pax_global_header 0000666 0000000 0000000 00000000064 13373742612 0014522 g ustar 00root root 0000000 0000000 52 comment=33b5452366a0126885517bd2f9e5ec9d0f86fb74
aika-java-0.24/ 0000775 0000000 0000000 00000000000 13373742612 0013273 5 ustar 00root root 0000000 0000000 aika-java-0.24/.gitignore 0000664 0000000 0000000 00000000324 13373742612 0015262 0 ustar 00root root 0000000 0000000 # Maven build files
target
*.log
maven-eclipse.xml
build.properties
site-content
*~
# IntelliJ IDEA files
.idea
.iws
*.iml
*.ipr
# Eclipse files
.settings
.classpath
.project
.externalToolBuilders
.checkstyle
aika-java-0.24/LICENSE.txt 0000664 0000000 0000000 00000026134 13373742612 0015124 0 ustar 00root root 0000000 0000000
Apache License
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http://www.apache.org/licenses/
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END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
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Copyright 2015-2017 Lukas Molzberger
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
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Unless required by applicable law or agreed to in writing, software
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aika-java-0.24/NOTICE.txt 0000664 0000000 0000000 00000000053 13373742612 0015013 0 ustar 00root root 0000000 0000000 AIKA
Copyright 2015-2017 Lukas Molzberger
aika-java-0.24/README.md 0000664 0000000 0000000 00000000707 13373742612 0014556 0 ustar 00root root 0000000 0000000 # aika
A good starting point to get familiar with this project are probably the following three test cases:
- SimplePatternMatchingTest (Demonstrates how a pattern like a word or a phrase can be matched)
- MutualExclusionTest (Demonstrates a negative feed back loop)
- NamedEntityRecognitionTest (A more complex example: recognize the name jackson cook)
- SuspensionTest (Demonstrates how neurons and logic nodes can be suspended to an external storage) aika-java-0.24/pom.xml 0000664 0000000 0000000 00000016776 13373742612 0014631 0 ustar 00root root 0000000 0000000 4.0.0org.aika-softwareaikajar0.24aikahttp://aika.networkAn artificial intelligence for knowledge acquisitionLukas MolzbergerLukas Molzbergercontact@aika-software.orgApache License, Version 2.0http://www.apache.org/licenses/LICENSE-2.0.txtrepohttps://github.com/aika-algorithm/aikascm:git:git://github.com/aika-algorithm/aika.gitscm:git:git@github.com:aika-algorithm/aika.gitaika-0.24ossrhhttps://oss.sonatype.org/content/repositories/snapshotsossrhhttps://oss.sonatype.org/service/local/staging/deploy/maven2/4.123.6.1org.apache.maven.pluginsmaven-compiler-plugin3.31.81.8utf-8org.apache.maven.pluginsmaven-surefire-plugin2.18.1always-Xmx8000m**/*Test.javaorg.apache.maven.pluginsmaven-javadoc-plugin2.10.3attach-javadocsjar-Xdoclint:noneorg.apache.maven.pluginsmaven-source-plugin2.2.1attach-sourcesjar-no-forkfalse../src/main/resources**/*false../src/test/resources**/*org.apache.commonscommons-math3${commons-math.version}org.slf4jslf4j-api1.7.25ch.qos.logbacklogback-core1.2.3testch.qos.logbacklogback-classic1.2.3testorg.slf4jjcl-over-slf4j1.7.25org.slf4jjul-to-slf4j1.7.25junitjunit${junit.version}testcommons-iocommons-io2.5testdeploy_artifactorg.apache.maven.pluginsmaven-gpg-plugin1.5sign-artifactsverifysignorg.sonatype.pluginsnexus-staging-maven-plugin1.6.7trueossrhhttps://oss.sonatype.org/true
aika-java-0.24/src/ 0000775 0000000 0000000 00000000000 13373742612 0014062 5 ustar 00root root 0000000 0000000 aika-java-0.24/src/main/ 0000775 0000000 0000000 00000000000 13373742612 0015006 5 ustar 00root root 0000000 0000000 aika-java-0.24/src/main/java/ 0000775 0000000 0000000 00000000000 13373742612 0015727 5 ustar 00root root 0000000 0000000 aika-java-0.24/src/main/java/network/ 0000775 0000000 0000000 00000000000 13373742612 0017420 5 ustar 00root root 0000000 0000000 aika-java-0.24/src/main/java/network/aika/ 0000775 0000000 0000000 00000000000 13373742612 0020325 5 ustar 00root root 0000000 0000000 aika-java-0.24/src/main/java/network/aika/AbstractNode.java 0000664 0000000 0000000 00000003603 13373742612 0023543 0 ustar 00root root 0000000 0000000 /*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package network.aika;
import network.aika.lattice.Node;
import network.aika.lattice.NodeActivation;
import network.aika.neuron.INeuron;
import network.aika.neuron.Neuron;
import java.io.DataInput;
import java.io.IOException;
/**
*
* @author Lukas Molzberger
*/
public abstract class AbstractNode
, A extends NodeActivation> implements Writable {
public volatile int lastUsedDocumentId = 0;
public volatile boolean modified;
public P provider;
/**
* Propagate an activation to the next node or the next neuron that is depending on the current node.
*
* @param act
*/
public abstract void propagate(A act);
public void setModified() {
modified = true;
}
public void suspend() {}
public void reactivate() {}
public static
AbstractNode read(DataInput in, P p) throws IOException {
AbstractNode n;
if(in.readBoolean()) {
n = INeuron.readNeuron(in, (Neuron) p);
} else {
n = Node.readNode(in, p);
}
return n;
}
}
aika-java-0.24/src/main/java/network/aika/ActivationFunction.java 0000664 0000000 0000000 00000001043 13373742612 0024775 0 ustar 00root root 0000000 0000000 package network.aika;
public enum ActivationFunction {
RECTIFIED_SCALED_LOGISTIC_SIGMOID(x -> Math.max(0.0, (2.0 * Utils.sigmoid(x)) - 1.0)),
RECTIFIED_HYPERBOLIC_TANGENT(x -> Math.max(0.0, Math.tanh(x))),
RECTIFIED_LINEAR_UNIT(x -> Math.max(0.0, x)),
LIMITED_RECTIFIED_LINEAR_UNIT(x -> Math.max(0.0, Math.min(1.0, x)));
Function f;
ActivationFunction(Function f) {
this.f = f;
}
public double f(double x) {
return f.f(x);
}
interface Function {
double f(double x);
}
}
aika-java-0.24/src/main/java/network/aika/Converter.java 0000664 0000000 0000000 00000026365 13373742612 0023153 0 ustar 00root root 0000000 0000000 /*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package network.aika;
import network.aika.lattice.AndNode;
import network.aika.lattice.Node;
import network.aika.lattice.OrNode;
import network.aika.neuron.INeuron;
import network.aika.neuron.Synapse;
import network.aika.neuron.relation.Relation;
import java.util.*;
/**
* Converts the synapse weights of a neuron into a boolean logic representation of this neuron.
*
* @author Lukas Molzberger
*/
public class Converter {
public static int MAX_AND_NODE_SIZE = 6;
public static Comparator SYNAPSE_COMP = (s1, s2) -> {
int r = Boolean.compare(
s2.linksAnyOutput() || s2.identity,
s1.linksAnyOutput() || s1.identity
);
if (r != 0) return r;
r = Double.compare(s2.weight, s1.weight);
if (r != 0) return r;
return Integer.compare(s1.id, s2.id);
};
private int threadId;
private INeuron neuron;
private Document doc;
private OrNode outputNode;
private Collection modifiedSynapses;
public static boolean convert(int threadId, Document doc, INeuron neuron, Collection modifiedSynapses) {
return new Converter(threadId, doc, neuron, modifiedSynapses).convert();
}
private Converter(int threadId, Document doc, INeuron neuron, Collection modifiedSynapses) {
this.doc = doc;
this.neuron = neuron;
this.threadId = threadId;
this.modifiedSynapses = modifiedSynapses;
}
private boolean convert() {
outputNode = neuron.node.get();
initInputNodesAndComputeWeightSums();
initSlotFlags();
if(neuron.biasSum + neuron.posDirSum + neuron.posRecSum <= 0.0) {
neuron.requiredSum = neuron.posDirSum + neuron.posRecSum;
outputNode.removeParents(threadId);
return false;
}
List candidates = prepareCandidates();
NodeContext nodeContext = null;
boolean noFurtherRefinement = false;
TreeSet reqSyns = new TreeSet<>(Synapse.INPUT_SYNAPSE_COMP);
double sum = 0.0;
neuron.requiredSum = 0.0;
if(neuron.numDisjunctiveSynapses == 0) {
double remainingSum = neuron.posDirSum;
int i = 0;
for (Synapse s : candidates) {
double v = s.getMaxInputValue();
final boolean isOptionalInput = sum + remainingSum - v + neuron.posRecSum + neuron.posPassiveSum + neuron.biasSum > 0.0;
final boolean maxAndNodesReached = i >= MAX_AND_NODE_SIZE;
if (isOptionalInput || maxAndNodesReached) {
break;
}
remainingSum -= v;
neuron.requiredSum += v;
reqSyns.add(s);
NodeContext nlNodeContext = expandNode(nodeContext, s);
if(nlNodeContext == null) {
break;
}
nodeContext = nlNodeContext;
i++;
sum += v;
final boolean sumOfSynapseWeightsAboveThreshold = sum + neuron.posRecSum + neuron.posPassiveSum + neuron.biasSum > 0.0;
if (sumOfSynapseWeightsAboveThreshold) {
noFurtherRefinement = true;
break;
}
}
outputNode.removeParents(threadId);
if (noFurtherRefinement || i == MAX_AND_NODE_SIZE) {
outputNode.addInput(nodeContext.getSynapseIds(), threadId, nodeContext.node, true);
} else {
for (Synapse s : candidates) {
double v = s.getMaxInputValue();
boolean belowThreshold = sum + v + remainingSum + neuron.posRecSum + neuron.posPassiveSum + neuron.biasSum <= 0.0;
if (belowThreshold) {
break;
}
if (!reqSyns.contains(s)) {
NodeContext nlNodeContext = expandNode(nodeContext, s);
if(nlNodeContext != null) {
outputNode.addInput(nlNodeContext.getSynapseIds(), threadId, nlNodeContext.node, true);
remainingSum -= v;
}
}
}
}
} else {
for (Synapse s : modifiedSynapses) {
if (s.isDisjunction && !s.isRecurrent) {
NodeContext nlNodeContext = expandNode(nodeContext, s);
outputNode.addInput(nlNodeContext.getSynapseIds(), threadId, nlNodeContext.node, false);
}
}
}
return true;
}
private void initSlotFlags() {
modifiedSynapses.forEach(s -> {
for(Integer slot: s.linksOutput()) {
neuron.slotHasInputs.add(slot);
}
for(Relation rel: s.relations.values()) {
rel.registerRequiredSlots(s.input);
}
});
}
private List prepareCandidates() {
Synapse syn = getBestSynapse(neuron.inputSynapses.values());
TreeSet alreadyCollected = new TreeSet<>();
ArrayList selectedCandidates = new ArrayList<>();
TreeMap relatedSyns = new TreeMap<>();
while(syn != null && selectedCandidates.size() < MAX_AND_NODE_SIZE) {
relatedSyns.remove(syn.id);
selectedCandidates.add(syn);
alreadyCollected.add(syn.id);
for(Integer relId: syn.relations.keySet()) {
if(!alreadyCollected.contains(relId)) {
Synapse rs = syn.output.getSynapseById(relId);
if(rs != null) {
relatedSyns.put(relId, rs);
}
}
}
syn = getBestSynapse(relatedSyns.values());
}
return selectedCandidates;
}
private Synapse getBestSynapse(Collection synapses) {
Synapse maxSyn = null;
for(Synapse s: synapses) {
if(!s.isNegative() && !s.isRecurrent && !s.inactive && !s.input.get().isPassiveInputNeuron()) {
if(maxSyn == null || SYNAPSE_COMP.compare(maxSyn, s) > 0) {
maxSyn = s;
}
}
}
return maxSyn;
}
public static final int DIRECT = 0;
public static final int RECURRENT = 1;
public static final int POSITIVE = 0;
public static final int NEGATIVE = 1;
private void initInputNodesAndComputeWeightSums() {
double[][] sumDelta = new double[2][2];
double posPassiveSumDelta = 0.0;
for (Synapse s : modifiedSynapses) {
if(s.toBeDeleted) {
s.update(doc, -s.weight, 0.0, s.limit);
}
INeuron in = s.input.get();
in.lock.acquireWriteLock();
try {
if (!s.inactive) {
sumDelta[s.isRecurrent ? RECURRENT : DIRECT][s.isNegative() ? NEGATIVE : POSITIVE] -= s.limit * s.weight;
sumDelta[s.isRecurrent ? RECURRENT : DIRECT][s.getNewWeight() <= 0.0 ? NEGATIVE : POSITIVE] += (s.limit + s.limitDelta) * s.getNewWeight();
if(in.isPassiveInputNeuron() && !s.isNegative()) {
posPassiveSumDelta -= !s.isNegative() ? (s.limit * s.weight) : 0.0;
posPassiveSumDelta += s.getNewWeight() > 0.0 ? ((s.limit + s.limitDelta) * s.getNewWeight()) : 0.0;
}
if(!s.isRecurrent) {
if (!s.isDisjunction(Synapse.State.OLD) && s.isDisjunction(Synapse.State.NEW)) {
neuron.numDisjunctiveSynapses++;
} else if (s.isDisjunction(Synapse.State.OLD) && !s.isDisjunction(Synapse.State.NEW)) {
neuron.numDisjunctiveSynapses--;
}
}
}
s.weight += s.weightDelta;
s.weightDelta = 0.0;
s.bias += s.biasDelta;
s.biasDelta = 0.0;
s.limit += s.limitDelta;
s.limitDelta = 0.0;
if (doc != null) {
s.committedInDoc = doc.id;
}
} finally {
in.lock.releaseWriteLock();
}
if(s.toBeDeleted) {
s.unlink();
}
}
neuron.bias += neuron.biasDelta;
neuron.biasDelta = 0.0;
neuron.biasSum += neuron.biasSumDelta;
neuron.biasSumDelta = 0.0;
assert Double.isFinite(neuron.biasSum);
neuron.posDirSum += sumDelta[DIRECT][POSITIVE];
neuron.negDirSum += sumDelta[DIRECT][NEGATIVE];
neuron.negRecSum += sumDelta[RECURRENT][NEGATIVE];
neuron.posRecSum += sumDelta[RECURRENT][POSITIVE];
neuron.posPassiveSum += posPassiveSumDelta;
neuron.setModified();
}
private NodeContext expandNode(NodeContext nc, Synapse s) {
if (nc == null) {
NodeContext nln = new NodeContext();
nln.node = s.input.get().outputNode.get();
nln.offsets = new Synapse[] {s};
return nln;
} else {
Relation[] relations = new Relation[nc.offsets.length];
for(int i = 0; i < nc.offsets.length; i++) {
Synapse linkedSynapse = nc.offsets[i];
relations[i] = s.getRelationById(linkedSynapse.id);
}
NodeContext nln = new NodeContext();
nln.offsets = new Synapse[nc.offsets.length + 1];
AndNode.Refinement ref = new AndNode.Refinement(new AndNode.RelationsMap(relations), s.input.get().outputNode);
AndNode.RefValue rv = nc.node.extend(threadId, doc, ref, null);
if(rv == null) {
return null;
}
nln.node = rv.child.get(doc);
for (int i = 0; i < nc.offsets.length; i++) {
nln.offsets[rv.offsets[i]] = nc.offsets[i];
}
for (int i = 0; i < nln.offsets.length; i++) {
if (nln.offsets[i] == null) {
nln.offsets[i] = s;
}
}
return nln;
}
}
private class NodeContext {
Node node;
Synapse[] offsets;
int[] getSynapseIds() {
int[] result = new int[offsets.length];
for(int i = 0; i < result.length; i++) {
result[i] = offsets[i].id;
}
return result;
}
}
}
aika-java-0.24/src/main/java/network/aika/DistanceFunction.java 0000664 0000000 0000000 00000001034 13373742612 0024426 0 ustar 00root root 0000000 0000000 package network.aika;
import network.aika.neuron.activation.Activation;
public enum DistanceFunction {
NONE((iAct, oAct) -> 1.0),
DEGRADING((iAct, oAct) -> 1.0 / ((double) (1 + Math.abs(iAct.getSlot(Activation.BEGIN).getDistance(oAct.getSlot(Activation.BEGIN))))));
Function f;
DistanceFunction(Function f) {
this.f = f;
}
public double f(Activation iAct, Activation oAct) {
return f.f(iAct, oAct);
}
interface Function {
double f(Activation iAct, Activation oAct);
}
}
aika-java-0.24/src/main/java/network/aika/Document.java 0000664 0000000 0000000 00000053063 13373742612 0022755 0 ustar 00root root 0000000 0000000 /*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package network.aika;
import network.aika.lattice.Node;
import network.aika.lattice.NodeActivation;
import network.aika.neuron.INeuron;
import network.aika.neuron.Synapse;
import network.aika.neuron.activation.Activation;
import network.aika.neuron.activation.Activation.Link;
import network.aika.neuron.activation.Candidate;
import network.aika.neuron.activation.Position;
import network.aika.neuron.activation.SearchNode;
import network.aika.lattice.Node.ThreadState;
import network.aika.neuron.activation.*;
import network.aika.neuron.activation.SearchNode.Decision;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.util.*;
import java.util.stream.Collectors;
import java.util.stream.Stream;
import static network.aika.neuron.activation.SearchNode.Decision.UNKNOWN;
/**
* The {@code Document} class represents a single document which may be either used for processing a text or as
* training input. A document consists of the raw text, the interpretations and the activations.
*
*
When the document is not needed any more, the method {@code clearActivations} must be called, since Aika only
* supports a single document per thread and model.
*
* @author Lukas Molzberger
*/
public class Document implements Comparable {
private static final Logger log = LoggerFactory.getLogger(Document.class);
public static int CLEANUP_INTERVAL = 500;
public static int MAX_ROUND = 20;
public static int ROUND_LIMIT = -1;
/**
* Experimental code: not working yet!
*/
public static boolean INCREMENTAL_MODE = false;
public final int id;
private final StringBuilder content;
public long visitedCounter = 1;
public int activationIdCounter = 0;
public int logicNodeActivationIdCounter = 0;
public int searchNodeIdCounter = 0;
public int searchStepCounter = 0;
public int positionIdCounter = 0;
public Model model;
public int threadId;
public Queue queue = new Queue();
public ValueQueue vQueue = new ValueQueue();
public UpperBoundQueue ubQueue = new UpperBoundQueue();
public Linker linker;
public TreeMap positions = new TreeMap<>();
public TreeSet activatedNodes = new TreeSet<>();
public TreeSet activatedNeurons = new TreeSet<>();
public TreeSet finallyActivatedNeurons = new TreeSet<>();
public TreeSet inputNeuronActivations = new TreeSet<>();
public TreeMap> modifiedWeights = new TreeMap<>();
public TreeMap searchNodeWeights = new TreeMap<>();
private TreeMap activationsBySlotAndPosition = new TreeMap<>((ak1, ak2) -> {
int r = Integer.compare(ak1.slot, ak2.slot);
if (r != 0) return r;
r = Position.compare(ak1.pos, ak2.pos);
if (r != 0) return r;
r = ak1.node.compareTo(ak2.node);
if (r != 0) return r;
return Integer.compare(ak1.actId, ak2.actId);
});
private TreeMap activationsByPosition = new TreeMap<>((ak1, ak2) -> {
int r = Position.compare(ak1.pos, ak2.pos);
if (r != 0) return r;
r = ak1.node.compareTo(ak2.node);
if (r != 0) return r;
return Integer.compare(ak1.actId, ak2.actId);
});
public TreeMap activationsById = new TreeMap<>();
private int lastProcessedActivationId = -1;
private int lastLinkedActivationId = -1;
public static class ActKey {
int slot;
Position pos;
Node node;
int actId;
public ActKey(int slot, Position pos, Node node, int actId) {
this.slot = slot;
this.pos = pos;
this.node = node;
this.actId = actId;
}
}
public TreeSet addedNodes = new TreeSet<>();
public ArrayList addedNodeActivations = new ArrayList<>();
public SearchNode selectedSearchNode;
public ArrayList candidates = new ArrayList<>();
public long createV;
public static Comparator ACTIVATIONS_OUTPUT_COMPARATOR = (act1, act2) -> {
int r = Position.compare(act1.getSlot(Activation.BEGIN), act2.getSlot(Activation.BEGIN));
if (r != 0) return r;
r = act1.node.compareTo(act2.node);
if (r != 0) return r;
return Integer.compare(act1.id, act2.id);
};
public Document(int id, String content, Model model, int threadId) {
this.id = id;
this.content = new StringBuilder(content);
this.model = model;
this.threadId = threadId;
this.linker = model.getLinkerFactory().createLinker(this);
}
public void append(String txt) {
content.append(txt);
}
public char charAt(int i) {
return content.charAt(i);
}
public String getContent() {
return content.toString();
}
public int length() {
return content.length();
}
public String toString() {
return content.toString();
}
public Position lookupFinalPosition(int pos) {
Position p = positions.get(pos);
if(p == null) {
p = new Position(this, pos);
positions.put(pos, p);
}
return p;
}
public String getText(Position begin, Position end) {
return getText(begin.getFinalPosition(), end.getFinalPosition());
}
public String getText(Integer begin, Integer end) {
if(begin != null && end != null) {
return content.substring(
Math.max(0, Math.min(begin, length())),
Math.max(0, Math.min(end, length()))
);
} else {
return "";
}
}
public void addActivation(Activation act) {
for(Map.Entry me : act.slots.entrySet()) {
Position pos = me.getValue();
if (pos != null && pos.getFinalPosition() != null) {
ActKey dak = new ActKey(me.getKey(), pos, act.node, act.id);
activationsBySlotAndPosition.put(dak, act);
activationsByPosition.put(dak, act);
}
}
activationsById.put(act.id, act);
}
public Collection getActivations(boolean onlyFinal) {
if(!onlyFinal) {
return activationsById.values();
} else {
return activationsById
.values()
.stream()
.filter(act -> act.isFinalActivation())
.collect(Collectors.toList());
}
}
public Collection getActivationsByPosition(int fromSlot, Position fromPos, boolean fromInclusive, int toSlot, Position toPos, boolean toInclusive) {
return activationsBySlotAndPosition.subMap(
new Document.ActKey(fromSlot, fromPos, Node.MIN_NODE, Integer.MIN_VALUE),
fromInclusive,
new Document.ActKey(toSlot, toPos, Node.MAX_NODE, Integer.MAX_VALUE),
toInclusive
).values();
}
public Collection getActivationsByPosition(Position fromPos, boolean fromInclusive, Position toPos, boolean toInclusive) {
return activationsByPosition.subMap(
new Document.ActKey(-1, fromPos, Node.MIN_NODE, Integer.MIN_VALUE),
fromInclusive,
new Document.ActKey(-1, toPos, Node.MAX_NODE, Integer.MAX_VALUE),
toInclusive
).values();
}
public Activation getNextActivation(Activation currentAct) {
Map.Entry me = currentAct == null ?
activationsById.firstEntry() :
activationsById.higherEntry(currentAct.id);
return me != null ? me.getValue() : null;
}
public int getNumberOfActivations() {
return activationsById.size();
}
@Override
public int compareTo(Document doc) {
return Integer.compare(id, doc.id);
}
public void propagate() {
boolean flag = true;
while(flag) {
queue.processChanges();
flag = ubQueue.process();
}
}
public void generateCandidates() {
TreeSet tmp = new TreeSet<>();
int i = 0;
if(!INCREMENTAL_MODE) {
candidates.clear();
}
for(Activation act: activationsById.subMap(INCREMENTAL_MODE ? lastProcessedActivationId : -1, false, Integer.MAX_VALUE, true).values()) {
if (act.decision == UNKNOWN && act.upperBound > 0.0) {
SearchNode.invalidateCachedDecision(act);
tmp.add(new Candidate(act, i++));
lastProcessedActivationId = Math.max(lastProcessedActivationId, act.id);
}
}
long v = visitedCounter++;
for(Activation act: inputNeuronActivations) {
act.markedHasCandidate = v;
}
while (!tmp.isEmpty()) {
int oldSize = tmp.size();
for (Candidate c : tmp) {
if (c.checkDependenciesSatisfied(v)) {
tmp.remove(c);
c.id = candidates.size();
candidates.add(c);
c.activation.markedHasCandidate = v;
break;
}
}
if(tmp.size() == oldSize) {
log.error("Cycle detected in the activations that is not marked recurrent.");
throw new RuntimeException("Cycle detected in the activations that is not marked recurrent.");
}
}
}
/**
* The method process needs to be called after all the input activations have been added to the
* network. It performs the search for the best interpretation.
*/
public void process() {
process(null);
}
public void process(Long timeoutInMilliSeconds) throws SearchNode.TimeoutException {
linker.lateLinking();
inputNeuronActivations.forEach(act -> vQueue.propagateActivationValue(0, act));
generateCandidates();
if(selectedSearchNode == null || !INCREMENTAL_MODE) {
selectedSearchNode = new SearchNode(this, null, null, 0);
}
SearchNode.search(this, selectedSearchNode, visitedCounter++, timeoutInMilliSeconds);
for(Activation act: activationsById.values()) {
if(act.isFinalActivation()) {
finallyActivatedNeurons.add(act.getINeuron());
}
}
if(SearchNode.COMPUTE_SOFT_MAX) {
computeSoftMax();
}
}
private void computeSoftMax() {
double norm = 0.0;
for(Double w: searchNodeWeights.values()) {
norm += Math.exp(w);
}
for(Activation act: activationsById.values()) {
if(act.searchStates != null) {
double avgValue = 0.0;
double avgPosValue = 0.0;
double avgP = 0.0;
double avgNet = 0.0;
double avgPosNet = 0.0;
for (Map.Entry me : searchNodeWeights.entrySet()) {
double p = Math.exp(me.getValue()) / norm;
Activation.State s = act.searchStates.get(me.getKey());
avgValue += p * s.value;
avgPosValue += p * s.posValue;
avgP += p * s.p;
avgNet += p * s.net;
avgPosNet += p * s.posNet;
}
act.avgState = new Activation.State(avgValue, avgPosValue, avgP, avgNet, avgPosNet, 0, 0.0);
}
}
}
public void dumpDebugCandidateStatistics() {
for (Candidate c : candidates) {
log.info(c.toString());
}
}
public void notifyWeightModified(Synapse synapse) {
Set is = modifiedWeights.get(synapse.output.get());
if(is == null) {
is = new TreeSet<>(Synapse.INPUT_SYNAPSE_COMP);
modifiedWeights.put(synapse.output.get(), is);
}
is.add(synapse);
}
/**
* Updates the model after the training step.
* It applies the weight and bias delta values and reflects the changes in the logic node structure.
*/
public void commit() {
modifiedWeights.forEach((n, inputSyns) -> Converter.convert(threadId, this, n, inputSyns));
modifiedWeights.clear();
}
/**
* Removes the activations of this document from the model again.
*/
public void clearActivations() {
activatedNeurons.forEach(n -> n.clearActivations(this));
activatedNodes.forEach(n -> n.clearActivations(this));
activationsById.clear();
addedNodeActivations.clear();
activatedNeurons.clear();
activatedNodes.clear();
addedNodes.clear();
if(model.lastCleanup[threadId] + CLEANUP_INTERVAL < id) {
model.lastCleanup[threadId] = id;
List> tmp;
synchronized(model.activeProviders) {
tmp = new ArrayList<>(model.activeProviders.values());
}
tmp.forEach(np -> {
AbstractNode an = np.getIfNotSuspended();
if (an != null && an instanceof Node) {
Node n = (Node) an;
Node.ThreadState th = n.threads[threadId];
if (th != null && th.lastUsed + CLEANUP_INTERVAL < id) {
n.threads[threadId] = null;
}
}
});
}
model.docs[threadId] = null;
}
public String generateOutputText() {
int oldLength = length();
TreeSet queue = new TreeSet<>(Comparator.comparingInt(p -> p.id));
for(Activation act: activationsById.values()) {
if(act.getINeuron().getOutputText() != null && act.getSlot(Activation.BEGIN).getFinalPosition() != null && act.getSlot(Activation.END).getFinalPosition() == null) {
queue.add(act.getSlot(Activation.BEGIN));
}
}
while(!queue.isEmpty()) {
Position pos = queue.pollFirst();
pos.getActivations(Activation.BEGIN)
.filter(act -> act.getINeuron().getOutputText() != null && act.isFinalActivation())
.forEach(act -> {
String outText = act.getINeuron().getOutputText();
Position nextPos = act.getSlot(Activation.END);
nextPos.setFinalPosition(pos.getFinalPosition() + outText.length());
content.replace(act.getSlot(Activation.BEGIN).getFinalPosition(), act.getSlot(Activation.END).getFinalPosition(), outText);
queue.add(nextPos);
});
}
return content.substring(oldLength, length());
}
public String activationsToString() {
return activationsToString(true, true, false);
}
public String activationsToString(boolean finalOnly, boolean withTextSnippet) {
return activationsToString(finalOnly, withTextSnippet, false);
}
public String activationsToString(boolean finalOnly, boolean withTextSnippet, boolean withLogic) {
Set acts = new TreeSet<>(ACTIVATIONS_OUTPUT_COMPARATOR);
acts.addAll(activationsById.values());
StringBuilder sb = new StringBuilder();
sb.append("Activation TYPE -");
if(finalOnly) {
sb.append(" Final Decision -");
} else {
sb.append(" Decision -");
}
sb.append(" Range" + (withTextSnippet ? " | Text Snippet" : ""));
sb.append(" | Identity -");
sb.append(" Neuron Label -");
sb.append((withLogic ? " Logic Layer -" : ""));
sb.append(" Upper Bound -");
if(finalOnly) {
sb.append(" Final Value | Final Weight | Final Norm -");
} else {
sb.append(" Simulation Rounds [Round | Value | Weight | Norm] -");
}
sb.append(" Input Value |");
sb.append(" Target Value");
sb.append("\n");
sb.append("\n");
for(Activation act: acts) {
if(act.upperBound <= 0.0 && (act.targetValue == null || act.targetValue <= 0.0)) {
continue;
}
sb.append(act.toString(finalOnly, withTextSnippet, withLogic));
sb.append("\n");
}
if(selectedSearchNode != null) {
sb.append("\n Final SearchNode:" + selectedSearchNode.id + " WeightSum:" + selectedSearchNode.accumulatedWeight + "\n");
}
return sb.toString();
}
public Stream getAllActivationsStream() {
return addedNodeActivations.stream();
}
public class Queue {
public final TreeSet queue = new TreeSet<>(new Comparator() {
@Override
public int compare(Node n1, Node n2) {
int r = Integer.compare(n1.level, n2.level);
if(r != 0) return r;
ThreadState th1 = n1.getThreadState(threadId, true);
ThreadState th2 = n2.getThreadState(threadId, true);
return Long.compare(th1.queueId, th2.queueId);
}
});
private long queueIdCounter = 0;
public void add(Node n) {
ThreadState th = n.getThreadState(threadId, true);
if(!th.isQueued) {
th.isQueued = true;
th.queueId = queueIdCounter++;
queue.add(n);
}
}
public void processChanges() {
while(!queue.isEmpty()) {
Node n = queue.pollFirst();
ThreadState th = n.getThreadState(threadId, true);
th.isQueued = false;
n.processChanges(Document.this);
}
}
}
public class UpperBoundQueue {
public final ArrayDeque queue = new ArrayDeque<>();
public void add(Link l) {
if(!l.synapse.isRecurrent) {
add(l.output);
}
}
public void add(Activation act) {
if(!act.ubQueued && act.inputValue == null) {
act.ubQueued = true;
queue.addLast(act);
}
}
public boolean process() {
boolean flag = false;
while(!queue.isEmpty()) {
flag = true;
Activation act = queue.pollFirst();
act.ubQueued = false;
act.processBounds();
}
return flag;
}
}
private static Comparator VALUE_QUEUE_COMP = (a, b) -> {
int r = Integer.compare(a.getSequence(), b.getSequence());
if(r != 0) return r;
return Integer.compare(a.id, b.id);
};
public class ValueQueue {
public final ArrayList> queue = new ArrayList<>();
public void propagateActivationValue(int round, Activation act) {
act.getOutputLinks(false)
.forEach(l -> add(l.synapse.isRecurrent ? round + 1 : round, l.output));
}
private void add(Activation act) {
add(0, act);
act.getOutputLinks(false)
.filter(l -> l.synapse.isRecurrent)
.forEach(l -> add(0, l.output));
}
public void add(int round, Activation act) {
if(act.rounds.isQueued(round) || act.decision == Decision.UNKNOWN) return;
TreeSet q;
if(round < queue.size()) {
q = queue.get(round);
} else {
assert round == queue.size();
q = new TreeSet<>(VALUE_QUEUE_COMP);
queue.add(q);
}
act.rounds.setQueued(round, true);
q.add(act);
}
public double process(SearchNode sn) {
long v = visitedCounter++;
if(sn.getParent() != null && sn.getParent().candidate != null) {
add(sn.getParent().candidate.activation);
}
double delta = 0.0;
for(int round = 0; round < queue.size(); round++) {
TreeSet q = queue.get(round);
while (!q.isEmpty()) {
Activation act = q.pollFirst();
act.rounds.setQueued(round, false);
delta += act.process(sn, round, v);
}
}
return delta;
}
}
public void dumpOscillatingActivations() {
activatedNeurons.stream()
.flatMap(n -> n.getActivations(this, false))
.filter(act -> act.rounds.getLastRound() != null && act.rounds.getLastRound() > MAX_ROUND - 5)
.forEach(act -> {
log.error(act.id + " " + act.slotsToString() + " " + act.decision + " " + act.rounds);
log.error(act.linksToString());
log.error("");
});
}
}
aika-java-0.24/src/main/java/network/aika/Model.java 0000664 0000000 0000000 00000020674 13373742612 0022241 0 ustar 00root root 0000000 0000000 /*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package network.aika;
import network.aika.lattice.InputNode;
import network.aika.lattice.Node;
import network.aika.neuron.INeuron;
import network.aika.neuron.Neuron;
import network.aika.Provider.SuspensionMode;
import network.aika.neuron.activation.Linker;
import network.aika.neuron.activation.SearchNode;
import java.lang.ref.WeakReference;
import java.util.*;
import java.util.concurrent.atomic.AtomicInteger;
import java.util.concurrent.atomic.AtomicLong;
/**
* The model consists of two layers. The first layer is the actual neural network consisting of neurons and synapses.
* The second layer is a pattern lattice containing a boolean logic representation of all the neurons. Whenever the
* synapse weights of a neuron are adjusted, then the underlying boolean logic representation of this neuron will be
* updated too.
*
*
The model supports parallel processing using a fixed number of threads.
*
* @author Lukas Molzberger
*/
public class Model {
public int numberOfThreads = 1;
public int[] lastCleanup;
public Document[] docs;
public SuspensionHook suspensionHook;
private WritableFactory nodeExtensionFactory;
private WritableFactory neuronExtensionFactory;
private WritableFactory synapseExtensionFactory;
private WritableFactory activationExtensionFactory;
public LinkerFactory linkerFactory = (doc) -> new Linker(doc);
public SearchNode.SkipSelectStep skipSelectStep = (act) -> false;
public AtomicInteger docIdCounter = new AtomicInteger(0);
public AtomicInteger currentId = new AtomicInteger(0);
// Important: the id field needs to be referenced by the provider!
public WeakHashMap>> providers = new WeakHashMap<>();
public Map> activeProviders = new TreeMap<>();
public Map passiveActivationFunctions = new TreeMap<>();
public int defaultThreadId = 0;
public static AtomicLong visitedCounter = new AtomicLong(1);
/**
* Creates a model with a single thread.
*/
public Model() {
this(null, 1);
}
public Model(SuspensionHook sh, int numberOfThreads) {
assert numberOfThreads >= 1;
this.numberOfThreads = numberOfThreads;
lastCleanup = new int[numberOfThreads];
docs = new Document[numberOfThreads];
suspensionHook = sh;
}
public SuspensionHook getSuspensionHook() {
return suspensionHook;
}
public void setSuspensionHook(SuspensionHook suspensionHook) {
this.suspensionHook = suspensionHook;
}
public WritableFactory getNodeExtensionFactory() {
return nodeExtensionFactory;
}
public void setNodeExtensionFactory(WritableFactory nodeExtensionFactory) {
this.nodeExtensionFactory = nodeExtensionFactory;
}
public WritableFactory getNeuronExtensionFactory() {
return neuronExtensionFactory;
}
public void setNeuronExtensionFactory(WritableFactory neuronExtensionFactory) {
this.neuronExtensionFactory = neuronExtensionFactory;
}
public WritableFactory getSynapseExtensionFactory() {
return synapseExtensionFactory;
}
public void setSynapseExtensionFactory(WritableFactory synapseExtensionFactory) {
this.synapseExtensionFactory = synapseExtensionFactory;
}
public WritableFactory getActivationExtensionFactory() {
return activationExtensionFactory;
}
public void setActivationExtensionFactory(WritableFactory activationExtensionFactory) {
this.activationExtensionFactory = activationExtensionFactory;
}
public LinkerFactory getLinkerFactory() {
return linkerFactory;
}
public void setLinkerFactory(LinkerFactory linkerFactory) {
this.linkerFactory = linkerFactory;
}
public SearchNode.SkipSelectStep getSkipSelectStep() {
return skipSelectStep;
}
public void setSkipSelectStep(SearchNode.SkipSelectStep skipSelectStep) {
this.skipSelectStep = skipSelectStep;
}
public Neuron createNeuron() {
return createNeuron(null);
}
public Neuron createNeuron(String label) {
return createNeuron(label, null);
}
public Neuron createNeuron(String label, String outputText) {
INeuron n = new INeuron(this, label, outputText);
InputNode iNode = InputNode.add(this, n);
iNode.setModified();
return n.provider;
}
public Document createDocument(String txt) {
return createDocument(txt, 0);
}
public Document createDocument(String txt, int threadId) {
Document doc = new Document(docIdCounter.addAndGet(1), txt, this, threadId);
if (txt != null) {
if (docs[threadId] != null) {
throw new RuntimeException("Two documents are using the same thread. Call clearActivations() first, before processing the next document.");
}
docs[threadId] = doc;
}
return doc;
}
public
> P lookupNodeProvider(int id) {
synchronized (providers) {
WeakReference> wr = providers.get(id);
if(wr != null) {
P p = (P) wr.get();
if (p != null) {
return p;
}
}
return (P) new Provider(this, id);
}
}
public Neuron lookupNeuron(int id) {
synchronized (providers) {
WeakReference> wr = providers.get(id);
if(wr != null) {
Neuron n = (Neuron) wr.get();
if (n != null) {
return n;
}
}
return new Neuron(this, id);
}
}
public void register(Provider p) {
synchronized (activeProviders) {
activeProviders.put(p.id, p);
}
}
public void unregister(Provider p) {
synchronized (activeProviders) {
activeProviders.remove(p.id);
}
}
/**
* Suspend all neurons and logic nodes whose last used document id is lower/older than {@param docId}.
*
* @param docId
*/
public void suspendUnusedNodes(int docId, SuspensionMode sm) {
docId = Math.min(docId, getOldestDocIdInProcessing());
List tmp;
synchronized (activeProviders) {
tmp = new ArrayList<>(activeProviders.values());
}
for (Provider p: tmp) {
suspend(docId, p, sm);
}
}
public int getOldestDocIdInProcessing() {
int oldestDocId = Integer.MAX_VALUE;
for(Document doc: docs) {
if(doc != null) oldestDocId = Math.min(oldestDocId, doc.id);
}
return oldestDocId;
}
/**
* Suspend all neurons and logic nodes in memory.
*
*/
public void suspendAll(SuspensionMode sm) {
suspendUnusedNodes(Integer.MAX_VALUE, sm);
}
private boolean suspend(int docId, Provider p, SuspensionMode sm) {
AbstractNode an = p.getIfNotSuspended();
if (an != null && an.lastUsedDocumentId < docId) {
p.suspend(sm);
return true;
}
return false;
}
public void removeProvider(Provider p) {
synchronized (activeProviders) {
activeProviders.remove(p.id);
}
synchronized (providers) {
providers.remove(p.id);
}
}
public interface WritableFactory {
Writable createObject();
}
public interface LinkerFactory {
Linker createLinker(Document doc);
}
}
aika-java-0.24/src/main/java/network/aika/PassiveInputFunction.java 0000664 0000000 0000000 00000000340 13373742612 0025325 0 ustar 00root root 0000000 0000000 package network.aika;
import network.aika.neuron.Synapse;
import network.aika.neuron.activation.Activation;
public interface PassiveInputFunction {
double getActivationValue(Synapse s, Activation outputActivation);
}
aika-java-0.24/src/main/java/network/aika/PatternDiscovery.java 0000664 0000000 0000000 00000006336 13373742612 0024505 0 ustar 00root root 0000000 0000000 /*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package network.aika;
import network.aika.Document;
import network.aika.lattice.AndNode;
import network.aika.lattice.NodeActivation;
import java.util.ArrayList;
/**
*
* @author Lukas Molzberger
*/
public class PatternDiscovery {
public interface CandidateCheck {
/**
* Check if node is an interesting pattern that might be considered for further processing.
*
* This property is required to be monotonic over the size of the pattern. In other words, if a pattern is
* interesting, then all its sub patterns also need to be interesting.
*
* @param act
* @return
*/
boolean check(NodeActivation act, NodeActivation secondAct);
}
public interface PatternCheck {
boolean check(AndNode andNode);
}
public interface Counter {
/**
* Updates the statistics of this node
*
* @param act
* @return
*/
void count(NodeActivation act);
}
public static class Config {
public CandidateCheck candidateCheck;
public PatternCheck patternCheck;
public Counter counter;
public Config setCandidateCheck(CandidateCheck candidateCheck) {
this.candidateCheck = candidateCheck;
return this;
}
public Config setPatternCheck(PatternCheck patternCheck) {
this.patternCheck = patternCheck;
return this;
}
/**
* The counter callback function should implement a customized counting function.
* The counting function should modify the custom meta object stored in the node.
* The NodeStatisticFactory is used to instantiate the custom meta object for a node.
*
* @param counter
* @return
*/
public Config setCounter(Counter counter) {
this.counter = counter;
return this;
}
}
public static void discover(Document doc, Config config) {
doc.createV = doc.visitedCounter++;
doc.getAllActivationsStream().forEach(act -> config.counter.count(act));
ArrayList activations = new ArrayList<>(doc.addedNodeActivations);
doc.addedNodeActivations.clear();
activations.forEach(act -> act.node.discover(act, config));
// doc.propagate();
doc.addedNodeActivations.forEach(act -> config.counter.count(act));
}
}
aika-java-0.24/src/main/java/network/aika/Provider.java 0000664 0000000 0000000 00000006703 13373742612 0022770 0 ustar 00root root 0000000 0000000 package network.aika;
import java.io.*;
import java.lang.ref.WeakReference;
import java.util.zip.GZIPInputStream;
import java.util.zip.GZIPOutputStream;
public class Provider implements Comparable> {
public Model model;
public Integer id;
private volatile T n;
public enum SuspensionMode {
SAVE,
DISCARD
}
public Provider(Model model, int id) {
this.model = model;
this.id = id;
if(model != null) {
synchronized (model.providers) {
model.providers.put(this.id, new WeakReference<>(this));
}
}
}
public Provider(Model model, T n) {
this.model = model;
this.n = n;
id = model.suspensionHook != null ? model.suspensionHook.getNewId() : model.currentId.addAndGet(1);
synchronized (model.providers) {
model.providers.put(id, new WeakReference<>(this));
if(n != null) {
model.register(this);
}
}
}
public boolean isSuspended() {
return n == null;
}
public T getIfNotSuspended() {
return n;
}
public synchronized T get() {
if (n == null) {
reactivate();
}
return n;
}
public synchronized T get(int lastUsedDocumentId) {
T n = get();
n.lastUsedDocumentId = Math.max(n.lastUsedDocumentId, lastUsedDocumentId);
return n;
}
/**
*
* @param doc The document is used to remember when this node has been used last.
* @return
*/
public T get(Document doc) {
return doc != null ? get(doc.id) : get();
}
public synchronized void suspend(SuspensionMode sm) {
if(n == null) return;
assert model.suspensionHook != null;
n.suspend();
model.unregister(this);
if(sm == SuspensionMode.SAVE) {
save();
}
n = null;
}
public void save() {
if (n.modified) {
ByteArrayOutputStream baos = new ByteArrayOutputStream();
try (
GZIPOutputStream gzipos = new GZIPOutputStream(baos);
DataOutputStream dos = new DataOutputStream(gzipos);) {
n.write(dos);
} catch (IOException e) {
throw new RuntimeException(e);
}
model.suspensionHook.store(id, baos.toByteArray());
}
n.modified = false;
}
private void reactivate() {
assert model.suspensionHook != null;
byte[] data = model.suspensionHook.retrieve(id);
ByteArrayInputStream bais = new ByteArrayInputStream(data);
try (
GZIPInputStream gzipis = new GZIPInputStream(bais);
DataInputStream dis = new DataInputStream(gzipis);) {
n = (T) AbstractNode.read(dis, this);
} catch (IOException e) {
throw new RuntimeException(e);
}
n.reactivate();
model.register(this);
}
@Override
public boolean equals(Object o) {
return id == ((Provider) o).id;
}
@Override
public int hashCode() {
return id;
}
public String toString() {
return "p(" + id + ":" + (n != null ? n.toString() : "SUSPENDED") + ")";
}
public int compareTo(Provider n) {
if (id < n.id) return -1;
else if (id > n.id) return 1;
else return 0;
}
}
aika-java-0.24/src/main/java/network/aika/ReadWriteLock.java 0000664 0000000 0000000 00000005412 13373742612 0023671 0 ustar 00root root 0000000 0000000 /*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package network.aika;
/**
*
* @author Lukas Molzberger
*/
public class ReadWriteLock {
private int readers = 0;
private int writers = 0;
private int writeRequests = 0;
private long writerThreadId = -1;
private int waitForReadLock = 0;
private int waitForWriteLock = 0;
private Object writeLock = new Object();
public void acquireWriteLock() {
try {
synchronized (this) {
writeRequests++;
while (readers > 0) {
wait();
}
}
long tid = Thread.currentThread().getId();
synchronized (writeLock) {
if(writerThreadId != tid) {
waitForWriteLock++;
while (writers > 0) {
writeLock.wait();
}
waitForWriteLock--;
writerThreadId = tid;
}
writers++;
}
} catch(InterruptedException e) {
e.printStackTrace();
}
}
public synchronized void acquireReadLock() {
try {
waitForReadLock++;
while (writeRequests > 0) {
wait();
}
waitForReadLock--;
readers++;
} catch(InterruptedException e) {
e.printStackTrace();
}
}
public void releaseWriteLock() {
synchronized (writeLock) {
writers--;
if(writers == 0) {
writerThreadId = -1;
if(waitForWriteLock > 0) {
writeLock.notify();
}
}
}
synchronized(this) {
writeRequests--;
if(writeRequests == 0 && waitForReadLock > 0) {
notifyAll();
}
}
}
public synchronized void releaseReadLock() {
readers--;
if(writeRequests > 0 && readers == 0) {
notifyAll();
}
}
} aika-java-0.24/src/main/java/network/aika/SuspensionHook.java 0000664 0000000 0000000 00000002474 13373742612 0024166 0 ustar 00root root 0000000 0000000 /*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package network.aika;
/**
*
* The suspension hook is used to suspend neurons and logic nodes to an external storage in order to reduce the memory footprint.
*
* !!! Important: When using the suspension hook, all references to a neuron or a logic node need to occur through a
* provider. Otherwise the reference might be outdated.
*
* @author Lukas Molzberger
*/
public interface SuspensionHook {
int getNewId();
void store(int id, byte[] data);
byte[] retrieve(int id);
Iterable getAllNodeIds();
}
aika-java-0.24/src/main/java/network/aika/Utils.java 0000664 0000000 0000000 00000005476 13373742612 0022304 0 ustar 00root root 0000000 0000000 /*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package network.aika;
import java.util.Comparator;
/**
*
* @author Lukas Molzberger
*/
public class Utils {
public static boolean contains(T[] in, T x, Comparator c) {
for(T y: in) {
if(c.compare(x, y) == 0) return true;
}
return false;
}
public static boolean compareNullSafe(Integer a, Integer b) {
if(a == null || b == null) return true;
return a >= b;
}
public static int compareInteger(Integer a, Integer b) {
if(a == null && b != null) return -1;
if(a != null && b == null) return 1;
if(a != null && b != null) {
return Integer.compare(a, b);
} else return 0;
}
public static double round(double x) {
return Math.round(x * 1000.0) / 1000.0;
}
public static Integer nullSafeAdd(Integer a, boolean fa, Integer b, boolean fb) {
if(b == null) return fa ? a : null;
if(a == null) return fb ? b : null;
else return a + b;
}
public static Integer nullSafeSub(Integer a, boolean fa, Integer b, boolean fb) {
if(b == null) return fa ? a : null;
if(a == null) return fb ? b : null;
return a - b;
}
public static Integer nullSafeMin(Integer a, Integer b) {
if(a == null) return b;
if(b == null) return a;
return Math.min(a, b);
}
public static Integer nullSafeMax(Integer a, Integer b) {
if(a == null) return b;
if(b == null) return a;
return Math.max(a, b);
}
public static Double nullSafeMax(Double a, Double b) {
if(a == null) return b;
if(b == null) return a;
return Math.max(a, b);
}
public static String collapseText(String txt, int length) {
if (txt.length() <= 2 * length) {
return txt;
} else {
return txt.substring(0, length) + "..." + txt.substring(txt.length() - length);
}
}
public static double sigmoid(double x) {
return 1.0 / (1.0 + Math.pow(Math.E, (-x)));
}
}
aika-java-0.24/src/main/java/network/aika/Writable.java 0000664 0000000 0000000 00000003034 13373742612 0022741 0 ustar 00root root 0000000 0000000 /*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package network.aika;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
public interface Writable {
/**
* Serialize the fields of this object to out.
*
* @param out DataOuput to serialize this object into.
* @throws IOException
*/
void write(DataOutput out) throws IOException;
/**
* Deserialize the fields of this object from in.
*
*
For efficiency, implementations should attempt to re-use storage in the
* existing object where possible.
*
* @param in DataInput to deseriablize this object from.
* @throws IOException
*/
void readFields(DataInput in, Model m) throws IOException;
} aika-java-0.24/src/main/java/network/aika/lattice/ 0000775 0000000 0000000 00000000000 13373742612 0021752 5 ustar 00root root 0000000 0000000 aika-java-0.24/src/main/java/network/aika/lattice/AndNode.java 0000664 0000000 0000000 00000055116 13373742612 0024135 0 ustar 00root root 0000000 0000000 /*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package network.aika.lattice;
import network.aika.Document;
import network.aika.Model;
import network.aika.Provider;
import network.aika.Writable;
import network.aika.neuron.relation.Relation;
import network.aika.neuron.activation.Activation;
import network.aika.PatternDiscovery;
import network.aika.lattice.InputNode.InputActivation;
import network.aika.lattice.AndNode.AndActivation;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.util.*;
/**
* The {@code InputNode} and the {@code AndNode} classes together form a pattern lattice, containing all
* possible substructures of any given conjunction. For example if we have the conjunction ABCD where A, B, C, D are
* the inputs, then the pattern lattice will contain the nodes ABCD, ABC, ABD, ACD, BCD, AB, AC, AD, BC, BD, CD,
* A, B, C, D. The pattern lattice is organized in layers, where each layer only contains conjunctions/patterns of the
* same size. These layers are connected through refinements. For example the and-node
* ABD on layer 3 is connected to the and-node ABCD on layer 4 via the refinement C.
*
* @author Lukas Molzberger
*/
public class AndNode extends Node {
public List parents;
public AndNode() {
parents = new ArrayList<>();
}
public AndNode(Model m, int level, List parents) {
super(m, level);
this.parents = parents;
}
private void init() {
for(Entry e: parents) {
e.rv.child = provider;
Node pn = e.rv.parent.get();
pn.addAndChild(e.ref, e.rv);
pn.setModified();
}
}
public void propagate(AndActivation act) {
apply(act);
}
@Override
public void cleanup() {
if(!isRemoved && !isRequired()) {
remove();
for(Entry e: parents) {
e.rv.parent.get().cleanup();
}
}
}
@Override
void processActivation(AndActivation act) {
if(act.isComplete()) {
super.processActivation(act);
}
}
@Override
void apply(AndActivation act) {
if (andChildren != null) {
for (Link fl : act.inputs) {
if(fl == null) continue;
InputActivation refAct = fl.refAct;
Refinement ref = fl.ref;
RefValue rv = fl.rv;
NodeActivation pAct = fl.input;
for (Link sl : pAct.outputsToAndNode.values()) {
InputActivation secondRefAct = sl.refAct;
Refinement secondRef = sl.ref;
RefValue secondRv = sl.rv;
NodeActivation secondAct = sl.output;
if (act != secondAct) {
Relation[] relations = new Relation[secondRef.relations.length() + 1];
for(int i = 0; i < secondRef.relations.length(); i++) {
relations[rv.offsets[i]] = secondRef.relations.get(i);
}
lock.acquireReadLock();
for(Map.Entry me: andChildren.subMap(
new Refinement(RelationsMap.MIN, secondRef.input),
new Refinement(RelationsMap.MAX, secondRef.input)).entrySet()) {
Refinement nRef = me.getKey();
RefValue nRv = me.getValue();
if(nRef.contains(secondRef, rv)) {
AndNode nlNode = nRv.child.get(act.doc);
AndActivation nlAct = lookupAndActivation(act, nRef);
if(nlAct == null) {
nlAct = new AndActivation(act.doc.logicNodeActivationIdCounter++, act.doc, nlNode);
nlAct.link(nRef, nRv, secondRefAct, act);
}
nlAct.node.addActivation(nlAct);
for(Entry secondNE: nlNode.parents) {
if(secondNE.rv.parent.get(act.doc) == secondAct.node && secondNE.ref.contains(ref, secondRv)) {
nlAct.link(secondNE.ref, secondNE.rv, refAct, secondAct);
break;
}
}
}
}
lock.releaseReadLock();
}
}
}
}
OrNode.processCandidate(this, act, false);
}
private AndActivation lookupAndActivation(NodeActivation input, Refinement ref) {
for (Link l : input.outputsToAndNode.values()) {
if(l.ref.compareTo(ref) == 0) {
return l.output;
}
}
return null;
}
@Override
public void discover(AndActivation act, PatternDiscovery.Config config) {
Document doc = act.doc;
for(Link fl : act.inputs) {
if(fl == null) continue;
for (Link sl : fl.input.outputsToAndNode.values()) {
AndActivation secondAct = sl.output;
if (secondAct.node instanceof AndNode) {
if (act != secondAct && config.candidateCheck.check(act, secondAct)) {
Activation iAct = act.getInputActivation(fl.rv.refOffset);
Activation secondIAct = secondAct.getInputActivation(sl.rv.refOffset);
List rels = InputNode.getRelations(iAct, secondIAct);
rels.add(null);
for(Relation rel: rels) {
Refinement nRef = createRefinement(fl.rv, sl.ref, rel);
AndNode.RefValue rv = extend(doc.threadId, doc, nRef, config);
if (rv != null) {
AndNode nln = rv.child.get();
nln.isDiscovered = true;
}
}
}
}
}
}
}
private Refinement createRefinement(RefValue firstRV, Refinement secondRef, Relation rel) {
Relation[] srm = secondRef.relations.relations;
RelationsMap rm = new RelationsMap();
rm.relations = new Relation[srm.length + 1];
for (int i = 0; i < srm.length; i++) {
rm.relations[firstRV.offsets[i]] = srm[i];
}
rm.relations[firstRV.refOffset] = rel;
return new Refinement(rm, secondRef.input);
}
public RefValue extend(int threadId, Document doc, Refinement firstRef, PatternDiscovery.Config patterDiscoverConfig) {
if(firstRef.relations.size() == 0) return null;
RefValue firstRV = getAndChild(firstRef);
if(firstRV != null) {
return firstRV;
}
int firstRefOffset = level;
Integer[] firstOffsets = new Integer[level];
for(int i = 0; i < firstOffsets.length; i++) {
firstOffsets[i] = i;
}
List nextLevelParents = new ArrayList<>();
for(Entry firstParent: parents) {
Node parentNode = firstParent.rv.parent.get(doc);
Relation[] secondParentRelations = new Relation[firstRef.relations.length() - 1];
for(int i = 0; i < firstRef.relations.length(); i++) {
Integer j = firstParent.rv.reverseOffsets[i];
if(j != null) {
secondParentRelations[j] = firstRef.relations.get(i);
}
}
Refinement secondParentRef = new Refinement(new RelationsMap(secondParentRelations), firstRef.input);
RefValue secondParentRV = patterDiscoverConfig != null ?
parentNode.getAndChild(secondParentRef) :
parentNode.extend(threadId, doc, secondParentRef, null);
if(secondParentRV == null) {
continue;
}
Relation[] secondRelations = new Relation[firstParent.ref.relations.length() + 1];
for(int i = 0; i < firstParent.ref.relations.length(); i++) {
int j = secondParentRV.offsets[i];
secondRelations[j] = firstParent.ref.relations.get(i);
}
Relation rel = firstRef.relations.get(firstParent.rv.refOffset);
if(rel != null) {
secondRelations[secondParentRV.refOffset] = rel.invert();
}
Refinement secondRef = new Refinement(new RelationsMap(secondRelations), firstParent.ref.input);
Integer[] secondOffsets = new Integer[secondParentRV.offsets.length + 1];
for(int i = 0; i < firstParent.rv.reverseOffsets.length; i++) {
Integer j = firstParent.rv.reverseOffsets[i];
if(j != null) {
secondOffsets[secondParentRV.offsets[j]] = i;
}
}
secondOffsets[secondParentRV.refOffset] = firstRefOffset;
nextLevelParents.add(new Entry(secondRef, new RefValue(secondOffsets, firstOffsets[firstParent.rv.refOffset], secondParentRV.child)));
}
firstRV = new RefValue(firstOffsets, firstRefOffset, provider);
nextLevelParents.add(new Entry(firstRef, firstRV));
return createAndNode(provider.model, doc, nextLevelParents, level + 1, patterDiscoverConfig) ? firstRV : null;
}
static boolean createAndNode(Model m, Document doc, List parents, int level, PatternDiscovery.Config patterDiscoverConfig) {
if (parents != null) {
// Locking needs to take place in a predefined order.
TreeSet> parentsForLocking = new TreeSet();
for(Entry e: parents) {
parentsForLocking.add(e.rv.parent);
}
for (Provider pn : parentsForLocking) {
pn.get().lock.acquireWriteLock();
}
try {
AndNode nln = new AndNode(m, level, parents);
if(patterDiscoverConfig != null && !patterDiscoverConfig.patternCheck.check(nln)) {
return false;
}
nln.init();
nln.postCreate(doc);
} finally {
for (Provider pn : parentsForLocking) {
pn.get().lock.releaseWriteLock();
}
}
}
return true;
}
@Override
public void changeNumberOfNeuronRefs(int threadId, long v, int d) {
super.changeNumberOfNeuronRefs(threadId, v, d);
parents.forEach(e -> e.rv.parent.get().changeNumberOfNeuronRefs(threadId, v, d));
}
@Override
public void reprocessInputs(Document doc) {
for(Entry e: parents) {
Node> pn = e.rv.parent.get();
for(NodeActivation act : pn.getActivations(doc)) {
act.repropagateV = markedCreated;
act.node.propagate(act);
}
}
}
@Override
public void remove() {
super.remove();
for(Entry e: parents) {
Node pn = e.rv.parent.get();
pn.lock.acquireWriteLock();
pn.removeAndChild(e.ref);
pn.setModified();
pn.lock.releaseWriteLock();
}
}
public String logicToString() {
StringBuilder sb = new StringBuilder();
sb.append("AND(" + level + ")[");
boolean first = true;
for(Entry e: parents) {
if(!first) {
sb.append(",");
}
first = false;
sb.append(e.ref);
}
sb.append("]");
return sb.toString();
}
@Override
public void write(DataOutput out) throws IOException {
out.writeBoolean(false);
out.writeChar('A');
super.write(out);
out.writeInt(parents.size());
for(Entry e: parents) {
e.ref.write(out);
e.rv.write(out);
}
}
@Override
public void readFields(DataInput in, Model m) throws IOException {
super.readFields(in, m);
int s = in.readInt();
for(int i = 0; i < s; i++) {
Refinement ref = Refinement.read(in, m);
RefValue rv = RefValue.read(in, m);
parents.add(new Entry(ref, rv));
}
}
/**
*
*/
public static class Refinement implements Comparable, Writable {
public RelationsMap relations;
public Provider input;
private Refinement() {}
public Refinement(RelationsMap relations, Provider input) {
this.relations = relations;
this.input = input;
}
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("(");
sb.append(relations);
sb.append(input.get().logicToString());
sb.append(")");
return sb.toString();
}
public void write(DataOutput out) throws IOException {
relations.write(out);
out.writeInt(input.id);
}
public void readFields(DataInput in, Model m) throws IOException {
relations = RelationsMap.read(in, m);
input = m.lookupNodeProvider(in.readInt());
}
public static Refinement read(DataInput in, Model m) throws IOException {
Refinement k = new Refinement();
k.readFields(in, m);
return k;
}
@Override
public int compareTo(Refinement ref) {
int r = input.compareTo(ref.input);
if(r != 0) return r;
return relations.compareTo(ref.relations);
}
public boolean contains(Refinement ref, RefValue rv) {
for(int i = 0; i < ref.relations.length(); i++) {
Relation ra = ref.relations.get(i);
Relation rb = relations.get(rv.offsets[i]);
if((ra == null && rb != null) || (ra != null && rb == null)) return false;
if(ra != null && rb != null && ra.compareTo(rb) != 0) {
return false;
}
}
return true;
}
}
public static class RelationsMap implements Comparable, Writable {
public static final RelationsMap MIN = new RelationsMap();
public static final RelationsMap MAX = new RelationsMap();
public Relation[] relations;
public RelationsMap() {}
public RelationsMap(Relation[] relations) {
this.relations = relations;
}
public void write(DataOutput out) throws IOException {
out.writeInt(relations.length);
for(int i = 0; i < relations.length; i++) {
Relation rel = relations[i];
out.writeBoolean(rel != null);
if(rel != null) {
rel.write(out);
}
}
}
public void readFields(DataInput in, Model m) throws IOException {
int l = in.readInt();
relations = new Relation[l];
for(int i = 0; i < l; i++) {
if(in.readBoolean()) {
relations[i] = Relation.read(in, m);
}
}
}
public static RelationsMap read(DataInput in, Model m) throws IOException {
RelationsMap k = new RelationsMap();
k.readFields(in, m);
return k;
}
public String toString() {
StringBuilder sb = new StringBuilder();
for(int i = 0; i < relations.length; i++) {
Relation rel = relations[i];
if(rel != null) {
sb.append(i + ":" + rel + ", ");
}
}
return sb.toString();
}
@Override
public int compareTo(RelationsMap rm) {
if (this == MIN) return -1;
if (rm == MIN) return 1;
if (this == MAX) return 1;
if (rm == MAX) return -1;
int r = Integer.compare(relations.length, rm.relations.length);
if(r != 0) return r;
for(int i = 0; i < relations.length; i++) {
Relation ra = relations[i];
Relation rb = rm.relations[i];
if(ra == null && rb == null) continue;
if(ra == null && rb != null) return -1;
if(ra != null && rb == null) return 1;
r = ra.compareTo(rb);
if(r != 0) return r;
}
return 0;
}
public int length() {
return relations.length;
}
public Relation get(int i) {
return relations[i];
}
public int size() {
if(relations.length == 0) return 0;
int count = 0;
for(int i = 0; i < relations.length; i++) {
if(relations[i] != null) count++;
}
return count;
}
public boolean isExact() {
for(Relation rel: relations) {
if(!rel.isExact()) {
return false;
}
}
return true;
}
}
public static class RefValue implements Writable {
public Integer[] offsets; // input offsets -> output offsets
public Integer[] reverseOffsets; // output offsets -> input offsets
public int refOffset;
public Provider parent;
public Provider child;
private RefValue() {}
public RefValue(Integer[] offsets, int refOffset, Provider parent) {
this.offsets = offsets;
reverseOffsets = new Integer[offsets.length + 1];
for(int i = 0; i < offsets.length; i++) {
reverseOffsets[offsets[i]] = i;
}
this.refOffset = refOffset;
this.parent = parent;
}
@Override
public void write(DataOutput out) throws IOException {
out.writeInt(offsets.length);
for(int i = 0; i < offsets.length; i++) {
Integer ofs = offsets[i];
out.writeBoolean(ofs != null);
out.writeInt(ofs);
}
out.writeInt(refOffset);
out.writeInt(parent.id);
out.writeInt(child.id);
}
public static RefValue read(DataInput in, Model m) throws IOException {
RefValue rv = new RefValue();
rv.readFields(in, m);
return rv;
}
@Override
public void readFields(DataInput in, Model m) throws IOException {
int l = in.readInt();
offsets = new Integer[l];
reverseOffsets = new Integer[l + 1];
for(int i = 0; i < l; i++) {
if(in.readBoolean()) {
Integer ofs = in.readInt();
offsets[i] = ofs;
reverseOffsets[ofs] = i;
}
}
refOffset = in.readInt();
parent = m.lookupNodeProvider(in.readInt());
child = m.lookupNodeProvider(in.readInt());
}
}
public static class Entry {
public Refinement ref;
public RefValue rv;
public Entry(Refinement ref, RefValue rv) {
this.ref = ref;
this.rv = rv;
}
}
public static class AndActivation extends NodeActivation {
public Link[] inputs;
public AndActivation(int id, Document doc, AndNode node) {
super(id, doc, node);
inputs = new Link[node.level];
}
public void link(Refinement ref, RefValue rv, InputActivation refAct, NodeActivation input) {
Link l = new Link(ref, rv, refAct, input, this);
inputs[rv.refOffset] = l;
input.outputsToAndNode.put(id, l);
}
public Activation getInputActivation(int i) {
Link l = inputs[i];
if(l != null) {
return l.refAct.input.input;
} else {
for(int j = 0; j < inputs.length; j++) {
if (j != i) {
l = inputs[j];
if(l != null) {
return l.input.getInputActivation(l.rv.reverseOffsets[i]);
}
}
}
return null;
}
}
public boolean isComplete() {
int numberOfLinks = 0;
for (Link l : inputs) {
if (l != null) numberOfLinks++;
}
return node.parents.size() == numberOfLinks;
}
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("A-ACT(");
boolean first = true;
for(int i = 0; i < inputs.length; i++) {
Activation iAct = getInputActivation(i);
if(iAct != null) {
if(!first) {
sb.append(",");
}
sb.append(i + ":" + iAct.getLabel() + " " + iAct.slotsToString() + " (" + iAct.id + ")");
first = false;
}
}
sb.append(")");
return sb.toString();
}
}
public static class Link {
public Refinement ref;
public RefValue rv;
public NodeActivation input;
public InputActivation refAct;
public AndActivation output;
public Link(Refinement ref, RefValue rv, InputActivation refAct, NodeActivation input, AndActivation output) {
this.ref = ref;
this.rv = rv;
this.refAct = refAct;
this.input = input;
this.output = output;
}
}
}
aika-java-0.24/src/main/java/network/aika/lattice/InputNode.java 0000664 0000000 0000000 00000027272 13373742612 0024534 0 ustar 00root root 0000000 0000000 /*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package network.aika.lattice;
import network.aika.Document;
import network.aika.Model;
import network.aika.Provider;
import network.aika.neuron.INeuron;
import network.aika.neuron.Neuron;
import network.aika.neuron.activation.Position;
import network.aika.neuron.relation.Relation;
import network.aika.neuron.activation.Activation;
import network.aika.PatternDiscovery;
import network.aika.lattice.AndNode.AndActivation;
import network.aika.lattice.InputNode.InputActivation;
import network.aika.lattice.AndNode.Refinement;
import network.aika.lattice.AndNode.RefValue;
import network.aika.lattice.AndNode.RelationsMap;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.util.*;
/**
* The {@code InputNode} class is the input layer for the boolean logic. The input-node has two sources of
* activations. First, it might be underlying logic node of an {@code InputNeuron} in which case the input
* activations come from the outside. The second option is that the activation come from the output of another neuron.
*
* @author Lukas Molzberger
*/
public class InputNode extends Node {
public Neuron inputNeuron;
public TreeMap nonExactAndChildren;
private long visitedDiscover;
public static final Relation[] CANDIDATE_RELATIONS = new Relation[] {
/* new RangeRelation(EQUALS),
new RangeRelation(BEGIN_TO_END_EQUALS),
new RangeRelation(END_TO_BEGIN_EQUALS),
new RangeRelation(BEGIN_EQUALS),
new RangeRelation(END_EQUALS),
new RangeRelation(CONTAINS),
new RangeRelation(CONTAINED_IN),
new AncestorRelation(AncestorRelation.Type.IS_DESCENDANT_OF),
new AncestorRelation(AncestorRelation.Type.IS_ANCESTOR_OF),
new AncestorRelation(AncestorRelation.Type.COMMON_ANCESTOR)*/
};
public InputNode() {
}
public InputNode(Model m) {
super(m, 1);
}
public static InputNode add(Model m, INeuron input) {
if (input.outputNode != null) {
return input.outputNode.get();
}
InputNode in = new InputNode(m);
if (input != null && in.inputNeuron == null) {
in.inputNeuron = input.provider;
input.outputNode = in.provider;
input.setModified();
}
return in;
}
public void addActivation(Activation inputAct) {
if(inputAct.repropagateV != null && inputAct.repropagateV != markedCreated) return;
InputActivation act = new InputActivation(inputAct.doc.logicNodeActivationIdCounter++, inputAct, this);
addActivation(act);
}
public void propagate(InputActivation act) {
apply(act);
}
public void reprocessInputs(Document doc) {
inputNeuron.get(doc).getActivations(doc, false).forEach(act -> {
act.repropagateV = markedCreated;
if(act.upperBound > 0.0) {
act.getINeuron().propagate(act);
}
});
}
void addAndChild(AndNode.Refinement ref, AndNode.RefValue child) {
super.addAndChild(ref, child);
if(!ref.relations.isExact()) {
if (nonExactAndChildren == null) {
nonExactAndChildren = new TreeMap<>();
}
AndNode.RefValue n = nonExactAndChildren.put(ref, child);
assert n == null;
}
}
void removeAndChild(AndNode.Refinement ref) {
super.removeAndChild(ref);
if(!ref.relations.isExact()) {
if (nonExactAndChildren != null) {
nonExactAndChildren.remove(ref);
if (nonExactAndChildren.isEmpty()) {
nonExactAndChildren = null;
}
}
}
}
public RefValue extend(int threadId, Document doc, Refinement ref, PatternDiscovery.Config patterDiscoveryConfig) {
if(ref.relations.size() == 0) return null;
Relation rel = ref.relations.get(0);
if(rel == null) {
return null;
}
RefValue rv = getAndChild(ref);
if(rv != null) {
return rv;
}
List nlParents = new ArrayList<>();
Refinement mirrorRef = new Refinement(new AndNode.RelationsMap(new Relation[]{rel.invert()}), provider);
nlParents.add(new AndNode.Entry(mirrorRef, new RefValue(new Integer[] {1}, 0, ref.input)));
rv = new RefValue(new Integer[] {0}, 1, provider);
nlParents.add(new AndNode.Entry(ref, rv));
return AndNode.createAndNode(provider.model, doc, nlParents, level + 1, patterDiscoveryConfig) ? rv : null;
}
/**
* @param act
*/
@Override
void apply(InputActivation act) {
try {
lock.acquireReadLock();
if (andChildren != null) {
TreeMap children;
if(andChildren.size() > 10) {
children = nonExactAndChildren;
applyExactRelations(act);
} else {
children = andChildren;
}
if(children != null) {
children.forEach((ref, rv) -> {
InputNode in = ref.input.getIfNotSuspended();
if (in != null) {
addNextLevelActivations(in, ref, rv.child.get(act.doc), act);
}
});
}
}
} finally {
lock.releaseReadLock();
}
OrNode.processCandidate(this, act, false);
}
private void applyExactRelations(InputActivation act) {
Activation iAct = act.input.input;
for (Map.Entry me : iAct.slots.entrySet()) {
for (Activation linkedAct : act.doc.getActivationsByPosition(me.getValue(), true, me.getValue(), true)) {
Provider in = linkedAct.getINeuron().outputNode;
for (Map.Entry mea : andChildren.subMap(
new Refinement(RelationsMap.MIN, in),
new Refinement(RelationsMap.MAX, in)).entrySet()) {
addNextLevelActivations(in.get(act.doc), mea.getKey(), mea.getValue().child.get(act.doc), act);
}
}
}
}
private static void addNextLevelActivations(InputNode secondNode, Refinement ref, AndNode nln, InputActivation act) {
Document doc = act.doc;
INeuron.ThreadState th = secondNode.inputNeuron.get().getThreadState(doc.threadId, false);
if (th == null || th.isEmpty()) return;
Activation iAct = act.input.input;
if(act.repropagateV != null && act.repropagateV != nln.markedCreated) return;
ref.relations.get(0).getActivations(secondNode.inputNeuron.get(doc), iAct)
.forEach(secondIAct -> {
if (secondIAct.outputToInputNode != null) {
InputActivation secondAct = secondIAct.outputToInputNode.output;
if (secondAct != null) {
// if (!Conflicts.isConflicting(iAct, secondIAct)) {
AndActivation oAct = new AndActivation(doc.logicNodeActivationIdCounter++, doc, nln);
for (AndNode.Entry e : nln.parents) {
boolean match = e.ref.compareTo(ref) == 0;
oAct.link(e.ref, e.rv, match ? secondAct : act, match ? act : secondAct);
}
nln.addActivation(oAct);
// }
}
}
}
);
}
@Override
public void discover(InputActivation act, PatternDiscovery.Config config) {
if(!act.input.input.isFinalActivation()) {
return;
}
Document doc = act.doc;
doc.getActivations(true).forEach(secondNAct -> {
InputActivation secondAct = secondNAct.outputToInputNode.output;
if (act != secondAct && config.candidateCheck.check(act, secondAct)) {
List relations = getRelations(act.input.input, secondNAct);
for(Relation r: relations) {
InputNode in = secondAct.node;
if (r != null) {
RelationsMap rm = new RelationsMap(new Relation[] {r});
Refinement ref = new Refinement(rm, in.provider);
AndNode.RefValue rv = extend(doc.threadId, doc, ref, config);
if (rv != null) {
AndNode nln = rv.child.get();
nln.isDiscovered = true;
}
}
}
}
});
}
public static List getRelations(Activation act1, Activation act2) {
ArrayList rels = new ArrayList<>();
for(Relation rel: CANDIDATE_RELATIONS) {
if(rel.test(act2, act1)) {
rels.add(rel);
break;
}
}
return rels;
}
@Override
public void cleanup() {
}
public String logicToString() {
StringBuilder sb = new StringBuilder();
sb.append("I");
sb.append("[");
if (inputNeuron != null) {
sb.append(inputNeuron.id);
if (inputNeuron.getLabel() != null) {
sb.append(",");
sb.append(inputNeuron.getLabel());
}
}
sb.append("]");
return sb.toString();
}
@Override
public void write(DataOutput out) throws IOException {
out.writeBoolean(false);
out.writeChar('I');
super.write(out);
out.writeBoolean(inputNeuron != null);
if (inputNeuron != null) {
out.writeInt(inputNeuron.id);
}
}
@Override
public void readFields(DataInput in, Model m) throws IOException {
super.readFields(in, m);
if (in.readBoolean()) {
inputNeuron = m.lookupNeuron(in.readInt());
}
}
public static class InputActivation extends NodeActivation {
public Link input;
public InputActivation(int id, Activation iAct, InputNode node) {
super(id, iAct.doc, node);
input = new Link(iAct, this);
iAct.outputToInputNode = input;
}
public Activation getInputActivation(int i) {
assert i == 0;
return input.input;
}
public String toString() {
return "I-ACT(" + input.input.getLabel() + " " + input.input.slotsToString() + ")";
}
}
public static class Link {
public Activation input;
public InputActivation output;
public Link(Activation iAct, InputActivation oAct) {
input = iAct;
output = oAct;
}
}
}
aika-java-0.24/src/main/java/network/aika/lattice/Node.java 0000664 0000000 0000000 00000027030 13373742612 0023504 0 ustar 00root root 0000000 0000000 /*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package network.aika.lattice;
import network.aika.*;
import network.aika.PatternDiscovery;
import network.aika.Document;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.io.*;
import java.util.*;
import java.util.concurrent.atomic.AtomicInteger;
/**
* The {@code Node} class is the abstract class for all the boolean logic nodes underneath the neural network layer.
* These nodes form a boolean representation for all the neurons of the neural network. Whenever changes occur to the
* synapse weights in the neural layer, then the structure of the boolean representation needs to be adjusted. Several
* neurons, however, might share common substructures in this boolean representation. The {@code InputNode} and
* the {@code AndNode} classes together form a pattern lattice, containing all possible substructures of any
* given conjunction. For example if we have the conjunction ABCD where A, B, C, D are the inputs then the
* pattern lattice will contain the nodes ABCD, ABC, ABD, ACD, BCD, AB, AC, AD, BC, BD, CD, A, B, C, D. The class
* {@code OrNode} is a disjunction of either input-nodes or and-nodes. The or-node is connected with one of
* the neurons.
*