From 5c513e4629f3642ad47b1828cea3b1a220fbe57f Mon Sep 17 00:00:00 2001 From: "Rifqi D. Panuluh" <69516665+nuluh@users.noreply.github.com> Date: Tue, 3 Jun 2025 20:12:11 +0700 Subject: [PATCH] Revert "Expose `maketitle` by just using `\input`" --- code/notebooks/stft.ipynb | 275 ++++++++++++++++---------- latex/frontmatter/acknowledgement.tex | 0 latex/frontmatter/glossaries.tex | 78 -------- latex/main.tex | 17 +- latex/thesis.cls | 20 +- 5 files changed, 189 insertions(+), 201 deletions(-) delete mode 100644 latex/frontmatter/acknowledgement.tex delete mode 100644 latex/frontmatter/glossaries.tex diff --git a/code/notebooks/stft.ipynb b/code/notebooks/stft.ipynb index e32eda5..7a86841 100644 --- a/code/notebooks/stft.ipynb +++ b/code/notebooks/stft.ipynb @@ -334,9 +334,8 @@ "metadata": {}, "outputs": [], "source": [ - "# len(ready_data1a)\n", - "# plt.pcolormesh(ready_data1[0])\n", - "ready_data1a[0].max().max()" + "len(ready_data1a)\n", + "# plt.pcolormesh(ready_data1[0])" ] }, { @@ -346,8 +345,7 @@ "outputs": [], "source": [ "for i in range(6):\n", - " plt.pcolormesh(ready_data1a[i], cmap=\"jet\", vmax=0.03, vmin=0.0)\n", - " plt.colorbar() \n", + " plt.pcolormesh(ready_data1a[i])\n", " plt.title(f'STFT Magnitude for case {i} sensor 1')\n", " plt.xlabel(f'Frequency [Hz]')\n", " plt.ylabel(f'Time [sec]')\n", @@ -537,8 +535,8 @@ "metadata": {}, "outputs": [], "source": [ - "len(y_data[0])\n", - "# y_data" + "# len(y_data[0])\n", + "y_data" ] }, { @@ -621,15 +619,137 @@ "metadata": {}, "outputs": [], "source": [ - "def train_and_evaluate_model(model, model_name, sensor_label, x_train, y_train, x_test, y_test):\n", - " model.fit(x_train, y_train)\n", - " y_pred = model.predict(x_test)\n", - " accuracy = accuracy_score(y_test, y_pred) * 100\n", - " return {\n", - " \"model\": model_name,\n", - " \"sensor\": sensor_label,\n", - " \"accuracy\": accuracy\n", - " }" + "accuracies1 = []\n", + "accuracies2 = []\n", + "\n", + "\n", + "# 1. Random Forest\n", + "rf_model1 = RandomForestClassifier()\n", + "rf_model1.fit(x_train1, y_train)\n", + "rf_pred1 = rf_model1.predict(x_test1)\n", + "acc1 = accuracy_score(y_test, rf_pred1) * 100\n", + "accuracies1.append(acc1)\n", + "# format with color coded if acc1 > 90\n", + "acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n", + "print(\"Random Forest Accuracy for sensor 1:\", acc1)\n", + "rf_model2 = RandomForestClassifier()\n", + "rf_model2.fit(x_train2, y_train)\n", + "rf_pred2 = rf_model2.predict(x_test2)\n", + "acc2 = accuracy_score(y_test, rf_pred2) * 100\n", + "accuracies2.append(acc2)\n", + "# format with color coded if acc2 > 90\n", + "acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n", + "print(\"Random Forest Accuracy for sensor 2:\", acc2)\n", + "# print(rf_pred)\n", + "# print(y_test)\n", + "\n", + "# 2. Bagged Trees\n", + "bagged_model1 = BaggingClassifier(estimator=DecisionTreeClassifier(), n_estimators=10)\n", + "bagged_model1.fit(x_train1, y_train)\n", + "bagged_pred1 = bagged_model1.predict(x_test1)\n", + "acc1 = accuracy_score(y_test, bagged_pred1) * 100\n", + "accuracies1.append(acc1)\n", + "# format with color coded if acc1 > 90\n", + "acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n", + "print(\"Bagged Trees Accuracy for sensor 1:\", acc1)\n", + "bagged_model2 = BaggingClassifier(estimator=DecisionTreeClassifier(), n_estimators=10)\n", + "bagged_model2.fit(x_train2, y_train)\n", + "bagged_pred2 = bagged_model2.predict(x_test2)\n", + "acc2 = accuracy_score(y_test, bagged_pred2) * 100\n", + "accuracies2.append(acc2)\n", + "# format with color coded if acc2 > 90\n", + "acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n", + "print(\"Bagged Trees Accuracy for sensor 2:\", acc2)\n", + "\n", + "# 3. Decision Tree\n", + "dt_model = DecisionTreeClassifier()\n", + "dt_model.fit(x_train1, y_train)\n", + "dt_pred1 = dt_model.predict(x_test1)\n", + "acc1 = accuracy_score(y_test, dt_pred1) * 100\n", + "accuracies1.append(acc1)\n", + "# format with color coded if acc1 > 90\n", + "acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n", + "print(\"Decision Tree Accuracy for sensor 1:\", acc1)\n", + "dt_model2 = DecisionTreeClassifier()\n", + "dt_model2.fit(x_train2, y_train)\n", + "dt_pred2 = dt_model2.predict(x_test2)\n", + "acc2 = accuracy_score(y_test, dt_pred2) * 100\n", + "accuracies2.append(acc2)\n", + "# format with color coded if acc2 > 90\n", + "acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n", + "print(\"Decision Tree Accuracy for sensor 2:\", acc2)\n", + "\n", + "# 4. KNeighbors\n", + "knn_model = KNeighborsClassifier()\n", + "knn_model.fit(x_train1, y_train)\n", + "knn_pred1 = knn_model.predict(x_test1)\n", + "acc1 = accuracy_score(y_test, knn_pred1) * 100\n", + "accuracies1.append(acc1)\n", + "# format with color coded if acc1 > 90\n", + "acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n", + "print(\"KNeighbors Accuracy for sensor 1:\", acc1)\n", + "knn_model2 = KNeighborsClassifier()\n", + "knn_model2.fit(x_train2, y_train)\n", + "knn_pred2 = knn_model2.predict(x_test2)\n", + "acc2 = accuracy_score(y_test, knn_pred2) * 100\n", + "accuracies2.append(acc2)\n", + "# format with color coded if acc2 > 90\n", + "acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n", + "print(\"KNeighbors Accuracy for sensor 2:\", acc2)\n", + "\n", + "# 5. Linear Discriminant Analysis\n", + "lda_model = LinearDiscriminantAnalysis()\n", + "lda_model.fit(x_train1, y_train)\n", + "lda_pred1 = lda_model.predict(x_test1)\n", + "acc1 = accuracy_score(y_test, lda_pred1) * 100\n", + "accuracies1.append(acc1)\n", + "# format with color coded if acc1 > 90\n", + "acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n", + "print(\"Linear Discriminant Analysis Accuracy for sensor 1:\", acc1)\n", + "lda_model2 = LinearDiscriminantAnalysis()\n", + "lda_model2.fit(x_train2, y_train)\n", + "lda_pred2 = lda_model2.predict(x_test2)\n", + "acc2 = accuracy_score(y_test, lda_pred2) * 100\n", + "accuracies2.append(acc2)\n", + "# format with color coded if acc2 > 90\n", + "acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n", + "print(\"Linear Discriminant Analysis Accuracy for sensor 2:\", acc2)\n", + "\n", + "# 6. Support Vector Machine\n", + "svm_model = SVC()\n", + "svm_model.fit(x_train1, y_train)\n", + "svm_pred1 = svm_model.predict(x_test1)\n", + "acc1 = accuracy_score(y_test, svm_pred1) * 100\n", + "accuracies1.append(acc1)\n", + "# format with color coded if acc1 > 90\n", + "acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n", + "print(\"Support Vector Machine Accuracy for sensor 1:\", acc1)\n", + "svm_model2 = SVC()\n", + "svm_model2.fit(x_train2, y_train)\n", + "svm_pred2 = svm_model2.predict(x_test2)\n", + "acc2 = accuracy_score(y_test, svm_pred2) * 100\n", + "accuracies2.append(acc2)\n", + "# format with color coded if acc2 > 90\n", + "acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n", + "print(\"Support Vector Machine Accuracy for sensor 2:\", acc2)\n", + "\n", + "# 7. XGBoost\n", + "xgboost_model = XGBClassifier()\n", + "xgboost_model.fit(x_train1, y_train)\n", + "xgboost_pred1 = xgboost_model.predict(x_test1)\n", + "acc1 = accuracy_score(y_test, xgboost_pred1) * 100\n", + "accuracies1.append(acc1)\n", + "# format with color coded if acc1 > 90\n", + "acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n", + "print(\"XGBoost Accuracy:\", acc1)\n", + "xgboost_model2 = XGBClassifier()\n", + "xgboost_model2.fit(x_train2, y_train)\n", + "xgboost_pred2 = xgboost_model2.predict(x_test2)\n", + "acc2 = accuracy_score(y_test, xgboost_pred2) * 100\n", + "accuracies2.append(acc2)\n", + "# format with color coded if acc2 > 90\n", + "acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n", + "print(\"XGBoost Accuracy:\", acc2)" ] }, { @@ -638,59 +758,8 @@ "metadata": {}, "outputs": [], "source": [ - "# Define models for sensor1\n", - "models_sensor1 = {\n", - " # \"Random Forest\": RandomForestClassifier(),\n", - " # \"Bagged Trees\": BaggingClassifier(estimator=DecisionTreeClassifier(), n_estimators=10),\n", - " # \"Decision Tree\": DecisionTreeClassifier(),\n", - " # \"KNN\": KNeighborsClassifier(),\n", - " # \"LDA\": LinearDiscriminantAnalysis(),\n", - " \"SVM\": SVC(),\n", - " \"XGBoost\": XGBClassifier()\n", - "}\n", - "\n", - "results_sensor1 = []\n", - "for name, model in models_sensor1.items():\n", - " res = train_and_evaluate_model(model, name, \"sensor1\", x_train1, y_train, x_test1, y_test)\n", - " results_sensor1.append(res)\n", - " print(f\"{name} on sensor1: Accuracy = {res['accuracy']:.2f}%\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "models_sensor2 = {\n", - " # \"Random Forest\": RandomForestClassifier(),\n", - " # \"Bagged Trees\": BaggingClassifier(estimator=DecisionTreeClassifier(), n_estimators=10),\n", - " # \"Decision Tree\": DecisionTreeClassifier(),\n", - " # \"KNN\": KNeighborsClassifier(),\n", - " # \"LDA\": LinearDiscriminantAnalysis(),\n", - " \"SVM\": SVC(),\n", - " \"XGBoost\": XGBClassifier()\n", - "}\n", - "\n", - "results_sensor2 = []\n", - "for name, model in models_sensor2.items():\n", - " res = train_and_evaluate_model(model, name, \"sensor2\", x_train2, y_train, x_test2, y_test)\n", - " results_sensor2.append(res)\n", - " print(f\"{name} on sensor2: Accuracy = {res['accuracy']:.2f}%\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "all_results = {\n", - " \"sensor1\": results_sensor1,\n", - " \"sensor2\": results_sensor2\n", - "}\n", - "\n", - "print(all_results)" + "print(accuracies1)\n", + "print(accuracies2)" ] }, { @@ -702,48 +771,36 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", - "def prepare_plot_data(results_dict):\n", - " # Gather unique model names\n", - " models_set = {entry['model'] for sensor in results_dict.values() for entry in sensor}\n", - " models = sorted(list(models_set))\n", - " \n", - " # Create dictionaries mapping sensor -> accuracy list ordered by model name\n", - " sensor_accuracies = {}\n", - " for sensor, entries in results_dict.items():\n", - " # Build a mapping: model -> accuracy for the given sensor\n", - " mapping = {entry['model']: entry['accuracy'] for entry in entries}\n", - " # Order the accuracies consistent with the sorted model names\n", - " sensor_accuracies[sensor] = [mapping.get(model, 0) for model in models]\n", - " \n", - " return models, sensor_accuracies\n", + "models = [rf_model, bagged_model, dt_model, knn_model, lda_model, svm_model, xgboost_model]\n", + "model_names = [\"Random Forest\", \"Bagged Trees\", \"Decision Tree\", \"KNN\", \"LDA\", \"SVM\", \"XGBoost\"]\n", "\n", - "def plot_accuracies(models, sensor_accuracies):\n", - " bar_width = 0.35\n", - " x = np.arange(len(models))\n", - " sensors = list(sensor_accuracies.keys())\n", - " \n", - " plt.figure(figsize=(10, 6))\n", - " # Assume two sensors for plotting grouped bars\n", - " plt.bar(x - bar_width/2, sensor_accuracies[sensors[0]], width=bar_width, color='blue', label=sensors[0])\n", - " plt.bar(x + bar_width/2, sensor_accuracies[sensors[1]], width=bar_width, color='orange', label=sensors[1])\n", - " \n", - " # Add text labels on top of bars\n", - " for i, (a1, a2) in enumerate(zip(sensor_accuracies[sensors[0]], sensor_accuracies[sensors[1]])):\n", - " plt.text(x[i] - bar_width/2, a1 + 0.1, f\"{a1:.2f}%\", ha='center', va='bottom', color='black')\n", - " plt.text(x[i] + bar_width/2, a2 + 0.1, f\"{a2:.2f}%\", ha='center', va='bottom', color='black')\n", - " \n", - " plt.xlabel('Model Name')\n", - " plt.ylabel('Accuracy (%)')\n", - " plt.title('Accuracy of Classifiers for Each Sensor')\n", - " plt.xticks(x, models)\n", - " plt.legend()\n", - " plt.ylim(0, 105)\n", - " plt.tight_layout()\n", - " plt.show()\n", + "bar_width = 0.35 # Width of each bar\n", + "index = np.arange(len(model_names)) # Index for the bars\n", "\n", - "# Use the functions\n", - "models, sensor_accuracies = prepare_plot_data(all_results)\n", - "plot_accuracies(models, sensor_accuracies)\n" + "# Plotting the bar graph\n", + "plt.figure(figsize=(14, 8))\n", + "\n", + "# Bar plot for Sensor 1\n", + "plt.bar(index, accuracies1, width=bar_width, color='blue', label='Sensor 1')\n", + "\n", + "# Bar plot for Sensor 2\n", + "plt.bar(index + bar_width, accuracies2, width=bar_width, color='orange', label='Sensor 2')\n", + "\n", + "# Add values on top of each bar\n", + "for i, acc1, acc2 in zip(index, accuracies1, accuracies2):\n", + " plt.text(i, acc1 + .1, f'{acc1:.2f}%', ha='center', va='bottom', color='black')\n", + " plt.text(i + bar_width, acc2 + 1, f'{acc2:.2f}%', ha='center', va='bottom', color='black')\n", + "\n", + "# Customize the plot\n", + "plt.xlabel('Model Name →')\n", + "plt.ylabel('Accuracy →')\n", + "plt.title('Accuracy of classifiers for Sensors 1 and 2 with 513 features')\n", + "plt.xticks(index + bar_width / 2, model_names) # Set x-tick positions\n", + "plt.legend()\n", + "plt.ylim(0, 100)\n", + "\n", + "# Show the plot\n", + "plt.show()\n" ] }, { diff --git a/latex/frontmatter/acknowledgement.tex b/latex/frontmatter/acknowledgement.tex deleted file mode 100644 index e69de29..0000000 diff --git a/latex/frontmatter/glossaries.tex b/latex/frontmatter/glossaries.tex deleted file mode 100644 index 3351622..0000000 --- a/latex/frontmatter/glossaries.tex +++ /dev/null @@ -1,78 +0,0 @@ -% % A new command that enables us to enter bi-lingual (Slovene and English) terms -% % syntax: \addterm[options]{label}{Slovene}{Slovene first use}{English}{Slovene -% % description} -% \newcommand{\addterm}[6][]{ -% \newglossaryentry{#2}{ -% name={#3 (angl.\ #5)}, -% first={#4 (\emph{#5})}, -% text={#3}, -% sort={#3}, -% description={#6}, -% #1 % pass additional options to \newglossaryentry -% } -% } - -% % A new command that enables us to enter (English) acronyms with bi-lingual -% % (Slovene and English) long versions -% % syntax: \addacronym[options]{label}{abbreviation}{Slovene long}{Slovene first -% % use long}{English long}{Slovene description} -% \newcommand{\addacronym}[7][]{ -% % Create the main glossary entry with \newacronym -% % \newacronym[key-val list]{label}{abbrv}{long} -% \newacronym[ -% name={#4 (angl.\ #6,\ #3)}, -% first={\emph{#5} (angl.\ \emph{#6},\ \emph{#3})}, -% sort={#4}, -% description={#7}, -% #1 % pass additional options to \newglossaryentry -% ] -% {#2}{#3}{#4} -% % Create a cross-reference from the abbreviation to the main glossary entry by -% % creating an auxiliary glossary entry (note: we set the label of this entry -% % to '_auxiliary' to avoid clashes) -% \newglossaryentry{#2_auxiliary}{ -% name={#3}, -% sort={#3}, -% description={\makefirstuc{#6}}, -% see=[See:]{#2} -% } -% } - -% % Change the text of the cross-reference links to the Slovene long version. -% \renewcommand*{\glsseeitemformat}[1]{\emph{\acrlong{#1}}.} - -% Define the Indonesian term and link it to the English term -\newglossaryentry{jaringansaraf}{ - name=Jaringan Saraf, - description={The Indonesian term for \gls{nn}} -} -% \newglossaryentry{pemelajaranmesin}{ -% name=Pemelajaran Mesin, -% description={Lihat \gls{machinelearning}} -% } - -% Define the English term and link it to its acronym -\newglossaryentry{neuralnetwork}{ - name=Neural Network, - description={A computational model inspired by the human brain, see \gls{nn}} -} - -% \newglossaryentry{machinelearning}{ -% name=Machine Learning, -% description={A program or system that trains a model from input data. The trained model can make useful predictions from new (never-before-seen) data drawn from the same distribution as the one used to train the model.}} -% \newglossaryentry{pemelajaranmesin}{ -% name={pemelajaran mesin (angl.\ #5)}, -% first={pemelajaran mesin (\emph{machine learning})}, -% text={pemelajaran mesin}, -% sort={ }, -% description={#6}, -% #1 % pass additional options to \newglossaryentry -% } -\longnewglossaryentry{machinelearning}{name={machine learning}} -{A program or system that trains a model from input data. The trained model can make useful predictions from new (never-before-seen) data drawn from the same distribution as the one used to train the model.} -\newterm[see={machinelearning}]{pemelajaranmesin} -% \newglossaryentry{pemelajaran mesin}{} -% \addterm{machinelearning}{pemelajaran mesin}{pemelajaran mesin}{machine learning}{A program or system that trains a model from input data. The trained model can make useful predictions from new (never-before-seen) data drawn from the same distribution as the one used to train the model.} -\newacronym - [description={statistical pattern recognition technique}] - {svm}{SVM}{support vector machine} \ No newline at end of file diff --git a/latex/main.tex b/latex/main.tex index e0e3784..35b5cb1 100644 --- a/latex/main.tex +++ b/latex/main.tex @@ -16,19 +16,22 @@ \input{preamble/macros} \begin{document} -\input{frontmatter/maketitle} -\input{frontmatter/maketitle_secondary} + +\maketitle \frontmatter -% \input{frontmatter/approval}\clearpage -% \input{frontmatter/originality}\clearpage -% \input{frontmatter/acknowledgement}\clearpage +\input{frontmatter/approval}\clearpage +\input{frontmatter/originality}\clearpage +\input{frontmatter/acknowledgement}\clearpage \tableofcontents \clearpage \mainmatter \pagestyle{fancyplain} +% Include content +\include{content/abstract} +\include{content/introduction} \include{chapters/01_introduction} -\include{chapters/id/02_literature_review/index} -\include{chapters/id/03_methodology/index} +\include{content/chapter2} +\include{content/conclusion} % Bibliography % \bibliographystyle{IEEEtran} diff --git a/latex/thesis.cls b/latex/thesis.cls index 10e9199..7aeb853 100644 --- a/latex/thesis.cls +++ b/latex/thesis.cls @@ -24,14 +24,15 @@ \RequirePackage{svg} % Allows including SVG images directly \RequirePackage{indentfirst} % Makes first paragraph after headings indented \RequirePackage{float} % Provides [H] option to force figure/table placement -\RequirePackage[style=apa, backend=biber, language=indonesian]{biblatex} + % Polyglossia set language -\setdefaultlanguage[variant=indonesian]{malay} % Proper Indonesian language setup -\setotherlanguage{english} % Enables English as secondary language -\DefineBibliographyStrings{english}{% % Customizes bibliography text - andothers={dkk\adddot}, % Changes "et al." to "dkk." - pages={hlm\adddot}, % Changes "pp." to "hlm." -} ++ \setdefaultlanguage[variant=indonesian]{malay} % Proper Indonesian language setup ++ \setotherlanguage{english} % Enables English as secondary language + ++ \DefineBibliographyStrings{english}{% % Customizes bibliography text ++ andothers={dkk\adddot}, % Changes "et al." to "dkk." ++ pages={hlm\adddot}, % Changes "pp." to "hlm." ++ } % Conditionally load the watermark package and settings \if@draftmark @@ -55,6 +56,8 @@ \setsansfont{Arial} \setmonofont{Courier New} +% Metadata commands +\input{metadata} \newcommand{\setthesisinfo}[7]{% \renewcommand{\thesistitle}{#1}% @@ -109,6 +112,9 @@ % \titlespacing*{\chapter}{0pt}{-10pt}{20pt} +% Redefine \maketitle +\renewcommand{\maketitle}{\input{frontmatter/maketitle}} + % Chapter & Section format \renewcommand{\cftchapfont}{\normalsize\MakeUppercase} % \renewcommand{\cftsecfont}{}