From aabf70395ee4f9d06a90e768328a6436758aad12 Mon Sep 17 00:00:00 2001
From: mkirsz <s1351949@sms.ed.ac.uk>
Date: Tue, 30 Jan 2024 10:50:05 +0000
Subject: [PATCH] Refactoring
---
m_blr_core.h | 59 +++++++++++++++++------------
m_core.h | 16 +-------
m_krr_core.h | 104 ++++++++++++++++++++++++++++++++++++++-------------
3 files changed, 115 insertions(+), 64 deletions(-)
diff --git a/m_blr_core.h b/m_blr_core.h
index bdd4980..433edbb 100644
--- a/m_blr_core.h
+++ b/m_blr_core.h
@@ -12,19 +12,14 @@
template
<class BF=Function_Base&>
class M_BLR_Core:
- public virtual M_Core,
- public virtual M_Predict,
- public virtual M_Train {
+ public virtual M_Core
+{
public:
- //using M_Core<BF>::weights;
- //using M_Core<BF>::trained;
- //using M_Core<BF>::config;
mat Sigma;
Config &config;
BF bf;
M_BLR_Core(Config &c):
config(c),
- //M_Core<BF>(c),
bf(c)
{
static_assert(std::is_base_of<BF_Base, BF>::value,
@@ -38,21 +33,50 @@ class M_BLR_Core:
M_BLR_Core(BF &bf, Config &c):
config(c),
- //M_Core<BF>(bf,c),
bf(bf)
{
if (dynamic_cast<BF_Base*>(&bf) == nullptr)
throw std::invalid_argument("Provided object is not of BF_Base type");
- //static_assert(std::is_same<Function_Base&, BF>::value,
- // "This constructor requires BF=Function_Base&\n");
-
init();
}
double predict(const rvec &v) const{
return bf.epredict(get_weights(),v);
};
+
+ private:
+ void init() {
+ if (config.exist("WEIGHTS")) {
+ weights.resize(config.size("WEIGHTS"));
+ config.get<t_type>("WEIGHTS",weights);
+ trained=true;
+ }
+
+ verbose=(config.get<int>("VERBOSE"));
+ }
+};
+template
+<class BF=Function_Base&>
+class M_BLR_Train:
+ public M_BLR_Core<BF>, // MUST NOT BE VIRUAL!!
+ public M_Train
+{
+
+ public:
+ using M_BLR_Core<BF>::trained;
+ using M_BLR_Core<BF>::config;
+ using M_BLR_Core<BF>::weights;
+ using M_BLR_Core<BF>::Sigma;
+
+ M_BLR_Train(Config &c):
+ M_BLR_Core<BF>(c)
+ {}
+
+ M_BLR_Train(BF &bf, Config &c):
+ M_BLR_Core<BF>(bf, c)
+ {}
+
void train(phi_type &Phi, const t_type &T) {
if(trained) {
throw std::runtime_error("This object is already trained!");
@@ -61,21 +85,10 @@ class M_BLR_Core:
trained=true;
}
t_type get_weights_uncertainty() const{
- double lambda=config.get<double>("LAMBDA");
+ double lambda=config.template get<double>("LAMBDA");
if(lambda >= 0) throw std::runtime_error(
"Sigma matrix is only computed for LAMBDA < 0");
return Sigma.diagonal();
}
-
- private:
- void init() {
- if (config.exist("WEIGHTS")) {
- weights.resize(config.size("WEIGHTS"));
- config.get<t_type>("WEIGHTS",weights);
- trained=true;
- }
-
- verbose=(config.get<int>("VERBOSE"));
- }
};
#endif
diff --git a/m_core.h b/m_core.h
index cced00f..56f827b 100644
--- a/m_core.h
+++ b/m_core.h
@@ -3,28 +3,14 @@
#include "../CORE/typedefs.h"
-//template
-//<class F=Function_Base&>
class M_Core {
public:
-// F f;
-// Config &config;
-//
-// M_Core(Config &c):
-// config(c),
-// f(c)
-// {}
-// M_Core(F &f, Config &c):
-// config(c),
-// f(f)
-// {}
int verbose;
bool trained=false;
t_type weights;
virtual ~M_Core() {}
- virtual double predict(const rvec &v)const=0;
virtual t_type get_weights_uncertainty()const=0;
bool is_trained() const {
@@ -36,6 +22,7 @@ class M_Core {
void set_weights(const t_type w) {
weights=w;
}
+ virtual double predict(const rvec &v)const=0;
};
class M_Predict {
public:
@@ -63,7 +50,6 @@ class M_Predict {
class M_Train {
public:
virtual ~M_Train() {}
-
virtual void train(phi_type &Phi, const t_type &T)=0;
};
#endif
diff --git a/m_krr_core.h b/m_krr_core.h
index 5ebf7db..2bf5bf2 100644
--- a/m_krr_core.h
+++ b/m_krr_core.h
@@ -13,22 +13,16 @@
template
<class K=Function_Base&>
class M_KRR_Core:
- public virtual M_Core,
- public virtual M_Predict,
- public virtual M_Train {
+ public virtual M_Core
+{
public:
- //using M_Core<K>::weights;
- //using M_Core<K>::trained;
- //using M_Core<K>::config;
mat Sigma;
Config &config;
K kernel;
- EKM<K> ekm;
+
M_KRR_Core(Config &c):
config(c),
- //M_Core<K>(c),
- kernel(c),
- ekm(c)
+ kernel(c)
{
static_assert(std::is_base_of<Kern_Base, K>::value,
"\nThe provided Kernel K is not of a Kernel type.\n\
@@ -40,9 +34,7 @@ class M_KRR_Core:
M_KRR_Core(K &kernel, Config &c):
config(c),
- //M_Core<K>(kernel, c),
- kernel(kernel),
- ekm(kernel)
+ kernel(kernel)
{
if (dynamic_cast<Kern_Base*>(&kernel) == nullptr)
throw std::invalid_argument("Provided object is not of Kern_Base type");
@@ -50,9 +42,46 @@ class M_KRR_Core:
init();
}
double predict(const rvec &v) const{
- return kernel.epredict(get_weights(),v);
+ return kernel.epredict(weights,v);
};
+
+ private:
+ void init() {
+ if (config.exist("WEIGHTS")) {
+ weights.resize(config.size("WEIGHTS"));
+ config.get<t_type>("WEIGHTS",weights);
+ trained=true;
+ }
+
+ verbose=(config.get<int>("VERBOSE"));
+ }
+};
+template
+<class K=Function_Base&>
+class M_KRR_Train:
+ public M_KRR_Core<K>, // MUST NOT BE VIRUAL!!
+ public M_Train
+{
+
+ public:
+ EKM<K> ekm;
+ using M_KRR_Core<K>::trained;
+ using M_KRR_Core<K>::config;
+ using M_KRR_Core<K>::kernel;
+ using M_KRR_Core<K>::weights;
+ using M_KRR_Core<K>::Sigma;
+
+ M_KRR_Train(Config &c):
+ M_KRR_Core<K>(c),
+ ekm(c)
+ {}
+
+ M_KRR_Train(K &kernel, Config &c):
+ M_KRR_Core<K>(kernel, c),
+ ekm(kernel)
+ {}
+
void train(phi_type &Phi, const t_type &T) {
if(trained) {
throw std::runtime_error("This object is already trained!");
@@ -69,21 +98,44 @@ class M_KRR_Core:
trained=true;
}
t_type get_weights_uncertainty() const{
- double lambda=config.get<double>("LAMBDA");
+ double lambda=config.template get<double>("LAMBDA");
if(lambda >= 0) throw std::runtime_error(
"Sigma matrix is only computed for LAMBDA < 0");
return Sigma.diagonal();
}
-
- private:
- void init() {
- if (config.exist("WEIGHTS")) {
- weights.resize(config.size("WEIGHTS"));
- config.get<t_type>("WEIGHTS",weights);
- trained=true;
- }
-
- verbose=(config.get<int>("VERBOSE"));
- }
};
+
+//template
+//<class K=Function_Base&>
+//class M_KRR_Predict:
+// public M_KRR_Core<K>, // MUST NOT BE VIRUAL!!
+// public virtual M_Predict
+//{
+//
+// public:
+// using M_KRR_Core<K>::config;
+// using M_KRR_Core<K>::kernel;
+// using M_KRR_Core<K>::weights;
+//
+// M_KRR_Predict(Config &c):
+// M_KRR_Core<K>(c)
+// {}
+//
+// M_KRR_Predict(K &kernel, Config &c):
+// M_KRR_Core<K>(kernel, c)
+// {}
+//
+// double epredict(const aed_rctype &aed) {
+// return kernel.epredict(weights,aed);
+// };
+//
+// double fpredict(const fd_type &fdij, const aed_rctype &aedi, const size_t k) {
+// return kernel.fpredict(weights,fdij,aedi,k);
+// }
+//
+// force_type fpredict(const fd_type &fdij, const aed_rctype &aedi) {
+// return kernel.fpredict(weights,fdij,aedi).array();
+// }
+//
+//};
#endif
--
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