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package de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections;
import java.util.Arrays;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2015
Ludwig-Maximilians-Universität München
Lehr- und Forschungseinheit für Datenbanksysteme
ELKI Development Team
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
import java.util.Random;
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.data.SparseNumberVector;
import de.lmu.ifi.dbs.elki.math.MathUtil;
import de.lmu.ifi.dbs.elki.math.random.RandomFactory;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.RandomParameter;
/**
* Abstract base class for random projection families.
*
* @author Erich Schubert
*/
public abstract class AbstractRandomProjectionFamily implements RandomProjectionFamily {
/**
* Random generator.
*/
protected Random random;
/**
* Constructor.
*/
public AbstractRandomProjectionFamily(RandomFactory random) {
super();
this.random = random.getSingleThreadedRandom();
}
/**
* Class to project using a matrix multiplication. This class is optimized for
* dense vector multiplications. In other words, the row dimensionality is the
* output dimensionality, the column dimensionality is the input
* dimensionality.
*
* It is <b>not thread safe</b> because it uses an internal buffer to store a
* local copy of the vector.
*
* @author Erich Schubert
*/
public static class MatrixProjection implements Projection {
/**
* Projection matrix.
*/
double[][] matrix;
/**
* Shared buffer to use during projections.
*/
private double[] buf;
/**
* Constructor.
*
* @param matrix Projection matrix ([output dim][input dim]).
*/
public MatrixProjection(double[][] matrix) {
super();
this.matrix = matrix;
this.buf = new double[matrix.length];
}
@Override
public double[] project(NumberVector in) {
return project(in, new double[matrix.length]);
}
@Override
public double[] project(NumberVector in, double[] ret) {
if(in instanceof SparseNumberVector) {
return projectSparse((SparseNumberVector) in, ret);
}
final int dim = MathUtil.min(buf.length, in.getDimensionality());
assert (ret.length >= matrix.length) : "Output buffer too small!";
// Copy vector into local buffer
for(int i = 0; i < dim; i++) {
buf[i] = in.doubleValue(i);
}
// Iterate over output dimensions:
for(int o = 0; o < matrix.length; o++) {
final double[] row = matrix[o];
double v = 0.;
for(int i = 0; i < dim; i++) {
v += row[i] * buf[i]; // Rows and input are aligned.
}
ret[o] = v;
}
// Fill excess dimensions.
for(int d = matrix.length; d < ret.length; d++) {
ret[d] = 0;
}
return ret;
}
/**
* Project, exploiting sparsity; but the transposed matrix layout would have
* been better. For projections where you expect sparse input, consider the
* opposite.
*
* @param in Input vector
* @param ret Projection buffer
* @return Projected data.
*/
private double[] projectSparse(SparseNumberVector in, double[] ret) {
Arrays.fill(ret, 0);
for(int iter = in.iter(); in.iterValid(iter); iter = in.iterAdvance(iter)) {
final int i = in.iterDim(iter);
final double val = in.iterDoubleValue(iter);
for(int o = 0; o < ret.length; o++) {
ret[o] += val * matrix[o][i]; // Not aligned.
}
}
return ret;
}
@Override
public int getOutputDimensionality() {
return matrix.length;
}
}
/**
* Parameterization interface (with the shared parameters)
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public abstract static class Parameterizer extends AbstractParameterizer {
/**
* Parameter for the random generator.
*/
public static final OptionID RANDOM_ID = new OptionID("randomproj.random", "Random generator seed.");
/**
* Random generator.
*/
protected RandomFactory random;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
RandomParameter rndP = new RandomParameter(RANDOM_ID);
if(config.grab(rndP)) {
random = rndP.getValue();
}
}
}
}
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