Examples of cern.colt.matrix.DoubleMatrix1D.toArray()

cern.colt.matrix.DoubleMatrix1D.toArray()
Constructs and returns a 1-dimensional array containing the cell values. The values are copied. So subsequent changes in values are not reflected in the matrix, and vice-versa. The returned array values has the form
for (int i=0; i < size(); i++) values[i] = get(i); @return an array filled with the values of the cells.

  testSort[3] = Double.NaN;
  testSort[4] = 1;
  DoubleMatrix1D doubleDense = new DenseDoubleMatrix1D(testSort);
  System.out.println("orig = "+doubleDense);
  doubleDense = doubleDense.viewSorted();
  doubleDense.toArray(testSort);
  System.out.println("sort = "+doubleDense);
  System.out.println("done\n");
}
/**
 */

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  testSort[3] = Double.NaN;
  testSort[4] = 1;
  DoubleMatrix1D doubleDense = new DenseDoubleMatrix1D(testSort);
  System.out.println("orig = "+doubleDense);
  doubleDense = doubleDense.viewSorted();
  doubleDense.toArray(testSort);
  System.out.println("sort = "+doubleDense);
  System.out.println("done\n");
}
/**
 */

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  {
    LUDecompositionQuick solver = new LUDecompositionQuick();
    DoubleMatrix2D matrix = toDoubleMatrix2D();
    solver.decompose(matrix);solver.setLU(matrix);
    DoubleMatrix1D result = toDoubleMatrix1D();
    solver.solve(result);result.toArray(j_x);    
  }
  
  /** The following two variables are not currently in use.
   * The amount of memory currently allocated on the solver. Negative will force allocation. This is necessary for initialisation. */
  //private static int currentMaxSize = -1;

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  {
    LUDecompositionQuick solver = new LUDecompositionQuick();
    DoubleMatrix2D matrix = toDoubleMatrix2D();
    solver.decompose(matrix);solver.setLU(matrix);
    DoubleMatrix1D result = toDoubleMatrix1D();
    solver.solve(result);result.toArray(j_x);    
  }
  
  /** The following two variables are not currently in use.
   * The amount of memory currently allocated on the solver. Negative will force allocation. This is necessary for initialisation. */
  //private static int currentMaxSize = -1;

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  {
    LUDecompositionQuick solver = new LUDecompositionQuick();
    DoubleMatrix2D matrix = toDoubleMatrix2D();
    solver.decompose(matrix);solver.setLU(matrix);
    DoubleMatrix1D result = toDoubleMatrix1D();
    solver.solve(result);result.toArray(j_x);    
  }
  
  /** The following two variables are not currently in use.
   * The amount of memory currently allocated on the solver. Negative will force allocation. This is necessary for initialisation. */
  //private static int currentMaxSize = -1;

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  {
    LUDecompositionQuick solver = new LUDecompositionQuick();
    DoubleMatrix2D matrix = toDoubleMatrix2D();
    solver.decompose(matrix);solver.setLU(matrix);
    DoubleMatrix1D result = toDoubleMatrix1D();
    solver.solve(result);result.toArray(j_x);    
  }
  
  /** The following two variables are not currently in use.
   * The amount of memory currently allocated on the solver. Negative will force allocation. This is necessary for initialisation. */
  //private static int currentMaxSize = -1;

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  {
    LUDecompositionQuick solver = new LUDecompositionQuick();
    DoubleMatrix2D matrix = toDoubleMatrix2D();
    solver.decompose(matrix);solver.setLU(matrix);
    DoubleMatrix1D result = toDoubleMatrix1D();
    solver.solve(result);result.toArray(j_x);    
  }
  
  /** The following two variables are not currently in use.
   * The amount of memory currently allocated on the solver. Negative will force allocation. This is necessary for initialisation. */
  //private static int currentMaxSize = -1;

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  {
    LUDecompositionQuick solver = new LUDecompositionQuick();
    DoubleMatrix2D matrix = toDoubleMatrix2D();
    solver.decompose(matrix);solver.setLU(matrix);
    DoubleMatrix1D result = toDoubleMatrix1D();
    solver.solve(result);result.toArray(j_x);    
  }
  
  /** The following two variables are not currently in use.
   * The amount of memory currently allocated on the solver. Negative will force allocation. This is necessary for initialisation. */
  //private static int currentMaxSize = -1;

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    final DoubleMatrix2D wDiag = DoubleFactory2D.sparse.diagonal(DoubleFactory1D.dense.make(w));
    final DoubleMatrix2D transpose = _algebra.transpose(matrix);
    final DoubleMatrix1D betasVector =
        _algebra.mult(_algebra.mult(_algebra.mult(_algebra.inverse(_algebra.mult(transpose, _algebra.mult(wDiag, matrix))), transpose), wDiag), vector);
    final double[] yModel = convertArray(_algebra.mult(matrix, betasVector).toArray());
    final double[] betas = convertArray(betasVector.toArray());
    return getResultWithStatistics(x, convertArray(wDiag.toArray()), y, betas, yModel, transpose, matrix, useIntercept);
  }


  private LeastSquaresRegressionResult getResultWithStatistics(final double[][] x, final double[][] w, final double[] y, final double[] betas,
      final double[] yModel, final DoubleMatrix2D transpose, final DoubleMatrix2D matrix, final boolean useIntercept) {

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    final DoubleMatrix1D vector = DoubleFactory1D.dense.make(indep);
    final DoubleMatrix2D w = DoubleFactory2D.dense.make(wArray);
    final DoubleMatrix2D transpose = _algebra.transpose(matrix);
    final DoubleMatrix1D betasVector = _algebra.mult(_algebra.mult(_algebra.mult(_algebra.inverse(_algebra.mult(transpose, _algebra.mult(w, matrix))), transpose), w), vector);
    final double[] yModel = convertArray(_algebra.mult(matrix, betasVector).toArray());
    final double[] betas = convertArray(betasVector.toArray());
    return getResultWithStatistics(x, y, betas, yModel, useIntercept);
  }


  private LeastSquaresRegressionResult getResultWithStatistics(final double[][] x, final double[] y, final double[] betas, final double[] yModel, final boolean useIntercept) {
    final int n = x.length;

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