[jira] [Commented] (RNG-111) Jenkins Small Fast generator
[ https://issues.apache.org/jira/browse/RNG-111?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16904075#comment-16904075 ] Abhishek Singh Dhadwal commented on RNG-111: Hello [~aherbert], I'd like to work on this issue. The github link is provided here : https://github.com/AbhishekSinghDhadwal/commons-rng/tree/feature-RNG-111 > Jenkins Small Fast generator > > > Key: RNG-111 > URL: https://issues.apache.org/jira/browse/RNG-111 > Project: Commons RNG > Issue Type: Improvement > Components: core >Affects Versions: 1.3 >Reporter: Alex D Herbert >Priority: Minor > > Implement Bob Jenkins' Small/Fast Chaotic PRNG. > [A small noncryptographic > PRNG|http://burtleburtle.net/bob/rand/smallprng.html] > Variants are provided for 32-bit and 64-bit output. The generators use bit > shifts to avalanche state and many variants are provided for different shift > combinations. However there is a recommended variant that has been more > extensively tested. A seeding routine is provided to ensure that generators > with short cycles are avoided. > This generator has no name but appears in PractRand as JSF (Jenkins Small > Fast). -- This message was sent by Atlassian JIRA (v7.6.14#76016)
[jira] [Commented] (RNG-94) Add RotateRotateMultiplyXorMultiplyXor generator
[
https://issues.apache.org/jira/browse/RNG-94?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16897321#comment-16897321
]
Abhishek Singh Dhadwal commented on RNG-94:
---
[~aherbert], hi, an implementational query. Should the classes be named RrMXMX,
rrmxmx(used by the author) or RRMXMX ?
> Add RotateRotateMultiplyXorMultiplyXor generator
>
>
> Key: RNG-94
> URL: https://issues.apache.org/jira/browse/RNG-94
> Project: Commons RNG
> Issue Type: New Feature
> Components: core
>Affects Versions: 1.3
>Reporter: Alex D Herbert
>Assignee: Alex D Herbert
>Priority: Minor
>
> The generators described on the MostlyMangling website [Better, stronger
> mixer and a test
> procedure|http://mostlymangling.blogspot.com/2019/01/better-stronger-mixer-and-test-procedure.html]
> show better output than the Stafford variant 13 mixing function used in
> SplitMix or the murmur hash 3 mixing function used in ThreadLocalRandom for a
> 64-bit based mixer function.
> {code}
> static inline uint64_t ror64(uint64_t v, int r) {
> return (v >> r) | (v << (64 - r));
> }
> // Old mixer, my rrmxmx
> uint64_t rrmxmx(uint64_t v) {
> v ^= ror64(v, 49) ^ ror64(v, 24);
> v *= 0x9FB21C651E98DF25L;
> v ^= v >> 28;
> v *= 0x9FB21C651E98DF25L;
> return v ^ v >> 28;
> }
> // New mixer, "rrxmrrxmsx_0", this is much more well behaved although
> // slightly slower doing 2 rotations instead of the shift in the middle
> // of rrmxmx.
> // With a unit counter starting at 0, it has passed 128 TB of
> // PractRand 0.94 -tf 2 without anomalies found past 2 TB.
> uint64_t rrxmrrxmsx_0(uint64_t v) {
> v ^= ror64(v, 25) ^ ror64(v, 50);
> v *= 0xA24BAED4963EE407UL;
> v ^= ror64(v, 24) ^ ror64(v, 49);
> v *= 9FB21C651E98DF25UL;
> return v ^ v >> 28;
> }
> {code}
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[jira] [Commented] (RNG-94) Add RotateRotateMultiplyXorMultiplyXor generator
[
https://issues.apache.org/jira/browse/RNG-94?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16897204#comment-16897204
]
Abhishek Singh Dhadwal commented on RNG-94:
---
Hi, I would like to work on this feature.
My work shall be stored in :
https://github.com/AbhishekSinghDhadwal/commons-rng/tree/feature-RNG-94
> Add RotateRotateMultiplyXorMultiplyXor generator
>
>
> Key: RNG-94
> URL: https://issues.apache.org/jira/browse/RNG-94
> Project: Commons RNG
> Issue Type: New Feature
> Components: core
>Affects Versions: 1.3
>Reporter: Alex D Herbert
>Assignee: Alex D Herbert
>Priority: Minor
>
> The generators described on the MostlyMangling website [Better, stronger
> mixer and a test
> procedure|http://mostlymangling.blogspot.com/2019/01/better-stronger-mixer-and-test-procedure.html]
> show better output than the Stafford variant 13 mixing function used in
> SplitMix or the murmur hash 3 mixing function used in ThreadLocalRandom for a
> 64-bit based mixer function.
> {code}
> static inline uint64_t ror64(uint64_t v, int r) {
> return (v >> r) | (v << (64 - r));
> }
> // Old mixer, my rrmxmx
> uint64_t rrmxmx(uint64_t v) {
> v ^= ror64(v, 49) ^ ror64(v, 24);
> v *= 0x9FB21C651E98DF25L;
> v ^= v >> 28;
> v *= 0x9FB21C651E98DF25L;
> return v ^ v >> 28;
> }
> // New mixer, "rrxmrrxmsx_0", this is much more well behaved although
> // slightly slower doing 2 rotations instead of the shift in the middle
> // of rrmxmx.
> // With a unit counter starting at 0, it has passed 128 TB of
> // PractRand 0.94 -tf 2 without anomalies found past 2 TB.
> uint64_t rrxmrrxmsx_0(uint64_t v) {
> v ^= ror64(v, 25) ^ ror64(v, 50);
> v *= 0xA24BAED4963EE407UL;
> v ^= ror64(v, 24) ^ ror64(v, 49);
> v *= 9FB21C651E98DF25UL;
> return v ^ v >> 28;
> }
> {code}
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[jira] [Comment Edited] (RNG-84) PCG
[ https://issues.apache.org/jira/browse/RNG-84?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16895365#comment-16895365 ] Abhishek Singh Dhadwal edited comment on RNG-84 at 7/29/19 3:34 PM: Hi, the PR has 4 commits at the moment, and when I attempt further rebasing, a plethora of merge conflicts seem to be present at almost every stage, and more come when I fix the aforementioned ones. Am I doing something wrong, or should I go through all of the commits ? Help would be highly appreciated. PS The PR shows that only the new added files/files intended to be altered, were altered. was (Author: abhi1507): Hi, the PR has 4 commits at the moment, and when I attempt further rebasing, a plethora of merge conflicts seem to be present at almost every stage, and more come when I fix the aforementioned ones. Am I doing something wrong, or should I go through all of the commits ? Help would be highly appreciated. > PCG > --- > > Key: RNG-84 > URL: https://issues.apache.org/jira/browse/RNG-84 > Project: Commons RNG > Issue Type: Sub-task > Components: core >Reporter: Gilles >Priority: Minor > Labels: gsoc2019 > > From the [author's web page|http://www.pcg-random.org]: > PCG is a family of simple fast space-efficient statistically good algorithms > for random number generation. Unlike many general-purpose RNGs, they are also > hard to predict. -- This message was sent by Atlassian JIRA (v7.6.14#76016)
[jira] [Commented] (RNG-84) PCG
[ https://issues.apache.org/jira/browse/RNG-84?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16895365#comment-16895365 ] Abhishek Singh Dhadwal commented on RNG-84: --- Hi, the PR has 4 commits at the moment, and when I attempt further rebasing, a plethora of merge conflicts seem to be present at almost every stage, and more come when I fix the aforementioned ones. Am I doing something wrong, or should I go through all of the commits ? Help would be highly appreciated. > PCG > --- > > Key: RNG-84 > URL: https://issues.apache.org/jira/browse/RNG-84 > Project: Commons RNG > Issue Type: Sub-task > Components: core >Reporter: Gilles >Priority: Minor > Labels: gsoc2019 > > From the [author's web page|http://www.pcg-random.org]: > PCG is a family of simple fast space-efficient statistically good algorithms > for random number generation. Unlike many general-purpose RNGs, they are also > hard to predict. -- This message was sent by Atlassian JIRA (v7.6.14#76016)
[jira] [Commented] (RNG-84) PCG
[ https://issues.apache.org/jira/browse/RNG-84?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16894737#comment-16894737 ] Abhishek Singh Dhadwal commented on RNG-84: --- Hi, it may be the lack of syncing between the original master branch and the master branch in my fork. I'll sync all the updates, and rebase, and let you know. Regards > PCG > --- > > Key: RNG-84 > URL: https://issues.apache.org/jira/browse/RNG-84 > Project: Commons RNG > Issue Type: Sub-task > Components: core >Reporter: Gilles >Priority: Minor > Labels: gsoc2019 > > From the [author's web page|http://www.pcg-random.org]: > PCG is a family of simple fast space-efficient statistically good algorithms > for random number generation. Unlike many general-purpose RNGs, they are also > hard to predict. -- This message was sent by Atlassian JIRA (v7.6.14#76016)
[jira] [Commented] (RNG-84) PCG
[ https://issues.apache.org/jira/browse/RNG-84?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16894729#comment-16894729 ] Abhishek Singh Dhadwal commented on RNG-84: --- I had rebased the original branch on which I was working on in my fork, such that the primary addition of all the LCG versions of the generators were present, and had rebased all the MCG versions that were required. At the moment there are five commits. Should I rebase them ? > PCG > --- > > Key: RNG-84 > URL: https://issues.apache.org/jira/browse/RNG-84 > Project: Commons RNG > Issue Type: Sub-task > Components: core >Reporter: Gilles >Priority: Minor > Labels: gsoc2019 > > From the [author's web page|http://www.pcg-random.org]: > PCG is a family of simple fast space-efficient statistically good algorithms > for random number generation. Unlike many general-purpose RNGs, they are also > hard to predict. -- This message was sent by Atlassian JIRA (v7.6.14#76016)
[jira] [Commented] (RNG-84) PCG
[ https://issues.apache.org/jira/browse/RNG-84?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16894712#comment-16894712 ] Abhishek Singh Dhadwal commented on RNG-84: --- Hi [~erans], I'd like to undertake this subtask. The PR pertaining to the request is : [here|[https://github.com/apache/commons-rng/pull/56|https://github.com/apache/commons-rng/pull/5]] . > PCG > --- > > Key: RNG-84 > URL: https://issues.apache.org/jira/browse/RNG-84 > Project: Commons RNG > Issue Type: Sub-task > Components: core >Reporter: Gilles >Priority: Minor > Labels: gsoc2019 > > From the [author's web page|http://www.pcg-random.org]: > PCG is a family of simple fast space-efficient statistically good algorithms > for random number generation. Unlike many general-purpose RNGs, they are also > hard to predict. -- This message was sent by Atlassian JIRA (v7.6.14#76016)
[jira] [Comment Edited] (RNG-84) PCG
[ https://issues.apache.org/jira/browse/RNG-84?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16894712#comment-16894712 ] Abhishek Singh Dhadwal edited comment on RNG-84 at 7/28/19 2:28 PM: Hi [~erans], I'd like to undertake this subtask. The PR pertaining to the request is : [https://github.com/apache/commons-rng/pull/56|https://github.com/apache/commons-rng/pull/5] was (Author: abhi1507): Hi [~erans], I'd like to undertake this subtask. The PR pertaining to the request is : [here|[https://github.com/apache/commons-rng/pull/56|https://github.com/apache/commons-rng/pull/5]] . > PCG > --- > > Key: RNG-84 > URL: https://issues.apache.org/jira/browse/RNG-84 > Project: Commons RNG > Issue Type: Sub-task > Components: core >Reporter: Gilles >Priority: Minor > Labels: gsoc2019 > > From the [author's web page|http://www.pcg-random.org]: > PCG is a family of simple fast space-efficient statistically good algorithms > for random number generation. Unlike many general-purpose RNGs, they are also > hard to predict. -- This message was sent by Atlassian JIRA (v7.6.14#76016)
[jira] [Comment Edited] (RNG-16) Linear congruential generators
[ https://issues.apache.org/jira/browse/RNG-16?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16865548#comment-16865548 ] Abhishek Singh Dhadwal edited comment on RNG-16 at 6/21/19 12:34 PM: - Hello, I shall be working on the task at hand. Upon discussion with Gilles and Alex Herbert, following are the details about the plan for implementation of the RNG. There shall be a base abstract class (AbstractLCG) which shall take inputs of a,c,m and the seed and return integer values as required. There shall be a child class (KnuthLewisLCG) which shall extend the aforementioned class with the values of a, c and m referred from [Numerical Recipes |https://en.wikipedia.org/wiki/Linear_congruential_generator#Parameters_in_common_use] The current questions/queries at hand are : * Will it pass the test suite? * Can using modulo 2^32 increase performance (to be tested using JMH) * Comparison between KnuthLewisDirect and the aforementioned child class The github repository where I will be working currently is : [https://github.com/AbhishekSinghDhadwal/commons-rng/tree/lcg] The test values (hexadecimal) obtained as per numerical recipes ( for seed = 0) are as follows : 3c6ef35f 47502932 d1ccf6e9 aaf95334 6252e503 9f2ec686 57fe6c2d a3d95fa8 81fdbee7 94f0af1a The generator should match these values. was (Author: abhi1507): Hello, I shall be working on the task at hand. Upon discussion with Gilles and Alex Herbert, following are the details about the plan for implementation of the RNG. There shall be a base abstract class (AbstractLCG) which shall take inputs of a,c,m and the seed and return integer values as required. There shall be a child class (KnuthLewisLCG) which shall extend the aforementioned class with the values of a, c and m referred from [Numerical Recipes |https://en.wikipedia.org/wiki/Linear_congruential_generator#Parameters_in_common_use] The current questions/queries at hand are : * Will it pass the test suite? * Can using modulo 2^32 increase performance (to be tested using JMH) * Comparison between KnuthLewisDirect and the aforementioned child class The github repository where I will be working currently is : [https://github.com/AbhishekSinghDhadwal/commons-rng] The test values (hexadecimal) obtained as per numerical recipes ( for seed = 0) are as follows : 3c6ef35f 47502932 d1ccf6e9 aaf95334 6252e503 9f2ec686 57fe6c2d a3d95fa8 81fdbee7 94f0af1a The generator should match these values. > Linear congruential generators > -- > > Key: RNG-16 > URL: https://issues.apache.org/jira/browse/RNG-16 > Project: Commons RNG > Issue Type: Sub-task >Reporter: Emmanuel Bourg >Priority: Minor > Labels: gsoc2019 > > This is a RFE for implementing linear congruential generators: > https://en.wikipedia.org/wiki/Linear_congruential_generator > This type of random generator is often used in language runtimes (Borland C, > GCC, Delphi, VB and even Java). Preconfigured generators using the same > parameters as these languages would be convenient for reproducing the same > number sequences in Java. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Comment Edited] (RNG-16) Linear congruential generators
[ https://issues.apache.org/jira/browse/RNG-16?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16865548#comment-16865548 ] Abhishek Singh Dhadwal edited comment on RNG-16 at 6/18/19 8:21 AM: Hello, I shall be working on the task at hand. Upon discussion with Gilles and Alex Herbert, following are the details about the plan for implementation of the RNG. There shall be a base abstract class (AbstractLCG) which shall take inputs of a,c,m and the seed and return integer values as required. There shall be a child class (KnuthLewisLCG) which shall extend the aforementioned class with the values of a, c and m referred from [Numerical Recipes |https://en.wikipedia.org/wiki/Linear_congruential_generator#Parameters_in_common_use] The current questions/queries at hand are : * Will it pass the test suite? * Can using modulo 2^32 increase performance (to be tested using JMH) * Comparison between KnuthLewisDirect and the aforementioned child class The github repository where I will be working currently is : [https://github.com/AbhishekSinghDhadwal/commons-rng] The test values (hexadecimal) obtained as per numerical recipes ( for seed = 0) are as follows : 3c6ef35f 47502932 d1ccf6e9 aaf95334 6252e503 9f2ec686 57fe6c2d a3d95fa8 81fdbee7 94f0af1a The generator should match these values. was (Author: abhi1507): Hello, I shall be working on the task at hand. Upon discussion with Gilles and Alex Herbert, following are the details about the plan for implementation of the RNG. There shall be a base abstract class (AbstractLCG) which shall take inputs of a,c,m and the seed and return integer values as required. There shall be a child class (KnuthLewisLCG) which shall extend the aforementioned class with the values of a, c and m referred from [Numerical Recipes |https://en.wikipedia.org/wiki/Linear_congruential_generator#Parameters_in_common_use] The current questions/queries at hand are : * Will it pass the test suite? * Can using modulo 2^32 increase performance (to be tested using JMH) * Comparison between KnuthLewisDirect and the aforementioned child class The github repository where I will be working currently is : [https://github.com/AbhishekSinghDhadwal/commons-rng] The test values (hexadecimal) obtained as per numerical recipes ( for seed = 0) are as follows : 0 3c6ef35f 47502932 d1ccf6e9 aaf95334 6252e503 9f2ec686 57fe6c2d a3d95fa8 81fdbee7 94f0af1a The generator should match these values. > Linear congruential generators > -- > > Key: RNG-16 > URL: https://issues.apache.org/jira/browse/RNG-16 > Project: Commons RNG > Issue Type: Sub-task >Reporter: Emmanuel Bourg >Priority: Minor > Labels: gsoc2019 > > This is a RFE for implementing linear congruential generators: > https://en.wikipedia.org/wiki/Linear_congruential_generator > This type of random generator is often used in language runtimes (Borland C, > GCC, Delphi, VB and even Java). Preconfigured generators using the same > parameters as these languages would be convenient for reproducing the same > number sequences in Java. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Comment Edited] (RNG-16) Linear congruential generators
[ https://issues.apache.org/jira/browse/RNG-16?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16865548#comment-16865548 ] Abhishek Singh Dhadwal edited comment on RNG-16 at 6/18/19 8:19 AM: Hello, I shall be working on the task at hand. Upon discussion with Gilles and Alex Herbert, following are the details about the plan for implementation of the RNG. There shall be a base abstract class (AbstractLCG) which shall take inputs of a,c,m and the seed and return integer values as required. There shall be a child class (KnuthLewisLCG) which shall extend the aforementioned class with the values of a, c and m referred from [Numerical Recipes |https://en.wikipedia.org/wiki/Linear_congruential_generator#Parameters_in_common_use] The current questions/queries at hand are : * Will it pass the test suite? * Can using modulo 2^32 increase performance (to be tested using JMH) * Comparison between KnuthLewisDirect and the aforementioned child class The github repository where I will be working currently is : [https://github.com/AbhishekSinghDhadwal/commons-rng] The test values (hexadecimal) obtained as per numerical recipes ( for seed = 0) are as follows : 0 3c6ef35f 47502932 d1ccf6e9 aaf95334 6252e503 9f2ec686 57fe6c2d a3d95fa8 81fdbee7 94f0af1a The generator should match these values. was (Author: abhi1507): Hello, I shall be working on the task at hand. Upon discussion with Gilles and Alex Herbert, following are the details about the plan for implementation of the RNG. There shall be a base abstract class (AbstractLCG) which shall take inputs of a,c,m and the seed and return integer values as required. There shall be a child class (KnuthLewisLCG) which shall extend the aforementioned class with the values of a, c and m referred from [Numerical Recipes |https://en.wikipedia.org/wiki/Linear_congruential_generator#Parameters_in_common_use] The current questions/queries at hand are : * Will it pass the test suite? * Can using modulo 2^32 increase performance (to be tested using JMH) * Comparison between KnuthLewisDirect and the aforementioned child class > Linear congruential generators > -- > > Key: RNG-16 > URL: https://issues.apache.org/jira/browse/RNG-16 > Project: Commons RNG > Issue Type: Sub-task >Reporter: Emmanuel Bourg >Priority: Minor > Labels: gsoc2019 > > This is a RFE for implementing linear congruential generators: > https://en.wikipedia.org/wiki/Linear_congruential_generator > This type of random generator is often used in language runtimes (Borland C, > GCC, Delphi, VB and even Java). Preconfigured generators using the same > parameters as these languages would be convenient for reproducing the same > number sequences in Java. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Commented] (RNG-16) Linear congruential generators
[ https://issues.apache.org/jira/browse/RNG-16?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16865548#comment-16865548 ] Abhishek Singh Dhadwal commented on RNG-16: --- Hello, I shall be working on the task at hand. Upon discussion with Gilles and Alex Herbert, following are the details about the plan for implementation of the RNG. There shall be a base abstract class (AbstractLCG) which shall take inputs of a,c,m and the seed and return integer values as required. There shall be a child class (KnuthLewisLCG) which shall extend the aforementioned class with the values of a, c and m referred from [Numerical Recipes |https://en.wikipedia.org/wiki/Linear_congruential_generator#Parameters_in_common_use] The current questions/queries at hand are : * Will it pass the test suite? * Can using modulo 2^32 increase performance (to be tested using JMH) * Comparison between KnuthLewisDirect and the aforementioned child class > Linear congruential generators > -- > > Key: RNG-16 > URL: https://issues.apache.org/jira/browse/RNG-16 > Project: Commons RNG > Issue Type: Sub-task >Reporter: Emmanuel Bourg >Priority: Minor > Labels: gsoc2019 > > This is a RFE for implementing linear congruential generators: > https://en.wikipedia.org/wiki/Linear_congruential_generator > This type of random generator is often used in language runtimes (Borland C, > GCC, Delphi, VB and even Java). Preconfigured generators using the same > parameters as these languages would be convenient for reproducing the same > number sequences in Java. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Issue Comment Deleted] (RNG-32) Implement more generators
[
https://issues.apache.org/jira/browse/RNG-32?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Abhishek Singh Dhadwal updated RNG-32:
--
Comment: was deleted
(was: Hello,
I shall be working on the task at hand. Upon discussion with Gilles and Alex
Herbert, following are the details about the plan for implementation of the RNG.
There shall be a base abstract class (AbstractLCG) which shall take inputs of
a,c,m and the seed and return integer values as required.
There shall be a child class (KnuthLewisLCG) which shall extend the
aforementioned class with the values of a, c and m referred from [Numerical
Recipes
|https://en.wikipedia.org/wiki/Linear_congruential_generator#Parameters_in_common_use]
The current questions/queries at hand are :
* Will it pass the test suite?
* Can using modulo 2^32 increase performance (to be tested using JMH)
* Comparison between KnuthLewisDirect and the aforementioned child class)
> Implement more generators
> -
>
> Key: RNG-32
> URL: https://issues.apache.org/jira/browse/RNG-32
> Project: Commons RNG
> Issue Type: Wish
>Reporter: Gilles
>Priority: Minor
> Labels: contributors, gsoc2019, scope
> Attachments: lsf.java
>
>
> Commons RNG is focused on pure-Java implementations of standard deterministic
> generators.
> Quite a few algorithms could be added, but priority is on fast generators
> that generate sequences of _pseudo-random_ numbers; i.e. the requirement is
> strong _uniformity_, but *not* strong _unpredictability_ (a.k.a. _true_
> random numbers).
> In particular, in Commons RNG, there is no provision for using an external
> entropy pool.
> Beware that some well-known (and much used) algorithms have been proven to
> fail spectacularly on the uniformity requirement.
> Would-be contributors should look at the {{commons-rng-core}} module for how
> to implement a generator, and at the {{commons-rng-examples}} module for how
> to test the uniformity requirement.
--
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[jira] [Commented] (RNG-32) Implement more generators
[
https://issues.apache.org/jira/browse/RNG-32?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16865545#comment-16865545
]
Abhishek Singh Dhadwal commented on RNG-32:
---
Hello,
I shall be working on the task at hand. Upon discussion with Gilles and Alex
Herbert, following are the details about the plan for implementation of the RNG.
There shall be a base abstract class (AbstractLCG) which shall take inputs of
a,c,m and the seed and return integer values as required.
There shall be a child class (KnuthLewisLCG) which shall extend the
aforementioned class with the values of a, c and m referred from [Numerical
Recipes
|https://en.wikipedia.org/wiki/Linear_congruential_generator#Parameters_in_common_use]
The current questions/queries at hand are :
* Will it pass the test suite?
* Can using modulo 2^32 increase performance (to be tested using JMH)
* Comparison between KnuthLewisDirect and the aforementioned child class
> Implement more generators
> -
>
> Key: RNG-32
> URL: https://issues.apache.org/jira/browse/RNG-32
> Project: Commons RNG
> Issue Type: Wish
>Reporter: Gilles
>Priority: Minor
> Labels: contributors, gsoc2019, scope
> Attachments: lsf.java
>
>
> Commons RNG is focused on pure-Java implementations of standard deterministic
> generators.
> Quite a few algorithms could be added, but priority is on fast generators
> that generate sequences of _pseudo-random_ numbers; i.e. the requirement is
> strong _uniformity_, but *not* strong _unpredictability_ (a.k.a. _true_
> random numbers).
> In particular, in Commons RNG, there is no provision for using an external
> entropy pool.
> Beware that some well-known (and much used) algorithms have been proven to
> fail spectacularly on the uniformity requirement.
> Would-be contributors should look at the {{commons-rng-core}} module for how
> to implement a generator, and at the {{commons-rng-examples}} module for how
> to test the uniformity requirement.
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[jira] [Commented] (RNG-17) Lagged Fibonacci generators
[ https://issues.apache.org/jira/browse/RNG-17?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16798222#comment-16798222 ] Abhishek Singh Dhadwal commented on RNG-17: --- Done and done. Thank you so much ! > Lagged Fibonacci generators > --- > > Key: RNG-17 > URL: https://issues.apache.org/jira/browse/RNG-17 > Project: Commons RNG > Issue Type: Sub-task >Reporter: Emmanuel Bourg >Priority: Minor > Labels: gsoc2019 > Attachments: scrcap2.png > > > This is a RFE to implement Lagged Fibonacci generators: > https://en.wikipedia.org/wiki/Lagged_Fibonacci_generator -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Issue Comment Deleted] (RNG-17) Lagged Fibonacci generators
[ https://issues.apache.org/jira/browse/RNG-17?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Abhishek Singh Dhadwal updated RNG-17: -- Comment: was deleted (was: !scrcap2.png! Sir, all of the links are showing the output as "not found". Any way I could solve this issue ?) > Lagged Fibonacci generators > --- > > Key: RNG-17 > URL: https://issues.apache.org/jira/browse/RNG-17 > Project: Commons RNG > Issue Type: Sub-task >Reporter: Emmanuel Bourg >Priority: Minor > Labels: gsoc2019 > Attachments: scrcap2.png > > > This is a RFE to implement Lagged Fibonacci generators: > https://en.wikipedia.org/wiki/Lagged_Fibonacci_generator -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Commented] (RNG-17) Lagged Fibonacci generators
[ https://issues.apache.org/jira/browse/RNG-17?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16798177#comment-16798177 ] Abhishek Singh Dhadwal commented on RNG-17: --- !scrcap2.png! Sir, all of the links are showing the output as "not found". Any way I could solve this issue ? > Lagged Fibonacci generators > --- > > Key: RNG-17 > URL: https://issues.apache.org/jira/browse/RNG-17 > Project: Commons RNG > Issue Type: Sub-task >Reporter: Emmanuel Bourg >Priority: Minor > Labels: gsoc2019 > Attachments: scrcap2.png > > > This is a RFE to implement Lagged Fibonacci generators: > https://en.wikipedia.org/wiki/Lagged_Fibonacci_generator -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Updated] (RNG-17) Lagged Fibonacci generators
[ https://issues.apache.org/jira/browse/RNG-17?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Abhishek Singh Dhadwal updated RNG-17: -- Attachment: scrcap2.png > Lagged Fibonacci generators > --- > > Key: RNG-17 > URL: https://issues.apache.org/jira/browse/RNG-17 > Project: Commons RNG > Issue Type: Sub-task >Reporter: Emmanuel Bourg >Priority: Minor > Labels: gsoc2019 > Attachments: scrcap2.png > > > This is a RFE to implement Lagged Fibonacci generators: > https://en.wikipedia.org/wiki/Lagged_Fibonacci_generator -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Commented] (RNG-17) Lagged Fibonacci generators
[ https://issues.apache.org/jira/browse/RNG-17?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16797786#comment-16797786 ] Abhishek Singh Dhadwal commented on RNG-17: --- I've read about the deficiencies in the wiki, but, they are a bit complicated in the language provided by the wiki page. Are there any other sources for the aforementioned data ? Also, I'm not part of the mailing list. Could you send a link for it ? > Lagged Fibonacci generators > --- > > Key: RNG-17 > URL: https://issues.apache.org/jira/browse/RNG-17 > Project: Commons RNG > Issue Type: Sub-task >Reporter: Emmanuel Bourg >Priority: Minor > Labels: gsoc2019 > > This is a RFE to implement Lagged Fibonacci generators: > https://en.wikipedia.org/wiki/Lagged_Fibonacci_generator -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Commented] (RNG-17) Lagged Fibonacci generators
[ https://issues.apache.org/jira/browse/RNG-17?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16797265#comment-16797265 ] Abhishek Singh Dhadwal commented on RNG-17: --- Hi [~erans]! I've completed reading the documentation . What should be my next move ? > Lagged Fibonacci generators > --- > > Key: RNG-17 > URL: https://issues.apache.org/jira/browse/RNG-17 > Project: Commons RNG > Issue Type: Sub-task >Reporter: Emmanuel Bourg >Priority: Minor > Labels: gsoc2019 > > This is a RFE to implement Lagged Fibonacci generators: > https://en.wikipedia.org/wiki/Lagged_Fibonacci_generator -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Commented] (RNG-17) Lagged Fibonacci generators
[ https://issues.apache.org/jira/browse/RNG-17?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16796254#comment-16796254 ] Abhishek Singh Dhadwal commented on RNG-17: --- Hi ! I would like to work on this issue as a part of GSoc'19. May I know what tasks do I need to perform in order to take part , or optimize this issue ? > Lagged Fibonacci generators > --- > > Key: RNG-17 > URL: https://issues.apache.org/jira/browse/RNG-17 > Project: Commons RNG > Issue Type: Sub-task >Reporter: Emmanuel Bourg >Priority: Minor > Labels: gsoc2019 > > This is a RFE to implement Lagged Fibonacci generators: > https://en.wikipedia.org/wiki/Lagged_Fibonacci_generator -- This message was sent by Atlassian JIRA (v7.6.3#76005)
