回首忆惘然
我已经实现了三种算法。第一个算法是Simple,使用最简单的嵌套循环方法,它具有O(N^5) 时间复杂度(对于我们的情况N,其中 是输入网格的一侧10),对于我们的输入大小10x10时间来说O(10^5)是相当好的。代码中的算法 ID 是algo = 0。如果您只想查看该算法,请跳转到------ Simple Algorithm代码内的行。第二种算法是Advanced,采用动态规划方法,其复杂度O(N^3)比第一种算法快得多。代码中的算法 ID 是algo = 1。跳转到------- Advanced Algorithm代码内的行。我实现的第三个算法Simple-ListComp只是为了好玩,它几乎与 相同Simple,相同的O(N^5)复杂性,但使用 Python 的列表理解而不是常规循环,这就是为什么它更短,也慢一点,因为没有使用一些优化。代码中的算法 ID 是algo = 2。跳转到------- Simple-ListComp Algorithm代码内的行以查看算法。除了算法之外,其余代码还实现检查结果的正确性(算法之间的双重检查)、打印结果、生成文本输入。代码分为解决任务函数solve()和测试函数test()。solve()函数有许多参数来允许配置函数的行为。所有主要代码行均由注释记录,阅读它们以了解如何使用代码。基本上,如果s变量包含带有网格元素的多行文本,就像您的问题一样,您只需运行solve(s, text = True)它就会解决任务并打印结果。algo = 0, check = False此外,您还可以通过给出求解函数的下一个参数(此处 0 表示算法 0 )从两个版本(0(简单)和 1(高级)和 2(简单列表计算))中选择算法。查看test()函数体以查看最简单的用法示例。算法默认输出到控制台的所有簇,从最大到最小,最大的用.符号表示,其余的用B, C, D, ...,Z符号表示。show_non_max = False如果您只想显示第一个(最大)簇,您可以在求解函数中设置参数。我将解释简单算法:基本上算法的作用是 - 它搜索所有可能的有角度的1s矩形并将其中最大的矩形的信息存储到ma二维数组中。Top-left这个矩形的点是(i, j), top-right- (i, k), bottom-left- (l, j + angle_offset), bottom-right- (l, k + angle_offset),所有 4 个角,这就是我们有这么多循环的原因。在外部两个i(行)、j(列)循环中,我们迭代整个网格,该(i, j)位置将是矩形top-left的点1s,我们需要迭代整个网格,因为所有可能的1s矩形可能位于整个网格的top-left任何点。(row, col)在循环开始时,j我们检查位置处的网格应(i, j)始终包含1,因为在循环内部我们仅搜索所有矩形1s。k循环遍历矩形的所有可能top-right位置。如果等于,我们应该跳出循环,因为没有必要进一步向右扩展,因为这样的矩形将始终包含。(i, k)1s(i, k)0k0在之前的循环中,我们固定了top-left矩形top-right的角点。现在我们需要搜索两个底角。为此,我们需要以不同角度向下延伸矩形,直到到达第一个0。off循环尝试以所有可能的角度向下延伸矩形(0(垂直垂直),+1(45从上到下向右移动的度数),-1(-45度)),off基本上是grid[y][x]“上方”的数字(对应于 by Y)grid[y + 1][x + off]。lY尝试以不同角度向下(方向)延伸矩形off。它被扩展到第一个,0因为它不能进一步扩展(因为每个这样的矩形已经包含0)。循环内部l有一个if grid[l][max(0, j + off * (l - i)) : min(k + 1 + off * (l - i), c)] != ones[:k - j + 1]:条件,基本上这if是为了检查矩形的最后一行是否包含所有内容,1如果不是,if则会跳出循环。此条件比较两个list切片是否相等。矩形的最后一行从点(l, j + angle_offset)(表达式max(0, j + off * (l - i)),最大限制为0 <= X)到点(l, k + angle_offset)(表达式min(k + 1 + off * (l - i), c),最小限制为X < c)。在循环内部l还有其他行,ry, rx = l, k + off * (l - i)计算bottom-right矩形的点,(ry, rx)即(l, k + angle_offset),该(ry, rx)位置用于存储在ma数组中找到的最大值,该数组存储所有找到的最大矩形,包含有关在点ma[ry][rx]处的矩形的信息。bottom-right(ry, rx)rv = (l + 1 - i, k + 1 - j, off)line 计算ma[ry][rx]数组条目的新的可能候选者,之所以可能,ma[ry][rx]是因为仅当新候选者具有更大的面积时才更新1s。这里元组rv[0]内的值rv包含height这样的矩形,rv[1]包含width这样的矩形(width等于矩形底行的长度),rv[2]包含这样的矩形的角度。条件if rv[0] * rv[1] > ma[ry][rx][0] * ma[ry][rx][1]:及其主体仅检查rv面积是否大于数组内的当前最大值ma[ry][rx],如果大于则更新此数组条目(ma[ry][rx] = rv)。我会提醒您,其中包含有关当前找到的最大面积矩形的ma[ry][rx]信息,该矩形具有、和。(width, height, angle)bottom-right(ry, rx)widthheightangle完毕!算法运行后,数组ma包含有关所有最大面积有角度的矩形(簇)的信息1s,以便所有簇都可以恢复并稍后打印到控制台。Largest of all such 1s-clusters 等于 some rv0 = ma[ry0][rx0],只需迭代一次 的所有元素ma并找到这样的点(ry0, rx0),以使ma[ry0][rx0][0] * ma[ry0][rx0][1](面积)最大。然后最大的簇将有bottom-rightpoint (ry0, rx0)、bottom-leftpoint (ry0, rx0 - rv0[1] + 1)、top-rightpoint (ry0 - rv0[0] + 1, rx0 - rv0[2] * (rv0[0] - 1))、top-leftpoint (ry0 - rv0[0] + 1, rx0 - rv0[1] + 1 - rv0[2] * (rv0[0] - 1))(这里只是角度偏移,即第一行与矩形的最后一行相比rv0[2] * (rv0[0] - 1)移动了多少)。X在线尝试一下!# ----------------- Main function solving task -----------------def solve( grid, *, algo = 1, # Choose algorithm, 0 - Simple, 1 - Advanced, 2 - Simple-ListComp check = True, # If True run all algorithms and check that they produce same results, otherwise run just chosen algorithm without checking text = False, # If true then grid is a multi-line text (string) having grid elements separated by spaces print_ = True, # Print results to console show_non_max = True, # When printing if to show all clusters, not just largest, as B, C, D, E... (chars from "cchars") cchars = ['.'] + [chr(ii) for ii in range(ord('B'), ord('Z') + 1)], # Clusters-chars, these chars are used to show clusters from largest to smallest one = None, # Value of "one" inside grid array, e.g. if you have grid with chars then one may be equal to "1" string. Defaults to 1 (for non-text) or "1" (for text). offs = [0, +1, -1], # All offsets (angles) that need to be checked, "off" is such that grid[i + 1][j + off] corresponds to next row of grid[i][j] debug = False, # If True, extra debug info is printed): # Preparing assert algo in [0, 1, 2], algo if text: grid = [l.strip().split() for l in grid.splitlines() if l.strip()] if one is None: one = 1 if not text else '1' r, c = len(grid), len(grid[0]) sgrid = '\n'.join([''.join([str(grid[ii][jj]) for jj in range(c)]) for ii in range(r)]) mas, ones = [], [one] * max(c, r) # ----------------- Simple Algorithm, O(N^5) Complexity ----------------- if algo == 0 or check: ma = [[(0, 0, 0) for jj in range(c)] for ii in range(r)] # Array containing maximal answers, Lower-Right corners for i in range(r): for j in range(c): if grid[i][j] != one: continue for k in range(j + 1, c): # Ensure at least 2 ones along X if grid[i][k] != one: break for off in offs: for l in range(i + 1, r): # Ensure at least 2 ones along Y if grid[l][max(0, j + off * (l - i)) : min(k + 1 + off * (l - i), c)] != ones[:k - j + 1]: l -= 1 break ry, rx = l, k + off * (l - i) rv = (l + 1 - i, k + 1 - j, off) if rv[0] * rv[1] > ma[ry][rx][0] * ma[ry][rx][1]: ma[ry][rx] = rv mas.append(ma) ma = None # ----------------- Advanced Algorithm using Dynamic Programming, O(N^3) Complexity ----------------- if algo == 1 or check: ma = [[(0, 0, 0) for jj in range(c)] for ii in range(r)] # Array containing maximal answers, Lower-Right corners for off in offs: d = [[(0, 0, 0) for jj in range(c)] for ii in range(c)] for i in range(r): f, d_ = 0, [[(0, 0, 0) for jj in range(c)] for ii in range(c)] for j in range(c): if grid[i][j] != one: f = j + 1 continue if f >= j: # Check that we have at least 2 ones along X continue df = [(0, 0, 0) for ii in range(c)] for k in range(j, -1, -1): t0 = d[j - off][max(0, k - off)] if 0 <= j - off < c and k - off < c else (0, 0, 0) if k >= f: t1 = (t0[0] + 1, t0[1], off) if t0 != (0, 0, 0) else (0, 0, 0) t2 = (1, j - k + 1, off) t0 = t1 if t1[0] * t1[1] >= t2[0] * t2[1] else t2 # Ensure that we have at least 2 ones along Y t3 = t1 if t1[0] > 1 else (0, 0, 0) if k < j and t3[0] * t3[1] < df[k + 1][0] * df[k + 1][1]: t3 = df[k + 1] df[k] = t3 else: t0 = d_[j][k + 1] if k < j and t0[0] * t0[1] < d_[j][k + 1][0] * d_[j][k + 1][1]: t0 = d_[j][k + 1] d_[j][k] = t0 if ma[i][j][0] * ma[i][j][1] < df[f][0] * df[f][1]: ma[i][j] = df[f] d = d_ mas.append(ma) ma = None # ----------------- Simple-ListComp Algorithm using List Comprehension, O(N^5) Complexity ----------------- if algo == 2 or check: ma = [ [ max([(0, 0, 0)] + [ (h, w, off) for h in range(2, i + 2) for w in range(2, j + 2) for off in offs if all( cr[ max(0, j + 1 - w - off * (h - 1 - icr)) : max(0, j + 1 - off * (h - 1 - icr)) ] == ones[:w] for icr, cr in enumerate(grid[max(0, i + 1 - h) : i + 1]) ) ], key = lambda e: e[0] * e[1]) for j in range(c) ] for i in range(r) ] mas.append(ma) ma = None # ----------------- Checking Correctness and Printing Results ----------------- if check: # Check that we have same answers for all algorithms masx = [[[cma[ii][jj][0] * cma[ii][jj][1] for jj in range(c)] for ii in range(r)] for cma in mas] assert all([masx[0] == e for e in masx[1:]]), 'Maximums of algorithms differ!\n\n' + sgrid + '\n\n' + ( '\n\n'.join(['\n'.join([' '.join([str(e1).rjust(2) for e1 in e0]) for e0 in cma]) for cma in masx]) ) ma = mas[0 if not check else algo] if print_: cchars = ['.'] + [chr(ii) for ii in range(ord('B'), ord('Z') + 1)] # These chars are used to show clusters from largest to smallest res = [[grid[ii][jj] for jj in range(c)] for ii in range(r)] mac = [[ma[ii][jj] for jj in range(c)] for ii in range(r)] processed = set() sid = 0 for it in range(r * c): sma = sorted( [(mac[ii][jj] or (0, 0, 0)) + (ii, jj) for ii in range(r) for jj in range(c) if (ii, jj) not in processed], key = lambda e: e[0] * e[1], reverse = True ) if len(sma) == 0 or sma[0][0] * sma[0][1] <= 0: break maxv = sma[0] if it == 0: maxvf = maxv processed.add((maxv[3], maxv[4])) show = True for trial in [True, False]: for i in range(maxv[3] - maxv[0] + 1, maxv[3] + 1): for j in range(maxv[4] - maxv[1] + 1 - (maxv[3] - i) * maxv[2], maxv[4] + 1 - (maxv[3] - i) * maxv[2]): if trial: if mac[i][j] is None: show = False break elif show: res[i][j] = cchars[sid] mac[i][j] = None if show: sid += 1 if not show_non_max and it == 0: break res = '\n'.join([''.join([str(res[ii][jj]) for jj in range(c)]) for ii in range(r)]) print( 'Max:\nArea: ', maxvf[0] * maxvf[1], '\nSize Row,Col: ', (maxvf[0], maxvf[1]), '\nLowerRight Row,Col: ', (maxvf[3], maxvf[4]), '\nAngle: ', ("-1", " 0", "+1")[maxvf[2] + 1], '\n', sep = '' ) print(res) if debug: # Print all computed maximums, for debug purposes for cma in [ma, mac]: print('\n' + '\n'.join([' '.join([f'({e0[0]}, {e0[1]}, {("-1", " 0", "+1")[e0[2] + 1]})' for e0_ in e for e0 in (e0_ or ('-', '-', 0),)]) for e in cma])) print(end = '-' * 28 + '\n') return ma# ----------------- Testing -----------------def test(): # Iterating over text inputs or other ways of producing inputs for s in [ """ 1 1 0 0 0 1 0 1 1 1 1 0 1 1 1 1 1 0 0 0 1 0 1 1 0 0 1 0 1 0 1 1 1 1 1 1 0 0 1 1 0 0 1 1 1 1 1 0 0 1 0 0 1 0 1 1 """, """ 1 0 1 1 0 1 0 0 0 1 1 0 1 0 0 1 1 1 0 0 0 0 0 1 0 1 1 1 0 1 0 1 0 1 1 1 1 0 1 1 1 1 0 0 0 1 0 0 0 1 1 1 0 1 0 1 """, """ 0 1 1 0 1 0 1 1 0 0 1 1 0 0 0 1 0 0 0 1 1 0 1 0 1 1 0 0 1 1 1 0 0 1 1 0 0 1 1 0 0 0 1 0 1 0 1 1 1 0 0 1 0 0 0 0 0 1 1 0 1 1 0 0 """ ]: solve(s, text = True)if __name__ == '__main__': test()输出:Max:Area: 8Size Row,Col: (4, 2)LowerRight Row,Col: (4, 7)Angle: 0CC000101CC1011..100010..001010..1BBB00..00BBBDD0010010DD----------------------------Max:Area: 6Size Row,Col: (3, 2)LowerRight Row,Col: (2, 1)Angle: -110..01000..01001..0000010BBB01010BBB1011CC0001000CC10101----------------------------Max:Area: 12Size Row,Col: (6, 2)LowerRight Row,Col: (5, 7)Angle: +10..0101100..0001000..010BB00..100BB00..0001010..1001000001101100----------------------------