int j;
Vector<Integer> deleteCols;
int[] todelete;
double[][] v;
Matrix corr;
EigenvalueDecomposition eig;
Matrix V;
m_TrainInstances = new Instances(instances);
// make a copy of the training data so that we can get the class
// column to append to the transformed data (if necessary)
m_TrainCopy = new Instances(m_TrainInstances);
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_NominalToBinaryFilter = new NominalToBinary();
m_NominalToBinaryFilter.setInputFormat(m_TrainInstances);
m_TrainInstances = Filter.useFilter(m_TrainInstances, m_NominalToBinaryFilter);
// delete any attributes with only one distinct value or are all missing
deleteCols = new Vector<Integer>();
for (i = 0; i < m_TrainInstances.numAttributes(); i++) {
if (m_TrainInstances.numDistinctValues(i) <= 1)
deleteCols.addElement(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();
todelete = new int [deleteCols.size()];
for (i = 0; i < deleteCols.size(); i++)
todelete[i] = ((Integer)(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();
fillCorrelation();
// get eigen vectors/values
corr = new Matrix(m_Correlation);
eig = corr.eig();
V = eig.getV();
v = new double[m_NumAttribs][m_NumAttribs];
for (i = 0; i < v.length; i++) {
for (j = 0; j < v[0].length; j++)
v[i][j] = V.get(i, j);
}
m_Eigenvectors = (double[][]) v.clone();
m_Eigenvalues = (double[]) eig.getRealEigenvalues().clone();
// any eigenvalues less than 0 are not worth anything --- change to 0
for (i = 0; i < m_Eigenvalues.length; i++) {
if (m_Eigenvalues[i] < 0)
m_Eigenvalues[i] = 0.0;