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dijkstra-runtime-analysis/codes/experiments/real_data/analysis/graph_distribution.py
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2026-03-02 20:55:36 +09:00

96 lines
2.4 KiB
Python

import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import random
def read_dimacs(file):
idx = 0
with open(f"{folder}/{file}", "r") as f:
for line in f:
line = line.strip()
if line.startswith("c"):
continue
elif line.startswith("p"):
_, _, n, e = line.split()
edges = int(e)
data = [0] * edges
elif line.startswith("a"):
_, u, v, w = line.split()
data[idx] = int(w)
idx += 1
return data
def visualize_weights(file):
data = read_dimacs(file)
sample = np.array(data)
save_folder = f"{res_folder}/{file}"
os.makedirs(save_folder, exist_ok=True)
plt.figure(figsize=(8, 5))
plt.hist(sample, bins=100)
plt.title("Edge Weight Distribution (Original Scale)")
plt.xlabel("Weight")
plt.ylabel("Frequency")
plt.tight_layout()
plt.savefig(f"{save_folder}/hist_original.png")
plt.close()
log_sample = np.log(sample[sample > 0])
plt.figure(figsize=(8, 5))
plt.hist(log_sample, bins=100)
plt.title("Log(Weight) Distribution")
plt.xlabel("log(Weight)")
plt.ylabel("Frequency")
plt.tight_layout()
plt.savefig(f"{save_folder}/hist_log.png")
plt.close()
plt.figure(figsize=(8, 5))
plt.hist(log_sample, bins=100, density=True)
plt.title("Log(Weight) Density")
plt.xlabel("log(Weight)")
plt.ylabel("Density")
plt.tight_layout()
plt.savefig(f"{save_folder}/hist_log_density.png")
plt.close()
return {
"file": file,
"edges": len(sample),
"mean": np.mean(sample),
"std": np.std(sample),
"min": np.min(sample),
"max": np.max(sample)
}
folder = "experiments/real_data/data/dimacs_data"
files = [
"USA-road-d.BAY.gr",
"USA-road-d.CAL.gr",
"USA-road-d.COL.gr",
"USA-road-d.CTR.gr",
"USA-road-d.E.gr",
"USA-road-d.FLA.gr",
"USA-road-d.LKS.gr",
"USA-road-d.NE.gr",
"USA-road-d.NW.gr",
"USA-road-d.NY.gr",
"USA-road-d.USA.gr",
"USA-road-d.W.gr"
]
res_folder = "results/real_data/derived/dimacs_graph_distribution"
os.makedirs(res_folder, exist_ok=True)
rows = []
for file in files:
row = visualize_weights(file)
rows.append(row)
df = pd.DataFrame(rows)
df.to_csv(f"{res_folder}/distribution.csv", index=False)