数字营销实训算法第五轮修改

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

@@ -10,6 +10,7 @@ import com.sztzjy.marketing.mapper.StuSpendingLevelMapper;
 import com.sztzjy.marketing.mapper.StuUserCommentMapper;
 import com.sztzjy.marketing.mapper.StuUserSalesAbilityMapper;
 import com.sztzjy.marketing.service.StuDigitalMarketingModelService;
+import com.sztzjy.marketing.util.BigDecimalUtils;
 import com.sztzjy.marketing.util.DataConverter;
 import com.sztzjy.marketing.util.ResultEntity;
 import com.sztzjy.marketing.util.algorithm.Apriori;
@@ -229,18 +230,17 @@ public class StuDigitalMarketingModelController {
     @PostMapping("/logistic")
     @AnonymousAccess
     public ResultEntity logistic(@RequestBody LogisticDTO logisticDTO) {
-        double[][] x = logisticDTO.getX();
+        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 lr = new LogisticRegression(x, y);
-        LogisticRegression model = new LogisticRegression(0.1);
+        double[] coefficients = lr.fit();
-        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.setIntercept(Double.parseDouble(df.format(coefficients[0])));
-        rAnalysisDTO.setSlope(Double.parseDouble(df.format(model.getCoefficient())));
+        rAnalysisDTO.setSlope(Double.parseDouble(df.format(coefficients[1])));
 
         return new ResultEntity(HttpStatus.OK,"成功",rAnalysisDTO);
     }
@@ -252,10 +252,8 @@ public class StuDigitalMarketingModelController {
     public ResultEntity prediction(@RequestBody LogisticPredictDTO logisticPredictDTO) {
 
             DecimalFormat df = new DecimalFormat("#.0");
-
+            BigDecimalUtils bigDecimalUtils=new BigDecimalUtils();
-            // 创建一个逻辑回归模型实例,0.1学习率
+            double predict = bigDecimalUtils.add(bigDecimalUtils.mul(logisticPredictDTO.getSlope(),logisticPredictDTO.getX()),logisticPredictDTO.getIntercept());
-            LogisticRegression model = new LogisticRegression(0.1);
-            double predict = model.predict(logisticPredictDTO.getLX(), logisticPredictDTO.getSlope(), logisticPredictDTO.getIntercept());//预测
             return new ResultEntity(HttpStatus.OK,"成功",df.format(predict));
 
 

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/LogisticPredictDTO.java

@@ -11,8 +11,7 @@ import org.ujmp.core.util.R;
 @EqualsAndHashCode(callSuper = true)
 @Data
 public class LogisticPredictDTO extends RAnalysisDTO {
-    private double raX;
 
-    private double lX;
+    private double x;
 
 }

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

@@ -21,6 +21,8 @@ import org.springframework.http.HttpStatus;
 import org.springframework.stereotype.Service;
 
 import javax.annotation.Resource;
+import java.io.BufferedReader;
+import java.io.FileReader;
 import java.io.IOException;
 import java.text.DecimalFormat;
 import java.time.Instant;
@@ -155,13 +157,16 @@ public class StuDigitalMarketingModelServiceImpl implements StuDigitalMarketingM
             List<Term> terms = HanLP.segment(string);
             // 创建词汇表
             Map<String, Integer> wordCount = new HashMap<>();
-            // 统计词频
+            // 读取停用词
+            Set<String> stopwords = this.loadStopwords("/usr/local/tianzeProject/digitalMarketing/jarAndDockerFile/停用词.txt");
+
+//            Set<String> stopwords = this.loadStopwords("D:\\project\\digital_marketing\\src\\main\\resources\\停用词.txt");
 
-            // 创建计数器
+            // 统计词频
             int count = 0;
             for (Term term : terms) {
                 String word = term.word.trim(); // 去除空格
-                if(!word.isEmpty()){
+                if (!word.isEmpty() && !stopwords.contains(word)) { // 过滤停用词
                     count++;
                     if (wordCount.containsKey(word)) {
                         wordCount.put(word, wordCount.get(word) + 1);
@@ -173,20 +178,22 @@ public class StuDigitalMarketingModelServiceImpl implements StuDigitalMarketingM
                     break;
                 }
             }
+
             // 将词汇表转换为列表，便于排序
             List<Map.Entry<String, Integer>> wordList = new ArrayList<>(wordCount.entrySet());
             // 按词频排序
             wordList.sort((o1, o2) -> o2.getValue().compareTo(o1.getValue()));
 
-            List<WordFrequencyDTO> list=new ArrayList<>();
+            List<WordFrequencyDTO> list = new ArrayList<>();
 
             for (Map.Entry<String, Integer> entry : wordList) {
-                WordFrequencyDTO frequencyDTO=new WordFrequencyDTO();
+                WordFrequencyDTO frequencyDTO = new WordFrequencyDTO();
                 frequencyDTO.setKeyword(entry.getKey());
                 frequencyDTO.setFrequency(entry.getValue());
                 list.add(frequencyDTO);
             }
-            return new ResultEntity<>(HttpStatus.OK,"成功",list);
+
+            return new ResultEntity<>(HttpStatus.OK, "成功", list);
 
         }
 //        if(modelType.equals(Constant.WORD_CLOUD)){ //词云分析
@@ -621,4 +628,16 @@ public class StuDigitalMarketingModelServiceImpl implements StuDigitalMarketingM
 
         return filteredAndConvertedList;
     }
+
+    // 读取停用词
+    Set<String> loadStopwords(String filePath) throws IOException {
+        Set<String> stopwords = new HashSet<>();
+        try (BufferedReader br = new BufferedReader(new FileReader(filePath))) {
+            String line;
+            while ((line = br.readLine()) != null) {
+                stopwords.add(line.trim());
+            }
+        }
+        return stopwords;
+    }
 }

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

@@ -1,121 +1,44 @@
 package com.sztzjy.marketing.util.algorithm;
 
-import com.sztzjy.marketing.util.BigDecimalUtils;
-
-import java.text.DecimalFormat;
-
 public class LogisticRegression {
-    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(double learningRate) {
-        this.learningRate = learningRate;
-    }
 
+    private double[] x;
 
 
+    private double[] y;
 
-    public LogisticRegression(double learningRate, double initialTheta0, double initialTheta1) {
+    public LogisticRegression(double[] x, double[] y) {
-        this.learningRate = learningRate;
+        this.x = x;
-        this.theta0 = initialTheta0;
+        this.y = y;
-        this.theta1 = initialTheta1;
-        this.currentEpoch = 0; // 在构造函数中初始化currentEpoch
     }
 
-    // 添加一个方法来获取截距项
+    // 计算线性回归参数
-    public double getIntercept() {
+    public double[] fit() {
-        return theta0;
+        if (x.length != y.length || x.length == 0) {
+            throw new IllegalArgumentException("Invalid input data");
         }
 
-    // 添加一个方法来获取第一个特征的系数
+        int n = x.length;
-    public double getCoefficient() {
+        double sumX = 0;
-        return theta1;
+        double sumY = 0;
-    }
+        double sumXY = 0;
+        double sumXX = 0;
 
-    // Sigmoid函数
+        // 计算各项和
-    private double sigmoid(double z) {
+        for (int i = 0; i < n; i++) {
-        return 1 / (1 + Math.exp(-z));
+            sumX += x[i];
+            sumY += y[i];
+            sumXY += x[i] * y[i];
+            sumXX += x[i] * x[i];
         }
 
-    // 预测函数
+        // 计算斜率和截距
-    public double predict(double x,double slopes,double intercept) {
+        double slope = (n * sumXY - sumX * sumY) / (n * sumXX - sumX * sumX);
-        BigDecimalUtils bigDecimalUtils=new BigDecimalUtils();
+        double intercept = (sumY - slope * sumX) / n;
-
-        Double mul = bigDecimalUtils.mul(intercept, x);
-
-        return bigDecimalUtils.add(slopes, mul);
 
+        return new double[] { intercept, slope };
     }
 
-    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 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) {
-//        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
-//
-//        DecimalFormat df = new DecimalFormat("#.0");
-//
-//        // 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 prediction = model.predict(2);
-//        System.out.println("x=3.5的预测： " + df.format(prediction));
-//    }
 }