HW3: Source and results for both of the scenarios added

2025-10-26 17:21:27 +02:00 · 2025-10-26 17:21:27 +02:00 · d1afa4ca45
commit d1afa4ca45
parent 5e17867857
43 changed files with 25380 additions and 0 deletions
--- a/.gitmodules
+++ b/.gitmodules
@ -4,3 +4,6 @@
 [submodule "Work 2/report/AUThReport"]
 	path = Work 2/report/AUThReport
 	url = ssh://git@git.hoo2.net:222/hoo2/AUThReport.git
+[submodule "Work 3/report/AUThReport"]
+	path = Work 3/report/AUThReport
+	url = ssh://git@git.hoo2.net:222/hoo2/AUThReport.git
--- a/3/report/.gitignore
+++ b/3/report/.gitignore
@ -0,0 +1,8 @@
+# LaTeX auxiliary files
+*.aux
+*.log
+*.out
+*.synctex.gz
+_minted*/*
+
+
--- a/3/report/AUThReport
+++ b/3/report/AUThReport
@ -0,0 +1 @@
+Subproject commit 74ec4b5f6c66382e5f1b6d2e6930897e4ed53ea6
--- a/3/report/Work3_report.pdf
+++ b/3/report/Work3_report.pdf
--- a/3/report/Work3_report.tex
+++ b/3/report/Work3_report.tex
@ -0,0 +1,205 @@
+%
+% !TEX TS-program = xelatex
+% !TEX encoding   = UTF-8 Unicode
+% !TEX spellcheck = el-GR
+%
+% Fuzzy Systems Assignment 3
+%
+% Requires compilation with pdfLaTeX or XeLaTeX
+%
+% authors:
+%   Χρήστος Χουτουρίδης ΑΕΜ 8997
+%   cchoutou@ece.auth.gr
+
+% Options:
+%
+% 1) mainlang=<language>
+%    Default:  english
+%    Set the default language of the document which affects hyphenations,
+%    localization (section, dates, etc...)
+%
+%    example: \documentclass[mainlang=greek]{AUThReport}
+%
+% 2) <language>
+%    Add hyphenation and typesetting support for other languages
+%    Currently supports: english, greek, german, frenc
+%
+%    example: \documentclass[english, greek]{AUThReport}
+%
+% 3) short: Requests a shorter title for the document
+%    Default: no short
+%
+%    example: \documentclass[short]{AUThReport}
+%
+\documentclass[a4paper, 11pt, mainlang=greek, english]{AUThReport/AUThReport}
+
+\CurrentDate{\today}
+
+% Greek report document setup suggestions
+%---------------------------------
+% \WorkGroup{Ομάδα Χ}
+
+\AuthorName{Χρήστος Χουτουρίδης}
+\AuthorAEM{8997}
+\AuthorMail{cchoutou@ece.auth.gr}
+
+%\CoAuthorName{Όνομα Επίθετο}
+%\CoAuthorAEM{1234}
+%\CoAuthorMail{xxx@ece.auth.gr}
+
+\DocTitle{Εργασία 3}
+\DocSubTitle{Επίλυση προβλήματος παλινδρόμησης με χρήση μοντέλων TSK}
+
+\Department{Τμήμα ΗΜΜΥ. Τομέας Ηλεκτρονικής}
+\ClassName{Ασαφή Συστήματα (Υπολογιστική Νοημοσύνη)}
+
+\InstructorName{Θεοχάρης Ιωάννης}
+\InstructorMail{theochar@ece.auth.gr}
+
+\CoInstructorName{Χαδουλός Χρήστος}
+\CoInstructorMail{christgc@auth.gr}
+
+
+\usepackage{float}
+\usepackage{minted}
+\usepackage{xcolor} %
+\usepackage{amsmath, amssymb, amsfonts}
+\usepackage{diagbox}
+%\usepackage{tabular}
+
+\setminted[cpp]{
+	fontsize=\small,
+	breaklines,
+	autogobble,
+	baselinestretch=1.1,
+	tabsize=2,
+	numbersep=8pt,
+	gobble=0
+}
+
+\newcommand{\repo}{https://git.hoo2.net/hoo2/FuzzySystems/src/branch/master/Work\%203}
+
+\begin{document}
+
+% Request a title page or header
+\InsertTitle
+%\InsertTitle[img/background.png][0.8\textwidth][2cm]
+
+\section{Εισαγωγή}
+
+Στην παρούσα εργασία καλούμαστε...
+ - Ας γράψουμε μια εισαγωγή βασισμένη κάπως ως πολύ πολύ μικρή περίλυψη των ουσιοδών πραγμάτων που είναι να υλοποιήσουμε, αλλά και την σημασία τους και τον ρόλο που παίζουν (πχ πληροφορίες για τα TSK μοντέλα και πως χρησιμοποιούντε (;!))
+
+
+\subsection{Παραδοτέα}
+Τα παραδοτέα της εργασίας αποτελούνται από:
+\begin{itemize}
+	\item Την παρούσα αναφορά.
+	\item Τον κατάλογο \textbf{source}, με τον κώδικα της matlab.
+	\item Το \href{\repo}{σύνδεσμο με το αποθετήριο} που περιέχει τον κώδικα της matlab καθώς και αυτόν της αναφοράς.
+\end{itemize}
+
+\section{Υλοποίηση}
+ - Ας αναφέρουμε ότι μπήκαμε στη λογική να κάνουμε κάποιες αφαιρέσεις, που θα βοηθούσαν στην διενέργεια καί των δύο υπο-tasks  που έχει η εργασία.
+ - Να πούμε ότι η εργασία μπορεί να εκτελεστεί εκτελώντας τα δύο scripts (scenario1 και scenario2) και ότι από αυτά τα script καλούνται οι συναρτήσεις.
+ - Να συνεχίσουμε με κάποια πάσα (κείμενο) για την υλοποίηση των συναρτήσεων προτού περάσουμε στα scripts που είναι τα ζητούμενα της εργασίας
+
+\subsection{Διαχωρισμός δοδομένων - split\_data()}
+ - Να εξηγήσουμε τη λειτουργία της συνάρτησης
+ - Να εξηγήσουμε τι ρόλο παίζει το seed και γιατί το χρησιμοποιήσαμε
+ - Να εξηγήσουμε γιατί αποφασίσαμε να επιστρέφουμε X και y
+
+\subsection{Προεπεξεργασία δοδομένων - preprocess\_data()}
+ - Ας ξεκινήσουμε αναφαίροντας το τι είναι και γιατί χρειάζεται
+ - Να εξηγήσουμε τη δική μας προσέγγιση (και ότι πειραματιστήκαμε με το z-score αλλα δεν το χρησιμοποιήσαμε)
+ - Να εξηγήσουμε την λειτουργία της συνάρτησης
+ -
+
+\subsection{Αξιολόγιση - evaluate()}
+ - Να εξηγήσουμε το βήμα evaluate και πως χρησιμοποιείται
+ - Να εξηγήσουμε τη δική μας προσέγγιση
+ - Να εξηγήσουμε πως δουλεύει η συνάρτηση
+
+
+\subsection{Εμφάνιση αποτελεσμάτων}
+ - Να αναφέρουμε συνοπτικά ότι υλοποιήσαμε 2 συναρτήσεις για να εμφανίζουμε και να αποθηκεύουμε συστηματικά τα plots για ευκολία.
+ - Να πούμε κάποια βασικά για την υλοποίηση αν έχουνε κάτι ενδιαφέρον μέσα
+
+
+\section {Σενάριο 1 - Εφαρμογή σε απλό Dataset}
+
+- Να ξεκινήσουμε αναφέροντας τι είναι το ζητούμενο εδώ
+- Να εξηγήσουμε τη δική μας προσέγγιση στη λύση (πως επιλέξαμε αρ. mf, αρ. εποχών, μετρικές κλπ)
+- Για τα 4 μοντέλα εκπαίδευσης να αναφέρουμε (παραθέτοντας και τις εικόνες) με τη σειρά
+
+\subsection {Συναρτήσεις συμμετοχής}
+- Αρχικά τις συναρτήσεις συμμετοχής των μοντέλων.
+\InsertFigure{!ht}{0.7}{fig:scn1_mf1}{../source/figures_scn1/model_1_mfs_all_inputs.png}{Περιγραφή...}
+\InsertFigure{!ht}{0.7}{fig:scn1_mf2}{../source/figures_scn1/model_2_mfs_all_inputs.png}{Περιγραφή...}
+\InsertFigure{!ht}{0.7}{fig:scn1_mf3}{../source/figures_scn1/model_3_mfs_all_inputs.png}{Περιγραφή...}
+\InsertFigure{!ht}{0.7}{fig:scn1_mf4}{../source/figures_scn1/model_4_mfs_all_inputs.png}{Περιγραφή...}
+ Να πούμε κάποια λόγια και μικρές παρατηρήσεις
+
+\subsection {Διαγράμματα μάθησης}
+- Έπειτα Να βάλουμε σαν τετράδα τα διαγράμματα μάθησης (πχ στο σχήμα χχ παραθέτουμε τα διαγράμματα)
+\begin{figure}[!ht]
+	\includegraphics[width=0.48\textwidth]{../source/figures_scn1/model_1_learning_curves.png}
+	\hspace{1em}
+	\includegraphics[width=0.48\textwidth]{../source/figures_scn1/model_2_learning_curves.png} \\
+	\vspace{1em}
+	\includegraphics[width=0.48\textwidth]{../source/figures_scn1/model_3_learning_curves.png}
+	\hspace{1em}
+	\includegraphics[width=0.48\textwidth]{../source/figures_scn1/model_4_learning_curves.png} \\
+	\caption{Σχεδίαση και απόκριση γραμμικού ελεγκτή.} % Προσάρμοσέ μου τα caption να να έχει ένα η κάθε εικόνα
+	\label{fig:scn1_learning_curves}
+\end{figure}
+ Να πούμε κάποια λόγια και μικρές παρατηρήσεις
+
+\subsection {Σφάλματα πρόβλεψης}
+- Έπειτα Να βάλουμε σαν τετράδα τα σφάλματα (πχ στο σχήμα χχ παραθέτουμε τα )
+\begin{figure}[!ht]
+	\includegraphics[width=0.48\textwidth]{../source/figures_scn1/model_1_error.png}
+	\hspace{1em}
+	\includegraphics[width=0.48\textwidth]{../source/figures_scn1/model_2_error.png} \\
+	\vspace{1em}
+	\includegraphics[width=0.48\textwidth]{../source/figures_scn1/model_3_error.png}
+	\hspace{1em}
+	\includegraphics[width=0.48\textwidth]{../source/figures_scn1/model_4_error.png} \\
+	\caption{Σχεδίαση και απόκριση γραμμικού ελεγκτή.} % Προσάρμοσέ μου τα caption να να έχει ένα η κάθε εικόνα
+	\label{fig:scn1_error}
+\end{figure}
+Να πούμε κάποια λόγια και μικρές παρατηρήσεις
+
+\subsection {Σύγκριση actual - predicted} % Ίσως θέλει προραρμογή ο τίτλος
+- Έπειτα Να βάλουμε σαν τετράδα τα σφάλματα (πχ στο σχήμα χχ παραθέτουμε τα )
+\begin{figure}[!ht]
+	\includegraphics[width=0.48\textwidth]{../source/figures_scn1/model_1_pred_vs_actual.png}
+	\hspace{1em}
+	\includegraphics[width=0.48\textwidth]{../source/figures_scn1/model_2_pred_vs_actual.png} \\
+	\vspace{1em}
+	\includegraphics[width=0.48\textwidth]{../source/figures_scn1/model_3_pred_vs_actual.png}
+	\hspace{1em}
+	\includegraphics[width=0.48\textwidth]{../source/figures_scn1/model_4_pred_vs_actual.png} \\
+	\caption{Σχεδίαση και απόκριση γραμμικού ελεγκτή.} % Προσάρμοσέ μου τα caption να να έχει ένα η κάθε εικόνα
+	\label{fig:scn1_predicted_vs_actual}
+\end{figure}
+Να πούμε κάποια λόγια και μικρές παρατηρήσεις
+
+\subsection {Συνολικά}
+Να αναφαίρουμε σε πίνακα τους δείκτες
+Metrics  |  Model-1    Model-2    Model-3    Model-4
+----------------------------------------------------
+MSE      |  16.926     17.141     12.895     30.475
+RMSE     |  4.1142     4.1401      3.591     5.5204
+R2       | 0.66468    0.66043    0.74454    0.39627
+NMSE     | 0.33532    0.33957    0.25546    0.60373
+NDEI     | 0.57907    0.58272    0.50543      0.777
+
+Να σχολιάσουμε τα αποτελέσματα
+
+\section {Σενάριο 2 - Dataset με υψηλή διαστασιμότητα}
+
+
+\section {Συμπεράσματα}
+
+\end{document}
--- a/3/report/img/FLC_Rules.png
+++ b/3/report/img/FLC_Rules.png
--- a/3/report/img/Terain.png
+++ b/3/report/img/Terain.png
--- a/3/source/.gitignore
+++ b/3/source/.gitignore
@ -0,0 +1,3 @@
+# Matlab auxiliary files
+*.asv
+
--- a/3/source/Datasets/airfoil_self_noise.dat
+++ b/3/source/Datasets/airfoil_self_noise.dat
--- a/3/source/Datasets/superconduct.csv
+++ b/3/source/Datasets/superconduct.csv
--- a/3/source/Scenario1.log
+++ b/3/source/Scenario1.log
@ -0,0 +1,620 @@
+
+Model 1: TSK0-2MF (gbellmf × 2 per input)
+=====================================
+Initial rules: 32
+
+ANFIS info:
+	Number of nodes: 92
+	Number of linear parameters: 32
+	Number of nonlinear parameters: 30
+	Total number of parameters: 62
+	Number of training data pairs: 902
+	Number of checking data pairs: 301
+	Number of fuzzy rules: 32
+
+
+Start training ANFIS ...
+
+1 	 4.25643 	 4.51914
+2 	 4.22775 	 4.48492
+3 	 4.1994 	 4.45216
+4 	 4.17142 	 4.42081
+Step size increases to 0.011000 after epoch 5.
+5 	 4.14386 	 4.39082
+6 	 4.11676 	 4.36215
+7 	 4.08753 	 4.33208
+8 	 4.05896 	 4.3035
+Step size increases to 0.012100 after epoch 9.
+9 	 4.0311 	 4.27637
+10 	 4.004 	 4.25063
+11 	 3.97511 	 4.22389
+12 	 3.94724 	 4.19874
+Step size increases to 0.013310 after epoch 13.
+13 	 3.92044 	 4.17515
+14 	 3.89473 	 4.15306
+15 	 3.86777 	 4.13049
+16 	 3.84221 	 4.10967
+Step size increases to 0.014641 after epoch 17.
+17 	 3.81806 	 4.09058
+18 	 3.79532 	 4.0732
+19 	 3.77194 	 4.05604
+20 	 3.75022 	 4.04091
+Step size increases to 0.016105 after epoch 21.
+21 	 3.73013 	 4.0278
+22 	 3.7116 	 4.0167
+23 	 3.69293 	 4.00682
+24 	 3.67596 	 3.99934
+Step size increases to 0.017716 after epoch 25.
+25 	 3.66054 	 3.99425
+26 	 3.64656 	 3.99151
+27 	 3.63269 	 3.99118
+28 	 3.62021 	 3.99352
+Step size increases to 0.019487 after epoch 29.
+29 	 3.60898 	 3.99839
+30 	 3.59883 	 4.00557
+31 	 3.58878 	 4.01587
+32 	 3.57974 	 4.02819
+Step size increases to 0.021436 after epoch 33.
+33 	 3.57161 	 4.04196
+34 	 3.56429 	 4.05654
+35 	 3.55707 	 4.07282
+36 	 3.55066 	 4.08823
+Step size increases to 0.023579 after epoch 37.
+37 	 3.54498 	 4.10185
+38 	 3.53995 	 4.1129
+39 	 3.53508 	 4.12181
+40 	 3.53083 	 4.12685
+Step size increases to 0.025937 after epoch 41.
+41 	 3.5271 	 4.12846
+42 	 3.52381 	 4.12733
+43 	 3.5206 	 4.12391
+44 	 3.51772 	 4.11867
+Step size increases to 0.028531 after epoch 45.
+45 	 3.51509 	 4.11214
+46 	 3.51267 	 4.10466
+47 	 3.51021 	 4.09579
+48 	 3.50794 	 4.08656
+Step size increases to 0.031384 after epoch 49.
+49 	 3.50585 	 4.07728
+50 	 3.50391 	 4.06815
+51 	 3.50193 	 4.05832
+52 	 3.50009 	 4.04887
+Step size increases to 0.034523 after epoch 53.
+53 	 3.49836 	 4.03931
+54 	 3.4968 	 4.03148
+55 	 3.49646 	 4.01735
+56 	 3.49577 	 4.01706
+Step size increases to 0.037975 after epoch 57.
+57 	 3.49528 	 4.00918
+58 	 3.4945 	 4.01098
+59 	 3.4945 	 4.0023
+60 	 3.49332 	 4.00347
+61 	 3.49319 	 3.99792
+62 	 3.49216 	 3.99805
+Step size increases to 0.041772 after epoch 63.
+63 	 3.49194 	 3.99353
+64 	 3.49123 	 3.99357
+65 	 3.49167 	 3.98955
+66 	 3.49062 	 3.98891
+67 	 3.49093 	 3.98663
+Step size decreases to 0.037595 after epoch 68.
+68 	 3.48995 	 3.98598
+69 	 3.49029 	 3.98279
+70 	 3.4883 	 3.98284
+71 	 3.48954 	 3.98056
+Step size decreases to 0.033836 after epoch 72.
+72 	 3.48769 	 3.98048
+73 	 3.48891 	 3.97664
+74 	 3.48635 	 3.97757
+75 	 3.4881 	 3.97462
+Step size decreases to 0.030452 after epoch 76.
+76 	 3.48584 	 3.97574
+77 	 3.48749 	 3.9711
+78 	 3.48473 	 3.97298
+79 	 3.4867 	 3.96937
+Step size decreases to 0.027407 after epoch 80.
+80 	 3.48431 	 3.97148
+81 	 3.48615 	 3.96648
+82 	 3.48339 	 3.96895
+83 	 3.48544 	 3.96508
+Step size decreases to 0.024666 after epoch 84.
+84 	 3.48307 	 3.96771
+85 	 3.48498 	 3.96281
+86 	 3.48233 	 3.96547
+87 	 3.48437 	 3.96168
+Step size decreases to 0.022200 after epoch 88.
+88 	 3.4821 	 3.96443
+89 	 3.484 	 3.95987
+90 	 3.48151 	 3.96249
+91 	 3.4835 	 3.95894
+Step size decreases to 0.019980 after epoch 92.
+92 	 3.48135 	 3.96161
+93 	 3.48319 	 3.95745
+94 	 3.4809 	 3.95993
+95 	 3.48275 	 3.95666
+Step size decreases to 0.017982 after epoch 96.
+96 	 3.48078 	 3.95917
+97 	 3.48248 	 3.95537
+98 	 3.48043 	 3.9577
+99 	 3.48207 	 3.95468
+Step size decreases to 0.016184 after epoch 100.
+100 	 3.48033 	 3.95705
+
+Designated epoch number reached. ANFIS training completed at epoch 100.
+
+Minimal training RMSE = 3.48033
+Minimal checking RMSE = 3.95468
+MSE : 16.9264
+RMSE: 4.11417
+R2  : 0.664677
+NMSE: 0.335323
+NDEI: 0.57907
+
+
+Model 2: TSK0-3MF (gbellmf × 3 per input)
+=====================================
+Initial rules: 243
+
+ANFIS info:
+	Number of nodes: 524
+	Number of linear parameters: 243
+	Number of nonlinear parameters: 45
+	Total number of parameters: 288
+	Number of training data pairs: 902
+	Number of checking data pairs: 301
+	Number of fuzzy rules: 243
+
+
+Start training ANFIS ...
+
+1 	 3.48949 	 6.18916
+2 	 3.45915 	 6.06247
+3 	 3.41872 	 5.91346
+4 	 3.38236 	 5.73296
+Step size increases to 0.011000 after epoch 5.
+5 	 3.353 	 5.57846
+6 	 3.32197 	 5.47698
+7 	 3.28593 	 5.3751
+8 	 3.2483 	 5.26731
+Step size increases to 0.012100 after epoch 9.
+9 	 3.20873 	 5.1596
+10 	 3.16709 	 5.06056
+11 	 3.11912 	 4.97062
+12 	 3.07004 	 4.90448
+Step size increases to 0.013310 after epoch 13.
+13 	 3.02187 	 4.86429
+14 	 2.97737 	 4.85458
+15 	 2.93582 	 4.88936
+16 	 2.90427 	 4.98087
+Step size increases to 0.014641 after epoch 17.
+17 	 2.88219 	 5.12211
+18 	 2.86471 	 5.26311
+19 	 2.84624 	 5.36588
+20 	 2.82829 	 5.46658
+Step size increases to 0.016105 after epoch 21.
+21 	 2.81115 	 5.55392
+22 	 2.79487 	 5.67287
+23 	 2.77853 	 5.64686
+24 	 2.76872 	 6.11556
+25 	 2.77206 	 5.14558
+26 	 2.75702 	 6.19896
+27 	 2.7602 	 5.14994
+Step size decreases to 0.014495 after epoch 28.
+28 	 2.74638 	 6.24052
+29 	 2.75098 	 5.11992
+30 	 2.73409 	 6.17768
+31 	 2.74283 	 5.10258
+Step size decreases to 0.013045 after epoch 32.
+32 	 2.72821 	 6.17703
+33 	 2.73726 	 5.07044
+34 	 2.7204 	 6.07671
+35 	 2.73153 	 5.05552
+Step size decreases to 0.011741 after epoch 36.
+36 	 2.7168 	 6.06383
+37 	 2.7272 	 5.03036
+38 	 2.71115 	 5.95635
+39 	 2.72224 	 5.02069
+Step size decreases to 0.010567 after epoch 40.
+40 	 2.70844 	 5.94394
+41 	 2.71842 	 5.00057
+42 	 2.70399 	 5.83984
+43 	 2.71392 	 4.99542
+Step size decreases to 0.009510 after epoch 44.
+44 	 2.70177 	 5.83091
+45 	 2.71053 	 4.9791
+46 	 2.69812 	 5.73434
+47 	 2.70652 	 4.97696
+Step size decreases to 0.008559 after epoch 48.
+48 	 2.69623 	 5.72879
+49 	 2.70354 	 4.96362
+50 	 2.69316 	 5.64101
+51 	 2.70004 	 4.96338
+Step size decreases to 0.007703 after epoch 52.
+52 	 2.69153 	 5.63813
+53 	 2.69744 	 4.95251
+54 	 2.68891 	 5.55924
+55 	 2.69442 	 4.9536
+Step size decreases to 0.006933 after epoch 56.
+56 	 2.68746 	 5.55841
+57 	 2.69215 	 4.94479
+58 	 2.6852 	 5.48794
+59 	 2.68954 	 4.94684
+Step size decreases to 0.006239 after epoch 60.
+60 	 2.68391 	 5.48866
+61 	 2.68755 	 4.93971
+62 	 2.68193 	 5.42588
+63 	 2.68528 	 4.94243
+Step size decreases to 0.005616 after epoch 64.
+64 	 2.68076 	 5.42767
+65 	 2.68353 	 4.93659
+66 	 2.67901 	 5.37175
+67 	 2.68155 	 4.93972
+Step size decreases to 0.005054 after epoch 68.
+68 	 2.67793 	 5.37416
+69 	 2.68 	 4.93481
+70 	 2.67637 	 5.32424
+71 	 2.67826 	 4.93813
+Step size decreases to 0.004549 after epoch 72.
+72 	 2.67537 	 5.32691
+73 	 2.67687 	 4.93383
+74 	 2.67396 	 5.28218
+75 	 2.67533 	 4.93716
+Step size decreases to 0.004094 after epoch 76.
+76 	 2.67302 	 5.28479
+77 	 2.67407 	 4.93317
+78 	 2.67174 	 5.24452
+79 	 2.6727 	 4.93637
+Step size decreases to 0.003684 after epoch 80.
+80 	 2.67084 	 5.24682
+81 	 2.67156 	 4.93247
+82 	 2.66967 	 5.21037
+83 	 2.67031 	 4.93543
+Step size decreases to 0.003316 after epoch 84.
+84 	 2.66881 	 5.21218
+85 	 2.66927 	 4.93142
+86 	 2.66772 	 5.17901
+87 	 2.66813 	 4.93412
+Step size decreases to 0.002984 after epoch 88.
+88 	 2.66689 	 5.1802
+89 	 2.66716 	 4.92983
+90 	 2.66588 	 5.14986
+91 	 2.66611 	 4.93226
+Step size decreases to 0.002686 after epoch 92.
+92 	 2.66507 	 5.15035
+93 	 2.6652 	 4.92758
+94 	 2.66412 	 5.12247
+95 	 2.66422 	 4.92977
+Step size decreases to 0.002417 after epoch 96.
+96 	 2.66333 	 5.1222
+97 	 2.66335 	 4.92461
+98 	 2.66244 	 5.09648
+99 	 2.66243 	 4.92661
+100 	 2.66165 	 5.09539
+
+Designated epoch number reached. ANFIS training completed at epoch 100.
+
+Minimal training RMSE = 2.66165
+Minimal checking RMSE = 4.85458
+MSE : 17.1406
+RMSE: 4.14012
+R2  : 0.660434
+NMSE: 0.339566
+NDEI: 0.582723
+
+
+Model 3: TSK1-2MF (gbellmf × 2 per input)
+=====================================
+Initial rules: 32
+
+ANFIS info:
+	Number of nodes: 92
+	Number of linear parameters: 192
+	Number of nonlinear parameters: 30
+	Total number of parameters: 222
+	Number of training data pairs: 902
+	Number of checking data pairs: 301
+	Number of fuzzy rules: 32
+
+
+Start training ANFIS ...
+
+1 	 2.85009 	 4.69235
+2 	 2.83691 	 4.61659
+3 	 2.82381 	 4.5385
+4 	 2.81077 	 4.45873
+Step size increases to 0.011000 after epoch 5.
+5 	 2.79775 	 4.37785
+6 	 2.78474 	 4.29631
+7 	 2.7704 	 4.2064
+8 	 2.756 	 4.11658
+Step size increases to 0.012100 after epoch 9.
+9 	 2.7415 	 4.02712
+10 	 2.72683 	 3.93818
+11 	 2.71042 	 3.84107
+12 	 2.69361 	 3.74462
+Step size increases to 0.013310 after epoch 13.
+13 	 2.67624 	 3.64883
+14 	 2.65807 	 3.5539
+15 	 2.63684 	 3.45151
+16 	 2.6138 	 3.35379
+Step size increases to 0.014641 after epoch 17.
+17 	 2.5883 	 3.2663
+18 	 2.55954 	 3.19899
+19 	 2.52297 	 3.16663
+20 	 2.47983 	 3.20424
+Step size increases to 0.016105 after epoch 21.
+21 	 2.42917 	 3.3412
+22 	 2.37416 	 3.59905
+23 	 2.34422 	 3.89625
+24 	 2.36179 	 3.60671
+25 	 2.33613 	 3.94345
+26 	 2.3545 	 3.59925
+Step size decreases to 0.014495 after epoch 27.
+27 	 2.32932 	 3.98109
+28 	 2.34864 	 3.58976
+29 	 2.31899 	 4.00715
+30 	 2.34073 	 3.59264
+Step size decreases to 0.013045 after epoch 31.
+31 	 2.3134 	 4.05635
+32 	 2.33536 	 3.58825
+33 	 2.30527 	 4.05997
+34 	 2.32785 	 3.59718
+Step size decreases to 0.011741 after epoch 35.
+35 	 2.30083 	 4.10646
+36 	 2.32309 	 3.59599
+37 	 2.29443 	 4.0886
+38 	 2.31615 	 3.60889
+Step size decreases to 0.010567 after epoch 39.
+39 	 2.29086 	 4.12772
+40 	 2.31186 	 3.61014
+41 	 2.2857 	 4.09768
+42 	 2.30547 	 3.62593
+Step size decreases to 0.009510 after epoch 43.
+43 	 2.28273 	 4.13163
+44 	 2.30157 	 3.62894
+45 	 2.27844 	 4.09721
+46 	 2.29579 	 3.64627
+Step size decreases to 0.008559 after epoch 47.
+47 	 2.27589 	 4.1275
+48 	 2.29224 	 3.65042
+49 	 2.27223 	 4.09275
+50 	 2.2871 	 3.6682
+Step size decreases to 0.007703 after epoch 51.
+51 	 2.27 	 4.11992
+52 	 2.28389 	 3.67307
+53 	 2.26682 	 4.08658
+54 	 2.27936 	 3.69061
+Step size decreases to 0.006933 after epoch 55.
+55 	 2.26483 	 4.11089
+56 	 2.27645 	 3.6959
+57 	 2.26204 	 4.07957
+58 	 2.27247 	 3.7128
+Step size decreases to 0.006239 after epoch 59.
+59 	 2.26024 	 4.10119
+60 	 2.26983 	 3.71823
+61 	 2.25776 	 4.07206
+62 	 2.26633 	 3.73423
+Step size decreases to 0.005616 after epoch 63.
+63 	 2.25611 	 4.09117
+64 	 2.26395 	 3.73959
+65 	 2.25389 	 4.06416
+66 	 2.26087 	 3.75453
+Step size decreases to 0.005054 after epoch 67.
+67 	 2.25236 	 4.08094
+68 	 2.25871 	 3.75961
+69 	 2.25036 	 4.0559
+70 	 2.25599 	 3.77339
+Step size decreases to 0.004549 after epoch 71.
+71 	 2.24893 	 4.07052
+72 	 2.25403 	 3.77802
+73 	 2.24711 	 4.04729
+74 	 2.25162 	 3.7906
+Step size decreases to 0.004094 after epoch 75.
+75 	 2.24576 	 4.05991
+76 	 2.24983 	 3.79463
+77 	 2.24409 	 4.03828
+78 	 2.24767 	 3.806
+Step size decreases to 0.003684 after epoch 79.
+79 	 2.2428 	 4.04906
+80 	 2.24604 	 3.80931
+81 	 2.24127 	 4.02885
+82 	 2.2441 	 3.81953
+Step size decreases to 0.003316 after epoch 83.
+83 	 2.24002 	 4.03794
+84 	 2.2426 	 3.82204
+85 	 2.2386 	 4.019
+86 	 2.24084 	 3.83118
+Step size decreases to 0.002984 after epoch 87.
+87 	 2.2374 	 4.02654
+88 	 2.23945 	 3.83283
+89 	 2.23608 	 4.00874
+90 	 2.23783 	 3.84101
+Step size decreases to 0.002686 after epoch 91.
+91 	 2.23491 	 4.01486
+92 	 2.23654 	 3.84179
+93 	 2.23367 	 3.99812
+94 	 2.23504 	 3.84916
+Step size decreases to 0.002417 after epoch 95.
+95 	 2.23253 	 4.00297
+96 	 2.23383 	 3.84907
+97 	 2.23136 	 3.98724
+98 	 2.23243 	 3.8558
+Step size decreases to 0.002176 after epoch 99.
+99 	 2.23025 	 3.99091
+100 	 2.23128 	 3.85487
+
+Designated epoch number reached. ANFIS training completed at epoch 100.
+
+Minimal training RMSE = 2.23025
+Minimal checking RMSE = 3.16663
+MSE : 12.8953
+RMSE: 3.591
+R2  : 0.744537
+NMSE: 0.255463
+NDEI: 0.505433
+
+
+Model 4: TSK1-3MF (gbellmf × 3 per input)
+=====================================
+Initial rules: 243
+
+ANFIS info:
+	Number of nodes: 524
+	Number of linear parameters: 1458
+	Number of nonlinear parameters: 45
+	Total number of parameters: 1503
+	Number of training data pairs: 902
+	Number of checking data pairs: 301
+	Number of fuzzy rules: 243
+
+Warning: Number of training data is smaller than number of modifiable parameters. 
+> In anfis>trainFIS (line 203)
+In anfis>anfisWithOptionObject (line 109)
+In anfis (line 68)
+In scenario1 (line 66) 
+
+Start training ANFIS ...
+
+1 	 2.18266 	 13.9384
+2 	 2.16266 	 14.302
+3 	 2.14218 	 14.771
+4 	 2.12092 	 15.3612
+Step size increases to 0.011000 after epoch 5.
+5 	 2.0985 	 16.0849
+6 	 2.07446 	 16.9408
+7 	 2.04563 	 17.9967
+8 	 2.01387 	 19.1103
+Step size increases to 0.012100 after epoch 9.
+9 	 1.97901 	 20.1856
+10 	 1.94113 	 21.1309
+11 	 1.89631 	 21.9439
+12 	 1.84864 	 22.4876
+Step size increases to 0.013310 after epoch 13.
+13 	 1.79858 	 22.7812
+14 	 1.74656 	 22.8849
+15 	 1.68767 	 22.8664
+16 	 1.62771 	 22.7684
+Step size increases to 0.014641 after epoch 17.
+17 	 1.56748 	 22.5829
+18 	 1.50801 	 22.2279
+19 	 1.44544 	 21.5009
+20 	 1.38899 	 20.2966
+Step size increases to 0.016105 after epoch 21.
+21 	 1.34211 	 18.5912
+22 	 1.30602 	 16.488
+23 	 1.27558 	 14.1361
+24 	 1.25531 	 13.5366
+Step size increases to 0.017716 after epoch 25.
+25 	 1.22833 	 11.9472
+26 	 1.20732 	 11.145
+27 	 1.17735 	 9.2776
+28 	 1.15854 	 8.56663
+Step size increases to 0.019487 after epoch 29.
+29 	 1.12679 	 6.41978
+30 	 1.12503 	 6.60586
+31 	 1.09297 	 4.9284
+32 	 1.10129 	 5.326
+33 	 1.06957 	 4.68211
+34 	 1.08124 	 4.74092
+Step size decreases to 0.017538 after epoch 35.
+35 	 1.05297 	 4.99736
+36 	 1.0659 	 4.73721
+37 	 1.03553 	 5.54205
+38 	 1.04981 	 5.0522
+Step size decreases to 0.015785 after epoch 39.
+39 	 1.02529 	 6.28831
+40 	 1.03688 	 5.44902
+41 	 1.009 	 7.02564
+42 	 1.02048 	 5.91912
+Step size decreases to 0.014206 after epoch 43.
+43 	 0.998267 	 8.03611
+44 	 1.00675 	 6.31568
+45 	 0.981053 	 8.87018
+46 	 0.992248 	 6.70868
+Step size decreases to 0.012786 after epoch 47.
+47 	 0.973147 	 9.9055
+48 	 0.983062 	 6.97916
+49 	 0.958957 	 10.505
+50 	 0.973683 	 7.26996
+Step size decreases to 0.011507 after epoch 51.
+51 	 0.954367 	 11.3465
+52 	 0.967501 	 7.4856
+53 	 0.942356 	 11.7345
+54 	 0.960308 	 7.75761
+Step size decreases to 0.010356 after epoch 55.
+55 	 0.939278 	 12.4896
+56 	 0.955315 	 7.97285
+57 	 0.928728 	 12.7622
+58 	 0.948976 	 8.26813
+Step size decreases to 0.009321 after epoch 59.
+59 	 0.926441 	 13.4981
+60 	 0.944515 	 8.50717
+61 	 0.91711 	 13.7127
+62 	 0.938576 	 8.84703
+Step size decreases to 0.008389 after epoch 63.
+63 	 0.915346 	 14.4517
+64 	 0.934437 	 9.12183
+65 	 0.907143 	 14.6391
+66 	 0.928786 	 9.51736
+Step size decreases to 0.007550 after epoch 67.
+67 	 0.905787 	 15.3775
+68 	 0.924938 	 9.83222
+69 	 0.898656 	 15.5517
+70 	 0.919613 	 10.2861
+Step size decreases to 0.006795 after epoch 71.
+71 	 0.89764 	 16.2705
+72 	 0.916096 	 10.6381
+73 	 0.891518 	 16.4343
+74 	 0.911178 	 11.1444
+Step size decreases to 0.006115 after epoch 75.
+75 	 0.890784 	 17.1082
+76 	 0.908033 	 11.5239
+77 	 0.885587 	 17.2586
+78 	 0.903579 	 12.0686
+Step size decreases to 0.005504 after epoch 79.
+79 	 0.885076 	 17.8654
+80 	 0.90081 	 12.4607
+81 	 0.8807 	 17.9986
+82 	 0.896814 	 13.0247
+Step size decreases to 0.004953 after epoch 83.
+83 	 0.880351 	 18.5253
+84 	 0.894379 	 13.4131
+85 	 0.876676 	 18.6391
+86 	 0.890789 	 13.9761
+Step size decreases to 0.004458 after epoch 87.
+87 	 0.876438 	 19.0838
+88 	 0.888624 	 14.3467
+89 	 0.873343 	 19.1782
+90 	 0.885379 	 14.8903
+Step size decreases to 0.004012 after epoch 91.
+91 	 0.873172 	 19.5467
+92 	 0.883427 	 15.2336
+93 	 0.870543 	 19.6236
+94 	 0.880489 	 15.7433
+Step size decreases to 0.003611 after epoch 95.
+95 	 0.870406 	 19.9261
+96 	 0.878716 	 16.0547
+97 	 0.868147 	 19.9882
+98 	 0.876081 	 16.5202
+Step size decreases to 0.003250 after epoch 99.
+99 	 0.868021 	 20.2362
+100 	 0.874473 	 16.7991
+
+Designated epoch number reached. ANFIS training completed at epoch 100.
+
+Minimal training RMSE = 0.868021
+Minimal checking RMSE = 4.68211
+MSE : 30.4753
+RMSE: 5.52045
+R2  : 0.396266
+NMSE: 0.603734
+NDEI: 0.777003
+
+    Metrics    Model_1    Model_2    Model_3    Model_4
+    _______    _______    _______    _______    _______
+
+     MSE        16.926     17.141     12.895     30.475
+     RMSE       4.1142     4.1401      3.591     5.5204
+     R2        0.66468    0.66043    0.74454    0.39627
+     NMSE      0.33532    0.33957    0.25546    0.60373
+     NDEI      0.57907    0.58272    0.50543      0.777
--- a/3/source/Scenario2.log
+++ b/3/source/Scenario2.log
--- a/3/source/evaluate.m
+++ b/3/source/evaluate.m
@ -0,0 +1,17 @@
+function [mse, rmse, r2, nmse, ndei] = evaluate(pred, truth)
+%EVALUATE Summary of this function goes here
+%   Detailed explanation goes here
+    
+    % MSE / RMSE
+    mse  = mean((truth - pred).^2);
+    rmse = sqrt(mse);
+
+    % R^2 - NMSE - NDEI
+    ss_res = sum((truth - pred).^2);
+    ss_tot = sum((truth - mean(truth)).^2);
+    
+    nmse   = ss_res / ss_tot;
+    ndei   = sqrt(nmse);
+    r2     = 1 - nmse;
+
+end
--- a/3/source/figures_scn1/model_1_error.png
+++ b/3/source/figures_scn1/model_1_error.png
--- a/3/source/figures_scn1/model_1_learning_curves.png
+++ b/3/source/figures_scn1/model_1_learning_curves.png
--- a/3/source/figures_scn1/model_1_mfs_all_inputs.png
+++ b/3/source/figures_scn1/model_1_mfs_all_inputs.png
--- a/3/source/figures_scn1/model_1_pred_vs_actual.png
+++ b/3/source/figures_scn1/model_1_pred_vs_actual.png
--- a/3/source/figures_scn1/model_2_error.png
+++ b/3/source/figures_scn1/model_2_error.png
--- a/3/source/figures_scn1/model_2_learning_curves.png
+++ b/3/source/figures_scn1/model_2_learning_curves.png
--- a/3/source/figures_scn1/model_2_mfs_all_inputs.png
+++ b/3/source/figures_scn1/model_2_mfs_all_inputs.png
--- a/3/source/figures_scn1/model_2_pred_vs_actual.png
+++ b/3/source/figures_scn1/model_2_pred_vs_actual.png
--- a/3/source/figures_scn1/model_3_error.png
+++ b/3/source/figures_scn1/model_3_error.png
--- a/3/source/figures_scn1/model_3_learning_curves.png
+++ b/3/source/figures_scn1/model_3_learning_curves.png
--- a/3/source/figures_scn1/model_3_mfs_all_inputs.png
+++ b/3/source/figures_scn1/model_3_mfs_all_inputs.png
--- a/3/source/figures_scn1/model_3_pred_vs_actual.png
+++ b/3/source/figures_scn1/model_3_pred_vs_actual.png
--- a/3/source/figures_scn1/model_4_error.png
+++ b/3/source/figures_scn1/model_4_error.png
--- a/3/source/figures_scn1/model_4_learning_curves.png
+++ b/3/source/figures_scn1/model_4_learning_curves.png
--- a/3/source/figures_scn1/model_4_mfs_all_inputs.png
+++ b/3/source/figures_scn1/model_4_mfs_all_inputs.png
--- a/3/source/figures_scn1/model_4_pred_vs_actual.png
+++ b/3/source/figures_scn1/model_4_pred_vs_actual.png
--- a/3/source/figures_scn2/scn2_cv_heatmap.png
+++ b/3/source/figures_scn2/scn2_cv_heatmap.png
--- a/3/source/figures_scn2/scn2_error_vs_features.png
+++ b/3/source/figures_scn2/scn2_error_vs_features.png
--- a/3/source/figures_scn2/scn2_error_vs_rules.png
+++ b/3/source/figures_scn2/scn2_error_vs_rules.png
--- a/3/source/figures_scn2/scn2_final_error_series.png
+++ b/3/source/figures_scn2/scn2_final_error_series.png
--- a/3/source/figures_scn2/scn2_final_learning_curves.png
+++ b/3/source/figures_scn2/scn2_final_learning_curves.png
--- a/3/source/figures_scn2/scn2_final_mfs_subset.png
+++ b/3/source/figures_scn2/scn2_final_mfs_subset.png
--- a/3/source/figures_scn2/scn2_final_pred_vs_actual.png
+++ b/3/source/figures_scn2/scn2_final_pred_vs_actual.png
--- a/3/source/plot_results1.m
+++ b/3/source/plot_results1.m
@ -0,0 +1,148 @@
+function plot_results1(init_fis, final_fis, trn_error, val_error, y_true, y_pred, model_id)
+%PLOT_RESULTS
+% Creates and saves:
+%   (A) One consolidated figure for membership functions (all inputs):
+%       - 2 x Ninputs tiled layout
+%       - Top row: BEFORE training (all MFs per input in one subplot)
+%       - Bottom row: AFTER training (all MFs per input in one subplot)
+%   (B) Learning curves (train & validation)
+%   (C) Predicted vs Actual (with y=x reference)
+%   (D) Prediction Error (time series + MAE)
+%
+% All figures are saved to ./figures as both PDF and PNG.
+%
+% Usage:
+%   plot_results(init_fis, final_fis, trn_error, val_error, y_true, y_pred, model_id)
+
+    % Input checks
+    if nargin < 7
+        error('plot_results: not enough inputs. Expected 7 arguments.');
+    end
+    if numel(y_true) ~= numel(y_pred)
+        error('plot_results: y_true and y_pred must have the same length.');
+    end
+    y_true = y_true(:);
+    y_pred = y_pred(:);
+
+    % Output directory 
+    outdir = fullfile('.', 'figures_scn1');
+    if ~exist(outdir, 'dir'); mkdir(outdir); end
+
+    % (A) MEMBERSHIP FUNCTIONS — ONE FIGURE, 2 x Ninputs (BEFORE / AFTER)
+    % =====================================================================
+    nInputs = numel(init_fis.Inputs);
+
+    % Precompute x/y for all inputs to keep consistent axes
+    Xb = cell(1, nInputs); Yb = cell(1, nInputs);   % BEFORE
+    Xa = cell(1, nInputs); Ya = cell(1, nInputs);   % AFTER
+    nMFb = zeros(1, nInputs); nMFa = zeros(1, nInputs);
+
+    for inIdx = 1:nInputs
+        [xb, yb] = plotmf(init_fis,  'input', inIdx);
+        [xa, ya] = plotmf(final_fis, 'input', inIdx);
+        Xb{inIdx} = xb; Yb{inIdx} = yb; nMFb(inIdx) = size(yb,2);
+        Xa{inIdx} = xa; Ya{inIdx} = ya; nMFa(inIdx) = size(ya,2);
+    end
+
+    % Create consolidated figure
+    f = figure('Name', sprintf('MFs — Model %d', model_id), 'Color','w');
+    tl = tiledlayout(f, 2, nInputs, 'TileSpacing','compact', 'Padding','compact');
+
+    % Titles for rows
+    rowTitles = {'BEFORE training', 'AFTER training'};
+
+    for inIdx = 1:nInputs
+        % BEFORE (top row)
+        nexttile(tl, inIdx);  % row 1, col inIdx
+        hold on; grid on;
+        xb = Xb{inIdx}; yb = Yb{inIdx}; kB = size(yb,2);
+        for k = 1:kB
+            plot(xb, yb(:,k), 'LineWidth', 1.5);
+        end
+        xlabel(sprintf('Input x%d', inIdx), 'Interpreter','latex');
+        ylabel('Membership', 'Interpreter','latex');
+        title(sprintf('%s — x%d', rowTitles{1}, inIdx), 'Interpreter','latex');
+
+        % Optional legend only on the first tile to reduce clutter
+        if inIdx == 1
+            lgdB = arrayfun(@(k) sprintf('MF %d', k), 1:kB, 'UniformOutput', false);
+            legend(lgdB, 'Location','best');
+        end
+
+        % AFTER (bottom row)
+        nexttile(tl, nInputs + inIdx); % row 2, col inIdx
+        hold on; grid on;
+        xa = Xa{inIdx}; ya = Ya{inIdx}; kA = size(ya,2);
+        for k = 1:kA
+            plot(xa, ya(:,k), 'LineWidth', 1.5);
+        end
+        xlabel(sprintf('Input x%d', inIdx), 'Interpreter','latex');
+        ylabel('Membership', 'Interpreter','latex');
+        title(sprintf('%s — x%d', rowTitles{2}, inIdx), 'Interpreter','latex');
+
+        if inIdx == 1
+            lgdA = arrayfun(@(k) sprintf('MF %d', k), 1:kA, 'UniformOutput', false);
+            legend(lgdA, 'Location','best');
+        end
+    end
+
+    % Super-title
+    sgtitle(tl, sprintf('Membership Functions — Model %d', model_id), 'Interpreter','latex');
+
+    % Save consolidated MF figure
+    save_figure(f, fullfile(outdir, sprintf('model_%d_mfs_all_inputs', model_id)));
+
+    % (B) LEARNING CURVES (train & validation)
+    % ============================================
+    f = figure('Name', sprintf('Learning Curves - Model %d', model_id), 'Color','w');
+    plot(trn_error, 'LineWidth', 1.5); hold on; grid on;
+    if ~isempty(val_error)
+        plot(val_error, 'LineWidth', 1.5);
+        legend('Train','Validation','Location','best');
+    else
+        legend('Train','Location','best');
+    end
+    xlabel('Epoch', 'Interpreter','latex');
+    ylabel('Error', 'Interpreter','latex');
+    title(sprintf('Learning Curves | Model %d', model_id), 'Interpreter','latex');
+    save_figure(f, fullfile(outdir, sprintf('model_%d_learning_curves', model_id)));
+
+    % (C) PREDICTED vs ACTUAL
+    % ============================================
+    f = figure('Name', sprintf('Predicted vs Actual - Model %d', model_id), 'Color','w');
+    plot(y_true, y_pred, '.', 'MarkerSize', 10); hold on; grid on;
+
+    mins = min([y_true; y_pred]);
+    maxs = max([y_true; y_pred]);
+    plot([mins maxs], [mins maxs], 'k-', 'LineWidth', 1);
+
+    xlabel('Actual', 'Interpreter','latex');
+    ylabel('Predicted', 'Interpreter','latex');
+    title(sprintf('Predicted vs Actual | Model %d', model_id), 'Interpreter','latex');
+    save_figure(f, fullfile(outdir, sprintf('model_%d_pred_vs_actual', model_id)));
+
+    % (D) PREDICTION ERROR
+    % ============================================
+    err = y_true - y_pred;
+    f = figure('Name', sprintf('Prediction Error - Model %d', model_id), 'Color','w');
+    plot(err, 'k'); grid on;
+    xlabel('Iteration', 'Interpreter','latex');
+    ylabel('Prediction Error', 'Interpreter','latex');
+    title('Prediction Error', 'Interpreter','latex');
+    mae = mean(abs(err));
+    try
+        subtitle(sprintf('Mean absolute error: %f', mae), 'Interpreter','latex');
+    catch
+        title(sprintf('Prediction Error (MAE=%.6f)', mae), 'Interpreter','latex');
+    end
+    save_figure(f, fullfile(outdir, sprintf('model_%d_error', model_id)));
+end
+
+% Helper: consistent saving (PDF + PNG, landscape)
+function save_figure(fig_handle, basepath)
+    set(fig_handle, 'PaperUnits','normalized');
+    set(fig_handle, 'PaperPosition', [0 0 1 1]);
+    set(fig_handle, 'PaperOrientation', 'landscape');
+    % print(fig_handle, '-dpdf',  [basepath '.pdf']);
+    print(fig_handle, '-dpng',  [basepath '.png']);
+end
--- a/3/source/plot_results2.m
+++ b/3/source/plot_results2.m
@ -0,0 +1,107 @@
+function plot_results2(init_fis, final_fis, trn_error, val_error, y_true, y_pred, ...
+                           feature_grid, radius_grid, cv_scores, cv_rules, sel_idx)
+% PLOTS FOR SCENARIO 2:
+% (1) CV heatmap (mean CV error over grid) with overlaid #rules
+% (2) Error vs rules (aggregated) & Error vs #features (best over radii)
+% (3) Final model: Learning curves, Predicted vs Actual, Residuals
+% (4) MFs (subset of selected inputs) BEFORE/AFTER
+%
+% All figures are saved under ./figures
+
+    outdir = fullfile('.', 'figures_scn2'); if ~exist(outdir,'dir'), mkdir(outdir); end
+
+    % (1) CV heatmap
+    f = figure('Color','w','Name','CV Heatmap');
+    imagesc(radius_grid, feature_grid, cv_scores);
+    set(gca,'YDir','normal'); xlabel('Cluster radius r_\alpha','Interpreter','latex');
+    ylabel('#Features','Interpreter','latex');
+    title('Mean CV Error','Interpreter','latex'); colorbar; grid on;
+    % overlay mean rules
+    hold on;
+    for i=1:numel(feature_grid)
+        for j=1:numel(radius_grid)
+            text(radius_grid(j), feature_grid(i), sprintf('%d', cv_rules(i,j)), ...
+                'HorizontalAlignment','center','Color','w','FontSize',8);
+        end
+    end
+    try
+        subtitle('Numbers show mean #rules','Interpreter','latex');
+    end
+    print(f, fullfile(outdir,'scn2_cv_heatmap'), '-dpng');
+
+    % (2a) Error vs #rules (aggregate by identical rule-counts)
+    uniq_rules = unique(cv_rules(:));
+    err_vs_rules = zeros(size(uniq_rules));
+    for k=1:numel(uniq_rules)
+        err_vs_rules(k) = mean(cv_scores(cv_rules == uniq_rules(k)));
+    end
+    f = figure('Color','w','Name','Error vs Rules');
+    plot(uniq_rules, err_vs_rules, 'o-','LineWidth',1.5); grid on;
+    xlabel('#Rules','Interpreter','latex'); ylabel('Mean CV Error','Interpreter','latex');
+    title('Error vs Rules','Interpreter','latex');
+    print(f, fullfile(outdir,'scn2_error_vs_rules'), '-dpng');
+
+    % (2b) Best CV error vs #features (min across radii)
+    [min_err_per_feat,~] = min(cv_scores,[],2);
+    f = figure('Color','w','Name','Error vs Features');
+    plot(feature_grid, min_err_per_feat, 's-','LineWidth',1.5); grid on;
+    xlabel('#Features','Interpreter','latex'); ylabel('Best CV Error','Interpreter','latex');
+    title('Best CV Error vs #Features','Interpreter','latex');
+    print(f, fullfile(outdir,'scn2_error_vs_features'), '-dpng');
+
+    % (3a) Learning curves
+    f = figure('Color','w','Name','Learning Curves');
+    plot(trn_error,'LineWidth',1.5); hold on; grid on;
+    if ~isempty(val_error)
+        plot(val_error,'LineWidth',1.5); legend('Train','Validation','Location','best');
+    else
+        legend('Train','Location','best');
+    end
+    xlabel('Epoch','Interpreter','latex'); ylabel('Error','Interpreter','latex');
+    title('Learning Curves','Interpreter','latex');
+    print(f, fullfile(outdir,'scn2_final_learning_curves'), '-dpng');
+
+    % (3b) Predicted vs Actual
+    y_true = y_true(:); y_pred = y_pred(:);
+    f = figure('Color','w','Name','Predicted vs Actual');
+    plot(y_true, y_pred, '.', 'MarkerSize', 10); hold on; grid on;
+    mins = min([y_true; y_pred]); maxs = max([y_true; y_pred]);
+    plot([mins maxs], [mins maxs], 'k-', 'LineWidth', 1);
+    xlabel('Actual','Interpreter','latex'); ylabel('Predicted','Interpreter','latex');
+    title('Predicted vs Actual','Interpreter','latex');
+    print(f, fullfile(outdir,'scn2_final_pred_vs_actual'), '-dpng');
+
+    % (3c) Residuals (time series)
+    err = y_true - y_pred;
+    f = figure('Color','w','Name','Prediction Error');
+    plot(err, 'k'); grid on;
+    xlabel('Sample','Interpreter','latex'); ylabel('Error','Interpreter','latex');
+    title('Prediction Error','Interpreter','latex');
+    mae = mean(abs(err));
+    try
+        subtitle(sprintf('Mean absolute error: %.6f', mae), 'Interpreter','latex');
+    end
+    print(f, fullfile(outdir,'scn2_final_error_series'), '-dpng');
+
+    % (4) MFs (subset of selected inputs) BEFORE/AFTER
+    % Show up to 3 selected inputs for clarity
+    nShow = min( min(3, numel(sel_idx)), numel(init_fis.Inputs) );
+    if nShow > 0
+        f = figure('Color','w','Name','MFs (subset)');
+        tl = tiledlayout(2, nShow, 'TileSpacing','compact', 'Padding','compact');
+        for k=1:nShow
+            inIdx = k; % first few selected
+            [xb,yb] = plotmf(init_fis,'input',inIdx);
+            [xa,ya] = plotmf(final_fis,'input',inIdx);
+            nexttile(tl,k); hold on; grid on; plot(xb,yb,'LineWidth',1.2);
+            title(sprintf('BEFORE x_{%d}', sel_idx(k)),'Interpreter','latex');
+            xlabel(sprintf('x_{%d}', sel_idx(k)),'Interpreter','latex'); ylabel('Membership','Interpreter','latex');
+
+            nexttile(tl,nShow+k); hold on; grid on; plot(xa,ya,'LineWidth',1.2);
+            title(sprintf('AFTER x_{%d}', sel_idx(k)),'Interpreter','latex');
+            xlabel(sprintf('x_{%d}', sel_idx(k)),'Interpreter','latex'); ylabel('Membership','Interpreter','latex');
+        end
+        sgtitle(tl, 'Membership Functions (subset)','Interpreter','latex');
+        print(f, fullfile(outdir,'scn2_final_mfs_subset'), '-dpng');
+    end
+end
--- a/3/source/preprocess_data.m
+++ b/3/source/preprocess_data.m
@ -0,0 +1,39 @@
+function [X_trn_s, X_val_s, X_chk_s, stats] = preprocess_data(X_trn, X_val, X_chk, mode)
+%SPLITSET Splits the data-set to train, eval,check data
+%   Randomly split the data set according to ratios   
+%    
+    
+    if nargin < 4, mode = 1; end  % 1: min-max, 2: z-score
+
+    switch mode
+        case 1  % Min–Max to [0,1] using TRAIN stats
+            xmin  = min(X_trn,[],1);
+            xmax  = max(X_trn,[],1);
+            range = xmax - xmin;
+            range(range==0) = 1;
+            X_trn_s = (X_trn - xmin) ./ range;
+            X_val_s = (X_val - xmin) ./ range;
+            X_chk_s = (X_chk - xmin) ./ range;
+            stats = struct( ...
+                'type', 'minmax', ...
+                'xmin', xmin, ...
+                'xmax', xmax ...
+            );
+
+        case 2  % Z-score using TRAIN stats (chatGPT gave me this one)
+            mu  = mean(X_trn,1);
+            sig = std(X_trn,0,1);
+            sig(sig==0) = 1;
+            X_trn_s = (X_trn - mu) ./ sig;
+            X_val_s = (X_val - mu) ./ sig;
+            X_chk_s = (X_chk - mu) ./ sig;
+            stats = struct(...
+                'type', 'zscore', ...
+                'mu', mu, ...
+                'sig', sig ...
+            );
+
+        otherwise
+            error('Unknown mode.');
+    end
+end
--- a/3/source/scenario1.m
+++ b/3/source/scenario1.m
@ -0,0 +1,94 @@
+%% Scenario1 (TSK - Airfoil Self-Noise)
+%
+% Assignment 3 in Fuzzy systems
+% 
+% author:
+%   Christos Choutouridis ΑΕΜ 8997
+%   cchoutou@ece.auth.gr
+%
+clear; clc; close all;
+
+
+% Configuration
+% --------------------------------
+config.mf_types    = ["constant", "constant", "linear", "linear"];
+config.num_mf      = [2, 3, 2, 3];  % #MFs per input
+config.model_names = ["TSK0-2MF", "TSK0-3MF", "TSK1-2MF", "TSK1-3MF"];
+config.Nepochs     = 100;
+
+rng(42,'twister');
+
+% Load dataset (Airfoil Self-Noise: 5 inputs, 1 target)
+data = load("Datasets/airfoil_self_noise.dat");
+% Split data: 60% train, 20% val, 20% check(test)
+[X_trn, y_trn, X_val, y_val, X_chk, y_chk] = split_data(data, [0.6 0.2 0.2], 42);
+% Preprocess inputs (mode=1 -> MinMax to [0,1])
+[X_trn, X_val, X_chk, stats] = preprocess_data(X_trn, X_val, X_chk, 1);
+
+% Pack data for anfis/evalfis convenience
+trn_data = [X_trn, y_trn];
+val_data = [X_val, y_val];
+chk_X    = X_chk;
+chk_y    = y_chk;
+
+
+% Results
+T = table(['MSE '; 'RMSE'; 'R2  '; 'NMSE'; 'NDEI'], 'VariableNames', {'Metrics'});
+
+all_models = cell(1,4);
+all_trnErr = cell(1,4);
+all_valErr = cell(1,4);
+
+for i = 1:length(config.mf_types)
+    fprintf('\n');
+    fprintf('Model %d: %s (gbellmf × %d per input)\n', i, config.model_names(i), config.num_mf(i));
+    fprintf('=====================================\n');
+
+    % Step 1 -  Initial FIS via Grid Partition
+    opt_g = genfisOptions("GridPartition", ...
+        "InputMembershipFunctionType", "gbellmf", ...
+        "NumMembershipFunctions", config.num_mf(i), ...
+        "OutputMembershipFunctionType", config.mf_types(i));
+
+    init_fis = genfis(X_trn, y_trn, opt_g);
+
+    % Info rules and MF
+    nRules = numel(init_fis.Rules);
+    fprintf('Initial rules: %d\n', nRules);
+
+    % Step 2 - Train with ANFIS (Hybrid: BP + LSE)
+    opt_a = anfisOptions( ...
+        "InitialFis", init_fis, ...
+        "ValidationData", val_data, ...
+        "EpochNumber", config.Nepochs, ...
+        "OptimizationMethod", 1 ... % 1=Hybrid
+    );
+    [trn_fis, trn_error, ~, val_fis, val_error] = anfis(trn_data, opt_a);
+
+    final_fis = val_fis;    % Select final
+
+    % Step 3 -  Evaluate on Check/Test set
+    y_hat = evalfis(final_fis, chk_X);
+    [mse, rmse, r2, nmse, ndei] = evaluate(y_hat, chk_y);
+
+    % Print & store metrics
+    fprintf('MSE : %g\n', mse);
+    fprintf('RMSE: %g\n', rmse);
+    fprintf('R2  : %g\n', r2);
+    fprintf('NMSE: %g\n', nmse);
+    fprintf('NDEI: %g\n', ndei);
+
+    T = addvars(T, [mse; rmse; r2; nmse; ndei], 'NewVariableNames', sprintf('Model_%d',i));
+
+    % Plots & figure exports
+    %   - MFs before/after
+    %   - Learning curves
+    %   - Predicted vs Actual
+    %   - Prediction Error
+    plot_results1(init_fis, final_fis, trn_error, val_error, y_chk, y_hat, i);
+
+end
+
+% Show and save table
+disp(T);
+writetable(T, 'scenario1_metrics.csv');
--- a/3/source/scenario1_metrics.csv
+++ b/3/source/scenario1_metrics.csv
@ -0,0 +1,6 @@
+Metrics,Model_1,Model_2,Model_3,Model_4
+MSE ,16.9264182248583,17.1406109263442,12.8952585467096,30.4753160187243
+RMSE,4.11417284819906,4.14012209075339,3.59099687367027,5.520445273592
+R2  ,0.664677433758077,0.660434146998977,0.744537141243185,0.396265586807705
+NMSE,0.335322566241923,0.339565853001023,0.255462858756815,0.603734413192295
+NDEI,0.579070432885261,0.582722792587541,0.505433337599347,0.777003483384917
--- a/3/source/scenario2.m
+++ b/3/source/scenario2.m
@ -0,0 +1,175 @@
+%% Scenario2 (TSK - Superconductivity, High-dimensional)
+%
+% Assignment 3 in Fuzzy systems
+% 
+% author:
+%   Christos Choutouridis ΑΕΜ 8997
+%   cchoutou@ece.auth.gr
+%
+% Notes:
+%   - 5-fold CV grid-search over (#features, SC radius) with careful
+%     no-leakage pipeline (scaling & ReliefF inside each fold)
+%   - Final training on Train with Validation on Val, Test on Test
+%   - Diagnostics: CV heatmap, error-vs-rules, error-vs-#features
+%   - Classic plots: learning curves, pred vs actual, residuals, MFs (subset)
+
+clear; clc; close all;
+
+% Configuration
+% --------------------------------
+% Grid of hyperparameters
+config.feature_grid = [5 8 11 15];
+config.radius_grid  = [0.25 0.5 0.75 1.0];
+config.K            = 5;  % 5-fold CV
+config.Nepochs      = 100;
+
+rng(42,'twister');
+
+fprintf('Scenario 2 — Superconduct\n');
+fprintf('===============================\n\n');
+
+% Load dataset
+fprintf('Loading dataset (superconduct.csv)...\n');
+data = load("Datasets/superconduct.csv");
+fprintf('   Done: %d samples, %d features + 1 target\n\n', size(data,1), size(data,2)-1);
+
+% Split 60/20/20 and scale using TRAIN stats
+fprintf('Splitting dataset into Train/Val/Test [0.6/0.2/0.2] ...\n');
+[X_trn, y_trn, X_val_raw, y_val, X_chk_raw, y_chk] = split_data(data, [0.6 0.2 0.2], 42);
+fprintf('   Done: Train=%d, Val=%d, Test=%d\n', ...
+        size(X_trn,1), size(X_val_raw,1), size(X_chk_raw,1));
+
+fprintf('Preprocessing (Min–Max scaling to [0,1]) ...\n');
+[X_trn_s, X_val_s, X_chk_s, scale_stats] = preprocess_data(X_trn, X_val_raw, X_chk_raw, 1);
+fprintf('   Done (mode = %s)\n\n', scale_stats.type);
+
+
+
+fprintf('Starting %d×%d grid-search (%d folds per point)...\n', ...
+        numel(config.feature_grid), numel(config.radius_grid), config.K);
+
+cv_scores = zeros(numel(config.feature_grid), numel(config.radius_grid));
+cv_rules  = zeros(numel(config.feature_grid), numel(config.radius_grid));
+
+Ntr = size(X_trn_s,1);
+folds = cvpartition(Ntr, "KFold", config.K);
+
+for fi = 1:numel(config.feature_grid)
+    kfeat = config.feature_grid(fi);
+    for rj = 1:numel(config.radius_grid)
+        rad = config.radius_grid(rj);
+        fprintf('   Combo: features=%2d | radius=%.2f ...\n', kfeat, rad);
+
+        fold_err  = zeros(config.K,1);
+        fold_rule = zeros(config.K,1);
+
+        for k = 1:config.K
+            fprintf('   Fold %d/%d ... ', k, config.K);
+
+            Itr = training(folds, k);
+            Ivl = test(folds, k);
+
+            % Raw (pre-global-scale) slices
+            x_tr_raw = X_trn(Itr,:);  y_tr_f = y_trn(Itr);
+            x_vl_raw = X_trn(Ivl,:);  y_vl_f = y_trn(Ivl);
+
+            % Per-fold scaling
+            [x_tr_f, x_vl_f, ~, ~] = preprocess_data(x_tr_raw, x_vl_raw, x_vl_raw, 1);
+
+            % Feature selection (ReliefF)
+            [idxF, ~] = relieff(x_tr_f, y_tr_f, 10);
+            kkeep = min(kfeat, size(x_tr_f,2));
+            sel = idxF(1:kkeep);
+
+            x_tr_f = x_tr_f(:, sel);
+            x_vl_f = x_vl_f(:, sel);
+
+            % Init FIS (SC)
+            gopt = genfisOptions("SubtractiveClustering", ...
+                                 "ClusterInfluenceRange", rad);
+            init_fis = genfis(x_tr_f, y_tr_f, gopt);
+
+            % Train (Hybrid)
+            aopt = anfisOptions("InitialFis", init_fis, ...
+                                "ValidationData", [x_vl_f y_vl_f], ...
+                                "EpochNumber", config.Nepochs, ...
+                                "OptimizationMethod", 1, ...
+                                "DisplayErrorValues", 0, ...
+                                "DisplayStepSize", 0);
+
+            [~, ~, ~, vl_fis, vl_err] = anfis([x_tr_f y_tr_f], aopt);
+
+            fold_err(k)  = min(vl_err);
+            fold_rule(k) = numel(vl_fis.Rules);
+            fprintf('   Done (val err=%.4g, rules=%d)\n', fold_err(k), fold_rule(k));
+        end
+
+        cv_scores(fi, rj) = mean(fold_err);
+        cv_rules(fi, rj)  = round(mean(fold_rule));
+        fprintf('   Mean CV error=%.4g | Mean rules=%d\n\n', ...
+                cv_scores(fi,rj), cv_rules(fi,rj));
+    end
+end
+
+fprintf('Grid-search completed.\n');
+
+% Pick best hyper-parameters
+[minErr, ix] = min(cv_scores(:));
+[fi_best, rj_best] = ind2sub(size(cv_scores), ix);
+best_feats = config.feature_grid(fi_best);
+best_rad   = config.radius_grid(rj_best);
+fprintf('\n');
+fprintf('   Best combo found: features=%d, radius=%.2f\n', best_feats, best_rad);
+fprintf('   Mean CV error   : %.4g\n', minErr);
+fprintf('===========================================\n\n');
+
+% Final training phase
+fprintf('Final training using best hyper-parameters...\n');
+[idx_all, ~] = relieff(X_trn_s, y_trn, 10);
+kkeep = min(best_feats, size(X_trn_s,2));
+sel_final = idx_all(1:kkeep);
+
+XtrF = X_trn_s(:, sel_final);
+XvlF = X_val_s(:, sel_final);
+XteF = X_chk_s(:, sel_final);
+
+fprintf('   Selected %d features (ReliefF top indices)\n', kkeep);
+fprintf('   Building initial FIS (SC radius=%.2f)...\n', best_rad);
+
+gopt = genfisOptions("SubtractiveClustering", "ClusterInfluenceRange", best_rad);
+init_fis = genfis(XtrF, y_trn, gopt);
+fprintf('   Initial FIS created with %d rules\n', numel(init_fis.Rules));
+
+aopt = anfisOptions("InitialFis", init_fis, ...
+                    "ValidationData", [XvlF y_val], ...
+                    "EpochNumber", config.Nepochs, ...
+                    "OptimizationMethod", 1, ...
+                    "DisplayErrorValues", 1, ...
+                    "DisplayStepSize", 0);
+
+fprintf('Training ANFIS (Hybrid optimization)...\n');
+[trn_fis, trn_error, ~, fin_fis, val_error] = anfis([XtrF y_trn], aopt);
+fprintf('   Done. Final FIS has %d rules.\n\n', numel(fin_fis.Rules));
+
+% Evaluation on TEST
+fprintf('Evaluating on Test set ...\n');
+y_hat = evalfis(fin_fis, XteF);
+[mse, rmse, r2, nmse, ndei] = evaluate(y_hat, y_chk);
+
+fprintf('\n');
+fprintf(' FINAL MODEL PERFORMANCE (Test set)\n');
+fprintf('-------------------------------------------\n');
+fprintf('  MSE : %g\n', mse);
+fprintf('  RMSE: %g\n', rmse);
+fprintf('  R2  : %g\n', r2);
+fprintf('  NMSE: %g\n', nmse);
+fprintf('  NDEI: %g\n', ndei);
+fprintf('\n');
+
+% Plots
+fprintf('Generating diagnostic plots ...\n');
+plot_results2( ...
+    init_fis, fin_fis, trn_error, val_error, y_chk, y_hat, ...
+    config.feature_grid, config.radius_grid, cv_scores, cv_rules, sel_final ...
+);
+fprintf('   All plots saved in ./figures_scn2\n');
--- a/3/source/split_data.m
+++ b/3/source/split_data.m
@ -0,0 +1,25 @@
+function [X_trn, y_trn, X_val, y_val, X_chk, y_chk] = split_data(data, ratios, seed)
+%SPLITSET Splits the data-set to train, eval,check data
+%   Randomly split the data set according to ratios   
+%
+
+    % Argument check and default values
+    if nargin < 2 || isempty(ratios), ratios = [0.6 0.2 0.2]; end
+    if nargin == 2, rng(42,  'twister'); end
+    if nargin > 2,  rng(seed,'twister'); end
+
+    N = size(data,1);           % Scramble the set
+    idx = randperm(N);
+
+    Ntrn = round(ratios(1)*N);  % Split positions
+    Nval = round(ratios(2)*N);
+    Itrn = idx(1:Ntrn);
+    Ival = idx(Ntrn+1:Ntrn+Nval);
+    Ichk = idx(Ntrn+Nval+1:end);
+
+    % Pick the data (in X and y)
+    X_trn = data(Itrn, 1:end-1);  y_trn = data(Itrn, end);
+    X_val = data(Ival, 1:end-1);  y_val = data(Ival, end);
+    X_chk = data(Ichk, 1:end-1);  y_chk = data(Ichk, end);
+
+end
				`@ -0,0 +1 @@`
				`Subproject commit 74ec4b5f6c66382e5f1b6d2e6930897e4ed53ea6`