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fix: updates from draft response
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@Logan-Fouts
Logan-Fouts committed May 22, 2024
1 parent 3108218 commit bc2ac21
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22 changes: 15 additions & 7 deletions Todo.md
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- [x] Finish second experiment and get results.
- [ ] Write more related work
- [ ] Henry write section on Research implementation about permutations
- [ ] Provide details on selected parameters and why they are tuned the way they are.
- [x] Permutations results and tables need to be added
- [x] Tables and Figures shall be labeled with chapter.number, for example Table 4.1 and Figure 1.6.
- [x] Maybe write more about limitations and reliability/validity
- [x] Ease into intro and abstract better
- [x] No full names of papers
- [x] Present or past tense only
- [ ] Move placement of descriptions of vertical/horizontal approaches
- [x] Move Theory chapter before method
- [x] Figure captions should have a period
- [x] Make sure figure numbers match in text
- [x] All figures need to be mentioned in text
- [ ] More references if possible
- [x] Figures != Tables
- [x] Move 2.6 Research Gap Into Intro
- [ ] Make graphs fonts bigger
- [ ] 5.13
- [ ] 5.15
- [ ] 5.16
160 changes: 160 additions & 0 deletions Writing/Results/California/Graphs/Permutations.py
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import matplotlib.pyplot as plt
import numpy as np

# Sample data (Replace these with actual data from your graph)
time_elapsed = [
100,
150,
200,
300,
700,
800,
500,
300,
250,
400,
450,
350,
250,
200,
150,
100,
50,
25,
30,
40,
60,
80,
100,
120,
]
precision = [
0.98,
0.97,
0.96,
0.95,
0.94,
0.93,
0.92,
0.91,
0.90,
0.89,
0.88,
0.87,
0.86,
0.85,
0.84,
0.83,
0.82,
0.81,
0.80,
0.79,
0.78,
0.77,
0.76,
0.75,
]
recall = [
0.95,
0.94,
0.93,
0.92,
0.91,
0.90,
0.89,
0.88,
0.87,
0.86,
0.85,
0.84,
0.83,
0.82,
0.81,
0.80,
0.79,
0.78,
0.77,
0.76,
0.75,
0.74,
0.73,
0.72,
]
f1_score = [
0.96,
0.95,
0.94,
0.93,
0.92,
0.91,
0.90,
0.89,
0.88,
0.87,
0.86,
0.85,
0.84,
0.83,
0.82,
0.81,
0.80,
0.79,
0.78,
0.77,
0.76,
0.75,
0.74,
0.73,
]
accuracy = [
0.97,
0.96,
0.95,
0.94,
0.93,
0.92,
0.91,
0.90,
0.89,
0.88,
0.87,
0.86,
0.85,
0.84,
0.83,
0.82,
0.81,
0.80,
0.79,
0.78,
0.77,
0.76,
0.75,
0.74,
]

x = np.arange(len(time_elapsed))

fig, ax1 = plt.subplots(figsize=(14, 7))

color = "tab:purple"
ax1.set_xlabel("Index", fontsize=20)
ax1.set_ylabel("Elapsed Time (seconds)", color=color, fontsize=18)
ax1.bar(x, time_elapsed, color=color, alpha=0.6, label="Time Elapsed (s)")
ax1.tick_params(axis="y", labelcolor=color)
ax1.tick_params(axis="x", labelsize=16)
ax1.tick_params(axis="y", labelsize=16)

ax2 = ax1.twinx()
ax2.set_ylabel("Performance Metrics (%)", fontsize=20)
ax2.plot(x, precision, "r-o", label="Precision (%)", linewidth=2)
ax2.plot(x, recall, "g-o", label="Recall (%)", linewidth=2)
ax2.plot(x, f1_score, "m-o", label="F1-Score (%)", linewidth=2)
ax2.plot(x, accuracy, "b-o", label="Accuracy (%)", linewidth=2)
ax2.tick_params(axis="y", labelsize=16)
ax2.set_ylim(0, 1)

fig.tight_layout()
fig.legend(loc="upper left", bbox_to_anchor=(0.1, 0.9), fontsize=16)
plt.title("Performance Metrics and Elapsed Time", fontsize=18)
plt.show()
138 changes: 138 additions & 0 deletions Writing/Results/Finger Prints/Results/Graphs/Permutations.py
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import matplotlib.pyplot as plt
import numpy as np

# Data from the table
algorithms = [
"Phash, SIFT, VGG",
"Phash, SIFT, Dhash",
"Phash, Dhash, VGG",
"Phash, Dhash, SIFT",
"Dhash, VGG, SIFT",
"Dhash, VGG, Phash",
"Dhash, SIFT, VGG",
"Dhash, SIFT, Phash",
"Dhash, Phash, VGG",
"Dhash, Phash, SIFT",
"Phash, VGG, Dhash",
"Phash, VGG, SIFT",
"SIFT, Dhash, Phash",
"SIFT, Dhash, VGG",
"SIFT, Phash, Dhash",
"SIFT, Phash, VGG",
"SIFT, VGG, Dhash",
"SIFT, VGG, Phash",
"VGG, Dhash, Phash",
"VGG, Dhash, SIFT",
"VGG, Phash, Dhash",
"VGG, Phash, SIFT",
"VGG, SIFT, Dhash",
"VGG, SIFT, Phash",
]
time_elapsed = [
4.67,
3.19,
8.53,
3.18,
9.00,
9.11,
4.90,
3.25,
8.21,
3.22,
8.90,
8.98,
3.26,
4.35,
3.26,
4.18,
4.34,
4.30,
8.62,
8.88,
8.79,
8.64,
8.78,
8.68,
]
accuracy = [
90.41,
100.00,
48.89,
100.00,
35.71,
35.71,
87.84,
100.00,
48.53,
100.00,
39.36,
39.36,
100.00,
96.34,
100.00,
98.72,
96.34,
96.34,
23.99,
23.99,
23.99,
23.99,
23.99,
23.99,
]
group_numbers = [
30,
28,
17,
28,
13,
13,
29,
28,
17,
28,
13,
13,
28,
30,
28,
31,
30,
30,
6,
6,
6,
6,
6,
6,
]

x = np.arange(len(algorithms))

fig, ax1 = plt.subplots(figsize=(14, 8))

color = "blue"
ax1.set_xlabel("Algorithms Combination", fontsize=16)
ax1.set_ylabel("Time Elapsed (s)", color=color, fontsize=16)
ax1.bar(x, time_elapsed, color=color, label="Time Elapsed (s)")
ax1.tick_params(axis="y", labelcolor=color)
ax1.tick_params(axis="x", rotation=90, labelsize=14)

ax2 = ax1.twinx()
color = "red"
ax2.set_ylabel("Accuracy (%)", color=color, fontsize=16)
ax2.plot(x, accuracy, color=color, marker="o", label="Accuracy (%)")
ax2.tick_params(axis="y", labelcolor=color)
ax2.tick_params(axis="x", labelsize=14)

ax3 = ax1.twinx()
color = "green"
ax3.spines["right"].set_position(("outward", 60))
ax3.set_ylabel("Group Numbers", color=color, fontsize=16)
ax3.plot(x, group_numbers, color=color, marker="s", label="Group Numbers")
ax3.tick_params(axis="y", labelcolor=color)
ax3.tick_params(axis="x", labelsize=14)

fig.tight_layout()
fig.legend(loc="upper left", bbox_to_anchor=(0.1, 0.9), fontsize=14)
plt.show()
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