mirror of https://github.com/dnomd343/klotski.git
Dnomd343
2 months ago
8 changed files with 6 additions and 21669 deletions
@ -1,71 +0,0 @@ |
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#!/usr/bin/env python3 |
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import re |
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# (type_id, pattern_id): (load_factor_a, coff, load_factor_b) |
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type data_type = dict[tuple[int, int], tuple[float, float, float]] |
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def load_data(lines: list[str]) -> data_type: |
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result = {} |
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key, items = (), [] |
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for line in lines: |
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if line.startswith('['): |
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match = re.match(r'^\[(\d+), (\d+)]$', line) |
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key = (int(match[1]), int(match[2])) |
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elif not line: |
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assert len(key) == 2 |
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assert items[0][0] == 1.0 |
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if len(items) == 1: |
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assert items[0][1] < 0.1 # skip low cases |
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else: |
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assert len(items) == 2 |
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result[key] = items[0][1], items[1][0], items[1][1] |
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key, items = (), [] |
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else: |
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match = re.match(r'^(\d\.\d{2}), (\d\.\d{6})$', line) |
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items.append((float(match[1]), float(match[2]))) |
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return result |
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def analyse_data(data: data_type) -> None: |
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data = {x: y for x, y in data.items() if y[0] >= 0.5} |
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times = set([int(x * 1000 / y) / 1000 for x, _, y in data.values()]) |
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print(sorted(times)) |
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type_a, type_b, type_c = [], [], [] |
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for group, (load_factor, coff, _) in data.items(): |
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if load_factor <= 0.55: |
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type_a.append((group, load_factor, coff)) |
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elif coff <= 1.3: |
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type_b.append((group, load_factor, coff)) |
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else: |
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type_c.append((group, load_factor, coff)) |
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type_c = sorted(type_c, key=lambda x: x[2]) |
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for item in type_c: |
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print(item) |
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# ((117, 0), 0.571359, 1.54) -> 4680 |
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# ((118, 0), 0.571298, 1.54) -> 37440 |
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# ((133, 0), 0.570803, 1.54) -> 149632 |
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# ((134, 0), 0.570558, 1.54) -> 299136 |
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# ((63, 0), 0.568915, 1.55) -> 582 |
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# ((136, 0), 0.565568, 1.55) -> 296520 |
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# ((112, 0), 0.563973, 1.56) -> 36960 |
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# ((113, 0), 0.563969, 1.56) -> 73920 |
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# ((197, 0), 0.714286, 1.6) -> 5 |
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# ((197, 1), 0.714286, 1.6) -> 5 |
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# ((197, 2), 0.714286, 1.6) -> 5 |
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# ((197, 3), 0.714286, 1.6) -> 5 |
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# ((197, 4), 0.714286, 1.6) -> 5 |
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# ((197, 5), 0.714286, 1.6) -> 5 |
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if __name__ == '__main__': |
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raw = open('load_factor.txt').read().splitlines() |
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analyse_data(load_data(raw)) |
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File diff suppressed because it is too large
@ -1,19 +0,0 @@ |
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#!/usr/bin/env python3 |
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import json |
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data_legacy = json.loads(open('legacy.json').read()) |
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data_next = json.loads(open('data.json').read()) |
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assert len(data_legacy) == len(data_next) |
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if __name__ == '__main__': |
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for info in data_legacy: |
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code = f'{info['code']}00' |
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info_next = data_next[code] |
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assert info['min_solution_step'] == info_next['min_step'] |
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assert info['farthest_step'] == info_next['max_step'] |
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assert info['min_solution_case'] == sorted([x[:7] for x in info_next['solutions']]) |
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assert info['farthest_case'] == sorted([x[:7] for x in info_next['furthest']]) |
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@ -1,65 +0,0 @@ |
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#!/usr/bin/env python3 |
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import re |
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import json |
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def split_item(raw: str) -> list[tuple[str, int]]: |
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assert raw[0] == '\n' |
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matched = [re.match(r'^([\dA-F]{9}) \((\d+)\)$', x) for x in raw[1:].splitlines()] |
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return [(x[1], int(x[2]) - 1) for x in matched] |
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def load_file(file_name: str) -> dict[str, dict]: |
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raw = open(file_name).read() |
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assert raw[0] == '[' |
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assert raw[-1] == '\n' |
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result = {} |
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for item in raw[1:-1].split('\n['): |
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item = item.split('--------') |
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assert len(item) == 7 |
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assert item[-1] == '' |
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assert item[2] == item[4] |
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assert item[2] == item[5] |
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code = re.match(r'^([\dA-F]{9})]\n$', item[0])[1] |
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min_solutions = split_item(item[1]) |
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assert len(min_solutions) in [0, 1] |
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solutions = split_item(item[3]) |
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assert len(set([x[1] for x in solutions])) in [0, 1] |
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assert len(set([x[0] for x in solutions])) == len(solutions) |
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if not min_solutions: |
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min_step = -1 |
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assert len(solutions) == 0 |
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else: |
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min_step = solutions[0][1] |
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assert min_solutions[0] in solutions |
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furthest = split_item(item[2]) |
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assert len(set([x[1] for x in furthest])) == 1 |
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assert len(set([x[0] for x in furthest])) == len(furthest) |
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result[code] = { |
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'min_step': min_step, |
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'max_step': furthest[0][1], |
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'solutions': [x[0] for x in solutions], |
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'furthest': [x[0] for x in furthest], |
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} |
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return result |
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def load_all(files: list[str]) -> dict[str, dict]: |
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data = {} |
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[data.update(load_file(x)) for x in files] |
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data = {x: data[x] for x in sorted(data)} |
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return data |
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if __name__ == '__main__': |
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content = json.dumps(load_all([ |
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'data_149.txt', 'data_154.txt', 'data_159.txt', |
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'data_164.txt', 'data_169.txt', 'data_174.txt' |
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])) |
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print(content) |
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