diff --git a/include/tadah/mlip/models/m_blr.h b/include/tadah/mlip/models/m_blr.h
index db4874b9a4562f00edf7549b1f656746a2b36247..809f08971af1591715ccf1a7aa6cd51883c31d8e 100644
--- a/include/tadah/mlip/models/m_blr.h
+++ b/include/tadah/mlip/models/m_blr.h
@@ -1,19 +1,19 @@
#ifndef M_BLR_H
#define M_BLR_H
-#include <tadah/mlip/models/m_tadah_base.h>
+#include <tadah/core/config.h>
#include <tadah/mlip/descriptors_calc.h>
#include <tadah/mlip/design_matrix/design_matrix.h>
#include <tadah/mlip/design_matrix/functions/dm_function_base.h>
+#include <tadah/mlip/memory/IMLIPWorkspaceManager.h>
+#include <tadah/mlip/models/m_tadah_base.h>
#include <tadah/mlip/normaliser.h>
#include <tadah/models/m_blr_train.h>
-#include <tadah/core/config.h>
-#include <tadah/mlip/memory/IMLIPWorkspaceManager.h>
+#include <iostream>
#include <limits>
#include <stdexcept>
#include <type_traits>
-#include <iostream>
namespace tadah {
namespace mlip {
@@ -21,34 +21,39 @@ namespace mlip {
* @class M_BLR
* @brief Bayesian Linear Regression (BLR).
*
- * This class implements Bayesian Linear Regression, a statistical method to make predictions using linear models with both linear and nonlinear features.
+ * This class implements Bayesian Linear Regression, a statistical method to
+ * make predictions using linear models with both linear and nonlinear features.
*
* **Model Supported Training Modes**:
- * - **LINEAR**: Uses Ordinary Least Squares or Ridge Regression for linear relationships.
- * - **NONLINEAR**: Utilizes basis functions to handle nonlinear input spaces, transforming input descriptors into higher-dimensional feature spaces. For example, polynomial transformations.
+ * - **LINEAR**: Uses Ordinary Least Squares or Ridge Regression for linear
+ * relationships.
+ * - **NONLINEAR**: Utilizes basis functions to handle nonlinear input spaces,
+ * transforming input descriptors into higher-dimensional feature spaces. For
+ * example, polynomial transformations.
*
* **Prediction**:
- * - Computes predictions as a weighted sum of basis functions applied to input vectors.
+ * - Computes predictions as a weighted sum of basis functions applied to input
+ * vectors.
*
* **Training**:
- * - Employs regularized least squares, allowing for optional regularization through the \f$\lambda\f$ parameter.
+ * - Employs regularized least squares, allowing for optional regularization
+ * through the \f$\lambda\f$ parameter.
* - Ordinary Least Squares (OLS) is a special case when \f$\lambda = 0\f$.
*
* **Configuration Options**:
- * - **LAMBDA**: Set to `0` for OLS, a positive value for specified regularization, or `-1` for automatic tuning using evidence approximation.
+ * - **LAMBDA**: Set to `0` for OLS, a positive value for specified
+ * regularization, or `-1` for automatic tuning using evidence approximation.
*
* @tparam BF DM_BF_Base child, Basis function
*/
-template
-<class BF=DM_Function_Base&>
-class M_BLR: public M_Tadah_Base, public tadah::models::M_BLR_Train<BF> {
+template <class BF = DM_Function_Base &>
+class M_BLR : public M_Tadah_Base, public tadah::models::M_BLR_Train<BF> {
public:
-
using tadah::models::M_BLR_Train<BF>::config;
using tadah::models::M_BLR_Train<BF>::bf;
- /**
+ /**
* @brief Initializes for training or prediction using a configuration.
*
* **Example**:
@@ -56,63 +61,63 @@ public:
* tadah::core::Config config("tadah::core::Config");
* M_BLR<tadah::models::BF_Linear> blr(config);
* \endcode
- *
+ *
* @param c Configuration object.
*/
- M_BLR(tadah::core::Config &c):
- tadah::models::M_BLR_Train<BF>(c),
- desmat(tadah::models::M_BLR_Train<BF>::bf,c)
- {
+ M_BLR(tadah::core::Config &c)
+ : tadah::models::M_BLR_Train<BF>(c),
+ desmat(tadah::models::M_BLR_Train<BF>::bf, c) {
norm = Normaliser(c);
}
- /**
- * @brief Initializes for training or prediction using a basis function and configuration.
- *
+ /**
+ * @brief Initializes for training or prediction using a basis function and
+ * configuration.
+ *
* @param bf Basis function.
* @param c Configuration object.
*/
- M_BLR(BF &bf, tadah::core::Config &c):
- tadah::models::M_BLR_Train<BF>(bf,c),
- desmat(bf,c)
- {
+ M_BLR(BF &bf, tadah::core::Config &c)
+ : tadah::models::M_BLR_Train<BF>(bf, c), desmat(bf, c) {
norm = Normaliser(c);
}
- M_BLR(BF &bf, tadah::core::Config &c, tadah::mlip::memory::IMLIPWorkspaceManager& workspaceManager):
- tadah::models::M_BLR_Train<BF>(bf,c,workspaceManager),
- desmat(bf,c,workspaceManager)
- {
+ M_BLR(BF &bf, tadah::core::Config &c,
+ tadah::mlip::memory::IMLIPWorkspaceManager &workspaceManager)
+ : tadah::models::M_BLR_Train<BF>(bf, c, workspaceManager),
+ desmat(bf, c, workspaceManager) {
norm = Normaliser(c);
}
- double epredict(const tadah::core::aed_type &aed) const{
- return bf.epredict(weights,aed);
+ double epredict(const tadah::core::aed_type &aed) const {
+ return bf.epredict(weights, aed);
};
- double fpredict(const tadah::core::fd_type &fdij, const tadah::core::aed_type &aedi, const size_t k) const{
- return bf.fpredict(weights,fdij,aedi,k);
+ double fpredict(const tadah::core::fd_type &fdij,
+ const tadah::core::aed_type &aedi, const size_t k) const {
+ return bf.fpredict(weights, fdij, aedi, k);
}
- tadah::core::force_type fpredict(const tadah::core::fd_type &fdij, const tadah::core::aed_type &aedi) const{
- return bf.fpredict(weights,fdij,aedi);
+ tadah::core::force_type fpredict(const tadah::core::fd_type &fdij,
+ const tadah::core::aed_type &aedi) const {
+ return bf.fpredict(weights, fdij, aedi);
}
void train(StDescriptorsDB &st_desc_db, const StructureDB &stdb) {
- if(config.template get<bool>("NORM"))
- norm = Normaliser(config,st_desc_db);
+ if (config.template get<bool>("NORM"))
+ norm = Normaliser(config, st_desc_db);
- desmat.build(st_desc_db,stdb);
+ desmat.build(st_desc_db, stdb);
train(desmat);
}
void train(const StructureDB &stdb, const NeighborListDB &nldb, DC_Base &dc) {
- if(config.template get<bool>("NORM")) {
+ if (config.template get<bool>("NORM")) {
- std::string force=config.template get<std::string>("FORCE");
- std::string stress=config.template get<std::string>("STRESS");
+ std::string force = config.template get<std::string>("FORCE");
+ std::string stress = config.template get<std::string>("STRESS");
config.remove("FORCE");
config.remove("STRESS");
@@ -121,7 +126,7 @@ public:
StDescriptorsDB st_desc_db_temp = dc.calc(stdb, nldb);
- if(config.template get<bool>("NORM")) {
+ if (config.template get<bool>("NORM")) {
norm = Normaliser(config);
norm.learn(st_desc_db_temp);
// norm.normalise(st_desc_db_temp);
@@ -133,24 +138,27 @@ public:
config.add("STRESS", stress);
}
- desmat.build(stdb,nldb,norm,dc);
+ desmat.build(stdb, nldb, norm, dc);
train(desmat);
}
- Structure predict(const tadah::core::Config &c, StDescriptors &std, const Structure &st, const StructureNeighborView &st_nb) {
- if(config.template get<bool>("NORM") && !std.normalised && bf.get_label()!="tadah::models::BF_Linear")
+ Structure predict(const tadah::core::Config &c, StDescriptors &std,
+ const Structure &st, const StructureNeighborView &st_nb) {
+ if (config.template get<bool>("NORM") && !std.normalised &&
+ bf.get_label() != "tadah::models::BF_Linear")
norm.normalise(std);
- return M_Tadah_Base::predict(c,std,st,st_nb);
+ return M_Tadah_Base::predict(c, std, st, st_nb);
}
- StructureDB predict(tadah::core::Config &c, const StructureDB &stdb, const NeighborListDB &nldb, DC_Base &dc) {
- return M_Tadah_Base::predict(c,stdb,nldb,dc);
+ StructureDB predict(tadah::core::Config &c, const StructureDB &stdb,
+ const NeighborListDB &nldb, DC_Base &dc) {
+ return M_Tadah_Base::predict(c, stdb, nldb, dc);
}
tadah::core::Config get_param_file() {
tadah::core::Config c = config;
- //c.remove("ALPHA");
- //c.remove("BETA");
+ // c.remove("ALPHA");
+ // c.remove("BETA");
c.remove("DBFILE");
c.remove("FORCE");
c.remove("STRESS");
@@ -161,28 +169,28 @@ public:
c.add("MODEL", label);
c.add("MODEL", bf.get_label());
- for (size_t i=0;i<weights.size();++i) {
+ for (size_t i = 0; i < weights.size(); ++i) {
c.add("WEIGHTS", weights(i));
}
- if(config.template get<bool>("NORM")) {
- for (size_t i=0;i<norm.mean.size();++i) {
+ if (config.template get<bool>("NORM")) {
+ for (size_t i = 0; i < norm.mean.size(); ++i) {
c.add("NMEAN", norm.mean[i]);
}
- for (size_t i=0;i<norm.std_dev.size();++i) {
+ for (size_t i = 0; i < norm.std_dev.size(); ++i) {
c.add("NSTDEV", norm.std_dev[i]);
}
}
c.clear_internal_keys();
return c;
}
- StructureDB predict(tadah::core::Config config_pred, StructureDB &stdb, DC_Base &dc,
- tadah::core::aed_type &predicted_error) {
+ StructureDB predict(tadah::core::Config config_pred, StructureDB &stdb,
+ DC_Base &dc, tadah::core::aed_type &predicted_error) {
- tadah::models::LinearRegressor::read_sigma(config_pred,Sigma);
- DesignMatrix<BF> dm(bf,config_pred);
- dm.scale=false; // do not scale energy, forces and stresses
- dm.build(stdb,norm,dc);
+ tadah::models::LinearRegressor::read_sigma(config_pred, Sigma);
+ DesignMatrix<BF> dm(bf, config_pred);
+ dm.scale = false; // do not scale energy, forces and stresses
+ dm.build(stdb, norm, dc);
predicted_error = T_MDMT_diag(dm.getPhi(), Sigma);
double pmean = sqrt(predicted_error.mean());
@@ -191,29 +199,35 @@ public:
tadah::core::aed_type Tpred = T_dgemv(dm.getPhi(), weights);
// Construct StructureDB object with predicted values
- StructureDB stdb_;
- stdb_.structures.resize(stdb.size());
- size_t i=0;
- for (size_t s=0; s<stdb.size(); ++s) {
- stdb_(s) = Structure(stdb(s));
-
- predicted_error(i) = (sqrt(predicted_error(i))-pmean)/stdb(s).natoms();
+ auto pinfo = stdb.exportPass1Info();
+ StructureDB stdb_(pinfo, true);
+ size_t i = 0;
+ for (size_t s = 0; s < stdb.size(); ++s) {
+ /*stdb_(s) = Structure(stdb(s));*/
+
+ predicted_error(i) =
+ (sqrt(predicted_error(i)) - pmean) / stdb(s).natoms();
stdb_(s).energy = Tpred(i++);
if (config_pred.get<bool>("FORCE")) {
- for (size_t a=0; a<stdb(s).natoms(); ++a) {
- for (size_t k=0; k<3; ++k) {
- stdb_(s).atoms[a].force[k] = Tpred(i++);
- predicted_error(i) = (sqrt(predicted_error(i))-pmean);
- }
+ for (size_t a = 0; a < stdb(s).natoms(); ++a) {
+ stdb_(s).fx(a) = Tpred(i);
+ predicted_error(i) = (sqrt(predicted_error(i)) - pmean);
+ i++;
+ stdb_(s).fy(a) = Tpred(i);
+ predicted_error(i) = (sqrt(predicted_error(i)) - pmean);
+ i++;
+ stdb_(s).fz(a) = Tpred(i);
+ predicted_error(i) = (sqrt(predicted_error(i)) - pmean);
+ i++;
}
}
if (config_pred.get<bool>("STRESS")) {
- for (size_t x=0; x<3; ++x) {
- for (size_t y=x; y<3; ++y) {
- stdb_(s).stress(x,y) = Tpred(i++);
- predicted_error(i) = (sqrt(predicted_error(i))-pmean);
- if (x!=y)
- stdb_(s).stress(y,x) = stdb_(s).stress(x,y);
+ for (size_t x = 0; x < 3; ++x) {
+ for (size_t y = x; y < 3; ++y) {
+ stdb_(s).stress(x, y) = Tpred(i++);
+ predicted_error(i) = (sqrt(predicted_error(i)) - pmean);
+ if (x != y)
+ stdb_(s).stress(y, x) = stdb_(s).stress(x, y);
}
}
}
@@ -221,7 +235,8 @@ public:
return stdb_;
}
StructureDB predict(StructureDB &stdb) {
- if(!trained) throw std::runtime_error("This object is not trained!\n\
+ if (!trained)
+ throw std::runtime_error("This object is not trained!\n\
Hint: check different predict() methods.");
tadah::core::phi_type &Phi = desmat.getPhi();
@@ -229,32 +244,35 @@ Hint: check different predict() methods.");
// compute energy, forces and stresses
tadah::core::aed_type Tpred = T_dgemv(Phi, weights);
- double eweightglob=config.template get<double>("EWEIGHT");
- double fweightglob=config.template get<double>("FWEIGHT");
- double sweightglob=config.template get<double>("SWEIGHT");
+ double eweightglob = config.template get<double>("EWEIGHT");
+ double fweightglob = config.template get<double>("FWEIGHT");
+ double sweightglob = config.template get<double>("SWEIGHT");
// Construct StructureDB object with predicted values
- StructureDB stdb_;
- stdb_.structures.resize(stdb.size());
- size_t s=0;
- size_t i=0;
- while (i<Phi.rows()) {
-
- stdb_(s).energy = Tpred(i++)*stdb(s).natoms()/eweightglob/stdb(s).eweight;
+ auto pinfo = stdb.exportPass1Info();
+ StructureDB stdb_(pinfo, true);
+ size_t s = 0;
+ size_t i = 0;
+ while (i < Phi.rows()) {
+
+ stdb_(s).energy =
+ Tpred(i++) * stdb(s).natoms() / eweightglob / stdb(s).eweight;
if (config.template get<bool>("FORCE")) {
- stdb_(s).atoms.resize(stdb(s).natoms());
- for (size_t a=0; a<stdb(s).natoms(); ++a) {
- for (size_t k=0; k<3; ++k) {
- stdb_(s).atoms[a].force[k] = Tpred(i++)/fweightglob/stdb(s).fweight;
- }
+ for (size_t a = 0; a < stdb(s).natoms(); ++a) {
+ stdb_(s).fx(a) = Tpred(i);
+ i++;
+ stdb_(s).fy(a) = Tpred(i);
+ i++;
+ stdb_(s).fz(a) = Tpred(i);
+ i++;
}
}
if (config.template get<bool>("STRESS")) {
- for (size_t x=0; x<3; ++x) {
- for (size_t y=x; y<3; ++y) {
- stdb_(s).stress(x,y) = Tpred(i++)/sweightglob/stdb(s).sweight;
- if (x!=y)
- stdb_(s).stress(y,x) = stdb_(s).stress(x,y);
+ for (size_t x = 0; x < 3; ++x) {
+ for (size_t y = x; y < 3; ++y) {
+ stdb_(s).stress(x, y) = Tpred(i++) / sweightglob / stdb(s).sweight;
+ if (x != y)
+ stdb_(s).stress(y, x) = stdb_(s).stress(x, y);
}
}
}
@@ -264,64 +282,63 @@ Hint: check different predict() methods.");
}
private:
- std::string label="M_BLR";
+ std::string label = "M_BLR";
DesignMatrix<BF> desmat;
// normalise weights such that when predict is called
// we can supply it with a non-normalised descriptor
- tadah::core::t_type convert_to_nweights(const tadah::core::t_type &weights) const {
- if(bf.get_label()!="tadah::models::BF_Linear") {
+ tadah::core::t_type
+ convert_to_nweights(const tadah::core::t_type &weights) const {
+ if (bf.get_label() != "tadah::models::BF_Linear") {
throw std::runtime_error("Cannot convert weights to nweights for\n\
non linear basis function\n");
}
tadah::core::t_type nw(weights.rows());
nw(0) = weights(0);
- for (size_t i=1; i<weights.size(); ++i) {
+ for (size_t i = 1; i < weights.size(); ++i) {
if (norm.std_dev[i] > std::numeric_limits<double>::min())
nw(i) = weights(i) / norm.std_dev[i];
else
nw(i) = weights(i);
- nw(0) -= norm.mean[i]*nw(i);
-
+ nw(0) -= norm.mean[i] * nw(i);
}
return nw;
}
// The opposite of convert_to_nweights()
tadah::core::t_type convert_to_weights(const tadah::core::t_type &nw) const {
- if(bf.get_label()!="tadah::models::BF_Linear") {
+ if (bf.get_label() != "tadah::models::BF_Linear") {
throw std::runtime_error("Cannot convert nweights to weights for\n\
non linear basis function\n");
}
// convert normalised weights back to "normal"
tadah::core::t_type w(nw.rows());
w(0) = nw(0);
- for (size_t i=1; i<nw.size(); ++i) {
+ for (size_t i = 1; i < nw.size(); ++i) {
if (norm.std_dev[i] > std::numeric_limits<double>::min())
w(i) = nw(i) * norm.std_dev[i];
else
w(i) = nw(i);
- w(0) += nw(i)*norm.mean[i];
+ w(0) += nw(i) * norm.mean[i];
}
return w;
}
- template <typename D>
- void train(D &desmat) {
- // TODO
+ template <typename D> void train(D &desmat) {
+ // TODO
// the train method destroys the Phi matrix
// In consequence, we cannot use it for quick prediction
// The simple solution, for now, is to make a copy of the Phi matrix
- //tadah::core::phi_type &Phi = desmat.Phi;
+ // tadah::core::phi_type &Phi = desmat.Phi;
tadah::core::phi_type Phi = desmat.getPhi();
tadah::core::t_type T = desmat.getT();
- //tadah::core::t_type &T = desmat.T;
- tadah::models::M_BLR_Train<BF>::train(Phi,T);
+ // tadah::core::t_type &T = desmat.T;
+ tadah::models::M_BLR_Train<BF>::train(Phi, T);
if (config.template get<bool>("NORM") &&
- bf.get_label()=="tadah::models::BF_Linear") {
+ bf.get_label() == "tadah::models::BF_Linear") {
weights = convert_to_nweights(weights);
}
}
@@ -334,6 +351,6 @@ non linear basis function\n");
using tadah::models::M_BLR_Train<BF>::weights;
using tadah::models::M_BLR_Train<BF>::Sigma;
};
-}
-}
+} // namespace mlip
+} // namespace tadah
#endif
diff --git a/include/tadah/mlip/models/m_krr.h b/include/tadah/mlip/models/m_krr.h
index 63f93b3792a2104d0f5741bc084227986f6265bf..e5907c1e737ccc0bbbb9710310cfd6eadb0ffef3 100644
--- a/include/tadah/mlip/models/m_krr.h
+++ b/include/tadah/mlip/models/m_krr.h
@@ -110,7 +110,7 @@ public:
train(desmat);
}
- void train(StructureDB &stdb, DC_Base &dc) {
+ void train(StructureDB &stdb, const NeighborListDB &nldb, DC_Base &dc) {
int modelN;
try {
modelN=config.template get<int>("MODEL",2);
@@ -121,9 +121,9 @@ public:
config.add("MODEL", modelN);
}
if (modelN==1)
- train1(stdb,dc);
+ train1(stdb,nldb,dc);
else if (modelN==2)
- train2(stdb,dc);
+ train2(stdb,nldb,dc);
else
throw
std::runtime_error(
@@ -191,14 +191,17 @@ public:
tadah::models::M_KRR_Train<K>::train2(basis.b, basis.T);
}
- Structure predict(const tadah::core::Config &c, StDescriptors &std, const Structure &st) {
- if(config.template get<bool>("NORM") && !std.normalised && kernel.get_label()!="tadah::models::Kern_Linear")
+ Structure predict(const tadah::core::Config &c, StDescriptors &std,
+ const Structure &st, const StructureNeighborView &st_nb) {
+ if (config.template get<bool>("NORM") && !std.normalised &&
+ kernel.get_label() != "tadah::models::Kern_Linear")
norm.normalise(std);
- return M_Tadah_Base::predict(c,std,st);
+ return M_Tadah_Base::predict(c, std, st, st_nb);
}
- StructureDB predict(tadah::core::Config &c, const StructureDB &stdb, DC_Base &dc) {
- return M_Tadah_Base::predict(c,stdb,dc);
+ StructureDB predict(tadah::core::Config &c, const StructureDB &stdb,
+ const NeighborListDB &nldb, DC_Base &dc) {
+ return M_Tadah_Base::predict(c, stdb, nldb, dc);
}
tadah::core::Config get_param_file() {
@@ -262,20 +265,23 @@ public:
tadah::core::aed_type Tpred = T_dgemv(dm.getPhi(), weights);
// Construct StructureDB object with predicted values
- StructureDB stdb_;
- stdb_.structures.resize(stdb.size());
+ auto pinfo = stdb.exportPass1Info();
+ StructureDB stdb_(pinfo, true);
size_t i=0;
for (size_t s=0; s<stdb.size(); ++s) {
- stdb_(s) = Structure(stdb(s));
-
stdb_(s).energy = Tpred(i++);
predicted_error(i) = (sqrt(predicted_error(i))-pmean)/stdb(s).natoms();
if (config_pred.get<bool>("FORCE")) {
for (size_t a=0; a<stdb(s).natoms(); ++a) {
- for (size_t k=0; k<3; ++k) {
- stdb_(s).atoms[a].force[k] = Tpred(i++);
- predicted_error(i) = (sqrt(predicted_error(i))-pmean);
- }
+ stdb_(s).fx(a) = Tpred(i);
+ predicted_error(i) = (sqrt(predicted_error(i)) - pmean);
+ i++;
+ stdb_(s).fy(a) = Tpred(i);
+ predicted_error(i) = (sqrt(predicted_error(i)) - pmean);
+ i++;
+ stdb_(s).fz(a) = Tpred(i);
+ predicted_error(i) = (sqrt(predicted_error(i)) - pmean);
+ i++;
}
}
if (config_pred.get<bool>("STRESS")) {
@@ -291,7 +297,7 @@ public:
}
return stdb_;
}
- StructureDB predict(StructureDB &stdb) {
+ StructureDB predict(StructureDB &stdb, const NeighborListDB &nldb) {
if(!trained) throw std::runtime_error("This object is not trained!\n\
Hint: check different predict() methods.");
@@ -305,19 +311,21 @@ Hint: check different predict() methods.");
double sweightglob=config.template get<double>("SWEIGHT");
// Construct StructureDB object with predicted values
- StructureDB stdb_;
- stdb_.structures.resize(stdb.size());
+ auto pinfo = stdb.exportPass1Info();
+ StructureDB stdb_(pinfo, true);
size_t s=0;
size_t i=0;
while (i<Phi.rows()) {
stdb_(s).energy = Tpred(i++)*stdb(s).natoms()/eweightglob/stdb(s).eweight;
if (config.template get<bool>("FORCE")) {
- stdb_(s).atoms.resize(stdb(s).natoms());
for (size_t a=0; a<stdb(s).natoms(); ++a) {
- for (size_t k=0; k<3; ++k) {
- stdb_(s).atoms[a].force[k] = Tpred(i++)/fweightglob/stdb(s).fweight;
- }
+ stdb_(s).fx(a) = Tpred(i);
+ i++;
+ stdb_(s).fy(a) = Tpred(i);
+ i++;
+ stdb_(s).fz(a) = Tpred(i);
+ i++;
}
}
if (config.template get<bool>("STRESS")) {
@@ -385,7 +393,6 @@ non linear kernel\n");
template <typename D>
void train(D &desmat) {
- // TODO see comments in M_BLR
tadah::core::phi_type Phi = desmat.getPhi();
tadah::core::t_type T = desmat.getT();
tadah::models::M_KRR_Train<K>::train(Phi,T);
diff --git a/include/tadah/mlip/models/m_tadah_base.h b/include/tadah/mlip/models/m_tadah_base.h
index bad261c7231d3c33b9ca30a201b94bc630280127..fe7a15e84384fef1d66f2fd4b262c4537aef3aae 100644
--- a/include/tadah/mlip/models/m_tadah_base.h
+++ b/include/tadah/mlip/models/m_tadah_base.h
@@ -89,7 +89,7 @@ public:
*
* @param dc is a DescriptorCalc object
*/
- virtual void train(StructureDB &, DC_Base &) {};
+ virtual void train(StructureDB &, const NeighborListDB &nldb, DC_Base &) {};
/** This will fit a model with precalculated StDescriptorsDB object.
@@ -107,5 +107,4 @@ public:
};
}
}
-//template<> inline Registry<M_Tadah_Base,DM_Function_Base&,tadah::core::Config&>::Map Registry<M_Tadah_Base,DM_Function_Base&,tadah::core::Config&>::registry{};
#endif