examples/src/main/java/org/apache/ignite/examples/ml/knn/KNNClassificationExample.java - ignite - Git at Google

 /*
  * 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.knn;

 import java.util.Arrays;
 import java.util.UUID;
 import javax.cache.Cache;
 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.cache.query.QueryCursor;
 import org.apache.ignite.cache.query.ScanQuery;
 import org.apache.ignite.configuration.CacheConfiguration;
 import org.apache.ignite.ml.dataset.impl.cache.CacheBasedDatasetBuilder;
 import org.apache.ignite.ml.knn.classification.KNNClassificationModel;
 import org.apache.ignite.ml.knn.classification.KNNClassificationTrainer;
 import org.apache.ignite.ml.knn.classification.KNNStrategy;
 import org.apache.ignite.ml.math.distances.EuclideanDistance;
 import org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector;
 import org.apache.ignite.thread.IgniteThread;

 /**
  * Run kNN multi-class classification trainer over distributed dataset.
  *
  * @see KNNClassificationTrainer
  */
 public class KNNClassificationExample {
     /** Run example. */
     public static void main(String[] args) throws InterruptedException {
         System.out.println();
         System.out.println(">>> kNN multi-class classification 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.");

             IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(),
                 KNNClassificationExample.class.getSimpleName(), () -> {
                 IgniteCache<Integer, double[]> dataCache = getTestCache(ignite);

                 KNNClassificationTrainer trainer = new KNNClassificationTrainer();

                 KNNClassificationModel knnMdl = trainer.fit(
                     new CacheBasedDatasetBuilder<>(ignite, dataCache),
                     (k, v) -> Arrays.copyOfRange(v, 1, v.length),
                     (k, v) -> v[0]
                 ).withK(3)
                     .withDistanceMeasure(new EuclideanDistance())
                     .withStrategy(KNNStrategy.WEIGHTED);

                 System.out.println(">>> ---------------------------------");
                 System.out.println(">>> | Prediction\t| Ground Truth\t|");
                 System.out.println(">>> ---------------------------------");

                 int amountOfErrors = 0;
                 int totalAmount = 0;

                 try (QueryCursor<Cache.Entry<Integer, double[]>> observations = dataCache.query(new ScanQuery<>())) {
                     for (Cache.Entry<Integer, double[]> observation : observations) {
                         double[] val = observation.getValue();
                         double[] inputs = Arrays.copyOfRange(val, 1, val.length);
                         double groundTruth = val[0];

                         double prediction = knnMdl.apply(new DenseLocalOnHeapVector(inputs));

                         totalAmount++;
                         if(groundTruth != prediction)
                             amountOfErrors++;

                         System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", prediction, groundTruth);
                     }

                     System.out.println(">>> ---------------------------------");

                     System.out.println("\n>>> Absolute amount of errors " + amountOfErrors);
                     System.out.println("\n>>> Accuracy " + (1 - amountOfErrors / (double)totalAmount));
                 }
             });

             igniteThread.start();
             igniteThread.join();
         }
     }

     /**
      * Fills cache with data and returns it.
      *
      * @param ignite Ignite instance.
      * @return Filled Ignite Cache.
      */
     private static IgniteCache<Integer, double[]> getTestCache(Ignite ignite) {
         CacheConfiguration<Integer, double[]> cacheConfiguration = new CacheConfiguration<>();
         cacheConfiguration.setName("TEST_" + UUID.randomUUID());
         cacheConfiguration.setAffinity(new RendezvousAffinityFunction(false, 10));

         IgniteCache<Integer, double[]> cache = ignite.createCache(cacheConfiguration);

         for (int i = 0; i < data.length; i++)
             cache.put(i, data[i]);

         return cache;
     }

     /** The Iris dataset. */
     private static final double[][] data = {
         {1, 5.1, 3.5, 1.4, 0.2},
         {1, 4.9, 3, 1.4, 0.2},
         {1, 4.7, 3.2, 1.3, 0.2},
         {1, 4.6, 3.1, 1.5, 0.2},
         {1, 5, 3.6, 1.4, 0.2},
         {1, 5.4, 3.9, 1.7, 0.4},
         {1, 4.6, 3.4, 1.4, 0.3},
         {1, 5, 3.4, 1.5, 0.2},
         {1, 4.4, 2.9, 1.4, 0.2},
         {1, 4.9, 3.1, 1.5, 0.1},
         {1, 5.4, 3.7, 1.5, 0.2},
         {1, 4.8, 3.4, 1.6, 0.2},
         {1, 4.8, 3, 1.4, 0.1},
         {1, 4.3, 3, 1.1, 0.1},
         {1, 5.8, 4, 1.2, 0.2},
         {1, 5.7, 4.4, 1.5, 0.4},
         {1, 5.4, 3.9, 1.3, 0.4},
         {1, 5.1, 3.5, 1.4, 0.3},
         {1, 5.7, 3.8, 1.7, 0.3},
         {1, 5.1, 3.8, 1.5, 0.3},
         {1, 5.4, 3.4, 1.7, 0.2},
         {1, 5.1, 3.7, 1.5, 0.4},
         {1, 4.6, 3.6, 1, 0.2},
         {1, 5.1, 3.3, 1.7, 0.5},
         {1, 4.8, 3.4, 1.9, 0.2},
         {1, 5, 3, 1.6, 0.2},
         {1, 5, 3.4, 1.6, 0.4},
         {1, 5.2, 3.5, 1.5, 0.2},
         {1, 5.2, 3.4, 1.4, 0.2},
         {1, 4.7, 3.2, 1.6, 0.2},
         {1, 4.8, 3.1, 1.6, 0.2},
         {1, 5.4, 3.4, 1.5, 0.4},
         {1, 5.2, 4.1, 1.5, 0.1},
         {1, 5.5, 4.2, 1.4, 0.2},
         {1, 4.9, 3.1, 1.5, 0.1},
         {1, 5, 3.2, 1.2, 0.2},
         {1, 5.5, 3.5, 1.3, 0.2},
         {1, 4.9, 3.1, 1.5, 0.1},
         {1, 4.4, 3, 1.3, 0.2},
         {1, 5.1, 3.4, 1.5, 0.2},
         {1, 5, 3.5, 1.3, 0.3},
         {1, 4.5, 2.3, 1.3, 0.3},
         {1, 4.4, 3.2, 1.3, 0.2},
         {1, 5, 3.5, 1.6, 0.6},
         {1, 5.1, 3.8, 1.9, 0.4},
         {1, 4.8, 3, 1.4, 0.3},
         {1, 5.1, 3.8, 1.6, 0.2},
         {1, 4.6, 3.2, 1.4, 0.2},
         {1, 5.3, 3.7, 1.5, 0.2},
         {1, 5, 3.3, 1.4, 0.2},
         {2, 7, 3.2, 4.7, 1.4},
         {2, 6.4, 3.2, 4.5, 1.5},
         {2, 6.9, 3.1, 4.9, 1.5},
         {2, 5.5, 2.3, 4, 1.3},
         {2, 6.5, 2.8, 4.6, 1.5},
         {2, 5.7, 2.8, 4.5, 1.3},
         {2, 6.3, 3.3, 4.7, 1.6},
         {2, 4.9, 2.4, 3.3, 1},
         {2, 6.6, 2.9, 4.6, 1.3},
         {2, 5.2, 2.7, 3.9, 1.4},
         {2, 5, 2, 3.5, 1},
         {2, 5.9, 3, 4.2, 1.5},
         {2, 6, 2.2, 4, 1},
         {2, 6.1, 2.9, 4.7, 1.4},
         {2, 5.6, 2.9, 3.6, 1.3},
         {2, 6.7, 3.1, 4.4, 1.4},
         {2, 5.6, 3, 4.5, 1.5},
         {2, 5.8, 2.7, 4.1, 1},
         {2, 6.2, 2.2, 4.5, 1.5},
         {2, 5.6, 2.5, 3.9, 1.1},
         {2, 5.9, 3.2, 4.8, 1.8},
         {2, 6.1, 2.8, 4, 1.3},
         {2, 6.3, 2.5, 4.9, 1.5},
         {2, 6.1, 2.8, 4.7, 1.2},
         {2, 6.4, 2.9, 4.3, 1.3},
         {2, 6.6, 3, 4.4, 1.4},
         {2, 6.8, 2.8, 4.8, 1.4},
         {2, 6.7, 3, 5, 1.7},
         {2, 6, 2.9, 4.5, 1.5},
         {2, 5.7, 2.6, 3.5, 1},
         {2, 5.5, 2.4, 3.8, 1.1},
         {2, 5.5, 2.4, 3.7, 1},
         {2, 5.8, 2.7, 3.9, 1.2},
         {2, 6, 2.7, 5.1, 1.6},
         {2, 5.4, 3, 4.5, 1.5},
         {2, 6, 3.4, 4.5, 1.6},
         {2, 6.7, 3.1, 4.7, 1.5},
         {2, 6.3, 2.3, 4.4, 1.3},
         {2, 5.6, 3, 4.1, 1.3},
         {2, 5.5, 2.5, 4, 1.3},
         {2, 5.5, 2.6, 4.4, 1.2},
         {2, 6.1, 3, 4.6, 1.4},
         {2, 5.8, 2.6, 4, 1.2},
         {2, 5, 2.3, 3.3, 1},
         {2, 5.6, 2.7, 4.2, 1.3},
         {2, 5.7, 3, 4.2, 1.2},
         {2, 5.7, 2.9, 4.2, 1.3},
         {2, 6.2, 2.9, 4.3, 1.3},
         {2, 5.1, 2.5, 3, 1.1},
         {2, 5.7, 2.8, 4.1, 1.3},
         {3, 6.3, 3.3, 6, 2.5},
         {3, 5.8, 2.7, 5.1, 1.9},
         {3, 7.1, 3, 5.9, 2.1},
         {3, 6.3, 2.9, 5.6, 1.8},
         {3, 6.5, 3, 5.8, 2.2},
         {3, 7.6, 3, 6.6, 2.1},
         {3, 4.9, 2.5, 4.5, 1.7},
         {3, 7.3, 2.9, 6.3, 1.8},
         {3, 6.7, 2.5, 5.8, 1.8},
         {3, 7.2, 3.6, 6.1, 2.5},
         {3, 6.5, 3.2, 5.1, 2},
         {3, 6.4, 2.7, 5.3, 1.9},
         {3, 6.8, 3, 5.5, 2.1},
         {3, 5.7, 2.5, 5, 2},
         {3, 5.8, 2.8, 5.1, 2.4},
         {3, 6.4, 3.2, 5.3, 2.3},
         {3, 6.5, 3, 5.5, 1.8},
         {3, 7.7, 3.8, 6.7, 2.2},
         {3, 7.7, 2.6, 6.9, 2.3},
         {3, 6, 2.2, 5, 1.5},
         {3, 6.9, 3.2, 5.7, 2.3},
         {3, 5.6, 2.8, 4.9, 2},
         {3, 7.7, 2.8, 6.7, 2},
         {3, 6.3, 2.7, 4.9, 1.8},
         {3, 6.7, 3.3, 5.7, 2.1},
         {3, 7.2, 3.2, 6, 1.8},
         {3, 6.2, 2.8, 4.8, 1.8},
         {3, 6.1, 3, 4.9, 1.8},
         {3, 6.4, 2.8, 5.6, 2.1},
         {3, 7.2, 3, 5.8, 1.6},
         {3, 7.4, 2.8, 6.1, 1.9},
         {3, 7.9, 3.8, 6.4, 2},
         {3, 6.4, 2.8, 5.6, 2.2},
         {3, 6.3, 2.8, 5.1, 1.5},
         {3, 6.1, 2.6, 5.6, 1.4},
         {3, 7.7, 3, 6.1, 2.3},
         {3, 6.3, 3.4, 5.6, 2.4},
         {3, 6.4, 3.1, 5.5, 1.8},
         {3, 6, 3, 4.8, 1.8},
         {3, 6.9, 3.1, 5.4, 2.1},
         {3, 6.7, 3.1, 5.6, 2.4},
         {3, 6.9, 3.1, 5.1, 2.3},
         {3, 5.8, 2.7, 5.1, 1.9},
         {3, 6.8, 3.2, 5.9, 2.3},
         {3, 6.7, 3.3, 5.7, 2.5},
         {3, 6.7, 3, 5.2, 2.3},
         {3, 6.3, 2.5, 5, 1.9},
         {3, 6.5, 3, 5.2, 2},
         {3, 6.2, 3.4, 5.4, 2.3},
         {3, 5.9, 3, 5.1, 1.8}
     };
 }
	/*
	* 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.knn;

	import java.util.Arrays;
	import java.util.UUID;
	import javax.cache.Cache;
	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.cache.query.QueryCursor;
	import org.apache.ignite.cache.query.ScanQuery;
	import org.apache.ignite.configuration.CacheConfiguration;
	import org.apache.ignite.ml.dataset.impl.cache.CacheBasedDatasetBuilder;
	import org.apache.ignite.ml.knn.classification.KNNClassificationModel;
	import org.apache.ignite.ml.knn.classification.KNNClassificationTrainer;
	import org.apache.ignite.ml.knn.classification.KNNStrategy;
	import org.apache.ignite.ml.math.distances.EuclideanDistance;
	import org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector;
	import org.apache.ignite.thread.IgniteThread;

	/**
	* Run kNN multi-class classification trainer over distributed dataset.
	*
	* @see KNNClassificationTrainer
	*/
	public class KNNClassificationExample {
	/** Run example. */
	public static void main(String[] args) throws InterruptedException {
	System.out.println();
	System.out.println(">>> kNN multi-class classification 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.");

	IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(),
	KNNClassificationExample.class.getSimpleName(), () -> {
	IgniteCache<Integer, double[]> dataCache = getTestCache(ignite);

	KNNClassificationTrainer trainer = new KNNClassificationTrainer();

	KNNClassificationModel knnMdl = trainer.fit(
	new CacheBasedDatasetBuilder<>(ignite, dataCache),
	(k, v) -> Arrays.copyOfRange(v, 1, v.length),
	(k, v) -> v[0]
	).withK(3)
	.withDistanceMeasure(new EuclideanDistance())
	.withStrategy(KNNStrategy.WEIGHTED);

	System.out.println(">>> ---------------------------------");
	System.out.println(">>> \| Prediction\t\| Ground Truth\t\|");
	System.out.println(">>> ---------------------------------");

	int amountOfErrors = 0;
	int totalAmount = 0;

	try (QueryCursor<Cache.Entry<Integer, double[]>> observations = dataCache.query(new ScanQuery<>())) {
	for (Cache.Entry<Integer, double[]> observation : observations) {
	double[] val = observation.getValue();
	double[] inputs = Arrays.copyOfRange(val, 1, val.length);
	double groundTruth = val[0];

	double prediction = knnMdl.apply(new DenseLocalOnHeapVector(inputs));

	totalAmount++;
	if(groundTruth != prediction)
	amountOfErrors++;

	System.out.printf(">>> \| %.4f\t\t\| %.4f\t\t\|\n", prediction, groundTruth);
	}

	System.out.println(">>> ---------------------------------");

	System.out.println("\n>>> Absolute amount of errors " + amountOfErrors);
	System.out.println("\n>>> Accuracy " + (1 - amountOfErrors / (double)totalAmount));
	}
	});

	igniteThread.start();
	igniteThread.join();
	}
	}

	/**
	* Fills cache with data and returns it.
	*
	* @param ignite Ignite instance.
	* @return Filled Ignite Cache.
	*/
	private static IgniteCache<Integer, double[]> getTestCache(Ignite ignite) {
	CacheConfiguration<Integer, double[]> cacheConfiguration = new CacheConfiguration<>();
	cacheConfiguration.setName("TEST_" + UUID.randomUUID());
	cacheConfiguration.setAffinity(new RendezvousAffinityFunction(false, 10));

	IgniteCache<Integer, double[]> cache = ignite.createCache(cacheConfiguration);

	for (int i = 0; i < data.length; i++)
	cache.put(i, data[i]);

	return cache;
	}

	/** The Iris dataset. */
	private static final double[][] data = {
	{1, 5.1, 3.5, 1.4, 0.2},
	{1, 4.9, 3, 1.4, 0.2},
	{1, 4.7, 3.2, 1.3, 0.2},
	{1, 4.6, 3.1, 1.5, 0.2},
	{1, 5, 3.6, 1.4, 0.2},
	{1, 5.4, 3.9, 1.7, 0.4},
	{1, 4.6, 3.4, 1.4, 0.3},
	{1, 5, 3.4, 1.5, 0.2},
	{1, 4.4, 2.9, 1.4, 0.2},
	{1, 4.9, 3.1, 1.5, 0.1},
	{1, 5.4, 3.7, 1.5, 0.2},
	{1, 4.8, 3.4, 1.6, 0.2},
	{1, 4.8, 3, 1.4, 0.1},
	{1, 4.3, 3, 1.1, 0.1},
	{1, 5.8, 4, 1.2, 0.2},
	{1, 5.7, 4.4, 1.5, 0.4},
	{1, 5.4, 3.9, 1.3, 0.4},
	{1, 5.1, 3.5, 1.4, 0.3},
	{1, 5.7, 3.8, 1.7, 0.3},
	{1, 5.1, 3.8, 1.5, 0.3},
	{1, 5.4, 3.4, 1.7, 0.2},
	{1, 5.1, 3.7, 1.5, 0.4},
	{1, 4.6, 3.6, 1, 0.2},
	{1, 5.1, 3.3, 1.7, 0.5},
	{1, 4.8, 3.4, 1.9, 0.2},
	{1, 5, 3, 1.6, 0.2},
	{1, 5, 3.4, 1.6, 0.4},
	{1, 5.2, 3.5, 1.5, 0.2},
	{1, 5.2, 3.4, 1.4, 0.2},
	{1, 4.7, 3.2, 1.6, 0.2},
	{1, 4.8, 3.1, 1.6, 0.2},
	{1, 5.4, 3.4, 1.5, 0.4},
	{1, 5.2, 4.1, 1.5, 0.1},
	{1, 5.5, 4.2, 1.4, 0.2},
	{1, 4.9, 3.1, 1.5, 0.1},
	{1, 5, 3.2, 1.2, 0.2},
	{1, 5.5, 3.5, 1.3, 0.2},
	{1, 4.9, 3.1, 1.5, 0.1},
	{1, 4.4, 3, 1.3, 0.2},
	{1, 5.1, 3.4, 1.5, 0.2},
	{1, 5, 3.5, 1.3, 0.3},
	{1, 4.5, 2.3, 1.3, 0.3},
	{1, 4.4, 3.2, 1.3, 0.2},
	{1, 5, 3.5, 1.6, 0.6},
	{1, 5.1, 3.8, 1.9, 0.4},
	{1, 4.8, 3, 1.4, 0.3},
	{1, 5.1, 3.8, 1.6, 0.2},
	{1, 4.6, 3.2, 1.4, 0.2},
	{1, 5.3, 3.7, 1.5, 0.2},
	{1, 5, 3.3, 1.4, 0.2},
	{2, 7, 3.2, 4.7, 1.4},
	{2, 6.4, 3.2, 4.5, 1.5},
	{2, 6.9, 3.1, 4.9, 1.5},
	{2, 5.5, 2.3, 4, 1.3},
	{2, 6.5, 2.8, 4.6, 1.5},
	{2, 5.7, 2.8, 4.5, 1.3},
	{2, 6.3, 3.3, 4.7, 1.6},
	{2, 4.9, 2.4, 3.3, 1},
	{2, 6.6, 2.9, 4.6, 1.3},
	{2, 5.2, 2.7, 3.9, 1.4},
	{2, 5, 2, 3.5, 1},
	{2, 5.9, 3, 4.2, 1.5},
	{2, 6, 2.2, 4, 1},
	{2, 6.1, 2.9, 4.7, 1.4},
	{2, 5.6, 2.9, 3.6, 1.3},
	{2, 6.7, 3.1, 4.4, 1.4},
	{2, 5.6, 3, 4.5, 1.5},
	{2, 5.8, 2.7, 4.1, 1},
	{2, 6.2, 2.2, 4.5, 1.5},
	{2, 5.6, 2.5, 3.9, 1.1},
	{2, 5.9, 3.2, 4.8, 1.8},
	{2, 6.1, 2.8, 4, 1.3},
	{2, 6.3, 2.5, 4.9, 1.5},
	{2, 6.1, 2.8, 4.7, 1.2},
	{2, 6.4, 2.9, 4.3, 1.3},
	{2, 6.6, 3, 4.4, 1.4},
	{2, 6.8, 2.8, 4.8, 1.4},
	{2, 6.7, 3, 5, 1.7},
	{2, 6, 2.9, 4.5, 1.5},
	{2, 5.7, 2.6, 3.5, 1},
	{2, 5.5, 2.4, 3.8, 1.1},
	{2, 5.5, 2.4, 3.7, 1},
	{2, 5.8, 2.7, 3.9, 1.2},
	{2, 6, 2.7, 5.1, 1.6},
	{2, 5.4, 3, 4.5, 1.5},
	{2, 6, 3.4, 4.5, 1.6},
	{2, 6.7, 3.1, 4.7, 1.5},
	{2, 6.3, 2.3, 4.4, 1.3},
	{2, 5.6, 3, 4.1, 1.3},
	{2, 5.5, 2.5, 4, 1.3},
	{2, 5.5, 2.6, 4.4, 1.2},
	{2, 6.1, 3, 4.6, 1.4},
	{2, 5.8, 2.6, 4, 1.2},
	{2, 5, 2.3, 3.3, 1},
	{2, 5.6, 2.7, 4.2, 1.3},
	{2, 5.7, 3, 4.2, 1.2},
	{2, 5.7, 2.9, 4.2, 1.3},
	{2, 6.2, 2.9, 4.3, 1.3},
	{2, 5.1, 2.5, 3, 1.1},
	{2, 5.7, 2.8, 4.1, 1.3},
	{3, 6.3, 3.3, 6, 2.5},
	{3, 5.8, 2.7, 5.1, 1.9},
	{3, 7.1, 3, 5.9, 2.1},
	{3, 6.3, 2.9, 5.6, 1.8},
	{3, 6.5, 3, 5.8, 2.2},
	{3, 7.6, 3, 6.6, 2.1},
	{3, 4.9, 2.5, 4.5, 1.7},
	{3, 7.3, 2.9, 6.3, 1.8},
	{3, 6.7, 2.5, 5.8, 1.8},
	{3, 7.2, 3.6, 6.1, 2.5},
	{3, 6.5, 3.2, 5.1, 2},
	{3, 6.4, 2.7, 5.3, 1.9},
	{3, 6.8, 3, 5.5, 2.1},
	{3, 5.7, 2.5, 5, 2},
	{3, 5.8, 2.8, 5.1, 2.4},
	{3, 6.4, 3.2, 5.3, 2.3},
	{3, 6.5, 3, 5.5, 1.8},
	{3, 7.7, 3.8, 6.7, 2.2},
	{3, 7.7, 2.6, 6.9, 2.3},
	{3, 6, 2.2, 5, 1.5},
	{3, 6.9, 3.2, 5.7, 2.3},
	{3, 5.6, 2.8, 4.9, 2},
	{3, 7.7, 2.8, 6.7, 2},
	{3, 6.3, 2.7, 4.9, 1.8},
	{3, 6.7, 3.3, 5.7, 2.1},
	{3, 7.2, 3.2, 6, 1.8},
	{3, 6.2, 2.8, 4.8, 1.8},
	{3, 6.1, 3, 4.9, 1.8},
	{3, 6.4, 2.8, 5.6, 2.1},
	{3, 7.2, 3, 5.8, 1.6},
	{3, 7.4, 2.8, 6.1, 1.9},
	{3, 7.9, 3.8, 6.4, 2},
	{3, 6.4, 2.8, 5.6, 2.2},
	{3, 6.3, 2.8, 5.1, 1.5},
	{3, 6.1, 2.6, 5.6, 1.4},
	{3, 7.7, 3, 6.1, 2.3},
	{3, 6.3, 3.4, 5.6, 2.4},
	{3, 6.4, 3.1, 5.5, 1.8},
	{3, 6, 3, 4.8, 1.8},
	{3, 6.9, 3.1, 5.4, 2.1},
	{3, 6.7, 3.1, 5.6, 2.4},
	{3, 6.9, 3.1, 5.1, 2.3},
	{3, 5.8, 2.7, 5.1, 1.9},
	{3, 6.8, 3.2, 5.9, 2.3},
	{3, 6.7, 3.3, 5.7, 2.5},
	{3, 6.7, 3, 5.2, 2.3},
	{3, 6.3, 2.5, 5, 1.9},
	{3, 6.5, 3, 5.2, 2},
	{3, 6.2, 3.4, 5.4, 2.3},
	{3, 5.9, 3, 5.1, 1.8}
	};
	}