GestureRecognitionToolkit  Version: 1.0 Revision: 04-03-15
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library for real-time gesture recognition.
GRT::ClassificationData Class Reference
Inheritance diagram for GRT::ClassificationData:
GRT::GRTBase

Public Member Functions

 ClassificationData (UINT numDimensions=0, string datasetName="NOT_SET", string infoText="")
 
 ClassificationData (const ClassificationData &rhs)
 
virtual ~ClassificationData ()
 
ClassificationDataoperator= (const ClassificationData &rhs)
 
ClassificationSampleoperator[] (const UINT &i)
 
const ClassificationSampleoperator[] (const UINT &i) const
 
void clear ()
 
bool setNumDimensions (UINT numDimensions)
 
bool setDatasetName (string datasetName)
 
bool setInfoText (string infoText)
 
bool setClassNameForCorrespondingClassLabel (string className, UINT classLabel)
 
bool setAllowNullGestureClass (bool allowNullGestureClass)
 
bool addSample (UINT classLabel, const VectorDouble &sample)
 
bool removeSample (const UINT index)
 
bool removeLastSample ()
 
bool reserve (const UINT N)
 
bool addClass (const UINT classLabel, const std::string className="NOT_SET")
 
UINT removeClass (const UINT classLabel)
 
UINT eraseAllSamplesWithClassLabel (const UINT classLabel)
 
bool relabelAllSamplesWithClassLabel (const UINT oldClassLabel, const UINT newClassLabel)
 
bool setExternalRanges (const vector< MinMax > &externalRanges, const bool useExternalRanges=false)
 
bool enableExternalRangeScaling (const bool useExternalRanges)
 
bool scale (const double minTarget, const double maxTarget)
 
bool scale (const vector< MinMax > &ranges, const double minTarget, const double maxTarget)
 
bool save (const string &filename) const
 
bool load (const string &filename)
 
bool saveDatasetToFile (const string &filename) const
 
bool loadDatasetFromFile (const string &filename)
 
bool saveDatasetToCSVFile (const string &filename) const
 
bool loadDatasetFromCSVFile (const string &filename, const UINT classLabelColumnIndex=0)
 
bool printStats () const
 
bool sortClassLabels ()
 
bool merge (const ClassificationData &labelledData)
 
ClassificationData partition (const UINT partitionPercentage, const bool useStratifiedSampling=false)
 
bool spiltDataIntoKFolds (const UINT K, const bool useStratifiedSampling=false)
 
ClassificationData getTrainingFoldData (const UINT foldIndex) const
 
ClassificationData getTestFoldData (const UINT foldIndex) const
 
ClassificationData getClassData (const UINT classLabel) const
 
ClassificationData getBootstrappedDataset (UINT numSamples=0) const
 
RegressionData reformatAsRegressionData () const
 
UnlabelledData reformatAsUnlabelledData () const
 
string getDatasetName () const
 
string getInfoText () const
 
string getStatsAsString () const
 
UINT getNumDimensions () const
 
UINT getNumSamples () const
 
UINT getNumClasses () const
 
UINT getMinimumClassLabel () const
 
UINT getMaximumClassLabel () const
 
UINT getClassLabelIndexValue (const UINT classLabel) const
 
string getClassNameForCorrespondingClassLabel (const UINT classLabel) const
 
vector< MinMaxgetRanges () const
 
vector< UINT > getClassLabels () const
 
vector< UINT > getNumSamplesPerClass () const
 
vector< ClassTrackergetClassTracker () const
 
MatrixDouble getClassHistogramData (const UINT classLabel, const UINT numBins) const
 
vector< MatrixDoublegetHistogramData (const UINT numBins) const
 
vector< ClassificationSamplegetClassificationData () const
 
VectorDouble getClassProbabilities () const
 
VectorDouble getClassProbabilities (const vector< UINT > &classLabels) const
 
VectorDouble getMean () const
 
VectorDouble getStdDev () const
 
MatrixDouble getClassMean () const
 
MatrixDouble getClassStdDev () const
 
MatrixDouble getCovarianceMatrix () const
 
vector< UINT > getClassDataIndexes (const UINT classLabel) const
 
MatrixDouble getDataAsMatrixDouble () const
 
- Public Member Functions inherited from GRT::GRTBase
 GRTBase (void)
 
virtual ~GRTBase (void)
 
bool copyGRTBaseVariables (const GRTBase *GRTBase)
 
string getClassType () const
 
string getLastWarningMessage () const
 
string getLastErrorMessage () const
 
string getLastInfoMessage () const
 
GRTBasegetGRTBasePointer ()
 
const GRTBasegetGRTBasePointer () const
 

Static Public Member Functions

static bool generateGaussDataset (const std::string filename, const UINT numSamples=10000, const UINT numClasses=10, const UINT numDimensions=3, const double range=10, const double sigma=1)
 
- Static Public Member Functions inherited from GRT::GRTBase
static string getGRTVersion (bool returnRevision=true)
 
static string getGRTRevison ()
 

Additional Inherited Members

- Protected Member Functions inherited from GRT::GRTBase
double SQR (const double &x) const
 
- Protected Attributes inherited from GRT::GRTBase
string classType
 
DebugLog debugLog
 
ErrorLog errorLog
 
InfoLog infoLog
 
TrainingLog trainingLog
 
TestingLog testingLog
 
WarningLog warningLog
 

Detailed Description

Definition at line 42 of file ClassificationData.h.

Constructor & Destructor Documentation

ClassificationData::ClassificationData ( UINT  numDimensions = 0,
string  datasetName = "NOT_SET",
string  infoText = "" 
)

Constructor, sets the name of the dataset and the number of dimensions of the training data. The name of the dataset should not contain any spaces.

Parameters
UINTnumDimensions: the number of dimensions of the training data, should be an unsigned integer greater than 0
stringdatasetName: the name of the dataset, should not contain any spaces
stringinfoText: some info about the data in this dataset, this can contain spaces

Definition at line 25 of file ClassificationData.cpp.

ClassificationData::ClassificationData ( const ClassificationData rhs)

Copy Constructor, copies the ClassificationData from the rhs instance to this instance

Parameters
constClassificationData &rhs: another instance of the ClassificationData class from which the data will be copied to this instance

Definition at line 40 of file ClassificationData.cpp.

ClassificationData::~ClassificationData ( )
virtual

Default Destructor

Definition at line 44 of file ClassificationData.cpp.

Member Function Documentation

bool ClassificationData::addClass ( const UINT  classLabel,
const std::string  className = "NOT_SET" 
)

This function adds the class with the classLabel to the class tracker. If the class tracker already contains the classLabel then the function will return false.

Parameters
constUINT classLabel: the class label you want to add to the classTracker
conststd::string className: the name associated with the new class
Returns
returns true if the classLabel was added, false otherwise

Definition at line 235 of file ClassificationData.cpp.

bool ClassificationData::addSample ( UINT  classLabel,
const VectorDouble &  sample 
)

Adds a new labelled sample to the dataset. The dimensionality of the sample should match the number of dimensions in the ClassificationData. The class label should be greater than zero (as zero is used as the default null rejection class label).

Parameters
UINTclassLabel: the class label of the corresponding sample
constUINT VectorDouble &sample: the new sample you want to add to the dataset. The dimensionality of this sample should match the number of dimensions in the ClassificationData
Returns
true if the sample was correctly added to the dataset, false otherwise

Definition at line 132 of file ClassificationData.cpp.

void ClassificationData::clear ( )

Clears any previous training data and counters

Definition at line 69 of file ClassificationData.cpp.

bool ClassificationData::enableExternalRangeScaling ( const bool  useExternalRanges)

Sets if the dataset should be scaled using an external range (if useExternalRanges == true) or the ranges of the dataset (if false). The external ranges need to be set FIRST before calling this function, otherwise it will return false.

Parameters
constbool useExternalRanges: sets if these ranges should be used to scale the dataset
Returns
returns true if the useExternalRanges variable was set, false otherwise

Definition at line 346 of file ClassificationData.cpp.

UINT ClassificationData::eraseAllSamplesWithClassLabel ( const UINT  classLabel)
Deprecated:
This function is now depreciated! You should use removeClass(const UINT classLabel) instead.

Deletes from the dataset all the samples with a specific class label.

Parameters
constUINT classLabel: the class label of the samples you wish to delete from the dataset
Returns
the number of samples deleted from the dataset

Definition at line 231 of file ClassificationData.cpp.

bool ClassificationData::generateGaussDataset ( const std::string  filename,
const UINT  numSamples = 10000,
const UINT  numClasses = 10,
const UINT  numDimensions = 3,
const double  range = 10,
const double  sigma = 1 
)
static

Generates a labeled dataset that can be used for basic training/testing/validation for ClassificationData.

Samples in the dataset will be generated based on K randomly select models, with Gaussian noise. K is set by the numClasses argument.

The range of each dimension will be [-range range]. Sigma controls the amount of Gaussian noise added.

The dataset will be saved to the file specified by filename.

Parameters
conststd::string filename: the name of the file the dataset will be saved to
constUINT numSamples: the total number of samples in the dataset
constUINT numClasses: the number of classes in the dataset
constUINT numDimensions: the number of dimensions in the dataset
constdouble range: the range the data will be sampled from, range will be [-range range] for each dimension
constdouble sigma: the amount of Gaussian noise
Returns
returns true if the dataset was created successfully, false otherwise

Definition at line 1427 of file ClassificationData.cpp.

ClassificationData ClassificationData::getBootstrappedDataset ( UINT  numSamples = 0) const

Gets a bootstrapped dataset from the current dataset. If the numSamples parameter is set to zero, then the size of the bootstrapped dataset will match the size of the current dataset, otherwise the size of the bootstrapped dataset will match the numSamples parameter.

Parameters
constUINT numSamples: the size of the bootstrapped dataset
Returns
returns a bootstrapped ClassificationData

Definition at line 1008 of file ClassificationData.cpp.

ClassificationData ClassificationData::getClassData ( const UINT  classLabel) const

Returns the all the data with the class label set by classLabel. The classLabel should be a valid classLabel, otherwise the dataset returned will be empty.

Parameters
constUINT classLabel: the class label of the class you want the data for
Returns
returns a dataset containing all the data with the matching classLabel

Definition at line 985 of file ClassificationData.cpp.

vector< UINT > ClassificationData::getClassDataIndexes ( const UINT  classLabel) const

Gets the indexes for all the samples in the current dataset belonging to the classLabel.

Parameters
constUINT classLabel: the classLabel of the class you want the indexes for
Returns
a vector< UINT > containing the indexes for all the samples in the current dataset belonging to the classLabel

Definition at line 1387 of file ClassificationData.cpp.

MatrixDouble ClassificationData::getClassHistogramData ( const UINT  classLabel,
const UINT  numBins 
) const

Computes a histogram for a specific class.

Parameters
constUINT classLabel: the class label of the class you want to compute the histogram data for
constUINT numBins: the number of bins in the histogram
Returns
a MatrixDouble of histogram data where each row represents a dimension and each column represents a histogram bin

Definition at line 1235 of file ClassificationData.cpp.

vector< ClassificationSample > GRT::ClassificationData::getClassificationData ( ) const
inline

Gets the classification data.

Returns
a vector of LabelledClassificationSamples

Definition at line 533 of file ClassificationData.h.

UINT ClassificationData::getClassLabelIndexValue ( const UINT  classLabel) const

Gets the index of the class label from the class tracker.

Returns
an unsigned int representing the index of the class label in the class tracker

Definition at line 1115 of file ClassificationData.cpp.

vector< UINT > ClassificationData::getClassLabels ( ) const

Gets the class label associated with class[i].

Returns
returns a vector of UINTs, where each element represents a class label.

Definition at line 1182 of file ClassificationData.cpp.

MatrixDouble ClassificationData::getClassMean ( ) const

Gets the mean values for each class in the dataset. This is returned in an [K N] matrix, where K is the number of classes in the dataset and N is the number of dimensions in the dataset.

Returns
a MatrixDouble with the mean values for each class in the dataset

Definition at line 1281 of file ClassificationData.cpp.

string ClassificationData::getClassNameForCorrespondingClassLabel ( const UINT  classLabel) const

Gets the name of the class with a given class label. If the class label does not exist then the string "CLASS_LABEL_NOT_FOUND" will be returned.

Returns
a string containing the name of the given class label or the string "CLASS_LABEL_NOT_FOUND" if the class label does not exist

Definition at line 1125 of file ClassificationData.cpp.

MatrixDouble ClassificationData::getClassStdDev ( ) const

Gets the standard deviation values for each class in the dataset. This is returned in an [K N] matrix, where K is the number of classes in the dataset and N is the number of dimensions in the dataset.

Returns
a MatrixDouble with the standard deviation values for each class in the dataset

Definition at line 1305 of file ClassificationData.cpp.

vector< ClassTracker > GRT::ClassificationData::getClassTracker ( ) const
inline

Gets the class tracker for each class in the dataset.

Returns
a vector of ClassTracker, one for each class in the dataset

Definition at line 508 of file ClassificationData.h.

MatrixDouble ClassificationData::getCovarianceMatrix ( ) const

Gets the covariance matrix across all the classes in the dataset. This is returned in an [N N] matrix, where N is the number of dimensions in the dataset.

Returns
a MatrixDouble with the covariance values for the dataset

Definition at line 1330 of file ClassificationData.cpp.

MatrixDouble ClassificationData::getDataAsMatrixDouble ( ) const

Gets the data as a MatrixDouble. This returns just the data, not the labels. This will be an M by N MatrixDouble, where M is the number of samples and N is the number of dimensions.

Returns
a MatrixDouble containing the data from the current dataset.

Definition at line 1412 of file ClassificationData.cpp.

string GRT::ClassificationData::getDatasetName ( ) const
inline

Gets the name of the dataset.

Returns
returns the name of the dataset

Definition at line 417 of file ClassificationData.h.

vector< MatrixDouble > ClassificationData::getHistogramData ( const UINT  numBins) const

Computes a histogram for each class in the dataset.

Parameters
constUINT numBins: the number of bins in the histogram
Returns
a vector of MatrixDouble, each element represents a class and is a MatrixDouble of histogram data where each row represents a dimension and each column represents a histogram bin

Definition at line 1347 of file ClassificationData.cpp.

string GRT::ClassificationData::getInfoText ( ) const
inline

Gets the infotext for the dataset

Returns
returns the infotext of the dataset

Definition at line 424 of file ClassificationData.h.

UINT ClassificationData::getMaximumClassLabel ( ) const

Gets the maximum class label in the dataset. If there are no values in the dataset then the value 0 will be returned.

Returns
an unsigned int representing the maximum class label in the dataset

Definition at line 1103 of file ClassificationData.cpp.

VectorDouble ClassificationData::getMean ( ) const

Gets the mean values across all classes in the dataset.

Returns
a vector containing the mean values across the entire dataset.

Definition at line 1206 of file ClassificationData.cpp.

UINT ClassificationData::getMinimumClassLabel ( ) const

Gets the minimum class label in the dataset. If there are no values in the dataset then the value 99999 will be returned.

Returns
an unsigned int representing the minimum class label in the dataset

Definition at line 1090 of file ClassificationData.cpp.

UINT GRT::ClassificationData::getNumClasses ( ) const
inline

Gets the number of classes.

Returns
an unsigned int representing the number of classes

Definition at line 452 of file ClassificationData.h.

UINT GRT::ClassificationData::getNumDimensions ( ) const
inline

Gets the number of dimensions of the labelled classification data.

Returns
an unsigned int representing the number of dimensions in the classification data

Definition at line 438 of file ClassificationData.h.

UINT GRT::ClassificationData::getNumSamples ( ) const
inline

Gets the number of samples in the classification data across all the classes.

Returns
an unsigned int representing the total number of samples in the classification data

Definition at line 445 of file ClassificationData.h.

vector< UINT > ClassificationData::getNumSamplesPerClass ( ) const

Gets the number of samples in each class.

Returns
returns a vector of UINTs, where each element represents the number of samples in that class.

Definition at line 1194 of file ClassificationData.cpp.

vector< MinMax > ClassificationData::getRanges ( ) const

Gets the ranges of the classification data.

Returns
a vector of minimum and maximum values for each dimension of the data

Definition at line 1161 of file ClassificationData.cpp.

string ClassificationData::getStatsAsString ( ) const

Gets the stats of the dataset as a string

Returns
returns the stats of this dataset as a string

Definition at line 1136 of file ClassificationData.cpp.

VectorDouble ClassificationData::getStdDev ( ) const

Gets the standard deviation values across all classes in the dataset.

Returns
a vector containing the standard deviation values across all classes in the dataset.

Definition at line 1220 of file ClassificationData.cpp.

ClassificationData ClassificationData::getTestFoldData ( const UINT  foldIndex) const

Returns the test dataset for the k-th fold for cross validation. The spiltDataIntoKFolds(UINT K) function should have been called once before using this function. The foldIndex should be in the range [0 K-1], where K is the number of folds the data was spilt into.

Parameters
constUINT foldIndex: the index of the fold you want the test data for, this should be in the range [0 K-1], where K is the number of folds the data was spilt into
Returns
returns a test dataset

Definition at line 954 of file ClassificationData.cpp.

ClassificationData ClassificationData::getTrainingFoldData ( const UINT  foldIndex) const

Returns the training dataset for the k-th fold for cross validation. The spiltDataIntoKFolds(UINT K) function should have been called once before using this function. The foldIndex should be in the range [0 K-1], where K is the number of folds the data was spilt into.

Parameters
constUINT foldIndex: the index of the fold you want the training data for, this should be in the range [0 K-1], where K is the number of folds the data was spilt into
Returns
returns a training dataset

Definition at line 918 of file ClassificationData.cpp.

bool ClassificationData::load ( const string &  filename)

Load the classification data from a file. If the file format ends in '.csv' then the function will try and load the data from a csv format. If this fails then it will try and load the data as a custom GRT file.

Parameters
conststring &filename: the name of the file the data will be loaded from
Returns
true if the data was loaded successfully, false otherwise

Definition at line 383 of file ClassificationData.cpp.

bool ClassificationData::loadDatasetFromCSVFile ( const string &  filename,
const UINT  classLabelColumnIndex = 0 
)

Loads the labelled classification data from a CSV file. This assumes the data is formatted with each row representing a sample. The class label should be the first column followed by the sample data as the following N columns, where N is the number of dimensions in the data. If the class label is not the first column, you should set the 2nd argument (UINT classLabelColumnIndex) to the column index that contains the class label.

Parameters
conststring &filename: the name of the file the data will be loaded from
constUINT classLabelColumnIndex: the index of the column containing the class label. Default value = 0
Returns
true if the data was loaded successfully, false otherwise

Definition at line 597 of file ClassificationData.cpp.

bool ClassificationData::loadDatasetFromFile ( const string &  filename)

Loads the labelled classification data from a custom file format.

Parameters
conststring &filename: the name of the file the data will be loaded from
Returns
true if the data was loaded successfully, false otherwise

Definition at line 437 of file ClassificationData.cpp.

bool ClassificationData::merge ( const ClassificationData labelledData)

Adds the data in the labelledData set to the current instance of the ClassificationData. The number of dimensions in both datasets must match. The names of the classes from the labelledData will be added to the current instance.

Parameters
constClassificationData &labelledData: the dataset to add to this dataset
Returns
returns true if the datasets were merged, false otherwise

Definition at line 782 of file ClassificationData.cpp.

ClassificationData & ClassificationData::operator= ( const ClassificationData rhs)

Sets the equals operator, copies the data from the rhs instance to this instance

Parameters
constClassificationData &rhs: another instance of the ClassificationData class from which the data will be copied to this instance
Returns
a reference to this instance of ClassificationData

Definition at line 47 of file ClassificationData.cpp.

ClassificationSample& GRT::ClassificationData::operator[] ( const UINT &  i)
inline

Array Subscript Operator, returns the ClassificationSample at index i. It is up to the user to ensure that i is within the range of [0 totalNumSamples-1]

Parameters
constUINT &i: the index of the training sample you want to access. Must be within the range of [0 totalNumSamples-1]
Returns
a reference to the i'th ClassificationSample

Definition at line 82 of file ClassificationData.h.

const ClassificationSample& GRT::ClassificationData::operator[] ( const UINT &  i) const
inline

Const Array Subscript Operator, returns the ClassificationSample at index i. It is up to the user to ensure that i is within the range of [0 totalNumSamples-1]

Parameters
constUINT &i: the index of the training sample you want to access. Must be within the range of [0 totalNumSamples-1]
Returns
a const reference to the i'th ClassificationSample

Definition at line 93 of file ClassificationData.h.

ClassificationData ClassificationData::partition ( const UINT  partitionPercentage,
const bool  useStratifiedSampling = false 
)

Partitions the dataset into a training dataset (which is kept by this instance of the ClassificationData) and a testing/validation dataset (which is returned as a new instance of a ClassificationData).

Parameters
constUINT partitionPercentage: sets the percentage of data which remains in this instance, the remaining percentage of data is then returned as the testing/validation dataset
constbool useStratifiedSampling: sets if the dataset should be broken into homogeneous groups first before randomly being spilt, default value is false
Returns
a new ClassificationData instance, containing the remaining data not kept but this instance

Definition at line 674 of file ClassificationData.cpp.

bool ClassificationData::printStats ( ) const

Prints the dataset info (such as its name and infoText) and the stats (such as the number of examples, number of dimensions, number of classes, etc.) to the std out.

Returns
returns true if the dataset info and stats were printed successfully, false otherwise

Definition at line 660 of file ClassificationData.cpp.

RegressionData ClassificationData::reformatAsRegressionData ( ) const

Reformats the ClassificationData as LabelledRegressionData to enable regression algorithms like the MLP to be used as a classifier. This sets the number of targets in the regression data equal to the number of classes in the classification data. The output target ouput of each regression sample will therefore be all zeros, except for the index matching the class label which will be 1. For this to work, the labelled classification data cannot have any samples with a class label of 0!

Returns
a new LabelledRegressionData instance, containing the reformated classification data

Definition at line 1038 of file ClassificationData.cpp.

UnlabelledData ClassificationData::reformatAsUnlabelledData ( ) const

Reformats the ClassificationData as UnlabelledData so the data can be used to train unsupervised training algorithms such as K-Means Clustering and Gaussian Mixture Models.

Returns
a new UnlabelledData instance, containing the reformated classification data

Definition at line 1073 of file ClassificationData.cpp.

bool ClassificationData::relabelAllSamplesWithClassLabel ( const UINT  oldClassLabel,
const UINT  newClassLabel 
)

Relabels all the samples with the class label A with the new class label B.

Parameters
constUINT oldClassLabel: the class label of the samples you want to relabel
constUINT newClassLabel: the class label the samples will be relabelled with
Returns
returns true if the samples were correctly relablled, false otherwise

Definition at line 288 of file ClassificationData.cpp.

UINT ClassificationData::removeClass ( const UINT  classLabel)

Deletes from the dataset all the samples with a specific class label.

Parameters
constUINT classLabel: the class label of the samples you wish to delete from the dataset
Returns
the number of samples deleted from the dataset

Definition at line 254 of file ClassificationData.cpp.

bool ClassificationData::removeLastSample ( )

Removes the last training sample added to the dataset.

Returns
true if the last sample was removed, false otherwise

Definition at line 212 of file ClassificationData.cpp.

bool ClassificationData::removeSample ( const UINT  index)

Removes the training sample at the specific index from the dataset.

Returns
true if the index is valid and the sample was removed, false otherwise

Definition at line 177 of file ClassificationData.cpp.

bool ClassificationData::reserve ( const UINT  N)

Reserves that the vector capacity be at least enough to contain N elements.

If N is greater than the current vector capacity, the function causes the container to reallocate its storage increasing its capacity to N (or greater).

Parameters
constUINT N: the new memory size
Returns
true if the memory was reserved successfully, false otherwise

Definition at line 222 of file ClassificationData.cpp.

bool ClassificationData::save ( const string &  filename) const

Saves the classification data to a file. If the file format ends in '.csv' then the data will be saved as comma-seperated-values, otherwise it will be saved to a custom GRT file (which contains the csv data with an additional header).

Parameters
conststring &filename: the name of the file the data will be saved to
Returns
true if the data was saved successfully, false otherwise

Definition at line 372 of file ClassificationData.cpp.

bool ClassificationData::saveDatasetToCSVFile ( const string &  filename) const

Saves the labelled classification data to a CSV file. This will save the class label as the first column and the sample data as the following N columns, where N is the number of dimensions in the data. Each row will represent a sample.

Parameters
conststring &filename: the name of the file the data will be saved to
Returns
true if the data was saved successfully, false otherwise

Definition at line 574 of file ClassificationData.cpp.

bool ClassificationData::saveDatasetToFile ( const string &  filename) const

Saves the labelled classification data to a custom file format.

Parameters
conststring &filename: the name of the file the data will be saved to
Returns
true if the data was saved successfully, false otherwise

Definition at line 394 of file ClassificationData.cpp.

bool ClassificationData::scale ( const double  minTarget,
const double  maxTarget 
)

Scales the dataset to the new target range.

Returns
true if the data was scaled correctly, false otherwise

Definition at line 354 of file ClassificationData.cpp.

bool ClassificationData::scale ( const vector< MinMax > &  ranges,
const double  minTarget,
const double  maxTarget 
)

Scales the dataset to the new target range, using the vector of ranges as the min and max source ranges.

Returns
true if the data was scaled correctly, false otherwise

Definition at line 359 of file ClassificationData.cpp.

bool ClassificationData::setAllowNullGestureClass ( bool  allowNullGestureClass)

Sets if the user can add samples to the dataset with the label matching the GRT_DEFAULT_NULL_CLASS_LABEL. If the allowNullGestureClass is set to true, then the user can add labels matching the default null class label (which is normally 0). If the allowNullGestureClass is set to false, then the user will not be able to add samples that have a class label matching the default null class label.

Returns
returns true if the allowNullGestureClass was set, false otherwise

Definition at line 127 of file ClassificationData.cpp.

bool ClassificationData::setClassNameForCorrespondingClassLabel ( string  className,
UINT  classLabel 
)

Sets the name of the class with the given class label. There should not be any spaces in the className. Will return true if the name is set, or false if the class label does not exist.

Returns
returns true if the name is set, or false if the class label does not exist

Definition at line 114 of file ClassificationData.cpp.

bool ClassificationData::setDatasetName ( string  datasetName)

Sets the name of the dataset. There should not be any spaces in the name. Will return true if the name is set, or false otherwise.

Returns
returns true if the name is set, or false otherwise

Definition at line 97 of file ClassificationData.cpp.

bool ClassificationData::setExternalRanges ( const vector< MinMax > &  externalRanges,
const bool  useExternalRanges = false 
)

Sets the external ranges of the dataset, also sets if the dataset should be scaled using these values. The dimensionality of the externalRanges vector should match the number of dimensions of this dataset.

Parameters
constvector< MinMax > &externalRanges: an N dimensional vector containing the min and max values of the expected ranges of the dataset.
constbool useExternalRanges: sets if these ranges should be used to scale the dataset, default value is false.
Returns
returns true if the external ranges were set, false otherwise

Definition at line 336 of file ClassificationData.cpp.

bool ClassificationData::setInfoText ( string  infoText)

Sets the info string. This can be any string with information about how the training data was recorded for example.

Parameters
stringinfoText: the infoText
Returns
true if the infoText was correctly updated, false otherwise

Definition at line 109 of file ClassificationData.cpp.

bool ClassificationData::setNumDimensions ( UINT  numDimensions)

Sets the number of dimensions in the training data. This should be an unsigned integer greater than zero. This will clear any previous training data and counters. This function needs to be called before any new samples can be added to the dataset, unless the numDimensions variable was set in the constructor or some data was already loaded from a file

Parameters
UINTnumDimensions: the number of dimensions of the training data. Must be an unsigned integer greater than zero
Returns
true if the number of dimensions was correctly updated, false otherwise

Definition at line 77 of file ClassificationData.cpp.

bool ClassificationData::sortClassLabels ( )

Sorts the class labels (in the class tracker) in ascending order.

Returns
returns true if the labels were sorted successfully, false otherwise

Definition at line 667 of file ClassificationData.cpp.

bool ClassificationData::spiltDataIntoKFolds ( const UINT  K,
const bool  useStratifiedSampling = false 
)

This function prepares the dataset for k-fold cross validation and should be called prior to calling the getTrainingFold(UINT foldIndex) or getTestingFold(UINT foldIndex) functions. It will spilt the dataset into K-folds, as long as K < M, where M is the number of samples in the dataset.

Parameters
constUINT K: the number of folds the dataset will be split into, K should be less than the number of samples in the dataset
constbool useStratifiedSampling: sets if the dataset should be broken into homogeneous groups first before randomly being spilt, default value is false
Returns
returns true if the dataset was split correctly, false otherwise

Definition at line 813 of file ClassificationData.cpp.


The documentation for this class was generated from the following files: