| /* |
| * 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.ignite.examples.ml.clustering; |
| |
| import java.util.concurrent.atomic.AtomicInteger; |
| import org.apache.ignite.Ignite; |
| import org.apache.ignite.IgniteCache; |
| import org.apache.ignite.Ignition; |
| import org.apache.ignite.cache.affinity.rendezvous.RendezvousAffinityFunction; |
| import org.apache.ignite.configuration.CacheConfiguration; |
| import org.apache.ignite.ml.clustering.gmm.GmmModel; |
| import org.apache.ignite.ml.clustering.gmm.GmmTrainer; |
| import org.apache.ignite.ml.dataset.feature.extractor.impl.LabeledDummyVectorizer; |
| import org.apache.ignite.ml.environment.LearningEnvironmentBuilder; |
| import org.apache.ignite.ml.math.Tracer; |
| import org.apache.ignite.ml.math.primitives.vector.VectorUtils; |
| import org.apache.ignite.ml.math.stat.MultivariateGaussianDistribution; |
| import org.apache.ignite.ml.structures.LabeledVector; |
| import org.apache.ignite.ml.util.generators.DataStreamGenerator; |
| import org.apache.ignite.ml.util.generators.primitives.scalar.GaussRandomProducer; |
| import org.apache.ignite.ml.util.generators.primitives.scalar.RandomProducer; |
| import org.apache.ignite.ml.util.generators.primitives.vector.VectorGeneratorsFamily; |
| |
| /** |
| * Example of using GMM clusterization algorithm. Gaussian Mixture Algorithm (GMM, see {@link GmmModel}, {@link |
| * GmmTrainer}) can be used for input dataset data distribution representation as mixture of multivariate gaussians. |
| * More info: https://en.wikipedia.org/wiki/Mixture_model#Gaussian_mixture_model . |
| * <p> |
| * In this example GMM are used for gaussians shape recovering - means and covariances of them. |
| */ |
| public class GmmClusterizationExample { |
| /** |
| * Runs example. |
| * |
| * @param args Command line arguments. |
| */ |
| public static void main(String[] args) { |
| System.out.println(); |
| System.out.println(">>> GMM clustering algorithm over cached dataset usage example started."); |
| // Start ignite grid. |
| try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) { |
| System.out.println(">>> Ignite grid started."); |
| |
| long seed = 0; |
| |
| IgniteCache<Integer, LabeledVector<Double>> dataCache = null; |
| try { |
| dataCache = ignite.createCache( |
| new CacheConfiguration<Integer, LabeledVector<Double>>("GMM_EXAMPLE_CACHE") |
| .setAffinity(new RendezvousAffinityFunction(false, 10)) |
| ); |
| |
| // Dataset consists of three gaussians where two from them are rotated onto PI/4. |
| DataStreamGenerator dataStream = new VectorGeneratorsFamily.Builder().add( |
| RandomProducer.vectorize( |
| new GaussRandomProducer(0, 2., seed++), |
| new GaussRandomProducer(0, 3., seed++) |
| ).rotate(Math.PI / 4).move(VectorUtils.of(10., 10.))).add( |
| RandomProducer.vectorize( |
| new GaussRandomProducer(0, 1., seed++), |
| new GaussRandomProducer(0, 2., seed++) |
| ).rotate(-Math.PI / 4).move(VectorUtils.of(-10., 10.))).add( |
| RandomProducer.vectorize( |
| new GaussRandomProducer(0, 3., seed++), |
| new GaussRandomProducer(0, 3., seed++) |
| ).move(VectorUtils.of(0., -10.)) |
| ).build(seed++).asDataStream(); |
| |
| AtomicInteger keyGen = new AtomicInteger(); |
| dataStream.fillCacheWithCustomKey(50000, dataCache, v -> keyGen.getAndIncrement()); |
| GmmTrainer trainer = new GmmTrainer(1); |
| |
| GmmModel mdl = trainer |
| .withMaxCountIterations(10) |
| .withMaxCountOfClusters(4) |
| .withEnvironmentBuilder(LearningEnvironmentBuilder.defaultBuilder().withRNGSeed(seed)) |
| .fit(ignite, dataCache, new LabeledDummyVectorizer<>()); |
| |
| System.out.println(">>> GMM means and covariances"); |
| for (int i = 0; i < mdl.countOfComponents(); i++) { |
| MultivariateGaussianDistribution distribution = mdl.distributions().get(i); |
| System.out.println(); |
| System.out.println("============"); |
| System.out.println("Component #" + i); |
| System.out.println("============"); |
| System.out.println("Mean vector = "); |
| Tracer.showAscii(distribution.mean()); |
| System.out.println(); |
| System.out.println("Covariance matrix = "); |
| Tracer.showAscii(distribution.covariance()); |
| } |
| |
| System.out.println(">>>"); |
| } |
| finally { |
| dataCache.destroy(); |
| } |
| } |
| finally { |
| System.out.flush(); |
| } |
| } |
| } |