/*
 * 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.activation.SearchNode;
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.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");
        Neuron wZimmermann = m.createNeuron("W-Zimmermann");

        Neuron eJoergForename = m.createNeuron("E-Joerg (Forename)");
        Neuron eJoergSurname = m.createNeuron("E-Joerg (Surname)");
        Neuron eZimmermannSurname = m.createNeuron("E-Zimmermann (Surname)");
        Neuron eZimmermannCompany = m.createNeuron("E-Zimmermann (Company)");

        Neuron suppr = m.createNeuron("SUPPR");

        Neuron.init(eJoergSurname, 5.0, INeuron.Type.EXCITATORY,
                new Synapse.Builder()
                        .setSynapseId(0)
                        .setNeuron(wJoerg)
                        .setWeight(10.0)
                        .setBias(-10.0),
                new Synapse.Builder()
                        .setSynapseId(1)
                        .setNeuron(suppr)
                        .setWeight(-60.0)
                        .setBias(0.0)
                        .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, INeuron.Type.EXCITATORY,
                new Synapse.Builder()
                        .setSynapseId(0)
                        .setNeuron(wZimmermann)
                        .setWeight(10.0)
                        .setBias(-10.0),
                new Synapse.Builder()
                        .setSynapseId(1)
                        .setNeuron(suppr)
                        .setWeight(-60.0)
                        .setBias(0.0)
                        .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, INeuron.Type.EXCITATORY,
                new Synapse.Builder()
                        .setSynapseId(0)
                        .setNeuron(wJoerg)
                        .setWeight(10.0)
                        .setBias(-10.0),
                new Synapse.Builder()
                        .setSynapseId(1)
                        .setNeuron(eZimmermannSurname)
                        .setWeight(10.0)
                        .setBias(-10.0)
                        .setRecurrent(true),
                new Synapse.Builder()
                        .setSynapseId(2)
                        .setNeuron(suppr)
                        .setWeight(-60.0)
                        .setBias(0.0)
                        .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, INeuron.Type.EXCITATORY,
                new Synapse.Builder()
                        .setSynapseId(0)
                        .setNeuron(wZimmermann)
                        .setWeight(10.0)
                        .setBias(-10.0),
                new Synapse.Builder()
                        .setSynapseId(1)
                        .setNeuron(eJoergForename)
                        .setWeight(10.0)
                        .setBias(-10.0)
                        .setRecurrent(true),
                new Synapse.Builder()
                        .setSynapseId(2)
                        .setNeuron(suppr)
                        .setWeight(-60.0)
                        .setBias(0.0)
                        .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, INeuron.Type.INHIBITORY,
                new Synapse.Builder()
                        .setSynapseId(0)
                        .setNeuron(eJoergForename)
                        .setWeight(10.0)
                        .setBias(0.0),
                new Synapse.Builder()
                        .setSynapseId(1)
                        .setNeuron(eJoergSurname)
                        .setWeight(10.0)
                        .setBias(0.0),
                new Synapse.Builder()
                        .setSynapseId(2)
                        .setNeuron(eZimmermannCompany)
                        .setWeight(10.0)
                        .setBias(0.0),
                new Synapse.Builder()
                        .setSynapseId(3)
                        .setNeuron(eZimmermannSurname)
                        .setWeight(10.0)
                        .setBias(0.0),
                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 = m.createDocument("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 = m.createDocument("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");
        Neuron inB = m.createNeuron("IN B");

        Neuron inhib = m.createNeuron("INHIB");

        Neuron nF = m.createNeuron("F");

        Neuron nC = Neuron.init(m.createNeuron("C"), 6.0, INeuron.Type.EXCITATORY,
                new Synapse.Builder()
                        .setSynapseId(0)
                        .setNeuron(inA)
                        .setWeight(10.0)
                        .setBias(-10.0),
                new Synapse.Builder()
                        .setSynapseId(1)
                        .setNeuron(inhib)
                        .setWeight(-100.0)
                        .setBias(0.0)
                        .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"), 7.0, INeuron.Type.EXCITATORY,
                new Synapse.Builder()
                        .setSynapseId(0)
                        .setNeuron(inA)
                        .setWeight(10.0)
                        .setBias(-10.0),
                new Synapse.Builder()
                        .setSynapseId(1)
                        .setNeuron(nF)
                        .setWeight(2.0)
                        .setBias(-2.0),
                new Synapse.Builder()
                        .setSynapseId(2)
                        .setNeuron(inhib)
                        .setWeight(-100.0)
                        .setBias(0.0)
                        .setRecurrent(true),
                new Relation.Builder()
                        .setFrom(1)
                        .setTo(0)
                        .setRelation(NONE),
                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"), 5.0, INeuron.Type.EXCITATORY,
                new Synapse.Builder()
                        .setSynapseId(0)
                        .setNeuron(inB)
                        .setWeight(10.0)
                        .setBias(-10.0),
                new Synapse.Builder()
                        .setSynapseId(1)
                        .setNeuron(inhib)
                        .setWeight(-100.0)
                        .setBias(0.0)
                        .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, INeuron.Type.EXCITATORY,
                new Synapse.Builder()
                        .setSynapseId(0)
                        .setNeuron(inB)
                        .setWeight(10.0)
                        .setBias(-10.0),
                new Synapse.Builder()
                        .setSynapseId(1)
                        .setNeuron(inhib)
                        .setWeight(-100.0)
                        .setBias(0.0)
                        .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, INeuron.Type.INHIBITORY,
                new Synapse.Builder()
                        .setSynapseId(0)
                        .setNeuron(nC)
                        .setWeight(10.0)
                        .setBias(0.0),
                new Synapse.Builder()
                        .setSynapseId(1)
                        .setNeuron(nD)
                        .setWeight(10.0)
                        .setBias(0.0),
                new Synapse.Builder()
                        .setSynapseId(2)
                        .setNeuron(nE)
                        .setWeight(10.0)
                        .setBias(0.0),
                new Synapse.Builder()
                        .setSynapseId(3)
                        .setNeuron(nF)
                        .setWeight(10.0)
                        .setBias(0.0),
                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 = m.createDocument("aaaa bbbb ");

        inA.addInput(doc, 0, 5);
        inB.addInput(doc, 5, 10);

        doc.process();

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

        Assert.assertFalse(nD.getActivations(doc, true).collect(Collectors.toList()).isEmpty());
    }


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

        Neuron inA = m.createNeuron("IN A");
        Neuron inB = m.createNeuron("IN B");

        Neuron inhib = m.createNeuron("INHIB");
        Neuron.init(inhib, 0.0, INeuron.Type.INHIBITORY);


        Neuron nE = m.createNeuron("E");
        Neuron nF = m.createNeuron("F");


        Neuron nC = Neuron.init(m.createNeuron("C"), 6.0, INeuron.Type.EXCITATORY,
                new Synapse.Builder()
                        .setSynapseId(0)
                        .setNeuron(inA)
                        .setWeight(10.0)
                        .setBias(-10.0),
                new Synapse.Builder()
                        .setSynapseId(1)
                        .setNeuron(inhib)
                        .setWeight(-100.0)
                        .setBias(0.0)
                        .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"), 5.0, INeuron.Type.EXCITATORY,
                new Synapse.Builder()
                        .setSynapseId(0)
                        .setNeuron(inA)
                        .setWeight(10.0)
                        .setBias(-10.0),
                new Synapse.Builder()
                        .setSynapseId(1)
                        .setNeuron(inhib)
                        .setWeight(-100.0)
                        .setBias(0.0)
                        .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)
                        .setBias(0.0),
                new Relation.Builder()
                        .setFrom(0)
                        .setTo(OUTPUT)
                        .setRelation(EQUALS)
        );

        Neuron.init(inhib,
                new Synapse.Builder()
                        .setSynapseId(1)
                        .setNeuron(nD)
                        .setWeight(10.0)
                        .setBias(0.0),
                new Relation.Builder()
                        .setFrom(1)
                        .setTo(OUTPUT)
                        .setRelation(EQUALS)
        );



        // Create the document even though the model is not yet complete

        Document doc = m.createDocument("aaaa bbbb ");

//        inA.addInput(doc, 0, 5);
//        inB.addInput(doc, 5, 10);

//        doc.process();

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


        // Complete the model

        Neuron.init(doc,
                nD,
                new Synapse.Builder()
                        .setSynapseId(2)
                        .setNeuron(nF)
                        .setWeight(2.0)
                        .setBias(0.0),
                new Relation.Builder()
                        .setFrom(2)
                        .setTo(0)
                        .setRelation(NONE)
        );

        Neuron.init(doc, nE, 5.0, INeuron.Type.EXCITATORY,
                new Synapse.Builder()
                        .setSynapseId(0)
                        .setNeuron(inB)
                        .setWeight(10.0)
                        .setBias(-10.0),
                new Synapse.Builder()
                        .setSynapseId(1)
                        .setNeuron(inhib)
                        .setWeight(-100.0)
                        .setBias(0.0)
                        .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, INeuron.Type.EXCITATORY,
                new Synapse.Builder()
                        .setSynapseId(0)
                        .setNeuron(inB)
                        .setWeight(10.0)
                        .setBias(-10.0),
                new Synapse.Builder()
                        .setSynapseId(1)
                        .setNeuron(inhib)
                        .setWeight(-100.0)
                        .setBias(0.0)
                        .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)
                        .setBias(0.0),
                new Relation.Builder()
                        .setFrom(2)
                        .setTo(OUTPUT)
                        .setRelation(EQUALS)
        );

        Neuron.init(
                doc,
                inhib,
                new Synapse.Builder()
                        .setSynapseId(3)
                        .setNeuron(nF)
                        .setWeight(10.0)
                        .setBias(0.0),
                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(true, true, true));

        Assert.assertFalse(nD.getActivations(doc, true).collect(Collectors.toList()).isEmpty());
    }


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

        Neuron in = m.createNeuron("IN");

        Neuron inhib = m.createNeuron("INHIB");
        Neuron out = m.createNeuron("OUT");


        Neuron.init(inhib, 0.0, ActivationFunction.LIMITED_RECTIFIED_LINEAR_UNIT, INeuron.Type.INHIBITORY,
                new Synapse.Builder()
                        .setSynapseId(0)
                        .setNeuron(out)
                        .setWeight(1.0)
                        .setBias(0.0),
                new Relation.Builder()
                        .setFrom(0)
                        .setTo(OUTPUT)
                        .setRelation(EQUALS)
        );

        Neuron.init(out, 5.0, ActivationFunction.RECTIFIED_HYPERBOLIC_TANGENT, INeuron.Type.EXCITATORY,
                new Synapse.Builder()
                        .setSynapseId(0)
                        .setNeuron(in)
                        .setWeight(20.0)
                        .setBias(-20.0),
                new Synapse.Builder()
                        .setSynapseId(1)
                        .setNeuron(in)
                        .setWeight(20.0)
                        .setBias(-20.0),
                new Synapse.Builder()
                        .setSynapseId(2)
                        .setNeuron(out)
                        .setWeight(-100.0)
                        .setBias(0.0)
                        .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)
        );


        Document doc = m.createDocument("aaaa");
        in.addInput(doc, 0, 1);
        in.addInput(doc, 1, 2);
        in.addInput(doc, 2, 3);
        in.addInput(doc, 3, 4);


        SearchNode.COMPUTE_SOFT_MAX = true;
        SearchNode.OPTIMIZE_SEARCH = false;

        doc.process();

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

        Assert.assertEquals(13, doc.searchNodeIdCounter);
    }
}
