mirror of
https://github.com/Relintai/pmlpp.git
synced 2024-11-13 13:57:19 +01:00
256 lines
8.2 KiB
C++
256 lines
8.2 KiB
C++
#ifndef MLPP_DATA_H
|
|
#define MLPP_DATA_H
|
|
|
|
/*************************************************************************/
|
|
/* data.h */
|
|
/*************************************************************************/
|
|
/* This file is part of: */
|
|
/* PMLPP Machine Learning Library */
|
|
/* https://github.com/Relintai/pmlpp */
|
|
/*************************************************************************/
|
|
/* Copyright (c) 2023-present Péter Magyar. */
|
|
/* Copyright (c) 2022-2023 Marc Melikyan */
|
|
/* */
|
|
/* Permission is hereby granted, free of charge, to any person obtaining */
|
|
/* a copy of this software and associated documentation files (the */
|
|
/* "Software"), to deal in the Software without restriction, including */
|
|
/* without limitation the rights to use, copy, modify, merge, publish, */
|
|
/* distribute, sublicense, and/or sell copies of the Software, and to */
|
|
/* permit persons to whom the Software is furnished to do so, subject to */
|
|
/* the following conditions: */
|
|
/* */
|
|
/* The above copyright notice and this permission notice shall be */
|
|
/* included in all copies or substantial portions of the Software. */
|
|
/* */
|
|
/* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, */
|
|
/* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF */
|
|
/* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.*/
|
|
/* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY */
|
|
/* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, */
|
|
/* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE */
|
|
/* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */
|
|
/*************************************************************************/
|
|
|
|
#include "core/math/math_defs.h"
|
|
|
|
#include "core/string/ustring.h"
|
|
#include "core/variant/array.h"
|
|
|
|
#include "core/object/reference.h"
|
|
|
|
#include "../lin_alg/mlpp_matrix.h"
|
|
#include "../lin_alg/mlpp_vector.h"
|
|
|
|
#include <string>
|
|
#include <tuple>
|
|
#include <vector>
|
|
|
|
class MLPPDataESimple : public Reference {
|
|
GDCLASS(MLPPDataESimple, Reference);
|
|
|
|
public:
|
|
Ref<MLPPVector> get_input();
|
|
void set_input(const Ref<MLPPVector> &val);
|
|
|
|
Ref<MLPPVector> get_output();
|
|
void set_output(const Ref<MLPPVector> &val);
|
|
|
|
void instance_data();
|
|
|
|
protected:
|
|
static void _bind_methods();
|
|
|
|
Ref<MLPPVector> _input;
|
|
Ref<MLPPVector> _output;
|
|
};
|
|
|
|
class MLPPDataSimple : public Reference {
|
|
GDCLASS(MLPPDataSimple, Reference);
|
|
|
|
public:
|
|
Ref<MLPPMatrix> get_input();
|
|
void set_input(const Ref<MLPPMatrix> &val);
|
|
|
|
Ref<MLPPVector> get_output();
|
|
void set_output(const Ref<MLPPVector> &val);
|
|
|
|
void instance_data();
|
|
|
|
protected:
|
|
static void _bind_methods();
|
|
|
|
Ref<MLPPMatrix> _input;
|
|
Ref<MLPPVector> _output;
|
|
};
|
|
|
|
class MLPPDataComplex : public Reference {
|
|
GDCLASS(MLPPDataComplex, Reference);
|
|
|
|
public:
|
|
Ref<MLPPMatrix> get_input();
|
|
void set_input(const Ref<MLPPMatrix> &val);
|
|
|
|
Ref<MLPPMatrix> get_output();
|
|
void set_output(const Ref<MLPPMatrix> &val);
|
|
|
|
void instance_data();
|
|
|
|
protected:
|
|
static void _bind_methods();
|
|
|
|
Ref<MLPPMatrix> _input;
|
|
Ref<MLPPMatrix> _output;
|
|
};
|
|
|
|
class MLPPData : public Reference {
|
|
GDCLASS(MLPPData, Reference);
|
|
|
|
public:
|
|
// Load Datasets
|
|
Ref<MLPPDataSimple> load_breast_cancer(const String &path);
|
|
Ref<MLPPDataSimple> load_breast_cancer_svc(const String &path);
|
|
Ref<MLPPDataComplex> load_iris(const String &path);
|
|
Ref<MLPPDataComplex> load_wine(const String &path);
|
|
Ref<MLPPDataComplex> load_mnist_train(const String &path);
|
|
Ref<MLPPDataComplex> load_mnist_test(const String &path);
|
|
Ref<MLPPDataSimple> load_california_housing(const String &path);
|
|
Ref<MLPPDataESimple> load_fires_and_crime(const String &path);
|
|
|
|
void set_data_supervised(int k, const String &file_name, Ref<MLPPMatrix> input_set, Ref<MLPPVector> output_set);
|
|
void set_data_unsupervised(int k, const String &file_name, Ref<MLPPMatrix> input_set);
|
|
void set_data_simple(const String &file_name, Ref<MLPPVector> input_set, Ref<MLPPVector> output_set);
|
|
|
|
struct SplitComplexData {
|
|
Ref<MLPPDataComplex> train;
|
|
Ref<MLPPDataComplex> test;
|
|
};
|
|
|
|
SplitComplexData train_test_split(Ref<MLPPDataComplex> data, real_t test_size);
|
|
Array train_test_split_bind(const Ref<MLPPDataComplex> &data, real_t test_size);
|
|
|
|
// Images
|
|
std::vector<std::vector<real_t>> rgb2gray(std::vector<std::vector<std::vector<real_t>>> input);
|
|
std::vector<std::vector<std::vector<real_t>>> rgb2ycbcr(std::vector<std::vector<std::vector<real_t>>> input);
|
|
std::vector<std::vector<std::vector<real_t>>> rgb2hsv(std::vector<std::vector<std::vector<real_t>>> input);
|
|
std::vector<std::vector<std::vector<real_t>>> rgb2xyz(std::vector<std::vector<std::vector<real_t>>> input);
|
|
std::vector<std::vector<std::vector<real_t>>> xyz2rgb(std::vector<std::vector<std::vector<real_t>>> input);
|
|
|
|
// Text-Based & NLP
|
|
std::string toLower(std::string text);
|
|
std::vector<char> split(std::string text);
|
|
Vector<String> split_sentences(String data);
|
|
Vector<String> remove_spaces(Vector<String> data);
|
|
Vector<String> remove_empty(Vector<String> data);
|
|
Vector<String> segment(String text);
|
|
Vector<int> tokenize(String text);
|
|
Vector<String> remove_stop_words(String text);
|
|
Vector<String> remove_stop_words_vec(Vector<String> segmented_data);
|
|
|
|
String stemming(String text);
|
|
|
|
enum BagOfWordsType {
|
|
BAG_OF_WORDS_TYPE_DEFAULT = 0,
|
|
BAG_OF_WORDS_TYPE_BINARY,
|
|
};
|
|
|
|
Ref<MLPPMatrix> bag_of_words(Vector<String> sentences, BagOfWordsType type = BAG_OF_WORDS_TYPE_DEFAULT);
|
|
Ref<MLPPMatrix> tfidf(Vector<String> sentences);
|
|
|
|
struct WordsToVecResult {
|
|
Ref<MLPPMatrix> word_embeddings;
|
|
Vector<String> word_list;
|
|
};
|
|
|
|
enum WordToVecType {
|
|
WORD_TO_VEC_TYPE_CBOW = 0,
|
|
WORD_TO_VEC_TYPE_SKIPGRAM,
|
|
};
|
|
|
|
WordsToVecResult word_to_vec(Vector<String> sentences, WordToVecType type, int windowSize, int dimension, real_t learning_rate, int max_epoch);
|
|
|
|
Ref<MLPPMatrix> lsa(Vector<String> sentences, int dim);
|
|
|
|
Vector<String> create_word_list(Vector<String> sentences);
|
|
|
|
// Extra
|
|
void setInputNames(std::string fileName, std::vector<std::string> &inputNames);
|
|
Ref<MLPPMatrix> feature_scaling(const Ref<MLPPMatrix> &X);
|
|
Ref<MLPPMatrix> mean_centering(const Ref<MLPPMatrix> &X);
|
|
Ref<MLPPMatrix> mean_normalization(const Ref<MLPPMatrix> &X);
|
|
Ref<MLPPMatrix> one_hot_rep(const Ref<MLPPVector> &temp_output_set, int n_class);
|
|
std::vector<real_t> reverseOneHot(std::vector<std::vector<real_t>> tempOutputSet);
|
|
|
|
template <class T>
|
|
std::vector<T> vecToSet(std::vector<T> inputSet) {
|
|
std::vector<T> setInputSet;
|
|
for (uint32_t i = 0; i < inputSet.size(); i++) {
|
|
bool new_element = true;
|
|
for (uint32_t j = 0; j < setInputSet.size(); j++) {
|
|
if (setInputSet[j] == inputSet[i]) {
|
|
new_element = false;
|
|
}
|
|
}
|
|
if (new_element) {
|
|
setInputSet.push_back(inputSet[i]);
|
|
}
|
|
}
|
|
return setInputSet;
|
|
}
|
|
|
|
template <class T>
|
|
Vector<T> vec_to_set(Vector<T> input_set) {
|
|
Vector<T> set_input_set;
|
|
|
|
for (int i = 0; i < input_set.size(); i++) {
|
|
bool new_element = true;
|
|
|
|
for (int j = 0; j < set_input_set.size(); j++) {
|
|
if (set_input_set[j] == input_set[i]) {
|
|
new_element = false;
|
|
}
|
|
}
|
|
|
|
if (new_element) {
|
|
set_input_set.push_back(input_set[i]);
|
|
}
|
|
}
|
|
|
|
return set_input_set;
|
|
}
|
|
|
|
Ref<MLPPVector> vec_to_setnv(const Ref<MLPPVector> &input_set) {
|
|
Vector<real_t> set_input_set;
|
|
|
|
for (int i = 0; i < input_set->size(); i++) {
|
|
bool new_element = true;
|
|
|
|
for (int j = 0; j < set_input_set.size(); j++) {
|
|
if (set_input_set[j] == input_set->element_get(i)) {
|
|
new_element = false;
|
|
}
|
|
}
|
|
|
|
if (new_element) {
|
|
set_input_set.push_back(input_set->element_get(i));
|
|
}
|
|
}
|
|
|
|
Ref<MLPPVector> ret;
|
|
ret.instance();
|
|
ret->set_from_vector(set_input_set);
|
|
|
|
return ret;
|
|
}
|
|
|
|
void load_default_suffixes();
|
|
void load_default_stop_words();
|
|
|
|
Vector<String> suffixes;
|
|
Vector<String> stop_words;
|
|
|
|
protected:
|
|
static void _bind_methods();
|
|
};
|
|
|
|
#endif /* Data_hpp */
|