1071 Speech Patterns (25分)

本文探讨了通过分析个人言语偏好来识别说话者身份的方法,特别关注于如何从文本样本中找出最常使用的词汇,这对于在线身份验证至关重要。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

People often have a preference among synonyms of the same word. For example, some may prefer "the police", while others may prefer "the cops". Analyzing such patterns can help to narrow down a speaker's identity, which is useful when validating, for example, whether it's still the same person behind an online avatar.

Now given a paragraph of text sampled from someone's speech, can you find the person's most commonly used word?

Input Specification:

Each input file contains one test case. For each case, there is one line of text no more than 1048576 characters in length, terminated by a carriage return \n. The input contains at least one alphanumerical character, i.e., one character from the set [0-9 A-Z a-z].

Output Specification:

For each test case, print in one line the most commonly occurring word in the input text, followed by a space and the number of times it has occurred in the input. If there are more than one such words, print the lexicographically smallest one. The word should be printed in all lower case. Here a "word" is defined as a continuous sequence of alphanumerical characters separated by non-alphanumerical characters or the line beginning/end.

Note that words are case insensitive.

Sample Input:

Can1: "Can a can can a can?  It can!"

Sample Output:

can 5

题意:找出现频次最多的单词,输出小写表示且输出次数

思路:神坑,最后一个节点:句子中最后一个字符不是非法字符时,就会造成少统计这个词语,需要在for循环后再判断一次,或者用个骚操作,把for循环中的i的条件变为等于s的长度,因为最后一个字符肯定是"\n",所以就可以骚一波了~

#include<iostream>

#include<cstring>
#include<unordered_map>
#include<map>

using namespace std;

int main(){
    unordered_map<string, int> un_map;
    // map<string, int> un_map;
    string s;
    getline(cin, s);
    string temp_s = "";
    bool flag = false;

    for(int i = 0; i <= s.length(); i++){
        
        if(isalnum(s[i])){
            if(isalpha(s[i])){

                temp_s += tolower(s[i]);
                
            }else
            {
                temp_s += s[i];
            }
            
            flag = true;

        }else
        {
            if(flag && temp_s != ""){
                un_map[temp_s]++;
                flag = false;
    
            }
            temp_s = "";
        }
        

    }
    int max = -1;
    string out_s = "";
    for(auto i : un_map){
        if(i.second > max){

            max = i.second;
            out_s = i.first;
        }
        else if(i.second == max && i.first < out_s)
        {
            out_s = i.first;
        }
        

    }
    printf("%s %d", out_s.c_str(), max);
    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
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值