2.Java中一个简单的机器学习例子 2.Java中一个简单的机器学习例

这是一个“Hello World”Java机器学习的例子。它只是给你一个Java的机器学习的味道。
环境 Java 1.6+ and Eclipse
第一步下载 Weka 库下载地址：: http://www.cs.waikato.ac.nz/ml/weka/snapshots/weka_snapshots.html
下载stable.XX.zip，解压缩文件，在Eclipse中将weka.jar添加到Java项目的库路径中。
第二步准备数据按照以下格式创建一个txt文件“weather.txt”：

@relation weather@attribute outlook {sunny, overcast, rainy} @attribute temperature numeric @attribute humidity numeric @attribute windy {TRUE, FALSE} @attribute play {yes, no}@data sunny,85,85,FALSE,no sunny,80,90,TRUE,no overcast,83,86,FALSE,yes rainy,70,96,FALSE,yes rainy,68,80,FALSE,yes rainy,65,70,TRUE,no overcast,64,65,TRUE,yes sunny,72,95,FALSE,no sunny,69,70,FALSE,yes rainy,75,80,FALSE,yes sunny,75,70,TRUE,yes overcast,72,90,TRUE,yes overcast,81,75,FALSE,yes rainy,71,91,TRUE,no

这个数据集来自weka下载包。它位于“/data/weather.numeric.arff”。文件扩展名是“arff”，但我们可以简单地使用“txt”。
第3步：使用Weka进行培训和测试此代码示例使用Weka提供的一组分类器。它在给定数据集上训练模型，并使用10分裂交叉验证进行测试。以后我会解释每个分类器，因为这是一个更复杂的主题。

import java.io.BufferedReader; import java.io.FileNotFoundException; import java.io.FileReader; import weka.classifiers.Classifier; import weka.classifiers.Evaluation; import weka.classifiers.evaluation.NominalPrediction; import weka.classifiers.rules.DecisionTable; import weka.classifiers.rules.PART; import weka.classifiers.trees.DecisionStump; import weka.classifiers.trees.J48; import weka.core.FastVector; import weka.core.Instances; public class WekaTest { public static BufferedReader readDataFile(String filename) { BufferedReader inputReader = null; try { inputReader = new BufferedReader(new FileReader(filename)); } catch (FileNotFoundException ex) { System.err.println("File not found: " + filename); } return inputReader; } public static Evaluation classify(Classifier model, Instances trainingSet, Instances testingSet) throws Exception { Evaluation evaluation = new Evaluation(trainingSet); model.buildClassifier(trainingSet); evaluation.evaluateModel(model, testingSet); return evaluation; } public static double calculateAccuracy(FastVector predictions) { double correct = 0; for (int i = 0; i < predictions.size(); i++) { NominalPrediction np = (NominalPrediction) predictions.elementAt(i); if (np.predicted() == np.actual()) { correct++; } } return 100 * correct / predictions.size(); } public static Instances[][] crossValidationSplit(Instances data, int numberOfFolds) { Instances[][] split = new Instances[2][numberOfFolds]; for (int i = 0; i < numberOfFolds; i++) { split[0][i] = data.trainCV(numberOfFolds, i); split[1][i] = data.testCV(numberOfFolds, i); } return split; } public static void main(String[] args) throws Exception { BufferedReader datafile = readDataFile("weather.txt"); Instances data = https://www.it610.com/article/new Instances(datafile); data.setClassIndex(data.numAttributes() - 1); // Do 10-split cross validation Instances[][] split = crossValidationSplit(data, 10); // Separate split into training and testing arrays Instances[] trainingSplits = split[0]; Instances[] testingSplits = split[1]; // Use a set of classifiers Classifier[] models = { new J48(), // a decision tree new PART(), new DecisionTable(),//decision table majority classifier new DecisionStump() //one-level decision tree }; // Run for each model for (int j = 0; j < models.length; j++) { // Collect every group of predictions for current model in a FastVector FastVector predictions = new FastVector(); // For each training-testing split pair, train and test the classifier for (int i = 0; i < trainingSplits.length; i++) { Evaluation validation = classify(models[j], trainingSplits[i], testingSplits[i]); predictions.appendElements(validation.predictions()); // Uncomment to see the summary for each training-testing pair. //System.out.println(models[j].toString()); } // Calculate overall accuracy of current classifier on all splits double accuracy = calculateAccuracy(predictions); // Print current classifier's name and accuracy in a complicated, // but nice-looking way. System.out.println("Accuracy of " + models[j].getClass().getSimpleName() + ": " + String.format("%.2f%%", accuracy) + "\n---------------------------------"); } } }

您的项目的包视图应该如下所示：