public void calcCost(Node n)
{
double c = 0;
long[] f = feature_cache_[n.fid];
for (int i = 0; i < f.Length && f[i] != -1; i++)
{
c += featureIndex.GetAlpha(f[i] + n.y);
}
n.cost = featureIndex.cost_factor_ * c;
}
public void calcCost(Node n)
{
double c = 0;
var f = feature_cache_[n.fid];
for (int i = 0; i < f.Length; ++i)
{
int fCurrent = (int)f[i];
if (fCurrent == -1)
{
break;
}
c += featureIndex.GetAlpha(fCurrent + n.y);
}
n.cost = featureIndex.cost_factor_ * c;
}
//Build feature set into indexed data
public bool BuildFeatureSetIntoIndex(string filename, double max_slot_usage_rate_threshold, int debugLevel, string strRetrainModelFileName)
{
Console.WriteLine("Building {0} features into index...", featureLexicalDict.Size);
IList<string> keyList;
IList<int> valList;
featureLexicalDict.GenerateLexicalIdList(out keyList, out valList);
if (debugLevel > 0)
{
Console.Write("Debug: Writing raw feature set into file...");
var filename_featureset_raw_format = filename + ".feature.raw_text";
var sw = new StreamWriter(filename_featureset_raw_format);
// save feature and its id into lists in raw format
for (var i = 0; i < keyList.Count; i++)
{
sw.WriteLine("{0}\t{1}", keyList[i], valList[i]);
}
sw.Close();
Console.WriteLine("Done.");
}
//Build feature index
var filename_featureset = filename + ".feature";
var da = new DoubleArrayTrieBuilder(thread_num_);
if (da.build(keyList, valList, max_slot_usage_rate_threshold) == false)
{
Console.WriteLine("Build lexical dictionary failed.");
return false;
}
//Save indexed feature set into file
da.save(filename_featureset);
if (strRetrainModelFileName == null || strRetrainModelFileName.Length == 0)
{
//Clean up all data
featureLexicalDict.Clear();
featureLexicalDict = null;
keyList = null;
valList = null;
GC.Collect();
//Create weight matrix
alpha_ = new double[feature_size() + 1];
}
else
{
Console.WriteLine();
Console.WriteLine("Loading the existed model for re-training...");
//Create weight matrix
alpha_ = new double[feature_size() + 1];
var modelReader = new ModelReader();
modelReader.LoadModel(strRetrainModelFileName);
if (modelReader.y_.Count == y_.Count)
{
for (var i = 0; i < keyList.Count; i++)
{
var index = modelReader.get_id(keyList[i]);
if (index < 0)
{
continue;
}
var size = (keyList[i][0] == 'U' ? y_.Count : y_.Count * y_.Count);
for (var j = 0; j < size; j++)
{
alpha_[valList[i] + j + 1] = modelReader.GetAlpha(index + j);
}
}
}
else
{
Console.WriteLine("The number of tags isn't equal between two models, it cannot be re-trained.");
}
//Clean up all data
featureLexicalDict.Clear();
featureLexicalDict = null;
keyList = null;
valList = null;
GC.Collect();
}
return true;
}