Leetcode题目索引

Best-First Search 算法研究 对于需要确保全局最优解的问题,可以考虑其他更复杂的搜索算法,如 A* 算法或 Dijkstra算法,这些算法结合了启发式函数和全局路径权重的考虑,能够更有效地搜索全局最优解。扩展 Arad 后,从 Arad 出发,可以到的三个城市为Zerind(374),Sibiu(253)和Timisoara(329),括号中为启发函数(直线距离)的值,选择最小值对应的城市,即下一个遍历的城市为。),它用于评估搜索节点的优先级。该算法在每一步选择下一个节点时,仅仅考虑当前节点的局部最优选择,而不考虑全局的最优选择。 阅读详情
1Two SumMedium
2Add Two NumbersMedium
3Longest Substring Without Repeating CharactersMedium
4Median of Two Sorted ArraysHard
5Longest Palindromic SubstringMedium
6ZigZag ConversionEasy
7Reverse IntegerEasy
8String to Integer (atoi)Easy
9Palindrome NumberEasy
10Regular Expression MatchingHard
11Container With Most WaterMedium
12Integer to RomanMedium
13Roman to IntegerEasy
14Longest Common PrefixEasy
153SumMedium
163Sum ClosestMedium
17Letter Combinations of a Phone NumberMedium
184SumMedium
19Remove Nth Node From End of ListEasy
20Valid ParenthesesEasy
21Merge Two Sorted ListsEasy
22Generate ParenthesesMedium
23Merge k Sorted ListsHard
24Swap Nodes in PairsMedium
25Reverse Nodes in k-GroupHard
26Remove Duplicates from Sorted ArrayEasy
27Remove ElementEasy
28Implement strStr()Easy
29Divide Two IntegersMedium
30Substring with Concatenation of All WordsHard
31Next PermutationMedium
32Longest Valid ParenthesesHard
33Search in Rotated Sorted ArrayHard
34Search for a RangeMedium
35Search Insert PositionMedium
36Valid SudokuEasy
37Sudoku SolverHard
38Count and SayEasy
39Combination SumMedium
40Combination Sum IIMedium
41First Missing PositiveHard
42Trapping Rain WaterHard
43Multiply StringsMedium
44Wildcard MatchingHard
45Jump Game IIHard
46PermutationsMedium
47Permutations IIHard
48Rotate ImageMedium
49AnagramsMedium
50Pow(x, n)Medium
51N-QueensHard
52N-Queens IIHard
53Maximum SubarrayMedium
54Spiral MatrixMedium
55Jump GameMedium
56Merge IntervalsHard
57Insert IntervalHard
58Length of Last WordEasy
59Spiral Matrix IIMedium
60Permutation SequenceMedium
61Rotate ListMedium
62Unique PathsMedium
63Unique Paths IIMedium
64Minimum Path SumMedium
65Valid NumberHard
66Plus OneEasy
67Add BinaryEasy
68Text JustificationHard
69Sqrt(x)Medium
70Climbing StairsEasy
71Simplify PathMedium
72Edit DistanceHard
73Set Matrix ZeroesMedium
74Search a 2D MatrixMedium
75Sort ColorsMedium
76Minimum Window SubstringHard
77CombinationsMedium
78SubsetsMedium
79Word SearchMedium
80Remove Duplicates from Sorted Array IIMedium
81Search in Rotated Sorted Array IIMedium
82Remove Duplicates from Sorted List IIMedium
83Remove Duplicates from Sorted ListEasy
84Largest Rectangle in HistogramHard
85Maximal RectangleHard
86Partition ListMedium
87Scramble StringHard
88Merge Sorted ArrayEasy
89Gray CodeMedium
90Subsets IIMedium
91Decode WaysMedium
92Reverse Linked List IIMedium
93Restore IP AddressesMedium
94Binary Tree Inorder TraversalMedium
95Unique Binary Search Trees IIMedium
96Unique Binary Search TreesMedium
97Interleaving StringHard
98Validate Binary Search TreeMedium
99Recover Binary Search TreeHard
100Same TreeEasy
101Symmetric TreeEasy
102Binary Tree Level Order TraversalEasy
103Binary Tree Zigzag Level Order TraversalMedium
104Maximum Depth of Binary TreeEasy
105Construct Binary Tree from Preorder and Inorder TraversalMedium
106Construct Binary Tree from Inorder and Postorder TraversalMedium
107Binary Tree Level Order Traversal IIEasy
108Convert Sorted Array to Binary Search TreeMedium
109Convert Sorted List to Binary Search TreeMedium
110Balanced Binary TreeEasy
111Minimum Depth of Binary TreeEasy
112Path SumEasy
113Path Sum IIMedium
114Flatten Binary Tree to Linked ListMedium
115Distinct SubsequencesHard
116Populating Next Right Pointers in Each NodeMedium
117Populating Next Right Pointers in Each Node IIHard
118Pascal's TriangleEasy
119Pascal's Triangle IIEasy
120TriangleMedium
121Best Time to Buy and Sell StockMedium
122Best Time to Buy and Sell Stock IIMedium
123Best Time to Buy and Sell Stock IIIHard
124Binary Tree Maximum Path SumHard
125Valid PalindromeEasy
126Word Ladder IIHard
127Word LadderMedium
128Longest Consecutive SequenceHard
129Sum Root to Leaf NumbersMedium
130Surrounded RegionsMedium
131Palindrome PartitioningMedium
132Palindrome Partitioning IIHard
133Clone GraphMedium
134Gas StationMedium
135CandyHard
136Single NumberMedium
137Single Number IIMedium
138Copy List with Random PointerHard
139Word BreakMedium
140Word Break IIHard
141Linked List CycleMedium
142Linked List Cycle IIMedium
143Reorder ListMedium
144Binary Tree Preorder TraversalMedium
145Binary Tree Postorder TraversalHard
146LRU CacheHard
147Insertion Sort ListMedium
148Sort ListMedium
149Max Points on a LineHard
150Evaluate Reverse Polish NotationMedium
151Reverse Words in a StringMedium
152Maximum Product SubarrayMedium
153Find Minimum in Rotated Sorted ArrayMedium
154Find Minimum in Rotated Sorted Array IIHard
155Min StackEasy
156Binary Tree Upside Down Medium
157Read N Characters Given Read4 Easy
158Read N Characters Given Read4 II - Call multiple times Hard
159Longest Substring with At Most Two Distinct Characters Hard
160Intersection of Two Linked ListsEasy
161One Edit Distance Medium
162Find Peak ElementMedium
163Missing Ranges Medium
164Maximum GapHard
165Compare Version NumbersEasy
166Fraction to Recurring DecimalMedium
167Two Sum II - Input array is sorted Medium
168Excel Sheet Column TitleEasy
169Majority ElementEasy
170Two Sum III - Data structure design Easy
171Excel Sheet Column NumberEasy
172Factorial Trailing ZeroesEasy
173Binary Search Tree IteratorMedium

(10-2)Bellman-Ford算法的局限性与改进 在搜索过程中,最佳优先搜索不保留之前访问的节点,因此可能会陷入无限循环或者无法找到解的情况,但它通常能够以较快的速度找到一个接近最优解的解。总的来说,最佳优先搜索算法的基本思想是在搜索过程中根据节点的启发式值选择下一个要扩展的节点,以期望能够更快地找到解决方案。(1)定义节点结构:创建一个表示节点的结构体或类,其中包括节点的位置坐标、距离起点的实际代价、到目标节点的启发式值等信息。(4)扩展节点:对于当前节点,根据问题的规则生成所有可能的后继节点,并计算它们的实际代价和启发式值。 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]); 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