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 * 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.
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package org.apache.ignite.ml.knn.ann;

import java.util.Arrays;
import java.util.HashMap;
import java.util.Iterator;
import java.util.List;
import java.util.Map;
import java.util.Set;
import java.util.TreeMap;
import org.apache.ignite.ml.Exporter;
import org.apache.ignite.ml.knn.NNClassificationModel;
import org.apache.ignite.ml.knn.classification.KNNModelFormat;
import org.apache.ignite.ml.knn.classification.NNStrategy;
import org.apache.ignite.ml.math.primitives.vector.Vector;
import org.apache.ignite.ml.structures.LabeledVector;
import org.apache.ignite.ml.structures.LabeledVectorSet;
import org.apache.ignite.ml.util.ModelTrace;
import org.jetbrains.annotations.NotNull;

/**
 * ANN model to predict labels in multi-class classification task.
 */
public class ANNClassificationModel extends NNClassificationModel  {
    /** */
    private static final long serialVersionUID = -127312378991350345L;

    /** The labeled set of candidates. */
    private final LabeledVectorSet candidates;

    /** Centroid statistics. */
    private final ANNClassificationTrainer.CentroidStat centroindsStat;

    /**
     * Build the model based on a candidates set.
     * @param centers The candidates set.
     * @param centroindsStat The stat about centroids.
     */
    public ANNClassificationModel(LabeledVectorSet centers,
        ANNClassificationTrainer.CentroidStat centroindsStat) {
       this.candidates = centers;
       this.centroindsStat = centroindsStat;
    }

    /** */
    public LabeledVectorSet getCandidates() {
        return candidates;
    }

    /** */
    public ANNClassificationTrainer.CentroidStat getCentroindsStat() {
        return centroindsStat;
    }

    /** {@inheritDoc} */
    @Override public Double apply(Vector v) {
            List neighbors = findKNearestNeighbors(v);
            return classify(neighbors, v, stgy);
    }

    /** */
    @Override public 

void saveModel(Exporter exporter, P path) { ANNModelFormat mdlData = new ANNModelFormat(k, distanceMeasure, stgy, candidates, centroindsStat); exporter.save(mdlData, path); } /** * The main idea is calculation all distance pairs between given vector and all centroids in candidates set, sorting * them and finding k vectors with min distance with the given vector. * * @param v The given vector. * @return K-nearest neighbors. */ private List findKNearestNeighbors(Vector v) { return Arrays.asList(getKClosestVectors(getDistances(v))); } /** * Iterates along entries in distance map and fill the resulting k-element array. * @param distanceIdxPairs The distance map. * @return K-nearest neighbors. */ @NotNull private LabeledVector[] getKClosestVectors( TreeMap> distanceIdxPairs) { LabeledVector[] res; if (candidates.rowSize() <= k) { res = new LabeledVector[candidates.rowSize()]; for (int i = 0; i < candidates.rowSize(); i++) res[i] = candidates.getRow(i); } else { res = new LabeledVector[k]; int i = 0; final Iterator iter = distanceIdxPairs.keySet().iterator(); while (i < k) { double key = iter.next(); Set idxs = distanceIdxPairs.get(key); for (Integer idx : idxs) { res[i] = candidates.getRow(idx); i++; if (i >= k) break; // go to next while-loop iteration } } } return res; } /** * Computes distances between given vector and each vector in training dataset. * * @param v The given vector. * @return Key - distanceMeasure from given features before features with idx stored in value. Value is presented * with Set because there can be a few vectors with the same distance. */ @NotNull private TreeMap> getDistances(Vector v) { TreeMap> distanceIdxPairs = new TreeMap<>(); for (int i = 0; i < candidates.rowSize(); i++) { LabeledVector labeledVector = candidates.getRow(i); if (labeledVector != null) { double distance = distanceMeasure.compute(v, labeledVector.features()); putDistanceIdxPair(distanceIdxPairs, i, distance); } } return distanceIdxPairs; } /** */ private double classify(List neighbors, Vector v, NNStrategy stgy) { Map clsVotes = new HashMap<>(); for (LabeledVector neighbor : neighbors) { TreeMap probableClsLb = ((ProbableLabel)neighbor.label()).clsLbls; double distance = distanceMeasure.compute(v, neighbor.features()); // we predict class label, not the probability vector (it need here another math with counting of votes) probableClsLb.forEach((label, probability) -> { double cnt = clsVotes.containsKey(label) ? clsVotes.get(label) : 0; clsVotes.put(label, cnt + probability * getClassVoteForVector(stgy, distance)); }); } return getClassWithMaxVotes(clsVotes); } /** {@inheritDoc} */ @Override public int hashCode() { int res = 1; res = res * 37 + k; res = res * 37 + distanceMeasure.hashCode(); res = res * 37 + stgy.hashCode(); res = res * 37 + candidates.hashCode(); return res; } /** {@inheritDoc} */ @Override public boolean equals(Object obj) { if (this == obj) return true; if (obj == null || getClass() != obj.getClass()) return false; ANNClassificationModel that = (ANNClassificationModel)obj; return k == that.k && distanceMeasure.equals(that.distanceMeasure) && stgy.equals(that.stgy) && candidates.equals(that.candidates); } /** {@inheritDoc} */ @Override public String toString() { return toString(false); } /** {@inheritDoc} */ @Override public String toString(boolean pretty) { return ModelTrace.builder("KNNClassificationModel", pretty) .addField("k", String.valueOf(k)) .addField("measure", distanceMeasure.getClass().getSimpleName()) .addField("strategy", stgy.name()) .addField("amount of candidates", String.valueOf(candidates.rowSize())) .toString(); } }





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