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Added uniform cost search algorithm for 2D grid based environment. There are start point, end point and a 20x20 grid for testing the UCS code.
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miltonbhowmick
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23184249da
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dcecedc2fe
228
graphs/uniform_search_cost.py
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228
graphs/uniform_search_cost.py
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from typing import List
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import heapq
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from copy import deepcopy
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class UniformCostSearch:
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def __init__(self, current, final, grid):
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self.m = len(grid[0])
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self.n = len(grid)
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# diagonal clockwise
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dxy1 = [
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(1, 1),
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(1, 0),
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(1, -1),
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(0, -1),
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(-1, -1),
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(-1, 0),
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(-1, 1),
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(0, 1),
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]
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# diagonal anti-clockwise
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dxy2 = [
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(-1, -1),
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(-1, 0),
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(0, -1),
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(0, 1),
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(1, 1),
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(1, 0),
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(1, -1),
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(-1, 1),
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]
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# start point and end point on same row and column right side
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dxy3 = [
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(0, -1),
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(-1, -1),
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(-1, 0),
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(0, 1),
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(1, 1),
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(1, 0),
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(1, -1),
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(-1, 1),
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]
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# start point and end point on same row and column left side
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dxy4 = [
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(0, 1),
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(1, 1),
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(1, 0),
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(1, -1),
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(0, -1),
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(-1, -1),
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(-1, 0),
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(-1, 1),
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]
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# start point and end point on same column and row down side
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dxy5 = [
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(1, 0),
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(0, 1),
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(1, 1),
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(1, -1),
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(0, -1),
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(-1, -1),
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(-1, 0),
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(-1, 1),
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]
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# start point and end point on same column and row up side
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dxy6 = [
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(0, -1),
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(0, 1),
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(1, 1),
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(1, 0),
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(1, -1),
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(-1, -1),
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(-1, 0),
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(-1, 1),
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]
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def get_shortest_path(self, start, end, dist, dxy):
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shortest_path = []
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curr_node = end
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while curr_node != start:
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shortest_path.append(curr_node)
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row, col = curr_node
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min_cell = float("inf")
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next_cell = None
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for dr, dc in dxy:
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if (
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0 <= row + dr < self.m
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and 0 <= col + dc < self.n
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and [row + dr, col + dc] not in shortest_path
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):
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if dist[row + dr][col + dc] <= min_cell:
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min_cell = dist[row + dr][col + dc]
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next_cell = [row + dr, col + dc]
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curr_node = next_cell
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shortest_path.append(start)
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return shortest_path
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def ucs(self, current, final, grid, prev, dxy, goal_answer):
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dist = [[float("inf") for _ in range(self.m)] for _ in range(self.n)]
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visited = [[0 for _ in range(self.m)] for _ in range(self.n)]
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x = current[0]
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y = current[1]
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dist[x][y] = 0
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final_cnt = 0
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heap = [(0, x, y)]
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while heap:
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d, x, y = heapq.heappop(heap)
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if visited[x][y]:
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continue
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visited[x][y] = 1
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if [x, y] in final:
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idxs = [ix for ix, iy in enumerate(final) if iy == [x, y]]
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if len(idxs) > 1:
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return None
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if goal_answer[idxs[0]] == 10**8:
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final_cnt += 1
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if goal_answer[idxs[0]] > d:
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goal_answer[idxs[0]] = d
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if final_cnt == len(final):
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path = self.get_shortest_path(current, final[0], dist, dxy)
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return path
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for dx, dy in dxy:
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if (
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0 <= x + dx < self.m
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and 0 <= y + dy < self.n
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and grid[x + dx][y + dy] != 1
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):
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weight = 1
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diagonal_weight = 1.414
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if (
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(dx == -1 and dy == -1)
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or (dx == 1 and dy == 1)
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or (dx == 1 and dy == -1)
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or (dx == -1 and dy == 1)
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):
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new_dist = d + diagonal_weight
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else:
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new_dist = d + weight
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if new_dist < dist[x + dx][y + dy]:
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dist[x + dx][y + dy] = new_dist
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prev[x + dx][y + dy] = (x, y)
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heapq.heappush(heap, (new_dist, x + dx, y + dy))
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def your_algorithm(
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self, start_point: List[int], end_point: List[int], grid: List[List[int]]
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) -> List[int]:
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prev = [[None for _ in range(self.m)] for _ in range(self.n)]
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dxy = []
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if start_point[1] - end_point[1] == 0 and start_point[0] - end_point[0] < 0:
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dxy = self.dxy5
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elif start_point[1] - end_point[1] == 0 and start_point[0] - end_point[0] > 0:
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dxy = self.dxy6
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elif start_point[0] - end_point[0] == 0 and start_point[1] - end_point[1] < 0:
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dxy = self.dxy4
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elif start_point[0] - end_point[0] == 0 and start_point[1] - end_point[1] > 0:
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dxy = self.dxy3
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elif start_point[0] - end_point[0] > 0:
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dxy = self.dxy2
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elif start_point[0] - end_point[0] < 0:
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dxy = self.dxy1
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goal_answer = []
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for cell in end_point:
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goal_answer.append(10**8)
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path = self.ucs(start_point, [end_point], grid, prev, dxy, goal_answer)
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return path
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def run():
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executed_object = UniformCostSearch(
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[0, 7],
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[19, 17],
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[
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0],
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[0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0],
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[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
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[0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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],
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)
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path_result = executed_object.your_algorithm(
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[0, 7],
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[19, 17],
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[
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0],
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[0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0],
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[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
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[0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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],
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)
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print(path_result)
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if __name__ == "__main__":
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run()
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