public bool Init(Dictionary dict)
{
if (dict == null)
{
return false;
}
else
{
m_pDict = dict;
m_cSegTxt = null;
return true;
}
}
//<summary>
//这是为了传进的直接是内容时的情况做准备的，上面那个API上面是为了传进文件名
//</summary>
public static String SegmentAPI_content(ref String content)
{
/* 这里，如果已经生成了双数组词典wc.txt，则不必要运行下面两行代码(即tt.DictLoad和tt.SaveArray)，
* 如果没有生成wc.txt，则运行下面的两行代码
* 下面两行的目的就是为了生成双数组字典wc.txt,
* by kosko 2011-3-17
*/
/*
tt.DictLoad(@"..\..\CoreDict.txt");
tt.SaveArray(@"..\..\CoreDict.txt",@"..\..\wc.txt", false);
*/
Dictionary tt = new Dictionary();
String dict = @"..\..\wc.txt";
tt.Load(dict);//这里加载的是wc.txt，而不是以前所谓的qq.txt,我把这里改成了相对路径，方便大家使用,modified by kosko,2011-3-17
//当是从BayesClassifierDemo那里启动时，是以BayesClassifierDemo下的目录为基准的
//System.Console.Out.WriteLine(tt.SearchWord("故宫"));
Segment seg = new Segment();
seg.Init(tt);
seg.LoadContent(content);
int iSegOutSize = 0;
String result = seg.TextSegment(ref iSegOutSize);
if (result == null)
{
System.Console.Out.WriteLine("error");
return null;
}
/*
string strTarget = filename;//是不是可以直接覆盖掉原文
FileStream ResultFile = File.OpenWrite(strTarget);
StreamWriter ResultWriter = new StreamWriter(ResultFile, Encoding.GetEncoding("UTF-8"));
ResultWriter.AutoFlush = true;
ResultWriter.Write(result);
ResultWriter.Close();
ResultFile.Close();
return strTarget;
*/
return result;
}
private Dictionary<string, double> makePrediction(String fileContent, SortedDictionary<string, ICategory> sd)
{
ExcludedWords m_ExcludedWords = new ExcludedWords();
m_ExcludedWords.InitDefault();
EnumerableCategory words_in_file = new EnumerableCategory("", m_ExcludedWords);
words_in_file.TeachCategory(fileContent);//理解naive bayes后，我终于理解了，这个就是提取待分类文本的特征(即属性词)
//万事俱备，只欠计算
Dictionary<string, double> score = new Dictionary<string, double>();
foreach (KeyValuePair<string, ICategory> cat in sd)
{
score.Add(cat.Key, 0.0);
}
foreach (KeyValuePair<string, int> kvp1 in words_in_file)
{
// PhraseCount pc_in_file = kvp1.Value;
String words_in_predictionfile = kvp1.Key;//算P(f1=x1|s=si)，其中words_in_predictionfile就是x1
foreach (KeyValuePair<string, ICategory> kvp in sd)
{
ICategory cat = kvp.Value;
int count = cat.GetPhraseCount(words_in_predictionfile);//这里每轮的words_in_predictionfile是待分类文本的特征词
if (0 < count)
{
score[kvp.Key] += System.Math.Log((double)count / (double)cat.TotalWords);//说到底还是按类别(cat1、cat2...)等分类统计概率,就是连乘P(f1=x1|s=si)
}
else//count==0,用0.01代替0防止log无意义
{
score[kvp.Key] += System.Math.Log(0.01 / (double)cat.TotalWords);
}
System.Diagnostics.Trace.WriteLine(words_in_predictionfile + "(" +
kvp.Key + ")" + score[kvp.Key]);
}
}
int total = 0;
foreach (Category cat in sd.Values)
{
total += cat.TotalWords;
}
foreach (KeyValuePair<string, ICategory> kvp in sd)//觉得这里写得很没意思，就是把cat1+cat2+cat3+cat4+cat5作为总和，然后分别用每个类别去除以这个总和，然后取对数
{//更重要的，这里的含义我真不理解，签名是把每个类别的单词处于该类别的count，然后取对数，相加，然后又加上一个类别除以类别之和取对数
//现在理解了，这就是算先验概率啊
ICategory cat = kvp.Value;
score[kvp.Key] += System.Math.Log((double)cat.TotalWords / (double)total);
}
return score;
}
//<summary>
//以一行一对数据的方式读取字典,最低时间是1.40s
//待我改过SortedDictionary->Dictionary后，可以做到0.276s,而且分类是正确的
//</summary>
/*
public SortedDictionary<string, ICategory> loadData(string filename)
{
SortedDictionary<string, ICategory> sd = new SortedDictionary<string, ICategory>();
if (!File.Exists(filename))
{
Console.WriteLine("{0} does not exist.", filename);
return null;
}
using (StreamReader sr = File.OpenText(filename))
{
String input, input1;
while ((input = sr.ReadLine()) != null)
{
String[] words = input.Split(' ');
if (words[0] == CLASSIFIER_BEGIN)
{
Category cat = new Category();
Dictionary<string,int> m_Phrases=new Dictionary<string,int>();
cat.Name = words[1];
int total = 0;
double totalTime = 0;
while ((input1 = sr.ReadLine()) != null)
{
String[] wordss = input1.Split(' ');
if (wordss[0] == CLASSIFIER_END)
break;
m_Phrases[wordss[0]] = Int32.Parse(wordss[1]);//估计这里能优化点
total += Int32.Parse(wordss[1]);
}
cat.m_Phrases = m_Phrases;
cat.TotalWords = total;
sd[words[1]] = cat;
}
}
sr.Close();
}
return sd;
}
*/
//<summary>
//以多对数据放一行的方式读取字典，最低要
//200:1.288s 300:1.322s 100;1.290 150:1.283
//我把各类别总数算出来后，还需要1.26s
//经过多次艰苦卓绝的测试，我终于发现，耗费时间的元凶在于 cat.m_Phrases[words[i]]，几乎95%的时间耗费在这个上
//改进一：我在loadData函数里做了一个SortedDictionary，然后每次直接用本地的SortedDictionary赋值，然后等全部完成后，再在循环外赋值给category那个类里的SortedDictionary,这样可以减少到0.73s
//改进二：我考察整个程序后，发现使用SoretedDictonary根本没有必要，而SortedDictionary非常耗费时间(排序O(nlog(n)))，所以我直接改成Dictionary,现在改进到了0.16s。哈哈哈哈
//</summary>
public SortedDictionary<string, ICategory> loadData(string filename)
{
SortedDictionary<string, ICategory> sd = new SortedDictionary<string, ICategory>();
Dictionary<string, int> m_Phrases=null;
if (!File.Exists(filename))
{
Console.WriteLine("{0} does not exist.", filename);
return null;
}
using (StreamReader sr = File.OpenText(filename))
{
String input;
//int total=0;
Category cat=null;
double totalTime = 0;
while ((input = sr.ReadLine()) != null)//每次读入data_count_per_time对数据(除了最后一行) 表示类别时另外起一行
{
String[] words = input.Split(' ');
if (words.Length != 2)
{
for (int i = 0; i < words.Length; i += 2)
{
m_Phrases[words[i]] = Int32.Parse(words[i + 1]);//估计这里能优化点
}
}
else if (words[0]==CLASSIFIER_BEGIN)
{
cat = new Category();
cat.Name = words[1];
m_Phrases = new Dictionary<string, int>();
}
else if (words[0]==CLASSIFIER_END)
{
cat.TotalWords = Int32.Parse(words[1]);
cat.m_Phrases = m_Phrases;
sd[cat.Name] = cat;
}
else
{
m_Phrases[words[0]] = Int32.Parse(words[1]);
}
}
sr.Close();
}
return sd;
}
