Dataset (Simbrain API)

java.lang.Object
- org.simbrain.util.projection.Dataset

```
public class Dataset
extends java.lang.Object
```
Dataset represents a set of n-dimensional points. Both the low and high dimensional data of the current ProjectionMethod are instances of this class. Dataset provides methods for working with such sets (e.g. open dataset up, adding points, checking their integrity, finding nearest neighbors of a point, calculating their interpoint distances, etc.). It is assumed that all points in a dataset have the same dimensionality.

Field Summary

Fields
Modifier and Type Field and Description

(package private) org.apache.log4j.Logger logger
Logger.

Fields
Modifier and Type	Field and Description
`(package private) org.apache.log4j.Logger`	`logger` Logger.

Constructor Summary

Constructors
Constructor and Description

Dataset(int dimensions)
Creates and instance of Dataset.

Dataset(int dimensions, int numPoints)
Creates an instance of Dataset.

Constructors
Constructor and Description
`Dataset(int dimensions)` Creates and instance of Dataset.
`Dataset(int dimensions, int numPoints)` Creates an instance of Dataset.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`addPoint(DataPoint point)` Add datapoint without checking whether it is unique or not.
`DataPoint`	`addPoint(DataPoint point, double tolerance)` Add a new datapoint to the dataset.
`void`	`clear()` Clear all data.
`void`	`exportToCSV(java.io.File theFile)` Save the current datast to a stored file.
`int`	`getClosestIndex(DataPoint point)` Returns the index of the closest point.
`double`	`getComponent(int datapointNumber, int dimension)` Get a specific coordinate of a specific datapoint.
`double`	`getCovariance(int i, int j)` Returns the covariance of the ith component of the dataset with respect to the jth component.
`Jama.Matrix`	`getCovarianceMatrix()` Returns a covariance matrix for the dataset.
`java.util.ArrayList<DataPoint>`	`getDatasetCopy()`
`int`	`getDimensions()`
`double`	`getDistance(DataPoint point1, DataPoint point2)` Returns the euclidean distance between two points.
`double`	`getDistance(int index1, int index2)` Get the distance between two points.
`double[][]`	`getDistances()` Returns a matrix of interpoint distances, between the points in the dataset.
`java.lang.String[][]`	`getDoubleStrings()` Returns a matrix of strings, one row for each datapoint, representing the dataset.
`int[]`	`getKNearestNeighbors(int k, DataPoint point)` returns k neighbors where the 0th item is the closest and the 1st item is the second closest etc.
`int`	`getKthVariantDimension(int k)` Returns the k'th most variant dimesion.
`DataPoint`	`getLastAddedPoint()` Returns the last point added to this dataset.
`double`	`getMaximumDistance()` Get the maximimum interpoint distance between points in the dataset.
`double`	`getMean(int d)` Returns the mean of the dataset on a given dimension.
`double`	`getMinimumDistance()` Get the minimum interpoint distance between points in the dataset.
`int`	`getNumPoints()`
`DataPoint`	`getPoint(int i)` Get a specified point in the dataset.
`double`	`getSumDistances()`
`void`	`mirror(Dataset other)` Makes this dataset a copy of the passed in dataset.
`void`	`perturbOverlappingPoints(double factor)` Find repeated points and perturb them slightly so they don't overlap.
`void`	`postOpenInit()` Initializes Dataset from persistent data.
`void`	`preSaveInit()` Initializes persistent data.
`void`	`printDataset()` Print out all points in the dataset Useful for debugging.
`void`	`randomize(int upperBound)` Randomize dataset to a value between 0 and upperBound.
`void`	`resultsToMaple(java.io.PrintStream ps)` Print out low dimensional points so maple can plot them Just does low dimension = 2.
`void`	`setPoint(int i, DataPoint point)` Set a specified point in the dataset.
`java.lang.String`	`toString()`

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Field Detail
  - logger
```
org.apache.log4j.Logger logger
```
    Logger.
- Constructor Detail
  - Dataset
```
public Dataset(int dimensions)
```
    Creates and instance of Dataset.
    
    Parameters:
    
    dimensions - dimension of dataset
  - Dataset
```
public Dataset(int dimensions,
               int numPoints)
```
    Creates an instance of Dataset.
    
    Parameters:
    
    dimensions - dimension of dataset
    
    numPoints - number of points
- Method Detail
  - getPoint
```
public DataPoint getPoint(int i)
```
    Get a specified point in the dataset.
    
    Parameters:
    
    i - index of the point to get
    
    Returns:
    
    the n-dimensional datapoint
  - addPoint
```
public DataPoint addPoint(DataPoint point,
                          double tolerance)
```
    Add a new datapoint to the dataset.
    
    Parameters:
    
    point - A point in the high dimensional space
    
    tolerance - forwarded to isUniquePoint; if -1 then add point regardless of whether it is unique or not
    
    Returns:
    
    null if point added, overlapping point otherwise
  - addPoint
```
public void addPoint(DataPoint point)
```
    Add datapoint without checking whether it is unique or not.
    
    Parameters:
    
    point - point to be added
  - setPoint
```
public void setPoint(int i,
                     DataPoint point)
```
    Set a specified point in the dataset.
    
    Parameters:
    
    i - the point to set
    
    point - the new n-dimensional point
  - getNumPoints
```
public int getNumPoints()
```
    Returns:
    
    the number of points in the dataset
  - clear
```
public void clear()
```
    Clear all data.
  - randomize
```
public void randomize(int upperBound)
```
    Randomize dataset to a value between 0 and upperBound.
    
    Parameters:
    
    upperBound - highest value to be used
  - getMinimumDistance
```
public double getMinimumDistance()
```
    Get the minimum interpoint distance between points in the dataset.
    
    Returns:
    
    minimum distance between any two points in the low-d dataset
  - getMaximumDistance
```
public double getMaximumDistance()
```
    Get the maximimum interpoint distance between points in the dataset.
    
    Returns:
    
    maximum distance between any two points in the low-d dataset
  - exportToCSV
```
public void exportToCSV(java.io.File theFile)
```
    Save the current datast to a stored file.
    
    Parameters:
    
    theFile - the file where data should be saved
  - perturbOverlappingPoints
```
public void perturbOverlappingPoints(double factor)
```
    Find repeated points and perturb them slightly so they don't overlap.
    
    Parameters:
    
    factor - Distance to perturb
  - resultsToMaple
```
public void resultsToMaple(java.io.PrintStream ps)
```
    Print out low dimensional points so maple can plot them Just does low dimension = 2.
    
    Parameters:
    
    ps -
  - getComponent
```
public double getComponent(int datapointNumber,
                           int dimension)
```
    Get a specific coordinate of a specific datapoint. Say, the second component of the third datapoint in a 5-dimensional dataset with 50 points.
    
    Parameters:
    
    datapointNumber - index of the point to get
    
    dimension - dimension of the desired component
    
    Returns:
    
    the value of of n'th component of the specified datapoint
  - getClosestIndex
```
public int getClosestIndex(DataPoint point)
```
    Returns the index of the closest point.
    
    Parameters:
    
    point - the point to check
    
    Returns:
    
    the index of the point closest to this one in the dataset
  - getKNearestNeighbors
```
public int[] getKNearestNeighbors(int k,
                                  DataPoint point)
```
    returns k neighbors where the 0th item is the closest and the 1st item is the second closest etc.
    
    Parameters:
    
    k - the number of points to retrieve
    
    point - the point to find neighbors for
    
    Returns:
    
    the indices of the neighbors
  - getDistance
```
public double getDistance(int index1,
                          int index2)
```
    Get the distance between two points.
    
    Parameters:
    
    index1 - index of point 1
    
    index2 - index of point 2
    
    Returns:
    
    distance between points 1 and 2
  - getDistance
```
public double getDistance(DataPoint point1,
                          DataPoint point2)
```
    Returns the euclidean distance between two points.
    
    Parameters:
    
    point1 - First point of distance
    
    point2 - Second point of distance
    
    Returns:
    
    the Euclidean distance between points 1 and 2
  - getDimensions
```
public int getDimensions()
```
    Returns:
    
    the dimensionality of the points in the dataset
  - getDistances
```
public double[][] getDistances()
```
    Returns a matrix of interpoint distances, between the points in the dataset. Note that the lower triangular duplicates the upper triangular
    
    Returns:
    
    a matrix of interpoint distances
  - getSumDistances
```
public double getSumDistances()
```
    Returns:
    
    the sum of the distances between points in the dataset
  - getMean
```
public double getMean(int d)
```
    Returns the mean of the dataset on a given dimension.
    
    Parameters:
    
    d - index of the dimension whose mean to get
    
    Returns:
    
    mean of dataset on dimension d
  - getCovariance
```
public double getCovariance(int i,
                            int j)
```
    Returns the covariance of the ith component of the dataset with respect to the jth component.
    
    Parameters:
    
    i - first dimension
    
    j - second dimension
    
    Returns:
    
    covariance of i with respect to j
  - getCovarianceMatrix
```
public Jama.Matrix getCovarianceMatrix()
```
    Returns a covariance matrix for the dataset.
    
    Returns:
    
    covariance matrix which describes how the data covary along each dimension
  - getKthVariantDimension
```
public int getKthVariantDimension(int k)
```
    Returns the k'th most variant dimesion. For example, the most variant dimension (k=1), or the least variant dimension (k=num_dimensions).
    
    Parameters:
    
    k - Number of variant dimension
    
    Returns:
    
    the k'th most variant dimension
  - getDatasetCopy
```
public java.util.ArrayList<DataPoint> getDatasetCopy()
```
    Returns:
    
    a reference to the dataset
  - mirror
```
public void mirror(Dataset other)
```
    Makes this dataset a copy of the passed in dataset.
    
    Parameters:
    
    other - the the dataset
  - printDataset
```
public void printDataset()
```
    Print out all points in the dataset Useful for debugging.
  - getDoubleStrings
```
public java.lang.String[][] getDoubleStrings()
```
    Returns a matrix of strings, one row for each datapoint, representing the dataset.
    
    Returns:
    
    a matrix of strings representing the dataset
  - preSaveInit
```
public void preSaveInit()
```
    Initializes persistent data. Initializes Dataset from persistent data.
  - postOpenInit
```
public void postOpenInit()
```
    Initializes Dataset from persistent data.
  - toString
```
public java.lang.String toString()
```
    Overrides:
    
    toString in class java.lang.Object
  - getLastAddedPoint
```
public DataPoint getLastAddedPoint()
```
    Returns the last point added to this dataset.
    
    Returns:
    
    the last added point

Class Dataset

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

logger

Constructor Detail

Dataset

Dataset

Method Detail

getPoint

addPoint

addPoint

setPoint

getNumPoints

clear

randomize

getMinimumDistance

getMaximumDistance

exportToCSV

perturbOverlappingPoints

resultsToMaple

getComponent

getClosestIndex

getKNearestNeighbors

getDistance

getDistance

getDimensions

getDistances

getSumDistances

getMean

getCovariance

getCovarianceMatrix

getKthVariantDimension

getDatasetCopy

mirror

printDataset

getDoubleStrings

preSaveInit

postOpenInit

toString

getLastAddedPoint