UVa 1593 - Alignment of Code

本文介绍了一种使用Java编程解决输出特定格式代码的方法,包括字符串数组的应用、边界控制和正则表达式的使用,旨在将多行代码按指定格式输出。

题目描述 : 输入若干行代码,按照要求格式输出,。各列单词尽量靠左,单词之间至少要一个空格。

思路 : 利用字符串数组找规律。  只要控制好边界其他的都很简单。  连测试用例都没有用,因为UVa网页老刷不出来。直接交代码QuickSubmit,只是担心会超时,但结果令人意外,竟然是AC。再来两道吧。      对了 我又不好意思的用了正则表达式。

代码 :

<p>import java.util.*;
import java.util.regex.Matcher;
import java.util.regex.Pattern;</p><p>public class Main1593 {</p><p> public static void main(String[] args) {
  Scanner scan = new Scanner(System.in);
  Pattern p = Pattern.compile("<a target=_blank href="file://\\S">\\S</a>+");
  String[][] str = new String[1005][1000];
  int rows = 0;
  int[] rowcnt = new int[1000];
  Arrays.fill(rowcnt, 0);
  while(scan.hasNextLine()) {
   String line = scan.nextLine();
   Matcher m = p.matcher(line);
   int cols = 0;
   while(m.find()) {
    rowcnt[rows] ++;
    str[rows][cols++] = m.group();
   }
   rows ++;
  }
  //System.out.println(rows);
  int[] maxlen = new int[850];
  Arrays.fill(maxlen, 0);
  for(int i=0; i<rows; i++) {
   for(int j=0; j<rowcnt[i]; j++) {
    maxlen[j] = max(maxlen[j], str[i][j].length());
   }
  }
  for(int i=0; i<rows; i++) {
   for(int j=0; j<rowcnt[i]; j++) {
    System.out.print(str[i][j]);
    if(j < rowcnt[i]-1)
    for(int k=0; k<=maxlen[j]-str[i][j].length(); k++) 
     System.out.print(" ");
   }
   System.out.println();
  }
 }
 public static int max(int a, int b) {
  if(a >= b)
   return a;
  else
   return b;
 }
}
</p>
### Soft-Alignment in Natural Language Processing Soft-alignment refers to a technique used primarily within the domains of natural language processing (NLP) and machine learning, where alignments between sequences are not strictly one-to-one but rather probabilistic or weighted across multiple elements[^2]. This approach allows models to capture more nuanced relationships between tokens from different sequences without enforcing rigid mapping constraints. In practice, soft-alignments can be implemented using attention mechanisms. Attention enables each position in an output sequence to attend over all positions in another input sequence with varying degrees of focus. The alignment scores determine how much weight should be given to corresponding parts when computing representations for target words during tasks like neural machine translation. #### Example Implementation Using Transformer Model's Self-Attention Mechanism Below is a simplified version demonstrating how self-attention could achieve soft-alignment: ```python import torch import torch.nn as nn class MultiHeadedAttention(nn.Module): def __init__(self, d_model, num_heads): super().__init__() assert d_model % num_heads == 0 self.d_k = d_model // num_heads self.num_heads = num_heads self.linears = clones(nn.Linear(d_model, d_model), 4) def forward(self, query, key, value, mask=None): batch_size = query.size(0) # Linear projections in batch from [batch_size, seq_len, d_model] -> [batch_size, h, seq_len, d_k] query, key, value = [ lin(x).view(batch_size, -1, self.num_heads, self.d_k).transpose(1, 2) for lin, x in zip(self.linears, (query, key, value)) ] # Apply attention on all the projected vectors in batch. x, _ = attention(query, key, value, mask=mask, dropout=self.dropout) # Concatenate heads and apply final linear transformation. x = ( x.transpose(1, 2) .contiguous() .view(batch_size, -1, self.h * self.d_k) ) del query, key, value return self.linears[-1](x) def attention(query, key, value, mask=None, dropout=None): "Compute 'Scaled Dot Product Attention'" d_k = query.size(-1) scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k) if mask is not None: scores = scores.masked_fill(mask == 0, -1e9) p_attn = F.softmax(scores, dim=-1) if dropout is not None: p_attn = dropout(p_attn) return torch.matmul(p_attn, value), p_attn ``` This code snippet illustrates part of what constitutes multi-headed attention—a core component facilitating soft-alignment by allowing queries to associate themselves softly with keys through scaled dot-product operations followed by softmax normalization. --related questions-- 1. How does hard-alignment differ fundamentally from soft-alignment? 2. Can you provide examples beyond NLP where soft-alignment proves beneficial? 3. What challenges arise specifically due to implementing soft-alignment techniques? 4. In which scenarios might traditional hard-alignment outperform its softer counterpart?
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