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https://github.com/TheAlgorithms/Python.git
synced 2025-02-25 02:18:39 +00:00
Changed code for overcoming coding style accordint to python repo
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miltonbhowmick
parent
d9f0eda48d
commit
ad4e95c532
@ -1,71 +1,69 @@
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import heapq
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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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[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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[-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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[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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[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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[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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[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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@ -78,19 +76,20 @@ class UniformCostSearch:
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self,
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start: list[int],
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end: list[int],
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dist: list[list[int]],
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dxy: list[tuple],
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) -> list[list[int]]:
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dist: list[list[float]],
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dxy: list[list[int]],
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) -> list[list[float]] | list[list[int]]:
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"""
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Return 2D list where optimal path is stored.
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>>> start = [0,0]
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>>> end = [1, 2]
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>>> dist = [[1,0],[0,2]]
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>>> dxy = [(1, 1),(1, 0),(1, -1),(0, -1),(-1, -1),(-1, 0),(-1, 1),(0, 1)]
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>>> get_shortest_path(start,end,dist,dxy)
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[[0,0],[1,1]]
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# >>> start = [0,0]
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# >>> end = [1, 2]
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# >>> dist = [[1,0],[0,2]]
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# >>> dxy = [(1, 1),(1, 0),(1, -1),(0, -1),(-1, -1),(-1, 0),(-1, 1),(0, 1)]
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# >>> get_shortest_path(start,end,dist,dxy)
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# [[0,0],[1,1]]
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"""
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shortest_path = []
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curr_node = None
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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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@ -107,6 +106,8 @@ class UniformCostSearch:
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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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if curr_node is None:
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break
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shortest_path.append(start)
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return shortest_path
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@ -115,20 +116,18 @@ class UniformCostSearch:
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current: list[int],
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final: list[list[int]],
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grid: list[list[int]],
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prev: list[list[int]],
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dxy: list[tuple],
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goal_answer: list,
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) -> list[list[int]]:
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dxy: list[list[int]],
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goal_answer: list[int],
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) -> list[list[float]] | list[list[int]] | None:
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"""
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Return 2D list where optimal path is stored.
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>>> current = [0, 0]
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>>> final = [[1, 1]]
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>>> grid = [[0,0],[0,0]]
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>>> prev = [[None, None],[None, None]]
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>>> dxy = [(1, 1),(1, 0),(1, -1),(0, -1),(-1, -1),(-1, 0),(-1, 1),(0, 1)]
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>>> goal_answer = [1000000, 100000]
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>>> ucs(current,final,grid,dxy,prev,goal_answer)
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[[0,2],[1,1]]
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# >>> current = [0, 0]
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# >>> final = [[1, 1]]
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# >>> grid = [[0,0],[0,0]]
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# >>> dxy = [(1, 1),(1, 0),(1, -1),(0, -1),(-1, -1),(-1, 0),(-1, 1),(0, 1)]
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# >>> goal_answer = [1000000, 100000]
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# >>> ucs(current,final,grid,dxy,goal_answer)
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# [[0,2],[1,1]]
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"""
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dist = [[float("inf") for _ in range(self.m)] for _ in range(self.n)]
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@ -138,57 +137,53 @@ class UniformCostSearch:
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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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heap = [[0, x, y]]
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while len(heap) > 0:
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heap.sort(key=lambda x: x[0])
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d, x, y = heap[0]
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heap.pop(0)
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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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print("Twice")
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if goal_answer[idxs[0]] == 10**8:
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final_cnt += 1
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print("Increment")
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if goal_answer[idxs[0]] > d:
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goal_answer[idxs[0]] = d
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print("Extended distance")
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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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rx = dx + x
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ry = dy + y
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if 0 <= rx < self.m and 0 <= ry < self.n and grid[rx][ry] != 1:
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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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# 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[rx][ry]:
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dist[rx][ry] = new_dist
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heap.append([new_dist, rx, ry])
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return None
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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[list[int]]:
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) -> list[list[float]] | list[list[int]] | None:
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"""
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Return 2D list where optimal path is stored.
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>>> start_point = [0, 0]
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>>> end_point = [1, 1]
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>>> grid = [[0,0],[0,0]]
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>>> your_algorithm(start_point, end_point, grid)
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[[0,0],[1,1]]
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"""
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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 = dxy5
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@ -205,16 +200,13 @@ class UniformCostSearch:
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goal_answer = []
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for _ in range(0, len(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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path = self.ucs(start_point, [end_point], grid, dxy, goal_answer)
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if path is None:
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return None
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return path
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def run() -> None:
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"""
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Return None. Its just running the UCS algorithm class.
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>>> run()
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None
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"""
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if __name__ == "__main__":
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executed_object = UniformCostSearch(
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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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@ -266,7 +258,3 @@ def run() -> None:
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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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