How to use

predict

method

in

smile.classification.DecisionTree

  @Override
  int predictFromModel(double[] data) {
    return classifierModel.predict(data);
  }
}

public int predict(double[] data) {
  return classifierModel.predict(data);
}

@Override
public int predict(double[] x) {
  double[] y = new double[k];
  for (int i = 0; i < trees.length; i++) {
    y[trees[i].predict(x)] += alpha[i];
  }
    
  return Math.whichMax(y);
}

@Override
public int predict(double[] x) {
  int[] y = new int[k];
  
  for (Tree tree : trees) {
    y[tree.tree.predict(x)]++;
  }
  
  return Math.whichMax(y);
}

@Override
public int predict(double[] x, double[] posteriori) {
  if (posteriori.length != k) {
    throw new IllegalArgumentException(String.format("Invalid posteriori vector size: %d, expected: %d", posteriori.length, k));
  }
  Arrays.fill(posteriori, 0.0);
  int[] y = new int[k];
  double[] pos = new double[k];
  for (Tree tree : trees) {
    y[tree.tree.predict(x, pos)]++;
    for (int i = 0; i < k; i++) {
      posteriori[i] += tree.weight * pos[i];
    }
  }
  Math.unitize1(posteriori);
  return Math.whichMax(y);
}    

/**
 * Predicts the class label of an instance and also calculate a posteriori
 * probabilities. Not supported.
 */
@Override
public int predict(double[] x, double[] posteriori) {
  Arrays.fill(posteriori, 0.0);
  for (int i = 0; i < trees.length; i++) {
    posteriori[trees[i].predict(x)] += alpha[i];
  }
  double sum = Math.sum(posteriori);
  for (int i = 0; i < k; i++) {
    posteriori[i] /= sum;
  }
  return Math.whichMax(posteriori);
}

/**
 * Test the model on a validation dataset.
 * 
 * @param x the test data set.
 * @param y the test data labels.
 * @param measures the performance measures of classification.
 * @return performance measures with first 1, 2, ..., decision trees.
 */
public double[][] test(double[][] x, int[] y, ClassificationMeasure[] measures) {
  int T = trees.size();
  int m = measures.length;
  double[][] results = new double[T][m];
  int n = x.length;
  int[] label = new int[n];
  double[][] prediction = new double[n][k];
  for (int i = 0; i < T; i++) {
    for (int j = 0; j < n; j++) {
      prediction[j][trees.get(i).tree.predict(x[j])]++;
      label[j] = Math.whichMax(prediction[j]);
    }
    for (int j = 0; j < m; j++) {
      results[i][j] = measures[j].measure(y, label);
    }
  }
  return results;
}

for (int i = 0; i < T; i++) {
  for (int j = 0; j < n; j++) {
    prediction[j] += alpha[i] * trees[i].predict(x[j]);
    label[j] = prediction[j] > 0 ? 1 : 0;
for (int i = 0; i < T; i++) {
  for (int j = 0; j < n; j++) {
    prediction[j][trees[i].predict(x[j])] += alpha[i];
    label[j] = Math.whichMax(prediction[j]);

/**
 * Test the model on a validation dataset.
 * 
 * @param x the test data set.
 * @param y the test data response values.
 * @return accuracies with first 1, 2, ..., decision trees.
 */
public double[] test(double[][] x, int[] y) {
  int T = trees.size();
  double[] accuracy = new double[T];
  int n = x.length;
  int[] label = new int[n];
  int[][] prediction = new int[n][k];
  Accuracy measure = new Accuracy();
  
  for (int i = 0; i < T; i++) {
    for (int j = 0; j < n; j++) {
      prediction[j][trees.get(i).tree.predict(x[j])]++;
      label[j] = Math.whichMax(prediction[j]);
    }
    accuracy[i] = measure.measure(y, label);
  }
  return accuracy;
}

for (int i = 0; i < T; i++) {
  for (int j = 0; j < n; j++) {
    prediction[j] += alpha[i] * trees[i].predict(x[j]);
    label[j] = prediction[j] > 0 ? 1 : 0;
for (int i = 0; i < T; i++) {
  for (int j = 0; j < n; j++) {
    prediction[j][trees[i].predict(x[j])] += alpha[i];
    label[j] = Math.whichMax(prediction[j]);

if (samples[i] == 0) {
  oob++;
  int p = tree.predict(x[i]);
  if (p == y[i]) correct++;
  synchronized (prediction[i]) {

err[i] = trees[t].predict(x[i]) != y[i];