【算法】Regular Expression Matching 正则匹配

JavaScript实现regular-expression-matching正则表达式匹配算法(附完整源码) JavaScript实现regular-expression-matching正则表达式匹配算法(附完整源码)regularExpressionMatching.js完整源代码regularExpressionMatching.test.js完整源代码 regularExpressionMatching.js完整源代码 const ZERO_OR_MORE_CHARS = '*'; const ANY_CHAR = '.'; export default function regularExpression 阅读详情

【算法】Regular Expression Matching 正则匹配

题目

Given an input string ( s ) and a pattern ( p ), implement regular expression matching with support for ‘.’ and ‘*’.

‘.’ Matches any single character.
‘*’ Matches zero or more of the preceding element.
The matching should cover the entire input string (not partial).

Note:

s could be empty and contains only lowercase letters a-z.
p could be empty and contains only lowercase letters a-z, and characters like . or *.
Example 1:

Input:

s = "aa"
p = "a"
Output: false
Explanation: "a" does not match the entire string "aa".

Example 2:

Input:
s = "aa"
p = "a*"
Output: true
Explanation: '*' means zero or more of the preceding element, 'a'. Therefore, by repeating 'a' once, it becomes "aa".

Example 3:

Input:
s = "ab"
p = ".*"
Output: true
Explanation: ".*" means "zero or more (*) of any character (.)".

Example 4:

Input:
s = "aab"
p = "c*a*b"
Output: true
Explanation: c can be repeated 0 times, a can be repeated 1 time. Therefore, it matches "aab".

Example 5:

Input:
s = "mississippi"
p = "mis*is*p*."
Output: false

给出一个字符串 s 和一个表达式 p,表达式 p 可以含有 " . " 和 " * "。

  • " . " 可以匹配一个任意字符。
  • " * "可以匹配 0 到无限个 " * " 前一位的字符,如 " a* " 可以匹配 0 到无限个 " a "。
  • 字符为 a ~ z
  • s 和 p 可以为空字符串

判断 s 和 p 是否匹配成功

解题思路

运用动态规划(dynamic programming)方法,dp[i][j] 表示 s 前 i 个字符与 p 前 j 个字符是否匹配,默认值为 false。找到 dp[i][j] 的表达式如下:

  1. 当 p[j - 1] 不为 *,且 s[i - 1] 与 p[j - 1] 匹配(包括字符相同和 p[j-1] 为 . )时,dp[i][j] = dp[i - 1][j - 1]
  2. 当 p[j - 1] 为 *,且 * 没有进行匹配时,dp[i][j] = dp[i][j - 2]
  3. 当 p[j - 1] 为 *,且 * 进行一次匹配时,dp[i][j] = dp[i - 1][j] && (s[i - 1] == p[j - 2] || p[j - 2] == ‘.’)

ps:需要注意的是这里 i j 表示的是长度,所以取第 i 为字符时应该用 s[i-1]

代码实现

Runtime: 20 ms
Memory: 20.7 MB

func isMatch(_ s: String, _ p: String) -> Bool {
        //将 String 转化成 Array ,方便获取字符
        let s = Array(s),p = Array(p)
        //m n 为 s p 的长度
        let m = s.count, n = p.count
        //dp[i][j] 表示 s 前 i 个字符与 p 前 j 个字符是否匹配,默认值为 false
        var dp = Array(repeating: Array(repeating: false, count: n+1), count: m+1)
        // s p 均为空时是匹配的,所以 dp[0][0] = true
        dp[0][0] = true
        //由于 p 为空时,只有当 s 也为空才为 true,其余情况皆为 false
        // 之前已经设置好 dp[0][0] = true ,所以 p 可以从 1 遍历到 n
        // 将 n 为 0 的情况直接跳过,返回结果
        if n > 0 {
            //遍历 s 的长度,从 0 到 m
            for i in 0 ... m {
                // 遍历 p 的长度,
                for j in 1 ... n {
                    //基本分为下面三种情况
                    //需要注意的是这里 i j 表示的是长度,所以取第 i 为字符时应该用 s[i-1]
                    //1.当 p[j - 1] 不为 *,且 s[i - 1] 与 p[j - 1] 匹配(包括字符相同和 p[j-1] 为 . )时,dp[i][j] = dp[i - 1][j - 1]
                    //2.当 p[j - 1] 为 *,且 * 没有进行匹配时,dp[i][j] = dp[i][j - 2]
                    //3.当 p[j - 1] 为 *,且 * 进行一次匹配时,dp[i][j] = dp[i - 1][j] && (s[i - 1] == p[j - 2] || p[j - 2] == '.')
                    
                    // 当 j 至少为 2 位,且 p[j-1] 为 * 时,这个 * 才合理
                    if (j > 1 && p[j-1] == "*") {
                        // 若 * 合理,则将上面情况 2 和情况 3 进行融合,只有满足其中一种情况,即匹配成功
                        dp[i][j] = dp[i][j - 2] || (i > 0 && (s[i-1]  == p[j-2] || p[j-2] == ".") && dp[i - 1][j])
                    }else{
                        // 若没有合理的 * ,则正常判断是否符合情况 1
                        dp[i][j] = i > 0 && dp[i - 1][j - 1] && (s[i-1] == p[j-1] || p[j-1] == ".")
                    }
                }
            }
        }
        //返回结果
        return dp[m][n];
    }

代码地址:https://github.com/sinianshou/EGSwiftLearning

go语言:实现regular-expression-matching正则表达式匹配算法(附完整源码) 阅读详情

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Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; 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")

Data-Structures-and-Algorithm-Analysis-in-Java.pdf

作者: Weiss, Mark A. 出版社: Addison Wesley Data Structures and Algorithm Analysis in Java is an “advanced algorithms” book that fits between traditional CS2 and Algorithms Analysis courses. In the old ACM Curriculum Guidelines, this course was known as CS7. This text is for readers who want to learn good programming and algorithm analysis skills simultaneously so that they can develop such programs with the maximum amo...

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