public static void initialize()
{
//read classifier
cls = (Classifier)weka.core.SerializationHelper.read("../../ClassifierNB.model");
EC = new ExperimentControl();
EC.Initialize();
}
public void Build()
{
this.featureIndex = new int[this.features.Count];
for(int i=0;(i< this.features.Count);i++)
{
for (int j = 0; (j < Extractor.ArffAttributeLabels.Length); j++)
if (((string)this.features[i]).Equals(Extractor.ArffAttributeLabels[j]))
{
this.featureIndex[i] = j;
break;
}
}
instances = new Instances(new StreamReader(this.filename));
instances.Class = instances.attribute(this.features.Count);
classifier = new J48();
classifier.buildClassifier(instances);
//setup the feature vector
//fvWekaAttributes = new FastVector(this.features.Count + 1);
//for (i = 0; (i < this.features.Count); i++)
// fvWekaAttributes.addElement(new weka.core.Attribute(this.features[i]));
//Setup the class attribute
//FastVector fvClassVal = new FastVector();
//for (i = 0; (i < this.classes.Count); i++)
// fvClassVal.addElement(this.classes[i]);
//weka.core.Attribute ClassAttribute = new weka.core.Attribute("activity", fvClassVal);
}
public Identifier(Boolean isAnnotated, WhatWhyTrainer wwt)
{
this.isAnnotated = isAnnotated;
this.wwt = wwt;
listWhoCandidates = new List<Candidate>();
listWhenCandidates = new List<Candidate>();
listWhereCandidates = new List<Candidate>();
listWhatCandidates = new List<List<Token>>();
listWhyCandidates = new List<List<Token>>();
listSecondaryWhatCandidates = new List<Candidate>();
listSecondaryWhyCandidates = new List<Candidate>();
fvPOS = new FastVector(Token.PartOfSpeechTags.Length);
foreach (String POS in Token.PartOfSpeechTags)
{
fvPOS.addElement(POS);
}
whoClassifier = (Classifier)SerializationHelper.read(@"..\..\IdentifierModels\who.model");
whenClassifier = (Classifier)SerializationHelper.read(@"..\..\IdentifierModels\when.model");
whereClassifier = (Classifier)SerializationHelper.read(@"..\..\IdentifierModels\where.model");
//whatClassifier = (Classifier)SerializationHelper.read(@"..\..\IdentifierModels\what.model");
//whyClassifier = (Classifier)SerializationHelper.read(@"..\..\IdentifierModels\why.model");
initializeAnnotations();
}
/* Use when the player logs in to initially create the classifier with data from server */
public void InitializeClassifier(String dataString)
{
try {
java.io.StringReader stringReader = new java.io.StringReader(dataString);
java.io.BufferedReader buffReader = new java.io.BufferedReader(stringReader);
playerData = new weka.core.Instances(buffReader);
/* State where in each Instance the class attribute is, if its not already specified by the file */
if (playerData.classIndex() == -1)
playerData.setClassIndex(playerData.numAttributes() - 1);
/* NAIVE BAYES */
//classifier = new weka.classifiers.bayes.NaiveBayes();
/* NEURAL NET */
//classifier = new weka.classifiers.functions.MultilayerPerceptron();
//((weka.classifiers.functions.MultilayerPerceptron)classifier).setHiddenLayers("12");
/* J48 TREE */
//classifier = new weka.classifiers.trees.J48();
/* IB1 NEAREST NEIGHBOUR */
//classifier = new weka.classifiers.lazy.IB1();
/* RANDOM FOREST */
classifier = new weka.classifiers.trees.RandomForest();
classifier.buildClassifier(playerData);
Debug.Log("Initialized Classifier");
}
catch (java.lang.Exception ex)
{
Debug.LogError(ex.getMessage());
}
}
/// <summary> Creates a deep copy of the given classifier using serialization.
///
/// </summary>
/// <param name="model">the classifier to copy
/// </param>
/// <returns> a deep copy of the classifier
/// </returns>
/// <exception cref="Exception">if an error occurs
/// </exception>
public static Classifier makeCopy(Classifier model)
{
return (Classifier) model.Clone();
}
/// <summary> Evaluates a classifier with the options given in an array of
/// strings. <p/>
///
/// Valid options are: <p/>
///
/// -t name of training file <br/>
/// Name of the file with the training data. (required) <p/>
///
/// -T name of test file <br/>
/// Name of the file with the test data. If missing a cross-validation
/// is performed. <p/>
///
/// -c class index <br/>
/// Index of the class attribute (1, 2, ...; default: last). <p/>
///
/// -x number of folds <br/>
/// The number of folds for the cross-validation (default: 10). <p/>
///
/// -s random number seed <br/>
/// Random number seed for the cross-validation (default: 1). <p/>
///
/// -m file with cost matrix <br/>
/// The name of a file containing a cost matrix. <p/>
///
/// -l name of model input file <br/>
/// Loads classifier from the given file. <p/>
///
/// -d name of model output file <br/>
/// Saves classifier built from the training data into the given file. <p/>
///
/// -v <br/>
/// Outputs no statistics for the training data. <p/>
///
/// -o <br/>
/// Outputs statistics only, not the classifier. <p/>
///
/// -i <br/>
/// Outputs detailed information-retrieval statistics per class. <p/>
///
/// -k <br/>
/// Outputs information-theoretic statistics. <p/>
///
/// -p <br/>
/// Outputs predictions for test instances (and nothing else). <p/>
///
/// -r <br/>
/// Outputs cumulative margin distribution (and nothing else). <p/>
///
/// -g <br/>
/// Only for classifiers that implement "Graphable." Outputs
/// the graph representation of the classifier (and nothing
/// else). <p/>
///
/// </summary>
/// <param name="classifier">machine learning classifier
/// </param>
/// <param name="options">the array of string containing the options
/// </param>
/// <throws> Exception if model could not be evaluated successfully </throws>
/// <returns> a string describing the results
/// </returns>
public static System.String evaluateModel(Classifier classifier, System.String[] options)
{
Instances train = null, tempTrain, test = null, template = null;
int seed = 1, folds = 10, classIndex = - 1;
System.String trainFileName, testFileName, sourceClass, classIndexString, seedString, foldsString, objectInputFileName, objectOutputFileName, attributeRangeString;
bool noOutput = false, printClassifications = false, trainStatistics = true, printMargins = false, printComplexityStatistics = false, printGraph = false, classStatistics = false, printSource = false;
System.Text.StringBuilder text = new System.Text.StringBuilder();
System.IO.StreamReader trainReader = null, testReader = null;
//UPGRADE_TODO: Class 'java.io.ObjectInputStream' was converted to 'System.IO.BinaryReader' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioObjectInputStream'"
System.IO.BinaryReader objectInputStream = null;
System.IO.Stream objectStream=null;
CostMatrix costMatrix = null;
System.Text.StringBuilder schemeOptionsText = null;
Range attributesToOutput = null;
long trainTimeStart = 0, trainTimeElapsed = 0, testTimeStart = 0, testTimeElapsed = 0;
Classifier classifierBackup;
try
{
// Get basic options (options the same for all schemes)
classIndexString = Utils.getOption('c', options);
if (classIndexString.Length != 0)
{
if (classIndexString.Equals("first"))
classIndex = 1;
else if (classIndexString.Equals("last"))
classIndex = - 1;
else
classIndex = System.Int32.Parse(classIndexString);
}
trainFileName = Utils.getOption('t', options);
objectInputFileName = Utils.getOption('l', options);
objectOutputFileName = Utils.getOption('d', options);
testFileName = Utils.getOption('T', options);
if (trainFileName.Length == 0)
{
if (objectInputFileName.Length == 0)
{
throw new System.Exception("No training file and no object " + "input file given.");
}
if (testFileName.Length == 0)
{
throw new System.Exception("No training file and no test " + "file given.");
}
}
else if ((objectInputFileName.Length != 0) && ((!(classifier is UpdateableClassifier)) || (testFileName.Length == 0)))
{
throw new System.Exception("Classifier not incremental, or no " + "test file provided: can't " + "use both train and model file.");
}
try
{
if (trainFileName.Length != 0)
{
//UPGRADE_TODO: The differences in the expected value of parameters for constructor 'java.io.BufferedReader.BufferedReader' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1092'"
//UPGRADE_WARNING: At least one expression was used more than once in the target code. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1181'"
//UPGRADE_TODO: Constructor 'java.io.FileReader.FileReader' was converted to 'System.IO.StreamReader' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073'"
trainReader = new System.IO.StreamReader(new System.IO.StreamReader(trainFileName, System.Text.Encoding.Default).BaseStream, new System.IO.StreamReader(trainFileName, System.Text.Encoding.Default).CurrentEncoding);
}
if (testFileName.Length != 0)
{
//UPGRADE_TODO: The differences in the expected value of parameters for constructor 'java.io.BufferedReader.BufferedReader' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1092'"
//UPGRADE_WARNING: At least one expression was used more than once in the target code. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1181'"
//UPGRADE_TODO: Constructor 'java.io.FileReader.FileReader' was converted to 'System.IO.StreamReader' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073'"
testReader = new System.IO.StreamReader(new System.IO.StreamReader(testFileName, System.Text.Encoding.Default).BaseStream, new System.IO.StreamReader(testFileName, System.Text.Encoding.Default).CurrentEncoding);
}
if (objectInputFileName.Length != 0)
{
//UPGRADE_TODO: Constructor 'java.io.FileInputStream.FileInputStream' was converted to 'System.IO.FileStream.FileStream' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioFileInputStreamFileInputStream_javalangString'"
objectStream= new System.IO.FileStream(objectInputFileName, System.IO.FileMode.Open, System.IO.FileAccess.Read);
if (objectInputFileName.EndsWith(".gz"))
{
//UPGRADE_ISSUE: Constructor 'java.util.zip.GZIPInputStream.GZIPInputStream' was not converted. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1000_javautilzipGZIPInputStream'"
objectStream= new ICSharpCode.SharpZipLib.GZip.GZipInputStream(objectStream);
}
//UPGRADE_TODO: Class 'java.io.ObjectInputStream' was converted to 'System.IO.BinaryReader' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioObjectInputStream'"
objectInputStream = new System.IO.BinaryReader(objectStream);
}
}
catch (System.Exception e)
{
//UPGRADE_TODO: The equivalent in .NET for method 'java.lang.Throwable.getMessage' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
throw new System.Exception("Can't open file " + e.Message + '.');
}
if (testFileName.Length != 0)
{
template = test = new Instances(testReader, 1);
if (classIndex != - 1)
{
test.ClassIndex = classIndex - 1;
}
else
{
test.ClassIndex = test.numAttributes() - 1;
}
if (classIndex > test.numAttributes())
{
throw new System.Exception("Index of class attribute too large.");
}
}
if (trainFileName.Length != 0)
{
if ((classifier is UpdateableClassifier) && (testFileName.Length != 0))
{
train = new Instances(trainReader, 1);
}
else
{
train = new Instances(trainReader);
}
template = train;
if (classIndex != - 1)
{
train.ClassIndex = classIndex - 1;
}
else
{
train.ClassIndex = train.numAttributes() - 1;
}
if ((testFileName.Length != 0) && !test.equalHeaders(train))
{
throw new System.ArgumentException("Train and test file not compatible!");
}
if (classIndex > train.numAttributes())
{
throw new System.Exception("Index of class attribute too large.");
}
}
if (template == null)
{
throw new System.Exception("No actual dataset provided to use as template");
}
seedString = Utils.getOption('s', options);
if (seedString.Length != 0)
{
seed = System.Int32.Parse(seedString);
}
foldsString = Utils.getOption('x', options);
if (foldsString.Length != 0)
{
folds = System.Int32.Parse(foldsString);
}
costMatrix = handleCostOption(Utils.getOption('m', options), template.numClasses());
classStatistics = Utils.getFlag('i', options);
noOutput = Utils.getFlag('o', options);
trainStatistics = !Utils.getFlag('v', options);
printComplexityStatistics = Utils.getFlag('k', options);
printMargins = Utils.getFlag('r', options);
printGraph = Utils.getFlag('g', options);
sourceClass = Utils.getOption('z', options);
printSource = (sourceClass.Length != 0);
// Check -p option
try
{
attributeRangeString = Utils.getOption('p', options);
}
catch (System.Exception e)
{
//UPGRADE_TODO: The equivalent in .NET for method 'java.lang.Throwable.getMessage' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
throw new System.Exception(e.Message + "\nNOTE: the -p option has changed. " + "It now expects a parameter specifying a range of attributes " + "to list with the predictions. Use '-p 0' for none.");
}
if (attributeRangeString.Length != 0)
{
// if no test file given, we cannot print predictions
if (testFileName.Length == 0)
throw new System.Exception("Cannot print predictions ('-p') without test file ('-T')!");
printClassifications = true;
if (!attributeRangeString.Equals("0"))
attributesToOutput = new Range(attributeRangeString);
}
// if no training file given, we don't have any priors
if ((trainFileName.Length == 0) && (printComplexityStatistics))
throw new System.Exception("Cannot print complexity statistics ('-k') without training file ('-t')!");
// If a model file is given, we can't process
// scheme-specific options
if (objectInputFileName.Length != 0)
{
Utils.checkForRemainingOptions(options);
}
else
{
// Set options for classifier
// if (classifier instanceof OptionHandler)
// {
// for (int i = 0; i < options.length; i++)
// {
// if (options[i].length() != 0)
// {
// if (schemeOptionsText == null)
// {
// schemeOptionsText = new StringBuffer();
// }
// if (options[i].indexOf(' ') != -1)
// {
// schemeOptionsText.append('"' + options[i] + "\" ");
// }
// else
// {
// schemeOptionsText.append(options[i] + " ");
// }
// }
// }
// ((OptionHandler)classifier).setOptions(options);
// }
}
Utils.checkForRemainingOptions(options);
}
catch (System.Exception e)
{
//UPGRADE_TODO: The equivalent in .NET for method 'java.lang.Throwable.getMessage' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
throw new System.Exception("\nWeka exception: " + e.Message + makeOptionString(classifier));
}
// Setup up evaluation objects
Evaluation trainingEvaluation = new Evaluation(new Instances(template, 0), costMatrix);
Evaluation testingEvaluation = new Evaluation(new Instances(template, 0), costMatrix);
if (objectInputFileName.Length != 0)
{
testingEvaluation.useNoPriors();
// Load classifier from file
//UPGRADE_WARNING: Method 'java.io.ObjectInputStream.readObject' was converted to 'SupportClass.Deserialize' which may throw an exception. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1101'"
//classifier = (Classifier) SupportClass.Deserialize(objectInputStream);
//FileStream fs = new FileStream("DataFile.dat", FileMode.Open);
try
{
BinaryFormatter formatter = new BinaryFormatter();
// Deserialize the hashtable from the file and
// assign the reference to the local variable.
// addresses = (Hashtable)formatter.Deserialize(fs);
classifier = (Classifier)formatter.Deserialize(objectStream);
}
catch (Exception e)
{
Console.WriteLine("Failed to deserialize. Reason: " + e.Message);
throw;
}
finally
{
objectStream.Close();
//fs.Close();
}
objectInputStream.Close();
}
// backup of fully setup classifier for cross-validation
classifierBackup = Classifier.makeCopy(classifier);
// Build the classifier if no object file provided
if ((classifier is UpdateableClassifier) && (testFileName.Length != 0) && (costMatrix == null) && (trainFileName.Length != 0))
{
// Build classifier incrementally
trainingEvaluation.Priors = train;
testingEvaluation.Priors = train;
trainTimeStart = (System.DateTime.Now.Ticks - 621355968000000000) / 10000;
if (objectInputFileName.Length == 0)
{
classifier.buildClassifier(train);
}
while (train.readInstance(trainReader))
{
trainingEvaluation.updatePriors(train.instance(0));
testingEvaluation.updatePriors(train.instance(0));
((UpdateableClassifier) classifier).updateClassifier(train.instance(0));
train.delete(0);
}
trainTimeElapsed = (System.DateTime.Now.Ticks - 621355968000000000) / 10000 - trainTimeStart;
trainReader.Close();
}
else if (objectInputFileName.Length == 0)
{
// Build classifier in one go
tempTrain = new Instances(train);
trainingEvaluation.Priors = tempTrain;
testingEvaluation.Priors = tempTrain;
trainTimeStart = (System.DateTime.Now.Ticks - 621355968000000000) / 10000;
classifier.buildClassifier(tempTrain);
trainTimeElapsed = (System.DateTime.Now.Ticks - 621355968000000000) / 10000 - trainTimeStart;
}
// Save the classifier if an object output file is provided
if (objectOutputFileName.Length != 0)
{
//UPGRADE_TODO: Constructor 'java.io.FileOutputStream.FileOutputStream' was converted to 'System.IO.FileStream.FileStream' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioFileOutputStreamFileOutputStream_javalangString'"
System.IO.Stream os = new System.IO.FileStream(objectOutputFileName, System.IO.FileMode.Create);
if (objectOutputFileName.EndsWith(".gz"))
{
//UPGRADE_ISSUE: Constructor 'java.util.zip.GZIPOutputStream.GZIPOutputStream' was not converted. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1000_javautilzipGZIPOutputStream'"
os = new ICSharpCode.SharpZipLib.GZip.GZipOutputStream(os);
}
//UPGRADE_TODO: Class 'java.io.ObjectOutputStream' was converted to 'System.IO.BinaryWriter' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioObjectOutputStream'"
System.IO.BinaryWriter objectOutputStream = new System.IO.BinaryWriter(os);
//UPGRADE_TODO: Method 'java.io.ObjectOutputStream.writeObject' was converted to 'SupportClass.Serialize' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioObjectOutputStreamwriteObject_javalangObject'"
//SupportClass.Serialize(objectOutputStream, classifier);
BinaryFormatter bformatter = new BinaryFormatter();
bformatter.Serialize(os, classifier);
objectOutputStream.Flush();
objectOutputStream.Close();
}
// If classifier is drawable output string describing graph
if ((classifier is Drawable) && (printGraph))
{
return ((Drawable) classifier).graph();
}
// Output the classifier as equivalent source
if ((classifier is Sourcable) && (printSource))
{
return wekaStaticWrapper((Sourcable) classifier, sourceClass);
}
// Output test instance predictions only
if (printClassifications)
{
return toPrintClassifications(classifier, new Instances(template, 0), testFileName, classIndex, attributesToOutput);
}
// Output model
if (!(noOutput || printMargins))
{
// if (classifier instanceof OptionHandler)
// {
// if (schemeOptionsText != null)
// {
// text.append("\nOptions: "+schemeOptionsText);
// text.append("\n");
// }
// }
//UPGRADE_TODO: The equivalent in .NET for method 'java.lang.Object.toString' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
text.Append("\n" + classifier.ToString() + "\n");
}
if (!printMargins && (costMatrix != null))
{
text.Append("\n=== Evaluation Cost Matrix ===\n\n").Append(costMatrix.ToString());
}
// Compute error estimate from training data
if ((trainStatistics) && (trainFileName.Length != 0))
{
if ((classifier is UpdateableClassifier) && (testFileName.Length != 0) && (costMatrix == null))
{
// Classifier was trained incrementally, so we have to
// reopen the training data in order to test on it.
//UPGRADE_TODO: The differences in the expected value of parameters for constructor 'java.io.BufferedReader.BufferedReader' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1092'"
//UPGRADE_WARNING: At least one expression was used more than once in the target code. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1181'"
//UPGRADE_TODO: Constructor 'java.io.FileReader.FileReader' was converted to 'System.IO.StreamReader' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073'"
trainReader = new System.IO.StreamReader(new System.IO.StreamReader(trainFileName, System.Text.Encoding.Default).BaseStream, new System.IO.StreamReader(trainFileName, System.Text.Encoding.Default).CurrentEncoding);
// Incremental testing
train = new Instances(trainReader, 1);
if (classIndex != - 1)
{
train.ClassIndex = classIndex - 1;
}
else
{
train.ClassIndex = train.numAttributes() - 1;
}
testTimeStart = (System.DateTime.Now.Ticks - 621355968000000000) / 10000;
while (train.readInstance(trainReader))
{
trainingEvaluation.evaluateModelOnce((Classifier) classifier, train.instance(0));
train.delete(0);
}
testTimeElapsed = (System.DateTime.Now.Ticks - 621355968000000000) / 10000 - testTimeStart;
trainReader.Close();
}
else
{
testTimeStart = (System.DateTime.Now.Ticks - 621355968000000000) / 10000;
trainingEvaluation.evaluateModel(classifier, train);
testTimeElapsed = (System.DateTime.Now.Ticks - 621355968000000000) / 10000 - testTimeStart;
}
// Print the results of the training evaluation
if (printMargins)
{
return trainingEvaluation.toCumulativeMarginDistributionString();
}
else
{
text.Append("\nTime taken to build model: " + Utils.doubleToString(trainTimeElapsed / 1000.0, 2) + " seconds");
text.Append("\nTime taken to test model on training data: " + Utils.doubleToString(testTimeElapsed / 1000.0, 2) + " seconds");
text.Append(trainingEvaluation.toSummaryString("\n\n=== Error on training" + " data ===\n", printComplexityStatistics));
if (template.classAttribute().Nominal)
{
if (classStatistics)
{
text.Append("\n\n" + trainingEvaluation.toClassDetailsString());
}
text.Append("\n\n" + trainingEvaluation.toMatrixString());
}
}
}
// Compute proper error estimates
if (testFileName.Length != 0)
{
// Testing is on the supplied test data
while (test.readInstance(testReader))
{
testingEvaluation.evaluateModelOnce((Classifier) classifier, test.instance(0));
test.delete(0);
}
testReader.Close();
text.Append("\n\n" + testingEvaluation.toSummaryString("=== Error on test data ===\n", printComplexityStatistics));
}
else if (trainFileName.Length != 0)
{
// Testing is via cross-validation on training data
//UPGRADE_TODO: The differences in the expected value of parameters for constructor 'java.util.Random.Random' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1092'"
System.Random random = new System.Random((System.Int32) seed);
// use untrained (!) classifier for cross-validation
classifier = Classifier.makeCopy(classifierBackup);
testingEvaluation.crossValidateModel(classifier, train, folds, random);
if (template.classAttribute().Numeric)
{
text.Append("\n\n\n" + testingEvaluation.toSummaryString("=== Cross-validation ===\n", printComplexityStatistics));
}
else
{
text.Append("\n\n\n" + testingEvaluation.toSummaryString("=== Stratified " + "cross-validation ===\n", printComplexityStatistics));
}
}
if (template.classAttribute().Nominal)
{
if (classStatistics)
{
text.Append("\n\n" + testingEvaluation.toClassDetailsString());
}
text.Append("\n\n" + testingEvaluation.toMatrixString());
}
return text.ToString();
}
/// <summary> Performs a (stratified if class is nominal) cross-validation
/// for a classifier on a set of instances. Now performs
/// a deep copy of the classifier before each call to
/// buildClassifier() (just in case the classifier is not
/// initialized properly).
///
/// </summary>
/// <param name="classifier">the classifier with any options set.
/// </param>
/// <param name="data">the data on which the cross-validation is to be
/// performed
/// </param>
/// <param name="numFolds">the number of folds for the cross-validation
/// </param>
/// <param name="random">random number generator for randomization
/// </param>
/// <throws> Exception if a classifier could not be generated </throws>
/// <summary> successfully or the class is not defined
/// </summary>
public virtual void crossValidateModel(Classifier classifier, Instances data, int numFolds, System.Random random)
{
// Make a copy of the data we can reorder
data = new Instances(data);
data.randomize(random);
if (data.classAttribute().Nominal)
{
data.stratify(numFolds);
}
// Do the folds
for (int i = 0; i < numFolds; i++)
{
Instances train = data.trainCV(numFolds, i, random);
Priors = train;
Classifier copiedClassifier = Classifier.makeCopy(classifier);
copiedClassifier.buildClassifier(train);
Instances test = data.testCV(numFolds, i);
evaluateModel(copiedClassifier, test);
}
m_NumFolds = numFolds;
}
/// <summary> Make up the help string giving all the command line options
///
/// </summary>
/// <param name="classifier">the classifier to include options for
/// </param>
/// <returns> a string detailing the valid command line options
/// </returns>
protected internal static System.String makeOptionString(Classifier classifier)
{
System.Text.StringBuilder optionsText = new System.Text.StringBuilder("");
// General options
optionsText.Append("\n\nGeneral options:\n\n");
optionsText.Append("-t <name of training file>\n");
optionsText.Append("\tSets training file.\n");
optionsText.Append("-T <name of test file>\n");
optionsText.Append("\tSets test file. If missing, a cross-validation");
optionsText.Append(" will be performed on the training data.\n");
optionsText.Append("-c <class index>\n");
optionsText.Append("\tSets index of class attribute (default: last).\n");
optionsText.Append("-x <number of folds>\n");
optionsText.Append("\tSets number of folds for cross-validation (default: 10).\n");
optionsText.Append("-s <random number seed>\n");
optionsText.Append("\tSets random number seed for cross-validation (default: 1).\n");
optionsText.Append("-m <name of file with cost matrix>\n");
optionsText.Append("\tSets file with cost matrix.\n");
optionsText.Append("-l <name of input file>\n");
optionsText.Append("\tSets model input file.\n");
optionsText.Append("-d <name of output file>\n");
optionsText.Append("\tSets model output file.\n");
optionsText.Append("-v\n");
optionsText.Append("\tOutputs no statistics for training data.\n");
optionsText.Append("-o\n");
optionsText.Append("\tOutputs statistics only, not the classifier.\n");
optionsText.Append("-i\n");
optionsText.Append("\tOutputs detailed information-retrieval");
optionsText.Append(" statistics for each class.\n");
optionsText.Append("-k\n");
optionsText.Append("\tOutputs information-theoretic statistics.\n");
optionsText.Append("-p <attribute range>\n");
optionsText.Append("\tOnly outputs predictions for test instances, along with attributes " + "(0 for none).\n");
optionsText.Append("-r\n");
optionsText.Append("\tOnly outputs cumulative margin distribution.\n");
if (classifier is Sourcable)
{
optionsText.Append("-z <class name>\n");
optionsText.Append("\tOnly outputs the source representation" + " of the classifier, giving it the supplied" + " name.\n");
}
if (classifier is Drawable)
{
optionsText.Append("-g\n");
optionsText.Append("\tOnly outputs the graph representation" + " of the classifier.\n");
}
// Get scheme-specific options
// if (classifier instanceof OptionHandler)
// {
// optionsText.append("\nOptions specific to "
// + classifier.getClass().getName()
// + ":\n\n");
// Enumeration enu = ((OptionHandler)classifier).listOptions();
// while (enu.hasMoreElements())
// {
// Option option = (Option) enu.nextElement();
// optionsText.append(option.synopsis() + '\n');
// optionsText.append(option.description() + "\n");
// }
// }
return optionsText.ToString();
}
/// <summary> Prints the predictions for the given dataset into a String variable.
///
/// </summary>
/// <param name="classifier the">classifier to use
/// </param>
/// <param name="train the">training data
/// </param>
/// <param name="testFileName the">name of the test file
/// </param>
/// <param name="classIndex the">class index
/// </param>
/// <param name="attributesToOutput the">indices of the attributes to output
/// </param>
/// <returns> the generated predictions for the attribute range
/// </returns>
/// <throws> Exception if test file cannot be opened </throws>
protected internal static System.String toPrintClassifications(Classifier classifier, Instances train, System.String testFileName, int classIndex, Range attributesToOutput)
{
System.Text.StringBuilder text = new System.Text.StringBuilder();
if (testFileName.Length != 0)
{
System.IO.StreamReader testReader = null;
try
{
//UPGRADE_TODO: The differences in the expected value of parameters for constructor 'java.io.BufferedReader.BufferedReader' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1092'"
//UPGRADE_WARNING: At least one expression was used more than once in the target code. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1181'"
//UPGRADE_TODO: Constructor 'java.io.FileReader.FileReader' was converted to 'System.IO.StreamReader' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073'"
testReader = new System.IO.StreamReader(new System.IO.StreamReader(testFileName, System.Text.Encoding.Default).BaseStream, new System.IO.StreamReader(testFileName, System.Text.Encoding.Default).CurrentEncoding);
}
catch (System.Exception e)
{
//UPGRADE_TODO: The equivalent in .NET for method 'java.lang.Throwable.getMessage' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
throw new System.Exception("Can't open file " + e.Message + '.');
}
Instances test = new Instances(testReader, 1);
if (classIndex != - 1)
{
test.ClassIndex = classIndex - 1;
}
else
{
test.ClassIndex = test.numAttributes() - 1;
}
int i = 0;
while (test.readInstance(testReader))
{
Instance instance = test.instance(0);
Instance withMissing = (Instance) instance.copy();
withMissing.Dataset = test;
double predValue = ((Classifier) classifier).classifyInstance(withMissing);
if (test.classAttribute().Numeric)
{
if (Instance.isMissingValue(predValue))
{
text.Append(i + " missing ");
}
else
{
text.Append(i + " " + predValue + " ");
}
if (instance.classIsMissing())
{
text.Append("missing");
}
else
{
text.Append(instance.classValue());
}
text.Append(" " + attributeValuesString(withMissing, attributesToOutput) + "\n");
}
else
{
if (Instance.isMissingValue(predValue))
{
text.Append(i + " missing ");
}
else
{
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1042'"
text.Append(i + " " + test.classAttribute().value_Renamed((int) predValue) + " ");
}
if (Instance.isMissingValue(predValue))
{
text.Append("missing ");
}
else
{
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1042'"
text.Append(classifier.distributionForInstance(withMissing)[(int) predValue] + " ");
}
text.Append(instance.toString(instance.classIndex()) + " " + attributeValuesString(withMissing, attributesToOutput) + "\n");
}
test.delete(0);
i++;
}
testReader.Close();
}
return text.ToString();
}
/// <summary> Evaluates the classifier on a single instance.
///
/// </summary>
/// <param name="classifier">machine learning classifier
/// </param>
/// <param name="instance">the test instance to be classified
/// </param>
/// <returns> the prediction made by the clasifier
/// </returns>
/// <throws> Exception if model could not be evaluated </throws>
/// <summary> successfully or the data contains string attributes
/// </summary>
public virtual double evaluateModelOnce(Classifier classifier, Instance instance)
{
Instance classMissing = (Instance) instance.copy();
double pred = 0;
classMissing.Dataset = instance.dataset();
classMissing.setClassMissing();
if (m_ClassIsNominal)
{
double[] dist = classifier.distributionForInstance(classMissing);
pred = Utils.maxIndex(dist);
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1042'"
if (dist[(int) pred] <= 0)
{
pred = Instance.missingValue();
}
updateStatsForClassifier(dist, instance);
}
else
{
pred = classifier.classifyInstance(classMissing);
updateStatsForPredictor(pred, instance);
}
return pred;
}
/// <summary> Returns the array of classifiers that have been built.</summary>
public virtual Classifier[][] classifiers()
{
Classifier[][] classifiers = new Classifier[m_NumClasses][];
for (int i = 0; i < m_NumClasses; i++)
{
classifiers[i] = new Classifier[m_NumGenerated];
}
for (int j = 0; j < m_NumClasses; j++)
{
for (int i = 0; i < m_NumGenerated; i++)
{
classifiers[j][i] = m_Classifiers[j][i];
}
}
return classifiers;
}
/// <summary> Creates a given number of deep copies of the given classifier using serialization.
///
/// </summary>
/// <param name="model">the classifier to copy
/// </param>
/// <param name="num">the number of classifier copies to create.
/// </param>
/// <returns> an array of classifiers.
/// </returns>
/// <exception cref="Exception">if an error occurs
/// </exception>
public static Classifier[] makeCopies(Classifier model, int num)
{
if (model == null)
{
throw new System.Exception("No model classifier set");
}
Classifier[] classifiers = new Classifier[num];
for (int i = 0; i < classifiers.Length; i++)
{
classifiers[i] = (Classifier)makeCopy(model);
}
return classifiers;
}
/// <summary>
/// Build the learning model for classification
/// </summary>
/// <param name="InstancesList">list of instances </param>
/// <param name="NumberofClusters">Number of Clusters</param>
/// <param name="TextBoxForFeedback">Text box for the results (can be NULL)</param>
/// <param name="PanelForVisualFeedback">Panel to display visual results if avalaible (can be NULL)</param>
public Classifier PerformTraining(FormForClassificationInfo WindowForClassificationParam, Instances InstancesList, /*int NumberofClusters,*/ RichTextBox TextBoxForFeedback,
Panel PanelForVisualFeedback, out weka.classifiers.Evaluation ModelEvaluation, bool IsCellular)
{
// weka.classifiers.Evaluation ModelEvaluation = null;
// FormForClassificationInfo WindowForClassificationParam = new FormForClassificationInfo(GlobalInfo);
ModelEvaluation = null;
// if (WindowForClassificationParam.ShowDialog() != System.Windows.Forms.DialogResult.OK) return null;
// weka.classifiers.Evaluation ModelEvaluation = new Evaluation(
cParamAlgo ClassifAlgoParams = WindowForClassificationParam.GetSelectedAlgoAndParameters();
if (ClassifAlgoParams == null) return null;
//this.Cursor = Cursors.WaitCursor;
// cParamAlgo ClassificationAlgo = WindowForClassificationParam.GetSelectedAlgoAndParameters();
cListValuesParam Parameters = ClassifAlgoParams.GetListValuesParam();
//Classifier this.CurrentClassifier = null;
// -------------------------- Classification -------------------------------
// create the instances
// InstancesList = this.ListInstances;
this.attValsWithoutClasses = new FastVector();
if (IsCellular)
for (int i = 0; i < cGlobalInfo.ListCellularPhenotypes.Count; i++)
this.attValsWithoutClasses.addElement(cGlobalInfo.ListCellularPhenotypes[i].Name);
else
for (int i = 0; i < cGlobalInfo.ListWellClasses.Count; i++)
this.attValsWithoutClasses.addElement(cGlobalInfo.ListWellClasses[i].Name);
InstancesList.insertAttributeAt(new weka.core.Attribute("Class", this.attValsWithoutClasses), InstancesList.numAttributes());
//int A = Classes.Count;
for (int i = 0; i < Classes.Count; i++)
InstancesList.get(i).setValue(InstancesList.numAttributes() - 1, Classes[i]);
InstancesList.setClassIndex(InstancesList.numAttributes() - 1);
weka.core.Instances train = new weka.core.Instances(InstancesList, 0, InstancesList.numInstances());
if (PanelForVisualFeedback != null)
PanelForVisualFeedback.Controls.Clear();
#region List classifiers
#region J48
if (ClassifAlgoParams.Name == "J48")
{
this.CurrentClassifier = new weka.classifiers.trees.J48();
((J48)this.CurrentClassifier).setMinNumObj((int)Parameters.ListDoubleValues.Get("numericUpDownMinInstLeaf").Value);
((J48)this.CurrentClassifier).setConfidenceFactor((float)Parameters.ListDoubleValues.Get("numericUpDownConfFactor").Value);
((J48)this.CurrentClassifier).setNumFolds((int)Parameters.ListDoubleValues.Get("numericUpDownNumFolds").Value);
((J48)this.CurrentClassifier).setUnpruned((bool)Parameters.ListCheckValues.Get("checkBoxUnPruned").Value);
((J48)this.CurrentClassifier).setUseLaplace((bool)Parameters.ListCheckValues.Get("checkBoxLaplacianSmoothing").Value);
((J48)this.CurrentClassifier).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeedNumber").Value);
((J48)this.CurrentClassifier).setSubtreeRaising((bool)Parameters.ListCheckValues.Get("checkBoxSubTreeRaising").Value);
// CurrentClassif.SetJ48Tree((J48)this.CurrentClassifier, Classes.Length);
this.CurrentClassifier.buildClassifier(train);
// display results training
// display tree
if (PanelForVisualFeedback != null)
{
GViewer GraphView = DisplayTree(GlobalInfo, ((J48)this.CurrentClassifier), IsCellular).gViewerForTreeClassif;
GraphView.Size = new System.Drawing.Size(PanelForVisualFeedback.Width, PanelForVisualFeedback.Height);
GraphView.Anchor = (AnchorStyles.Bottom | AnchorStyles.Top | AnchorStyles.Left | AnchorStyles.Right);
PanelForVisualFeedback.Controls.Clear();
PanelForVisualFeedback.Controls.Add(GraphView);
}
}
#endregion
#region Random Tree
else if (ClassifAlgoParams.Name == "RandomTree")
{
this.CurrentClassifier = new weka.classifiers.trees.RandomTree();
if ((bool)Parameters.ListCheckValues.Get("checkBoxMaxDepthUnlimited").Value)
((RandomTree)this.CurrentClassifier).setMaxDepth(0);
else
((RandomTree)this.CurrentClassifier).setMaxDepth((int)Parameters.ListDoubleValues.Get("numericUpDownMaxDepth").Value);
((RandomTree)this.CurrentClassifier).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeed").Value);
((RandomTree)this.CurrentClassifier).setMinNum((double)Parameters.ListDoubleValues.Get("numericUpDownMinWeight").Value);
if ((bool)Parameters.ListCheckValues.Get("checkBoxIsBackfitting").Value)
{
((RandomTree)this.CurrentClassifier).setNumFolds((int)Parameters.ListDoubleValues.Get("numericUpDownBackFittingFolds").Value);
}
else
{
((RandomTree)this.CurrentClassifier).setNumFolds(0);
}
this.CurrentClassifier.buildClassifier(train);
//string StringForTree = ((RandomTree)this.CurrentClassifier).graph().Remove(0, ((RandomTree)this.CurrentClassifier).graph().IndexOf("{") + 2);
//Microsoft.Msagl.GraphViewerGdi.GViewer GraphView = new GViewer();
//GraphView.Graph = GlobalInfo.WindowHCSAnalyzer.ComputeAndDisplayGraph(StringForTree);//.Remove(StringForTree.Length - 3, 3));
//GraphView.Size = new System.Drawing.Size(panelForGraphicalResults.Width, panelForGraphicalResults.Height);
//GraphView.Anchor = (AnchorStyles.Bottom | AnchorStyles.Top | AnchorStyles.Left | AnchorStyles.Right);
//this.panelForGraphicalResults.Controls.Clear();
//this.panelForGraphicalResults.Controls.Add(GraphView);
}
#endregion
#region Random Forest
else if (ClassifAlgoParams.Name == "RandomForest")
{
this.CurrentClassifier = new weka.classifiers.trees.RandomForest();
if ((bool)Parameters.ListCheckValues.Get("checkBoxMaxDepthUnlimited").Value)
((RandomForest)this.CurrentClassifier).setMaxDepth(0);
else
((RandomForest)this.CurrentClassifier).setMaxDepth((int)Parameters.ListDoubleValues.Get("numericUpDownMaxDepth").Value);
((RandomForest)this.CurrentClassifier).setNumTrees((int)Parameters.ListDoubleValues.Get("numericUpDownNumTrees").Value);
((RandomForest)this.CurrentClassifier).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeed").Value);
this.CurrentClassifier.buildClassifier(train);
}
#endregion
#region KStar
else if (ClassifAlgoParams.Name == "KStar")
{
this.CurrentClassifier = new weka.classifiers.lazy.KStar();
((KStar)this.CurrentClassifier).setGlobalBlend((int)Parameters.ListDoubleValues.Get("numericUpDownGlobalBlend").Value);
((KStar)this.CurrentClassifier).setEntropicAutoBlend((bool)Parameters.ListCheckValues.Get("checkBoxBlendAuto").Value);
this.CurrentClassifier.buildClassifier(train);
}
#endregion
#region SVM
else if (ClassifAlgoParams.Name == "SVM")
{
this.CurrentClassifier = new weka.classifiers.functions.SMO();
((SMO)this.CurrentClassifier).setC((double)Parameters.ListDoubleValues.Get("numericUpDownC").Value);
((SMO)this.CurrentClassifier).setKernel(WindowForClassificationParam.GeneratedKernel);
((SMO)this.CurrentClassifier).setRandomSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeed").Value);
this.CurrentClassifier.buildClassifier(train);
}
#endregion
#region KNN
else if (ClassifAlgoParams.Name == "KNN")
{
this.CurrentClassifier = new weka.classifiers.lazy.IBk();
string OptionDistance = " -K " + (int)Parameters.ListDoubleValues.Get("numericUpDownKNN").Value + " -W 0 ";
string WeightType = (string)Parameters.ListTextValues.Get("comboBoxDistanceWeight").Value;
switch (WeightType)
{
case "No Weighting":
OptionDistance += "";
break;
case "1/Distance":
OptionDistance += "-I";
break;
case "1-Distance":
OptionDistance += "-F";
break;
default:
break;
}
OptionDistance += " -A \"weka.core.neighboursearch.LinearNNSearch -A \\\"weka.core.";
string DistanceType = (string)Parameters.ListTextValues.Get("comboBoxDistance").Value;
// OptionDistance += " -A \"weka.core.";
switch (DistanceType)
{
case "Euclidean":
OptionDistance += "EuclideanDistance";
break;
case "Manhattan":
OptionDistance += "ManhattanDistance";
break;
case "Chebyshev":
OptionDistance += "ChebyshevDistance";
break;
default:
break;
}
if (!(bool)Parameters.ListCheckValues.Get("checkBoxNormalize").Value)
OptionDistance += " -D";
OptionDistance += " -R ";
OptionDistance += "first-last\\\"\"";
((IBk)this.CurrentClassifier).setOptions(weka.core.Utils.splitOptions(OptionDistance));
//((IBk)this.CurrentClassifier).setKNN((int)Parameters.ListDoubleValues.Get("numericUpDownKNN").Value);
this.CurrentClassifier.buildClassifier(train);
}
#endregion
#region Multilayer Perceptron
else if (ClassifAlgoParams.Name == "Perceptron")
{
this.CurrentClassifier = new weka.classifiers.functions.MultilayerPerceptron();
((MultilayerPerceptron)this.CurrentClassifier).setMomentum((double)Parameters.ListDoubleValues.Get("numericUpDownMomentum").Value);
((MultilayerPerceptron)this.CurrentClassifier).setLearningRate((double)Parameters.ListDoubleValues.Get("numericUpDownLearningRate").Value);
((MultilayerPerceptron)this.CurrentClassifier).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeed").Value);
((MultilayerPerceptron)this.CurrentClassifier).setTrainingTime((int)Parameters.ListDoubleValues.Get("numericUpDownTrainingTime").Value);
((MultilayerPerceptron)this.CurrentClassifier).setNormalizeAttributes((bool)Parameters.ListCheckValues.Get("checkBoxNormAttribute").Value);
((MultilayerPerceptron)this.CurrentClassifier).setNormalizeNumericClass((bool)Parameters.ListCheckValues.Get("checkBoxNormNumericClasses").Value);
this.CurrentClassifier.buildClassifier(train);
}
#endregion
#region ZeroR
else if (ClassifAlgoParams.Name == "ZeroR")
{
this.CurrentClassifier = new weka.classifiers.rules.OneR();
this.CurrentClassifier.buildClassifier(train);
}
#endregion
#region OneR
else if (ClassifAlgoParams.Name == "OneR")
{
this.CurrentClassifier = new weka.classifiers.rules.OneR();
((OneR)this.CurrentClassifier).setMinBucketSize((int)Parameters.ListDoubleValues.Get("numericUpDownMinBucketSize").Value);
this.CurrentClassifier.buildClassifier(train);
}
#endregion
#region Naive Bayes
else if (ClassifAlgoParams.Name == "NaiveBayes")
{
this.CurrentClassifier = new weka.classifiers.bayes.NaiveBayes();
((NaiveBayes)this.CurrentClassifier).setUseKernelEstimator((bool)Parameters.ListCheckValues.Get("checkBoxKernelEstimator").Value);
this.CurrentClassifier.buildClassifier(train);
}
#endregion
#region Logistic
else if (ClassifAlgoParams.Name == "Logistic")
{
this.CurrentClassifier = new weka.classifiers.functions.Logistic();
((Logistic)this.CurrentClassifier).setUseConjugateGradientDescent((bool)Parameters.ListCheckValues.Get("checkBoxUseConjugateGradientDescent").Value);
((Logistic)this.CurrentClassifier).setRidge((double)Parameters.ListDoubleValues.Get("numericUpDownRidge").Value);
this.CurrentClassifier.buildClassifier(train);
}
#endregion
//weka.classifiers.functions.SMO
//BayesNet
#endregion
if (TextBoxForFeedback != null)
{
TextBoxForFeedback.Clear();
TextBoxForFeedback.AppendText(this.CurrentClassifier.ToString());
}
TextBoxForFeedback.AppendText("\n" + (InstancesList.numAttributes() - 1) + " attributes:\n\n");
for (int IdxAttributes = 0; IdxAttributes < InstancesList.numAttributes() - 1; IdxAttributes++)
{
TextBoxForFeedback.AppendText(IdxAttributes + "\t: " + InstancesList.attribute(IdxAttributes).name() + "\n");
}
#region evaluation of the model and results display
if ((WindowForClassificationParam.numericUpDownFoldNumber.Enabled) && (TextBoxForFeedback != null))
{
TextBoxForFeedback.AppendText("\n-----------------------------\nModel validation\n-----------------------------\n");
ModelEvaluation = new weka.classifiers.Evaluation(InstancesList);
ModelEvaluation.crossValidateModel(this.CurrentClassifier, InstancesList, (int)WindowForClassificationParam.numericUpDownFoldNumber.Value, new java.util.Random(1));
TextBoxForFeedback.AppendText(ModelEvaluation.toSummaryString());
TextBoxForFeedback.AppendText("\n-----------------------------\nConfusion Matrix:\n-----------------------------\n");
double[][] ConfusionMatrix = ModelEvaluation.confusionMatrix();
string NewLine = "";
for (int i = 0; i < ConfusionMatrix[0].Length; i++)
{
NewLine += "c" + i + "\t";
}
TextBoxForFeedback.AppendText(NewLine + "\n\n");
for (int j = 0; j < ConfusionMatrix.Length; j++)
{
NewLine = "";
for (int i = 0; i < ConfusionMatrix[0].Length; i++)
{
NewLine += ConfusionMatrix[j][i] + "\t";
}
// if
TextBoxForFeedback.AppendText(NewLine + "| c" + j + " <=> " + cGlobalInfo.ListCellularPhenotypes[j].Name + "\n");
}
}
#endregion
return this.CurrentClassifier;
}
public cClusteringObject(Classifier Model, Evaluation Evaluation, int NumFolds)
{
this.FoldNumber = NumFolds;
this.Model = Model;
this.Evaluation = Evaluation;
}
public async Task <string> classifyTest(weka.classifiers.Classifier cl)
{
string a = "";
double rate = 0;
try
{
//instsTest = Instances.mergeInstances(ins,null);
/*if (ins.classIndex() == -1)
* ins.setClassIndex(insts.numAttributes() - 1);*/
System.Console.WriteLine("Performing " + percentSplit + "% split evaluation.");
weka.filters.Filter normalized = new weka.filters.unsupervised.attribute.Normalize();
normalized.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, normalized);
//randomize the order of the instances in the dataset.
weka.filters.Filter myRandom = new weka.filters.unsupervised.instance.Randomize();
myRandom.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, myRandom);
//replace missing values
weka.filters.Filter replaceMissingValues = new weka.filters.unsupervised.attribute.ReplaceMissingValues();
replaceMissingValues.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, replaceMissingValues);
int trainSize = insts.numInstances() * percentSplit / 100;
int testSize = insts.numInstances() - trainSize;
weka.core.Instances train = new weka.core.Instances(insts, 0, trainSize);
cl.buildClassifier(train);
//double label = cl.classifyInstance(instsTest.instance(0));
double label = cl.classifyInstance(ins);
ins.setClassValue(label);
//instsTest.instance(0).setClassValue(label);
a = ins.toString(ins.numAttributes() - 1);
weka.core.SerializationHelper.write("mymodel.model", cl);
int numCorrect = 0;
for (int i = trainSize; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
double predictedClass = cl.classifyInstance(currentInst);
if (predictedClass == insts.instance(i).classValue())
{
numCorrect++;
}
}
rate = (double)((double)numCorrect / (double)testSize * 100.0);
}
catch (java.lang.Exception ex)
{
//ex.printStackTrace();
rate = -1;
}
return(rate.ToString() + ";" + a ?? "");
}
/// <summary>
/// 初始化分类器
/// </summary>
private void init()
{
object[] arr = SerializationHelper.readAll("Resources/ibk.save");
cl = (Classifier)arr[0];
header = (Instances)arr[1];
}
public static void GenerarModelo()
{
// Fuente de datos
DataSource source = new DataSource(DirectorioDatosPrueba());
// convertimos a instancias
Instances data = source.getDataSet();
// generamos una copia
Instances dataCopy = new Instances(data);
// hay qeu decirle cual es el atributo de clase
if (data.classIndex() == -1)
{
data.setClassIndex(data.numAttributes() - 1);
}
#region Discretizacion
//// DISCRETIZACION
//bool bHasNonNominal = true;
//bool bHasMissingValues = true;
//weka.filters.unsupervised.attribute.Discretize unSupervised = new weka.filters.unsupervised.attribute.Discretize();
//weka.filters.supervised.attribute.Discretize supervised = new weka.filters.supervised.attribute.Discretize();
////Instances dataCopy = new Instances(data);
//// Quitamos las missing classes de las instancias
////data.deleteWithMissingClass();
////
////data = NormalizeDataset(data);
//// si no hay missing values
//if (bHasNonNominal)
//{
// // Para los que son nominales y no son missing values
// weka.filters.supervised.attribute.Discretize supervised2 = new weka.filters.supervised.attribute.Discretize();
// bool k = supervised2.getUseKononenko();
// supervised2.setUseKononenko(true);
// k = supervised2.getUseKononenko();
// k= supervised2.setInputFormat(data);
// data = weka.filters.Filter.useFilter(data, supervised2);
//}
//// si hay missing valyes
//if (bHasMissingValues)
//{
// //System.err.println("Warning: filling in missing values in data set");
// weka.filters.unsupervised.attribute.ReplaceMissingValues m_MissingValuesFilter = new weka.filters.unsupervised.attribute.ReplaceMissingValues();
// m_MissingValuesFilter.setInputFormat(data);
// data = weka.filters.Filter.useFilter(data, m_MissingValuesFilter);
//}
//// EQUAL FREQUENCIES
//// creamos el objeto para discretizar
//weka.filters.unsupervised.attribute.Discretize discretizer = new weka.filters.unsupervised.attribute.Discretize();
//// Le decimos que use el metodo equal frequecies
//discretizer.setUseEqualFrequency(true);
//// ponemos el numero de bins que nos salga de las narices
//discretizer.setBins(3);
//// Este metodo es de la clase PotentialClassIgnorer
//discretizer.setIgnoreClass(true);//Set the IgnoreClass value. Set this to true if the class index is to be unset before the filter is applied.
//// Sets the format of the input instances.
//bool val = discretizer.setInputFormat(data);
//// Aplicamos la discretizacion
//data = Filter.useFilter(data, discretizer);
//// FIND NUM BINS
//// creamos el discretizador
//weka.filters.unsupervised.attribute.Discretize discretizer2 = new weka.filters.unsupervised.attribute.Discretize();
//// le decimos que use este metodo
//discretizer2.setFindNumBins(true);
//// esto es opcional
//discretizer2.setIgnoreClass(true);
////
//discretizer2.setInputFormat(data);
////
//data = Filter.useFilter(data, discretizer2);
#endregion
#region Modelos
#region Cadenas de texto
// hay qeu decirle cual es el atributo de clase
if (data.classIndex() == -1)
{
data.setClassIndex(data.numAttributes() - 1);
}
string k2 = "weka.classifiers.bayes.BayesNet -D -Q weka.classifiers.bayes.net.search.local.K2 -- -P 1 -S BAYES -E weka.classifiers.bayes.net.estimate.SimpleEstimator -- -A 0.5";
string HillClimber = "weka.classifiers.bayes.BayesNet -D -Q weka.classifiers.bayes.net.search.local.HillClimber -- -P 1 -S BAYES -E weka.classifiers.bayes.net.estimate.SimpleEstimator -- -A 0.5";
string TAN = "weka.classifiers.bayes.BayesNet -D -Q weka.classifiers.bayes.net.search.local.TAN -- -S BAYES -E weka.classifiers.bayes.net.estimate.SimpleEstimator -- -A 0.5";
string bayesNetworkNaiveBayes = "weka.classifiers.bayes.NaiveBayes";
string j48 = "weka.classifiers.trees.J48 -C 0.25 -M 2";
string rF = "weka.classifiers.trees.RandomForest -P 100 -I 100 -num-slots 1 -K 0 -M 1.0 -V 0.001 -S 1";
string knn = "weka.classifiers.lazy.IBk -K " + 1.ToString() + " -W 0 -A \"weka.core.neighboursearch.LinearNNSearch -A \\\"weka.core.EuclideanDistance -R first-last\\\"\"";
string ann = "weka.classifiers.functions.MultilayerPerceptron -L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a";
string polykernel = "weka.classifiers.functions.SMO -C 1.0 -L 0.001 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.PolyKernel -E 1.0 -C 250007\" -calibrator \"weka.classifiers.functions.Logistic -R 1.0E-8 -M -1 -num-decimal-places 4\"";
string rbfkernell = "weka.classifiers.functions.SMO -C 1.0 -L 0.001 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.RBFKernel -G 0.01 -C 250007\" -calibrator \"weka.classifiers.functions.Logistic -R 1.0E-8 -M -1 -num-decimal-places 4\"";
string normalized = "weka.classifiers.functions.SMO -C 1.0 -L 0.001 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.NormalizedPolyKernel -E 2.0 -C 250007\" -calibrator \"weka.classifiers.functions.Logistic -R 1.0E-8 -M -1 -num-decimal-places 4\"";
string puk = "weka.classifiers.functions.SMO -C 1.0 -L 0.001 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.Puk -O 1.0 -S 1.0 -C 250007\" -calibrator \"weka.classifiers.functions.Logistic -R 1.0E-8 -M -1 -num-decimal-places 4\"";
#endregion
#region Clasificador
// Separamos la cadena de texto del clasificador en un array
string[] options = Utils.splitOptions("weka.classifiers.functions.SMO -C 1.0 -L 0.001 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.PolyKernel -E 1.0 -C 250007\" -calibrator \"weka.classifiers.functions.Logistic -R 1.0E-8 -M -1 -num-decimal-places 4\"");
string className = options[0];
options[0] = string.Empty;
// Creamos el clasificador
weka.classifiers.Classifier scheme = (Classifier)Utils.forName(java.lang.Class.forName("weka.classifiers.Classifier"), className, options);
#endregion
#region Evaluar
#region Percentage split
// porcentaje que queremos para predecir
int trainingPercent = 80;
// sacamos el numero de isntancias que se corresponden a ese procentaje
int trainSize = (int)Math.Round(data.numInstances() * ((double)trainingPercent / (double)100));
// sacamos el porentajhe que queremos para probar
int testSize = data.numInstances() - trainSize;
// aleatoreizamos el dataset para asegurarnos que las instancias no estan ordenadas
data.randomize(new java.util.Random(DateTime.Now.Ticks));
// Separamos las instancias en 2 grupos (entrenar con unas y predecri con otras)
Instances train = new Instances(data, 0, trainSize);
Instances test = new Instances(data, trainSize, testSize);
// Generamos el clasificador con el training y lo evaluamos con el test
Classifier clsCopyTest = AbstractClassifier.makeCopy(scheme);
clsCopyTest.buildClassifier(train);
Evaluation evalTest = new Evaluation(data);
evalTest.evaluateModel(clsCopyTest, test);
Console.WriteLine();
Console.WriteLine("Correct % = " + evalTest.pctCorrect());
Console.WriteLine("Incorrect % = " + evalTest.pctIncorrect());
Console.WriteLine("AUC = " + evalTest.areaUnderROC(1));
Console.WriteLine("kappa = " + evalTest.kappa());
Console.WriteLine("MAE = " + evalTest.meanAbsoluteError());
Console.WriteLine("RMSE = " + evalTest.rootMeanSquaredError());
Console.WriteLine("RAE = " + evalTest.relativeAbsoluteError());
Console.WriteLine("RRSE = " + evalTest.rootRelativeSquaredError());
Console.WriteLine("Precision = " + evalTest.precision(1));
Console.WriteLine("Recall = " + evalTest.recall(1));
Console.WriteLine("fMeasure = " + evalTest.fMeasure(1));
Console.WriteLine("Error Rate = " + evalTest.errorRate());
double[][] conf = evalTest.confusionMatrix();
#endregion
#region Cross Validation
long seed = DateTime.Now.Ticks;
int folds = 15;
// randomizamos los datos en funcion de la semilla
java.util.Random rand = new java.util.Random(seed);
data.randomize(rand);
// estratificamso el dataset
if (data.classAttribute().isNominal())
{
data.stratify(folds);
}
// crear evaluador
Evaluation eval3 = new Evaluation(data);
// iteramos en funcion de los diferentes folds con los que querams hacer el CV para ver si mejora con mas folds
for (int n = 0; n < folds; n++)
{
//dividimos aleatroiamente el numero de instancias en train y test en funcion de la validacion
Instances train2 = data.trainCV(folds, n);
Instances test2 = data.testCV(folds, n);
// creamos y evaluamos el clasificador
Classifier clsCopy = AbstractClassifier.makeCopy(scheme);
clsCopy.buildClassifier(train);
eval3.evaluateModel(clsCopy, test);
}
// output evaluation
Console.WriteLine();
//Console.WriteLine(eval3.toMatrixString("=== Confusion matrix for fold " + (n + 1) + "/" + folds + " ===\n"));
Console.WriteLine("Correct % = " + eval3.pctCorrect());
Console.WriteLine("Incorrect % = " + eval3.pctIncorrect());
Console.WriteLine("AUC = " + eval3.areaUnderROC(1));
Console.WriteLine("kappa = " + eval3.kappa());
Console.WriteLine("MAE = " + eval3.meanAbsoluteError());
Console.WriteLine("RMSE = " + eval3.rootMeanSquaredError());
Console.WriteLine("RAE = " + eval3.relativeAbsoluteError());
Console.WriteLine("RRSE = " + eval3.rootRelativeSquaredError());
Console.WriteLine("Precision = " + eval3.precision(1));
Console.WriteLine("Recall = " + eval3.recall(1));
Console.WriteLine("fMeasure = " + eval3.fMeasure(1));
Console.WriteLine("Error Rate = " + eval3.errorRate());
// Matriz de confusion
Console.WriteLine(eval3.toMatrixString("=== Overall Confusion Matrix ===\n"));
#endregion
#region Predecir
#region Clasificar
// Le decimos cual es la clase sobre la que tiene que predecir
data.setClassIndex(data.numAttributes() - 1);
// Cogemos el numero de clases de ese atributo
int numClasses = data.numClasses();
// Pintamos por consola cuales son las clases de un atributo
for (int i = 0; i < numClasses; i++)
{
// Pintamos por consola
String classValue = data.classAttribute().value(i);
Console.WriteLine("Class Value " + i + " is " + classValue);
}
//creamos el clasificador
scheme.buildClassifier(data);
// TODO: aqui deberiamos cargar los nuevos datosque queremos que el modelo clasifique
// nosotors lo hacemos con el Dtaset almacenado en la variable data2 que es el mismo que data
// Le decimos que prediga sobre la ultima clase
//data2.setClassIndex(data2.numAttributes() - 1);
// Pintamos la cabecera
Console.WriteLine("===================");
Console.WriteLine("Actual Class, Predicted Class");
// Clasificamos cada instancia del dataset data2
for (int i = 0; i < data.numInstances(); i++)
{
// coge el numero de la clase a la que pertenece esa instancia para un atributo (en el caso de Beta 0-1-2-3)
double actualClass = data.instance(i).classValue();
//valor para esa clase en concreto (en el caso de B cogeria el intervalo correspondiente a la clase dentro de los 4 intervalos en los que puede estar)
String actual = data.classAttribute().value((int)actualClass);
// cogemos la instancia completa dle nuevo dataser
Instance newInst = (Instance)data.instance(i).copy();
newInst.setClassMissing();
// TODO: hay que llamar a esto de forma recursiva, llamar a un metodo desde el otro
// claificamso la instancia
double predNB = scheme.classifyInstance(newInst);
double[] distribution = scheme.distributionForInstance(newInst);
// cogemos la clase a la que se ha predicho que pretende la instancia
String predString = data.classAttribute().value((int)predNB);
Console.WriteLine(actual + ", " + predString);
}
#endregion
#region Regresion
////set class index to the last attribute
//data.setClassIndex(data.numAttributes() - 1);
////build model
//SMOreg smo = new SMOreg();
//smo.buildClassifier(data);
////output model
//Console.WriteLine(smo);
//// Cargar nuevos datos para predecir
////set class index to the last attribute
//data2.setClassIndex(data2.numAttributes() - 1);
////loop through the new dataset and make predictions
//Console.WriteLine("===================");
//Console.WriteLine("Actual Class, SMO Predicted");
//for (int i = 0; i < data2.numInstances(); i++)
//{
// //get class double value for current instance
// double actualValue = data2.instance(i).classValue();
// //get Instance object of current instance
// Instance newInst = data2.instance(i);
// //call classifyInstance, which returns a double value for the class
// double predSMO = smo.classifyInstance(newInst);
// Console.WriteLine(actualValue + ", " + predSMO);
//}
#endregion
#endregion
#endregion
// Lo construimos
scheme.buildClassifier(data);
// Pintar datos en consola
//Console.WriteLine(eval.toSummaryString());
//Console.WriteLine(System.Environment.NewLine);
//Console.WriteLine(eval.toClassDetailsString());
#endregion
}
/// <summary> Evaluates the classifier on a given set of instances. Note that
/// the data must have exactly the same format (e.g. order of
/// attributes) as the data used to train the classifier! Otherwise
/// the results will generally be meaningless.
///
/// </summary>
/// <param name="classifier">machine learning classifier
/// </param>
/// <param name="data">set of test instances for evaluation
/// </param>
/// <returns> the predictions
/// </returns>
/// <throws> Exception if model could not be evaluated </throws>
/// <summary> successfully
/// </summary>
public virtual double[] evaluateModel(Classifier classifier, Instances data)
{
double[] predictions = new double[data.numInstances()];
for (int i = 0; i < data.numInstances(); i++)
{
predictions[i] = evaluateModelOnce((Classifier) classifier, data.instance(i));
}
return predictions;
}
public MITesDataCollectionForm(string dataDirectory, string arffFile, bool isHierarchical)
{
//where data is being stored
this.dataDirectory = dataDirectory;
//Initialize high resolution unix timer
UnixTime.InitializeTime();
//Initialize and start GUI progress thread
progressMessage = null;
aProgressThread = new Thread(new ThreadStart(ProgressThread));
aProgressThread.Start();
#region Load Configuration files
//load the activity and sensor configuration files
progressMessage = "Loading XML protocol and sensors ...";
AXML.Reader reader = new AXML.Reader(Constants.MASTER_DIRECTORY, dataDirectory);
#if (!PocketPC)
if (reader.validate() == false)
{
throw new Exception("Error Code 0: XML format error - activities.xml does not match activities.xsd!");
}
else
{
#endif
this.annotation = reader.parse();
this.annotation.DataDirectory = dataDirectory;
SXML.Reader sreader = new SXML.Reader(Constants.MASTER_DIRECTORY, dataDirectory);
#if (!PocketPC)
if (sreader.validate() == false)
{
throw new Exception("Error Code 0: XML format error - sensors.xml does not match sensors.xsd!");
}
else
{
#endif
this.sensors = sreader.parse(Constants.MAX_CONTROLLERS);
progressMessage += " Completed\r\n";
//TODO: remove BT components
progressMessage += "Loading configuration file ...";
MITesFeatures.core.conf.ConfigurationReader creader = new MITesFeatures.core.conf.ConfigurationReader(dataDirectory);
this.configuration = creader.parse();
progressMessage += " Completed\r\n";
#if (!PocketPC)
}
}
#endif
#endregion Load Configuration files
#region Initialize External Data Reception Channels
//Initialize 1 master decoder
this.masterDecoder = new MITesDecoder();
//Initialize the software mode
isExtracting = false;
isCollectingDetailedData = false;
isPlotting = true;
isClassifying = true;
#region Initialize Feature Extraction
this.isExtracting = false;
if (this.sensors.TotalReceivers > 0) // if there is at least 1 MIT
//Extractor.Initialize(this.mitesDecoders[0], dataDirectory, this.annotation, this.sensors, this.configuration);
Extractor.Initialize(this.masterDecoder, dataDirectory, this.annotation, this.sensors, this.configuration);
else if (this.sensors.Sensors.Count > 0) // only built in
Extractor.Initialize(this.masterDecoder, dataDirectory, this.annotation, this.sensors, this.configuration);
#endregion Initialize Feature Extraction
labelIndex = new Hashtable();
instances = new Instances(new StreamReader(arffFile));
instances.Class = instances.attribute(Extractor.ArffAttributeLabels.Length);
classifier = new J48();
if (!File.Exists("model.xml"))
{
classifier.buildClassifier(instances);
TextWriter tc = new StreamWriter("model.xml");
classifier.toXML(tc);
tc.Flush();
tc.Close();
}
else
classifier.buildClassifier("model.xml", instances);
fvWekaAttributes = new FastVector(Extractor.ArffAttributeLabels.Length + 1);
for (int i = 0; (i < Extractor.ArffAttributeLabels.Length); i++)
fvWekaAttributes.addElement(new weka.core.Attribute(Extractor.ArffAttributeLabels[i]));
FastVector fvClassVal = new FastVector();
labelCounters = new int[((AXML.Category)this.annotation.Categories[0]).Labels.Count + 1];
activityLabels = new string[((AXML.Category)this.annotation.Categories[0]).Labels.Count + 1];
for (int i = 0; (i < ((AXML.Category)this.annotation.Categories[0]).Labels.Count); i++)
{
labelCounters[i] = 0;
string label = "";
int j = 0;
for (j = 0; (j < this.annotation.Categories.Count - 1); j++)
label += ((AXML.Label)((AXML.Category)this.annotation.Categories[j]).Labels[i]).Name.Replace(' ', '_') + "_";
label += ((AXML.Label)((AXML.Category)this.annotation.Categories[j]).Labels[i]).Name.Replace(' ', '_');
activityLabels[i] = label;
labelIndex.Add(label, i);
fvClassVal.addElement(label);
}
weka.core.Attribute ClassAttribute = new weka.core.Attribute("activity", fvClassVal);
isClassifying = true;
this.aMITesActivityCounters = new Hashtable();
if (!((this.sensors.Sensors.Count == 1) && (this.sensors.HasBuiltinSensors)))
{
//Initialize arrays to store USB and Bluetooth controllers
this.mitesControllers = new MITesReceiverController[this.sensors.TotalWiredReceivers];
#if (PocketPC)
this.bluetoothControllers = new BluetoothController[this.sensors.TotalBluetoothReceivers];
//this.ts = new Thread[this.sensors.TotalBluetoothReceivers];
#endif
//Initialize array to store Bluetooth connection status
//this.bluetoothConnectionStatus = new bool[this.sensors.TotalBluetoothReceivers];
//Initialize a decoder for each sensor
this.mitesDecoders = new MITesDecoder[this.sensors.TotalReceivers];
#if (PocketPC)
#region Bluetooth reception channels initialization
//Initialize and search for wockets connections
progressMessage += "Initializing Bluetooth receivers ... searching " + this.sensors.TotalBluetoothReceivers + " BT receivers\r\n";
//Try to initialize all Bluetooth receivers 10 times then exit
int initializationAttempt = 0;
while (initializationAttempt <= 10)
{
if (InitializeBluetoothReceivers() == false)
{
initializationAttempt++;
if (initializationAttempt == 10)
{
MessageBox.Show("Exiting: Some Bluetooth receivers in your configuration were not initialized.");
Application.Exit();
System.Diagnostics.Process.GetCurrentProcess().Kill();
}
else
progressMessage += "Failed to initialize all BT connections. Retrying (" + initializationAttempt + ")...\r\n";
}
else
break;
Thread.Sleep(2000);
}
#endregion Bluetooth reception channels initialization
#endif
#region USB reception channels initialization
if (InitializeUSBReceivers() == false)
{
MessageBox.Show("Exiting: Some USB receivers in your configuration were not initialized.");
#if (PocketPC)
Application.Exit();
System.Diagnostics.Process.GetCurrentProcess().Kill();
#else
Environment.Exit(0);
#endif
}
#endregion USB reception channels initialization
}
//}
#endregion Initialize External Data Reception Channels
#if (PocketPC)
#region Initialize Builtin Data Reception Channels
if (InitializeBuiltinReceivers() == false)
{
MessageBox.Show("Exiting: A built in receiver channel was not found.");
Application.Exit();
System.Diagnostics.Process.GetCurrentProcess().Kill();
}
#endregion Initialize Builtin Data Reception Channels
#endif
#region Initialize GUI Components
//initialize the interface components
InitializeComponent();
//Initialize GUI timers
progressMessage += "Initializing Timers ...";
InitializeTimers();
progressMessage += " Completed\r\n";
//Initialize different GUI components
progressMessage += "Initializing GUI ...";
InitializeInterface();
progressMessage += " Completed\r\n";
this.isPlotting = true;
//count the number of accelerometers
if (this.sensors.IsHR)
this.maxPlots = this.sensors.Sensors.Count - 1;
else
this.maxPlots = this.sensors.Sensors.Count;
SetFormPositions();
if (this.sensors.TotalReceivers > 0)
aMITesPlotter = new MITesScalablePlotter(this.panel1, MITesScalablePlotter.DeviceTypes.IPAQ, maxPlots, this.masterDecoder, GetGraphSize(false));
else
aMITesPlotter = new MITesScalablePlotter(this.panel1, MITesScalablePlotter.DeviceTypes.IPAQ, maxPlots, this.masterDecoder, GetGraphSize(false));
//Override the resize event
#if (PocketPC)
this.Resize += new EventHandler(OnResize);
#else
this.form1.Resize += new EventHandler(OnResizeForm1);
this.form1.FormClosing += new FormClosingEventHandler(form_FormClosing);
this.form2.Resize += new EventHandler(OnResizeForm2);
this.form2.FormClosing += new FormClosingEventHandler(form_FormClosing);
this.form3.Resize += new EventHandler(OnResizeForm3);
this.form3.FormClosing += new FormClosingEventHandler(form_FormClosing);
this.form4.Resize += new EventHandler(OnResizeForm4);
this.form4.FormClosing += new FormClosingEventHandler(form_FormClosing);
#endif
//Initialize the quality interface
progressMessage += "Initializing MITes Quality GUI ...";
InitializeQualityInterface();
progressMessage += " Completed\r\n";
//Remove classifier tabs
#if (PocketPC)
this.tabControl1.TabPages.RemoveAt(4);
this.tabControl1.SelectedIndex = 0;
#else
this.ShowForms();
#endif
#endregion Initialize GUI Components
#region Initialize Quality Tracking variables
InitializeQuality();
#endregion Initialize Quality Tracking variables
#region Initialize Logging
InitializeLogging(dataDirectory);
#endregion Initialize Logging
#region Initialize CSV Storage (PC Only)
#if (!PocketPC)
//create some counters for activity counts
averageX = new int[this.sensors.MaximumSensorID + 1];
averageY = new int[this.sensors.MaximumSensorID + 1];
averageZ = new int[this.sensors.MaximumSensorID + 1];
averageRawX = new int[this.sensors.MaximumSensorID + 1];
averageRawY = new int[this.sensors.MaximumSensorID + 1];
averageRawZ = new int[this.sensors.MaximumSensorID + 1];
prevX = new int[this.sensors.MaximumSensorID + 1];
prevY = new int[this.sensors.MaximumSensorID + 1];
prevZ = new int[this.sensors.MaximumSensorID + 1];
acCounters = new int[this.sensors.MaximumSensorID + 1];
activityCountWindowSize = 0;
activityCountCSVs = new StreamWriter[this.sensors.MaximumSensorID + 1];
samplingCSVs = new StreamWriter[this.sensors.MaximumSensorID + 1];
averagedRaw = new StreamWriter[this.sensors.MaximumSensorID + 1];
masterCSV = new StreamWriter(dataDirectory + "\\MITesSummaryData.csv");
hrCSV = new StreamWriter(dataDirectory + "\\HeartRate_MITes.csv");
string csv_line1 = "UnixTimeStamp,TimeStamp,X,Y,Z";
string csv_line2 = "UnixTimeStamp,TimeStamp,Sampling";
string hr_csv_header = "UnixTimeStamp,TimeStamp,HR";
string master_csv_header = "UnixTimeStamp,TimeStamp";
foreach (Category category in this.annotation.Categories)
master_csv_header += "," + category.Name;
foreach (Sensor sensor in this.sensors.Sensors)
{
int sensor_id = Convert.ToInt32(sensor.ID);
string location = sensor.Location.Replace(' ', '-');
if (sensor_id > 0) //exclude HR
{
activityCountCSVs[sensor_id] = new StreamWriter(dataDirectory + "\\MITes_" + sensor_id.ToString("00") + "_ActivityCount_" + location + ".csv");
activityCountCSVs[sensor_id].WriteLine(csv_line1);
averagedRaw[sensor_id] = new StreamWriter(dataDirectory + "\\MITes_" + sensor_id.ToString("00") + "_1s-RawMean_" + location + ".csv");
averagedRaw[sensor_id].WriteLine(csv_line1);
samplingCSVs[sensor_id] = new StreamWriter(dataDirectory + "\\MITes_" + sensor_id.ToString("00") + "_SampleRate_" + location + ".csv");
samplingCSVs[sensor_id].WriteLine(csv_line2);
master_csv_header += ",MITes" + sensor_id.ToString("00") + "_SR," + "MITes" + sensor_id.ToString("00") + "_AVRaw_X," +
"MITes" + sensor_id.ToString("00") + "_AVRaw_Y," + "MITes" + sensor_id.ToString("00") + "_AVRaw_Z," + "MITes" + sensor_id.ToString("00") + "_AC_X," +
"MITes" + sensor_id.ToString("00") + "_AC_Y," + "MITes" + sensor_id.ToString("00") + "_AC_Z";
}
}
master_csv_header += ",HR";
this.masterCSV.WriteLine(master_csv_header);
this.hrCSV.WriteLine(hr_csv_header);
#endif
#endregion Initialize CSV Storage (PC Only)
#region Start Collecting Data
//if (this.sensors.TotalReceivers > 0)
// isStartedReceiver = true;
//Start the built in polling thread
#if (PocketPC)
if (this.sensors.HasBuiltinSensors)
{
this.pollingThread = new Thread(new ThreadStart(this.pollingData));
this.pollingThread.Priority = ThreadPriority.Lowest;
this.pollingThread.Start();
}
#endif
//Terminate the progress thread
progressThreadQuit = true;
//Enable all timer functions
this.readDataTimer.Enabled = true;
this.qualityTimer.Enabled = true;
if (this.sensors.IsHR)
this.HRTimer.Enabled = true;
#endregion Start Collecting Data
}