기본 콘텐츠로 건너뛰기

Spring 다른 웹사이트 요청


ConnectService.java
import java.io.BufferedReader;
import java.io.DataOutputStream;
import java.io.IOException;
import java.io.InputStreamReader;
import java.net.HttpURLConnection;
import java.net.URL;

import org.springframework.stereotype.Service;

@Service
public class ConnectService {
  private final String USER_AGENT = "Mozilla/5.0";
  private final String DATA = "test data";
 
  public String get(String url) throws IOException {
    URL url2 = new URL(url);
    HttpURLConnection connection = (HttpURLConnection) url2.openConnection();
   
    connection.setRequestMethod("GET");    // GET 방식 요청
    connection.setRequestProperty("User-Agent", USER_AGENT);
   
    int responseCode = connection.getResponseCode();
   
    BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(connection.getInputStream()));
    StringBuffer stringBuffer = new StringBuffer();
    String inputLine;
   
    while ((inputLine = bufferedReader.readLine()) != null)  {
      stringBuffer.append(inputLine);
    }
    bufferedReader.close();
   
    String response = stringBuffer.toString();
    System.out.println("response:"+response);
   
    return response;
  }
 
  public String post(String url) throws IOException {
    URL url2 = new URL(url);
    HttpURLConnection connection = (HttpURLConnection) url2.openConnection();
   
    connection.setRequestMethod("POST");     // POST 방식 요청
    connection.setRequestProperty("User-Agent", USER_AGENT);
    connection.setDoOutput(true);
   
    DataOutputStream outputStream = new DataOutputStream(connection.getOutputStream());
    outputStream.writeBytes(DATA);
    outputStream.flush();
    outputStream.close();
   
    int responseCode = connection.getResponseCode();
    System.out.println("resposneCode:"+responseCode);
   
    BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(connection.getInputStream()));
    StringBuffer stringBuffer = new StringBuffer();
    String inputLine;
   
    while ((inputLine = bufferedReader.readLine()) != null)  {
      stringBuffer.append(inputLine);
    }
    bufferedReader.close();
   
    String response = stringBuffer.toString();
    System.out.println("response:"+response);
   
    return response;
  }
}

ConnectController.java
import java.io.IOException;
import java.util.HashMap;
import java.util.Map;

import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Controller;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.ResponseBody;

import com.example.demo.connect.ConnectService;

@Controller
@RequestMapping("/connect")
public class ConnectController {
  @Autowired
  private ConnectService svc;
 
  @GetMapping("/json")
  @ResponseBody
  public String json() throws IOException {
    String response = svc.get("http://localhost:7878/json");
    return response;
  }
}


이 블로그의 인기 게시물

Blogger

코드 하이라이트 사이트 http://hilite.me/ 코드 <!-- 나만의 공간 --> <style id='daru_css' type='text/css'> .code {      overflow: auto;      height: 200px;      background-color: rgb(239,239,239);      border-radius: 10px;      padding: 5px 10px; } .code::-webkit-scrollbar-thumb {      background-color: grey;      border: 1px solid transparent;      border-radius: 10px;      background-clip: padding-box;   } .code::-webkit-scrollbar {      width: 15px; } </style> <!-- 나만의 공간 -->

Python Sklearn make_blobs

from sklearn.datasets import make_blobs 예제 X, y = make_blobs(n_samples=500, centers=3, n_features=2, random_state=0) # 500개의 점을 3개로 모이게 한다, 변수는 2개, 무작위 상태는 0 X.shape, y.shape # ((500, 2), (500,)) plt.scatter(X[:,0],X[:,1],c=y,s=5) plt.show() # 학습 데이터 나누기 from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=.25, random_state=0) x_train.shape, x_test.shape, y_train.shape, y_test.shape # ((375, 2), (125, 2), (375,), (125,)) # 지도 학습 하기 from sklearn.linear_model import LogisticRegression logisticReg = LogisticRegression(max_iter=5000) # 기본 반복 100 logisticReg.fit(x_train, y_train) # 추정하기 pred = logisticReg.predict(X) # 결정계수 logisticReg.score(x_test, y_test) # 0.92 # 한글 깨짐 없이 나오게 설정 from matplotlib import rcParams # 인코딩 폰트 설정 rcParams['font.family'] = 'New Gulim' rcParams['font.size'] = 10 # 산점도 plt.figure(figsize=(10,4)) plt.subplot(1,2, 1) plt.scatter(X[:,0],X[:,1],c=y) plt.title('정답') plt.su...

Python 문법

제곱 c = c**2; 주석 # 주석 함수 # 함수 형식 def hello(): # 함수 선언     print("여기는 함수") # 함수 실행문 hello() # 함수 호출 #결과: 여기는 함수 def add(a,b): # 매개변수에 자료형이 필요없다     c = a+b     print(f"{a} + {b} = {c}") add(3,5) #결과 : 3 + 5 = 8 if문 if a > b:     print("a가 큽니다") 객체의 정보 dir(객체) 객체의 주소 id(객체) 생략 if 'a' == 'a':     pass # 생략 else:     pass # 생략 enumerate for i,v in enumerate(range(20, 26)):     print(i,v) display display(df)