From 4a9095d9b041ac8ede85f81214a94eb71a7e5703 Mon Sep 17 00:00:00 2001
From: "@t2652009480" <2652009480@qq.com>
Date: Sat, 17 Aug 2024 18:00:53 +0800
Subject: [PATCH] =?UTF-8?q?=E6=95=B0=E5=AD=97=E8=90=A5=E9=94=80=E5=AE=9E?=
=?UTF-8?q?=E8=AE=AD=E7=AE=97=E6=B3=95=E7=AC=AC=E4=B8=80=E8=BD=AE=E4=BF=AE?=
=?UTF-8?q?=E6=94=B9?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
---
.../StuDigitalMarketingModelController.java | 94 +++++++-----
.../marketing/entity/StuUserLoginActive.java | 12 +-
.../entity/StuUserLoginActiveExample.java | 30 ++--
.../marketing/entity/dto/AprioriDTO.java | 23 +++
.../entity/dto/DescriptiveStatistics.java | 2 +-
.../marketing/entity/dto/LogisticDTO.java | 2 +-
.../marketing/entity/dto/RAnalysisDTO.java | 13 ++
.../mapper/StuUserBehaviorMapper.java | 6 +-
.../mapper/StuUserLoginActiveMapper.java | 2 -
.../StuDigitalMarketingModelService.java | 2 +-
.../StuDigitalMarketingModelServiceImpl.java | 51 +++++--
.../marketing/util/algorithm/Apriori.java | 62 +++++---
.../sztzjy/marketing/util/algorithm/Data.java | 11 ++
.../algorithm/DescriptiveStatisticsUtil.java | 11 +-
.../marketing/util/algorithm/KMeans.java | 50 ++++---
.../util/algorithm/KMeansResult.java | 84 +++++++----
.../util/algorithm/LinearRegression.java | 19 ++-
.../util/algorithm/LogisticRegression.java | 140 ++++++++++++------
.../marketing/util/excel/ImportExcelUtil.java | 1 +
.../mappers/StuSpendingLevelMapper.xml | 4 +-
.../mappers/StuUserBehaviorMapper.xml | 10 +-
.../mappers/StuUserLoginActiveMapper.xml | 24 +--
22 files changed, 425 insertions(+), 228 deletions(-)
create mode 100644 src/main/java/com/sztzjy/marketing/entity/dto/AprioriDTO.java
create mode 100644 src/main/java/com/sztzjy/marketing/entity/dto/RAnalysisDTO.java
create mode 100644 src/main/java/com/sztzjy/marketing/util/algorithm/Data.java
diff --git a/src/main/java/com/sztzjy/marketing/controller/stu/StuDigitalMarketingModelController.java b/src/main/java/com/sztzjy/marketing/controller/stu/StuDigitalMarketingModelController.java
index a14a448..4b1555a 100644
--- a/src/main/java/com/sztzjy/marketing/controller/stu/StuDigitalMarketingModelController.java
+++ b/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)));
diff --git a/src/main/java/com/sztzjy/marketing/entity/StuUserLoginActive.java b/src/main/java/com/sztzjy/marketing/entity/StuUserLoginActive.java
index b29dbf0..b804953 100644
--- a/src/main/java/com/sztzjy/marketing/entity/StuUserLoginActive.java
+++ b/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() {
diff --git a/src/main/java/com/sztzjy/marketing/entity/StuUserLoginActiveExample.java b/src/main/java/com/sztzjy/marketing/entity/StuUserLoginActiveExample.java
index 2fa6933..18d09a4 100644
--- a/src/main/java/com/sztzjy/marketing/entity/StuUserLoginActiveExample.java
+++ b/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;
}
diff --git a/src/main/java/com/sztzjy/marketing/entity/dto/AprioriDTO.java b/src/main/java/com/sztzjy/marketing/entity/dto/AprioriDTO.java
new file mode 100644
index 0000000..e1c0d1a
--- /dev/null
+++ b/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;
+}
diff --git a/src/main/java/com/sztzjy/marketing/entity/dto/DescriptiveStatistics.java b/src/main/java/com/sztzjy/marketing/entity/dto/DescriptiveStatistics.java
index 56342a3..d0379ae 100644
--- a/src/main/java/com/sztzjy/marketing/entity/dto/DescriptiveStatistics.java
+++ b/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;
diff --git a/src/main/java/com/sztzjy/marketing/entity/dto/LogisticDTO.java b/src/main/java/com/sztzjy/marketing/entity/dto/LogisticDTO.java
index b32dad1..b5cc2f5 100644
--- a/src/main/java/com/sztzjy/marketing/entity/dto/LogisticDTO.java
+++ b/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;
diff --git a/src/main/java/com/sztzjy/marketing/entity/dto/RAnalysisDTO.java b/src/main/java/com/sztzjy/marketing/entity/dto/RAnalysisDTO.java
new file mode 100644
index 0000000..d2cae68
--- /dev/null
+++ b/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;
+}
diff --git a/src/main/java/com/sztzjy/marketing/mapper/StuUserBehaviorMapper.java b/src/main/java/com/sztzjy/marketing/mapper/StuUserBehaviorMapper.java
index 1568b0a..9b908ac 100644
--- a/src/main/java/com/sztzjy/marketing/mapper/StuUserBehaviorMapper.java
+++ b/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);
}
\ No newline at end of file
diff --git a/src/main/java/com/sztzjy/marketing/mapper/StuUserLoginActiveMapper.java b/src/main/java/com/sztzjy/marketing/mapper/StuUserLoginActiveMapper.java
index 61fcd79..76b0ecc 100644
--- a/src/main/java/com/sztzjy/marketing/mapper/StuUserLoginActiveMapper.java
+++ b/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);
}
\ No newline at end of file
diff --git a/src/main/java/com/sztzjy/marketing/service/StuDigitalMarketingModelService.java b/src/main/java/com/sztzjy/marketing/service/StuDigitalMarketingModelService.java
index 8a5357b..6b286c3 100644
--- a/src/main/java/com/sztzjy/marketing/service/StuDigitalMarketingModelService.java
+++ b/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);
diff --git a/src/main/java/com/sztzjy/marketing/service/impl/StuDigitalMarketingModelServiceImpl.java b/src/main/java/com/sztzjy/marketing/service/impl/StuDigitalMarketingModelServiceImpl.java
index 28ed716..da51f31 100644
--- a/src/main/java/com/sztzjy/marketing/service/impl/StuDigitalMarketingModelServiceImpl.java
+++ b/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
diff --git a/src/main/java/com/sztzjy/marketing/util/algorithm/Apriori.java b/src/main/java/com/sztzjy/marketing/util/algorithm/Apriori.java
index 96db4dd..dc93ee8 100644
--- a/src/main/java/com/sztzjy/marketing/util/algorithm/Apriori.java
+++ b/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) {
diff --git a/src/main/java/com/sztzjy/marketing/util/algorithm/Data.java b/src/main/java/com/sztzjy/marketing/util/algorithm/Data.java
new file mode 100644
index 0000000..9a2c9b6
--- /dev/null
+++ b/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;
+ }
+}
\ No newline at end of file
diff --git a/src/main/java/com/sztzjy/marketing/util/algorithm/DescriptiveStatisticsUtil.java b/src/main/java/com/sztzjy/marketing/util/algorithm/DescriptiveStatisticsUtil.java
index e25e1eb..83f9e7d 100644
--- a/src/main/java/com/sztzjy/marketing/util/algorithm/DescriptiveStatisticsUtil.java
+++ b/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
diff --git a/src/main/java/com/sztzjy/marketing/util/algorithm/KMeans.java b/src/main/java/com/sztzjy/marketing/util/algorithm/KMeans.java
index 804853e..34e54ea 100644
--- a/src/main/java/com/sztzjy/marketing/util/algorithm/KMeans.java
+++ b/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;
}
diff --git a/src/main/java/com/sztzjy/marketing/util/algorithm/KMeansResult.java b/src/main/java/com/sztzjy/marketing/util/algorithm/KMeansResult.java
index 958747d..fa0f6c9 100644
--- a/src/main/java/com/sztzjy/marketing/util/algorithm/KMeansResult.java
+++ b/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);
diff --git a/src/main/java/com/sztzjy/marketing/util/algorithm/LinearRegression.java b/src/main/java/com/sztzjy/marketing/util/algorithm/LinearRegression.java
index 4948516..a370d24 100644
--- a/src/main/java/com/sztzjy/marketing/util/algorithm/LinearRegression.java
+++ b/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));
}
}
\ No newline at end of file
diff --git a/src/main/java/com/sztzjy/marketing/util/algorithm/LogisticRegression.java b/src/main/java/com/sztzjy/marketing/util/algorithm/LogisticRegression.java
index 363d34b..d0917af 100644
--- a/src/main/java/com/sztzjy/marketing/util/algorithm/LogisticRegression.java
+++ b/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));
}
}
\ No newline at end of file
diff --git a/src/main/java/com/sztzjy/marketing/util/excel/ImportExcelUtil.java b/src/main/java/com/sztzjy/marketing/util/excel/ImportExcelUtil.java
index 9270e72..95ec016 100644
--- a/src/main/java/com/sztzjy/marketing/util/excel/ImportExcelUtil.java
+++ b/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;
diff --git a/src/main/resources/mappers/StuSpendingLevelMapper.xml b/src/main/resources/mappers/StuSpendingLevelMapper.xml
index 6a3b65b..388134f 100644
--- a/src/main/resources/mappers/StuSpendingLevelMapper.xml
+++ b/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>
\ No newline at end of file
diff --git a/src/main/resources/mappers/StuUserBehaviorMapper.xml b/src/main/resources/mappers/StuUserBehaviorMapper.xml
index 96a6c43..70541be 100644
--- a/src/main/resources/mappers/StuUserBehaviorMapper.xml
+++ b/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>
\ No newline at end of file
diff --git a/src/main/resources/mappers/StuUserLoginActiveMapper.xml b/src/main/resources/mappers/StuUserLoginActiveMapper.xml
index 6c9580f..7e8c225 100644
--- a/src/main/resources/mappers/StuUserLoginActiveMapper.xml
+++ b/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>
\ No newline at end of file