zhuangh

class Solution {
public:  
    vector<vector<int>> updateMatrix(vector<vector<int>>& matrix) {
        int n = matrix.size();
        int m = 0;
        if(n>0) m = matrix[0].size();        
        int max_dist = 100000;                
        queue< pair<int, int> > q;
                                 
        for(int i = 0; i < n; i++){

zhuangh

class Solution {
public:
    vector<vector<int>> updateMatrix(vector<vector<int>>& matrix) {
        int n = matrix.size();
        int m = 0;
        if( n > 0 ) m = matrix[0].size();
        int max_dist = 100000;  
        vector< vector<int> > dist(n, vector<int> (m, max_dist));
                                 

zhuangh

class Solution {
public:
    int largestRectangleArea(vector<int>& heights) {
        int space = 0;
        int maxh = 0;
        int n = heights.size();
        stack<int> s;        
        int i = 0;
        int max_space = 0;
        while(i<n){

zhuangh

### Visualize your network's feature maps here.
### Feel free to use as many code cells as needed.
 
# image_input: the test image being fed into the network to produce the feature maps
# tf_activation: should be a tf variable name used during your training procedure that represents the calculated state of a specific weight layer
# activation_min/max: can be used to view the activation contrast in more detail, by default matplot sets min and max to the actual min and max values of the output
# plt_num: used to plot out multiple different weight feature map sets on the same block, just extend the plt number for each new feature map entry
def outputFeatureMap(image_input, tf_activation, activation_min=-1, activation_max=-1 ,plt_num=1):
    # Here make sure to preprocess your image_input in a way your network expects
    # with size, normalization, ect if needed

zhuangh

class Solution {
public:
    int ladderLength(string beginWord, string endWord, vector<string>& wordList) {
        queue<string> q;
        unordered_set<string> m, dict;
        q.push(beginWord);
        m.insert(beginWord);
        for(const auto & it : wordList)
            dict.insert(it);   
        int level = 1;        

zhuangh

class STNode{
public:
    long l, r, m;
    bool tracked;
    STNode * lnode, * rnode;
    STNode(int ll, int rr):l(ll), r(rr), lnode(nullptr), rnode(nullptr){
        m = (l+r)/2;
        tracked = false;
    }

zhuangh

class STNode{
public:
    long l, r, m;
    bool tracked;
    shared_ptr<STNode> lnode, rnode;
    STNode(int ll, int rr):l(ll), r(rr), lnode(nullptr), rnode(nullptr){
        m = (l+r)/2;
        tracked = false;
    }
    

zhuangh

/**
 * Definition for an interval.
 * struct Interval {
 *     int start;
 *     int end;
 *     Interval() : start(0), end(0) {}
 *     Interval(int s, int e) : start(s), end(e) {}
 * };
 */

zhuangh

#include<string>
#include<iostream>
#include<vector>

using namespace std;

class ShortPalindromes{
public:
	
	string solve(const string & s, int i, int j, 

zhuangh

class Vector2D {
private:
    vector<vector<int>>::iterator row, iBegin, iEnd;
    vector<int>::iterator col;
public:
    Vector2D(vector<vector<int>>& vec2d) {
        iBegin = row = vec2d.begin(); 
        iEnd = vec2d.end();
        if(vec2d.size())
            col = row->begin();