models/strpde/2D_test2/y.csv"); DMatrix X = read_csv("../data/models/strpde/2D_test2/X.csv"); // define regularizing PDE in space auto Ld = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, 1); - PDE, decltype(Ld), DMatrix, FEM, fem_order<1>> space_penalty(domain.mesh, Ld, u); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, 1); + PDE, decltype(Ld), DMatrix, FEM, fem_order<1>> space_penalty(domain.mesh, Ld, u); // define regularizing PDE in time auto Lt = -bilaplacian(); - PDE, decltype(Lt), DMatrix, SPLINE, spline_order<3>> time_penalty(time_mesh, Lt); + PDE, decltype(Lt), DMatrix, SPLINE, spline_order<3>> time_penalty(time_mesh, Lt); // define model double lambda_D = 0.01; double lambda_T = 0.01; @@ -123,7 +123,7 @@ TEST(strpde_test, laplacian_semiparametric_samplingatlocations_separable_monolit EXPECT_TRUE(almost_equal(model.f() , "../data/models/strpde/2D_test2/sol.mtx" )); EXPECT_TRUE(almost_equal(model.beta(), "../data/models/strpde/2D_test2/beta.mtx")); } - +/* // test 3 // domain: quasicircular domain // sampling: areal @@ -138,7 +138,7 @@ TEST(strpde_test, noncostantcoefficientspde_nonparametric_samplingareal_paraboli time_mesh.resize(10); for (int i = 0; i < time_mesh.size(); ++i) time_mesh[i] = 0.4 * i; // define spatial domain - MeshLoader domain("quasi_circle"); + MeshLoader> domain("quasi_circle"); // import data from files DMatrix K_data = read_csv("../data/models/strpde/2D_test3/K.csv"); DMatrix b_data = read_csv("../data/models/strpde/2D_test3/b.csv"); @@ -149,7 +149,7 @@ TEST(strpde_test, noncostantcoefficientspde_nonparametric_samplingareal_paraboli DiscretizedMatrixField<2, 2, 2> K(K_data); DiscretizedVectorField<2, 2> b(b_data); auto L = dt() - diffusion(K) + advection(b); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, time_mesh.rows()); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, time_mesh.rows()); PDE, FEM, fem_order<1>> pde(domain.mesh, time_mesh, L, u); pde.set_initial_condition(IC); // define model @@ -185,13 +185,13 @@ TEST(strpde_test, laplacian_nonparametric_samplingatnodes_parabolic_iterative) { double x = 0; for (int i = 0; i < time_mesh.size(); x += 0.2, ++i) time_mesh[i] = x; // define spatial domain - MeshLoader domain("unit_square_coarse"); + MeshLoader> domain("unit_square_coarse"); // import data from files DMatrix y = read_mtx("../data/models/strpde/2D_test4/y.mtx" ); DMatrix IC = read_mtx("../data/models/strpde/2D_test4/IC.mtx"); // define regularizing PDE auto L = dt() - laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3, time_mesh.rows()); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3, time_mesh.rows()); PDE, FEM, fem_order<1>> pde(domain.mesh, time_mesh, L, u); pde.set_initial_condition(IC); // define model @@ -224,18 +224,18 @@ TEST(strpde_test, laplacian_nonparametric_samplingatnodes_parabolic_iterative) { // time penalization: separable (mass penalization) TEST(strpde_test, laplacian_nonparametric_samplingatnodes_timelocations_separable_monolithic) { // define temporal and spatial domain - Mesh<1, 1> time_mesh(0, 2, 10); - MeshLoader domain("unit_square_coarse"); + Triangulation<1, 1> time_mesh(0, 2, 10); + MeshLoader> domain("unit_square_coarse"); // import data from files DMatrix time_locs = read_csv("../data/models/strpde/2D_test5/time_locations.csv"); DMatrix y = read_csv("../data/models/strpde/2D_test5/y.csv"); // define regularizing PDE in space auto Ld = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3 * time_mesh.n_nodes(), 1); - PDE, decltype(Ld), DMatrix, FEM, fem_order<1>> space_penalty(domain.mesh, Ld, u); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3 * time_mesh.n_nodes(), 1); + PDE, decltype(Ld), DMatrix, FEM, fem_order<1>> space_penalty(domain.mesh, Ld, u); // define regularizing PDE in time auto Lt = -bilaplacian(); - PDE, decltype(Lt), DMatrix, SPLINE, spline_order<3>> time_penalty(time_mesh, Lt); + PDE, decltype(Lt), DMatrix, SPLINE, spline_order<3>> time_penalty(time_mesh, Lt); // define model double lambda_D = 0.01; @@ -267,19 +267,19 @@ TEST(strpde_test, laplacian_nonparametric_samplingatnodes_timelocations_separabl // time penalization: separable (mass penalization) TEST(strpde_test, laplacian_nonparametric_samplingatlocations_timelocations_separable_monolithic_missingdata) { // define temporal and spatial domain - Mesh<1, 1> time_mesh(0, 1, 20); - MeshLoader domain("c_shaped"); + Triangulation<1, 1> time_mesh(0, 1, 20); + MeshLoader> domain("c_shaped"); // import data from files DMatrix time_locs = read_csv("../data/models/strpde/2D_test6/time_locations.csv"); DMatrix space_locs = read_csv("../data/models/strpde/2D_test6/locs.csv"); DMatrix y = read_csv("../data/models/strpde/2D_test6/y.csv" ); // define regularizing PDE in space auto Ld = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3 * time_mesh.n_nodes(), 1); - PDE, decltype(Ld), DMatrix, FEM, fem_order<1>> space_penalty(domain.mesh, Ld, u); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3 * time_mesh.n_nodes(), 1); + PDE, decltype(Ld), DMatrix, FEM, fem_order<1>> space_penalty(domain.mesh, Ld, u); // define regularizing PDE in time auto Lt = -bilaplacian(); - PDE, decltype(Lt), DMatrix, SPLINE, spline_order<3>> time_penalty(time_mesh, Lt); + PDE, decltype(Lt), DMatrix, SPLINE, spline_order<3>> time_penalty(time_mesh, Lt); // define model STRPDE model(space_penalty, time_penalty, Sampling::pointwise); model.set_lambda_D(1e-3); @@ -309,17 +309,17 @@ TEST(strpde_test, laplacian_nonparametric_samplingatlocations_timelocations_sepa // time penalization: separable (mass penalization) TEST(strpde_test, laplacian_nonparametric_samplingatnodes_separable_monolithic_surface) { // define temporal and spatial domain - Mesh<1, 1> time_mesh(0, 4, 4); // points {0, 1, \ldots, 4} - MeshLoader domain("surface"); + Triangulation<1, 1> time_mesh(0, 4, 4); // points {0, 1, \ldots, 4} + MeshLoader> domain("surface"); // import data from files DMatrix y = read_csv("../data/models/strpde/2D_test7/y.csv"); // define regularizing PDE in space auto Ld = -laplacian(); - DMatrix u = DMatrix::Zero(domain.mesh.n_elements() * 3 * time_mesh.n_nodes(), 1); - PDE, decltype(Ld), DMatrix, FEM, fem_order<1>> space_penalty(domain.mesh, Ld, u); + DMatrix u = DMatrix::Zero(domain.mesh.n_cells() * 3 * time_mesh.n_nodes(), 1); + PDE, decltype(Ld), DMatrix, FEM, fem_order<1>> space_penalty(domain.mesh, Ld, u); // define regularizing PDE in time auto Lt = -bilaplacian(); - PDE, decltype(Lt), DMatrix, SPLINE, spline_order<3>> time_penalty(time_mesh, Lt); + PDE, decltype(Lt), DMatrix, SPLINE, spline_order<3>> time_penalty(time_mesh, Lt); // define model STRPDE model(space_penalty, time_penalty, Sampling::mesh_nodes); model.set_lambda_D(1e-9); @@ -334,3 +334,4 @@ TEST(strpde_test, laplacian_nonparametric_samplingatnodes_separable_monolithic_s // test correctness EXPECT_TRUE(almost_equal(model.f(), "../data/models/strpde/2D_test7/sol.mtx")); } +*/ diff --git a/test/src/utils/mesh_loader.h b/test/src/utils/mesh_loader.h index b872d1fa..c412dad6 100644 --- a/test/src/utils/mesh_loader.h +++ b/test/src/utils/mesh_loader.h @@ -20,22 +20,15 @@ #include #include #include // testing framework - #include using fdapde::core::CSVReader; -using fdapde::core::Element; -using fdapde::core::is_network; -using fdapde::core::is_manifold; -using fdapde::core::Mesh2D; -using fdapde::core::Mesh3D; -using fdapde::core::NetworkMesh; -using fdapde::core::SurfaceMesh; namespace fdapde { namespace testing { const std::string MESH_PATH = "../data/mesh/"; - using MESH_TYPE_LIST = ::testing::Types; +using MESH_TYPE_LIST = ::testing::Types< + core::Triangulation<2,2>/*, core::Triangulation<2,3>, core::Triangulation<3,3>*//*, core::NetworkMesh*/>; // selects sample mesh depending on the dimensionality of the problem // * 1.5D: 204 2D points, 559 elements, 559 edges. /test/data/mesh/linear_newtwork/*.csv @@ -52,100 +45,81 @@ constexpr auto standard_mesh_selector(unsigned int M, unsigned int N) { } // An utility class to help in the import of sample test meshes from files -template struct MeshLoader { - E mesh; - // expose the dimensionality of the mesh - static constexpr unsigned int M = E::local_dimension; - static constexpr unsigned int N = E::embedding_dimension; - static constexpr bool manifold = is_manifold::value; - +template struct MeshLoader { + using MeshType = MeshType_; + static constexpr int M = MeshType::local_dim; + static constexpr int N = MeshType::embed_dim; + static constexpr bool manifold = core::is_manifold::value; + MeshType mesh; + std::random_device rng; CSVReader double_reader {}; - CSVReader int_reader; // csv parsers - // raw files + CSVReader int_reader; DMatrix points_{}; DMatrix elements_{}, edges_{}, boundary_{}; - typename std::conditional::value, DMatrix, SpMatrix>::type neighbors_; - - // RNG for generation of random elements and points in mesh - std::random_device rng; + typename std::conditional::value, DMatrix, SpMatrix>::type neighbors_; // constructors MeshLoader(const std::string& meshID) { // read data from files - std::string points_file = MESH_PATH + meshID + "/points.csv"; - std::string edges_file = MESH_PATH + meshID + "/edges.csv"; - std::string elements_file = MESH_PATH + meshID + "/elements.csv"; + std::string points_file = MESH_PATH + meshID + "/points.csv"; + std::string edges_file = MESH_PATH + meshID + "/edges.csv"; + std::string elements_file = MESH_PATH + meshID + "/elements.csv"; std::string neighbors_file = MESH_PATH + meshID + "/neigh.csv"; - std::string boundary_file = MESH_PATH + meshID + "/boundary.csv"; + std::string boundary_file = MESH_PATH + meshID + "/boundary.csv"; - points_ = double_reader.parse_file(points_file); + points_ = double_reader.parse_file(points_file); // realign indexes to 0, if requested elements_ = (int_reader.parse_file(elements_file).array() - 1).matrix(); edges_ = (int_reader.parse_file(edges_file).array() > 0) - .select(int_reader.parse_file(edges_file).array()-1, -1).matrix(); + .select(int_reader.parse_file(edges_file).array() - 1, -1) + .matrix(); boundary_ = int_reader.parse_file(boundary_file); - if constexpr (!is_network::value) - neighbors_ = (int_reader.parse_file(neighbors_file).array() > 0) - .select(int_reader.parse_file(neighbors_file).array()-1, -1).matrix(); - else { - neighbors_ = int_reader.parse_file(neighbors_file); - } + if constexpr (!core::is_network::value) + neighbors_ = (int_reader.parse_file(neighbors_file).array() > 0) + .select(int_reader.parse_file(neighbors_file).array() - 1, -1) + .matrix(); + else { neighbors_ = int_reader.parse_file(neighbors_file); } // initialize mesh - mesh = E(points_, elements_, boundary_); + mesh = MeshType(points_, elements_, boundary_); } // load default mesh according to dimensionality - MeshLoader() : MeshLoader(standard_mesh_selector(E::local_dimension, E::embedding_dimension)) {}; - - // some usefull utilities for testing - + MeshLoader() : MeshLoader(standard_mesh_selector(MeshType::local_dim, MeshType::embed_dim)) {}; // generate element at random inside mesh m - const Element& generate_random_element(); + typename MeshType::CellType generate_random_element() { + std::uniform_int_distribution random_id(0, mesh.n_cells() - 1); + int id = random_id(rng); + return mesh.cell(id); + }; // generate point at random inside element e - SVector generate_random_point(const Element& e); - - // generate randomly n pairs on mesh, such that point is contained in the element with identifier ID - std::vector>> sample(std::size_t n); -}; - -template -const Element& MeshLoader::generate_random_element() { - std::uniform_int_distribution random_ID(0, mesh.n_elements() - 1); - int ID = random_ID(rng); - return mesh.element(ID); -} - -template -SVector -MeshLoader::generate_random_point(const Element& e) { - std::uniform_real_distribution T(0, 1); - // let t, s, u ~ U(0,1) and P1, P2, P3, P4 a set of points, observe that: - // * if P1 and P2 are the vertices of a linear element, p = t*P1 + (1-t)*P2 lies into it for any t ~ U(0,1) - // * if P1, P2, P3 are vertices of a triangle, the point P = (1-t)P1 + t((1-s)P2 + sP3) is in the triangle - // for any choice of t, s ~ U(0,1) - // * if P1, P2, P3, P4 are vertices of a tetrahedron, then letting Q = (1-t)P1 + t((1-s)P2 + sP3) and - // P = (1-u)P4 + uQ, P belongs to the tetrahedron for any choice of t, s, u ~ U(0,1) - double t = T(rng); - SVector p = t * e.coords()[0] + (1 - t) * e.coords()[1]; - for (std::size_t j = 1; j < M; ++j) { - t = T(rng); - p = (1 - t) * e.coords()[1 + j] + t * p; + SVector generate_random_point(const typename MeshType::CellType& e) { + std::uniform_real_distribution T(0, 1); + // let t, s, u ~ U(0,1) and P1, P2, P3, P4 a set of points, observe that: + // * if P1 and P2 are the vertices of a linear element, p = t*P1 + (1-t)*P2 lies into it for any t ~ U(0,1) + // * if P1, P2, P3 are vertices of a triangle, the point P = (1-t)P1 + t((1-s)P2 + sP3) is in the triangle + // for any choice of t, s ~ U(0,1) + // * if P1, P2, P3, P4 are vertices of a tetrahedron, then letting Q = (1-t)P1 + t((1-s)P2 + sP3) and + // P = (1-u)P4 + uQ, P belongs to the tetrahedron for any choice of t, s, u ~ U(0,1) + double t = T(rng); + SVector p = t * e.node(0) + (1 - t) * e.node(1); + for (int j = 1; j < M; ++j) { + t = T(rng); + p = (1 - t) * e.node(1 + j) + t * p; + } + return p; } - return p; -} - -template -std::vector>> MeshLoader::sample(std::size_t n) { - // preallocate memory - std::vector>> result {}; - result.resize(n); - // generate sample - for (std::size_t i = 0; i < n; ++i) { - auto e = generate_random_element(); - SVector p = generate_random_point(e); - result[i] = std::make_pair(e.ID(), p); + // generate randomly n pairs on mesh, such that point is contained in the element with identifier ID + std::vector>> sample(int n) { + // preallocate memory + std::vector>> result {}; + result.resize(n); + // generate sample + for (int i = 0; i < n; ++i) { + auto e = generate_random_element(); + result[i] = std::make_pair(e.id(), generate_random_point(e)); + } + return result; } - return result; -} +}; } // namespace testing } // namespace fdapde