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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This version represents the developer version, the
"bleeding edge" of development, you could say. New functionality gets added
to this version.
/*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see
* -M <num>
* minimum allowable standard deviation for normal density computation
* (default 1e-6)
*
*
*
* -W <clusterer name>
* Clusterer to wrap.
* (default weka.clusterers.SimpleKMeans)
*
*
*
* Options specific to clusterer weka.clusterers.SimpleKMeans:
*
*
*
* -N <num>
* number of clusters.
* (default 2).
*
*
*
* -V
* Display std. deviations for centroids.
*
*
*
* -M
* Replace missing values with mean/mode.
*
*
*
* -S <num>
* Random number seed.
* (default 10)
*
*
*
*
* Options after "--" are passed on to the base clusterer.
*
* @author Richard Kirkby ([email protected])
* @author Mark Hall ([email protected])
* @author Eibe Frank ([email protected])
* @version $Revision: 10203 $
*/
public class MakeDensityBasedClusterer extends AbstractDensityBasedClusterer
implements NumberOfClustersRequestable, OptionHandler,
WeightedInstancesHandler {
/** for serialization */
static final long serialVersionUID = -5643302427972186631L;
/** holds training instances header information */
private Instances m_theInstances;
/** prior probabilities for the fitted clusters */
private double[] m_priors;
/** normal distributions fitted to each numeric attribute in each cluster */
private double[][][] m_modelNormal;
/** discrete distributions fitted to each discrete attribute in each cluster */
private DiscreteEstimator[][] m_model;
/** default minimum standard deviation */
private double m_minStdDev = 1e-6;
/** The clusterer being wrapped */
private Clusterer m_wrappedClusterer = new weka.clusterers.SimpleKMeans();
/** globally replace missing values */
private ReplaceMissingValues m_replaceMissing;
/**
* Default constructor.
*
*/
public MakeDensityBasedClusterer() {
super();
}
/**
* Contructs a MakeDensityBasedClusterer wrapping a given Clusterer.
*
* @param toWrap the clusterer to wrap around
*/
public MakeDensityBasedClusterer(Clusterer toWrap) {
setClusterer(toWrap);
}
/**
* Returns a string describing classifier
*
* @return a description suitable for displaying in the explorer/experimenter
* gui
*/
public String globalInfo() {
return "Class for wrapping a Clusterer to make it return a distribution "
+ "and density. Fits normal distributions and discrete distributions "
+ "within each cluster produced by the wrapped clusterer. Supports the "
+ "NumberOfClustersRequestable interface only if the wrapped Clusterer "
+ "does.";
}
/**
* String describing default clusterer.
*
* @return the default clusterer classname
*/
protected String defaultClustererString() {
return SimpleKMeans.class.getName();
}
/**
* Set the number of clusters to generate.
*
* @param n the number of clusters to generate
* @throws Exception if the wrapped clusterer has not been set, or if the
* wrapped clusterer does not implement this facility.
*/
@Override
public void setNumClusters(int n) throws Exception {
if (m_wrappedClusterer == null) {
throw new Exception("Can't set the number of clusters to generate - "
+ "no clusterer has been set yet.");
}
if (!(m_wrappedClusterer instanceof NumberOfClustersRequestable)) {
throw new Exception("Can't set the number of clusters to generate - "
+ "wrapped clusterer does not support this facility.");
}
((NumberOfClustersRequestable) m_wrappedClusterer).setNumClusters(n);
}
/**
* Returns default capabilities of the clusterer (i.e., of the wrapper
* clusterer).
*
* @return the capabilities of this clusterer
*/
@Override
public Capabilities getCapabilities() {
if (m_wrappedClusterer != null) {
return m_wrappedClusterer.getCapabilities();
}
Capabilities result = super.getCapabilities();
result.disableAll();
result.enable(Capability.NO_CLASS);
return result;
}
/**
* Builds a clusterer for a set of instances.
*
* @param data the instances to train the clusterer with
* @throws Exception if the clusterer hasn't been set or something goes wrong
*/
@Override
public void buildClusterer(Instances data) throws Exception {
// can clusterer handle the data?
getCapabilities().testWithFail(data);
m_replaceMissing = new ReplaceMissingValues();
m_replaceMissing.setInputFormat(data);
data = weka.filters.Filter.useFilter(data, m_replaceMissing);
m_theInstances = new Instances(data, 0);
if (m_wrappedClusterer == null) {
throw new Exception("No clusterer has been set");
}
m_wrappedClusterer.buildClusterer(data);
m_model = new DiscreteEstimator[m_wrappedClusterer.numberOfClusters()][data
.numAttributes()];
m_modelNormal = new double[m_wrappedClusterer.numberOfClusters()][data
.numAttributes()][2];
double[][] weights = new double[m_wrappedClusterer.numberOfClusters()][data
.numAttributes()];
m_priors = new double[m_wrappedClusterer.numberOfClusters()];
for (int i = 0; i < m_wrappedClusterer.numberOfClusters(); i++) {
m_priors[i] = 1.0; // laplace correction
for (int j = 0; j < data.numAttributes(); j++) {
if (data.attribute(j).isNominal()) {
m_model[i][j] = new DiscreteEstimator(data.attribute(j).numValues(),
true);
}
}
}
Instance inst = null;
// Compute mean, etc.
int[] clusterIndex = new int[data.numInstances()];
for (int i = 0; i < data.numInstances(); i++) {
inst = data.instance(i);
int cluster = m_wrappedClusterer.clusterInstance(inst);
m_priors[cluster] += inst.weight();
for (int j = 0; j < data.numAttributes(); j++) {
if (!inst.isMissing(j)) {
if (data.attribute(j).isNominal()) {
m_model[cluster][j].addValue(inst.value(j), inst.weight());
} else {
m_modelNormal[cluster][j][0] += inst.weight() * inst.value(j);
weights[cluster][j] += inst.weight();
}
}
}
clusterIndex[i] = cluster;
}
for (int j = 0; j < data.numAttributes(); j++) {
if (data.attribute(j).isNumeric()) {
for (int i = 0; i < m_wrappedClusterer.numberOfClusters(); i++) {
if (weights[i][j] > 0) {
m_modelNormal[i][j][0] /= weights[i][j];
}
}
}
}
// Compute standard deviations
for (int i = 0; i < data.numInstances(); i++) {
inst = data.instance(i);
for (int j = 0; j < data.numAttributes(); j++) {
if (!inst.isMissing(j)) {
if (data.attribute(j).isNumeric()) {
double diff = m_modelNormal[clusterIndex[i]][j][0] - inst.value(j);
m_modelNormal[clusterIndex[i]][j][1] += inst.weight() * diff * diff;
}
}
}
}
for (int j = 0; j < data.numAttributes(); j++) {
if (data.attribute(j).isNumeric()) {
for (int i = 0; i < m_wrappedClusterer.numberOfClusters(); i++) {
if (weights[i][j] > 0) {
m_modelNormal[i][j][1] = Math.sqrt(m_modelNormal[i][j][1]
/ weights[i][j]);
} else if (weights[i][j] <= 0) {
m_modelNormal[i][j][1] = Double.MAX_VALUE;
}
if (m_modelNormal[i][j][1] <= m_minStdDev) {
m_modelNormal[i][j][1] = data.attributeStats(j).numericStats.stdDev;
if (m_modelNormal[i][j][1] <= m_minStdDev) {
m_modelNormal[i][j][1] = m_minStdDev;
}
}
}
}
}
Utils.normalize(m_priors);
}
/**
* Returns the cluster priors.
*
* @return the cluster priors
*/
@Override
public double[] clusterPriors() {
double[] n = new double[m_priors.length];
System.arraycopy(m_priors, 0, n, 0, n.length);
return n;
}
/**
* Computes the log of the conditional density (per cluster) for a given
* instance.
*
* @param inst the instance to compute the density for
* @return an array containing the estimated densities
* @throws Exception if the density could not be computed successfully
*/
@Override
public double[] logDensityPerClusterForInstance(Instance inst)
throws Exception {
int i, j;
double logprob;
double[] wghts = new double[m_wrappedClusterer.numberOfClusters()];
m_replaceMissing.input(inst);
inst = m_replaceMissing.output();
for (i = 0; i < m_wrappedClusterer.numberOfClusters(); i++) {
logprob = 0;
for (j = 0; j < inst.numAttributes(); j++) {
if (!inst.isMissing(j)) {
if (inst.attribute(j).isNominal()) {
logprob += Math.log(m_model[i][j].getProbability(inst.value(j)));
} else { // numeric attribute
logprob += logNormalDens(inst.value(j), m_modelNormal[i][j][0],
m_modelNormal[i][j][1]);
}
}
}
wghts[i] = logprob;
}
return wghts;
}
/** Constant for normal distribution. */
private static double m_normConst = 0.5 * Math.log(2 * Math.PI);
/**
* Density function of normal distribution.
*
* @param x input value
* @param mean mean of distribution
* @param stdDev standard deviation of distribution
* @return the density
*/
private double logNormalDens(double x, double mean, double stdDev) {
double diff = x - mean;
return -(diff * diff / (2 * stdDev * stdDev)) - m_normConst
- Math.log(stdDev);
}
/**
* Returns the number of clusters.
*
* @return the number of clusters generated for a training dataset.
* @throws Exception if number of clusters could not be returned successfully
*/
@Override
public int numberOfClusters() throws Exception {
return m_wrappedClusterer.numberOfClusters();
}
/**
* Returns a description of the clusterer.
*
* @return a string containing a description of the clusterer
*/
@Override
public String toString() {
if (m_priors == null) {
return "No clusterer built yet!";
}
StringBuffer text = new StringBuffer();
text.append("MakeDensityBasedClusterer: \n\nWrapped clusterer: "
+ m_wrappedClusterer.toString());
text.append("\nFitted estimators (with ML estimates of variance):\n");
for (int j = 0; j < m_priors.length; j++) {
text.append("\nCluster: " + j + " Prior probability: "
+ Utils.doubleToString(m_priors[j], 4) + "\n\n");
for (int i = 0; i < m_model[0].length; i++) {
text.append("Attribute: " + m_theInstances.attribute(i).name() + "\n");
if (m_theInstances.attribute(i).isNominal()) {
if (m_model[j][i] != null) {
text.append(m_model[j][i].toString());
}
} else {
text.append("Normal Distribution. Mean = "
+ Utils.doubleToString(m_modelNormal[j][i][0], 4) + " StdDev = "
+ Utils.doubleToString(m_modelNormal[j][i][1], 4) + "\n");
}
}
}
return text.toString();
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for displaying in the
* explorer/experimenter gui
*/
public String clustererTipText() {
return "the clusterer to wrap";
}
/**
* Sets the clusterer to wrap.
*
* @param toWrap the clusterer
*/
public void setClusterer(Clusterer toWrap) {
m_wrappedClusterer = toWrap;
}
/**
* Gets the clusterer being wrapped.
*
* @return the clusterer
*/
public Clusterer getClusterer() {
return m_wrappedClusterer;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for displaying in the
* explorer/experimenter gui
*/
public String minStdDevTipText() {
return "set minimum allowable standard deviation";
}
/**
* Set the minimum value for standard deviation when calculating normal
* density. Reducing this value can help prevent arithmetic overflow resulting
* from multiplying large densities (arising from small standard deviations)
* when there are many singleton or near singleton values.
*
* @param m minimum value for standard deviation
*/
public void setMinStdDev(double m) {
m_minStdDev = m;
}
/**
* Get the minimum allowable standard deviation.
*
* @return the minumum allowable standard deviation
*/
public double getMinStdDev() {
return m_minStdDev;
}
/**
* Returns an enumeration describing the available options..
*
* @return an enumeration of all the available options.
*/
@Override
public Enumeration© 2015 - 2024 Weber Informatics LLC | Privacy Policy