package utils;
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import java.util.HashMap;
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/**
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* @Auther: Forrest
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* @Date: 2020/6/8 14:05
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* @Description:
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*/
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public class AlgsUtils {
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private HashMap<String, Object> model = new HashMap<String, Object>();
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public HashMap<String, Object> createPredictHashmap(HashMap<String, Object> models) {
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if ((models.containsKey("iail/mdk/model"))) {
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if (((String) ((HashMap) models.get("iail/mdk/model")).get("param1")).isEmpty()) {
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String aaa = "error";
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model.put("param1", aaa);
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} else {
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String model_train = (String) ((HashMap) models.get("iail/mdk/model")).get("param1");
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model.put("param1", model_train);
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}
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} else {
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model = models;
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}
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return model;
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}
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public HashMap<String, Object> createPredictHashmapplus(HashMap<String, Object> models) {
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if (models != null && models.containsKey("models")) {
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if (((String) ((HashMap) models.get("models")).get("paramFile")).isEmpty()) {
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String aaa = "error";
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model.put("param1", aaa);
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} else {
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String model_train = (String) ((HashMap) models.get("models")).get("paramFile");
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model.put("paramFile", model_train);
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if (((HashMap) models.get("models")).containsKey("dim")) {
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Object dim = ((HashMap) models.get("models")).get("dim");
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model.put("dim", dim);
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}
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}
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} else {
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model = models;
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}
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return model;
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}
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private HashMap<String, Object> eval_pre = new HashMap<String, Object>();
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/**
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* 对返回码进行转换
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*
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* @param models
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* @return
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*/
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public int reverseResultCode(HashMap<String, Object> models) {
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if ((models.containsKey("result_code"))) {
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return Integer.parseInt((String) models.get("result_code"));
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}
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return -2;
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}
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/**
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* 对评价指标进行转换
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*
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* @param models
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* @return
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*/
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public HashMap<String, Object> reverseEval(HashMap<String, Object> models) {
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if ((models.containsKey("eval"))) {
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if (((HashMap) models.get("eval")).containsKey("MAE")) {
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double MAE = Double.parseDouble((String) ((HashMap) models.get("eval")).get("MAE"));
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eval_pre.put("MAE", MAE);
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}
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if (((HashMap) models.get("eval")).containsKey("MAPE")) {
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double MAPE = Double.parseDouble((String) ((HashMap) models.get("eval")).get("MAPE"));
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eval_pre.put("MAPE", MAPE);
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}
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if (((HashMap) models.get("eval")).containsKey("RMSE")) {
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double MAE = Double.parseDouble((String) ((HashMap) models.get("eval")).get("RMSE"));
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eval_pre.put("RMSE", MAE);
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}
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}
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return eval_pre;
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}
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/**
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* 对models里面的参数进行转换
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*/
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private HashMap<String, Object> train_result_models = new HashMap<String, Object>();
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public HashMap<String, Object> reverseModels(HashMap<String, Object> train_result) {
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if (train_result.containsKey("models")) {
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train_result_models = (HashMap) train_result.get("models");
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if (((HashMap) train_result.get("models")).containsKey("dim")) {
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double dim = Double.parseDouble((String) ((HashMap) train_result.get("models")).get("dim"));
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train_result_models.put("dim", dim);
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}
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train_result.put("models", train_result_models);
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}
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return train_result;
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}
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/**
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* 获取二维数组行列数
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*
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* @param arr
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* @return
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*/
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public int[] getColAndRow(double[][] arr) {
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int row = arr.length;
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int col = arr[0].length;
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int[] result = new int[2];
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result[0] = row;
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result[1] = col;
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return result;
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}
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/**
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* 两个二维数组进行合并
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*
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* @param data
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* @param refs
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* @return
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*/
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public double[][] getMathergeArr(double[][] data, double[][] refs) {
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int[] dataRowAndCol = getColAndRow(data);
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int rowData = dataRowAndCol[0];
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int colData = dataRowAndCol[1];
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int[] refsRowAndCol = getColAndRow(refs);
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int rowrefs = refsRowAndCol[0];
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int colrefs = refsRowAndCol[1];
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double[][] newData = new double[rowData + rowrefs][colData];
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for (int i = 0; i < rowData; i++) {
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for (int j = 0; j < colData; j++) {
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newData[i][j] = data[i][j];
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}
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}
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for (int i = 0; i < rowrefs; i++) {
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for (int j = 0; j < colrefs; j++) {
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newData[i + rowData][j] = refs[i][j];
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}
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}
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return newData;
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}
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/**
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* 对训练方法进行处理,实现评价指标的转换
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*/
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public HashMap<String, Object> trainUtil(HashMap<String, Object> train_result, HashMap<String, Object> eval, String time) {
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if (train_result.containsKey("eval")) {
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eval = (HashMap<String, Object>) train_result.get("eval");
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eval.put("time", time);
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train_result.put("eval", eval);
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}
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train_result.put("result_code", reverseResultCode(train_result));
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return train_result;
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}
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/**
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* 对预测方法进行处理
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*/
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// public HashMap<String,Object> predictUtil(){
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//
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// }
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}
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