Refine lab6 assets and report comparison
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Artem
239
lab6/aco.py
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239
lab6/aco.py
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import math
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import random
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import struct
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import zlib
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from dataclasses import dataclass
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from typing import List, Sequence, Tuple
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City = Tuple[float, float]
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Tour = List[int]
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def euclidean_distance(c1: City, c2: City) -> float:
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return math.hypot(c1[0] - c2[0], c1[1] - c2[1])
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def build_distance_matrix(cities: Sequence[City]) -> list[list[float]]:
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size = len(cities)
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matrix = [[0.0 for _ in range(size)] for _ in range(size)]
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for i in range(size):
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for j in range(i + 1, size):
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dist = euclidean_distance(cities[i], cities[j])
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matrix[i][j] = matrix[j][i] = dist
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return matrix
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def _write_png(filename: str, pixels: list[list[tuple[int, int, int]]]) -> None:
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height = len(pixels)
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width = len(pixels[0]) if height else 0
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def chunk(chunk_type: bytes, data: bytes) -> bytes:
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return (
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struct.pack(">I", len(data))
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+ chunk_type
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+ data
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+ struct.pack(">I", zlib.crc32(chunk_type + data) & 0xFFFFFFFF)
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)
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raw = b"".join(b"\x00" + bytes([c for px in row for c in px]) for row in pixels)
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png = b"\x89PNG\r\n\x1a\n"
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ihdr = struct.pack(">IIBBBBB", width, height, 8, 2, 0, 0, 0)
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png += chunk(b"IHDR", ihdr)
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png += chunk(b"IDAT", zlib.compress(raw, 9))
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png += chunk(b"IEND", b"")
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with open(filename, "wb") as f:
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f.write(png)
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def _scale_points(points: Sequence[tuple[float, float]], size: int = 800, margin: int = 20):
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xs = [p[0] for p in points]
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ys = [p[1] for p in points]
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min_x, max_x = min(xs), max(xs)
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min_y, max_y = min(ys), max(ys)
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scale_x = (size - 2 * margin) / (max_x - min_x + 1e-9)
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scale_y = (size - 2 * margin) / (max_y - min_y + 1e-9)
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return [
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(
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int((x - min_x) * scale_x + margin),
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int((y - min_y) * scale_y + margin),
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)
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for x, y in points
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]
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def _draw_line(pixels: list[list[tuple[int, int, int]]], p1: tuple[int, int], p2: tuple[int, int], color: tuple[int, int, int]):
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x1, y1 = p1
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x2, y2 = p2
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dx = abs(x2 - x1)
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dy = -abs(y2 - y1)
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sx = 1 if x1 < x2 else -1
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sy = 1 if y1 < y2 else -1
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err = dx + dy
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while True:
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if 0 <= x1 < len(pixels[0]) and 0 <= y1 < len(pixels):
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pixels[y1][x1] = color
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if x1 == x2 and y1 == y2:
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break
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e2 = 2 * err
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if e2 >= dy:
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err += dy
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x1 += sx
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if e2 <= dx:
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err += dx
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y1 += sy
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def _draw_circle(pixels: list[list[tuple[int, int, int]]], center: tuple[int, int], radius: int, color: tuple[int, int, int]):
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cx, cy = center
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for y in range(cy - radius, cy + radius + 1):
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for x in range(cx - radius, cx + radius + 1):
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if 0 <= x < len(pixels[0]) and 0 <= y < len(pixels):
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if (x - cx) ** 2 + (y - cy) ** 2 <= radius ** 2:
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pixels[y][x] = color
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def plot_tour(cities: Sequence[City], tour: Sequence[int], save_path: str) -> None:
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ordered = [cities[i] for i in tour] + [cities[tour[0]]]
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points = _scale_points(ordered)
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width = height = 820
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pixels = [[(255, 255, 255) for _ in range(width)] for _ in range(height)]
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for i in range(len(points) - 1):
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_draw_line(pixels, points[i], points[i + 1], (0, 120, 200))
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# draw cities
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city_points = _scale_points(cities)
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for p in city_points:
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_draw_circle(pixels, p, 4, (200, 50, 50))
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_write_png(save_path, pixels)
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def plot_history(best_lengths: Sequence[float], save_path: str) -> None:
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if not best_lengths:
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return
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width, height, margin = 820, 400, 20
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pixels = [[(255, 255, 255) for _ in range(width)] for _ in range(height)]
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n = len(best_lengths)
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min_len, max_len = min(best_lengths), max(best_lengths)
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span = max_len - min_len if max_len != min_len else 1
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def to_point(idx: int, value: float) -> tuple[int, int]:
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x = margin + int((width - 2 * margin) * idx / max(1, n - 1))
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y = height - margin - int((height - 2 * margin) * (value - min_len) / span)
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return x, y
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prev = to_point(0, best_lengths[0])
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for i, v in enumerate(best_lengths[1:], start=1):
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cur = to_point(i, v)
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_draw_line(pixels, prev, cur, (30, 30, 30))
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prev = cur
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_write_png(save_path, pixels)
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@dataclass
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class ACOConfig:
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cities: Sequence[City]
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n_ants: int
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n_iterations: int
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alpha: float = 1.0
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beta: float = 5.0
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rho: float = 0.5
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q: float = 1.0
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seed: int | None = None
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@dataclass
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class ACOResult:
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best_tour: Tour
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best_length: float
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history: List[float]
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class AntColonyOptimizer:
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def __init__(self, config: ACOConfig):
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self.config = config
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if config.seed is not None:
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random.seed(config.seed)
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self.cities = config.cities
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self.dist_matrix = build_distance_matrix(config.cities)
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n = len(config.cities)
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self.pheromone = [[1.0 if i != j else 0.0 for j in range(n)] for i in range(n)]
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def _choose_next_city(self, current: int, unvisited: set[int]) -> int:
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candidates = list(unvisited)
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weights = []
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for nxt in candidates:
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tau = self.pheromone[current][nxt] ** self.config.alpha
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eta = (1.0 / (self.dist_matrix[current][nxt] + 1e-12)) ** self.config.beta
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weights.append(tau * eta)
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total = sum(weights)
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probs = [w / total for w in weights]
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return random.choices(candidates, weights=probs, k=1)[0]
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def _build_tour(self, start: int) -> Tour:
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n = len(self.cities)
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tour = [start]
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unvisited = set(range(n))
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unvisited.remove(start)
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current = start
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while unvisited:
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nxt = self._choose_next_city(current, unvisited)
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tour.append(nxt)
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unvisited.remove(nxt)
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current = nxt
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return tour
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def _tour_length(self, tour: Sequence[int]) -> float:
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return sum(
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self.dist_matrix[tour[i]][tour[(i + 1) % len(tour)]]
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for i in range(len(tour))
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)
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def run(self) -> ACOResult:
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best_tour: Tour = []
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best_length = float("inf")
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best_history: list[float] = []
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for _ in range(self.config.n_iterations):
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tours: list[Tour] = []
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lengths: list[float] = []
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for _ in range(self.config.n_ants):
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start_city = random.randrange(len(self.cities))
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tour = self._build_tour(start_city)
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length = self._tour_length(tour)
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tours.append(tour)
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lengths.append(length)
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if length < best_length:
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best_length = length
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best_tour = tour
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for i in range(len(self.pheromone)):
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for j in range(len(self.pheromone)):
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self.pheromone[i][j] *= 1 - self.config.rho
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for tour, length in zip(tours, lengths):
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deposit = self.config.q / length
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for i in range(len(tour)):
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a, b = tour[i], tour[(i + 1) % len(tour)]
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self.pheromone[a][b] += deposit
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self.pheromone[b][a] += deposit
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best_history.append(best_length)
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return ACOResult(best_tour=best_tour, best_length=best_length, history=best_history)
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def run_aco(config: ACOConfig) -> ACOResult:
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optimizer = AntColonyOptimizer(config)
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return optimizer.run()
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