【重点BFS】LeetCode 127. Word Ladder

若依(ruoyivue)实现文档在线查看 1、Kkfileview部署 第一步:从kkfileview官方发行版本下载安装包,安装包下载路径: https://gitee.com/kekingcn/file-online-preview/releases, 第二步:解压kkFileView-x.x.x文件(Windows用.zip包,Linux/MacOS用.tar.gz包) 第三步:打开解压后文件夹的bin目录,运行startup脚本(Windows下以管理员身份运行startup.bat,Linux以root用户运行startup.sh) 第四 阅读详情

LeetCode 127. Word Ladder

Solution1:我的超过40%的AC的答案
原先利用BFS做但是内存溢出未能AC;进过修改加上了标记是否访问过的visited数组,可以AC啦~~~

class Solution {
public:
    int ladderLength(string beginWord, string endWord, vector<string>& wordList) {
        if (find(wordList.begin(), wordList.end(), endWord) == wordList.end())
            return 0;
        queue<string> str_queue;
        str_queue.push(beginWord);
        int level = 2, cur = 1, next = 0;
        vector<int> visited(wordList.size(), 0);
        while (cur) { 
            string temp = str_queue.front();
            for (int i = 0; i < wordList.size(); i++) {
                if (! visited[i] && diff_n_char(temp, wordList[i]) == 1) {
                    visited[i] = 1;
                    if (wordList[i] == endWord)
                        return level;
                    str_queue.push(wordList[i]);
                    next++;
                }
            }
            str_queue.pop();
            cur--;
            if (!cur) {
                cur = next;
                next = 0;
                level++;
            }                
        }
        return 0;
    }

private:
    int diff_n_char(string& s1, string& s2) {
        int diff = 0;
        for (int i = 0; i < s1.size(); i++) {
            if (s1[i] != s2[i])
                diff++;
        }
        return diff;
    }
};

Solution2:
单向BFS,参考自花花酱:http://zxi.mytechroad.com/blog/searching/127-word-ladder/

class Solution {
public:
    int ladderLength(string beginWord, string endWord, vector<string>& wordList) {
        unordered_set<string> dict(wordList.begin(), wordList.end());        
        if (!dict.count(endWord)) return 0;

        queue<string> q;
        q.push(beginWord);

        int l = beginWord.length();
        int step = 0;

        while (!q.empty()) {
            ++step;
            for (int size = q.size(); size > 0; size--) {                
                string w = q.front();                
                q.pop();
                for (int i = 0; i < l; i++) {                
                    char ch = w[i];
                    for (int j = 'a'; j <= 'z'; j++) {
                        w[i] = j;
                        // Found the solution
                        if (w == endWord) return step + 1;
                        // Not in dict, skip it
                        if (!dict.count(w)) continue;
                        // Remove new word from dict
                        dict.erase(w);
                        // Add new word into queue
                        q.push(w);                    
                    }
                    w[i] = ch;
                }
            }
        }
        return 0;
    }
};

//仿写答案
class Solution {
public:
    int ladderLength(string beginWord, string endWord, vector<string>& wordList) {
        unordered_set<string> word_set(wordList.begin(), wordList.end());
        if (!word_set.count(endWord)) //endWord不在wordList中
            return 0;
        queue<string> word_q;
        word_q.push(beginWord);
        int level = 1, len_word = beginWord.size();
        while (!word_q.empty()) {
            level++;
            for (int i = word_q.size(); i > 0; i--) {
                //倒着计数很聪明,简化了后面queue增、删元素引起的边界问题
                string w = word_q.front();
                word_q.pop();
                for (int j = 0; j < len_word; j++) {
                    char temp_ch = w[j];
                    for (int k = 'a'; k <= 'z'; k++) {
                        w[j] = k;
                        if (w == endWord) return level;
                        if (!word_set.count(w)) continue;
                        word_set.erase(w);
                        word_q.push(w);
                    }
                    w[j] = temp_ch;
                }
            }
        }
        return 0;
    }
};

Solution3:
双向BFS,参考自花花酱:http://zxi.mytechroad.com/blog/searching/127-word-ladder/

class Solution {
public:
    int ladderLength(string beginWord, string endWord, vector<string>& wordList) {
        unordered_set<string> dict(wordList.begin(), wordList.end());        
        if (!dict.count(endWord)) return 0;

        int l = beginWord.length();

        unordered_set<string> q1{beginWord};
        unordered_set<string> q2{endWord};

        int step = 0;

        while (!q1.empty() && !q2.empty()) {
            ++step;

            // Always expend the smaller queue first
            if (q1.size() > q2.size())
                std::swap(q1, q2);

            unordered_set<string> q;

            for (string w : q1) {
                for (int i = 0; i < l; i++) {
                    char ch = w[i];
                    for (int j = 'a'; j <= 'z'; j++) {
                        w[i] = j;
                        if (q2.count(w)) return step + 1;
                        if (!dict.count(w)) continue;                        
                        dict.erase(w);
                        q.insert(w);
                    }
                    w[i] = ch;
                }
            }

            std::swap(q, q1);
        }

        return 0;
    }
};
第 2 章:PassThrough 直通滤波 通过设置某个维度(如 x、y、z 或 rgb)的取值范围,仅保留该范围内的点,从而达到**点云裁剪(裁边)**或过滤异常值的目的。所有在该范围之外的点会被移除(可选是否保留);点云中包含背景、地面或传感器噪声,需要过滤;通过 z 轴过滤,只保留桌面上方的物体;多轴组合过滤(如同时过滤 x、y、z);可用于地面剔除、车载点云预处理等场景。不支持一次过滤多个字段,可多次调用;仅处理某一层或某一区域的点云数据。原始点云中包含地面、墙面、背景等;在区域裁剪、前景提取中广泛使用;设定一个轴(如z轴); 阅读详情

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B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. 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plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

leetcode127.Word Ladder (单双向BFS)

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思维决定一切 1万+
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