com.davidbracewell.ml.classification.lazy.SparseKNN.java Source code

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Here is the source code for com.davidbracewell.ml.classification.lazy.SparseKNN.java

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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.ml.classification.lazy;

import com.davidbracewell.collection.InvertedIndex;
import com.davidbracewell.collection.Sorting;
import com.davidbracewell.math.distance.DistanceMeasure;
import com.davidbracewell.math.linear.VectorMap;
import com.davidbracewell.ml.Feature;
import com.davidbracewell.ml.Instance;
import com.davidbracewell.ml.classification.ClassificationModel;
import com.davidbracewell.ml.classification.ClassificationResult;
import com.davidbracewell.tuple.Pair;
import com.google.common.collect.MinMaxPriorityQueue;
import com.google.common.collect.Ordering;

/**
 * @author David B. Bracewell
 */
public class SparseKNN extends ClassificationModel {

    private static final long serialVersionUID = 1670966911919829066L;
    int K;
    InvertedIndex<Instance, Feature> index;
    DistanceMeasure distanceMeasure;

    @Override
    protected ClassificationResult classifyImpl(Instance instance) {
        final MinMaxPriorityQueue<Pair<Double, Double>> neighbors = MinMaxPriorityQueue
                .orderedBy(Ordering.from(Sorting.<Double, Double>mapEntryComparator(false, true))).maximumSize(K)
                .create();
        for (Instance inst : index.query(instance)) {
            double distance = distanceMeasure.calculate(inst, instance, VectorMap.VALID_VALUES.FINITE);
            neighbors.add(Pair.of(inst.getTargetValue(), distance));
        }
        double[] p = new double[getTargetFeature().alphabetSize()];
        for (Pair<Double, Double> pair : neighbors) {
            p[pair.getFirst().intValue()] += 1d / (pair.getSecond() + 0.00000001);
        }
        return new ClassificationResult(getTargetFeature(), p);
    }

    @Override
    public boolean isTrained() {
        return index != null;
    }

}//END OF KNN