Mahout is a top-level project of the Apache Foundation, an open-source machine learning library that provides a wealth of machine learning algorithms and tools. Mahout's goal is to enable developers to carry out machine learning and data mining tasks in the Big data environment. The Maven coordinates of Mahout are: ``` <dependency> <groupId>org.apache.mahout</groupId> <artifactId>mahout-core</artifactId> <version>0.13.0</version> </dependency> ``` This Maven coordinate represents the core functionality of using Mahout. The following is a sample code for implementing vector machine classification using Mahout: ```java import org.apache.mahout.classifier.sgd.L1; import org.apache.mahout.classifier.sgd.OnlineLogisticRegression; import org.apache.mahout.math.DenseVector; import org.apache.mahout.math.Vector; public class SVMClassification { public static void main(String[] args) { //Create a training dataset Vector[] trainData = new Vector[4]; trainData[0] = new DenseVector(new double[]{0, 0}); trainData[1] = new DenseVector(new double[]{0, 1}); trainData[2] = new DenseVector(new double[]{1, 0}); trainData[3] = new DenseVector(new double[]{1, 1}); //Create tags for training data int[] trainLabels = new int[]{0, 1, 1, 0}; //Create an online Logistic regression model and set super parameters OnlineLogisticRegression model = new OnlineLogisticRegression(2, 2, new L1()); model.learningRate(0.1); model.lambda(0.01); //Train on the model for (int i = 0; i < trainData.length; i++) { model.train(trainLabels[i], trainData[i]); } //Create test data Vector testData = new DenseVector(new double[]{0.5, 0.5}); //Using models for prediction double score = model.classifyScalar(testData); System. out. println ("predicted result:"+score); } } ``` This sample uses Mahout to implement a simple vector machine classifier, with a training dataset consisting of 4 samples, each with 2 features. The labels are 0 and 1, respectively. Then an online Logistic regression model is created and trained. Finally, use the model to predict the test data and output the prediction results. Summary: By using Mahout's machine learning library, we can easily implement vector machine classification algorithms. Mahout provides a wealth of machine learning algorithms and tools, which makes the task of machine learning and data mining in the Big data environment easier.

Maven coordinates of dependent class libraries: ```xml <dependency> <groupId>org.apache.mahout</groupId> <artifactId>mahout-core</artifactId> <version>0.13.0</version> </dependency> ``` Mahout is an open source framework for machine learning and data mining, which includes many algorithms and tools for recommendation engines. Mahout's Collaborative filtering recommendation module is based on user behavior data, and predicts the possible interests and recommendations of users by analyzing their historical behavior and similarity with other users. If there is a dependent dataset, you can store the dataset in a file. Each line represents the interaction information between a user and an item. The format can be user ID, item ID, and interaction intensity (such as rating). Here is an example dataset: ``` 1,101,5 1,102,3 1,103,2 2,101,2 2,102,2 2,103,5 3,101,2 3,103,4 ``` The following is a complete sample code recommended for Collaborative filtering using Mahout: ```java import org.apache.mahout.cf.taste.common.TasteException; import org.apache.mahout.cf.taste.impl.model.file.FileDataModel; import org.apache.mahout.cf.taste.impl.neighborhood.NearestNUserNeighborhood; import org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender; import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity; import org.apache.mahout.cf.taste.model.DataModel; import org.apache.mahout.cf.taste.neighborhood.UserNeighborhood; import org.apache.mahout.cf.taste.recommender.RecommendedItem; import org.apache.mahout.cf.taste.similarity.UserSimilarity; import java.io.File; import java.io.IOException; import java.util.List; public class CollaborativeFilteringExample { public static void main(String[] args) { try { //Load Dataset DataModel dataModel = new FileDataModel(new File("data.csv")); //Building similarity measures UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel); //Building user neighbors UserNeighborhood neighborhood = new NearestNUserNeighborhood(3, similarity, dataModel); //Build recommender GenericUserBasedRecommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, similarity); //Generate Top N recommendations for user ID 1 List<RecommendedItem> recommendations = recommender.recommend(1, 3); //Print recommendation results for (RecommendedItem recommendation : recommendations) { System.out.println(recommendation); } }Catch (IOException | TasteException e){ e.printStackTrace(); } } } ``` This example first loads the dataset and then constructs a user similarity measure based on Pearson correlation coefficient. Next, a user filter was initialized based on neighbor size, similarity measurement, and data model. Finally, use the recommended method to generate Top N recommended items for a given user and print them out. Summary: Mahout is a powerful recommendation engine framework. It is very convenient to use Mahout for Collaborative filtering recommendation in Java. We can easily implement Collaborative filtering recommendation algorithms by loading datasets, building similarity measures, building user neighbors, and building recommenders. The above sample code provides a basic example to demonstrate how to use Mahout for recommendations. Using Mahout's Collaborative filtering recommendation module can help us achieve an efficient personalized recommendation system.

Maven coordinates of dependent class libraries: ```xml <dependency> <groupId>org.apache.mahout</groupId> <artifactId>mahout-math</artifactId> <version>0.13.0</version> </dependency> ``` Mahout is a machine learning library based on Apache Hadoop, which provides some common machine learning algorithms and tools, including random number generators. Mahout's random number generator class library is' org. apache. mahout. path. random. RandomAdapter '. It is an adapter class that wraps Java's native' java. util. Random 'class into Mahout's random number generator. The specific information of the dependent dataset is not clear. You can generate a sample dataset by using the random number generator. The following is an example of Java code generated by Mahout random number generator and printed: ```java import org.apache.mahout.math.random.RandomAdaptor; public class RandomGeneratorExample { public static void main(String[] args) { //Create a Mahout random number generator RandomAdaptor random = new RandomAdaptor(); //Generate random numbers and print them out for (int i = 0; i < 10; i++) { double randomValue = random.nextDouble(); System.out.println(randomValue); } } } ``` Running the above code will generate 10 random numbers and print them out. Summary: This article introduces the basic steps of using Mahout random number generator in Java. Firstly, we need to add Maven coordinates for Mahout path dependencies. Then, we generate random numbers by creating a RandomAdapter object, and on top of this object, we call the nextDouble() method to obtain the random numbers. Finally, we can handle these random numbers according to actual needs. Mahout's random number generator provides a convenient way to generate random numbers and can be extended as needed.

Maven coordinates of dependent class libraries: ```xml <dependency> <groupId>org.apache.mahout</groupId> <artifactId>mahout-mr</artifactId> <version>0.13.0</version> </dependency> ``` Mahout is a Java class library for large-scale machine learning, providing many algorithms and tools for tasks such as data mining, recommendation systems, clustering, classification, and regression. It is widely used for processing and analyzing large-scale datasets, with characteristics of parallelism and scalability. When using Mahout to preprocess data, we can use the 'org. apache. math. stats. DescriptiveStatistics' class to perform data normalization preprocessing operations. ``` import org.apache.mahout.math.stats.DescriptiveStatistics; public class DataNormalizationExample { public static void main(String[] args) { //Sample Dataset double[] data = {1, 2, 3, 4, 5}; //Create a DescriptiveStatistics object DescriptiveStatistics stats = new DescriptiveStatistics(); //Add data to statistical objects for (double value : data) { stats.addValue(value); } //Obtain maximum and minimum values double min = stats.getMinValue(); double max = stats.getMaxValue(); //Normalize data for (int i = 0; i < data.length; i++) { data[i] = (data[i] - min) / (max - min); } //Print normalized data for (double value : data) { System.out.println(value); } } } ``` The above example code demonstrates how to use the Mahout library to normalize data. Firstly, we created a 'DescriptiveStatistics' object and added data to it. Next, we use the 'getMinValue()' and 'getMaxValue()' methods to obtain the minimum and maximum values of the dataset. Then, we normalize the data and print the results. Summary: Mahout is a powerful Java class library that provides many algorithms and tools for large-scale machine learning tasks. When performing data preprocessing, Mahout's' DescriptiveStatistics' class can be used for data normalization operations to ensure that the data is within the same scale range. Normalizing the data may help improve the performance and accuracy of the algorithm.

Maven coordinates and brief introduction: For the functionality of using Mahout for model serialization and deserialization, we need to add the following dependencies to the Pom.xml file of the Maven project: ```xml <dependency> <groupId>org.apache.mahout</groupId> <artifactId>mahout-math</artifactId> <version>0.14.0</version> </dependency> <dependency> <groupId>org.apache.mahout</groupId> <artifactId>mahout-hdfs</artifactId> <version>0.14.0</version> </dependency> <dependency> <groupId>org.apache.mahout</groupId> <artifactId>mahout-integration</artifactId> <version>0.14.0</version> </dependency> ``` Mahout is an open source machine learning library that provides many algorithms and tools for large-scale dataset processing. Mahout math provides commonly used mathematical tools and matrix calculations, Mahout hdfs provides integration with Hadoop file systems, and Mahout integration provides integration with other open source libraries and tools. Dataset information: In this example, we will use a simple dataset to demonstrate how to serialize and deserialize the Mahout model. We will use a CSV file called "dataset. csv", which contains some sample data. Complete Java code example: ```java import org.apache.mahout.classifier.AbstractVectorClassifier; import org.apache.mahout.classifier.sgd.OnlineLogisticRegression; import org.apache.mahout.math.RandomAccessSparseVector; import org.apache.mahout.math.Vector; import org.apache.mahout.math.VectorWritable; import java.io.*; import java.util.Arrays; public class MahoutModelSerializationExample { private static final String MODEL_FILE = "model.mahout"; public static void main(String[] args) { //Create a simple model AbstractVectorClassifier model = createModel(); //Serialize the model and save it to a file serializeModel(model, MODEL_FILE); //Deserialize models from files AbstractVectorClassifier deserializedModel = deserializeModel(MODEL_FILE); //Output the predicted results of the deserialized model String prediction = predict(deserializedModel, new double[]{1.0, 2.0}); System.out.println("Deserialized model prediction: " + prediction); } public static AbstractVectorClassifier createModel() { //Create an OnlineLogisticRegression object AbstractVectorClassifier logisticRegression = new OnlineLogisticRegression(2, 2); logisticRegression.train(Arrays.asList( new Pair<>(new float[]{0.1f, 0.2f}, 0), new Pair<>(new float[]{0.3f, 0.4f}, 1) )); return logisticRegression; } public static void serializeModel(AbstractVectorClassifier model, String filename) { try (FileOutputStream fos = new FileOutputStream(filename); BufferedOutputStream bos = new BufferedOutputStream(fos); DataOutputStream dos = new DataOutputStream(bos)) { VectorWritable.writeVector(dos, model.getParameters().viewPart(0, 2)); } catch (IOException e) { e.printStackTrace(); } } public static AbstractVectorClassifier deserializeModel(String filename) { AbstractVectorClassifier model = null; try (FileInputStream fis = new FileInputStream(filename); BufferedInputStream bis = new BufferedInputStream(fis); DataInputStream dis = new DataInputStream(bis)) { Vector parameters = VectorWritable.readVector(dis); model = new OnlineLogisticRegression().modelWithParameters(parameters, true); }Catch (IOException | ClassNotFoundException e){ e.printStackTrace(); } return model; } public static String predict(AbstractVectorClassifier model, double[] features) { Vector vector = new RandomAccessSparseVector(features.length); vector.assign(features); int predictedLabel = model.classifyFull(vector).maxValueIndex(); return Integer.toString(predictedLabel); } } ``` Summary: In this example, we first created a simple Mahout model that uses the OnlineLogisticRegression object to train and predict two types of data. Then, we serialize the model and save it to a file. Next, we reconstruct the model by deserializing it from the file. Finally, we use the deserialized model for prediction to verify its accuracy. By using Mahout's Mahout math, Mahout hdfs, and Mahout integration class libraries, we can easily serialize machine learning models into files and deserialize them back into model objects when needed. This ability can improve the portability and reusability of the model.