HW3: Source and results for both of the scenarios added
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[submodule "Work 2/report/AUThReport"]
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[submodule "Work 2/report/AUThReport"]
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path = Work 2/report/AUThReport
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path = Work 2/report/AUThReport
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url = ssh://git@git.hoo2.net:222/hoo2/AUThReport.git
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url = ssh://git@git.hoo2.net:222/hoo2/AUThReport.git
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[submodule "Work 3/report/AUThReport"]
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path = Work 3/report/AUThReport
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url = ssh://git@git.hoo2.net:222/hoo2/AUThReport.git
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# LaTeX auxiliary files
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Subproject commit 74ec4b5f6c66382e5f1b6d2e6930897e4ed53ea6
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% !TEX TS-program = xelatex
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% !TEX spellcheck = el-GR
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%
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% Fuzzy Systems Assignment 3
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% Requires compilation with pdfLaTeX or XeLaTeX
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% authors:
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% Χρήστος Χουτουρίδης ΑΕΜ 8997
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% cchoutou@ece.auth.gr
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%---------------------------------
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% \WorkGroup{Ομάδα Χ}
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\AuthorName{Χρήστος Χουτουρίδης}
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\AuthorAEM{8997}
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\AuthorMail{cchoutou@ece.auth.gr}
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%\CoAuthorName{Όνομα Επίθετο}
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%\CoAuthorAEM{1234}
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%\CoAuthorMail{xxx@ece.auth.gr}
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\DocTitle{Εργασία 3}
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\DocSubTitle{Επίλυση προβλήματος παλινδρόμησης με χρήση μοντέλων TSK}
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\Department{Τμήμα ΗΜΜΥ. Τομέας Ηλεκτρονικής}
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\ClassName{Ασαφή Συστήματα (Υπολογιστική Νοημοσύνη)}
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\InstructorName{Θεοχάρης Ιωάννης}
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\InstructorMail{theochar@ece.auth.gr}
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\CoInstructorName{Χαδουλός Χρήστος}
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\CoInstructorMail{christgc@auth.gr}
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\usepackage{float}
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\usepackage{minted}
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\usepackage{xcolor} %
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\usepackage{amsmath, amssymb, amsfonts}
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\usepackage{diagbox}
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%\usepackage{tabular}
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\setminted[cpp]{
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fontsize=\small,
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}
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\newcommand{\repo}{https://git.hoo2.net/hoo2/FuzzySystems/src/branch/master/Work\%203}
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\begin{document}
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% Request a title page or header
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\InsertTitle
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%\InsertTitle[img/background.png][0.8\textwidth][2cm]
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\section{Εισαγωγή}
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Στην παρούσα εργασία καλούμαστε...
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- 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 (;!))
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\subsection{Παραδοτέα}
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Τα παραδοτέα της εργασίας αποτελούνται από:
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\begin{itemize}
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\item Την παρούσα αναφορά.
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\item Τον κατάλογο \textbf{source}, με τον κώδικα της matlab.
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\item Το \href{\repo}{σύνδεσμο με το αποθετήριο} που περιέχει τον κώδικα της matlab καθώς και αυτόν της αναφοράς.
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\end{itemize}
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\section{Υλοποίηση}
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- Ας αναφέρουμε ότι μπήκαμε στη λογική να κάνουμε κάποιες αφαιρέσεις, που θα βοηθούσαν στην διενέργεια καί των δύο υπο-tasks που έχει η εργασία.
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- Να πούμε ότι η εργασία μπορεί να εκτελεστεί εκτελώντας τα δύο scripts (scenario1 και scenario2) και ότι από αυτά τα script καλούνται οι συναρτήσεις.
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- Να συνεχίσουμε με κάποια πάσα (κείμενο) για την υλοποίηση των συναρτήσεων προτού περάσουμε στα scripts που είναι τα ζητούμενα της εργασίας
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\subsection{Διαχωρισμός δοδομένων - split\_data()}
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- Να εξηγήσουμε τη λειτουργία της συνάρτησης
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- Να εξηγήσουμε τι ρόλο παίζει το seed και γιατί το χρησιμοποιήσαμε
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- Να εξηγήσουμε γιατί αποφασίσαμε να επιστρέφουμε X και y
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\subsection{Προεπεξεργασία δοδομένων - preprocess\_data()}
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- Ας ξεκινήσουμε αναφαίροντας το τι είναι και γιατί χρειάζεται
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- Να εξηγήσουμε τη δική μας προσέγγιση (και ότι πειραματιστήκαμε με το z-score αλλα δεν το χρησιμοποιήσαμε)
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- Να εξηγήσουμε την λειτουργία της συνάρτησης
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-
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\subsection{Αξιολόγιση - evaluate()}
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- Να εξηγήσουμε το βήμα evaluate και πως χρησιμοποιείται
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- Να εξηγήσουμε τη δική μας προσέγγιση
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- Να εξηγήσουμε πως δουλεύει η συνάρτηση
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\subsection{Εμφάνιση αποτελεσμάτων}
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- Να αναφέρουμε συνοπτικά ότι υλοποιήσαμε 2 συναρτήσεις για να εμφανίζουμε και να αποθηκεύουμε συστηματικά τα plots για ευκολία.
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- Να πούμε κάποια βασικά για την υλοποίηση αν έχουνε κάτι ενδιαφέρον μέσα
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\section {Σενάριο 1 - Εφαρμογή σε απλό Dataset}
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- Να ξεκινήσουμε αναφέροντας τι είναι το ζητούμενο εδώ
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- Να εξηγήσουμε τη δική μας προσέγγιση στη λύση (πως επιλέξαμε αρ. mf, αρ. εποχών, μετρικές κλπ)
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- Για τα 4 μοντέλα εκπαίδευσης να αναφέρουμε (παραθέτοντας και τις εικόνες) με τη σειρά
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\subsection {Συναρτήσεις συμμετοχής}
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- Αρχικά τις συναρτήσεις συμμετοχής των μοντέλων.
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\InsertFigure{!ht}{0.7}{fig:scn1_mf1}{../source/figures_scn1/model_1_mfs_all_inputs.png}{Περιγραφή...}
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\InsertFigure{!ht}{0.7}{fig:scn1_mf2}{../source/figures_scn1/model_2_mfs_all_inputs.png}{Περιγραφή...}
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\InsertFigure{!ht}{0.7}{fig:scn1_mf3}{../source/figures_scn1/model_3_mfs_all_inputs.png}{Περιγραφή...}
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\InsertFigure{!ht}{0.7}{fig:scn1_mf4}{../source/figures_scn1/model_4_mfs_all_inputs.png}{Περιγραφή...}
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Να πούμε κάποια λόγια και μικρές παρατηρήσεις
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\subsection {Διαγράμματα μάθησης}
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- Έπειτα Να βάλουμε σαν τετράδα τα διαγράμματα μάθησης (πχ στο σχήμα χχ παραθέτουμε τα διαγράμματα)
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\begin{figure}[!ht]
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\includegraphics[width=0.48\textwidth]{../source/figures_scn1/model_1_learning_curves.png}
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\hspace{1em}
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\includegraphics[width=0.48\textwidth]{../source/figures_scn1/model_2_learning_curves.png} \\
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\vspace{1em}
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\includegraphics[width=0.48\textwidth]{../source/figures_scn1/model_3_learning_curves.png}
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\hspace{1em}
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\includegraphics[width=0.48\textwidth]{../source/figures_scn1/model_4_learning_curves.png} \\
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\caption{Σχεδίαση και απόκριση γραμμικού ελεγκτή.} % Προσάρμοσέ μου τα caption να να έχει ένα η κάθε εικόνα
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\label{fig:scn1_learning_curves}
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\end{figure}
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Να πούμε κάποια λόγια και μικρές παρατηρήσεις
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\subsection {Σφάλματα πρόβλεψης}
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- Έπειτα Να βάλουμε σαν τετράδα τα σφάλματα (πχ στο σχήμα χχ παραθέτουμε τα )
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\begin{figure}[!ht]
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\includegraphics[width=0.48\textwidth]{../source/figures_scn1/model_1_error.png}
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\hspace{1em}
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\includegraphics[width=0.48\textwidth]{../source/figures_scn1/model_2_error.png} \\
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\vspace{1em}
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\includegraphics[width=0.48\textwidth]{../source/figures_scn1/model_3_error.png}
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\hspace{1em}
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\includegraphics[width=0.48\textwidth]{../source/figures_scn1/model_4_error.png} \\
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\caption{Σχεδίαση και απόκριση γραμμικού ελεγκτή.} % Προσάρμοσέ μου τα caption να να έχει ένα η κάθε εικόνα
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\label{fig:scn1_error}
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\end{figure}
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Να πούμε κάποια λόγια και μικρές παρατηρήσεις
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\subsection {Σύγκριση actual - predicted} % Ίσως θέλει προραρμογή ο τίτλος
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- Έπειτα Να βάλουμε σαν τετράδα τα σφάλματα (πχ στο σχήμα χχ παραθέτουμε τα )
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\begin{figure}[!ht]
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\includegraphics[width=0.48\textwidth]{../source/figures_scn1/model_1_pred_vs_actual.png}
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\hspace{1em}
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\includegraphics[width=0.48\textwidth]{../source/figures_scn1/model_2_pred_vs_actual.png} \\
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\vspace{1em}
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\includegraphics[width=0.48\textwidth]{../source/figures_scn1/model_3_pred_vs_actual.png}
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\hspace{1em}
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\includegraphics[width=0.48\textwidth]{../source/figures_scn1/model_4_pred_vs_actual.png} \\
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\caption{Σχεδίαση και απόκριση γραμμικού ελεγκτή.} % Προσάρμοσέ μου τα caption να να έχει ένα η κάθε εικόνα
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\label{fig:scn1_predicted_vs_actual}
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\end{figure}
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Να πούμε κάποια λόγια και μικρές παρατηρήσεις
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\subsection {Συνολικά}
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Να αναφαίρουμε σε πίνακα τους δείκτες
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Metrics | Model-1 Model-2 Model-3 Model-4
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----------------------------------------------------
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MSE | 16.926 17.141 12.895 30.475
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RMSE | 4.1142 4.1401 3.591 5.5204
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R2 | 0.66468 0.66043 0.74454 0.39627
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NMSE | 0.33532 0.33957 0.25546 0.60373
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NDEI | 0.57907 0.58272 0.50543 0.777
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Να σχολιάσουμε τα αποτελέσματα
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\section {Σενάριο 2 - Dataset με υψηλή διαστασιμότητα}
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\section {Συμπεράσματα}
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\end{document}
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*.asv
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Model 1: TSK0-2MF (gbellmf × 2 per input)
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=====================================
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Initial rules: 32
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ANFIS info:
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Number of nodes: 92
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Number of linear parameters: 32
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Number of nonlinear parameters: 30
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Total number of parameters: 62
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Number of training data pairs: 902
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Number of checking data pairs: 301
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Number of fuzzy rules: 32
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Start training ANFIS ...
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1 4.25643 4.51914
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2 4.22775 4.48492
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3 4.1994 4.45216
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4 4.17142 4.42081
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Step size increases to 0.011000 after epoch 5.
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5 4.14386 4.39082
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6 4.11676 4.36215
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7 4.08753 4.33208
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8 4.05896 4.3035
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Step size increases to 0.012100 after epoch 9.
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9 4.0311 4.27637
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10 4.004 4.25063
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11 3.97511 4.22389
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12 3.94724 4.19874
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Step size increases to 0.013310 after epoch 13.
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13 3.92044 4.17515
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14 3.89473 4.15306
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15 3.86777 4.13049
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16 3.84221 4.10967
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Step size increases to 0.014641 after epoch 17.
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17 3.81806 4.09058
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18 3.79532 4.0732
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19 3.77194 4.05604
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20 3.75022 4.04091
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Step size increases to 0.016105 after epoch 21.
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21 3.73013 4.0278
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22 3.7116 4.0167
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23 3.69293 4.00682
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24 3.67596 3.99934
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Step size increases to 0.017716 after epoch 25.
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25 3.66054 3.99425
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26 3.64656 3.99151
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27 3.63269 3.99118
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28 3.62021 3.99352
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|
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
|
||||||
1163
Work 3/source/Scenario2.log
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1163
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Load Diff
17
Work 3/source/evaluate.m
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17
Work 3/source/evaluate.m
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@ -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
|
||||||
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@ -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
|
||||||
107
Work 3/source/plot_results2.m
Normal file
107
Work 3/source/plot_results2.m
Normal file
@ -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
|
||||||
39
Work 3/source/preprocess_data.m
Normal file
39
Work 3/source/preprocess_data.m
Normal file
@ -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
|
||||||
94
Work 3/source/scenario1.m
Normal file
94
Work 3/source/scenario1.m
Normal file
@ -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');
|
||||||
6
Work 3/source/scenario1_metrics.csv
Normal file
6
Work 3/source/scenario1_metrics.csv
Normal file
@ -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
|
||||||
|
175
Work 3/source/scenario2.m
Normal file
175
Work 3/source/scenario2.m
Normal file
@ -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');
|
||||||
25
Work 3/source/split_data.m
Normal file
25
Work 3/source/split_data.m
Normal file
@ -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
|
||||||
Loading…
x
Reference in New Issue
Block a user