Merge branch 'master' of http://118.31.7.2:3000/tzjy-code/digital_marketing

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

@@ -30,7 +30,7 @@ import java.math.BigDecimal;
 import java.text.DecimalFormat;
 import java.util.*;
 
-import static com.sztzjy.marketing.util.algorithm.KMeans.readIrisData;
+import static com.sztzjy.marketing.util.algorithm.KMeans.*;
 import static com.sztzjy.marketing.util.algorithm.KMeansResult.*;
 
 /**
@@ -62,8 +62,9 @@ public class StuDigitalMarketingModelController {
     @PostMapping("/viewMetrics")
     @AnonymousAccess
     public ResultEntity viewMetrics(@ApiParam("用户ID") String userId,
-                                    @ApiParam("表格名") String tableName) {
-        List<String> list=modelService.viewMetrics(userId,tableName);
+                                    @ApiParam("表格名") String tableName,
+                                    @ApiParam("算法名称") String algorithmName) {
+        List<String> list=modelService.viewMetrics(userId,tableName,algorithmName);
         return new ResultEntity(HttpStatus.OK,"成功",list);
     }
 
@@ -112,7 +113,9 @@ public class StuDigitalMarketingModelController {
         Integer k = clusterScatterPlotDTO.getK();
         List<Map<String, Object>> deduplicatedDataList = clusterScatterPlotDTO.getDeduplicatedDataList();
 
-        List<KMeans.Point> irisData = readIrisData(deduplicatedDataList);
+        List<String> strings = convertToDelimitedStringList(deduplicatedDataList);
+
+        List<KMeans.Point> irisData = readIrisData(strings);
 
         //获取数据集
         KMeans kMeans = new KMeans(k, t, irisData);
@@ -148,12 +151,16 @@ public class StuDigitalMarketingModelController {
     @ApiOperation("聚类分析--分析结果")
     @PostMapping("/clusterAnalysisResult")
     @AnonymousAccess
-    public ResultEntity clusterAnalysisResult(@ApiParam("簇数") Integer k,
-                                              @ApiParam("最大迭代次数") Integer t,
-                                              @ApiParam("最大迭代次数") String userId,
-                                              @RequestParam(required = false) @RequestPart MultipartFile file) {
+    public ResultEntity clusterAnalysisResult(@RequestBody ClusterScatterPlotDTO clusterScatterPlotDTO) {
+
+        Integer t = clusterScatterPlotDTO.getT();
+        Integer k = clusterScatterPlotDTO.getK();
+        List<Map<String, Object>> deduplicatedDataList = clusterScatterPlotDTO.getDeduplicatedDataList();
+
+        List<String> strings = convertToDelimitedStringList(deduplicatedDataList);
+
         //初始化数据
-        ArrayList<ArrayList<Float>> arrayLists = readTable(file);
+        ArrayList<ArrayList<Float>> arrayLists = readTable(strings);
 
 
         //随机选择 k 个数据点作为初始聚类中心
@@ -163,7 +170,7 @@ public class StuDigitalMarketingModelController {
         ArrayList<ArrayList<ArrayList<Float>>> kmeans = kmeans(k, t, centerList);
 
 
-        return new ResultEntity(HttpStatus.OK);
+        return new ResultEntity(HttpStatus.OK,kmeans);
     }
 
 
@@ -171,20 +178,23 @@ public class StuDigitalMarketingModelController {
     @ApiOperation("关联规则挖掘")
     @PostMapping("/apriori")
     @AnonymousAccess
-    public ResultEntity apriori(@ApiParam("最小支持度阀值") double support,
-                                @ApiParam("最小置信度") double confidence,
-                                @ApiParam("用户ID") String userId,
-                                @RequestParam(required = false) @RequestPart MultipartFile file) throws IOException {
+    public ResultEntity apriori(@RequestBody AprioriDTO aprioriDTO) throws IOException {
+
+        double support = aprioriDTO.getSupport();
+        double confidence = aprioriDTO.getConfidence();
+        String userId = aprioriDTO.getUserId();
+        List<Map<String, Object>> deduplicatedDataList = aprioriDTO.getDeduplicatedDataList();
 
 
+        List<String> strings = convertToDelimitedStringList(deduplicatedDataList);
         //初始化事务数据库、项目集、候选集再进行剪枝
-        Apriori.init(file,support);
+        Apriori.init(strings,support);
 
         //迭代求出最终的候选频繁集
         Apriori.iteration(Apriori.C,Apriori.L,support);
 
         //根据最终的关联集，根据公式计算出各个关联事件
-        List<AssociationRulesDTO> connection = Apriori.connection(confidence);
+        List<String> connection = Apriori.connection(confidence);
 
 
         return new ResultEntity(HttpStatus.OK,"成功",connection);
@@ -202,26 +212,38 @@ public class StuDigitalMarketingModelController {
         LinearRegression regression=new LinearRegression();
         regression.fit(x,y);
 
+        double[] slopes = regression.getSlopes();
+
+        RAnalysisDTO rAnalysisDTO=new RAnalysisDTO();
+        rAnalysisDTO.setIntercept(regression.getIntercept());
+        for (int i = 0; i < slopes.length; i++) {
+            rAnalysisDTO.setSlopes(slopes[i]);
+        }
 
 
-        return new ResultEntity(HttpStatus.OK,"成功",regression);
+        return new ResultEntity(HttpStatus.OK,"成功",rAnalysisDTO);
     }
 
 
-//    @ApiOperation("回归分析--逻辑回归")
-//    @PostMapping("/logistic")
-//    @AnonymousAccess
-//    public ResultEntity logistic(@RequestBody LogisticDTO logisticDTO) {
-//        double[] x = logisticDTO.getX();
-//        double[] y = logisticDTO.getY();
-//
-//        // 创建线性回归模型
-//        LogisticRegression regression=new LogisticRegression();
-//        regression.fit(x,y);
-//
-//
-//        return new ResultEntity(HttpStatus.OK,"成功",regression);
-//    }
+    @ApiOperation("回归分析--逻辑回归")
+    @PostMapping("/logistic")
+    @AnonymousAccess
+    public ResultEntity logistic(@RequestBody LogisticDTO logisticDTO) {
+        double[][] x = logisticDTO.getX();
+        double[] y = logisticDTO.getY();
+
+        // 创建一个逻辑回归模型实例,0.1学习率
+        LogisticRegression model = new LogisticRegression(0.1);
+        model.train(x,y,1000); //训练模型
+
+        DecimalFormat df = new DecimalFormat("#.0");
+        RAnalysisDTO rAnalysisDTO=new RAnalysisDTO();
+
+        rAnalysisDTO.setIntercept(Double.parseDouble(df.format(model.getIntercept())));
+        rAnalysisDTO.setSlopes(Double.parseDouble(df.format(model.getCoefficient())));
+
+        return new ResultEntity(HttpStatus.OK,"成功",rAnalysisDTO);
+    }
 
 
     @ApiOperation("情感分析/文本挖掘")
@@ -237,7 +259,7 @@ public class StuDigitalMarketingModelController {
     @ApiOperation("批量导入")
     @PostMapping("/batchImport")
     @AnonymousAccess
-    public ResultEntity batchImport(@RequestPart MultipartFile file) {
+    public ResultEntity batchImport(@RequestParam(required = false) @RequestPart MultipartFile file) {
 
         //验证文件类型
         if (!file.getOriginalFilename().substring(file.getOriginalFilename().lastIndexOf(".")).equals(".xls") && !file.getOriginalFilename().substring(file.getOriginalFilename().lastIndexOf(".")).equals(".xlsx")) {
@@ -262,10 +284,10 @@ public class StuDigitalMarketingModelController {
 
                 StuSpendingLevel stuSpendingLevel=new StuSpendingLevel();
                 stuSpendingLevel.setId(Integer.valueOf((String) list.get(0)));
-                stuSpendingLevel.setGender(Integer.valueOf((String) list.get(0)));
-                stuSpendingLevel.setAge(Integer.valueOf((String) list.get(0)));
-                stuSpendingLevel.setAnnualIncome(Integer.valueOf((String) list.get(0)));
-                stuSpendingLevel.setSpendingScore(Integer.valueOf((String) list.get(0)));
+                stuSpendingLevel.setGender(Integer.valueOf((String) list.get(1)));
+                stuSpendingLevel.setAge(Integer.valueOf((String) list.get(2)));
+                stuSpendingLevel.setAnnualIncome(Integer.valueOf((String) list.get(3)));
+                stuSpendingLevel.setSpendingScore(Integer.valueOf((String) list.get(4)));
 
 
 

src/main/java/com/sztzjy/marketing/entity/StuUserLoginActive.java

@@ -2,7 +2,6 @@ package com.sztzjy.marketing.entity;
 
 import java.util.Date;
 
-import io.swagger.annotations.ApiModel;
 import io.swagger.annotations.ApiModelProperty;
 /**
  * 用户登录活跃表
@@ -10,7 +9,6 @@ import io.swagger.annotations.ApiModelProperty;
  * @author whb
  * stu_user_login_active
  */
-@ApiModel("用户登录活跃表")
 public class StuUserLoginActive {
     @ApiModelProperty("id")
     private Integer id;
@@ -48,8 +46,8 @@ public class StuUserLoginActive {
     @ApiModelProperty("登录频次")
     private Integer loginFrequency;
 
-    @ApiModelProperty("登录时长")
-    private String loginDuration;
+    @ApiModelProperty("登录时长(分钟)")
+    private Double loginDuration;
 
     @ApiModelProperty("创建时间")
     private Date createTime;
@@ -153,12 +151,12 @@ public class StuUserLoginActive {
         this.loginFrequency = loginFrequency;
     }
 
-    public String getLoginDuration() {
+    public Double getLoginDuration() {
         return loginDuration;
     }
 
-    public void setLoginDuration(String loginDuration) {
-        this.loginDuration = loginDuration == null ? null : loginDuration.trim();
+    public void setLoginDuration(Double loginDuration) {
+        this.loginDuration = loginDuration;
     }
 
     public Date getCreateTime() {

src/main/java/com/sztzjy/marketing/entity/StuUserLoginActiveExample.java

@@ -925,62 +925,52 @@ public class StuUserLoginActiveExample {
             return (Criteria) this;
         }
 
-        public Criteria andLoginDurationEqualTo(String value) {
+        public Criteria andLoginDurationEqualTo(Double value) {
             addCriterion("login_duration =", value, "loginDuration");
             return (Criteria) this;
         }
 
-        public Criteria andLoginDurationNotEqualTo(String value) {
+        public Criteria andLoginDurationNotEqualTo(Double value) {
             addCriterion("login_duration <>", value, "loginDuration");
             return (Criteria) this;
         }
 
-        public Criteria andLoginDurationGreaterThan(String value) {
+        public Criteria andLoginDurationGreaterThan(Double value) {
             addCriterion("login_duration >", value, "loginDuration");
             return (Criteria) this;
         }
 
-        public Criteria andLoginDurationGreaterThanOrEqualTo(String value) {
+        public Criteria andLoginDurationGreaterThanOrEqualTo(Double value) {
             addCriterion("login_duration >=", value, "loginDuration");
             return (Criteria) this;
         }
 
-        public Criteria andLoginDurationLessThan(String value) {
+        public Criteria andLoginDurationLessThan(Double value) {
             addCriterion("login_duration <", value, "loginDuration");
             return (Criteria) this;
         }
 
-        public Criteria andLoginDurationLessThanOrEqualTo(String value) {
+        public Criteria andLoginDurationLessThanOrEqualTo(Double value) {
             addCriterion("login_duration <=", value, "loginDuration");
             return (Criteria) this;
         }
 
-        public Criteria andLoginDurationLike(String value) {
-            addCriterion("login_duration like", value, "loginDuration");
-            return (Criteria) this;
-        }
-
-        public Criteria andLoginDurationNotLike(String value) {
-            addCriterion("login_duration not like", value, "loginDuration");
-            return (Criteria) this;
-        }
-
-        public Criteria andLoginDurationIn(List<String> values) {
+        public Criteria andLoginDurationIn(List<Double> values) {
             addCriterion("login_duration in", values, "loginDuration");
             return (Criteria) this;
         }
 
-        public Criteria andLoginDurationNotIn(List<String> values) {
+        public Criteria andLoginDurationNotIn(List<Double> values) {
             addCriterion("login_duration not in", values, "loginDuration");
             return (Criteria) this;
         }
 
-        public Criteria andLoginDurationBetween(String value1, String value2) {
+        public Criteria andLoginDurationBetween(Double value1, Double value2) {
             addCriterion("login_duration between", value1, value2, "loginDuration");
             return (Criteria) this;
         }
 
-        public Criteria andLoginDurationNotBetween(String value1, String value2) {
+        public Criteria andLoginDurationNotBetween(Double value1, Double value2) {
             addCriterion("login_duration not between", value1, value2, "loginDuration");
             return (Criteria) this;
         }

src/main/java/com/sztzjy/marketing/entity/dto/AprioriDTO.java

@@ -0,0 +1,23 @@
+package com.sztzjy.marketing.entity.dto;
+
+import io.swagger.annotations.ApiModelProperty;
+import lombok.Data;
+
+import java.util.List;
+import java.util.Map;
+
+/**
+ * @author tz
+ * @date 2024/8/17 14:16
+ */
+@Data
+public class AprioriDTO {
+    @ApiModelProperty("最小支持度阀值")
+    private double support;
+    @ApiModelProperty("最小置信度")
+    private double confidence;
+    @ApiModelProperty("用户ID")
+    private String userId;
+    @ApiModelProperty("数据集")
+    private List<Map<String, Object>> deduplicatedDataList;
+}

src/main/java/com/sztzjy/marketing/entity/dto/DescriptiveStatistics.java

@@ -18,7 +18,7 @@ public class DescriptiveStatistics {
     private double median;
 
     @ApiModelProperty("众数")
-    private List<Double> mode;
+    private double mode;
 
     @ApiModelProperty("标准差")
     private double standardDeviation;

src/main/java/com/sztzjy/marketing/entity/dto/LogisticDTO.java

@@ -11,7 +11,7 @@ import lombok.Data;
 public class LogisticDTO {
 
     @ApiModelProperty("自变量x")
-    private double[] x;
+    private double[][] x;
 
     @ApiModelProperty("因变量y")
     private double[] y;

src/main/java/com/sztzjy/marketing/entity/dto/RAnalysisDTO.java

@@ -0,0 +1,13 @@
+package com.sztzjy.marketing.entity.dto;
+
+import lombok.Data;
+
+/**
+ * @author tz
+ * @date 2024/8/17 16:30
+ */
+@Data
+public class RAnalysisDTO {
+    private double intercept;
+    private double slopes;
+}

src/main/java/com/sztzjy/marketing/mapper/StuUserBehaviorMapper.java

@@ -3,10 +3,8 @@ package com.sztzjy.marketing.mapper;
 import com.sztzjy.marketing.entity.StuUserBehavior;
 import com.sztzjy.marketing.entity.StuUserBehaviorExample;
 import java.util.List;
-
-import org.apache.ibatis.annotations.Mapper;
 import org.apache.ibatis.annotations.Param;
-@Mapper
+
 public interface StuUserBehaviorMapper {
     long countByExample(StuUserBehaviorExample example);
 
@@ -29,6 +27,4 @@ public interface StuUserBehaviorMapper {
     int updateByPrimaryKeySelective(StuUserBehavior record);
 
     int updateByPrimaryKey(StuUserBehavior record);
-
-    List<StuUserBehavior> selectByFields(List<String> fieldList, String table);
 }

src/main/java/com/sztzjy/marketing/mapper/StuUserLoginActiveMapper.java

@@ -27,6 +27,4 @@ public interface StuUserLoginActiveMapper {
     int updateByPrimaryKeySelective(StuUserLoginActive record);
 
     int updateByPrimaryKey(StuUserLoginActive record);
-
-    List<StuUserLoginActive> selectByFields(List<String> fieldList, String table);
 }

src/main/java/com/sztzjy/marketing/service/StuDigitalMarketingModelService.java

@@ -20,7 +20,7 @@ public interface StuDigitalMarketingModelService {
 
     ResultEntity descriptiveStatistics(Map<String, List<Double>> map, List<String> statistic, String userId);
 
-    List<String> viewMetrics(String userId, String tableName);
+    List<String> viewMetrics(String userId, String tableName,String algorithmName);
 
     ResultEntity viewAnalyzeData(AnalyzeDataDTO analyzeDataDTO);
 

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

@@ -19,6 +19,7 @@ import org.springframework.stereotype.Service;
 
 import javax.annotation.Resource;
 import java.io.IOException;
+import java.text.DecimalFormat;
 import java.time.Instant;
 import java.time.LocalDateTime;
 import java.time.ZoneId;
@@ -190,13 +191,14 @@ public class StuDigitalMarketingModelServiceImpl implements StuDigitalMarketingM
 
                 }
                 if(statistic.get(i).equals("众数")){
-                    List<Double> mode = statisticsUtil.getMode(map.get(key));
+                    Double mode = statisticsUtil.getMode(map.get(key));
                     statistics.setMode(mode);
 
                 }
                 if(statistic.get(i).equals("标准差")){
                     double standardDeviation = statisticsUtil.getStandardDeviation(map.get(key));
-                    statistics.setStandardDeviation(standardDeviation);
+                    DecimalFormat df = new DecimalFormat("#.0");
+                    statistics.setStandardDeviation(Double.parseDouble(df.format(standardDeviation)));
 
                 }
                 if(statistic.get(i).equals("方差")){
@@ -211,12 +213,24 @@ public class StuDigitalMarketingModelServiceImpl implements StuDigitalMarketingM
                 }
                 if(statistic.get(i).equals("峰度")){
                     double kurtosis = statisticsUtil.getKurtosis(map.get(key));
-                    statistics.setKurtosis(kurtosis);
+
+                    if(Double.isNaN(kurtosis)){
+                        statistics.setKurtosis(0);
+                    }else {
+                        statistics.setKurtosis(kurtosis);
+
+                    }
 
                 }
                 if(statistic.get(i).equals("偏度")){
                     double skewness = statisticsUtil.getSkewness(map.get(key));
-                    statistics.setSkewness(skewness);
+
+                    if(Double.isNaN(skewness)){
+                        statistics.setSkewness(0);
+                    }else {
+                        statistics.setSkewness(skewness);
+                    }
+
 
                 }
                 if(statistic.get(i).equals("最大值")){
@@ -248,7 +262,7 @@ public class StuDigitalMarketingModelServiceImpl implements StuDigitalMarketingM
     }
 
     @Override
-    public List<String> viewMetrics(String userId, String tableName) {
+    public List<String> viewMetrics(String userId, String tableName,String algorithmName) {
         List<String> list=new ArrayList<>();
 
         if(tableName.equals(Constant.YHSXB)){
@@ -258,14 +272,33 @@ public class StuDigitalMarketingModelServiceImpl implements StuDigitalMarketingM
             list.add("role_age");
         }
         if(tableName.equals(Constant.YHDLHYB)){
-            list=indicatorsMapper.getYHDLHYB();
+//            list=indicatorsMapper.getYHDLHYB();
+            list.add("id");
+            list.add("login_frequency");
+            list.add("login_duration");
 
         }
         if(tableName.equals(Constant.YHXFNLB)){
-            list=indicatorsMapper.getYHXFNLB();
+//            list=indicatorsMapper.getYHXFNLB();
+                if(algorithmName.equals("关联规则挖掘")){
+                    list.add("consumer_goods");
+                } else {
+                list.add("id");
+                list.add("consumer_amount");
+                list.add("consumer_number");
+                list.add("consumer_past_seven_days_amount");
+                list.add("consumer_past_seven_days_number");
+            }
+
         }
         if(tableName.equals(Constant.YHPLB) || tableName.equals(Constant.YHXWB)){
-            list=indicatorsMapper.getYHPLB();
+//            list=indicatorsMapper.getYHPLB();
+                if(algorithmName.equals("关联规则挖掘")){
+                    list.add("goods_name");
+            }else {
+                list.add("id");
+                list.add("user_behavior_type");
+            }
         }
         return list;
     }
@@ -285,7 +318,7 @@ public class StuDigitalMarketingModelServiceImpl implements StuDigitalMarketingM
         }
 
         if(analyzeDataDTO.getTableName().equals(Constant.YHXFNLB)){ //查询用户消费能力表
-            table="stu_spending_level";
+            table="stu_user_consumption_ability";
         }
 
         if(analyzeDataDTO.getTableName().equals(Constant.YHPLB) || analyzeDataDTO.getTableName().equals(Constant.YHXWB)){ //查询用户评论或行为表

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

@@ -30,25 +30,39 @@ public class Apriori {
 	// 用于存取候选集每次计算结果（即存放所有的频繁项集L），最后计算关联规则，就不用再次遍历事务数据库。
 	static HashMap<ArrayList<String>, Integer> L_ALL = new HashMap<ArrayList<String>, Integer>();
 	//从文件中读取内容,返回事务集
-	public static ArrayList<ArrayList<String>> readTable(MultipartFile file) {
-		ArrayList<ArrayList<String>> t = new ArrayList<>();
-		try (InputStream inputStream = file.getInputStream();
-			 Workbook workbook = new XSSFWorkbook(inputStream)) {
-			Sheet sheet = workbook.getSheetAt(0);
-			for (Row row : sheet) {
-				ArrayList<String> rowData = new ArrayList<>();
-				for (Cell cell : row) {
-					rowData.add(cell.getStringCellValue());
-				}
-				t.add(rowData);
+
+	public static ArrayList<ArrayList<String>> readTable(List<String> inputList) {
+		ArrayList<ArrayList<String>> result = new ArrayList<>();
+		for (String line : inputList) {
+			String[] values = line.split(",");
+			ArrayList<String> rowData = new ArrayList<>();
+			for (String value : values) {
+				rowData.add(value);
 			}
-		} catch (IOException e) {
-			e.printStackTrace();
-			System.out.println("文件读取失败！");
+			result.add(rowData);
 		}
-		System.out.println("\nD:" + t);
-		return t;
+		System.out.println("\nD:" + result);
+		return result;
 	}
+//	public static ArrayList<ArrayList<String>> readTable(MultipartFile file) {
+//		ArrayList<ArrayList<String>> t = new ArrayList<>();
+//		try (InputStream inputStream = file.getInputStream();
+//			 Workbook workbook = new XSSFWorkbook(inputStream)) {
+//			Sheet sheet = workbook.getSheetAt(0);
+//			for (Row row : sheet) {
+//				ArrayList<String> rowData = new ArrayList<>();
+//				for (Cell cell : row) {
+//					rowData.add(cell.getStringCellValue());
+//				}
+//				t.add(rowData);
+//			}
+//		} catch (IOException e) {
+//			e.printStackTrace();
+//			System.out.println("文件读取失败！");
+//		}
+//		System.out.println("\nD:" + t);
+//		return t;
+//	}
 
 	//剪枝步：从候选集C中删除小于最小支持度的，并放入频繁集L中
 	public static void pruning(HashMap<ArrayList<String>, Integer> C,HashMap<ArrayList<String>, Integer> L,double min_support) {
@@ -70,9 +84,9 @@ public class Apriori {
 	/**
 	 * 初始化事务数据库、项目集、候选集
 	 */
-	public static void init(MultipartFile file,double min_support) throws IOException {
+	public static void init( List<String> strings,double min_support) throws IOException {
 //		//将文件中的数据放入集合D中
-		D = readTable(file);
+		D = readTable(strings);
 		// 扫描事务数据库。生成项目集，支持度=该元素在事务数据库出现的次数/事务数据库的事务数
 		for (int i = 0; i < D.size(); i++) {
 			for (int j = 0; j < D.get(i).size(); j++) {
@@ -198,10 +212,12 @@ public class Apriori {
 	/**
 	 * 根据最终的关联集，根据公式计算出各个关联事件
 	 */
-	public static List<AssociationRulesDTO> connection(double min_confident) {
+	public static List<String> connection(double min_confident) {
 
 		List<AssociationRulesDTO> list=new ArrayList<>();
 
+		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
 			ArrayList<ArrayList<String>> key_allSubset = getSubset(key);
 			//得到所有频繁集中每个集合的子集
@@ -235,8 +251,14 @@ public class Apriori {
 
 								list.add(associationRulesDTO);
 
+
+
 								System.out.print(item_pre + "==>" + item_post );// 则是一个关联事件
 								System.out.println("==>" + confident);
+
+								String string=item_pre + "==>" + item_post+"==>" + confident;
+
+								lists.add(string);
 							}
 						}
  
@@ -244,7 +266,7 @@ public class Apriori {
 				}
 			}
 		}
-		return list;
+		return lists;
 	}
 	
 	public static void main(String[] args) {

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

@@ -0,0 +1,11 @@
+package com.sztzjy.marketing.util.algorithm;
+
+public class Data {
+    public double[] features;
+    public double label;
+
+    public Data(double[] features, double label) {
+        this.features = features;
+        this.label = label;
+    }
+}

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

@@ -32,7 +32,7 @@ public class DescriptiveStatisticsUtil {
 
     // 计算众数
 
-    public List<Double> getMode(List<Double> data) {
+    public Double getMode(List<Double> data) {
         HashMap map = new HashMap();
         double imode = 0;
         for (int i = 0; i < data.size(); i++) {
@@ -57,7 +57,10 @@ public class DescriptiveStatisticsUtil {
                 lst.add(key);
             }
         }
-        return lst;
+
+        Object object = lst.get(0);
+        Double v = Double.valueOf(object.toString());
+        return v;
     }
 
 
@@ -90,6 +93,7 @@ public class DescriptiveStatisticsUtil {
     public double getKurtosis(List<Double> data) {
         DescriptiveStatistics stats = new DescriptiveStatistics();
         for (double value : data) {
+
             stats.addValue(value);
         }
         return stats.getKurtosis();
@@ -101,7 +105,8 @@ public class DescriptiveStatisticsUtil {
         for (double value : data) {
             stats.addValue(value);
         }
-        return stats.getSkewness();
+        double skewness = stats.getSkewness();
+        return skewness;
     }
 
     // 获取最大值

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

@@ -6,10 +6,9 @@ import java.io.BufferedReader;
 import java.io.FileReader;
 import java.io.IOException;
 import java.io.InputStreamReader;
-import java.util.ArrayList;
-import java.util.List;
-import java.util.Map;
-import java.util.Random;
+import java.util.*;
+import java.util.stream.Collectors;
+import java.util.stream.Stream;
 
 public class KMeans {
 
@@ -134,7 +133,7 @@ public class KMeans {
         }
     }
 
-//    public static void main(String[] args) {
+    public static void main(String[] args) {
 //        List<KMeans.Point> irisData = readIrisData("D:\\home\\marketing\\Iris.txt");
 //        int k = 3; // 假设聚为3类
 //        int maxIterations = 100;
@@ -151,22 +150,35 @@ public class KMeans {
 //        for (int i = 0; i < centroids.size(); i++) {
 //            System.out.println("Centroid for cluster " + i + ": " + centroids.get(i));
 //        }
-//    }
 
-    public static List<KMeans.Point> readIrisData(List<Map<String, Object>> deduplicatedDataList) {
-        List<KMeans.Point> points = new ArrayList<>();
-        for (Map<String, Object> data : deduplicatedDataList) {
-            // 假设每个Map都有"x"和"y"键，并且它们的值是Double类型
-            // 这里没有错误处理，你可能需要添加一些来确保键存在且值可以转换为Double
-            Double x = (Double) data.get("x");
-            Double y = (Double) data.get("y");
-
-            if (x != null && y != null) {
-                points.add(new KMeans.Point(x, y));
-            } else {
-                // 处理缺少x或y的情况，比如记录日志或抛出异常
-                System.err.println("数据中缺少x或y: " + data);
+    }
+
+    public static List<String> convertToDelimitedStringList(List<Map<String, Object>> nestedList) {
+        List<String> flatList = new ArrayList<>();
+        for (Map<String, Object> innerMap : nestedList) {
+            StringBuilder sb = new StringBuilder();
+            for (Object value : innerMap.values()) {
+                if (sb.length() > 0) {
+                    sb.append(",");
+                }
+                sb.append(value);
             }
+            flatList.add(sb.toString());
+        }
+        return flatList;
+    }
+
+
+    public static List<KMeans.Point> readIrisData(List<String> data) {
+        List<KMeans.Point> points = new ArrayList<>();
+        for (String line : data) {
+            String[] values = line.split(",");
+
+            double x = Double.parseDouble(values[0]);
+            double y = Double.parseDouble(values[1]);
+
+
+            points.add(new KMeans.Point(x, y));
         }
         return points;
     }

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

@@ -8,6 +8,7 @@ import java.io.FileInputStream;
 import java.io.InputStreamReader;
 import java.util.ArrayList;
 import java.util.HashSet;
+import java.util.List;
 import java.util.Set;
 
 /**
@@ -32,33 +33,48 @@ public class KMeansResult {
     static ArrayList<ArrayList<Float>> randomList = new ArrayList<ArrayList<Float>>();
 
     // 读取文件中的数据，储存到集合中
-    public static ArrayList<ArrayList<Float>> readTable(MultipartFile file){
+    public static ArrayList<ArrayList<Float>> readTable(List<String> data) {
         table.clear();
-        ArrayList<Float> d = null;
 
-        try (BufferedReader br = new BufferedReader(new InputStreamReader(file.getInputStream()))) {
 
-
-            // 跳过标题行
-            br.readLine();
-
-            String str = null;
-            while((str = br.readLine()) != null) {
-                d = new ArrayList<Float>();
-                String[] str1 = str.split(",");
-                for(int i = 0; i < str1.length ; i++) {
-                    d.add(Float.parseFloat(str1[i]));
-                }
-                table.add(d);
+        for (String line : data) {
+            ArrayList<Float> d = new ArrayList<Float>();
+            String[] str1 = line.split(",");
+            for (String str2 : str1) {
+                d.add(Float.parseFloat(str2));
             }
-//			System.out.println(table);
-
-        } catch (Exception e) {
-            e.printStackTrace();
-            System.out.println("文件不存在！");
+            table.add(d);
         }
+
         return table;
     }
+//    public static ArrayList<ArrayList<Float>> readTable(MultipartFile file){
+//        table.clear();
+//        ArrayList<Float> d = null;
+//
+//        try (BufferedReader br = new BufferedReader(new InputStreamReader(file.getInputStream()))) {
+//
+//
+//            // 跳过标题行
+//            br.readLine();
+//
+//            String str = null;
+//            while((str = br.readLine()) != null) {
+//                d = new ArrayList<Float>();
+//                String[] str1 = str.split(",");
+//                for(int i = 0; i < str1.length ; i++) {
+//                    d.add(Float.parseFloat(str1[i]));
+//                }
+//                table.add(d);
+//            }
+////			System.out.println(table);
+//
+//        } catch (Exception e) {
+//            e.printStackTrace();
+//            System.out.println("文件不存在！");
+//        }
+//        return table;
+//    }
 
     // 随机产生k个初始聚类中心
     public static ArrayList<ArrayList<Float>> randomList(Integer k,ArrayList<ArrayList<Float>> randomList) {
@@ -66,15 +82,16 @@ public class KMeansResult {
 
         int[] list = new int[k];
 
-        // Generate k distinct random numbers
+        // 生成k个不同的随机数
+        int size = table.size(); //生成的随机数不能大于table中元素的个数
         do {
             for (int i = 0; i < k; i++) {
-                list[i] = (int)(Math.random() * 30);
+                list[i] = (int)(Math.random() * size);
             }
         } while (hasDuplicates(list));
 
 
-        // For testing purposes, we can take the first k elements from the data set as initial cluster centers
+        // 将数据集中的前k个元素作为初始集群中心
         for (int i = 0; i < k; i++) {
             centerList.add(table.get(list[i]));
         }
@@ -193,11 +210,24 @@ public class KMeansResult {
     private static ArrayList<Float> calculateCentroid(ArrayList<ArrayList<Float>> cluster) {
         float sum1 = 0, sum2 = 0, sum3 = 0, sum4 = 0;
         for (ArrayList<Float> point : cluster) {
-            sum1 += point.get(1);
-            sum2 += point.get(2);
-            sum3 += point.get(3);
-            sum4 += point.get(4);
+            for (int i = 0; i < point.size(); i++) {
+                if(i==0){
+                    sum1 += point.get(0);
+                }
+                if(i==1){
+                    sum2 += point.get(1);
+                }
+                if(i==2){
+                    sum3 += point.get(2);
+                }
+                if(i==3){
+                    sum4 += point.get(3);
+                }
+
+            }
+
         }
+
         float size = cluster.size();
         ArrayList<Float> centroid = new ArrayList<>();
         centroid.add(0f);

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

@@ -1,6 +1,8 @@
 package com.sztzjy.marketing.util.algorithm;
 
 import java.text.DecimalFormat;
+import java.util.ArrayList;
+import java.util.List;
 
 public class LinearRegression {
     private double intercept;
@@ -76,14 +78,21 @@ public class LinearRegression {
         // 创建线性回归模型
         LinearRegression lr = new LinearRegression();
         lr.fit(x, y);
- 
+
+        DecimalFormat df = new DecimalFormat("#.0");
+
+        List<String> list=new ArrayList<>();
+        for (int i = 0; i < lr.getSlopes().length; i++) {
+            String format = df.format(lr.getSlopes()[i]);
+            list.add(format);
+        }
         // 输出模型参数
-        System.out.println("截距: " + lr.getIntercept());
-        System.out.println("斜率: " + java.util.Arrays.toString(lr.getSlopes()));
+        System.out.println("常数项值: " + df.format(lr.getIntercept()));
+        System.out.println("第一个特征系数值: " + list.get(0));
  
         // 预测新数据
-        double[] newX = {2, 45};
+        double[] newX = {2, 88};
         double predictedY = lr.predict(newX);
-        System.out.println("输入x: " + java.util.Arrays.toString(newX) + "预测y=" + predictedY);
+        System.out.println("输入x: " + java.util.Arrays.toString(newX) + "预测y=" + df.format(predictedY));
     }
 }

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

@@ -1,71 +1,121 @@
 package com.sztzjy.marketing.util.algorithm;
 
-import java.util.Arrays;
+import com.sztzjy.marketing.util.BigDecimalUtils;
+
+import java.text.DecimalFormat;
 
 public class LogisticRegression {
-    private double[] weights;
-    private double bias;
+    private double theta0; // 截距项
+    private double theta1; // 斜率
+    private double learningRate; // 学习率
+
+    private int currentEpoch; // 声明currentEpoch变量
 
-    public LogisticRegression(int numFeatures) {
-        weights = new double[numFeatures];
-        bias = 0;
+    public LogisticRegression(double learningRate) {
+        this.learningRate = learningRate;
     }
 
-    public void fit(double[][] X, double[] y, double learningRate, int numIterations) {
-        int numSamples = X.length;
-        int numFeatures = X[0].length;
 
-        for (int iter = 0; iter < numIterations; iter++) {
-            double[] gradientWeights = new double[numFeatures];
-            double gradientBias = 0;
 
-            for (int i = 0; i < numSamples; i++) {
-                double dotProduct = dotProduct(X[i], weights) + bias;
-                double prediction = sigmoid(dotProduct);
-                double error = y[i] - prediction;
 
-                for (int j = 0; j < numFeatures; j++) {
-                    gradientWeights[j] += error * X[i][j];
-                }
-                gradientBias += error;
-            }
+    public LogisticRegression(double learningRate, double initialTheta0, double initialTheta1) {
+        this.learningRate = learningRate;
+        this.theta0 = initialTheta0;
+        this.theta1 = initialTheta1;
+        this.currentEpoch = 0; // 在构造函数中初始化currentEpoch
+    }
 
-            for (int j = 0; j < numFeatures; j++) {
-                weights[j] += learningRate * gradientWeights[j] / numSamples;
-            }
-            bias += learningRate * gradientBias / numSamples;
-        }
+    // 添加一个方法来获取截距项
+    public double getIntercept() {
+        return theta0;
     }
 
-    public double predict(double[] x) {
-        double dotProduct = dotProduct(x, weights) + bias;
-        return sigmoid(dotProduct);
+    // 添加一个方法来获取第一个特征的系数
+    public double getCoefficient() {
+        return theta1;
     }
 
-    private double sigmoid(double x) {
-        return 1 / (1 + Math.exp(-x));
+    // Sigmoid函数
+    private double sigmoid(double z) {
+        return 1 / (1 + Math.exp(-z));
     }
 
-    private double dotProduct(double[] x, double[] y) {
-        double sum = 0;
-        for (int i = 0; i < x.length; i++) {
-            sum += x[i] * y[i];
+    // 预测函数
+    public double predict(double x) {
+        BigDecimalUtils bigDecimalUtils=new BigDecimalUtils();
+
+        Double mul = bigDecimalUtils.mul(this.theta0, x);
+
+        return bigDecimalUtils.add(this.theta1, mul);
+
+    }
+
+    private double dotProduct(double[] a, double[] b) {
+        double result = 0.0;
+        for (int i = 0; i < a.length; i++) {
+            result += a[i] * b[i];
         }
-        return sum;
+        return result;
+    }
+
+    // 梯度下降更新参数
+    public void updateParameters(double x, double y) {
+        double h = predict(x);
+        double error = y - h;
+        theta1 += learningRate * error * h * (1 - h) * x;
+        theta0 += learningRate * error * h * (1 - h);
+    }
+    private double exponentialDecayLearningRate(double currentEpoch, int totalEpochs) {
+        return learningRate * Math.exp(-(currentEpoch / totalEpochs));
+    }
+
+    // 训练模型
+    public void train(double[][] data, double[] labels, int epochs) {
+        double learningRateCurrentEpoch = exponentialDecayLearningRate(currentEpoch, epochs);
+        for (int epoch = 0; epoch < epochs; epoch++) {
+            for (int i = 0; i < data.length; i++) {
+                double x = data[i][0];
+                double y = labels[i];
+                double h = sigmoid(theta0 + theta1 * x);
+                double error = y - h;
+                theta1 += learningRateCurrentEpoch * error * h * (1 - h) * x;
+                theta0 += learningRateCurrentEpoch * error * h * (1 - h);
+            }
+            // 可以添加打印语句查看训练情况
+//            System.out.println("Epoch: " + (epoch + 1) + ", Loss: " + calculateLoss(data, labels));
+        }
+    }
+    public double calculateLoss(double[][] data, double[] labels) {
+        double loss = 0;
+        for (int i = 0; i < data.length; i++) {
+            double x = data[i][0];
+            double y = labels[i];
+            double h = sigmoid(theta0 + theta1 * x);
+            loss += -y * Math.log(h) - (1 - y) * Math.log(1 - h);
+        }
+        return loss / data.length;
     }
 
     public static void main(String[] args) {
-        double[][] X = {{1, 1}, {1, 2}, {2, 1}, {2, 3}};
-        double[] y = {0, 0, 0, 1};
+        LogisticRegression model = new LogisticRegression(0.1); // 创建一个逻辑回归模型实例
+
+        double[][] data = {{1}, {2}, {3}, {4}, {5}};
+        double[] labels = {0, 0, 1, 1, 1}; // 假设这是一个二分类问题
+
+//        double[][] data = {{1, 19}, {1, 21}, {2, 20}, {2, 23}, {2, 31}};
+//        double[] labels = {15, 15, 16, 16, 17};
+
+         model.train(data, labels, 1000); // 训练模型
 
-        LogisticRegression model = new LogisticRegression(2);
-        model.fit(X, y, 0.01, 10000);
+        DecimalFormat df = new DecimalFormat("#.0");
 
-        System.out.println("Weights: " + Arrays.toString(model.weights));
-        System.out.println("Bias: " + model.bias);
+        // 获取并打印截距项和系数
+        double intercept = model.getIntercept();
+        double coefficient = model.getCoefficient();
+        System.out.println("常数项值: " + df.format(intercept));
+        System.out.println("系数值 " + df.format(coefficient));
 
-        double[] newSample = {1, 2};
-        double prediction = model.predict(newSample);
-        System.out.println("Prediction for " + Arrays.toString(newSample) + ": " + prediction);
+        double prediction = model.predict(2);
+        System.out.println("x=3.5的预测： " + df.format(prediction));
     }
 }

src/main/java/com/sztzjy/marketing/util/excel/ImportExcelUtil.java

@@ -6,6 +6,7 @@ import org.apache.poi.ss.usermodel.*;
 import org.apache.poi.xssf.usermodel.XSSFWorkbook;
 import org.springframework.stereotype.Component;
 
+import java.io.IOException;
 import java.io.InputStream;
 import java.math.BigDecimal;
 import java.text.DecimalFormat;

src/main/resources/mappers/StuSpendingLevelMapper.xml

@@ -209,10 +209,10 @@
     where id = #{id,jdbcType=INTEGER}
   </update>
   <insert id="addList">
-    INSERT INTO tch_start_course_name_list (id, gender, age, annual_income, spending_score)
+    INSERT INTO stu_spending_level (id, gender, age, annual_income, spending_score)
     VALUES
     <foreach collection="stuSpendingLevels" item="stuSpendingLevel" separator=",">
-      (#{stuSpendingLevel.id}, #{stuSpendingLevel.gender}, #{stuSpendingLevel.age}, #{stuSpendingLevel.annual_income}, #{stuSpendingLevel.spending_score})
+      (#{stuSpendingLevel.id}, #{stuSpendingLevel.gender}, #{stuSpendingLevel.age}, #{stuSpendingLevel.annualIncome}, #{stuSpendingLevel.spendingScore})
     </foreach>
   </insert>
 </mapper>

src/main/resources/mappers/StuUserBehaviorMapper.xml

@@ -235,8 +235,7 @@
       <include refid="Example_Where_Clause" />
     </if>
   </select>
-
-    <update id="updateByExampleSelective" parameterType="map">
+  <update id="updateByExampleSelective" parameterType="map">
     update stu_user_behavior
     <set>
       <if test="record.id != null">
@@ -384,11 +383,4 @@
       update_time = #{updateTime,jdbcType=TIMESTAMP}
     where id = #{id,jdbcType=INTEGER}
   </update>
-  <select id="selectByFields" resultType="com.sztzjy.marketing.entity.StuUserBehavior" resultMap="BaseResultMap">
-    SELECT
-    <foreach collection="fieldList" item="field" separator=",">
-      ${field}
-    </foreach>
-    FROM ${table}
-  </select>
 </mapper>

src/main/resources/mappers/StuUserLoginActiveMapper.xml

@@ -14,7 +14,7 @@
     <result column="frequent_login_location" jdbcType="VARCHAR" property="frequentLoginLocation" />
     <result column="last_login_date" jdbcType="TIMESTAMP" property="lastLoginDate" />
     <result column="login_frequency" jdbcType="INTEGER" property="loginFrequency" />
-    <result column="login_duration" jdbcType="VARCHAR" property="loginDuration" />
+    <result column="login_duration" jdbcType="DOUBLE" property="loginDuration" />
     <result column="create_time" jdbcType="TIMESTAMP" property="createTime" />
     <result column="update_time" jdbcType="TIMESTAMP" property="updateTime" />
   </resultMap>
@@ -122,7 +122,7 @@
       #{userName,jdbcType=VARCHAR}, #{studentId,jdbcType=VARCHAR}, #{stuClass,jdbcType=VARCHAR}, 
       #{major,jdbcType=VARCHAR}, #{school,jdbcType=VARCHAR}, #{roleName,jdbcType=VARCHAR}, 
       #{frequentLoginLocation,jdbcType=VARCHAR}, #{lastLoginDate,jdbcType=TIMESTAMP}, 
-      #{loginFrequency,jdbcType=INTEGER}, #{loginDuration,jdbcType=VARCHAR}, #{createTime,jdbcType=TIMESTAMP}, 
+      #{loginFrequency,jdbcType=INTEGER}, #{loginDuration,jdbcType=DOUBLE}, #{createTime,jdbcType=TIMESTAMP}, 
       #{updateTime,jdbcType=TIMESTAMP})
   </insert>
   <insert id="insertSelective" parameterType="com.sztzjy.marketing.entity.StuUserLoginActive">
@@ -212,7 +212,7 @@
         #{loginFrequency,jdbcType=INTEGER},
       </if>
       <if test="loginDuration != null">
-        #{loginDuration,jdbcType=VARCHAR},
+        #{loginDuration,jdbcType=DOUBLE},
       </if>
       <if test="createTime != null">
         #{createTime,jdbcType=TIMESTAMP},
@@ -228,8 +228,7 @@
       <include refid="Example_Where_Clause" />
     </if>
   </select>
-
-    <update id="updateByExampleSelective" parameterType="map">
+  <update id="updateByExampleSelective" parameterType="map">
     update stu_user_login_active
     <set>
       <if test="record.id != null">
@@ -269,7 +268,7 @@
         login_frequency = #{record.loginFrequency,jdbcType=INTEGER},
       </if>
       <if test="record.loginDuration != null">
-        login_duration = #{record.loginDuration,jdbcType=VARCHAR},
+        login_duration = #{record.loginDuration,jdbcType=DOUBLE},
       </if>
       <if test="record.createTime != null">
         create_time = #{record.createTime,jdbcType=TIMESTAMP},
@@ -296,7 +295,7 @@
       frequent_login_location = #{record.frequentLoginLocation,jdbcType=VARCHAR},
       last_login_date = #{record.lastLoginDate,jdbcType=TIMESTAMP},
       login_frequency = #{record.loginFrequency,jdbcType=INTEGER},
-      login_duration = #{record.loginDuration,jdbcType=VARCHAR},
+      login_duration = #{record.loginDuration,jdbcType=DOUBLE},
       create_time = #{record.createTime,jdbcType=TIMESTAMP},
       update_time = #{record.updateTime,jdbcType=TIMESTAMP}
     <if test="_parameter != null">
@@ -340,7 +339,7 @@
         login_frequency = #{loginFrequency,jdbcType=INTEGER},
       </if>
       <if test="loginDuration != null">
-        login_duration = #{loginDuration,jdbcType=VARCHAR},
+        login_duration = #{loginDuration,jdbcType=DOUBLE},
       </if>
       <if test="createTime != null">
         create_time = #{createTime,jdbcType=TIMESTAMP},
@@ -364,16 +363,9 @@
       frequent_login_location = #{frequentLoginLocation,jdbcType=VARCHAR},
       last_login_date = #{lastLoginDate,jdbcType=TIMESTAMP},
       login_frequency = #{loginFrequency,jdbcType=INTEGER},
-      login_duration = #{loginDuration,jdbcType=VARCHAR},
+      login_duration = #{loginDuration,jdbcType=DOUBLE},
       create_time = #{createTime,jdbcType=TIMESTAMP},
       update_time = #{updateTime,jdbcType=TIMESTAMP}
     where id = #{id,jdbcType=INTEGER}
   </update>
-  <select id="selectByFields" resultType="com.sztzjy.marketing.entity.StuUserLoginActive" resultMap="BaseResultMap">
-    SELECT
-    <foreach collection="fieldList" item="field" separator=",">
-      ${field}
-    </foreach>
-    FROM ${table}
-  </select>
 </mapper>