公式-特征值

1、 开发特征值公式

blade-service/blade-manager/src/main/java/com/mixsmart/utils/CustomFunction.java

@@ -11,6 +11,7 @@ import com.jfireel.expression.node.impl.VariableNode;
 import com.jfireel.expression.token.Token;
 import org.apache.commons.collections4.MapUtils;
 import org.springblade.common.utils.BaseUtils;
+import org.springblade.core.log.exception.ServiceException;
 import org.springblade.core.redis.cache.BladeRedis;
 import org.springblade.core.tool.utils.*;
 import org.springblade.manager.dto.ParamElements;
@@ -23,6 +24,7 @@ import java.text.ParseException;
 import java.text.SimpleDateFormat;
 import java.time.LocalDate;
 import java.time.LocalDateTime;
+import java.time.ZoneOffset;
 import java.util.*;
 import java.util.concurrent.atomic.AtomicInteger;
 import java.util.concurrent.atomic.AtomicReference;
@@ -1187,7 +1189,7 @@ public class CustomFunction {
         }else{
             localDateTime= LocalDateTime.now();
         }
-      return localDateTime.atZone(java.time.ZoneOffset.UTC)
+      return localDateTime.atZone(ZoneOffset.UTC)
               .toInstant()
               .toEpochMilli();
     }
@@ -3062,7 +3064,37 @@ public class CustomFunction {
         toDay(list).forEach(System.out::println);
     }*/
 
+    /**
+     * 特征值计算
+     */
+    public static Object eigenvalue(Object data){
 
+        //默认只有10组数据
+        if (data != null ) {
+            List<Object> datas = obj2ListNe(data);
+            datas = datas.size() > 10 ? datas.subList(0, datas.size()/2) : datas;
+            int total = datas.size();
+            //换算系数
+            double conversionFactor;
+            //获取换算系数
+            if(total >= 10 && total <= 15){
+                conversionFactor = 1.695;
+            }else if(total >= 16 && total <= 24){
+                conversionFactor = 1.645;
+            }else if(total>=25){
+                conversionFactor = 1.595;
+            }else{
+                throw new ServiceException("数据组数小于10");
+            }
+            List<String> _datas = datas.stream().map(StringUtils::handleNull).collect(Collectors.toList());
+            //获取平均值
+            double avgVal = _datas.stream().mapToDouble(Double::parseDouble).average().orElse(0D);
+            //获取标准差
+            double sqrt = Math.sqrt(_datas.stream().mapToDouble(Double::parseDouble).map(e -> Math.pow(e - avgVal, 2)).sum() / (total-1));
+            return avgVal - (conversionFactor * sqrt);
+        }
+        return null;
+    }
 
 
 }