1071 Speech Patterns (25 分)

本文展示了一个使用C++进行文本处理和单词频率统计的示例程序。该程序读取文本文件,将所有字符转换为小写,过滤掉非字母数字字符,并统计每个单词出现的次数,最后输出出现频率最高的单词及其数量。

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#include<bits/stdc++.h>
using namespace std;
int main()
{
	#ifndef ONLINE_JUDGE
	freopen("in.txt","r",stdin);
	#endif
	string s;
	getline(cin,s);
	transform(s.begin(),s.end(),s.begin(),::tolower);
	map<string,int> hash;
	string temp;
	for(int i=0;i<s.length();i++){
		if((s[i]<='9'&&s[i]>='0')||(s[i]>='a'&&s[i]<='z')){
			temp=temp+s[i];
		}
		if(!isalnum(s[i])||i==s.length()-1){//如果不加i==s.length()-1这一句,如果最后一个字符是字母或数字,就会忽略掉最后一个字母
			if(temp!="")
			hash[temp]++;
			temp.erase(0,temp.size());
		}
	}
	map<string,int>::iterator it;
	string maxstring;
	int maxnum=0;
	for(it=hash.begin();it!=hash.end();it++){
		if(it->second>maxnum){
			maxnum=it->second;
			maxstring=it->first;
		}
	}
	cout<<maxstring<<' '<<maxnum;
	return 0;
}
  

 

### Sequence Classification in Computer Science In computer science, sequence classification refers to a type of problem where models are trained to assign labels or categories to sequences of data points. This task is crucial across various domains including natural language processing (NLP), bioinformatics, speech recognition, and more. #### Definition and Importance Sequence classification involves predicting discrete class labels for entire sequences rather than individual elements within them. For instance, identifying whether an email message should be classified as spam based on its content constitutes one application area[^2]. The ability to automate such classifications significantly impacts how efficiently systems handle large volumes of sequential data. #### Techniques Used Several techniques have been developed specifically targeting this challenge: - **Recurrent Neural Networks (RNNs)**: These networks maintain internal states that allow information from previous steps to influence future predictions, making RNN suitable candidates when dealing with temporal dependencies present in many types of sequences. - **Convolutional Neural Networks (CNNs)** applied over time-series-like structures also prove effective due to their capacity to capture local patterns while being computationally efficient compared to traditional methods like Hidden Markov Models used earlier in fields such as computational biology[^3]. - **Transformers**: Introduced initially for NLP tasks but now widely adopted beyond text-based applications because they excel at handling long-range dependencies without suffering vanishing gradient problems associated with deep recurrent architectures. ```python import torch.nn as nn class SimpleLSTM(nn.Module): def __init__(self, input_dim, hidden_dim, output_dim): super(SimpleLSTM, self).__init__() self.lstm = nn.LSTM(input_dim, hidden_dim, batch_first=True) self.fc = nn.Linear(hidden_dim, output_dim) def forward(self, x): lstm_out, _ = self.lstm(x) out = self.fc(lstm_out[:, -1, :]) return out ``` This code snippet demonstrates a simple LSTM model which could serve as
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