private static void Test_BPMIncremental(
int nClass, int totalFeatures, double noisePrec, int maxItemsInBatch,
int nChunks, string trainingFile, Vector[] testData)
{
Console.WriteLine("\n------- BPM Train Incremental -------");
VectorGaussian[] wInfer = new VectorGaussian[nClass];
BPM bpmIncremental = new BPM(nClass, totalFeatures, noisePrec);
bpmIncremental.TrainingEngine.ShowProgress = false;
bpmIncremental.TestEngine.ShowProgress = false;
int LocToStart = 0;
for (int c = 0; c < nChunks; c++)
{
List<Vector>[] dataChunks = DataFromFile.Read(trainingFile, nClass, maxItemsInBatch, ref LocToStart);
wInfer = bpmIncremental.TrainIncremental(dataChunks);
}
#if ShowWeights
for (int i = 0; i < wInfer.GetLength(0); i++)
{
Console.WriteLine(wInfer[i].ToString());
}
#endif
Console.WriteLine("\nPredictions:");
Discrete[] predictions = bpmIncremental.Test(testData);
foreach (Discrete pred in predictions)
Console.WriteLine(pred);
Console.WriteLine();
}
private static void Test_BPM(int nClass, int totalFeatures, double noisePrec, string fileName, Vector[] testData)
{
Console.WriteLine("\n------- BPM -------");
List<Vector>[] data = DataFromFile.Read(fileName, nClass);
BPM bpm = new BPM(nClass, totalFeatures, noisePrec);
bpm.TrainingEngine.ShowProgress = false;
bpm.TestEngine.ShowProgress = false;
VectorGaussian[] wInfer = bpm.Train(data);
#if ShowWeights
for (int i = 0; i < wInfer.GetLength(0); i++)
{
Console.WriteLine(wInfer[i].ToString());
}
#endif
Discrete[] predictions = bpm.Test(testData);
Console.WriteLine("\nPredictions:");
foreach (Discrete pred in predictions)
Console.WriteLine(pred);
Console.WriteLine();
}
/// <summary>
/// Runs the BPM using dense features.
/// </summary>
/// <param name="numClasses">The number of classes</param>
/// <param name="noisePrecision">The noise precision</param>
/// <param name="trainingSetFile">The file containing the training set</param>
/// <param name="testSet">The test set</param>
private static void RunBPM(int numClasses, double noisePrecision, string trainingSetFile, bool labelAtEnd, bool addBias, Vector[] testSet)
{
Console.WriteLine("------- BPM -------");
int[] labels;
Vector[] featureVectors = DataUtils.Read(trainingSetFile, labelAtEnd, addBias, out labels);
BPM bpm = new BPM(numClasses, noisePrecision);
VectorGaussian[] posteriorWeights = bpm.Train(featureVectors, labels);
#if ShowWeights
Console.WriteLine("Weights=" + StringUtil.ArrayToString(posteriorWeights));
#endif
Console.WriteLine("\nPredictions:");
Discrete[] predictions = bpm.Test(testSet);
foreach (Discrete prediction in predictions)
Console.WriteLine(prediction);
Console.WriteLine();
}
/// <summary>
/// Runs the BPM using dense features and incremental training.
/// </summary>
/// <param name="numClasses">The number of classes</param>
/// <param name="noisePrecision">The noise precision</param>
/// <param name="trainingSetFile">The file containing the training set</param>
/// <param name="testSet">The test set</param>
/// <param name="maxItemsPerChunk">The maximum number of items per chunk</param>
/// <param name="numChunks">The total number of chunks</param>
private static void RunIncrementalBPM(int numClasses, double noisePrecision, string trainingSetFile, bool labelAtEnd, bool addBias, Vector[] testSet, int maxItemsPerChunk, int numChunks)
{
Console.WriteLine("\n------- Incremental BPM -------");
BPM bpm = new BPM(numClasses, noisePrecision);
int[] labels;
Vector[] featureVectors;
VectorGaussian[] posteriorWeights = new VectorGaussian[numClasses];
int locationToStart = 0;
for (int c = 0; c < numChunks; c++)
{
featureVectors = DataUtils.Read(trainingSetFile, maxItemsPerChunk, labelAtEnd, addBias, out labels, ref locationToStart);
posteriorWeights = bpm.Train(featureVectors, labels);
}
#if ShowWeights
Console.WriteLine("Weights=" + StringUtil.ArrayToString(posteriorWeights));
#endif
Console.WriteLine("\nPredictions:");
Discrete[] predictions = bpm.Test(testSet);
foreach (Discrete pred in predictions)
Console.WriteLine(pred);
Console.WriteLine();
}
private void begin_Click(object sender, EventArgs e)
{
if (_maxSensitivity < _startSensitivity)
{
ShowDialog(@"The maximum sensitivity must be greater than or equal to the minimum sensitivity.",
"Error", false);
return;
}
if (!_performingCalcs)
{
//Disable input changes
ChangeStatusOfInputs(false);
beginButton.Text = @"Cancel";
_bpm = new BPM(_numOfClasses, _noisePrecision);
try
{
_trainingModel = FileUtils.ReadFile(_trainingFilePath, _labelAtStartOfLine, _noOfFeatures, _addBias);
_testModel = FileUtils.ReadFile(_testFilePath, _labelAtStartOfLine, _noOfFeatures, _addBias);
_writer = new StreamWriter(_resultsFilePath, _appendToFile);
_totalRuns = (int) Math.Ceiling(_noOfRuns*(1 + ((_maxSensitivity - _startSensitivity)/_sensitivityIncrement)));
_last = DateTime.Now;
bw.WorkerReportsProgress = true;
bw.WorkerSupportsCancellation = true;
bw.DoWork += bw_DoWork;
bw.ProgressChanged += bw_ProgressChanged;
bw.RunWorkerCompleted += bw_RunWorkerCompleted;
bw.RunWorkerAsync();
progressBar1.Maximum = _totalRuns;
_performingCalcs = true;
_prevRem = _totalRuns;
}
catch (Exception exception)
{
ShowDialog("Sorry, there was an error reading the input data" + exception.GetType(), "Error", true);
beginButton.Text = @"Begin processing";
ChangeStatusOfInputs(true);
}
}
else
{
bw.CancelAsync();
beginButton.Text = @"Begin processing";
//Tidy up
statusLabel.Text = @"";
}
}