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| 1 | + |
| 2 | +# You are given two lists of closed intervals, firstList and secondList, where firstList[i] = [starti, endi] and secondList[j] = [startj, endj]. Each list of intervals is pairwise disjoint and in sorted order. |
| 3 | + |
| 4 | +# Return the intersection of these two interval lists. |
| 5 | + |
| 6 | +# A closed interval [a, b] (with a <= b) denotes the set of real numbers x with a <= x <= b. |
| 7 | + |
| 8 | +# The intersection of two closed intervals is a set of real numbers that are either empty or represented as a closed interval. For example, the intersection of [1, 3] and [2, 4] is [2, 3]. |
| 9 | + |
| 10 | +# Input: firstList = [[0,2],[5,10],[13,23],[24,25]], secondList = [[1,5],[8,12],[15,24],[25,26]] |
| 11 | +# Output: [[1,2],[5,5],[8,10],[15,23],[24,24],[25,25]] |
| 12 | +def solution(firstList, secondList): |
| 13 | + result = [] |
| 14 | + |
| 15 | + i, j = 0, 0 |
| 16 | + |
| 17 | + while i < len(firstList) and j < len(secondList): |
| 18 | + newstart = max(firstList[i][0], secondList[j][0]) |
| 19 | + newend = min(firstList[i][1], secondList[j][1]) |
| 20 | + |
| 21 | + if newstart <= newend: |
| 22 | + result.append((newstart, newend)) |
| 23 | + |
| 24 | + if firstList[i][1] < secondList[j][1]: |
| 25 | + i += 1 |
| 26 | + else: |
| 27 | + j += 1 |
| 28 | + |
| 29 | + return result |
| 30 | + |
| 31 | +firstList = [[0,2],[5,10],[13,23],[24,25]] |
| 32 | +secondList = [[1,5],[8,12],[15,24],[25,26]] |
| 33 | +solution(firstList, secondList) |
| 34 | + |
| 35 | +i = 0, j = 0, (newstart, newend) = (1, 2), result = [(1,2)]. i += 1 = 1 |
| 36 | +i = 1, j = 0, (newstart, newend) = (5, 5), result = [(1,2), (5, 5)]. j += 1 = 1 |
| 37 | +i = 1, j = 1, (newstart, newend) = (8, 10), result = [(1,2), (5, 5), (8,10)]. i += 1 = 2 |
| 38 | +i = 2, j = 1, (newstart, newend) = (13, 12), result = [(1,2), (5, 5), (8,10)]. j += 1 = 2 |
| 39 | +// |
| 40 | +i = 2, j = 2, (newstart, newend) = (13, 12), result = [(1,2), (5, 5), (8,10)]. j += 1 = 2 |
| 41 | + |
| 42 | + |
| 43 | +# Given an n x n binary matrix grid, return the length of the shortest clear path in the matrix. If there is no clear path, return -1. |
| 44 | + |
| 45 | +# A clear path in a binary matrix is a path from the top-left cell (i.e., (0, 0)) to the bottom-right cell (i.e., (n - 1, n - 1)) such that: |
| 46 | + |
| 47 | +# All the visited cells of the path are 0. |
| 48 | +# All the adjacent cells of the path are 8-directionally connected (i.e., they are different and they share an edge or a corner). |
| 49 | +# The length of a clear path is the number of visited cells of this path. |
| 50 | + |
| 51 | +# Input: grid = [[0,1], |
| 52 | + # [1,0] |
| 53 | + # ] |
| 54 | +# Output: 2 |
| 55 | + |
| 56 | + |
| 57 | +DIR = {(0,1),(1,0),(-1,0),(0,-1),(1,1),(1,-1),(-1,-1),(-1,1)} |
| 58 | +from collections import deque |
| 59 | +def solution(grid): |
| 60 | + n = len(grid) |
| 61 | + if n == 0: |
| 62 | + return -1 |
| 63 | + |
| 64 | + if grid[0][0] == 1 or grid[n-1][n-1] == 1: |
| 65 | + return -1 |
| 66 | + |
| 67 | + queue = deque((0, 0, 1)) # (x, y, dist) |
| 68 | + visited = set([(0,0)]) # (x, y) visited |
| 69 | + |
| 70 | + while queue: |
| 71 | + x, y, dist = queue.popleft() |
| 72 | + if x == n-1 and y == n-1: |
| 73 | + return dist |
| 74 | + |
| 75 | + for dx, dy in DIR: |
| 76 | + nx, ny = x+dx, y+dy |
| 77 | + if 0 <= nx < n and 0 <= ny < n and grid[nx][ny] == 0 and (nx,ny) not in visited: |
| 78 | + queue.append((nx,ny,dist+1)) |
| 79 | + visited.add(nx, ny) |
| 80 | + 6 |
| 81 | + return -1 |
| 82 | + |
| 83 | +solution( [[0,1], |
| 84 | + [1,0] |
| 85 | + ]) # 2 |
| 86 | + |
| 87 | +n = 2 |
| 88 | +queue = [(0,0,1)] |
| 89 | + |
| 90 | +x = 0, y = 0, dist = 1 |
| 91 | + - queue = ((1,1,2)), visited = {(1,1)} |
| 92 | + |
| 93 | +x = 1, y = 1, dist = 2 |
| 94 | + - return 2 |
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