package PCI.ASQ.nlp;

import java.io.IOException;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.Iterator;
import java.util.List;
import java.util.Map;

import AVQ.ASQ.OVQ.OSQ.VSQ.obj.WordFrequency;
import ESU.list.List_ESU;
import OCI.ME.analysis.C.A;
import OEI.ME.analysis.E.CogsBinaryForest_AE;
//import OEU.LYG4DQS4D.LYG9DWithDoubleQuickSort4D;
import OEU.LYG4DQS4D.LYG9DWithDoubleTopSort4D;
import SVQ.stable.StablePOS;
//import OSI.PCI.ASQ.statistic.LYG4DWithDoubleQuickSort4D;
//作者,著作权人： 罗瑶光，浏阳
public class NLPTopicMatch{
  //<<NLP Algorithm of Matching The POS Scored Sentence>>.
  //This prediction algorithm mostly used for matching 
  //the best sample sentence by using score method.  
  //Theory: Yaoguang.Luo 20191228
  //Application: Yaoguang.Luo
  //Attention: need Deta Parser API OSS 
  public static String NLPBestSentenceMatch(String searchString
      , String[] sampleSentences) throws IOException {
    //init the deta mixed parser engine.
    CogsBinaryForest_AE _A= new CogsBinaryForest_AE();
    _A.IV_Mixed();
    //init the nlp POS(part of speech) functions.
    Map<String, String> nlp = _A.getPosCnToCn();
    List<String> keys= _A.parserMixedString(searchString);
    //find a appear frequency from the keys of search string.
    Map<String, WordFrequency> keyMap= _A.getWordFrequencyMap(keys);
    //get a POS score rights array from keyMap.
    String[] stringKeys= List_ESU.listToArray(keys);
    // I create a new algorithm of 'list to array' in my Data Swap Project. 20191228 Yaoguang. Luo
    //  public static String[] listToArray(List<String> list) {
    //    String[] output= new String[list.size()];
    //    int outputPoint= 0;
    //    Iterator<String> iterator= list.iterator();
    //    while(iterator.hasNext()) {
    //      output[outputPoint++]= iterator.next();
    //    }
    //    return output;
    //  }
    double[] scoreRights= getNLPBestSentencesMatchScoreRights(searchString
        , sampleSentences, _A, nlp, keyMap);
    //loop score array
    double[] matchScore= new double[sampleSentences.length];
    double max= 0;
    int maxPoint= 0;
    for(int i= 0; i< sampleSentences.length; i++) {
      List<String> matchList= _A.parserMixedString(sampleSentences[i]);
      Map<String, WordFrequency> matchMap= _A.getWordFrequencyMap(matchList);
      for(int j= 0; j< stringKeys.length; j++) {
        if(matchMap.containsKey(stringKeys[j])) {
          matchScore[i]+= scoreRights[j]* matchMap.get(stringKeys[j]).getFrequency();
        }
      }
      if(max< matchScore[i]) {
        max= matchScore[i];
        maxPoint= i;
      }
    }
    //output
    return sampleSentences[maxPoint];  
  }

  //<<NLP Algorithm of Matching The POS Scored Sentences>>.
  //This prediction algorithm mostly used for matching the 
  //ranged sample sentences by using score method.  
  //Theory: Yaoguang.Luo 20191229
  //Application: Yaoguang.Luo
  //Attention: need Deta Parser API OSS 
  //sortStackRange: for the sort stacks filter scale
  //filterRate: for how many sets need to delete 
  public static List<String> NLPBestSentencesMatch(String searchString
      , String[] sampleSentences, int sortStackRange, double filterRate) throws IOException {
    double[] matchScore= getNLPBestSentencesMatchScore(searchString, sampleSentences);
    //get iden key 
    Map<Double, List<String>> tempBase= new HashMap<>(); 
    for(int i= 0; i< matchScore.length; i++) {
      List<String> tempList;
      if(tempBase.containsKey(matchScore[i])) {
        tempList= tempBase.get(matchScore[i]);
      }else {
        tempList= new ArrayList<>();
      }
      tempList.add(sampleSentences[i]);
      tempBase.put(matchScore[i], tempList);
    }
    //sort
    //matchScore= new LYG4DWithDoubleQuickSort4D().sort(matchScore, sortStackRange);
    matchScore= new LYG9DWithDoubleTopSort4D().sort(matchScore, sortStackRange, 4);//算法应用更新
    double filterCount= filterRate* matchScore.length;
    //filter
    List<String> output= new ArrayList<>();
    for(int i= (int)filterCount; i< matchScore.length; i++) {
      if(tempBase.containsKey(matchScore[i])) {
        Iterator<String> iterator= tempBase.get(matchScore[i]).iterator();
        while(iterator.hasNext()) {
          output.add(iterator.next());  
        }
        tempBase.remove(matchScore[i]);
      }
    }
    //output
    return output;  
  }

  public static double[] getNLPBestSentencesMatchScore(String searchString
      , String[] sampleSentences) throws IOException {
    //init the deta mixed parser engine.
    CogsBinaryForest_AE _A= new CogsBinaryForest_AE();
    _A.IV_Mixed();
    //init the nlp POS(part of speech) functions.
    Map<String, String> nlp = _A.getPosCnToCn();
    List<String> keys= _A.parserMixedString(searchString);
    //find a appear frequency from the keys of search string.
    Map<String, WordFrequency> keyMap= _A.getWordFrequencyMap(keys);
    //get a POS score rights array from keyMap.
    String[] stringKeys= List_ESU.listToArray(keys);
    double[] scoreRights= getNLPBestSentencesMatchScoreRights(searchString
        , sampleSentences, _A, nlp, keyMap);
    //loop score array
    double[] matchScore= new double[sampleSentences.length];
    for(int i= 0; i< sampleSentences.length; i++) {
      List<String> matchList= _A.parserMixedString(sampleSentences[i]);
      Map<String, WordFrequency> matchMap= _A.getWordFrequencyMap(matchList);
      for(int j= 0; j< stringKeys.length; j++) {
        if(matchMap.containsKey(stringKeys[j])) {
          matchScore[i]+= scoreRights[j]* matchMap.get(stringKeys[j]).getFrequency();
        }
      }
    }
    return matchScore;
  }

  public static double[] getNLPBestSentencesMatchScoreRights(String searchString
      , String[] sampleSentences, A _A, Map<String, String> nlp
      , Map<String, WordFrequency> keyMap) throws IOException {
    double[] scoreRights= new double[keyMap.size()];
    int scoreRightsPoint= 0;
    Iterator<String> keyIterator= keyMap.keySet().iterator();
    while(keyIterator.hasNext()) {
      String key= keyIterator.next();
      if(nlp.containsKey(key)) {
        String pos= nlp.get(key);
        //init rights of POS {30,20,10,3,1}
        if(pos.contains(StablePOS.NLP_CI_MING)) {// n.
          scoreRights[scoreRightsPoint]= 30* keyMap.get(key).getFrequency();
        }else if(pos.contains(StablePOS.NLP_CI_DONG)) {//v
          scoreRights[scoreRightsPoint]= 20* keyMap.get(key).getFrequency();
        }else if(pos.contains(StablePOS.NLP_CI_XING_RONG)) {//adj
          scoreRights[scoreRightsPoint]= 10* keyMap.get(key).getFrequency();
        }else {
          scoreRights[scoreRightsPoint]= 3* keyMap.get(key).getFrequency();
        }
      }else {
        scoreRights[scoreRightsPoint]= 1* keyMap.get(key).getFrequency();
      }
      scoreRightsPoint++;
    }
    return scoreRights;
  }
}