数字营销实训算法第三轮修改

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

@@ -429,11 +429,17 @@ public class StuDigitalMarketingModelServiceImpl implements StuDigitalMarketingM
 
         List<Map<String,Object>> attributes = stuTableNameMapper.selectByFields(fieldList,table);
 
-        for (int i = 0; i < attributes.size(); i++) {
-            if(attributes.get(i).size()<fieldList.size()){
-                Collections.rotate(attributes,-1);
+
+
+            for (int i = 0; i < attributes.size(); i++) {
+                if(attributes.get(i)!=null){
+                    if(attributes.get(i).size()<fieldList.size()){
+                        Collections.rotate(attributes,-1);
+                    }
+                }
             }
-        }
+
+
 
 
 
@@ -482,6 +488,20 @@ public class StuDigitalMarketingModelServiceImpl implements StuDigitalMarketingM
         //判断缺失值处理方式
         if(method.equals("缺失值处理--剔除数据")){
 
+            int a=0;
+            List<Integer> list=new ArrayList<>();
+            for (int i = 0; i < deduplicatedDataList.size(); i++) {
+                a = deduplicatedDataList.get(0).size();
+                if(deduplicatedDataList.get(i).size()<a){
+                    list.add(i);
+                }
+            }
+
+            // 反转列表
+            Collections.reverse(list);
+            for (int i = 0; i < list.size(); i++) {
+                deduplicatedDataList.remove(deduplicatedDataList.get(list.get(i)));
+            }
 
 
             deduplicatedDataList.removeIf(record -> record.containsValue(null));
@@ -503,6 +523,21 @@ public class StuDigitalMarketingModelServiceImpl implements StuDigitalMarketingM
 //                }
 //            }
 
+            int a=0;
+            List<Integer> list=new ArrayList<>();
+            for (int i = 0; i < deduplicatedDataList.size(); i++) {
+                a = deduplicatedDataList.get(0).size();
+                if(deduplicatedDataList.get(i).size()<a){
+                    list.add(i);
+                }
+            }
+
+            // 反转列表
+            Collections.reverse(list);
+            for (int i = 0; i < list.size(); i++) {
+                deduplicatedDataList.remove(deduplicatedDataList.get(list.get(i)));
+            }
+
             //众数代替
             for (Map<String, Object> record : deduplicatedDataList) {
                 for (Map.Entry<String, Object> entry : record.entrySet()) {

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

@@ -16,6 +16,7 @@ import java.io.IOException;
 import java.io.InputStream;
 import java.io.InputStreamReader;
 import java.nio.charset.StandardCharsets;
+import java.text.DecimalFormat;
 import java.util.*;
 
 public class Apriori {
@@ -251,12 +252,13 @@ public class Apriori {
 								if (confident > min_confident) {// 如果事件的置信度大于最小置信度
 									//封装对象返回
 
+									DecimalFormat df = new DecimalFormat("#.0");
 									AssociationRulesDTO associationRulesDTO=new AssociationRulesDTO();
 									associationRulesDTO.setCorrelation(item_pre);
 									associationRulesDTO.setAssociated(item_post);
-									associationRulesDTO.setConfidenceLevel(confident);
+									associationRulesDTO.setConfidenceLevel(Double.parseDouble(df.format(confident)));
 
-									String string=item_pre + "==>" + item_post+"==>" + confident;
+									String string=item_pre + "==>" + item_post+"==>" + Double.parseDouble(df.format(confident));
 									associationRulesDTO.setRule(string);
 
 									list.add(associationRulesDTO);

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

@@ -146,7 +146,9 @@ public class KMeansResult {
     private static double calculateEuclideanDistance(ArrayList<Float> point1, ArrayList<Float> point2) {
         double sum = 0;
         for (int i = 1; i < point1.size(); i++) {
-            sum += Math.pow(point1.get(i) - point2.get(i), 2);
+            if(i<=point2.size()){
+                sum += Math.pow(point1.get(i) - point2.get(i), 2);
+            }
         }
         return Math.sqrt(sum);
     }