pax_global_header00006660000000000000000000000064135254301200014505gustar00rootroot0000000000000052 comment=7e6b916a2d2bd9e4051e3e50ad9248a8b250fd96 aika-java-1.4.0/000077500000000000000000000000001352543012000133335ustar00rootroot00000000000000aika-java-1.4.0/.gitignore000066400000000000000000000003241352543012000153220ustar00rootroot00000000000000# 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-1.4.0/LICENSE.txt000066400000000000000000000261341352543012000151640ustar00rootroot00000000000000 Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. 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You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. 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While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. 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 "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. 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 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. aika-java-1.4.0/NOTICE.txt000066400000000000000000000000531352543012000150530ustar00rootroot00000000000000AIKA Copyright 2015-2017 Lukas Molzberger aika-java-1.4.0/README.md000066400000000000000000000007071352543012000146160ustar00rootroot00000000000000# 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-1.4.0/pom.xml000066400000000000000000000161531352543012000146560ustar00rootroot00000000000000 4.0.0 org.aika-software aika jar 1.4.0 aika http://aika.network An artificial intelligence for knowledge acquisition Lukas Molzberger Lukas Molzberger contact@aika-software.org Apache License, Version 2.0 http://www.apache.org/licenses/LICENSE-2.0.txt repo https://github.com/aika-algorithm/aika scm:git:git://github.com/aika-algorithm/aika.git scm:git:git@github.com:aika-algorithm/aika.git aika-1.4.0 ossrh https://oss.sonatype.org/content/repositories/snapshots ossrh https://oss.sonatype.org/service/local/staging/deploy/maven2/ 4.12 3.6.1 1.2.3 1.7.25 org.apache.maven.plugins maven-compiler-plugin 3.3 11 11 utf-8 org.apache.maven.plugins maven-surefire-plugin 2.18.1 always -Xmx8000m **/*Test.java org.apache.maven.plugins maven-javadoc-plugin attach-javadocs jar -Xdoclint:none org.apache.maven.plugins maven-source-plugin 2.2.1 attach-sources jar-no-fork false ../src/main/resources **/* false ../src/test/resources **/* org.apache.commons commons-math3 ${commons-math.version} org.slf4j slf4j-api ${slf4j.version} ch.qos.logback logback-core ${logback.version} test ch.qos.logback logback-classic ${logback.version} test junit junit ${junit.version} test deploy_artifact org.apache.maven.plugins maven-gpg-plugin 1.5 sign-artifacts verify sign org.sonatype.plugins nexus-staging-maven-plugin 1.6.7 true ossrh https://oss.sonatype.org/ true aika-java-1.4.0/src/000077500000000000000000000000001352543012000141225ustar00rootroot00000000000000aika-java-1.4.0/src/main/000077500000000000000000000000001352543012000150465ustar00rootroot00000000000000aika-java-1.4.0/src/main/java/000077500000000000000000000000001352543012000157675ustar00rootroot00000000000000aika-java-1.4.0/src/main/java/network/000077500000000000000000000000001352543012000174605ustar00rootroot00000000000000aika-java-1.4.0/src/main/java/network/aika/000077500000000000000000000000001352543012000203655ustar00rootroot00000000000000aika-java-1.4.0/src/main/java/network/aika/AbstractNode.java000066400000000000000000000033571352543012000236110ustar00rootroot00000000000000/* * 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

> implements Writable { public volatile int lastUsedDocumentId = 0; public volatile boolean modified; protected P provider; public P getProvider() { return provider; } 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 = p.getModel().readNeuron(in, (Neuron) p); } else { n = Node.readNode(in, p); } return n; } } aika-java-1.4.0/src/main/java/network/aika/ActivationFunction.java000066400000000000000000000011631352543012000250400ustar00rootroot00000000000000package network.aika; /** * * @author Lukas Molzberger */ public enum ActivationFunction { NULL_FUNCTION(x -> 0.0), 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); } public static double sigmoid(double x) { return 1.0 / (1.0 + Math.pow(Math.E, (-x))); } } aika-java-1.4.0/src/main/java/network/aika/Document.java000066400000000000000000000431351352543012000230140ustar00rootroot00000000000000/* * 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.Converter; import network.aika.lattice.Node; import network.aika.lattice.NodeActivation; import network.aika.lattice.NodeQueue; import network.aika.neuron.INeuron; import network.aika.neuron.Synapse; import network.aika.neuron.activation.Activation; import network.aika.neuron.activation.Activation.OscillatingActivationsException; import network.aika.neuron.activation.Position; import network.aika.neuron.activation.link.Linker; import network.aika.neuron.activation.search.SearchNode; import network.aika.neuron.activation.search.SearchNode.TimeoutException; import network.aika.neuron.activation.*; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import java.util.*; import java.util.stream.Collectors; import static network.aika.neuron.INeuron.Type.*; import static network.aika.neuron.activation.Activation.CANDIDATE_COMP; import static network.aika.neuron.activation.search.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; private final int id; private final StringBuilder content; private long visitedCounter = 1; private int activationIdCounter = 0; private int nodeActivationIdCounter = 0; public int searchNodeIdCounter = 0; public int searchStepCounter = 0; public int positionIdCounter = 0; private Model model; private int threadId; private NodeQueue nodeQueue = new NodeQueue(this); private ValueQueue valueQueue = new ValueQueue(); private UpperBoundQueue ubQueue = new UpperBoundQueue(); private Linker linker; private TreeMap positions = new TreeMap<>(); private TreeSet activatedNodes = new TreeSet<>(); private TreeSet activatedNeurons = new TreeSet<>(); private TreeSet finallyActivatedNeurons = new TreeSet<>(); private TreeSet inputNeuronActivations = new TreeSet<>(); private TreeMap> modifiedWeights = 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.neuron.compareTo(ak2.neuron); 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.neuron.compareTo(ak2.neuron); if (r != 0) return r; return Integer.compare(ak1.actId, ak2.actId); }); private TreeMap activationsById = new TreeMap<>(); private static class ActKey { int slot; Position pos; INeuron neuron; int actId; public ActKey(int slot, Position pos, INeuron neuron, int actId) { this.slot = slot; this.pos = pos; this.neuron = neuron; 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.lookupSlot(Activation.BEGIN), act2.lookupSlot(Activation.BEGIN)); if (r != 0) return r; r = act1.getINeuron().compareTo(act2.getINeuron()); if (r != 0) return r; return Integer.compare(act1.getId(), act2.getId()); }; public Document(Model model, String content) { this(model, content, 0); } public Document(Model model, String content, int threadId) { this(model, model.getNewDocumentId(), content, threadId); } public Document(Model model, int id, String content, int threadId) { this.id = id; this.content = new StringBuilder(content); this.model = model; this.threadId = threadId; this.linker = initLinker(); model.acquireThread(threadId, this); } protected Linker initLinker() { return new Linker(this); } public int getId() { return id; } public Model getModel() { return model; } public Linker getLinker() { return linker; } public ValueQueue getValueQueue() { return valueQueue; } public long getNewVisitedId() { return visitedCounter++; } public int getNewActivationId() { return activationIdCounter++; } public int getNewNodeActivationId() { return nodeActivationIdCounter++; } public void addActivatedNode(Node n) { activatedNodes.add(n); } public void addInputNeuronActivation(Activation act) { inputNeuronActivations.add(act); } public void addFinallyActivatedNeuron(INeuron n) { finallyActivatedNeurons.add(n); } public void addActivatedNeuron(INeuron n) { activatedNeurons.add(n); } public int getThreadId() { return threadId; } 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 UpperBoundQueue getUpperBoundQueue() { return ubQueue; } public NodeQueue getNodeQueue() { return nodeQueue; } 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) { if(begin == null || end == null) { return ""; } 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.getSlots().entrySet()) { Position pos = me.getValue(); if (pos != null && pos.getFinalPosition() != null) { ActKey dak = new ActKey(me.getKey(), pos, act.getINeuron(), act.getId()); activationsBySlotAndPosition.put(dak, act); activationsByPosition.put(dak, act); } } activationsById.put(act.getId(), 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, INeuron.MIN_NEURON, Integer.MIN_VALUE), fromInclusive, new Document.ActKey(toSlot, toPos, INeuron.MAX_NEURON, Integer.MAX_VALUE), toInclusive ).values(); } public Collection getActivationsByPosition(Position fromPos, boolean fromInclusive, Position toPos, boolean toInclusive) { return activationsByPosition.subMap( new Document.ActKey(-1, fromPos, INeuron.MIN_NEURON, Integer.MIN_VALUE), fromInclusive, new Document.ActKey(-1, toPos, INeuron.MAX_NEURON, Integer.MAX_VALUE), toInclusive ).values(); } public Activation getNextActivation(Activation currentAct) { Map.Entry me = currentAct == null ? activationsById.firstEntry() : activationsById.higherEntry(currentAct.getId()); 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) { nodeQueue.process(); flag = ubQueue.process(); } } public void generateCandidates() throws CyclicDependencyException { TreeSet tmp = new TreeSet<>(CANDIDATE_COMP); int i = 0; for (Activation act : activationsById.values()) { if (act.getType() == EXCITATORY && act.getDecision() == UNKNOWN && act.getUpperBound() > 0.0) { act.setCandidateId(i++); tmp.add(act); } } long v = visitedCounter++; for(Activation act: inputNeuronActivations) { act.markHasCandidate(v); } while (!tmp.isEmpty()) { int oldSize = tmp.size(); for (Activation act : tmp) { if (act.checkDependenciesSatisfied(v)) { tmp.remove(act); act.setCandidateId(candidates.size()); candidates.add(act); act.markHasCandidate(v); break; } } if(tmp.size() == oldSize) { log.info(activationsToString()); throw new CyclicDependencyException(); } } } /** * 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() throws TimeoutException, CyclicDependencyException, OscillatingActivationsException { process(null); } public void process(Long timeoutInMilliSeconds) throws TimeoutException, CyclicDependencyException, OscillatingActivationsException { linker.lateLinking(); inputNeuronActivations.forEach(act -> { valueQueue.propagateActivationValue(act, null, true, true); }); generateCandidates(); selectedSearchNode = new SearchNode(this, null, null, 0); selectedSearchNode.updateActivations(this); storeFinalState(); SearchNode rootNode = selectedSearchNode; SearchNode.search(this, selectedSearchNode, visitedCounter++, timeoutInMilliSeconds); for(Activation act: activationsById.values()) { if(act.isFinalActivation()) { finallyActivatedNeurons.add(act.getINeuron()); } } if(SearchNode.COMPUTE_SOFT_MAX) { SearchNode.computeCachedFactor(rootNode); computeOptionProbabilities(); } } public void storeFinalState() { for(Activation act: activationsById.values()) { act.finalOption = act.currentOption; } } private void computeOptionProbabilities() { for (Activation act : activationsById.values()) { act.computeOptionProbabilities(); } } public void dumpDebugCandidateStatistics() { for (Activation act : candidates) { log.info(act.searchStateToString()); } } public void notifyWeightModified(Synapse synapse) { Set is = modifiedWeights.get(synapse.getOutput().get()); if(is == null) { is = new TreeSet<>(Synapse.INPUT_SYNAPSE_COMP); modifiedWeights.put(synapse.getOutput().get(), is); } is.add(synapse); } public Map> getModifiedWeights() { return modifiedWeights; } /** * 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) -> { n.commit(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; n.clearThreadState(threadId, id - CLEANUP_INTERVAL); } }); } model.docs[threadId] = null; } public String generateOutputText() { int oldLength = length(); TreeSet queue = new TreeSet<>(Comparator.comparingInt(p -> p.getId())); for(Activation act: activationsById.values()) { if(act.getINeuron().getOutputText() != null && act.lookupSlot(Activation.BEGIN).getFinalPosition() != null && act.lookupSlot(Activation.END).getFinalPosition() == null) { queue.add(act.lookupSlot(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.lookupSlot(Activation.END); nextPos.setFinalPosition(pos.getFinalPosition() + outText.length()); content.replace(act.lookupSlot(Activation.BEGIN).getFinalPosition(), act.lookupSlot(Activation.END).getFinalPosition(), outText); queue.add(nextPos); }); } return content.substring(oldLength, length()); } public String activationsToString() { Set acts = new TreeSet<>(ACTIVATIONS_OUTPUT_COMPARATOR); acts.addAll(activationsById.values()); StringBuilder sb = new StringBuilder(); sb.append("Id -"); sb.append(" Decision -"); sb.append(" Range | Text Snippet"); sb.append(" | Identity -"); sb.append(" Neuron Label -"); sb.append(" Upper Bound -"); sb.append(" Value | Net | Weight -"); sb.append(" Input Value |"); sb.append(" Target Value"); sb.append("\n"); sb.append("\n"); for(Activation act: acts) { if(act.getUpperBound() <= 0.0 && (act.getTargetValue() == null || act.getTargetValue() <= 0.0)) { continue; } sb.append(act.toStringDetailed()); sb.append("\n"); } if(selectedSearchNode != null) { sb.append("\n Final SearchNode:" + selectedSearchNode.getId() + " WeightSum:" + selectedSearchNode.getAccumulatedWeight() + "\n"); } return sb.toString(); } public static class CyclicDependencyException extends RuntimeException { public CyclicDependencyException() { super("Cycle detected in the activations that is not marked recurrent."); } } } aika-java-1.4.0/src/main/java/network/aika/Model.java000066400000000000000000000171621352543012000222770ustar00rootroot00000000000000/* * 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.neuron.INeuron; import network.aika.neuron.INeuron.Type; import network.aika.neuron.Neuron; import network.aika.Provider.SuspensionMode; import network.aika.neuron.Synapse; import network.aika.neuron.activation.search.SearchNode; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; 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; 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 Neuron createNeuron(Type type) { return createNeuron(null, type); } public Neuron createNeuron(String label, Type type) { return createNeuron(label, type, type.getDefaultActivationFunction(), null); } public Neuron createNeuron(String label, Type type, ActivationFunction actF) { return new INeuron(this, label, null, type, actF).getProvider(); } public Neuron createNeuron(String label, Type type, String outputText) { return new INeuron(this, label, outputText, type, type.getDefaultActivationFunction()).getProvider(); } public Neuron createNeuron(String label, Type type, ActivationFunction actF, String outputText) { return new INeuron(this, label, outputText, type, actF).getProvider(); } public INeuron readNeuron(DataInput in, Neuron p) throws IOException { INeuron n = new INeuron(p); n.readFields(in, this); return n; } public Synapse readSynapse(DataInput in) throws IOException { Synapse s = new Synapse(); s.readFields(in, this); return s; } public void writeSynapse(Synapse s, DataOutput out) throws IOException { s.write(out); } public int getNewDocumentId() { return docIdCounter.addAndGet(1); } public void acquireThread(int threadId, Document doc) { if (docs[threadId] != null) { throw new StaleDocumentException(); } docs[threadId] = doc; } public Collection getActiveNeurons() { List tmp = new ArrayList<>(); for(Provider p: activeProviders.values()) { if(p instanceof Neuron) { tmp.add((Neuron) p); } } return tmp; } 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 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.getId()); } 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 static class StaleDocumentException extends RuntimeException { public StaleDocumentException() { super("Two documents are using the same thread. Call clearActivations() first, before processing the next document."); } } } aika-java-1.4.0/src/main/java/network/aika/PassiveInputFunction.java000066400000000000000000000004071352543012000253710ustar00rootroot00000000000000package network.aika; import network.aika.neuron.Synapse; import network.aika.neuron.activation.Activation; /** * * @author Lukas Molzberger */ public interface PassiveInputFunction { double getActivationValue(Synapse s, Activation outputActivation); } aika-java-1.4.0/src/main/java/network/aika/Provider.java000066400000000000000000000071501352543012000230250ustar00rootroot00000000000000package network.aika; import java.io.*; import java.lang.ref.WeakReference; import java.util.zip.GZIPInputStream; import java.util.zip.GZIPOutputStream; /** * * @author Lukas Molzberger */ public class Provider implements Comparable> { protected Model model; protected 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 Integer getId() { return id; } public Model getModel() { return model; } 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.getId()) : 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-1.4.0/src/main/java/network/aika/ReadWriteLock.java000066400000000000000000000054121352543012000237310ustar00rootroot00000000000000/* * 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-1.4.0/src/main/java/network/aika/SuspensionHook.java000066400000000000000000000024741352543012000242260ustar00rootroot00000000000000/* * 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-1.4.0/src/main/java/network/aika/Utils.java000066400000000000000000000032041352543012000223270ustar00rootroot00000000000000/* * 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 Utils { public static double round(double x) { return Math.round(x * 1000.0) / 1000.0; } 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 String addPadding(String s, int targetSize) { StringBuilder sb = new StringBuilder(); sb.append(s); for(int i = s.length(); i < targetSize; i++) { sb.append(' '); } return sb.toString(); } public static Integer max(Integer a, Integer b) { if(a == null) return b; if(b == null) return a; return Math.max(a, b); } } aika-java-1.4.0/src/main/java/network/aika/Writable.java000066400000000000000000000030341352543012000230010ustar00rootroot00000000000000/* * 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-1.4.0/src/main/java/network/aika/lattice/000077500000000000000000000000001352543012000220125ustar00rootroot00000000000000aika-java-1.4.0/src/main/java/network/aika/lattice/AndNode.java000066400000000000000000000261401352543012000241700ustar00rootroot00000000000000/* * 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.lattice.activation.AndActivation; import network.aika.lattice.activation.InputActivation; import network.aika.lattice.refinement.RefValue; import network.aika.lattice.refinement.Refinement; import network.aika.lattice.refinement.RelationsMap; import network.aika.neuron.relation.Relation; 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(); } } @Override protected void propagate(AndActivation act) { if (andChildren != null) { for (AndActivation.Link fl : act.inputs) { if(fl == null) continue; NodeActivation pAct = fl.input; for (AndActivation.Link sl : pAct.outputsToAndNode.values()) { NodeActivation secondAct = sl.output; if (act != secondAct) { applyIntern(act, fl.refAct, fl.ref, fl.rv, secondAct, sl.refAct, sl.ref, sl.rv); } } } } propagateToOrNode(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); } } private void applyIntern(AndActivation act, InputActivation refAct, Refinement ref, RefValue rv, NodeActivation secondAct, InputActivation secondRefAct, Refinement secondRef, RefValue secondRv) { Document doc = act.getDocument(); 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(doc); AndActivation nlAct = lookupAndActivation(act, nRef); if(nlAct == null) { nlAct = new AndActivation(doc, nlNode); nlAct.link(nRef, nRv, secondRefAct, act); } nlAct.getNode().addActivation(nlAct); for(Entry secondNE: nlNode.parents) { if(secondNE.rv.parent.get(doc) == secondAct.getNode() && secondNE.ref.contains(ref, secondRv)) { nlAct.link(secondNE.ref, secondNE.rv, refAct, secondAct); break; } } } } lock.releaseReadLock(); } private AndActivation lookupAndActivation(NodeActivation input, Refinement ref) { for (AndActivation.Link l : input.outputsToAndNode.values()) { if(l.ref.compareTo(ref) == 0) { return l.output; } } return null; } RefValue expand(int threadId, Document doc, Refinement firstRef) { if(!firstRef.isConvertible()) 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); Refinement secondParentRef = new Refinement(getParentRelations(firstRef, firstParent), firstRef.input); RefValue secondParentRV = parentNode.expand(threadId, doc, secondParentRef); if(secondParentRV == null) { continue; } Refinement secondRef = new Refinement(getRelations(firstRef, firstParent, secondParentRV), firstParent.ref.input); RefValue secondRV = new RefValue(getOffsets(firstRefOffset, firstParent, secondParentRV), firstOffsets[firstParent.rv.refOffset], secondParentRV.child); nextLevelParents.add(new Entry(secondRef, secondRV)); } firstRV = new RefValue(firstOffsets, firstRefOffset, provider); nextLevelParents.add(new Entry(firstRef, firstRV)); return createAndNode(provider.getModel(), doc, nextLevelParents, level + 1) ? firstRV : null; } private RelationsMap getParentRelations(Refinement firstRef, Entry firstParent) { 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); } } return new RelationsMap(secondParentRelations); } private static RelationsMap getRelations(Refinement firstRef, Entry firstParent, RefValue secondParentRV) { 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(); } return new RelationsMap(secondRelations); } private static Integer[] getOffsets(int firstRefOffset, Entry firstParent, RefValue secondParentRV) { 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; return secondOffsets; } static boolean createAndNode(Model m, Document doc, List parents, int level) { 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); 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.getNode().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 Entry { public Refinement ref; public RefValue rv; public Entry(Refinement ref, RefValue rv) { this.ref = ref; this.rv = rv; } } } aika-java-1.4.0/src/main/java/network/aika/lattice/Converter.java000066400000000000000000000232321352543012000246260ustar00rootroot00000000000000/* * 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.lattice.refinement.RefValue; import network.aika.lattice.refinement.Refinement; import network.aika.lattice.refinement.RelationsMap; import network.aika.neuron.INeuron; import network.aika.neuron.INeuron.SynapseSummary; import network.aika.neuron.Synapse; import network.aika.neuron.relation.Relation; import java.util.*; import static network.aika.neuron.Synapse.State.CURRENT; /** * 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 = 10; public static Comparator SYNAPSE_COMP = (s1, s2) -> { int r = Boolean.compare( s2.linksAnyOutput() || s2.isIdentity(), s1.linksAnyOutput() || s1.isIdentity() ); if (r != 0) return r; r = Double.compare(s2.getWeight(), s1.getWeight()); if (r != 0) return r; return Integer.compare(s1.getId(), s2.getId()); }; 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.getInputNode().get(); SynapseSummary ss = neuron.getSynapseSummary(); if(neuron.getTotalBias(CURRENT) + ss.getPosDirSum() + ss.getPosRecSum() <= 0.0) { outputNode.removeParents(threadId); return false; } switch(neuron.getType()) { case EXCITATORY: if(hasOnlyWeakSynapses()) { convertWeakSynapses(); } else { convertConjunction(); } break; case INHIBITORY: convertDisjunction(); break; case INPUT: break; } return true; } private void convertConjunction() { SynapseSummary ss = neuron.getSynapseSummary(); outputNode.removeParents(threadId); List candidates = prepareCandidates(); double sum = 0.0; NodeContext nodeContext = null; double remainingSum = ss.getPosDirSum(); int i = 0; boolean optionalInputMode = false; for (Synapse s : candidates) { double v = s.getMaxInputValue(); boolean belowThreshold = sum + v + remainingSum + ss.getPosRecSum() + ss.getPosPassiveSum() + neuron.getTotalBias(CURRENT) <= 0.0; if (belowThreshold) { return; } if(sum + remainingSum - v + ss.getPosRecSum() + ss.getPosPassiveSum() + neuron.getTotalBias(CURRENT) > 0.0) { optionalInputMode = true; } if (!optionalInputMode) { NodeContext nlNodeContext = expandNode(nodeContext, s); if (nlNodeContext == null) { return; } nodeContext = nlNodeContext; remainingSum -= v; sum += v; i++; } else { NodeContext nlNodeContext = expandNode(nodeContext, s); if (nlNodeContext != null) { outputNode.addInput(nlNodeContext.getSynapseIds(), threadId, nlNodeContext.node, true); remainingSum -= v; } } final boolean sumOfSynapseWeightsAboveThreshold = sum + ss.getPosRecSum() + ss.getPosPassiveSum() + neuron.getTotalBias(CURRENT) > 0.0; final boolean maxAndNodesReached = i >= MAX_AND_NODE_SIZE; if (sumOfSynapseWeightsAboveThreshold || maxAndNodesReached) { break; } } if(nodeContext != null && !optionalInputMode) { outputNode.addInput(nodeContext.getSynapseIds(), threadId, nodeContext.node, true); } } private boolean hasOnlyWeakSynapses() { for(Synapse s: neuron.getInputSynapses()) { if(!s.isWeak(CURRENT)) { return false; } } return true; } private void convertWeakSynapses() { TreeSet synapsesSortedByWeight = new TreeSet<>((s1, s2) -> { int r = Double.compare(s2.getWeight(), s1.getWeight()); if(r != 0) return r; return SYNAPSE_COMP.compare(s1, s2); }); synapsesSortedByWeight.addAll(neuron.getInputSynapses()); double sum = 0.0; for (Synapse s : synapsesSortedByWeight) { if (!s.isRecurrent()) { sum += s.getWeight(); NodeContext nlNodeContext = expandNode(null, s); outputNode.addInput(nlNodeContext.getSynapseIds(), threadId, nlNodeContext.node, true); if(sum > neuron.getBias()) { // break; // siehe: AIKA-1 } } } } private void convertDisjunction() { for (Synapse s : modifiedSynapses) { if (!s.isRecurrent() && !s.isWeak(CURRENT)) { NodeContext nlNodeContext = expandNode(null, s); outputNode.addInput(nlNodeContext.getSynapseIds(), threadId, nlNodeContext.node, false); } } } private List prepareCandidates() { Synapse syn = getStrongestSynapse(neuron.getInputSynapses()); if(syn == null) { return Collections.EMPTY_LIST; } TreeSet alreadyCollected = new TreeSet<>(); ArrayList selectedCandidates = new ArrayList<>(); TreeMap relatedSyns = new TreeMap<>(); while(syn != null && selectedCandidates.size() < MAX_AND_NODE_SIZE) { relatedSyns.remove(syn.getId()); selectedCandidates.add(syn); alreadyCollected.add(syn.getId()); for(Map.Entry me: syn.getRelations().entrySet()) { Integer relId = me.getKey(); Relation rel = me.getValue(); if(!alreadyCollected.contains(relId)) { Synapse rs = syn.getOutput().getSynapseById(relId); if(rs != null) { relatedSyns.put(relId, rs); } } } syn = getStrongestSynapse(relatedSyns.values()); } return selectedCandidates; } private Synapse getStrongestSynapse(Collection synapses) { Synapse maxSyn = null; for(Synapse s: synapses) { if(!s.isNegative(CURRENT) && !s.isRecurrent() && !s.isInactive() && !s.getInput().get().isPassiveInputNeuron()) { if(maxSyn == null || SYNAPSE_COMP.compare(maxSyn, s) > 0) { maxSyn = s; } } } return maxSyn; } private NodeContext expandNode(NodeContext nc, Synapse s) { if (nc == null) { NodeContext nln = new NodeContext(); nln.node = s.getInput().get().getOutputNode().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.getId()); } NodeContext nln = new NodeContext(); nln.offsets = new Synapse[nc.offsets.length + 1]; Refinement ref = new Refinement(new RelationsMap(relations), s.getInput().get().getOutputNode()); RefValue rv = nc.node.expand(threadId, doc, ref); 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].getId(); } return result; } } } aika-java-1.4.0/src/main/java/network/aika/lattice/InputNode.java000066400000000000000000000201401352543012000245570ustar00rootroot00000000000000/* * 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.lattice.activation.AndActivation; import network.aika.lattice.activation.InputActivation; import network.aika.lattice.refinement.RefValue; import network.aika.lattice.refinement.Refinement; import network.aika.lattice.refinement.RelationsMap; import network.aika.neuron.Neuron; import network.aika.neuron.activation.Position; import network.aika.neuron.relation.Relation; import network.aika.neuron.activation.Activation; 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 static int CHILD_NODE_THRESHOLD = 10; private Neuron inputNeuron; private TreeMap nonExactAndChildren; public InputNode() { } public InputNode(Model m) { super(m, 1); } public void setInputNeuron(Neuron n) { inputNeuron = n; } public void addActivation(Activation inputAct) { // if(inputAct.repropagateV != null && inputAct.repropagateV != markedCreated) return; InputActivation act = new InputActivation(inputAct, this); addActivation(act); } public void reprocessInputs(Document doc) { inputNeuron.get(doc).getActivations(doc, false).forEach(act -> { // act.repropagateV = markedCreated; if(act.getUpperBound() > 0.0) { act.getINeuron().propagate(act); } }); } void addAndChild(Refinement ref, RefValue child) { super.addAndChild(ref, child); if(!ref.relations.isExact()) { if (nonExactAndChildren == null) { nonExactAndChildren = new TreeMap<>(); } RefValue n = nonExactAndChildren.put(ref, child); assert n == null; } } void removeAndChild(Refinement ref) { super.removeAndChild(ref); if(!ref.relations.isExact()) { if (nonExactAndChildren != null) { nonExactAndChildren.remove(ref); if (nonExactAndChildren.isEmpty()) { nonExactAndChildren = null; } } } } public RefValue expand(int threadId, Document doc, Refinement ref) { if(!ref.isConvertible()) 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 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.getModel(), doc, nlParents, level + 1) ? rv : null; } /** * @param act */ @Override protected void propagate(InputActivation act) { try { lock.acquireReadLock(); if (andChildren != null) { TreeMap children; if(andChildren.size() > CHILD_NODE_THRESHOLD) { children = nonExactAndChildren; propagateWithExactRelations(act); } else { children = andChildren; } if(children != null) { children.forEach((ref, rv) -> { InputNode in = ref.input.getIfNotSuspended(); if (in != null) { in.addNextLevelActivations(ref, rv.child.get(act.getDocument()), act); } }); } } } finally { lock.releaseReadLock(); } propagateToOrNode(act); } private void propagateWithExactRelations(InputActivation act) { Activation iAct = act.input; Document doc = act.getDocument(); for (Map.Entry me : iAct.getSlots().entrySet()) { for (Activation linkedAct : act.getDocument().getActivationsByPosition(me.getValue(), true, me.getValue(), true)) { Provider in = linkedAct.getINeuron().getOutputNode(); for (Map.Entry mea : andChildren.subMap( new Refinement(RelationsMap.MIN, in), new Refinement(RelationsMap.MAX, in)).entrySet()) { in.get(doc).addNextLevelActivations(mea.getKey(), mea.getValue().child.get(doc), act); } } } } private void addNextLevelActivations(Refinement ref, AndNode nln, InputActivation act) { Document doc = act.getDocument(); if(inputNeuron.get().isEmpty(doc)) return; Activation iAct = act.input; if(act.repropagateV != null && act.repropagateV != nln.markedCreated) return; ref.relations.get(0).getActivations(inputNeuron.get(doc), iAct) .filter(secondIAct -> secondIAct.getOutputNodeActivation() != null) .map(secondIAct -> secondIAct.getOutputNodeActivation()) .filter(secondAct -> secondAct != null && secondAct.registered) .forEach(secondAct -> { // if (!Conflicts.isConflicting(iAct, secondIAct)) { AndActivation oAct = new AndActivation(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 cleanup() { } public String logicToString() { StringBuilder sb = new StringBuilder(); sb.append("I"); sb.append("["); if (inputNeuron != null) { sb.append(inputNeuron.getId()); 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.getId()); } } @Override public void readFields(DataInput in, Model m) throws IOException { super.readFields(in, m); if (in.readBoolean()) { inputNeuron = m.lookupNeuron(in.readInt()); } } } aika-java-1.4.0/src/main/java/network/aika/lattice/Node.java000066400000000000000000000312431352543012000235450ustar00rootroot00000000000000/* * 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.Document; import network.aika.lattice.refinement.OrEntry; import network.aika.lattice.refinement.RefValue; import network.aika.lattice.refinement.Refinement; 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. *

*

Each logic node has a set of activations. The activations are stored in the thread local data structure * {@code ThreadState}. * * @author Lukas Molzberger */ public abstract class Node> extends AbstractNode> implements Comparable { public static final Node MIN_NODE = new InputNode(); public static final Node MAX_NODE = new InputNode(); TreeMap andChildren; TreeSet orChildren; public int level; private AtomicInteger numberOfNeuronRefs = new AtomicInteger(0); volatile boolean isRemoved; // Only the children maps are locked. protected ReadWriteLock lock = new ReadWriteLock(); private ThreadState[] threads; long markedCreated; /** * Propagate an activation to the next node or the next neuron that is depending on the current node. * * @param act */ protected abstract void propagate(A act); /** * The {@code ThreadState} is a thread local data structure containing the activations of a single document for * a specific logic node. */ private static class ThreadState { public long lastUsed; public Document doc; public List added; public List activations; public long visited; public boolean isQueued = false; public long queueId; public ThreadState() { added = new ArrayList<>(); activations = new ArrayList<>(); } } private ThreadState getThreadState(int threadId, boolean create) { ThreadState th = threads[threadId]; if (th == null) { if (!create) return null; th = new ThreadState(); threads[threadId] = th; } th.lastUsed = provider.getModel().docIdCounter.get(); return th; } public void clearThreadState(int threadId, int deleteDocId) { Node.ThreadState th = threads[threadId]; if (th != null && th.lastUsed < deleteDocId) { threads[threadId] = null; } } abstract RefValue expand(int threadId, Document doc, Refinement ref); public abstract void reprocessInputs(Document doc); public abstract void cleanup(); public abstract String logicToString(); protected Node() { } public Node(Model m, int level) { threads = new ThreadState[m.numberOfThreads]; provider = new Provider(m, this); this.level = level; setModified(); } public void postCreate(Document doc) { if(doc != null) { markedCreated = doc.createV; doc.addedNodes.add(this); } } void addOrChild(OrEntry rv) { lock.acquireWriteLock(); if (orChildren == null) { orChildren = new TreeSet<>(); } orChildren.add(rv); lock.releaseWriteLock(); } void removeOrChild(OrEntry rv) { lock.acquireWriteLock(); if (orChildren != null) { orChildren.remove(rv); if (orChildren.isEmpty()) { orChildren = null; } } lock.releaseWriteLock(); } void addAndChild(Refinement ref, RefValue child) { if (andChildren == null) { andChildren = new TreeMap<>(); } if(!andChildren.containsKey(ref)) { andChildren.put(ref, child); } } void removeAndChild(Refinement ref) { if (andChildren != null) { andChildren.remove(ref); if (andChildren.isEmpty()) { andChildren = null; } } } void processActivation(A act) { register(act); propagate(act); } public void register(A act) { if(act.registered) { return; } Document doc = act.getDocument(); assert act.getNode() == this; ThreadState th = getThreadState(doc.getThreadId(), true); if(th.doc == null) { th.doc = doc; doc.addActivatedNode(act.getNode()); } if(th.doc != doc) { throw new Model.StaleDocumentException(); } th.activations.add(act); doc.addedNodeActivations.add(act); act.registered = true; } public void clearActivations(Document doc) { clearActivations(doc.getThreadId()); } public void clearActivations(int threadId) { ThreadState th = getThreadState(threadId, false); if (th == null) return; th.activations.clear(); th.added.clear(); th.doc = null; } public void clearActivations() { for (int i = 0; i < provider.getModel().numberOfThreads; i++) { clearActivations(i); } } /** * Process all added or removed activation for this logic node. * * @param doc */ public void processChanges(Document doc) { ThreadState th = getThreadState(doc.getThreadId(), true); List tmpAdded = th.added; th.added = new ArrayList<>(); tmpAdded.forEach(act -> processActivation(act)); } protected void propagateToOrNode(NodeActivation inputAct) { Document doc = inputAct.getDocument(); try { lock.acquireReadLock(); if (orChildren != null) { for (OrEntry oe : orChildren) { oe.child.get(doc).addActivation(oe, inputAct); } } } finally { lock.releaseReadLock(); } } /** * Add a new activation to this logic node and further propagate this activation through the network. * This activation, however, will not be added immediately. This method only adds a request to the activations * queue in the document. The activation will be added when the method {@code Node.processChanges(Document doc)} * is called. * * @param act */ public void addActivation(A act) { ThreadState th = getThreadState(act.getThreadId(), true); if(th.doc == null) { th.doc = act.doc; act.doc.addActivatedNode(act.getNode()); } if(th.doc != act.doc) { throw new Model.StaleDocumentException(); } th.added.add(act); act.getDocument().getNodeQueue().add(this); } public void remove() { assert !isRemoved; lock.acquireWriteLock(); setModified(); while (andChildren != null && !andChildren.isEmpty()) { andChildren.firstEntry().getValue().child.get().remove(); } while (orChildren != null && !orChildren.isEmpty()) { orChildren.pollFirst().child.get().remove(); } lock.releaseWriteLock(); isRemoved = true; } RefValue getAndChild(Refinement ref) { lock.acquireReadLock(); RefValue result = andChildren != null ? andChildren.get(ref) : null; lock.releaseReadLock(); return result; } public boolean isRequired() { return numberOfNeuronRefs.get() > 0; } protected void changeNumberOfNeuronRefs(int threadId, long v, int d) { ThreadState th = getThreadState(threadId, true); if (th.visited == v) return; th.visited = v; numberOfNeuronRefs.addAndGet(d); } public Collection getActivations(Document doc) { ThreadState th = getThreadState(doc.getThreadId(), false); if (th == null) return Collections.EMPTY_LIST; return th.activations; } public String getNeuronLabel() { return ""; } public boolean isQueued(int threadId, long queueId) { ThreadState th = getThreadState(threadId, true); if (!th.isQueued) { th.isQueued = true; th.queueId = queueId; } return false; } public void setNotQueued(int threadId) { ThreadState th = getThreadState(threadId, false); if(th == null) return; th.isQueued = false; } public static int compareRank(int threadId, 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); } public String toString() { if(this == MIN_NODE) return "MIN_NODE"; if(this == MAX_NODE) return "MAX_NODE"; StringBuilder sb = new StringBuilder(); sb.append(getNeuronLabel()); sb.append(" - "); sb.append(logicToString()); return sb.toString(); } public int compareTo(Node n) { if (this == n) return 0; if (this == MIN_NODE) return -1; if (n == MIN_NODE) return 1; if (this == MAX_NODE) return 1; if (n == MAX_NODE) return -1; return provider.compareTo(n.provider); } @Override public void write(DataOutput out) throws IOException { out.writeInt(level); out.writeInt(numberOfNeuronRefs.get()); if (andChildren != null) { out.writeInt(andChildren.size()); for (Map.Entry me : andChildren.entrySet()) { me.getKey().write(out); me.getValue().write(out); } } else { out.writeInt(0); } if (orChildren != null) { out.writeInt(orChildren.size()); for (OrEntry oe : orChildren) { oe.write(out); } } else { out.writeInt(0); } } @Override public void readFields(DataInput in, Model m) throws IOException { level = in.readInt(); numberOfNeuronRefs.set(in.readInt()); int s = in.readInt(); for (int i = 0; i < s; i++) { addAndChild(Refinement.read(in, m), RefValue.read(in, m)); } s = in.readInt(); for (int i = 0; i < s; i++) { if (orChildren == null) { orChildren = new TreeSet<>(); } orChildren.add(OrEntry.read(in, m)); } threads = new ThreadState[m.numberOfThreads]; } public static Node readNode(DataInput in, Provider p) throws IOException { char type = in.readChar(); Node n = null; switch (type) { case 'I': n = new InputNode(); break; case 'A': n = new AndNode(); break; case 'O': n = new OrNode(); break; } n.provider = p; n.readFields(in, p.getModel()); return n; } } aika-java-1.4.0/src/main/java/network/aika/lattice/NodeActivation.java000066400000000000000000000037441352543012000255740ustar00rootroot00000000000000/* * 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.lattice.activation.AndActivation; import network.aika.lattice.activation.OrActivation; import network.aika.neuron.activation.Activation; import java.util.*; /** * * @author Lukas Molzberger */ public abstract class NodeActivation implements Comparable> { public final int id; private final T node; protected final Document doc; public Long repropagateV; public boolean registered; public TreeMap outputsToAndNode = new TreeMap<>(); public TreeMap outputsToOrNode = new TreeMap<>(); public NodeActivation(Document doc, T node) { this.id = doc.getNewNodeActivationId(); this.doc = doc; this.node = node; } public T getNode() { return node; } public Document getDocument() { return doc; } public int getThreadId() { return doc.getThreadId(); } public abstract Activation getInputActivation(int i); @Override public int compareTo(NodeActivation act) { return Integer.compare(id, act.id); } } aika-java-1.4.0/src/main/java/network/aika/lattice/NodeQueue.java000066400000000000000000000030321352543012000245450ustar00rootroot00000000000000/* * 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 java.util.TreeSet; /** * * @author Lukas Molzberger */ public class NodeQueue { private Document doc; private final TreeSet queue = new TreeSet<>( (n1, n2) -> Node.compareRank(doc.getThreadId(), n1, n2) ); private long queueIdCounter = 0; public NodeQueue(Document doc) { this.doc = doc; } public void add(Node n) { if(!n.isQueued(doc.getThreadId(), queueIdCounter++)) { queue.add(n); } } public void process() { while(!queue.isEmpty()) { Node n = queue.pollFirst(); n.setNotQueued(doc.getThreadId()); n.processChanges(doc); } } } aika-java-1.4.0/src/main/java/network/aika/lattice/OrNode.java000066400000000000000000000215051352543012000240460ustar00rootroot00000000000000/* * 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.lattice.activation.OrActivation; import network.aika.lattice.refinement.OrEntry; import network.aika.lattice.refinement.RefValue; import network.aika.lattice.refinement.Refinement; import network.aika.neuron.INeuron; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.activation.*; import network.aika.Document; import network.aika.neuron.activation.link.Direction; import network.aika.neuron.activation.link.Link; import network.aika.neuron.relation.Relation; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; import java.util.*; import java.util.function.Predicate; import static network.aika.neuron.Synapse.OUTPUT; /** * While several neurons might share a the same input-node or and-node, there is always a always a one-to-one relation * between or-nodes and neurons. The only exceptions are the input neurons which have a one-to-one relation with the * input-node. The or-nodes form a disjunction of one or more input-nodes or and-nodes. * * @author Lukas Molzberger */ public class OrNode extends Node { private static final Logger log = LoggerFactory.getLogger(OrNode.class); TreeSet andParents = new TreeSet<>(); private Neuron outputNeuron = null; public OrNode() {} public OrNode(Model m) { super(m, -1); // Or-node activations always need to be processed first! } @Override public RefValue expand(int threadId, Document doc, Refinement ref) { throw new UnsupportedOperationException(); } protected void addActivation(OrEntry oe, NodeActivation inputAct) { OrActivation.Link ol = new OrActivation.Link(oe, inputAct); Document doc = inputAct.getDocument(); INeuron n = outputNeuron.get(doc); Activation act = lookupActivation(ol, l -> { Synapse s = l.getSynapse(); if(!s.isIdentity()) return true; Integer i = oe.revSynapseIds.get(s.getId()); Activation iAct = doc.getLinker().computeInputActivation(s, inputAct.getInputActivation(i)); return i != null && l.getInput() == iAct; }); if(act == null) { OrActivation orAct = new OrActivation(doc, this); register(orAct); act = new Activation(doc, n, getSlots(oe, inputAct)); act.setInputNodeActivation(orAct); orAct.setOutputAct(act); } OrActivation orAct = act.getInputNodeActivation(); propagate(orAct); orAct.link(ol); ol.linkOutputActivation(act); } private Activation lookupActivation(OrActivation.Link ol, Predicate filter) { for(Link l: ol.getInputLinks(outputNeuron)) { Synapse syn = l.getSynapse(); Map rels = syn.getRelations(); for(Map.Entry me: rels.entrySet()) { Integer relSynId = me.getKey(); Relation rel = me.getValue(); Activation existingAct = null; if(relSynId != OUTPUT) { Synapse s = outputNeuron.getSynapseById(relSynId); if (s != null) { existingAct = rel .invert() .getActivations(s.getInput().get(), l.getInput()) .flatMap(act -> act.getLinksBySynapse(Direction.OUTPUT, s)) .map(rl -> rl.getOutput()) .findFirst() .orElse(null); } } else { existingAct = rel .invert() .getActivations(outputNeuron.get(), l.getInput()) .findFirst() .orElse(null); } if(existingAct != null && existingAct.match(filter)) { return existingAct; } } } return null; } private SortedMap getSlots(OrEntry oe, NodeActivation inputAct) { SortedMap slots = new TreeMap<>(); for(int i = 0; i < oe.synapseIds.length; i++) { int synapseId = oe.synapseIds[i]; Synapse s = outputNeuron.getSynapseById(synapseId); if(s != null) { for (Map.Entry me : s.getRelations().entrySet()) { Relation rel = me.getValue(); if (me.getKey() == Synapse.OUTPUT) { Activation iAct = inputAct.getInputActivation(i); rel.mapSlots(slots, iAct); } } } } return slots; } @Override protected void propagate(OrActivation act) { act.getDocument().getUpperBoundQueue().add(act.getOutputAct()); } @Override public void cleanup() { } @Override public void reprocessInputs(Document doc) { for (OrEntry oe : andParents) { Node> pn = oe.parent.get(); for (NodeActivation act : pn.getActivations(doc)) { act.repropagateV = markedCreated; act.getNode().propagate(act); } } } void addInput(int[] synapseIds, int threadId, Node in, boolean andMode) { in.changeNumberOfNeuronRefs(threadId, provider.getModel().visitedCounter.addAndGet(1), 1); OrEntry oe = new OrEntry(synapseIds, in.getProvider(), provider); in.addOrChild(oe); in.setModified(); if(andMode) { lock.acquireWriteLock(); setModified(); andParents.add(oe); lock.releaseWriteLock(); } } void remove(int threadId) { outputNeuron.get().remove(); super.remove(); try { lock.acquireReadLock(); removeParents(threadId); } finally { lock.releaseReadLock(); } } void removeParents(int threadId) { for (OrEntry oe : andParents) { Node pn = oe.parent.get(); pn.changeNumberOfNeuronRefs(threadId, provider.getModel().visitedCounter.addAndGet(1), -1); pn.removeOrChild(oe); pn.setModified(); } andParents.clear(); } @Override protected void changeNumberOfNeuronRefs(int threadId, long v, int d) { throw new UnsupportedOperationException(); } public String logicToString() { StringBuilder sb = new StringBuilder(); sb.append("OR["); boolean first = true; int i = 0; for(OrEntry oe : andParents) { if (!first) { sb.append(","); } first = false; sb.append(oe.parent.get().logicToString()); if (i > 2) { sb.append(",..."); break; } i++; } sb.append("]"); return sb.toString(); } @Override public void write(DataOutput out) throws IOException { out.writeBoolean(false); out.writeChar('O'); super.write(out); out.writeInt(outputNeuron.getId()); out.writeInt(andParents.size()); for(OrEntry oe: andParents) { oe.write(out); } } @Override public void readFields(DataInput in, Model m) throws IOException { super.readFields(in, m); outputNeuron = m.lookupNeuron(in.readInt()); int s = in.readInt(); for(int i = 0; i < s; i++) { andParents.add(OrEntry.read(in, m)); } } public String getNeuronLabel() { String l = outputNeuron.getLabel(); return l != null ? l : ""; } public void setOutputNeuron(Neuron n) { outputNeuron = n; } public Neuron getOutputNeuron() { return outputNeuron; } } aika-java-1.4.0/src/main/java/network/aika/lattice/activation/000077500000000000000000000000001352543012000241535ustar00rootroot00000000000000aika-java-1.4.0/src/main/java/network/aika/lattice/activation/AndActivation.java000066400000000000000000000065041352543012000275470ustar00rootroot00000000000000/* * 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.activation; import network.aika.Document; import network.aika.lattice.AndNode; import network.aika.lattice.NodeActivation; import network.aika.lattice.refinement.RefValue; import network.aika.lattice.refinement.Refinement; import network.aika.neuron.activation.Activation; /** * * @author Lukas Molzberger */ public class AndActivation extends NodeActivation { public Link[] inputs; public AndActivation(Document doc, AndNode node) { super(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; } 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 getNode().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.getId() + ")"); 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-1.4.0/src/main/java/network/aika/lattice/activation/InputActivation.java000066400000000000000000000027461352543012000301500ustar00rootroot00000000000000/* * 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.activation; import network.aika.lattice.InputNode; import network.aika.lattice.NodeActivation; import network.aika.neuron.activation.Activation; /** * * @author Lukas Molzberger */ public class InputActivation extends NodeActivation { public Activation input; public InputActivation(Activation iAct, InputNode node) { super(iAct.getDocument(), node); input = iAct; iAct.setOutputNodeActivation(this); } public Activation getInputActivation(int i) { assert i == 0; return input; } public String toString() { return "I-ACT(" + input.getLabel() + " " + input.slotsToString() + ")"; } }aika-java-1.4.0/src/main/java/network/aika/lattice/activation/OrActivation.java000066400000000000000000000065561352543012000274340ustar00rootroot00000000000000/* * 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.activation; import network.aika.Document; import network.aika.lattice.NodeActivation; import network.aika.lattice.OrNode; import network.aika.lattice.refinement.OrEntry; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.activation.Activation; import network.aika.neuron.activation.link.Linker; import java.util.*; /** * * @author Lukas Molzberger */ public class OrActivation extends NodeActivation { private Map orInputs = new TreeMap<>(); private Activation outputAct; public OrActivation(Document doc, OrNode node) { super(doc, node); } public Activation getOutputAct() { return outputAct; } public void setOutputAct(Activation outputAct) { this.outputAct = outputAct; } @Override public Activation getInputActivation(int i) { throw new UnsupportedOperationException(); } public void link(Link l) { l.setOutput(this); orInputs.put(l.input.id, l); l.input.outputsToOrNode.put(id, l); } public static class Link { public OrEntry oe; private NodeActivation input; private OrActivation output; public Link(OrEntry oe, NodeActivation input) { this.oe = oe; this.input = input; } public int size() { return oe.synapseIds.length; } public int get(int i) { return oe.synapseIds[i]; } public void linkOutputActivation(Activation act) { Linker l = act.getDocument().getLinker(); for (int i = 0; i < size(); i++) { int synId = get(i); Synapse s = act.getSynapseById(synId); if(s != null) { Activation iAct = input.getInputActivation(i); l.link(s, iAct, act); } } l.process(); } public Collection getInputLinks(Neuron n) { List inputActs = new ArrayList<>(); for (int i = 0; i < size(); i++) { int synId = get(i); Synapse s = n.getSynapseById(synId); Activation iAct = input.getInputActivation(i); inputActs.add(new network.aika.neuron.activation.link.Link(s, iAct, null)); } return inputActs; } public void setOutput(OrActivation output) { this.output = output; } } } aika-java-1.4.0/src/main/java/network/aika/lattice/refinement/000077500000000000000000000000001352543012000241465ustar00rootroot00000000000000aika-java-1.4.0/src/main/java/network/aika/lattice/refinement/OrEntry.java000066400000000000000000000062621352543012000264210ustar00rootroot00000000000000/* * 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.refinement; import network.aika.Model; import network.aika.Provider; import network.aika.Writable; import network.aika.lattice.Node; import network.aika.lattice.OrNode; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; import java.util.TreeMap; /** * * @author Lukas Molzberger */ public class OrEntry implements Comparable, Writable { public int[] synapseIds; public TreeMap revSynapseIds = new TreeMap<>(); public Provider parent; public Provider child; private OrEntry() {} public OrEntry(int[] synapseIds, Provider parent, Provider child) { this.synapseIds = synapseIds; for(int ofs = 0; ofs < synapseIds.length; ofs++) { revSynapseIds.put(synapseIds[ofs], ofs); } this.parent = parent; this.child = child; } @Override public void write(DataOutput out) throws IOException { out.writeInt(synapseIds.length); for(int i = 0; i < synapseIds.length; i++) { Integer ofs = synapseIds[i]; out.writeBoolean(ofs != null); out.writeInt(ofs); } out.writeInt(parent.getId()); out.writeInt(child.getId()); } public static OrEntry read(DataInput in, Model m) throws IOException { OrEntry rv = new OrEntry(); rv.readFields(in, m); return rv; } @Override public void readFields(DataInput in, Model m) throws IOException { int l = in.readInt(); synapseIds = new int[l]; for(int i = 0; i < l; i++) { if(in.readBoolean()) { Integer ofs = in.readInt(); synapseIds[i] = ofs; revSynapseIds.put(ofs, i); } } parent = m.lookupNodeProvider(in.readInt()); child = m.lookupNodeProvider(in.readInt()); } @Override public int compareTo(OrEntry oe) { int r = child.compareTo(oe.child); if(r != 0) return r; r = parent.compareTo(oe.parent); if(r != 0) return r; r = Integer.compare(synapseIds.length, oe.synapseIds.length); if(r != 0) return r; for(int i = 0; i < synapseIds.length; i++) { r = Integer.compare(synapseIds[i], oe.synapseIds[i]); if(r != 0) return r; } return 0; } } aika-java-1.4.0/src/main/java/network/aika/lattice/refinement/RefValue.java000066400000000000000000000055611352543012000265310ustar00rootroot00000000000000/* * 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.refinement; import network.aika.Model; import network.aika.Provider; import network.aika.Writable; import network.aika.lattice.AndNode; import network.aika.lattice.Node; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; /** * * @author Lukas Molzberger */ public 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.getId()); out.writeInt(child.getId()); } 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()); } } aika-java-1.4.0/src/main/java/network/aika/lattice/refinement/Refinement.java000066400000000000000000000056741352543012000271210ustar00rootroot00000000000000/* * 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.refinement; import network.aika.Model; import network.aika.Provider; import network.aika.Writable; import network.aika.lattice.InputNode; import network.aika.neuron.relation.Relation; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; /** * * @author Lukas Molzberger */ public 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 boolean isConvertible() { for(Relation rel: relations.relations) { if(rel != null) return true; } return false; } 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.getId()); } 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; } } aika-java-1.4.0/src/main/java/network/aika/lattice/refinement/RelationsMap.java000066400000000000000000000073211352543012000274120ustar00rootroot00000000000000/* * 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.refinement; import network.aika.Model; import network.aika.Writable; import network.aika.neuron.relation.Relation; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; /** * * @author Lukas Molzberger */ public 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; } } aika-java-1.4.0/src/main/java/network/aika/neuron/000077500000000000000000000000001352543012000216735ustar00rootroot00000000000000aika-java-1.4.0/src/main/java/network/aika/neuron/INeuron.java000066400000000000000000000651661352543012000241330ustar00rootroot00000000000000/* * 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.neuron; import network.aika.*; import network.aika.lattice.OrNode; import network.aika.neuron.activation.Activation; import network.aika.neuron.activation.Position; import network.aika.lattice.InputNode; import network.aika.neuron.relation.Relation; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import java.io.*; import java.util.*; import java.util.stream.Collectors; import java.util.stream.Stream; import static network.aika.ActivationFunction.*; import static network.aika.neuron.INeuron.Type.*; import static network.aika.neuron.Synapse.State.CURRENT; import static network.aika.neuron.Synapse.State.NEXT; /** * The {@code INeuron} class represents a internal neuron implementation in Aikas neural network and is connected to other neurons through * input synapses and output synapses. The activation value of a neuron is calculated by computing the weighted sum * (input act. value * synapse weight) of the input synapses, adding the bias to it and sending the resulting value * through a transfer function (the upper part of tanh). *

*

The neuron does not store its activationsBySlotAndPosition by itself. The activation objects are stored within the * logic nodes. To access the activationsBySlotAndPosition of this neuron simply use the member variable {@code node} or use * the method {@code getFinalActivations(Document doc)} to ge the final activationsBySlotAndPosition of this neuron. * * @author Lukas Molzberger */ public class INeuron extends AbstractNode implements Comparable { private static final Logger log = LoggerFactory.getLogger(INeuron.class); public static double WEIGHT_TOLERANCE = 0.001; public static final INeuron MIN_NEURON = new INeuron(); public static final INeuron MAX_NEURON = new INeuron(); private String label; private Type type; public enum Type { INPUT(NULL_FUNCTION), EXCITATORY(RECTIFIED_HYPERBOLIC_TANGENT), INHIBITORY(RECTIFIED_LINEAR_UNIT); private ActivationFunction defaultActivationFunction; Type(ActivationFunction defaultActivationFunction) { this.defaultActivationFunction = defaultActivationFunction; } public ActivationFunction getDefaultActivationFunction() { return defaultActivationFunction; } } private String outputText; private volatile double bias; private volatile double biasDelta; private SynapseSummary synapseSummary = new SynapseSummary(); ActivationFunction activationFunction; private volatile int synapseIdCounter = 0; // synapseId -> relation private Map outputRelations = new TreeMap<>(); // A synapse is stored only in one direction, depending on the synapse weight. TreeMap inputSynapses = new TreeMap<>(Synapse.INPUT_SYNAPSE_COMP); TreeMap outputSynapses = new TreeMap<>(Synapse.OUTPUT_SYNAPSE_COMP); TreeMap passiveInputSynapses = null; private Provider outputNode; private Provider inputNode; ReadWriteLock lock = new ReadWriteLock(); PassiveInputFunction passiveInputFunction = null; private ThreadState[] threads; /** * The {@code ThreadState} is a thread local data structure containing the activationsBySlotAndPosition of a single document for * a specific logic node. */ private static class ThreadState { public long lastUsed; public Document doc; private TreeMap activationsBySlotAndPosition; private TreeMap activations; public int minLength = Integer.MAX_VALUE; public int maxLength = 0; public ThreadState() { activationsBySlotAndPosition = new TreeMap<>(); activations = new TreeMap<>(); } } public void setOutputNode(Provider node) { outputNode = node; } public Integer getId() { return provider.getId(); } public String getLabel() { return label; } public Type getType() { return type; } public void setType(Type t) { this.type = t; } public Provider getOutputNode() { return outputNode; } public Provider getInputNode() { return inputNode; } public SynapseSummary getSynapseSummary() { return synapseSummary; } public Map getOutputRelations() { return outputRelations; } public Collection getInputSynapses() { return inputSynapses.values(); } public Synapse getMaxInputSynapse(Synapse.State state) { if(type != EXCITATORY) { return null; } Synapse maxSyn = null; for(Synapse s: getInputSynapses()) { if(!s.isInactive()) { if(maxSyn == null || maxSyn.getNewWeight() < s.getNewWeight()) { maxSyn = s; } } } return maxSyn; } public Collection getOutputSynapses() { return outputSynapses.values(); } public Collection getPassiveInputSynapses() { if(passiveInputSynapses == null) { return Collections.emptyList(); } return passiveInputSynapses.values(); } public ActivationFunction getActivationFunction() { return activationFunction; } public void setActivationFunction(ActivationFunction actF) { activationFunction = actF; } public Stream getActivations(Document doc) { ThreadState th = getThreadState(doc.getThreadId(), false); if(th == null) { return Stream.empty(); } return th.activations.values().stream(); } public boolean isEmpty(Document doc) { ThreadState th = getThreadState(doc.getThreadId(), false); if(th == null) { return true; } return th.activationsBySlotAndPosition.isEmpty(); } public int size(Document doc) { ThreadState th = getThreadState(doc.getThreadId(), false); if(th == null) { return 0; } return th.activations.size(); } public void clearActivations(Document doc) { ThreadState th = getThreadState(doc.getThreadId(), false); if(th == null) { return; } th.activationsBySlotAndPosition.clear(); th.activations.clear(); th.doc = null; } public Stream getActivations(Document doc, int slot, Position pos, boolean onlyFinal) { return getActivations(doc, slot, pos, true, slot, pos, false) .filter(act -> !onlyFinal || act.isFinalActivation()); } public void clearActivations() { for (int i = 0; i < provider.getModel().numberOfThreads; i++) { clearActivations(i); } } public void clearActivations(int threadId) { ThreadState th = getThreadState(threadId, false); if (th == null) return; th.activationsBySlotAndPosition.clear(); th.activations.clear(); th.doc = null; } public Model getModel() { return provider.getModel(); } public Stream getActivations(Document doc, int fromSlot, Position fromPos, boolean fromInclusive, int toSlot, Position toPos, boolean toInclusive) { ThreadState th = getThreadState(doc.getThreadId(), false); if(th == null) { return Stream.empty(); } return th.activationsBySlotAndPosition.subMap( new INeuron.ActKey(fromSlot, fromPos, Integer.MIN_VALUE), fromInclusive, new INeuron.ActKey(toSlot, toPos, Integer.MAX_VALUE), toInclusive ).values() .stream(); } public Stream getActivations(Document doc, boolean onlyFinal) { return onlyFinal ? getActivations(doc) .filter(act -> act.isFinalActivation()) : getActivations(doc); } public Collection getActivations(Document doc, SortedMap slots) { Integer firstSlot = slots.firstKey(); Position firstPos = slots.get(firstSlot); return getActivations(doc, firstSlot, firstPos, true, firstSlot, firstPos, true) .filter( act -> { for(Map.Entry me: slots.entrySet()) { Position pos = me.getValue(); if(pos.getFinalPosition() != null && pos.compare(act.lookupSlot(me.getKey())) != 0) { return false; } } return true; }) .collect(Collectors.toList()); } private static class ActKey implements Comparable { int slot; Position pos; int actId; public ActKey(int slot, Position pos, int actId) { this.slot = slot; this.pos = pos; this.actId = actId; } @Override public int compareTo(ActKey ak) { int r = Integer.compare(slot, ak.slot); if(r != 0) return r; r = pos.compare(ak.pos); if(r != 0) return r; return Integer.compare(actId, ak.actId); } } private ThreadState getThreadState(int threadId, boolean create) { ThreadState th = threads[threadId]; if (th == null) { if (!create) return null; th = new ThreadState(); threads[threadId] = th; } th.lastUsed = provider.getModel().docIdCounter.get(); return th; } private INeuron() { } public INeuron(Neuron p) { provider = p; threads = new INeuron.ThreadState[p.getModel().numberOfThreads]; } public INeuron(Model m, String label, Type type, ActivationFunction actF) { this(m, label, null, type, actF); } public INeuron(Model m, String label, String outputText, Type type, ActivationFunction actF) { this.label = label; this.type = type; this.activationFunction = actF; setOutputText(outputText); threads = new ThreadState[m.numberOfThreads]; provider = new Neuron(m, this); OrNode node = new OrNode(m); InputNode iNode = new InputNode(m); node.setOutputNeuron(provider); inputNode = node.getProvider(); iNode.setInputNeuron(provider); outputNode = iNode.getProvider(); setModified(); iNode.setModified(); } public void setOutputText(String outputText) { this.outputText = outputText; } public String getOutputText() { return outputText; } /** * Propagate an input activation into the network. * * @param doc The current document * @param input */ public Activation addInput(Document doc, Activation.Builder input) { Activation act = getActivation(doc, input); if (act == null) { act = createActivation(doc, input.getSlots(doc)); } act.setInputState(input); doc.addInputNeuronActivation(act); doc.addFinallyActivatedNeuron(act.getINeuron()); if(getType() != INPUT) { doc.getLinker().linkInput(act); doc.getLinker().process(); } propagate(act); doc.propagate(); return act; } protected Activation createActivation(Document doc, Map slots) { return new Activation(doc, this, slots); } private Activation getActivation(Document doc, Activation.Builder input) { Integer firstSlot = input.positions.firstKey(); Position firstPos = doc.lookupFinalPosition(input.positions.get(firstSlot)); x: for(Activation a: getActivations(doc, firstSlot, firstPos, true, firstSlot, firstPos, true).collect(Collectors.toList())) { for(Map.Entry me: input.positions.entrySet()) { Position pos = a.lookupSlot(me.getKey()); if(pos == null || me.getValue().compareTo(pos.getFinalPosition()) != 0) { continue x; } } return a; } return null; } public double getTotalBias(Synapse.State state) { switch(type) { case EXCITATORY: return getBias(state) - synapseSummary.getPosSum(state); case INHIBITORY: return getBias(state); } return getBias(state); } public void commit(Collection modifiedSynapses) { for (Synapse s : modifiedSynapses) { INeuron in = s.getInput().get(); in.lock.acquireWriteLock(); try { synapseSummary.updateSynapse(s); } finally { in.lock.releaseWriteLock(); } } bias += biasDelta; biasDelta = 0.0; for (Synapse s : modifiedSynapses) { s.commit(); } synapseSummary.commit(); setModified(); } // TODO public void remove() { clearActivations(); for (Synapse s : inputSynapses.values()) { INeuron in = s.getInput().get(); in.provider.lock.acquireWriteLock(); in.provider.activeOutputSynapses.remove(s); in.provider.lock.releaseWriteLock(); } provider.lock.acquireReadLock(); for (Synapse s : provider.activeOutputSynapses.values()) { INeuron out = s.getOutput().get(); out.lock.acquireWriteLock(); out.inputSynapses.remove(s); out.lock.releaseWriteLock(); } provider.lock.releaseReadLock(); } public synchronized int getNewSynapseId() { setModified(); return synapseIdCounter++; } public synchronized void registerSynapseId(Integer synId) { if(synId >= synapseIdCounter) { setModified(); synapseIdCounter = synId + 1; } } public void propagate(Activation act) { Document doc = act.getDocument(); outputNode.get(doc).addActivation(act); } public int compareTo(INeuron n) { if (this == n) return 0; if (this == MIN_NEURON) return -1; if (n == MIN_NEURON) return 1; if (this == MAX_NEURON) return 1; if (n == MAX_NEURON) return -1; if (getId() < n.getId()) return -1; else if (getId() > n.getId()) return 1; else return 0; } @Override public void write(DataOutput out) throws IOException { out.writeBoolean(true); out.writeBoolean(label != null); if(label != null) { out.writeUTF(label); } out.writeBoolean(type != null); if(type != null) { out.writeUTF(type.name()); } out.writeBoolean(outputText != null); if(outputText != null) { out.writeUTF(outputText); } out.writeDouble(bias); synapseSummary.write(out); out.writeBoolean(activationFunction != null); if(activationFunction != null) { out.writeUTF(activationFunction.name()); } out.writeInt(outputNode.getId()); out.writeBoolean(inputNode != null); if (inputNode != null) { out.writeInt(inputNode.getId()); } out.writeInt(synapseIdCounter); for (Synapse s : inputSynapses.values()) { if (s.getInput() != null) { out.writeBoolean(true); getModel().writeSynapse(s, out); out.writeBoolean(passiveInputSynapses != null && passiveInputSynapses.containsKey(s)); } } out.writeBoolean(false); for (Synapse s : outputSynapses.values()) { if (s.getOutput() != null) { out.writeBoolean(true); getModel().writeSynapse(s, out); } } out.writeBoolean(false); if(outputRelations != null) { out.writeInt(outputRelations.size()); for (Map.Entry me : outputRelations.entrySet()) { out.writeInt(me.getKey()); me.getValue().write(out); } } else { out.writeInt(0); } } @Override public void readFields(DataInput in, Model m) throws IOException { if(in.readBoolean()) { label = in.readUTF(); } if(in.readBoolean()) { type = Type.valueOf(in.readUTF()); } if(in.readBoolean()) { outputText = in.readUTF(); } bias = in.readDouble(); synapseSummary = SynapseSummary.read(in, m); if(in.readBoolean()) { activationFunction = ActivationFunction.valueOf(in.readUTF()); } outputNode = m.lookupNodeProvider(in.readInt()); if (in.readBoolean()) { Integer nId = in.readInt(); inputNode = m.lookupNodeProvider(nId); } synapseIdCounter = in.readInt(); while (in.readBoolean()) { Synapse syn = m.readSynapse(in); inputSynapses.put(syn, syn); if(in.readBoolean()) { registerPassiveInputSynapse(syn); } } while (in.readBoolean()) { Synapse syn = m.readSynapse(in); outputSynapses.put(syn, syn); } int l = in.readInt(); if(l > 0) { outputRelations = new TreeMap<>(); for(int i = 0; i < l; i++) { Integer relId = in.readInt(); Relation r = Relation.read(in, m); outputRelations.put(relId, r); } } passiveInputFunction = m.passiveActivationFunctions.get(getId()); } @Override public void suspend() { for (Synapse s : inputSynapses.values()) { s.getInput().removeActiveOutputSynapse(s); } for (Synapse s : outputSynapses.values()) { s.getOutput().removeActiveInputSynapse(s); } provider.lock.acquireReadLock(); for (Synapse s : provider.activeInputSynapses.values()) { s.getInput().removeActiveOutputSynapse(s); } for (Synapse s : provider.activeOutputSynapses.values()) { s.getOutput().removeActiveInputSynapse(s); } provider.lock.releaseReadLock(); } @Override public void reactivate() { provider.lock.acquireReadLock(); for (Synapse s : provider.activeInputSynapses.values()) { s.getInput().addActiveOutputSynapse(s); } for (Synapse s : provider.activeOutputSynapses.values()) { s.getOutput().addActiveInputSynapse(s); } provider.lock.releaseReadLock(); for (Synapse s : inputSynapses.values()) { s.getInput().addActiveOutputSynapse(s); if (!s.getInput().isSuspended()) { s.getOutput().addActiveInputSynapse(s); } } for (Synapse s : outputSynapses.values()) { s.getOutput().addActiveInputSynapse(s); if (!s.getOutput().isSuspended()) { s.getInput().addActiveOutputSynapse(s); } } } public void setBias(double b) { biasDelta = b - bias; } public void updateBiasDelta(double biasDelta) { this.biasDelta += biasDelta; } public double getBias() { return bias; } private double getBias(Synapse.State state) { return state == CURRENT ? bias : bias + biasDelta; } public double getNewBias() { return bias + biasDelta; } public double getBiasDelta() { return biasDelta; } public void register(Activation act) { Document doc = act.getDocument(); ThreadState th = getThreadState(act.getThreadId(), true); if(th.doc == null) { th.doc = doc; doc.addActivatedNeuron(act.getINeuron()); } if(th.doc != doc) { throw new Model.StaleDocumentException(); } Integer l = act.length(); if(l != null) { th.minLength = Math.min(th.minLength, l); th.maxLength = Math.max(th.maxLength, l); } for(Map.Entry me: act.getSlots().entrySet()) { ActKey ak = new ActKey(me.getKey(), me.getValue(), act.getId()); th.activationsBySlotAndPosition.put(ak, act); th.activations.put(act.getId(), act); } for(Map.Entry me: act.getSlots().entrySet()) { me.getValue().addActivation(me.getKey(), act); } doc.addActivation(act); } public boolean isPassiveInputNeuron() { return passiveInputFunction != null; } public void registerPassiveInputSynapse(Synapse s) { if(passiveInputSynapses == null) { passiveInputSynapses = new TreeMap<>(Synapse.INPUT_SYNAPSE_COMP); } passiveInputSynapses.put(s, s); } public String toString() { return label; } protected String toDetailedString() { return typeToString() + " " + label + " B:" + Utils.round(bias); } public String typeToString() { return getType().toString(); } public String toStringWithSynapses() { SortedSet is = new TreeSet<>(Comparator.comparing(s -> s.getInput().getId())); if(type == EXCITATORY) { is.addAll(inputSynapses.values()); } else if(type == INHIBITORY) { is.addAll(getProvider().getActiveInputSynapses()); } StringBuilder sb = new StringBuilder(); sb.append(toDetailedString()); sb.append("\n"); for (Synapse s : is) { sb.append(" "); sb.append(s.toString()); sb.append("\n"); } return sb.toString(); } public static class SynapseSummary implements Writable { private volatile double posDirSum; private volatile double negDirSum; private volatile double negRecSum; private volatile double posRecSum; private volatile double posPassiveSum; private volatile double posDirSumDelta = 0.0; private volatile double negDirSumDelta = 0.0; private volatile double negRecSumDelta = 0.0; private volatile double posRecSumDelta = 0.0; private volatile double posPassiveSumDelta = 0.0; public double getPosDirSum() { return posDirSum; } public double getNegDirSum() { return negDirSum; } public double getNegRecSum() { return negRecSum; } public double getPosRecSum() { return posRecSum; } public double getPosPassiveSum() { return posPassiveSum; } public double getPosSum(Synapse.State state) { return getPosDirSum(state) + getPosRecSum(state); } private double getPosDirSum(Synapse.State state) { return state == CURRENT ? posDirSum : posDirSum + posDirSumDelta; } private double getPosRecSum(Synapse.State state) { return state == CURRENT ? posRecSum : posRecSum + posRecSumDelta; } private double getPosPassiveSum(Synapse.State state) { return state == CURRENT ? posPassiveSum : posPassiveSum + posPassiveSumDelta; } public void updateSynapse(Synapse s) { if (!s.isInactive(CURRENT)) { updateSynapse(CURRENT, -1.0, s); } if (!s.isInactive(NEXT)) { updateSynapse(NEXT, 1.0, s); } } private void updateSynapse(Synapse.State state, double sign, Synapse s) { updateSum(s.isRecurrent(), s.isNegative(state), sign * (s.getLimit(state) * s.getWeight(state))); posDirSumDelta += sign * s.computeMaxRelationWeights(); if(s.getInput().get().isPassiveInputNeuron() && !s.isNegative(state)) { posPassiveSumDelta += sign * (!s.isNegative(state) ? (s.getLimit(state) * s.getWeight(state)) : 0.0); } } private void updateSum(boolean rec, boolean neg, double delta) { if(!rec) { if(!neg) { posDirSumDelta += delta; } else { negDirSumDelta += delta; } } else { if(!neg) { posRecSumDelta += delta; } else { negRecSumDelta += delta; } } } public void commit() { posDirSum += posDirSumDelta; negDirSum += negDirSumDelta; posRecSum += posRecSumDelta; negRecSum += negRecSumDelta; posPassiveSum += posPassiveSumDelta; posDirSumDelta = 0.0; negDirSumDelta = 0.0; negRecSumDelta = 0.0; posDirSumDelta = 0.0; posPassiveSumDelta = 0.0; } public static SynapseSummary read(DataInput in, Model m) throws IOException { SynapseSummary ss = new SynapseSummary(); ss.readFields(in, m); return ss; } @Override public void write(DataOutput out) throws IOException { out.writeDouble(posDirSum); out.writeDouble(negDirSum); out.writeDouble(negRecSum); out.writeDouble(posRecSum); out.writeDouble(posPassiveSum); } @Override public void readFields(DataInput in, Model m) throws IOException { posDirSum = in.readDouble(); negDirSum = in.readDouble(); negRecSum = in.readDouble(); posRecSum = in.readDouble(); posPassiveSum = in.readDouble(); } } } aika-java-1.4.0/src/main/java/network/aika/neuron/Neuron.java000066400000000000000000000264451352543012000240170ustar00rootroot00000000000000/* * 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.neuron; import network.aika.*; import network.aika.lattice.Converter; import network.aika.neuron.activation.Activation; import network.aika.neuron.activation.Position; import network.aika.neuron.relation.Relation; import network.aika.neuron.INeuron.Type; import java.util.*; import java.util.function.Predicate; import java.util.stream.Stream; /** * The {@code Neuron} class is a proxy implementation for the real neuron implementation in the class {@code INeuron}. * Aika uses the provider pattern to store and reload rarely used neurons or logic nodes. * * @author Lukas Molzberger */ public class Neuron extends Provider { public static final Neuron MIN_NEURON = new Neuron(null, Integer.MIN_VALUE); public static final Neuron MAX_NEURON = new Neuron(null, Integer.MAX_VALUE); ReadWriteLock lock = new ReadWriteLock(); NavigableMap inputSynapsesById = new TreeMap<>(); NavigableMap activeInputSynapses = new TreeMap<>(Synapse.INPUT_SYNAPSE_COMP); NavigableMap activeOutputSynapses = new TreeMap<>(Synapse.OUTPUT_SYNAPSE_COMP); public Neuron(Model m, int id) { super(m, id); } public Neuron(Model m, INeuron n) { super(m, n); } public String getLabel() { return get().getLabel(); } public Type getType() { return get().getType(); } public void setType(Type t) { get().setType(t); } public void setActivationFunction(ActivationFunction actF) { get().setActivationFunction(actF); } /** * Propagate an input activation into the network. * * @param doc The current document * @param begin The range begin * @param end The range end */ public Activation addInput(Document doc, int begin, int end) { return addInput(doc, new Activation.Builder() .setRange(begin, end) ); } /** * Propagate an input activation into the network. * * @param doc The current document * @param inputAct */ public Activation addInput(Document doc, Activation.Builder inputAct) { return get(doc).addInput(doc, inputAct); } public static Neuron init(Neuron n, Builder... inputs) { return init(null, n, inputs); } public static Neuron init(Document doc, Neuron n, Builder... inputs) { if(n.init(doc, null, getSynapseBuilders(inputs), getRelationBuilders(inputs))) { return n; } else return null; } /** * Creates a neuron with the given bias. * * @param n * @param bias * @param inputs * @return */ public static Neuron init(Neuron n, double bias, Builder... inputs) { return init(n, bias, getSynapseBuilders(inputs), getRelationBuilders(inputs)); } /** * Creates a neuron with the given bias. * * @param n * @param bias * @param inputs * @return */ public static Neuron init(Document doc, Neuron n, double bias, Builder... inputs) { return init(doc, n, bias, getSynapseBuilders(inputs), getRelationBuilders(inputs)); } /** * Initializes a neuron with the given bias. * * @param n * @param bias * @param synapseBuilders * @param relationBuilders * @return */ public static Neuron init(Neuron n, double bias, Collection synapseBuilders, Collection relationBuilders) { if(n.init((Document) null, bias, synapseBuilders, relationBuilders)) return n; return null; } public static Neuron init(Neuron n, double bias, Collection inputs) { if(n.init((Document) null, bias, getSynapseBuilders(inputs), getRelationBuilders(inputs))) return n; return null; } /** * Initializes a neuron with the given bias. * * @param n * @param bias * @param synapseBuilders * @param relationBuilders * @return */ public static Neuron init(Document doc, Neuron n, double bias, Collection synapseBuilders, Collection relationBuilders) { if(n.init(doc, bias, synapseBuilders, relationBuilders)) return n; return null; } public static Neuron init(Document doc, Neuron n, double bias, Collection inputs) { if(n.init(doc, bias, getSynapseBuilders(inputs), getRelationBuilders(inputs))) return n; return null; } private boolean init(Document doc, Double bias, Collection synapseBuilders, Collection relationBuilders) { INeuron n = get(); if(bias != null) { n.setBias(bias); } ArrayList modifiedSynapses = new ArrayList<>(); // s.link requires an updated n.biasSumDelta value. synapseBuilders.forEach(input -> { Synapse s = input.getSynapse(this); s.update(doc, input.weight, input.limit); modifiedSynapses.add(s); }); modifiedSynapses.forEach(s -> s.link()); relationBuilders.forEach(input -> input.connect(this)); n.commit(modifiedSynapses); return Converter.convert(model.defaultThreadId, doc, n, modifiedSynapses); } public PassiveInputFunction getPassiveInputFunction() { return get().passiveInputFunction; } public void setPassiveInputFunction(PassiveInputFunction f) { get().passiveInputFunction = f; model.passiveActivationFunctions.put(id, f); for(Synapse s: get().outputSynapses.values()) { s.getOutput().get().registerPassiveInputSynapse(s); } } public void setOutputText(String outputText) { get().setOutputText(outputText); } public Synapse getSynapseById(int synapseId) { return inputSynapsesById.get(synapseId); } /** * {@code getFinalActivations} is a convenience method to retrieve all activations of the given neuron that * are part of the final interpretation. Before calling this method, the {@code doc.process()} needs to * be called first. {@code getFinalActivations} requires that the {@code doc.process()} method has been called first. * * @param doc The current document * @return A collection with all final activations of this neuron. */ public Stream getActivations(Document doc, boolean onlyFinal) { INeuron n = getIfNotSuspended(); if(n == null) return Stream.empty(); return n.getActivations(doc, onlyFinal); } public Stream getActivations(Document doc, int slot, Position pos, boolean onlyFinal) { INeuron n = getIfNotSuspended(); if(n == null) return Stream.empty(); return n.getActivations(doc, slot, pos, onlyFinal); } public Activation getActivation(Document doc, int begin, int end, boolean onlyFinal) { return getActivations(doc, Activation.BEGIN, doc.lookupFinalPosition(begin), onlyFinal) .filter(act -> act.lookupSlot(Activation.END).getFinalPosition() == end) .findFirst() .orElse(null); } public Synapse selectInputSynapse(Neuron inputNeuron, Predicate filter) { lock.acquireWriteLock(); Synapse synapse = activeInputSynapses.subMap( new Synapse(inputNeuron, this, Integer.MIN_VALUE), true, new Synapse(inputNeuron, this, Integer.MAX_VALUE), true ) .keySet() .stream() .filter(filter) .findAny() .orElse(null); lock.releaseWriteLock(); return synapse; } void addActiveInputSynapse(Synapse s) { lock.acquireWriteLock(); activeInputSynapses.put(s, s); inputSynapsesById.put(s.getId(), s); lock.releaseWriteLock(); } void removeActiveInputSynapse(Synapse s) { lock.acquireWriteLock(); activeInputSynapses.remove(s); inputSynapsesById.remove(s.getId()); lock.releaseWriteLock(); } void addActiveOutputSynapse(Synapse s) { lock.acquireWriteLock(); activeOutputSynapses.put(s, s); lock.releaseWriteLock(); } void removeActiveOutputSynapse(Synapse s) { lock.acquireWriteLock(); activeOutputSynapses.remove(s); lock.releaseWriteLock(); } public String toString() { if(this == MIN_NEURON) return "MIN_NEURON"; if(this == MAX_NEURON) return "MAX_NEURON"; return super.toString(); } public int getNewSynapseId() { return get().getNewSynapseId(); } public void registerSynapseId(int synId) { get().registerSynapseId(synId); } /** * Active input synapses are those synapses that are currently available in the main memory. * * @return */ public Collection getActiveInputSynapses() { return activeInputSynapses.values(); } public Collection getActiveOutputSynapses() { return activeOutputSynapses.values(); } private static Collection getSynapseBuilders(Collection builders) { ArrayList result = new ArrayList<>(); for(Builder b: builders) { if(b instanceof Synapse.Builder) { result.add((Synapse.Builder) b); } } return result; } private static Collection getRelationBuilders(Collection builders) { ArrayList result = new ArrayList<>(); for(Builder b: builders) { if(b instanceof Relation.Builder) { result.add((Relation.Builder) b); } } return result; } private static Collection getSynapseBuilders(Builder... builders) { ArrayList result = new ArrayList<>(); for(Builder b: builders) { if(b instanceof Synapse.Builder) { result.add((Synapse.Builder) b); } } return result; } private static Collection getRelationBuilders(Builder... builders) { ArrayList result = new ArrayList<>(); for(Builder b: builders) { if(b instanceof Relation.Builder) { result.add((Relation.Builder) b); } } return result; } public interface Builder { void registerSynapseIds(Neuron n); } }aika-java-1.4.0/src/main/java/network/aika/neuron/Synapse.java000066400000000000000000000373321352543012000241700ustar00rootroot00000000000000/* * 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.neuron; import network.aika.*; import network.aika.Document; import network.aika.neuron.activation.link.Link; import network.aika.neuron.relation.Relation; import network.aika.Writable; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; import java.util.*; import static network.aika.neuron.INeuron.Type.EXCITATORY; import static network.aika.neuron.INeuron.Type.INHIBITORY; import static network.aika.neuron.Synapse.State.CURRENT; import static network.aika.neuron.Synapse.State.NEXT; /** * The {@code Synapse} class connects two neurons with each other. When propagating an activation signal, the * weight of the synapse is multiplied with the activation value of the input neurons activation. The result is then added * to the output neurons weighted sum to compute the output activation value. In contrast to a conventional neural network * synapses in Aika do not just propagate the activation value from one neuron to the next, but also structural information * like the text range and the relational id (e.g. word position) and also the interpretation to which the input * activation belongs. To determine in which way the structural information should be propagated synapses in Aika possess * a few more properties. * *

The properties {@code relativeRid} and {@code absoluteRid} determine either the relative difference between * the {@code rid} of the input activation and the rid of the output activation or require a fixed rid as input. * *

The properties range match, range mapping and range output manipulate the ranges. The range match determines whether * the input range begin or end is required to be equal, greater than or less than the range of the output activation. * The range mapping can be used to map for example an input range end to an output range begin. Usually this simply maps * begin to begin and end to end. The range output property is a boolean flag which determines whether the input range * should be propagated to the output activation. * *

Furthermore, the property {@code isRecurrent} specifies if this synapse is a recurrent feedback link. Recurrent * feedback links can be either negative or positive depending on the weight of the synapse. Recurrent feedback links * kind of allow to use future information as inputs of a current neuron. Aika allows this by making assumptions about * the recurrent input neuron. The class {@code SearchNode} modifies these assumptions until the best interpretation * for this document is found. * * * @author Lukas Molzberger */ public class Synapse implements Writable { public static final int OUTPUT = -1; public static double TOLERANCE = 0.0000001; public static final Comparator INPUT_SYNAPSE_COMP = (s1, s2) -> { int r = s1.input.compareTo(s2.input); if (r != 0) return r; return Integer.compare(s1.id, s2.id); }; public static final Comparator OUTPUT_SYNAPSE_COMP = (s1, s2) -> { int r = s1.output.compareTo(s2.output); if (r != 0) return r; return Integer.compare(s1.id, s2.id); }; private Neuron input; private Neuron output; private Integer id; private boolean isRecurrent; private boolean identity; private Map relations = new TreeMap<>(); private boolean inactive; private boolean inactiveNew; private double weight; private double weightDelta; private double limit = 1.0; private double limitDelta; public Synapse() { } public Synapse(Neuron input, Neuron output, Integer id) { this.id = id; this.input = input; this.output = output; } public Neuron getInput() { return input; } public Neuron getOutput() { return output; } public Integer getId() { return id; } public boolean isRecurrent() { return isRecurrent; } public void setRecurrent(boolean recurrent) { isRecurrent = recurrent; } public boolean isIdentity() { return identity; } public void setIdentity(boolean identity) { this.identity = identity; } public Map getRelations() { return relations; } public void setRelations(Map relations) { this.relations = relations; } public boolean isInactive() { return inactive; } public boolean isInactive(State s) { return s == CURRENT ? inactive : inactiveNew; } public void setInactive(State s, boolean inactive) { if(s == CURRENT) { this.inactive = inactive; } else if(s == NEXT) { this.inactiveNew = inactive; } } public double getWeight() { return weight; } public double getLimit() { return limit; } public double getNewWeight() { return weight + weightDelta; } public double getNewLimit() { return limit + limitDelta; } public double getWeight(State s) { return s == CURRENT ? weight : getNewWeight(); } public double getLimit(State s) { return s == CURRENT ? limit : getNewLimit(); } public double getWeightDelta() { return weightDelta; } public double getLimitDelta() { return limitDelta; } public void link() { verify(); INeuron in = input.get(); INeuron out = output.get(); boolean dir = in.getId() < out.getId(); (dir ? in : out).lock.acquireWriteLock(); (dir ? out : in).lock.acquireWriteLock(); input.lock.acquireWriteLock(); input.activeOutputSynapses.put(this, this); input.lock.releaseWriteLock(); output.lock.acquireWriteLock(); output.activeInputSynapses.put(this, this); output.inputSynapsesById.put(id, this); output.lock.releaseWriteLock(); removeLinkInternal(in, out); if(out.getType() == EXCITATORY) { out.inputSynapses.put(this, this); out.setModified(); } if(out.getType() == INHIBITORY){ in.outputSynapses.put(this, this); in.setModified(); } out.registerSynapseId(id); if(in.isPassiveInputNeuron()) { out.registerPassiveInputSynapse(this); } (dir ? in : out).lock.releaseWriteLock(); (dir ? out : in).lock.releaseWriteLock(); } public void unlink() { INeuron in = input.get(); INeuron out = output.get(); boolean dir = input.getId() < out.getId(); (dir ? in : out).lock.acquireWriteLock(); (dir ? out : in).lock.acquireWriteLock(); input.lock.acquireWriteLock(); input.activeOutputSynapses.remove(this); input.lock.releaseWriteLock(); output.lock.acquireWriteLock(); output.activeInputSynapses.remove(this); output.inputSynapsesById.remove(id); output.lock.releaseWriteLock(); removeLinkInternal(in, out); (dir ? in : out).lock.releaseWriteLock(); (dir ? out : in).lock.releaseWriteLock(); } private void verify() { if((isRecurrent || isNegative(CURRENT)) && output.getType() == INHIBITORY) { throw new InvalidInhibitoryNeuronSynapse(); } } private void removeLinkInternal(INeuron in, INeuron out) { if(out.getType() == EXCITATORY) { if(out.inputSynapses.remove(this) != null) { out.setModified(); } } if(out.getType() == INHIBITORY) { if(in.outputSynapses.remove(this) != null) { in.setModified(); } } } public double getMaxInputValue() { return limit * weight; } public boolean exists() { if(input.get().outputSynapses.containsKey(this)) return true; if(output.get().inputSynapses.containsKey(this)) return true; return false; } public void commit() { weight += weightDelta; weightDelta = 0.0; limit += limitDelta; limitDelta = 0.0; inactive = inactiveNew; } public boolean isZero() { return Math.abs(weight) < TOLERANCE; } public double computeRelationWeights(Link l) { return 0; } public double computeMaxRelationWeights() { return 0; } public enum State { NEXT, CURRENT } public boolean isWeak(State state) { double w = getLimit(state) * getWeight(state); INeuron n = output.get(); switch(n.getType()) { case EXCITATORY: return w < n.getBias(); case INHIBITORY: return w < -n.getBias(); case INPUT: return false; } return false; } public void updateDelta(Document doc, double weightDelta, double limitDelta) { this.weightDelta += weightDelta; this.limitDelta += limitDelta; if(doc != null) { doc.notifyWeightModified(this); } } public void update(Document doc, double weight, double limit) { this.weightDelta = weight - this.weight; this.limitDelta = limit - this.limit; if(doc != null) { doc.notifyWeightModified(this); } } public boolean isNegative(State s) { return getWeight(s) < 0.0; } public Relation getRelationById(Integer id) { return relations.get(id); } public String toString() { return "S ID:" + id + " NW:" + Utils.round(getNewWeight()) + " rec:" + isRecurrent + " " + input + "->" + output; } @Override public void write(DataOutput out) throws IOException { out.writeInt(id); out.writeBoolean(isRecurrent); out.writeBoolean(identity); out.writeInt(input.getId()); out.writeInt(output.getId()); out.writeInt(relations.size()); for(Map.Entry me: relations.entrySet()) { out.writeInt(me.getKey()); me.getValue().write(out); } out.writeDouble(weight); out.writeDouble(limit); out.writeBoolean(inactive); } @Override public void readFields(DataInput in, Model m) throws IOException { id = in.readInt(); isRecurrent = in.readBoolean(); identity = in.readBoolean(); input = m.lookupNeuron(in.readInt()); output = m.lookupNeuron(in.readInt()); int l = in.readInt(); for(int i = 0; i < l; i++) { Integer relId = in.readInt(); relations.put(relId, Relation.read(in, m)); } weight = in.readDouble(); limit = in.readDouble(); inactive = in.readBoolean(); } public static Synapse createOrReplace(Document doc, Integer synapseId, Neuron inputNeuron, Neuron outputNeuron, SynapseFactory synapseFactory) { outputNeuron.get(doc); inputNeuron.get(doc); outputNeuron.lock.acquireWriteLock(); Synapse synapse = outputNeuron.inputSynapsesById.get(synapseId); if(synapse != null) { synapse.unlink(); } outputNeuron.lock.releaseWriteLock(); if(synapse == null) { synapse = synapseFactory.createSynapse(inputNeuron, outputNeuron, synapseId); } else { synapse.input = inputNeuron; synapse.output = outputNeuron; } if (synapseId == null) { synapse.id = outputNeuron.get(doc).getNewSynapseId(); } synapse.link(); return synapse; } public boolean linksAnyOutput() { // Output Relations are always PositionRelations. return relations.get(OUTPUT) != null; } /** * The {@code Builder} class is just a helper class which is used to initialize a neuron. Most of the parameters of this class * will be mapped to a input synapse for this neuron. * * @author Lukas Molzberger */ public static class Builder implements Neuron.Builder { private boolean recurrent; private Neuron neuron; double weight; double limit = 1.0; private boolean identity; private Integer synapseId; /** * The property recurrent specifies if input is a recurrent feedback link. Recurrent * feedback links can be either negative or positive depending on the weight of the synapse. Recurrent feedback links * kind of allow to use future information as inputs of a current neuron. Aika allows this by making assumptions about * the recurrent input neuron. The class SearchNode modifies these assumptions until the best interpretation * for this document is found. * * @param recurrent * @return */ public Builder setRecurrent(boolean recurrent) { this.recurrent = recurrent; return this; } /** * Determines the input neuron. * * @param neuron * @return */ public Builder setNeuron(Neuron neuron) { assert neuron != null; this.neuron = neuron; return this; } /** * Determines the input neuron. * * @param neuron * @return */ public Builder setNeuron(INeuron neuron) { assert neuron != null; this.neuron = neuron.getProvider(); return this; } /** * The synapse weight of this input. * * @param weight * @return */ public Builder setWeight(double weight) { this.weight = weight; return this; } public Builder setLimit(double limit) { this.limit = limit; return this; } public Builder setIdentity(boolean identity) { this.identity = identity; return this; } public Builder setSynapseId(int synapseId) { assert synapseId >= 0; this.synapseId = synapseId; return this; } @Override public void registerSynapseIds(Neuron n) { n.registerSynapseId(synapseId); } public Synapse getSynapse(Neuron outputNeuron) { Synapse s = createOrReplace(null, synapseId, neuron, outputNeuron, getSynapseFactory()); s.isRecurrent = recurrent; s.identity = identity; return s; } protected SynapseFactory getSynapseFactory() { return (input, output, id) -> new Synapse(input, output, id); } } public interface SynapseFactory { Synapse createSynapse(Neuron input, Neuron output, Integer id); } public class InvalidInhibitoryNeuronSynapse extends RuntimeException { public InvalidInhibitoryNeuronSynapse() { super("An inhibitory neuron is not allowed to have recurrent or negative input synapses."); } } } aika-java-1.4.0/src/main/java/network/aika/neuron/activation/000077500000000000000000000000001352543012000240345ustar00rootroot00000000000000aika-java-1.4.0/src/main/java/network/aika/neuron/activation/Activation.java000066400000000000000000000722361352543012000270120ustar00rootroot00000000000000/* * 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.neuron.activation; import network.aika.ActivationFunction; import network.aika.Document; import network.aika.Utils; import network.aika.lattice.activation.InputActivation; import network.aika.lattice.activation.OrActivation; import network.aika.neuron.INeuron; import network.aika.neuron.INeuron.SynapseSummary; import network.aika.neuron.INeuron.Type; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.activation.link.Direction; import network.aika.neuron.activation.link.Link; import network.aika.neuron.activation.search.Decision; import network.aika.neuron.activation.search.Option; import network.aika.neuron.activation.search.SearchNode; import java.util.*; import java.util.function.Predicate; import java.util.stream.Stream; import static network.aika.neuron.INeuron.Type.*; import static network.aika.neuron.activation.search.Decision.*; import static network.aika.neuron.activation.link.Link.INPUT_COMP; import static network.aika.neuron.activation.link.Link.OUTPUT_COMP; import static network.aika.neuron.Synapse.State.CURRENT; import static network.aika.neuron.activation.State.ZERO; /** * The {@code Activation} class is the most central class in Aika. On the one hand it stores the activation value * for a given neuron in the {@code State} substructure. On the other hand it specifies where this activation is * located within the document and to which interpretation it belongs. The {@code Activation.Key} therefore * consists of the logic node to which this activation belongs. If this logic node is an or-node, then this activation * automatically also belongs to the neuron as well. Furthermore, the key contains the char range within the document * and the relational id (rid). The relational id might be used to store the word pos for instance. Lastly, the key * contain the interpretation node of this activation, specifying to which interpretation this activation belongs. * *

The activations are linked to each other on two levels. The fields {@code inputs} and {@code outputs} * contain the activation links within the logic layer. The fields {@code inputLinks} and * {@code outputLinks} contain the links on the neural layer. * * @author Lukas Molzberger */ public class Activation implements Comparable { public static int BEGIN = 0; public static int END = 1; public static final Comparator ACTIVATION_ID_COMP = Comparator.comparingInt(act -> act.id); public static int MAX_SELF_REFERENCING_DEPTH = 5; public static boolean DEBUG_OUTPUT = false; public static final Activation MIN_ACTIVATION = new Activation(Integer.MIN_VALUE); public static final Activation MAX_ACTIVATION = new Activation(Integer.MAX_VALUE); private int id; private INeuron neuron; private Document doc; private long visited = 0; private Map slots = new TreeMap<>(); private OrActivation inputNodeActivation; private InputActivation outputNodeActivation; private TreeMap inputLinks = new TreeMap<>(INPUT_COMP); private TreeMap outputLinks = new TreeMap<>(OUTPUT_COMP); private double upperBound; private double lowerBound; public Option rootOption = new Option(null, this, null); public Option currentOption = rootOption; public Option finalOption; boolean ubQueued = false; private long markedHasCandidate; private Double targetValue; private Double inputValue; private Integer sequence; private Integer candidateId; public long markedAncDesc; public boolean blocked; /** * The cached decision is used to avoid having to explore the same currentSearchState twice even though nothing that * influences this currentSearchState has changed. */ public Decision cachedDecision = UNKNOWN; public double alternativeCachedWeightSum; /** * The cached search node is used to avoid having to recompute the activation values and weights that are associated * with this search node. */ public SearchNode cachedSearchNode; public SearchNode bestChildNode; public int[] debugCounts = new int[3]; public int[] debugDecisionCounts = new int[3]; public static Comparator CANDIDATE_COMP = (act1, act2) -> { Iterator> ita = act1.getSlots().entrySet().iterator(); Iterator> itb = act2.getSlots().entrySet().iterator(); Map.Entry mea; Map.Entry meb; while (ita.hasNext() || itb.hasNext()) { mea = ita.hasNext() ? ita.next() : null; meb = itb.hasNext() ? itb.next() : null; if (mea == null && meb == null) { break; } else if (mea == null && meb != null) { return -1; } else if (mea != null && meb == null) { return 1; } int r = Integer.compare(mea.getKey(), meb.getKey()); if (r != 0) return r; r = Position.compare(act1.lookupSlot(mea.getKey()), act2.lookupSlot(meb.getKey())); if (r != 0) return r; } int r = Integer.compare(act1.getSequence(), act2.getSequence()); if (r != 0) return r; return Integer.compare(act1.getCandidateId(), act2.getCandidateId()); }; private Activation(int id) { this.id = id; } public Activation(Document doc, INeuron neuron, Map slots) { this.id = doc.getNewActivationId(); this.doc = doc; this.neuron = neuron; this.slots = slots; neuron.register(this); } public void setInputNodeActivation(OrActivation inputNodeActivation) { this.inputNodeActivation = inputNodeActivation; } public OrActivation getInputNodeActivation() { return inputNodeActivation; } public InputActivation getOutputNodeActivation() { return outputNodeActivation; } public void setOutputNodeActivation(InputActivation outputNodeActivation) { this.outputNodeActivation = outputNodeActivation; } public Map getSlots() { return slots; } public Position lookupSlot(int slot) { Position pos = slots.get(slot); if(pos == null) { pos = new Position(doc); slots.put(slot, pos); } return pos; } public Integer length() { int min = Integer.MAX_VALUE; int max = Integer.MIN_VALUE; for(Position pos: slots.values()) { if(pos.getFinalPosition() == null) { return null; } min = Math.min(min, pos.getFinalPosition()); max = Math.max(max, pos.getFinalPosition()); } if(min > max) return 0; return max - min; } public int getId() { return id; } public Document getDocument() { return doc; } public int getThreadId() { return doc.getThreadId(); } public long getNewVisitedId() { return doc.getNewVisitedId(); } public long getVisitedId() { return visited; } public boolean checkVisited(long v) { if(visited == v) return false; visited = v; return true; } public String getLabel() { return getINeuron().getLabel(); } public Type getType() { return getINeuron().getType(); } public String getText() { return doc.getText(lookupSlot(BEGIN), lookupSlot(END)); } public INeuron getINeuron() { return neuron; } public Neuron getNeuron() { return neuron.getProvider(); } public Synapse getSynapseById(int synapseId) { return getNeuron().getSynapseById(synapseId); } public boolean checkDependenciesSatisfied(long v) { return !getInputLinks() .anyMatch(l -> l.getInput().markedHasCandidate != v && !l.isRecurrent() && l.getInput().getUpperBound() > 0.0); } public void markHasCandidate(long v) { markedHasCandidate = v; for(Link l: outputLinks.values()) { if(l.getOutput().getType() == INHIBITORY) { l.getOutput().markHasCandidate(v); } } } public double getUpperBound() { return upperBound; } public void setUpperBound(double ub) { upperBound = ub; } public double getLowerBound() { return lowerBound; } public void setLowerBound(double lb) { lowerBound = lb; } public Double getTargetValue() { return targetValue; } public Double getInputValue() { return inputValue; } public Decision getDecision() { return currentOption.decision; } public Decision getNextDecision(Option parent, SearchNode sn) { if(sn == null) { return UNKNOWN; } else if(this == sn.getActivation()) { return sn.getDecision(); } else { return parent.decision; } } public Decision getFinalDecision() { return finalOption.decision; } public void addLink(Direction dir, Link l) { getLinks(dir.getInverted()).put(l, l); // TODO: Warum inverted? } public TreeMap getLinks(Direction dir) { switch(dir) { case INPUT: return inputLinks; case OUTPUT: return outputLinks; } return null; } public Link getLinkBySynapseId(int synapseId) { for(Link l: inputLinks.values()) { if(l.getSynapse().getId() == synapseId) { return l; } } return null; } public Stream getInputLinks() { return inputLinks.values().stream(); } public Stream getOutputLinks() { return outputLinks.values().stream(); } public Link getInputLink(Link l) { return inputLinks.get(l); } public Stream getLinksBySynapse(Direction dir, Synapse syn) { return getLinks(dir).subMap( new Link(syn, MIN_ACTIVATION, MIN_ACTIVATION), new Link(syn, MAX_ACTIVATION, MAX_ACTIVATION)) .values() .stream(); } public double process(SearchNode sn) throws OscillatingActivationsException, RecursiveDepthExceededException { State oldState = currentOption.getState(); State s = computeValueAndWeight(sn); if (currentOption.searchNode != sn) { if((currentOption.decision != UNKNOWN && currentOption.getState().equalsWithWeights(s))) { return 0.0; } if(this == sn.getActivation() && s.getPreferredDecision() != sn.getDecision()) { return 0.0; } saveState(sn); } if (currentOption.setState(s)) { doc.getValueQueue().propagateActivationValue(this, sn, !oldState.lowerBoundEquals(s), !oldState.upperBoundEquals(s)); } return s.weight - oldState.weight; } public State computeValueAndWeight(SearchNode sn) throws RecursiveDepthExceededException { INeuron n = getINeuron(); INeuron.SynapseSummary ss = n.getSynapseSummary(); double net = n.getTotalBias(CURRENT); double netUB = net; Integer fired = null; for (InputState is: getInputStates(sn)) { Synapse s = is.l.getSynapse(); Activation iAct = is.l.getInput(); if (iAct == this) continue; double x = Math.min(s.getLimit(), is.s.value) * s.getWeight(); net += x; netUB += Math.min(s.getLimit(), is.s.ub) * s.getWeight(); net += s.computeRelationWeights(is.l); if (!s.isRecurrent() && !s.isNegative(CURRENT)) { fired = Utils.max(fired, iAct.currentOption.getState().fired); } } for(Synapse s : n.getPassiveInputSynapses()) { double x = s.getWeight() * s.getInput().getPassiveInputFunction().getActivationValue(s, this); net += x; netUB += x; } return new State( n.getActivationFunction().f(net), n.getActivationFunction().f(netUB), net, net > 0.0 ? (fired != null ? fired : 0) + (getType() == EXCITATORY ? 1 : 0) : null, Math.max(0.0, Math.min(-ss.getNegRecSum(), net)) ); } public void processBounds() throws RecursiveDepthExceededException { double oldUpperBound = upperBound; computeBounds(); if(Math.abs(upperBound - oldUpperBound) > 0.01) { for(Link l: outputLinks.values()) { doc.getUpperBoundQueue().add(l); } } if (oldUpperBound <= 0.0 && upperBound > 0.0 && !blocked) { getINeuron().propagate(this); } } public void computeBounds() throws RecursiveDepthExceededException { INeuron n = getINeuron(); SynapseSummary ss = n.getSynapseSummary(); double ub = n.getTotalBias(CURRENT) + ss.getPosRecSum(); double lb = n.getTotalBias(CURRENT) + ss.getPosRecSum(); for (Link l : inputLinks.values()) { Synapse s = l.getSynapse(); if(s.isInactive()) { continue; } Activation iAct = l.getInput(); if (iAct == this) continue; double x = s.getWeight(); if (s.isNegative(CURRENT)) { if (!s.isRecurrent()) { ub += Math.min(s.getLimit(), iAct.lowerBound) * x; } lb += s.getLimit() * x; } else { ub += Math.min(s.getLimit(), iAct.upperBound) * x; lb += Math.min(s.getLimit(), iAct.lowerBound) * x; double rlw = s.computeRelationWeights(l); ub += rlw; lb += rlw; } } for(Synapse s : n.getPassiveInputSynapses()) { double x = s.getWeight() * s.getInput().getPassiveInputFunction().getActivationValue(s, this); ub += x; lb += x; } upperBound = n.getActivationFunction().f(ub); lowerBound = n.getActivationFunction().f(lb); } private List getInputStates(SearchNode sn) { ArrayList tmp = new ArrayList<>(); Synapse lastSynapse = null; InputState maxInputState = null; for (Link l : inputLinks.values()) { if(l.isInactive()) { continue; } if (lastSynapse != null && lastSynapse != l.getSynapse()) { tmp.add(maxInputState); maxInputState = null; } State s = l.getInput().getInputState(l.getSynapse(), this, sn); if (maxInputState == null || maxInputState.s.value < s.value) { maxInputState = new InputState(l, s); } lastSynapse = l.getSynapse(); } if (maxInputState != null) { tmp.add(maxInputState); } return tmp; } public void setInputState(Builder input) { rootOption.decision = SELECTED; rootOption.p = 1.0; rootOption.setState(new State(input.value, input.value, input.net, input.fired, 0.0)); currentOption = rootOption; finalOption = rootOption; inputValue = input.value; upperBound = input.value; lowerBound = input.value; targetValue = input.targetValue; } private static class InputState { public InputState(Link l, State s) { this.l = l; this.s = s; } Link l; State s; } private State getInputState(Synapse s, Activation act, SearchNode sn) { State is = currentOption.getState(); if(s.isNegative(CURRENT)) { if(!checkSelfReferencing(act)) { is = new State(is.ub, is.value, 0.0, null, 0.0); } else { is = ZERO; } } if(act.getType() == INHIBITORY) { return is; } else { Decision nd = act.getNextDecision(act.currentOption, sn); if (nd == SELECTED) { return new State(is.ub, is.ub, 0.0, 0, 0.0); } else if (nd == EXCLUDED) { return new State(is.value, is.value, 0.0, 0, 0.0); } } return null; } public boolean needsPropagation(SearchNode sn, boolean lowerBoundChange, boolean upperBoundChange) { if(getType() == INHIBITORY) { return upperBoundChange || lowerBoundChange; } else if(getType() == EXCITATORY) { Decision nd = getNextDecision(currentOption, sn); if(nd == SELECTED) { return upperBoundChange; } else if(nd == EXCLUDED) { return lowerBoundChange; } } return false; } public boolean checkSelfReferencing(Activation act) { Activation act1 = getInputExcitatoryActivation(); if(act1 == null) { return false; } else if(act == act1) { return true; } Integer f1 = act1.currentOption.getState().fired; Integer f2 = act.currentOption.getState().fired; if(f1 == null) { return false; } else if (f2 != null && f1 > f2) { return act1.checkSelfReferencingRecursiveStep(act, 0); } else { return act.checkSelfReferencingRecursiveStep(act1, 0); } } private boolean checkSelfReferencingRecursiveStep(Activation act, int depth) { if (this == act) { return true; } // The activation at depth 0 might not yet be computed. if(depth > 0 && currentOption.getState().value <= 0.0) { return false; } if(depth > MAX_SELF_REFERENCING_DEPTH) { return false; } if(getType() == INHIBITORY) { Link strongestLink = getStrongestLink(); if (strongestLink == null) { return false; } return strongestLink.getInput().checkSelfReferencingRecursiveStep(act, depth + 1); } else { for (Link l : inputLinks.values()) { Synapse s = l.getSynapse(); if(!s.isWeak(CURRENT) && !s.isNegative(CURRENT) && l.getInput().checkSelfReferencingRecursiveStep(act, depth + 1)) { return true; } } return false; } } private Activation getInputExcitatoryActivation() { if(getType() != INHIBITORY) { return this; } else { Link l = getStrongestLink(); if(l == null) { return null; } return l.getInput().getInputExcitatoryActivation(); } } private Link getStrongestLink() { if(inputLinks.size() == 1) { return inputLinks.firstEntry().getValue(); } return inputLinks .values() .stream() .filter(l -> l.getInput().currentOption.getState().value > 0.0) .max(Comparator.comparing(l -> l.getInput().currentOption.getState().value)) .orElse(null); } public List getFinalInputActivationLinks() { ArrayList results = new ArrayList<>(); for (Link l : inputLinks.values()) { if (l.getInput().isFinalActivation()) { results.add(l); } } return results; } public List getFinalOutputActivationLinks() { ArrayList results = new ArrayList<>(); for (Link l : outputLinks.values()) { if (l.getOutput().isFinalActivation()) { results.add(l); } } return results; } public boolean isFinalActivation() { return getFinalState().value > 0.0; } public State getFinalState() { return finalOption != null ? finalOption.getState() : ZERO; } public double getValue() { return getFinalState().value; } public Integer getSequence() { if (sequence != null) return sequence; sequence = 0; inputLinks .values() .stream() .filter(l -> !l.isRecurrent()) .forEach(l -> sequence = Math.max(sequence, l.getInput().getSequence() + 1)); return sequence; } public Integer getCandidateId() { return candidateId; } public void setCandidateId(Integer candidateId) { this.candidateId = candidateId; } public boolean match(Predicate filter) { Synapse ls = null; boolean matched = false; for(Link l: inputLinks.navigableKeySet()) { Synapse s = l.getSynapse(); if(ls != null && ls != s) { if(!matched) { return false; } matched = false; } if(filter.test(l)) { matched = true; } ls = s; } return matched; } public void computeOptionProbabilities() { rootOption.traverse((o) -> o.computeRemainingWeight()); final double[] offset = new double[] {Double.MAX_VALUE}; rootOption.traverse(o -> offset[0] = Math.min(offset[0], Math.log(o.cacheFactor) + o.remainingWeight)); final double[] norm = new double[] {0.0}; rootOption.traverse(o -> norm[0] += Math.log(o.cacheFactor) + o.remainingWeight - offset[0]); rootOption.traverse(o -> { if (o.getAct().getType() != INPUT && o.decision == SELECTED) { o.p = norm[0] != 0.0 ? Math.exp(Math.log(o.cacheFactor) + o.remainingWeight - offset[0]) / norm[0] : 1.0; } }); } public String toString() { return id + " " + getNeuron().getId() + ":" + getINeuron().typeToString() + " " + getLabel() + " " + slotsToString() + " " + identityToString() + " - " + " UB:" + Utils.round(upperBound) + (inputValue != null ? " IV:" + Utils.round(inputValue) : "") + (targetValue != null ? " TV:" + Utils.round(targetValue) : "") + " V:" + (currentOption != null ? Utils.round(currentOption.getState().value) : "-") + " FV:" + (finalOption != null ? Utils.round(finalOption.getState().value) : "-"); } public String searchStateToString() { return id + " " + getNeuron().getId() + ":" + getLabel() + " " + " CD:" + cachedDecision + " LIMITED:" + debugCounts[SearchNode.DebugState.LIMITED.ordinal()] + " CACHED:" + debugCounts[SearchNode.DebugState.CACHED.ordinal()] + " EXPLORE:" + debugCounts[SearchNode.DebugState.EXPLORE.ordinal()] + " SELECTED:" + debugDecisionCounts[0] + " EXCLUDED:" + debugDecisionCounts[1]; } public String toStringDetailed() { StringBuilder sb = new StringBuilder(); sb.append(Utils.addPadding("" + id, 3) + " "); sb.append(Utils.addPadding(getINeuron().typeToString(), 10) + " - "); sb.append(Utils.addPadding(getType() == EXCITATORY ? "" + (getFinalDecision() != null ? getFinalDecision() : "X") : "", 8) + " - "); sb.append(slotsToString()); sb.append(" \""); if (getINeuron().getOutputText() != null) { sb.append(Utils.collapseText(getINeuron().getOutputText(), 7)); } else { sb.append(Utils.collapseText(doc.getText(lookupSlot(BEGIN), lookupSlot(END)), 7)); } sb.append("\""); sb.append(identityToString()); sb.append(" - "); sb.append(getLabel()); if(DEBUG_OUTPUT) { sb.append(" - UB:"); sb.append(Utils.round(upperBound)); } if(SearchNode.COMPUTE_SOFT_MAX) { sb.append(" Exp:"); sb.append(getExpectedState()); } sb.append(" - "); State fs = getFinalState(); if (fs != null) { sb.append(fs); } if (inputValue != null) { sb.append(" - IV:" + Utils.round(inputValue)); } if (targetValue != null) { sb.append(" - TV:" + Utils.round(targetValue)); } return sb.toString(); } public State getExpectedState() { double value = 0.0; double ub = 0.0; double net = 0.0; for (Option option : getOptions()) { double p = option.p; State s = option.getState(); value += p * s.value; ub += p * s.ub; net += p * s.net; } return new State(value, ub, net, 0, 0.0); } public Collection

*

Before the search is started a set of initial refinements is generated from the conflicts within the document. * In other words, if there are no conflicts in a given document, then no search is needed. In this case the final interpretation * will simply be the set of all interpretation nodes. The initial refinements are then expanded, meaning all interpretation nodes that are consistent * with this refinement are added to the refinement. The initial refinements are then propagated along the search path as refinement candidates. * * @author Lukas Molzberger */ public class SearchNode implements Comparable { private static final Logger log = LoggerFactory.getLogger(SearchNode.class); public static int MAX_SEARCH_STEPS = Integer.MAX_VALUE; public static boolean OPTIMIZE_SEARCH = true; public static boolean COMPUTE_SOFT_MAX = false; private int id; private SearchNode parent; private Decision decision; private Activation act; private int level; public Branch selected = new Branch(); public Branch excluded = new Branch(); private double weightDelta; private double accumulatedWeight = 0.0; private Map modifiedActs = new TreeMap<>(Activation.ACTIVATION_ID_COMP); private Step step = Step.INIT; private Decision currentChildDecision = UNKNOWN; private long processVisited; private boolean bestPath; private int cachedCount = 1; private int cachedFactor = 1; private DebugState debugState; private enum Step { INIT, SELECT, POST_SELECT, EXCLUDE, POST_EXCLUDE, FINAL } public enum DebugState { CACHED, LIMITED, EXPLORE } public SearchNode(Document doc, Decision d, SearchNode p, int level) { id = doc.searchNodeIdCounter++; decision = d; parent = p; this.level = level; } public Branch getBranch(Decision d) { switch(d) { case SELECTED: return selected; case EXCLUDED: return excluded; } return null; } public SearchNode getAlternative() { return parent.getBranch(decision.getInverted()).child; } public void updateActivations(Document doc) throws OscillatingActivationsException { Activation act = getActivation(); weightDelta = doc.getValueQueue().process(this); if (act != null && followPath()) { act.cachedSearchNode = this; if(COMPUTE_SOFT_MAX) { modifiedActs.values().forEach(o -> o.link()); } } if (parent != null) { accumulatedWeight = weightDelta + parent.accumulatedWeight; } } public boolean followPath() { return getActivation().currentOption.searchNode == this && decision == getActivation().currentOption.getState().getPreferredDecision(); } public int getId() { return id; } public Map getModifiedActivations() { return modifiedActs; } public double getAccumulatedWeight() { return accumulatedWeight; } public Activation getActivation() { return parent != null ? parent.act : null; } /** * Searches for the best interpretation for the given document. *

* This implementation of the algorithm is iterative to prevent stack overflow errors from happening. * Depending on the document the search tree might be getting very deep. * * @param doc * @param root */ public static void search(Document doc, SearchNode root, long v, Long timeoutInMilliSeconds) throws TimeoutException, RecursiveDepthExceededException, OscillatingActivationsException { SearchNode sn = root; double returnWeight = 0.0; double returnWeightSum = 0.0; long startTime = System.currentTimeMillis(); do { if (sn.processVisited != v) { sn.step = Step.INIT; sn.processVisited = v; } switch (sn.step) { case INIT: if (sn.level >= doc.candidates.size()) { checkTimeoutCondition(timeoutInMilliSeconds, startTime); returnWeight = sn.processResult(doc); returnWeightSum = returnWeight; sn.step = Step.FINAL; sn = sn.parent; } else { sn.initStep(doc); sn.step = Step.SELECT; } break; case SELECT: if (sn.prepareStep(doc, SELECTED)) { sn.step = Step.POST_SELECT; sn = sn.selected.child; // Recursive Step } else { sn.step = Step.EXCLUDE; } break; case POST_SELECT: sn.selected.postStep(returnWeight, returnWeightSum); sn.step = Step.SELECT; break; case EXCLUDE: if (sn.prepareStep(doc, EXCLUDED)) { sn.step = Step.POST_EXCLUDE; sn = sn.excluded.child; // Recursive Step } else { sn.step = Step.FINAL; } break; case POST_EXCLUDE: sn.excluded.postStep(returnWeight, returnWeightSum); sn.step = Step.SELECT; break; case FINAL: returnWeight = sn.finalStep(); returnWeightSum = sn.getWeightSum(); sn = sn.parent; break; default: } } while (sn != null); } public void setWeight(double w) { for (Option sc : modifiedActs.values()) { sc.setWeight(w); } } private static void checkTimeoutCondition(Long timeoutInMilliSeconds, long startTime) throws TimeoutException { if (timeoutInMilliSeconds != null && System.currentTimeMillis() > startTime + timeoutInMilliSeconds) { throw new TimeoutException("Interpretation search took too long: " + (System.currentTimeMillis() - startTime) + "ms"); } } public double getWeightSum() { return selected.weightSum + excluded.weightSum; } private void initStep(Document doc) throws RecursiveDepthExceededException { act = doc.candidates.get(level); if (OPTIMIZE_SEARCH) { Decision cd = getCachedDecision(); if(cd != null && cd != UNKNOWN) { getBranch(cd).weightSum = act.alternativeCachedWeightSum; if (COMPUTE_SOFT_MAX) { SearchNode asn = act.cachedSearchNode.getAlternative(); if (asn != null) { asn.cachedCount++; } } } } if (doc.searchStepCounter > MAX_SEARCH_STEPS) { dumpDebugState(); throw new RuntimeException("Max search step exceeded."); } doc.searchStepCounter++; storeDebugInfos(); } private Decision getCachedDecision() { return act.cachedDecision; } private boolean prepareStep(Document doc, Decision d) throws OscillatingActivationsException { Branch b = getBranch(d); if(b.visited) { return false; } b.visited = true; if (OPTIMIZE_SEARCH && getCachedDecision() == d.getInverted() && (selected.searched || d == SELECTED)) { // In case there is a tie between two cached conflicting activations. return false; } SearchNode child = new SearchNode(doc, d, this, level + 1); if (b.prepareStep(doc, child)) return false; if (d == SELECTED && act.cachedDecision == UNKNOWN) { invalidateCachedDecisions(); } act.debugDecisionCounts[d.ordinal()]++; return true; } private double finalStep() { Decision d = selected.weight >= excluded.weight ? SELECTED : EXCLUDED; if (selected.searched && excluded.searched) { act.cachedDecision = d; act.alternativeCachedWeightSum = getBranch(act.cachedDecision).weightSum; } Branch b = getBranch(d); SearchNode cn = b.child; if (cn != null && cn.bestPath) { act.bestChildNode = cn; bestPath = true; } if(!COMPUTE_SOFT_MAX) { if(!bestPath) { b.cleanup(); } getBranch(d.getInverted()).cleanup(); } return b.weight; } private void invalidateCachedDecisions() { act.getOutputLinks() .filter(l -> !l.isNegative(CURRENT)) .forEach(l -> invalidateCachedDecision(l.getOutput())); } public static void invalidateCachedDecision(Activation act) { if (act != null && act.cachedDecision == EXCLUDED) { act.cachedDecision = UNKNOWN; SearchNode pn = act.cachedSearchNode.parent; if(pn != null) { pn.selected.repeat(); } } act.getInputLinks() .filter(l -> l.isRecurrent() && l.isNegative(CURRENT)) .map(l -> l.getInput()) .filter(c -> c.cachedDecision == SELECTED) .forEach(c -> c.cachedDecision = UNKNOWN); } private double processResult(Document doc) { double accNW = accumulatedWeight; if (level > doc.selectedSearchNode.level || accNW > getSelectedAccumulatedWeight(doc)) { doc.selectedSearchNode = this; doc.storeFinalState(); bestPath = true; } else { bestPath = false; } /* if(COMPUTE_SOFT_MAX) { dumpDebugState(); System.out.println(accumulatedWeight); System.out.println(); } */ return accumulatedWeight; } public static void computeCachedFactor(SearchNode sn) { while (sn != null) { switch (sn.currentChildDecision) { case UNKNOWN: sn.currentChildDecision = SELECTED; if (sn.selected.child != null) { sn = sn.selected.child; sn.computeCacheFactor(); } break; case SELECTED: sn.currentChildDecision = EXCLUDED; if (sn.excluded.child != null) { sn = sn.excluded.child; sn.computeCacheFactor(); } break; case EXCLUDED: sn = sn.parent; break; } } } private void computeCacheFactor() { cachedFactor = (parent != null ? parent.cachedFactor : 1) * cachedCount; for (Option sc : modifiedActs.values()) { sc.setCacheFactor(cachedFactor); } } private double getSelectedAccumulatedWeight(Document doc) { return doc.selectedSearchNode != null ? doc.selectedSearchNode.accumulatedWeight : -1.0; } public void changeState(Activation.Mode m) { modifiedActs .values() .forEach(sc -> sc.restoreState(m)); } @Override public int compareTo(SearchNode sn) { return Integer.compare(id, sn.id); } public Decision getDecision() { return decision; } private void storeDebugInfos() { debugState = getDebugState(); act.debugCounts[debugState.ordinal()]++; } private DebugState getDebugState() { if (!selected.searched || !excluded.searched) { return DebugState.LIMITED; } else if (getCachedDecision() != UNKNOWN) { return DebugState.CACHED; } else { return DebugState.EXPLORE; } } public void dumpDebugState() { SearchNode n = this; String weights = ""; Decision decision = UNKNOWN; while (n != null && n.level >= 0) { log.info( n.level + " " + n.debugState + " DECISION:" + decision + weights + " " + (n.act != null ? n.act.toString() : "") + " MOD-ACTS:" + n.modifiedActs.size() ); decision = n.decision; weights = " AW:" + Utils.round(n.accumulatedWeight) + " DW:" + Utils.round(n.weightDelta); n = n.parent; } } public String toString() { return "id:" + id + " actId:" + (act != null ? act.getId() : "-") + " Decision:" + getDecision() + " curDec:" + currentChildDecision; } public static class TimeoutException extends RuntimeException { public TimeoutException(String message) { super(message); } } } aika-java-1.4.0/src/main/java/network/aika/neuron/relation/000077500000000000000000000000001352543012000235105ustar00rootroot00000000000000aika-java-1.4.0/src/main/java/network/aika/neuron/relation/AncestorRelation.java000066400000000000000000000224101352543012000276260ustar00rootroot00000000000000/* * 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.neuron.relation; import network.aika.neuron.INeuron; import network.aika.neuron.Neuron; import network.aika.neuron.activation.Activation; import network.aika.neuron.activation.Position; import java.util.*; import java.util.stream.Stream; /** * * @author Lukas Molzberger */ public abstract class AncestorRelation extends Relation { public static AncestorRelation COMMON_ANCESTOR = new CommonAncestor(); public static AncestorRelation IS_DESCENDANT_OF = new IsDescendantOf(); public static AncestorRelation IS_ANCESTOR_OF = new IsAncestorOf(); public static AncestorRelation NOT_DESCENDANT_OF = new NotDescendantOf(); public static AncestorRelation NOT_ANCESTOR_OF = new NotAncestorOf(); AncestorRelation() {} private static void collectCommonAncestor(Collection results, INeuron n, Activation linkedAct, long v) { if(linkedAct.getVisitedId() == v) return; collectContains(results, n, linkedAct, v); linkedAct.getInputLinks() .filter(l -> l.isIdentity()) .forEach(l -> collectCommonAncestor(results, n, l.getInput(), v)); } private static void collectContains(Collection results, INeuron n, Activation linkedAct, long v) { if(!linkedAct.checkVisited(v)) return; if(linkedAct.getINeuron() == n) { results.add(linkedAct); } linkedAct.getOutputLinks() .filter(l ->l.isIdentity()) .forEach(l -> collectContains(results, n, l.getOutput(), v)); } private static void collectContainedIn(Collection results, INeuron n, Activation linkedAct, long v) { if(!linkedAct.checkVisited(v)) return; if(linkedAct.getINeuron() == n) { results.add(linkedAct); } linkedAct.getInputLinks() .filter(l -> l.isIdentity()) .forEach(l -> collectContainedIn(results, n, l.getInput(), v)); } @Override public void mapSlots(Map slots, Activation act) { } private static boolean contains(Activation actA, Activation actB, long v) { if(!actA.checkVisited(v)) return false; if(actA == actB) return true; return actA.getInputLinks() .filter(l -> l.isIdentity()) .anyMatch(l -> contains(l.getInput(), actB, v)); } private static boolean hasCommonAncestor(Activation act, Activation linkedAct) { long v = act.getNewVisitedId(); markAncestors(linkedAct, v); return hasCommonAncestor(act, v, act.getNewVisitedId()); } private static void markAncestors(Activation act, long v) { if(!act.checkVisited(v)) return; act.markedAncDesc = v; act.getInputLinks() .filter(l -> l.isIdentity()) .forEach(l -> markAncestors(l.getInput(), v)); } private static void markDescendants(Activation act, long v) { if(!act.checkVisited(v)) return; act.markedAncDesc = v; act.getOutputLinks() .filter(l -> l.isIdentity()) .forEach(l -> markDescendants(l.getInput(), v)); } private static boolean hasCommonAncestor(Activation act, long v1, long v2) { if(!act.checkVisited(v2)) return false; if(act.markedAncDesc == v1) return true; return act.getInputLinks() .filter(l -> l.isIdentity()) .anyMatch(l -> hasCommonAncestor(l.getInput(), v1, v2)); } @Override public boolean isExact() { return false; } public static class CommonAncestor extends AncestorRelation { public static int TYPE = 50; static { registerRelation(TYPE, () -> COMMON_ANCESTOR); } public CommonAncestor() { } @Override public int getType() { return TYPE; } @Override public Relation invert() { return new CommonAncestor(); } @Override public boolean test(Activation act, Activation linkedAct, boolean allowUndefined) { return hasCommonAncestor(act, linkedAct); } @Override public Stream getActivations(INeuron n, Activation linkedAct) { List results = new ArrayList<>(); collectCommonAncestor(results, n, linkedAct, linkedAct.getNewVisitedId()); return results.stream(); } public String toString() { return "COMMON-ANCESTOR"; } } public static class IsDescendantOf extends AncestorRelation { public static int TYPE = 51; static { registerRelation(TYPE, () -> IS_DESCENDANT_OF); } public IsDescendantOf() { } @Override public int getType() { return TYPE; } @Override public Relation invert() { return new IsAncestorOf(); } @Override public boolean test(Activation act, Activation linkedAct, boolean allowUndefined) { return contains(act, linkedAct, act.getNewVisitedId()); } @Override public Stream getActivations(INeuron n, Activation linkedAct) { List results = new ArrayList<>(); collectContains(results, n, linkedAct, linkedAct.getNewVisitedId()); return results.stream(); } public String toString() { return "DESCENDANT-OF"; } } public static class IsAncestorOf extends AncestorRelation { public static int TYPE = 52; static { registerRelation(TYPE, () -> IS_ANCESTOR_OF); } public IsAncestorOf() { } @Override public int getType() { return TYPE; } @Override public Relation invert() { return new IsDescendantOf(); } @Override public boolean test(Activation act, Activation linkedAct, boolean allowUndefined) { return contains(linkedAct, act, act.getNewVisitedId()); } @Override public Stream getActivations(INeuron n, Activation linkedAct) { List results = new ArrayList<>(); collectContainedIn(results, n, linkedAct, linkedAct.getNewVisitedId()); return results.stream(); } public String toString() { return "ANCESTOR-OF"; } } public static class NotDescendantOf extends AncestorRelation { public static int TYPE = 53; static { registerRelation(TYPE, () -> NOT_DESCENDANT_OF); } public NotDescendantOf() { } @Override public int getType() { return TYPE; } @Override public Relation invert() { return new NotAncestorOf(); } @Override public boolean test(Activation act, Activation linkedAct, boolean allowUndefined) { return !contains(act, linkedAct, act.getNewVisitedId()); } @Override public Stream getActivations(INeuron n, Activation linkedAct) { long v = linkedAct.getNewVisitedId(); markDescendants(linkedAct, v); return n.getActivations(linkedAct.getDocument()) .filter(act -> act.markedAncDesc != v); } public String toString() { return "NOT-DESCENDANT-OF"; } } public static class NotAncestorOf extends AncestorRelation { public static int TYPE = 54; static { registerRelation(TYPE, () -> NOT_ANCESTOR_OF); } public NotAncestorOf() { } @Override public int getType() { return TYPE; } @Override public Relation invert() { return new NotDescendantOf(); } @Override public boolean test(Activation act, Activation linkedAct, boolean allowUndefined) { return !contains(linkedAct, act, act.getNewVisitedId()); } @Override public Stream getActivations(INeuron n, Activation linkedAct) { long v = linkedAct.getNewVisitedId(); markAncestors(linkedAct, v); return n.getActivations(linkedAct.getDocument()) .filter(act -> act.markedAncDesc != v); } public String toString() { return "NOT-ANCESTOR-OF"; } } } aika-java-1.4.0/src/main/java/network/aika/neuron/relation/MultiRelation.java000066400000000000000000000124431352543012000271470ustar00rootroot00000000000000/* * 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.neuron.relation; import network.aika.Model; import network.aika.neuron.INeuron; import network.aika.neuron.Neuron; import network.aika.neuron.activation.Activation; import network.aika.neuron.activation.Position; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; import java.util.*; import java.util.stream.Stream; /** * * @author Lukas Molzberger */ public class MultiRelation extends Relation { public static final int ID = 1; private SortedMap relations; static { registerRelation(ID, () -> new MultiRelation()); } public MultiRelation() { relations = new TreeMap<>(); } public MultiRelation(Relation... rels) { relations = new TreeMap<>(); for(Relation r: rels) { relations.put(r, r); } } public MultiRelation(SortedMap rels) { relations = rels; } public Collection getLeafRelations() { return relations.values(); } public int size() { return relations.size(); } public Relation getRelation(Relation r) { return relations.get(r); } public void addRelation(Relation r) { relations.put(r, r); } public void removeRelation(Relation r) { relations.remove(r); } @Override public int getType() { return ID; } @Override public boolean test(Activation act, Activation linkedAct, boolean allowUndefined) { for (Relation rel : relations.values()) { if (!rel.test(act, linkedAct, allowUndefined)) { return false; } } return true; } @Override public Relation invert() { SortedMap invRels = new TreeMap<>(); for(Relation rel: relations.values()) { Relation ir = rel.invert(); invRels.put(ir, ir); } return new MultiRelation(invRels); } @Override public void mapSlots(Map slots, Activation act) { for(Relation rel: relations.values()) { rel.mapSlots(slots, act); } } @Override public boolean isExact() { for(Relation rel: relations.values()) { if(rel.isExact()) { return true; } } return false; } @Override public Stream getActivations(INeuron n, Activation linkedAct) { if(relations.isEmpty()) { return n.getActivations(linkedAct.getDocument()); } else { return relations.firstKey() .getActivations(n, linkedAct) .filter(act -> { for (Relation rel : relations.values()) { if (!rel.test(act, linkedAct, false)) { return false; } } return true; }); } } @Override public int compareTo(Relation rel) { int r = super.compareTo(rel); if(r != 0) return r; MultiRelation mr = (MultiRelation) rel; r = Integer.compare(relations.size(), mr.relations.size()); if(r != 0) return r; Iterator ita = relations.values().iterator(); Iterator itb = mr.relations.values().iterator(); while(ita.hasNext() || itb.hasNext()) { Relation a = ita.next(); Relation b = itb.next(); r = a.compareTo(b); if(r != 0) return r; } return 0; } @Override public void write(DataOutput out) throws IOException { super.write(out); out.writeInt(relations.size()); for(Relation rel: relations.values()) { rel.write(out); } } @Override public void readFields(DataInput in, Model m) throws IOException { super.readFields(in, m); int l = in.readInt(); for(int i = 0; i < l; i++) { Relation r = Relation.read(in, m); relations.put(r, r); } } public String toString() { StringBuilder sb = new StringBuilder(); sb.append("MULTI("); boolean first = true; for(Relation rel: relations.values()) { if(!first) { sb.append(", "); } first = false; sb.append(rel.toString()); } sb.append(")"); return sb.toString(); } } aika-java-1.4.0/src/main/java/network/aika/neuron/relation/PositionRelation.java000066400000000000000000000212611352543012000276570ustar00rootroot00000000000000/* * 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.neuron.relation; import network.aika.Model; import network.aika.neuron.INeuron; import network.aika.neuron.activation.Activation; import network.aika.neuron.activation.Position; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; import java.util.Map; import java.util.Set; import java.util.stream.Stream; /** * * @author Lukas Molzberger */ public abstract class PositionRelation extends Relation { public int fromSlot; public int toSlot; public PositionRelation() { } public PositionRelation(int fromSlot, int toSlot) { this.fromSlot = fromSlot; this.toSlot = toSlot; } @Override public boolean test(Activation act, Activation linkedAct, boolean allowUndefined) { Position toPos = linkedAct.lookupSlot(toSlot); if(allowUndefined && toPos == null) { return true; } return test(act.lookupSlot(fromSlot), toPos); } public abstract boolean test(Position a, Position b); @Override public void mapSlots(Map slots, Activation act) { } @Override public int compareTo(Relation rel) { int r = super.compareTo(rel); if(r != 0) return r; PositionRelation pr = (PositionRelation) rel; r = Integer.compare(fromSlot, pr.fromSlot); if(r != 0) return r; r = Integer.compare(toSlot, pr.toSlot); return r; } @Override public void write(DataOutput out) throws IOException { super.write(out); out.writeInt(fromSlot); out.writeInt(toSlot); } @Override public void readFields(DataInput in, Model m) throws IOException { super.readFields(in, m); fromSlot = in.readInt(); toSlot = in.readInt(); } @Override public Stream getActivations(INeuron n, Activation linkedAct) { Position pos = linkedAct.lookupSlot(toSlot); if(pos == null) { return Stream.empty(); } return getActivations(n, pos) .filter(act -> test(act, linkedAct, false)); } public abstract Stream getActivations(INeuron n, Position pos); public static class Equals extends PositionRelation { public static int TYPE = 0; static { registerRelation(TYPE, () -> new Equals()); } public Equals() {} public Equals(int fromSlot, int toSlot) { super(fromSlot, toSlot); } @Override public int getType() { return TYPE; } @Override public Relation invert() { return new Equals(toSlot, fromSlot); } @Override public boolean test(Position a, Position b) { return a == b; } @Override public void mapSlots(Map slots, Activation act) { slots.put(toSlot, act.lookupSlot(fromSlot)); } @Override public boolean isExact() { return true; } @Override public Stream getActivations(INeuron n, Position pos) { return n.getActivations(pos.getDocument(), fromSlot, pos, true, fromSlot, pos, true ); } public String toString() { return "EQUALS(" + fromSlot + "," + toSlot + ")"; } } public static class LessThan extends PositionRelation { public static int TYPE = 10; private boolean orEquals; private int maxLength = Integer.MAX_VALUE; static { registerRelation(TYPE, () -> new LessThan()); } public LessThan() { } public LessThan(int fromSlot, int toSlot, boolean orEquals) { super(fromSlot, toSlot); this.orEquals = orEquals; } public LessThan(int fromSlot, int toSlot, boolean orEquals, int maxLength) { super(fromSlot, toSlot); this.orEquals = orEquals; this.maxLength = maxLength; } @Override public int getType() { return TYPE; } @Override public Relation invert() { return new GreaterThan(toSlot, fromSlot, orEquals, maxLength); } @Override public boolean isExact() { return false; } @Override public boolean test(Position a, Position b) { if(a == b) { return orEquals; } return a.getFinalPosition() != null && b.getFinalPosition() != null && a.getFinalPosition() < b.getFinalPosition() && (b.getFinalPosition() - a.getFinalPosition() < maxLength); } @Override public Stream getActivations(INeuron n, Position pos) { return n.getActivations( pos.getDocument(), fromSlot, new Position(pos.getDocument(), maxLength != Integer.MAX_VALUE ? pos.getFinalPosition() - maxLength : Integer.MIN_VALUE), true, fromSlot, pos, orEquals ); } @Override public void write(DataOutput out) throws IOException { super.write(out); out.writeBoolean(orEquals); out.writeInt(maxLength); } @Override public void readFields(DataInput in, Model m) throws IOException { super.readFields(in, m); orEquals = in.readBoolean(); maxLength = in.readInt(); } public String toString() { return "LT" + (orEquals ? "E" : "") + "(" + fromSlot + "," + toSlot + ")"; } } public static class GreaterThan extends PositionRelation { public static int TYPE = 11; private boolean orEquals; private int maxLength = Integer.MAX_VALUE; static { registerRelation(TYPE, () -> new GreaterThan()); } public GreaterThan() { } public GreaterThan(int fromSlot, int toSlot, boolean orEquals) { super(fromSlot, toSlot); this.orEquals = orEquals; } public GreaterThan(int fromSlot, int toSlot, boolean orEquals, int maxLength) { super(fromSlot, toSlot); this.orEquals = orEquals; this.maxLength = maxLength; } @Override public int getType() { return TYPE; } @Override public Relation invert() { return new LessThan(toSlot, fromSlot, orEquals, maxLength); } @Override public boolean isExact() { return false; } @Override public boolean test(Position a, Position b) { if(a == b) { return orEquals; } return a.getFinalPosition() != null && b.getFinalPosition() != null && a.getFinalPosition() > b.getFinalPosition() && (a.getFinalPosition() - b.getFinalPosition() < maxLength); } @Override public Stream getActivations(INeuron n, Position pos) { return n.getActivations( pos.getDocument(), fromSlot, pos, orEquals, fromSlot, new Position(pos.getDocument(), maxLength != Integer.MAX_VALUE ? pos.getFinalPosition() + maxLength : Integer.MAX_VALUE), true ); } @Override public void write(DataOutput out) throws IOException { super.write(out); out.writeBoolean(orEquals); out.writeInt(maxLength); } @Override public void readFields(DataInput in, Model m) throws IOException { super.readFields(in, m); orEquals = in.readBoolean(); maxLength = in.readInt(); } public String toString() { return "GT" + (orEquals ? "E" : "") + "(" + fromSlot + "," + toSlot + ")"; } } } aika-java-1.4.0/src/main/java/network/aika/neuron/relation/Relation.java000066400000000000000000000216421352543012000261350ustar00rootroot00000000000000/* * 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.neuron.relation; import network.aika.Model; import network.aika.Writable; import network.aika.neuron.INeuron; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.activation.Activation; import network.aika.neuron.activation.Position; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; import java.util.*; import java.util.stream.Stream; import static network.aika.neuron.INeuron.Type.INHIBITORY; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.activation.Activation.BEGIN; import static network.aika.neuron.activation.Activation.END; import static network.aika.neuron.relation.PositionRelation.Equals; import static network.aika.neuron.relation.PositionRelation.LessThan; import static network.aika.neuron.relation.PositionRelation.GreaterThan; /** * * @author Lukas Molzberger */ public abstract class Relation implements Comparable, Writable { public static Map relationRegistry = new TreeMap<>(); public static Relation EQUALS = new MultiRelation( new Equals(BEGIN, BEGIN), new Equals(END, END) ); public static Relation BEGIN_EQUALS = new Equals(BEGIN, BEGIN); public static Relation END_EQUALS = new Equals(END, END); public static Relation BEGIN_TO_END_EQUALS = new Equals(BEGIN, END); public static Relation END_TO_BEGIN_EQUALS = new Equals(END, BEGIN); public static Relation CONTAINS = new MultiRelation( new LessThan(BEGIN, BEGIN, true), new GreaterThan(END, END, true, Integer.MAX_VALUE) ); public static Relation CONTAINED_IN = new MultiRelation( new GreaterThan(BEGIN, BEGIN, true), new LessThan(END, END, true, Integer.MAX_VALUE) ); public static Relation OVERLAPS = new MultiRelation( new LessThan(BEGIN, END, false), new GreaterThan(END, BEGIN, false, Integer.MAX_VALUE) ); public static Relation BEFORE = new LessThan(END, BEGIN, true); public static Relation AFTER = new GreaterThan(BEGIN, END, true); public static Relation ANY = new Any(); @Override public int compareTo(Relation rel) { return Integer.compare(getType(), rel.getType()); } public static void registerRelation(int relationType, RelationFactory rf) { relationRegistry.put(relationType, rf); } public interface RelationFactory { Relation create(); } public abstract int getType(); public abstract boolean test(Activation act, Activation linkedAct, boolean allowUndefined); public abstract Relation invert(); public abstract void mapSlots(Map slots, Activation act); public Relation() { } public Collection getLeafRelations() { return Collections.singletonList(this); } public Relation getRelation(Relation r) { if(compareTo(r) == 0) { return this; } return null; } public void setFromSynapseId(int fromSynapseId) { } public void setToSynapseId(int toSynapseId) { } public void write(DataOutput out) throws IOException { out.writeInt(getType()); } public static Relation read(DataInput in, Model m) throws IOException { RelationFactory rf = relationRegistry.get(in.readInt()); Relation rel = rf.create(); rel.readFields(in, m); return rel; } @Override public void readFields(DataInput in, Model m) throws IOException { } public abstract boolean isExact(); public abstract Stream getActivations(INeuron n, Activation linkedAct); public void link(Neuron n, int from, int to) { addRelation(to, from, n, this); addRelation(from, to, n, invert()); } public static void addRelation(Integer synId, Integer targetSynId, Neuron n, Relation rel) { addRelation(getRelationsMap(targetSynId, n), synId, targetSynId, n, rel); } public static void addRelation(Map relMap, Integer synId, Integer targetSynId, Neuron n, Relation r) { if(targetSynId == OUTPUT) { Synapse s = n.getSynapseById(synId); if(s == null || (r.isExact() && n.getType() == INHIBITORY && !s.isInactive())) { return; } } Relation existingRel = relMap.get(synId); if (existingRel == null) { relMap.put(synId, r); } else if(existingRel instanceof MultiRelation) { MultiRelation mr = (MultiRelation) existingRel; mr.addRelation(r); } else if(existingRel.compareTo(r) != 0) { MultiRelation mr = new MultiRelation(); mr.addRelation(existingRel); mr.addRelation(r); relMap.put(synId, mr); } } public static void removeRelation(Integer synId, Integer targetSynId, Neuron n, Relation r) { removeRelation(getRelationsMap(targetSynId, n), synId, r); } public static void removeRelation(Map relMap, Integer synId, Relation r) { Relation existingRel = relMap.get(synId); if(existingRel == null) { return; } else if(existingRel.compareTo(r) == 0) { relMap.remove(synId); } else if(existingRel instanceof MultiRelation) { MultiRelation mr = (MultiRelation) existingRel; mr.removeRelation(r); if(mr.size() == 1) { relMap.put(synId, mr.getLeafRelations().iterator().next()); } } } public static Map getRelationsMap(int synapseId, Neuron n) { if(synapseId == OUTPUT) { return n.get().getOutputRelations(); } else { Synapse s = n.getSynapseById(synapseId); return s.getRelations(); } } public static class Builder implements Neuron.Builder { protected int from; protected int to; protected Relation relation; /** * This parameter allows to specify whether the relations connected to thy synapse refer to the activation of the * input neuron (that's the default) or if the should refer to one of the input activations of the input neuron. * In this case the synapseId of the input neuron needs to be specified which leads to the desired input activation. * * @param synapseId * @return */ public Builder setFrom(int synapseId) { assert synapseId >= -1; from = synapseId; return this; } public Builder setTo(int synapseId) { assert synapseId >= -1; to = synapseId; return this; } public Builder setRelation(Relation rel) { this.relation = rel; return this; } public Relation getRelation() { return relation; } public void connect(Neuron n) { getRelation().link(n, from, to); } @Override public void registerSynapseIds(Neuron n) { n.registerSynapseId(from); n.registerSynapseId(to); relation.setFromSynapseId(from); relation.setToSynapseId(to); } } public static class Any extends Relation { public static int ID = 100; static { registerRelation(ID, () -> Relation.ANY); } public Any() { } @Override public int getType() { return ID; } @Override public boolean test(Activation act, Activation linkedAct, boolean allowUndefined) { return true; } @Override public Relation invert() { return this; } @Override public void mapSlots(Map slots, Activation act) { } @Override public boolean isExact() { return false; } @Override public Stream getActivations(INeuron n, Activation linkedAct) { return n.getActivations(linkedAct.getDocument()); } } } aika-java-1.4.0/src/site/000077500000000000000000000000001352543012000150665ustar00rootroot00000000000000aika-java-1.4.0/src/site/blog.html000066400000000000000000000101601352543012000166750ustar00rootroot00000000000000 Blog - Aika

Blog

9. August 2019

How to efficiently propagate activations in a massive neural network

An event-driven approach

In traditional neural networks using the sigmoid activation function, all neurons are more or less activated. There is no clear case of an inactive neuron here. ...

3. August 2019

On adding negative feedback synapses to a neural network

The missing link in deep neural networks

The special thing about adding negative recurrent synapses to a neural network is that they introduce inner states within the network. ...

9. December 2018

On integrating symbolic inference into deep neural networks

What makes biological neural networks so superior to their technical counterparts? Is there anything we have overlooked so far?

Deep neural networks have been a tremendous success story over the last couple of years. Many advances in the field of AI, such as recognizing real world objects, fluently translating natural language or playing GO ...

25. October 2017

Aika: Ein semantisches neuronales Netzwerk

Wenn es darum geht Informationen aus natürlichsprachigen Texten zu extrahieren, stehen einem verschiedene Möglichkeiten zur Verfügung. Eine der ältesten und wohl auch am häufigsten genutzten Möglichkeiten ist die der regulären Ausdrücke. Hier werden exakte Muster definiert und in einem Textstring gematcht. Probleme bereiten diese allerdings, wenn kompliziertere semantische Muster gefunden werden sollen oder wenn verschiedene Muster aufeinander aufbauen oder miteinander interagieren sollen. ...

20. June 2017

Machine-Learning-Bibliothek hilft dabei Texte zu verstehen

Volltextsuche ist ein Kernbestandteil des Internetzeitalters, nichtsdestotrotz lässt sie bis heute viel zu Wünschen übrig. Sie ist ganz hervorragend dazu geeignet, um exakte Worttreffer in einer großen Menge an Dokumenten zu finden. Was allerdings bis heute noch nicht zuverlässig funktioniert ist, ein Wort nur in seiner gewünschten Bedeutung zu finden. Genau hier kann die Java-Bibliothek Aika weiterhelfen. ...

aika-java-1.4.0/src/site/css/000077500000000000000000000000001352543012000156565ustar00rootroot00000000000000aika-java-1.4.0/src/site/css/style.css000066400000000000000000000130211352543012000175250ustar00rootroot00000000000000/* Website template by freewebsitetemplates.com */ @font-face { font-family: 'OpenSans'; src: url('../fonts/OpenSans-Regular.eot'); src: local('☺'), url('../fonts/OpenSans-Regular.woff') format('woff'), url('../fonts/OpenSans-Regular.ttf') format('truetype'), url('../fonts/OpenSans-Regular.svg') format('svg'); font-weight: normal; font-style: normal; } @font-face { font-family: 'Play'; src: url('../fonts/Play-Regular.eot'); src: local('☺'), url('../fonts/Play-Regular.woff') format('woff'), url('../fonts/Play-Regular.ttf') format('truetype'), url('../fonts/Play-Regular.svg') format('svg'); font-weight: normal; font-style: normal; } body { background-color: #fff; font-family: 'OpenSans'; margin: 0; } img { border: 0; } .clearfix:after, #contents:after { clear:both; content:""; display:block; height:1%; line-height:0; visibility:hidden; } /*------------------------------ HEADER ------------------------------*/ #header { background-color: #eee; border-bottom: 1px solid #e6e6e6; padding: 27px 0; } @media (min-width:500px) { #header > div, #footer > div { width: 980px; margin: 0 auto; padding: 0 20px; } } @media (max-width:499px) { #header > div, #footer > div { /* height: 60%;*/ margin: 0 auto; padding: 0 20px; } } /** Logo **/ #header .logo { float: left; margin-right: 20px; } #header .logo a { background: url(../images/logo.svg) no-repeat center top; color: #000; display: block; font: 15px/30px 'Play'; height: 20px; width: 140px; padding-top: 68px; text-decoration: none; text-transform: uppercase; } /** Navigation **/ #navigation { display: inline-block; list-style: none; line-height: 100px; margin: 0; padding: 0; } #navigation ul { display: inline-block; list-style: none; margin: 0; padding: 0; } @media (min-width:500px) { #navigation li { float: left; padding-left: 15px; padding-right: 15px; text-align: center; } } @media (max-width:499px) { #navigation li { float: left; width: 300px; height: 60px; text-align: center; } } #navigation li a { color: #818181; font-size: 20px; line-height: 30px; text-decoration: none; } #navigation li a:hover { color: #000; } #navigation li.active a { color: #f99600; } .parse-button { position: absolute; margin-top: 15px; padding: 10px; font-size: 10pt; } .text-form { width: auto; } .text-field { width: auto; } .text-input { width: 100%; height: 100px; font-size: 12pt; } .v-spacer { padding-top: 60px; } /*------------------------------ CONTENTS ------------------------------*/ @media (min-width:500px) { #contents { min-height: 510px; width: 880px; margin: 0 auto; padding: 54px 40px; } } @media (max-width:499px) { #contents { min-height: 510px; width: 80%; margin: 50px auto; padding: 54px auto; } } .prettyprint-code { width: 810px; } h1 { color: #3e3e3e; font-size: 30px; font-weight: normal; line-height: 30px; margin: 0 0 30px; } h2 { color: #2c2c2c; font-size: 24px; font-weight: normal; line-height: 24px; margin: 0 0 12px; } p { color: #585858; font-size: 16px; line-height: 24px; margin: 0 0 30px; } p a { color: #585858; } #tagline h1 { margin-left: 20px; } #tagline > div { float: left; width: 400px; margin: 0 20px; } @media (min-width:500px) { #contents .features { width: 810px; margin: 0 auto; } } @media (max-width:499px) { #contents .features { width: 80%; margin: 0 auto; } } .features > div { display: inline-block; margin: 0 0 30px; } .features > div img { float: left; margin-right: 20px; margin-top: 36px; } .date { float: left; height: 78px; width: 70px; margin-right: 20px; border: 1px solid #d5d5d5; text-align: center; } .date p { margin: 12px 0 0; } .date p span { display: block; font-size: 30px; margin-bottom: 6px; } .author { color: #585858; display: block; font-size: 12px; } .more { background-color: #727272; color: #fff; display: inline-block; font-size: 14px; 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!Ø$""$¸&(È€8ð¨`Ј@ø° h Ø H ¸p(#H˜PÀx0è XÈ€ hÈÈÈÈÈȨ8¨¨¨¨ð@@@@@@ pà˜˜˜˜˜˜Àxxxxxx€€È˜È˜È˜8888𨨨¨¨ÐˆˆˆˆˆÀÀÀÀ@x@x@x¨ h0 h0 h0 è è è è Ø  Ø  Ø        p€p((( è Ø 5ø6°7h8 5ø6°7h8 8 ;¸;¸rFtvx(z|@~€‚„*†,ˆ.&HŠŒ2*tz||*€.>>>>>>>>>FFFFFttvvvvvvvvv((((z||||||||@~€€€„„„„„********ˆˆˆ.....&&&&&&&&&&HHHHŒŒŒ„¨hhhhsjQ¤,w® Ïç=h%ò 6 Qé k ± m¤Hc ê By ° Ìøg Î 3  ~  Ý D  ¿  g Î| ô'Q‹ ºØ x á  >  ‘  ö  N ‹ Ì  z# á!• þ*‹ ù Q  ª   b Ÿ Ý 1 š ·B   ] º    o ¬ é B « Í -ižÄ ëT±    j  Ç  * u ³ ú U ¿ " ¢ ö  X ¤ ³ Ý zì J¦  ¢E .; ,ƒ ÚÈ 4Copyright (c) 2011 by Jonas Hecksher, Playtypeª, e-types AS. All rights reserved.Copyright (c) 2011 by Jonas Hecksher, Playtype», e-types AS. All rights reserved.PlayPlayRegularRegularJonasHecksher,Playtype,e-typesAS: Play: 2011JonasHecksher,Playtype,e-typesAS: Play: 2011PlayPlayVersion 1.002Version 1.002PlayPlayPlay is a trademark of Jonas Hecksher, Playtypeª, e-types AS.Play is a trademark of Jonas Hecksher, Playtype», e-types AS.Jonas Hecksher, Playtypeª, e-types ASJonas Hecksher, Playtype», e-types ASJonas HecksherJonas HecksherCopyright (c) 2011 by Jonas Hecksher, Playtypeª, e-types AS. All rights reserved.Copyright (c) 2011 by Jonas Hecksher, Playtype», e-types AS. All rights reserved.http://www.playtype.comhttp://www.playtype.comhttp://www.e-types.comhttp://www.e-types.comCopyright (c) 2011, Jonas Hecksher, Playtypes, e-types AS. All rights reserved. Available under SIL OFL v1.1.Copyright (c) 2011, Jonas Hecksher, Playtypes, e-types AS. All rights reserved. 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All rights reserved.Copyright (c) 2011 by Jonas Hecksher, Playtype», e-types AS. All rights reserved.PlayPlayRegularRegularJonasHecksher,Playtype,e-typesAS: Play: 2011JonasHecksher,Playtype,e-typesAS: Play: 2011PlayPlayVersion 1.002Version 1.002PlayPlayPlay is a trademark of Jonas Hecksher, Playtypeª, e-types AS.Play is a trademark of Jonas Hecksher, Playtype», e-types AS.Jonas Hecksher, Playtypeª, e-types ASJonas Hecksher, Playtype», e-types ASJonas HecksherJonas HecksherCopyright (c) 2011 by Jonas Hecksher, Playtypeª, e-types AS. All rights reserved.Copyright (c) 2011 by Jonas Hecksher, Playtype», e-types AS. All rights reserved.http://www.playtype.comhttp://www.playtype.comhttp://www.e-types.comhttp://www.e-types.comCopyright (c) 2011, Jonas Hecksher, Playtypes, e-types AS. All rights reserved. Available under SIL OFL v1.1.Copyright (c) 2011, Jonas Hecksher, Playtypes, e-types AS. All rights reserved. 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!Ø$""$¸&(È€8ð¨`Ј@ø° h Ø H ¸p(#H˜PÀx0è XÈ€ hÈÈÈÈÈȨ8¨¨¨¨ð@@@@@@ pà˜˜˜˜˜˜Àxxxxxx€€È˜È˜È˜8888𨨨¨¨ÐˆˆˆˆˆÀÀÀÀ@x@x@x¨ h0 h0 h0 è è è è Ø  Ø  Ø        p€p((( è Ø 5ø6°7h8 5ø6°7h8 8 ;¸;¸rFtvx(z|@~€‚„*†,ˆ.&HŠŒ2*tz||*€.>>>>>>>>>FFFFFttvvvvvvvvv((((z||||||||@~€€€„„„„„********ˆˆˆ.....&&&&&&&&&&HHHHŒŒŒ„¨hhhhsjQ¤,w® Ïç=h%ò 6 Qé k ± m¤Hc ê By ° Ìøg Î 3  ~  Ý D  ¿  g Î| ô'Q‹ ºØ x á  >  ‘  ö  N ‹ Ì  z# á!• þ*‹ ù Q  ª   b Ÿ Ý 1 š ·B   ] º    o ¬ é B « Í -ižÄ ëT±    j  Ç  * u ³ ú U ¿ " ¢ ö  X ¤ ³ Ý zì J¦  ¢E .; ,ƒ ÚÈ 4Copyright (c) 2011 by Jonas Hecksher, Playtypeª, e-types AS. All rights reserved.Copyright (c) 2011 by Jonas Hecksher, Playtype», e-types AS. All rights reserved.PlayPlayRegularRegularJonasHecksher,Playtype,e-typesAS: Play: 2011JonasHecksher,Playtype,e-typesAS: Play: 2011PlayPlayVersion 1.002Version 1.002PlayPlayPlay is a trademark of Jonas Hecksher, Playtypeª, e-types AS.Play is a trademark of Jonas Hecksher, Playtype», e-types AS.Jonas Hecksher, Playtypeª, e-types ASJonas Hecksher, Playtype», e-types ASJonas HecksherJonas HecksherCopyright (c) 2011 by Jonas Hecksher, Playtypeª, e-types AS. All rights reserved.Copyright (c) 2011 by Jonas Hecksher, Playtype», e-types AS. All rights reserved.http://www.playtype.comhttp://www.playtype.comhttp://www.e-types.comhttp://www.e-types.comCopyright (c) 2011, Jonas Hecksher, Playtypes, e-types AS. All rights reserved. Available under SIL OFL v1.1.Copyright (c) 2011, Jonas Hecksher, Playtypes, e-types AS. All rights reserved. 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Inhibitory
Neuron
[Not supported by viewer]
Excitatory
Neuron
PA
[Not supported by viewer]
Excitatory
Neuron
PB
[Not supported by viewer]
Excitatory
Neuron
PC
[Not supported by viewer]
Output
Neuron
[Not supported by viewer]aika-java-1.4.0/src/site/images/divider.png000066400000000000000000000017641352543012000204770ustar00rootroot00000000000000‰PNG  IHDR ¾ŽçïtEXtSoftwareAdobe ImageReadyqÉe<hiTXtXML:com.adobe.xmp £¹+h"IDATxÚbøÿÿ?0ëèè„‚X`.¢‚® ÀôÐç¬P*IEND®B`‚aika-java-1.4.0/src/site/images/example.svg000066400000000000000000003135371352543012000205230ustar00rootroot00000000000000 aika-java-1.4.0/src/site/images/favicon.png000066400000000000000000000017431352543012000204730ustar00rootroot00000000000000‰PNG  IHDR õ”;¼zTXtmxGraphModelM“Ýκ8ƯÆd÷„@CÊ·(ˆo_Ï ʇ€\ý¿®›ì& yžù1Ì”N7¢ñX Òà à_îõ·ãF47èJš&Ý[æx†ÿŠI›Óy`2@ì!ð,,B¦â0ÞJ3¡?Ÿ ŽñÍ'ã'MT8qûÉô]tØo€ÁtCêO-g5ý¤÷ž>XÄ–Žç€ÂKœÀ‹ ü¤EÚ“ÿ}°à„ûÐöÛ ÌÉàÔ/Á9iÿó~I¢5Äʯ£!¥óšdQx¶]ÌŠ4© D;ííusóþœWö;®fç}}EÊV„¼j.5Y&F ìáÅÖ²ÔF;Ðe{µ ‘à $D׶è#è„ OFpÕ»ˆ šú«‹ÒB¡d©%c|õ~¼²ƒjmŒÖêy¤RÛF8É‹-ï¬æDaµÄžœ%UËfµÊ]vt[§å«»ÒÚÙž¿y²cƒÛ`$c®Ó*RF %óÖ¢;ÿüV’ՀΡ µj*N>{“\ÂjK´vÿ“®ƒYap–ÐOXKqrœ²Yð¾xxÿ Mî¥Áo¨ýóD¡ÄR#×ñï°[n|×ÉùÃÝ¿ïÜ™:(R6ÜÐóâZµæħ{©=DD *‚¸mݶ<²?·z¡;Yä› 9€ë9(ËŠÄÓíÒ» Ã!€zÍOÅzYu4¹·qÛ–·#êgíp¹Ü²NSB¿Ø¹ÿL ÔËÏ(~»öøõÿÞFÑú?i]ÓâIDAT(S­‘=JaEÏ$•Á}ˆD'… "‚…`­V2#XÄÖÞ »|c¥ ¬lÔNDfÁÊUX¨Ô+P˜˜ _}Þy?WLPš€e4¼u×dª~‚k]²Öí·pl‘D8 ÄÇãá4ŸÅ>EêcÈö@4TÍiÞÀä&Ö=ò:hƒ?Ái± œóé‘ÞÁG¨¾B¯õT…Ó||ZúùýAT[£75 /—VØ$Ä7ì>ν^‚®ÉâÃ*œI¹ã[c›³¹—ÒžäûÈ‹„öÎïÿHè3á sÖA wIEND®B`‚aika-java-1.4.0/src/site/images/jackson-cook-diagram.svg000066400000000000000000000617661352543012000230570ustar00rootroot00000000000000
Entity
Jackson
(forename)
[Not supported by viewer]
Entity
Jackson (city)
[Not supported by viewer]
Inhibit
Inhibit
Word
Jackson
[Not supported by viewer]
Word
Cook
[Not supported by viewer]
AND
[Not supported by viewer]
AND
[Not supported by viewer]
OR
[Not supported by viewer]
Entity
Cook
(profession)
[Not supported by viewer]
Enitity
Cook (surname)
[Not supported by viewer]
Category
forename
[Not supported by viewer]
Category
surname
[Not supported by viewer]
OR
[Not supported by viewer]
OR
[Not supported by viewer]
next word
next word
previous word
previous word
AND
[Not supported by viewer]
AND
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Recurrent
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About Aika

Aika is an artificial neural network designed specifically for the processing of natural language texts. A key feature of the Aika algorithm is the ability to evaluate and process various interpretations of the individual sections of a text. Aika combines several ideas and approaches from the field of AI such as artificial neural networks, frequent pattern mining and logic based expert systems. It can be applied to a broad spectrum of text analysis task such as word sense disambiguation, entity resolution, named entity recognition, text classification and information extraction.

Aika allows to model linguistic concepts like words, word meanings (entities), categories (e.g. person name, city), grammatical word types and so on as neurons in the neural network. By choosing appropriate synapse weights, these neurons can take on different functions within the network. For instance neurons whose synapse weights are chosen to mimic a logical AND can be used to match an exact phrase. On the other hand neurons with an OR characteristic can be used to connect large lists of word entity neurons to determine a category like 'city' or 'profession'.

Aika is able to generate multiple mutually exclusive interpretations of a word, phrase, or sentence, and select the most likely interpretation. For example a neuron representing a specific meaning of a given word can be linked through a negatively weighted synapse to a neuron representing an alternative meaning of this word. In this case these neurons would exclude each other. Such synapses might even be cyclic. Aika can resolve such recurrent feedback links by making tentative assumptions and performing a search for the highest ranking interpretation.

In contrast to conventional neural networks, Aika propagates activation objects through its network, not just activation values. These activation objects refer to a specific text segment and a given interpretation.

Aika consists of two layers. The neural layer, containing all the neurons and continuously weighted synapses and underneath that the discrete logic layer, containing a boolean representation of all the neurons. The logic layer uses a frequent pattern lattice to efficiently store the individual logic nodes. This architecture allows Aika to process extremely large networks in which only neurons that are activated by a logic node need to compute their weighted sum and their activation value. This means that the fast majority of neurons stays inactive during the processing of a given text.

To prevent that the whole network needs to stay in memory during processing, Aika uses the provider pattern to suspend individual neurons or logic nodes to an external storage like a mongo database.

One of the first use cases implemented with Aika is named entity recognition (NER). For this case, lists of forenames, surnames, cities, professions, company names and common words have been translated into a weighted neural network. These lists are partially overlapping because many surnames are also city names, professions or common words. In addition some common knowledge rules were added, such as forenames often precede surnames or articles rarely precede city names in German (e.g. 'die Halle' does not mean the city Halle, but the noun 'Halle'). These rules can arbitrarily depend on each other and thus form a network through which activations can be propagated. Aika is able to accurately recognize named entities within texts and predict the correct meaning of the name.

Aika is implemented in Java and made available as an open source free software library under the Apache 2 license.

aika-java-1.4.0/src/site/inference.html000066400000000000000000000510711352543012000177160ustar00rootroot00000000000000 Inference - Aika

How it works

The Artificial Neural Network

Aika is a neural network simulation algorithm, that uses neurons to represent a wide variety of linguistic concepts and connects them via synapses. In contrast to most other neural network architectures, Aika does not employ layers to structure its network. Synapses can connect arbitrary neurons with each other, but the network is not fully connected either.
Like other artificial neural networks (ANN) the synapses are weighted. By choosing the weights and the threshold (i.e. the bias) accordingly, neurons can take on the characteristics of boolean logic gates such as an and-gate or an or-gate. To compute the activation value of a neuron, the weighted sum over its input synapses is computed. Then the bias value \(b\) is added to this sum and the result is sent through an activation function \(\varphi\).

$$net_j = {b_j + \sum\limits_{i=0}^N{x_i w_{ij}}}$$ $$y_j = \varphi (net_j)$$ Depending on the type of neuron, different activation functions are used. One commonly used activation function in Aika is the rectified hyperbolic tangent function, which is basically the positive half of the \(\tanh()\) function. \[\varphi(x) = \Bigg \{ {0 \atop \tanh(x)} {: x \leq 0 \atop : x > 0}\]

The activation functions are chosen in a way that they clearly distinguish between active and inactive neurons. Only activated neurons are processed. These activations are expressed not only by a real valued number but also by an activation object.

Activations

The advantage of having activation objects is that, through them, Aika is able to cope with the relational structure of natural language text by making the activation relate to a specific segment of text. In a way these activations can be seen as text annotations that specify their start and end character.
Words, phrases and sentences are in a relation to each other through their sequential order. The assignment of text ranges and word positions to activations is a simple yet powerful representation of the relational structure of text and avoids some of the shortcomings of other representations such as bag of words or sliding window. Since the activations are propagated along through the network, synapses need to be able to manipulate the text range and the word position while the activations are passed on to the next neuron.

Interpretations

One common problem when processing text is that of cyclic dependencies. In the example 'jackson cook' it is impossible to decide which word has to be resolved first, the forename or the surname, since both depend on each other. The word jackson can be recognized as a forename when the next word is a surname and the word cook can be recognized as a surname when the previous word is a forename. To tackle this problem Aika employs non-monotonic logic and is therefore able to derive multiple interpretations that are mutually exclusive. These interpretations are then weighted and only the strongest interpretation is returned as the result.
Consider the following network, which is able to determine whether a word, which has been recognized in a text, is a forename, surname, city name, or profession. If for instance the word "jackson" has been recognized in a text, it will trigger further activations in the two jackson entity neurons. Since both are connected through the inhibiting neuron only one of them can be active in the end. Aika will therefore generate two interpretations. But these interpretations are not limited to a single neuron. For instance if the word neuron cook gets activated too, the jackson forename entity and the cook surname entity will be part of the same interpretation. The forename and surname category links in this example form a positive feedback loop which reinforces this interpretation.

New interpretations are spawned if both input and output of a negative recurrent synapse get activated. In this case a conflict is generated. An interpretation is a conflict free set of activations. Therefore, if there are no conflicts during the processing of an input data set, only one interpretation will exist and the search for the best interpretation will end immediately. On the other hand, if there are conflicts between activations, a search needs to be performed which selects or excludes individual activations and tests how these changes affect the overall weights sum of all activations. This sum is also called the objective function \(f\) and can be stated in the following way: $$f = \sum\limits_{j \in Acts}{\min (-g_j, net_j)}$$ $$g_j = \sum\limits_{i = 0, w_{ij} < 0, w_{ij} \in Recurrent}^N{w_{ij}}$$ The value \(g_j\) is simply the sum of all negative feedback synapses. The intention behind this objective function is to measure a neurons ability to overcome the inhibiting input signals of other neurons.

During the search, the individual activation values of each neuron are adjusted according to the current search path. Selected activations are computed and propagated through the network, excluded activations are set to zero. To reduce costs of the interpretation search several optimizations have been implemented, so that the search avoids to follow branches that have already been computed. Furthermore, the computation results in each search node are cached if possible. A stack trace of the resulting binary search tree looks as shown in the box below. This list shows a single path from a leaf node to the root node. Each search node is associated with a single activation. The nodes are ordered in a way, that the causality of non recurrent synapses is respected. Depending on the path each activation is either selected or excluded. Decisions on lower level nodes might limit the available choices for higher level nodes. The values DW and DN are the weight delta and norm delta for this search node. The values AW and AN are the accumulated weights and norms up to this node. The values LIMITED, CACHED, EXPLORE, SELECTED, EXCLUDED, SIM-CACHED, SIM-COMPUTED and MODIFIED are simply statistics for different events during the search. 

				        
	7 RANGE:(12,16) C-surname
	AW:9.998 AN:20.3 DW:9.998 DN:0.0
	LIMITED DECISION:SELECTED CONFLICT:false LIMITED:5 CACHED:0 EXPLORE:0 SELECTED:2 EXCLUDED:3
	SIM-CACHED:0 SIM-COMPUTED:5 MODIFIED:1

	6 RANGE:(12,16) INHIBIT
	AW:0.0 AN:20.3 DW:0.0 DN:0.0
	LIMITED DECISION:SELECTED CONFLICT:false LIMITED:5 CACHED:0 EXPLORE:0 SELECTED:4 EXCLUDED:1
	SIM-CACHED:0 SIM-COMPUTED:5 MODIFIED:2

	5 RANGE:(12,16) E-cook (surname)
	AW:0.0 AN:20.3 DW:0.0 DN:5.0
	CACHED  DECISION:SELECTED CONFLICT:true  LIMITED:2 CACHED:1 EXPLORE:1 SELECTED:2 EXCLUDED:3
	SIM-CACHED:0 SIM-COMPUTED:5 MODIFIED:1

	4 RANGE:(12,16) E-cook (profession)
	AW:0.0 AN:15.3 DW:0.0 DN:4.5
	EXPLORE DECISION:EXCLUDED CONFLICT:true  LIMITED:0 CACHED:0 EXPLORE:2 SELECTED:2 EXCLUDED:2
	SIM-CACHED:0 SIM-COMPUTED:4 MODIFIED:0

	3 RANGE:(4,11)  C-forename
	AW:0.0 AN:10.8 DW:0.0 DN:0.0
	LIMITED DECISION:SELECTED CONFLICT:false LIMITED:2 CACHED:0 EXPLORE:0 SELECTED:1 EXCLUDED:1
	SIM-CACHED:0 SIM-COMPUTED:2 MODIFIED:0

	2 RANGE:(4,11)  INHIBIT
	AW:0.0 AN:10.8 DW:0.0 DN:0.0
	LIMITED DECISION:SELECTED CONFLICT:false LIMITED:2 CACHED:0 EXPLORE:0 SELECTED:2 EXCLUDED:0
	SIM-CACHED:0 SIM-COMPUTED:2 MODIFIED:1

	1 RANGE:(4,11)  E-jackson (forename)
	AW:0.0 AN:10.8 DW:0.0 DN:5.0
	EXPLORE DECISION:SELECTED CONFLICT:true  LIMITED:1 CACHED:0 EXPLORE:1 SELECTED:1 EXCLUDED:1
	SIM-CACHED:0 SIM-COMPUTED:2 MODIFIED:0

	0 RANGE:(4,11)  E-jackson (city)
	AW:0.0 AN:5.8 DW:0.0 DN:5.8
	EXPLORE DECISION:EXCLUDED CONFLICT:true  LIMITED:0 CACHED:0 EXPLORE:1 SELECTED:1 EXCLUDED:1
	SIM-CACHED:0 SIM-COMPUTED:2 MODIFIED:0

Synapses

Synapses in Aika consist not just of a weight value but also properties that specify relations between synapses. These relations can either be used to imply an constrained on the matching input text ranges or the dependency structure of the input activations. Biological neurons seem to achieve such a relation matching through the timing of the firing patterns of their action potentials.
The complete list of synapse properties looks as follows: 

				        
                new Synapse.Builder()
                        .setSynapseId(0)
                        .setNeuron(inputNeuronA)
                        .setWeight(10.0)
                        .setBias(-10.0)
                        .setRecurrent(false)
                new Synapse.Builder()
                        .setSynapseId(1)
                        .setNeuron(inputNeuronB)
                        .setWeight(10.0)
                        .setBias(-10.0)
                        .setRecurrent(false)
                new Relation.Builder()
                        .setFrom(0)
                        .setTo(1)
                        .setRelation(new Equals(END, BEGIN)),
                new Relation.Builder()
                        .setFrom(0)
                        .setTo(OUTPUT)
                        .setRelation(new Equals(BEGIN, BEGIN)),
                new Relation.Builder()
                        .setFrom(1)
                        .setTo(OUTPUT)
                        .setRelation(new Equals(END, END))

Unlike other ANNs, Aika allows to specify a bias value per synapse. These bias values are simply summed up and added to the neurons bias value.
The property recurrent states whether this synapse is a feedback loop or not. Depending on the weight of this synapse such a feedback loop might either be positive or negative. This property is an important indicator for the interpretation search.
Range relations define a relation either to another synapse or to the output range of this activation. The range relations consist of four comparison operations between the between the begin and end of the current synapses input activation and the linked synapses input activation.

The next property is the range output which consists of two boolean values. If these are set to true, the range begin and the range end are propagated to the output activation.
The last two properties are used to establish a dependency structure among activations. The instance relation here defines whether this synapse and the linked synapse have a common ancestor or are depending on each other in either direction. During the evaluation of this relation only those synapses are followed which are marked with the identity flag. Depending on the weight of the synapse and the bias sum of the outgoing neuron, Aika distinguishes two types synapses: conjunctive synapses and disjunctive synapses. Conjunctive synapses are stored in the output neuron as inputs and disjunctive synapses are stored in the input neuron as outputs. The reason for this is that disjunctive neurons like the inhibitory neuron or the category neurons may have a huge number of input synapses, which makes it expensive to load them from disk. By storing those synapses on the input neuron side, only those synapses need to stay in memory, that are needed by an currently activated input neuron.

Neuron Types

There are two main types of neurons in Aika: excitatory neurons and inhibitory neurons. The biological role models for those neurons are the spiny pyramidal cell and the aspiny stellate cell in the cerebral cortex. The pyramidal cells usually exhibit an excitatory characteristic and some of them possess long ranging axons that connect to other parts of the brain. The stellate cells on the other hand are usually inhibitory interneurons with short axons which form circuits with nearby neurons.
Those two types of neurons also have a different electrical signature. Stellate cells usually react to a constant depolarising current by firing action potentials. This occurs with a relatively constant frequency during the entire stimulus. In contrast, most pyramidal cells are unable to maintain a constant firing rate. Instead, they are firing quickly at the beginning of the stimulus and then reduce the frequency even if the stimulus stays strong. This slowdown over time is called adaption.
Aika tries to mimic this behaviour by using different activation functions for the different types of neurons. Since Aika is not a spiking neural network like the biological counterpart, we only have the neurons activation value which can roughly be interpreted as the firing frequency of a spiking neuron. In a sense the earlier described activation function based on the rectified tanh function quite nicely captures the adaption behaviour of a pyramidal cell. An increase of a weak signal has a strong effect on the neurons output, while an increase on an already strong signal has almost no effect. Furthermore, if the input of the neuron does not surpass a certain threshold then the neuron will not fire at all. For inhibitory neurons Aika uses the rectified linear unit function (ReLU).

$$y = \max(0, x)$$

Especially for strongly disjunctive neurons like the inhibitory neuron, ReLU has the advantage of propagating its input signal exactly as it is, without distortion or loss of information.

The Pattern Lattice

One design goal of the Aika algorithm is, to be able to efficiently simulate huge networks with millions of neurons. However, this is only possible if only a very small part of the neurons become active for any given input data set. One prerequisite for achieving this goal is the earlier mentioned distinction between active and inactive neurons. If one neuron gets activated, only those outgoing neurons are required to be computed, that would exceed their threshold based on this new input. Depending on the synapse weights and bias settings of the outgoing neuron it may either fire directly (for or-gate like neurons) or it may require other input neurons to fire first (for and-gate like neurons). If the neuron is an and-gate like neuron then there is an efficient way to determine only those outgoing neurons that might be activated by a new input signal.
This is where the pattern lattice comes into play. The pattern lattice is used to store all sub patterns of a given input pattern. If for instance a neuron requires the inputs \(A\), \(B\) and \(C\) to be activated before the neuron itself can be fired, then the following pattern lattice would emerge.

As with the neural network, activations can be propagated within the pattern lattice as well. If only the two inputs \(A\) and \(B\) are activated by an incoming signal, then the pattern node \(AB\) would be activated as well, but not the pattern node \(ABC\). The advantage of this data structure is that only minimal refinements have to be tested from one level in the pattern lattice to the next. If all neurons dependent on input \(A\) were tested directly, the number of outgoing neurons might be so large that many unnecessary calculations would be required. But before we can build the pattern lattice, we first have to convert the weighted inputs of the neuron into a boolean representation. Here we only need to distinguish between active and inactive neurons. Hence let us consider a neuron with a heaviside activation function (\(\theta(x) = 1\) if \(x > 0\) otherwise \(0\)).

$$b = -0.5$$ $$w_0 = 1.0$$ $$w_1 = 0.3$$ $$w_2 = 0.3$$ $$y = \theta \Bigg({b + \sum\limits_{i=0}^N{x_i w_i}}\Bigg)$$

This example neuron can then be reformulated into a disjunctive normal form (-0.5 + 1.0 > 0 OR -0.5 + 0.3 + 0.3 > 0) boolean expression:

$$y = x_0 \vee (x_1 \wedge x_2)$$

A boolean expression in disjunctive normal form consists of a set of conjunctions that are connected by a disjunction. That means we can store these conjunctions as nodes within the pattern lattice and connect them by a disjunction node. The activation of this disjunction node is then the precondition before the associated neuron can be activated. So the weighted sum of the neuron still needs to be computed, but only when the disjunction node gets activated. Another advantage of the pattern lattice is that large parts of the lattice can be shared by multiple neurons.

aika-java-1.4.0/src/site/resources.html000066400000000000000000000267061352543012000200010ustar00rootroot00000000000000 Resources - Aika

Resources

Get Involved

Every contribution is welcome and needed to make Aika better. If you would like to get involved, please contact me.

Related Resources

Jumble : A Job Search Engine whose tagging is powered by Aika.
KDnuggests : A list of Text Analysis, Text Mining and Information Retrieval software.

Download

Version 1.4.0

API Documentation

JavaDoc v1.4.0

Release Notes

Aika Version 1.4.0 (2019-08-16)

  • Reduced memory consumption.
  • Implementation to compute the average activation of an activation object over all interpretation options.
  • Linking of interpretation options.

Aika Version 1.3.0 (2019-07-31)

  • Major simplification of the interpretation search.
  • Code reorganization.

Aika Version 1.2.0 (2019-06-07)

  • Removed synapse bias. Bias now depends on the neuron type.
  • Exception Handling refactoring.
  • Removed factory methods.
  • Refactored position propagation.
  • Bugfixes

Aika Version 1.1.0 (2019-02-28)

  • Major cleanup refactoring
  • Bugfixes

Aika Version 1.0.0 (2018-12-09)

  • Bugfixes

Aika Version 0.25 (2018-12-02)

  • Bugfixes

Aika Version 0.24 (2018-11-17)

  • Refactoring of the relation architecture.
  • Bugfixes

Aika Version 0.23 (2018-11-14)

  • The Range class has now been replaced with by a slots concept. Previously, an activation had a range with a begin and an end position. Now activations can possess an arbitrary number of positions.
  • The synapse bias is now used to decide whether a synapse is conjunctive or disjunctive.

Aika Version 0.22 (2018-10-31)

  • API Refactoring: Synapse relations are now established through a separate builder class.
  • The range positions are now optionally variable. This feature is required for text generation. In this use case the positions are not known in advance and need to be computed during processing.
  • Introduced passive neurons. Passive neurons are only evaluated if the connected output neuron requires it. Passive neurons act basically like callback functions.
  • Optimization of the interpretation search.
  • Lots of bug fixes.

Aika Version 0.17 (2018-05-14)

  • Introduction of synapse relations. Previously the relation between synapses was implicitly modeled through word positions (RIDs). Now it is possible to explicitly model relations like: The end position of input activation 1 equals to the begin position of input activation 2. Two types of relations are currently supported, range relations and instance relations. Range relations compare the input activation range of a given synapse with that of the linked synapse. Instance relations also compare the input activations of two synapses, but instead of the ranges the dependency relations of these activations are compared.
  • Removed the norm term from the interpretation objective function.
  • Introduced an optional distance function to synapses. It allows to model a weakening signal depending on the distance of the activation ranges.
  • Example implementation of a context free grammar.
  • Example implementation for co-reference resolution.
  • Work on an syllable identification experiment based on the meta network implementation.

Aika Version 0.15 (2018-03-16)

  • Simplified interpretation handling by removing the InterpretationNode class and moving the remaining logic to the Activation class.
  • Moved the activation linking and activation selection code to separate classes.
  • Ongoing work on the training algorithms.

Aika Version 0.14 (2018-02-04)

  • Caching of partially computed states in the neural network during the interpretation search.
  • Refactoring of the interpretation search. The search is now iterative to prevent stack overflows. The debugging output is much more detailed now.
  • Ongoing work on the training algorithms.

Aika Version 0.13 (2018-01-20)

  • API cleanups: Input -> Synapse.Builder, Activation.Builder
  • Optimization and simplification of the interpretation search.
  • Refactoring of the range matching within synapses.
  • Ongoing work on the training algorithms.

Aika Version 0.12 (2017-12-12)

  • Memory optimization: Disjunctive synapses are now stored on the input neuron side.
  • The bias delta value in a neuron input is now an absolute value.
  • Bug fixes, code cleanups, code readability improvements, lambda expression usage, convenience functions.

Aika Version 0.11 (2017-11-26)

  • Optimization for the search for the best interpretation.
  • Optimization of the checkSelfReferencing function.
  • Fixes for the training and pattern discovery functions.

Aika Version 0.10 (2017-11-03)

  • Simplification: Activations are now only added during processing never removed. However, they might be suppressed if they are conflicting with other activations.
  • Removed some old experimental training code and provided two APIs for training and pattern discovery. The APIs allow to implement heuristics when deciding which synapses should be created or which patterns should be selected.
  • Experimental support for text generation.

Aika Version 0.9 (2017-10-22)

  • Rewrite of the conversion of synapse weights to logic nodes.
  • Optimization of the interpretation search.
  • Fixes for a few deadlocks.

Aika Version 0.8 (2017-09-17)

  • Optimization of the interpretation search using an upper bound on the interpretation weights.
  • Support for very large models with millions of neurons by suspending rarely used neurons to disk.

Aika Version 0.7 (2017-08-06)

  • Refactoring of the range model. Now the range begin and the range end can be treated independently of each other. Synapses now have three properties: range match, range output and range mapping.
  • The Iteration class has been merged into the document class.
  • Performance optimizations for the interpretation search in the SearchNode class.
  • Test case fixes
  • Class renaming: Option -> InterprNode, ExpandNode -> SearchNode
  • Lots of javadoc

Aika Version 0.6 (2017-07-01)

  • Mainly optimizations

Aika Version 0.5 (2017-06-01)

  • Simplification of the algorithm!
  • Lots of optimizations!
  • New unified model for weights and neuron activation values.
  • Options are now generated for all neuron activations.
  • Disjunctions of options are now supported.

Aika Version 0.2 (2016-07-09)

  • Annotation of text ranges instead of individual characters.
  • Use of the relational id for pattern matching instead of character positions.
  • Use of a lattice to represent text ranges.
  • Allow a single neuron to suppress multiple inputs.
  • Lots of optimizations!

Aika Version 0.1 (2016-02-27)

  • Implementation of a symmetric lattice structure to represent the different interpretations that arise from the usage of non-monotonic logic.
  • Implementation of an algorithm to determine the interpretation with the highest associated weight sum. This algorithm efficiently searches for non-conflicting combinations of options by limiting the search space with an upper bound.

Acknowledgments and Support

YourKit supports open source projects with its full-featured Java Profiler. YourKit, LLC is the creator of YourKit Java Profiler and YourKit .NET Profiler, innovative and intelligent tools for profiling Java and .NET applications.

Developers

Lukas Molzberger

Contact

Lukas Molzberger
Weiherstrasse 16
53111 Bonn

contact@aika-software.org

aika-java-1.4.0/src/site/usage.html000066400000000000000000000561721352543012000170730ustar00rootroot00000000000000 Examples - Aika

Usage

Maven POM

The following dependency includes the Aika software library to an existing maven project.

<dependency>
    <groupId>org.aika-software</groupId>
    <artifactId>aika</artifactId>
    <version>1.4.0</version>
</dependency>

Named Entity Recognition / Entity Resolution example

This example demonstrates the recognition of the words 'jackson cook' as forename and surname. If these words would have occurred independently, they would have been recognized as city or profession based on the example code. The complete code for this example can be found in the class NamedEntityRecognitionTest.
Now, the first step is to create an empty model. The following map is used as a dictionary to look up the individual word input neurons. 

				
    Model m = new Model();

    HashMap<String, Neuron> inputNeurons = new HashMap<>();

    for(String word: new String[] {"jackson", "cook"}) {
        Neuron in = m.createNeuron("W-" + word, INPUT);

        inputNeurons.put(word, in);
    }

These three neurons are just placeholders that will be wired up later on. 

				
    Neuron forenameCategory = m.createNeuron("C-forename", INHIBITORY, LIMITED_RECTIFIED_LINEAR_UNIT);
    Neuron surnameCategory = m.createNeuron("C-surname", INHIBITORY, LIMITED_RECTIFIED_LINEAR_UNIT);
    Neuron inhibitingN = m.createNeuron("INHIB", INHIBITORY, LIMITED_RECTIFIED_LINEAR_UNIT);

The following code snippet is the crucial section of this example. It shows how the first entity neuron is created and connected by its input synapses. The entity neurons represent the concrete meanings of the input words. As can be seen here the individual inputs have a few more properties than we are used to from other neural networks. The reason for this is that Aika not only propagates the input activation value to the next neuron but also the text range, the word position.
This entity neuron has three inputs. The first one simply checks if the word 'cook' occurred in the text. The second one is a positive feedback loop and checks if the previous word was a forename. The last one is a negative feedback loop. It makes sure that only one meaning is assigned to each word.
The other entity neurons are created in a similar fashion which is therefore not shown here. 

				
    Neuron cookSurnameEntity = Neuron.init(
            m.createNeuron("E-cook (surname)", EXCITATORY, RECTIFIED_HYPERBOLIC_TANGENT),
            6.0, // adjusts the bias
            new Synapse.Builder() // Requires the word to be recognized
                    .setSynapseId(0)
                    .setNeuron(inputNeurons.get("cook"))
                    .setWeight(10.0)
                    .setRecurrent(false),
            new Synapse.Builder() // The previous word needs to be a forename
                    .setSynapseId(1)
                    .setNeuron(forenameCategory)
                    .setWeight(10.0)
                    .setRecurrent(true), // this input is a positive feedback loop

                    // This neuron may be suppressed by the E-cook (profession) neuron, but there is no
                    // self suppression taking place even though 'E-cook (surname)' is also contained
                    // in the inhibitingN.
            new Synapse.Builder()
                    .setSynapseId(2)
                    .setNeuron(inhibitingN)
                    .setWeight(-100.0)
                    .setRecurrent(true), // this input is a negative feedback loop
            new Relation.Builder()  // references the previous word
                    .setFrom(1)  // From Synpase Id
                    .setTo(0)    // To Synapse Id
                    .setRelation(END_TO_BEGIN_EQUALS),
            new Relation.Builder()
                    .setFrom(2)
                    .setTo(0)
                    .setRelation(OVERLAPS),
            new Relation.Builder()
                    .setFrom(0)
                    .setTo(OUTPUT)
                    .setRelation(EQUALS)
    );

The category neurons in this example only have input. For a real application there would be a long list of inputs. 

				
    Neuron.init(
            forenameCategory,
            0.0,
            new Synapse.Builder() // In this example there is only one forename considered.
                    .setSynapseId(0)
                    .setNeuron(jacksonForenameEntity)
                    .setWeight(1.0),
            new Relation.Builder()
                    .setFrom(0)
                    .setTo(OUTPUT)
                    .setRelation(EQUALS)
    );
    Neuron.init(
            surnameCategory,
            0.0,
            new Synapse.Builder()
                    .setSynapseId(0)
                    .setNeuron(cookSurnameEntity)
                    .setWeight(1.0),
            new Relation.Builder()
                    .setFrom(0)
                    .setTo(OUTPUT)
                    .setRelation(EQUALS)
    );

The last neuron is the suppressing neuron. It is used as a mediator in order to avoid having to connect all of these neurons with each other. Self suppression does not take place. 

				
    Neuron.init(
            inhibitingN,
            0.0,
            new Synapse.Builder().setNeuron(cookProfessionEntity)
                    .setSynapseId(0)
                    .setWeight(1.0),
            new Synapse.Builder()
                    .setSynapseId(1)
                    .setNeuron(cookSurnameEntity)
                    .setWeight(1.0),
            new Synapse.Builder()
                    .setSynapseId(2)
                    .setNeuron(jacksonCityEntity)
                    .setWeight(1.0),
            new Synapse.Builder()
                    .setSynapseId(3)
                    .setNeuron(jacksonForenameEntity)
                    .setWeight(1.0),
            new Relation.Builder()
                    .setFrom(0)
                    .setTo(OUTPUT)
                    .setRelation(EQUALS),
            new Relation.Builder()
                    .setFrom(1)
                    .setTo(OUTPUT)
                    .setRelation(EQUALS),
            new Relation.Builder()
                    .setFrom(2)
                    .setTo(OUTPUT)
                    .setRelation(EQUALS),
            new Relation.Builder()
                    .setFrom(3)
                    .setTo(OUTPUT)
                    .setRelation(EQUALS)
        );

Now that the model is complete, we can start processing an actual text. 

				
    Document doc = m.createDocument("mr. jackson cook was born in new york ");

    int i = 0;
    for(String w: doc.getContent().split(" ")) {
        int j = i + w.length();

        // Feed the individual words as inputs into the network.
        inputNeurons.get(w).addInput(doc, i, j);
        i = j + 1;
    }

    // Search for the best interpretation of this text.
    doc.process();

    System.out.println(doc.activationsToString());

    doc.clearActivations();

The output of this example looks as following. Each line in the table represents a neuron activation. In this example, slots simply specify the range begin (slot 0) and the range end (slot 1). 

				

Activation ID - Neuron Type - Final Decision - Slots (Ranges) | Identity - Neuron Label - Logic Layer - Upper Bound -
Value | Sum | Weight - Input Value | Target Value

0   INPUT      -          - (0:0, 1:4) "mr. " () - W-mr. - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
4   INHIBITORY -          - (0:4, 1:12) "jackson " () - C-forename - V:1.0 UB:1.0 Net:1.0 W:0.0
5   INHIBITORY -          - (0:4, 1:12) "jackson " () - INHIB - V:1.0 UB:1.0 Net:1.0 W:0.0
1   INPUT      -          - (0:4, 1:12) "jackson " () - W-jackson - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
2   EXCITATORY - SELECTED - (0:4, 1:12) "jackson " () - E-jackson (forename) - V:1.0 UB:1.0 Net:6.0 W:6.0
3   EXCITATORY - EXCLUDED - (0:4, 1:12) "jackson " () - E-jackson (city) - V:0.0 UB:0.0 Net:-95.0 W:0.0
9   INHIBITORY -          - (0:12, 1:17) "cook " () - C-surname - V:1.0 UB:1.0 Net:1.0 W:0.0
10  INHIBITORY -          - (0:12, 1:17) "cook " () - INHIB - V:1.0 UB:1.0 Net:1.0 W:0.0
6   INPUT      -          - (0:12, 1:17) "cook " () - W-cook - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
7   EXCITATORY - SELECTED - (0:12, 1:17) "cook " () - E-cook (surname) - V:1.0 UB:1.0 Net:6.0 W:6.0
8   EXCITATORY - EXCLUDED - (0:12, 1:17) "cook " () - E-cook (profession) - V:0.0 UB:0.0 Net:-95.0 W:0.0
11  INPUT      -          - (0:17, 1:21) "was " () - W-was - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
12  INPUT      -          - (0:21, 1:26) "born " () - W-born - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
13  INPUT      -          - (0:26, 1:29) "in " () - W-in - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
14  INPUT      -          - (0:29, 1:33) "new " () - W-new - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
15  INPUT      -          - (0:33, 1:38) "york " () - W-york - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0

 Final SearchNode:6  WeightSum:12.0


Mutual exclusion example

				
    @Test
    public void testMutualExclusion() {
        Model m = new Model();

        // Create the input neurons for the network.
        Neuron inA = m.createNeuron("IN-A", INPUT);
        Neuron inB = m.createNeuron("IN-B", INPUT);
        Neuron inC = m.createNeuron("IN-C", INPUT);

        // Instantiate the inhibitory neuron. Its inputs will be added later on.
        Neuron inhibN = m.createNeuron("INHIB");

        // Create three neurons that might be suppressed by the inhibitory neuron.
        Neuron pA = Neuron.init(
                m.createNeuron("A", EXCITATORY),
                3.0,
                new Synapse.Builder()
                        .setSynapseId(0)
                        .setNeuron(inA)
                        .setWeight(10.0)
                        .setRecurrent(false),
                new Synapse.Builder()
                        .setSynapseId(1)
                        .setNeuron(inhibN)
                        .setWeight(-100.0)
                        .setRecurrent(true),
                new Relation.Builder()
                        .setFrom(1)
                        .setTo(0)
                        .setRelation(EQUALS),
                new Relation.Builder()
                        .setFrom(0)
                        .setTo(OUTPUT)
                        .setRelation(EQUALS)
        );

        Neuron pB = Neuron.init(
                m.createNeuron("B", EXCITATORY),
                5.0,
                new Synapse.Builder()
                        .setSynapseId(0)
                        .setNeuron(inB)
                        .setWeight(10.0)
                        .setRecurrent(false),
                new Synapse.Builder()
                        .setSynapseId(1)
                        .setNeuron(inhibN)
                        .setWeight(-100.0)
                        .setRecurrent(true),
                new Relation.Builder()
                        .setFrom(1)
                        .setTo(0)
                        .setRelation(EQUALS),
                new Relation.Builder()
                        .setFrom(0)
                        .setTo(OUTPUT)
                        .setRelation(EQUALS)
        );

        Neuron pC = Neuron.init(
                m.createNeuron("C", EXCITATORY),
                2.0,
                new Synapse.Builder()
                        .setSynapseId(0)
                        .setNeuron(inC)
                        .setWeight(10.0)
                        .setRecurrent(false),
                new Synapse.Builder()
                        .setSynapseId(1)
                        .setNeuron(inhibN)
                        .setWeight(-100.0)
                        .setRecurrent(true),
                new Relation.Builder()
                        .setFrom(1)
                        .setTo(0)
                        .setRelation(EQUALS),
                new Relation.Builder()
                        .setFrom(0)
                        .setTo(OUTPUT)
                        .setRelation(EQUALS)
        );

        // Finally addInput all the inputs to the suppressing neuron.
        Neuron.init(
                inhibN,
                0.0,
                new Synapse.Builder()
                        .setSynapseId(0)
                        .setNeuron(pA)
                        .setWeight(1.0)
                        .setRecurrent(false),
                new Synapse.Builder()
                        .setSynapseId(1)
                        .setNeuron(pB)
                        .setWeight(1.0)
                        .setRecurrent(false),
                new Synapse.Builder()
                        .setSynapseId(2)
                        .setNeuron(pC)
                        .setWeight(1.0)
                        .setRecurrent(false),
                new Relation.Builder()
                        .setFrom(0)
                        .setTo(OUTPUT)
                        .setRelation(EQUALS),
                new Relation.Builder()
                        .setFrom(1)
                        .setTo(OUTPUT)
                        .setRelation(EQUALS),
                new Relation.Builder()
                        .setFrom(2)
                        .setTo(OUTPUT)
                        .setRelation(EQUALS)
        );

        Neuron outN = Neuron.init(
                m.createNeuron("OUT", EXCITATORY),
                0.0,
                new Synapse.Builder()
                         .setSynapseId(0)
                        .setNeuron(pB)
                        .setWeight(1.0)
                        .setRecurrent(false),
                new Relation.Builder()
                        .setFrom(0)
                        .setTo(OUTPUT)
                        .setRelation(EQUALS)
        );

        // Now that the model is complete, apply it to a document.

        Document doc = m.createDocument("foobar", 0);

        // Add input activations starting at char 0 and ending at char 1
        // These inputs will be immediately propagated through the network.
        inA.addInput(doc, 0, 1);
        inB.addInput(doc, 0, 1);
        inC.addInput(doc, 0, 1);

        // Computes the best interpretation
        doc.process();

        System.out.println(doc.activationsToString());

        Assert.assertEquals(0, pA.getActivations(doc, true).count());
        Assert.assertEquals(1, pB.getActivations(doc, true).count());
        Assert.assertEquals(0, pC.getActivations(doc, true).count());

        Assert.assertEquals(1, outN.getActivations(doc, true).count());
        doc.clearActivations();
    }
                
			
				
 Activation ID - Neuron Type - Final Decision - Slots | Identity - Neuron Label - Upper Bound -
 Value | Net | Weight - Input Value | Target Value

0   INPUT      -          - (0:0, 1:1) "f" () - IN-A - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
3   INPUT      -          - (0:0, 1:1) "f" () - IN-B - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
6   INPUT      -          - (0:0, 1:1) "f" () - IN-C - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
2   INHIBITORY -          - (0:0, 1:1) "f" () - INHIB - V:1.0 UB:1.0 Net:1.0 W:0.0
1   EXCITATORY - EXCLUDED - (0:0, 1:1) "f" () - A - V:0.0 UB:0.0 Net:-97.0 W:0.0
4   EXCITATORY - SELECTED - (0:0, 1:1) "f" () - B - V:1.0 UB:1.0 Net:5.0 W:5.0
7   EXCITATORY - EXCLUDED - (0:0, 1:1) "f" () - C - V:0.0 UB:0.0 Net:-98.0 W:0.0
5   INHIBITORY -          - (0:0, 1:1) "f" () - OUT - V:0.762 UB:0.762 Net:1.0 W:0.0

Final SearchNode:10  WeightSum:5.0


Pattern matching example

				
    @Test
    public void testPatternMatching() {
        Model m = new Model();

        Map<Character, Neuron> inputNeurons = new HashMap<>();

        // Create an input neuron for every letter in this example.
        for(char c: new char[] {'a', 'b', 'c', 'd', 'e', 'f'}) {
            Neuron in = m.createNeuron(c + "", INPUT);

            inputNeurons.put(c, in);
        }

        Neuron pattern = Neuron.init(
                m.createNeuron("BCDE", EXCITATORY),
                5.0,
                new Synapse.Builder()
                        .setSynapseId(0)
                        .setNeuron(inputNeurons.get('b'))
                        .setWeight(10.0)
                        .setRecurrent(false),
                new Synapse.Builder()
                        .setSynapseId(1)
                        .setNeuron(inputNeurons.get('c'))
                        .setWeight(10.0)
                        .setRecurrent(false),
                new Synapse.Builder()
                        .setSynapseId(2)
                        .setNeuron(inputNeurons.get('d'))
                        .setWeight(10.0)
                        .setRecurrent(false),
                new Synapse.Builder()
                        .setSynapseId(3)
                        .setNeuron(inputNeurons.get('e'))
                        .setWeight(10.0)
                        .setRecurrent(false),
                new Relation.Builder()
                        .setFrom(0)
                        .setTo(1)
                        .setRelation(END_TO_BEGIN_EQUALS),
                new Relation.Builder()
                        .setFrom(1)
                        .setTo(2)
                        .setRelation(END_TO_BEGIN_EQUALS),
                new Relation.Builder()
                        .setFrom(2)
                        .setTo(3)
                        .setRelation(END_TO_BEGIN_EQUALS),
                new Relation.Builder()
                        .setFrom(0)
                        .setTo(OUTPUT)
                        .setRelation(BEGIN_EQUALS),
                new Relation.Builder()
                        .setFrom(3)
                        .setTo(OUTPUT)
                        .setRelation(END_EQUALS)
        );


        // Create a simple text document.
        Document doc = m.createDocument("a b c d e ");

        // Then add the characters
        for(int i = 0; i < doc.length(); i++) {
            char c = doc.getContent().charAt(i);
            if(c != ' ') {
                inputNeurons.get(c).addInput(doc, i, i + 2);
            }
        }

        // Computes the best interpretation
        doc.process();

        Assert.assertEquals(1, pattern.getActivations(doc, false).count());


        System.out.println("All activations:");
        System.out.println(doc.activationsToString());
        System.out.println();

        doc.clearActivations();
    }
				
			
				
Activation ID - Neuron Type - Final Decision - Slots | Identity - Neuron Label - Upper Bound -
Value | Net | Weight - Input Value | Target Value

0   INPUT      -          - (0:0, 1:2) "a " () - a - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
1   INPUT      -          - (0:2, 1:4) "b " () - b - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
5   EXCITATORY - SELECTED - (0:2, 1:10) "b c d e " () - BCDE - V:1.0 UB:1.0 Net:5.0 W:0.0
2   INPUT      -          - (0:4, 1:6) "c " () - c - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
3   INPUT      -          - (0:6, 1:8) "d " () - d - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0
4   INPUT      -          - (0:8, 1:10) "e " () - e - V:1.0 UB:1.0 Net:0.0 W:0.0 - IV:1.0

 Final SearchNode:1  WeightSum:0.0
aika-java-1.4.0/src/test/000077500000000000000000000000001352543012000151015ustar00rootroot00000000000000aika-java-1.4.0/src/test/java/000077500000000000000000000000001352543012000160225ustar00rootroot00000000000000aika-java-1.4.0/src/test/java/network/000077500000000000000000000000001352543012000175135ustar00rootroot00000000000000aika-java-1.4.0/src/test/java/network/aika/000077500000000000000000000000001352543012000204205ustar00rootroot00000000000000aika-java-1.4.0/src/test/java/network/aika/lattice/000077500000000000000000000000001352543012000220455ustar00rootroot00000000000000aika-java-1.4.0/src/test/java/network/aika/lattice/ActivationsTest.java000066400000000000000000000075411352543012000260430ustar00rootroot00000000000000/* * 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.lattice.activation.OrActivation; import network.aika.neuron.INeuron; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.activation.Activation; import network.aika.neuron.activation.Position; import network.aika.neuron.relation.Relation; import org.junit.Assert; import org.junit.Test; import java.util.Map; import java.util.TreeMap; import static network.aika.neuron.INeuron.Type.EXCITATORY; import static network.aika.neuron.INeuron.Type.INPUT; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.activation.Activation.BEGIN; import static network.aika.neuron.activation.Activation.END; import static network.aika.neuron.relation.Relation.EQUALS; /** * * @author Lukas Molzberger */ public class ActivationsTest { @Test public void addActivationsTest() { Model m = new Model(); Neuron inA = m.createNeuron("A", INPUT); Neuron pA = Neuron.init(m.createNeuron("pA", EXCITATORY), 0.5, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(1.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Document doc = new Document(m, "aaaaaaaaaa", 0); inA.addInput(doc, 0, 1); inA.addInput(doc, 2, 3); Assert.assertNotNull(pA.getActivation(doc, 0, 1, false)); Assert.assertNull(pA.getActivation(doc, 1, 2, false)); Assert.assertNotNull(pA.getActivation(doc, 2, 3, false)); inA.addInput(doc, 1, 2); Assert.assertNotNull(pA.getActivation(doc, 0, 1, false)); Assert.assertNotNull(pA.getActivation(doc, 1, 2, false)); Assert.assertNotNull(pA.getActivation(doc, 2, 3, false)); } @Test public void testGetActivationReturnsFirstFired() { Model m = new Model(); Neuron in = m.createNeuron("A", INPUT); OrNode inNode = in.get().getInputNode().get(); Document doc = new Document(m, "aaaaaaaaaa", 0); inNode.processActivation(createActivation(doc, inNode)); inNode.processActivation(createActivation(doc, inNode)); inNode.processActivation(createActivation(doc, inNode)); inNode.processActivation(createActivation(doc, inNode)); inNode.processActivation(createActivation(doc, inNode)); } private OrActivation createActivation(Document doc, OrNode inNode) { Map slots = new TreeMap<>(); slots.put(BEGIN, doc.lookupFinalPosition(0)); slots.put(END, doc.lookupFinalPosition(1)); Activation act = new Activation(doc, inNode.getOutputNeuron().get(), slots); OrActivation orAct = new OrActivation(doc, inNode); act.setInputNodeActivation(orAct); orAct.setOutputAct(act); return orAct; } } aika-java-1.4.0/src/test/java/network/aika/lattice/ConverterTest.java000066400000000000000000000357051352543012000255310ustar00rootroot00000000000000/* * 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.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.lattice.Converter; import network.aika.neuron.INeuron; import network.aika.neuron.relation.Relation; import org.junit.Assert; import org.junit.Test; import java.util.stream.Collectors; import static network.aika.ActivationFunction.RECTIFIED_HYPERBOLIC_TANGENT; import static network.aika.neuron.INeuron.Type.*; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.relation.Relation.EQUALS; /** * * @author Lukas Molzberger */ public class ConverterTest { @Test public void testConverter() { Model m = new Model(); Neuron inA = m.createNeuron("A", INPUT); Neuron inB = m.createNeuron("B", INPUT); Neuron inC = m.createNeuron("C", INPUT); Neuron inD = m.createNeuron("D", INPUT); Neuron out = Neuron.init(m.createNeuron("ABCD", EXCITATORY), 0.5, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(4.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(3.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(2) .setNeuron(inC) .setWeight(2.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(3) .setNeuron(inD) .setWeight(1.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(2) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(3) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(3) .setTo(OUTPUT) .setRelation(EQUALS) ); System.out.println(out.get().getInputNode().get().logicToString()); Assert.assertEquals(1, out.get().getInputNode().get().andParents.size()); out.get().setBias(1.5); out.get().commit(out.get().getInputSynapses()); Converter.convert(0, null, out.get(), out.get().getInputSynapses()); System.out.println(out.get().getInputNode().get().logicToString()); Assert.assertEquals(1, out.get().getInputNode().get().andParents.size()); } @Test public void testConverter1() { Model m = new Model(); Neuron inA = m.createNeuron("A", INPUT); Neuron inB = m.createNeuron("B", INPUT); Neuron inC = m.createNeuron("C", INPUT); Neuron inD = m.createNeuron("D", INPUT); Neuron out = Neuron.init(m.createNeuron("ABCD", INHIBITORY), -5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(2) .setNeuron(inC) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(3) .setNeuron(inD) .setWeight(1.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(2) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(3) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(3) .setTo(OUTPUT) .setRelation(EQUALS) ); System.out.println(out.get().getInputNode().get().logicToString()); Assert.assertEquals(1, inA.get().getOutputNode().get().orChildren.size()); } @Test public void testConverter2() { Model m = new Model(); Neuron inA = m.createNeuron("A", INPUT); Neuron inB = m.createNeuron("B", INPUT); Neuron inC = m.createNeuron("C", INPUT); Neuron inD = m.createNeuron("D", INPUT); Neuron inE = m.createNeuron("E", INPUT); Neuron out = Neuron.init(m.createNeuron("ABCD", EXCITATORY), 3.5, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(5.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(5.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(2) .setNeuron(inC) .setWeight(2.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(3) .setNeuron(inD) .setWeight(2.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(4) .setNeuron(inE) .setWeight(0.5) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(2) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(3) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(4) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(3) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(4) .setTo(OUTPUT) .setRelation(EQUALS) ); System.out.println(out.get().getInputNode().get().logicToString()); Assert.assertEquals(2, out.get().getInputNode().get().andParents.size()); out.getSynapseById(3).updateDelta(null, -1.5, 0.0); out.get().commit(out.get().getInputSynapses()); Converter.convert( 0, null, out.get(), out.get().getInputSynapses()); System.out.println(out.get().getInputNode().get().logicToString()); Assert.assertEquals(1, out.get().getInputNode().get().andParents.size()); } @Test public void testConverter3() { Model m = new Model(); Neuron inA = m.createNeuron("A", INPUT); Neuron inB = m.createNeuron("B", INPUT); Neuron inC = m.createNeuron("C", INPUT); Neuron inD = m.createNeuron("D", INPUT); Neuron out = Neuron.init(m.createNeuron("ABCD", EXCITATORY), 5.5, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(50.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(3.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(2) .setNeuron(inC) .setWeight(2.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(3) .setNeuron(inD) .setWeight(1.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(2) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(3) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(3) .setTo(OUTPUT) .setRelation(EQUALS) ); System.out.println(out.get().getInputNode().get().logicToString()); Assert.assertEquals(3, out.get().getInputNode().get().andParents.size()); } @Test public void testDuplicates() { Model m = new Model(); Neuron in = m.createNeuron("IN", INPUT); Neuron out = Neuron.init(m.createNeuron("OUT", EXCITATORY), 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(in) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(in) .setWeight(10.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Document doc = new Document(m, "IN"); in.addInput(doc, 0, 2); Assert.assertFalse(out.getActivations(doc, false).collect(Collectors.toList()).isEmpty()); } @Test public void testOnlyOptionalInputs() { Model model = new Model(); Neuron inA = model.createNeuron("A", INPUT); Neuron inB = model.createNeuron("B", INPUT); Neuron inC = model.createNeuron("C", INPUT); Neuron testNeuron = model.createNeuron("Test", EXCITATORY); Neuron.init(testNeuron, 2.1, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(2.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(2.0), new Synapse.Builder() .setSynapseId(2) .setNeuron(inC) .setWeight(2.0), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(EQUALS) ); Document doc = new Document(model, "Bla"); inB.addInput(doc, 0, 3); inC.addInput(doc, 0, 3); doc.process(); Assert.assertNotNull(testNeuron.getActivation(doc, 0, 3, false)); } } aika-java-1.4.0/src/test/java/network/aika/lattice/PatternLatticeTest.java000066400000000000000000000134061352543012000264770ustar00rootroot00000000000000/* * 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.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.INeuron; import network.aika.neuron.relation.Relation; import org.junit.Assert; import org.junit.Test; import java.util.stream.Collectors; import static network.aika.neuron.INeuron.Type.EXCITATORY; import static network.aika.neuron.INeuron.Type.INPUT; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.relation.Relation.BEGIN_EQUALS; import static network.aika.neuron.relation.Relation.END_EQUALS; import static network.aika.neuron.relation.Relation.EQUALS; /** * * @author Lukas Molzberger */ public class PatternLatticeTest { @Test public void testPredefinedPatterns() { Model m = new Model(); Neuron inA = m.createNeuron("A", INPUT); Neuron inB = m.createNeuron("B", INPUT); Neuron inC = m.createNeuron("C", INPUT); Neuron.init(m.createNeuron("ABC", EXCITATORY), 0.001, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(2) .setNeuron(inC) .setWeight(1.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(2) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(EQUALS) ); Document doc = new Document(m, "", 0); InputNode pA = inA.get().getOutputNode().get(); InputNode pB = inB.get().getOutputNode().get(); InputNode pC = inC.get().getOutputNode().get(); AndNode pAB = pA.andChildren.firstEntry().getValue().child.get(); Assert.assertNotNull(pAB.getProvider()); AndNode pBC = pC.andChildren.firstEntry().getValue().child.get(); Assert.assertNotNull(pBC.getProvider()); Assert.assertEquals(1, pAB.andChildren.size()); Assert.assertEquals(1, pBC.andChildren.size()); AndNode pABC = pAB.andChildren.firstEntry().getValue().child.get(); Assert.assertNotNull(pABC); Assert.assertEquals(2, pABC.parents.size()); } @Test public void testMultipleActivation() { Model m = new Model(); Neuron inA = m.createNeuron("A", INPUT); Neuron inB = m.createNeuron("B", INPUT); Neuron inC = m.createNeuron("C", INPUT); Neuron nABC = Neuron.init(m.createNeuron("ABC", EXCITATORY), 0.001, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(2) .setNeuron(inC) .setWeight(1.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(2) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(BEGIN_EQUALS), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(END_EQUALS) ); Document doc = new Document(m, "aaaaaaaaaa", 0); inA.addInput(doc, 0, 1); inB.addInput(doc, 0, 1); inC.addInput(doc, 0, 1); Assert.assertFalse(nABC.getActivations(doc, false).collect(Collectors.toList()).isEmpty()); } } aika-java-1.4.0/src/test/java/network/aika/network/000077500000000000000000000000001352543012000221115ustar00rootroot00000000000000aika-java-1.4.0/src/test/java/network/aika/network/ActivationOutputsTest.java000066400000000000000000000150411352543012000273220ustar00rootroot00000000000000/* * 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.network; import network.aika.Document; import network.aika.Model; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.activation.Activation; import network.aika.neuron.activation.link.Link; import network.aika.neuron.relation.Relation; import org.junit.Assert; import org.junit.Test; import java.util.stream.Stream; import static network.aika.neuron.INeuron.Type.EXCITATORY; import static network.aika.neuron.INeuron.Type.INPUT; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.relation.Relation.BEGIN_EQUALS; import static network.aika.neuron.relation.Relation.END_EQUALS; import static network.aika.neuron.relation.Relation.EQUALS; /** * * @author Lukas Molzberger */ public class ActivationOutputsTest { @Test public void addActivationsTest() { Model m = new Model(); Neuron inA = m.createNeuron("A", INPUT); Neuron inB = m.createNeuron("B", INPUT); Neuron pAB = Neuron.init(m.createNeuron("pAB", EXCITATORY), 0.001, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(1.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(Relation.EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(BEGIN_EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(END_EQUALS) ); Document doc = new Document(m, "aaaaaaaaaa", 0); inA.addInput(doc, 0, 1); inB.addInput(doc, 0, 1); Activation inA1 = inA.getActivation(doc, 0, 1, false); Activation inB1 = inB.getActivation(doc, 0, 1, false); Assert.assertTrue(containsOutputActivation(inA1.getOutputLinks(), pAB.getActivation(doc, 0, 1, false))); Assert.assertTrue(containsOutputActivation(inB1.getOutputLinks(), pAB.getActivation(doc, 0, 1, false))); Activation actAB = pAB.getActivation(doc, 0, 1, false); Assert.assertEquals( inA.getActivation(doc, 0, 1, false), selectInputActivation(actAB.getInputLinks(), inA) ); actAB = pAB.getActivation(doc, 0, 1, false); Assert.assertEquals( inB.getActivation(doc, 0, 1, false), selectInputActivation(actAB.getInputLinks(), inB) ); Assert.assertTrue(containsOutputActivation(inA1.getOutputLinks(), pAB.getActivation(doc, 0, 1, false))); Assert.assertTrue(containsOutputActivation(inB1.getOutputLinks(), pAB.getActivation(doc, 0, 1, false))); actAB = pAB.getActivation(doc, 0, 1, false); Assert.assertEquals( inA.getActivation(doc, 0, 1, false), selectInputActivation(actAB.getInputLinks(), inA) ); actAB = pAB.getActivation(doc, 0, 1, false); Assert.assertEquals( inB.getActivation(doc, 0, 1, false), selectInputActivation(actAB.getInputLinks(), inB) ); } private Activation selectInputActivation(Stream acts, Neuron n) { return acts.filter(l -> l.getInput().getNeuron().compareTo(n) == 0).map(l -> l.getInput()) .findAny() .orElse(null); } public boolean containsOutputActivation(Stream outputActivations, Activation oAct) { return outputActivations.anyMatch(l -> l.getOutput() == oAct); } @Test public void simpleAddActivationTest1() { Model m = new Model(); Document doc = new Document(m, "aaaaaaaaaa", 0); Neuron inA = m.createNeuron("A", INPUT); Neuron outB = Neuron.init(m.createNeuron("B", EXCITATORY), 0.5, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(1.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); inA.addInput(doc, new Activation.Builder() .setRange(0, 1) ); Activation outB1 = outB.getActivation(doc, 0, 1, false); Assert.assertTrue(containsOutputActivation(inA.getActivation(doc, 0, 1, false).getOutputLinks(), outB1)); } @Test public void removeRemoveDestinationActivation() { Model m = new Model(); Document doc = new Document(m, "aaaaaaaaaa", 0); Neuron inA = m.createNeuron("A", INPUT); Neuron outB = Neuron.init(m.createNeuron("B", EXCITATORY), 0.001, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(1.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); inA.addInput(doc, new Activation.Builder() .setRange(0, 1) ); Activation outB1 = outB.getActivation(doc, 0, 1, false); Assert.assertTrue(containsOutputActivation(inA.getActivation(doc, 0, 1, false).getOutputLinks(), outB1)); } } aika-java-1.4.0/src/test/java/network/aika/network/AddSynapseTest.java000066400000000000000000000056601352543012000256560ustar00rootroot00000000000000/* * 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.network; import network.aika.ActivationFunction; import network.aika.Document; import network.aika.Model; import network.aika.neuron.INeuron; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.activation.Activation; import network.aika.neuron.relation.Relation; import org.junit.Assert; import org.junit.Test; import java.util.TreeMap; import java.util.stream.Collectors; import static network.aika.ActivationFunction.RECTIFIED_HYPERBOLIC_TANGENT; import static network.aika.neuron.INeuron.Type.INHIBITORY; import static network.aika.neuron.INeuron.Type.INPUT; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.relation.Relation.EQUALS; /** * * @author Lukas Molzberger */ public class AddSynapseTest { @Test public void testAddSynapse() { Model m = new Model(); Neuron n = m.createNeuron("OR", INHIBITORY, RECTIFIED_HYPERBOLIC_TANGENT); TreeMap inputNeurons = new TreeMap<>(); int i = 0; for(String l: new String[] {"A", "B", "C", "D"}) { Neuron in = m.createNeuron(l, INPUT); inputNeurons.put(l, in); int synId = i++; Neuron.init(n, new Synapse.Builder() .setSynapseId(synId) .setNeuron(in) .setWeight(10.0), new Relation.Builder() .setFrom(synId) .setTo(OUTPUT) .setRelation(EQUALS) ); } Document doc = new Document(m, " "); i = 0; for(Neuron in: inputNeurons.values()) { in.addInput(doc, new Activation.Builder() .setRange(i * 2, (i * 2) + 1) ); i++; } System.out.println(doc.activationsToString()); doc.process(); System.out.println(doc.activationsToString()); Assert.assertEquals(4, n.getActivations(doc, true).collect(Collectors.toList()).size()); } } aika-java-1.4.0/src/test/java/network/aika/network/AncestorRelationTest.java000066400000000000000000000203651352543012000270760ustar00rootroot00000000000000package network.aika.network; import network.aika.Document; import network.aika.Model; import network.aika.neuron.INeuron; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.activation.Activation; import network.aika.neuron.relation.Relation; import org.junit.Assert; import org.junit.Ignore; import org.junit.Test; import java.util.stream.Collectors; import static network.aika.neuron.INeuron.Type.EXCITATORY; import static network.aika.neuron.INeuron.Type.INPUT; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.relation.AncestorRelation.COMMON_ANCESTOR; import static network.aika.neuron.relation.AncestorRelation.IS_ANCESTOR_OF; import static network.aika.neuron.relation.AncestorRelation.NOT_ANCESTOR_OF; import static network.aika.neuron.relation.Relation.EQUALS; public class AncestorRelationTest { @Test public void testAncestorRelation() { Model m = new Model(); Neuron inA = m.createNeuron("A", INPUT); Neuron inB = Neuron.init(m.createNeuron("B", EXCITATORY), 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(10.0) .setRecurrent(false) .setIdentity(true), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron outC = Neuron.init(m.createNeuron("C", EXCITATORY), 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(10.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(IS_ANCESTOR_OF), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS) ); Document doc = new Document(m, "aaaaaaaaaa", 0); inA.addInput(doc, 0, 1); Activation outC1 = outC.getActivation(doc, 0, 1, false); doc.process(); System.out.println(doc.activationsToString()); Assert.assertNotNull(outC1); } @Test public void testAncestorRelation1() { Model m = new Model(); Document doc = new Document(m, "aaaaaaaaaa", 0); Neuron inA = m.createNeuron("A", INPUT); Neuron nB = Neuron.init(m.createNeuron("B", EXCITATORY), 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(10.0) .setRecurrent(false) .setIdentity(true), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron nC = Neuron.init(m.createNeuron("C", EXCITATORY), 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(10.0) .setRecurrent(false) .setIdentity(true), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron nD = Neuron.init(m.createNeuron("D", EXCITATORY), 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(nB) .setWeight(10.0) .setRecurrent(false) .setSynapseId(0), new Synapse.Builder() .setSynapseId(1) .setNeuron(nC) .setWeight(10.0) .setRecurrent(false) .setSynapseId(1), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(COMMON_ANCESTOR), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); inA.addInput(doc, 0, 1); doc.process(); Assert.assertFalse(nD.getActivations(doc, true).collect(Collectors.toList()).isEmpty()); } @Test public void testAncestorRelation2() { Model m = new Model(); Document doc = new Document(m, "aaaaaaaaaa", 0); Neuron inA = m.createNeuron("A", INPUT); Neuron inB = m.createNeuron("B", INPUT); Neuron nC = Neuron.init(m.createNeuron("C", EXCITATORY), 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(10.0) .setRecurrent(false) .setSynapseId(0), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(10.0) .setRecurrent(false) .setSynapseId(1), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(COMMON_ANCESTOR), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); inA.addInput(doc, 0, 1); inB.addInput(doc, 1, 2); doc.process(); Assert.assertTrue(nC.getActivations(doc, true).collect(Collectors.toList()).isEmpty()); } @Test public void testNotAncestorOfRelation() { Model m = new Model(); Neuron inA = m.createNeuron("A", INPUT); Neuron inB = m.createNeuron("B", INPUT); /* Neuron.setWeight(inB, 5.0, INeuron.Type.EXCITATORY, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(10.0) .setBias(-10.0) .setRecurrent(false) .setIdentity(true), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); */ Neuron outC = Neuron.init(m.createNeuron("C", EXCITATORY), 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(10.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(NOT_ANCESTOR_OF), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS) ); Document doc = new Document(m, "aaaaaaaaaa", 0); inA.addInput(doc, 0, 1); inB.addInput(doc, 0, 1); Activation outC1 = outC.getActivation(doc, 0, 1, false); doc.process(); System.out.println(doc.activationsToString()); Assert.assertNotNull(outC1); } } aika-java-1.4.0/src/test/java/network/aika/network/AsymmetricConflictTest.java000066400000000000000000000060731352543012000274210ustar00rootroot00000000000000package network.aika.network; import network.aika.Document; import network.aika.Model; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.relation.Relation; import org.junit.Assert; import org.junit.Ignore; import org.junit.Test; import static network.aika.ActivationFunction.RECTIFIED_LINEAR_UNIT; import static network.aika.neuron.INeuron.Type.EXCITATORY; import static network.aika.neuron.INeuron.Type.INPUT; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.relation.Relation.EQUALS; public class AsymmetricConflictTest { @Test public void testAsymmetricConflict() { Model m = new Model(); Neuron in = m.createNeuron("IN", INPUT); Neuron inNeg = m.createNeuron("IN-NEG", INPUT); Neuron outA = m.createNeuron("OUT-A", EXCITATORY, RECTIFIED_LINEAR_UNIT); Neuron outB = m.createNeuron("OUT-B", EXCITATORY, RECTIFIED_LINEAR_UNIT); Neuron.init(outA, 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(in) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(outB) .setWeight(-100.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(2) .setNeuron(inNeg) .setWeight(-100.0) .setRecurrent(true), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(2) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(outB, 4.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(in) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(outA) .setWeight(-100.0) .setRecurrent(true), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Document doc = new Document(m, "X "); in.addInput(doc, 0, 1); inNeg.addInput(doc, 0, 1); doc.process(); System.out.println(doc.activationsToString()); Assert.assertEquals(0, outA.getActivations(doc, true).count()); Assert.assertEquals(1, outB.getActivations(doc, true).count()); } } aika-java-1.4.0/src/test/java/network/aika/network/AsymmetricSuppressionTest.java000066400000000000000000000036661352543012000302170ustar00rootroot00000000000000package network.aika.network; import network.aika.Document; import network.aika.Model; import network.aika.neuron.INeuron; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.relation.Relation; import org.junit.Assert; import org.junit.Ignore; import org.junit.Test; import java.util.stream.Collectors; import static network.aika.neuron.INeuron.Type.EXCITATORY; import static network.aika.neuron.INeuron.Type.INPUT; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.relation.Relation.EQUALS; public class AsymmetricSuppressionTest { @Test public void testAsymmetricSuppression() { Model m = new Model(); Neuron inA = m.createNeuron("A", INPUT); Neuron inB = m.createNeuron("B", INPUT); Neuron outN = Neuron.init(m.createNeuron("OUT", EXCITATORY), 10.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(-100.0) .setRecurrent(true), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS) ); Document doc = new Document(m, "a"); inA.addInput(doc, 0, 1); inB.addInput(doc, 0, 1); doc.process(); System.out.println(doc.activationsToString()); Assert.assertTrue(outN.getActivations(doc, true).collect(Collectors.toList()).isEmpty()); doc.clearActivations(); } } aika-java-1.4.0/src/test/java/network/aika/network/InterpretationSearchTest.java000066400000000000000000000524231352543012000277570ustar00rootroot00000000000000/* * 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.network; import network.aika.Document; import network.aika.Model; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.activation.search.SearchNode; import network.aika.neuron.relation.Relation; import org.junit.Assert; import org.junit.Test; import java.util.stream.Collectors; import static network.aika.ActivationFunction.LIMITED_RECTIFIED_LINEAR_UNIT; import static network.aika.neuron.INeuron.Type.*; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.relation.Relation.*; /** * * @author Lukas Molzberger */ public class InterpretationSearchTest { @Test public void testJoergZimmermann() { Model m = new Model(); Neuron wJoerg = m.createNeuron("W-Joerg", INPUT); Neuron wZimmermann = m.createNeuron("W-Zimmermann", INPUT); Neuron eJoergForename = m.createNeuron("E-Joerg (Forename)", EXCITATORY); Neuron eJoergSurname = m.createNeuron("E-Joerg (Surname)", EXCITATORY); Neuron eZimmermannSurname = m.createNeuron("E-Zimmermann (Surname)", EXCITATORY); Neuron eZimmermannCompany = m.createNeuron("E-Zimmermann (Company)", EXCITATORY); Neuron suppr = m.createNeuron("SUPPR", INHIBITORY); Neuron.init(eJoergSurname, 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(wJoerg) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(suppr) .setWeight(-60.0) .setRecurrent(true), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(eZimmermannCompany, 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(wZimmermann) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(suppr) .setWeight(-60.0) .setRecurrent(true), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(eJoergForename, 6.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(wJoerg) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(eZimmermannSurname) .setWeight(10.0) .setRecurrent(true), new Synapse.Builder() .setSynapseId(2) .setNeuron(suppr) .setWeight(-60.0) .setRecurrent(true), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(END_TO_BEGIN_EQUALS), new Relation.Builder() .setFrom(2) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(eZimmermannSurname, 6.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(wZimmermann) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(eJoergForename) .setWeight(10.0) .setRecurrent(true), new Synapse.Builder() .setSynapseId(2) .setNeuron(suppr) .setWeight(-60.0) .setRecurrent(true), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(BEGIN_TO_END_EQUALS), new Relation.Builder() .setFrom(2) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(suppr, 0.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(eJoergForename) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(eJoergSurname) .setWeight(10.0), new Synapse.Builder() .setSynapseId(2) .setNeuron(eZimmermannCompany) .setWeight(10.0), new Synapse.Builder() .setSynapseId(3) .setNeuron(eZimmermannSurname) .setWeight(10.0), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(3) .setTo(OUTPUT) .setRelation(EQUALS) ); Document doc = new Document(m, "Joerg Zimmermann"); wJoerg.addInput(doc, 0, 6); wZimmermann.addInput(doc, 6, 16); doc.process(); System.out.println(doc.activationsToString()); Assert.assertTrue(eZimmermannCompany.getActivations(doc, true).collect(Collectors.toList()).isEmpty()); Assert.assertFalse(eZimmermannSurname.getActivations(doc, true).collect(Collectors.toList()).isEmpty()); doc.clearActivations(); doc = new Document(m, "Joerg Zimmermann Joerg Zimmermann"); wJoerg.addInput(doc, 0, 6); wZimmermann.addInput(doc, 6, 17); wJoerg.addInput(doc, 17, 23); wZimmermann.addInput(doc, 23, 33); doc.process(); System.out.println(doc.activationsToString()); Assert.assertEquals(0, eZimmermannCompany.getActivations(doc, true).collect(Collectors.toList()).size()); Assert.assertEquals(2, eZimmermannSurname.getActivations(doc, true).collect(Collectors.toList()).size()); doc.clearActivations(); } @Test public void testBackwardReferencingSynapses() { Model m = new Model(); Neuron inA = m.createNeuron("IN A", INPUT); Neuron inB = m.createNeuron("IN B", INPUT); Neuron inhib = m.createNeuron("INHIB", INHIBITORY); Neuron nF = m.createNeuron("F", EXCITATORY); Neuron nC = Neuron.init(m.createNeuron("C", EXCITATORY), 6.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(inhib) .setWeight(-100.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron nD = Neuron.init(m.createNeuron("D", EXCITATORY), 7.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(nF) .setWeight(2.0), new Synapse.Builder() .setSynapseId(2) .setNeuron(inhib) .setWeight(-100.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(ANY), new Relation.Builder() .setFrom(2) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron nE = Neuron.init(m.createNeuron("E", EXCITATORY), 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inB) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(inhib) .setWeight(-100.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(nF, 6.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inB) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(inhib) .setWeight(-100.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(inhib, 0.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(nC) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(nD) .setWeight(10.0), new Synapse.Builder() .setSynapseId(2) .setNeuron(nE) .setWeight(10.0), new Synapse.Builder() .setSynapseId(3) .setNeuron(nF) .setWeight(10.0), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(3) .setTo(OUTPUT) .setRelation(EQUALS) ); Document doc = new Document(m, "aaaa bbbb "); inA.addInput(doc, 0, 5); inB.addInput(doc, 5, 10); doc.process(); System.out.println(doc.activationsToString()); Assert.assertFalse(nD.getActivations(doc, true).collect(Collectors.toList()).isEmpty()); } @Test public void testReversePositiveFeedbackSynapse() { Model m = new Model(); Neuron inA = m.createNeuron("IN A", INPUT); Neuron inB = m.createNeuron("IN B", INPUT); Neuron inhib = m.createNeuron("INHIB", INHIBITORY); Neuron.init(inhib, 0.0); Neuron nE = m.createNeuron("E", EXCITATORY); Neuron nF = m.createNeuron("F", EXCITATORY); Neuron nC = Neuron.init(m.createNeuron("C", EXCITATORY), 6.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(inhib) .setWeight(-100.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron nD = Neuron.init(m.createNeuron("D", EXCITATORY), 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(inhib) .setWeight(-100.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(inhib, new Synapse.Builder() .setSynapseId(0) .setNeuron(nC) .setWeight(10.0), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(inhib, new Synapse.Builder() .setSynapseId(1) .setNeuron(nD) .setWeight(10.0), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS) ); // Create the document even though the model is not yet complete Document doc = new Document(m, "aaaa bbbb "); // inA.addInput(doc, 0, 5); // inB.addInput(doc, 5, 10); // doc.process(); System.out.println(doc.activationsToString()); // Complete the model Neuron.init(doc, nD, 7.0, new Synapse.Builder() .setSynapseId(2) .setNeuron(nF) .setWeight(2.0), new Relation.Builder() .setFrom(2) .setTo(0) .setRelation(ANY) ); Neuron.init(doc, nE, 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inB) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(inhib) .setWeight(-100.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(doc, nF, 6.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inB) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(inhib) .setWeight(-100.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init( doc, inhib, new Synapse.Builder() .setSynapseId(2) .setNeuron(nE) .setWeight(10.0), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init( doc, inhib, new Synapse.Builder() .setSynapseId(3) .setNeuron(nF) .setWeight(10.0), new Relation.Builder() .setFrom(3) .setTo(OUTPUT) .setRelation(EQUALS) ); inA.addInput(doc, 0, 5); inB.addInput(doc, 5, 10); doc.propagate(); doc.process(); System.out.println(doc.activationsToString()); Assert.assertFalse(nD.getActivations(doc, true).collect(Collectors.toList()).isEmpty()); } @Test public void testAvoidUnnecessaryExcludeSteps() { Model m = new Model(); Neuron in = m.createNeuron("IN", INPUT); Neuron inhib = m.createNeuron("INHIB", INHIBITORY, LIMITED_RECTIFIED_LINEAR_UNIT); Neuron out = m.createNeuron("OUT", EXCITATORY); Neuron.init(inhib, 0.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(out) .setWeight(1.0), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(out, 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(in) .setWeight(20.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(in) .setWeight(20.0), new Synapse.Builder() .setSynapseId(2) .setNeuron(out) .setWeight(-100.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(BEGIN_TO_END_EQUALS), new Relation.Builder() .setFrom(2) .setTo(Synapse.OUTPUT) .setRelation(OVERLAPS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(BEGIN_EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(END_EQUALS) ); SearchNode.COMPUTE_SOFT_MAX = true; SearchNode.OPTIMIZE_SEARCH = false; Document doc = new Document(m, "aaaa"); in.addInput(doc, 0, 1); in.addInput(doc, 1, 2); in.addInput(doc, 2, 3); in.addInput(doc, 3, 4); doc.process(); System.out.println(doc.activationsToString()); Assert.assertEquals(13, doc.searchNodeIdCounter); SearchNode.COMPUTE_SOFT_MAX = false; } } aika-java-1.4.0/src/test/java/network/aika/network/LinkerTest.java000066400000000000000000000117741352543012000250520ustar00rootroot00000000000000package network.aika.network; import network.aika.Document; import network.aika.Model; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.INeuron; import network.aika.neuron.relation.Relation; import org.junit.Assert; import org.junit.Test; import static network.aika.neuron.INeuron.Type.*; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.relation.Relation.ANY; import static network.aika.neuron.relation.Relation.EQUALS; public class LinkerTest { /* @Test public void testLinker() { testSynapse(new Range(doc, 0, 20), new Range(5, 15), Relation.IS_ANCESTOR_OF, true); testSynapse(new Range(0, 10), new Range(0, 10), Relation.EQUALS, true); // Overlaps testSynapse(new Range(0, 10), new Range(5, 15), Relation.OVERLAPS, true); testSynapse(new Range(0, 20), new Range(5, 15), Relation.OVERLAPS, true); testSynapse(new Range(0, 5), new Range(10, 15), Relation.OVERLAPS, false); // Overlaps but does not contain testSynapse(new Range(0, 10), new Range(5, 15), Relation.create(GREATER_THAN, LESS_THAN, GREATER_THAN, GREATER_THAN), true); testSynapse(new Range(0, 20), new Range(5, 15), Relation.create(GREATER_THAN, LESS_THAN, GREATER_THAN, GREATER_THAN), false); testSynapse(new Range(5, 15), new Range(0, 20), Relation.create(GREATER_THAN, LESS_THAN, GREATER_THAN, GREATER_THAN), false); } */ public void testSynapse(int beginA, int endA, int beginB, int endB, Relation rr, boolean targetValue) { for (int dir = 0; dir < 2; dir++) { Model m = new Model(); Neuron na = m.createNeuron("A", INPUT); Neuron nb = m.createNeuron("B", INPUT); Neuron nc = m.createNeuron("C", EXCITATORY); Neuron.init(nc, 6.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(na) .setWeight(1.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(nb) .setWeight(10.0), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(rr), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS) ); Document doc = new Document(m, "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"); if (dir == 0) { na.addInput(doc, beginA, endA); nb.addInput(doc, beginB, endB); } else { nb.addInput(doc, beginB, endB); na.addInput(doc, beginA, endA); } doc.process(); Assert.assertEquals(targetValue, na.get().getActivations(doc, false).iterator().next().getOutputLinks().count() != 0); } } @Test public void testLinkInputActivationExcitatory() { Model m = new Model(); Neuron na = m.createNeuron("A", INPUT); Neuron nb = m.createNeuron("B", EXCITATORY); Neuron.init(nb, -0.5, new Synapse.Builder() .setSynapseId(1) .setNeuron(na) .setWeight(10.0), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS) ); Document doc = new Document(m, "X"); na.addInput(doc, 0, 1); nb.addInput(doc, 0, 1); Assert.assertTrue(nb.getActivations(doc, false).iterator().next().getInputLinks().findAny().isPresent()); } @Test public void testLinkReverseFromOutputActivation() { Model m = new Model(); Neuron ina = m.createNeuron("A", INPUT); Neuron inb = m.createNeuron("B", INPUT); Neuron out = m.createNeuron("Out", INHIBITORY); Neuron.init(out, 0.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(ina) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(inb) .setWeight(10.0), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(ANY), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS) ); Document doc = new Document(m, "X"); ina.addInput(doc, 0, 1); inb.addInput(doc, 0, 1); doc.process(); System.out.println(doc.activationsToString()); Assert.assertEquals(2, out.getActivations(doc, false).findFirst().get().getInputLinks().count()); } } aika-java-1.4.0/src/test/java/network/aika/network/MutualExclusionTest.java000066400000000000000000000175431352543012000267670ustar00rootroot00000000000000/* * 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.network; import network.aika.ActivationFunction; import network.aika.Document; import network.aika.Model; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.INeuron; import network.aika.neuron.relation.Relation; import org.junit.Assert; import org.junit.Test; import java.util.stream.Collectors; import static network.aika.ActivationFunction.LIMITED_RECTIFIED_LINEAR_UNIT; import static network.aika.ActivationFunction.RECTIFIED_HYPERBOLIC_TANGENT; import static network.aika.neuron.INeuron.Type.*; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.relation.Relation.EQUALS; /** * * @author Lukas Molzberger */ public class MutualExclusionTest { /** * |----------------------------| * | ------ | * -*| & | | * A ---| PA |------\ ------- | * ------ \ | | | * \-| OR | | * ------ | | | * B ---| & |----------| All |----| * -*| PB | -| | | * | ------ / ------- | * |----------------------------| * / | * ------ / | * C ---| & |----/ | * -*| PC | | * | ------ | * |----------------------------| */ @Test public void testMutualExclusion() { Model m = new Model(); // Create the input neurons for the network. Neuron inA = m.createNeuron("IN-A", INPUT); Neuron inB = m.createNeuron("IN-B", INPUT); Neuron inC = m.createNeuron("IN-C", INPUT); // Instantiate the inhibitory neuron. Its inputs will be added later on. Neuron inhibN = m.createNeuron("INHIB", INHIBITORY, LIMITED_RECTIFIED_LINEAR_UNIT); // Create three neurons that might be suppressed by the inhibitory neuron. Neuron pA = Neuron.init( m.createNeuron("A", EXCITATORY), 3.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inhibN) .setWeight(-100.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron pB = Neuron.init( m.createNeuron("B", EXCITATORY), 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inB) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inhibN) .setWeight(-100.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron pC = Neuron.init( m.createNeuron("C", EXCITATORY), 2.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inC) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inhibN) .setWeight(-100.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); // Finally addInput all the inputs to the suppressing neuron. Neuron.init( inhibN, 0.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(pA) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(pB) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(2) .setNeuron(pC) .setWeight(1.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron outN = Neuron.init(m.createNeuron("OUT", INHIBITORY, RECTIFIED_HYPERBOLIC_TANGENT), 0.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(pB) .setWeight(1.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); // Now that the model is complete, apply it to a document. Document doc = new Document(m, "foobar", 0); // Add input activations starting at char 0 and ending at char 1 // These inputs will be immediately propagated through the network. inA.addInput(doc, 0, 1); inB.addInput(doc, 0, 1); inC.addInput(doc, 0, 1); // Computes the best interpretation doc.process(); System.out.println(doc.activationsToString()); Assert.assertTrue(pA.getActivations(doc, true).collect(Collectors.toList()).isEmpty()); Assert.assertFalse(pB.getActivations(doc, true).collect(Collectors.toList()).isEmpty()); Assert.assertTrue(pC.getActivations(doc, true).collect(Collectors.toList()).isEmpty()); Assert.assertFalse(outN.getActivations(doc, true).collect(Collectors.toList()).isEmpty()); doc.clearActivations(); } } aika-java-1.4.0/src/test/java/network/aika/network/NegationTest.java000066400000000000000000000624611352543012000253710ustar00rootroot00000000000000/* * 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.network; import network.aika.Document; import network.aika.Model; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.lattice.OrNode; import network.aika.neuron.relation.PositionRelation; import network.aika.neuron.relation.Relation; import org.junit.Assert; import org.junit.Test; import static network.aika.neuron.INeuron.Type.*; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.activation.Activation.BEGIN; import static network.aika.neuron.activation.Activation.END; import static network.aika.neuron.relation.Relation.*; /** * * @author Lukas Molzberger */ public class NegationTest { @Test public void testTwoNegativeInputs1() { Model m = new Model(); Neuron inA = m.createNeuron("A", INPUT); Neuron inB = m.createNeuron("B", INPUT); Neuron inC = m.createNeuron("C", INPUT); Neuron abcN = m.createNeuron("ABC", EXCITATORY); Neuron.init(abcN, 5.5, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(-10.0) .setRecurrent(true), new Synapse.Builder() .setSynapseId(2) .setNeuron(inC) .setWeight(-10.0) .setRecurrent(true), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(CONTAINS), new Relation.Builder() .setFrom(0) .setTo(2) .setRelation(CONTAINS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Document doc = new Document(m, "aaaaaaaaaaa", 0); inA.addInput(doc, 0, 11); Assert.assertNotNull(abcN.getActivation(doc, 0, 11, false)); inB.addInput(doc, 2, 7); inC.addInput(doc, 4, 9); doc.process(); System.out.println(doc.activationsToString()); Assert.assertNotNull(abcN.getActivation(doc, 0, 11, false)); } @Test public void testTwoNegativeInputs2() { Model m = new Model(); Neuron inA = m.createNeuron("A", INPUT); Neuron inB = m.createNeuron("B", INPUT); Neuron inC = m.createNeuron("C", INPUT); Neuron abcN = m.createNeuron("ABC", EXCITATORY); Neuron outN = Neuron.init(m.createNeuron("OUT", EXCITATORY), 1.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(abcN) .setWeight(1.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(abcN, 0.001, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(-1.0) .setRecurrent(true), new Synapse.Builder() .setSynapseId(2) .setNeuron(inC) .setWeight(-1.0) .setRecurrent(true), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(ANY), new Relation.Builder() .setFrom(0) .setTo(2) .setRelation(ANY), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Document doc = new Document(m, "aaaaaaaaaaa", 0); inA.addInput(doc, 0, 11); inB.addInput(doc, 2, 7); inC.addInput(doc, 4, 9); doc.process(); System.out.println(doc.activationsToString()); // Assert.assertNull(Activation.get(t, outN.node, 0, new Range(0, 11), Range.Relation.EQUALS, null, null, null)); } @Test public void testSimpleNegation1() { Model m = new Model(); Neuron inA = m.createNeuron("A", INPUT); Neuron asN = m.createNeuron("AS", EXCITATORY); Neuron inS = m.createNeuron("S", INPUT); Neuron outN = Neuron.init(m.createNeuron("OUT", INHIBITORY), 0.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(asN) .setWeight(1.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(asN, 0.001, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inS) .setWeight(-1.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Document doc = new Document(m, "aaaaaaaaaaa", 0); inS.addInput(doc, 3, 8); System.out.println(doc.activationsToString()); inA.addInput(doc, 0, 11); doc.process(); System.out.println(doc.activationsToString()); Assert.assertNotNull(outN.getActivation(doc, 0, 11, false)); doc.clearActivations(); } @Test public void testSimpleNegation2() { Model m = new Model(); Neuron inA = m.createNeuron("A", INPUT); Neuron asN = m.createNeuron("AS", EXCITATORY); Neuron inS = m.createNeuron("S", INPUT); Neuron outN = Neuron.init(m.createNeuron("OUT", INHIBITORY), 0.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(asN) .setWeight(1.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(asN, 0.001, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inS) .setWeight(-1.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(CONTAINS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Document doc = new Document(m, "aaaaaaaaaaa", 0); inS.addInput(doc, 3, 8); inA.addInput(doc, 0, 11); doc.process(); System.out.println(doc.activationsToString()); Assert.assertNotNull(outN.getActivation(doc, 0, 11, false)); doc.clearActivations(); } @Test public void testSimpleNegation3() { Model m = new Model(); Neuron inA = m.createNeuron("A", INPUT); Neuron asN = m.createNeuron("AS", EXCITATORY); Neuron inS = m.createNeuron("S", INPUT); Neuron outN = Neuron.init(m.createNeuron("OUT", INHIBITORY), 0.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(asN) .setWeight(1.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(asN, 0.001, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inS) .setWeight(-1.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(CONTAINS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Document doc = new Document(m, "aaaaaaaaaaa", 0); inA.addInput(doc, 0, 11); inS.addInput(doc, 3, 8); doc.process(); System.out.println(doc.activationsToString()); Assert.assertNotNull(outN.getActivation(doc, 0, 11, false)); doc.clearActivations(); } @Test public void testNegation1() { Model m = new Model(); Neuron inA = m.createNeuron("A", INPUT); Neuron inB = m.createNeuron("B", INPUT); Neuron asN = m.createNeuron("AS", EXCITATORY); Neuron absN = m.createNeuron("ABS", EXCITATORY); Neuron inS = Neuron.init(m.createNeuron("S", INHIBITORY), 0.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(asN) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(absN) .setWeight(1.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(asN, 0.001, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inS) .setWeight(-1.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(CONTAINS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(absN, 0.001, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(2) .setNeuron(inS) .setWeight(-1.0) .setRecurrent(true), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(new PositionRelation.GreaterThan(END, BEGIN, false)), new Relation.Builder() .setFrom(2) .setTo(0) .setRelation(OVERLAPS), new Relation.Builder() .setFrom(2) .setTo(1) .setRelation(OVERLAPS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(BEGIN_EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(END_EQUALS) ); { Document doc = new Document(m, "aaaaaaaaaa", 0); inA.addInput(doc, 0, 6); inB.addInput(doc, 0, 6); doc.process(); System.out.println(doc.activationsToString()); Assert.assertNotNull(inS.getActivation(doc, 0, 6, false)); Assert.assertEquals(2, inS.getActivation(doc, 0, 6, false).getInputLinks().count()); doc.clearActivations(); } { Document doc = new Document(m, "aaaaaaaaaa", 0); inA.addInput(doc, 0, 6); inB.addInput(doc, 3, 9); doc.process(); System.out.println(doc.activationsToString()); Assert.assertNotNull(inS.getActivation(doc, 0, 9, false)); Assert.assertEquals(1, inS.getActivation(doc, 0, 6, false).getInputLinks().count()); Assert.assertEquals(1, inS.getActivation(doc, 0, 9, false).getInputLinks().count()); doc.clearActivations(); } } @Test public void testNegation2() { Model m = new Model(); Neuron inA = m.createNeuron("A", INPUT); Neuron inB = m.createNeuron("B", INPUT); Neuron inC = m.createNeuron("C", INPUT); Neuron asN = m.createNeuron("AS", EXCITATORY); Neuron ascN = m.createNeuron("ASC", EXCITATORY); Neuron bsN = m.createNeuron("BS", EXCITATORY); Neuron inS = Neuron.init(m.createNeuron("S", INHIBITORY), 0.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(asN) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(ascN) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(2) .setNeuron(bsN) .setWeight(1.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(asN, 0.001, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inS) .setWeight(-1.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(ascN, 0.001, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inC) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(2) .setNeuron(inS) .setWeight(-1.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(bsN, 0.001, new Synapse.Builder() .setSynapseId(0) .setNeuron(inB) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inS) .setWeight(-1.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron outA = Neuron.init(m.createNeuron("OUT A", EXCITATORY), 0.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(asN) .setWeight(1.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron outAC = Neuron.init(m.createNeuron("OUT AC", EXCITATORY), 0.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(ascN) .setWeight(1.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron outB = Neuron.init(m.createNeuron("OUT B", EXCITATORY), 1.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(bsN) .setWeight(1.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Document doc = new Document(m, "aaaaaaaaaa", 0); inA.addInput(doc, 0, 6); inB.addInput(doc, 0, 6); inC.addInput(doc, 0, 6); doc.process(); System.out.println(doc.activationsToString()); } /** * * ----- * A ---| & |------ * -*| C | | ------ * | ------ | G---| & | * \ | | H |----- * \/-----------------| | * /\-----------------| | * / | ------ * | ------ | * -*| & |------ * B ---| D | * ------ * */ @Test public void testOptions() { Model m = new Model(); Neuron inA = m.createNeuron("A", INPUT); Neuron inB = m.createNeuron("B", INPUT); Neuron pC = m.createNeuron("C", EXCITATORY); Neuron pD = m.createNeuron("D", EXCITATORY); Neuron.init(pC, 0.5, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(2.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(pD) .setWeight(-2.0) .setRecurrent(true), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(pD, 0.5, new Synapse.Builder() .setSynapseId(0) .setNeuron(inB) .setWeight(2.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(pC) .setWeight(-2.0) .setRecurrent(true), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron inG = m.createNeuron("G", INPUT); OrNode inGNode = inG.get().getInputNode().get(); Neuron pH = Neuron.init(m.createNeuron("H", EXCITATORY), 0.001, new Synapse.Builder() .setSynapseId(0) .setNeuron(pC) .setWeight(2.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(pD) .setWeight(2.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(2) .setNeuron(inG) .setWeight(2.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(EQUALS) ); Document doc = new Document(m, "aaaaaaaaaa", 0); inA.addInput(doc, 0, 1); inB.addInput(doc, 0, 1); inG.addInput(doc, 0, 1); doc.process(); System.out.println(doc.activationsToString()); Assert.assertNotNull(pC.getActivation(doc, 0, 1, false)); Assert.assertNotNull(pD.getActivation(doc, 0, 1, false)); // Die Optionen 0 und 2 stehen in Konflikt. Da sie aber jetzt in Oder Optionen eingebettet sind, werden sie nicht mehr ausgefiltert. // Assert.assertNull(pH.node.getFirstActivation(t)); } } aika-java-1.4.0/src/test/java/network/aika/network/NextWordTest.java000066400000000000000000000034301352543012000253660ustar00rootroot00000000000000package network.aika.network; import network.aika.Document; import network.aika.Model; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.INeuron; import network.aika.neuron.relation.Relation; import org.junit.Test; import static network.aika.neuron.INeuron.Type.EXCITATORY; import static network.aika.neuron.INeuron.Type.INPUT; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.relation.Relation.*; public class NextWordTest { @Test public void testMatchTheWord() { Model m = new Model(null, 1); Neuron inA = m.createNeuron("A", INPUT); Neuron inB = m.createNeuron("B", INPUT); Neuron abN = Neuron.init(m.createNeuron("AB", EXCITATORY), 6.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(10.0), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(END_TO_BEGIN_EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(BEGIN_EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(END_EQUALS) ); Document doc = new Document(m, "aaaa bbbb ", 0); inA.addInput(doc, 0, 5); inB.addInput(doc, 5, 10); doc.process(); System.out.println(doc.activationsToString()); } } aika-java-1.4.0/src/test/java/network/aika/network/OptionalAndTest.java000066400000000000000000000207471352543012000260360ustar00rootroot00000000000000/* * 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.network; import network.aika.Document; import network.aika.Model; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.relation.Relation; import org.junit.Assert; import org.junit.Test; import static network.aika.neuron.INeuron.Type.*; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.relation.Relation.*; /** * * @author Lukas Molzberger */ public class OptionalAndTest { @Test public void testOptionalAnd() { Model m = new Model(null, 2); Neuron wordEssen = m.createNeuron("word:essen", INPUT); Neuron wordHamburg = m.createNeuron("word:hamburg", INPUT); Neuron wordGehen = m.createNeuron("word:gehen", INPUT); Neuron upperCase = m.createNeuron("upper case", INPUT); Neuron suppr = m.createNeuron("SUPPRESS", INHIBITORY); Neuron hintNoun = Neuron.init(m.createNeuron("HINT-NOUN", INHIBITORY), 0.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(wordEssen) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(wordHamburg) .setWeight(1.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron hintVerb = Neuron.init(m.createNeuron("HINT-VERB", INHIBITORY), 0.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(wordEssen) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(wordGehen) .setWeight(1.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron noun = Neuron.init(m.createNeuron("NOUN", EXCITATORY), 0.501, new Synapse.Builder() .setSynapseId(0) .setNeuron(hintNoun) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(upperCase) .setWeight(0.5) .setRecurrent(false), new Synapse.Builder() .setSynapseId(2) .setNeuron(suppr) .setWeight(-1.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(0) .setRelation(OVERLAPS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron verb = Neuron.init(m.createNeuron("VERB", EXCITATORY), 0.001, new Synapse.Builder() .setSynapseId(0) .setNeuron(hintVerb) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(suppr) .setWeight(-1.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(OVERLAPS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(suppr, 0.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(noun) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(verb) .setWeight(1.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS) ); Document doc1 = new Document(m, "Essen"); Document doc2 = new Document(m, "essen", 1); for(Document doc: new Document[] {doc1, doc2}) { String txt = doc.getContent(); int begin = txt.toLowerCase().indexOf("essen"); int end = begin + 5; wordEssen.addInput(doc, begin, end); if(Character.isUpperCase(txt.charAt(begin))) { upperCase.addInput(doc, begin, end); } doc.process(); System.out.println(doc.activationsToString()); System.out.println(); doc.clearActivations(); } } @Test public void testOnlyOptionalInputs() { Model model = new Model(); Neuron inA = model.createNeuron("A", INPUT); Neuron inB = model.createNeuron("B", INPUT); Neuron inC = model.createNeuron("C", INPUT); Neuron testNeuron = model.createNeuron("Test", EXCITATORY); Neuron.init(testNeuron, 2.1, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(2.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(2.0), new Synapse.Builder() .setSynapseId(2) .setNeuron(inC) .setWeight(2.0), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(BEGIN_EQUALS), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(END_EQUALS), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(END_TO_BEGIN_EQUALS), new Relation.Builder() .setFrom(1) .setTo(2) .setRelation(END_TO_BEGIN_EQUALS) ); Document doc = new Document(model, "ABC"); inA.addInput(doc, 0, 1); inB.addInput(doc, 1, 2); inC.addInput(doc, 2, 3); doc.process(); Assert.assertEquals(1, testNeuron.getActivations(doc, false).count()); } } aika-java-1.4.0/src/test/java/network/aika/network/OverlappingOrTest.java000066400000000000000000000107261352543012000264110ustar00rootroot00000000000000/* * 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.network; import network.aika.Document; import network.aika.Model; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.INeuron; import network.aika.neuron.relation.PositionRelation; import network.aika.neuron.relation.Relation; import org.junit.Assert; import org.junit.Test; import java.util.HashMap; import java.util.Map; import static network.aika.neuron.INeuron.Type.EXCITATORY; import static network.aika.neuron.INeuron.Type.INPUT; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.relation.Relation.*; import static network.aika.neuron.activation.Activation.BEGIN; import static network.aika.neuron.activation.Activation.END; /** * * @author Lukas Molzberger */ public class OverlappingOrTest { @Test public void testOverlappingOr() { Model m = new Model(); Map inputNeurons = new HashMap<>(); // Create an input neuron and a recurrent neuron for every letter in this example. for(char c: new char[] {'a', 'b', 'c', 'd', 'e'}) { Neuron in = m.createNeuron(c + "", INPUT); inputNeurons.put(c, in); } // Create a pattern neuron with the recurrent neurons as input. The number that are // given in the inputs are the recurrent ids (relativeRid) which specify the relative position // of the inputs relative to each other. The following flag specifies whether this relativeRid is // relative or absolute. Neuron pattern = Neuron.init( m.createNeuron("BCD", EXCITATORY), 3.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inputNeurons.get('b')) .setWeight(4.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inputNeurons.get('c')) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(2) .setNeuron(inputNeurons.get('d')) .setWeight(4.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(END_TO_BEGIN_EQUALS), new Relation.Builder() .setFrom(0) .setTo(2) .setRelation(new PositionRelation.LessThan(END, BEGIN, false)), new Relation.Builder() .setFrom(1) .setTo(2) .setRelation(END_TO_BEGIN_EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(BEGIN_EQUALS), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(END_EQUALS) ); Document doc = new Document(m, "a b c d e ", 0); for(int i = 0; i < doc.length(); i++) { char c = doc.getContent().charAt(i); if(c != ' ') { inputNeurons.get(c).addInput(doc, i, i + 2); } } // Computes the selected option doc.process(); Assert.assertEquals(1, pattern.get().size(doc)); System.out.println("All activations:"); System.out.println(doc.activationsToString()); System.out.println(); doc.clearActivations(); } } aika-java-1.4.0/src/test/java/network/aika/network/PassiveInputNeuronTest.java000066400000000000000000000037011352543012000274360ustar00rootroot00000000000000package network.aika.network; import network.aika.Document; import network.aika.Model; import network.aika.neuron.INeuron; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.activation.Activation; import network.aika.neuron.relation.Relation; import org.junit.Assert; import org.junit.Test; import static network.aika.neuron.INeuron.Type.EXCITATORY; import static network.aika.neuron.INeuron.Type.INPUT; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.relation.Relation.EQUALS; public class PassiveInputNeuronTest { @Test public void testPassiveInputNeuron() { Model m = new Model(); Document doc = new Document(m, "aaaaaaaaaa", 0); Neuron inA = m.createNeuron("A", INPUT); Neuron inB = m.createNeuron("B", INPUT); inB.setPassiveInputFunction((s, oAct) -> 1.0); Neuron out = Neuron.init( m.createNeuron("OUT", EXCITATORY), 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(10.0), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); inA.addInput(doc, new Activation.Builder() .setRange(0, 1) ); Activation outAct = out.getActivation(doc, 0, 1, false); doc.process(); System.out.println(doc.activationsToString()); Assert.assertTrue(outAct.isFinalActivation()); } } aika-java-1.4.0/src/test/java/network/aika/network/PatternMatchingTest.java000066400000000000000000000527731352543012000267220ustar00rootroot00000000000000/* * 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.network; import network.aika.ActivationFunction; import network.aika.Document; import network.aika.Model; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.activation.Activation; import network.aika.neuron.INeuron; import network.aika.neuron.relation.Relation; import org.junit.Assert; import org.junit.Test; import java.util.HashMap; import java.util.Map; import java.util.stream.Collectors; import static network.aika.neuron.INeuron.Type.EXCITATORY; import static network.aika.neuron.INeuron.Type.INPUT; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.relation.Relation.*; /** * * @author Lukas Molzberger */ public class PatternMatchingTest { @Test public void testPatternMatching1() { Model m = new Model(); Map inputNeurons = new HashMap<>(); // Create an input neuron and a recurrent neuron for every letter in this example. for(char c: new char[] {'a', 'b', 'c', 'd', 'e'}) { Neuron in = m.createNeuron(c + "", INPUT); inputNeurons.put(c, in); } // Create a pattern neuron with the relational neurons as input. The numbers that are // given in the inputs are the recurrent ids (relativeRid) which specify the relative position // of the inputs relative to each other. The following flag specifies whether this relativeRid // is relative or absolute. Neuron pattern = Neuron.init( m.createNeuron("BCD", EXCITATORY), 1.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inputNeurons.get('b')) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inputNeurons.get('c')) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(2) .setNeuron(inputNeurons.get('d')) .setWeight(10.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(END_TO_BEGIN_EQUALS), new Relation.Builder() .setFrom(1) .setTo(2) .setRelation(END_TO_BEGIN_EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(BEGIN_EQUALS), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(END_EQUALS) ); // Create a simple text document. Document doc = new Document(m, "a b c d e ", 0); // Then add the characters int wordPos = 0; for(int i = 0; i < doc.length(); i++) { char c = doc.getContent().charAt(i); if(c != ' ') { inputNeurons.get(c).addInput(doc, i, i + 2); } else { wordPos++; } } // Computes the selected option doc.process(); Assert.assertEquals(1, pattern.get().size(doc)); System.out.println("Output activation:"); INeuron n = pattern.get(); for(Activation act: n.getActivations(doc, false).collect(Collectors.toList())) { System.out.println("Text Range: " + act.slotsToString()); System.out.println("Neuron: " + act.getLabel()); System.out.println(); } System.out.println("All activations:"); System.out.println(doc.activationsToString()); System.out.println(); doc.clearActivations(); } @Test public void testPatternMatching2() { Model m = new Model(); Map inputNeurons = new HashMap<>(); // Create an input neuron and a recurrent neuron for every letter in this example. for(char c: new char[] {'a', 'b', 'c', 'd', 'e', 'f'}) { Neuron in = m.createNeuron(c + "", INPUT); inputNeurons.put(c, in); } // Create a pattern neuron with the relational neurons as input. The numbers that are // given in the inputs are the recurrent ids (relativeRid) which specify the relative position // of the inputs relative to each other. The following flag specifies whether this relativeRid // is relative or absolute. Neuron pattern = Neuron.init( m.createNeuron("BCDE", EXCITATORY), 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inputNeurons.get('b')) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inputNeurons.get('c')) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(2) .setNeuron(inputNeurons.get('d')) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(3) .setNeuron(inputNeurons.get('e')) .setWeight(10.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(END_TO_BEGIN_EQUALS), new Relation.Builder() .setFrom(1) .setTo(2) .setRelation(END_TO_BEGIN_EQUALS), new Relation.Builder() .setFrom(2) .setTo(3) .setRelation(END_TO_BEGIN_EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(BEGIN_EQUALS), new Relation.Builder() .setFrom(3) .setTo(OUTPUT) .setRelation(END_EQUALS) ); // Create a simple text document. Document doc = new Document(m, "a b c d e ", 0); // Then add the characters int wordPos = 0; for(int i = 0; i < doc.length(); i++) { char c = doc.getContent().charAt(i); if(c != ' ') { inputNeurons.get(c).addInput(doc, i, i + 2); } else { wordPos++; } } // Computes the best interpretation doc.process(); Assert.assertEquals(1, pattern.getActivations(doc, false).collect(Collectors.toList()).size()); System.out.println("Output activation:"); INeuron n = pattern.get(); for(Activation act: n.getActivations(doc, false).collect(Collectors.toList())) { System.out.println("Text Range: " + act.slotsToString()); System.out.println("Neuron: " + act.getLabel()); System.out.println(); } System.out.println("All activations:"); System.out.println(doc.activationsToString()); System.out.println(); doc.clearActivations(); } @Test public void testPatternMatching3() { Model m = new Model(); Neuron inA = m.createNeuron("A", INPUT); Neuron inB = m.createNeuron("B", INPUT); Neuron inC = m.createNeuron("C", INPUT); Neuron pattern = Neuron.init( m.createNeuron("ABC", EXCITATORY), 1.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(2) .setNeuron(inC) .setWeight(10.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(2) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(BEGIN_EQUALS), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(END_EQUALS) ); Document doc = new Document(m, "X", 0); inA.addInput(doc, 0, 1); inB.addInput(doc, 0, 1); inC.addInput(doc, 0, 1); // Computes the selected option doc.process(); Assert.assertEquals(1, pattern.get().size(doc)); System.out.println("Output activation:"); INeuron n = pattern.get(); for(Activation act: n.getActivations(doc, false).collect(Collectors.toList())) { System.out.println("Text Range: " + act.slotsToString()); System.out.println("Neuron: " + act.getLabel()); System.out.println(); } System.out.println("All activations:"); System.out.println(doc.activationsToString()); System.out.println(); doc.clearActivations(); } @Test public void testPatternMatching4() { Model m = new Model(); Neuron inA = m.createNeuron("A", INPUT); Neuron inB = m.createNeuron("B", INPUT); Neuron inC = m.createNeuron("C", INPUT); Neuron inD = m.createNeuron("D", INPUT); Neuron pattern = Neuron.init( m.createNeuron("ABCD", EXCITATORY), 1.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(2) .setNeuron(inC) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(3) .setNeuron(inD) .setWeight(10.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(2) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(3) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(2) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(3) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(3) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(BEGIN_EQUALS), new Relation.Builder() .setFrom(3) .setTo(OUTPUT) .setRelation(END_EQUALS) ); Document doc = new Document(m, "X", 0); inA.addInput(doc, 0, 1); inB.addInput(doc, 0, 1); inC.addInput(doc, 0, 1); inD.addInput(doc, 0, 1); // Computes the selected option doc.process(); Assert.assertEquals(1, pattern.get().size(doc)); System.out.println("Output activation:"); INeuron n = pattern.get(); for(Activation act: n.getActivations(doc, false).collect(Collectors.toList())) { System.out.println("Text Range: " + act.slotsToString()); System.out.println("Neuron: " + act.getLabel()); System.out.println(); } System.out.println("All activations:"); System.out.println(doc.activationsToString()); System.out.println(); doc.clearActivations(); } @Test public void testPatternMatching5() { Model m = new Model(); Neuron word = m.createNeuron("Word", INPUT); Map inputNeurons = new HashMap<>(); // Create an input neuron and a recurrent neuron for every letter in this example. for(char c: new char[] {'a', 'b', 'c', 'd', 'e'}) { Neuron in = m.createNeuron(c + "", INPUT); inputNeurons.put(c, in); } // Create a pattern neuron with the relational neurons as input. The numbers that are // given in the inputs are the recurrent ids (relativeRid) which specify the relative position // of the inputs relative to each other. The following flag specifies whether this relativeRid // is relative or absolute. Neuron pattern = Neuron.init( m.createNeuron("BCD", EXCITATORY), 1.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inputNeurons.get('b')) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inputNeurons.get('c')) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(2) .setNeuron(inputNeurons.get('d')) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(3) .setNeuron(word) .setWeight(10.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(END_TO_BEGIN_EQUALS), new Relation.Builder() .setFrom(1) .setTo(2) .setRelation(END_TO_BEGIN_EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(BEGIN_EQUALS), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(END_EQUALS), new Relation.Builder() .setFrom(3) .setTo(OUTPUT) .setRelation(EQUALS) ); // Create a simple text document. Document doc = new Document(m, "a b c d e ", 0); word.addInput(doc, 2, 8); // Then add the characters int wordPos = 0; for(int i = 0; i < doc.length(); i++) { char c = doc.getContent().charAt(i); if(c != ' ') { inputNeurons.get(c).addInput(doc, i, i + 2); } else { wordPos++; } } // Computes the selected option doc.process(); Assert.assertEquals(1, pattern.get().size(doc)); System.out.println("Output activation:"); INeuron n = pattern.get(); for(Activation act: n.getActivations(doc, false).collect(Collectors.toList())) { System.out.println("Text Range: " + act.slotsToString()); System.out.println("Neuron: " + act.getLabel()); System.out.println(); } System.out.println("All activations:"); System.out.println(doc.activationsToString()); System.out.println(); doc.clearActivations(); } @Test public void testPatternMatching6() { Model m = new Model(); Neuron word = m.createNeuron("Word", INPUT); Map inputNeurons = new HashMap<>(); // Create an input neuron and a recurrent neuron for every letter in this example. for(char c: new char[] {'a', 'b', 'c', 'd', 'e'}) { Neuron in = m.createNeuron(c + "", INPUT); inputNeurons.put(c, in); } // Create a pattern neuron with the relational neurons as input. The numbers that are // given in the inputs are the recurrent ids (relativeRid) which specify the relative position // of the inputs relative to each other. The following flag specifies whether this relativeRid // is relative or absolute. Neuron pattern = Neuron.init( m.createNeuron("BCD", EXCITATORY), 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inputNeurons.get('b')) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inputNeurons.get('c')) .setWeight(2.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(2) .setNeuron(inputNeurons.get('d')) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(3) .setNeuron(word) .setWeight(10.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(END_TO_BEGIN_EQUALS), new Relation.Builder() .setFrom(1) .setTo(2) .setRelation(END_TO_BEGIN_EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(BEGIN_EQUALS), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(END_EQUALS), new Relation.Builder() .setFrom(3) .setTo(0) .setRelation(BEGIN_EQUALS), new Relation.Builder() .setFrom(3) .setTo(2) .setRelation(END_EQUALS) ); // Create a simple text document. Document doc = new Document(m, "a b c d e ", 0); word.addInput(doc, 2, 8); // Then add the characters int wordPos = 0; for(int i = 0; i < doc.length(); i++) { char c = doc.getContent().charAt(i); if(c != ' ') { inputNeurons.get(c).addInput(doc, i, i + 2); } else { wordPos++; } } // Computes the selected option doc.process(); Assert.assertEquals(1, pattern.get().size(doc)); System.out.println("Output activation:"); INeuron n = pattern.get(); for(Activation act: n.getActivations(doc, false).collect(Collectors.toList())) { System.out.println("Text Range: " + act.slotsToString()); System.out.println("Neuron: " + act.getLabel()); System.out.println(); } System.out.println("All activations:"); System.out.println(doc.activationsToString()); System.out.println(); doc.clearActivations(); } } aika-java-1.4.0/src/test/java/network/aika/network/PositionRelationTest.java000066400000000000000000000164441352543012000271270ustar00rootroot00000000000000package network.aika.network; import network.aika.Document; import network.aika.Model; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.INeuron; import network.aika.neuron.activation.Activation; import network.aika.neuron.relation.MultiRelation; import network.aika.neuron.relation.PositionRelation; import network.aika.neuron.relation.Relation; import org.junit.Assert; import org.junit.Test; import java.util.ArrayList; import java.util.HashMap; import java.util.List; import java.util.Map; import java.util.stream.Collectors; import static network.aika.neuron.INeuron.Type.EXCITATORY; import static network.aika.neuron.INeuron.Type.INPUT; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.activation.Activation.BEGIN; import static network.aika.neuron.activation.Activation.END; import static network.aika.neuron.relation.Relation.*; import static network.aika.neuron.relation.PositionRelation.Equals; import static network.aika.neuron.relation.PositionRelation.LessThan; import static network.aika.neuron.relation.PositionRelation.GreaterThan; import static network.aika.neuron.relation.Relation.EQUALS; public class PositionRelationTest { @Test public void testRangeRelation() { Model m = new Model(); Neuron inA = m.createNeuron("A", INPUT); Neuron inB = m.createNeuron("B", INPUT); Neuron outC = Neuron.init(m.createNeuron("C", EXCITATORY), 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(10.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS) ); Document doc = new Document(m, "aaaaaaaaaa", 0); inA.addInput(doc, 0, 1); inB.addInput(doc, 0, 1); Activation outC1 = outC.getActivation(doc, 0, 1, false); doc.process(); System.out.println(doc.activationsToString()); Assert.assertNotNull(outC1); } @Test public void testABCPattern() { Model m = new Model(); Neuron inA = m.createNeuron("A", INPUT); Neuron inB = m.createNeuron("B", INPUT); Neuron inC = m.createNeuron("C", INPUT); Neuron outD = Neuron.init(m.createNeuron("D" ,EXCITATORY), 0.001, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(2) .setNeuron(inC) .setWeight(1.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(END_TO_BEGIN_EQUALS), new Relation.Builder() .setFrom(1) .setTo(2) .setRelation(END_TO_BEGIN_EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(BEGIN_EQUALS), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(END_EQUALS) ); Document doc = new Document(m, "aaaaaaaaaa", 0); inA.addInput(doc, 0, 1); inB.addInput(doc, 1, 2); inC.addInput(doc, 2, 3); Activation outD1 = outD.getActivation(doc, 0, 3, false); Assert.assertNotNull(outD1); } @Test public void testHuettenheim() { Model m = new Model(); HashMap chars = new HashMap<>(); for (char c = 'a'; c <= 'z'; c++) { Neuron rec = m.createNeuron("IN-" + c, INPUT); chars.put(c, rec); } String word = "Huettenheim"; List inputs = new ArrayList<>(); for (int i = 0; i < word.length(); i++) { char c = word.toLowerCase().charAt(i); Neuron rec = chars.get(c); if (rec != null) { boolean begin = i == 0; boolean end = i + 1 == word.length(); inputs.add(new Synapse.Builder() .setSynapseId(i) .setNeuron(rec) .setWeight(begin || end ? 2.0 : 1.0) .setRecurrent(false) ); if(begin) { inputs.add( new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(BEGIN_EQUALS) ); } if(end) { inputs.add( new Relation.Builder() .setFrom(i) .setTo(OUTPUT) .setRelation(END_EQUALS) ); } if(!end) { inputs.add(new Relation.Builder() .setFrom(i) .setTo(i + 1) .setRelation(END_TO_BEGIN_EQUALS) ); } else { inputs.add(new Relation.Builder() .setFrom(i) .setTo(0) .setRelation(new PositionRelation.GreaterThan(END, BEGIN, false)) ); } } } Neuron n = Neuron.init(m.createNeuron("PATTERN", EXCITATORY), 0.5, inputs.toArray(new Neuron.Builder[inputs.size()])); System.out.println(n.get().getInputNode().get().logicToString()); Document doc = new Document(m, "abc Huettenheim cba", 0); for (int i = 0; i < doc.length(); i++) { char c = doc.getContent().toLowerCase().charAt(i); Neuron inputNeuron = chars.get(c); if (inputNeuron != null) { inputNeuron.addInput(doc, i, i + 1); } } doc.process(); System.out.println(doc.activationsToString()); assert n.get().getActivations(doc, false).collect(Collectors.toList()).size() >= 1; } } aika-java-1.4.0/src/test/java/network/aika/network/PositiveFeedbackLoopTest.java000066400000000000000000000051671352543012000276660ustar00rootroot00000000000000package network.aika.network; import network.aika.Document; import network.aika.Model; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.INeuron; import network.aika.neuron.relation.Relation; import org.junit.Assert; import org.junit.Test; import java.util.stream.Collectors; import static network.aika.neuron.INeuron.Type.EXCITATORY; import static network.aika.neuron.INeuron.Type.INPUT; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.relation.Relation.EQUALS; public class PositiveFeedbackLoopTest { @Test public void testSimplePosFeedbackLoop() { Model m = new Model(); Neuron inA = m.createNeuron("IN A", INPUT); Neuron inB = m.createNeuron("IN B", INPUT); Neuron nC = m.createNeuron("N C", EXCITATORY); Neuron nD = m.createNeuron("N D", EXCITATORY); Neuron.init(nC, 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(nD) .setWeight(10.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(nD, 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inB) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(nC) .setWeight(10.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Document doc = new Document(m, "Bla "); inA.addInput(doc, 0, 3); inB.addInput(doc, 0, 3); doc.process(); Assert.assertFalse(nC.getActivations(doc, true).collect(Collectors.toList()).isEmpty()); Assert.assertFalse(nD.getActivations(doc, true).collect(Collectors.toList()).isEmpty()); } } aika-java-1.4.0/src/test/java/network/aika/network/SameNeuronRelationTest.java000066400000000000000000000037421352543012000273740ustar00rootroot00000000000000package network.aika.network; import network.aika.Model; import network.aika.neuron.INeuron; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.relation.Relation; import org.junit.Test; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.relation.Relation.EQUALS; public class SameNeuronRelationTest { @Test public void testSameNeuronRelation() { Model m = new Model(); Neuron inA = m.createNeuron("IN-A", INeuron.Type.INPUT); Neuron inB = m.createNeuron("IN-B", INeuron.Type.INPUT); Neuron inC = m.createNeuron("IN-C", INeuron.Type.INPUT); Neuron middle = m.createNeuron("MIDDLE", INeuron.Type.INHIBITORY); Neuron out = m.createNeuron("OUT", INeuron.Type.EXCITATORY); Neuron.init(middle, 0.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(1.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(1.0), new Synapse.Builder() .setSynapseId(2) .setNeuron(inC) .setWeight(1.0), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(out, 2.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(middle) .setWeight(5.0) ); } } aika-java-1.4.0/src/test/java/network/aika/network/SimpleWeightsTest.java000066400000000000000000000101641352543012000264020ustar00rootroot00000000000000/* * 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.network; import network.aika.Document; import network.aika.Model; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.INeuron; import network.aika.neuron.relation.Relation; import org.junit.Test; import static network.aika.neuron.INeuron.Type.EXCITATORY; import static network.aika.neuron.INeuron.Type.INPUT; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.relation.Relation.EQUALS; /** * * @author Lukas Molzberger */ public class SimpleWeightsTest { @Test public void testWeightsOR() { Model m = new Model(); Neuron inA = m.createNeuron("A", INPUT); Neuron inB = m.createNeuron("B", INPUT); Neuron pC = m.createNeuron("C", EXCITATORY); Neuron.init(pC, 0.7, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(0.3) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(0.4) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS) ); { Document doc = new Document(m, "aaaaaaaaaa", 0); inA.addInput(doc, 0, 6); doc.process(); System.out.println(doc.activationsToString()); doc.clearActivations(); } { Document doc = new Document(m, "aaaaaaaaaa", 0); inA.addInput(doc, 0, 6); inB.addInput(doc, 0, 6); doc.process(); System.out.println(doc.activationsToString()); doc.clearActivations(); } } @Test public void testWeightsAND() { Model m = new Model(); Neuron inA = m.createNeuron("A", INPUT); Neuron inB = m.createNeuron("B", INPUT); Neuron pC = m.createNeuron("C", EXCITATORY); Neuron.init(pC, 0.01, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(3.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(3.0) .setRecurrent(false), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); { Document doc = new Document(m, "aaaaaaaaaa", 0); inA.addInput(doc, 0, 6); inB.addInput(doc, 0, 6); doc.process(); System.out.println(doc.activationsToString()); doc.clearActivations(); } } } aika-java-1.4.0/src/test/java/network/aika/network/SinglePositionSuppression.java000066400000000000000000000301351352543012000301770ustar00rootroot00000000000000package network.aika.network; import network.aika.ActivationFunction; import network.aika.Document; import network.aika.Model; import network.aika.neuron.INeuron; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.activation.Activation; import network.aika.neuron.relation.AncestorRelation; import network.aika.neuron.relation.PositionRelation; import network.aika.neuron.relation.Relation; import org.junit.Test; import static network.aika.neuron.INeuron.Type.*; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.relation.AncestorRelation.IS_ANCESTOR_OF; import static network.aika.neuron.relation.AncestorRelation.IS_DESCENDANT_OF; import static network.aika.neuron.relation.Relation.END_TO_BEGIN_EQUALS; import static network.aika.neuron.relation.Relation.EQUALS; public class SinglePositionSuppression { @Test public void testSinglePositionSuppression() { Model m = new Model(); Neuron inABegin = m.createNeuron("IN-A-BEGIN", EXCITATORY); Neuron inAEnd = m.createNeuron("IN-A-END", EXCITATORY); Neuron.init(inAEnd, 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inABegin) .setIdentity(true) .setWeight(10.0), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(END_TO_BEGIN_EQUALS) ); Neuron inBBegin = m.createNeuron("IN-B-BEGIN", EXCITATORY); Neuron inBEnd = m.createNeuron("IN-B-END", EXCITATORY); Neuron.init(inBEnd, 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inBBegin) .setIdentity(true) .setWeight(10.0), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(END_TO_BEGIN_EQUALS) ); Neuron inCBegin = m.createNeuron("IN-C-BEGIN", EXCITATORY); Neuron inCEnd = m.createNeuron("IN-C-END", EXCITATORY); Neuron.init(inCEnd, 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inCBegin) .setIdentity(true) .setWeight(10.0), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(END_TO_BEGIN_EQUALS) ); Neuron inDBegin = m.createNeuron("IN-D-BEGIN", EXCITATORY); Neuron inDEnd = m.createNeuron("IN-D-END", EXCITATORY); Neuron.init(inDEnd, 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inDBegin) .setIdentity(true) .setWeight(10.0), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(END_TO_BEGIN_EQUALS) ); Neuron mBegin = m.createNeuron("M-BEGIN", EXCITATORY); Neuron mEnd = m.createNeuron("M-END", EXCITATORY); Neuron nBegin = m.createNeuron("N-BEGIN", EXCITATORY); Neuron nEnd = m.createNeuron("N-END", EXCITATORY); Neuron.init(mBegin, 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inABegin) .setWeight(5.0), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(new PositionRelation.Equals(0, 0)), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(new AncestorRelation.IsAncestorOf()), new Synapse.Builder() .setSynapseId(1) .setNeuron(inAEnd) .setWeight(5.0), new Relation.Builder() .setFrom(1) .setTo(2) .setRelation(new PositionRelation.Equals(0, 0)), new Synapse.Builder() .setSynapseId(2) .setNeuron(inBBegin) .setWeight(4.0), new Relation.Builder() .setFrom(2) .setTo(3) .setRelation(new AncestorRelation.IsAncestorOf()), new Synapse.Builder() .setSynapseId(3) .setNeuron(inBEnd) .setWeight(4.0), new Relation.Builder() .setFrom(3) .setTo(4) .setRelation(new PositionRelation.Equals(0, 0)), new Synapse.Builder() .setSynapseId(4) .setNeuron(inCBegin) .setWeight(3.0), new Relation.Builder() .setFrom(4) .setTo(5) .setRelation(new AncestorRelation.IsAncestorOf()), new Synapse.Builder() .setSynapseId(5) .setNeuron(inCEnd) .setWeight(3.0) ); Neuron.init(mEnd, 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inABegin) .setWeight(3.0), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(new AncestorRelation.IsAncestorOf()), new Synapse.Builder() .setSynapseId(1) .setNeuron(inAEnd) .setWeight(3.0), new Relation.Builder() .setFrom(1) .setTo(2) .setRelation(new PositionRelation.Equals(0, 0)), new Synapse.Builder() .setSynapseId(2) .setNeuron(inBBegin) .setWeight(4.0), new Relation.Builder() .setFrom(2) .setTo(3) .setRelation(new AncestorRelation.IsAncestorOf()), new Synapse.Builder() .setSynapseId(3) .setNeuron(inBEnd) .setWeight(4.0), new Relation.Builder() .setFrom(3) .setTo(4) .setRelation(new PositionRelation.Equals(0, 0)), new Synapse.Builder() .setSynapseId(4) .setNeuron(inCBegin) .setWeight(5.0), new Relation.Builder() .setFrom(4) .setTo(5) .setRelation(new AncestorRelation.IsAncestorOf()), new Synapse.Builder() .setSynapseId(5) .setNeuron(inCEnd) .setWeight(5.0), new Relation.Builder() .setFrom(5) .setTo(OUTPUT) .setRelation(new PositionRelation.Equals(0, 0)) ); Neuron.init(nBegin, 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inBBegin) .setWeight(5.0), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(new PositionRelation.Equals(0, 0)), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(new AncestorRelation.IsAncestorOf()), new Synapse.Builder() .setSynapseId(1) .setNeuron(inBEnd) .setWeight(5.0), new Relation.Builder() .setFrom(1) .setTo(2) .setRelation(new PositionRelation.Equals(0, 0)), new Synapse.Builder() .setSynapseId(2) .setNeuron(inCBegin) .setWeight(4.0), new Relation.Builder() .setFrom(2) .setTo(3) .setRelation(new AncestorRelation.IsAncestorOf()), new Synapse.Builder() .setSynapseId(3) .setNeuron(inCEnd) .setWeight(4.0), new Relation.Builder() .setFrom(3) .setTo(4) .setRelation(new PositionRelation.Equals(0, 0)), new Synapse.Builder() .setSynapseId(4) .setNeuron(inDBegin) .setWeight(3.0), new Relation.Builder() .setFrom(4) .setTo(5) .setRelation(new AncestorRelation.IsAncestorOf()), new Synapse.Builder() .setSynapseId(5) .setNeuron(inDEnd) .setWeight(3.0) ); Neuron.init(nEnd, 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inBBegin) .setWeight(3.0), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(new AncestorRelation.IsAncestorOf()), new Synapse.Builder() .setSynapseId(1) .setNeuron(inBEnd) .setWeight(3.0), new Relation.Builder() .setFrom(1) .setTo(2) .setRelation(new PositionRelation.Equals(0, 0)), new Synapse.Builder() .setSynapseId(2) .setNeuron(inCBegin) .setWeight(4.0), new Relation.Builder() .setFrom(2) .setTo(3) .setRelation(new AncestorRelation.IsAncestorOf()), new Synapse.Builder() .setSynapseId(3) .setNeuron(inCEnd) .setWeight(4.0), new Relation.Builder() .setFrom(3) .setTo(4) .setRelation(new PositionRelation.Equals(0, 0)), new Synapse.Builder() .setSynapseId(4) .setNeuron(inDBegin) .setWeight(5.0), new Relation.Builder() .setFrom(4) .setTo(5) .setRelation(new AncestorRelation.IsAncestorOf()), new Synapse.Builder() .setSynapseId(5) .setNeuron(inDEnd) .setWeight(5.0), new Relation.Builder() .setFrom(5) .setTo(OUTPUT) .setRelation(new PositionRelation.Equals(0, 0)) ); Document doc = new Document(m, "abcd"); Activation actInABegin = inABegin.addInput(doc, 0, 1); Activation actInBBegin = inBBegin.addInput(doc, 1, 2); Activation actInCBegin = inCBegin.addInput(doc, 2, 3); Activation actInDBegin = inDBegin.addInput(doc, 3, 4); doc.process(); System.out.println(doc.activationsToString()); } } aika-java-1.4.0/src/test/java/network/aika/network/SoftMaxTest.java000066400000000000000000000072021352543012000251760ustar00rootroot00000000000000package network.aika.network; import network.aika.Document; import network.aika.Model; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.activation.Activation; import network.aika.neuron.activation.search.SearchNode; import network.aika.neuron.relation.Relation; import org.junit.Test; import static network.aika.ActivationFunction.LIMITED_RECTIFIED_LINEAR_UNIT; import static network.aika.neuron.INeuron.Type.*; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.relation.Relation.EQUALS; public class SoftMaxTest { @Test public void testSoftMax() { SearchNode.COMPUTE_SOFT_MAX = true; SearchNode.OPTIMIZE_SEARCH = false; double[][] ra = initModel(new double[][]{{1.0, 1.0}, {0.5, 5.0}}); SearchNode.OPTIMIZE_SEARCH = true; double[][] rb = initModel(new double[][]{{1.0, 1.0}, {0.5, 5.0}}); SearchNode.COMPUTE_SOFT_MAX = false; } public double[][] initModel(double[][] x) { Model m = new Model(); Neuron inhib = m.createNeuron("INHIBITORY", INHIBITORY, LIMITED_RECTIFIED_LINEAR_UNIT); Neuron.init(inhib, 0.0); Neuron[] inputs = new Neuron[]{ m.createNeuron("INPUT A", INPUT), m.createNeuron("INPUT B", INPUT) }; Neuron[][] output = new Neuron[2][2]; int j = 0; for (double[] y : x) { int i = 0; for (double a : y) { Neuron n = Neuron.init(m.createNeuron(j + "-" + i, EXCITATORY), a, new Synapse.Builder() .setSynapseId(0) .setNeuron(inputs[j]) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(inhib) .setWeight(-100.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); output[j][i] = n; int inhibSynId = inhib.getNewSynapseId(); Neuron.init(inhib, new Synapse.Builder() .setSynapseId(inhibSynId) .setNeuron(n) .setWeight(1.0), new Relation.Builder() .setFrom(inhibSynId) .setTo(OUTPUT) .setRelation(EQUALS) ); i++; } j++; } Document doc = new Document(m , "A B "); inputs[0].addInput(doc, 0, 1); inputs[1].addInput(doc, 2, 3); doc.process(); System.out.println(doc.activationsToString()); System.out.println(); double[][] results = new double[2][2]; for(j = 0; j < 2; j++) { for(int i = 0; i < 2; i++) { Neuron n = output[j][i]; Activation act = j == 0 ? n.getActivation(doc, 0, 1, false) : n.getActivation(doc, 2, 3, false); } } System.out.println(); System.out.println(); return results; } } aika-java-1.4.0/src/test/java/network/aika/network/SuspensionTest.java000066400000000000000000000152731352543012000257720ustar00rootroot00000000000000/* * 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.network; import network.aika.Document; import network.aika.Model; import network.aika.Provider; import network.aika.SuspensionHook; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.INeuron; import network.aika.neuron.relation.Relation; import org.junit.Assert; import org.junit.Test; import java.util.Map; import java.util.TreeMap; import java.util.concurrent.atomic.AtomicInteger; import java.util.stream.Collectors; import static network.aika.neuron.INeuron.Type.EXCITATORY; import static network.aika.neuron.INeuron.Type.INPUT; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.relation.Relation.*; /** * * @author Lukas Molzberger */ public class SuspensionTest { @Test public void testSuspendInputNeuron() { Model m = new Model(new DummySuspensionHook(), 1); Neuron n = m.createNeuron("A", INPUT); n.get().getInputNode().suspend(Provider.SuspensionMode.SAVE); n.suspend(Provider.SuspensionMode.SAVE); int id = n.getId(); // Reactivate n = m.lookupNeuron(id); Document doc = new Document(m, "Bla"); n.addInput(doc, 0, 1); } @Test public void testSuspendAndNeuron() { Model m = new Model(new DummySuspensionHook(), 1); Neuron inA = m.createNeuron("A", INPUT); Neuron inB = m.createNeuron("B", INPUT); int idA = inA.getId(); int idB = inB.getId(); Neuron nC = Neuron.init(m.createNeuron("C", EXCITATORY), 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(10.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(END_TO_BEGIN_EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(BEGIN_EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(END_EQUALS) ); Neuron outD = Neuron.init(m.createNeuron("D", EXCITATORY), 6.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(nC) .setWeight(10.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); m.suspendAll(Provider.SuspensionMode.SAVE); Assert.assertTrue(outD.isSuspended()); // Reactivate Document doc = new Document(m, "Bla"); inA = m.lookupNeuron(idA); inA.addInput(doc, 0, 1); inB = m.lookupNeuron(idB); inB.addInput(doc, 1, 2); doc.process(); System.out.println(doc.activationsToString()); Assert.assertFalse(outD.getActivations(doc, true).collect(Collectors.toList()).isEmpty()); } public static class DummySuspensionHook implements SuspensionHook { public AtomicInteger currentId = new AtomicInteger(0); Map storage = new TreeMap<>(); @Override public int getNewId() { return currentId.addAndGet(1); } @Override public void store(int id, byte[] data) { storage.put(id, data); } @Override public byte[] retrieve(int id) { return storage.get(id); } @Override public Iterable getAllNodeIds() { return storage.keySet(); } } @Test public void testSuspendNegativeNeuron() { Model m = new Model(new DummySuspensionHook(), 1); Neuron inA = m.createNeuron("A", INPUT); Neuron inB = m.createNeuron("B", INPUT); int idA = inA.getId(); int idB = inB.getId(); Neuron nC = Neuron.init(m.createNeuron("C", EXCITATORY), 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inA) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(-100.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(EQUALS) ); Neuron outD = Neuron.init(m.createNeuron("D", EXCITATORY), 6.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(nC) .setWeight(10.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); m.suspendAll(Provider.SuspensionMode.SAVE); Assert.assertTrue(outD.isSuspended()); // Reactivate Document doc = new Document(m, "Bla"); inA = m.lookupNeuron(idA); inA.addInput(doc, 0, 1); inB = m.lookupNeuron(idB); Assert.assertEquals(2, nC.get().getInputSynapses().size()); } } aika-java-1.4.0/src/test/java/network/aika/network/WeakConflictTest.java000066400000000000000000000043671352543012000261770ustar00rootroot00000000000000package network.aika.network; import network.aika.ActivationFunction; import network.aika.Document; import network.aika.Model; import network.aika.neuron.INeuron; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.relation.Relation; import org.junit.Assert; import org.junit.Ignore; import org.junit.Test; import static network.aika.ActivationFunction.RECTIFIED_LINEAR_UNIT; import static network.aika.neuron.INeuron.Type.EXCITATORY; import static network.aika.neuron.INeuron.Type.INPUT; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.relation.Relation.EQUALS; public class WeakConflictTest { @Ignore @Test public void testWeakConflict() { Model m = new Model(); Neuron in = m.createNeuron("IN", INPUT); Neuron na = m.createNeuron("A", EXCITATORY, RECTIFIED_LINEAR_UNIT); Neuron nb = m.createNeuron("B", EXCITATORY, RECTIFIED_LINEAR_UNIT); Neuron.init(na, 10.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(in) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(nb) .setWeight(-2.0) .setRecurrent(true), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(nb, 10.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(in) .setWeight(10.0), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Document doc = new Document(m,"X "); in.addInput(doc, 0, 1); doc.process(); System.out.println(doc.activationsToString()); Assert.assertEquals(1, na.getActivations(doc, true).count()); } } aika-java-1.4.0/src/test/java/network/aika/network/WeakInputProcessingTest.java000066400000000000000000000173601352543012000275670ustar00rootroot00000000000000/* * 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.network; import network.aika.Document; import network.aika.Model; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.activation.Activation; import network.aika.neuron.INeuron; import network.aika.neuron.relation.Relation; import org.junit.Assert; import org.junit.Test; import static network.aika.neuron.INeuron.Type.*; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.relation.Relation.EQUALS; import static network.aika.neuron.relation.Relation.OVERLAPS; /** * * @author Lukas Molzberger */ public class WeakInputProcessingTest { @Test public void testWeakInputProcessing() { Model m = new Model(); Neuron strongInput = m.createNeuron("Strong Input", INPUT); Neuron weakInputA = m.createNeuron("Weak Input A", INPUT); Neuron weakInputB = m.createNeuron("Weak Input B", INPUT); Neuron weakInputC = m.createNeuron("Weak Input C", INPUT); Neuron suppr = m.createNeuron("suppr", INHIBITORY); Neuron patternA = Neuron.init( m.createNeuron("Pattern A", EXCITATORY), 5.4, new Synapse.Builder() .setSynapseId(0) .setNeuron(strongInput) .setWeight(50.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(weakInputA) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(2) .setNeuron(suppr) .setWeight(-60.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(0) .setRelation(OVERLAPS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron patternB = Neuron.init( m.createNeuron("Pattern B", EXCITATORY), 5.4, new Synapse.Builder() .setSynapseId(0) .setNeuron(strongInput) .setWeight(50.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(weakInputB) .setWeight(1.5) .setRecurrent(false), new Synapse.Builder() .setSynapseId(2) .setNeuron(suppr) .setWeight(-60.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron patternC = Neuron.init( m.createNeuron("Pattern C", EXCITATORY), 5.4, new Synapse.Builder() .setSynapseId(0) .setNeuron(strongInput) .setWeight(50.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(weakInputC) .setWeight(0.5) .setRecurrent(false), new Synapse.Builder() .setSynapseId(2) .setNeuron(suppr) .setWeight(-60.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(suppr, 0.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(patternA) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(patternB) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(2) .setNeuron(patternC) .setWeight(10.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(EQUALS) ); Document doc = new Document(m, "a "); strongInput.addInput(doc,0,1); weakInputB.addInput(doc, 0, 1); doc.process(); System.out.println(doc.activationsToString()); Activation act = patternA.getActivation(doc, 0, 1, false); Assert.assertTrue(act.getValue() < 0.5); act = patternB.getActivation(doc, 0, 1, false); Assert.assertTrue(act.getValue() > 0.5); act = patternC.getActivation(doc, 0, 1, false); Assert.assertTrue(act.getValue() < 0.5); } } aika-java-1.4.0/src/test/java/network/aika/network/WeightsTest.java000066400000000000000000000177001352543012000252330ustar00rootroot00000000000000/* * 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.network; import network.aika.Document; import network.aika.Model; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.INeuron; import network.aika.neuron.relation.Relation; import org.junit.Test; import static network.aika.neuron.INeuron.Type.*; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.relation.Relation.CONTAINED_IN; import static network.aika.neuron.relation.Relation.EQUALS; /** * * @author Lukas Molzberger */ public class WeightsTest { Neuron inAA; Neuron inBA; Neuron inCA; Neuron inAB; Neuron inBB; Neuron inCB; Neuron pDA; Neuron pDB; @Test public void testAndWithMultipleIO() { Model m = new Model(); Neuron pSuppr = m.createNeuron("SUPPR", INHIBITORY); inAA = m.createNeuron("AA", INPUT); inBA = m.createNeuron("BA", INPUT); inCA = m.createNeuron("CA", INPUT); Neuron pOrA = m.createNeuron("pOrA", INHIBITORY); Neuron.init(pOrA, 0.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inAA) .setWeight(3.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inBA) .setWeight(4.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS) ); pDA = m.createNeuron("DA", EXCITATORY); Neuron.init(pDA, 1.401, new Synapse.Builder() .setSynapseId(0) .setNeuron(pOrA) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inCA) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(2) .setNeuron(pSuppr) .setWeight(-2.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(0) .setRelation(CONTAINED_IN), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS) ); inAB = m.createNeuron("AB", INPUT); inBB = m.createNeuron("BB", INPUT); inCB = m.createNeuron("CB", INPUT); Neuron pOrB = m.createNeuron("pOrB", INHIBITORY); Neuron.init(pOrB, 0.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inAB) .setWeight(2.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inBB) .setWeight(5.0) .setRecurrent(false), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS) ); pDB = m.createNeuron("DB", EXCITATORY); Neuron.init(pDB, 0.401, new Synapse.Builder() .setSynapseId(0) .setNeuron(pOrB) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inCB) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(2) .setNeuron(pSuppr) .setWeight(-2.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(0) .setRelation(CONTAINED_IN), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(pSuppr, 0.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(pDA) .setWeight(1.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(pDB) .setWeight(1.0) .setRecurrent(false), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS) ); testVariant(m, 9); // 17 for (int i = 0; i < 32; i++) { System.out.println("Variant:" + i); testVariant(m, i); } } private void testVariant(Model m, int i) { Document doc = new Document(m, "aaaaaaaaaa", 0); if(getBit(i, 0)) { inAA.addInput(doc, 0, 6); } if(getBit(i, 1)) { inBA.addInput(doc, 0, 6); } if(getBit(i, 2)) { inCA.addInput(doc, 0, 6); } if(getBit(i, 3)) { inAB.addInput(doc, 0, 6); } if(getBit(i, 4)) { inBB.addInput(doc, 0, 6); } inCB.addInput(doc, 0, 6); doc.process(); doc.clearActivations(); } private boolean getBit(int i, int pos) { return ((i >> pos) & 1) > 0; } } aika-java-1.4.0/src/test/java/network/aika/nlp/000077500000000000000000000000001352543012000212115ustar00rootroot00000000000000aika-java-1.4.0/src/test/java/network/aika/nlp/ContextFreeGrammarTest.java000066400000000000000000000167271352543012000264660ustar00rootroot00000000000000package network.aika.nlp; //http://ccl.pku.edu.cn/doubtfire/NLP/Parsing/Introduction/Grammars%20and%20Parsing.htm import network.aika.ActivationFunction; import network.aika.Document; import network.aika.Model; import network.aika.neuron.INeuron; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.relation.PositionRelation; import network.aika.neuron.relation.Relation; import org.junit.Assert; import org.junit.Before; import org.junit.Test; import java.util.ArrayList; import java.util.List; import java.util.Map; import java.util.TreeMap; import static network.aika.ActivationFunction.LIMITED_RECTIFIED_LINEAR_UNIT; import static network.aika.ActivationFunction.RECTIFIED_HYPERBOLIC_TANGENT; import static network.aika.neuron.INeuron.Type.*; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.relation.Relation.*; public class ContextFreeGrammarTest { Model m; Neuron I; Neuron S; Neuron NP; Neuron VP; Neuron NP_ART_ADJ_N; Neuron NP_ART_N; Neuron NP_ADJ_N; Neuron VP_AUX_V_NP; Neuron VP_V_NP; Neuron ART; Neuron N; Neuron ADJ; Neuron V; Neuron AUX; Map dictionary = new TreeMap<>(); @Before public void init() { m = new Model(); I = m.createNeuron("Inhibitory", INHIBITORY, LIMITED_RECTIFIED_LINEAR_UNIT); S = m.createNeuron("Sentence", EXCITATORY); NP = m.createNeuron("Noun Phrase", INHIBITORY, LIMITED_RECTIFIED_LINEAR_UNIT); VP = m.createNeuron("Verb Phrase", INHIBITORY, LIMITED_RECTIFIED_LINEAR_UNIT); NP_ART_ADJ_N = m.createNeuron("NP <- ART ADJ N", EXCITATORY); NP_ART_N = m.createNeuron("NP <- ART N", EXCITATORY); NP_ADJ_N = m.createNeuron("NP <- ADJ N", EXCITATORY); VP_AUX_V_NP = m.createNeuron("VP <- AUX V NP", EXCITATORY); VP_V_NP = m.createNeuron("VP <- V NP", EXCITATORY); ART = m.createNeuron("Article", INHIBITORY, RECTIFIED_HYPERBOLIC_TANGENT); N = m.createNeuron("Noun", INHIBITORY, RECTIFIED_HYPERBOLIC_TANGENT); ADJ = m.createNeuron("Adjective", INHIBITORY, RECTIFIED_HYPERBOLIC_TANGENT); V = m.createNeuron("Verb", INHIBITORY, RECTIFIED_HYPERBOLIC_TANGENT); AUX = m.createNeuron("Auxiliary", INHIBITORY, RECTIFIED_HYPERBOLIC_TANGENT); for(Neuron n: new Neuron[] {I, NP, VP}) { Neuron.init(n, 0.0); } initOrNeuron(I, NP, VP, ART, N, ADJ, V, AUX); initOrNeuron(NP, NP_ART_ADJ_N, NP_ART_N, NP_ADJ_N); initOrNeuron(VP, VP_AUX_V_NP, VP_V_NP); initAndNeuron(S, 6.0, NP, VP); initAndNeuron(NP_ART_ADJ_N, 9.0, ART, ADJ, N); initAndNeuron(NP_ART_N, 6.0, ART, N); initAndNeuron(NP_ADJ_N, 6.0, ADJ, N); initAndNeuron(VP_AUX_V_NP, 9.0, AUX, V, NP); initAndNeuron(VP_V_NP, 6.0, V, NP); initWord("the", ART); initWord("large", ADJ); initWord("can", AUX, N, V); initWord("hold", N, V); initWord("water", N, V); } private void initWord(String word, Neuron... wordTypes) { Neuron wordN = m.createNeuron("W-" + word, INPUT); dictionary.put(word, wordN); for (Neuron wordType : wordTypes) { Neuron entity = m.createNeuron("E-" + word + "(" + wordType.getLabel() + ")", EXCITATORY); Neuron.init(entity, 2.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(wordN) .setWeight(10) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(I) .setWeight(-50.0) .setRecurrent(true), new Relation.Builder() .setFrom(0) .setTo(1) .setRelation(OVERLAPS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); int wtSynId = wordType.getNewSynapseId(); Neuron.init(wordType, new Synapse.Builder() .setSynapseId(wtSynId) .setNeuron(entity) .setWeight(3.0), new Relation.Builder() .setFrom(wtSynId) .setTo(OUTPUT) .setRelation(EQUALS) ); } } private void initOrNeuron(Neuron orN, Neuron... inputs) { for (Neuron n : inputs) { int synId = orN.getNewSynapseId(); Neuron.init(orN, new Synapse.Builder() .setSynapseId(synId) .setNeuron(n) .setWeight(1.0), new Relation.Builder() .setFrom(synId) .setTo(OUTPUT) .setRelation(EQUALS) ); } } private void initAndNeuron(Neuron andN, double weight, Neuron... inputs) { List in = new ArrayList<>(); for(int i = 0; i < inputs.length; i++) { boolean begin = i == 0; boolean end = i + 1 == inputs.length; in.add(new Synapse.Builder() .setSynapseId(i) .setNeuron(inputs[i]) .setWeight(10.0) .setIdentity(true) ); if(begin) { in.add( new Relation.Builder() .setFrom(i) .setTo(OUTPUT) .setRelation(BEGIN_EQUALS) ); } if(end) { in.add( new Relation.Builder() .setFrom(i) .setTo(OUTPUT) .setRelation(END_EQUALS) ); } if(!begin) { in.add(new Relation.Builder() .setFrom(i) .setTo(i - 1) .setRelation(BEGIN_TO_END_EQUALS) ); } } in.add(new Synapse.Builder() .setSynapseId(inputs.length) .setNeuron(I) .setWeight(-100.0) .setRecurrent(true) ); in.add(new Relation.Builder() .setFrom(inputs.length) .setTo(Synapse.OUTPUT) .setRelation(OVERLAPS) ); Neuron.init(andN, weight, in.toArray(new Neuron.Builder[in.size()])); } public Document parse(String txt) { Document doc = new Document(m, txt); int i = 0; for(String word: txt.split(" ")) { int j = i + word.length(); Neuron wn = dictionary.get(word); if(wn != null) { wn.addInput(doc, i, j + 1); } i = j + 1; } doc.process(); return doc; } @Test public void parseSentence() { Document doc = parse("the large can can hold the water "); System.out.println(doc.activationsToString()); Assert.assertNotNull(S.getActivation(doc, 0, 33, true)); } }aika-java-1.4.0/src/test/java/network/aika/nlp/GenerateTextTest.java000066400000000000000000000260331352543012000253170ustar00rootroot00000000000000/* * 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.nlp; import network.aika.ActivationFunction; import network.aika.Document; import network.aika.Model; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.INeuron; import network.aika.neuron.relation.Relation; import org.junit.Assert; import org.junit.Test; import static network.aika.ActivationFunction.LIMITED_RECTIFIED_LINEAR_UNIT; import static network.aika.neuron.INeuron.Type.*; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.relation.AncestorRelation.IS_DESCENDANT_OF; import static network.aika.neuron.relation.Relation.*; /** * * @author Lukas Molzberger */ public class GenerateTextTest { @Test public void testTwoWords() { Model m = new Model(); // The suppressing neuron should prevent that two neurons output overlapping text. Neuron inhib = m.createNeuron("INHIB", INHIBITORY, LIMITED_RECTIFIED_LINEAR_UNIT); // Some input neurons Neuron inA = m.createNeuron("IN-A", INPUT); Neuron inB = m.createNeuron("IN-B", INPUT); Neuron inOut = m.createNeuron("IN-OUT", INPUT); Neuron phraseA = m.createNeuron("PHRASE-A", EXCITATORY); Neuron phraseB = m.createNeuron("PHRASE-B", EXCITATORY); Neuron outputFrame = m.createNeuron("OUTPUT-FRAME", EXCITATORY); // Four test words that should be used to generate a text. Neuron outA = m.createNeuron("OUT-A", EXCITATORY, "aaaaaaa "); Neuron outB = m.createNeuron("OUT-B", EXCITATORY, "bbb "); Neuron outC = m.createNeuron("OUT-C", EXCITATORY, "ccccccccc "); Neuron outD = m.createNeuron("OUT-D", EXCITATORY, "ddddd "); // Word aaaaaaa is only added to the resulting text if input a is active and this neuron // is not suppressed by another neuron. Output aaaaaaa may start a text. Neuron.init(phraseA, 4.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(outputFrame) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(inA) .setWeight(10.0), new Synapse.Builder() .setSynapseId(2) .setNeuron(inhib) .setWeight(-100.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(ANY), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(phraseB, 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(outputFrame) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(inB) .setWeight(10.0), new Synapse.Builder() .setSynapseId(2) .setNeuron(inhib) .setWeight(-100.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(ANY), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(outputFrame, 4.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inOut) .setWeight(10.0), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(END_TO_BEGIN_EQUALS) ); Neuron.init(outA, 4.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(phraseA) .setWeight(10.0) .setIdentity(true), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(BEGIN_EQUALS) ); // OutC is only activated if the previous word was outA. Neuron.init(outC, 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(phraseA) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(outA) .setWeight(10.0), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(IS_DESCENDANT_OF), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(END_EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(END_TO_BEGIN_EQUALS) ); // Word bbb is only added to the resulting text if input b is active and this neuron // is not suppressed by another neuron. Output bbb may start a text. // Neuron outB has a slightly higher weight than outA. Neuron.init(outB, 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(phraseB) .setWeight(10.0) .setIdentity(true), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(BEGIN_EQUALS) ); // OutD is only activated if the previous word was outB. Neuron.init(outD, 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(phraseB) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(outB) .setWeight(10.0), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(IS_DESCENDANT_OF), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(END_EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(END_TO_BEGIN_EQUALS) ); // All outputs suppress each other. Neuron.init(inhib, 0.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(phraseA) .setWeight(1.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(phraseB) .setWeight(1.0), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS) ); Document doc = new Document(m, "Bla"); // Add both input a and input b. inA.addInput(doc, 0, 3); inB.addInput(doc, 0, 3); inOut.addInput(doc, 0, 3); // Search for the best interpretation. doc.process(); System.out.println(doc.activationsToString()); System.out.println(); String outputText = doc.generateOutputText(); // Generate the output text. System.out.println(outputText); Assert.assertEquals("bbb ddddd ", outputText); System.out.println(doc.activationsToString()); } @Test public void intermediateOutputNeuron() { Model m = new Model(); Neuron in = m.createNeuron("IN", INPUT); Neuron intermediate = m.createNeuron("INTERMEDIATE", EXCITATORY); Neuron outA = m.createNeuron("OUT A", EXCITATORY, "aaaaaaa "); Neuron outB = m.createNeuron("OUT B", EXCITATORY, "bbb "); Neuron.init(intermediate, 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(in) .setWeight(10.0) .setIdentity(true), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(END_TO_BEGIN_EQUALS) ); Neuron.init(outA, 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(intermediate) .setWeight(10.0), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init(outB, 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(intermediate) .setWeight(10.0), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(END_TO_BEGIN_EQUALS) ); Document doc = new Document(m, "in "); in.addInput(doc, 0, 3); doc.process(); String outputText = doc.generateOutputText(); System.out.println(doc.activationsToString()); Assert.assertEquals("aaaaaaa bbb ", outputText); } } aika-java-1.4.0/src/test/java/network/aika/nlp/NamedEntityRecognitionTest.java000066400000000000000000000260441352543012000273440ustar00rootroot00000000000000/* * 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.nlp; import network.aika.ActivationFunction; import network.aika.Document; import network.aika.Model; import network.aika.neuron.Neuron; import network.aika.neuron.Synapse; import network.aika.neuron.activation.Activation; import network.aika.neuron.INeuron; import network.aika.neuron.relation.Relation; import org.junit.Assert; import org.junit.Test; import java.util.HashMap; import java.util.stream.Collectors; import static network.aika.ActivationFunction.LIMITED_RECTIFIED_LINEAR_UNIT; import static network.aika.ActivationFunction.RECTIFIED_HYPERBOLIC_TANGENT; import static network.aika.neuron.INeuron.Type.*; import static network.aika.neuron.Synapse.OUTPUT; import static network.aika.neuron.relation.Relation.*; /** * * @author Lukas Molzberger */ public class NamedEntityRecognitionTest { // This test demonstrates the recognition of the words 'jackson cook' as forename and surname // even though each individual word would have been recognized as city or profession // respectively. @Test public void testNamedEntityRecognition() { Model m = new Model(null, 1); // number of threads Neuron forenameCategory = m.createNeuron("C-forename", INHIBITORY, LIMITED_RECTIFIED_LINEAR_UNIT); Neuron surnameCategory = m.createNeuron("C-surname", INHIBITORY, LIMITED_RECTIFIED_LINEAR_UNIT); Neuron inhibitingN = m.createNeuron("INHIB", INHIBITORY, LIMITED_RECTIFIED_LINEAR_UNIT); // The word input neurons which do not yet possess a relational id. HashMap inputNeurons = new HashMap<>(); String[] words = new String[] { "mr.", "jackson", "cook", "was", "born", "in", "new", "york" }; for(String word: words) { Neuron in = m.createNeuron("W-" + word, INPUT); inputNeurons.put(word, in); } // The entity neurons represent the concrete meanings of the input words. // The helper function 'initAndNeuron' computes the required bias for a // conjunction of the inputs. Neuron cookSurnameEntity = Neuron.init( m.createNeuron("E-cook (surname)", EXCITATORY), 6.0, // adjusts the bias new Synapse.Builder() // Requires the word to be recognized .setSynapseId(0) .setNeuron(inputNeurons.get("cook")) .setWeight(10.0) // This input requires the input activation to have an // activation value of at least 0.9 .setRecurrent(false), new Synapse.Builder() // The previous word needs to be a forename .setSynapseId(1) .setNeuron(forenameCategory) .setWeight(10.0) .setRecurrent(true), // this input is a positive feedback loop // This neuron may be suppressed by the E-cook (profession) neuron, but there is no // self suppression taking place even though 'E-cook (surname)' is also contained // in the inhibitingN. new Synapse.Builder() .setSynapseId(2) .setNeuron(inhibitingN) .setWeight(-100.0) .setRecurrent(true), // this input is a negative feedback loop new Relation.Builder() // references the previous word .setFrom(1) .setTo(0) .setRelation(END_TO_BEGIN_EQUALS), new Relation.Builder() .setFrom(2) .setTo(0) .setRelation(OVERLAPS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron cookProfessionEntity = Neuron.init( m.createNeuron("E-cook (profession)", EXCITATORY), 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inputNeurons.get("cook")) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(inhibitingN) .setWeight(-100.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(OVERLAPS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron jacksonForenameEntity = Neuron.init( m.createNeuron("E-jackson (forename)", EXCITATORY), 6.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inputNeurons.get("jackson")) .setWeight(10.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(surnameCategory) .setWeight(10.0) .setRecurrent(true), new Synapse.Builder() .setSynapseId(2) .setNeuron(inhibitingN) .setWeight(-100.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(BEGIN_TO_END_EQUALS), new Relation.Builder() .setFrom(2) .setTo(0) .setRelation(OVERLAPS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron jacksonCityEntity = Neuron.init( m.createNeuron("E-jackson (city)", EXCITATORY), 5.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(inputNeurons.get("jackson")) .setWeight(10.0) .setRecurrent(false), new Synapse.Builder() .setSynapseId(1) .setNeuron(inhibitingN) .setWeight(-100.0) .setRecurrent(true), new Relation.Builder() .setFrom(1) .setTo(0) .setRelation(OVERLAPS), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init( forenameCategory, 0.0, new Synapse.Builder() // In this example there is only one forename considered. .setSynapseId(0) .setNeuron(jacksonForenameEntity) .setWeight(1.0), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init( surnameCategory, 0.0, new Synapse.Builder() .setSynapseId(0) .setNeuron(cookSurnameEntity) .setWeight(1.0), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS) ); Neuron.init( inhibitingN, 0.0, new Synapse.Builder().setNeuron(cookProfessionEntity) .setSynapseId(0) .setWeight(1.0), new Synapse.Builder() .setSynapseId(1) .setNeuron(cookSurnameEntity) .setWeight(1.0), new Synapse.Builder() .setSynapseId(2) .setNeuron(jacksonCityEntity) .setWeight(1.0), new Synapse.Builder() .setSynapseId(3) .setNeuron(jacksonForenameEntity) .setWeight(1.0), new Relation.Builder() .setFrom(0) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(1) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(2) .setTo(OUTPUT) .setRelation(EQUALS), new Relation.Builder() .setFrom(3) .setTo(OUTPUT) .setRelation(EQUALS) ); // Now that the model is complete, start processing an actual text. Document doc = new Document(m, "mr. jackson cook was born in new york "); int i = 0; for(String w: doc.getContent().split(" ")) { int j = i + w.length(); // Feed the individual words as inputs into the network. inputNeurons.get(w).addInput(doc, i, j + 1); i = j + 1; } // Search for the best interpretation of this text. doc.process(); System.out.println(doc.activationsToString()); System.out.println(); System.out.println("Activations of the Surname Category:"); for(Activation act: surnameCategory.getActivations(doc, true).collect(Collectors.toList())) { System.out.print(act.slotsToString() + " "); System.out.print(act.getLabel() + " "); System.out.print(act.getValue()); } Assert.assertFalse(jacksonForenameEntity.getActivations(doc, true).collect(Collectors.toList()).isEmpty()); Assert.assertFalse(cookSurnameEntity.getActivations(doc, true).collect(Collectors.toList()).isEmpty()); doc.clearActivations(); } }