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# Prerequisites
*.d
# Compiled Object files
*.slo
*.lo
*.o
*.obj
# Precompiled Headers
*.gch
*.pch
# Compiled Dynamic libraries
*.so
*.dylib
*.dll
# Fortran module files
*.mod
*.smod
# Compiled Static libraries
*.lai
*.la
*.a
*.lib
# Executables
*.exe
*.out
*.app

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if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<http://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<http://www.gnu.org/philosophy/why-not-lgpl.html>.

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CC=g++
CFLAGS=-Wall -O3 -std=c++11 -fopenmp
BASESRC=test/debug_cluster.cpp
BASEHDR=src/mdimension.hpp src/mmatrix.hpp src/meigen.hpp src/mining.hpp src/learning.hpp src/clustering.hpp
BASEOBJ=$(BASESRC:.cpp=.o)
BASEFLD=bin
BASEEXE=debug_cluster
all: $(BASESRC) $(BASEEXE)
$(BASEEXE): $(BASEOBJ)
$(CC) $(CFLAGS) $(BASEOBJ) -o $(BASEFLD)/$@
.cpp.o: $(BASEHDR)
$(CC) -c $(CFLAGS) $< -o $@
clean:
find ./test/ -name "*.o" -delete
find ./$(BASEFLD) -name $(BASEEXE) -delete

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# data-learning
Approach for implementing a library concerning data mining (PCA, MDS, etc.), machine learning (Perceptron, SVM, etc.) and clustering (k-means, etc.). Can be used in future projects, for instance in the field of genomic selection.
In lib-folder, all necessary code files can be found. Using $ ~ make , you can compile the test file from the src-folder.
Important is to include mmatrix.hpp, the "mathematical matrix" class and clustering, mining or learning.
All functions are defined within the scope data_learning::< category >::< class >, e.g. data_learning::clustering::kmeans
This personal project is being developed in freetime, so at some points there will be many changes, but at some other points in the future, there wont be changes within a few days.
As of 2021 I start continuing on this project; a plan besides finishing cpu implementation is to get it run on AMD GPUs with OpenCL.
If there are questions, please feel free to contact me:
lhahn@data-learning.de
Best regards,
Lars Hahn

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#ifndef _CLUSTERING_HPP_
#define _CLUSTERING_HPP_
/*===Libraries================================================================*/
#include <algorithm>
#include <ctime>
#include <stdexcept>
#include <string>
#include <unordered_set>
#include <vector>
#include "mmatrix.hpp"
/*===Classes-Structurres======================================================*/
namespace data_learning{
/*---clustering-------------------------------------------------------------*/
namespace clustering{
static bool _Seeded = false;
/*---KMeans-Clutering---------------------------------------------------*/
template<typename T = double>
class kmeans{
private:
mmatrix<T> _DataMatrix;
mmatrix<T> _Prototypes;
mmatrix<T> _Assignments;
std::size_t _K;
static double _Threshold;
public:
kmeans(std::size_t K);
kmeans(mmatrix<T> && Mat, std::size_t K);
kmeans(mmatrix<T> & Mat, std::size_t K);
void initial_prototypes(mmatrix<T> & Mat);
void data_matrix(mmatrix<T> && Mat);
void data_matrix(mmatrix<T> & Mat);
void threshold(T thresh);
double cluster();
std::vector<double> clustering(std::size_t Steps = 1e3);
mmatrix<T> data_matrix();
mmatrix<T> prototypes();
mmatrix<T> assignments();
T threshold();
std::size_t k();
std::vector<std::size_t> labels();
std::vector< std::vector<std::size_t> > label_clusters();
std::vector< mmatrix<T> > clusters();
void reset();
private:
mmatrix<T> initial_proto();
void initial_assign();
void initialisation();
};
/*---Expectation-Maximisation-Clustering--------------------------------*/
template<typename T = double>
class emcluster{
private:
mmatrix<T> _DataMatrix;
mmatrix<T> _Prototypes;
mmatrix<T> _Assignments;
mmatrix<T> _ClusterProb;
std::size_t _K;
double _Sigma;
double _SigmaInit;
static double _Threshold;
public:
emcluster(std::size_t K, double sigma = 1e-4);
emcluster(mmatrix<T> && Mat, std::size_t K, double sigma = 1e-4);
emcluster(mmatrix<T> & Mat, std::size_t K, double sigma = 1e-4);
void initial_prototypes(mmatrix<T> & Mat);
void data_matrix(mmatrix<T> && Mat);
void data_matrix(mmatrix<T> & Mat);
void probability_matrix(mmatrix<T> && Mat);
void probability_matrix(mmatrix<T> & Mat);
void threshold(T thresh);
void sigma(double sigma);
double cluster();
std::vector<double> clustering(std::size_t Steps = 1e3);
mmatrix<T> data_matrix();
mmatrix<T> prototypes();
mmatrix<T> assignments();
mmatrix<T> probability_matrix();
T threshold();
double sigma();
std::size_t k();
std::vector<std::size_t> labels();
std::vector< std::vector<std::size_t> > label_clusters();
std::vector< mmatrix<T> > clusters();
void reset();
private:
void init_kmeans();
void initialisation();
};
template<typename T>
double emcluster<T>::_Threshold = 1e-4;
template<typename T>
emcluster<T>::emcluster(std::size_t K, double sigma):_K(K),_Sigma(sigma),_SigmaInit(sigma){
if(_K < 2){
throw std::out_of_range("K, number of prototypes, has to be greater than 1.");
}
if(!clustering::_Seeded){
std::srand(std::time(NULL));
clustering::_Seeded = true;
}
}
template<typename T>
emcluster<T>::emcluster(mmatrix<T> && Mat, std::size_t K, double sigma):_K(K),_Sigma(sigma),_SigmaInit(sigma){
if(_K < 2){
throw std::out_of_range("K, number of prototypes, has to be greater than 1.");
}
if(!clustering::_Seeded){
std::srand(std::time(NULL));
clustering::_Seeded = true;
}
data_matrix(Mat);
}
template<typename T>
emcluster<T>::emcluster(mmatrix<T> & Mat, std::size_t K, double sigma):_K(K),_Sigma(sigma),_SigmaInit(sigma){
if(_K < 2){
throw std::out_of_range("K, number of prototypes, has to be greater than 1.");
}
if(!clustering::_Seeded){
std::srand(std::time(NULL));
clustering::_Seeded = true;
}
data_matrix(Mat);
}
template<typename T>
void emcluster<T>::initial_prototypes(mmatrix<T> & Mat){
if(Mat.row_size() != _K){
throw std::out_of_range("Initial prototypes row size("+
std::to_string(Mat.row_size())+") differs from number K("+
std::to_string(_K)+")");
}
if(Mat.col_size() != _DataMatrix.col_size() && _DataMatrix.col_size() != 0){
throw std::out_of_range("initial prototypes col size("+
std::to_string(Mat.col_size())+") differs from data col size ("+
std::to_string(_DataMatrix.col_size())+").");
}
_Prototypes = Mat;
initialisation();
}
template<typename T>
void emcluster<T>::data_matrix(mmatrix<T> && Mat){
data_matrix(Mat);
}
template<typename T>
void emcluster<T>::data_matrix(mmatrix<T> & Mat){
_DataMatrix = Mat;
init_kmeans();
}
template<typename T>
void emcluster<T>::probability_matrix(mmatrix<T> && Mat){
probability_matrix(Mat);
}
template<typename T>
void emcluster<T>::probability_matrix(mmatrix<T> & Mat){
_ClusterProb = Mat;
initialisation();
}
template<typename T>
void emcluster<T>::threshold(T thresh){
_Threshold = thresh;
}
template<typename T>
void emcluster<T>::sigma(double sigma){
_Sigma = sigma;
_SigmaInit = sigma;
}
template<typename T>
double emcluster<T>::cluster(){
double NewSigma = 1.0/(2.0*_Sigma);
for(std::size_t i = 0; i < _DataMatrix.row_size(); i++){
mmatrix<T> DataProtoDist = mmatrix<T>::vector_norms(_Prototypes - _DataMatrix[i],mmatrix<T>::euclids).transpose()*NewSigma;
DataProtoDist -= mmatrix<T>::mins(DataProtoDist);
//Final Step: _Assignment(i,:) = exp(DataProtoDist * Pj*-1)/sum(Pj*DataProtoDist*-1)[0][0]
}
_ClusterProb = mmatrix<T>::sums(_Assignments.transposition())/(T)_DataMatrix.row_size();
for(std::size_t i = 0; i < _K; i++){
mmatrix<T> NewProto = _DataMatrix.vec_entry_mult(_Assignments.transposition()[i]);
_Prototypes[i] = (mmatrix<T>::sums(NewProto.transposition())/mmatrix<T>::sum(_Assignments.transposition()[i]))[0];
}
NewSigma = 0;
for(std::size_t i = 0; i < _DataMatrix.row_size(); i++){
NewSigma += (double)(mmatrix<T>::vector_norms(_Prototypes - _DataMatrix[i],mmatrix<T>::euclids)*_Assignments[i])[0][0];
}
_Sigma = NewSigma/(_DataMatrix.row_size()*_DataMatrix.col_size());
}
template<typename T>
std::vector<double> emcluster<T>::clustering(std::size_t Steps){
std::vector<double> ReconstError;
ReconstError.push_back(cluster());
for(std::size_t i = 1; i < Steps; i++){
ReconstError.push_back(cluster());
if(((ReconstError[i-1]-ReconstError[i])/ReconstError[i-1]) < _Threshold){
break;
}
}
return ReconstError;
}
template<typename T>
mmatrix<T> emcluster<T>::data_matrix(){
return _DataMatrix;
}
template<typename T>
mmatrix<T> emcluster<T>::prototypes(){
return _Prototypes;
}
template<typename T>
mmatrix<T> emcluster<T>::assignments(){
return _Assignments;
}
template<typename T>
mmatrix<T> emcluster<T>::probability_matrix(){
return _ClusterProb;
}
template<typename T>
T emcluster<T>::threshold(){
return _Threshold;
}
template<typename T>
double emcluster<T>::sigma(){
return _Sigma;
}
template<typename T>
std::size_t emcluster<T>::k(){
return _K;
}
template<typename T>
std::vector<std::size_t> emcluster<T>::labels(){
std::vector<std::size_t> Labels(_Assignments.row_size());
for(std::size_t i = 0; i < _Assignments; i++){
Labels[i] = std::max_element(_Assignments[i].begin(),
_Assignments[i].end())-_Assignments[i].begin();
}
return labels;
}
template<typename T>
std::vector< std::vector<std::size_t> > emcluster<T>::label_clusters(){
std::vector< std::vector<std::size_t> > Clusters(_K);
for(std::size_t i = 0; i < _Assignments.row_size(); i++){
Clusters[(std::max_element(_Assignments[i].begin(),
_Assignments[i].end())-_Assignments[i].begin())].push_back(i);
}
return Clusters;
}
template<typename T>
std::vector< mmatrix<T> > emcluster<T>::clusters(){
std::vector< mmatrix<T> > Clusters(_K);
std::vector< std::vector<std::size_t> > cluster_label = label_clusters();
for(std::size_t i = 0; i < _K; i++){
Clusters[i].push_back(_Prototypes[i]);
for(std::size_t j = 0; j < cluster_label[i].size(); j++){
Clusters[i].push_back(_DataMatrix[cluster_label[i][j]]);
}
}
return Clusters;
}
template<typename T>
void emcluster<T>::reset(){
_Sigma = _SigmaInit;
init_kmeans();
}
template<typename T>
void emcluster<T>::init_kmeans(){
kmeans<T> tmp_cluster(_DataMatrix,_K);
tmp_cluster.clustering();
_Prototypes = tmp_cluster.prototypes();
_Assignments = tmp_cluster.assignments();
}
template<typename T>
void emcluster<T>::initialisation(){
if(_Prototypes.row_size() != _K){
throw std::out_of_range("Initial prototypes row size("+
std::to_string(_Prototypes.row_size())+
") differs from number K("+std::to_string(_K)+")");
}
if(_Prototypes.row_size() != _DataMatrix.col_size() && _DataMatrix.col_size() != 0){
throw std::out_of_range("initial prototypes col size("+
std::to_string(_Prototypes.col_size())+
") differs from data col size ("+
std::to_string(_DataMatrix.col_size())+").");
}
if(_ClusterProb.row_size() == _K){
_ClusterProb.transpose();
}
if(_ClusterProb.col_size() != _K){
throw std::out_of_range("Initial probabilities col size("+
std::to_string(_ClusterProb.col_size())+
") differs from number K("+std::to_string(_K)+")");
}
}
/*---Topograpic-Vector-Quantorisation-----------------------------------*/
template<typename T = double>
class tvq{
};
/*---KMeans-------------------------------------------------------------*/
template<typename T>
double kmeans<T>::_Threshold = 1e-4;
template<typename T>
kmeans<T>::kmeans(std::size_t K):_K(K){
if(_K < 2){
throw std::out_of_range("K, number of prototypes, has to be greater than 1.");
}
if(!clustering::_Seeded){
std::srand(std::time(NULL));
clustering::_Seeded = true;
}
}
template<typename T>
kmeans<T>::kmeans(mmatrix<T> && Mat, std::size_t K):_K(K){
if(_K < 2){
throw std::out_of_range("K, number of prototypes, has to be greater than 1.");
}
if(!clustering::_Seeded){
std::srand(std::time(NULL));
clustering::_Seeded = true;
}
data_matrix(Mat);
}
template<typename T>
kmeans<T>::kmeans(mmatrix<T> & Mat, std::size_t K):_K(K){
if(_K < 2){
throw std::out_of_range("K, number of prototypes, has to be greater than 1.");
}
if(!clustering::_Seeded){
std::srand(std::time(NULL));
clustering::_Seeded = true;
}
data_matrix(Mat);
}
template<typename T>
void kmeans<T>::initial_prototypes(mmatrix<T> & Mat){
if(Mat.row_size() != _K){
throw std::out_of_range("Initial prototypes row size("+
std::to_string(Mat.row_size())+") differs from number K("+
std::to_string(_K)+")");
}
if(Mat.col_size() != _DataMatrix.col_size() && _DataMatrix.col_size() != 0){
throw std::out_of_range("initial prototypes col size("+
std::to_string(Mat.col_size())+") differs from data col size ("+
std::to_string(_DataMatrix.col_size())+").");
}
_Prototypes = Mat;
initial_assign();
}
template<typename T>
void kmeans<T>::data_matrix(mmatrix<T> && Mat){
data_matrix(Mat);
}
template<typename T>
void kmeans<T>::data_matrix(mmatrix<T> & Mat){
_DataMatrix = Mat;
initialisation();
}
template<typename T>
void kmeans<T>::threshold(T thresh){
_Threshold = thresh;
}
template<typename T>
double kmeans<T>::cluster(){
double ReconstError = 0;
for(std::size_t i = 0; i < _DataMatrix.row_size(); i++){
mmatrix<T> Assignment = mmatrix<T>::vector_norms(_Prototypes - _DataMatrix[i], mmatrix<T>::euclids);
_Assignments[i][std::max_element(_Assignments[i].begin(),_Assignments[i].end())-_Assignments[i].begin()] = T(0);
std::size_t Idx = std::max_element(Assignment[0].begin(),Assignment[0].end()) - Assignment[0].begin();
_Assignments[i][Idx] = T(1);
}
for(std::size_t i = 0; i < _K; i++){
mmatrix<T> NewProto = _DataMatrix.vec_entry_mult(_Assignments.transposition()[i]);
_Prototypes[i] = (mmatrix<T>::sums(NewProto.transposition())/mmatrix<T>::sum(_Assignments.transposition()[i]))[0];
}
for(std::size_t i = 0; i < _DataMatrix.row_size(); i++){
ReconstError += (double)(mmatrix<T>::vector_norms(_Prototypes - _DataMatrix[i],mmatrix<T>::euclids)*_Assignments[i])[0][0];
}
return ReconstError*_DataMatrix.row_size();
}
template<typename T>
std::vector<double> kmeans<T>::clustering(std::size_t Steps){
std::vector<double> ReconstError;
std::size_t Idx = 0, Try = 0;
double Gradient = 1, Diff = 1;
while(Try < Steps){
ReconstError.push_back(cluster());
Try++, Idx++;
if(std::abs(mmatrix<T>::min(mmatrix<T>::maxs(_Assignments.transposition()))) < 1e-10){
while(std::abs(mmatrix<T>::min(mmatrix<T>::maxs(_Assignments.transposition()))) < 1e-10){
ReconstError.clear();
initialisation();
ReconstError.push_back(cluster());
}
Idx = 1;
Gradient = 1;
}
if(Idx > 1){
Gradient = std::fabs((ReconstError[Idx-2]-ReconstError[Idx-1])/(ReconstError[Idx-2]));
}
if(Idx > 2){
Diff = std::fabs(ReconstError[Idx-1]-ReconstError[Idx-3]);
}
if(Gradient < _Threshold || std::fabs(ReconstError[Idx-1]) == 0 || Diff == 0 ){
break;
}
}
return ReconstError;
}
template<typename T>
mmatrix<T> kmeans<T>::data_matrix(){
return _DataMatrix;
}
template<typename T>
mmatrix<T> kmeans<T>::prototypes(){
return _Prototypes;
}
template<typename T>
mmatrix<T> kmeans<T>::assignments(){
return _Assignments;
}
template<typename T>
T kmeans<T>::threshold(){
return _Threshold;
}
template<typename T>
std::size_t kmeans<T>::k(){
return _K;
}
template<typename T>
std::vector<std::size_t> kmeans<T>::labels(){
std::vector<std::size_t> Labels(_Assignments.row_size());
for(std::size_t i = 0; i < _Assignments; i++){
Labels[i] = std::max_element(_Assignments[i].begin(),_Assignments[i].end())-_Assignments[i].begin();
}
return labels;
}
template<typename T>
std::vector< std::vector<std::size_t> > kmeans<T>::label_clusters(){
std::vector< std::vector<std::size_t> > Clusters(_K);
for(std::size_t i = 0; i < _Assignments.row_size(); i++){
Clusters[(std::max_element(_Assignments[i].begin(),_Assignments[i].end())-_Assignments[i].begin())].push_back(i);
}
return Clusters;
}
template<typename T>
std::vector< mmatrix<T> > kmeans<T>::clusters(){
std::vector< mmatrix<T> > Clusters(_K);
std::vector< std::vector<std::size_t> > cluster_label = label_clusters();
for(std::size_t i = 0; i < _K; i++){
Clusters[i].push_back(_Prototypes[i]);
for(std::size_t j = 0; j < cluster_label[i].size(); j++){
Clusters[i].push_back(_DataMatrix[cluster_label[i][j]]);
}
}
return Clusters;
}
template<typename T>
void kmeans<T>::reset(){
_Prototypes = initial_proto();
initial_assign();
}
template<typename T>
mmatrix<T> kmeans<T>::initial_proto(){
mmatrix<T> Protos;
std::unordered_set<std::size_t> Indices;
std::size_t i = 0, idx;
while(i < _K){
idx = std::fmod(std::rand(),_DataMatrix.row_size());
auto Search = Indices.find(idx);
if(Search == Indices.end()){
Protos.push_back(_DataMatrix[idx]);
Indices.insert(idx);
i++;
}
}
return Protos;
}
template<typename T>
void kmeans<T>::initial_assign(){
mmatrix<T> Assignment;
std::size_t Idx;
for(std::size_t i = 0; i < _DataMatrix.row_size(); i++){
Assignment = mmatrix<T>::vector_norms(_Prototypes - _DataMatrix[i], mmatrix<T>::euclids);
Idx = std::max_element(Assignment[0].begin(),Assignment[0].end()) - Assignment[0].begin();
_Assignments[i][Idx] = T(1);
}
}
template<typename T>
void kmeans<T>::initialisation(){
do{
_Assignments = mmatrix<T>(_DataMatrix.row_size(), _K);
_Prototypes = initial_proto();
initial_assign();
}while(std::abs(mmatrix<T>::min(mmatrix<T>::maxs(_Assignments.transposition()))) <1e-10);
}
};
};
/*===Variables================================================================*/
/*===Prototypes===============================================================*/
/*===Main=====================================================================*/
#endif

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#ifndef _LEARNING_HPP_
#define _LEARNING_HPP_
/*===Libraries================================================================*/
/*===Classes-Structurres======================================================*/
namespace data_learning{
/*---machine-learning-------------------------------------------------------*/
namespace learning{
class perceptron{
};
class centroid{
};
class svm{
};
class neighbour{
};
class neuronal{
};
class kernel{
};
class forest{
};
};
};
/*===Variables================================================================*/
/*===Prototypes===============================================================*/
/*===Main=====================================================================*/
#endif

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#ifndef _MDIMENSION_HPP_
#define _MDIMENSION_HPP_
/*===Libraries================================================================*/
#include <iostream>
#include <cstdlib>
#include <stdexcept>
#include <string>
#include "mdimension.hpp"
/*===Classes-Structurres======================================================*/
class mdimension{
public:
// size(Mat) == _Row x _Col
std::size_t Row;
std::size_t Col;
public:
mdimension();
mdimension(std::size_t Size);
mdimension(std::size_t R, std::size_t C);
mdimension(const mdimension & Dim);
mdimension(const mdimension && Dim);
std::string to_string() const;
bool empty() const;
void swap();
bool operator==(const mdimension && Dim) const;
bool operator!=(const mdimension && Dim) const;
bool operator==(const mdimension & Dim) const;
bool operator!=(const mdimension & Dim) const;
mdimension& operator=(const mdimension && Dim);
mdimension& operator=(const mdimension & Dim);
mdimension operator*(const mdimension && DimL) const;
mdimension& operator*=(const mdimension && DimL);
mdimension operator*(const mdimension & DimL) const;
mdimension& operator*=(const mdimension & DimL);
};
/*===Variables================================================================*/
/*===Prototypes===============================================================*/
/*===Main=====================================================================*/
mdimension::mdimension():Row(0),Col(0){
}
mdimension::mdimension(std::size_t Size):Row(Size),Col(Size){
}
mdimension::mdimension(std::size_t R, std::size_t C):Row(R),Col(C){
}
mdimension::mdimension(const mdimension & Dim){
Row = Dim.Row;
Col = Dim.Col;
}
mdimension::mdimension(const mdimension && Dim){
Row = Dim.Row;
Col = Dim.Col;
}
std::string mdimension::to_string() const{
std::string Str = "<" + std::to_string(Row) + "x"+std::to_string(Col) + ">";
return Str;
}
bool mdimension::empty() const{
return ((Row + Col) == 0);
}
void mdimension::swap(){
std::size_t Tmp = std::move(Row);
Row = std::move(Col);
Col = std::move(Tmp);
}
bool mdimension::operator==(const mdimension && Dim) const{
return operator==(Dim);
}
bool mdimension::operator!=(const mdimension && Dim) const{
return operator!=(Dim);
}
bool mdimension::operator==(const mdimension & Dim) const{
return (Row - Dim.Row + Col - Dim.Col) == 0;
}
bool mdimension::operator!=(const mdimension & Dim) const{
return (Row - Dim.Row + Col - Dim.Col) != 0;
}
mdimension& mdimension::operator=(const mdimension && Dim){
return operator=(Dim);
}
mdimension& mdimension::operator=(const mdimension & Dim){
Row = Dim.Row;
Col = Dim.Col;
return *this;
}
mdimension mdimension::operator*(const mdimension && Dim) const{
return operator*(Dim);
}
mdimension& mdimension::operator*=(const mdimension && Dim){
return operator*=(Dim);
}
mdimension mdimension::operator*(const mdimension & Dim) const{
mdimension NewDim(0);
if((Col - Dim.Row) == 0){
NewDim.Row = Row;
NewDim.Col = Dim.Col;
}
else{
throw std::out_of_range("Matrix dimensions "+to_string() + " * "
+ Dim.to_string() + " are not conforming, " + std::to_string(Col)
+ " != " +std::to_string(Dim.Row) + ".");
}
return NewDim;
}
mdimension& mdimension::operator*=(const mdimension & Dim){
if((Col - Dim.Row) == 0){
Col = Dim.Col;
}
else{
throw std::out_of_range("Matrix dimensions "+to_string() + " * "
+ Dim.to_string() + " are not conforming, " + std::to_string(Col)
+ " != " +std::to_string(Dim.Row) + ".");
}
return *this;
}
#endif

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#ifndef _MEIGEN_HPP_
#define _MEIGEN_HPP_
/*===Libraries================================================================*/
#define _USE_MATH_DEFINES
#include <cmath>
#include <algorithm>
#include <cstdlib>
#include <ctime>
/*===Classes-Structurres======================================================*/
template<typename T>
class mmatrix;
template<typename T = double>
class meigen{
private:
mmatrix<T> _EigenVector;
T _EigenValue;
static double _Threshold;
static T _RMin;
static T _RMax;
static std::size_t _MaxPowerVal;
static bool _Seeded;
public:
meigen();
meigen(mmatrix<T> && Vector, T Value);
meigen(mmatrix<T> & Vector, T Value);
mmatrix<T> vector();
T value();
static meigen<T> power_iteration(mmatrix<T> && SqrMatrix, std::function<T(mmatrix<T>)> const& Norm = mmatrix<T>::euclid);
static meigen<T> power_iteration(mmatrix<T> & SqrMatrix, std::function<T(mmatrix<T>)> const& Norm = mmatrix<T>::euclid);
static meigen<T> power_iteration(mmatrix<T> && SqrMatrix, mmatrix<T> && InitVector, std::function<T(mmatrix<T>)> const& Norm = mmatrix<T>::euclid);
static meigen<T> power_iteration(mmatrix<T> & SqrMatrix, mmatrix<T> & InitVector, std::function<T(mmatrix<T>)> const& Norm = mmatrix<T>::euclid);
static double threshold();
static void threshold(double thresh);
static std::size_t power_counter();
static void power_counter(std::size_t MaxCtr);
};
/*===Variables================================================================*/
template<typename T>
double meigen<T>::_Threshold = 1e-4;
template<typename T>
T meigen<T>::_RMax = T(1000);
template<typename T>
T meigen<T>::_RMin = T(0);
template<typename T>
std::size_t meigen<T>::_MaxPowerVal = 1e5;
template<typename T>
bool meigen<T>::_Seeded = false;
/*===Prototypes===============================================================*/
/*===Main=====================================================================*/
template<typename T>
meigen<T>::meigen(){
if(!_Seeded){
std::srand(std::time(NULL));
_Seeded = true;
}
};
template<typename T>
meigen<T>::meigen(mmatrix<T> && Vector, T Value){
_EigenVector = Vector;
_EigenValue = Value;
if(!_Seeded){
std::srand(std::time(NULL));
_Seeded = true;
}
}
template<typename T>
meigen<T>::meigen(mmatrix<T> & Vector, T Value){
_EigenVector = Vector;
_EigenValue = Value;
if(!_Seeded){
std::srand(std::time(NULL));
_Seeded = true;
}
}
template<typename T>
mmatrix<T> meigen<T>::vector(){
return _EigenVector;
}
template<typename T>
T meigen<T>::value(){
return _EigenValue;
}
template<typename T>
meigen<T> meigen<T>::power_iteration(mmatrix<T> && SqrMatrix, std::function<T(mmatrix<T>)> const& Norm){
return power_iteration(SqrMatrix, Norm);
}
template<typename T>
meigen<T> meigen<T>::power_iteration(mmatrix<T> & SqrMatrix, std::function<T(mmatrix<T>)> const& Norm){
std::vector<T> RandVec(SqrMatrix.col_size());
std::transform(RandVec.begin(), RandVec.end(), RandVec.begin(),[](T & Tmp){
return std::fmod(std::rand(),(_RMax-_RMin + 1)) + _RMin;
});
mmatrix<T> RandInitVector(RandVec);
return power_iteration(SqrMatrix, RandInitVector, Norm);
}
template<typename T>
meigen<T> meigen<T>::power_iteration(mmatrix<T> && SqrMatrix, mmatrix<T> && InitVector, std::function<T(mmatrix<T>)> const& Norm){
power_iteration(SqrMatrix, InitVector, Norm);
}
template<typename T>
meigen<T> meigen<T>::power_iteration(mmatrix<T> & SqrMatrix, mmatrix<T> & InitVector, std::function<T(mmatrix<T>)> const& Norm){
if(SqrMatrix.row_size() != SqrMatrix.col_size()){
throw std::out_of_range("power_iteration: Matrix has to be square matix, but it is "+SqrMatrix.size().to_string()+".");
}
mmatrix<T> PreVec = InitVector/Norm(InitVector), EigVec(PreVec * SqrMatrix);
EigVec /= Norm(EigVec);
double deg = std::acos((PreVec*EigVec.transposition())[0][0])*180.0 / M_PI;
std::size_t RoundCtr = 0;
while(deg > _Threshold && RoundCtr < _MaxPowerVal){
RoundCtr++;
PreVec = EigVec;
EigVec = PreVec * SqrMatrix;
EigVec /= Norm(EigVec);
deg = std::acos((PreVec*EigVec.transposition())[0][0])*180.0 / M_PI;
}
T EigVal = (EigVec * (SqrMatrix * EigVec.transposition()))[0][0];
return meigen(EigVec, EigVal);
}
template<typename T>
double meigen<T>::threshold(){
return _Threshold;
}
template<typename T>
void meigen<T>::threshold(double Thresh){
_Threshold = Thresh;
}
template<typename T>
std::size_t meigen<T>::power_counter(){
return _MaxPowerVal;
}
template<typename T>
void meigen<T>::power_counter(std::size_t MaxCtr){
_MaxPowerVal = MaxCtr;
}
#endif

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#ifndef _MINING_HPP_
#define _MINING_HPP_
/*===Libraries================================================================*/
#include <cmath>
#include <vector>
#include "mmatrix.hpp"
#include "meigen.hpp"
namespace data_learning{
/*---data-mining------------------------------------------------------------*/
namespace mining{
template<typename T = double>
class pca{
private:
mmatrix<T> _DataMatrix;
mmatrix<T> _CovMatrix;
std::vector< meigen<T> > _Eigens;
public:
pca();
pca(mmatrix<T> && Mat);
pca(mmatrix<T> & Mat);
void data_matrix(mmatrix<T> && Mat);
void data_matrix(mmatrix<T> & Mat);
mmatrix<T> data_matrix();
mmatrix<T> cov_matrix();
std::vector< meigen<T> > eigen(std::size_t EigNumber = 0, std::function<T(mmatrix<T>)> const& Norm = mmatrix<T>::euclid);
mmatrix<T> eigen_spectrum(std::size_t EigNumber = 0, bool normalise = true, std::function<T(mmatrix<T>)> const& Norm = mmatrix<T>::euclid);
mmatrix<T> loadings(std::size_t LoadNumber = 0, std::function<T(mmatrix<T>)> const& Norm = mmatrix<T>::euclid);
mmatrix<T> loading(std::size_t LoadIdx, std::function<T(mmatrix<T>)> const& Norm = mmatrix<T>::euclid);
mmatrix<T> principle_components(std::size_t CompNumber = 0, std::function<T(mmatrix<T>)> const& Norm = mmatrix<T>::euclid);
mmatrix<T> principle_component(std::size_t CompIdx, std::function<T(mmatrix<T>)> const& Norm = mmatrix<T>::euclid);
private:
void calc_eigen(std::size_t EigNumber, std::function<T(mmatrix<T>)> const& Norm = mmatrix<T>::euclid);
};
template<typename T>
pca<T>::pca(){
}
template<typename T>
pca<T>::pca(mmatrix<T> && Mat){
data_matrix(Mat);
}
template<typename T>
pca<T>::pca(mmatrix<T> & Mat){
data_matrix(Mat);
}
template<typename T>
void pca<T>::data_matrix(mmatrix<T> && Mat){
data_matrix(Mat);
}
template<typename T>
void pca<T>::data_matrix(mmatrix<T> & Mat){
_Eigens.clear();
_CovMatrix.clear();
_DataMatrix = Mat;
}
template<typename T>
mmatrix<T> pca<T>::data_matrix(){
return _DataMatrix;
}
template<typename T>
mmatrix<T> pca<T>::cov_matrix(){
if(_CovMatrix.size().Row == 0 || _CovMatrix.size().Col == 0){
_CovMatrix = mmatrix<T>::covariance(_DataMatrix);
}
return _CovMatrix;
}
template<typename T>
std::vector< meigen<T> > pca<T>::eigen(std::size_t EigNumber, std::function<T(mmatrix<T>)> const& Norm){
if(_Eigens.size() < EigNumber || EigNumber == 0){
if(EigNumber == 0){
EigNumber = _DataMatrix.col_size();
}
calc_eigen(EigNumber, Norm);
}
return _Eigens;
}
template<typename T>
mmatrix<T> pca<T>::eigen_spectrum(std::size_t EigNumber, bool normalise, std::function<T(mmatrix<T>)> const& Norm){
if(_Eigens.size() < EigNumber || EigNumber == 0){
if(EigNumber == 0){
EigNumber = _DataMatrix.col_size();
}
calc_eigen(EigNumber, Norm);
}
mmatrix<T> EigSpec = mmatrix<T>(1,EigNumber);
T Sum = T();
for(std::size_t i = 0; i < EigNumber; i++){
EigSpec[0][i] = _Eigens[i].value();
Sum += _Eigens[i].value();
}
if(normalise){
std::transform(EigSpec[0].begin(), EigSpec[0].end(), EigSpec[0].begin(), [Sum](T & Val){
return Val/Sum;
});
}
return EigSpec;
}
template<typename T>
mmatrix<T> pca<T>::loadings(std::size_t LoadNumber, std::function<T(mmatrix<T>)> const& Norm){
if(_Eigens.size() < LoadNumber || LoadNumber == 0){
if(LoadNumber == 0){
LoadNumber = _DataMatrix.col_size();
}
calc_eigen(LoadNumber, Norm);
}
mmatrix<T> Loadings = mmatrix<T>();
for(meigen<T> Eigen : _Eigens){
Loadings.push_back(Eigen.vector());
}
return Loadings;
}
template<typename T>
mmatrix<T> pca<T>::loading(std::size_t LoadIdx, std::function<T(mmatrix<T>)> const& Norm){
if(_Eigens.size() < LoadIdx){
calc_eigen(LoadIdx, Norm);
}
return _Eigens[LoadIdx].vector();
}
template<typename T>
mmatrix<T> pca<T>::principle_components(std::size_t CompNumber, std::function<T(mmatrix<T>)> const& Norm){
if(_Eigens.size() < CompNumber || CompNumber == 0){
if(CompNumber == 0){
CompNumber = _DataMatrix.col_size();
}
calc_eigen(CompNumber, Norm);
}
mmatrix<T> PrinComp = _DataMatrix*loadings().transposition();
return PrinComp;
}
template<typename T>
mmatrix<T> pca<T>::principle_component(std::size_t CompIdx, std::function<T(mmatrix<T>)> const& Norm){
if(_Eigens.size() < CompIdx){
calc_eigen(CompIdx, Norm);
}
return _DataMatrix*_Eigens[CompIdx].vector().transposition();
}
template<typename T>
void pca<T>::calc_eigen(std::size_t EigNumber, std::function<T(mmatrix<T>)> const& Norm){
if(_CovMatrix.size().Row == 0 || _CovMatrix.size().Col == 0){
_CovMatrix = mmatrix<T>::covariance(_DataMatrix);
}
if(EigNumber == 0){
EigNumber = _CovMatrix.row_size();
}
if(_Eigens.size() == 0){
_Eigens = mmatrix<T>::eigen(_CovMatrix, EigNumber, Norm);
}
else if(_Eigens.size() < EigNumber){
std::vector< meigen<T> > remainEigen = mmatrix<T>::eigen(mmatrix<T>::reduced_covariance(_CovMatrix, _Eigens),EigNumber-_Eigens.size(), Norm);
_Eigens.insert(_Eigens.end(),remainEigen.begin(),remainEigen.end());
}
}
/*----------------------------------------------------------------------*/
template<typename T = double>
class mds{
private:
mmatrix<T> _DistMatrix;
mmatrix<T> _GramianMatrix;
std::vector< meigen<T> > _Eigens;
public:
mds();
mds(mmatrix<T> && Mat);
mds(mmatrix<T> & Mat);
void dist_matrix(mmatrix<T> && Mat);
void dist_matrix(mmatrix<T> & Mat);
void data_matrix(mmatrix<T> && Mat, std::function<T(mmatrix<T>)> const& Norm = mmatrix<T>::euclid);
void data_matrix(mmatrix<T> & Mat, std::function<T(mmatrix<T>)> const& Norm = mmatrix<T>::euclid);
mmatrix<T> dist_matrix();
mmatrix<T> gramian_matrix();
std::vector< meigen<T> > eigen(std::size_t EigNumber = 0, std::function<T(mmatrix<T>)> const& Norm = mmatrix<T>::euclid);
mmatrix<T> eigen_spectrum(std::size_t EigNumber = 0, bool normalise = true, std::function<T(mmatrix<T>)> const& Norm = mmatrix<T>::euclid);
mmatrix<T> loadings(std::size_t LoadNumber = 0, std::function<T(mmatrix<T>)> const& Norm = mmatrix<T>::euclid);
mmatrix<T> loading(std::size_t LoadIdx, std::function<T(mmatrix<T>)> const& Norm = mmatrix<T>::euclid);
mmatrix<T> principle_components(std::size_t CompNumber = 0, std::function<T(mmatrix<T>)> const& Norm = mmatrix<T>::euclid);
mmatrix<T> principle_component(std::size_t CompIdx, std::function<T(mmatrix<T>)> const& Norm = mmatrix<T>::euclid);
private:
void calc_eigen(std::size_t EigNumber, std::function<T(mmatrix<T>)> const& Norm = mmatrix<T>::euclid);
};
template<typename T>
mds<T>::mds(){
}
template<typename T>
mds<T>::mds(mmatrix<T> && Mat){
data_matrix(Mat);
}
template<typename T>
mds<T>::mds(mmatrix<T> & Mat){
data_matrix(Mat);
}
template<typename T>
void mds<T>::dist_matrix(mmatrix<T> && Mat){
_Eigens.clear();
_GramianMatrix.clear();
_DistMatrix = Mat;
}
template<typename T>
void mds<T>::dist_matrix(mmatrix<T> & Mat){
_Eigens.clear();
_GramianMatrix.clear();
_DistMatrix = Mat;
}
template<typename T>
void mds<T>::data_matrix(mmatrix<T> && Mat, std::function<T(mmatrix<T>)> const& Norm){
data_matrix(Mat,Norm);
}
template<typename T>
void mds<T>::data_matrix(mmatrix<T> & Mat, std::function<T(mmatrix<T>)> const& Norm){
_Eigens.clear();
_GramianMatrix.clear();
_DistMatrix = mmatrix<T>::vectorwise_distance(Mat,Mat,Norm);
std::ofstream asdf("dist.dat");
asdf << _DistMatrix.to_string() << std::endl;
asdf.close();
}
template<typename T>
mmatrix<T> mds<T>::dist_matrix(){
return _DistMatrix;
}
template<typename T>
mmatrix<T> mds<T>::gramian_matrix(){
if(_GramianMatrix.size().Row == 0 || _GramianMatrix.size().Col == 0){
_GramianMatrix = mmatrix<T>::gramian(_DistMatrix);
}
return _GramianMatrix;
}
template<typename T>
std::vector< meigen<T> > mds<T>::eigen(std::size_t EigNumber, std::function<T(mmatrix<T>)> const& Norm){
if(_Eigens.size() < EigNumber || EigNumber == 0){
calc_eigen(EigNumber, Norm);
}
return _Eigens;
}
template<typename T>
mmatrix<T> mds<T>::eigen_spectrum(std::size_t EigNumber, bool normalise, std::function<T(mmatrix<T>)> const& Norm){
if(_Eigens.size() < EigNumber || EigNumber == 0){
calc_eigen(EigNumber, Norm);
}
mmatrix<T> EigSpec = mmatrix<T>(1,EigNumber);
T Sum = T();
for(std::size_t i = 0; i < EigNumber; i++){
EigSpec[0][i] = _Eigens[i].value();
Sum += _Eigens[i].value();
}
if(normalise){
std::transform(EigSpec[0].begin(), EigSpec[0].end(), EigSpec[0].begin(), [Sum](T & Val){
return Val/Sum;
});
}
return EigSpec;
}
template<typename T>
mmatrix<T> mds<T>::loadings(std::size_t LoadNumber, std::function<T(mmatrix<T>)> const& Norm){
if(_Eigens.size() < LoadNumber || LoadNumber == 0){
calc_eigen(LoadNumber, Norm);
}
mmatrix<T> Loadings = mmatrix<T>();
for(meigen<T> Eigen : _Eigens){
Loadings.push_back(Eigen.vector());
}
return Loadings;
}
template<typename T>
mmatrix<T> mds<T>::loading(std::size_t LoadIdx, std::function<T(mmatrix<T>)> const& Norm){
if(_Eigens.size() < LoadIdx){
calc_eigen(LoadIdx, Norm);
}
return _Eigens[LoadIdx].vector();
}
template<typename T>
mmatrix<T> mds<T>::principle_components(std::size_t CompNumber, std::function<T(mmatrix<T>)> const& Norm){
return loadings(CompNumber, Norm).transposition();
}
template<typename T>
mmatrix<T> mds<T>::principle_component(std::size_t CompIdx, std::function<T(mmatrix<T>)> const& Norm){
return loading(CompIdx, Norm).transposition();
}
template<typename T>
void mds<T>::calc_eigen(std::size_t EigNumber, std::function<T(mmatrix<T>)> const& Norm){
if(_GramianMatrix.size().Row == 0 || _GramianMatrix.size().Col == 0){
_GramianMatrix = mmatrix<T>::gramian(_DistMatrix);
std::ofstream asdf("gram.dat");
asdf << _GramianMatrix.to_string() << std::endl;
asdf.close();
}
if(EigNumber == 0){
EigNumber = _GramianMatrix.row_size();
}
if(_Eigens.size() == 0){
_Eigens = mmatrix<T>::eigen(_GramianMatrix, EigNumber, Norm);
}
else if(_Eigens.size() < EigNumber){
std::vector< meigen<T> > remainEigen = mmatrix<T>::eigen(mmatrix<T>::reduced_covariance(_GramianMatrix, _Eigens),EigNumber-_Eigens.size(), Norm);
_Eigens.insert(_Eigens.end(),remainEigen.begin(),remainEigen.end());
}
}
/*----------------------------------------------------------------------*/
template<typename T = double>
class sammon{
private:
mmatrix<T> _DataMatrix;
std::vector< meigen<T> > _Eigens;
public:
sammon();
sammon(mmatrix<T> && Mat);
sammon(mmatrix<T> & Mat);
void set_matrix(mmatrix<T> && Mat);
void set_matrix(mmatrix<T> & Mat);
};
template<typename T>
sammon<T>::sammon(){
}
template<typename T>
sammon<T>::sammon(mmatrix<T> && Mat){
set_matrix(Mat);
}
template<typename T>
sammon<T>::sammon(mmatrix<T> & Mat){
set_matrix(Mat);
}
template<typename T>
void sammon<T>::set_matrix(mmatrix<T> && Mat){
_Eigens.clear();
_DataMatrix = Mat;
}
template<typename T>
void sammon<T>::set_matrix(mmatrix<T> & Mat){
_Eigens.clear();
_DataMatrix = Mat;
}
/*----------------------------------------------------------------------*/
class kernelreg{
};
};
};
/*===Variables================================================================*/
/*===Prototypes===============================================================*/
/*===Main=====================================================================*/
#endif

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/*===Libraries================================================================*/
#include <iostream>
#include <vector>
#include <fstream>
#include <string>
#include <sstream>
#include <omp.h>
#include "../src/mmatrix.hpp"
#include "../src/mining.hpp"
#include "../src/learning.hpp"
#include "../src/clustering.hpp"
/*===Classes-Structurres======================================================*/
/*===Variables================================================================*/
/*===Prototypes===============================================================*/
void debug_kmeans();
void debug_pca();
void debug_mds();
void debug_matrix();
void split(const std::string &s, char delim, std::vector<double> &elems);
std::vector<double> split(const std::string &s, char delim);
/*===Main=====================================================================*/
int main(){
debug_kmeans();
//debug_matrix();
//debug_pca();
//debug_mds();
return 0;
}
/*---clustering-------------------------------------------------------------*/
void debug_kmeans(){
mmatrix<double> DataMat;
std::vector<double> data;
std::string Line;
std::size_t K;
std::ifstream Input("data-test/Cluster.dat");
if(Input.is_open()){
while(std::getline(Input,Line)){
if(Line.size() != 0){
data = split(Line, ' ');
DataMat.push_back(data);
}
}
}
Input.close();
K = 4;
data_learning::clustering::kmeans<double> KMeans = data_learning::clustering::kmeans<double>(DataMat,K);
std::ofstream Output("data-test/Cls_ErrDev.dat");
std::vector<double> ErrDev = KMeans.clustering();
for(std::size_t i = 0; i < ErrDev.size(); i++){
Output << ErrDev[i] << std::endl;
}
Output.close();
Output = std::ofstream("data-test/clustered.dat");
std::vector< mmatrix<double> > Clusters = KMeans.clusters();
for(std::size_t i = 0; i < Clusters.size(); i++){
Output << Clusters[i].to_string() << std::endl << std::endl;
}
Output.close();
}
/*---data-mining------------------------------------------------------------*/
void debug_pca(){
mmatrix<double> DataMat, EigSpec, PrinComp, EigenVectors;
std::vector<double> data;
std::string Line;
std::ifstream Input("data-test/Hidden1.dat");
while(!Input.eof()){
std::getline(Input,Line);
data = split(Line, ' ');
if(data.size() != 0){
DataMat.push_back(data);
}
}
Input.close();
data_learning::mining::pca<double> PCA = data_learning::mining::pca<double>(DataMat);
unsigned EigNumb = 10;
std::ofstream Output("data-test/EigenSpectum_PCA.dat");
EigSpec = PCA.eigen_spectrum();
for(unsigned i = 0; i < EigSpec.col_size(); i++){
Output << EigSpec[0][i] << std::endl;
}
Output.close();
Output = std::ofstream("data-test/PrincipleComponents_PCA.dat");
PrinComp = PCA.principle_components(EigNumb);
for(unsigned i = 0; i < PrinComp.row_size(); i++){
for(unsigned j = 0; j < PrinComp.col_size()-1; j++){
Output << PrinComp[i][j] << "\t";
}
Output << PrinComp[i][PrinComp.col_size()-1] << std::endl;
}
Output.close();
Output = std::ofstream("data-test/Loadings_PCA.dat");
EigenVectors = PCA.loadings();
for(unsigned i = 0; i < EigenVectors.row_size(); i++){
for(unsigned j = 0; j < EigenVectors.col_size()-1; j++){
Output << EigenVectors[i][j] << "\t";
}
Output << EigenVectors[i][EigenVectors.col_size()-1] << std::endl;
}
Output.close();
}
void debug_mds(){
mmatrix<double> DataMat, EigSpec, PrinComp;
std::vector<double> data;
std::string Line;
mmatrix<double>::thread(4);
std::ifstream Input("data-test/Hidden.dat");
while(!Input.eof()){
std::getline(Input,Line);
data = split(Line, ' ');
if(data.size() != 0){
DataMat.push_back(data);
}
}
Input.close();
data_learning::mining::mds<double> MDS = data_learning::mining::mds<double>(DataMat);
std::ofstream Output("data-test/EigenSpectum_MDS.dat");
EigSpec = MDS.eigen_spectrum(2);
for(unsigned i = 0; i < EigSpec.col_size(); i++){
Output << EigSpec[0][i] << std::endl;
}
Output.close();
Output = std::ofstream("data-test/PrincipleComponents_MDS.dat");
PrinComp = MDS.principle_components(2);
for(unsigned i = 0; i < PrinComp.row_size(); i++){
for(unsigned j = 0; j < PrinComp.col_size()-1; j++){
Output << PrinComp[i][j] << "\t";
}
Output << PrinComp[i][PrinComp.col_size()-1] << std::endl;
}
Output.close();
}
/*---Auxilliary---------------------------------------------------------------*/
void debug_matrix(){
mmatrix<double> Mat1 = {{1,2,3},{4,5,6},{7,8,9}};
mmatrix<double> Mat2 = {{0,1,2},{3,4,5},{6,7,8}};
mmatrix<double> Vec1 = {10.0,11.0,12.0};
std::vector<double> Vec2 = {11.0,12.0,13.0};
double factor = 3;
std::cout << "Matrix 1:\n" << Mat1.to_string() << "\nMatrix2:\n" << Mat2.to_string() << std::endl << std::endl;
std::cout << "Vec 1:\n" << Vec1.to_string() << "\nVec2:\n";
for(auto Val : Vec2){
std::cout << Val << " ";
}
std::cout << std::endl << std::endl;
std::cout << "M1+ M2 : \n" << (Mat1+Mat2).to_string() << std::endl << std::endl;
Mat1 += Mat2;
std::cout << "M1+=M2 : \n" << Mat1.to_string() << std::endl << std::endl;
std::cout << "M1- M2 : \n" << (Mat1-Mat2).to_string() << std::endl << std::endl;
Mat1 -= Mat2;
std::cout << "M1-=M2 : \n" << Mat1.to_string() << std::endl << std::endl;
std::cout << "M1* M2 : \n" << (Mat1*Mat2).to_string() << std::endl << std::endl;
Mat1 *= Mat2;
std::cout << "M1* " << factor << " : \n" << (Mat1*factor).to_string() << std::endl << std::endl;
Mat1 *= factor;
std::cout << "M1*=" << factor << " : \n" << Mat1.to_string() << std::endl << std::endl;
std::cout << "M1* V1': \n" << (Mat1*Vec1.transposition()).to_string() << std::endl << std::endl;
std::cout << "M1* V2 : \n" << (Mat1*Vec2).to_string() << std::endl << std::endl;
Mat1 *= Vec1.transposition();
std::cout << "M1*=V1 : \n" << Mat1.to_string() << std::endl << std::endl;
Mat1.transpose();
Mat1 *= Vec2;
std::cout << "M1'*=V2 : \n" << Mat1.to_string() << std::endl << std::endl;
}
void split(const std::string &s, char delim, std::vector<double> &elems){
std::stringstream ss;
ss.str(s);
std::string item;
while (std::getline(ss, item, delim)) {
if(item.empty() == false){
elems.push_back(std::stof(item));
}
}
}
std::vector<double> split(const std::string &s, char delim){
std::vector<double> elems;
split(s, delim, elems);
return elems;
}

179
test/debug_cluster.cpp Executable file
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/*===Libraries================================================================*/
#include <iostream>
#include <vector>
#include <fstream>
#include <string>
#include <sstream>
#include <omp.h>
#include "../src/mmatrix.hpp"
#include "../src/mining.hpp"
#include "../src/learning.hpp"
#include "../src/clustering.hpp"
/*===Classes-Structurres======================================================*/
/*===Variables================================================================*/
/*===Prototypes===============================================================*/
void debug_kmeans();
void debug_pca();
void debug_mds();
void split(const std::string &s, char delim, std::vector<double> &elems);
std::vector<double> split(const std::string &s, char delim);
/*===Main=====================================================================*/
int main(){
//debug_kmeans();
//debug_matrix();
debug_pca();
//debug_mds();
return 0;
}
/*---clustering-------------------------------------------------------------*/
void debug_kmeans(){
mmatrix<double> DataMat;
std::vector<double> data;
std::string Line;
std::size_t K;
std::ifstream Input("data-test/Cluster.dat");
if(Input.is_open()){
while(std::getline(Input,Line)){
if(Line.size() != 0){
data = split(Line, ' ');
DataMat.push_back(data);
}
}
}
Input.close();
K = 4;
data_learning::clustering::kmeans<double> KMeans = data_learning::clustering::kmeans<double>(DataMat,K);
std::ofstream Output("data-test/Cls_ErrDev.dat");
std::vector<double> ErrDev = KMeans.clustering();
for(std::size_t i = 0; i < ErrDev.size(); i++){
Output << ErrDev[i] << std::endl;
}
Output.close();
Output = std::ofstream("data-test/clustered.dat");
std::vector< mmatrix<double> > Clusters = KMeans.clusters();
for(std::size_t i = 0; i < Clusters.size(); i++){
Output << Clusters[i].to_string() << std::endl << std::endl;
}
Output.close();
}
/*---data-mining------------------------------------------------------------*/
void debug_pca(){
mmatrix<double> DataMat, EigSpec, PrinComp, EigenVectors;
std::vector<double> data;
std::string Line;
std::ifstream Input("test_data/Hidden2.dat");
while(!Input.eof()){
std::getline(Input,Line);
data = split(Line, ' ');
if(data.size() != 0){
DataMat.push_back(data);
}
}
Input.close();
data_learning::mining::pca<double> PCA = data_learning::mining::pca<double>(DataMat);
unsigned EigNumb = 10;
std::ofstream Output("test_data/EigenSpectum_PCA.dat");
EigSpec = PCA.eigen_spectrum();
for(unsigned i = 0; i < EigSpec.col_size(); i++){
Output << EigSpec[0][i] << std::endl;
}
Output.close();
Output = std::ofstream("test_data/PrincipleComponents_PCA.dat");
PrinComp = PCA.principle_components(EigNumb);
for(unsigned i = 0; i < PrinComp.row_size(); i++){
for(unsigned j = 0; j < PrinComp.col_size()-1; j++){
Output << PrinComp[i][j] << "\t";
}
Output << PrinComp[i][PrinComp.col_size()-1] << std::endl;
}
Output.close();
Output = std::ofstream("test_data/Loadings_PCA.dat");
EigenVectors = PCA.loadings();
for(unsigned i = 0; i < EigenVectors.row_size(); i++){
for(unsigned j = 0; j < EigenVectors.col_size()-1; j++){
Output << EigenVectors[i][j] << "\t";
}
Output << EigenVectors[i][EigenVectors.col_size()-1] << std::endl;
}
Output.close();
}
void debug_mds(){
mmatrix<double> DataMat, EigSpec, PrinComp;
std::vector<double> data;
std::string Line;
mmatrix<double>::thread(4);
std::ifstream Input("data-test/Hidden.dat");
while(!Input.eof()){
std::getline(Input,Line);
data = split(Line, ' ');
if(data.size() != 0){
DataMat.push_back(data);
}
}
Input.close();
data_learning::mining::mds<double> MDS = data_learning::mining::mds<double>(DataMat);
std::ofstream Output("data-test/EigenSpectum_MDS.dat");
EigSpec = MDS.eigen_spectrum(2);
for(unsigned i = 0; i < EigSpec.col_size(); i++){
Output << EigSpec[0][i] << std::endl;
}
Output.close();
Output = std::ofstream("data-test/PrincipleComponents_MDS.dat");
PrinComp = MDS.principle_components(2);
for(unsigned i = 0; i < PrinComp.row_size(); i++){
for(unsigned j = 0; j < PrinComp.col_size()-1; j++){
Output << PrinComp[i][j] << "\t";
}
Output << PrinComp[i][PrinComp.col_size()-1] << std::endl;
}
Output.close();
}
/*---Auxilliary---------------------------------------------------------------*/
void split(const std::string &s, char delim, std::vector<double> &elems){
std::stringstream ss;
ss.str(s);
std::string item;
while (std::getline(ss, item, delim)) {
if(item.empty() == false){
elems.push_back(std::stof(item));
}
}
}
std::vector<double> split(const std::string &s, char delim){
std::vector<double> elems;
split(s, delim, elems);
return elems;
}

94
test/debug_dimension.cpp Executable file
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/*===Libraries================================================================*/
#include <iostream>
#include <vector>
#include <string>
#include "../src/mdimension.hpp"
/*===Classes-Structurres======================================================*/
/*===Variables================================================================*/
/*===Prototypes===============================================================*/
void constructors();
void operators();
/*===Main=====================================================================*/
int main(){
constructors();
operators();
}
/**
* Check if constructors are working and modification is still possible.
*/
void constructors(){
std::cout << std::endl << std::string(80,'#') << std::endl;
std::cout << "#\t\tA) Dimension Constructors \t\t\t\t #" << std::endl;
std::cout << std::string(80,'#') << std::endl << "Reset\n" << std::endl;
mdimension DimEmpt;
std::cout << "DimHc: " << DimEmpt.to_string() << std::endl << std::endl;
mdimension DimSq1(10);
std::cout << "DimSq1(10): " << DimSq1.to_string() << std::endl << std::endl;
mdimension DimSq2(2,5);
std::cout << "DimSq2(10): " << DimSq2.to_string() << std::endl << std::endl;
mdimension DimDef;
DimDef.Row = 4;
DimDef.Col = 5;
std::cout << "DimDef: " << DimDef.to_string() << std::endl;
mdimension DimCopy(DimDef);
std::cout << "DimDCopy(DimDef): " << DimCopy.to_string() << std::endl;
DimCopy = DimSq1;
std::cout << "DimCopy = DimSq1: " << DimCopy.to_string() << std::endl;
}
void operators(){
std::cout << std::endl << std::string(80,'#') << std::endl;
std::cout << "#\t\tB) Dimension Operations \t\t\t\t #" << std::endl;
std::cout << std::string(80,'#') << std::endl << "Reset\n" << std::endl;
mdimension DimEmpt;
mdimension DimSq(2,5);
mdimension DimDef(5,2);
mdimension DimCopy(DimDef);
std::cout << "DimEmpt: " << DimEmpt.to_string() << std::endl << std::endl;
std::cout << "DimDef: " << DimDef.to_string() << std::endl << std::endl;
std::cout << "DimSq: " << DimSq.to_string() << std::endl << std::endl;
std::cout << "DimCopy: " << DimCopy.to_string() << std::endl << std::endl;
std::cout << std::endl << std::string(80,'#') << std::endl;
std::cout << "#\t\tB1) Unary Operations\t\t\t\t\t #" << std::endl;
std::cout << std::string(80,'#') << std::endl << "Reset\n" << std::endl;
DimDef.swap();
std::cout << "DimCopy.swap() = " << DimCopy.to_string() << std::endl << std::endl;
std::cout << "DimCopy.empty = " << DimCopy.empty() << std::endl << std::endl;
DimCopy = mdimension(DimDef);
std::cout << std::endl << std::string(80,'#') << std::endl;
std::cout << "#\t\tB2) Boolean Operations\t\t\t\t\t #" << std::endl;
std::cout << std::string(80,'#') << std::endl << "Reset\n" << std::endl;
std::cout << "DimDef == DimCopy: " << (DimDef == DimCopy) << std::endl << std::endl;
std::cout << "DimCopy != DimEmpt: " << (DimCopy != DimEmpt) << std::endl << std::endl;
DimCopy = mdimension(DimDef);
std::cout << std::endl << std::string(80,'#') << std::endl;
std::cout << "#\t\tB3) Multiplication\t\t\t\t\t #" << std::endl;
std::cout << std::string(80,'#') << std::endl << "Reset\n" << std::endl;
DimSq.swap();
DimCopy = DimDef * DimSq;
std::cout << "DimCopy = DimDef * DimSq.swap() \n" << DimCopy.to_string() << std::endl << std::endl;
DimSq *= DimCopy;
std::cout << "DimSq *= DimCopy: \n" << DimSq.to_string() << std::endl << std::endl;
}

385
test/debug_matrix.cpp Executable file
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/*===Libraries================================================================*/
#include <cassert>
#include <cstdlib>
#include <iostream>
#include <omp.h>
#include "../src/mmatrix.hpp"
#include "../src/meigen.hpp"
/*===Classes-Structurres======================================================*/
/*===Variables================================================================*/
/*===Prototypes===============================================================*/
void constructors();
void memory();
void push_back();
void operators();
void static_operations();
void static_norms();
/*===Main=====================================================================*/
int main(){
mmatrix<double>::thread(omp_get_max_threads());
constructors();
memory();
push_back();
operators();
static_operations();
static_norms();
}
/**
* Check if constructors are working and modification is still possible.
*/
void constructors(){
std::cout << std::endl << std::string(80,'#') << std::endl;
std::cout << "#\t\tA) Matrix Constructors \t\t\t\t\t #" << std::endl;
std::cout << std::string(80,'#') << std::endl << "Reset\n" << std::endl;
mmatrix<double> MatrixHc = {{1,2,3},{4,5,6},{7,8,9}};
std::cout << "MatrixHc = {{1,2,3},{4,5,6},{7,8,9}}: \n" << MatrixHc.to_string() << std::endl << std::endl;
mmatrix<double> MatrixHcV = {10,11,12};
std::cout << "MatrixHcV = {1,2,3}: \n" << MatrixHcV.to_string() << std::endl << std::endl;
mmatrix<double> MatrixDef;
std::cout << "MatrixDef \n" << MatrixDef.to_string() << std::endl;
mmatrix<double> MatrixDim1(MatrixHc.size());
std::cout << "MatrixDim1(MatrixHc.size()) \n" << MatrixDim1.to_string() << std::endl << std::endl;
mmatrix<double> MatrixDim2(MatrixHc.row_size(),MatrixHc.col_size());
std::cout << "MatrixDim2(MatrixHc.row_size(),MatrixHc.col_size()) \n" << MatrixDim2.to_string() << std::endl << std::endl;
mmatrix<double> MatrixVec(MatrixHcV[0]);
std::cout << "MatrixVec(MatrixHcV[0]): \n" << MatrixVec.to_string() << std::endl << std::endl;
mmatrix<double> MatrixVecVec(MatrixHc.vector_matrix());
std::cout << "MatrixVecVec(MatrixHc.vetor_matrix()): \n" << MatrixVecVec.to_string() << std::endl << std::endl;
mmatrix<double> MatrixCopy(MatrixHc);
std::cout << "MatrixCopy(MatrixHcV): \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixDef.push_back(MatrixHcV[0]);
for(auto Vec : MatrixHc){
MatrixDef.push_back(Vec);
}
std::cout << "MatrixDef[i] = MatrixHc[i] for all i: \n" << MatrixCopy.to_string() << std::endl << std::endl;
for(std::size_t i = 0; i < MatrixHc.row_size(); i++){
for(std::size_t j = 0; j < MatrixHc.col_size(); j++){
assert(MatrixDim1[i][j] == 0.0);
MatrixDim1[i][j] = MatrixHc[i][j];
MatrixDim2[i][j] = MatrixHc[i][j];
}
}
std::cout << "MatrixDim1,2[i][j] = MatrixHc[i][j] for all i,j: \n" << MatrixDim1.to_string() << std::endl << std::endl;
}
void memory(){
std::cout << std::endl << std::string(80,'#') << std::endl;
std::cout << "#\t\tB) Matrix Memory Functions \t\t\t\t #" << std::endl;
std::cout << std::string(80,'#') << std::endl << "Reset\n" << std::endl;
mmatrix<double> MatrixHc = {{1,2,3},{4,5,6},{7,8,9}};
mmatrix<double> Matrix0;
std::cout << "MatrixHc: \n" << MatrixHc.to_string() << std::endl << std::endl;
std::cout << "Matrix0: \n" << Matrix0.to_string() << std::endl << std::endl;
Matrix0.resize(MatrixHc.size(),1);
std::cout << "Matrix0.resize(MatrixHc.size(),1): \n" << Matrix0.to_string() << std::endl << std::endl;
Matrix0.resize(MatrixHc.row_size()*2,MatrixHc.col_size()*2,2);
std::cout << "Matrix0.resize(MatrixHc.row_size()*2,MatrixHc.col_size()*2,2): \n" << Matrix0.to_string() << std::endl << std::endl;
Matrix0.resize(MatrixHc.row_size()-1,MatrixHc.col_size()-1,3);
std::cout << "Matrix0.resize(MatrixHc.row_size()-1,MatrixHc.col_size()-2,3): \n" << Matrix0.to_string() << std::endl << std::endl;
Matrix0.reserve(10);
std::cout << "Matrix0.reserve(10): \n" << Matrix0.to_string() << std::endl << std::endl;
Matrix0.resize(10,-1.337);
std::cout << "Matrix0.resize(10,-1.337): \n" << Matrix0.to_string() << std::endl << std::endl;
}
void push_back(){
std::cout << std::endl << std::string(80,'#') << std::endl;
std::cout << "#\t\tC) Matrix Matrix/Vector push_back \t\t\t #" << std::endl;
std::cout << std::string(80,'#') << std::endl << "Reset\n" << std::endl;
mmatrix<double> MatrixDef;
std::vector<double> VectorVec = {1,2,3};
std::vector< std::vector<double> > VectorVecVec = {{1,2,3},{4,5,6},{7,8,9}};
std::cout << "MatrixDef: \n" << MatrixDef.to_string() << std::endl << std::endl;
std::cout << "VectorVec: \n1 2 3" << std::endl << std::endl;
std::cout << "VectorVecVec: \n1 2 3\n4 5 6\n7 8 9" << std::endl << std::endl;
MatrixDef.push_back(VectorVecVec);
std::cout << "MatrixDef.push_back(VectorVecVec): \n" << MatrixDef.to_string() << std::endl << std::endl;
MatrixDef.push_back_row(VectorVec);
std::cout << "MatrixDef.push_back_row(VectorVec): \n" << MatrixDef.to_string() << std::endl << std::endl;
MatrixDef = (MatrixDef.transposition()*MatrixDef);
MatrixDef.push_back_col(VectorVecVec[2]);
std::cout << "(MatrixDef'*MatrixDef).push_back_col(VectorVecVec[2]): \n" << MatrixDef.to_string() << std::endl << std::endl;
}
void operators(){
std::cout << std::endl << std::string(80,'#') << std::endl;
std::cout << "#\t\tD) Matrix Matrix/Vector Operators \t\t\t #" << std::endl;
std::cout << std::string(80,'#') << std::endl << "Reset\n" << std::endl;
mmatrix<double> MatrixHc = {{1,2,3},{4,5,6},{7,8,9}};
mmatrix<double> MatrixHcV = {10,11,12};
mmatrix<double> MatrixDef;
mmatrix<double> MatrixDim(MatrixHc.size());
mmatrix<double> MatrixVec(MatrixHcV[0]);
mmatrix<double> MatrixCopy(MatrixHc);
std::vector<double> VectorVec = {1,2,3};
MatrixDef.push_back(MatrixHcV[0]);
for(auto Vec : MatrixHc){
MatrixDef.push_back(Vec);
}
for(std::size_t i = 0; i < MatrixHc.row_size(); i++){
for(std::size_t j = 0; j < MatrixHc.col_size(); j++){
assert(MatrixDim[i][j] == 0.0);
MatrixDim[i][j] = MatrixHc[i][j];
}
}
std::cout << "MatrixCopy: \n" << MatrixCopy.to_string() << std::endl << std::endl;
std::cout << "MatrixDim: \n" << MatrixDim.to_string() << std::endl << std::endl;
std::cout << "MatrixHc: \n" << MatrixHc.to_string() << std::endl << std::endl;
std::cout << "MatrixDef: \n" << MatrixDef.to_string() << std::endl << std::endl;
std::cout << "MatrixHcV: \n" << MatrixHcV.to_string() << std::endl << std::endl;
std::cout << "MatrixVec: \n" << MatrixVec.to_string() << std::endl << std::endl;
std::cout << "VectorVec: \n" << (char)((char)VectorVec[0]+'0') << " " << (char)((char)VectorVec[1]+'0') << " " << (char)((char)VectorVec[2]+'0') << std::endl << std::endl;
std::cout << std::endl << std::string(80,'#') << std::endl;
std::cout << "#\t\tD1) Matrix */ Matrix Multiplication\t\t\t #" << std::endl;
std::cout << std::string(80,'#') << std::endl << "Reset\n" << std::endl;
MatrixCopy = MatrixCopy * MatrixDim;
std::cout << "MatrixCopy = MatrixCopy * MatrixDim: \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = MatrixCopy * MatrixHc;
std::cout << "MatrixCopy = MatrixCopy * MatrixHc: \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = MatrixCopy * MatrixVec.transposition();
std::cout << "MatrixCopy = MatrixCopy * MatrixVec': \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = MatrixCopy * MatrixCopy.transposition()/1000000;
std::cout << "MatrixCopy = MatrixCopy * MatrixCopy'/1000000: \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = mmatrix<double>(MatrixHc);
std::cout << std::endl << std::string(80,'#') << std::endl;
std::cout << "#\t\tD2) Matrix */= Matrix Multiplication\t\t\t #" << std::endl;
std::cout << std::string(80,'#') << std::endl << "Reset\n" << std::endl;
MatrixCopy *= MatrixDim;
std::cout << "MatrixCopy *= MatrixDim: \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy *= MatrixHc;
std::cout << "MatrixCopy *= MatrixHc: \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy *= MatrixVec.transposition();
std::cout << "MatrixCopy *= MatrixVec': \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy *= MatrixCopy.transposition()/1000000;
std::cout << "MatrixCopy *= MatrixCopy'/1000000: \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = mmatrix<double>(MatrixHc);
std::cout << std::endl << std::string(80,'#') << std::endl;
std::cout << "#\t\tD3) Matrix +- Matrix Addition\t\t\t\t #" << std::endl;
std::cout << std::string(80,'#') << std::endl << "Reset\n" << std::endl;
MatrixCopy = MatrixCopy*MatrixCopy.transposition() + MatrixCopy*MatrixCopy.transposition();
std::cout << "MatrixCopy = MatrixCopy*MatrixCopy' + MatrixCopy*MatrixCopy': \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = MatrixCopy+MatrixHc;
std::cout << "MatrixCopy = MatrixCopy+MatrixHc: \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = MatrixCopy*MatrixVec.transposition()+MatrixVec.transposition();
std::cout << "MatrixCopy = MatrixCopy*MatrixVec' + MatrixVec': \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = MatrixCopy.transposition()-MatrixVec;
std::cout << "MatrixCopy = MatrixCopy'-MatrixVec: \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = MatrixCopy.transposition()*MatrixVec - MatrixHc;
std::cout << "MatrixCopy = MatrixCopy'*MatrixVec - MatrixHc: \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = mmatrix<double>(MatrixHc);
std::cout << std::endl << std::string(80,'#') << std::endl;
std::cout << "#\t\tD4) Matrix +-= Matrix Addition\t\t\t\t #" << std::endl;
std::cout << std::string(80,'#') << std::endl << "Reset\n" << std::endl;
MatrixCopy += MatrixCopy.transposition();
std::cout << "MatrixCopy += MatrixCopy': \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy -= MatrixCopy*MatrixCopy.transposition();
std::cout << "MatrixCopy -= MatrixCopy*MatrixCopy': \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy += MatrixCopy*MatrixCopy;
std::cout << "MatrixCopy += MatrixCopy*MatrixCopy: \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy -= MatrixCopy.transposition()*MatrixCopy.transposition();
std::cout << "MatrixCopy -= MatrixCopy'*MatrixCopy': \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = mmatrix<double>(MatrixHc);
std::cout << std::endl << std::string(80,'#') << std::endl;
std::cout << "#\t\tD5) Matrix * Vector Multiplication\t\t\t #" << std::endl;
std::cout << std::string(80,'#') << std::endl << "Reset\n" << std::endl;
MatrixCopy = MatrixCopy * MatrixVec.transposition();
std::cout << "MatrixCopy = MatrixCopy * MatrixVec': \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = MatrixCopy * MatrixVec;
std::cout << "MatrixCopy = MatrixCopy * MatrixVec: \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = MatrixCopy * MatrixDef.transposition();
std::cout << "MatrixCopy = MatrixCopy * MatrixDef': \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = MatrixCopy.transposition()*MatrixHc.transposition();
std::cout << "MatrixCopy = MatrixCopy' * MatrixHc': \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = MatrixHc*MatrixVec.transposition()*VectorVec;
std::cout << "MatrixHc = (MatrixCopy*MatrixVec) * VectorVec: \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = mmatrix<double>(MatrixHc);
std::cout << std::endl << std::string(80,'#') << std::endl;
std::cout << "#\t\tD6) Matrix *= Vector Multiplication\t\t\t #" << std::endl;
std::cout << std::string(80,'#') << std::endl << "Reset\n" << std::endl;
MatrixCopy *= MatrixVec.transposition();
std::cout << "MatrixCopy *= MatrixVec': \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy *= VectorVec;
std::cout << "MatrixCopy *= VectorVec: \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = mmatrix<double>(MatrixHc);
std::cout << std::endl << std::string(80,'#') << std::endl;
std::cout << "#\t\tD7) Matrix *= -or- * Matrix Entry-Multiplication\t #" << std::endl;
std::cout << std::string(80,'#') << std::endl << "Reset\n" << std::endl;
MatrixCopy = MatrixCopy.entry_mult(MatrixCopy.transposition());
std::cout << "MatrixCopy = MatrixCopy .* MatrixCopy': \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = MatrixCopy.vec_entry_mult(VectorVec);
std::cout << "MatrixCopy = MatrixCopy .* VectorVec: \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy.equal_entry_mult(MatrixCopy.transposition());
std::cout << "MatrixCopy .*= MatrixCopy': \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy.equal_vec_entry_mult(VectorVec);
std::cout << "MatrixCopy .*= VectorVec: \n" << MatrixCopy.to_string() << std::endl << std::endl;
}
void static_operations(){
std::cout << std::endl << std::string(80,'#') << std::endl;
std::cout << "#\t\tE) Static Matrix Operations\t\t\t\t #" << std::endl;
std::cout << std::string(80,'#') << std::endl << "Reset\n" << std::endl;
mmatrix<double> MatrixHc = {{1,2,3},{4,5,6},{7,8,9}};
mmatrix<double> MatrixVec = {1,2,3};
mmatrix<double> MatrixCopy(MatrixVec);
std::function<double (double)> function;
std::function<mmatrix<double> (mmatrix<double>)> mat_function;
std::cout << "MatrixHc: \n" << MatrixHc.to_string() << std::endl << std::endl;
std::cout << "MatrixVec: \n" << MatrixVec.to_string() << std::endl << std::endl;
std::cout << "MatrixCopy: \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = mmatrix<double>::repmat(MatrixCopy,3,1);
std::cout << "MatrixCopy = mmatrix::repmat(MatrixCopy,3,1): \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = mmatrix<double>::repmat(mmatrix<double>(MatrixCopy),3,3);
std::cout << "MatrixCopy = mmatrix::repmat(MatrixCopy,3,1): \n" << MatrixCopy.to_string() << std::endl << std::endl;
function = [](double Val){return Val*Val;};
mmatrix<double>::transform(MatrixHc, function);
std::cout << "std::function<T (T)> function = [](double Val){return Val*Val}" << std::endl;
std::cout << "mmatrix::transform(MatrixHc, function): \n" << MatrixHc.to_string() << std::endl << std::endl;
mat_function = [](mmatrix<double> Mat){
for(auto iter = Mat.begin(); iter != Mat.end(); iter++){
for(auto jter = iter->begin(); jter != iter->end(); jter++){
*jter = sqrt(*jter);
}
}
return Mat;
};
mmatrix<double>::transform(MatrixHc,mat_function);
std::cout << "std::function<T (T)> mat_function = [](mmatrix<double> Val){...sqrt for each value...}" << std::endl;
std::cout << "mmatrix::transform(MatrixHc, mat_function): \n" << MatrixHc.to_string() << std::endl << std::endl;
mmatrix<double>::transform(MatrixHc.begin(),MatrixHc.end(),function);
std::cout << "mmatrix::transform(MatrixHc.begin(),MatrixHc.end(), function): \n" << MatrixHc.to_string() << std::endl << std::endl;
std::cout << "mmatrix::max(MatrixHc): \n" << mmatrix<double>::max(MatrixHc) << std::endl << std::endl;
std::cout << "mmatrix::min(MatrixHc): \n" << mmatrix<double>::min(MatrixHc) << std::endl << std::endl;
std::cout << "mmatrix::sum(MatrixHc): \n" << mmatrix<double>::sum(MatrixHc) << std::endl << std::endl;
MatrixCopy = mmatrix<double>::maxs(MatrixHc);
std::cout << "MatrixCopy = mmatrix::maxs(MatrixHc): \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = mmatrix<double>::mins(MatrixHc);
std::cout << "MatrixCopy = mmatrix::mins(MatrixHc): \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = mmatrix<double>::sums(MatrixHc);
std::cout << "MatrixCopy = mmatrix::sums(MatrixHc): \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = mmatrix<double>::covariance(MatrixHc);
std::cout << "MatrixCopy = mmatrix::covariance(MatrixHc): \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = mmatrix<double>::gramian(MatrixHc);
std::cout << "MatrixCopy = mmatrix::gramian(MatrixHc): \n" << MatrixCopy.to_string() << std::endl << std::endl;
}
void static_norms(){
std::cout << std::endl << std::string(80,'#') << std::endl;
std::cout << "#\t\tF) Static Normalisation Operations\t\t\t #" << std::endl;
std::cout << std::string(80,'#') << std::endl << "Reset\n" << std::endl;
mmatrix<double> MatrixHc = {{1,2,3},{4,5,6},{7,8,9}};
mmatrix<double> MatrixVec = {1,2,3};
mmatrix<double> MatrixCopy(MatrixVec);
double Norm;
std::cout << "MatrixHc: \n" << MatrixHc.to_string() << std::endl << std::endl;
std::cout << "MatrixVec: \n" << MatrixVec.to_string() << std::endl << std::endl;
std::cout << "MatrixCopy: \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = mmatrix<double>::vector_norms(MatrixHc,1);
std::cout << "MatrixCopy = mmatrix::vector_norms(MatrixHc,1): \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = mmatrix<double>::vector_norms(MatrixHc,2);
std::cout << "MatrixCopy = mmatrix::vector_norms(MatrixHc,2): \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = mmatrix<double>::vector_norms(MatrixHc,200);
std::cout << "MatrixCopy = mmatrix::vector_norms(MatrixHc,200): \n" << MatrixCopy.to_string() << std::endl << std::endl;
Norm = mmatrix<double>::vector_norm(MatrixCopy,1);
std::cout << "Norm = mmatrix::vector_norm(MatrixCopy,1): \n" << Norm << std::endl << std::endl;
Norm = mmatrix<double>::vector_norm(MatrixCopy,2);
std::cout << "Norm = mmatrix::vector_norm(MatrixCopy,2): \n" << Norm << std::endl << std::endl;
Norm = mmatrix<double>::vector_norm(MatrixCopy,200);
std::cout << "Norm = mmatrix::vector_norm(MatrixCopy,200): \n" << Norm << std::endl << std::endl;
MatrixCopy = mmatrix<double>::vector_norms(MatrixHc,mmatrix<double>::euclids);
std::cout << "MatrixCopy = mmatrix::vector_norms(MatrixHc,mmatrix::euclids): \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = mmatrix<double>::vector_norms(MatrixHc,mmatrix<double>::taxicaps);
std::cout << "MatrixCopy = mmatrix::vector_norms(MatrixHc,mmatrix::taxicaps): \n" << MatrixCopy.to_string() << std::endl << std::endl;
Norm = mmatrix<double>::vector_norm(MatrixCopy,mmatrix<double>::taxicap);
std::cout << "Norm = mmatrix::vector_norm(MatrixCopy,mmatrix::taxicap): \n" << Norm << std::endl << std::endl;
Norm = mmatrix<double>::vector_norm(MatrixCopy,mmatrix<double>::euclid);
std::cout << "Norm = mmatrix::vector_norm(MatrixCopy,mmatrix::euclid): \n" << Norm << std::endl << std::endl;
MatrixCopy = MatrixHc*2;
std::cout << "MatrixHc: \n" << MatrixHc.to_string() << std::endl << std::endl;
std::cout << "MatrixCopy: \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = mmatrix<double>::vectorwise_distance(MatrixCopy,MatrixHc,1);
std::cout << "MatrixCopy = mmatrix::vectorwise_distance(MatrixCopy,MatrixHc,1): \n" << MatrixCopy.to_string() << std::endl << std::endl;
MatrixCopy = mmatrix<double>::vectorwise_distance(MatrixCopy,MatrixHc,1);
std::cout << "MatrixCopy = mmatrix::vectorwise_distance(MatrixCopy,MatrixHc,1): \n" << MatrixCopy.to_string() << std::endl << std::endl;
Norm = mmatrix<double>::distance(MatrixCopy,MatrixHc,2);
std::cout << "Norm = mmatrix::distance(MatrixCopy,MatrixHc,2): \n" << Norm << std::endl << std::endl;
std::vector< meigen<double> > Eigens = mmatrix<double>::eigen(MatrixHc,MatrixHc.col_size(),mmatrix<double>::euclid);
std::cout << "Eigen = mmatrix::eigen(MatrixHc,MatrixHc.col_size(),mmatrix::euclid):" << std::endl;
int Ctr = 0;
for(auto & Eigen : Eigens){
std::cout << "\tEigen.vector() " << Ctr << ": \n\t" << Eigen.vector().to_string() << std::endl;
std::cout << "\tEigen.value() " << Ctr++ << " \n\t" << Eigen.value() << std::endl;
}
std::cout << std::endl;
}

40
test_data-creation/cluster.py Executable file
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import random
def random_data(nData, nDim, muVec, sigVec):
if type(muVec) != list:
muVec = [muVec]*nDim
elif len(muVec) != nDim:
pass
if type(sigVec) != list:
sigVec = [sigVec]*nDim;
elif len(muVec) != nDiM:
pass
DataPoints = []
for n in range(nData):
point = [0]*nDim
for i in range(nDim):
point[i] = random.gauss(muVec[i],sigVec[i])
DataPoints.append(point)
return DataPoints
def point_to_string(Point):
return " ".join(str(x) for x in Point)
#---MAIN-----------------------------------------------------------------------#
nData = 1000
nDim = 2
sigma = 7
Data = []
Data.extend(random_data(nData,nDim,[165,60],sigma))
#Data.extend(random_data(nData,nDim,[0,1],sigma))
#Data.extend(random_data(nData,nDim,[1,0],sigma))
Data.extend(random_data(nData,nDim,[185,80],sigma))
File = open("Cluster.dat","w")
for Point in Data:
File.write(point_to_string(Point)+"\n")
File.close()

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test_data-creation/pca.py Executable file
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import math,random
from numpy import matrix as mat
def point_to_string(Point):
return " ".join(str(x) for x in Point)
def rotation(Point,Degree):
if type(Point) != list:
return Point
if type(Degree) != list:
Degree = [math.radians(Degree)]*len(Point);
elif len(Point) != len(Degree) and len(Degree) != 3:
return Point
for i in range(len(Degree)):
Degree[i] = math.radians(Degree[i])
RotX = mat(([1,0,0],[0,math.cos(Degree[0]),-math.sin(Degree[0])],[0,math.sin(Degree[0]),math.cos(Degree[0])]))
RotY = mat(([math.cos(Degree[0]),0,math.sin(Degree[0])],[0,1,0],[-math.sin(Degree[0]),0,math.cos(Degree[0])]))
RotZ = mat(([math.cos(Degree[0]),-math.sin(Degree[0]),0],[math.sin(Degree[0]),math.cos(Degree[0]),0],[0,0,1]))
return (Point*(RotZ*RotY*RotX)).tolist()[0]
#===MAIN=======================================================================#
nDataPart = 100;
nDim = 10
DataPoints = []
rotDeg = 45;
#--- d ------------------------------------------------------------------------#
xOff = 0;
yOff = 0;
for i in range(nDataPart):
yOff += 1/nDataPart
Point = []
#for p in range(2):
# Point.append(random.random())
Point.extend(rotation([xOff,yOff,0],rotDeg));
#for p in range(nDim-2-3):
# Point.append(random.random())
DataPoints.append(Point)
yOff = 0;
for i in range(nDataPart):
yOff += 1/nDataPart/1.5;
Point = []
#for p in range(2):
# Point.append(random.random())
Point.extend(rotation([-0.1*math.cos(yOff)+(1/13)+1e-3+xOff,0.3*math.sin(yOff)+math.sin(1/4),0],rotDeg))
#for p in range(nDim-2-3):
# Point.append(random.random())
DataPoints.append(Point);
Point = []
#for p in range(2):
# Point.append(random.random())
Point.extend(rotation([-0.1*math.cos(yOff)+(1/13)+1e-3+xOff,-0.3*math.sin(yOff)+math.sin(1/4),0],rotDeg))
#for p in range(nDim-2-3):
# Point.append(random.random())
DataPoints.append(Point);
#--- a ------------------------------------------------------------------------#
xOff += 3e-2
yOff = 0;
for i in range(nDataPart):
yOff += 1/nDataPart/2;
Point = []
#for p in range(2):
#Point.append(random.random())
Point.extend(rotation([xOff,yOff,0],rotDeg));
#for p in range(nDim-2-3):
#Point.append(random.random())
DataPoints.append(Point)
yOff = 0;
for i in range(nDataPart):
yOff += 1/nDataPart/1.5;
Point = []
#for p in range(2):
#Point.append(random.random())
Point.extend(rotation([-0.1*math.cos(yOff)+(1/13)+1e-3+xOff,0.3*math.sin(yOff)+math.sin(1/4),0],rotDeg))
#for p in range(nDim-2-3):
#Point.append(random.random())
DataPoints.append(Point);
Point = []
#for p in range(2):
# Point.append(random.random())
Point.extend(rotation([-0.1*math.cos(yOff)+(1/13)+1e-3+xOff,-0.3*math.sin(yOff)+math.sin(1/4),0],rotDeg))
#for p in range(nDim-2-3):
# Point.append(random.random())
DataPoints.append(Point);
#--- t ------------------------------------------------------------------------#
xOff += 3e-2
xOff -= 1.2e-2
yOff = 0.025;
tickOff = xOff- 3e-2/4
for i in range(nDataPart):
yOff += 1/nDataPart/1.25;
Point = []
#for p in range(2):
# Point.append(random.random())
Point.extend(rotation([xOff,yOff,0],rotDeg));
#for p in range(nDim-2-3):
# Point.append(random.random())
DataPoints.append(Point)
yOff = 0.55;
for i in range(nDataPart):
tickOff += 3e-2/nDataPart/2;
Point = []
#for p in range(2):
# Point.append(random.random())
Point.extend(rotation([tickOff,yOff,0],rotDeg));
#for p in range(nDim-2-3):
# Point.append(random.random())
DataPoints.append(Point)
yOff = 0.75;
xOff += 0.7e-2
for i in range(nDataPart):
yOff += 1/nDataPart;
Point = []
#for p in range(2):
# Point.append(random.random())
Point.extend(rotation([-7e-3*math.sin(yOff)+xOff,-7e-2*math.cos(yOff),0],rotDeg))
#for p in range(nDim-2-3):
# Point.append(random.random())
DataPoints.append(Point);
Point = []
#for p in range(2):
# Point.append(random.random())
Point.extend(rotation([7e-3*math.sin(yOff)+xOff-8e-3,-7e-2*math.cos(yOff),0],rotDeg))
#for p in range(nDim-2-3):
# Point.append(random.random())
DataPoints.append(Point);
xOff -= 0.7e-2
xOff += 1.2e-2
#--- a ------------------------------------------------------------------------#
xOff += 3e-2
yOff = 0;
for i in range(nDataPart):
yOff += 1/nDataPart/2;
Point = []
#for p in range(2):
# Point.append(random.random())
Point.extend(rotation([xOff,yOff,0],rotDeg));
#for p in range(nDim-2-3):
# Point.append(random.random())
DataPoints.append(Point)
yOff = 0;
for i in range(nDataPart):
yOff += 1/nDataPart/1.5;
Point = []
#for p in range(2):
# Point.append(random.random())
Point.extend(rotation([-0.1*math.cos(yOff)+(1/13)+1e-3+xOff,0.3*math.sin(yOff)+math.sin(1/4),0],rotDeg))
#for p in range(nDim-2-3):
# Point.append(random.random())
DataPoints.append(Point);
Point = []
#for p in range(2):
# Point.append(random.random())
Point.extend(rotation([-0.1*math.cos(yOff)+(1/13)+1e-3+xOff,-0.3*math.sin(yOff)+math.sin(1/4),0],rotDeg))
#for p in range(nDim-2-3):
# Point.append(random.random())
DataPoints.append(Point);
File = open("Hidden.dat","w")
for Point in DataPoints:
File.write(point_to_string(Point)+"\n")
File.close()

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test_data/.Rhistory Executable file
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setwd("~/Projekte/data-learning/test_data")
data = read.table("PrincipleComponents_PCA.dat",header=FALSE)
plot(data$V1,data$V2)
data = read.table("PrincipleComponents_PCA.dat",header=FALSE)
plot(data$V1,data$V2)
data = read.table("PrincipleComponents_PCA.dat",header=FALSE)
plot(data$V1,data$V2)
plot(data$V1,data$V2)

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test_data/Cluster.dat Executable file
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test_data/Hidden.dat Executable file
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test_data/Hidden1.dat Executable file
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test_data/Hidden2.dat Executable file
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