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247 lines
8.2 KiB
C++
247 lines
8.2 KiB
C++
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#ifndef MLPP_DATA_H
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#define MLPP_DATA_H
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//
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// Data.hpp
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// MLP
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//
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// Created by Marc Melikyan on 11/4/20.
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//
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#include "core/math/math_defs.h"
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#include "core/string/ustring.h"
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#include "core/variant/array.h"
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#include "core/object/reference.h"
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#include "../lin_alg/mlpp_matrix.h"
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#include "../lin_alg/mlpp_vector.h"
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#include <string>
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#include <tuple>
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#include <vector>
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class MLPPDataESimple : public Reference {
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GDCLASS(MLPPDataESimple, Reference);
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public:
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Ref<MLPPVector> get_input();
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void set_input(const Ref<MLPPVector> &val);
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Ref<MLPPVector> get_output();
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void set_output(const Ref<MLPPVector> &val);
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void instance_data();
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protected:
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static void _bind_methods();
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Ref<MLPPVector> _input;
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Ref<MLPPVector> _output;
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};
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class MLPPDataSimple : public Reference {
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GDCLASS(MLPPDataSimple, Reference);
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public:
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Ref<MLPPMatrix> get_input();
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void set_input(const Ref<MLPPMatrix> &val);
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Ref<MLPPVector> get_output();
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void set_output(const Ref<MLPPVector> &val);
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void instance_data();
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protected:
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static void _bind_methods();
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Ref<MLPPMatrix> _input;
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Ref<MLPPVector> _output;
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};
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class MLPPDataComplex : public Reference {
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GDCLASS(MLPPDataComplex, Reference);
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public:
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Ref<MLPPMatrix> get_input();
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void set_input(const Ref<MLPPMatrix> &val);
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Ref<MLPPMatrix> get_output();
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void set_output(const Ref<MLPPMatrix> &val);
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void instance_data();
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protected:
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static void _bind_methods();
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Ref<MLPPMatrix> _input;
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Ref<MLPPMatrix> _output;
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};
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class MLPPData : public Reference {
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GDCLASS(MLPPData, Reference);
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public:
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// Load Datasets
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Ref<MLPPDataSimple> load_breast_cancer(const String &path);
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Ref<MLPPDataSimple> load_breast_cancer_svc(const String &path);
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Ref<MLPPDataComplex> load_iris(const String &path);
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Ref<MLPPDataComplex> load_wine(const String &path);
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Ref<MLPPDataComplex> load_mnist_train(const String &path);
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Ref<MLPPDataComplex> load_mnist_test(const String &path);
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Ref<MLPPDataSimple> load_california_housing(const String &path);
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Ref<MLPPDataESimple> load_fires_and_crime(const String &path);
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void set_data_supervised(int k, const String &file_name, Ref<MLPPMatrix> input_set, Ref<MLPPVector> output_set);
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void set_data_unsupervised(int k, const String &file_name, Ref<MLPPMatrix> input_set);
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void set_data_simple(const String &file_name, Ref<MLPPVector> input_set, Ref<MLPPVector> output_set);
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struct SplitComplexData {
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Ref<MLPPDataComplex> train;
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Ref<MLPPDataComplex> test;
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};
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SplitComplexData train_test_split(Ref<MLPPDataComplex> data, real_t test_size);
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Array train_test_split_bind(const Ref<MLPPDataComplex> &data, real_t test_size);
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// Load Datasets
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std::tuple<std::vector<std::vector<real_t>>, std::vector<real_t>> loadBreastCancer();
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std::tuple<std::vector<std::vector<real_t>>, std::vector<real_t>> loadBreastCancerSVC();
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std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> loadIris();
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std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> loadWine();
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std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> loadMnistTrain();
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std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> loadMnistTest();
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std::tuple<std::vector<std::vector<real_t>>, std::vector<real_t>> loadCaliforniaHousing();
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std::tuple<std::vector<real_t>, std::vector<real_t>> loadFiresAndCrime();
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std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> trainTestSplit(std::vector<std::vector<real_t>> inputSet, std::vector<std::vector<real_t>> outputSet, real_t testSize);
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// Supervised
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void setData(int k, std::string fileName, std::vector<std::vector<real_t>> &inputSet, std::vector<real_t> &outputSet);
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void printData(std::vector<std::string> inputName, std::string outputName, std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet);
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// Unsupervised
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void setData(int k, std::string fileName, std::vector<std::vector<real_t>> &inputSet);
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void printData(std::vector<std::string> inputName, std::vector<std::vector<real_t>> inputSet);
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// Simple
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void setData(std::string fileName, std::vector<real_t> &inputSet, std::vector<real_t> &outputSet);
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void printData(std::string &inputName, std::string &outputName, std::vector<real_t> &inputSet, std::vector<real_t> &outputSet);
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// Images
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std::vector<std::vector<real_t>> rgb2gray(std::vector<std::vector<std::vector<real_t>>> input);
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std::vector<std::vector<std::vector<real_t>>> rgb2ycbcr(std::vector<std::vector<std::vector<real_t>>> input);
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std::vector<std::vector<std::vector<real_t>>> rgb2hsv(std::vector<std::vector<std::vector<real_t>>> input);
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std::vector<std::vector<std::vector<real_t>>> rgb2xyz(std::vector<std::vector<std::vector<real_t>>> input);
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std::vector<std::vector<std::vector<real_t>>> xyz2rgb(std::vector<std::vector<std::vector<real_t>>> input);
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// Text-Based & NLP
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std::string toLower(std::string text);
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std::vector<char> split(std::string text);
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std::vector<std::string> splitSentences(std::string data);
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std::vector<std::string> removeSpaces(std::vector<std::string> data);
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std::vector<std::string> removeNullByte(std::vector<std::string> data);
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std::vector<std::string> segment(std::string text);
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std::vector<real_t> tokenize(std::string text);
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std::vector<std::string> removeStopWords(std::string text);
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std::vector<std::string> removeStopWords(std::vector<std::string> segmented_data);
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std::string stemming(std::string text);
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std::vector<std::vector<real_t>> BOW(std::vector<std::string> sentences, std::string = "Default");
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std::vector<std::vector<real_t>> TFIDF(std::vector<std::string> sentences);
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std::tuple<std::vector<std::vector<real_t>>, std::vector<std::string>> word2Vec(std::vector<std::string> sentences, std::string type, int windowSize, int dimension, real_t learning_rate, int max_epoch);
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struct WordsToVecResult {
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std::vector<std::vector<real_t>> word_embeddings;
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std::vector<std::string> word_list;
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};
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WordsToVecResult word_to_vec(std::vector<std::string> sentences, std::string type, int windowSize, int dimension, real_t learning_rate, int max_epoch);
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std::vector<std::vector<real_t>> LSA(std::vector<std::string> sentences, int dim);
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std::vector<std::string> createWordList(std::vector<std::string> sentences);
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// Extra
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void setInputNames(std::string fileName, std::vector<std::string> &inputNames);
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std::vector<std::vector<real_t>> featureScaling(std::vector<std::vector<real_t>> X);
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std::vector<std::vector<real_t>> meanNormalization(std::vector<std::vector<real_t>> X);
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std::vector<std::vector<real_t>> meanCentering(std::vector<std::vector<real_t>> X);
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std::vector<std::vector<real_t>> oneHotRep(std::vector<real_t> tempOutputSet, int n_class);
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std::vector<real_t> reverseOneHot(std::vector<std::vector<real_t>> tempOutputSet);
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Ref<MLPPMatrix> mean_centering(const Ref<MLPPMatrix> &X);
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Ref<MLPPMatrix> one_hot_rep(const Ref<MLPPVector> &temp_output_set, int n_class);
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template <class T>
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std::vector<T> vecToSet(std::vector<T> inputSet) {
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std::vector<T> setInputSet;
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for (uint32_t i = 0; i < inputSet.size(); i++) {
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bool new_element = true;
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for (uint32_t j = 0; j < setInputSet.size(); j++) {
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if (setInputSet[j] == inputSet[i]) {
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new_element = false;
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}
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}
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if (new_element) {
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setInputSet.push_back(inputSet[i]);
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}
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}
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return setInputSet;
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}
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template <class T>
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Vector<T> vec_to_set(Vector<T> input_set) {
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Vector<T> set_input_set;
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for (int i = 0; i < input_set.size(); i++) {
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bool new_element = true;
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for (int j = 0; j < set_input_set.size(); j++) {
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if (set_input_set[j] == input_set[i]) {
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new_element = false;
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}
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}
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if (new_element) {
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set_input_set.push_back(input_set[i]);
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}
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}
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return set_input_set;
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}
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Ref<MLPPVector> vec_to_setnv(const Ref<MLPPVector> &input_set) {
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Vector<real_t> set_input_set;
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for (int i = 0; i < input_set->size(); i++) {
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bool new_element = true;
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for (int j = 0; j < set_input_set.size(); j++) {
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if (set_input_set[j] == input_set->element_get(i)) {
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new_element = false;
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}
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}
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if (new_element) {
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set_input_set.push_back(input_set->element_get(i));
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}
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}
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Ref<MLPPVector> ret;
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ret.instance();
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ret->set_from_vector(set_input_set);
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return ret;
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}
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protected:
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static void _bind_methods();
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};
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#endif /* Data_hpp */
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