数字营销实训算法第7轮修改

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

@@ -241,7 +241,7 @@ public class StuDigitalMarketingModelController {
         LogisticRegression lr = new LogisticRegression(x, y);
         double[] coefficients = lr.fit();
 
-        DecimalFormat df = new DecimalFormat("#.0");
+        DecimalFormat df = new DecimalFormat("#.00");
         RAnalysisDTO rAnalysisDTO=new RAnalysisDTO();
 
         rAnalysisDTO.setIntercept(Double.parseDouble(df.format(coefficients[0])));
@@ -250,6 +250,21 @@ public class StuDigitalMarketingModelController {
         return new ResultEntity(HttpStatus.OK,"成功",rAnalysisDTO);
     }
 
+    @ApiOperation("回归分析--走势图")
+    @PostMapping("/trendChart")
+    @AnonymousAccess
+    public ResultEntity trendChart(@RequestBody LogisticDTO logisticDTO) {
+        double[] x = logisticDTO.getX();
+        double[] y = logisticDTO.getY();
+
+        LogisticRegression lr = new LogisticRegression(x, y);
+        double[][] coefficients = lr.getCoordinatesOnLine();
+
+
+
+        return new ResultEntity(HttpStatus.OK,"成功",coefficients);
+    }
+
 
     @ApiOperation("线性回归/逻辑回归--预测")
     @PostMapping("/prediction")

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

@@ -1,5 +1,7 @@
 package com.sztzjy.marketing.util.algorithm;
 
+import java.util.Arrays;
+
 public class LogisticRegression {
 
     private double[] x;
@@ -39,6 +41,19 @@ public class LogisticRegression {
         return new double[] { intercept, slope };
     }
 
+    public double[][] getCoordinatesOnLine() {
+        double[] parameters = fit();
+        double intercept = parameters[0];
+        double slope = parameters[1];
+
+        double[][] coordinates = new double[x.length][2];
 
+        for (int i = 0; i < x.length; i++) {
+            coordinates[i][0] = x[i];
+            coordinates[i][1] = slope * x[i] + intercept;
+        }
+
+        return coordinates;
+    }
 
 }