Text Justification leetcode

本文介绍了一个文本对齐算法,该算法能够将给定的一组单词按照指定长度进行左对齐和右对齐处理,确保每行字符数固定,并均匀分布单词间的空格。特别地,文章还讨论了如何处理无法整除的空格数以及最后一行的特殊处理方式。

Text Justification

Apr 3 '12

3100 / 15986

Given an array of words and a length L, format the text such that each line has exactly L characters and is fully (left and right) justified.

You should pack your words in a greedy approach; that is, pack as many words as you can in each line. Pad extra spaces ' ' when necessary so that each line has exactly L characters.

Extra spaces between words should be distributed as evenly as possible. If the number of spaces on a line do not divide evenly between words, the empty slots on the left will be assigned more spaces than the slots on the right.

For the last line of text, it should be left justified and no extra space is inserted between words.

For example,
words:
["This", "is", "an", "example", "of", "text", "justification."]
L:
16.

Return the formatted lines as:

[

   "This    is    an",

   "example  of text",

   "justification.  "

]


Note: Each word is guaranteed not to exceed L in length.



,大体思路ok了,细节仍有错误


package String;  

import java.util.ArrayList;

/** 
 * @Title: Text_Just.java 
 * @Package String 
 * @Description: TODO
 * @author nutc
 * @date 2013-9-6 上午9:50:57 
 * @version V1.0 
 */
public class Text_Just {
	
	public static void main(String args[]){
		Text_Just t = new Text_Just();
		String[] s = {"a","b","c","d","e"};
		ArrayList<String> list = t.fullJustify(s, 3);
		System.out.println(list);
	}
	    public ArrayList<String> fullJustify(String[] words, int L) {


	        if(words==null||words.length==0) return null;
	        ArrayList<String> list = new ArrayList<String>();
	        
	        
	        int i=0,j=0,now = 0;
	        while(i<words.length){
	            j = i;
	            now = 0;
	            now += words[i].length();
	            while(++i<words.length && (words[i].length()+1+now)<=L){
	                now += words[i].length()+1;//保证两个
	            }
	            i--; //i  还是要剪掉的!
	            if(i==j){
	                StringBuilder sb = new StringBuilder(words[i]);
	                for(int k=1;k<=L-now;k++)
	                    sb.append(" ");
	                list.add(sb.toString());
	            }else{
	                int divide = (L-now+(i-j))/(i-j);
	                int add = (L-now+(i-j))%(i-j);
	                StringBuilder sb = new StringBuilder(words[j]);
	                for(int k = 1;k<=(divide+add);k++){
	                    sb.append(" ");
	                }
	                for(int k=j+1;k<i;k++){
	                    sb.append(words[k]);
	                    for(int m = 1;m<=divide;m++){
	                        sb.append(" ");
	                    }
	                }
	                sb.append(words[i]);
	                list.add(sb.toString());
	            }
	            i++;
	        }
	        return list;
	        
	    }
}
 


内容概要:本文介绍了一个基于MATLAB实现的无人机三维路径规划项目,采用蚁群算法(ACO)与多层感知机(MLP)相结合的混合模型(ACO-MLP)。该模型通过三维环境离散化建模,利用ACO进行全局路径搜索,并引入MLP对环境特征进行自适应学习与启发因子优化,实现路径的动态调整与多目标优化。项目解决了高维空间建模、动态障碍规避、局部最优陷阱、算法实时性及多目标权衡等关键技术难题,结合并行计算与参数自适应机制,提升了路径规划的智能性、安全性和工程适用性。文中提供了详细的模型架构、核心算法流程及MATLAB代码示例,涵盖空间建模、信息素更新、MLP训练与融合优化等关键步骤。; 适合人群:具备一定MATLAB编程基础,熟悉智能优化算法与神经网络的高校学生、科研人员及从事无人机路径规划相关工作的工程师;适合从事智能无人系统、自动驾驶、机器人导航等领域的研究人员; 使用场景及目标:①应用于复杂三维环境下的无人机路径规划,如城市物流、灾害救援、军事侦察等场景;②实现飞行安全、能耗优化、路径平滑与实时避障等多目标协同优化;③为智能无人系统的自主决策与环境适应能力提供算法支持; 阅读建议:此资源结合理论模型与MATLAB实践,建议读者在理解ACO与MLP基本原理的基础上,结合代码示例进行仿真调试,重点关注ACO-MLP融合机制、多目标优化函数设计及参数自适应策略的实现,以深入掌握混合智能算法在工程中的应用方法。
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