es.uam.eps.ir.relison.diffusion.selections.CountSelectionMechanism Maven / Gradle / Ivy
/*
* Copyright (C) 2020 Information Retrieval Group at Universidad Autónoma
* de Madrid, http://ir.ii.uam.es
*
* This Source Code Form is subject to the terms of the Mozilla Public
* License, v. 2.0. If a copy of the MPL was not distributed with this
* file, You can obtain one at http://mozilla.org/MPL/2.0/.
*/
package es.uam.eps.ir.relison.diffusion.selections;
import es.uam.eps.ir.relison.diffusion.data.Data;
import es.uam.eps.ir.relison.diffusion.data.PropagatedInformation;
import es.uam.eps.ir.relison.diffusion.simulation.SimulationState;
import es.uam.eps.ir.relison.diffusion.simulation.UserState;
import java.io.Serializable;
import java.util.*;
import java.util.stream.Collectors;
/**
* Selects the propagated pieces. This algorithm propagates a certain number of information pieces each
* iteration, chosen randomly from the own pieces of information and the received ones.
*
* @author Javier Sanz-Cruzado ([email protected])
* @author Pablo Castells ([email protected])
*
* @param type of the users.
* @param type of the information.
* @param type of the parameters.
*/
public class CountSelectionMechanism extends AbstractSelectionMechanism
{
/**
* Number of own information pieces to propagate for each user and iteration.
*/
private final int numOwn;
/**
* Number of received information to propagate for each user and iteration.
*/
private final int numPropagate;
/**
* Number of information pieces to repropagate for each user and iteration.
*/
private final int numRepropagate;
/**
* Random number generator
*/
protected final Random rng;
/**
* Constructor.
* @param numOwn number of own information pieces to propagate for each user and iteration.
* @param numPropagate number of received information to propagate for each user and iteration.
* @param numRepropagate number of information pieces to repropagate for each user and iteration.
*/
public CountSelectionMechanism(int numOwn, int numPropagate, int numRepropagate)
{
this.numOwn = numOwn;
this.numPropagate = numPropagate;
this.numRepropagate = numRepropagate;
this.rng = new Random();
}
/**
* Constructor.
* @param numOwn number of own information pieces to propagate for each user and iteration.
* @param numPropagate number of received information to propagate for each user and iteration.
*/
public CountSelectionMechanism(int numOwn, int numPropagate)
{
this(numOwn, numPropagate, SelectionConstants.NONE);
}
@Override
protected List getOwnInformation(UserState user, Data data, SimulationState state, int numIter, Long timestamp)
{
List ownInfo = user.getOwnInformation().collect(Collectors.toCollection(ArrayList::new));
int userId = data.getUserIndex().object2idx(user.getUserId());
return this.getPropagatedInformation(userId, this.numOwn, numIter, ownInfo);
}
@Override
protected List getReceivedInformation(UserState user, Data data, SimulationState state, int numIter, Long timestamp)
{
List recInfo = user.getReceivedInformation().collect(Collectors.toCollection(ArrayList::new));
int userId = data.getUserIndex().object2idx(user.getUserId());
return this.getPropagatedInformation(userId, this.numPropagate, numIter, recInfo);
}
@Override
protected List getRepropagatedInformation(UserState user, Data data, SimulationState state, int numIter, Long timestamp)
{
List repInfo = user.getPropagatedInformation().collect(Collectors.toCollection(ArrayList::new));
int userId = data.getUserIndex().object2idx(user.getUserId());
return this.getPropagatedInformation(userId, this.numRepropagate, numIter, repInfo);
}
/**
* Obtains a subset of the information pieces in a list to repropagate.
* @param userId the identifier of the user.
* @param count the (maximum) number of information pieces to retrieve from that list.
* @param numIter the current iteration number.
* @param propInfo the list of propagated information we want to obtain some pieces from.
* @return the list of information pieces to propagate.
*/
protected List getPropagatedInformation(int userId, int count, int numIter, List propInfo)
{
List propagatedPieces = new ArrayList<>();
Set setInfo = new HashSet<>();
int size = propInfo.size();
// If we have a number of pieces that we want to retrieve:
if(count != SelectionConstants.NONE)
{
if(count == SelectionConstants.ALL || size <= count) // If there are not enough pieces, we add them all.
{
propInfo.forEach(info -> propagatedPieces.add(new PropagatedInformation(info.getInfoId(), numIter, userId)));
}
else // We select a subset at random:
{
while(setInfo.size() < this.numOwn)
{
setInfo.add(propInfo.get(rng.nextInt(size)).getInfoId());
}
setInfo.forEach(idx -> propagatedPieces.add(new PropagatedInformation(idx, numIter, userId)));
}
}
return propagatedPieces;
}
/**
* Gets the number of own information pieces to propagate.
* @return the number of own information pieces to propagate
*/
public int getNumOwn()
{
return numOwn;
}
/**
* Gets the number of received information pieces to propagate.
* @return the number of received information pieces to propagate.
*/
public int getNumReceived()
{
return numPropagate;
}
/**
* Gets the number of propagated information pieces to repropagate.
* @return the number of propagated information pieces to repropagate.
*/
public int getNumRepropagate()
{
return numRepropagate;
}
}