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package org.nd4j.linalg.dimensionalityreduction;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.random.impl.GaussianDistribution;
import org.nd4j.linalg.api.rng.Random;
import org.nd4j.linalg.exception.ND4JIllegalStateException;
import org.nd4j.linalg.factory.Nd4j;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
/**
* Created by huitseeker on 7/28/17.
*/
public class RandomProjection {
private int components;
private Random rng;
private double eps;
private boolean autoMode;
public RandomProjection(double eps, Random rng){
this.rng = rng;
this.eps = eps;
this.autoMode = true;
}
public RandomProjection(double eps){
this(eps, Nd4j.getRandom());
}
public RandomProjection(int components, Random rng){
this.rng = rng;
this.components = components;
this.autoMode = false;
}
public RandomProjection(int components){
this(components, Nd4j.getRandom());
}
/**
* 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
* @param n Number of samples. If an array is given, it will compute
* a safe number of components array-wise.
* @param eps Maximum distortion rate as defined by the Johnson-Lindenstrauss lemma.
* Will compute array-wise if an array is given.
* @return
*/
public static List johnsonLindenstraussMinDim(int[] n, double... eps){
Boolean basicCheck = n == null || n.length == 0 || eps == null || eps.length == 0;
if (basicCheck)
throw new IllegalArgumentException("Johnson-Lindenstrauss dimension estimation requires > 0 components and at least a relative error");
for (double epsilon: eps){
if (epsilon <= 0 || epsilon >= 1) {
throw new IllegalArgumentException("A relative error should be in ]0, 1[");
}
}
List res = new ArrayList(n.length * eps.length);
for (double epsilon : eps){
double denom = (Math.pow(epsilon, 2) / 2 - Math.pow(epsilon, 3) / 3);
for (int components: n){
res.add((int) (4 * Math.log(components) / denom));
}
}
return res;
}
public static List johnsonLindenStraussMinDim(int n, double... eps){
return johnsonLindenstraussMinDim(new int[]{n}, eps);
}
/**
* Generate a dense Gaussian random matrix.
*
* The n' components of the random matrix are drawn from
* N(0, 1.0 / n').
*
* @param shape
* @param rng
* @return
*/
private INDArray gaussianRandomMatrix(int[] shape, Random rng){
Nd4j.checkShapeValues(shape);
INDArray res = Nd4j.create(shape);
GaussianDistribution op1 = new GaussianDistribution(res, 0.0, 1.0 / Math.sqrt(shape[0]));
Nd4j.getExecutioner().exec(op1, rng);
return res;
}
private int[] projectionMatrixShape;
private INDArray _projectionMatrix;
private INDArray getProjectionMatrix(int[] shape, Random rng){
if (! Arrays.equals(projectionMatrixShape, shape) || _projectionMatrix == null)
_projectionMatrix = gaussianRandomMatrix(shape, rng);
return _projectionMatrix;
}
/**
*
* Compute the target shape of the projection matrix
* @param shape the shape of the data tensor
* @param eps the relative error used in the Johnson-Lindenstrauss estimation
* @param auto whether to use JL estimation for user specification
* @param targetDimension the target size for the
*
*/
private static int[] targetShape(int[] shape, double eps, int targetDimension, boolean auto){
int components = targetDimension;
if (auto) components = johnsonLindenStraussMinDim(shape[0], eps).get(0);
// JL or user spec edge cases
if (auto && (components <= 0 || components > shape[1])){
throw new ND4JIllegalStateException(String.format("Estimation led to a target dimension of %d, which is invalid", components));
}
return new int[]{ shape[1], components};
}
/**
* Compute the target shape of a suitable projection matrix
* @param X the Data tensor
* @param eps the relative error used in the Johnson-Lindenstrauss estimation
* @return the shape of the projection matrix to use
*/
protected static int[] targetShape(INDArray X, double eps) {
return targetShape(X.shape(), eps, -1, true);
}
/**
* Compute the target shape of a suitable projection matrix
* @param X the Data Tensor
* @param targetDimension a desired dimension
* @return the shape of the projection matrix to use
*/
protected static int[] targetShape(INDArray X, int targetDimension) {
return targetShape(X.shape(), -1, targetDimension, false);
}
/**
* Create a copy random projection by using matrix product with a random matrix
* @param data
* @return the projected matrix
*/
public INDArray project(INDArray data){
int[] tShape = targetShape(data.shape(), eps, components, autoMode);
return data.mmul(getProjectionMatrix(tShape, this.rng));
}
/**
* Create a copy random projection by using matrix product with a random matrix
*
* @param data
* @param result a placeholder result
* @return
*/
public INDArray project(INDArray data, INDArray result){
int[] tShape = targetShape(data.shape(), eps, components, autoMode);
return data.mmuli(getProjectionMatrix(tShape, this.rng), result);
}
/**
* Create an in-place random projection by using in-place matrix product with a random matrix
* @param data
* @return the projected matrix
*/
public INDArray projecti(INDArray data){
int[] tShape = targetShape(data.shape(), eps, components, autoMode);
return data.mmuli(getProjectionMatrix(tShape, this.rng));
}
/**
* Create an in-place random projection by using in-place matrix product with a random matrix
*
* @param data
* @param result a placeholder result
* @return
*/
public INDArray projecti(INDArray data, INDArray result){
int[] tShape = targetShape(data.shape(), eps, components, autoMode);
return data.mmuli(getProjectionMatrix(tShape, this.rng), result);
}
}
