Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.Matrix.inverse()

Package de.lmu.ifi.dbs.elki.math.linearalgebra

Class de.lmu.ifi.dbs.elki.math.linearalgebra.Matrix

Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.Matrix.inverse()

de.lmu.ifi.dbs.elki.math.linearalgebra.Matrix.inverse()
Matrix inverse or pseudoinverse @return inverse(A) if A is square, pseudoinverse otherwise.

    assert (E.getRowDimensionality() == E.getColumnDimensionality());
    for(int col = 0; col < E.getColumnDimensionality(); col++) {
      assert (E.get(col, col) == 0.0);
      E.set(col, col, 1.0);
    }
    E = E.inverse().timesEquals(1 - c);


    // Split the matrix into columns
    for(int i = 0; i < ids.size(); i++) {
      DBID id = ids.get(i);
      // Note: matrix times ith unit vector = ith column

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    int dimensionality = means.get(0).getDimensionality();
    for(int i = 0; i < k; i++) {
      Matrix m = Matrix.identity(dimensionality, dimensionality);
      covarianceMatrices.add(m);
      normDistrFactor.add(1.0 / Math.sqrt(Math.pow(MathUtil.TWOPI, dimensionality) * m.det()));
      invCovMatr.add(m.inverse());
      clusterWeights.add(1.0 / k);
      if(logger.isDebuggingFinest()) {
        StringBuffer msg = new StringBuffer();
        msg.append(" model ").append(i).append(":\n");
        msg.append(" mean:    ").append(means.get(i)).append("\n");

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    assert (E.getRowDimensionality() == E.getColumnDimensionality());
    for(int col = 0; col < E.getColumnDimensionality(); col++) {
      assert (E.get(col, col) == 0.0);
      E.set(col, col, 1.0);
    }
    E = E.inverse().timesEquals(1 - c);


    // Split the matrix into columns
    for(int i = 0; i < ids.size(); i++) {
      DBID id = ids.get(i);
      // Note: matrix times ith unit vector = ith column

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    final int dimensionality = means.get(0).getDimensionality();
    for(int i = 0; i < k; i++) {
      Matrix m = Matrix.identity(dimensionality, dimensionality);
      covarianceMatrices.add(m);
      normDistrFactor.add(1.0 / Math.sqrt(Math.pow(MathUtil.TWOPI, dimensionality) * m.det()));
      invCovMatr.add(m.inverse());
      clusterWeights.add(1.0 / k);
      if(logger.isDebuggingFinest()) {
        StringBuffer msg = new StringBuffer();
        msg.append(" model ").append(i).append(":\n");
        msg.append(" mean:    ").append(means.get(i)).append("\n");

View Full Code Here

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