List of usage examples for weka.classifiers.functions Logistic toString
@Override
public String toString()
From source file:classify.Classifier.java
/** * @param args the command line arguments */// w w w. j a va 2 s. co m public static void main(String[] args) { //read in data try { DataSource input = new DataSource("no_missing_values.csv"); Instances data = input.getDataSet(); //Instances data = readFile("newfixed.txt"); missingValuesRows(data); setAttributeValues(data); data.setClassIndex(data.numAttributes() - 1); //boosting AdaBoostM1 boosting = new AdaBoostM1(); boosting.setNumIterations(25); boosting.setClassifier(new DecisionStump()); //build the classifier boosting.buildClassifier(data); //evaluate using 10-fold cross validation Evaluation e1 = new Evaluation(data); e1.crossValidateModel(boosting, data, 10, new Random(1)); DecimalFormat nf = new DecimalFormat("0.000"); System.out.println("Results of Boosting with Decision Stumps:"); System.out.println(boosting.toString()); System.out.println("Results of Cross Validation:"); System.out.println("Number of correctly classified instances: " + e1.correct() + " (" + nf.format(e1.pctCorrect()) + "%)"); System.out.println("Number of incorrectly classified instances: " + e1.incorrect() + " (" + nf.format(e1.pctIncorrect()) + "%)"); System.out.println("TP Rate: " + nf.format(e1.weightedTruePositiveRate() * 100) + "%"); System.out.println("FP Rate: " + nf.format(e1.weightedFalsePositiveRate() * 100) + "%"); System.out.println("Precision: " + nf.format(e1.weightedPrecision() * 100) + "%"); System.out.println("Recall: " + nf.format(e1.weightedRecall() * 100) + "%"); System.out.println(); System.out.println("Confusion Matrix:"); for (int i = 0; i < e1.confusionMatrix().length; i++) { for (int j = 0; j < e1.confusionMatrix()[0].length; j++) { System.out.print(e1.confusionMatrix()[i][j] + " "); } System.out.println(); } System.out.println(); System.out.println(); System.out.println(); //logistic regression Logistic l = new Logistic(); l.buildClassifier(data); e1 = new Evaluation(data); e1.crossValidateModel(l, data, 10, new Random(1)); System.out.println("Results of Logistic Regression:"); System.out.println(l.toString()); System.out.println("Results of Cross Validation:"); System.out.println("Number of correctly classified instances: " + e1.correct() + " (" + nf.format(e1.pctCorrect()) + "%)"); System.out.println("Number of incorrectly classified instances: " + e1.incorrect() + " (" + nf.format(e1.pctIncorrect()) + "%)"); System.out.println("TP Rate: " + nf.format(e1.weightedTruePositiveRate() * 100) + "%"); System.out.println("FP Rate: " + nf.format(e1.weightedFalsePositiveRate() * 100) + "%"); System.out.println("Precision: " + nf.format(e1.weightedPrecision() * 100) + "%"); System.out.println("Recall: " + nf.format(e1.weightedRecall() * 100) + "%"); System.out.println(); System.out.println("Confusion Matrix:"); for (int i = 0; i < e1.confusionMatrix().length; i++) { for (int j = 0; j < e1.confusionMatrix()[0].length; j++) { System.out.print(e1.confusionMatrix()[i][j] + " "); } System.out.println(); } } catch (Exception ex) { //data couldn't be read, so end program System.out.println("Exception thrown, program ending."); } }