/// <summary>
/// erzeugt einen WEKA-kompatiblen Datensatz
/// </summary>
/// <param name="classNames">Aufzählung der Klassennamen die im Datensatz verwendet werden sollen</param>
/// <param name="featureNames">Namen der Merkmale</param>
/// <param name="name">Name des Datensatzes</param>
/// <param name="classAttributeName">>Name für das Klassenattribut</param>
/// <returns></returns>
public static Instances CreateInstances(IEnumerable <string> classNames, IEnumerable <string> featureNames, string name = "data", string classAttributeName = "class")
{
var features = new ArrayList();
foreach (var featureName in featureNames)
{
var attribute = new Attribute(featureName);
features.add(attribute);
}
Attribute classAttribute = null;
if (null != classNames)
{
var classNamesList = new List <string>(classNames);
classNamesList.Sort();
var classValues = new ArrayList();
foreach (var className in classNamesList)
{
classValues.add(className);
}
classAttribute = new Attribute(classAttributeName, classValues);
features.add(classAttribute);
}
var dataset = new Instances(name, features, 1);
if (null != classAttribute)
{
dataset.setClass(classAttribute);
}
return(dataset);
}
public HashSet <HtmlNode> RunOnTestSeenSet()
{
HashSet <HtmlNode> classifierSelectedNodes = new HashSet <HtmlNode>();
InitTestSeen();
foreach (string featureString in FeaturesUsed)
{
HashSet <HtmlNode> resNodes = DomPool.TESTSeenRunXpathQuery(useNormalPerformanceQUERY(featureString));
foreach (HtmlNode nd in resNodes)
{
if (!testSeenAllNodes.Contains(nd))
{
continue;
}
testSeenNodeFeatures[nd].Add(featureString);
}
}
FastVector fvWekaAttributes = GetDataSetAtts();
Instances testSet = new Instances("TestSeenSet", fvWekaAttributes, 10);
testSet.setClassIndex(fvWekaAttributes.size() - 1);
foreach (HtmlNode currNode in testSeenAllNodes)
{
Instance item = new SparseInstance(fvWekaAttributes.size());
for (int i = 0; i < fvWekaAttributes.size() - 1; i++)
{
weka.core.Attribute currFeature = (weka.core.Attribute)fvWekaAttributes.elementAt(i);
if (testSeenNodeFeatures[currNode].Contains(currFeature.name()))
{
item.setValue(currFeature, 1);
}
else
{
item.setValue(currFeature, 0);
}
}
//set the class
weka.core.Attribute classFeature = (weka.core.Attribute)fvWekaAttributes.elementAt(fvWekaAttributes.size() - 1);
//string rightVal = DomPool.TargetNodes.Contains(currNode) ? "yes" : "no";
item.setDataset(testSet);
double classifierdv = classifierTree.classifyInstance(item);
string classifierVal = classFeature.value((int)classifierdv);
if (classifierVal.Equals("yes"))
{
classifierSelectedNodes.Add(currNode);
}
testSet.add(item);
}
return(classifierSelectedNodes);
}
/// <summary> Generates the classifier.
///
/// </summary>
/// <param name="instances">set of instances serving as training data
/// </param>
/// <exception cref="Exception">if the classifier has not been generated successfully
/// </exception>
public override void buildClassifier(Instances instances)
{
double sumOfWeights = 0;
m_Class = instances.classAttribute();
m_ClassValue = 0;
switch (instances.classAttribute().type())
{
case weka.core.Attribute.NUMERIC:
m_Counts = null;
break;
case weka.core.Attribute.NOMINAL:
m_Counts = new double[instances.numClasses()];
for (int i = 0; i < m_Counts.Length; i++)
{
m_Counts[i] = 1;
}
sumOfWeights = instances.numClasses();
break;
default:
throw new System.Exception("ZeroR can only handle nominal and numeric class" + " attributes.");
}
System.Collections.IEnumerator enu = instances.enumerateInstances();
//UPGRADE_TODO: Method 'java.util.Enumeration.hasMoreElements' was converted to 'System.Collections.IEnumerator.MoveNext' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javautilEnumerationhasMoreElements'"
while (enu.MoveNext())
{
//UPGRADE_TODO: Method 'java.util.Enumeration.nextElement' was converted to 'System.Collections.IEnumerator.Current' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javautilEnumerationnextElement'"
Instance instance = (Instance) enu.Current;
if (!instance.classIsMissing())
{
if (instances.classAttribute().Nominal)
{
//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'"
m_Counts[(int) instance.classValue()] += instance.weight();
}
else
{
m_ClassValue += instance.weight() * instance.classValue();
}
sumOfWeights += instance.weight();
}
}
if (instances.classAttribute().Numeric)
{
if (Utils.gr(sumOfWeights, 0))
{
m_ClassValue /= sumOfWeights;
}
}
else
{
m_ClassValue = Utils.maxIndex(m_Counts);
Utils.normalize(m_Counts, sumOfWeights);
}
}
public FastVector GetDataSetAtts()
{
if (_fvWekaAttributes != null)
{
return(_fvWekaAttributes);
}
// Declare features
FastVector fvWekaAttributes = new FastVector(DomPool.SelectorFeatures.Count() + 1);
foreach (Feature currFeature in DomPool.SelectorFeatures)
{
weka.core.Attribute feature = new weka.core.Attribute(currFeature.ToString());
fvWekaAttributes.addElement(feature);
}
// Declare the class attribute along with its values
FastVector fvClassVal = new FastVector(2);
fvClassVal.addElement("yes");
fvClassVal.addElement("no");
weka.core.Attribute ClassAttribute = new weka.core.Attribute("theClass", fvClassVal);
// Declare the feature vector
fvWekaAttributes.addElement(ClassAttribute);
_fvWekaAttributes = fvWekaAttributes;
return(_fvWekaAttributes);
}
public List <double> testMLPUsingWeka(string[] attributeArray, string[] classNames, double[] dataValues, string classHeader, string defaultclass, string modelName, int hiddelLayers = 7, double learningRate = 0.03, double momentum = 0.4, int decimalPlaces = 2, int trainingTime = 1000)
{
java.util.ArrayList classLabel = new java.util.ArrayList();
foreach (string className in classNames)
{
classLabel.Add(className);
}
weka.core.Attribute classHeaderName = new weka.core.Attribute(classHeader, classLabel);
java.util.ArrayList attributeList = new java.util.ArrayList();
foreach (string attribute in attributeArray)
{
weka.core.Attribute newAttribute = new weka.core.Attribute(attribute);
attributeList.Add(newAttribute);
}
attributeList.add(classHeaderName);
weka.core.Instances data = new weka.core.Instances("TestInstances", attributeList, 0);
data.setClassIndex(data.numAttributes() - 1);
// Set instance's values for the attributes
weka.core.Instance inst_co = new DenseInstance(data.numAttributes());
for (int i = 0; i < data.numAttributes() - 1; i++)
{
inst_co.setValue(i, dataValues.ElementAt(i));
}
inst_co.setValue(classHeaderName, defaultclass);
data.add(inst_co);
java.io.File path = new java.io.File("/models/");
weka.classifiers.functions.MultilayerPerceptron clRead = loadModel(modelName, path);
clRead.setHiddenLayers(hiddelLayers.ToString());
clRead.setLearningRate(learningRate);
clRead.setMomentum(momentum);
clRead.setNumDecimalPlaces(decimalPlaces);
clRead.setTrainingTime(trainingTime);
weka.filters.Filter myRandom = new weka.filters.unsupervised.instance.Randomize();
myRandom.setInputFormat(data);
data = weka.filters.Filter.useFilter(data, myRandom);
double classValue = clRead.classifyInstance(data.get(0));
double[] predictionDistribution = clRead.distributionForInstance(data.get(0));
List <double> predictionDistributions = new List <double>();
for (int predictionDistributionIndex = 0;
predictionDistributionIndex < predictionDistribution.Count();
predictionDistributionIndex++)
{
string classValueString1 = classLabel.get(predictionDistributionIndex).ToString();
double prob = predictionDistribution[predictionDistributionIndex] * 100;
predictionDistributions.Add(prob);
}
List <double> prediction = new List <double>();
prediction.Add(classValue);
prediction.AddRange(predictionDistributions);
return(prediction);
}
public VectorClassif(int nbTags)
{
tagsNb = nbTags;
ArrayList nomi = new ArrayList();
nomi.add("0");
nomi.add("1");
ArrayList attr = new ArrayList();
weka.core.Attribute stringAttr = new weka.core.Attribute("todoString", (List)null);
attr.add(stringAttr);
for (int i = 1; i <= nbTags; i++) {
attr.add(new weka.core.Attribute("label" + i, nomi));
}
oDataSet = new Instances("Todo-Instances", attr, 500);
}
/// <summary>
/// Adds teta results of gini results to the list
/// Change the attributes of the arff file
/// Adds the attributes to arff file
/// </summary>
/// <param name="insts"></param>
/// <param name="result"></param>
/// <param name="path"></param>
private void CreateNewDataset(weka.core.Instances insts, List <double[]> result, string path)
{
//Tetaları Listeye Ekle
List <List <string> > lst = new List <List <string> >();
for (int i = 0; i < insts.numInstances(); i++)
{
lst.Add(new List <string>());
for (int j = 0; j < insts.instance(i).numValues() - 1; j++)
{
string value = insts.instance(i).toString(j);
for (int k = 0; k < categories[j].Length; k++)
{
if (insts.instance(i).toString(j) == categories[j][k])
{
lst[lst.Count - 1].Add(String.Format("{0:0.00}", result[j][k]));
break;
}
}
}
}
//Attiribute Değiştir
for (int i = 0; i < insts.numAttributes() - 1; i++)
{
string name = insts.attribute(i).name().ToString();
insts.deleteAttributeAt(i);
weka.core.Attribute att = new weka.core.Attribute(name);
insts.insertAttributeAt(att, i);
}
//Attiributeları yaz
for (int i = 0; i < insts.numInstances(); i++)
{
for (int j = 0; j < insts.instance(i).numValues() - 1; j++)
{
insts.instance(i).setValue(j, Convert.ToDouble(lst[i][j]));
}
}
if (File.Exists(path))
{
File.Delete(path);
}
var saver = new ArffSaver();
saver.setInstances(insts);
saver.setFile(new java.io.File(path));
saver.writeBatch();
}
public Runtime AddNumericAttribute(string name, double[] values)
{
if (values.Length != NumInstances)
{
throw new ArgumentException("Values should be " + NumInstances + " in length.");
}
var att = new weka.core.Attribute(name);
var attidx = NumAttributes;
InsertAttributeAt(new PmlAttribute(att), attidx);
for (int j = 0; j < values.Length; j++)
{
this[j].SetValue(attidx, values[j]);
}
return(this);
}
public async Task makeNewInstance()
{
int a = 0;
ins = new DenseInstance(insts.numAttributes());
ins.setDataset(insts);
foreach (trainedData item in myData)
{
if (item.isNumber == false)
{
weka.core.Attribute m = insts.attribute(item.name);
ins.setValue(m, item.value);
a++;
}
else
{
}
}
}
public Runtime AddNominalAttribute(string name, string[] values)
{
if (values.Length != NumInstances)
{
throw new ArgumentException("Values should be " + NumInstances + " in length.");
}
var labels = values.Distinct().ToArray();
var att = new weka.core.Attribute(name, labels.ToArrayList());
var attidx = NumAttributes;
InsertAttributeAt(new PmlAttribute(att), attidx);
for (int j = 0; j < values.Length; j++)
{
this[j].SetValue(attidx, values[j]);
}
return(this);
}
public string testHybridEmotionUsingWeka(string[] attributeArray, string[] classNames, double[] dataValues, string classHeader, string defaultclass, string modelName)
{
java.util.ArrayList classLabel = new java.util.ArrayList();
foreach (string className in classNames)
{
classLabel.Add(className);
}
weka.core.Attribute classHeaderName = new weka.core.Attribute(classHeader, classLabel);
java.util.ArrayList attributeList = new java.util.ArrayList();
foreach (string attribute in attributeArray)
{
weka.core.Attribute newAttribute = new weka.core.Attribute(attribute);
attributeList.Add(newAttribute);
}
attributeList.add(classHeaderName);
weka.core.Instances data = new weka.core.Instances("TestInstances", attributeList, 0);
data.setClassIndex(data.numAttributes() - 1);
// Set instance's values for the attributes
weka.core.Instance inst_co = new DenseInstance(data.numAttributes());
for (int i = 0; i < data.numAttributes() - 1; i++)
{
inst_co.setValue(i, dataValues.ElementAt(i));
}
inst_co.setValue(classHeaderName, defaultclass);
data.add(inst_co);
java.io.File path = new java.io.File("/models/");
weka.classifiers.meta.Bagging clRead = loadBaggingModel(modelName, path);
weka.filters.Filter myRandom = new weka.filters.unsupervised.instance.Randomize();
myRandom.setInputFormat(data);
data = weka.filters.Filter.useFilter(data, myRandom);
double classValue = clRead.classifyInstance(data.get(0));
string classValueString = classLabel.get(Int32.Parse(classValue.ToString())).ToString();
return(classValueString);
}
//code taken from here http://stackoverflow.com/questions/9616872/classification-of-instances-in-weka/14876081#14876081
// This creates the data set's attributes vector
public static FastVector CreateFastVector(int size)
{
var fv = new FastVector();
weka.core.Attribute att;
foreach (int key in TrainingTesting_SharedVariables._trainTopIGFeatures)
{
if (key != TrainingTesting_SharedVariables._trainTopIGFeatures[TrainingTesting_SharedVariables._trainTopIGFeatures.Length - 1])
{
att = new weka.core.Attribute("att_" + (key + 1).ToString());
fv.addElement(att);
}
}
{
var classValues = new FastVector(1); //it doesnt matter if its 3 or 1, when addElement is used the fastvector grows.
List <string> labels = GuiPreferences.Instance.getLabels();
GuiPreferences.Instance.setLog("automatically! adding " + (labels.Count - 1).ToString() + " classes 2 -> " + (labels.Count + 1).ToString() + " to fast vector, based on protocol labels");
//baseline is ignored, we start from the second event in the protocol
for (int l = 1; l < labels.Count; l++)
{
classValues.addElement((l + 1).ToString());
}
//classValues.addElement("2");
//classValues.addElement("3");
//classValues.addElement("4");
//classValues.addElement("5");
var classAttribute = new weka.core.Attribute("class", classValues);
fv.addElement(classAttribute);
}
return(fv);
}
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
}
public static void CreateArffFiles()
{
java.util.ArrayList atts;
java.util.ArrayList attsRel;
java.util.ArrayList attVals;
java.util.ArrayList attValsRel;
Instances data;
Instances dataRel;
double[] vals;
double[] valsRel;
int i;
// 1. set up attributes
atts = new java.util.ArrayList();
// - numeric
atts.Add(new weka.core.Attribute("att1"));
// - nominal
attVals = new java.util.ArrayList();
for (i = 0; i < 5; i++)
{
attVals.add("val" + (i + 1));
}
weka.core.Attribute nominal = new weka.core.Attribute("att2", attVals);
atts.add(nominal);
// - string
atts.add(new weka.core.Attribute("att3", (java.util.ArrayList)null));
// - date
atts.add(new weka.core.Attribute("att4", "yyyy-MM-dd"));
// - relational
attsRel = new java.util.ArrayList();
// -- numeric
attsRel.add(new weka.core.Attribute("att5.1"));
// -- nominal
attValsRel = new java.util.ArrayList();
for (i = 0; i < 5; i++)
{
attValsRel.Add("val5." + (i + 1));
}
attsRel.add(new weka.core.Attribute("att5.2", attValsRel));
dataRel = new Instances("att5", attsRel, 0);
atts.add(new weka.core.Attribute("att5", dataRel, 0));
// 2. create Instances object
data = new Instances("MyRelation", atts, 0);
// 3. fill with data
// first instance
vals = new double[data.numAttributes()];
// - numeric
vals[0] = Math.PI;
// - nominal
vals[1] = attVals.indexOf("val3");
// - string
vals[2] = data.attribute(2).addStringValue("This is a string!");
// - date
vals[3] = data.attribute(3).parseDate("2001-11-09");
// - relational
dataRel = new Instances(data.attribute(4).relation(), 0);
// -- first instance
valsRel = new double[2];
valsRel[0] = Math.PI + 1;
valsRel[1] = attValsRel.indexOf("val5.3");
weka.core.Instance inst = new DenseInstance(2);
inst.setValue(1, valsRel[0]);
inst.setValue(1, valsRel[1]);
dataRel.add(inst);
// -- second instance
valsRel = new double[2];
valsRel[0] = Math.PI + 2;
valsRel[1] = attValsRel.indexOf("val5.2");
dataRel.add(inst);
vals[4] = data.attribute(4).addRelation(dataRel);
// add
weka.core.Instance inst2 = new DenseInstance(4);
inst2.setValue(1, vals[0]);
inst2.setValue(1, vals[1]);
inst2.setValue(1, vals[2]);
inst2.setValue(1, vals[3]);
data.add(inst2);
// second instance
vals = new double[data.numAttributes()]; // important: needs NEW array!
// - numeric
vals[0] = Math.E;
// - nominal
vals[1] = attVals.indexOf("val1");
// - string
vals[2] = data.attribute(2).addStringValue("And another one!");
// - date
vals[3] = data.attribute(3).parseDate("2000-12-01");
// - relational
dataRel = new Instances(data.attribute(4).relation(), 0);
// -- first instance
valsRel = new double[2];
valsRel[0] = Math.E + 1;
valsRel[1] = attValsRel.indexOf("val5.4");
dataRel.add(inst);
// -- second instance
valsRel = new double[2];
valsRel[0] = Math.E + 2;
valsRel[1] = attValsRel.indexOf("val5.1");
dataRel.add(inst);
vals[4] = data.attribute(4).addRelation(dataRel);
// add
data.add(inst2);
data.setClassIndex(data.numAttributes() - 1);
// 4. output data
for (int x = 0; x < data.numInstances(); x++)
{
weka.core.Instance ins = data.instance(x);
System.Console.WriteLine(ins.value(x).ToString());
}
return;
}
public void LearnModel()
{
Init();
foreach (Feature currFeature in DomPool.SelectorFeatures)
{
String featureString = currFeature.ToString();
HashSet <HtmlNode> resNodes = DomPool.RunXpathQuery(featureString);
foreach (HtmlNode nd in resNodes)
{
if (!allNodes.Contains(nd))
{
continue;
}
nodeFeatures[nd].Add(featureString);
}
}
FastVector fvWekaAttributes = GetDataSetAtts();
Instances trainingSet = new Instances("TS", fvWekaAttributes, 10);
trainingSet.setClassIndex(fvWekaAttributes.size() - 1);
foreach (HtmlNode currNode in allNodes)
{
Instance item = new SparseInstance(fvWekaAttributes.size());
for (int i = 0; i < fvWekaAttributes.size() - 1; i++)
{
weka.core.Attribute currFeature = (weka.core.Attribute)fvWekaAttributes.elementAt(i);
if (nodeFeatures[currNode].Contains(currFeature.name()))
{
item.setValue(currFeature, 1);
}
else
{
item.setValue(currFeature, 0);
}
}
//set the class
weka.core.Attribute classFeature = (weka.core.Attribute)fvWekaAttributes.elementAt(fvWekaAttributes.size() - 1);
item.setValue(classFeature, (DomPool.TargetNodes.Contains(currNode)?"yes":"no"));
item.setDataset(trainingSet);
if (DomPool.TargetNodes.Contains(currNode))
{
for (int t = 0; t < (DomPool.NonTargetNodes.Count() / DomPool.TargetNodes.Count()); t++)
{
trainingSet.add(new SparseInstance(item));
}
}
else
{
trainingSet.add(item);
}
}
//String[] options = new String[2];
//options = new string[] { "-C", "0.05" }; // unpruned tree
NaiveBayes cls = new NaiveBayes(); // new instance of tree
//cls.setOptions(weka.core.Utils.splitOptions("-C 1.0 -L 0.0010 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.PolyKernel -C 250007 -E 1.0\""));
//cls.setOptions(options); // set the options
cls.buildClassifier(trainingSet); // build classifier
//save the resulting classifier
classifier = cls;
// Reader treeDot = new StringReader(tree.graph());
// TreeBuild treeBuild = new TreeBuild();
// Node treeRoot = treeBuild.create(treeDot);
FeaturesUsed = new HashSet <string>();
foreach (Feature f in DomPool.SelectorFeatures)
{
FeaturesUsed.Add(f.ToString());
}
}
/// <summary> Builds the boosted classifier</summary>
public virtual void buildClassifier(Instances data)
{
m_RandomInstance = new Random(m_Seed);
Instances boostData;
int classIndex = data.classIndex();
if (data.classAttribute().Numeric)
{
throw new Exception("LogitBoost can't handle a numeric class!");
}
if (m_Classifier == null)
{
throw new System.Exception("A base classifier has not been specified!");
}
if (!(m_Classifier is WeightedInstancesHandler) && !m_UseResampling)
{
m_UseResampling = true;
}
if (data.checkForStringAttributes())
{
throw new Exception("Cannot handle string attributes!");
}
if (m_Debug)
{
System.Console.Error.WriteLine("Creating copy of the training data");
}
m_NumClasses = data.numClasses();
m_ClassAttribute = data.classAttribute();
// Create a copy of the data
data = new Instances(data);
data.deleteWithMissingClass();
// Create the base classifiers
if (m_Debug)
{
System.Console.Error.WriteLine("Creating base classifiers");
}
m_Classifiers = new Classifier[m_NumClasses][];
for (int j = 0; j < m_NumClasses; j++)
{
m_Classifiers[j] = Classifier.makeCopies(m_Classifier, this.NumIterations);
}
// Do we want to select the appropriate number of iterations
// using cross-validation?
int bestNumIterations = this.NumIterations;
if (m_NumFolds > 1)
{
if (m_Debug)
{
System.Console.Error.WriteLine("Processing first fold.");
}
// Array for storing the results
double[] results = new double[this.NumIterations];
// Iterate throught the cv-runs
for (int r = 0; r < m_NumRuns; r++)
{
// Stratify the data
data.randomize(m_RandomInstance);
data.stratify(m_NumFolds);
// Perform the cross-validation
for (int i = 0; i < m_NumFolds; i++)
{
// Get train and test folds
Instances train = data.trainCV(m_NumFolds, i, m_RandomInstance);
Instances test = data.testCV(m_NumFolds, i);
// Make class numeric
Instances trainN = new Instances(train);
trainN.ClassIndex = - 1;
trainN.deleteAttributeAt(classIndex);
trainN.insertAttributeAt(new weka.core.Attribute("'pseudo class'"), classIndex);
trainN.ClassIndex = classIndex;
m_NumericClassData = new Instances(trainN, 0);
// Get class values
int numInstances = train.numInstances();
double[][] tmpArray = new double[numInstances][];
for (int i2 = 0; i2 < numInstances; i2++)
{
tmpArray[i2] = new double[m_NumClasses];
}
double[][] trainFs = tmpArray;
double[][] tmpArray2 = new double[numInstances][];
for (int i3 = 0; i3 < numInstances; i3++)
{
tmpArray2[i3] = new double[m_NumClasses];
}
double[][] trainYs = tmpArray2;
for (int j = 0; j < m_NumClasses; j++)
{
for (int k = 0; k < numInstances; k++)
{
trainYs[k][j] = (train.instance(k).classValue() == j)?1.0 - m_Offset:0.0 + (m_Offset / (double) m_NumClasses);
}
}
// Perform iterations
double[][] probs = initialProbs(numInstances);
m_NumGenerated = 0;
double sumOfWeights = train.sumOfWeights();
for (int j = 0; j < this.NumIterations; j++)
{
performIteration(trainYs, trainFs, probs, trainN, sumOfWeights);
Evaluation eval = new Evaluation(train);
eval.evaluateModel(this, test);
results[j] += eval.correct();
}
}
}
// Find the number of iterations with the lowest error
//UPGRADE_TODO: The equivalent in .NET for field 'java.lang.Double.MAX_VALUE' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
double bestResult = - System.Double.MaxValue;
for (int j = 0; j < this.NumIterations; j++)
{
if (results[j] > bestResult)
{
bestResult = results[j];
bestNumIterations = j;
}
}
if (m_Debug)
{
System.Console.Error.WriteLine("Best result for " + bestNumIterations + " iterations: " + bestResult);
}
}
// Build classifier on all the data
int numInstances2 = data.numInstances();
double[][] trainFs2 = new double[numInstances2][];
for (int i4 = 0; i4 < numInstances2; i4++)
{
trainFs2[i4] = new double[m_NumClasses];
}
double[][] trainYs2 = new double[numInstances2][];
for (int i5 = 0; i5 < numInstances2; i5++)
{
trainYs2[i5] = new double[m_NumClasses];
}
for (int j = 0; j < m_NumClasses; j++)
{
for (int i = 0, k = 0; i < numInstances2; i++, k++)
{
trainYs2[i][j] = (data.instance(k).classValue() == j)?1.0 - m_Offset:0.0 + (m_Offset / (double) m_NumClasses);
}
}
// Make class numeric
data.ClassIndex = - 1;
data.deleteAttributeAt(classIndex);
data.insertAttributeAt(new weka.core.Attribute("'pseudo class'"), classIndex);
data.ClassIndex = classIndex;
m_NumericClassData = new Instances(data, 0);
// Perform iterations
double[][] probs2 = initialProbs(numInstances2);
double logLikelihood = CalculateLogLikelihood(trainYs2, probs2);
m_NumGenerated = 0;
if (m_Debug)
{
System.Console.Error.WriteLine("Avg. log-likelihood: " + logLikelihood);
}
double sumOfWeights2 = data.sumOfWeights();
for (int j = 0; j < bestNumIterations; j++)
{
double previousLoglikelihood = logLikelihood;
performIteration(trainYs2, trainFs2, probs2, data, sumOfWeights2);
logLikelihood = CalculateLogLikelihood(trainYs2, probs2);
if (m_Debug)
{
System.Console.Error.WriteLine("Avg. log-likelihood: " + logLikelihood);
}
if (System.Math.Abs(previousLoglikelihood - logLikelihood) < m_Precision)
{
return ;
}
}
}
public void LearnModel()
{
Init();
foreach (Feature currFeature in DomPool.SelectorFeatures)
{
String featureString = currFeature.ToString();
HashSet <HtmlNode> resNodes = DomPool.RunXpathQuery(featureString);
foreach (HtmlNode nd in resNodes)
{
if (!allNodes.Contains(nd))
{
continue;
}
nodeFeatures[nd].Add(featureString);
}
}
FastVector fvWekaAttributes = GetDataSetAtts();
Instances trainingSet = new Instances("TS", fvWekaAttributes, 100);
trainingSet.setClassIndex(fvWekaAttributes.size() - 1);
foreach (HtmlNode currNode in allNodes)
{
Instance item = new SparseInstance(fvWekaAttributes.size());
for (int i = 0; i < fvWekaAttributes.size() - 1; i++)
{
weka.core.Attribute currFeature = (weka.core.Attribute)fvWekaAttributes.elementAt(i);
if (nodeFeatures[currNode].Contains(currFeature.name()))
{
item.setValue(currFeature, 1);
}
else
{
item.setValue(currFeature, 0);
}
}
//set the class
weka.core.Attribute classFeature = (weka.core.Attribute)fvWekaAttributes.elementAt(fvWekaAttributes.size() - 1);
item.setValue(classFeature, (DomPool.TargetNodes.Contains(currNode)?"yes":"no"));
item.setDataset(trainingSet);
if (DomPool.TargetNodes.Contains(currNode))
{
for (int t = 0; t < (DomPool.NonTargetNodes.Count() / DomPool.TargetNodes.Count()); t++)
{
trainingSet.add(new SparseInstance(item));
}
}
else
{
trainingSet.add(item);
}
}
String[] options = new String[2];
options[0] = "-C"; // unpruned tree
options[1] = "0.1";
J48 tree = new J48(); // new instance of tree
tree.setOptions(options); // set the options
tree.buildClassifier(trainingSet); // build classifier
//save the resulting classifier
classifierTree = tree;
Reader treeDot = new StringReader(tree.graph());
TreeBuild treeBuild = new TreeBuild();
Node treeRoot = treeBuild.create(treeDot);
FeaturesUsed = getTreeFeatures(treeRoot);
}
public List <double> testSMOUsingWeka(string[] attributeArray, string[] classNames, double[] dataValues, string classHeader, string defaultclass, string modelName, int hiddelLayers = 7, double learningRate = 0.03, double momentum = 0.4, int decimalPlaces = 2, int trainingTime = 1000)
{
java.util.ArrayList classLabel = new java.util.ArrayList();
foreach (string className in classNames)
{
classLabel.Add(className);
}
weka.core.Attribute classHeaderName = new weka.core.Attribute(classHeader, classLabel);
java.util.ArrayList attributeList = new java.util.ArrayList();
foreach (string attribute in attributeArray)
{
weka.core.Attribute newAttribute = new weka.core.Attribute(attribute);
attributeList.Add(newAttribute);
}
attributeList.add(classHeaderName);
weka.core.Instances data = new weka.core.Instances("TestInstances", attributeList, 0);
data.setClassIndex(data.numAttributes() - 1);
// Set instance's values for the attributes
weka.core.Instance inst_co = new DenseInstance(data.numAttributes());
for (int i = 0; i < data.numAttributes() - 1; i++)
{
inst_co.setValue(i, Math.Round(dataValues.ElementAt(i), 5));
}
inst_co.setValue(classHeaderName, defaultclass);
data.add(inst_co);
weka.core.Instance currentInst = data.get(0);
int j = 0;
//foreach (float value in dataValues)
//{
// // double roundedValue = Math.Round(value);
// //var rounded = Math.Floor(value * 100) / 100;
// if (array.ElementAt(j) != value)
// {
// System.Console.WriteLine("Masla occur");
// }
// j++;
//}
//double predictedClass = cl.classifyInstance(data.get(0));
weka.classifiers.functions.SMO clRead = new weka.classifiers.functions.SMO();
try
{
java.io.File path = new java.io.File("/models/");
clRead = loadSMOModel(modelName, path);
}
catch (Exception e)
{
//string p1 = Assembly.GetExecutingAssembly().Location;
string ClassifierName = Path.GetFileName(Path.GetFileName(modelName));
string Path1 = HostingEnvironment.MapPath(@"~//libs//models//" + ClassifierName);
//string Path1 = HostingEnvironment.MapPath(@"~//libs//models//FusionCustomized.model");
clRead = (weka.classifiers.functions.SMO)weka.core.SerializationHelper.read(modelName);
}
// weka.classifiers.functions.SMO clRead = loadSMOModel(modelName, path);
clRead.setBatchSize("100");
clRead.setCalibrator(new weka.classifiers.functions.Logistic());
clRead.setKernel(new weka.classifiers.functions.supportVector.PolyKernel());
clRead.setEpsilon(1.02E-12);
clRead.setC(1.0);
clRead.setDebug(false);
clRead.setChecksTurnedOff(false);
clRead.setFilterType(new SelectedTag(weka.classifiers.functions.SMO.FILTER_NORMALIZE, weka.classifiers.functions.SMO.TAGS_FILTER));
double classValue = clRead.classifyInstance(data.get(0));
double[] predictionDistribution = clRead.distributionForInstance(data.get(0));
//for (int predictionDistributionIndex = 0;
// predictionDistributionIndex < predictionDistribution.Count();
// predictionDistributionIndex++)
//{
// string classValueString1 = classLabel.get(predictionDistributionIndex).ToString();
// double prob= predictionDistribution[predictionDistributionIndex]*100;
// System.Console.WriteLine(classValueString1 + ":" + prob);
//}
List <double> prediction = new List <double>();
prediction.Add(classValue);
//prediction.AddRange(predictionDistribution);
return(prediction);
}
