instances.java

一个数据挖掘软件ALPHAMINERR的整个过程的JAVA版源代码

  public final void renameAttributeValue(int att, int val, String name) {

    Attribute newAtt = (Attribute)attribute(att).copy();
    FastVector newVec = new FastVector(numAttributes());

    newAtt.setValue(val, name);
    for (int i = 0; i < numAttributes(); i++) {
      if (i == att) {
	newVec.addElement(newAtt);
      } else {
	newVec.addElement(attribute(i));
      }
    }
    m_Attributes = newVec;
  }

  /**
   * Renames the value of a nominal (or string) attribute value. This
   * change only affects this dataset.
   *
   * @param att the attribute
   * @param val the value
   * @param name the new name
   */
  public final void renameAttributeValue(Attribute att, String val, 
                                         String name) {

    int v = att.indexOfValue(val);
    if (v == -1) throw new IllegalArgumentException(val + " not found");
    renameAttributeValue(att.index(), v, name);
  }

  /**
   * Creates a new dataset of the same size using random sampling
   * with replacement.
   *
   * @param random a random number generator
   * @return the new dataset
   */
  public final Instances resample(Random random) {

    Instances newData = new Instances(this, numInstances());
    while (newData.numInstances() < numInstances()) {
      newData.add(instance(random.nextInt(numInstances())));
    }
    return newData;
  }

  /**
   * Creates a new dataset of the same size using random sampling
   * with replacement according to the current instance weights. The
   * weights of the instances in the new dataset are set to one.
   *
   * @param random a random number generator
   * @return the new dataset
   */
  public final Instances resampleWithWeights(Random random) {

    double [] weights = new double[numInstances()];
    for (int i = 0; i < weights.length; i++) {
      weights[i] = instance(i).weight();
    }
    return resampleWithWeights(random, weights);
  }


  /**
   * Creates a new dataset of the same size using random sampling
   * with replacement according to the given weight vector. The
   * weights of the instances in the new dataset are set to one.
   * The length of the weight vector has to be the same as the
   * number of instances in the dataset, and all weights have to
   * be positive.
   *
   * @param random a random number generator
   * @param weights the weight vector
   * @return the new dataset
   * @exception IllegalArgumentException if the weights array is of the wrong
   * length or contains negative weights.
   */
  public final Instances resampleWithWeights(Random random, 
					     double[] weights) {

    if (weights.length != numInstances()) {
      throw new IllegalArgumentException("weights.length != numInstances.");
    }
    Instances newData = new Instances(this, numInstances());
    if (numInstances() == 0) {
      return newData;
    }
    double[] probabilities = new double[numInstances()];
    double sumProbs = 0, sumOfWeights = Utils.sum(weights);
    for (int i = 0; i < numInstances(); i++) {
      sumProbs += random.nextDouble();
      probabilities[i] = sumProbs;
    }
    Utils.normalize(probabilities, sumProbs / sumOfWeights);

    // Make sure that rounding errors don't mess things up
    probabilities[numInstances() - 1] = sumOfWeights;
    int k = 0; int l = 0;
    sumProbs = 0;
    while ((k < numInstances() && (l < numInstances()))) {
      if (weights[l] < 0) {
	throw new IllegalArgumentException("Weights have to be positive.");
      }
      sumProbs += weights[l];
      while ((k < numInstances()) &&
	     (probabilities[k] <= sumProbs)) { 
	newData.add(instance(l));
	newData.instance(k).setWeight(1);
	k++;
      }
      l++;
    }
    return newData;
  }

  /** 
   * Sets the class attribute.
   *
   * @param att attribute to be the class
   */
  public final void setClass(Attribute att) {

    m_ClassIndex = att.index();
  }

  /** 
   * Sets the class index of the set.
   * If the class index is negative there is assumed to be no class.
   * (ie. it is undefined)
   *
   * @param classIndex the new class index
   * @exception IllegalArgumentException if the class index is too big or < 0
   */
  public final void setClassIndex(int classIndex) {

    if (classIndex >= numAttributes()) {
      throw new IllegalArgumentException("Invalid class index: " + classIndex);
    }
    m_ClassIndex = classIndex;
  }
  
  //<<18/03/2005, Frank J. Xu
  //Sets the class index of scoring dataset without target attribute.
  /** 
   * Sets the class index of the set.
   * If the class index is negative there is assumed to be no class.
   * (ie. it is undefined)
   *
   * @param classIndex the new class index
   * @exception IllegalArgumentException if the class index is too big or < 0
   */
  public final void setScoringDataClassIndex(int classIndex) {

    if ((classIndex-1) >= numAttributes()) {
      throw new IllegalArgumentException("Invalid class index: " + classIndex);
    }
    m_ClassIndex = classIndex;
  }  
  //18/03/2005, Frank J. Xu>>

  /**
   * Sets the relation's name.
   *
   * @param newName the new relation name.
   */
  public final void setRelationName(String newName) {
    
    m_RelationName = newName;
  }

  /**
   * Sorts the instances based on an attribute. For numeric attributes, 
   * instances are sorted in ascending order. For nominal attributes, 
   * instances are sorted based on the attribute label ordering 
   * specified in the header. Instances with missing values for the 
   * attribute are placed at the end of the dataset.
   *
   * @param attIndex the attribute's index
   */
  public final void sort(int attIndex) {

    int i,j;

    // move all instances with missing values to end
    j = numInstances() - 1;
    i = 0;
    while (i <= j) {
      if (instance(j).isMissing(attIndex)) {
	j--;
      } else {
	if (instance(i).isMissing(attIndex)) {
	  swap(i,j);
	  j--;
	}
	i++;
      }
    }
    quickSort(attIndex, 0, j);
  }

  /**
   * Sorts the instances based on an attribute. For numeric attributes, 
   * instances are sorted into ascending order. For nominal attributes, 
   * instances are sorted based on the attribute label ordering 
   * specified in the header. Instances with missing values for the 
   * attribute are placed at the end of the dataset.
   *
   * @param att the attribute
   */
  public final void sort(Attribute att) {

    sort(att.index());
  }

  /**
   * Stratifies a set of instances according to its class values 
   * if the class attribute is nominal (so that afterwards a 
   * stratified cross-validation can be performed).
   *
   * @param numFolds the number of folds in the cross-validation
   * @exception UnassignedClassException if the class is not set
   */
  public final void stratify(int numFolds) {
    
    if (numFolds <= 0) {
      throw new IllegalArgumentException("Number of folds must be greater than 1");
    }
    if (m_ClassIndex < 0) {
      throw new UnassignedClassException("Class index is negative (not set)!");
    }
    if (classAttribute().isNominal()) {

      // sort by class
      int index = 1;
      while (index < numInstances()) {
	Instance instance1 = instance(index - 1);
	for (int j = index; j < numInstances(); j++) {
	  Instance instance2 = instance(j);
	  if ((instance1.classValue() == instance2.classValue()) ||
	      (instance1.classIsMissing() && 
	       instance2.classIsMissing())) {
	    swap(index,j);
	    index++;
	  }
	}
	index++;
      }
      stratStep(numFolds);
    }
  }
 
  /**
   * Computes the sum of all the instances' weights.
   *
   * @return the sum of all the instances' weights as a double
   */
  public final double sumOfWeights() {
    
    double sum = 0;

    for (int i = 0; i < numInstances(); i++) {
      sum += instance(i).weight();
    }
    return sum;
  }

  /**
   * Creates the test set for one fold of a cross-validation on 
   * the dataset.
   *
   * @param numFolds the number of folds in the cross-validation. Must
   * be greater than 1.
   * @param numFold 0 for the first fold, 1 for the second, ...
   * @return the test set as a set of weighted instances
   * @exception IllegalArgumentException if the number of folds is less than 2
   * or greater than the number of instances.
   */
  public Instances testCV(int numFolds, int numFold) {

    int numInstForFold, first, offset;
    Instances test;
    
    if (numFolds < 2) {
      throw new IllegalArgumentException("Number of folds must be at least 2!");
    }
    if (numFolds > numInstances()) {
      throw new IllegalArgumentException("Can't have more folds than instances!");
    }
    numInstForFold = numInstances() / numFolds;
    if (numFold < numInstances() % numFolds){
      numInstForFold++;
      offset = numFold;
    }else
      offset = numInstances() % numFolds;
    test = new Instances(this, numInstForFold);
    first = numFold * (numInstances() / numFolds) + offset;
    copyInstances(first, test, numInstForFold);
    return test;
  }
 
  /**
   * Returns the dataset as a string in ARFF format. Strings
   * are quoted if they contain whitespace characters, or if they
   * are a question mark.
   *
   * @return the dataset in ARFF format as a string
   */
  public final String toString() {
    
    StringBuffer text = new StringBuffer();
    
    text.append(ARFF_RELATION).append(" ").
      append(Utils.quote(m_RelationName)).append("\n\n");
    for (int i = 0; i < numAttributes(); i++) {
      text.append(attribute(i)).append("\n");
    }
    text.append("\n").append(ARFF_DATA).append("\n");
    for (int i = 0; i < numInstances(); i++) {
      text.append(instance(i));
      if (i < numInstances() - 1) {
	text.append('\n');
      }
    }
    return text.toString();
  }

  /**
   * Creates the training set for one fold of a cross-validation 
   * on the dataset. The data is subsequently randomized based
   * on the given random number generator.
   *
   * @param numFolds the number of folds in the cross-validation. Must
   * be greater than 1.
   * @param numFold 0 for the first fold, 1 for the second, ...
   * @return the training set 
   * @exception IllegalArgumentException if the number of folds is less than 2
   * or greater than the number of instances.
   */
  public Instances trainCV(int numFolds, int numFold) {

    int numInstForFold, first, offset;
    Instances train;
 
    if (numFolds < 2) {
      throw new IllegalArgumentException("Number of folds must be at least 2!");
    }
    if (numFolds > numInstances()) {
      throw new IllegalArgumentException("Can't have more folds than instances!");
    }
    numInstForFold = numInstances() / numFolds;
    if (numFold < numInstances() % numFolds) {
      numInstForFold++;
      offset = numFold;
    }else
      offset = numInstances() % numFolds;
    train = new Instances(this, numInstances() - numInstForFold);
    first = numFold * (numInstances() / numFolds) + offset;
    copyInstances(0, train, first);
    copyInstances(first + numInstForFold, train,
		  numInstances() - first - numInstForFold);

    return train;
  }

  /**
   * Creates the training set for one fold of a cross-validation 
   * on the dataset. The data is subsequently randomized based
   * on the given random number generator.
   *
   * @param numFolds the number of folds in the cross-validation. Must
   * be greater than 1.
   * @param numFold 0 for the first fold, 1 for the second, ...
   * @param random the random number generator
   * @return the training set 
   * @exception IllegalArgumentException if the number of folds is less than 2
   * or greater than the number of instances.
   */
  public Instances trainCV(int numFolds, int numFold, Random random) {

    Instances train = trainCV(numFolds, numFold);
    train.randomize(random);
    return train;
  }

  /**
   * Computes the variance for a numeric attribute.
   *
   * @param attIndex the numeric attribute
   * @return the variance if the attribute is numeric
   * @exception IllegalArgumentException if the attribute is not numeric
   */
  public final double variance(int attIndex) {
  
    double sum = 0, sumSquared = 0, sumOfWeights = 0;

    if (!attribute(attIndex).isNumeric()) {
      throw new IllegalArgumentException("Can't compute variance because attribute is " +
			  "not numeric!");
    }
    for (int i = 0; i < numInstances(); i++) {
      if (!instance(i).isMissing(attIndex)) {
	sum += instance(i).weight() * 
	  instance(i).value(attIndex);
	sumSquared += instance(i).weight() * 
	  instance(i).value(attIndex) *
	  instance(i).value(attIndex);
	sumOfWeights += instance(i).weight();
      }
    }
    if (sumOfWeights <= 1) {
      return 0;
    }
    double result = (sumSquared - (sum * sum / sumOfWeights)) / 
      (sumOfWeights - 1);

    // We don't like negative variance
    if (result < 0) {
      return 0;
    } else {
      return result;
    }
  }