public static void toARFF(string aDataDirectory, string masterDirectory, int maxControllers, string sourceFile, int annotators,string[] filter)
{
int featureVectorIndex = 0;
MITesDecoder aMITesDecoder = new MITesDecoder();
MITesLoggerReader aMITesLoggerReader = new MITesLoggerReader(aMITesDecoder, aDataDirectory);
SXML.SensorAnnotation sannotation = null;
MITesFeatures.core.conf.GeneralConfiguration configuration = null;
AXML.Annotation[] aannotation = new AXML.Annotation[annotators];
AXML.Reader[] readers=new AXML.Reader[annotators];
AXML.Annotation intersection = null;
AXML.Annotation[] difference = new AXML.Annotation[annotators];
AXML.Annotation realtimeAnnotation = new AXML.Reader(masterDirectory, aDataDirectory).parse();
realtimeAnnotation.RemoveData(filter);
for (int i = 0; (i < annotators); i++)
{
readers[i] = new AXML.Reader(masterDirectory, aDataDirectory, sourceFile + i + ".xml");
aannotation[i] = readers[i].parse();
aannotation[i].RemoveData(filter);
aannotation[i].DataDirectory = aDataDirectory;
if (intersection == null)
intersection = aannotation[i];
else
intersection = intersection.Intersect(aannotation[i]);
}
for (int i = 0; (i < annotators); i++)
difference[i] = aannotation[i].Difference(intersection);
SXML.Reader sreader = new SXML.Reader(masterDirectory, aDataDirectory);
sannotation = sreader.parse(maxControllers);
MITesFeatures.core.conf.ConfigurationReader creader = new MITesFeatures.core.conf.ConfigurationReader(aDataDirectory);
configuration = creader.parse();
Extractor.Initialize(aMITesDecoder, aDataDirectory, aannotation[0], sannotation,configuration);
TextWriter tw = new StreamWriter(aDataDirectory + "\\output-"+sourceFile+".arff");
tw.WriteLine("@RELATION wockets");
tw.WriteLine(Extractor.GetArffHeader());
tw.WriteLine("@ATTRIBUTE INDEX NUMERIC");
tw.WriteLine("@ATTRIBUTE ANNOTATORS_AGREE NUMERIC");
Hashtable recorded_activities = new Hashtable();
for (int k = 0; (k < annotators); k++)
{
//tw.Write("@ATTRIBUTE annotator"+k+" {unknown");
//tw.Write("@ATTRIBUTE annotator" + k + " {");
for (int i = 0; (i < aannotation[k].Data.Count); i++)
{
AXML.AnnotatedRecord record = ((AXML.AnnotatedRecord)aannotation[k].Data[i]);
string activity = "";
for (int j = 0; (j < record.Labels.Count); j++)
{
if (j == record.Labels.Count - 1)
activity += ((AXML.Label)record.Labels[j]).Name;
else
activity += ((AXML.Label)record.Labels[j]).Name + "_";
}
activity = activity.Replace("none", "").Replace('-', '_').Replace(':', '_').Replace('%', '_').Replace('/', '_');
activity = Regex.Replace(activity, "[_]+", "_");
activity = Regex.Replace(activity, "^[_]+", "");
activity = Regex.Replace(activity, "[_]+$", "");
//only output activity labels that have not been seen
if (recorded_activities.Contains(activity) == false)
{
//tw.Write("," + activity);
recorded_activities[activity] = activity;
}
}
}
for (int k = 0; (k < annotators); k++)
{
tw.Write("@ATTRIBUTE annotator" + k + " {unknown,flapping,rocking,flaprock}\n");
//foreach ( DictionaryEntry de in recorded_activities )
// tw.Write("," + (string) de.Key);
//tw.WriteLine("}");
}
tw.Write("@ATTRIBUTE realtime {unknown,flapping,rocking,flaprock}\n");
//foreach (DictionaryEntry de in recorded_activities)
// tw.Write("," + (string)de.Key);
//tw.WriteLine("}");
tw.WriteLine("@DATA");
bool isData = aMITesLoggerReader.GetSensorData(10);
int channel = 0, x = 0, y = 0, z = 0;
double unixtimestamp = 0.0;
int[] differenceIndex = new int[annotators];
AXML.AnnotatedRecord[] annotatedRecord = new AXML.AnnotatedRecord[annotators];
int intersectionIndex = 0;
string intersection_activity = "unknown";
AXML.AnnotatedRecord intersectionRecord = ((AXML.AnnotatedRecord)intersection.Data[intersectionIndex]);
string[] current_activity = new string[annotators];
string realtime_activity = "unknown";
int realtimeIndex = 0;
AXML.AnnotatedRecord realtimeRecord = ((AXML.AnnotatedRecord)realtimeAnnotation.Data[realtimeIndex]);
for (int i = 0; (i < annotators); i++)
{
differenceIndex[i] = 0;
annotatedRecord[i] = ((AXML.AnnotatedRecord)difference[i].Data[differenceIndex[i]]);
current_activity[i] = "unknown";
}
do
{
//decode the frame
channel = aMITesDecoder.GetSomeMITesData()[0].channel;
x = aMITesDecoder.GetSomeMITesData()[0].x;
y = aMITesDecoder.GetSomeMITesData()[0].y;
z = aMITesDecoder.GetSomeMITesData()[0].z;
unixtimestamp = aMITesDecoder.GetSomeMITesData()[0].unixTimeStamp;
double lastTimeStamp = Extractor.StoreMITesWindow();
for (int i = 0; (i < annotators); i++)
{
if (unixtimestamp > annotatedRecord[i].EndUnix)
{
current_activity[i] = "unknown";
if (differenceIndex[i] < difference[i].Data.Count - 1)
{
differenceIndex[i] = differenceIndex[i]+1;
annotatedRecord[i] = ((AXML.AnnotatedRecord)difference[i].Data[differenceIndex[i]]);
}
}
}
if (unixtimestamp > realtimeRecord.EndUnix)
{
realtime_activity = "unknown";
realtimeIndex++;
if (realtimeIndex < realtimeAnnotation.Data.Count)
realtimeRecord = ((AXML.AnnotatedRecord)realtimeAnnotation.Data[realtimeIndex]);
}
if (unixtimestamp > intersectionRecord.EndUnix)
{
intersection_activity = "unknown";
if (intersectionIndex < intersection.Data.Count - 1)
{
intersectionIndex = intersectionIndex + 1;
intersectionRecord = ((AXML.AnnotatedRecord)intersection.Data[intersectionIndex]);
}
}
for (int i = 0; (i < annotators); i++)
{
if ((lastTimeStamp >= annotatedRecord[i].StartUnix) &&
(lastTimeStamp <= annotatedRecord[i].EndUnix) && current_activity[i].Equals("unknown"))
{
current_activity[i] = "";
for (int j = 0; (j < annotatedRecord[i].Labels.Count); j++)
{
if (j == annotatedRecord[i].Labels.Count - 1)
current_activity[i] += ((AXML.Label)annotatedRecord[i].Labels[j]).Name;
else
current_activity[i] += ((AXML.Label)annotatedRecord[i].Labels[j]).Name + "_";
}
current_activity[i] = current_activity[i].Replace("none", "").Replace('-', '_').Replace(':', '_').Replace('%', '_').Replace('/', '_');
current_activity[i] = Regex.Replace(current_activity[i], "[_]+", "_");
current_activity[i] = Regex.Replace(current_activity[i], "^[_]+", "");
current_activity[i] = Regex.Replace(current_activity[i], "[_]+$", "");
}
else if (lastTimeStamp > annotatedRecord[i].EndUnix)
current_activity[i] = "unknown";
}
if ((lastTimeStamp >= realtimeRecord.StartUnix) &&
(lastTimeStamp <= realtimeRecord.EndUnix) && realtime_activity.Equals("unknown"))
{
realtime_activity = "";
for (int j = 0; (j < realtimeRecord.Labels.Count); j++)
{
if (j == realtimeRecord.Labels.Count - 1)
realtime_activity += ((AXML.Label)realtimeRecord.Labels[j]).Name;
else
realtime_activity += ((AXML.Label)realtimeRecord.Labels[j]).Name + "_";
}
realtime_activity = realtime_activity.Replace("none", "").Replace('-', '_').Replace(':', '_').Replace('%', '_').Replace('/', '_');
realtime_activity = Regex.Replace(realtime_activity, "[_]+", "_");
realtime_activity = Regex.Replace(realtime_activity, "^[_]+", "");
realtime_activity = Regex.Replace(realtime_activity, "[_]+$", "");
}
else if (lastTimeStamp > realtimeRecord.EndUnix)
realtime_activity = "unknown";
if ((lastTimeStamp >= intersectionRecord.StartUnix) &&
(lastTimeStamp <= intersectionRecord.EndUnix) && intersection_activity.Equals("unknown"))
{
intersection_activity = "";
for (int j = 0; (j < intersectionRecord.Labels.Count); j++)
{
if (j == intersectionRecord.Labels.Count - 1)
intersection_activity += ((AXML.Label)intersectionRecord.Labels[j]).Name;
else
intersection_activity += ((AXML.Label)intersectionRecord.Labels[j]).Name + "_";
}
intersection_activity = intersection_activity.Replace("none", "").Replace('-', '_').Replace(':', '_').Replace('%', '_').Replace('/', '_');
intersection_activity = Regex.Replace(intersection_activity, "[_]+", "_");
intersection_activity = Regex.Replace(intersection_activity, "^[_]+", "");
intersection_activity = Regex.Replace(intersection_activity, "[_]+$", "");
}
if ((Extractor.GenerateFeatureVector(lastTimeStamp)))
{
string activity_suffix = "," + featureVectorIndex;
if (intersection_activity.Equals("unknown") == true) //disagreement or agreement unknown
{
if ((current_activity[0] == "unknown") && (current_activity[1] == "unknown"))
activity_suffix += ",1";
else
activity_suffix += ",0";
for (int i = 0; (i < annotators); i++)
activity_suffix += "," + current_activity[i];
}
else
{
activity_suffix += ",1";
for (int i = 0; (i < annotators); i++)
activity_suffix += "," + intersection_activity;
}
string arffSample = Extractor.toString() + activity_suffix;
//if (activity_suffix.Contains("unknown") == false)
//{
tw.WriteLine(arffSample+","+realtime_activity);
featureVectorIndex++;
//}
}
} while (isData = aMITesLoggerReader.GetSensorData(10));
tw.Close();
}
public static void toARFF(string aDataDirectory, string masterDirectory, int maxControllers, string[] filter)
{
MITesDecoder aMITesDecoder = new MITesDecoder();
MITesLoggerReader aMITesLoggerReader = new MITesLoggerReader(aMITesDecoder, aDataDirectory);
AXML.Annotation aannotation=null;
SXML.SensorAnnotation sannotation=null;
GeneralConfiguration configuration = new ConfigurationReader(aDataDirectory).parse();
AXML.Reader reader = new AXML.Reader(masterDirectory, aDataDirectory);
// if (reader.validate() == false)
// throw new Exception("Error Code 0: XML format error - activities.xml does not match activities.xsd!");
//else
// {
aannotation = reader.parse();
aannotation.RemoveData(filter);
aannotation.DataDirectory = aDataDirectory;
SXML.Reader sreader = new SXML.Reader(masterDirectory, aDataDirectory);
// if (sreader.validate() == false)
// throw new Exception("Error Code 0: XML format error - sensors.xml does not match sensors.xsd!");
// else
sannotation = sreader.parse(maxControllers);
//}
Extractor.Initialize(aMITesDecoder, aDataDirectory, aannotation, sannotation,configuration);
TextWriter tw = new StreamWriter(aDataDirectory + "\\realtime-output.arff");
tw.WriteLine("@RELATION wockets");
tw.WriteLine(Extractor.GetArffHeader());
tw.Write("@ATTRIBUTE activity {unknown");
Hashtable recorded_activities = new Hashtable();
for (int i=0;(i<aannotation.Data.Count);i++)
{
AXML.AnnotatedRecord record = ((AXML.AnnotatedRecord)aannotation.Data[i]);
string activity="";
for (int j = 0; (j <record.Labels.Count); j++)
{
if (j==record.Labels.Count-1)
activity+=((AXML.Label)record.Labels[j]).Name;
else
activity += ((AXML.Label)record.Labels[j]).Name+"_";
}
activity = activity.Replace("none", "").Replace('-', '_').Replace(':', '_').Replace('%', '_').Replace('/', '_');
activity = Regex.Replace(activity, "[_]+", "_");
activity = Regex.Replace(activity, "^[_]+", "");
activity = Regex.Replace(activity, "[_]+$", "");
//only output activity labels that have not been seen
if (recorded_activities.Contains(activity) == false)
{
tw.Write("," + activity);
recorded_activities[activity] = activity;
}
}
tw.WriteLine("}");
tw.WriteLine("@DATA");
bool isData = aMITesLoggerReader.GetSensorData(10);
int channel = 0, x = 0, y = 0, z = 0;
double unixtimestamp = 0.0;
int activityIndex = 0;
AXML.AnnotatedRecord annotatedRecord=((AXML.AnnotatedRecord)aannotation.Data[activityIndex]);
string current_activity = "unknown";
TextWriter tww = new StreamWriter("test.csv");
do
{
//decode the frame
channel = aMITesDecoder.GetSomeMITesData()[0].channel;
x = aMITesDecoder.GetSomeMITesData()[0].x;
y = aMITesDecoder.GetSomeMITesData()[0].y;
z = aMITesDecoder.GetSomeMITesData()[0].z;
unixtimestamp = aMITesDecoder.GetSomeMITesData()[0].unixTimeStamp;
tww.WriteLine(unixtimestamp + "," + x + "," + y + "," + z);
double lastTimeStamp = Extractor.StoreMITesWindow();
//DateTime dt = new DateTime(1970, 1, 1, 0, 0, 0, 0);
//dt=dt.AddMilliseconds(unixtimestamp);
//DateTime dt2 = new DateTime(1970, 1, 1, 0, 0, 0, 0);
//dt2 = dt2.AddMilliseconds(annotatedRecord.EndUnix);
if (unixtimestamp > annotatedRecord.EndUnix)
{
current_activity = "unknown";
if (activityIndex < aannotation.Data.Count-1)
{
activityIndex++;
annotatedRecord = ((AXML.AnnotatedRecord)aannotation.Data[activityIndex]);
}
}
if ((lastTimeStamp >= annotatedRecord.StartUnix) &&
(lastTimeStamp <= annotatedRecord.EndUnix) && current_activity.Equals("unknown"))
{
current_activity = "";
for (int j = 0; (j < annotatedRecord.Labels.Count); j++)
{
if (j == annotatedRecord.Labels.Count - 1)
current_activity += ((AXML.Label)annotatedRecord.Labels[j]).Name;
else
current_activity += ((AXML.Label)annotatedRecord.Labels[j]).Name + "_";
}
current_activity = current_activity.Replace("none", "").Replace('-', '_').Replace(':', '_').Replace('%', '_').Replace('/', '_');
current_activity = Regex.Replace(current_activity, "[_]+", "_");
current_activity = Regex.Replace(current_activity, "^[_]+", "");
current_activity = Regex.Replace(current_activity, "[_]+$", "");
}
//if (lastTimeStamp>=
//if (current_activity.Equals("unknown") == false)
//{
if ((Extractor.GenerateFeatureVector(lastTimeStamp)))
{
string arffSample = Extractor.toString() + "," + current_activity;
tw.WriteLine(arffSample);
}
//}
} while (isData = aMITesLoggerReader.GetSensorData(10));
tww.Close();
tw.Close();
}
public static void Initialize2(MITesDecoder aMITesDecoder, string aDataDirectory,
AXML.Annotation aannotation, SXML.SensorAnnotation sannotation,Hashtable calibrations)//, string masterDirectory)
{
Extractor.aannotation = aannotation;
Extractor.sannotation = sannotation;
Extractor.calibrations = calibrations;
// count the sensors for feature extraction and identify their indicies in
// sensor annotation - at the moment only accelerometers are used
Extractor.sensorIndicies = new Hashtable();
Extractor.extractorSensorCount = 0;
foreach (SXML.Sensor sensor in sannotation.Sensors)
{
int channel = Convert.ToInt32(sensor.ID);
if (channel > 0) // if accelerometer
{
Extractor.sensorIndicies[channel] = extractorSensorCount;
Extractor.extractorSensorCount++;
}
}
//load sensor data
//SXML.Reader sreader = new SXML.Reader(masterDirectory, aDataDirectory);
//Extractor.sannotation = sreader.parse();
//load configuration
ConfigurationReader creader = new ConfigurationReader(aDataDirectory);
Extractor.dconfiguration = creader.parse();
//load annotation data
//AXML.Reader reader = new AXML.Reader(masterDirectory, aDataDirectory + "\\" + AXML.Reader.DEFAULT_XML_FILE);
//Extractor.aannotation = reader.parse();
//CHANGE: gathers training samples based on the first category only
Extractor.trainingTime = new Hashtable();//int[((AXML.Category)Extractor.aannotation.Categories[0]).Labels.Count];
//for (int i = 0; (i < Extractor.trainingTime.Length); i++)
foreach (AXML.Label label in ((AXML.Category)Extractor.aannotation.Categories[0]).Labels)
Extractor.trainingTime.Add(label.Name, 0);
//Extractor.trainingTime[i] = 0;
Extractor.trainingCompleted = false;
Extractor.inputRowSize = Extractor.extractorSensorCount * 3;
Extractor.fftInterpolationPower = dconfiguration.FFTInterpolatedPower;
Extractor.fftMaximumFrequencies = dconfiguration.FFTMaximumFrequencies;
//Extractor.trainingTimePerClass = configuration.TrainingTime;
//Extractor.trainingWaitTime = configuration.TrainingWaitTime;
Extractor.inputColumnSize = (int)Math.Pow(2, Extractor.fftInterpolationPower);
int MOTION_DIRECTIONS = 3;
int ORIENTATIONS = 8;
int meansIndex = 0;
int varianceIndex = meansIndex + Extractor.extractorSensorCount + 1; // means 1 per sensors + add 1 for total mean
int orientationIndex = varianceIndex + Extractor.extractorSensorCount + 1; //variances 1 per sensor + add 1 for total variance
int closestOrientationIndex = orientationIndex + (Extractor.extractorSensorCount * ORIENTATIONS); // for 8 orientations, score each sensor to the orientations
int orientationSensorsIndex = closestOrientationIndex + Extractor.extractorSensorCount; // 1 discrete orientation per sensor
int sensorsCorrelationIndex = orientationSensorsIndex + (Extractor.extractorSensorCount - 1) * Extractor.extractorSensorCount; // n* n-1 relative orientations between sensors
int sensorPercentMotionIndex = sensorsCorrelationIndex + ((Extractor.inputRowSize * Extractor.inputRowSize) - Extractor.inputRowSize) / 2; //number of correlation coefficients between axes
int sensorMotionTypeIndex = sensorPercentMotionIndex + (Extractor.extractorSensorCount * MOTION_DIRECTIONS * 3);
Extractor.num_features = Extractor.extractorSensorCount; // number of means
Extractor.num_features += 1; //total mean;
Extractor.num_features += Extractor.extractorSensorCount; // number of variances
Extractor.num_features += 1; //total variance;
Extractor.num_features += Extractor.extractorSensorCount * ORIENTATIONS; // number of orientation scores
Extractor.num_features += Extractor.extractorSensorCount; // best orientation
Extractor.num_features += (((Extractor.extractorSensorCount - 1) * Extractor.extractorSensorCount)/2); // n* n-1 relative orientations between sensors
Extractor.num_features += ((Extractor.inputRowSize * Extractor.inputRowSize) - Extractor.inputRowSize) / 2; //correlation coefficients off-di
Extractor.num_features += (Extractor.extractorSensorCount * MOTION_DIRECTIONS * 3); //percent of motion in each motion direction for the 3 axes
Extractor.num_features += (Extractor.extractorSensorCount * MOTION_DIRECTIONS * 3); //Type of motion in each motion direction for the 3 axes
Extractor.features = new double[Extractor.num_features];
//Extractor.arffAttriburesLabels = new string[Extractor.num_features];
//Extractor.attributeLocation = new Hashtable();
Extractor.standardized = new double[inputRowSize][];
for (int i = 0; (i < inputRowSize); i++)
Extractor.standardized[i] = new double[Extractor.inputColumnSize];//input[0].Length];
Extractor.means = new double[inputRowSize];
Extractor.variances = new double[inputRowSize];
//inputFFT = new int[Extractor.inputColumnSize];
//FFT.Initialize(fftInterpolationPower, fftMaximumFrequencies);
Extractor.aMITesDecoder = aMITesDecoder;
//Create the ARFF File header
string arffHeader = "@RELATION wockets\n\n" + Extractor.GetArffHeader2();//sannotation.Sensors.Count * 3, configuration.FFTMaximumFrequencies);
arffHeader += "@ATTRIBUTE activity {";
foreach (AXML.Label label in ((AXML.Category)Extractor.aannotation.Categories[0]).Labels)
arffHeader += label.Name.Replace(' ', '_') + ",";
arffHeader += "unknown}\n";
arffHeader += "\n@DATA\n\n";
//Calculating windowing parameters
//total number of points per interpolated window
Extractor.INTERPOLATED_SAMPLING_RATE_PER_WINDOW = (int)Math.Pow(2, dconfiguration.FFTInterpolatedPower); //128;
//space between interpolated samples
Extractor.INTERPOLATED_SAMPLES_SPACING = (double)dconfiguration.WindowTime / INTERPOLATED_SAMPLING_RATE_PER_WINDOW;
//expected sampling rate per MITes
//Extractor.EXPECTED_SAMPLING_RATE = dconfiguration.ExpectedSamplingRate / sannotation.Sensors.Count; //samples per second
//expected samples per window
//Extractor.EXPECTED_WINDOW_SIZE = (int)(EXPECTED_SAMPLING_RATE * (dconfiguration.WindowTime / 1000.0)); // expectedSamplingRate per window
//what would be considered a good sampling rate
//Extractor.EXPECTED_GOOD_SAMPLING_RATE = EXPECTED_WINDOW_SIZE - (int)(dconfiguration.MaximumNonconsecutiveFrameLoss * EXPECTED_WINDOW_SIZE); //number of packets lost per second
//space between samples
//Extractor.EXPECTED_SAMPLES_SPACING = (double)dconfiguration.WindowTime / EXPECTED_WINDOW_SIZE;
Extractor.EXPECTED_SAMPLING_RATES = new int[Extractor.extractorSensorCount];
Extractor.EXPECTED_WINDOW_SIZES = new int[Extractor.extractorSensorCount];
Extractor.EXPECTED_GOOD_SAMPLING_RATES = new int[Extractor.extractorSensorCount];
Extractor.EXPECTED_SAMPLES_SPACING = new double[Extractor.extractorSensorCount];
//foreach (SXML.Sensor sensor in sannotation.Sensors)
foreach (DictionaryEntry sensorEntry in Extractor.sensorIndicies)
{
//Get the channel and index in data array for only
// extractor sensors (sensors that will be used to compute
// features i.e. accelerometers)
int channel = (int)sensorEntry.Key;
int sensorIndex = (int)sensorEntry.Value;
SXML.Sensor sensor = ((SXML.Sensor)sannotation.Sensors[(int)sannotation.SensorsIndex[channel]]);
int receiverID = Convert.ToInt32(sensor.Receiver);
Extractor.EXPECTED_SAMPLING_RATES[sensorIndex] = dconfiguration.ExpectedSamplingRate / sannotation.NumberSensors[receiverID];
Extractor.EXPECTED_WINDOW_SIZES[sensorIndex] = (int)(Extractor.EXPECTED_SAMPLING_RATES[sensorIndex] * (dconfiguration.WindowTime / 1000.0));
Extractor.EXPECTED_GOOD_SAMPLING_RATES[sensorIndex] = Extractor.EXPECTED_WINDOW_SIZES[sensorIndex] - (int)(dconfiguration.MaximumNonconsecutiveFrameLoss * Extractor.EXPECTED_WINDOW_SIZES[sensorIndex]);
Extractor.EXPECTED_SAMPLES_SPACING[sensorIndex] = (double)dconfiguration.WindowTime / Extractor.EXPECTED_WINDOW_SIZES[sensorIndex];
}
//window counters and delimiters
Extractor.next_window_end = 0;
Extractor.total_window_count = 0;
Extractor.num_feature_windows = 0;
//data quality variables
Extractor.isAcceptableLossRate = true;
Extractor.isAcceptableConsecutiveLoss = true;
Extractor.unacceptable_window_count = 0;
Extractor.unacceptable_consecutive_window_loss_count = 0;
//2 D array that stores Sensor axes + time stamps on each row X expected WINDOW SIZE
Extractor.data = new double[Extractor.extractorSensorCount * 4][]; // 1 row for each axis
// 2D array that stores Sensor axes X INTERPOLATED WINDOW SIZE
Extractor.interpolated_data = new double[Extractor.extractorSensorCount * 3][];
// array to store the y location for each axes as data is received
// will be different for every sensor of course
Extractor.y_index = new int[Extractor.extractorSensorCount];
//Initialize expected data array
foreach (DictionaryEntry sensorEntry in Extractor.sensorIndicies)
{
//Get the channel and index in data array for only
// extractor sensors (sensors that will be used to compute
// features i.e. accelerometers)
int channel = (int)sensorEntry.Key;
int sensorIndex = (int)sensorEntry.Value;
int adjusted_sensor_index = sensorIndex * 4;
//Initialize 4 rows x,y,z timestamp
for (int j = 0; j < 4; j++)
{
Extractor.data[adjusted_sensor_index] = new double[EXPECTED_WINDOW_SIZES[sensorIndex]];
for (int k = 0; (k < EXPECTED_WINDOW_SIZES[sensorIndex]); k++)
Extractor.data[adjusted_sensor_index][k] = 0;
adjusted_sensor_index++;
}
}
//Here it is equal across all sensors, so we do not need to consider
//the sampling rate of each sensor separately
for (int j = 0; (j < (Extractor.extractorSensorCount * 3)); j++)
{
Extractor.interpolated_data[j] = new double[INTERPOLATED_SAMPLING_RATE_PER_WINDOW];
for (int k = 0; (k < INTERPOLATED_SAMPLING_RATE_PER_WINDOW); k++)
Extractor.interpolated_data[j][k] = 0;
}
//Initialize y index for each sensor
for (int j = 0; (j < Extractor.extractorSensorCount); j++)
Extractor.y_index[j] = 0;
}
public static void Initialize(MITesDecoder aMITesDecoder, string aDataDirectory,
AXML.Annotation aannotation,SXML.SensorAnnotation sannotation)//, string masterDirectory)
{
Extractor.aannotation = aannotation;
Extractor.sannotation = sannotation;
//load sensor data
//SXML.Reader sreader = new SXML.Reader(masterDirectory, aDataDirectory);
//Extractor.sannotation = sreader.parse();
//load configuration
ConfigurationReader creader = new ConfigurationReader(aDataDirectory);
Extractor.dconfiguration = creader.parse();
//load annotation data
//AXML.Reader reader = new AXML.Reader(masterDirectory, aDataDirectory + "\\" + AXML.Reader.DEFAULT_XML_FILE);
//Extractor.aannotation = reader.parse();
//CHANGE: gathers training samples based on the first category only
Extractor.trainingTime = new Hashtable();//int[((AXML.Category)Extractor.aannotation.Categories[0]).Labels.Count];
//for (int i = 0; (i < Extractor.trainingTime.Length); i++)
foreach (AXML.Label label in ((AXML.Category)Extractor.aannotation.Categories[0]).Labels)
Extractor.trainingTime.Add(label.Name, 0);
//Extractor.trainingTime[i] = 0;
Extractor.trainingCompleted = false;
Extractor.inputRowSize = sannotation.Sensors.Count * 3;
Extractor.fftInterpolationPower = dconfiguration.FFTInterpolatedPower;
Extractor.fftMaximumFrequencies = dconfiguration.FFTMaximumFrequencies;
//Extractor.trainingTimePerClass = configuration.TrainingTime;
//Extractor.trainingWaitTime = configuration.TrainingWaitTime;
Extractor.inputColumnSize = (int)Math.Pow(2, Extractor.fftInterpolationPower);
Extractor.num_features = Extractor.inputRowSize; // number of distances
Extractor.num_features += 1; //total mean;
Extractor.num_features += Extractor.inputRowSize; // number of variances
Extractor.num_features += Extractor.inputRowSize; // number of ranges
Extractor.num_features += 2 * Extractor.fftMaximumFrequencies * Extractor.inputRowSize; // number of fft magnitudes and frequencies
Extractor.num_features += Extractor.inputRowSize; // number of energies
Extractor.num_features += ((Extractor.inputRowSize * Extractor.inputRowSize) - Extractor.inputRowSize) / 2; //correlation coefficients off-di
Extractor.features = new double[Extractor.num_features];
Extractor.arffAttriburesLabels = new string[Extractor.num_features];
Extractor.standardized = new double[inputRowSize][];
for (int i = 0; (i < inputRowSize); i++)
Extractor.standardized[i] = new double[Extractor.inputColumnSize];//input[0].Length];
Extractor.means = new double[inputRowSize];
inputFFT = new int[Extractor.inputColumnSize];
FFT.Initialize(fftInterpolationPower, fftMaximumFrequencies);
Extractor.aMITesDecoder = aMITesDecoder;
//Create the ARFF File header
string arffHeader = "@RELATION wockets\n\n" + Extractor.GetArffHeader();//sannotation.Sensors.Count * 3, configuration.FFTMaximumFrequencies);
arffHeader += "@ATTRIBUTE activity {";
foreach (AXML.Label label in ((AXML.Category)Extractor.aannotation.Categories[0]).Labels)
arffHeader += label.Name.Replace(' ', '_') + ",";
arffHeader += "unknown}\n";
arffHeader += "\n@DATA\n\n";
//Calculating windowing parameters
//total number of points per interpolated window
Extractor.INTERPOLATED_SAMPLING_RATE_PER_WINDOW = (int)Math.Pow(2, dconfiguration.FFTInterpolatedPower); //128;
//expected sampling rate per MITes
Extractor.EXPECTED_SAMPLING_RATE = dconfiguration.ExpectedSamplingRate / sannotation.Sensors.Count; //samples per second
//expected samples per window
Extractor.EXPECTED_WINDOW_SIZE = (int)(EXPECTED_SAMPLING_RATE * (dconfiguration.WindowTime / 1000.0)); // expectedSamplingRate per window
//what would be considered a good sampling rate
Extractor.EXPECTED_GOOD_SAMPLING_RATE = EXPECTED_WINDOW_SIZE - (int)(dconfiguration.MaximumNonconsecutiveFrameLoss * EXPECTED_WINDOW_SIZE); //number of packets lost per second
//space between samples
Extractor.EXPECTED_SAMPLES_SPACING = (double)dconfiguration.WindowTime / EXPECTED_WINDOW_SIZE;
//space between interpolated samples
Extractor.INTERPOLATED_SAMPLES_SPACING = (double)dconfiguration.WindowTime / INTERPOLATED_SAMPLING_RATE_PER_WINDOW;
//window counters and delimiters
Extractor.next_window_end = 0;
Extractor.total_window_count = 0;
Extractor.num_feature_windows = 0;
//data quality variables
Extractor.isAcceptableLossRate = true;
Extractor.isAcceptableConsecutiveLoss = true;
Extractor.unacceptable_window_count = 0;
Extractor.unacceptable_consecutive_window_loss_count = 0;
//2 D array that stores Sensor axes + time stamps on each row X expected WINDOW SIZE
Extractor.data = new double[Extractor.sannotation.Sensors.Count * 4][]; // 1 row for each axis
// 2D array that stores Sensor axes X INTERPOLATED WINDOW SIZE
Extractor.interpolated_data = new double[Extractor.sannotation.Sensors.Count * 3][];
// array to store the y location for each axes as data is received
// will be different for every sensor of course
Extractor.y_index = new int[Extractor.sannotation.Sensors.Count];
//Initialize expected data array
for (int j = 0; (j < (Extractor.sannotation.Sensors.Count * 4)); j++)
{
Extractor.data[j] = new double[EXPECTED_WINDOW_SIZE];
for (int k = 0; (k < EXPECTED_WINDOW_SIZE); k++)
Extractor.data[j][k] = 0;
}
//Initialize interpolated data array
for (int j = 0; (j < (Extractor.sannotation.Sensors.Count * 3)); j++)
{
Extractor.interpolated_data[j] = new double[INTERPOLATED_SAMPLING_RATE_PER_WINDOW];
for (int k = 0; (k < INTERPOLATED_SAMPLING_RATE_PER_WINDOW); k++)
Extractor.interpolated_data[j][k] = 0;
}
//Initialize y index for each sensor
for (int j = 0; (j < Extractor.sannotation.Sensors.Count); j++)
Extractor.y_index[j] = 0;
}
