org.apache.mahout.knn.BruteSpeedCheck.java Source code

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Here is the source code for org.apache.mahout.knn.BruteSpeedCheck.java

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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.
 */

package org.apache.mahout.knn;

import com.google.common.collect.Lists;
import org.apache.mahout.common.distance.EuclideanDistanceMeasure;
import org.apache.mahout.knn.search.BruteSearch;
import org.apache.mahout.math.ConstantVector;
import org.apache.mahout.math.DenseMatrix;
import org.apache.mahout.math.Matrix;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.random.MultiNormal;
import org.apache.mahout.math.random.Sampler;
import org.apache.mahout.math.WeightedVector;

import java.util.List;

public class BruteSpeedCheck {
    private static final int VECTOR_DIMENSION = 250;
    private static final int REFERENCE_SIZE = 10000;
    private static final int QUERY_SIZE = 100;

    public static void main(String[] args) {
        Sampler<Vector> rand = new MultiNormal(new ConstantVector(1, VECTOR_DIMENSION));
        List<WeightedVector> referenceVectors = Lists.newArrayListWithExpectedSize(REFERENCE_SIZE);
        for (int i = 0; i < REFERENCE_SIZE; ++i) {
            referenceVectors.add(new WeightedVector(rand.sample(), 1, i));
        }
        System.out.printf("Generated reference matrix.\n");

        List<WeightedVector> queryVectors = Lists.newArrayListWithExpectedSize(QUERY_SIZE);
        for (int i = 0; i < QUERY_SIZE; ++i) {
            queryVectors.add(new WeightedVector(rand.sample(), 1, i));
        }
        System.out.printf("Generated query matrix.\n");

        for (int threads : new int[] { 1, 2, 3, 4, 5, 6, 10, 20, 50 }) {
            for (int block : new int[] { 1, 10, 50 }) {
                BruteSearch search = new BruteSearch(new EuclideanDistanceMeasure());
                search.addAll(referenceVectors);
                long t0 = System.nanoTime();
                search.search(queryVectors, block, threads);
                long t1 = System.nanoTime();
                System.out.printf("%d\t%d\t%.2f\n", threads, block, (t1 - t0) / 1e9);
            }
        }
    }
}