| { | |
| "version": "1.2", | |
| "variables": [ | |
| {"label":"X1", "interface":"knob", "type":"real", "range":[0,5], "rad-abs": 0.0}, | |
| {"label":"X2", "interface":"knob", "type":"real", "range":[0,3], "rad-abs": 0.0}, | |
| {"label":"F1", "interface":"output", "type":"real"}, | |
| {"label":"F2", "interface":"output", "type":"real"} | |
| ], | |
| "alpha": "(X1-5)*(X1-5)+X2*X2-25 and -(X1-8)*(X1-8)-(X2+3)*(X2+3)+7.7", | |
| "objectives": { |
This project demonstrates global optimization techniques using SciPy on the Eggholder function, a complex mathematical function commonly used for testing optimization algorithms.
The Eggholder function is a challenging optimization problem with many local minima, making it an excellent benchmark for testing global optimization algorithms.
Global minimum: f(x*) = -959.6407, at x* = (512, 404.2319)
| #!/usr/bin/python3.12 | |
| def visualize_quadtree(quadtree, points, query_rect=None, found_points=None, | |
| filename="quadtree", title="Quadtree Visualization"): | |
| """Visualize the quadtree structure with points.""" | |
| fig, ax = plt.subplots(figsize=(12, 12)) | |
| # Draw all quadtree boundaries | |
| boundaries = quadtree.get_all_boundaries() | |
| for boundary in boundaries: | |
| x = boundary.x - boundary.width/2 |
VF2 tends to perform better on graphs that are sparse (fewer connections), small, or have a regular structure (e.g., 2D meshes or bounded valence graphs with low valence). In some cases, Nauty cannot find a solution for these regular graphs if they exceed a certain size (a few dozen nodes).
Nauty (via pynauty) is generally the better algorithm for dense or large randomly connected graphs.
| #!/usr/bin/python3.14 | |
| import arxiv,sh | |
| from json import load, loads, dump, dumps, JSONDecodeError | |
| from sys import argv | |
| from os import makedirs, listdir, getcwd | |
| from os.path import join, isdir, isfile, realpath, dirname | |
| from typing import List, Tuple | |
| from pydantic import RootModel | |
| from operator import itemgetter | |
| from rich import print as rprint |
| #!/usr/bin/python3.12 | |
| import json | |
| import pandas as pd | |
| # Read the JSON file | |
| with open('funds.json', 'r', encoding='utf-8') as f: | |
| data = json.load(f) | |
| # Extract the result array | |
| funds_list = data['funds']['result'] |
| #!/usr/bin/python3.12 | |
| # coding: utf-8 | |
| # author: github.com/vuiseng9 | |
| # dependency ubt1804: graphviz libgraphviz-dev libgl1-mesa-glx | |
| import os, sys | |
| import pygraphviz | |
| from glob import glob | |
| from natsort import natsorted | |
| from rich import print as rprint |
| #!/usr/bin/python3.14 | |
| """Query GitHub API""" | |
| from datetime import datetime | |
| from urllib.request import urlopen | |
| from pprint import pformat | |
| from sys import argv | |
| import json | |
| from os import popen | |
| def user_time(login): |
| #!/usr/bin/python3.12 | |
| import questionary | |
| from questionary import Style | |
| from rich.console import Console | |
| console_256=Console(color_system="256") | |
| fancy = Style([ | |
| ('qmark', 'fg:#673ab7 bold'), | |
| ('question', 'bold'), | |
| ('answer', 'fg:#ff00ff bg:#ffffff nobold'), | |
| ('pointer', 'fg:#673ab7 bold'), |
| MODULE=disjoint_set_c | |
| VER=12 | |
| EXT=cpython-3$(VER)-$(HOSTTYPE)-gnu.so | |
| SO=$(MODULE).$(EXT) | |
| %.$(EXT): %_setup.py %.pyx | |
| python3.$(VER) $< build_ext -i | |
| strip $@ | |
| -rm -rf build | |
| all: $(SO) | |
| clean: |