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A single jar with all the Cognitive Foundry components.
/*
* File: DefaultDataDistribution.java
* Authors: Justin Basilico
* Company: Sandia National Laboratories
* Project: Incremental Learning Core
*
* Copyright June 15, 2011, Sandia Corporation.
* Under the terms of Contract DE-AC04-94AL85000, there is a non-exclusive
* license for use of this work by or on behalf of the U.S. Government. Export
* of this program may require a license from the United States Government.
*
*/
package gov.sandia.cognition.statistics.distribution;
import gov.sandia.cognition.factory.Factory;
import gov.sandia.cognition.learning.algorithm.AbstractBatchAndIncrementalLearner;
import gov.sandia.cognition.math.MutableDouble;
import gov.sandia.cognition.statistics.AbstractDataDistribution;
import gov.sandia.cognition.statistics.DataDistribution;
import gov.sandia.cognition.statistics.DistributionEstimator;
import gov.sandia.cognition.statistics.DistributionWeightedEstimator;
import gov.sandia.cognition.util.AbstractCloneableSerializable;
import gov.sandia.cognition.util.ArgumentChecker;
import gov.sandia.cognition.util.WeightedValue;
import java.util.LinkedHashMap;
import java.util.Map;
/**
* A default implementation of {@code ScalarDataDistribution} that uses a
* backing map.
*
* @param
* Type of Key in the distribution
* @author Justin Basilico
* @since 3.1.2
*/
public class DefaultDataDistribution
extends AbstractDataDistribution
{
/**
* Default initial capacity, {@value}.
*/
public static final int DEFAULT_INITIAL_CAPACITY = 10;
/**
* Total of the counts in the distribution
*/
protected double total;
/**
* Default constructor
*/
public DefaultDataDistribution()
{
this( DEFAULT_INITIAL_CAPACITY );
}
/**
* Creates a new instance of DefaultDataDistribution
* @param initialCapacity
* Initial capacity of the Map
*/
public DefaultDataDistribution(
int initialCapacity)
{
this( new LinkedHashMap( initialCapacity), 0.0 );
}
/**
* Creates a new instance of DefaultDataDistribution
* @param other
* DataDistribution to copy
*/
public DefaultDataDistribution(
final DataDistribution other)
{
this(new LinkedHashMap(other.size()), 0.0);
this.incrementAll(other);
}
/**
* Creates a new instance of ScalarDataDistribution
* @param data
* Data to create the distribution
*/
public DefaultDataDistribution(
final Iterable data )
{
this();
this.incrementAll(data);
}
/**
* Creates a new instance of
* @param map
* Backing Map that stores the data
* @param total
* Sum of all values in the Map
*/
protected DefaultDataDistribution(
final Map map,
final double total)
{
super( map );
this.total = total;
}
@Override
public DefaultDataDistribution clone()
{
DefaultDataDistribution clone =
(DefaultDataDistribution) super.clone();
// We have to manually reset "total" because super.super.clone
// calls "incrementAll", which will, in turn, increment the total
// So we'd end up with twice the total.
clone.total = this.total;
return clone;
}
@Override
public double increment(
KeyType key,
final double value)
{
final MutableDouble entry = this.map.get(key);
double newValue = 0.0;
double delta;
if( entry == null )
{
if( value > 0.0 )
{
// It's best to avoid this.set() here as it could mess up
// our total tracker in some subclasses...
// Also it's more efficient this way (avoid another get)
this.map.put( key, new MutableDouble(value) );
newValue = value;
delta = value;
}
else
{
delta = 0.0;
}
}
else
{
if( entry.value+value >= 0.0 )
{
delta = value;
entry.value += value;
newValue = entry.value;
}
else
{
delta = -entry.value;
entry.value = 0.0;
}
}
this.total += delta;
return newValue;
}
@Override
public void set(
final KeyType key,
final double value)
{
// I decided not to call super.set because it would result in me
// having to perform an extra call to this.map.get
final MutableDouble entry = this.map.get(key);
if( entry == null )
{
// Only need to allocate if it's not null
if( value > 0.0 )
{
this.map.put( key, new MutableDouble( value ) );
this.total += value;
}
}
else if( value > 0.0 )
{
this.total += value - entry.value;
entry.value = value;
}
else
{
this.total -= entry.value;
entry.value = 0.0;
}
}
@Override
public double getTotal()
{
return this.total;
}
@Override
public void clear()
{
super.clear();
this.total = 0.0;
}
@Override
public DistributionEstimator> getEstimator()
{
return new DefaultDataDistribution.Estimator();
}
@Override
public DataDistribution.PMF getProbabilityFunction()
{
return new DefaultDataDistribution.PMF(this);
}
/**
* Gets the average value of all keys in the distribution, that is, the
* total value divided by the number of keys (even zero-value keys)
* @return
* Average value of all keys in the distribution
*/
public double getMeanValue()
{
final int ds = this.getDomainSize();
if( ds > 0 )
{
return this.getTotal() / ds;
}
else
{
return 0.0;
}
}
/**
* PMF of the DefaultDataDistribution
* @param
* Type of Key in the distribution
*/
public static class PMF
extends DefaultDataDistribution
implements DataDistribution.PMF
{
/**
* Default constructor
*/
public PMF()
{
super();
}
/**
* Copy constructor
* @param other
* ScalarDataDistribution to copy
*/
public PMF(
final DataDistribution other)
{
super(other);
}
/**
* Creates a new instance of DefaultDataDistribution
* @param initialCapacity
* Initial capacity of the Map
*/
public PMF(
int initialCapacity)
{
super( initialCapacity );
}
/**
* Creates a new instance of ScalarDataDistribution
* @param data
* Data to create the distribution
*/
public PMF(
final Iterable data )
{
super( data );
}
@Override
public double logEvaluate(
KeyType input)
{
return this.getLogFraction(input);
}
@Override
public Double evaluate(
KeyType input)
{
return this.getFraction(input);
}
@Override
public DefaultDataDistribution.PMF getProbabilityFunction()
{
return this;
}
}
/**
* Estimator for a DefaultDataDistribution
* @param
* Type of Key in the distribution
*/
public static class Estimator
extends AbstractBatchAndIncrementalLearner>
implements DistributionEstimator>
{
/**
* Default constructor
*/
public Estimator()
{
super();
}
@Override
public DefaultDataDistribution.PMF createInitialLearnedObject()
{
return new DefaultDataDistribution.PMF();
}
@Override
public void update(
final DefaultDataDistribution.PMF target,
final KeyType data)
{
target.increment(data, 1.0);
}
}
/**
* A weighted estimator for a DefaultDataDistribution
* @param
* Type of Key in the distribution
*/
public static class WeightedEstimator
extends AbstractBatchAndIncrementalLearner, DefaultDataDistribution.PMF>
implements DistributionWeightedEstimator>
{
/**
* Default constructor
*/
public WeightedEstimator()
{
super();
}
@Override
public DefaultDataDistribution.PMF createInitialLearnedObject()
{
return new DefaultDataDistribution.PMF();
}
@Override
public void update(
final DefaultDataDistribution.PMF target,
final WeightedValue data)
{
target.increment( data.getValue(), data.getWeight() );
}
}
/**
* A factory for {@code DefaultDataDistribution} objects using some given
* initial capacity for them.
*
* @param
* The type of data for the factory.
*/
public static class DefaultFactory
extends AbstractCloneableSerializable
implements Factory>
{
/** The initial domain capacity. */
protected int initialDomainCapacity;
/**
* Creates a new {@code DefaultFactory} with a default
* initial domain capacity.
*/
public DefaultFactory()
{
this(DEFAULT_INITIAL_CAPACITY);
}
/**
* Creates a new {@code DefaultFactory} with a given
* initial domain capacity.
*
* @param initialDomainCapacity
* The initial capacity for the domain. Must be positive.
*/
public DefaultFactory(
final int initialDomainCapacity)
{
super();
this.setInitialDomainCapacity(initialDomainCapacity);
}
@Override
public DefaultDataDistribution create()
{
// Create the histogram.
return new DefaultDataDistribution(
this.getInitialDomainCapacity());
}
/**
* Gets the initial domain capacity.
*
* @return
* The initial domain capacity. Must be positive.
*/
public int getInitialDomainCapacity()
{
return this.initialDomainCapacity;
}
/**
* Sets the initial domain capacity.
*
* @param initialDomainCapacity
* The initial domain capacity. Must be positive.
*/
public void setInitialDomainCapacity(
final int initialDomainCapacity)
{
ArgumentChecker.assertIsPositive("initialDomainCapacity",
initialDomainCapacity);
this.initialDomainCapacity = initialDomainCapacity;
}
}
}