com.davidbracewell.apollo.ml.clustering.flat.OneShotClustering Maven / Gradle / Ivy

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/*
 * (c) 2005 David B. Bracewell
 *
 * 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 com.davidbracewell.apollo.ml.clustering.flat;

import com.davidbracewell.apollo.Optimum;
import com.davidbracewell.apollo.linear.NDArray;
import com.davidbracewell.apollo.ml.Instance;
import com.davidbracewell.apollo.ml.clustering.Clusterer;
import com.davidbracewell.apollo.stat.measure.Measure;
import com.davidbracewell.stream.StreamingContext;
import com.davidbracewell.tuple.Tuple2;
import lombok.NonNull;

import java.util.Arrays;

import static com.davidbracewell.tuple.Tuples.$;

/**
 * Clustering specific to one-shot clustering
 *
 * @author David B. Bracewell
 */
class OneShotClustering extends FlatClustering {
   private static final long serialVersionUID = 1L;

   public OneShotClustering(Clusterer clusterer, Measure measure) {
      super(clusterer, measure);
   }


   @Override
   public int hardCluster(@NonNull Instance instance) {
      return Optimum.MINIMUM.optimum(softCluster(instance)).v1;
   }

   @Override
   public double[] softCluster(Instance instance) {
      double[] distances = new double[size()];
      Arrays.fill(distances, Double.POSITIVE_INFINITY);
      NDArray nDArray = getPreprocessors().apply(instance).toVector(getEncoderPair());
      Tuple2 best = StreamingContext.local().stream(this)
                                                     .parallel()
                                                     .map(cluster -> {
                                                             double d = 0;
                                                             for (NDArray jj : cluster) {
                                                                d += getMeasure().calculate(nDArray, jj);
                                                             }
                                                             return $(cluster.getId(), d);
                                                          }
                                                         ).min((t1, t2) -> Double.compare(t1.v2, t2.v2))
                                                     .orElse($(-1, 0.0));

      if (best.v1 != -1) {
         distances[best.v1] = best.v2;
      }
      return distances;
   }

}//END OF OneShotClustering