org.carrot2.matrix.factorization.IterativeMatrixFactorizationBase Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of carrot2-mini Show documentation
Show all versions of carrot2-mini Show documentation
Carrot2 search results clustering framework. Minimal functional subset
(core algorithms and infrastructure, no document sources).
/*
* Carrot2 project.
*
* Copyright (C) 2002-2019, Dawid Weiss, Stanisław Osiński.
* All rights reserved.
*
* Refer to the full license file "carrot2.LICENSE"
* in the root folder of the repository checkout or at:
* http://www.carrot2.org/carrot2.LICENSE
*/
package org.carrot2.matrix.factorization;
import org.carrot2.mahout.math.function.Functions;
import org.carrot2.mahout.math.matrix.DoubleMatrix2D;
import org.carrot2.matrix.MatrixUtils;
import org.carrot2.matrix.factorization.seeding.ISeedingStrategy;
import org.carrot2.matrix.factorization.seeding.RandomSeedingStrategy;
import com.carrotsearch.hppc.sorting.IndirectComparator;
/**
* Base functionality for {@link IIterativeMatrixFactorization}s.
*/
abstract class IterativeMatrixFactorizationBase extends MatrixFactorizationBase implements
IIterativeMatrixFactorization
{
/** The desired number of base vectors */
protected int k;
protected static int DEFAULT_K = 15;
/** The maximum number of iterations the algorithm is allowed to run */
protected int maxIterations;
protected static final int DEFAULT_MAX_ITERATIONS = 15;
/**
* If the percentage decrease in approximation error becomes smaller than
* stopThreshold, the algorithm will stop. Note: calculation of
* approximation error is quite costly. Setting the threshold to -1 turns off
* approximation error calculation and hence makes the algorithm do the maximum number
* of iterations.
*/
protected double stopThreshold;
protected static double DEFAULT_STOP_THRESHOLD = -1.0;
/** Seeding strategy */
protected ISeedingStrategy seedingStrategy;
protected static final ISeedingStrategy DEFAULT_SEEDING_STRATEGY = new RandomSeedingStrategy(
0);
/** Order base vectors according to their 'activity'? */
protected boolean ordered;
protected static final boolean DEFAULT_ORDERED = false;
/** Current approximation error */
protected double approximationError;
/** Approximation errors during subsequent iterations */
protected double [] approximationErrors;
/** Iteration counter */
protected int iterationsCompleted;
/** Sorting aggregates */
protected double [] aggregates;
/**
*/
public IterativeMatrixFactorizationBase(DoubleMatrix2D A)
{
super(A);
this.k = DEFAULT_K;
this.maxIterations = DEFAULT_MAX_ITERATIONS;
this.stopThreshold = DEFAULT_STOP_THRESHOLD;
this.seedingStrategy = DEFAULT_SEEDING_STRATEGY;
this.ordered = DEFAULT_ORDERED;
this.approximationErrors = null;
this.approximationError = -1;
this.iterationsCompleted = 0;
}
/**
* Sets the number of base vectors k .
*
* @param k the number of base vectors
*/
public void setK(int k)
{
this.k = k;
}
/**
* Returns the number of base vectors k .
*/
public int getK()
{
return k;
}
/**
* @return true if the decrease in the approximation error is smaller than the
* stopThreshold
*/
protected boolean updateApproximationError()
{
if (approximationErrors == null)
{
approximationErrors = new double [maxIterations + 1];
}
// Approximation error
double newApproximationError = MatrixUtils.frobeniusNorm(U.zMult(V, null, 1, 0,
false, true).assign(A, Functions.MINUS));
approximationErrors[iterationsCompleted] = newApproximationError;
if ((approximationError - newApproximationError) / approximationError < stopThreshold)
{
approximationError = newApproximationError;
return true;
}
else
{
approximationError = newApproximationError;
return false;
}
}
/**
* Orders U and V matrices according to the 'activity' of base vectors.
*/
protected void order()
{
DoubleMatrix2D VT = V.viewDice();
aggregates = new double [VT.rows()];
for (int i = 0; i < aggregates.length; i++)
{
aggregates[i] = VT.viewRow(i).aggregate(Functions.PLUS, Functions.SQUARE);
}
final IndirectComparator.DescendingDoubleComparator comparator = new IndirectComparator.DescendingDoubleComparator(
aggregates);
V = MatrixUtils.sortedRowsView(VT, comparator).viewDice();
U = MatrixUtils.sortedRowsView(U.viewDice(), comparator).viewDice();
}
/**
* Returns current {@link ISeedingStrategy}.
*/
public ISeedingStrategy getSeedingStrategy()
{
return seedingStrategy;
}
/**
* Sets new {@link ISeedingStrategy}.
*/
public void setSeedingStrategy(ISeedingStrategy seedingStrategy)
{
this.seedingStrategy = seedingStrategy;
}
/**
* Returns the maximum number of iterations the algorithm is allowed to run.
*/
public int getMaxIterations()
{
return maxIterations;
}
/**
* Sets the maximum number of iterations the algorithm is allowed to run.
*/
public void setMaxIterations(int maxIterations)
{
this.maxIterations = maxIterations;
}
/**
* Returns the algorithms stopThreshold. If the percentage decrease in
* approximation error becomes smaller than stopThreshold, the algorithm
* will stop.
*/
public double getStopThreshold()
{
return stopThreshold;
}
/**
* Sets the algorithms stopThreshold. If the percentage decrease in
* approximation error becomes smaller than stopThreshold, the algorithm
* will stop.
*
* Note: calculation of approximation error is quite costly. Setting the threshold to
* -1 turns off calculation of the approximation error and hence makes the algorithm
* do the maximum allowed number of iterations.
*/
public void setStopThreshold(double stopThreshold)
{
this.stopThreshold = stopThreshold;
}
/**
*/
public double getApproximationError()
{
return approximationError;
}
/**
*/
public double [] getApproximationErrors()
{
return approximationErrors;
}
public int getIterationsCompleted()
{
return iterationsCompleted;
}
/**
* Returns true when the factorization is set to generate an ordered
* basis.
*/
public boolean isOrdered()
{
return ordered;
}
/**
* Set to true to generate an ordered basis.
*/
public void setOrdered(boolean ordered)
{
this.ordered = ordered;
}
/**
* Returns column aggregates for a sorted factorization, and null for an
* unsorted factorization.
*/
public double [] getAggregates()
{
return aggregates;
}
}