数字营销实训算法第三轮修改

src/main/java/com/sztzjy/marketing/controller/stu/StuExperimentalTrainingController.java

@@ -14,6 +14,7 @@ import com.sztzjy.marketing.mapper.StuAssessmentQuestionDetailsMapper;
 import com.sztzjy.marketing.mapper.StuImageRecognitionTrainingMapper;
 import com.sztzjy.marketing.mapper.StuLearningAssessmentMapper;
 import com.sztzjy.marketing.service.StuFiveGTrainingService;
+import com.sztzjy.marketing.service.UserBehaviorProfilingAnaService;
 import com.sztzjy.marketing.util.ConvertUtil;
 import com.sztzjy.marketing.util.ResultEntity;
 import io.swagger.annotations.Api;
@@ -24,6 +25,7 @@ import org.springframework.http.HttpStatus;
 import org.springframework.web.bind.annotation.*;
 
 
+import javax.annotation.Resource;
 import javax.servlet.http.HttpServletResponse;
 import java.util.*;
 import java.util.stream.Collectors;
@@ -51,6 +53,8 @@ public class StuExperimentalTrainingController {
 
     @Autowired
     private StuImageRecognitionTrainingMapper stuImageRecognitionTrainingMapper;
+    @Resource
+    UserBehaviorProfilingAnaService behaviorProfilingAnaService;
 
     @ApiOperation("什么是大数据/获取题目")
     @GetMapping("/bigDataTopic")
@@ -230,7 +234,7 @@ public class StuExperimentalTrainingController {
         }
 
 
-//        stuDigitalIndustrializationService.practicalTrainingSubmission(answers.get(0).getUserId(),"大数据","什么是大数据",count);
+        behaviorProfilingAnaService.practicalTrainingSubmission(answers.get(0).getUserId(),"什么是大数据",count);
 
 
 

src/main/java/com/sztzjy/marketing/service/impl/StuDigitalMarketingModelServiceImpl.java

@@ -1,6 +1,8 @@
 package com.sztzjy.marketing.service.impl;
 
 
+import com.google.gson.Gson;
+import com.google.gson.reflect.TypeToken;
 import com.hankcs.hanlp.HanLP;
 import com.hankcs.hanlp.seg.common.Term;
 import com.sztzjy.marketing.config.Constant;
@@ -88,7 +90,8 @@ public class StuDigitalMarketingModelServiceImpl implements StuDigitalMarketingM
         if(modelType.equals(Constant.COMMENT_EXTRACTION)){ //评论观点抽取
             String commentUrl = "https://aip.baidubce.com/rpc/2.0/nlp/v2/comment_tag?charset=UTF-8&access_token="+ getAccessToken();
 
-            RequestBody body = RequestBody.create(mediaType, "{\"text\":\""+content+"\",\"type\":8}");
+
+            RequestBody body = RequestBody.create(mediaType, "{\"text\":\""+content+"\",\"type\":\""+id+"\"}");
 
             Request request = new Request.Builder()
                     .url(commentUrl)
@@ -98,8 +101,26 @@ public class StuDigitalMarketingModelServiceImpl implements StuDigitalMarketingM
                     .build();
             Response response = HTTP_CLIENT.newCall(request).execute();
 
+            String string = response.body().string();
+            // Gson对象
+            Gson gson = new Gson();
+            Map<String, Object> data = gson.fromJson(string, new TypeToken<Map<String, Object>>(){}.getType());
+//            Object object = data.get("items");
+//
+//
+//            if(object instanceof List){
+//                ArrayList<String> tagList = (ArrayList<String>) object;
+//                if (tagList.isEmpty()) {
+//                    return new ResultEntity(HttpStatus.ACCEPTED,"请重新选择评论类型");
+//                }
+//            }
+            if(data.containsKey("error_msg")){
+                return new ResultEntity(HttpStatus.ACCEPTED,"评论异常");
+            }
             if (response.body() != null) {
-                return new ResultEntity(HttpStatus.OK,"成功",response.body().string());
+                return new ResultEntity(HttpStatus.OK, "成功", string);
+            }else {
+                return new ResultEntity(HttpStatus.ACCEPTED,"评论异常");
             }
 
         }
@@ -114,8 +135,17 @@ public class StuDigitalMarketingModelServiceImpl implements StuDigitalMarketingM
                     .build();
             Response response = HTTP_CLIENT.newCall(request).execute();
 
+            String string = response.body().string();
+            // Gson对象
+            Gson gson = new Gson();
+            Map<String, Object> data = gson.fromJson(string, new TypeToken<Map<String, Object>>(){}.getType());
+
+            if(data.containsKey("error_msg")){
+                return new ResultEntity(HttpStatus.ACCEPTED,"评论异常");
+            }
+
             if (response.body() != null) {
-                return new ResultEntity(HttpStatus.OK, "成功", response.body().string());
+                return new ResultEntity(HttpStatus.OK, "成功", string);
             }
 
 
@@ -171,15 +201,9 @@ public class StuDigitalMarketingModelServiceImpl implements StuDigitalMarketingM
     }
 
     // 过滤标点符号的函数
-    private String filterPunctuation(String originalContent) {
+    private String filterPunctuation(String content) {
-        // 创建一个正则表达式，用于匹配所有标点符号
+        //标点符号
-        String punctuationPattern = "[\\p{Punct}]";
+        return content.replaceAll("[，。、？！：;,.?!:'“”’‘]", "");
-        // 创建一个 Pattern 对象
-        Pattern pattern = Pattern.compile(punctuationPattern);
-        // 创建一个 Matcher 对象
-        Matcher matcher = pattern.matcher(originalContent);
-        // 使用 Matcher 的 replaceAll 方法替换所有匹配的标点符号为 ""
-        return matcher.replaceAll("");
     }
 
     @Override

src/main/java/com/sztzjy/marketing/util/algorithm/Apriori.java

@@ -41,7 +41,7 @@ public class Apriori {
 			}
 			result.add(rowData);
 		}
-		System.out.println("\nD:" + result);
+//		System.out.println("\nD:" + result);
 		return result;
 	}
 //	public static ArrayList<ArrayList<String>> readTable(MultipartFile file) {
@@ -161,10 +161,10 @@ public class Apriori {
 			}
 			str = C.toString();
 			
-			 System.out.println("候选"+t+"项集：C: \n"+C);
+//			 System.out.println("候选"+t+"项集：C: \n"+C);
 			// 二、剪枝步
 			pruning(C, L,min_support);
-			 System.out.println("频繁"+t+"项集：L: \n"+L+"\n");
+//			 System.out.println("频繁"+t+"项集：L: \n"+L+"\n");
 			 str = L.toString();
 			 
 			 //System.out.println("===");
@@ -219,11 +219,18 @@ public class Apriori {
 		List<String> lists=new ArrayList<>();
 
 		for (ArrayList<String> key : L_ALL.keySet()) {// 对最终的关联集各个事件进行判断
+			if (key != null && !key.isEmpty() && L_ALL != null) {
 				ArrayList<ArrayList<String>> key_allSubset = getSubset(key);
 				//得到所有频繁集中每个集合的子集
 				// System.out.println(key_allSubset);
 				for (int i = 0; i < key_allSubset.size(); i++) {
 					ArrayList<String> item_pre = key_allSubset.get(i);//得到一个真子集
+					if(item_pre.size()>1){
+						continue;
+					}
+					if(item_pre.isEmpty()){
+						continue;
+					}
 					if (0 < item_pre.size() && item_pre.size() < key.size()) {// 判断是否是非空真子集
 						// 各个非空互补真子集之间形成关联事件
 						double item_pre_support = L_ALL.get(item_pre);//得到真子集的支持度度
@@ -256,8 +263,8 @@ public class Apriori {
 
 
 
-								System.out.print(item_pre + "==>" + item_post );// 则是一个关联事件
+//								System.out.print(item_pre + "==>" + item_post );// 则是一个关联事件
-								System.out.println("==>" + confident);
+//								System.out.println("==>" + confident);
 
 
 
@@ -269,6 +276,8 @@ public class Apriori {
 					}
 				}
 			}
+
+		}
 		return list;
 	}
 	

src/main/java/com/sztzjy/marketing/util/algorithm/KMeansResult.java

@@ -228,13 +228,26 @@ public class KMeansResult {
 
         }
 
+        int a=0;
+        for (ArrayList<Float> point : cluster) {
+            a=point.size();
+        }
         float size = cluster.size();
         ArrayList<Float> centroid = new ArrayList<>();
-        centroid.add(0f);
+        for (int i = 0; i < a; i++) {
+            if(i==0){
                 centroid.add(sum1 / size);
+            }
+            if(i==1){
                 centroid.add(sum2 / size);
+            }
+            if(i==2){
                 centroid.add(sum3 / size);
+            }
+            if(i==3){
                 centroid.add(sum4 / size);
+            }
+        }
         return centroid;
     }