Compare commits
9 Commits
latex/91-b
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latex/docu
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3
.gitignore
vendored
3
.gitignore
vendored
@@ -1,5 +1,4 @@
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# Ignore CSV files in the data directory and all its subdirectories
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data/**/*.csv
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.venv/
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*.pyc
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*.egg-info/
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*.pyc
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3
.vscode/settings.json
vendored
3
.vscode/settings.json
vendored
@@ -1,4 +1,3 @@
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{
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"python.analysis.extraPaths": ["./code/src/features"],
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"jupyter.notebookFileRoot": "${workspaceFolder}/code"
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"python.analysis.extraPaths": ["./code/src/features"]
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}
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@@ -16,8 +16,3 @@ The repository is private and access is restricted only to those who have been g
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All contents of this repository, including the thesis idea, code, and associated data, are copyrighted © 2024 by Rifqi Panuluh. Unauthorized use or duplication is prohibited.
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[LICENSE](https://github.com/nuluh/thesis?tab=License-1-ov-file#readme)
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## How to Run `stft.ipynb`
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1. run `pip install -e .` in root project first
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2. run the notebook
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@@ -155,7 +155,7 @@
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"import pandas as pd\n",
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"import numpy as np\n",
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"from scipy.signal import stft, hann\n",
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"# from multiprocessing import Pool\n",
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"from multiprocessing import Pool\n",
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"\n",
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"# Function to compute and append STFT data\n",
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"def process_stft(args):\n",
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@@ -321,9 +321,9 @@
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"ready_data1a = []\n",
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"ready_data1 = []\n",
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"for file in os.listdir('D:/thesis/data/converted/raw/sensor1'):\n",
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" ready_data1a.append(pd.read_csv(os.path.join('D:/thesis/data/converted/raw/sensor1', file)))\n",
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" ready_data1.append(pd.read_csv(os.path.join('D:/thesis/data/converted/raw/sensor1', file)))\n",
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"# colormesh give title x is frequency and y is time and rotate/transpose the data\n",
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"# Plotting the STFT Data"
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]
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@@ -334,9 +334,8 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# len(ready_data1a)\n",
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"# plt.pcolormesh(ready_data1[0])\n",
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"ready_data1a[0].max().max()"
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"ready_data1[0]\n",
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"plt.pcolormesh(ready_data1[0])"
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]
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},
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{
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@@ -346,8 +345,7 @@
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"outputs": [],
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"source": [
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"for i in range(6):\n",
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" plt.pcolormesh(ready_data1a[i], cmap=\"jet\", vmax=0.03, vmin=0.0)\n",
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" plt.colorbar() \n",
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" plt.pcolormesh(ready_data1[i])\n",
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" plt.title(f'STFT Magnitude for case {i} sensor 1')\n",
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" plt.xlabel(f'Frequency [Hz]')\n",
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" plt.ylabel(f'Time [sec]')\n",
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@@ -360,9 +358,9 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"ready_data2a = []\n",
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"ready_data2 = []\n",
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"for file in os.listdir('D:/thesis/data/converted/raw/sensor2'):\n",
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" ready_data2a.append(pd.read_csv(os.path.join('D:/thesis/data/converted/raw/sensor2', file)))"
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" ready_data2.append(pd.read_csv(os.path.join('D:/thesis/data/converted/raw/sensor2', file)))"
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]
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},
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{
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@@ -371,8 +369,8 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"print(len(ready_data1a))\n",
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"print(len(ready_data2a))"
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"print(len(ready_data1))\n",
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"print(len(ready_data2))"
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]
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},
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{
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@@ -381,16 +379,10 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"x1a = 0\n",
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"print(type(ready_data1a[0]))\n",
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"ready_data1a[0].iloc[:,0]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Checking length of the total array"
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"x1 = 0\n",
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"print(type(ready_data1[0]))\n",
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"ready_data1[0].iloc[:,0]\n",
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"# x1 = x1 + ready_data1[0].shape[0]"
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]
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},
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{
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@@ -399,14 +391,16 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"x1a = 0\n",
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"print(type(x1a))\n",
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"for i in range(len(ready_data1a)):\n",
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" print(type(ready_data1a[i].shape[0]))\n",
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" x1a = x1a + ready_data1a[i].shape[0]\n",
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" print(type(x1a))\n",
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"x1 = 0\n",
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"print(type(x1))\n",
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"for i in range(len(ready_data1)):\n",
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" # print(ready_data1[i].shape)\n",
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" # print(ready_data1[i].)\n",
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" print(type(ready_data1[i].shape[0]))\n",
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" x1 = x1 + ready_data1[i].shape[0]\n",
|
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" print(type(x1))\n",
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"\n",
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"print(x1a)"
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"print(x1)"
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]
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},
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{
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@@ -415,20 +409,13 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"x2a = 0\n",
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"x2 = 0\n",
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"\n",
|
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"for i in range(len(ready_data2a)):\n",
|
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" print(ready_data2a[i].shape)\n",
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" x2a = x2a + ready_data2a[i].shape[0]\n",
|
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"for i in range(len(ready_data2)):\n",
|
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" print(ready_data2[i].shape)\n",
|
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" x2 = x2 + ready_data2[i].shape[0]\n",
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"\n",
|
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"print(x2a)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Flatten 6 array into one array"
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"print(x2)"
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]
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},
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{
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@@ -437,22 +424,14 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# Combine all dataframes in ready_data1a into a single dataframe\n",
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"if ready_data1a: # Check if the list is not empty\n",
|
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" # Use pandas concat function instead of iterative concatenation\n",
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" combined_data = pd.concat(ready_data1a, axis=0, ignore_index=True)\n",
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" \n",
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" print(f\"Type of combined data: {type(combined_data)}\")\n",
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" print(f\"Shape of combined data: {combined_data.shape}\")\n",
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" \n",
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" # Display the combined dataframe\n",
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" combined_data\n",
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"else:\n",
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" print(\"No data available in ready_data1a list\")\n",
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" combined_data = pd.DataFrame()\n",
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"\n",
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"# Store the result in x1a for compatibility with subsequent code\n",
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"x1a = combined_data"
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"x1 = ready_data1[0]\n",
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"# print(x1)\n",
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"print(type(x1))\n",
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"for i in range(len(ready_data1) - 1):\n",
|
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" #print(i)\n",
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" x1 = np.concatenate((x1, ready_data1[i + 1]), axis=0)\n",
|
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"# print(x1)\n",
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"pd.DataFrame(x1)"
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]
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},
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{
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@@ -461,29 +440,34 @@
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||||
"metadata": {},
|
||||
"outputs": [],
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"source": [
|
||||
"# Combine all dataframes in ready_data1a into a single dataframe\n",
|
||||
"if ready_data2a: # Check if the list is not empty\n",
|
||||
" # Use pandas concat function instead of iterative concatenation\n",
|
||||
" combined_data = pd.concat(ready_data2a, axis=0, ignore_index=True)\n",
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" \n",
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" print(f\"Type of combined data: {type(combined_data)}\")\n",
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" print(f\"Shape of combined data: {combined_data.shape}\")\n",
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" \n",
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" # Display the combined dataframe\n",
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" combined_data\n",
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||||
"else:\n",
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||||
" print(\"No data available in ready_data1a list\")\n",
|
||||
" combined_data = pd.DataFrame()\n",
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||||
"x2 = ready_data2[0]\n",
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"\n",
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||||
"# Store the result in x1a for compatibility with subsequent code\n",
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"x2a = combined_data"
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"for i in range(len(ready_data2) - 1):\n",
|
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" #print(i)\n",
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" x2 = np.concatenate((x2, ready_data2[i + 1]), axis=0)\n",
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||||
"pd.DataFrame(x2)"
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||||
]
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||||
},
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{
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||||
"cell_type": "markdown",
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||||
"cell_type": "code",
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||||
"execution_count": null,
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||||
"metadata": {},
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"outputs": [],
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"source": [
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"### Creating the label"
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"print(x1.shape)\n",
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"print(x2.shape)"
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]
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},
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{
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||||
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"y_1 = [1,1,1,1]\n",
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"y_2 = [0,1,1,1]\n",
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"y_3 = [1,0,1,1]\n",
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"y_4 = [1,1,0,0]"
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]
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},
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{
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@@ -506,41 +490,39 @@
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"metadata": {},
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||||
"outputs": [],
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"source": [
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"y_data = [y_1, y_2, y_3, y_4, y_5, y_6]\n",
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"y_data = [y_1, y_2, y_3, y_4, y_5, y_6]"
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]
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||||
},
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{
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||||
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"for i in range(len(y_data)):\n",
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" print(ready_data1[i].shape[0])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"for i in range(len(y_data)):\n",
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" y_data[i] = [y_data[i]]*ready_data1[i].shape[0]\n",
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" y_data[i] = np.array(y_data[i])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
|
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"outputs": [],
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"source": [
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"y_data"
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]
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},
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{
|
||||
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
|
||||
"outputs": [],
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"source": [
|
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"for i in range(len(y_data)):\n",
|
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" print(ready_data1a[i].shape[0])"
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]
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},
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{
|
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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||||
"outputs": [],
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"source": [
|
||||
"import numpy as np\n",
|
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"for i in range(len(y_data)):\n",
|
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" y_data[i] = [y_data[i]]*ready_data1a[i].shape[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"len(y_data[0])\n",
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"# y_data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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@@ -570,10 +552,10 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from src.ml.model_selection import create_ready_data\n",
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"X1a, y = create_ready_data('D:/thesis/data/converted/raw/sensor1')\n",
|
||||
"X2a, y = create_ready_data('D:/thesis/data/converted/raw/sensor2')"
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||||
"x_train1, x_test1, y_train, y_test = train_test_split(x1, y, test_size=0.2, random_state=2)\n",
|
||||
"x_train2, x_test2, y_train, y_test = train_test_split(x2, y, test_size=0.2, random_state=2)"
|
||||
]
|
||||
},
|
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{
|
||||
@@ -583,17 +565,6 @@
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"outputs": [],
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||||
"source": [
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"\n",
|
||||
"x_train1, x_test1, y_train, y_test = train_test_split(X1a, y, test_size=0.2, random_state=2)\n",
|
||||
"x_train2, x_test2, y_train, y_test = train_test_split(X2a, y, test_size=0.2, random_state=2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.metrics import accuracy_score\n",
|
||||
"from sklearn.ensemble import RandomForestClassifier, BaggingClassifier\n",
|
||||
"from sklearn.tree import DecisionTreeClassifier\n",
|
||||
@@ -621,15 +592,130 @@
|
||||
"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_model = RandomForestClassifier()\n",
|
||||
"rf_model.fit(x_train1, y_train)\n",
|
||||
"rf_pred1 = rf_model.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_model.fit(x_train2, y_train)\n",
|
||||
"rf_pred2 = rf_model.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_model = BaggingClassifier(estimator=DecisionTreeClassifier(), n_estimators=10)\n",
|
||||
"bagged_model.fit(x_train1, y_train)\n",
|
||||
"bagged_pred1 = bagged_model.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_model.fit(x_train2, y_train)\n",
|
||||
"bagged_pred2 = bagged_model.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_model.fit(x_train2, y_train)\n",
|
||||
"dt_pred2 = dt_model.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_model.fit(x_train2, y_train)\n",
|
||||
"knn_pred2 = knn_model.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_model.fit(x_train2, y_train)\n",
|
||||
"lda_pred2 = lda_model.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_model.fit(x_train2, y_train)\n",
|
||||
"svm_pred2 = svm_model.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_model.fit(x_train2, y_train)\n",
|
||||
"xgboost_pred2 = xgboost_model.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 +724,8 @@
|
||||
"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 +737,36 @@
|
||||
"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"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -764,10 +787,51 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from src.ml.model_selection import create_ready_data\n",
|
||||
"def spectograph(data_dir: str):\n",
|
||||
" # print(os.listdir(data_dir))\n",
|
||||
" for damage in os.listdir(data_dir):\n",
|
||||
" # print(damage)\n",
|
||||
" d = os.path.join(data_dir, damage)\n",
|
||||
" # print(d)\n",
|
||||
" for file in os.listdir(d):\n",
|
||||
" # print(file)\n",
|
||||
" f = os.path.join(d, file)\n",
|
||||
" print(f)\n",
|
||||
" # sensor1 = pd.read_csv(f, skiprows=1, sep=';')\n",
|
||||
" # sensor2 = pd.read_csv(f, skiprows=1, sep=';')\n",
|
||||
"\n",
|
||||
"X1b, y = create_ready_data('D:/thesis/data/converted/raw_B/sensor1')\n",
|
||||
"X2b, y = create_ready_data('D:/thesis/data/converted/raw_B/sensor2')"
|
||||
" # df1 = pd.DataFrame()\n",
|
||||
"\n",
|
||||
" # df1['s1'] = sensor1[sensor1.columns[-1]]\n",
|
||||
" # df1['s2'] = sensor2[sensor2.columns[-1]]\n",
|
||||
"ed\n",
|
||||
" # # Combined Plot for sensor 1 and sensor 2 from data1 file in which motor is operated at 800 rpm\n",
|
||||
"\n",
|
||||
" # plt.plot(df1['s2'], label='sensor 2')\n",
|
||||
" # plt.plot(df1['s1'], label='sensor 1')\n",
|
||||
" # plt.xlabel(\"Number of samples\")\n",
|
||||
" # plt.ylabel(\"Amplitude\")\n",
|
||||
" # plt.title(\"Raw vibration signal\")\n",
|
||||
" # plt.legend()\n",
|
||||
" # plt.show()\n",
|
||||
"\n",
|
||||
" # from scipy import signal\n",
|
||||
" # from scipy.signal.windows import hann\n",
|
||||
"\n",
|
||||
" # vibration_data = df1['s1']\n",
|
||||
"\n",
|
||||
" # # Applying STFT\n",
|
||||
" # window_size = 1024\n",
|
||||
" # hop_size = 512\n",
|
||||
" # window = hann(window_size) # Creating a Hanning window\n",
|
||||
" # frequencies, times, Zxx = signal.stft(vibration_data, window=window, nperseg=window_size, noverlap=window_size - hop_size)\n",
|
||||
"\n",
|
||||
" # # Plotting the STFT Data\n",
|
||||
" # plt.pcolormesh(times, frequencies, np.abs(Zxx), shading='gouraud')\n",
|
||||
" # plt.title(f'STFT Magnitude for case 1 signal sensor 1 ')\n",
|
||||
" # plt.ylabel('Frequency [Hz]')\n",
|
||||
" # plt.xlabel('Time [sec]')\n",
|
||||
" # plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -776,115 +840,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.metrics import accuracy_score, classification_report\n",
|
||||
"# 4. Validate on Dataset B\n",
|
||||
"y_pred_svm = svm_model.predict(X1b)\n",
|
||||
"\n",
|
||||
"# 5. Evaluate\n",
|
||||
"print(\"Accuracy on Dataset B:\", accuracy_score(y, y_pred_svm))\n",
|
||||
"print(classification_report(y, y_pred_svm))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.metrics import accuracy_score, classification_report\n",
|
||||
"# 4. Validate on Dataset B\n",
|
||||
"y_pred = rf_model2.predict(X2b)\n",
|
||||
"\n",
|
||||
"# 5. Evaluate\n",
|
||||
"print(\"Accuracy on Dataset B:\", accuracy_score(y, y_pred))\n",
|
||||
"print(classification_report(y, y_pred))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_predict = svm_model2.predict(X2b.iloc[[5312],:])\n",
|
||||
"print(y_predict)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y[5312]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Confusion Matrix"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"cm = confusion_matrix(y, y_pred_svm) # -> ndarray\n",
|
||||
"\n",
|
||||
"# get the class labels\n",
|
||||
"labels = svm_model.classes_\n",
|
||||
"\n",
|
||||
"# Plot\n",
|
||||
"disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
|
||||
"disp.plot(cmap=plt.cm.Blues) # You can change colormap\n",
|
||||
"plt.title(\"SVM Sensor1 CM Train w/ Dataset A Val w/ Dataset B\")\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Self-test CM"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 1. Predict sensor 1 on Dataset A\n",
|
||||
"y_train_pred = svm_model.predict(x_train1)\n",
|
||||
"\n",
|
||||
"# 2. Import confusion matrix tools\n",
|
||||
"from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"# 3. Create and plot confusion matrix\n",
|
||||
"cm_train = confusion_matrix(y_train, y_train_pred)\n",
|
||||
"labels = svm_model.classes_\n",
|
||||
"\n",
|
||||
"disp = ConfusionMatrixDisplay(confusion_matrix=cm_train, display_labels=labels)\n",
|
||||
"disp.plot(cmap=plt.cm.Blues)\n",
|
||||
"plt.title(\"Confusion Matrix: Train & Test on Dataset A\")\n",
|
||||
"plt.show()\n"
|
||||
"spectograph('D:/thesis/data/converted/raw')"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
@@ -1,57 +0,0 @@
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import os
|
||||
from sklearn.model_selection import train_test_split as sklearn_split
|
||||
|
||||
|
||||
def create_ready_data(
|
||||
stft_data_path: str,
|
||||
stratify: np.ndarray = None,
|
||||
) -> tuple:
|
||||
"""
|
||||
Create a stratified train-test split from STFT data.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
stft_data_path : str
|
||||
Path to the directory containing STFT data files (e.g. 'data/converted/raw/sensor1')
|
||||
stratify : np.ndarray, optional
|
||||
Labels to use for stratified sampling
|
||||
|
||||
Returns:
|
||||
--------
|
||||
tuple
|
||||
(X_train, X_test, y_train, y_test) - Split datasets
|
||||
"""
|
||||
ready_data = []
|
||||
for file in os.listdir(stft_data_path):
|
||||
ready_data.append(pd.read_csv(os.path.join(stft_data_path, file)))
|
||||
|
||||
y_data = [i for i in range(len(ready_data))]
|
||||
|
||||
# Combine all dataframes in ready_data into a single dataframe
|
||||
if ready_data: # Check if the list is not empty
|
||||
# Use pandas concat function instead of iterative concatenation
|
||||
combined_data = pd.concat(ready_data, axis=0, ignore_index=True)
|
||||
|
||||
print(f"Type of combined data: {type(combined_data)}")
|
||||
print(f"Shape of combined data: {combined_data.shape}")
|
||||
else:
|
||||
print("No data available in ready_data list")
|
||||
combined_data = pd.DataFrame()
|
||||
|
||||
# Store the result in x1a for compatibility with subsequent code
|
||||
X = combined_data
|
||||
|
||||
for i in range(len(y_data)):
|
||||
y_data[i] = [y_data[i]] * ready_data[i].shape[0]
|
||||
y_data[i] = np.array(y_data[i])
|
||||
|
||||
if y_data:
|
||||
# Use numpy concatenate function instead of iterative concatenation
|
||||
y = np.concatenate(y_data, axis=0)
|
||||
else:
|
||||
print("No labels available in y_data list")
|
||||
y = np.array([])
|
||||
|
||||
return X, y
|
||||
@@ -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 '<original label>_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}
|
||||
@@ -1,30 +1,31 @@
|
||||
\begin{titlepage}
|
||||
\centering
|
||||
\vspace*{1cm}
|
||||
|
||||
{\fontsize{14pt}{16pt}\selectfont \textbf{\MakeUppercase{Tugas Akhir}}\par}
|
||||
\vspace{1.5cm}
|
||||
|
||||
{\fontsize{14pt}{16pt}\selectfont \textbf{\MakeUppercase{\thetitle}}\par}
|
||||
\vspace{1.5cm}
|
||||
|
||||
\includegraphics[width=5cm]{frontmatter/img/logo.png}
|
||||
\vspace{1.5cm}
|
||||
|
||||
|
||||
\textbf{Disusun oleh:} \\
|
||||
{\fontsize{14pt}{16pt}\selectfont \textbf{\theauthor}} \\
|
||||
{\fontsize{14pt}{16pt}\selectfont \textbf{\studentid}} \\
|
||||
|
||||
|
||||
\vfill
|
||||
|
||||
{\fontsize{12pt}{14pt}\selectfont
|
||||
\textbf{\program} \\
|
||||
\textbf{\faculty} \\
|
||||
\textbf{\university} \\
|
||||
\textbf{\yearofsubmission}
|
||||
}
|
||||
|
||||
\end{titlepage}%
|
||||
|
||||
\centering
|
||||
\vspace*{1cm}
|
||||
|
||||
{\fontsize{14pt}{16pt}\selectfont \textbf{\MakeUppercase{Tugas Akhir}}\par}
|
||||
\vspace{1.5cm}
|
||||
|
||||
{\fontsize{14pt}{16pt}\selectfont \textbf{\MakeUppercase{\thesistitle}}\par}
|
||||
\vspace{1.5cm}
|
||||
|
||||
\includegraphics[width=5cm]{frontmatter/img/logo.png}
|
||||
\vspace{1.5cm}
|
||||
|
||||
|
||||
\textbf{Disusun oleh:} \\
|
||||
{\fontsize{14pt}{16pt}\selectfont \textbf{\studentname}} \\
|
||||
{\fontsize{14pt}{16pt}\selectfont \textbf{\studentid}} \\
|
||||
|
||||
|
||||
\vfill
|
||||
|
||||
{\fontsize{12pt}{14pt}\selectfont
|
||||
\textbf{\program} \\
|
||||
\textbf{\faculty} \\
|
||||
\textbf{\university} \\
|
||||
\textbf{\yearofsubmission}
|
||||
}
|
||||
|
||||
\end{titlepage}%
|
||||
|
||||
|
||||
@@ -1,29 +0,0 @@
|
||||
\begin{titlepage}
|
||||
\centering
|
||||
{\fontsize{14pt}{16pt}\selectfont \textbf{\MakeUppercase{Tugas Akhir}}\par}
|
||||
\vspace{1.5cm}
|
||||
|
||||
{\fontsize{14pt}{16pt}\selectfont \textbf{\MakeUppercase{\thetitle}}\par}
|
||||
\vspace{1cm}
|
||||
{\normalsize\selectfont Diajukan guna melengkapi persyaratan untuk memenuhi gelar Sarjana Teknik di Program Studi Teknik Sipil, Fakultas Teknik, Universitas Muhammadiyah Yogyakarta\par}
|
||||
\vspace{1.5cm}
|
||||
|
||||
\includegraphics[width=5cm]{frontmatter/img/logo.png}
|
||||
\vspace{1.5cm}
|
||||
|
||||
|
||||
\textbf{Disusun oleh:} \\
|
||||
{\fontsize{14pt}{16pt}\selectfont \textbf{\theauthor}} \\
|
||||
{\fontsize{14pt}{16pt}\selectfont \textbf{\studentid}} \\
|
||||
|
||||
|
||||
\vfill
|
||||
|
||||
{\fontsize{12pt}{14pt}\selectfont
|
||||
\textbf{\program} \\
|
||||
\textbf{\faculty} \\
|
||||
\textbf{\university} \\
|
||||
\textbf{\yearofsubmission}
|
||||
}
|
||||
|
||||
\end{titlepage}%
|
||||
@@ -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}
|
||||
|
||||
@@ -16,7 +16,6 @@
|
||||
\RequirePackage{geometry}
|
||||
\RequirePackage{setspace}
|
||||
\RequirePackage{graphicx}
|
||||
\RequirePackage{hyperref}
|
||||
\RequirePackage{etoolbox}
|
||||
\RequirePackage{tocloft}
|
||||
\RequirePackage{tocbibind}
|
||||
@@ -24,14 +23,16 @@
|
||||
\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}
|
||||
\RequirePackage{hyperref}
|
||||
|
||||
% 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}{}
|
||||
@@ -158,15 +164,42 @@
|
||||
\renewcommand{\cftafterlottitle}{\hfill}
|
||||
% \renewcommand{\cfttoctitlefont}{\bfseries\MakeUppercase}
|
||||
% \renewcommand{\cftaftertoctitle}{\vskip 2em}
|
||||
\makeatletter
|
||||
% Extracting the Year from \today
|
||||
\newcommand{\theyear}{%
|
||||
\expandafter\@car\expandafter\@gobble\the\year\@nil
|
||||
}
|
||||
|
||||
% % Apply a custom fancyhdr layout only on the first page of each \chapter, and use no header/footer elsewhere
|
||||
% % \let\oldchapter\chapter
|
||||
% % \renewcommand{\chapter}{%
|
||||
% % \cleardoublepage
|
||||
% % \pagestyle{fancyplainchapter}%
|
||||
% % \oldchapter
|
||||
% % \thispagestyle{fancyplainchapter} % ensure chapter start page uses it
|
||||
% % \pagestyle{fancyplain}% switch for subsequent pages
|
||||
% % }
|
||||
% Declare internal macros as initially empty
|
||||
\newcommand{\@authorid}{}
|
||||
\newcommand{\@firstadvisor}{}
|
||||
\newcommand{\@secondadvisor}{}
|
||||
\newcommand{\@headdepartement}{}
|
||||
\newcommand{\@headdepartementid}{}
|
||||
\newcommand{\@faculty}{}
|
||||
\newcommand{\@program}{}
|
||||
\newcommand{\@university}{}
|
||||
\newcommand{\@yearofsubmission}{}
|
||||
|
||||
% Define user commands to set these values.
|
||||
\newcommand{\authorid}[1]{\gdef\@authorid{#1}}
|
||||
\newcommand{\firstadvisor}[1]{\gdef\@firstadvisor{#1}}
|
||||
\newcommand{\secondadvisor}[1]{\gdef\@secondadvisor{#1}}
|
||||
\newcommand{\headdepartement}[1]{\gdef\@headdepartement{#1}}
|
||||
\newcommand{\headdepartementid}[1]{\gdef\@headdepartementid{#1}}
|
||||
\newcommand{\faculty}[1]{\gdef\@faculty{#1}}
|
||||
\newcommand{\program}[1]{\gdef\@program{#1}}
|
||||
\newcommand{\university}[1]{\gdef\@university{#1}}
|
||||
% \newcommand{\yearofsubmission}[1]{\gdef\@yearofsubmission{#1}}
|
||||
|
||||
% Now expose robust “the‑” getters to access the values
|
||||
\newcommand{\theauthorid}{\@authorid}
|
||||
\newcommand{\thefirstadvisor}{\@firstadvisor}
|
||||
\newcommand{\thesecondadvisor}{\@secondadvisor}
|
||||
\newcommand{\theheaddepartement}{\@headdepartement}
|
||||
\newcommand{\theheaddepartementid}{\@headdepartementid}
|
||||
\newcommand{\thefaculty}{\@faculty}
|
||||
\newcommand{\theprogram}{\@program}
|
||||
\newcommand{\theuniversity}{\@university}
|
||||
\makeatother
|
||||
\endinput
|
||||
Reference in New Issue
Block a user