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

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

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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");
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      if(logger.isDebuggingFinest()) {
        StringBuffer msg = new StringBuffer();
        msg.append(" model ").append(i).append(":\n");
        msg.append(" mean:    ").append(means.get(i)).append("\n");
        msg.append(" m:\n").append(FormatUtil.format(m, "        ")).append("\n");
        msg.append(" m.det(): ").append(m.det()).append("\n");
        msg.append(" cluster weight: ").append(clusterWeights.get(i)).append("\n");
        msg.append(" normDistFact:   ").append(normDistrFactor.get(i)).append("\n");
        logger.debugFine(msg.toString());
      }
    }
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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");
View Full Code Here
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      if(logger.isDebuggingFinest()) {
        StringBuffer msg = new StringBuffer();
        msg.append(" model ").append(i).append(":\n");
        msg.append(" mean:    ").append(means.get(i)).append("\n");
        msg.append(" m:\n").append(FormatUtil.format(m, "        ")).append("\n");
        msg.append(" m.det(): ").append(m.det()).append("\n");
        msg.append(" cluster weight: ").append(clusterWeights.get(i)).append("\n");
        msg.append(" normDistFact:   ").append(normDistrFactor.get(i)).append("\n");
        logger.debugFine(msg.toString());
      }
    }
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