jsat

Class SimpleDataSet

java.lang.Object

jsat.DataSet<SimpleDataSet>

jsat.SimpleDataSet

public class SimpleDataSet
extends DataSet<SimpleDataSet>

SimpleData Set is a basic implementation of a data set. Has no assumptions about the task that is going to be performed.

Constructor Summary

Constructors

Constructor and Description
SimpleDataSet(CategoricalData[] categories, int numNumericalValues)
SimpleDataSet(java.util.List<DataPoint> dataPoints)

Method Summary

Modifier and Type	Method and Description
void	add(DataPoint dp) Adds a new datapoint to this set.
ClassificationDataSet	asClassificationDataSet(int index) Converts this dataset into one meant for classification problems.
RegressionDataSet	asRegressionDataSet(int index) Converts this dataset into one meant for regression problems.
java.util.List<DataPoint>	getBackingList()
DataPoint	getDataPoint(int i) Returns the i'th data point in this set.
int	getSampleSize() Returns the number of data points in this data set
SimpleDataSet	getTwiceShallowClone() Returns a new version of this data set that is of the same type, and contains a different listing pointing to shallow data point copies.
void	setDataPoint(int i, DataPoint dp) Replaces an already existing data point with the one given.
SimpleDataSet	shallowClone() Returns a new version of this data set that is of the same type, and contains a different list pointing to the same data points.

Methods inherited from class jsat.DataSet

applyTransform, applyTransform, applyTransform, applyTransform, countMissingValues, cvSet, cvSet, getCategories, getCategoryName, getColumnMeanVariance, getDataMatrix, getDataMatrixView, getDataPointIterator, getDataPoints, getDataVectors, getDataWeights, getMissingDropped, getNumCategoricalVars, getNumericColumn, getNumericColumns, getNumericColumns, getNumericName, getNumFeatures, getNumNumericalVars, getOnlineColumnStats, getOnlineDenseStats, getSparsityStats, randomSplit, randomSplit, replaceNumericFeatures, setNumericName

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

SimpleDataSet

public SimpleDataSet(java.util.List<DataPoint> dataPoints)

SimpleDataSet

public SimpleDataSet(CategoricalData[] categories,
                     int numNumericalValues)

Method Detail

getDataPoint

public DataPoint getDataPoint(int i)

Description copied from class: DataSet

Returns the i'th data point in this set. The order will never chance so long as no data points are added or removed from the set.

Specified by:

getDataPoint in class DataSet<SimpleDataSet>

Parameters:

i - the i'th data point in this set

Returns:

the i'th data point in this set

setDataPoint

public void setDataPoint(int i,
                         DataPoint dp)

Description copied from class: DataSet

Replaces an already existing data point with the one given. Any values associated with the data point, but not apart of it, will remain intact.

Specified by:

setDataPoint in class DataSet<SimpleDataSet>

Parameters:

i - the i'th dataPoint to set.

dp - the data point to set at the specified index

add

public void add(DataPoint dp)

Adds a new datapoint to this set.

Parameters:

dp - the datapoint to add

getSampleSize

public int getSampleSize()

Description copied from class: DataSet

Returns the number of data points in this data set

Specified by:

getSampleSize in class DataSet<SimpleDataSet>

Returns:

the number of data points in this data set

asClassificationDataSet

public ClassificationDataSet asClassificationDataSet(int index)

Converts this dataset into one meant for classification problems. The given categorical feature index is removed from the data and made the target variable for the classification problem.

Parameters:

index - the classification variable index, should be in the range [0, DataSet.getNumCategoricalVars())

Returns:

a new dataset where one categorical variable is removed and made the target of a classification dataset

asRegressionDataSet

public RegressionDataSet asRegressionDataSet(int index)

Converts this dataset into one meant for regression problems. The given numeric feature index is removed from the data and made the target variable for the regression problem.

Parameters:

index - the regression variable index, should be in the range [0, DataSet.getNumNumericalVars())

Returns:

a new dataset where one numeric variable is removed and made the target of a regression dataset

getBackingList

public java.util.List<DataPoint> getBackingList()

Returns:

direct access to the list that backs this data set.

shallowClone

public SimpleDataSet shallowClone()

Description copied from class: DataSet

Returns a new version of this data set that is of the same type, and contains a different list pointing to the same data points.

Specified by:

shallowClone in class DataSet<SimpleDataSet>

Returns:

a shallow copy of this data set

getTwiceShallowClone

public SimpleDataSet getTwiceShallowClone()

Description copied from class: DataSet

Returns a new version of this data set that is of the same type, and contains a different listing pointing to shallow data point copies. Because the data point object contains the weight itself, the weight is not shared - while the vector and array information is. This allows altering the weights of the data points while preserving the original weights.
Altering the list or weights of the returned data set will not be reflected in the original. Altering the feature values will.

Overrides:

getTwiceShallowClone in class DataSet<SimpleDataSet>

Returns:

a shallow copy of shallow data point copies for this data set.