Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.LinearEquationSystem

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

Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.LinearEquationSystem

de.lmu.ifi.dbs.elki.math.linearalgebra.LinearEquationSystem
Class for systems of linear equations. @author Elke Achtert

   * @throws NonNumericFeaturesException if the linear equation system is not
   *         compatible with values initialized during normalization
   */
  public LinearEquationSystem getNormalizedLinearEquationSystem(Normalization<?> normalization) throws NonNumericFeaturesException {
    if(normalization != null) {
      LinearEquationSystem lq = normalization.transform(linearEquationSystem);
      lq.solveByTotalPivotSearch();
      return lq;
    }
    else {
      return linearEquationSystem;
    }

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          TextWriterStreamNormalizing<V> nout = (TextWriterStreamNormalizing<V>) out;
          LinearEquationSystem lq = getNormalizedLinearEquationSystem(nout.getNormalization());
          out.commentPrint("Linear Equation System: ");
          out.commentPrintLn(lq.equationsToString(nf));
        } else { */
        LinearEquationSystem lq = getNormalizedLinearEquationSystem(null);
        out.commentPrint("Linear Equation System: ");
        out.commentPrintLn(lq.equationsToString(nf));
        //}
      }
    }
    catch(NonNumericFeaturesException e) {
      LoggingUtil.exception(e);

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    // System.out.print("beta: ");
    // for (double d : beta)
    // System.out.print(d + " ");
    // System.out.println();
    // Solve the equation system (Gauss-Jordan)
    LinearEquationSystem ls = new LinearEquationSystem(covmat, beta);
    ls.solveByTotalPivotSearch();
    // update covmat with the inverse
    covmat = ls.getCoefficents();
    // and deltaparams with the solution vector
    deltaparams = ls.getRHS();
    // deltaparams = beta;
    // System.out.print("deltaparams: ");
    // for (double d : deltaparams)
    // System.out.print(d + " ");
    // System.out.println();

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          }
        }
      }
      // dim == minDim
      else {
        LinearEquationSystem les = runDerivator(relation, dim - 1, interval.getIDs());
        Cluster<Model> c = new Cluster<Model>(interval.getIDs(), new LinearEquationModel(les));
        res.addCluster(c);
        noiseIDs.removeDBIDs(interval.getIDs());
        clusterIDs.addDBIDs(interval.getIDs());
        processedIDs.addAll(interval.getIDs());
      }


      // Rebuild heap
      List<IntegerPriorityObject<CASHInterval>> heapVector = heap.toSortedArrayList();
      for(IntegerPriorityObject<CASHInterval> pair : heapVector) {
        CASHInterval currentInterval = pair.getObject();
        currentInterval.removeIDs(clusterIDs);
        if(currentInterval.getIDs().size() >= minPts) {
          heap.add(new IntegerPriorityObject<CASHInterval>(currentInterval.priority(), currentInterval));
        }
      }


      if(progress != null) {
        progress.setProcessed(processedIDs.size(), logger);
      }
    }


    // put noise to clusters
    if(!noiseIDs.isEmpty()) {
      if(dim == noiseDim) {
        Cluster<Model> c = new Cluster<Model>(noiseIDs, true, ClusterModel.CLUSTER);
        res.addCluster(c);
        processedIDs.addAll(noiseIDs);
      }
      else if(noiseIDs.size() >= minPts) {
        LinearEquationSystem les = runDerivator(fulldatabase, dim - 1, noiseIDs);
        Cluster<Model> c = new Cluster<Model>(noiseIDs, true, new LinearEquationModel(les));
        res.addCluster(c);
        processedIDs.addAll(noiseIDs);
      }
    }

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      DependencyDerivator<DoubleVector, DoubleDistance> derivator = null;
      Class<DependencyDerivator<DoubleVector, DoubleDistance>> cls = ClassGenericsUtil.uglyCastIntoSubclass(DependencyDerivator.class);
      derivator = parameters.tryInstantiate(cls);


      CorrelationAnalysisSolution<DoubleVector> model = derivator.run(derivatorDB);
      LinearEquationSystem les = model.getNormalizedLinearEquationSystem(null);
      return les;
    }
    catch(UnableToComplyException e) {
      throw new IllegalStateException("Initialization of the database for the derivator failed: " + e);
    }

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        }
        rhs[row[r]] = rhs[row[r]] + sum;
      }
    }


    LinearEquationSystem lq = new LinearEquationSystem(coeff, rhs, row, col);
    return lq;
  }

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        }
        rhs[row[r]] = rhs[row[r]] + sum;
      }
    }


    LinearEquationSystem lq = new LinearEquationSystem(coeff, rhs, row, col);
    return lq;
  }

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      double[][] a = new double[transposedWeakEigenvectors.getRowDimensionality()][transposedWeakEigenvectors.getColumnDimensionality()];
      double[][] we = transposedWeakEigenvectors.getArrayRef();
      double[] b = B.getArrayRef();
      System.arraycopy(we, 0, a, 0, transposedWeakEigenvectors.getRowDimensionality());


      LinearEquationSystem lq = new LinearEquationSystem(a, b);
      lq.solveByTotalPivotSearch();


      sol = new CorrelationAnalysisSolution<V>(lq, db, strongEigenvectors, pcares.getWeakEigenvectors(), pcares.similarityMatrix(), centroid);


      if(logger.isDebuggingFine()) {
        StringBuilder log = new StringBuilder();
        log.append("Solution:\n");
        log.append("Standard deviation ").append(sol.getStandardDeviation());
        log.append(lq.equationsToString(NF.getMaximumFractionDigits()));
        logger.debugFine(log.toString());
      }
    }
    return sol;
  }

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        }
        rhs[row[r]] = rhs[row[r]] + sum;
      }
    }


    LinearEquationSystem lq = new LinearEquationSystem(coeff, rhs, row, col);
    return lq;
  }

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        }
        rhs[row[r]] = rhs[row[r]] + sum;
      }
    }


    LinearEquationSystem lq = new LinearEquationSystem(coeff, rhs, row, col);
    return lq;
  }

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