package demo_thesis;
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 < n`enter code here`umberOfFolds; 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("C:\\Users\\user\\Desktop\\demo_thesis\\src\\input_file\\weather.txt");
Instances data = 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---------------------------------");
}
}
}
我已将weka jar文件集成到java包中。我已经使用了这个代码。但是在行谓词.appendElements(validation.predictions())中有一个错误显示;给出一个错误,比如“找不到符号符号:方法预测()”。我使用过Netbeans IDE 8.2和Jdk 1.8。我在很多方面尝试过,但无法解决它。我该如何解决错误?