Catalano.MachineLearning.Dataset

Class DatasetRegression

java.lang.Object

Catalano.MachineLearning.Dataset.DatasetRegression

All Implemented Interfaces:

IDataset<double[][],double[]>, java.io.Serializable

public class DatasetRegression
extends java.lang.Object
implements IDataset<double[][],double[]>

Dataset for regression.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
DatasetRegression(java.lang.String filepath) Initializes a new instance of the DatasetRegression class.
DatasetRegression(java.lang.String name, double[][] input, double[] output) Initializes a new instance of the DatasetClassification class.
DatasetRegression(java.lang.String name, double[][] input, double[] output, DecisionVariable[] attributes) Initializes a new instance of the DatasetClassification class.
DatasetRegression(java.lang.String filepath, java.lang.String name) Initializes a new instance of the DatasetRegression class.
DatasetRegression(java.lang.String filepath, java.lang.String name, boolean ignoreAttributeInfo) Initializes a new instance of the DatasetRegression class.
DatasetRegression(java.lang.String filepath, java.lang.String name, boolean ignoreAttributeInfo, int classIndex) Initializes a new instance of the DatasetRegression class.

Method Summary

Modifier and Type	Method and Description
static DatasetRegression	FromCSV(java.lang.String filepath, java.lang.String name) Read dataset from a CSV structure.
static DatasetRegression	FromCSV(java.lang.String filepath, java.lang.String name, boolean ignoreAttributeInfo) Read dataset from a CSV structure.
static DatasetRegression	FromCSV(java.lang.String filepath, java.lang.String name, boolean ignoreAttributeInfo, int classIndex) Construct a dataset from an CSV file.
DecisionVariable[]	getAllDecisionVariables() Get the decision variables including the output.
int	getClassIndex() Get class index.
DecisionVariable[]	getDecisionVariables() Get the decision variables.
double[][]	getInput() Get the input data.
java.lang.String	getName() Get the name of the dataset.
int	getNumberOfAttributes() Get the number of attributes.
int	getNumberOfContinuous() Get the number of type continuous.
int	getNumberOfDiscrete() Get the number of type discrete.
int	getNumberOfInstances() Get the number of instances.
double[]	getOutput() Get the output data.
StatisticsDataset[]	getStatistics() Get the statistics from the dataset.
void	KeepAttributes(int[] indexes) Keep the selected attributes and remove the rest.
double[][]	Normalize() Normalize all continuous data.
double[][]	Normalize(double min, double max) Normalize all continuous data.
void	RemoveAttribute(int index) Remove an attribute.
void	RemoveAttributes(int[] indexes) Remove selected attributes.
void	setClassIndex(int classIndex) Set class index.
void	setInput(double[][] input, DecisionVariable[] variables) Set the input data.
DatasetRegression	Split(float percentage) Split the percentange of the dataset in Trainning and the rest in Validation.
DatasetRegression	Split(float percentage, java.lang.String name) Split the percentange of the dataset in Trainning and the rest in Validation.
double[][]	Standartize() Standartize all continuous data.
void	WriteAsARFF(java.lang.String filename) Write dataset as ARFF file.
void	WriteAsARFF(java.lang.String filename, int decimalPlaces) Write dataset as ARFF file.
void	WriteAsCSV(java.lang.String filename) Write the dataset as CSV file.
void	WriteAsCSV(java.lang.String filename, int decimalPlaces) Write the dataset as CSV file.
void	WriteAsCSV(java.lang.String filename, int decimalPlaces, char delimiter) Write the dataset as CSV file.
void	WriteAsCSV(java.lang.String filename, int decimalPlaces, char delimiter, java.lang.String newLine) Write the dataset as CSV file.
void	WriteAsCSV(java.lang.String filename, int decimalPlaces, char delimiter, java.lang.String newLine, boolean writeHeader) Write the dataset as CSV file.

Methods inherited from class java.lang.Object

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

Constructor Detail

DatasetRegression

public DatasetRegression(java.lang.String filepath)

Initializes a new instance of the DatasetRegression class.

Parameters:

filepath - File path.

DatasetRegression

public DatasetRegression(java.lang.String filepath,
                         java.lang.String name)

Initializes a new instance of the DatasetRegression class.

Parameters:

filepath - File path.

name - Name of the dataset.

DatasetRegression

public DatasetRegression(java.lang.String filepath,
                         java.lang.String name,
                         boolean ignoreAttributeInfo)

Initializes a new instance of the DatasetRegression class.

Parameters:

filepath - File path.

name - Name of the data.

ignoreAttributeInfo - Ignore attribute information.

DatasetRegression

public DatasetRegression(java.lang.String filepath,
                         java.lang.String name,
                         boolean ignoreAttributeInfo,
                         int classIndex)

Initializes a new instance of the DatasetRegression class.

Parameters:

filepath - File path.

name - Name of the dataset.

ignoreAttributeInfo - Ignore attribute information.

classIndex - Output index.

DatasetRegression

public DatasetRegression(java.lang.String name,
                         double[][] input,
                         double[] output)

Initializes a new instance of the DatasetClassification class.

Parameters:

name - Name of the dataset.

input - Input data.

output - Output data.

DatasetRegression

public DatasetRegression(java.lang.String name,
                         double[][] input,
                         double[] output,
                         DecisionVariable[] attributes)

Initializes a new instance of the DatasetClassification class.

Parameters:

name - Name of the dataset.

attributes - Decision variables.

input - Input data.

output - Output data.

Method Detail

getName

public java.lang.String getName()

Get the name of the dataset.

Returns:

Name.

getInput

public double[][] getInput()

Get the input data.

Specified by:

getInput in interface IDataset<double[][],double[]>

Returns:

Input data.

getOutput

public double[] getOutput()

Get the output data.

Specified by:

getOutput in interface IDataset<double[][],double[]>

Returns:

Output data.

getClassIndex

public int getClassIndex()

Get class index.

Returns:

Class index.

setClassIndex

public void setClassIndex(int classIndex)

Set class index.

Parameters:

classIndex - Class index.

getDecisionVariables

public DecisionVariable[] getDecisionVariables()

Get the decision variables.

Specified by:

getDecisionVariables in interface IDataset<double[][],double[]>

Returns:

Decision Variables.

getAllDecisionVariables

public DecisionVariable[] getAllDecisionVariables()

Get the decision variables including the output.

Returns:

Decision Variables.

getNumberOfInstances

public int getNumberOfInstances()

Get the number of instances.

Returns:

Number of instances.

getNumberOfAttributes

public int getNumberOfAttributes()

Get the number of attributes.

Returns:

Number of attributes.

getNumberOfContinuous

public int getNumberOfContinuous()

Get the number of type continuous.

Returns:

Number of type continuous.

getNumberOfDiscrete

public int getNumberOfDiscrete()

Get the number of type discrete.

Returns:

Number of type discrete.

FromCSV

public static DatasetRegression FromCSV(java.lang.String filepath,
                                        java.lang.String name)

Read dataset from a CSV structure. The last column of CSV is interpreted as output.

Parameters:

filepath - File path.

name - Name of the dataset.

Returns:

Classification Dataset.

FromCSV

public static DatasetRegression FromCSV(java.lang.String filepath,
                                        java.lang.String name,
                                        boolean ignoreAttributeInfo)

Read dataset from a CSV structure. The last column of CSV is interpreted as output.

Parameters:

filepath - File path.

name - Name of the dataset.

Returns:

Classification Dataset.

FromCSV

public static DatasetRegression FromCSV(java.lang.String filepath,
                                        java.lang.String name,
                                        boolean ignoreAttributeInfo,
                                        int classIndex)

Construct a dataset from an CSV file.

Parameters:

filepath - File.

name - Name of the dataset.

ignoreAttributeInfo - Ignore attribute information.

classIndex - Index of the attribute for to be setup as output.

Returns:

ClassificationDataset.

Normalize

public double[][] Normalize()

Normalize all continuous data. Default: (0..1).

Returns:

Range of the normalization.

Normalize

public double[][] Normalize(double min,
                            double max)

Normalize all continuous data.

Parameters:

min - Minimum.

max - Maximum.

Returns:

Range of the normalization.

RemoveAttribute

public void RemoveAttribute(int index)

Remove an attribute.

Parameters:

index - Index of the attribute.

RemoveAttributes

public void RemoveAttributes(int[] indexes)

Remove selected attributes.

Parameters:

indexes - Indexes of the attributes.

KeepAttributes

public void KeepAttributes(int[] indexes)

Keep the selected attributes and remove the rest.

Parameters:

indexes - Indexes of the attributes.

Split

public DatasetRegression Split(float percentage)

Split the percentange of the dataset in Trainning and the rest in Validation.

Parameters:

percentage - Percentage.

Returns:

Validation dataset.

Split

public DatasetRegression Split(float percentage,
                               java.lang.String name)

Split the percentange of the dataset in Trainning and the rest in Validation.

Parameters:

percentage - Percentage.

name - Name of the validation dataset.

Returns:

Validation dataset.

Standartize

public double[][] Standartize()

Standartize all continuous data. x = (x - u) / s

Returns:

Range of standartization.

getStatistics

public StatisticsDataset[] getStatistics()

Description copied from interface: IDataset

Get the statistics from the dataset.

Specified by:

getStatistics in interface IDataset<double[][],double[]>

Returns:

Statistics.

WriteAsCSV

public void WriteAsCSV(java.lang.String filename)

Write the dataset as CSV file.

Parameters:

filename - Filename.

WriteAsCSV

public void WriteAsCSV(java.lang.String filename,
                       int decimalPlaces)

Write the dataset as CSV file.

Parameters:

filename - Filename.

decimalPlaces - Decimal places for continous data.

WriteAsCSV

public void WriteAsCSV(java.lang.String filename,
                       int decimalPlaces,
                       char delimiter)

Write the dataset as CSV file.

Parameters:

filename - Filename.

decimalPlaces - Decimal places for continous data.

delimiter - Delimiter.

WriteAsCSV

public void WriteAsCSV(java.lang.String filename,
                       int decimalPlaces,
                       char delimiter,
                       java.lang.String newLine)

Write the dataset as CSV file.

Parameters:

filename - Filename.

decimalPlaces - Decimal places.

delimiter - Delimiter.

newLine - Newline char.

WriteAsCSV

public void WriteAsCSV(java.lang.String filename,
                       int decimalPlaces,
                       char delimiter,
                       java.lang.String newLine,
                       boolean writeHeader)

Write the dataset as CSV file.

Parameters:

filename - Filename.

decimalPlaces - Decimal places.

delimiter - Delimiter.

newLine - Newline char.

writeHeader - Write the header of the dataset.

WriteAsARFF

public void WriteAsARFF(java.lang.String filename)

Write dataset as ARFF file.

Parameters:

filename - Filename.

WriteAsARFF

public void WriteAsARFF(java.lang.String filename,
                        int decimalPlaces)

Write dataset as ARFF file.

Parameters:

filename - Filename.

decimalPlaces - Decimal places.

setInput

public void setInput(double[][] input,
                     DecisionVariable[] variables)

Description copied from interface: IDataset

Set the input data.

Specified by:

setInput in interface IDataset<double[][],double[]>

Parameters:

input - Input.