基于Python实现北京PM2.5浓度的预测分析项目源码（期末大作业）.zip

package BP; import java.io.File; import java.text.DecimalFormat; import java.util.Arrays; import java.util.Scanner; import java.util.Vector; public class BpDeepTest { //存储从文件中读取的原始数据 public static Vector<String> indata = new Vector<String>(); //存储预处理和归一化后的训练集 public static Vector<double[]> data = new Vector<double[]>(); static double[] max = new double[]{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}; static double[] min = new double[]{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}; static double[] weigth = new double[]{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}; //加载测试的数据文件 public static boolean loadData(String url) { try { Scanner in = new Scanner(new File(url));//读入文件 while (in.hasNextLine()) { String str = in.nextLine();//将文件的每一行存到str的临时变量中 indata.add(str);//将每一个样本点的数据追加到Vector 中 } return true; } catch (Exception e) { //如果出错返回false return false; } } public static void pretreatment(Vector<String> indata) { //数据预处理，将原始数据中的每一个属性值提取出来并进行归一化存放到Vector<double[]> data中 Vector<double[]> temdata = new Vector<double[]>(); int i = 1; String t; while (i < indata.size()) {//取出indata中的每一行值 double[] tem = new double[14]; t = indata.get(i); String[] sourceStrArray = t.split(",", 16);//使用字符串分割函数提取出各属性值 for (int j = 0; j < 13; j++) { tem[j] = Double.parseDouble(sourceStrArray[j + 2]); //将每一个的样本的各属性值类型转换后依次存入到double[]数组中 if (tem[j] > max[j]) max[j] = tem[j]; if (tem[j] < min[j]) min[j] = tem[j]; } switch (sourceStrArray[12]) { case "Very High": { tem[13] = 1; break; } case "High": { tem[13] = 2; break; } case "Moderate": { tem[13] = 3; break; } case "Low": { tem[13] = 4; break; } case "Very low": { tem[13] = 5; break; } default: break; } temdata.add(tem);//将每一个样本加入到temdata中 i++; } /*******以下部分对数据进行归一化处理**********/ for (int r = 0; r < max.length; r++) { weigth[r] = max[r] - min[r]; } for (int r = 0; r < temdata.size(); r++) { double[] t1 = temdata.get(r); for (int j = 0; j < t1.length - 1; j++) { t1[j] = t1[j] / weigth[j]; } data.add(t1); } } public static String Show_air_quality(double[] result) {//根据结果返回空气质量 String rt = ""; int NO = 0; double max = 0; for (int i = 0; i < result.length; i++) { if (result[i] >= max) { max = result[i]; NO = i; } } switch (NO) { case 0: { rt = "Very high"; break; } case 1: { rt = "High"; break; } case 2: { rt = "Moderate"; break; } case 3: { rt = "Low"; break; } case 4: { rt = "Very low"; break; } default: break; } return rt; } public static void main(String[] args) { long startTime = System.currentTimeMillis();//或得程序开始运行时间 loadData("AirQualityUCI.data");//载入训练数据 pretreatment(indata);//预处理数据 double[][] train_data = new double[data.size()][data.get(0).length - 1];//构建训练样本集 int r = 0; while (r < data.size()) { double[] tem = data.get(r); for (int j = 0; j < tem.length - 1; j++) { train_data[r][j] = tem[j]; } r++; } double[][] target = new double[data.size()][5];//构建训练样本集的结果集 r = 0; while (r < data.size()) { int t = (int) data.get(r)[13]; switch (t) { case 1: { target[r] = new double[]{1.0, 0.0, 0.0, 0.0, 0.0}; break; } case 2: { target[r] = new double[]{0.0, 1.0, 0.0, 0.0, 0.0}; break; } case 3: { target[r] = new double[]{0.0, 0.0, 1.0, 0.0, 0.0}; break; } case 4: { target[r] = new double[]{0.0, 0.0, 0.0, 1.0, 0.0}; break; } case 5: { target[r] = new double[]{0.0, 0.0, 0.0, 0.0, 1.0}; break; } default: break; } r++; } BP bp1 = new BP(13, 13, 5);//新建一个神经网络 for (int s = 0; s < 10000; s++) {//循环训练10000次 for (int i = 0; i < data.size(); i++) { //训练 bp1.train(train_data[i], target[i]); } int correct = 0; for (int j = 0; j < data.size(); j++) { //测试 double[] result = bp1.test(train_data[j]); double max = 0; int NO = 0; for (int i = 0; i < result.length; i++) { if (result[i] >= max) { max = result[i]; NO = i; } } if (target[j][NO] == 1.0) { correct++; } else if(s==9999)//输出训练10000次后测试的错误结果 System.out.println("第"+(s+1)+"次训练后，第"+j+"号测试用例预测错误--------------"); } double b=(correct * 1.0 / data.size()) * 100;//计算正确率 DecimalFormat df = new DecimalFormat( "0.00 ");//设置输出精度 System.out.println("第 " + (s+1) + " 次训练后，使用训练集检测的正确率为==" +df.format(b) + "%"); } double[] x = new double[]{-200,883,-200,1.3,530,63,997,46,1102,617,13.7,68.2,1.0611}; System.out.print("使用测试用例" + Arrays.toString(x) + " 根据神经网络计算预计空气质量为："); for(int i=0;i<x.length;i++) x[i]=x[i]/weigth[i];//对数据归一化 double[] result = bp1.test(x); System.out.println(Show_air_quality(result)); System.out.println("程序运行时间为：" + (System.currentTimeMillis() - startTime) * 1.0 / 1000 + " s"); } }