DigitalNet

Documentation of 'umontreal.iro.lecuyer.hups.DigitalNet' Java class DigitalNet

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java.lang.Object
- umontreal.iro.lecuyer.hups.PointSet
- - umontreal.iro.lecuyer.hups.DigitalNet

Direct Known Subclasses:: DigitalNetBase2, DigitalNetFromFile, DigitalSequence

public class DigitalNet
extends PointSet

This class provides the basic structures for storing and manipulating linear digital nets in base b, for an arbitrary base b >= 2. We recall that a net contains n = b^k points in s dimensions, where the ith point u_i, for i = 0,..., b^k - 1, is defined as follows:

i	=	∑_r=0^k-1a_{i, r}b^r,
(u_{i, j, 1} u_{i, j, 2} …)^T	=	C_j (a_{i, 0} a_{i, 1} … a_{i, k-1})^T,
u_{i, j}	=	∑_r=1^∞u_{i, j, r}b^-r,
u_i	=	(u_{i, 0}, …, u_{i, s-1}).

In our implementation, the matrices C_j are r×k, so the expansion of u_{i, j} is truncated to its first r terms. The points are stored implicitly by storing the generator matrices C_j in a large two-dimensional array of integers, with srk elements.

The points u_i are enumerated using the Gray code technique. With this technique, the b-ary representation of i, a_i = (a_{i, 0},..., a_{i, k-1}), is replaced by a Gray code representation of i, g_i = (g_{i, 0},..., g_{i, k-1}). The Gray code g_i used here is defined by g_{i, k-1} = a_{i, k-1} and g_{i, ν} = (a_{i, ν} - a_{i, ν+1})mod b for ν = 0,..., k - 2. It has the property that g_i = (g_{i, 0},..., g_{i, k-1}) and g_i+1 = (g_{i+1, 0},..., g_{i+1, k-1}) differ only in the position of the smallest index ν such that a_{i, ν} < b - 1, and we have g_{i+1, ν} = (g_{i, ν} +1)mod b in that position.

This Gray code representation permits a more efficient enumeration of the points by the iterators. It changes the order in which the points u_i are enumerated, but the first b^m points remain the same for every integer m. The ith point of the sequence with the Gray enumeration is the i'th point of the original enumeration, where i' is the integer whose b-ary representation a_i' is given by the Gray code g_i. To enumerate all the points successively, we never need to compute the Gray codes explicitly. It suffices to know the position ν of the Gray code digit that changes at each step, and this can be found quickly from the b-ary representation a_i. The digits of each coordinate j of the current point can be updated by adding column ν of the generator matrix C_j to the old digits, modulo b.

One should avoid using the method getCoordinate(i, j) for arbitrary values of i and j, because this is much slower than using an iterator to access successive coordinates.

Digital nets can be randomized in various ways. Several types of randomizations specialized for nets are implemented directly in this class.

A simple but important randomization is the random digital shift in base b, defined as follows: replace each digit u_{i, j, ν} in the third equation above by (u_{i, j, ν} + d_{j, ν})mod b, where the d_{j, ν}'s are i.i.d. uniform over {0,..., b - 1}. This is equivalent to applying a single random shift to all the points in a formal series representation of their coordinates. In practice, the digital shift is truncated to w digits, for some integer w >= r. Applying a digital shift does not change the equidistribution and (t, m, s)-net properties of a point set. Moreover, with the random shift, each point is uniformly distributed over the unit hypercube (but the points are not independent, of course).

A second class of randomizations specialized for digital nets are the linear matrix scrambles, which multiply the matrices C_j by a random invertible matrix M_j, modulo b. There are several variants, depending on how M_j is generated, and on whether C_j is multiplied on the left or on the right. In our implementation, the linear matrix scrambles are incorporated directly into the matrices C_j, so they do not slow down the enumeration of points. Methods are available for applying linear matrix scrambles and for removing these randomizations. These methods generate the appropriate random numbers and make the corresponding changes to the C_j's. A copy of the original C_j's is maintained, so the point set can be returned to its original unscrambled state at any time. When a new linear matrix scramble is applied, it is always applied to the original generator matrices. The method resetGeneratorMatrices removes the current matrix scramble by resetting the generator matrices to their original state. On the other hand, the method eraseOriginalGeneratorMatrices replaces the original generator matrices by the current ones, making the changes permanent. This is useful if one wishes to apply two or more linear matrix scrambles on top of each other.

Linear matrix scrambles are usually combined with a random digital shift; this combination is called an affine matrix scramble. These two randomizations are applied via separate methods. The linear matrix scrambles are incorporated into the matrices C_j whereas the digital random shift is stored and applied separately, independently of the other scramblings.

Applying a digital shift or a linear matrix scramble to a digital net invalidates all iterators for that randomized point, because each iterator uses a cached copy of the current point, which is updated only when the current point index of that iterator changes, and the update also depends on the cached copy of the previous point. After applying any kind of scrambling, the iterators must be reinitialized to the initial point by invoking resetCurPointIndex or reinstantiated by the iterator method (this is not done automatically).

Constructor Summary

Constructors
Constructor and Description

DigitalNet()
Empty constructor.

Constructors
Constructor and Description
`DigitalNet()` Empty constructor.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`addRandomShift(int d1, int d2, RandomStream stream)` Adds a random digital shift to all the points of the point set, using stream `stream` to generate the random numbers.
`void`	`addRandomShift(RandomStream stream)` Same as `addRandomShift(0, dim, stream)`, where `dim` is the dimension of the digital net.
`void`	`clearRandomShift()` Erases the current random shift, if any.
`void`	`eraseOriginalGeneratorMatrices()` Erases the original generator matrices and replaces them by the current ones.
`double`	`getCoordinate(int i, int j)` Returns u_{i, j}, the coordinate j of the point i.
`double`	`getCoordinateNoGray(int i, int j)` Returns u_{i, j}, the coordinate j of point i, the points being enumerated in the standard order (no Gray code).
`void`	`iBinomialMatrixScramble(RandomStream stream)` Applies the i-binomial matrix scramble proposed by Tezuka .
`void`	`iBinomialMatrixScrambleFaurePermut(RandomStream stream, int sb)` Similar to `iBinomialMatrixScramble` except that the diagonal elements of each matrix M_j are chosen as in `leftMatrixScrambleFaurePermut`.
`void`	`iBinomialMatrixScrambleFaurePermutAll(RandomStream stream, int sb)` Similar to `iBinomialMatrixScrambleFaurePermut` except that the elements under the diagonal are also chosen from the same restricted set as the diagonal elements.
`void`	`iBinomialMatrixScrambleFaurePermutDiag(RandomStream stream, int sb)` Similar to `iBinomialMatrixScrambleFaurePermut` except that all the off-diagonal elements are 0.
`PointSetIterator`	`iterator()` Constructs and returns a point set iterator.
`PointSetIterator`	`iteratorNoGray()` This iterator does not use the Gray code.
`void`	`leftMatrixScramble(RandomStream stream)` Applies a linear scramble by multiplying each C_j on the left by a w×w nonsingular lower-triangular matrix M_j, as suggested by Matoušek and implemented by Hong and Hickernell.
`void`	`leftMatrixScrambleDiag(RandomStream stream)` Similar to `leftMatrixScramble` except that all the off-diagonal elements of the M_j are 0.
`void`	`leftMatrixScrambleFaurePermut(RandomStream stream, int sb)` Similar to `leftMatrixScramble` except that the diagonal elements of each matrix M_j are chosen from a restricted set of the best integers as calculated by Faure.
`void`	`leftMatrixScrambleFaurePermutAll(RandomStream stream, int sb)` Similar to `leftMatrixScrambleFaurePermut` except that the elements under the diagonal are also chosen from the same restricted set as the diagonal elements.
`void`	`leftMatrixScrambleFaurePermutDiag(RandomStream stream, int sb)` Similar to `leftMatrixScrambleFaurePermut` except that all off-diagonal elements are 0.
`void`	`printGeneratorMatrices(int s)` Prints the generator matrices in standard form for dimensions 1 to s.
`void`	`resetGeneratorMatrices()` Restores the original generator matrices.
`void`	`rightMatrixScramble(RandomStream stream)` Applies a linear scramble by multiplying each C_j on the right by a single k×k nonsingular upper-triangular matrix M, as suggested by Faure and Tezuka.
`void`	`stripedMatrixScramble(RandomStream stream)` Applies the striped matrix scramble proposed by Owen.
`void`	`stripedMatrixScrambleFaurePermutAll(RandomStream stream, int sb)` Similar to `stripedMatrixScramble` except that the elements on and under the diagonal of each matrix M_j are chosen as in `leftMatrixScrambleFaurePermut`.
`java.lang.String`	`toString()` Formats a string that contains information about the point set.
`void`	`unrandomize()` By default, this method simply calls `clearRandomShift()`.

Methods inherited from class umontreal.iro.lecuyer.hups.PointSet
addRandomShift, addRandomShift, formatPoints, formatPoints, formatPoints, formatPoints, formatPointsBase, formatPointsBase, formatPointsBase, formatPointsBase, formatPointsNumbered, formatPointsNumbered, getDimension, getNumPoints, getStream, randomize, randomize, randomize, randomize, randomize, setStream

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - DigitalNet
```
public DigitalNet()
```
    Empty constructor.
- Method Detail
  - getCoordinate
```
public double getCoordinate(int i,
                            int j)
```
    Description copied from class: PointSet
    
    Returns u_{i, j}, the coordinate j of the point i.
    
    Specified by:
    
    getCoordinate in class PointSet
    
    Parameters:
    
    i - index of the point to look for
    
    j - index of the coordinate to look for
    
    Returns:
    
    the value of u_{i, j}
  - iterator
```
public PointSetIterator iterator()
```
    Description copied from class: PointSet
    
    Constructs and returns a point set iterator. The default implementation returns an iterator that uses the method getCoordinate (i,j) to iterate over the points and coordinates, but subclasses can reimplement it for better efficiency.
    
    Overrides:
    
    iterator in class PointSet
    
    Returns:
    
    point set iterator for the point set
  - getCoordinateNoGray
```
public double getCoordinateNoGray(int i,
                                  int j)
```
    Returns u_{i, j}, the coordinate j of point i, the points being enumerated in the standard order (no Gray code).
    
    Parameters:
    
    i - point index
    
    j - coordinate index
    
    Returns:
    
    the value of u_{i, j}
  - iteratorNoGray
```
public PointSetIterator iteratorNoGray()
```
    This iterator does not use the Gray code. Thus the points are enumerated in the order of their first coordinate before randomization.
  - addRandomShift
```
public void addRandomShift(int d1,
                           int d2,
                           RandomStream stream)
```
    Adds a random digital shift to all the points of the point set, using stream stream to generate the random numbers. For each coordinate j from d1 to d2-1, the shift vector (d_{j, 0},..., d_{j, k-1}) is generated uniformly over {0,..., b - 1}^k and added modulo b to the digits of all the points. After adding a digital shift, all iterators must be reconstructed or reset to zero.
    
    Overrides:
    
    addRandomShift in class PointSet
    
    Parameters:
    
    stream - random number stream used to generate uniforms
  - addRandomShift
```
public void addRandomShift(RandomStream stream)
```
    Same as addRandomShift(0, dim, stream), where dim is the dimension of the digital net.
    
    Overrides:
    
    addRandomShift in class PointSet
    
    Parameters:
    
    stream - random number stream used to generate uniforms
  - clearRandomShift
```
public void clearRandomShift()
```
    Description copied from class: PointSet
    
    Erases the current random shift, if any.
    
    Overrides:
    
    clearRandomShift in class PointSet
  - toString
```
public java.lang.String toString()
```
    Description copied from class: PointSet
    
    Formats a string that contains information about the point set.
    
    Overrides:
    
    toString in class PointSet
    
    Returns:
    
    string representation of the point set information
  - leftMatrixScramble
```
public void leftMatrixScramble(RandomStream stream)
```
    Applies a linear scramble by multiplying each C_j on the left by a w×w nonsingular lower-triangular matrix M_j, as suggested by Matoušek and implemented by Hong and Hickernell. The diagonal entries of each matrix M_j are generated uniformly over {1,..., b - 1}, the entries below the diagonal are generated uniformly over {0,..., b - 1}, and all these entries are generated independently. This means that in base b = 2, all diagonal elements are equal to 1.
    
    Parameters:
    
    stream - random number stream used to generate the randomness
  - leftMatrixScrambleDiag
```
public void leftMatrixScrambleDiag(RandomStream stream)
```
    Similar to leftMatrixScramble except that all the off-diagonal elements of the M_j are 0.
    
    Parameters:
    
    stream - random number stream used to generate the randomness
  - leftMatrixScrambleFaurePermut
```
public void leftMatrixScrambleFaurePermut(RandomStream stream,
                                          int sb)
```
    Similar to leftMatrixScramble except that the diagonal elements of each matrix M_j are chosen from a restricted set of the best integers as calculated by Faure. They are generated uniformly over the first sb elements of array F, where F is made up of a permutation of the integers [1..(b - 1)]. These integers are sorted by increasing order of the upper bounds of the extreme discrepancy for the given integer.
    
    Parameters:
    
    stream - random number stream used to generate the randomness
    
    sb - Only the first sb elements of F are used
  - leftMatrixScrambleFaurePermutDiag
```
public void leftMatrixScrambleFaurePermutDiag(RandomStream stream,
                                              int sb)
```
    Similar to leftMatrixScrambleFaurePermut except that all off-diagonal elements are 0.
    
    Parameters:
    
    stream - random number stream used to generate the randomness
    
    sb - Only the first sb elements of F are used
  - leftMatrixScrambleFaurePermutAll
```
public void leftMatrixScrambleFaurePermutAll(RandomStream stream,
                                             int sb)
```
    Similar to leftMatrixScrambleFaurePermut except that the elements under the diagonal are also chosen from the same restricted set as the diagonal elements.
    
    Parameters:
    
    stream - random number stream used to generate the randomness
    
    sb - Only the first sb elements of F are used
  - iBinomialMatrixScramble
```
public void iBinomialMatrixScramble(RandomStream stream)
```
    Applies the i-binomial matrix scramble proposed by Tezuka . This multiplies each C_j on the left by a w×w nonsingular lower-triangular matrix M_j as in leftMatrixScramble, but with the additional constraint that all entries on any given diagonal or subdiagonal of M_j are identical.
    
    Parameters:
    
    stream - random number stream used as generator of the randomness
  - iBinomialMatrixScrambleFaurePermut
```
public void iBinomialMatrixScrambleFaurePermut(RandomStream stream,
                                               int sb)
```
    Similar to iBinomialMatrixScramble except that the diagonal elements of each matrix M_j are chosen as in leftMatrixScrambleFaurePermut.
    
    Parameters:
    
    stream - random number stream used to generate the randomness
    
    sb - Only the first sb elements of F are used
  - iBinomialMatrixScrambleFaurePermutDiag
```
public void iBinomialMatrixScrambleFaurePermutDiag(RandomStream stream,
                                                   int sb)
```
    Similar to iBinomialMatrixScrambleFaurePermut except that all the off-diagonal elements are 0.
    
    Parameters:
    
    stream - random number stream used to generate the randomness
    
    sb - Only the first sb elements of F are used
  - iBinomialMatrixScrambleFaurePermutAll
```
public void iBinomialMatrixScrambleFaurePermutAll(RandomStream stream,
                                                  int sb)
```
    Similar to iBinomialMatrixScrambleFaurePermut except that the elements under the diagonal are also chosen from the same restricted set as the diagonal elements.
    
    Parameters:
    
    stream - random number stream used to generate the randomness
    
    sb - Only the first sb elements of F are used
  - stripedMatrixScramble
```
public void stripedMatrixScramble(RandomStream stream)
```
    Applies the striped matrix scramble proposed by Owen. It multiplies each C_j on the left by a w×w nonsingular lower-triangular matrix M_j as in leftMatrixScramble, but with the additional constraint that in any column, all entries below the diagonal are equal to the diagonal entry, which is generated randomly over {1,..., b - 1}. Note that for b = 2, the matrices M_j become deterministic, with all entries on and below the diagonal equal to 1.
    
    Parameters:
    
    stream - random number stream used as generator of the randomness
  - stripedMatrixScrambleFaurePermutAll
```
public void stripedMatrixScrambleFaurePermutAll(RandomStream stream,
                                                int sb)
```
    Similar to stripedMatrixScramble except that the elements on and under the diagonal of each matrix M_j are chosen as in leftMatrixScrambleFaurePermut.
    
    Parameters:
    
    stream - random number stream used as generator of the randomness
    
    sb - Only the first sb elements of F are used
  - rightMatrixScramble
```
public void rightMatrixScramble(RandomStream stream)
```
    Applies a linear scramble by multiplying each C_j on the right by a single k×k nonsingular upper-triangular matrix M, as suggested by Faure and Tezuka. The diagonal entries of the matrix M are generated uniformly over {1,..., b - 1}, the entries above the diagonal are generated uniformly over {0,..., b - 1}, and all the entries are generated independently. The effect of this scramble is only to change the order in which the points are generated. If one computes the average value of a function over all the points of a given digital net, or over a number of points that is a power of the basis, then this scramble makes no difference.
    
    Parameters:
    
    stream - random number stream used as generator of the randomness
  - unrandomize
```
public void unrandomize()
```
    Description copied from class: PointSet
    
    By default, this method simply calls clearRandomShift().
    
    Overrides:
    
    unrandomize in class PointSet
  - resetGeneratorMatrices
```
public void resetGeneratorMatrices()
```
    Restores the original generator matrices. This removes the current linear matrix scrambles.
  - eraseOriginalGeneratorMatrices
```
public void eraseOriginalGeneratorMatrices()
```
    Erases the original generator matrices and replaces them by the current ones. The current linear matrix scrambles thus become permanent. This is useful if we want to apply several scrambles in succession to a given digital net.
  - printGeneratorMatrices
```
public void printGeneratorMatrices(int s)
```
    Prints the generator matrices in standard form for dimensions 1 to s.

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Class DigitalNet

Constructor Summary

Method Summary

Methods inherited from class umontreal.iro.lecuyer.hups.PointSet

Methods inherited from class java.lang.Object

Constructor Detail

DigitalNet

Method Detail

getCoordinate

iterator

getCoordinateNoGray

iteratorNoGray

addRandomShift

addRandomShift

clearRandomShift

toString

leftMatrixScramble

leftMatrixScrambleDiag

leftMatrixScrambleFaurePermut

leftMatrixScrambleFaurePermutDiag

leftMatrixScrambleFaurePermutAll

iBinomialMatrixScramble

iBinomialMatrixScrambleFaurePermut

iBinomialMatrixScrambleFaurePermutDiag

iBinomialMatrixScrambleFaurePermutAll

stripedMatrixScramble

stripedMatrixScrambleFaurePermutAll

rightMatrixScramble

unrandomize

resetGeneratorMatrices

eraseOriginalGeneratorMatrices

printGeneratorMatrices