/*
 * 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 org.apache.samoa.moa.clusterers;

import java.util.Collections;
import java.util.LinkedList;
import java.util.List;
import java.util.Random;

import org.apache.samoa.instances.Instance;
import org.apache.samoa.instances.Instances;
import org.apache.samoa.instances.InstancesHeader;
import org.apache.samoa.moa.cluster.Clustering;
import org.apache.samoa.moa.core.Measurement;
import org.apache.samoa.moa.core.ObjectRepository;
import org.apache.samoa.moa.core.StringUtils;
import org.apache.samoa.moa.options.AbstractOptionHandler;
import org.apache.samoa.moa.tasks.TaskMonitor;

import com.github.javacliparser.FlagOption;
import com.github.javacliparser.IntOption;

public abstract class AbstractClusterer extends AbstractOptionHandler
    implements Clusterer {

  @Override
  public String getPurposeString() {
    return "MOA Clusterer: " + getClass().getCanonicalName();
  }

  protected InstancesHeader modelContext;

  protected double trainingWeightSeenByModel = 0.0;

  protected int randomSeed = 1;

  protected IntOption randomSeedOption;

  public FlagOption evaluateMicroClusteringOption;

  protected Random clustererRandom;

  protected Clustering clustering;

  public AbstractClusterer() {
    if (isRandomizable()) {
      this.randomSeedOption = new IntOption("randomSeed", 'r',
          "Seed for random behaviour of the Clusterer.", 1);
    }

    if (implementsMicroClusterer()) {
      this.evaluateMicroClusteringOption =
          new FlagOption("evaluateMicroClustering", 'M',
              "Evaluate the underlying microclustering instead of the macro clustering");
    }
  }

  @Override
  public void prepareForUseImpl(TaskMonitor monitor,
      ObjectRepository repository) {
    if (this.randomSeedOption != null) {
      this.randomSeed = this.randomSeedOption.getValue();
    }
    if (!trainingHasStarted()) {
      resetLearning();
    }
    clustering = new Clustering();
  }

  public void setModelContext(InstancesHeader ih) {
    if ((ih != null) && (ih.classIndex() < 0)) {
      throw new IllegalArgumentException(
          "Context for a Clusterer must include a class to learn");
    }
    if (trainingHasStarted()
        && (this.modelContext != null)
        && ((ih == null) || !contextIsCompatible(this.modelContext, ih))) {
      throw new IllegalArgumentException(
          "New context is not compatible with existing model");
    }
    this.modelContext = ih;
  }

  public InstancesHeader getModelContext() {
    return this.modelContext;
  }

  public void setRandomSeed(int s) {
    this.randomSeed = s;
    if (this.randomSeedOption != null) {
      // keep option consistent
      this.randomSeedOption.setValue(s);
    }
  }

  public boolean trainingHasStarted() {
    return this.trainingWeightSeenByModel > 0.0;
  }

  public double trainingWeightSeenByModel() {
    return this.trainingWeightSeenByModel;
  }

  public void resetLearning() {
    this.trainingWeightSeenByModel = 0.0;
    if (isRandomizable()) {
      this.clustererRandom = new Random(this.randomSeed);
    }
    resetLearningImpl();
  }

  public void trainOnInstance(Instance inst) {
    if (inst.weight() > 0.0) {
      this.trainingWeightSeenByModel += inst.weight();
      trainOnInstanceImpl(inst);
    }
  }

  public Measurement[] getModelMeasurements() {
    List<Measurement> measurementList = new LinkedList<>();
    measurementList.add(new Measurement("model training instances",
        trainingWeightSeenByModel()));
    measurementList.add(new Measurement("model serialized size (bytes)",
        measureByteSize()));
    Measurement[] modelMeasurements = getModelMeasurementsImpl();
    if (modelMeasurements != null) {
      Collections.addAll(measurementList, modelMeasurements);
    }
    // add average of sub-model measurements
    Clusterer[] subModels = getSubClusterers();
    if ((subModels != null) && (subModels.length > 0)) {
      List<Measurement[]> subMeasurements = new LinkedList<>();
      for (Clusterer subModel : subModels) {
        if (subModel != null) {
          subMeasurements.add(subModel.getModelMeasurements());
        }
      }
      Measurement[] avgMeasurements = Measurement
          .averageMeasurements(subMeasurements
              .toArray(new Measurement[subMeasurements.size()][]));
      Collections.addAll(measurementList, avgMeasurements);
    }
    return measurementList.toArray(new Measurement[measurementList.size()]);
  }

  public void getDescription(StringBuilder out, int indent) {
    StringUtils.appendIndented(out, indent, "Model type: ");
    out.append(this.getClass().getName());
    StringUtils.appendNewline(out);
    Measurement.getMeasurementsDescription(getModelMeasurements(), out,
        indent);
    StringUtils.appendNewlineIndented(out, indent, "Model description:");
    StringUtils.appendNewline(out);
    if (trainingHasStarted()) {
      getModelDescription(out, indent);
    } else {
      StringUtils.appendIndented(out, indent,
          "Model has not been trained.");
    }
  }

  public Clusterer[] getSubClusterers() {
    return null;
  }

  @Override
  public Clusterer copy() {
    return (Clusterer) super.copy();
  }

  // public boolean correctlyClassifies(Instance inst) {
  // return Utils.maxIndex(getVotesForInstance(inst)) == (int) inst
  // .classValue();
  // }

  public String getClassNameString() {
    return InstancesHeader.getClassNameString(this.modelContext);
  }

  public String getClassLabelString(int classLabelIndex) {
    return InstancesHeader.getClassLabelString(this.modelContext,
        classLabelIndex);
  }

  public String getAttributeNameString(int attIndex) {
    return InstancesHeader.getAttributeNameString(this.modelContext,
        attIndex);
  }

  public String getNominalValueString(int attIndex, int valIndex) {
    return InstancesHeader.getNominalValueString(this.modelContext,
        attIndex, valIndex);
  }

  // originalContext notnull
  // newContext notnull
  public static boolean contextIsCompatible(InstancesHeader originalContext,
      InstancesHeader newContext) {
    // rule 1: num classes can increase but never decrease
    // rule 2: num attributes can increase but never decrease
    // rule 3: num nominal attribute values can increase but never decrease
    // rule 4: attribute types must stay in the same order (although class
    // can
    // move; is always skipped over)
    // attribute names are free to change, but should always still represent
    // the original attributes
    if (newContext.numClasses() < originalContext.numClasses()) {
      return false; // rule 1
    }
    if (newContext.numAttributes() < originalContext.numAttributes()) {
      return false; // rule 2
    }
    int oPos = 0;
    int nPos = 0;
    while (oPos < originalContext.numAttributes()) {
      if (oPos == originalContext.classIndex()) {
        oPos++;
        if (!(oPos < originalContext.numAttributes())) {
          break;
        }
      }
      if (nPos == newContext.classIndex()) {
        nPos++;
      }
      if (originalContext.attribute(oPos).isNominal()) {
        if (!newContext.attribute(nPos).isNominal()) {
          return false; // rule 4
        }
        if (newContext.attribute(nPos).numValues() < originalContext
            .attribute(oPos).numValues()) {
          return false; // rule 3
        }
      } else {
        assert (originalContext.attribute(oPos).isNumeric());
        if (!newContext.attribute(nPos).isNumeric()) {
          return false; // rule 4
        }
      }
      oPos++;
      nPos++;
    }
    return true; // all checks clear
  }

  // reason for ...Impl methods:
  // ease programmer burden by not requiring them to remember calls to super
  // in overridden methods & will produce compiler errors if not overridden

  public abstract void resetLearningImpl();

  public abstract void trainOnInstanceImpl(Instance inst);

  protected abstract Measurement[] getModelMeasurementsImpl();

  public abstract void getModelDescription(StringBuilder out, int indent);

  protected static int modelAttIndexToInstanceAttIndex(int index,
      Instance inst) {
    return inst.classIndex() > index ? index : index + 1;
  }

  protected static int modelAttIndexToInstanceAttIndex(int index,
      Instances insts) {
    return insts.classIndex() > index ? index : index + 1;
  }

  public boolean implementsMicroClusterer() {
    return false;
  }

  public boolean keepClassLabel() {
    return false;
  }

  public Clustering getMicroClusteringResult() {
    return null;
  }
}