IGNITE-9297: Review ML examples javadocs as of release 2.7 this closes #4560
Project: http://git-wip-us.apache.org/repos/asf/ignite/repo Commit: http://git-wip-us.apache.org/repos/asf/ignite/commit/fea694fe Tree: http://git-wip-us.apache.org/repos/asf/ignite/tree/fea694fe Diff: http://git-wip-us.apache.org/repos/asf/ignite/diff/fea694fe Branch: refs/heads/master Commit: fea694fefb96ef9bf66940b7e230d1d0abab1c08 Parents: 0a19d01 Author: Oleg Ignatenko <oignate...@gridgain.com> Authored: Mon Aug 20 14:47:37 2018 +0300 Committer: Yury Babak <yba...@gridgain.com> Committed: Mon Aug 20 14:47:37 2018 +0300 ---------------------------------------------------------------------- .../examples/ml/MLExamplesCommonArgs.java | 31 ---- .../clustering/KMeansClusterizationExample.java | 42 ++--- .../AlgorithmSpecificDatasetExample.java | 30 ++-- .../ml/dataset/CacheBasedDatasetExample.java | 29 +--- .../ml/dataset/LocalDatasetExample.java | 55 +++---- .../ignite/examples/ml/genetic/change/Coin.java | 62 +++---- .../OptimizeMakeChangeFitnessFunction.java | 66 ++++---- .../change/OptimizeMakeChangeGAExample.java | 163 +++++++++---------- .../OptimizeMakeChangeTerminateCriteria.java | 52 +++--- .../helloworld/HelloWorldFitnessFunction.java | 39 ++--- .../genetic/helloworld/HelloWorldGAExample.java | 100 ++++++------ .../helloworld/HelloWorldTerminateCriteria.java | 61 +++---- .../examples/ml/genetic/knapsack/Item.java | 43 ++--- .../knapsack/KnapsackFitnessFunction.java | 43 ++--- .../ml/genetic/knapsack/KnapsackGAExample.java | 157 +++++++++--------- .../knapsack/KnapsackTerminateCriteria.java | 68 ++++---- .../ignite/examples/ml/genetic/movie/Movie.java | 51 +++--- .../ml/genetic/movie/MovieFitnessFunction.java | 70 ++++---- .../ml/genetic/movie/MovieGAExample.java | 141 ++++++++-------- .../genetic/movie/MovieTerminateCriteria.java | 57 +++---- .../ml/knn/ANNClassificationExample.java | 17 +- .../ml/knn/KNNClassificationExample.java | 41 ++--- .../examples/ml/knn/KNNRegressionExample.java | 51 +++--- .../examples/ml/nn/MLPTrainerExample.java | 16 +- .../ml/preprocessing/BinarizationExample.java | 31 ++-- .../ml/preprocessing/ImputingExample.java | 34 ++-- .../ImputingWithMostFrequentValuesExample.java | 38 ++--- .../ml/preprocessing/MinMaxScalerExample.java | 35 ++-- .../ml/preprocessing/NormalizationExample.java | 31 ++-- .../LinearRegressionLSQRTrainerExample.java | 40 ++--- ...ssionLSQRTrainerWithMinMaxScalerExample.java | 47 ++---- .../LinearRegressionSGDTrainerExample.java | 42 ++--- .../LogisticRegressionSGDTrainerExample.java | 43 ++--- ...gressionMultiClassClassificationExample.java | 46 ++---- .../ml/selection/cv/CrossValidationExample.java | 8 +- .../split/TrainTestDatasetSplitterExample.java | 43 ++--- .../binary/SVMBinaryClassificationExample.java | 43 ++--- .../SVMMultiClassClassificationExample.java | 45 ++--- ...ecisionTreeClassificationTrainerExample.java | 17 +- .../DecisionTreeRegressionTrainerExample.java | 12 +- .../GDBOnTreesClassificationTrainerExample.java | 10 +- .../GDBOnTreesRegressionTrainerExample.java | 14 +- .../RandomForestClassificationExample.java | 25 +-- .../RandomForestRegressionExample.java | 25 +-- .../ml/tutorial/Step_1_Read_and_Learn.java | 22 ++- .../examples/ml/tutorial/Step_2_Imputing.java | 21 ++- .../examples/ml/tutorial/Step_3_Categorial.java | 24 ++- .../Step_3_Categorial_with_One_Hot_Encoder.java | 26 ++- .../ml/tutorial/Step_4_Add_age_fare.java | 22 ++- .../examples/ml/tutorial/Step_5_Scaling.java | 40 +++-- .../ignite/examples/ml/tutorial/Step_6_KNN.java | 40 +++-- .../ml/tutorial/Step_7_Split_train_test.java | 43 +++-- .../ignite/examples/ml/tutorial/Step_8_CV.java | 57 ++++--- .../ml/tutorial/Step_8_CV_with_Param_Grid.java | 53 ++++-- .../ml/tutorial/Step_9_Go_to_LogReg.java | 44 +++-- .../examples/ml/tutorial/TitanicUtils.java | 2 +- .../ml/tutorial/TutorialStepByStepExample.java | 4 +- .../ignite/examples/ml/util/DatasetHelper.java | 57 +++++++ .../examples/ml/util/MLExamplesCommonArgs.java | 31 ++++ .../ignite/examples/ml/util/TestCache.java | 77 +++++++++ .../ignite/examples/ml/util/package-info.java | 22 +++ .../testsuites/IgniteExamplesMLTestSuite.java | 2 +- .../ml/knn/regression/KNNRegressionModel.java | 2 +- 63 files changed, 1428 insertions(+), 1275 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/ignite/blob/fea694fe/examples/src/main/java/org/apache/ignite/examples/ml/MLExamplesCommonArgs.java ---------------------------------------------------------------------- diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/MLExamplesCommonArgs.java b/examples/src/main/java/org/apache/ignite/examples/ml/MLExamplesCommonArgs.java deleted file mode 100644 index 701894b..0000000 --- a/examples/src/main/java/org/apache/ignite/examples/ml/MLExamplesCommonArgs.java +++ /dev/null @@ -1,31 +0,0 @@ -/* - * 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; - -/** - * Some common arguments for examples in ML module. - */ -public class MLExamplesCommonArgs { - /** - * Unattended argument. - */ - public static String UNATTENDED = "unattended"; - - /** Empty args for ML examples. */ - public static final String[] EMPTY_ARGS_ML = new String[] {"--" + UNATTENDED}; -} http://git-wip-us.apache.org/repos/asf/ignite/blob/fea694fe/examples/src/main/java/org/apache/ignite/examples/ml/clustering/KMeansClusterizationExample.java ---------------------------------------------------------------------- diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/clustering/KMeansClusterizationExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/clustering/KMeansClusterizationExample.java index e1b9844..5304a70 100644 --- a/examples/src/main/java/org/apache/ignite/examples/ml/clustering/KMeansClusterizationExample.java +++ b/examples/src/main/java/org/apache/ignite/examples/ml/clustering/KMeansClusterizationExample.java @@ -18,27 +18,32 @@ package org.apache.ignite.examples.ml.clustering; 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.examples.ml.util.TestCache; import org.apache.ignite.ml.clustering.kmeans.KMeansModel; import org.apache.ignite.ml.clustering.kmeans.KMeansTrainer; -import org.apache.ignite.ml.knn.classification.KNNClassificationTrainer; import org.apache.ignite.ml.math.Tracer; import org.apache.ignite.ml.math.primitives.vector.VectorUtils; import org.apache.ignite.ml.math.primitives.vector.impl.DenseVector; import org.apache.ignite.thread.IgniteThread; /** - * Run kNN multi-class classification trainer over distributed dataset. - * - * @see KNNClassificationTrainer + * Run KMeans clustering algorithm ({@link KMeansTrainer}) 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 KMeans 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 KMeansClusterizationExample { /** Run example. */ @@ -51,7 +56,7 @@ public class KMeansClusterizationExample { IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(), KMeansClusterizationExample.class.getSimpleName(), () -> { - IgniteCache<Integer, double[]> dataCache = getTestCache(ignite); + IgniteCache<Integer, double[]> dataCache = new TestCache(ignite).get(data); KMeansTrainer trainer = new KMeansTrainer() .withSeed(7867L); @@ -94,6 +99,8 @@ public class KMeansClusterizationExample { System.out.println("\n>>> Absolute amount of errors " + amountOfErrors); System.out.println("\n>>> Accuracy " + (1 - amountOfErrors / (double)totalAmount)); + + System.out.println(">>> KMeans clustering algorithm over cached dataset usage example completed."); } }); @@ -102,25 +109,6 @@ public class KMeansClusterizationExample { } } - /** - * 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 = { {0, 5.1, 3.5, 1.4, 0.2}, http://git-wip-us.apache.org/repos/asf/ignite/blob/fea694fe/examples/src/main/java/org/apache/ignite/examples/ml/dataset/AlgorithmSpecificDatasetExample.java ---------------------------------------------------------------------- diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/dataset/AlgorithmSpecificDatasetExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/dataset/AlgorithmSpecificDatasetExample.java index 1229fb1..de7f0e9 100644 --- a/examples/src/main/java/org/apache/ignite/examples/ml/dataset/AlgorithmSpecificDatasetExample.java +++ b/examples/src/main/java/org/apache/ignite/examples/ml/dataset/AlgorithmSpecificDatasetExample.java @@ -36,20 +36,28 @@ import org.apache.ignite.ml.math.primitives.vector.VectorUtils; /** * Example that shows how to implement your own algorithm (gradient descent trainer for linear regression) which uses * dataset as an underlying infrastructure. - * + * <p> + * Code in this example launches Ignite grid and fills the cache with simple test data.</p> + * <p> + * After that it creates an algorithm specific dataset to perform linear regression as described in more detail below.</p> + * <p> + * Finally, this example trains linear regression model using gradient descent and outputs the result.</p> + * <p> + * You can change the test data used in this example and re-run it to explore this functionality further.</p> + * <p> * The common idea behind using algorithm specific datasets is to write a simple local version algorithm at first, then - * find operations which involves data manipulations, and finally define algorithm specific version of the dataset - * extended by introducing these new operations. As result your algorithm will work with extended dataset (based on - * {@link DatasetWrapper}) in a sequential manner. - * + * find operations which involve data manipulations, and finally define algorithm specific version of the dataset + * extended by introducing these new operations. As a result your algorithm will work with extended dataset (based on + * {@link DatasetWrapper}) in a sequential manner.</p> + * <p> * In this example we need to implement gradient descent. This is iterative method that involves calculation of gradient - * on every step. In according with the common idea we defines + * on every step. In according with the common idea we define * {@link AlgorithmSpecificDatasetExample.AlgorithmSpecificDataset} - extended version of {@code Dataset} with - * {@code gradient} method. As result our gradient descent method looks like a simple loop where every iteration + * {@code gradient} method. As a result our gradient descent method looks like a simple loop where every iteration * includes call of the {@code gradient} method. In the example we want to keep iteration number as well for logging. * Iteration number cannot be recovered from the {@code upstream} data and we need to keep it in the custom * partition {@code context} which is represented by - * {@link AlgorithmSpecificDatasetExample.AlgorithmSpecificPartitionContext} class. + * {@link AlgorithmSpecificDatasetExample.AlgorithmSpecificPartitionContext} class.</p> */ public class AlgorithmSpecificDatasetExample { /** Run example. */ @@ -59,7 +67,7 @@ public class AlgorithmSpecificDatasetExample { IgniteCache<Integer, Person> persons = createCache(ignite); - // Creates a algorithm specific dataset to perform linear regression. Here we defines the way features and + // Creates a algorithm specific dataset to perform linear regression. Here we define the way features and // labels are extracted, and partition data and context are created. try (AlgorithmSpecificDataset dataset = DatasetFactory.create( ignite, @@ -169,12 +177,12 @@ public class AlgorithmSpecificDatasetExample { private int iteration; /** */ - public int getIteration() { + int getIteration() { return iteration; } /** */ - public void setIteration(int iteration) { + void setIteration(int iteration) { this.iteration = iteration; } } http://git-wip-us.apache.org/repos/asf/ignite/blob/fea694fe/examples/src/main/java/org/apache/ignite/examples/ml/dataset/CacheBasedDatasetExample.java ---------------------------------------------------------------------- diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/dataset/CacheBasedDatasetExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/dataset/CacheBasedDatasetExample.java index b5a7059..3f75540 100644 --- a/examples/src/main/java/org/apache/ignite/examples/ml/dataset/CacheBasedDatasetExample.java +++ b/examples/src/main/java/org/apache/ignite/examples/ml/dataset/CacheBasedDatasetExample.java @@ -17,13 +17,13 @@ package org.apache.ignite.examples.ml.dataset; -import java.util.Arrays; 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.examples.ml.dataset.model.Person; +import org.apache.ignite.examples.ml.util.DatasetHelper; import org.apache.ignite.ml.dataset.DatasetFactory; import org.apache.ignite.ml.dataset.primitive.SimpleDataset; import org.apache.ignite.ml.math.primitives.vector.VectorUtils; @@ -31,6 +31,13 @@ import org.apache.ignite.ml.math.primitives.vector.VectorUtils; /** * Example that shows how to create dataset based on an existing Ignite Cache and then use it to calculate {@code mean} * and {@code std} values as well as {@code covariance} and {@code correlation} matrices. + * <p> + * Code in this example launches Ignite grid and fills the cache with simple test data.</p> + * <p> + * After that it creates the dataset based on the data in the cache and uses Dataset API to find and output + * various statistical metrics of the data.</p> + * <p> + * You can change the test data used in this example and re-run it to explore this functionality further.</p> */ public class CacheBasedDatasetExample { /** Run example. */ @@ -46,25 +53,7 @@ public class CacheBasedDatasetExample { persons, (k, v) -> VectorUtils.of(v.getAge(), v.getSalary()) )) { - // Calculation of the mean value. This calculation will be performed in map-reduce manner. - double[] mean = dataset.mean(); - System.out.println("Mean \n\t" + Arrays.toString(mean)); - - // Calculation of the standard deviation. This calculation will be performed in map-reduce manner. - double[] std = dataset.std(); - System.out.println("Standard deviation \n\t" + Arrays.toString(std)); - - // Calculation of the covariance matrix. This calculation will be performed in map-reduce manner. - double[][] cov = dataset.cov(); - System.out.println("Covariance matrix "); - for (double[] row : cov) - System.out.println("\t" + Arrays.toString(row)); - - // Calculation of the correlation matrix. This calculation will be performed in map-reduce manner. - double[][] corr = dataset.corr(); - System.out.println("Correlation matrix "); - for (double[] row : corr) - System.out.println("\t" + Arrays.toString(row)); + new DatasetHelper(dataset).describe(); } System.out.println(">>> Cache Based Dataset example completed."); http://git-wip-us.apache.org/repos/asf/ignite/blob/fea694fe/examples/src/main/java/org/apache/ignite/examples/ml/dataset/LocalDatasetExample.java ---------------------------------------------------------------------- diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/dataset/LocalDatasetExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/dataset/LocalDatasetExample.java index e3af738..282e76b 100644 --- a/examples/src/main/java/org/apache/ignite/examples/ml/dataset/LocalDatasetExample.java +++ b/examples/src/main/java/org/apache/ignite/examples/ml/dataset/LocalDatasetExample.java @@ -17,12 +17,10 @@ package org.apache.ignite.examples.ml.dataset; -import java.util.Arrays; import java.util.HashMap; import java.util.Map; -import org.apache.ignite.Ignite; -import org.apache.ignite.Ignition; import org.apache.ignite.examples.ml.dataset.model.Person; +import org.apache.ignite.examples.ml.util.DatasetHelper; import org.apache.ignite.ml.dataset.DatasetFactory; import org.apache.ignite.ml.dataset.primitive.SimpleDataset; import org.apache.ignite.ml.math.primitives.vector.VectorUtils; @@ -30,48 +28,35 @@ import org.apache.ignite.ml.math.primitives.vector.VectorUtils; /** * Example that shows how to create dataset based on an existing local storage and then use it to calculate {@code mean} * and {@code std} values as well as {@code covariance} and {@code correlation} matrices. + * <p> + * Code in this example the storage with simple test data.</p> + * <p> + * After that it creates the dataset based on the data in the storage and uses Dataset API to find and output + * various statistical metrics of the data.</p> + * <p> + * You can change the test data used in this example and re-run it to explore this functionality further.</p> */ public class LocalDatasetExample { /** Run example. */ public static void main(String[] args) throws Exception { - try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) { - System.out.println(">>> Local Dataset example started."); + System.out.println(">>> Local Dataset example started."); - Map<Integer, Person> persons = createCache(ignite); + Map<Integer, Person> persons = createCache(); - // Creates a local simple dataset containing features and providing standard dataset API. - try (SimpleDataset<?> dataset = DatasetFactory.createSimpleDataset( - persons, - 2, - (k, v) -> VectorUtils.of(v.getAge(), v.getSalary()) - )) { - // Calculation of the mean value. This calculation will be performed in map-reduce manner. - double[] mean = dataset.mean(); - System.out.println("Mean \n\t" + Arrays.toString(mean)); - - // Calculation of the standard deviation. This calculation will be performed in map-reduce manner. - double[] std = dataset.std(); - System.out.println("Standard deviation \n\t" + Arrays.toString(std)); - - // Calculation of the covariance matrix. This calculation will be performed in map-reduce manner. - double[][] cov = dataset.cov(); - System.out.println("Covariance matrix "); - for (double[] row : cov) - System.out.println("\t" + Arrays.toString(row)); - - // Calculation of the correlation matrix. This calculation will be performed in map-reduce manner. - double[][] corr = dataset.corr(); - System.out.println("Correlation matrix "); - for (double[] row : corr) - System.out.println("\t" + Arrays.toString(row)); - } - - System.out.println(">>> Local Dataset example completed."); + // Creates a local simple dataset containing features and providing standard dataset API. + try (SimpleDataset<?> dataset = DatasetFactory.createSimpleDataset( + persons, + 2, + (k, v) -> VectorUtils.of(v.getAge(), v.getSalary()) + )) { + new DatasetHelper(dataset).describe(); } + + System.out.println(">>> Local Dataset example completed."); } /** */ - private static Map<Integer, Person> createCache(Ignite ignite) { + private static Map<Integer, Person> createCache() { Map<Integer, Person> persons = new HashMap<>(); persons.put(1, new Person("Mike", 42, 10000)); http://git-wip-us.apache.org/repos/asf/ignite/blob/fea694fe/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/Coin.java ---------------------------------------------------------------------- diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/Coin.java b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/Coin.java index 4944a6b..14b7553 100644 --- a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/Coin.java +++ b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/Coin.java @@ -20,62 +20,69 @@ package org.apache.ignite.examples.ml.genetic.change; import java.io.Serializable; /** - * POJO to model a coin + * POJO to model a coin. */ public class Coin implements Serializable { - /** Define Coin Type */ - public static enum CoinType { - PENNY, QUARTER, NICKEL, DIME + /** Define Coin Type. */ + public enum CoinType { + /** */ + PENNY, + /** */ + QUARTER, + /** */ + NICKEL, + /** */ + DIME } - ; + /** Number of coins. */ + private int numOfCoins; - /** number of coins */ - private int numberOfCoins = 0; - - /** CoinType */ - private CoinType coinType = null; + /** Coin type. */ + private CoinType coinType; /** - * @param coinType Type of coin - * @param numberOfCoins Number of coins + * Create instance. + * + * @param coinType Type of coin. + * @param numOfCoins Number of coins. */ - public Coin(CoinType coinType, int numberOfCoins) { + Coin(CoinType coinType, int numOfCoins) { this.coinType = coinType; - this.numberOfCoins = numberOfCoins; + this.numOfCoins = numOfCoins; } /** - * Retrieve the number of coins + * Retrieve the number of coins. * - * @return Number of coins + * @return Number of coins. */ - public int getNumberOfCoins() { - return numberOfCoins; + public int getNumOfCoins() { + return numOfCoins; } /** - * Set the number of coins + * Set the number of coins. * - * @param numberOfCoins Number of coins + * @param numOfCoins Number of coins. */ - public void setNumberOfCoins(int numberOfCoins) { - this.numberOfCoins = numberOfCoins; + public void setNumOfCoins(int numOfCoins) { + this.numOfCoins = numOfCoins; } /** - * Retrieve Coin type + * Retrieve Coin type. * - * @return Coin type + * @return Coin type. */ public CoinType getCoinType() { return coinType; } /** - * Set Coin type + * Set Coin type. * - * @param coinType Coin Type + * @param coinType Coin type. */ public void setCoinType(CoinType coinType) { this.coinType = coinType; @@ -83,7 +90,6 @@ public class Coin implements Serializable { /** {@inheritDoc} */ @Override public String toString() { - return "Coin [numberOfCoins=" + numberOfCoins + ", coinType=" + coinType + "]"; + return "Coin [numOfCoins=" + numOfCoins + ", coinType=" + coinType + "]"; } - } http://git-wip-us.apache.org/repos/asf/ignite/blob/fea694fe/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/OptimizeMakeChangeFitnessFunction.java ---------------------------------------------------------------------- diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/OptimizeMakeChangeFitnessFunction.java b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/OptimizeMakeChangeFitnessFunction.java index 34626be..36b5777 100644 --- a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/OptimizeMakeChangeFitnessFunction.java +++ b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/OptimizeMakeChangeFitnessFunction.java @@ -22,83 +22,75 @@ import org.apache.ignite.ml.genetic.Gene; import org.apache.ignite.ml.genetic.IFitnessFunction; /** - * This example demonstrates how to create a IFitnessFunction <br/> - * - * Your IFitness function will vary depending on your particular use case. <br/> - * - * For this fitness function, we simply want to calculate the value of <br/> - * - * an individual solution relative to other solutions. <br/> + * This example demonstrates how to create a {@link IFitnessFunction}. + * <p> + * Your fitness function will vary depending on your particular use case. For this fitness function, we simply want + * to calculate the value of an individual solution relative to other solutions.</p> */ public class OptimizeMakeChangeFitnessFunction implements IFitnessFunction { - /** target amount */ - int targetAmount = 0; + /** Target amount. */ + private int targetAmount; /** - * @param targetAmount Amount of change + * @param targetAmount Amount of change. */ public OptimizeMakeChangeFitnessFunction(int targetAmount) { this.targetAmount = targetAmount; } /** - * Calculate fitness + * Calculate fitness. * - * @param genes Genes - * @return Fitness value + * @param genes List of genes. + * @return Fitness value. */ public double evaluate(List<Gene> genes) { - int changeAmount = getAmountOfChange(genes); int totalCoins = getTotalNumberOfCoins(genes); int changeDifference = Math.abs(targetAmount - changeAmount); double fitness = (99 - changeDifference); - if (changeAmount == targetAmount) { + if (changeAmount == targetAmount) fitness += 100 - (10 * totalCoins); - } return fitness; - } /** - * Calculate amount of change + * Calculate amount of change. * - * @param genes Genes - * @return Amount of change + * @param genes List of genes. + * @return Amount of change. */ private int getAmountOfChange(List<Gene> genes) { - Gene quarterGene = (Gene)genes.get(0); - Gene dimeGene = (Gene)genes.get(1); - Gene nickelGene = (Gene)genes.get(2); - Gene pennyGene = (Gene)genes.get(3); + Gene quarterGene = genes.get(0); + Gene dimeGene = genes.get(1); + Gene nickelGene = genes.get(2); + Gene pennyGene = genes.get(3); - int numQuarters = ((Coin)quarterGene.getVal()).getNumberOfCoins(); - int numDimes = ((Coin)dimeGene.getVal()).getNumberOfCoins(); - int numNickels = ((Coin)nickelGene.getVal()).getNumberOfCoins(); - int numPennies = ((Coin)pennyGene.getVal()).getNumberOfCoins(); + int numQuarters = ((Coin)quarterGene.getVal()).getNumOfCoins(); + int numDimes = ((Coin)dimeGene.getVal()).getNumOfCoins(); + int numNickels = ((Coin)nickelGene.getVal()).getNumOfCoins(); + int numPennies = ((Coin)pennyGene.getVal()).getNumOfCoins(); return (numQuarters * 25) + (numDimes * 10) + (numNickels * 5) + numPennies; } /** - * Return the total number of coins + * Return the total number of coins. * - * @param genes Genes - * @return Number of coins + * @param genes List of genes. + * @return Number of coins. */ private int getTotalNumberOfCoins(List<Gene> genes) { - - int totalNumberOfCoins = 0; + int totalNumOfCoins = 0; for (Gene gene : genes) { - int numberOfCoins = ((Coin)gene.getVal()).getNumberOfCoins(); - totalNumberOfCoins = totalNumberOfCoins + numberOfCoins; - + int numOfCoins = ((Coin)gene.getVal()).getNumOfCoins(); + totalNumOfCoins = totalNumOfCoins + numOfCoins; } - return totalNumberOfCoins; + return totalNumOfCoins; } } http://git-wip-us.apache.org/repos/asf/ignite/blob/fea694fe/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/OptimizeMakeChangeGAExample.java ---------------------------------------------------------------------- diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/OptimizeMakeChangeGAExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/OptimizeMakeChangeGAExample.java index 1e80d6d..ed392c29 100644 --- a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/OptimizeMakeChangeGAExample.java +++ b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/OptimizeMakeChangeGAExample.java @@ -30,110 +30,101 @@ import org.apache.ignite.ml.genetic.parameter.GAConfiguration; import org.apache.ignite.ml.genetic.parameter.GAGridConstants; /** - * This example demonstrates how to use the GAGrid framework. <br/> - * - * This example is inspired by JGAP's "Minimize Make Change" example. <br/> - * + * This example demonstrates how to use the {@link GAGrid} framework. It is inspired by + * <a href="https://github.com/martin-steghoefer/jgap/blob/master/examples/src/examples/MinimizingMakeChange.java";> + * JGAP's "Minimize Make Change"</a> example. + * <p> * In this example, the objective is to calculate the minimum number of coins that equal user specified amount of - * change - * - * ie: -DAMOUNTCHANGE - * - * mvn exec:java -Dexec.mainClass="org.apache.ignite.examples.ml.genetic.change.OptimizeMakeChangeGAExample" - * -DAMOUNTCHANGE=75 - * - * <p> Remote nodes should always be started with special configuration file which enables P2P class loading: {@code - * 'ignite.{sh|bat} examples/config/example-ignite.xml'}.</p> <p> Alternatively you can run ExampleNodeStartup in - * another JVM which will start node with {@code examples/config/example-ignite.xml} configuration.</p> + * change ie: {@code -DAMOUNTCHANGE}.</p> + * <p> + * {@code mvn exec:java -Dexec.mainClass="org.apache.ignite.examples.ml.genetic.change.OptimizeMakeChangeGAExample" + * -DAMOUNTCHANGE=75}</p> + * <p> + * Code in this example launches Ignite grid, prepares simple test data (gene pool) and configures GA grid.</p> + * <p> + * After that it launches the process of evolution on GA grid and outputs the progress and results.</p> + * <p> + * You can change the test data and parameters of GA grid used in this example and re-run it to explore + * this functionality further.</p> + * <p> + * Remote nodes should always be started with special configuration file which enables P2P class loading: {@code + * 'ignite.{sh|bat} examples/config/example-ignite.xml'}.</p> + * <p> + * Alternatively you can run ExampleNodeStartup in another JVM which will start node with + * {@code examples/config/example-ignite.xml} configuration.</p> */ public class OptimizeMakeChangeGAExample { - /** Ignite instance */ - private static Ignite ignite = null; - - /** GAGrid */ - private static GAGrid gaGrid = null; - - /** GAConfiguration */ - private static GAConfiguration gaConfig = null; - - /** amount of change */ - private static String sAmountChange = null; - - /** Ignite logger */ - private static IgniteLogger logger = null; - /** * Executes example. * - * Specify value for -DAMOUNTCHANGE JVM system variable + * Specify value for {@code -DAMOUNTCHANGE} JVM system variable. * * @param args Command line arguments, none required. */ public static void main(String args[]) { + System.out.println(">>> OptimizeMakeChange GA grid example started."); + System.setProperty("IGNITE_QUIET", "false"); - sAmountChange = "75"; + String sAmountChange = "75"; - StringBuffer sbErrorMessage = new StringBuffer(); - sbErrorMessage.append("AMOUNTCHANGE System property not set. Please provide a valid value between 1 and 99. "); - sbErrorMessage.append(" "); - sbErrorMessage.append("IE: -DAMOUNTCHANGE=75"); - sbErrorMessage.append("\n"); - sbErrorMessage.append("Using default value: 75"); + StringBuilder sbErrorMsg = new StringBuilder(); + sbErrorMsg.append("AMOUNTCHANGE System property not set. Please provide a valid value between 1 and 99. "); + sbErrorMsg.append(" "); + sbErrorMsg.append("IE: -DAMOUNTCHANGE=75"); + sbErrorMsg.append("\n"); + sbErrorMsg.append("Using default value: 75"); - //Check if -DAMOUNTCHANGE JVM system variable is provided - if (System.getProperty("AMOUNTCHANGE") == null) { - System.out.println(sbErrorMessage); - } - else { + //Check if -DAMOUNTCHANGE JVM system variable is provided. + if (System.getProperty("AMOUNTCHANGE") == null) + System.out.println(sbErrorMsg); + else sAmountChange = System.getProperty("AMOUNTCHANGE"); - } try { + // Create an Ignite instance as you would in any other use case. + Ignite ignite = Ignition.start("examples/config/example-ignite.xml"); - //Create an Ignite instance as you would in any other use case. - ignite = Ignition.start("examples/config/example-ignite.xml"); - - logger = ignite.log(); + IgniteLogger log = ignite.log(); - // Create GAConfiguration - gaConfig = new GAConfiguration(); + // Create GAConfiguration. + GAConfiguration gaCfg = new GAConfiguration(); - // set Gene Pool + // Set Gene Pool. List<Gene> genes = getGenePool(); - // set selection method - gaConfig.setSelectionMtd(GAGridConstants.SELECTION_METHOD.SELECTON_METHOD_ELETISM); - gaConfig.setElitismCnt(10); + // Set selection method. + gaCfg.setSelectionMtd(GAGridConstants.SELECTION_METHOD.SELECTON_METHOD_ELETISM); + gaCfg.setElitismCnt(10); - // set the Chromosome Length to '4' since we have 4 coins. - gaConfig.setChromosomeLen(4); + // Set the Chromosome Length to '4' since we have 4 coins. + gaCfg.setChromosomeLen(4); - // set population size - gaConfig.setPopulationSize(500); + // Set population size. + gaCfg.setPopulationSize(500); - // initialize gene pool - gaConfig.setGenePool(genes); + // Initialize gene pool. + gaCfg.setGenePool(genes); - // set Truncate Rate - gaConfig.setTruncateRate(.10); + // Set Truncate Rate. + gaCfg.setTruncateRate(.10); - // set Cross Over Rate - gaConfig.setCrossOverRate(.50); + // Set Cross Over Rate. + gaCfg.setCrossOverRate(.50); - // set Mutation Rate - gaConfig.setMutationRate(.50); + // Set Mutation Rate. + gaCfg.setMutationRate(.50); - // create and set Fitness function + // Create and set Fitness function. OptimizeMakeChangeFitnessFunction function = new OptimizeMakeChangeFitnessFunction(new Integer(sAmountChange)); - gaConfig.setFitnessFunction(function); + gaCfg.setFitnessFunction(function); - // create and set TerminateCriteria + // Create and set TerminateCriteria. OptimizeMakeChangeTerminateCriteria termCriteria = new OptimizeMakeChangeTerminateCriteria(ignite); ChromosomeCriteria chromosomeCriteria = new ChromosomeCriteria(); - List values = new ArrayList(); + List<String> values = new ArrayList<>(); values.add("coinType=QUARTER"); values.add("coinType=DIME"); @@ -142,40 +133,41 @@ public class OptimizeMakeChangeGAExample { chromosomeCriteria.setCriteria(values); - gaConfig.setChromosomeCriteria(chromosomeCriteria); - gaConfig.setTerminateCriteria(termCriteria); + gaCfg.setChromosomeCriteria(chromosomeCriteria); + gaCfg.setTerminateCriteria(termCriteria); - // initialize GAGrid - gaGrid = new GAGrid(gaConfig, ignite); + // Initialize GAGrid. + GAGrid gaGrid = new GAGrid(gaCfg, ignite); - logger.info("##########################################################################################"); + log.info("##########################################################################################"); - logger.info("Calculating optimal set of coins where amount of change is " + sAmountChange); + log.info("Calculating optimal set of coins where amount of change is " + sAmountChange); - logger.info("##########################################################################################"); + log.info("##########################################################################################"); - Chromosome fittestChromosome = gaGrid.evolve(); + Chromosome chromosome = gaGrid.evolve(); - Ignition.stop(true); + System.out.println(">>> Evolution result: " + chromosome); - ignite = null; + Ignition.stop(true); + System.out.println(">>> OptimizeMakeChange GA grid example completed."); } catch (Exception e) { - System.out.println(e); + System.out.println(e.getMessage()); + e.printStackTrace(); } - } /** - * Helper routine to initialize Gene pool + * Helper routine to initialize Gene pool. * - * In typical usecase genes may be stored in database. + * In typical use case genes may be stored in database. * - * @return List of Genes + * @return List of Genes. */ private static List<Gene> getGenePool() { - List<Gene> list = new ArrayList(); + List<Gene> list = new ArrayList<>(); Gene quarterGene1 = new Gene(new Coin(Coin.CoinType.QUARTER, 3)); Gene quarterGene2 = new Gene(new Coin(Coin.CoinType.QUARTER, 2)); @@ -212,5 +204,4 @@ public class OptimizeMakeChangeGAExample { return list; } - } http://git-wip-us.apache.org/repos/asf/ignite/blob/fea694fe/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/OptimizeMakeChangeTerminateCriteria.java ---------------------------------------------------------------------- diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/OptimizeMakeChangeTerminateCriteria.java b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/OptimizeMakeChangeTerminateCriteria.java index 2080ac3..1d6612c 100644 --- a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/OptimizeMakeChangeTerminateCriteria.java +++ b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/change/OptimizeMakeChangeTerminateCriteria.java @@ -26,57 +26,59 @@ import org.apache.ignite.ml.genetic.parameter.ITerminateCriteria; import org.apache.ignite.ml.genetic.utils.GAGridUtils; /** - * Terminate Condition implementation for OptimizeMakeChangeGATest <br/> + * Terminate Condition implementation for {@link OptimizeMakeChangeGAExample}. */ public class OptimizeMakeChangeTerminateCriteria implements ITerminateCriteria { - /** Ignite logger */ - private IgniteLogger igniteLogger = null; - /** Ignite instance */ - private Ignite ignite = null; + /** Ignite logger. */ + private IgniteLogger igniteLog; + /** Ignite instance. */ + private Ignite ignite; /** - * @param ignite Ignite + * Create class instance. + * + * @param ignite Ignite instance. */ public OptimizeMakeChangeTerminateCriteria(Ignite ignite) { this.ignite = ignite; - this.igniteLogger = ignite.log(); + this.igniteLog = ignite.log(); } /** - * @param fittestChromosome Most fit chromosome at for the nth generation - * @param averageFitnessScore Average fitness score as of the nth generation - * @param currentGeneration Current generation - * @return Boolean value + * Check whether termination condition is met. + * + * @param fittestChromosome Most fit chromosome at for the nth generation. + * @param averageFitnessScore Average fitness score as of the nth generation. + * @param currGeneration Current generation. + * @return Status whether condition is met or not. */ public boolean isTerminationConditionMet(Chromosome fittestChromosome, double averageFitnessScore, - int currentGeneration) { + int currGeneration) { boolean isTerminate = true; - igniteLogger.info("##########################################################################################"); - igniteLogger.info("Generation: " + currentGeneration); - igniteLogger.info("Fittest is Chromosome Key: " + fittestChromosome); - igniteLogger.info("Chromsome: " + fittestChromosome); + igniteLog.info("##########################################################################################"); + igniteLog.info("Generation: " + currGeneration); + igniteLog.info("Fittest is Chromosome Key: " + fittestChromosome); + igniteLog.info("Chromosome: " + fittestChromosome); printCoins(GAGridUtils.getGenesInOrderForChromosome(ignite, fittestChromosome)); - igniteLogger.info("Avg Chromsome Fitness: " + averageFitnessScore); - igniteLogger.info("##########################################################################################"); + igniteLog.info("Avg Chromosome Fitness: " + averageFitnessScore); + igniteLog.info("##########################################################################################"); - if (!(currentGeneration > 5)) { + if (!(currGeneration > 5)) isTerminate = false; - } return isTerminate; } /** - * Helper to print change detail + * Helper to print change details. * - * @param genes List if Genes + * @param genes List if Genes. */ private void printCoins(List<Gene> genes) { for (Gene gene : genes) { - igniteLogger.info("Coin Type: " + ((Coin)gene.getVal()).getCoinType().toString()); - igniteLogger.info("Number of Coins: " + ((Coin)gene.getVal()).getNumberOfCoins()); + igniteLog.info("Coin Type: " + ((Coin)gene.getVal()).getCoinType().toString()); + igniteLog.info("Number of Coins: " + ((Coin)gene.getVal()).getNumOfCoins()); } - } } http://git-wip-us.apache.org/repos/asf/ignite/blob/fea694fe/examples/src/main/java/org/apache/ignite/examples/ml/genetic/helloworld/HelloWorldFitnessFunction.java ---------------------------------------------------------------------- diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/helloworld/HelloWorldFitnessFunction.java b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/helloworld/HelloWorldFitnessFunction.java index 190bd2b..a339ff5 100644 --- a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/helloworld/HelloWorldFitnessFunction.java +++ b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/helloworld/HelloWorldFitnessFunction.java @@ -22,41 +22,30 @@ import org.apache.ignite.ml.genetic.Gene; import org.apache.ignite.ml.genetic.IFitnessFunction; /** - * This example demonstrates how to create a IFitnessFunction - * - * Your IFitness function will vary depending on your particular use case. - * - * For this fitness function, we simply want to calculate the value of - * - * an individual solution relative to other solutions. - * - * - * To do this, we simply increase fitness score by '1' for each character - * - * that is correct position. - * - * For our solution, our genetic algorithm will continue until - * - * we achieve a fitness score of '11', as 'HELLO WORLD' contains '11' characters. + * This example demonstrates how to create a {@link IFitnessFunction}. + * <p> + * Your fitness function will vary depending on your particular use case. For this fitness function, we simply want + * to calculate the value of an individual solution relative to other solutions. + * <p> + * To do this, we increase fitness score by '1' for each character that is in correct position.</p> + * <p> + * For our solution, genetic algorithm will continue until we achieve a fitness score of '11', as 'HELLO WORLD' + * contains 11 characters.</p> */ public class HelloWorldFitnessFunction implements IFitnessFunction { - /** Optimal target solution */ - private String targetString = "HELLO WORLD"; - /** - * Calculate fitness + * Calculate fitness. * - * @param genes List of Genes - * @return Fitness value + * @param genes List of Genes. + * @return Fitness value. */ public double evaluate(List<Gene> genes) { - double matches = 0; for (int i = 0; i < genes.size(); i++) { - if (((Character)(genes.get(i).getVal())).equals(targetString.charAt(i))) { + String targetStr = "HELLO WORLD"; + if (genes.get(i).getVal().equals(targetStr.charAt(i))) matches = matches + 1; - } } return matches; } http://git-wip-us.apache.org/repos/asf/ignite/blob/fea694fe/examples/src/main/java/org/apache/ignite/examples/ml/genetic/helloworld/HelloWorldGAExample.java ---------------------------------------------------------------------- diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/helloworld/HelloWorldGAExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/helloworld/HelloWorldGAExample.java index 839471a..3182d2f 100644 --- a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/helloworld/HelloWorldGAExample.java +++ b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/helloworld/HelloWorldGAExample.java @@ -27,99 +27,99 @@ import org.apache.ignite.ml.genetic.Gene; import org.apache.ignite.ml.genetic.parameter.GAConfiguration; /** - * This example demonstrates how to use the GAGrid framework. - * - * In this example, we want to evolve a string of 11 characters such that the word 'HELLO WORLD'. - * - * is found. - * - * - * How To Run: - * - * mvn exec:java -Dexec.mainClass="org.apache.ignite.examples.ml.genetic.helloworld.HelloWorldGAExample" - * - * <p> Remote nodes should always be started with special configuration file which enables P2P class loading: {@code - * 'ignite.{sh|bat} examples/config/example-ignite.xml'}.</p> <p> Alternatively you can run ExampleNodeStartup in - * another JVM which will start node with {@code examples/config/example-ignite.xml} configuration.</p> + * This example demonstrates how to use the {@link GAGrid} framework. In this example, we want to evolve a string + * of 11 characters such that the word 'HELLO WORLD' is found. + * <p> + * Code in this example launches Ignite grid, prepares simple test data (gene pool) and configures GA grid.</p> + * <p> + * After that it launches the process of evolution on GA grid and outputs the progress and results.</p> + * <p> + * You can change the test data and parameters of GA grid used in this example and re-run it to explore + * this functionality further.</p> + * <p> + * How to run from command line:</p> + * <p> + * {@code mvn exec:java -Dexec.mainClass="org.apache.ignite.examples.ml.genetic.helloworld.HelloWorldGAExample"}</p> + * <p> + * Remote nodes should always be started with special configuration file which enables P2P class loading: {@code + * 'ignite.{sh|bat} examples/config/example-ignite.xml'}.</p> + * <p> + * Alternatively you can run ExampleNodeStartup in another JVM which will start node with + * {@code examples/config/example-ignite.xml} configuration.</p> */ public class HelloWorldGAExample { - /** Ignite instance */ - private static Ignite ignite = null; - /** GAGrid */ - private static GAGrid gaGrid = null; - /** GAConfiguration */ - private static GAConfiguration gaConfig = null; - /** * Executes example. * * @param args Command line arguments, none required. */ public static void main(String args[]) { + System.out.println(">>> HelloWorld GA grid example started."); + System.setProperty("IGNITE_QUIET", "false"); try { + // Create an Ignite instance as you would in any other use case. + Ignite ignite = Ignition.start("examples/config/example-ignite.xml"); - //Create an Ignite instance as you would in any other use case. + // Create GAConfiguration. + GAConfiguration gaCfg = new GAConfiguration(); - ignite = Ignition.start("examples/config/example-ignite.xml"); - - // Create GAConfiguration - gaConfig = new GAConfiguration(); - - // set Gene Pool + // Set Gene Pool. List<Gene> genes = getGenePool(); - // set the Chromosome Length to '11' since 'HELLO WORLD' contains 11 characters. - gaConfig.setChromosomeLen(11); + // Set the Chromosome Length to '11' since 'HELLO WORLD' contains 11 characters. + gaCfg.setChromosomeLen(11); - // initialize gene pool - gaConfig.setGenePool(genes); + // Initialize gene pool. + gaCfg.setGenePool(genes); - // create and set Fitness function + // Create and set Fitness function. HelloWorldFitnessFunction function = new HelloWorldFitnessFunction(); - gaConfig.setFitnessFunction(function); + gaCfg.setFitnessFunction(function); - // create and set TerminateCriteria + // Create and set TerminateCriteria. HelloWorldTerminateCriteria termCriteria = new HelloWorldTerminateCriteria(ignite); - gaConfig.setTerminateCriteria(termCriteria); + gaCfg.setTerminateCriteria(termCriteria); ignite.log(); - gaGrid = new GAGrid(gaConfig, ignite); - // evolve the population - Chromosome fittestChromosome = gaGrid.evolve(); + GAGrid gaGrid = new GAGrid(gaCfg, ignite); - Ignition.stop(true); + // Evolve the population. + Chromosome chromosome = gaGrid.evolve(); + + System.out.println(">>> Evolution result: " + chromosome); - ignite = null; + Ignition.stop(true); + System.out.println(">>> HelloWorld GA grid example completed."); } catch (Exception e) { - System.out.println(e); + System.out.println(e.getMessage()); + e.printStackTrace(); } - } /** - * Helper routine to initialize Gene pool + * Helper routine to initialize Gene pool. * - * In typical usecase genes may be stored in database. + * In typical use case genes may be stored in database. * - * @return List<Gene> + * @return List of Gene objects. */ private static List<Gene> getGenePool() { - List<Gene> list = new ArrayList(); + List<Gene> list = new ArrayList<>(); char[] chars = { 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', ' '}; - for (int i = 0; i < chars.length; i++) { - Gene gene = new Gene(new Character(chars[i])); + for (char aChar : chars) { + Gene gene = new Gene(aChar); list.add(gene); } + return list; } - } http://git-wip-us.apache.org/repos/asf/ignite/blob/fea694fe/examples/src/main/java/org/apache/ignite/examples/ml/genetic/helloworld/HelloWorldTerminateCriteria.java ---------------------------------------------------------------------- diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/helloworld/HelloWorldTerminateCriteria.java b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/helloworld/HelloWorldTerminateCriteria.java index 41809d4..f149da1 100644 --- a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/helloworld/HelloWorldTerminateCriteria.java +++ b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/helloworld/HelloWorldTerminateCriteria.java @@ -26,62 +26,63 @@ import org.apache.ignite.ml.genetic.parameter.ITerminateCriteria; import org.apache.ignite.ml.genetic.utils.GAGridUtils; /** - * Represents the terminate condition for HelloWorld Genetic algorithm - * - * Class terminates Genetic algorithm when fitnessScore > 10 + * Represents the terminate condition for {@link HelloWorldGAExample}. + * <p> + * Class terminates Genetic algorithm when fitness score is more than 10.</p> */ public class HelloWorldTerminateCriteria implements ITerminateCriteria { - /** Ignite logger */ - private IgniteLogger igniteLogger = null; - /** Ignite instance */ - private Ignite ignite = null; + /** Ignite logger. */ + private IgniteLogger igniteLog; + /** Ignite instance. */ + private Ignite ignite; /** - * @param ignite Ignite + * Create class instance. + * + * @param ignite Ignite instance. */ public HelloWorldTerminateCriteria(Ignite ignite) { this.ignite = ignite; - this.igniteLogger = ignite.log(); + this.igniteLog = ignite.log(); } /** - * @param fittestChromosome Most fit chromosome at for the nth generation - * @param averageFitnessScore Average fitness score as of the nth generation - * @param currentGeneration Current generation - * @return Boolean value + * Check whether termination condition is met. + * + * @param fittestChromosome Most fit chromosome at for the nth generation. + * @param averageFitnessScore Average fitness score as of the nth generation. + * @param currGeneration Current generation. + * @return Status whether condition is met or not. */ public boolean isTerminationConditionMet(Chromosome fittestChromosome, double averageFitnessScore, - int currentGeneration) { + int currGeneration) { boolean isTerminate = true; - igniteLogger.info("##########################################################################################"); - igniteLogger.info("Generation: " + currentGeneration); - igniteLogger.info("Fittest is Chromosome Key: " + fittestChromosome); - igniteLogger.info("Chromosome: " + fittestChromosome); + igniteLog.info("##########################################################################################"); + igniteLog.info("Generation: " + currGeneration); + igniteLog.info("Fittest is Chromosome Key: " + fittestChromosome); + igniteLog.info("Chromosome: " + fittestChromosome); printPhrase(GAGridUtils.getGenesInOrderForChromosome(ignite, fittestChromosome)); - igniteLogger.info("Avg Chromosome Fitness: " + averageFitnessScore); - igniteLogger.info("##########################################################################################"); + igniteLog.info("Avg Chromosome Fitness: " + averageFitnessScore); + igniteLog.info("##########################################################################################"); - if (!(fittestChromosome.getFitnessScore() > 10)) { + if (!(fittestChromosome.getFitnessScore() > 10)) isTerminate = false; - } return isTerminate; } /** - * Helper to print Phrase + * Helper to print phrase. * - * @param List of Genes + * @param genes List of Genes. */ private void printPhrase(List<Gene> genes) { + StringBuilder sbPhrase = new StringBuilder(); - StringBuffer sbPhrase = new StringBuffer(); - - for (Gene gene : genes) { + for (Gene gene : genes) sbPhrase.append(((Character)gene.getVal()).toString()); - } - igniteLogger.info(sbPhrase.toString()); - } + igniteLog.info(sbPhrase.toString()); + } } http://git-wip-us.apache.org/repos/asf/ignite/blob/fea694fe/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/Item.java ---------------------------------------------------------------------- diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/Item.java b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/Item.java index f64cb17..43a387c 100644 --- a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/Item.java +++ b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/Item.java @@ -20,52 +20,54 @@ package org.apache.ignite.examples.ml.genetic.knapsack; import java.io.Serializable; /** - * POJO to model an Item + * POJO to model an Item. */ public class Item implements Serializable { - /** weight of item in lbs. */ + /** Weight of item in lbs. */ private double weight; - /** value of item */ - private double value; - /** name of item */ + /** Value of item. */ + private double val; + /** Name of item. */ private String name; /** - * Get the weight + * Get the weight. * - * @return Weight + * @return Weight. */ public double getWeight() { return weight; } /** - * Set the weight + * Set the weight. * - * @param weight Weight + * @param weight Weight. */ public void setWeight(double weight) { this.weight = weight; } /** - * Get the value + * Get the value. * - * @return Value + * @return Value. */ - public double getValue() { - return value; + public double getVal() { + return val; } /** - * @param value Value + * Set the value. + * + * @param val Value. */ - public void setValue(double value) { - this.value = value; + public void setVal(double val) { + this.val = val; } /** - * Get the name + * Get the name. * * @return Name */ @@ -74,9 +76,9 @@ public class Item implements Serializable { } /** - * Set the name + * Set the name. * - * @param name Name + * @param name Name. */ public void setName(String name) { this.name = name; @@ -84,7 +86,6 @@ public class Item implements Serializable { /** {@inheritDoc} */ @Override public String toString() { - return "Item [weight=" + weight + ", value=" + value + ", name=" + name + "]"; + return "Item [weight=" + weight + ", value=" + val + ", name=" + name + "]"; } - } http://git-wip-us.apache.org/repos/asf/ignite/blob/fea694fe/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/KnapsackFitnessFunction.java ---------------------------------------------------------------------- diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/KnapsackFitnessFunction.java b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/KnapsackFitnessFunction.java index bf24edf..e7c72b0 100644 --- a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/KnapsackFitnessFunction.java +++ b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/KnapsackFitnessFunction.java @@ -23,47 +23,40 @@ import org.apache.ignite.ml.genetic.Gene; import org.apache.ignite.ml.genetic.IFitnessFunction; /** - * This example demonstrates how to create a IFitnessFunction - * - * Your IFitnessFunction will vary depending on your particular use case. - * - * For this fitness function, we simply want to calculate the weight and value of - * - * an individual solution relative to other solutions. - * - * - * To do this, we total the weights and values of all the genes within a chromosome. + * This example demonstrates how to create a {@link IFitnessFunction}. + * <p> + * Your fitness function will vary depending on your particular use case. For this fitness function, we simply want + * to calculate the weight and value of an individual solution relative to other solutions.</p> + * <p> + * To do this, we total the weights and values of all the genes within a chromosome.</p> */ public class KnapsackFitnessFunction implements IFitnessFunction { - /** weight capacity of knapsack */ - private double maximumWeight = 20; - /** - * Calculate fitness + * Calculate fitness. * - * @param genes List of Genes - * @return Fitness value + * @param genes List of Genes. + * @return Fitness value. */ public double evaluate(List<Gene> genes) { - - double value = 0; + double val = 0; double weight = 0; - List<Long> dups = new ArrayList<Long>(); + List<Long> duplicates = new ArrayList<>(); int badSolution = 1; - for (Gene agene : genes) { - weight = weight + ((Item)(agene.getVal())).getWeight(); - value = value + ((Item)(agene.getVal())).getValue(); + for (Gene gene : genes) { + weight = weight + ((Item)(gene.getVal())).getWeight(); + val = val + ((Item)(gene.getVal())).getVal(); - if (dups.contains(agene.id()) || (weight > maximumWeight)) { + double maximumWeight = 20; + if (duplicates.contains(gene.id()) || (weight > maximumWeight)) { badSolution = 0; break; } else - dups.add(agene.id()); + duplicates.add(gene.id()); } - return (value * badSolution); + return (val * badSolution); } } http://git-wip-us.apache.org/repos/asf/ignite/blob/fea694fe/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/KnapsackGAExample.java ---------------------------------------------------------------------- diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/KnapsackGAExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/KnapsackGAExample.java index 7578226..873c1c6 100644 --- a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/KnapsackGAExample.java +++ b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/KnapsackGAExample.java @@ -21,269 +21,273 @@ import java.util.ArrayList; import java.util.List; import org.apache.ignite.Ignite; import org.apache.ignite.Ignition; +import org.apache.ignite.ml.genetic.Chromosome; import org.apache.ignite.ml.genetic.GAGrid; import org.apache.ignite.ml.genetic.Gene; import org.apache.ignite.ml.genetic.parameter.GAConfiguration; /** - * This example demonstrates how to use the GAGrid framework. - * - * Example demonstrates Knapsack Problem: Given a set of 30 items, each with a weight and a value, pack 10 items in - * knapsack so that the total weight is less <= 20 lbs. and the total value is maximized. - * - * - * How To Run: - * - * mvn exec:java -Dexec.mainClass="org.apache.ignite.examples.ml.genetic.knapsack.KnapsackGAExample" - * - * <p> Remote nodes should always be started with special configuration file which enables P2P class loading: {@code - * 'ignite.{sh|bat} examples/config/example-ignite.xml'}.</p> <p> Alternatively you can run ExampleNodeStartup in - * another JVM which will start node with {@code examples/config/example-ignite.xml} configuration.</p> + * This example demonstrates how to use the {@link GAGrid} framework. It shows working with Knapsack Problem: + * Given a set of 30 items, each with a weight and a value, pack 10 items in knapsack so that the total weight + * is less or equal 20 lbs, and the total value is maximized. + * <p> + * Code in this example launches Ignite grid, prepares simple test data (gene pool) and configures GA grid.</p> + * <p> + * After that it launches the process of evolution on GA grid and outputs the progress and results.</p> + * <p> + * You can change the test data and parameters of GA grid used in this example and re-run it to explore + * this functionality further.</p> + * <p> + * How to run from command line:</p> + * <p> + * {@code mvn exec:java -Dexec.mainClass="org.apache.ignite.examples.ml.genetic.knapsack.KnapsackGAExample"}</p> + * <p> + * Remote nodes should always be started with special configuration file which enables P2P class loading: {@code + * 'ignite.{sh|bat} examples/config/example-ignite.xml'}.</p> + * <p> + * Alternatively you can run ExampleNodeStartup in another JVM which will start node with + * {@code examples/config/example-ignite.xml} configuration.</p> */ public class KnapsackGAExample { - /** Ignite instance */ - private static Ignite ignite = null; - /** GAGrid */ - private static GAGrid gaGrid = null; - /** GAConfiguration */ - private static GAConfiguration gaConfig = null; - /** * @param args Command line arguments, none required. */ public static void main(String args[]) { + System.out.println(">>> Knapsack GA grid example started."); + System.setProperty("IGNITE_QUIET", "false"); try { + // Create an Ignite instance as you would in any other use case. + Ignite ignite = Ignition.start("examples/config/example-ignite.xml"); - //Create an Ignite instance as you would in any other use case. - ignite = Ignition.start("examples/config/example-ignite.xml"); + // Create GAConfiguration. + GAConfiguration gaCfg = new GAConfiguration(); - // Create GAConfiguration - gaConfig = new GAConfiguration(); - - // set Gene Pool + // Set Gene Pool. List<Gene> genes = getGenePool(); - // set the Chromosome Length to '10' since our knapsack may contain a total of 10 items. - gaConfig.setChromosomeLen(10); + // Set the Chromosome Length to '10' since our knapsack may contain a total of 10 items. + gaCfg.setChromosomeLen(10); - // initialize gene pool - gaConfig.setGenePool(genes); + // Initialize gene pool. + gaCfg.setGenePool(genes); - // create and set Fitness function + // Create and set Fitness function. KnapsackFitnessFunction function = new KnapsackFitnessFunction(); - gaConfig.setFitnessFunction(function); + gaCfg.setFitnessFunction(function); - // create and set TerminateCriteria + // Create and set TerminateCriteria. KnapsackTerminateCriteria termCriteria = new KnapsackTerminateCriteria(ignite); - gaConfig.setTerminateCriteria(termCriteria); + gaCfg.setTerminateCriteria(termCriteria); ignite.log(); - gaGrid = new GAGrid(gaConfig, ignite); - // evolve the population - gaGrid.evolve(); + GAGrid gaGrid = new GAGrid(gaCfg, ignite); - Ignition.stop(true); + // Evolve the population. + Chromosome chromosome = gaGrid.evolve(); + + System.out.println(">>> Evolution result: " + chromosome); - ignite = null; + Ignition.stop(true); + System.out.println(">>> Knapsack GA grid example completed."); } catch (Exception e) { - System.out.println(e); + System.out.println(e.getMessage()); + e.printStackTrace(); } - } /** - * Helper routine to initialize Gene pool + * Helper routine to initialize Gene pool. * - * In typical usecase genes may be stored in database. + * In typical use case genes may be stored in database. * - * @return List<Gene> + * @return List of Gene objects. */ private static List<Gene> getGenePool() { - List<Gene> list = new ArrayList<Gene>(); + List<Gene> list = new ArrayList<>(); Item item1 = new Item(); item1.setName("Swiss Army Knife"); item1.setWeight(0.08125); - item1.setValue(15); + item1.setVal(15); Gene gene1 = new Gene(item1); Item item2 = new Item(); item2.setName("Duct Tape"); item2.setWeight(1.3); - item2.setValue(3); + item2.setVal(3); Gene gene2 = new Gene(item2); Item item3 = new Item(); item3.setName("Rope (50 feet)"); item3.setWeight(7); - item3.setValue(10); + item3.setVal(10); Gene gene3 = new Gene(item3); Item item4 = new Item(); item4.setName("Satellite phone"); item4.setWeight(2); - item4.setValue(8); + item4.setVal(8); Gene gene4 = new Gene(item4); Item item5 = new Item(); item5.setName("Elmer's Glue"); item5.setWeight(0.25); - item5.setValue(2); + item5.setVal(2); Gene gene5 = new Gene(item5); Item item6 = new Item(); item6.setName("Toilet Paper Roll"); item6.setWeight(.5); - item6.setValue(4); + item6.setVal(4); Gene gene6 = new Gene(item6); Item item7 = new Item(); item7.setName("Binoculars"); item7.setWeight(3); - item7.setValue(5); + item7.setVal(5); Gene gene7 = new Gene(item7); Item item8 = new Item(); item8.setName("Compass"); item8.setWeight(0.0573202); - item8.setValue(15); + item8.setVal(15); Gene gene8 = new Gene(item8); Item item9 = new Item(); - item9.setName("Jug (prefilled with water)"); + item9.setName("Jug (pre-filled with water)"); item9.setWeight(4); - item9.setValue(6); + item9.setVal(6); Gene gene9 = new Gene(item9); Item item10 = new Item(); item10.setName("Flashlight"); item10.setWeight(2); - item10.setValue(4); + item10.setVal(4); Gene gene10 = new Gene(item10); Item item11 = new Item(); item11.setName("Box of paper clips"); item11.setWeight(.9); - item11.setValue(2); + item11.setVal(2); Gene gene11 = new Gene(item11); Item item12 = new Item(); item12.setName("Gloves (1 pair)"); item12.setWeight(.8125); - item12.setValue(3); + item12.setVal(3); Gene gene12 = new Gene(item12); Item item13 = new Item(); item13.setName("Scissors"); item13.setWeight(0.2); - item13.setValue(2); + item13.setVal(2); Gene gene13 = new Gene(item13); Item item14 = new Item(); item14.setName("Signal Flair (4pk)"); item14.setWeight(4); - item14.setValue(5); + item14.setVal(5); Gene gene14 = new Gene(item14); Item item15 = new Item(); item15.setName("Water Purifying System"); item15.setWeight(0.5125); - item15.setValue(4); + item15.setVal(4); Gene gene15 = new Gene(item15); Item item16 = new Item(); item16.setName("Whistle"); item16.setWeight(0.075); - item16.setValue(2); + item16.setVal(2); Gene gene16 = new Gene(item16); Item item17 = new Item(); item17.setName("Sleeping Bag"); item17.setWeight(0.38125); - item17.setValue(4); + item17.setVal(4); Gene gene17 = new Gene(item17); Item item18 = new Item(); item18.setName("Insect Repellent"); item18.setWeight(1.15); - item18.setValue(3); + item18.setVal(3); Gene gene18 = new Gene(item18); Item item19 = new Item(); item19.setName("Trowel"); item19.setWeight(0.31875); - item19.setValue(3); + item19.setVal(3); Gene gene19 = new Gene(item19); Item item20 = new Item(); item20.setName("Lighter"); item20.setWeight(.2); - item20.setValue(4); + item20.setVal(4); Gene gene20 = new Gene(item20); Item item21 = new Item(); item21.setName("Safety Horn"); item21.setWeight(.21); - item21.setValue(3); + item21.setVal(3); Gene gene21 = new Gene(item21); Item item22 = new Item(); item22.setName("Headlamp"); item22.setWeight(.8); - item22.setValue(4); + item22.setVal(4); Gene gene22 = new Gene(item22); Item item23 = new Item(); item23.setName("Freeze Dried Food Kit"); item23.setWeight(2); - item23.setValue(6); + item23.setVal(6); Gene gene23 = new Gene(item23); Item item24 = new Item(); item24.setName("Sunscreen"); item24.setWeight(.5); - item24.setValue(4); + item24.setVal(4); Gene gene24 = new Gene(item24); Item item25 = new Item(); item25.setName("Trekking Pole (Adjustable)"); item25.setWeight(1.3); - item25.setValue(4); + item25.setVal(4); Gene gene25 = new Gene(item25); Item item26 = new Item(); item26.setName("Counter Assault Bear Spray"); item26.setWeight(.5); - item26.setValue(4); + item26.setVal(4); Gene gene26 = new Gene(item26); Item item27 = new Item(); item27.setName("Insect Spray"); item27.setWeight(.5); - item27.setValue(3); + item27.setVal(3); Gene gene27 = new Gene(item27); Item item28 = new Item(); item28.setName("Hand sanitizer"); item28.setWeight(.625); - item28.setValue(3); + item28.setVal(3); Gene gene28 = new Gene(item28); Item item29 = new Item(); item29.setName("Mirror"); item29.setWeight(.5); - item29.setValue(3); + item29.setVal(3); Gene gene29 = new Gene(item29); Item item30 = new Item(); item30.setName("First Aid Kit"); item30.setWeight(3); - item30.setValue(6); + item30.setVal(6); Gene gene30 = new Gene(item30); list.add(gene1); @@ -319,5 +323,4 @@ public class KnapsackGAExample { return list; } - } http://git-wip-us.apache.org/repos/asf/ignite/blob/fea694fe/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/KnapsackTerminateCriteria.java ---------------------------------------------------------------------- diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/KnapsackTerminateCriteria.java b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/KnapsackTerminateCriteria.java index 487f8a7..1bba159 100644 --- a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/KnapsackTerminateCriteria.java +++ b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/knapsack/KnapsackTerminateCriteria.java @@ -26,54 +26,60 @@ import org.apache.ignite.ml.genetic.parameter.ITerminateCriteria; import org.apache.ignite.ml.genetic.utils.GAGridUtils; /** - * Represents the terminate condition for Knapsack Genetic algorithm - * - * Class terminates Genetic algorithm when once GA Grid has performed 30 generations. + * Represents the terminate condition for {@link KnapsackGAExample}. + * <p> + * Class terminates Genetic algorithm when once GA Grid has performed 30 generations.</p> */ public class KnapsackTerminateCriteria implements ITerminateCriteria { - /** Ignite instance */ - private static Ignite ignite = null; + /** Ignite instance. */ + private Ignite ignite; - /** Ignite logger */ - private IgniteLogger igniteLogger = null; + /** Ignite logger. */ + private IgniteLogger igniteLog; /** - * @param ignite Ignite + * Create class instance. + * + * @param ignite Ignite instance. */ public KnapsackTerminateCriteria(Ignite ignite) { this.ignite = ignite; - this.igniteLogger = this.ignite.log(); + this.igniteLog = this.ignite.log(); } /** - * @param fittestChromosome Most fit chromosome at for the nth generation - * @param averageFitnessScore Average fitness score as of the nth generation - * @param currentGeneration Current generation - * @return Boolean value + * Check whether termination condition is met. + * + * @param fittestChromosome Most fit chromosome at for the nth generation. + * @param averageFitnessScore Average fitness score as of the nth generation. + * @param currGeneration Current generation. + * @return Status whether condition is met or not. */ public boolean isTerminationConditionMet(Chromosome fittestChromosome, double averageFitnessScore, - int currentGeneration) { + int currGeneration) { boolean isTerminate = true; - igniteLogger.info("##########################################################################################"); - igniteLogger.info("Generation: " + currentGeneration); - igniteLogger.info("Fittest is Chromosome Key: " + fittestChromosome); - igniteLogger.info("Total value is: " + fittestChromosome.getFitnessScore()); - igniteLogger.info("Total weight is: " + calculateTotalWeight(GAGridUtils.getGenesInOrderForChromosome(ignite, fittestChromosome))); - igniteLogger.info("Avg Chromosome Fitness: " + averageFitnessScore); - igniteLogger.info("Chromosome: " + fittestChromosome); + igniteLog.info("##########################################################################################"); + igniteLog.info("Generation: " + currGeneration); + igniteLog.info("Fittest is Chromosome Key: " + fittestChromosome); + igniteLog.info("Total value is: " + fittestChromosome.getFitnessScore()); + igniteLog.info("Total weight is: " + calculateTotalWeight(GAGridUtils.getGenesInOrderForChromosome(ignite, fittestChromosome))); + igniteLog.info("Avg Chromosome Fitness: " + averageFitnessScore); + igniteLog.info("Chromosome: " + fittestChromosome); printItems(GAGridUtils.getGenesInOrderForChromosome(ignite, fittestChromosome)); - igniteLogger.info("##########################################################################################"); + igniteLog.info("##########################################################################################"); - if (!(currentGeneration > 29)) + if (!(currGeneration > 29)) isTerminate = false; return isTerminate; } /** - * @param genes List of Genes - * @return double value + * Calculate total weight. + * + * @param genes List of Genes. + * @return Calculated value. */ private double calculateTotalWeight(List<Gene> genes) { double totalWeight = 0; @@ -84,16 +90,16 @@ public class KnapsackTerminateCriteria implements ITerminateCriteria { } /** - * Helper to print items in knapsack + * Helper to print items in knapsack. * - * @param genes List of Genes + * @param genes List of Genes. */ private void printItems(List<Gene> genes) { for (Gene gene : genes) { - igniteLogger.info("------------------------------------------------------------------------------------------"); - igniteLogger.info("Name: " + ((Item)gene.getVal()).getName().toString()); - igniteLogger.info("Weight: " + ((Item)gene.getVal()).getWeight()); - igniteLogger.info("Value: " + ((Item)gene.getVal()).getValue()); + igniteLog.info("------------------------------------------------------------------------------------------"); + igniteLog.info("Name: " + ((Item)gene.getVal()).getName()); + igniteLog.info("Weight: " + ((Item)gene.getVal()).getWeight()); + igniteLog.info("Value: " + ((Item)gene.getVal()).getVal()); } } } http://git-wip-us.apache.org/repos/asf/ignite/blob/fea694fe/examples/src/main/java/org/apache/ignite/examples/ml/genetic/movie/Movie.java ---------------------------------------------------------------------- diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/movie/Movie.java b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/movie/Movie.java index 38d27ff2..2eebe5e 100644 --- a/examples/src/main/java/org/apache/ignite/examples/ml/genetic/movie/Movie.java +++ b/examples/src/main/java/org/apache/ignite/examples/ml/genetic/movie/Movie.java @@ -23,102 +23,104 @@ import java.util.List; * POJO to model a movie. */ public class Movie { - /** name of movie */ + /** Name of movie. */ private String name; - /** genre of movie */ + /** Genre of movie. */ private List genre; - /** rating of movie */ + /** Rating of movie. */ private String rating; - /** IMDB rating */ + /** IMDB rating. */ private double imdbRating; - /** year of movie */ + /** Year of movie. */ private String year; /** - * Get the year + * Get the year. * - * @return Year + * @return Year. */ public String getYear() { return year; } /** - * Set the year + * Set the year. * - * @param year Year + * @param year Year. */ public void setYear(String year) { this.year = year; } /** - * Get the IMDB rating + * Get the <a href="https://en.wikipedia.org/wiki/IMDb";>IMDB rating</a>. * - * @return IMDB rating + * @return IMDB rating. */ public double getImdbRating() { return imdbRating; } /** - * Set the IMDB rating + * Set the IMDB rating. * - * @param imdbRating IMDB rating + * @param imdbRating IMDB rating. */ public void setImdbRating(double imdbRating) { this.imdbRating = imdbRating; } /** - * Get the name of movie + * Get the name of movie. * - * @return Name of movie + * @return Name of movie. */ public String getName() { return name; } /** - * Set the name of movie + * Set the name of movie. * - * @param name Movie name + * @param name Movie name. */ public void setName(String name) { this.name = name; } /** - * @return List of genres + * Get movie genres. + * + * @return List of genres. */ public List getGenre() { return genre; } /** - * Set the genre + * Set the genre. * - * @param genre Genre of movie + * @param genre List of genres of movie. */ public void setGenre(List genre) { this.genre = genre; } /** - * Get the rating of the movie + * Get the rating of the movie. * - * @return Movie rating + * @return Movie rating. */ public String getRating() { return rating; } /** - * Set the rating of the movie + * Set the rating of the movie. * - * @param rating Movie rating + * @param rating Movie rating. */ public void setRating(String rating) { this.rating = rating; @@ -129,5 +131,4 @@ public class Movie { return "Movie [name=" + name + ", genre=" + genre + ", rating=" + rating + ", imdbRating=" + imdbRating + ", year=" + year + "]"; } - }
