public HugeFeatureLexicalDict(int thread_num, uint shrinkMemLoad)
{
SHRINK_AVALI_MEM_LOAD = shrinkMemLoad;
arrayFeatureFreq = new VarBigArray <FeatureFreq>(1024 * 1024);
arrayFeatureFreqSize = 0;
md5 = new AdvUtils.Security.Cryptography.MD5();
parallelOption = new ParallelOptions();
parallelOption.MaxDegreeOfParallelism = thread_num;
}
public void Clear()
{
arrayFeatureFreq.Clear();
arrayFeatureFreq = null;
}
public void Process(string strModelFileName, string strShrinkedModelFileName, int thread_num_ = 1)
{
var sr = new StreamReader(strModelFileName);
string strLine;
//读入版本号
strLine = sr.ReadLine();
var version = uint.Parse(strLine.Split(':')[1].Trim());
if (version == CRFSharp.Utils.MODEL_TYPE_SHRINKED)
{
Console.WriteLine("The input model has been shrinked");
return;
}
//读入cost_factor
strLine = sr.ReadLine();
var cost_factor_ = double.Parse(strLine.Split(':')[1].Trim());
//读入maxid
strLine = sr.ReadLine();
var maxid_ = long.Parse(strLine.Split(':')[1].Trim());
//读入xsize
strLine = sr.ReadLine();
var xsize_ = uint.Parse(strLine.Split(':')[1].Trim());
//读入空行
strLine = sr.ReadLine();
//读入待标注的标签
var y_ = new List<string>();
while (true)
{
strLine = sr.ReadLine();
if (strLine.Length == 0)
{
break;
}
y_.Add(strLine);
}
//读入unigram和bigram模板
var unigram_templs_ = new List<string>();
var bigram_templs_ = new List<string>();
while (sr.EndOfStream == false)
{
strLine = sr.ReadLine();
if (strLine.Length == 0)
{
break;
}
if (strLine[0] == 'U')
{
unigram_templs_.Add(strLine);
}
if (strLine[0] == 'B')
{
bigram_templs_.Add(strLine);
}
}
sr.Close();
//Load all features alpha data
var filename_alpha = strModelFileName + ".alpha";
var filename_shrink_alpha = strShrinkedModelFileName + ".alpha";
var sr_alpha = new StreamReader(filename_alpha);
var br_alpha = new BinaryReader(sr_alpha.BaseStream);
var sw_alpha = new StreamWriter(filename_shrink_alpha);
var bw_alpha = new BinaryWriter(sw_alpha.BaseStream);
long shrinked_alpha_size = 0;
//Only reserve non-zero feature weights and save them into file as two-tuples format
var alpha_ = new FixedBigArray<double>(maxid_ + 1, 0);
for (long i = 0; i < maxid_; i++)
{
alpha_[i] = br_alpha.ReadSingle();
if (alpha_[i] != 0)
{
bw_alpha.Write(i);
bw_alpha.Write((float)alpha_[i]);
shrinked_alpha_size++;
}
}
br_alpha.Close();
bw_alpha.Close();
//Only reserved lexical feature whose weights is non-zero
var varValue = new VarBigArray<int>(1024);
var varFeature = new VarBigArray<string>(1024);
var feaCnt = 0;
var filename_feature = strModelFileName + ".feature.raw_text";
var sr_fea = new StreamReader(filename_feature);
while (sr_fea.EndOfStream == false)
{
strLine = sr_fea.ReadLine();
var items = strLine.Split('\t');
var strFeature = items[0];
var key = int.Parse(items[1]);
var size = (strFeature[0] == 'U' ? y_.Count : y_.Count * y_.Count);
var hasAlpha = false;
for (var i = key; i < key + size; i++)
{
if (alpha_[i] != 0)
{
hasAlpha = true;
break;
}
}
if (hasAlpha == true)
{
varFeature[feaCnt] = strFeature;
varValue[feaCnt] = key;
feaCnt++;
}
}
sr_fea.Close();
Console.WriteLine("Shrink feature size from {0} to {1}", maxid_, shrinked_alpha_size);
maxid_ = shrinked_alpha_size;
//Build new lexical feature
var val = new FixedBigArray<int>(feaCnt, 0);
var fea = new FixedBigArray<string>(feaCnt, 0);
for (var i = 0; i < feaCnt; i++)
{
fea[i] = varFeature[i];
val[i] = varValue[i];
}
varFeature = null;
varValue = null;
var da = new DoubleArrayTrieBuilder(thread_num_);
if (da.build(fea, val, 0.95) == false)
{
Console.WriteLine("Build lexical dictionary failed.");
return;
}
da.save(strShrinkedModelFileName + ".feature");
var tofs = new StreamWriter(strShrinkedModelFileName);
// header
tofs.WriteLine("version: " + CRFSharp.Utils.MODEL_TYPE_SHRINKED);
tofs.WriteLine("cost-factor: " + cost_factor_);
tofs.WriteLine("maxid: " + maxid_);
tofs.WriteLine("xsize: " + xsize_);
tofs.WriteLine();
// y
for (var i = 0; i < y_.Count; ++i)
{
tofs.WriteLine(y_[i]);
}
tofs.WriteLine();
// template
for (var i = 0; i < unigram_templs_.Count; ++i)
{
tofs.WriteLine(unigram_templs_[i]);
}
for (var i = 0; i < bigram_templs_.Count; ++i)
{
tofs.WriteLine(bigram_templs_[i]);
}
tofs.Close();
}
public void Process(string strModelFileName, string strShrinkedModelFileName, int thread_num_ = 1)
{
var sr = new StreamReader(strModelFileName);
string strLine;
//读入版本号
strLine = sr.ReadLine();
var version = uint.Parse(strLine.Split(':')[1].Trim());
if (version == CRFSharp.Utils.MODEL_TYPE_SHRINKED)
{
Console.WriteLine("The input model has been shrinked");
return;
}
//读入cost_factor
strLine = sr.ReadLine();
var cost_factor_ = double.Parse(strLine.Split(':')[1].Trim());
//读入maxid
strLine = sr.ReadLine();
var maxid_ = long.Parse(strLine.Split(':')[1].Trim());
//读入xsize
strLine = sr.ReadLine();
var xsize_ = uint.Parse(strLine.Split(':')[1].Trim());
//读入空行
strLine = sr.ReadLine();
//读入待标注的标签
var y_ = new List <string>();
while (true)
{
strLine = sr.ReadLine();
if (strLine.Length == 0)
{
break;
}
y_.Add(strLine);
}
//读入unigram和bigram模板
var unigram_templs_ = new List <string>();
var bigram_templs_ = new List <string>();
while (sr.EndOfStream == false)
{
strLine = sr.ReadLine();
if (strLine.Length == 0)
{
break;
}
if (strLine[0] == 'U')
{
unigram_templs_.Add(strLine);
}
if (strLine[0] == 'B')
{
bigram_templs_.Add(strLine);
}
}
sr.Close();
//Load all features alpha data
var filename_alpha = strModelFileName + ".alpha";
var filename_shrink_alpha = strShrinkedModelFileName + ".alpha";
var sr_alpha = new StreamReader(filename_alpha);
var br_alpha = new BinaryReader(sr_alpha.BaseStream);
var sw_alpha = new StreamWriter(filename_shrink_alpha);
var bw_alpha = new BinaryWriter(sw_alpha.BaseStream);
long shrinked_alpha_size = 0;
//Only reserve non-zero feature weights and save them into file as two-tuples format
var alpha_ = new FixedBigArray <double>(maxid_ + 1, 0);
for (long i = 0; i < maxid_; i++)
{
alpha_[i] = br_alpha.ReadSingle();
if (alpha_[i] != 0)
{
bw_alpha.Write(i);
bw_alpha.Write((float)alpha_[i]);
shrinked_alpha_size++;
}
}
br_alpha.Close();
bw_alpha.Close();
//Only reserved lexical feature whose weights is non-zero
var varValue = new VarBigArray <int>(1024);
var varFeature = new VarBigArray <string>(1024);
var feaCnt = 0;
var filename_feature = strModelFileName + ".feature.raw_text";
var sr_fea = new StreamReader(filename_feature);
while (sr_fea.EndOfStream == false)
{
strLine = sr_fea.ReadLine();
var items = strLine.Split('\t');
var strFeature = items[0];
var key = int.Parse(items[1]);
var size = (strFeature[0] == 'U' ? y_.Count : y_.Count * y_.Count);
var hasAlpha = false;
for (var i = key; i < key + size; i++)
{
if (alpha_[i] != 0)
{
hasAlpha = true;
break;
}
}
if (hasAlpha == true)
{
varFeature[feaCnt] = strFeature;
varValue[feaCnt] = key;
feaCnt++;
}
}
sr_fea.Close();
Console.WriteLine("Shrink feature size from {0} to {1}", maxid_, shrinked_alpha_size);
maxid_ = shrinked_alpha_size;
//Build new lexical feature
var val = new FixedBigArray <int>(feaCnt, 0);
var fea = new FixedBigArray <string>(feaCnt, 0);
for (var i = 0; i < feaCnt; i++)
{
fea[i] = varFeature[i];
val[i] = varValue[i];
}
varFeature = null;
varValue = null;
var da = new DoubleArrayTrieBuilder(thread_num_);
if (da.build(fea, val, 0.95) == false)
{
Console.WriteLine("Build lexical dictionary failed.");
return;
}
da.save(strShrinkedModelFileName + ".feature");
var tofs = new StreamWriter(strShrinkedModelFileName);
// header
tofs.WriteLine("version: " + CRFSharp.Utils.MODEL_TYPE_SHRINKED);
tofs.WriteLine("cost-factor: " + cost_factor_);
tofs.WriteLine("maxid: " + maxid_);
tofs.WriteLine("xsize: " + xsize_);
tofs.WriteLine();
// y
for (var i = 0; i < y_.Count; ++i)
{
tofs.WriteLine(y_[i]);
}
tofs.WriteLine();
// template
for (var i = 0; i < unigram_templs_.Count; ++i)
{
tofs.WriteLine(unigram_templs_[i]);
}
for (var i = 0; i < bigram_templs_.Count; ++i)
{
tofs.WriteLine(bigram_templs_[i]);
}
tofs.Close();
}
