/*
    * 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.commons.math4.examples.kmeans.image;
  
  import java.util.concurrent.Callable;
  import java.time.Instant;
  import java.time.Duration;
  
  import picocli.CommandLine;
  import picocli.CommandLine.Option;
  import picocli.CommandLine.Command;
  
  import org.apache.commons.rng.UniformRandomProvider;
  import org.apache.commons.rng.simple.RandomSource;
  import org.apache.commons.math4.legacy.ml.distance.DistanceMeasure;
  import org.apache.commons.math4.legacy.ml.distance.EuclideanDistance;
  import org.apache.commons.math4.legacy.ml.clustering.Clusterer;
  import org.apache.commons.math4.legacy.ml.clustering.KMeansPlusPlusClusterer;
  import org.apache.commons.math4.legacy.ml.clustering.ElkanKMeansPlusPlusClusterer;
  
  /**
   * Application class.
   */
  @Command(description = "Run the application",
           mixinStandardHelpOptions = true)
  public final class StandAlone implements Callable<Void> {
      /** The k parameter. */
      @Option(names = { "-k" }, paramLabel = "K", required = true,
              description = "Number of clusters.")
      private int numClusters;
      /** The maximal number of iterations. */
      @Option(names = { "-i", "--iterations" }, paramLabel = "N",
              description = "Allowed number of iterations (default: ${DEFAULT-VALUE}).")
      private int maxIter = 2000;
      /** The input file. */
      @Option(names = { "--image" }, paramLabel = "FILE", required = true,
              description = "Input file name.")
      private String inputFile;
      /** The output prefix. */
      @Option(names = { "-o", "--output" }, paramLabel = "PREFIX", required = true,
              description = "Prefix (path) for the output files.")
      private String outputPrefix;
  
      /**
       * Program entry point.
       *
       * @param args Command line arguments and options.
       */
      public static void main(String[] args) {
          CommandLine.call(new StandAlone(), args);
      }
  
      @Override
      public Void call() {
          final ImageData image = ImageData.load(inputFile);
          final UniformRandomProvider rng = RandomSource.MWC_256.create();
          final DistanceMeasure distance = new EuclideanDistance();
  
          cluster(image,
                  new ElkanKMeansPlusPlusClusterer<>(numClusters,
                                                     maxIter,
                                                     distance,
                                                     rng),
                  "elkan");
          cluster(image,
                  new KMeansPlusPlusClusterer<>(numClusters,
                                                maxIter,
                                                distance,
                                                rng),
                  "kmeans");
  
          return null;
      }
  
      /**
       * Perform clustering and write results.
       *
       * @param image Input.
       * @param algo Algorithm to do the clustering.
       * @param id Identifier for output file name.
       */
      private void cluster(ImageData image,
                           Clusterer<ImageData.PixelClusterable> algo,
                           String id) {
         final String dot = ".";
         final String out = new StringBuilder()
             .append(outputPrefix)
             .append(dot)
             .append("k_")
             .append(numClusters)
             .append(dot)
             .append(id)
             .append(dot)
             .toString();
 
         final Instant start = Instant.now();
         image.write(algo.cluster(image.getPixels()), out);
         //CHECKSTYLE: stop all
         System.out.println("time=" + Duration.between(start, Instant.now()).toMillis());
         //CHECKSTYLE: resume all
     }
 }