Package org.nd4j.linalg.dimensionalityreduction

Class RandomProjection

java.lang.Object

org.nd4j.linalg.dimensionalityreduction.RandomProjection

public class RandomProjection
extends Object

Constructor Summary

Constructors

Constructor	Description
RandomProjection(double eps)
RandomProjection(double eps, Random rng)
RandomProjection(int components)
RandomProjection(int components, Random rng)

Method Summary

Modifier and Type	Method	Description
static List<Integer>	johnsonLindenstraussMinDim(int[] n, double... eps)	Find a safe number of components to project this to, through the Johnson-Lindenstrauss lemma The minimum number n' of components to guarantee the eps-embedding is given by: n' >= 4 log(n) / (eps² / 2 - eps³ / 3) see http://cseweb.ucsd.edu/~dasgupta/papers/jl.pdf §2.1
static List<Long>	johnsonLindenstraussMinDim(long[] n, double... eps)
static List<Integer>	johnsonLindenStraussMinDim(int n, double... eps)
static List<Long>	johnsonLindenStraussMinDim(long n, double... eps)
INDArray	project(INDArray data)	Create a copy random projection by using matrix product with a random matrix
INDArray	project(INDArray data, INDArray result)	Create a copy random projection by using matrix product with a random matrix
INDArray	projecti(INDArray data)	Create an in-place random projection by using in-place matrix product with a random matrix
INDArray	projecti(INDArray data, INDArray result)	Create an in-place random projection by using in-place matrix product with a random matrix
static long[]	targetShape(INDArray X, double eps)	Compute the target shape of a suitable projection matrix
protected static long[]	targetShape(INDArray X, int targetDimension)	Compute the target shape of a suitable projection matrix

Methods inherited from class java.lang.Object

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

Constructor Detail

RandomProjection

public RandomProjection(double eps,
                        Random rng)

RandomProjection

public RandomProjection(double eps)

RandomProjection

public RandomProjection(int components,
                        Random rng)

RandomProjection

public RandomProjection(int components)

Method Detail

johnsonLindenstraussMinDim

public static List<Integer> johnsonLindenstraussMinDim(int[] n,
                                                       double... eps)

Find a safe number of components to project this to, through the Johnson-Lindenstrauss lemma The minimum number n' of components to guarantee the eps-embedding is given by: n' >= 4 log(n) / (eps² / 2 - eps³ / 3) see http://cseweb.ucsd.edu/~dasgupta/papers/jl.pdf §2.1

Parameters:

n - Number of samples. If an array is given, it will compute a safe number of components array-wise.

eps - Maximum distortion rate as defined by the Johnson-Lindenstrauss lemma. Will compute array-wise if an array is given.

Returns:

johnsonLindenstraussMinDim

public static List<Long> johnsonLindenstraussMinDim(long[] n,
                                                    double... eps)

johnsonLindenStraussMinDim

public static List<Integer> johnsonLindenStraussMinDim(int n,
                                                       double... eps)

johnsonLindenStraussMinDim

public static List<Long> johnsonLindenStraussMinDim(long n,
                                                    double... eps)

targetShape

public static long[] targetShape(INDArray X,
                                 double eps)

Compute the target shape of a suitable projection matrix

Parameters:

X - the Data tensor

eps - the relative error used in the Johnson-Lindenstrauss estimation

Returns:

the shape of the projection matrix to use

targetShape

protected static long[] targetShape(INDArray X,
                                    int targetDimension)

Compute the target shape of a suitable projection matrix

Parameters:

X - the Data Tensor

targetDimension - a desired dimension

Returns:

the shape of the projection matrix to use

project

public INDArray project(INDArray data)

Create a copy random projection by using matrix product with a random matrix

Parameters:

data -

Returns:

the projected matrix

project

public INDArray project(INDArray data,
                        INDArray result)

Create a copy random projection by using matrix product with a random matrix

Parameters:

data -

result - a placeholder result

Returns:

projecti

public INDArray projecti(INDArray data)

Create an in-place random projection by using in-place matrix product with a random matrix

Parameters:

data -

Returns:

the projected matrix

projecti

public INDArray projecti(INDArray data,
                         INDArray result)

Create an in-place random projection by using in-place matrix product with a random matrix

Parameters:

data -

result - a placeholder result

Returns: