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weka.attributeSelection.PrincipalComponents Maven / Gradle / Ivy
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
*
*
* Valid options are:
*
*
*
* -C
* Center (rather than standardize) the
* data and compute PCA using the covariance (rather
* than the correlation) matrix.
*
*
*
* -R
* Retain enough PC attributes to account
* for this proportion of variance in the original data.
* (default = 0.95)
*
*
*
* -O
* Transform through the PC space and
* back to the original space.
*
*
*
* -A
* Maximum number of attributes to include in
* transformed attribute names. (-1 = include all)
*
*
*
*
* @author Mark Hall ([email protected] )
* @author Gabi Schmidberger ([email protected] )
* @version $Revision: 12659 $
*/
public class PrincipalComponents extends UnsupervisedAttributeEvaluator
implements AttributeTransformer, OptionHandler {
/** for serialization */
private static final long serialVersionUID = -3675307197777734007L;
/** The data to transform analyse/transform */
private Instances m_trainInstances;
/** Keep a copy for the class attribute (if set) */
private Instances m_trainHeader;
/** The header for the transformed data format */
private Instances m_transformedFormat;
/** The header for data transformed back to the original space */
private Instances m_originalSpaceFormat;
/** Data has a class set */
private boolean m_hasClass;
/** Class index */
private int m_classIndex;
/** Number of attributes */
private int m_numAttribs;
/** Number of instances */
private int m_numInstances;
/** Correlation/covariance matrix for the original data */
private UpperSymmDenseMatrix m_correlation;
private double[] m_means;
private double[] m_stdDevs;
/**
* If true, center (rather than standardize) the data and compute PCA from
* covariance (rather than correlation) matrix.
*/
private boolean m_center = false;
/**
* Will hold the unordered linear transformations of the (normalized) original
* data
*/
private double[][] m_eigenvectors;
/** Eigenvalues for the corresponding eigenvectors */
private double[] m_eigenvalues = null;
/** Sorted eigenvalues */
private int[] m_sortedEigens;
/** sum of the eigenvalues */
private double m_sumOfEigenValues = 0.0;
/** Filters for original data */
private ReplaceMissingValues m_replaceMissingFilter;
private NominalToBinary m_nominalToBinFilter;
private Remove m_attributeFilter;
private Center m_centerFilter;
private Standardize m_standardizeFilter;
/** The number of attributes in the pc transformed data */
private int m_outputNumAtts = -1;
/**
* the amount of variance to cover in the original data when retaining the
* best n PC's
*/
private double m_coverVariance = 0.95;
/**
* transform the data through the pc space and back to the original space ?
*/
private boolean m_transBackToOriginal = false;
/** maximum number of attributes in the transformed attribute name */
private int m_maxAttrsInName = 5;
/**
* holds the transposed eigenvectors for converting back to the original space
*/
private double[][] m_eTranspose;
/**
* Returns a string describing this attribute transformer
*
* @return a description of the evaluator suitable for displaying in the
* explorer/experimenter gui
*/
public String globalInfo() {
return "Performs a principal components analysis and transformation of "
+ "the data. Use in conjunction with a Ranker search. Dimensionality "
+ "reduction is accomplished by choosing enough eigenvectors to "
+ "account for some percentage of the variance in the original data---"
+ "default 0.95 (95%). Attribute noise can be filtered by transforming "
+ "to the PC space, eliminating some of the worst eigenvectors, and "
+ "then transforming back to the original space.";
}
/**
* Returns an enumeration describing the available options.
*
*
* @return an enumeration of all the available options.
**/
@Override
public Enumeration listOptions() {
Vector newVector = new Vector (4);
newVector.addElement(new Option("\tCenter (rather than standardize) the"
+ "\n\tdata and compute PCA using the covariance (rather"
+ "\n\t than the correlation) matrix.", "C", 0, "-C"));
newVector.addElement(new Option("\tRetain enough PC attributes to account "
+ "\n\tfor this proportion of variance in " + "the original data.\n"
+ "\t(default = 0.95)", "R", 1, "-R"));
newVector.addElement(new Option("\tTransform through the PC space and "
+ "\n\tback to the original space.", "O", 0, "-O"));
newVector.addElement(new Option(
"\tMaximum number of attributes to include in "
+ "\n\ttransformed attribute names. (-1 = include all)", "A", 1, "-A"));
return newVector.elements();
}
/**
* Parses a given list of options.
*
*
* Valid options are:
*
*
*
* -C
* Center (rather than standardize) the
* data and compute PCA using the covariance (rather
* than the correlation) matrix.
*
*
*
* -R
* Retain enough PC attributes to account
* for this proportion of variance in the original data.
* (default = 0.95)
*
*
*
* -O
* Transform through the PC space and
* back to the original space.
*
*
*
* -A
* Maximum number of attributes to include in
* transformed attribute names. (-1 = include all)
*
*
*
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
@Override
public void setOptions(String[] options) throws Exception {
resetOptions();
String optionString;
optionString = Utils.getOption('R', options);
if (optionString.length() != 0) {
Double temp;
temp = Double.valueOf(optionString);
setVarianceCovered(temp.doubleValue());
}
optionString = Utils.getOption('A', options);
if (optionString.length() != 0) {
setMaximumAttributeNames(Integer.parseInt(optionString));
}
setTransformBackToOriginal(Utils.getFlag('O', options));
setCenterData(Utils.getFlag('C', options));
}
/**
* Reset to defaults
*/
private void resetOptions() {
m_coverVariance = 0.95;
m_sumOfEigenValues = 0.0;
m_transBackToOriginal = false;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for displaying in the
* explorer/experimenter gui
*/
public String centerDataTipText() {
return "Center (rather than standardize) the data. PCA will "
+ "be computed from the covariance (rather than correlation) " + "matrix";
}
/**
* Set whether to center (rather than standardize) the data. If set to true
* then PCA is computed from the covariance rather than correlation matrix.
*
* @param center true if the data is to be centered rather than standardized
*/
public void setCenterData(boolean center) {
m_center = center;
}
/**
* Get whether to center (rather than standardize) the data. If true then PCA
* is computed from the covariance rather than correlation matrix.
*
* @return true if the data is to be centered rather than standardized.
*/
public boolean getCenterData() {
return m_center;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for displaying in the
* explorer/experimenter gui
*/
public String varianceCoveredTipText() {
return "Retain enough PC attributes to account for this proportion of "
+ "variance.";
}
/**
* Sets the amount of variance to account for when retaining principal
* components
*
* @param vc the proportion of total variance to account for
*/
public void setVarianceCovered(double vc) {
m_coverVariance = vc;
}
/**
* Gets the proportion of total variance to account for when retaining
* principal components
*
* @return the proportion of variance to account for
*/
public double getVarianceCovered() {
return m_coverVariance;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for displaying in the
* explorer/experimenter gui
*/
public String maximumAttributeNamesTipText() {
return "The maximum number of attributes to include in transformed attribute names.";
}
/**
* Sets maximum number of attributes to include in transformed attribute
* names.
*
* @param m the maximum number of attributes
*/
public void setMaximumAttributeNames(int m) {
m_maxAttrsInName = m;
}
/**
* Gets maximum number of attributes to include in transformed attribute
* names.
*
* @return the maximum number of attributes
*/
public int getMaximumAttributeNames() {
return m_maxAttrsInName;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for displaying in the
* explorer/experimenter gui
*/
public String transformBackToOriginalTipText() {
return "Transform through the PC space and back to the original space. "
+ "If only the best n PCs are retained (by setting varianceCovered < 1) "
+ "then this option will give a dataset in the original space but with "
+ "less attribute noise.";
}
/**
* Sets whether the data should be transformed back to the original space
*
* @param b true if the data should be transformed back to the original space
*/
public void setTransformBackToOriginal(boolean b) {
m_transBackToOriginal = b;
}
/**
* Gets whether the data is to be transformed back to the original space.
*
* @return true if the data is to be transformed back to the original space
*/
public boolean getTransformBackToOriginal() {
return m_transBackToOriginal;
}
/**
* Gets the current settings of PrincipalComponents
*
* @return an array of strings suitable for passing to setOptions()
*/
@Override
public String[] getOptions() {
Vector options = new Vector();
if (getCenterData()) {
options.add("-C");
}
options.add("-R");
options.add("" + getVarianceCovered());
options.add("-A");
options.add("" + getMaximumAttributeNames());
if (getTransformBackToOriginal()) {
options.add("-O");
}
return options.toArray(new String[0]);
}
/**
* Returns the capabilities of this evaluator.
*
* @return the capabilities of this evaluator
* @see Capabilities
*/
@Override
public Capabilities getCapabilities() {
Capabilities result = super.getCapabilities();
result.disableAll();
// attributes
result.enable(Capability.NOMINAL_ATTRIBUTES);
result.enable(Capability.NUMERIC_ATTRIBUTES);
result.enable(Capability.DATE_ATTRIBUTES);
result.enable(Capability.MISSING_VALUES);
// class
result.enable(Capability.NOMINAL_CLASS);
result.enable(Capability.UNARY_CLASS);
result.enable(Capability.NUMERIC_CLASS);
result.enable(Capability.DATE_CLASS);
result.enable(Capability.MISSING_CLASS_VALUES);
result.enable(Capability.NO_CLASS);
return result;
}
/**
* Initializes principal components and performs the analysis
*
* @param data the instances to analyse/transform
* @throws Exception if analysis fails
*/
@Override
public void buildEvaluator(Instances data) throws Exception {
// can evaluator handle data?
getCapabilities().testWithFail(data);
buildAttributeConstructor(data);
}
private void buildAttributeConstructor(Instances data) throws Exception {
m_eigenvalues = null;
m_outputNumAtts = -1;
m_attributeFilter = null;
m_nominalToBinFilter = null;
m_sumOfEigenValues = 0.0;
m_trainInstances = new Instances(data);
// make a copy of the training data so that we can get the class
// column to append to the transformed data (if necessary)
m_trainHeader = new Instances(m_trainInstances, 0);
m_replaceMissingFilter = new ReplaceMissingValues();
m_replaceMissingFilter.setInputFormat(m_trainInstances);
m_trainInstances =
Filter.useFilter(m_trainInstances, m_replaceMissingFilter);
/*
* if (m_normalize) { m_normalizeFilter = new Normalize();
* m_normalizeFilter.setInputFormat(m_trainInstances); m_trainInstances =
* Filter.useFilter(m_trainInstances, m_normalizeFilter); }
*/
m_nominalToBinFilter = new NominalToBinary();
m_nominalToBinFilter.setInputFormat(m_trainInstances);
m_trainInstances = Filter.useFilter(m_trainInstances, m_nominalToBinFilter);
// delete any attributes with only one distinct value or are all missing
Vector deleteCols = new Vector();
for (int i = 0; i < m_trainInstances.numAttributes(); i++) {
if (m_trainInstances.numDistinctValues(i) <= 1) {
deleteCols.addElement(new Integer(i));
}
}
if (m_trainInstances.classIndex() >= 0) {
// get rid of the class column
m_hasClass = true;
m_classIndex = m_trainInstances.classIndex();
deleteCols.addElement(new Integer(m_classIndex));
}
// remove columns from the data if necessary
if (deleteCols.size() > 0) {
m_attributeFilter = new Remove();
int[] todelete = new int[deleteCols.size()];
for (int i = 0; i < deleteCols.size(); i++) {
todelete[i] = (deleteCols.elementAt(i)).intValue();
}
m_attributeFilter.setAttributeIndicesArray(todelete);
m_attributeFilter.setInvertSelection(false);
m_attributeFilter.setInputFormat(m_trainInstances);
m_trainInstances = Filter.useFilter(m_trainInstances, m_attributeFilter);
}
// can evaluator handle the processed data ? e.g., enough attributes?
getCapabilities().testWithFail(m_trainInstances);
m_numInstances = m_trainInstances.numInstances();
m_numAttribs = m_trainInstances.numAttributes();
fillCovariance();
SymmDenseEVD evd = SymmDenseEVD.factorize(m_correlation);
m_eigenvectors = Matrices.getArray(evd.getEigenvectors());
m_eigenvalues = evd.getEigenvalues();
/*
* for (int i = 0; i < m_numAttribs; i++) { for (int j = 0; j <
* m_numAttribs; j++) { System.err.println(v[i][j] + " "); }
* System.err.println(d[i]); }
*/
// any eigenvalues less than 0 are not worth anything --- change to 0
for (int i = 0; i < m_eigenvalues.length; i++) {
if (m_eigenvalues[i] < 0) {
m_eigenvalues[i] = 0.0;
}
}
m_sortedEigens = Utils.sort(m_eigenvalues);
m_sumOfEigenValues = Utils.sum(m_eigenvalues);
m_transformedFormat = setOutputFormat();
if (m_transBackToOriginal) {
m_originalSpaceFormat = setOutputFormatOriginal();
// new ordered eigenvector matrix
int numVectors =
(m_transformedFormat.classIndex() < 0) ? m_transformedFormat
.numAttributes() : m_transformedFormat.numAttributes() - 1;
double[][] orderedVectors =
new double[m_eigenvectors.length][numVectors + 1];
// try converting back to the original space
for (int i = m_numAttribs - 1; i > (m_numAttribs - numVectors - 1); i--) {
for (int j = 0; j < m_numAttribs; j++) {
orderedVectors[j][m_numAttribs - i] =
m_eigenvectors[j][m_sortedEigens[i]];
}
}
// transpose the matrix
int nr = orderedVectors.length;
int nc = orderedVectors[0].length;
m_eTranspose = new double[nc][nr];
for (int i = 0; i < nc; i++) {
for (int j = 0; j < nr; j++) {
m_eTranspose[i][j] = orderedVectors[j][i];
}
}
}
}
/**
* Returns just the header for the transformed data (ie. an empty set of
* instances. This is so that AttributeSelection can determine the structure
* of the transformed data without actually having to get all the transformed
* data through transformedData().
*
* @return the header of the transformed data.
* @throws Exception if the header of the transformed data can't be
* determined.
*/
@Override
public Instances transformedHeader() throws Exception {
if (m_eigenvalues == null) {
throw new Exception("Principal components hasn't been built yet");
}
if (m_transBackToOriginal) {
return m_originalSpaceFormat;
} else {
return m_transformedFormat;
}
}
/**
* Return the header of the training data after all filtering - i.e missing
* values and nominal to binary.
*
* @return the header of the training data after all filtering.
*/
public Instances getFilteredInputFormat() {
return new Instances(m_trainInstances, 0);
}
/**
* Return the correlation/covariance matrix
*
* @return the correlation or covariance matrix
*/
public double[][] getCorrelationMatrix() {
return Matrices.getArray(m_correlation);
}
/**
* Return the unsorted eigenvectors
*
* @return the unsorted eigenvectors
*/
public double[][] getUnsortedEigenVectors() {
return m_eigenvectors;
}
/**
* Return the eigenvalues corresponding to the eigenvectors
*
* @return the eigenvalues
*/
public double[] getEigenValues() {
return m_eigenvalues;
}
/**
* Gets the transformed training data.
*
* @return the transformed training data
* @throws Exception if transformed data can't be returned
*/
@Override
public Instances transformedData(Instances data) throws Exception {
if (m_eigenvalues == null) {
throw new Exception("Principal components hasn't been built yet");
}
Instances output = null;
if (m_transBackToOriginal) {
output = new Instances(m_originalSpaceFormat);
} else {
output = new Instances(m_transformedFormat);
}
for (int i = 0; i < data.numInstances(); i++) {
Instance converted = convertInstance(data.instance(i));
output.add(converted);
}
return output;
}
/**
* Evaluates the merit of a transformed attribute. This is defined to be 1
* minus the cumulative variance explained. Merit can't be meaningfully
* evaluated if the data is to be transformed back to the original space.
*
* @param att the attribute to be evaluated
* @return the merit of a transformed attribute
* @throws Exception if attribute can't be evaluated
*/
@Override
public double evaluateAttribute(int att) throws Exception {
if (m_eigenvalues == null) {
throw new Exception("Principal components hasn't been built yet!");
}
if (m_transBackToOriginal) {
return 1.0; // can't evaluate back in the original space!
}
// return 1-cumulative variance explained for this transformed att
double cumulative = 0.0;
for (int i = m_numAttribs - 1; i >= m_numAttribs - att - 1; i--) {
cumulative += m_eigenvalues[m_sortedEigens[i]];
}
return 1.0 - cumulative / m_sumOfEigenValues;
}
private void fillCovariance() throws Exception {
// first store the means
m_means = new double[m_trainInstances.numAttributes()];
m_stdDevs = new double[m_trainInstances.numAttributes()];
for (int i = 0; i < m_trainInstances.numAttributes(); i++) {
m_means[i] = m_trainInstances.meanOrMode(i);
m_stdDevs[i] =
Math.sqrt(Utils.variance(m_trainInstances.attributeToDoubleArray(i)));
}
// just center the data or standardize it?
if (m_center) {
m_centerFilter = new Center();
m_centerFilter.setInputFormat(m_trainInstances);
m_trainInstances = Filter.useFilter(m_trainInstances, m_centerFilter);
} else {
m_standardizeFilter = new Standardize();
m_standardizeFilter.setInputFormat(m_trainInstances);
m_trainInstances = Filter.useFilter(m_trainInstances, m_standardizeFilter);
}
// now compute the covariance matrix
m_correlation = new UpperSymmDenseMatrix(m_numAttribs);
for (int i = 0; i < m_numAttribs; i++) {
for (int j = i; j < m_numAttribs; j++) {
double cov = 0;
for (Instance inst : m_trainInstances) {
cov += inst.value(i) * inst.value(j);
}
cov /= m_trainInstances.numInstances() - 1;
m_correlation.set(i, j, cov);
}
}
}
/**
* Return a summary of the analysis
*
* @return a summary of the analysis.
*/
private String principalComponentsSummary() {
StringBuffer result = new StringBuffer();
double cumulative = 0.0;
Instances output = null;
int numVectors = 0;
try {
output = setOutputFormat();
numVectors =
(output.classIndex() < 0) ? output.numAttributes() : output
.numAttributes() - 1;
} catch (Exception ex) {
}
// tomorrow
String corrCov = (m_center) ? "Covariance " : "Correlation ";
result
.append(corrCov + "matrix\n" + matrixToString(Matrices.getArray(m_correlation)) + "\n\n");
result.append("eigenvalue\tproportion\tcumulative\n");
for (int i = m_numAttribs - 1; i > (m_numAttribs - numVectors - 1); i--) {
cumulative += m_eigenvalues[m_sortedEigens[i]];
result.append(Utils
.doubleToString(m_eigenvalues[m_sortedEigens[i]], 9, 5)
+ "\t"
+ Utils.doubleToString(
(m_eigenvalues[m_sortedEigens[i]] / m_sumOfEigenValues), 9, 5)
+ "\t"
+ Utils.doubleToString((cumulative / m_sumOfEigenValues), 9, 5)
+ "\t"
+ output.attribute(m_numAttribs - i - 1).name() + "\n");
}
result.append("\nEigenvectors\n");
for (int j = 1; j <= numVectors; j++) {
result.append(" V" + j + '\t');
}
result.append("\n");
for (int j = 0; j < m_numAttribs; j++) {
for (int i = m_numAttribs - 1; i > (m_numAttribs - numVectors - 1); i--) {
result.append(Utils.doubleToString(
m_eigenvectors[j][m_sortedEigens[i]], 7, 4) + "\t");
}
result.append(m_trainInstances.attribute(j).name() + '\n');
}
if (m_transBackToOriginal) {
result.append("\nPC space transformed back to original space.\n"
+ "(Note: can't evaluate attributes in the original " + "space)\n");
}
return result.toString();
}
/**
* Returns a description of this attribute transformer
*
* @return a String describing this attribute transformer
*/
@Override
public String toString() {
if (m_eigenvalues == null) {
return "Principal components hasn't been built yet!";
} else {
return "\tPrincipal Components Attribute Transformer\n\n"
+ principalComponentsSummary();
}
}
/**
* Return a matrix as a String
*
* @param matrix that is decribed as a string
* @return a String describing a matrix
*/
public static String matrixToString(double[][] matrix) {
StringBuffer result = new StringBuffer();
int last = matrix.length - 1;
for (int i = 0; i <= last; i++) {
for (int j = 0; j <= last; j++) {
result.append(Utils.doubleToString(matrix[i][j], 6, 2) + " ");
if (j == last) {
result.append('\n');
}
}
}
return result.toString();
}
/**
* Convert a pc transformed instance back to the original space
*
* @param inst the instance to convert
* @return the processed instance
* @throws Exception if something goes wrong
*/
private Instance convertInstanceToOriginal(Instance inst) throws Exception {
double[] newVals = null;
if (m_hasClass) {
newVals = new double[m_numAttribs + 1];
} else {
newVals = new double[m_numAttribs];
}
if (m_hasClass) {
// class is always appended as the last attribute
newVals[m_numAttribs] = inst.value(inst.numAttributes() - 1);
}
for (int i = 0; i < m_eTranspose[0].length; i++) {
double tempval = 0.0;
for (int j = 1; j < m_eTranspose.length; j++) {
tempval += (m_eTranspose[j][i] * inst.value(j - 1));
}
newVals[i] = tempval;
if (!m_center) {
newVals[i] *= m_stdDevs[i];
}
newVals[i] += m_means[i];
}
if (inst instanceof SparseInstance) {
return new SparseInstance(inst.weight(), newVals);
} else {
return new DenseInstance(inst.weight(), newVals);
}
}
/**
* Transform an instance in original (unormalized) format. Convert back to the
* original space if requested.
*
* @param instance an instance in the original (unormalized) format
* @return a transformed instance
* @throws Exception if instance cant be transformed
*/
@Override
public Instance convertInstance(Instance instance) throws Exception {
if (m_eigenvalues == null) {
throw new Exception("convertInstance: Principal components not "
+ "built yet");
}
double[] newVals = new double[m_outputNumAtts];
Instance tempInst = (Instance) instance.copy();
if (!instance.dataset().equalHeaders(m_trainHeader)) {
throw new Exception("Can't convert instance: header's don't match: "
+ "PrincipalComponents\n"
+ instance.dataset().equalHeadersMsg(m_trainHeader));
}
m_replaceMissingFilter.input(tempInst);
m_replaceMissingFilter.batchFinished();
tempInst = m_replaceMissingFilter.output();
/*
* if (m_normalize) { m_normalizeFilter.input(tempInst);
* m_normalizeFilter.batchFinished(); tempInst = m_normalizeFilter.output();
* }
*/
m_nominalToBinFilter.input(tempInst);
m_nominalToBinFilter.batchFinished();
tempInst = m_nominalToBinFilter.output();
if (m_attributeFilter != null) {
m_attributeFilter.input(tempInst);
m_attributeFilter.batchFinished();
tempInst = m_attributeFilter.output();
}
if (!m_center) {
m_standardizeFilter.input(tempInst);
m_standardizeFilter.batchFinished();
tempInst = m_standardizeFilter.output();
} else {
m_centerFilter.input(tempInst);
m_centerFilter.batchFinished();
tempInst = m_centerFilter.output();
}
if (m_hasClass) {
newVals[m_outputNumAtts - 1] = instance.value(instance.classIndex());
}
double cumulative = 0;
for (int i = m_numAttribs - 1; i >= 0; i--) {
double tempval = 0.0;
for (int j = 0; j < m_numAttribs; j++) {
tempval += (m_eigenvectors[j][m_sortedEigens[i]] * tempInst.value(j));
}
newVals[m_numAttribs - i - 1] = tempval;
cumulative += m_eigenvalues[m_sortedEigens[i]];
if ((cumulative / m_sumOfEigenValues) >= m_coverVariance) {
break;
}
}
if (!m_transBackToOriginal) {
if (instance instanceof SparseInstance) {
return new SparseInstance(instance.weight(), newVals);
} else {
return new DenseInstance(instance.weight(), newVals);
}
} else {
if (instance instanceof SparseInstance) {
return convertInstanceToOriginal(new SparseInstance(instance.weight(),
newVals));
} else {
return convertInstanceToOriginal(new DenseInstance(instance.weight(),
newVals));
}
}
}
/**
* Set up the header for the PC->original space dataset
*
* @return the output format
* @throws Exception if something goes wrong
*/
private Instances setOutputFormatOriginal() throws Exception {
ArrayList attributes = new ArrayList();
for (int i = 0; i < m_numAttribs; i++) {
String att = m_trainInstances.attribute(i).name();
attributes.add(new Attribute(att));
}
if (m_hasClass) {
attributes.add((Attribute) m_trainHeader.classAttribute().copy());
}
Instances outputFormat =
new Instances(m_trainHeader.relationName() + "->PC->original space",
attributes, 0);
// set the class to be the last attribute if necessary
if (m_hasClass) {
outputFormat.setClassIndex(outputFormat.numAttributes() - 1);
}
return outputFormat;
}
/**
* Set the format for the transformed data
*
* @return a set of empty Instances (header only) in the new format
* @throws Exception if the output format can't be set
*/
private Instances setOutputFormat() throws Exception {
if (m_eigenvalues == null) {
return null;
}
double cumulative = 0.0;
ArrayList attributes = new ArrayList();
for (int i = m_numAttribs - 1; i >= 0; i--) {
StringBuffer attName = new StringBuffer();
// build array of coefficients
double[] coeff_mags = new double[m_numAttribs];
for (int j = 0; j < m_numAttribs; j++) {
coeff_mags[j] = -Math.abs(m_eigenvectors[j][m_sortedEigens[i]]);
}
int num_attrs =
(m_maxAttrsInName > 0) ? Math.min(m_numAttribs, m_maxAttrsInName)
: m_numAttribs;
// this array contains the sorted indices of the coefficients
int[] coeff_inds;
if (m_numAttribs > 0) {
// if m_maxAttrsInName > 0, sort coefficients by decreasing magnitude
coeff_inds = Utils.sort(coeff_mags);
} else {
// if m_maxAttrsInName <= 0, use all coeffs in original order
coeff_inds = new int[m_numAttribs];
for (int j = 0; j < m_numAttribs; j++) {
coeff_inds[j] = j;
}
}
// build final attName string
for (int j = 0; j < num_attrs; j++) {
double coeff_value = m_eigenvectors[coeff_inds[j]][m_sortedEigens[i]];
if (j > 0 && coeff_value >= 0) {
attName.append("+");
}
attName.append(Utils.doubleToString(coeff_value, 5, 3)
+ m_trainInstances.attribute(coeff_inds[j]).name());
}
if (num_attrs < m_numAttribs) {
attName.append("...");
}
attributes.add(new Attribute(attName.toString()));
cumulative += m_eigenvalues[m_sortedEigens[i]];
if ((cumulative / m_sumOfEigenValues) >= m_coverVariance) {
break;
}
}
if (m_hasClass) {
attributes.add((Attribute) m_trainHeader.classAttribute().copy());
}
Instances outputFormat =
new Instances(m_trainInstances.relationName() + "_principal components",
attributes, 0);
// set the class to be the last attribute if necessary
if (m_hasClass) {
outputFormat.setClassIndex(outputFormat.numAttributes() - 1);
}
m_outputNumAtts = outputFormat.numAttributes();
return outputFormat;
}
/**
* Returns the revision string.
*
* @return the revision
*/
@Override
public String getRevision() {
return RevisionUtils.extract("$Revision: 12659 $");
}
/**
* Main method for testing this class
*
* @param argv should contain the command line arguments to the
* evaluator/transformer (see AttributeSelection)
*/
public static void main(String[] argv) {
runEvaluator(new PrincipalComponents(), argv);
}
}
