public void Setup(bool debug)
{
var readModel = new ReadModel(InputModelFile);
var temp = new ReadModel(string.Concat(InputModelFile, ".featuresToK"));
_weightVector = new WeightVector(temp.GetFeatureToKdDictionary());
foreach (var pair in readModel.ModelIterator())
{
_weightVector.Add(pair);
}
_tags = new Tags(_tagList);
_viterbiForGlobalLinearModel = new ViterbiForGlobalLinearModel(_weightVector, _tags);
// read input file in a class and per line iterator.
var inputData = new ReadInputData(InputTestFile);
var writeModel = new WriteModel(_outputTestFile);
foreach (var line in inputData.GetSentence())
{
List<string> debugList;
var outputTags = _viterbiForGlobalLinearModel.Decode(line, debug, out debugList);
if (debug)
{
writeModel.WriteDataWithTagDebug(line, outputTags, debugList);
}
else
{
writeModel.WriteDataWithTag(line, outputTags);
}
}
writeModel.Flush();
}
public void StartMapping()
{
var inputData = new ReadInputData(_inputFile);
foreach (var line in inputData.GetSentence())
{
var inputTags = new List<string>(line.Count);
for (var j = 0; j < line.Count; j++)
{
var split = line[j].Split(new char[] {' '});
line[j] = split[0];
inputTags.Add(split[1]);
}
GenerateMappingForSentence(line);
}
inputData.Reset();
}
public void ReadInputs(string inputFile)
{
var inputData = new ReadInputData(inputFile);
foreach (var line in inputData.GetSentence())
{
var inputTags = new List<string>(line.Count);
var inputList = new List<string>(line.Count);
for (var j = 0; j < line.Count; j++)
{
var split = line[j].Split(new char[] { ' ' });
inputList.Add(split[0]);
inputTags.Add(split[1]);
}
InputSentences.Add(inputList);
TagsList.Add(inputTags);
}
inputData.Reset();
}
public void Setup(bool debug)
{
Init();
// read input file in a class and per line iterator.
var inputData = new ReadInputData(InputTestFile);
var writeModel = new WriteModel(_outputTestFile);
foreach (var line in inputData.GetSentence())
{
List<string> debugList;
var outputTags = ViterbiForGLM.DecodeNew(line, debug, out debugList);
if (debug)
{
writeModel.WriteDataWithTagDebug(line, outputTags, debugList);
}
else
{
writeModel.WriteDataWithTag(line, outputTags);
}
}
writeModel.Flush();
}
public void Train(string[] inputFiles)
{
for (int fileNum = 0; fileNum < inputFiles.Length; fileNum++)
{
var inputFile = inputFiles[fileNum];
Console.WriteLine(DateTime.Now + " " + inputFile + " training ");
const int iterationCount = 10;
for (var i = 0; i < iterationCount; i++)
{
// Console.WriteLine(DateTime.Now + " " + inputFile + " training iteration: " + i);
var inputData = new ReadInputData(inputFile);
foreach (var line in inputData.GetSentence())
{
var inputTags = new List<string>(line.Count);
for (var j = 0; j < line.Count; j++)
{
var split = line[j].Split(new char[] {' '});
line[j] = split[0];
inputTags.Add(split[1]);
}
List<string> temp;
var outputTags = _viterbiForGlobalLinearModel.DecodeNew(line, false, out temp);
if (Match(inputTags, outputTags)) continue;
var inputFeature = (new FeatureWrapper(inputTags, line)).NextFeature().GetEnumerator();
var outputFeature = new FeatureWrapper(outputTags, line).NextFeature().GetEnumerator();
while (inputFeature.MoveNext() && outputFeature.MoveNext())
{
if (inputFeature.Current.Key.Equals(outputFeature.Current.Key))
continue;
var inputAdd = 1*Features.GetWeight(inputFeature.Current.Value);
var outputRemove = -1*Features.GetWeight(outputFeature.Current.Value);
WeightVector.AddToKey(inputFeature.Current.Value, inputAdd);
WeightVector.AddToKey(outputFeature.Current.Value, outputRemove);
}
}
AvgWeightVector.AddWeightVector(WeightVector);
inputData.Reset();
}
AvgWeightVector.DividebyNum(iterationCount);
}
Console.WriteLine(DateTime.Now+" training is complete");
}
public void Train()
{
for (var i = 0; i < 1; i++)
{
Console.WriteLine(DateTime.Now+" training iteration: "+ i);
var inputData = new ReadInputData(_inputFile);
foreach (var line in inputData.GetSentence())
{
var inputTags = new List<string>(line.Count);
for(var j = 0; j < line.Count;j++)
{
var split = line[j].Split(new char[] {' '});
line[j] = split[0];
inputTags.Add(split[1]);
}
List<string> temp;
var outputTags = _viterbiForGlobalLinearModel.Decode(line, false, out temp);
if (Match(inputTags, outputTags)) continue;
var inputFeature = (new FeatureWrapper(inputTags, line)).NextFeature().GetEnumerator();
var outputFeature= new FeatureWrapper(outputTags, line).NextFeature().GetEnumerator();
while (inputFeature.MoveNext() && outputFeature.MoveNext())
{
if (inputFeature.Current.Key.Equals(outputFeature.Current.Key))
continue;
WeightVector.AddToKey(inputFeature.Current.Value,
1 * Features.GetWeight(inputFeature.Current.Value));
WeightVector.AddToKey(outputFeature.Current.Value,
-1 * Features.GetWeight(inputFeature.Current.Value));
}
}
inputData.Reset();
}
// _weightVector.NormalizeAllWeights(100);
Console.WriteLine(DateTime.Now+" training is complete");
}
