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A single jar with all the Cognitive Foundry components.
/*
* File: SimplePatternRecognizer.java
* Authors: Justin Basilico
* Company: Sandia National Laboratories
* Project: Cognitive Framework Lite
*
* Copyright March 14, 2006, 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. See CopyrightHistory.txt for
* complete details.
*
*
*/
package gov.sandia.cognition.framework.lite;
import gov.sandia.cognition.framework.CognitiveModuleState;
import gov.sandia.cognition.framework.DefaultSemanticNetwork;
import gov.sandia.cognition.framework.SemanticLabel;
import gov.sandia.cognition.framework.SemanticNetwork;
import gov.sandia.cognition.math.matrix.VectorFactory;
import gov.sandia.cognition.math.matrix.Matrix;
import gov.sandia.cognition.math.matrix.MatrixFactory;
import gov.sandia.cognition.math.matrix.Vector;
import gov.sandia.cognition.util.AbstractCloneableSerializable;
import gov.sandia.cognition.util.ObjectUtil;
import java.util.ArrayList;
import java.util.Collection;
import java.util.HashMap;
import java.util.List;
/**
* The SimplePatternRecognizer class implements a simple version of the
* PatternRecognizerLite interface. All it does is use a matrix multiply to
* come up with the next set of values.
*
* This implementation is for demonstration purposes only and is in no way
* psychologically plausible.
*
* @author Justin Basilico
* @since 1.0
*/
public class SimplePatternRecognizer
extends AbstractCloneableSerializable
implements MutablePatternRecognizerLite
{
// Note: This class does not make use of the getters pattern because it
// is expected to be high-performance and instead uses direct access to
// the member variables.
/** The nodes in the recognition network, sorted by how they are to appear
* in the vector. */
private ArrayList nodes = null;
/** The mapping of semantic labels to their index in the vector. */
private HashMap nodeToIDMap = null;
/** The matrix underlying the pattern recognizer. */
private Matrix matrix = null;
/** True if the set of nodes has changed since the last update. */
private boolean nodesChangedSinceLastUpdate = false;
/**
* Creates a new, empty instance of SimplePatternRecognizer.
*/
public SimplePatternRecognizer()
{
this(new DefaultSemanticNetwork());
}
/**
* Creates a new instance of SimplePatternRecognizer.
*
* @param network The network to create the pattern recognizer from.
*/
public SimplePatternRecognizer(
SemanticNetwork network)
{
super();
// First get the nodes out of the given network.
this.setNodes(new ArrayList(network.getNodes()));
this.buildNodeToIDMap();
// Create the matrix corresponding to the network connections.
int numNodes = this.nodes.size();
Matrix weights = MatrixFactory.getDefault().createMatrix(numNodes, numNodes);
for ( int i = 0; i < numNodes; i++)
{
SemanticLabel labelI = nodes.get(i);
// Get the outgoing links for the i-th label.
for ( SemanticLabel labelJ : network.getOutLinks(labelI) )
{
// Get the link to j.
int j = nodeToIDMap.get(labelJ);
double weight = network.getAssociation(labelI, labelJ);
if ( weight == 0.0 )
{
// The weight is zero so do not add it.
continue;
}
// Set the matrix entry.
weights.setElement(j, i, weight);
}
}
this.setMatrix(weights);
this.setNodesChangedSinceLastUpdate(false);
}
/**
* Creates a new instance of SimplePatternRecognizer.
*
* @param other The SimplePatternRecognizer to copy.
*/
public SimplePatternRecognizer(
SimplePatternRecognizer other)
{
super();
this.setNodes(new ArrayList(other.nodes));
this.setNodeToIDMap(
new HashMap(other.nodeToIDMap));
this.setMatrix(other.matrix.clone());
this.setNodesChangedSinceLastUpdate(other.nodesChangedSinceLastUpdate);
}
/**
* {@inheritDoc}
*
* @return {@inheritDoc}
*/
@Override
public SimplePatternRecognizer clone()
{
final SimplePatternRecognizer clone = (SimplePatternRecognizer)
super.clone();
clone.nodes = new ArrayList(this.nodes);
clone.nodeToIDMap = new HashMap(this.nodeToIDMap);
clone.matrix = ObjectUtil.cloneSafe(this.matrix);
return clone;
}
/**
* {@inheritDoc}
*
* @return {@inheritDoc}
*/
public SimplePatternRecognizerState initialState()
{
return new SimplePatternRecognizerState(this.nodes);
}
/**
* {@inheritDoc}
*
* @param state {@inheritDoc}
* @param inputs {@inheritDoc}
* @return {@inheritDoc}
*/
public Vector recognize(
CognitiveModuleState state,
Vector inputs)
{
if ( state == null || !(state instanceof SimplePatternRecognizerState) )
{
throw new IllegalArgumentException("Invalid state.");
}
SimplePatternRecognizerState recognizerState =
(SimplePatternRecognizerState) state;
Vector stateVector = recognizerState.getStateVector();
// If we've changed the underlying network, or the size of the
// state vector has changed, then we need to rebuild the map
// and create a new state vector.
if ( this.nodesChangedSinceLastUpdate ||
stateVector.getDimensionality() != this.getInputDimensionality() )
{
this.nodesChangedSinceLastUpdate = false;
Vector oldState = stateVector;
Vector newState = this.createEmptyInputVector();
stateVector = newState;
List oldLabels = recognizerState.getLabels();
int numOldLabels = oldLabels.size();
for (int oldIndex = 0; oldIndex < numOldLabels; oldIndex++)
{
int newIndex = this.getIndex(oldLabels.get(oldIndex));
if ( newIndex >= 0 )
{
double value = oldState.getElement(oldIndex);
newState.setElement(newIndex, value);
}
}
recognizerState.setLabels(this.nodes);
recognizerState.setStateVector(newState);
}
// Let the recognition equation be:
// x(n+1) = A*x(n) + u(n)
// Note that this is a dynamical system with an identity control-gain
// matrix (feedforward), where the inputs are the same dimension are the
// state.
Vector result = this.matrix.times(stateVector);
result.plusEquals(inputs);
recognizerState.setStateVector(result);
return result;
}
/**
* {@inheritDoc}
*
* @param label {@inheritDoc}
*/
public void addNode(
SemanticLabel label)
{
int index = this.getIndex(label);
if ( index >= 0 )
{
return;
}
index = this.nodes.size();
this.nodes.add(label);
this.nodeToIDMap.put(label, index);
int numNodes = index + 1;
Matrix newMatrix = MatrixFactory.getDefault().createMatrix(numNodes, numNodes);
newMatrix.setSubMatrix(0, 0, this.matrix);
this.setMatrix(newMatrix);
this.setNodesChangedSinceLastUpdate(true);
}
/**
* {@inheritDoc}
*
* @param label {@inheritDoc}
*/
public void removeNode(
SemanticLabel label)
{
// Get the index of the label.
int index = this.getIndex(label);
if ( index < 0 )
{
// The node is not in the recognizer, so there is nothing to do.
return;
}
// Remove the node and rebuild the ID map.
int oldNumNodes = this.nodes.size();
this.nodes.remove(index);
this.buildNodeToIDMap();
// Remove the row and column for the node from the matrix.
int newNumNodes = oldNumNodes - 1;
Matrix oldMatrix = this.matrix;
Matrix newMatrix =
MatrixFactory.getDefault().createMatrix(newNumNodes, newNumNodes);
this.matrix = newMatrix;
// Copy over the matrix values.
for (int oldI = 0; oldI < oldNumNodes; oldI++)
{
int newI = oldI;
if ( oldI == index )
{
// This is the row we ignore.
continue;
}
else if ( oldI > index )
{
newI = oldI - 1;
}
for (int oldJ = 0; oldJ < oldNumNodes; oldJ++)
{
int newJ = oldJ;
if ( oldJ == index )
{
// This is the column we ignore.
continue;
}
else if ( oldJ > index )
{
newJ = oldJ - 1;
}
newMatrix.setElement(newI, newJ,
oldMatrix.getElement(oldI, oldJ));
}
}
this.setNodesChangedSinceLastUpdate(true);
}
/**
* Gets the association between two nodes in the network.
*
* @param from The label the association is from
* @param to The label the association is to.
* @return The association weight between the two labels or zero if
* one of the labels is not in the recognizer.
*/
public double getAssociation(
SemanticLabel from,
SemanticLabel to)
{
int fromIndex = this.getIndex(from);
int toIndex = this.getIndex(to);
if ( fromIndex < 0 || toIndex < 0 )
{
// Error: Bad labels.
return 0.0;
}
else
{
// Return the value.
return this.matrix.getElement(toIndex, fromIndex);
}
}
/**
* {@inheritDoc}
*
* @param from {@inheritDoc}
* @param to {@inheritDoc}
* @param weight {@inheritDoc}
*/
public void setAssociation(
SemanticLabel from,
SemanticLabel to,
double weight)
{
int fromIndex = this.getIndex(from);
int toIndex = this.getIndex(to);
if ( fromIndex < 0 || toIndex < 0 )
{
// Error: Bad labels.
return;
}
this.matrix.setElement(toIndex, fromIndex, weight);
}
/**
* Gets the index in the vector of the given label.
*
* @param label The label to get the index of.
* @return The index assigned to the label or -1 if the label is
* not in the recognizer.
*/
public int getIndex(
SemanticLabel label)
{
Integer index = this.nodeToIDMap.get(label);
if ( index == null )
{
return -1;
}
else
{
return index;
}
}
/**
* {@inheritDoc}
*
* @param label {@inheritDoc}
* @return {@inheritDoc}
*/
public boolean isLabel(
SemanticLabel label)
{
return this.nodeToIDMap.containsKey(label);
}
/**
* {@inheritDoc}
*
* @param label {@inheritDoc}
* @return {@inheritDoc}
*/
public boolean isInputLabel(
SemanticLabel label)
{
return this.isLabel(label);
}
/**
* {@inheritDoc}
*
* @param label {@inheritDoc}
* @return {@inheritDoc}
*/
public boolean isOutputLabel(
SemanticLabel label)
{
return this.isLabel(label);
}
/**
* {@inheritDoc}
*
* @return {@inheritDoc}
*/
public Collection getAllLabels()
{
return this.nodes;
}
/**
* {@inheritDoc}
*
* @param label {@inheritDoc}
* @param inputLabel {@inheritDoc}
* @return {@inheritDoc}
*/
public boolean trySetInputLabel(
SemanticLabel label,
boolean inputLabel)
{
// Labels must always be input labels.
return inputLabel && this.isLabel(label);
}
/**
* {@inheritDoc}
*
* @param label {@inheritDoc}
* @param outputLabel {@inheritDoc}
* @return {@inheritDoc}
*/
public boolean trySetOutputLabel(
SemanticLabel label,
boolean outputLabel)
{
// Labels must always be output labels.
return outputLabel && this.isLabel(label);
}
/**
* Builds the map of nodes to identifiers from the list of nodes.
*/
protected void buildNodeToIDMap()
{
this.setNodeToIDMap(new HashMap());
int numNodes = this.nodes.size();
// Map the nodes to their index into the vector.
for ( int i = 0; i < numNodes; i++)
{
this.nodeToIDMap.put(this.nodes.get(i), i);
}
}
/**
* {@inheritDoc}
*
* @return {@inheritDoc}
*/
public Vector createEmptyInputVector()
{
return VectorFactory.getDefault().createVector(this.getInputDimensionality());
}
/**
* {@inheritDoc}
*
* @return {@inheritDoc}
*/
public int getInputDimensionality()
{
return this.nodes.size();
}
/**
* {@inheritDoc}
*
* @return {@inheritDoc}
*/
public int getOutputDimensionality()
{
return this.nodes.size();
}
/**
* {@inheritDoc}
*
* @return {@inheritDoc}
*/
public Collection getInputLabels()
{
return this.nodes;
}
/**
* {@inheritDoc}
*
* @return {@inheritDoc}
*/
public Collection getOutputLabels()
{
return this.nodes;
}
/**
* {@inheritDoc}
*
* @return {@inheritDoc}
*/
public SemanticNetwork getNetwork()
{
return new SimplePatternRecognizer.Network();
}
/**
* Sets the nodes in the recognizer.
*
* @param nodes The new list of nodes.
*/
protected void setNodes(
ArrayList nodes)
{
this.nodes = nodes;
}
/**
* Sets the mapping of nodes to their vector indices.
*
* @param nodeToIDMap The new mapping of nodes to vector indices.
*/
protected void setNodeToIDMap(
HashMap nodeToIDMap)
{
this.nodeToIDMap = nodeToIDMap;
}
/**
* Sets the underlying matrix.
*
* @param matrix The new underlying matrix.
*/
protected void setMatrix(
Matrix matrix)
{
this.matrix = matrix;
}
/**
* Sets whether or not the nodes have changed since the last update.
*
* @param nodesChangedSinceLastUpdate Whether or not the nodes have
* changed since the last update.
*/
private void setNodesChangedSinceLastUpdate(
boolean nodesChangedSinceLastUpdate)
{
this.nodesChangedSinceLastUpdate = nodesChangedSinceLastUpdate;
}
/**
* The Network inner class is an implementation of the
* SemanticNetwork interface that pulls its data off of the
* SimplePatternRecognizer.
*
* @author Justin Basilico
* @since 1.0
*/
private class Network
extends java.lang.Object
implements SemanticNetwork
{
/**
* Creates a new instance of Network.
*/
private Network()
{
super();
}
/**
* {@inheritDoc}
*
* @return {@inheritDoc}
*/
public int getNumNodes()
{
return nodes.size();
}
/**
* {@inheritDoc}
*
* @param label {@inheritDoc}
* @return {@inheritDoc}
*/
public boolean isNode(
SemanticLabel label)
{
return nodes.contains(label);
}
/**
* {@inheritDoc}
*
* @return {@inheritDoc}
*/
public Collection getNodes()
{
return nodes;
}
/**
* {@inheritDoc}
*
* @param nodeLabel {@inheritDoc}
* @return {@inheritDoc}
*/
public Collection getOutLinks(
SemanticLabel nodeLabel)
{
// First see if the node is in the network at all.
Integer nodeIDInteger = nodeToIDMap.get(nodeLabel);
if ( nodeIDInteger == null )
{
// Not in the network.
return null;
}
// We are going to return a list of the labels that the node is
// connected to.
ArrayList result = new ArrayList();
// Convert the node ID to an integer.
int nodeID = nodeIDInteger.intValue();
// TO DO: Change this to make use of sparseness once it is implemented.
int numNodes = this.getNumNodes();
for (int j = 0; j < numNodes; j++)
{
// Get the connection to the other node.
double value = matrix.getElement(j, nodeID);
if ( value != 0.0 )
{
// The connection weight is
result.add(nodes.get(j));
}
}
return result;
}
/**
* {@inheritDoc}
*
* @param from {@inheritDoc}
* @param to {@inheritDoc}
* @return {@inheritDoc}
*/
public double getAssociation(
SemanticLabel from,
SemanticLabel to)
{
Integer fromID = nodeToIDMap.get(from);
Integer toID = nodeToIDMap.get(to);
if ( fromID == null || toID == null )
{
return 0.0;
}
else
{
return matrix.getElement(toID, fromID);
}
}
}
}