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Commit 03d4e990 authored by Martin Beseda's avatar Martin Beseda
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DEL: Deleted .orig files

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src/DataSet/DataSet.cpp.orig deleted 100644 → 0
+ 0
− 205
View file @ 8e84c0b5
//
// Created by martin on 7/13/18.
//
#include "DataSetSerialization.h"
namespace lib4neuro {
DataSet::DataSet(std::string file_path) {
std::ifstream ifs(file_path);
boost::archive::text_iarchive ia(ifs);
ia >> *this;
ifs.close();
}
<<<<<<< HEAD
=======
DataSet::DataSet() {
}
>>>>>>> 5fd0f3dcf535edfc3e3f4dd7b4cb256f2e95e8fe
DataSet::DataSet(std::vector<std::pair<std::vector<double>, std::vector<double>>> *data_ptr) {
this->n_elements = data_ptr->size();
this->data = *data_ptr;
this->input_dim = this->data[0].first.size();
this->output_dim = this->data[0].second.size();
//TODO check the complete data set for input/output dimensions
}
DataSet::DataSet(double lower_bound, double upper_bound, unsigned int size, double output) {
std::vector<std::pair<std::vector<double>, std::vector<double>>> new_data_vec;
this->data = new_data_vec;
this->n_elements = 0;
this->input_dim = 1;
this->output_dim = 1;
this->add_isotropic_data(lower_bound, upper_bound, size, output);
}
DataSet::DataSet(std::vector<double> &bounds, unsigned int no_elems_in_one_dim,
std::vector<double> (*output_func)(std::vector<double> &), unsigned int output_dim) {
std::vector<std::pair<std::vector<double>, std::vector<double>>> new_data_vec;
this->data = new_data_vec;
this->input_dim = bounds.size() / 2;
this->output_dim = output_dim;
this->n_elements = 0;
this->add_isotropic_data(bounds, no_elems_in_one_dim, output_func);
}
void DataSet::add_data_pair(std::vector<double> &inputs, std::vector<double> &outputs) {
if (inputs.size() != this->input_dim) {
throw InvalidDimension("Bad input dimension.");
} else if (outputs.size() != this->output_dim) {
throw InvalidDimension("Bad output dimension.");
}
this->n_elements++;
this->data.emplace_back(std::make_pair(inputs, outputs));
}
void DataSet::add_isotropic_data(double lower_bound, double upper_bound, unsigned int size, double output) {
if (this->input_dim != 1 || this->output_dim != 1) {
throw InvalidDimension("Cannot add data with dimensionality 1:1 when the data set "
"is of different dimensionality!");
}
double frac = (upper_bound - lower_bound) / (size - 1);
std::vector<double> inp, out;
out = {output};
for (unsigned int i = 0; i < size; ++i) {
inp = {frac * i};
this->data.emplace_back(std::make_pair(inp, out));
}
this->n_elements += size;
}
void DataSet::add_isotropic_data(std::vector<double> &bounds, unsigned int no_elems_in_one_dim,
std::vector<double> (*output_func)(std::vector<double> &)) {
// TODO add check of dataset dimensions
std::vector<std::vector<double>> grid;
std::vector<double> tmp;
double frac;
for (unsigned int i = 0; i < bounds.size(); i += 2) {
frac = (bounds[i] + bounds[i + 1]) / (no_elems_in_one_dim - 1);
tmp.clear();
for (double j = bounds[i]; j <= bounds[i + 1]; j += frac) {
tmp.emplace_back(j);
}
grid.emplace_back(tmp);
}
grid = this->cartesian_product(&grid);
for (auto vec : grid) {
this->n_elements++;
this->data.emplace_back(std::make_pair(vec, output_func(vec)));
}
}
std::vector<std::pair<std::vector<double>, std::vector<double>>> *DataSet::get_data() {
return &(this->data);
}
size_t DataSet::get_n_elements() {
return this->n_elements;
}
size_t DataSet::get_input_dim() {
return this->input_dim;
}
size_t DataSet::get_output_dim() {
return this->output_dim;
}
<<<<<<< HEAD
void DataSet::print_data() {
if (n_elements) {
for (auto p : this->data) {
/* INPUT */
for (auto v : std::get<0>(p)) {
std::cout << v << " ";
}
std::cout << "-> ";
/* OUTPUT */
for (auto v : std::get<1>(p)) {
std::cout << v << " ";
}
=======
void DataSet::print_data() {
if (n_elements) {
for (auto p : this->data) {
/* INPUT */
for (auto v : std::get<0>(p)) {
std::cout << v << " ";
}
std::cout << "-> ";
/* OUTPUT */
for (auto v : std::get<1>(p)) {
std::cout << v << " ";
}
>>>>>>> 5fd0f3dcf535edfc3e3f4dd7b4cb256f2e95e8fe
std::cout << std::endl;
}
}
}
void DataSet::store_text(std::string &file_path) {
//TODO check if stream was successfully opened
std::ofstream ofs(file_path);
boost::archive::text_oarchive oa(ofs);
oa << *this;
ofs.close();
}
template<class T>
std::vector<std::vector<T>> DataSet::cartesian_product(const std::vector<std::vector<T>> *v) {
std::vector<std::vector<double>> v_combined_old, v_combined, v_tmp;
std::vector<double> tmp;
for (const auto &e : v->at(0)) {
tmp = {e};
v_combined.emplace_back(tmp);
}
for (unsigned int i = 1; i < v->size(); i++) { // Iterate through remaining vectors of 'v'
v_combined_old = v_combined;
v_combined.clear();
for (const auto &e : v->at(i)) {
for (const auto &vec : v_combined_old) {
tmp = vec;
tmp.emplace_back(e);
/* Add only unique elements */
if (std::find(v_combined.begin(), v_combined.end(), tmp) == v_combined.end()) {
v_combined.emplace_back(tmp);
}
}
}
}
return v_combined;
}
}
\ No newline at end of file
src/DataSet/DataSet.h.orig deleted 100644 → 0
+ 0
− 177
View file @ 8e84c0b5
//
// Created by martin on 7/13/18.
//
#ifndef INC_4NEURO_DATASET_H
#define INC_4NEURO_DATASET_H
#include <iostream>
#include <fstream>
#include <utility>
#include <vector>
#include <string>
#include <functional>
#include "../settings.h"
#include "../Exception/Exceptions.h"
namespace lib4neuro {
/**
* Class representing data, which can be used for training
* and testing purposes.
*/
class DataSet {
private:
/**
* Number of elements in the data set
*/
size_t n_elements;
/**
* Dimension of the input
*/
size_t input_dim = 0;
/**
* Dimension of the output
*/
size_t output_dim = 0;
/**
* Stored data in the format of pairs of corresponding
* input and output vectors
*/
std::vector<std::pair<std::vector<double>, std::vector<double>>> data;
template<class T>
std::vector<std::vector<T>> cartesian_product(const std::vector<std::vector<T>> *v);
public:
/**
* Struct used to access private properties from
* the serialization function
*/
struct access;
/**
* Constructor reading data from the file
* @param file_path Path to the file with stored data set
*/
LIB4NEURO_API DataSet(std::string file_path);
<<<<<<< HEAD
=======
LIB4NEURO_API DataSet();
>>>>>>> 5fd0f3dcf535edfc3e3f4dd7b4cb256f2e95e8fe
/**
* Constructor accepting data vector
* @param data_ptr Pointer to the vector containing data
*/
LIB4NEURO_API DataSet(std::vector<std::pair<std::vector<double>, std::vector<double>>> *data_ptr);
/**
* Creates a new data set with input values equidistantly positioned
* over the certain interval and the output value
* being constant
*
* Both input and output are 1-dimensional
*
* @todo add bounds as vectors for multi-dimensional data-sets
*
* @param lower_bound Lower bound of the input data interval
* @param upper_bound Upper bound of the input data interval
* @param size Number of input-output pairs generated
* @param output Constant output value
*/
LIB4NEURO_API DataSet(double lower_bound, double upper_bound, unsigned int size, double output);
/**
*
* @param bounds
* @param no_elems_in_one_dim
* @param output_func
* @param output_dim
*/
LIB4NEURO_API DataSet(std::vector<double> &bounds, unsigned int no_elems_in_one_dim,
std::vector<double> (*output_func)(std::vector<double> &), unsigned int output_dim);
/**
* Getter for number of elements
* @return Number of elements in the data set
*/
LIB4NEURO_API size_t get_n_elements();
/**
* Returns the input dimension
* @return Input dimension
*/
LIB4NEURO_API size_t get_input_dim();
/**
* Return the output dimension
* @return Output dimension
*/
LIB4NEURO_API size_t get_output_dim();
/**
* Getter for the data structure
* @return Vector of data
*/
LIB4NEURO_API std::vector<std::pair<std::vector<double>, std::vector<double>>> *get_data();
/**
* Adds a new pair of data to the data set
* @param inputs Vector of input data
* @param outputs Vector of output data corresponding to the input data
*/
LIB4NEURO_API void add_data_pair(std::vector<double> &inputs, std::vector<double> &outputs);
//TODO expand method to generate multiple data types - chebyshev etc.
/**
* Adds a new data with input values equidistantly positioned
* over the certain interval and the output value
* being constant
*
* Both input and output are 1-dimensional
*
* @param lower_bound Lower bound of the input data interval
* @param upper_bound Upper bound of the input data interval
* @param size Number of input-output pairs generated
* @param output Constant output value
*/
LIB4NEURO_API void add_isotropic_data(double lower_bound, double upper_bound, unsigned int size, double output);
/**
* Adds a new data with input values equidistantly positioned
* over the certain interval and the output value
* being constant
*
* Input can have arbitrary many dimensions,
* output can be an arbitrary function
*
* @param bounds Odd values are lower bounds and even values are corresponding upper bounds
* @param size Number of input-output pairs generated
* @param output_func Function determining output value
*/
LIB4NEURO_API void add_isotropic_data(std::vector<double> &bounds, unsigned int no_elems_in_one_dim,
std::vector<double> (*output_func)(std::vector<double> &));
//TODO Chebyshev - ch. interpolation points, i-th point = cos(i*alpha) from 0 to pi
/**
* Prints the data set
*/
LIB4NEURO_API void print_data();
/**
* Stores the DataSet object to the binary file
*/
LIB4NEURO_API void store_text(std::string &file_path);
};
}
#endif //INC_4NEURO_DATASET_H
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