Package edu.berkeley.spectralHMM.matrix

Examples of edu.berkeley.spectralHMM.matrix.EigenSystem

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    if (eigenFunction != null)  {
      return eigenFunction;
    }
   
    // if eigenfunction not found in the cache, check if the eigensystem for the w coefficients was solved earlier
    EigenSystem eigenSystem = findEigenSystem (metaArgs);
   
    assert (matrixCutoff == eigenSystem.eigenVectors.length);
   
    // eigensystem exists, now compute the eigenfunction using it
    eigenFunction = BigDecimal.ZERO.setScale(mc.getPrecision());
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    BigDecimal squaredEigenLength = t.get(n);
    if (squaredEigenLength != null)  {
      return squaredEigenLength ;
    }
   
    EigenSystem eigenSystem = findEigenSystem(metaArgs);
   
    assert(matrixCutoff == eigenSystem.eigenVectors.length);
   
    squaredEigenLength = BigDecimal.ZERO;
    // TODO Should this be summing till maxM?
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  public static BigDecimal getC (BigDecimal alpha, BigDecimal beta, BigDecimal hetF, BigDecimal homF, int matrixCutoff, int maxM, MathContext mc) throws EigenRefineException, EigenRefineError {
   
    // get the eigensystem
    // TODO we don't need maxM for this eigensystem actually
    EigenfunctionMetaArguments metaArgs = new EigenfunctionMetaArguments (alpha, beta, hetF, homF, matrixCutoff, maxM, mc);
    EigenSystem theSystem = findEigenSystem (metaArgs);
   
    // initialize return value
    BigDecimal result = BigDecimal.ZERO;
    // initialize the running value of the "multinomial"
    BigDecimal multi = BigDecimal.ONE;
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        return mp.getValue();
      }
    }

    // eigensystem not already solved, so do it now
    EigenSystem eigenSystem;

    // first let us create a coefficient matrix
    // for solving the system appropriately later (especially making 0 an eigenvalue), we need some extra precision
    int extraPrecision = Math.max(0, (int)(3. * Math.log10(1. * metaArgs.matrixCutoff / 100.) + 1)) + 5;
    System.out.println("# Old precision = " + metaArgs.mc.getPrecision());
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    return eigenSystem;
  }
 
  public static BigDecimal getEigenValue(BigDecimal alpha, BigDecimal beta, BigDecimal hetF, BigDecimal homF, int matrixCutoff, int maxM, int n, MathContext mc) throws EigenRefineException, EigenRefineError {
    EigenfunctionMetaArguments metaArgs = new EigenfunctionMetaArguments(alpha, beta, hetF, homF, matrixCutoff, maxM, mc);
    EigenSystem eigenSystem = findEigenSystem(metaArgs);

    return eigenSystem.eigenValues[n];
  }
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    return eigenSystem.eigenValues[n];
  }

  public static BigDecimal getEigenVectorEntry(BigDecimal alpha, BigDecimal beta, BigDecimal hetF, BigDecimal homF, int matrixCutoff, int maxM, int n, int m, MathContext mc) throws EigenRefineException, EigenRefineError {
    EigenfunctionMetaArguments metaArgs = new EigenfunctionMetaArguments(alpha, beta, hetF, homF, matrixCutoff, maxM, mc);
    EigenSystem eigenSystem = findEigenSystem(metaArgs);
   
    return eigenSystem.eigenVectors[n][m];
  }
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    return eigenSystem.eigenVectors[n][m];
  }
 
  public static BigDecimal[] getEigenVectorCopy(BigDecimal alpha, BigDecimal beta, BigDecimal hetF, BigDecimal homF, int matrixCutoff, int maxM, int n, MathContext mc) throws EigenRefineException, EigenRefineError {
    EigenfunctionMetaArguments metaArgs = new EigenfunctionMetaArguments(alpha, beta, hetF, homF, matrixCutoff, maxM, mc);
    EigenSystem eigenSystem = findEigenSystem(metaArgs);
   
    return Arrays.copyOf (eigenSystem.eigenVectors[n], eigenSystem.eigenVectors[n].length);
  }
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  }

  public static BigDecimal[][] getEigenVectorMatrixCopy(BigDecimal alpha, BigDecimal beta, BigDecimal hetF, BigDecimal homF, int matrixCutoff, int numRows, int numCols, MathContext mc) throws EigenRefineException, EigenRefineError {
    // maxM is numCols-1
    EigenfunctionMetaArguments metaArgs = new EigenfunctionMetaArguments(alpha, beta, hetF, homF, matrixCutoff, numCols-1, mc);
    EigenSystem eigenSystem = findEigenSystem(metaArgs);
   
    BigDecimal[][] result = new BigDecimal[numRows][numCols];
    for (int i=0; i<numRows; i++) {
      for (int j=0; j<numCols; j++)  {
        result[i][j] = eigenSystem.eigenVectors[i][j];
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    if (eigenFunctionDerivative != null)  {
      return eigenFunctionDerivative;
    }
   
    // if eigenfunction not found in the cache, check if the eigensystem for the w coefficients was solved earlier
    EigenSystem eigenSystem = findEigenSystem (metaArgs);
   
    assert (matrixCutoff == eigenSystem.eigenVectors.length);
   
    // gets a bit more complicated due to product rule
   
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Related Classes of edu.berkeley.spectralHMM.matrix.EigenSystem

  • edu.berkeley.spectralHMM.oneD.SelectionEigenfunktion
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