public void When_GivenNonExistentModelPath_Then_ShouldThrowFileNotFoundException()
{
// Arrange
var testTarget = new ModelReader(
_testModelPath + "some nonexistant garbage");
// Act
testTarget.LoadModel();
}
public void When_GivenValidModelPath_Then_ShouldLoadModel()
{
// Arrange
var testTarget = new ModelReader(_testModelPath);
int expectedFeatureSize = 470517; // figured out based on model metadata
// Act
testTarget.LoadModel();
// Assert
Assert.AreEqual(testTarget.feature_size(), expectedFeatureSize);
}
public void When_GivenValidModelLoader_Then_ShouldLoadModel()
{
// Arrange
Func <string, Stream> fakeLoader = modelName =>
new MemoryStream(File.ReadAllBytes(modelName));
var testTarget = new ModelReader(fakeLoader, _testModelPath);
int expectedFeatureSize = 470517; // figured out based on model metadata
// Act
testTarget.LoadModel();
// Assert
Assert.AreEqual(testTarget.feature_size(), expectedFeatureSize);
}
//Build feature set into indexed data
public bool BuildFeatureSetIntoIndex(string filename, double max_slot_usage_rate_threshold, int debugLevel)
{
IList <string> keyList;
IList <int> valList;
featureLexicalDict.GenerateLexicalIdList(out keyList, out valList);
if (debugLevel > 0)
{
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();
}
//Build feature index
var filename_featureset = filename + ".feature";
var da = new CRFLite.Utils.DoubleArrayTrieBuilder(thread_num_);
if (da.build(keyList, valList, max_slot_usage_rate_threshold) == false)
{
return(false);
}
//Save indexed feature set into file
da.save(filename_featureset);
if (string.IsNullOrWhiteSpace(modelFileName))
{
//Clean up all data
featureLexicalDict.Clear();
featureLexicalDict = null;
keyList = null;
valList = null;
GC.Collect();
//Create weight matrix
alpha_ = new double[feature_size() + 1];
}
else
{
//Create weight matrix
alpha_ = new double[feature_size() + 1];
var modelReader = new ModelReader(this.modelFileName);
modelReader.LoadModel();
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
{
}
//Clean up all data
featureLexicalDict.Clear();
featureLexicalDict = null;
keyList = null;
valList = null;
GC.Collect();
}
return(true);
}
/// <summary>
/// Loads an encoded model using the specified delegate.
/// Using this overload you can read the model e.g.
/// from network, zipped archives or other locations, as you wish.
/// </summary>
/// <param name="modelLoader">
/// Allows reading the model from arbitrary formats and sources.
/// </param>
/// <param name="modelFilename">
/// The model file name, as used by the given <paramref name="modelLoader"/>
/// for file resolution.
/// </param>
/// <returns></returns>
public void LoadModel(Func <string, Stream> modelLoader, string modelFilename)
{
this._modelReader = new ModelReader(modelLoader, modelFilename);
_modelReader.LoadModel();
}
/// <summary>
/// Load encoded model from file
/// </summary>
/// <param name="modelFilename">
/// The model path.
/// </param>
/// <returns></returns>
public void LoadModel(string modelFilename)
{
_modelReader = new ModelReader(modelFilename);
_modelReader.LoadModel();
}
//Load encoded model form file
public bool LoadModel(string strModelFileName)
{
modelReader = new ModelReader();
return(modelReader.LoadModel(strModelFileName));
}
/// <summary>
/// Load encoded model from file
/// </summary>
/// <param name="strModelFileName"></param>
/// <returns></returns>
public void LoadModel(string strModelFileName)
{
modelReader = new ModelReader();
modelReader.LoadModel(strModelFileName);
}
