examples/src/main/java/org/apache/ignite/examples/ml/inference/exchange/ANNClassificationExportImportExample.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.inference.exchange;

 import java.io.IOException;
 import java.nio.file.Files;
 import java.nio.file.Path;
 import java.util.Arrays;
 import java.util.UUID;
 import javax.cache.Cache;
 import org.apache.commons.math3.util.Precision;
 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.feature.extractor.Vectorizer;
 import org.apache.ignite.ml.dataset.feature.extractor.impl.DoubleArrayVectorizer;
 import org.apache.ignite.ml.knn.NNClassificationModel;
 import org.apache.ignite.ml.knn.ann.ANNClassificationModel;
 import org.apache.ignite.ml.knn.ann.ANNClassificationTrainer;
 import org.apache.ignite.ml.math.distances.EuclideanDistance;
 import org.apache.ignite.ml.math.distances.ManhattanDistance;
 import org.apache.ignite.ml.math.primitives.vector.impl.DenseVector;

 /**
  * Run ANN multi-class classification trainer ({@link ANNClassificationTrainer}) over distributed dataset.
  * <p>
  * Code in this example launches Ignite grid and fills the cache with test data points (based on the
  * <a href="https://en.wikipedia.org/wiki/Iris_flower_data_set"></a>Iris dataset</a>).</p>
  * <p>
  * After that it trains the model based on the specified data using
  * <a href="https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm">kNN</a> algorithm.</p>
  * <p>
  * Finally, this example loops over the test set of data points, applies the trained model to predict what cluster does
  * this point belong to, and compares prediction to expected outcome (ground truth).</p>
  * <p>
  * You can change the test data used in this example and re-run it to explore this algorithm further.</p>
  */
 public class ANNClassificationExportImportExample {
     /**
      * Run example.
      */
     public static void main(String[] args) throws IOException {
         System.out.println();
         System.out.println(">>> ANN 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.");

             IgniteCache<Integer, double[]> dataCache = null;
             Path jsonMdlPath = null;
             try {
                 dataCache = getTestCache(ignite);

                 ANNClassificationTrainer trainer = new ANNClassificationTrainer()
                     .withDistance(new ManhattanDistance())
                     .withK(50)
                     .withMaxIterations(1000)
                     .withEpsilon(1e-2);

                 ANNClassificationModel mdl = (ANNClassificationModel) trainer.fit(
                     ignite,
                     dataCache,
                     new DoubleArrayVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.FIRST)
                 ).withK(5)
                     .withDistanceMeasure(new EuclideanDistance())
                     .withWeighted(true);

                 System.out.println("\n>>> Exported ANN model: " + mdl.toString(true));

                 double accuracy = evaluateModel(dataCache, mdl);

                 System.out.println("\n>>> Accuracy for exported ANN model:" + accuracy);

                 jsonMdlPath = Files.createTempFile(null, null);
                 mdl.toJSON(jsonMdlPath);

                 ANNClassificationModel modelImportedFromJSON = ANNClassificationModel.fromJSON(jsonMdlPath);

                 System.out.println("\n>>> Imported ANN model: " + modelImportedFromJSON.toString(true));

                 accuracy = evaluateModel(dataCache, modelImportedFromJSON);

                 System.out.println("\n>>> Accuracy for imported ANN model:" + accuracy);

                 System.out.println(">>> ANN multi-class classification algorithm over cached dataset usage example completed.");
             }
             finally {
                 if (dataCache != null)
                     dataCache.destroy();
                 if (jsonMdlPath != null)
                     Files.deleteIfExists(jsonMdlPath);
             }
         }
         finally {
             System.out.flush();
         }
     }

     /** */
     private static double evaluateModel(IgniteCache<Integer, double[]> dataCache, NNClassificationModel knnMdl) {
         int amountOfErrors = 0;
         int totalAmount = 0;

         double accuracy;
         try (QueryCursor<Cache.Entry<Integer, double[]>> observations = dataCache.query(new ScanQuery<>())) {
             System.out.println(">>> ---------------------------------");
             System.out.println(">>> | Prediction\t| Ground Truth\t|");
             System.out.println(">>> ---------------------------------");

             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.predict(new DenseVector(inputs));

                 totalAmount++;
                 if (!Precision.equals(groundTruth, prediction, Precision.EPSILON))
                     amountOfErrors++;

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

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

             accuracy = 1 - amountOfErrors / (double) totalAmount;

         }
         return accuracy;
     }

     /**
      * 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 k = 0; k < 10; k++) { // multiplies the Iris dataset k times.
             for (int i = 0; i < data.length; i++)
                 cache.put(k * 10000 + i, mutate(data[i], k));
         }

         return cache;
     }

     /**
      * Tiny changing of data depending on k parameter.
      *
      * @param datum The vector data.
      * @param k     The passed parameter.
      * @return The changed vector data.
      */
     private static double[] mutate(double[] datum, int k) {
         for (int i = 0; i < datum.length; i++)
             datum[i] += k / 100000;
         return datum;
     }

     /**
      * 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.inference.exchange;

	import java.io.IOException;
	import java.nio.file.Files;
	import java.nio.file.Path;
	import java.util.Arrays;
	import java.util.UUID;
	import javax.cache.Cache;
	import org.apache.commons.math3.util.Precision;
	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.feature.extractor.Vectorizer;
	import org.apache.ignite.ml.dataset.feature.extractor.impl.DoubleArrayVectorizer;
	import org.apache.ignite.ml.knn.NNClassificationModel;
	import org.apache.ignite.ml.knn.ann.ANNClassificationModel;
	import org.apache.ignite.ml.knn.ann.ANNClassificationTrainer;
	import org.apache.ignite.ml.math.distances.EuclideanDistance;
	import org.apache.ignite.ml.math.distances.ManhattanDistance;
	import org.apache.ignite.ml.math.primitives.vector.impl.DenseVector;

	/**
	* Run ANN multi-class classification trainer ({@link ANNClassificationTrainer}) over distributed dataset.
	* <p>
	* Code in this example launches Ignite grid and fills the cache with test data points (based on the
	* <a href="https://en.wikipedia.org/wiki/Iris_flower_data_set"></a>Iris dataset</a>).</p>
	* <p>
	* After that it trains the model based on the specified data using
	* <a href="https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm">kNN</a> algorithm.</p>
	* <p>
	* Finally, this example loops over the test set of data points, applies the trained model to predict what cluster does
	* this point belong to, and compares prediction to expected outcome (ground truth).</p>
	* <p>
	* You can change the test data used in this example and re-run it to explore this algorithm further.</p>
	*/
	public class ANNClassificationExportImportExample {
	/**
	* Run example.
	*/
	public static void main(String[] args) throws IOException {
	System.out.println();
	System.out.println(">>> ANN 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.");

	IgniteCache<Integer, double[]> dataCache = null;
	Path jsonMdlPath = null;
	try {
	dataCache = getTestCache(ignite);

	ANNClassificationTrainer trainer = new ANNClassificationTrainer()
	.withDistance(new ManhattanDistance())
	.withK(50)
	.withMaxIterations(1000)
	.withEpsilon(1e-2);

	ANNClassificationModel mdl = (ANNClassificationModel) trainer.fit(
	ignite,
	dataCache,
	new DoubleArrayVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.FIRST)
	).withK(5)
	.withDistanceMeasure(new EuclideanDistance())
	.withWeighted(true);

	System.out.println("\n>>> Exported ANN model: " + mdl.toString(true));

	double accuracy = evaluateModel(dataCache, mdl);

	System.out.println("\n>>> Accuracy for exported ANN model:" + accuracy);

	jsonMdlPath = Files.createTempFile(null, null);
	mdl.toJSON(jsonMdlPath);

	ANNClassificationModel modelImportedFromJSON = ANNClassificationModel.fromJSON(jsonMdlPath);

	System.out.println("\n>>> Imported ANN model: " + modelImportedFromJSON.toString(true));

	accuracy = evaluateModel(dataCache, modelImportedFromJSON);

	System.out.println("\n>>> Accuracy for imported ANN model:" + accuracy);

	System.out.println(">>> ANN multi-class classification algorithm over cached dataset usage example completed.");
	}
	finally {
	if (dataCache != null)
	dataCache.destroy();
	if (jsonMdlPath != null)
	Files.deleteIfExists(jsonMdlPath);
	}
	}
	finally {
	System.out.flush();
	}
	}

	/** */
	private static double evaluateModel(IgniteCache<Integer, double[]> dataCache, NNClassificationModel knnMdl) {
	int amountOfErrors = 0;
	int totalAmount = 0;

	double accuracy;
	try (QueryCursor<Cache.Entry<Integer, double[]>> observations = dataCache.query(new ScanQuery<>())) {
	System.out.println(">>> ---------------------------------");
	System.out.println(">>> \| Prediction\t\| Ground Truth\t\|");
	System.out.println(">>> ---------------------------------");

	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.predict(new DenseVector(inputs));

	totalAmount++;
	if (!Precision.equals(groundTruth, prediction, Precision.EPSILON))
	amountOfErrors++;

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

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

	accuracy = 1 - amountOfErrors / (double) totalAmount;

	}
	return accuracy;
	}

	/**
	* 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 k = 0; k < 10; k++) { // multiplies the Iris dataset k times.
	for (int i = 0; i < data.length; i++)
	cache.put(k * 10000 + i, mutate(data[i], k));
	}

	return cache;
	}

	/**
	* Tiny changing of data depending on k parameter.
	*
	* @param datum The vector data.
	* @param k The passed parameter.
	* @return The changed vector data.
	*/
	private static double[] mutate(double[] datum, int k) {
	for (int i = 0; i < datum.length; i++)
	datum[i] += k / 100000;
	return datum;
	}

	/**
	* 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}
	};
	}