/// <summary>
/// 找到导入库和现有库的不同
/// </summary>
/// <param name="NewDicFile">导入库文件</param>
/// <param name="Encoding">导入库文件编码</param>
/// <param name="DicFormat">导入库文件格式</param>
/// <param name="SourceDict">原库对象</param>
/// <param name="OddLines">输出没有词性标注且现有库中也没有的词行</param>
/// <param name="NewWords">输出新词或现有词的新词性</param>
/// <param name="ExistWords">输出重复词，且词性也相同</param>
/// <param name="MaxFrqRate">重复词的最大词频比例</param>
/// <param name="MinFrqRate">重复词的最小词频比例</param>
/// <param name="AvgFrqRate">重复词的平均词频比例</param>
public static void FindDifferent(string NewDicFile, Encoding Encoding, DictionaryFormat DicFormat, WordDictionary SourceDict,
out string[] OddLines, out WordDictionary.DicWordInfo[] NewWords, out WordDictionary.DicWordInfo[] ExistWords,
out double MaxFrqRate, out double MinFrqRate, out double AvgFrqRate)
{
//初始化
MaxFrqRate = double.MinValue; MinFrqRate = double.MaxValue; decimal SumFrqRate = 0;
//const string[] CheckPos = new string[] { "n", "ns", "nr", "ng", "v", "j", "m", "vn", "a", "q" };
//准备词性转换
Dictionary<string, string> PosTrans = getPosTransformMap(DicFormat);
//加载词库
Dictionary<string, WordDictionary.DicWordInfo> OldWords = SourceDict.ToWordDictionary(); ;
//内存词组
List<string> Odds = new List<string>(OldWords.Count / 2);
List<WordDictionary.DicWordInfo> Exists = new List<SharpICTCLAS.WordDictionary.DicWordInfo>(OldWords.Count / 2);
List<WordDictionary.DicWordInfo> News = new List<WordDictionary.DicWordInfo>(OldWords.Count / 2);
//加载词库并统计库内有的词的词频，以估算词频转换的比例关系
foreach (string Line in File.ReadAllLines(NewDicFile, Encoding))
{
string Word;
int Frq;
string Poses;
switch (DicFormat)
{
case DictionaryFormat.SogouW2006:
string[] s = Line.Split('\t', ' ');
Word = s[0];
Frq = s.Length == 1 ? -1 : int.Parse(s[1]);
Poses = s.Length < 2 ? null : s[2];
break;
case DictionaryFormat.ExcelCSV:
default:
int p1 = Line.IndexOf(',');
int p2 = Line.IndexOf(',', p1 + 1);
Word = Line.Substring(0, p1);
Frq = int.Parse(Line.Substring(p1 + 1, p2 - p1 - 1));
Poses = Line.Substring(p2 + 1).Trim('"').Trim();
break;
}
if (string.IsNullOrEmpty(Poses))
{
if (!OldWords.ContainsKey(Word.ToLower())) Odds.Add(Line);
continue;
}
foreach (string InputPos in Poses.TrimEnd(',').Split(','))
{
if (string.IsNullOrEmpty(InputPos)) continue;
//如果映射表中没有，则保留原始词性字母
string Pos = PosTrans.ContainsKey(InputPos.ToLower()) ? PosTrans[InputPos.ToLower()] : InputPos.ToLower();
//是否存在
if (OldWords.ContainsKey(Word.ToLower()) && OldWords[Word.ToLower()].Pos.Contains(Pos))
{
int SourceFrq = OldWords[Word.ToLower()].Frequence;
double FrqR = SourceFrq == 0 ? Frq : (double)Frq / SourceFrq;
if (FrqR > MaxFrqRate) MaxFrqRate = FrqR;
if (FrqR < MinFrqRate) MinFrqRate = FrqR;
SumFrqRate += (decimal)FrqR;
Exists.Add(new WordDictionary.DicWordInfo(Word, Pos, Frq));
}
else //新词或新词性
{
News.Add(new WordDictionary.DicWordInfo(Word, Pos, Frq));
}
}
}
//平均频度转换倍数
AvgFrqRate = Exists.Count > 0 ? Convert.ToDouble(SumFrqRate / Exists.Count) : 0;
OddLines = Odds.ToArray();
NewWords = News.ToArray();
ExistWords = Exists.ToArray();
}