如何将文本拆分成最小化解决方案的块?

时间:2016-09-28 14:47:07

标签: python string algorithm split computer-science

概述

我有一组可用的有效块,可以用来分割文本(如果可能的话)。

如何使用这些块拆分给定文本,例如结果将根据生成的块数进行优化(最小化)?

TEST SUITE

if __name__ == "__main__":
    import random
    import sys

    random.seed(1)

    # 1) Testing robustness
    examples = []
    sys.stdout.write("Testing correctness...")
    N = 50
    large_number = "3141592653589793238462643383279502884197169399375105820974944592307816406286208998628034825342117067982148086513282306647093844609550582231725359408128481"
    for i in range(100):
        for j in range(i):
            choices = random.sample(range(i), j)
            examples.append((choices, large_number))

    for (choices, large_number) in examples:
        get_it_done(choices, large_number)
    sys.stdout.write("OK")

    # 2) Testing correctness
    examples = [
        # Example1 ->
        # Solution ['012345678910203040506070', '80', '90', '100', '200', '300', '400', '500', '600', '700', '800', '900']
        (
            [
                "0", "1", "2", "3", "4", "5", "6", "7", "8", "9",
                "10", "20", "30", "40", "50", "60", "70", "80", "90",
                "100", "200", "300", "400", "500", "600", "700", "800", "900",
                "012345678910203040506070"
            ],
            "0123456789102030405060708090100200300400500600700800900"
        ),
        # Example2
        ## Solution ['100']
        (
            ["0", "1", "10", "100"],
            "100"
        ),
        # Example3
        ## Solution ['101234567891020304050', '6070809010020030040050', '0600700800900']
        (
            [
                "10", "20", "30", "40", "50", "60", "70", "80", "90",
                "012345678910203040506070",
                "101234567891020304050",
                "6070809010020030040050",
                "0600700800900"
            ],
            "10123456789102030405060708090100200300400500600700800900"
        ),
        # Example4
        ### Solution ['12', '34', '56', '78', '90']
        (
            [
                "12", "34", "56", "78", "90",
                "890",
            ],
            "1234567890"
        ),
        # Example5
        ## Solution ['12', '34']
        (
            [
                "1", "2", "3",
                "12", "23", "34"
            ],
            "1234"
        ),
        # Example6
        ## Solution ['100', '10']
        (
            ["0", "1", "10", "100"],
            "10010"
        )
    ]

    score = 0
    for (choices, large_number) in examples:
        res = get_it_done(choices, large_number)
        flag = "".join(res) == large_number
        print("{0}\n{1}\n{2} --> {3}".format(
            large_number, "".join(res), res, flag))
        print('-' * 80)
        score += flag

    print(
        "Score: {0}/{1} = {2:.2f}%".format(score, len(examples), score / len(examples) * 100))

    # 3) TODO: Testing optimization, it should provide (if possible)
    #          minimal cases

问题

如何在不使用强力方法的情况下在python上解决这个问题?

4 个答案:

答案 0 :(得分:7)

使用动态编程,您可以构建列表(l0, l1, l2, ... ln-1),其中n是输入字符串中的字符数,li是您需要到达的最小块数输入字符串的字符i。整体结构如下:

minValues := list with n infinity entries
for i from 0 to n-1
    for every choice c that is a suffix of input[0..i]
        if i - len(c) < 0
            newVal = 1
        else
            newVal = minValues[i - len(c)] + 1
        end if
        if(newVal < minValues[i])
            minValues[i] = newVal
            //optionally record the used chunk
        end if
    next
next

然后,整个字符串的最小块数为ln-1。您可以通过跟踪列表(需要记录使用过的块)来获取实际的块。

使用trie(反向选择字符串)可以加速检索作为后缀的选项。最差情况的复杂性仍为O(n * c * lc),其中n是输入字符串的长度,c是选择的数量,lc是最大长度选择。但是,这种复杂性仅适用于嵌套后缀的选项(例如0100100010 ...)。在这种情况下,trie将退化为列表。平均而言,运行时间应该少得多。假设从trie中检索到的选择的数量总是一个很小的常数,它是O(n * lc)(实际上,lc因子可能也更小。)

以下是一个例子:

choices = ["0","1","10","100"]
text = "10010"

algorithm step    content of minValues
                   0      1       2        3      4
---------------------------------------------------------
initialize        (∞,     ∞ ,     ∞ ,      ∞ ,    ∞     )
i = 0, c = "1"    (1 "1", ∞ ,     ∞ ,      ∞ ,    ∞     )
i = 1, c = "0"    (1 "1", 2 "0",  ∞ ,      ∞ ,    ∞     )
i = 1, c = "10"   (1 "1", 1 "10", ∞ ,      ∞ ,    ∞     )
i = 2, c = "0"    (1 "1", 1 "10", 2 "0",   ∞ ,    ∞     )
i = 2, c = "100"  (1 "1", 1 "10", 1 "100", ∞ ,    ∞     )
i = 3, c = "1"    (1 "1", 1 "10", 1 "100", 2 "1", ∞     )
i = 4, c = "0"    (1 "1", 1 "10", 1 "100", 2 "1", 3 "0" )
i = 4, c = "10"   (1 "1", 1 "10", 1 "100", 2 "1", 2 "10")

含义:我们可以用2个块组成字符串。追溯以相反的顺序给出块:“10”,“100”。

答案 1 :(得分:4)

对不起,实施有点hacky。但我认为它总能找到最佳答案。 (虽然没有证明。)这是python中一个快速而完整的实现,并为所有提议的用例返回正确的答案。

该算法是递归的,其工作原理如下:

  1. 从文本开头开始。
  2. 找到可用作第一个块的匹配块。
  3. 对于每个匹配的块,递归地从步骤1开始,其余的文本(即从开头删除的块)并收集解决方案
  4. 返回最短的收集解决方案
  5. 算法完成后,所有可能的路径(以及不可能的路径,即末尾不匹配)应该只被遍历一次。

    为了有效地执行第2步,我为选择构建了一个patricia树,以便可以快速查找匹配文本开头的可能块。

    def get_seq_in_tree(tree, choice):
        if type(tree)!=dict:
            if choice == tree:
                return [choice]
            return []
        for i in range(1, len(choice)+1):
            if choice[:i] in tree:
                return [choice[:i]] + get_seq_in_tree(tree[choice[:i]], choice[i:])
        return []
    
    def seq_can_end_here(tree, seq):
        res = []
        last = tree
        for e, c in enumerate(seq):
            if '' in last[c]:
                res.append(e+1)
            last = last[c]
        return res
    
    def build_tree(choices):
        tree = {}
        choices = sorted(choices)
        for choice in choices:
            last = tree
            for c in choice:
                if c not in last:
                    last[c] = {}
                last = last[c]
            last['']=None
        return tree
    
    solution_cache = {}
    ncalls = 0
    
    def solve(tree, number):
        global solution_cache
        global ncalls
        ncalls +=1
    
        # take every path only once
        if number in solution_cache: 
            return solution_cache[number]
    
        solutions = []
        seq =  get_seq_in_tree(tree, number)
        endings = seq_can_end_here(tree, seq)
        for i in reversed(endings):
            current_solution = []
            current_solution.append(number[:i])
            if i == len(number):
                solutions.append(current_solution)
            else:
                next_solution = solve(tree, number[i:])
                if next_solution:
                    solutions.append(current_solution + next_solution)
        if not solutions:
            return None
    
        shortest_solution = sorted([(len(solution), solution) for solution in solutions])[0][1]
    
        solution_cache[number] = shortest_solution
        return shortest_solution
    
    def get_it_done(choices, number):
        tree = build_tree(choices)
        solution = solve(tree, number)
        return solution
    
    
    if __name__ == "__main__":
    
        examples = [
            # Example1 ->
            # Solution ['012345678910203040506070', '80', '90', '100', '200', '300', '400', '500', '600', '700', '800', '900']
            (
                [
                    "0", "1", "2", "3", "4", "5", "6", "7", "8", "9",
                    "10", "20", "30", "40", "50", "60", "70", "80", "90",
                    "100", "200", "300", "400", "500", "600", "700", "800", "900",
                    "012345678910203040506070"
                ],
                "0123456789102030405060708090100200300400500600700800900"
            ),
            ## Example2
            ## Solution ['100']
            (
                ["0", "1", "10", "100"],
                "100"
            ),
            ## Example3
            ## Solution ['101234567891020304050', '6070809010020030040050', '0600700800900']
            (
                [
                    "10", "20", "30", "40", "50", "60", "70", "80", "90",
                    "012345678910203040506070",
                    "101234567891020304050",
                    "6070809010020030040050",
                    "0600700800900"
                ],
                "10123456789102030405060708090100200300400500600700800900"
            ),
            ### Example4
            ### Solution ['12', '34', '56', '78', '90']
            (
                [
                    "12", "34", "56", "78", "90",
                    "890",
                ],
                "1234567890"
            ),
            ## Example5
            ## Solution ['12', '34']
            (
                [
                    "1", "2", "3",
                    "12", "23", "34"
                ],
                "1234"
            ),
            # Example6
            ## Solution ['100', '10']
            (
                ["0", "1", "10", "100"],
                "10010"
            )
        ]
    
        score = 0
        for (choices, large_number) in examples:
            res = get_it_done(choices, large_number)
            flag = "".join(res) == large_number
            print("{0}\n{1}\n{2} --> {3}".format(
                large_number, "".join(res), res, flag))
            print('-' * 80)
            score += flag
    
        print("Score: {0}/{1} = {2:.2f}%".format(score, len(examples), score / len(examples) * 100))
    

    我猜复杂性类似于O(L * N * log(C)),其中L是文本的长度,N是词汇量的大小,C是选择的数量。

    编辑:包含缺少的测试用例。

答案 2 :(得分:2)

def find_shortest_path(graph, start, end, path=[]):
    path = path + [start]
    if start == end:
        return path
    if start not in graph:
        return None
    shortest = None
    for node in graph[start]:
        if node not in path:
            newpath = find_shortest_path(graph, node, end, path)
            if newpath:
                if not shortest or len(newpath) < len(shortest):
                    shortest = newpath
    return shortest


def get_it_done(choices, number):
    mapping = {}
    graph = {} 

    for choice in choices:
        if choice in number:
            _from = number.index(choice)
            _to = _from + len(choice)
            mapping.setdefault((_from, _to), choice)

    items = sorted(mapping.items(), key=lambda x: x[0])
    for _range, value in items:
        _from, _to = _range
        graph.setdefault(_from, []).append(_to)
    start = 0
    end = _range[1] #this is hack, works only in python 2.7
    path = find_shortest_path(graph, start, end) 
    ranges = [tuple(path[i:i+2]) for i in range(len(path) - 1)]
    if len(ranges) == 1:
        items = sorted(choices, key=len, reverse=True)
        number_length = len(number) 
        result = ''
        for item in items:
            result += item
            if len(result) == number_length: 
                return result 
    return [mapping[_range] for _range in ranges]


if __name__ == "__main__":
    examples = [
        # Example1 ->
        # Solution ['012345678910203040506070', '80', '90', '100', '200', '300', '400', '500', '600', '700', '800', '900']
        (
            [
                "0", "1", "2", "3", "4", "5", "6", "7", "8", "9",
                "10", "20", "30", "40", "50", "60", "70", "80", "90",
                "100", "200", "300", "400", "500", "600", "700", "800", "900",
                "012345678910203040506070"
            ],
            "0123456789102030405060708090100200300400500600700800900"
        ),
        ## Example2
        ## Solution ['100']
        (
            ["0", "1", "10", "100"],
            "100"
        ),
        ## Example3
        ## Solution ['101234567891020304050', '6070809010020030040050', '0600700800900']
        (
            [
                "10", "20", "30", "40", "50", "60", "70", "80", "90",
                "012345678910203040506070",
                "101234567891020304050",
                "6070809010020030040050",
                "0600700800900"
            ],
            "10123456789102030405060708090100200300400500600700800900"
        ),
        ### Example4
        ### Solution ['12', '34', '56', '78', '90']
        (
            [
                "12", "34", "56", "78", "90",
                "890",
            ],
            "1234567890"
        ),
        ## Example5
        ## Solution ['12', '34']
        (
            [
                "1", "2", "3",
                "12", "23", "34"
            ],
            "1234"
        ),
        # Example6
        ## Solution ['100', '10']
        (
            ["0", "1", "10", "100"],
            "10010"
        )
    ]

    score = 0
    for (choices, large_number) in examples:
        res = get_it_done(choices, large_number)
        flag = "".join(res) == large_number
        print("{0}\n{1}\n{2} --> {3}".format(
            large_number, "".join(res), res, flag))
        print('-' * 80)
        score += flag

    print(
        "Score: {0}/{1} = {2:.2f}%".format(score, len(examples), score / len(examples) * 100))

get_it_done函数首先在mapping创建,其中键是choice中每个number的出现范围。然后按mapping dict的每个键中的第一项对其进行排序。下一步是创建graph。然后使用find_shortest_path函数,我们可以找到以最佳方式构建结果的最短路径。最后,我们可以再次使用mapping,根据其范围返回choices。如果有一个范围,我们就会遇到所有数字都包含相同的两个值的情况,因此规则是不同的。我们可以直接从choices(按降序排序)收集数字,直到结果的长度与number的长度相同。

答案 3 :(得分:-3)

def find_shortest_path(graph, start, end, path=[]):
    path = path + [start]
    if start == end:
        return path
    if start not in graph:
        return None
    shortest = None
    for node in graph[start]:
        if node not in path:
            newpath = find_shortest_path(graph, node, end, path)
            if newpath:
                if not shortest or len(newpath) < len(shortest):
                    shortest = newpath
    return shortest


def get_it_done(choices, number):
    mapping = {}
    graph = {} 

    for choice in choices:
        if choice in number:
            _from = number.index(choice)
            _to = _from + len(choice)
            mapping.setdefault((_from, _to), choice)

    items = sorted(mapping.items(), key=lambda x: x[0])
    for _range, value in items:
        _from, _to = _range
        graph.setdefault(_from, []).append(_to)
    start = 0
    end = _range[1] #this is hack, works only in python 2.7
    path = find_shortest_path(graph, start, end) 
    ranges = [tuple(path[i:i+2]) for i in range(len(path) - 1)]
    if len(ranges) == 1:
        return [mapping[(start, graph[start][-1])]]
    return [mapping[_range] for _range in ranges]


if __name__ == "__main__":
    examples = [
        # Example1 ->
        # Solution ['012345678910203040506070', '80', '90', '100', '200', '300', '400', '500', '600', '700', '800', '900']
        (
            [
                "0", "1", "2", "3", "4", "5", "6", "7", "8", "9",
                "10", "20", "30", "40", "50", "60", "70", "80", "90",
                "100", "200", "300", "400", "500", "600", "700", "800", "900",
                "012345678910203040506070"
            ],
            "0123456789102030405060708090100200300400500600700800900"
        ),
        ## Example2
        ## Solution ['100']
        (
            ["0", "1", "10", "100"],
            "100"
        ),
        ## Example3
        ## Solution ['101234567891020304050', '6070809010020030040050', '0600700800900']
        (
            [
                "10", "20", "30", "40", "50", "60", "70", "80", "90",
                "012345678910203040506070",
                "101234567891020304050",
                "6070809010020030040050",
                "0600700800900"
            ],
            "10123456789102030405060708090100200300400500600700800900"
        ),
        ### Example4
        ### Solution ['12', '34', '56', '78', '90']
        (
            [
                "12", "34", "56", "78", "90",
                "890",
            ],
            "1234567890"
        ),
        ## Example5
        ## Solution ['12', '34']
        (
            [
                "1", "2", "3",
                "12", "23", "34"
            ],
            "1234"
        )
    ]

    for (choices, large_number) in examples:
        res = get_it_done(choices, large_number)
        print("{0}\n{1}\n{2} --> {3}".format(
            large_number, "".join(res), res, "".join(res) == large_number))
        print('-' * 80)
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