const 前往下载 = “ http://icourse8.com/wmdxrgzn.html ”；Chapter information
0- promo
01- complex system
02- big data and machine learning
03- artificial intelligence in three stages
04- higher mathematics - the element and limit
05- application of complex network economics
06- and supervised machine learning algorithms
07- Alpha dogs and reinforcement learning algorithm
08- mathematics - two important limit Theorem
09- Higher Mathematics - derivative
10- Bayesian) O / k $ D, B, G & F
11- Higher Mathematics - Taylor expansion
12- Higher Mathematics - partial derivatives
13- higher mathematics - integral
14- Higher Mathematics - normal
15 naive Bayesian and maximum likelihood estimation
16- linear algebra - linear spaces and linear transformations
17- scientific and statistical data (on
18 linear Algebra - matrix, equivalence classes and determinant
19-Python base course (on)
20 linear Algebra - eigenvalue and eigenvector
21 supervised learning framework
22-Python base course (lower)
23 is the PCA-, dimensionality reduction method of introducing
24 - Science and statistics data (lower)
25-database operation the Python, Python crawler
26- linear classifier
27-Python advanced (upper)
28-Scikit Learn1-
29- entropy, logistic regression, the SVM is introduced
30-Python into order (under)
31- tree
32- basis of the data presented
33 cloud computing preliminary
34-D-Park combat
35 fourth paradigm Share
36- to random tree forest
37- Advanced data presented
38- reinforcement learning (on)
39- reinforcement learning (under)
40-SVM and neural network introduced
41- integrated model summary and GDBT understand its derivative applications
42- neural network
43 supervised learning - return
44- supervised learning - classification
45- convolution neural network infrastructure and network
46- time series forecasting
47- artificial intelligence and financial applications
48- depth study entry-purpose computer vision papers
visual depth learning introductory computer architecture articles 49-
Getting Started 50 computer visual depth study to optimize chapter
51 visual depth learning computer data entry papers
52- depth study of computer vision Starter Kit chapter
53- personalized recommendation algorithm for
54-Pig and Spark consolidate
55- artificial Intelligence and design
56- neural networks
57- Nonlinear dynamics
58- HFT order flow model
59- block chain revolution 7
60- statistical physics topics (a)
61- statistical physics topics (two
62- complex network Introduction
63-ABM Introduction and modeling financial markets
64- understand complex systems Ising model
65 complexity of the financial markets
power-law distribution of the widespread occurrence of 66-
67- natural heuristics
68- machine learning
69- model visualization project management
70-the Iteration Value Networks
70- latest playback
71- nonlinear dynamical systems (on
72- nonlinear dynamical system (under)
73- import natural language processing
physical transport processes on complex networks of 74-
75-RNN and LSTM
76- Talking about artificial intelligence entrepreneurial
77- depth of learning other subjects
78- course summary
class Solution {
public List<List<Integer>> threeSum(int[] nums) {
Arrays.sort(nums);
List<List<Integer>> ls = new ArrayList<>();
for (int i = 0; i < nums.length - 2; i++) {
if (i == 0 || (i > 0 && nums[i] != nums[i - 1])) { // 跳过可能重复的答案
int l = i + 1, r = nums.length - 1, sum = 0 - nums[i];
while (l < r) {
if (nums[l] + nums[r] == sum) {
ls.add(Arrays.asList(nums[i], nums[l], nums[r]));
while (l < r && nums[l] == nums[l + 1]) l++;
while (l < r && nums[r] == nums[r - 1]) r--;
l++;
r--;
} else if (nums[l] + nums[r] < sum) {
while (l < r && nums[l] == nums[l + 1]) l++; // 跳过重复值
l++;
} else {
while (l < r && nums[r] == nums[r - 1]) r--;
r--;
}
}
}
}
return ls;
}
}