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David Vojtek authoredDavid Vojtek authored
DataSet.h 5.34 KiB
//
// 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);
/**
* 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