数字营销实训算法第一轮修改
@t2652009480
7 months ago
parent
c67e2436b9
commit
4a9095d9b0
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package com.sztzjy.marketing.entity.dto;
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import io.swagger.annotations.ApiModelProperty;
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import lombok.Data;
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import java.util.List;
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import java.util.Map;
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/**
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* @author tz
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* @date 2024/8/17 14:16
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*/
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@Data
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public class AprioriDTO {
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@ApiModelProperty("最小支持度阀值")
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private double support;
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@ApiModelProperty("最小置信度")
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private double confidence;
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@ApiModelProperty("用户ID")
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private String userId;
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@ApiModelProperty("数据集")
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private List<Map<String, Object>> deduplicatedDataList;
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}
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package com.sztzjy.marketing.entity.dto;
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import lombok.Data;
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/**
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* @author tz
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* @date 2024/8/17 16:30
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*/
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@Data
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public class RAnalysisDTO {
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private double intercept;
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private double slopes;
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}
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package com.sztzjy.marketing.util.algorithm;
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public class Data {
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public double[] features;
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public double label;
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public Data(double[] features, double label) {
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this.features = features;
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this.label = label;
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}
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}
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@ -1,71 +1,121 @@
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package com.sztzjy.marketing.util.algorithm;
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import java.util.Arrays;
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import com.sztzjy.marketing.util.BigDecimalUtils;
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import java.text.DecimalFormat;
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public class LogisticRegression {
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private double[] weights;
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private double bias;
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private double theta0; // 截距项
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private double theta1; // 斜率
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private double learningRate; // 学习率
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private int currentEpoch; // 声明currentEpoch变量
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public LogisticRegression(int numFeatures) {
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weights = new double[numFeatures];
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bias = 0;
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public LogisticRegression(double learningRate) {
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this.learningRate = learningRate;
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}
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public void fit(double[][] X, double[] y, double learningRate, int numIterations) {
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int numSamples = X.length;
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int numFeatures = X[0].length;
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for (int iter = 0; iter < numIterations; iter++) {
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double[] gradientWeights = new double[numFeatures];
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double gradientBias = 0;
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for (int i = 0; i < numSamples; i++) {
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double dotProduct = dotProduct(X[i], weights) + bias;
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double prediction = sigmoid(dotProduct);
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double error = y[i] - prediction;
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for (int j = 0; j < numFeatures; j++) {
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gradientWeights[j] += error * X[i][j];
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}
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gradientBias += error;
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}
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public LogisticRegression(double learningRate, double initialTheta0, double initialTheta1) {
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this.learningRate = learningRate;
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this.theta0 = initialTheta0;
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this.theta1 = initialTheta1;
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this.currentEpoch = 0; // 在构造函数中初始化currentEpoch
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}
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for (int j = 0; j < numFeatures; j++) {
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weights[j] += learningRate * gradientWeights[j] / numSamples;
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}
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bias += learningRate * gradientBias / numSamples;
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}
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// 添加一个方法来获取截距项
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public double getIntercept() {
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return theta0;
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}
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public double predict(double[] x) {
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double dotProduct = dotProduct(x, weights) + bias;
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return sigmoid(dotProduct);
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// 添加一个方法来获取第一个特征的系数
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public double getCoefficient() {
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return theta1;
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}
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private double sigmoid(double x) {
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return 1 / (1 + Math.exp(-x));
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// Sigmoid函数
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private double sigmoid(double z) {
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return 1 / (1 + Math.exp(-z));
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}
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private double dotProduct(double[] x, double[] y) {
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double sum = 0;
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for (int i = 0; i < x.length; i++) {
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sum += x[i] * y[i];
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// 预测函数
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public double predict(double x) {
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BigDecimalUtils bigDecimalUtils=new BigDecimalUtils();
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Double mul = bigDecimalUtils.mul(this.theta0, x);
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return bigDecimalUtils.add(this.theta1, mul);
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}
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private double dotProduct(double[] a, double[] b) {
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double result = 0.0;
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for (int i = 0; i < a.length; i++) {
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result += a[i] * b[i];
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}
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return sum;
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return result;
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}
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// 梯度下降更新参数
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public void updateParameters(double x, double y) {
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double h = predict(x);
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double error = y - h;
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theta1 += learningRate * error * h * (1 - h) * x;
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theta0 += learningRate * error * h * (1 - h);
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}
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private double exponentialDecayLearningRate(double currentEpoch, int totalEpochs) {
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return learningRate * Math.exp(-(currentEpoch / totalEpochs));
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}
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// 训练模型
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public void train(double[][] data, double[] labels, int epochs) {
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double learningRateCurrentEpoch = exponentialDecayLearningRate(currentEpoch, epochs);
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for (int epoch = 0; epoch < epochs; epoch++) {
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for (int i = 0; i < data.length; i++) {
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double x = data[i][0];
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double y = labels[i];
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double h = sigmoid(theta0 + theta1 * x);
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double error = y - h;
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theta1 += learningRateCurrentEpoch * error * h * (1 - h) * x;
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theta0 += learningRateCurrentEpoch * error * h * (1 - h);
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}
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// 可以添加打印语句查看训练情况
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// System.out.println("Epoch: " + (epoch + 1) + ", Loss: " + calculateLoss(data, labels));
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}
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}
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public double calculateLoss(double[][] data, double[] labels) {
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double loss = 0;
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for (int i = 0; i < data.length; i++) {
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double x = data[i][0];
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double y = labels[i];
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double h = sigmoid(theta0 + theta1 * x);
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loss += -y * Math.log(h) - (1 - y) * Math.log(1 - h);
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}
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return loss / data.length;
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}
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public static void main(String[] args) {
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double[][] X = {{1, 1}, {1, 2}, {2, 1}, {2, 3}};
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double[] y = {0, 0, 0, 1};
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LogisticRegression model = new LogisticRegression(0.1); // 创建一个逻辑回归模型实例
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double[][] data = {{1}, {2}, {3}, {4}, {5}};
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double[] labels = {0, 0, 1, 1, 1}; // 假设这是一个二分类问题
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// double[][] data = {{1, 19}, {1, 21}, {2, 20}, {2, 23}, {2, 31}};
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// double[] labels = {15, 15, 16, 16, 17};
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model.train(data, labels, 1000); // 训练模型
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LogisticRegression model = new LogisticRegression(2);
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model.fit(X, y, 0.01, 10000);
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DecimalFormat df = new DecimalFormat("#.0");
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System.out.println("Weights: " + Arrays.toString(model.weights));
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System.out.println("Bias: " + model.bias);
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// 获取并打印截距项和系数
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double intercept = model.getIntercept();
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double coefficient = model.getCoefficient();
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System.out.println("常数项值: " + df.format(intercept));
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System.out.println("系数值 " + df.format(coefficient));
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double[] newSample = {1, 2};
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double prediction = model.predict(newSample);
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System.out.println("Prediction for " + Arrays.toString(newSample) + ": " + prediction);
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double prediction = model.predict(2);
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System.out.println("x=3.5的预测： " + df.format(prediction));
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}
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}
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