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Otmar Ertl updated MATH-1307:
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Attachment: BaseRandomGeneratorFloatGenerationPerformanceTest.java
I have played around with the
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Otmar Ertl commented on MATH-1296:
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Gilles, I agree, it is the documentation that needs to be fixed.
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Otmar Ertl resolved MATH-1254.
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Resolution: Fixed
Fix Version/s: 3.6
4.0
4.0:
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Otmar Ertl commented on MATH-1253:
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Provided that there is no overflow, I do not think that my proposal is
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Otmar Ertl commented on MATH-1285:
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Added definition of distribution to javadoc.
3.6:
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Otmar Ertl resolved MATH-1269.
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Resolution: Fixed
Fix Version/s: 3.6
4.0
> FastMath.exp may return NaN for
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Otmar Ertl commented on MATH-1269:
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fixed
3.6: 8aecb842d32464a98eaab722da84902f8126957b
4.0:
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Otmar Ertl commented on MATH-1285:
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Thanks for reporting this. I have changed the reference to Wikipedia's
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Otmar Ertl resolved MATH-1285.
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Resolution: Fixed
Fix Version/s: 3.6
4.0
> Description API ZipfDistribution
>
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Otmar Ertl reopened MATH-1285:
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Reopened issue because javadoc needs to be further improved.
> Description API ZipfDistribution
>
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Otmar Ertl updated MATH-1269:
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Attachment: MATH-1269-fix-tempC-infinity.patch
One more proposal. The problem is the calculation of
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Otmar Ertl resolved MATH-1277.
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Resolution: Fixed
Fix Version/s: 3.6
4.0
> Incorrect Kendall Tau calc due to
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Otmar Ertl commented on MATH-1277:
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Thanks for reporting! Fixed bug in following commits:
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Otmar Ertl resolved MATH-1276.
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Resolution: Fixed
Fix Version/s: 3.6
4.0
> Override
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Otmar Ertl commented on MATH-1276:
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Gilles, thank you for your suggestions. I have split the patch into
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Otmar Ertl updated MATH-1276:
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Flags: Patch
> Override GeometricDistribution.inverseCumulativeProbability() to speed up
> sampling
>
Otmar Ertl created MATH-1276:
Summary: Override
GeometricDistribution.inverseCumulativeProbability() to speed up sampling
Key: MATH-1276
URL: https://issues.apache.org/jira/browse/MATH-1276
Project:
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Otmar Ertl updated MATH-1276:
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Attachment: MATH-1276.patch
Any objections to the proposed patch?
> Override
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Otmar Ertl commented on MATH-1246:
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Sounds ok to me, due to its slow convergence avoiding Monte Carlo as
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Otmar Ertl resolved MATH-1274.
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Resolution: Fixed
> Represent Kolmogorov-Smirnov statistic as long value
>
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Otmar Ertl commented on MATH-1274:
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Applied the patch after renaming the methods to integralXxx as
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Otmar Ertl updated MATH-1274:
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Fix Version/s: 3.6
4.0
> Represent Kolmogorov-Smirnov statistic as long value
>
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Otmar Ertl commented on MATH-1246:
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The p-value is the probability that the observed KS-statistic is
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Otmar Ertl updated MATH-1274:
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Attachment: MATH-1274.patch
> Represent Kolmogorov-Smirnov statistic as long value
>
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Otmar Ertl updated MATH-1274:
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Flags: Patch
> Represent Kolmogorov-Smirnov statistic as long value
>
Otmar Ertl created MATH-1274:
Summary: Represent Kolmogorov-Smirnov statistic as long value
Key: MATH-1274
URL: https://issues.apache.org/jira/browse/MATH-1274
Project: Commons Math
Issue Type:
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https://issues.apache.org/jira/browse/MATH-1246?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=14745495#comment-14745495
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Otmar Ertl commented on MATH-1246:
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After some research I have the feeling we are discussing how to define
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Otmar Ertl edited comment on MATH-1246 at 9/15/15 2:00 PM:
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After some research I
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Otmar Ertl commented on MATH-1246:
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I still have doubts:
# There is a difference, if there are ties within
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Otmar Ertl commented on MATH-1269:
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+1 for 2nd patch, I have run the test over the range (MAX_LONG -
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Otmar Ertl commented on MATH-1246:
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The Monte Carlo approach can be modified by simultaneously sampling D.
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Otmar Ertl commented on MATH-1269:
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I have an objection. The patch changes the result of
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Otmar Ertl commented on MATH-1246:
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I am thinking of another way to treat ties:
The probability that two
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Otmar Ertl commented on MATH-1269:
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In my opinion simply changing NaNs to infinity can only be a
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Otmar Ertl edited comment on MATH-1246 at 9/9/15 9:19 PM:
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I am thinking of
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Otmar Ertl commented on MATH-1262:
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+1 for updating Javadoc and adding a note that memory needs are
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Otmar Ertl edited comment on MATH-1262 at 9/6/15 8:46 AM:
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-This
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Otmar Ertl commented on MATH-1262:
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The paper [C.Schwartz, Numerical Calculation of Bessel Functions,
Otmar Ertl created MATH-1269:
Summary: FastMath.exp may return NaN
Key: MATH-1269
URL: https://issues.apache.org/jira/browse/MATH-1269
Project: Commons Math
Issue Type: Bug
Affects Versions:
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Otmar Ertl updated MATH-1269:
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Summary: FastMath.exp may return NaN for non-NaN arguments (was:
FastMath.exp may return NaN)
>
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Otmar Ertl updated MATH-1269:
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Description: I have observed that FastMath.exp(709.8125) returns NaN.
However, the exponential function
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Otmar Ertl commented on MATH-1262:
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This
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Otmar Ertl commented on MATH-1262:
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The current implementation allocates a double array
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Otmar Ertl resolved MATH-1220.
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Resolution: Fixed
More efficient sample() method for ZipfDistribution
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Otmar Ertl commented on MATH-1220:
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Introduced rejection-inversion sampling approach with
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Otmar Ertl commented on MATH-990:
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Applied patch in following commits:
*
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Otmar Ertl updated MATH-990:
Fix Version/s: 3.6
Improve performance of MathArrays.sortInPlace
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Otmar Ertl resolved MATH-990.
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Resolution: Fixed
Improve performance of MathArrays.sortInPlace
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Otmar Ertl commented on MATH-990:
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I think I do not have the rights to reassign this issue.
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Otmar Ertl commented on MATH-1258:
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I have just added the missing entries to changes.xml.
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Otmar Ertl resolved MATH-1258.
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Resolution: Fixed
Fix Version/s: 3.6
4.0
compute() method in classes in
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Otmar Ertl commented on MATH-1258:
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Thanks! It worked now.
compute() method in classes
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Otmar Ertl commented on MATH-1258:
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Committed my patch:
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Otmar Ertl commented on MATH-1220:
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If there are not any concerns, I will commit
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Otmar Ertl updated MATH-1258:
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Attachment: patch.diff
I also do not like throwing an ArrayIndexOutOfBoundsException due to 2 reasons:
*
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Otmar Ertl commented on MATH-1257:
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+1 for the patch, it eliminates one source of error.
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Otmar Ertl updated MATH-1220:
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Attachment: patch_v3
I have revised the patch that makes use of rejection-inversion sampling. I have
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Otmar Ertl updated MATH-1254:
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Attachment: seed628.patch
Unit test CorrelatedRandomVectorGeneratorTest is flaky
Otmar Ertl created MATH-1254:
Summary: Unit test CorrelatedRandomVectorGeneratorTest is flaky
Key: MATH-1254
URL: https://issues.apache.org/jira/browse/MATH-1254
Project: Commons Math
Issue
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Otmar Ertl commented on MATH-1253:
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The given example is problematic anyway, because the
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Otmar Ertl commented on MATH-1242:
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Fixed shuffle algorithm and added unit test in the
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Otmar Ertl resolved MATH-1242.
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Resolution: Fixed
Speed up KolmogorovSmirnovTest.monteCarloP()
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Otmar Ertl updated MATH-1242:
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Attachment: bug_fix_and_unit_test.patch
Here is an update of the fix. I have moved the shuffle algorithm
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Otmar Ertl updated MATH-1242:
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Attachment: patch
There is a bug in the simplified Fisher-Yates shuffle algorithm. The random
number r
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Otmar Ertl commented on MATH-1242:
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Your modifications look fine. Thanks!
Speed up
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Otmar Ertl updated MATH-1242:
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Attachment: patch
Speed up KolmogorovSmirnovTest.monteCarloP()
Otmar Ertl created MATH-1242:
Summary: Speed up KolmogorovSmirnovTest.monteCarloP()
Key: MATH-1242
URL: https://issues.apache.org/jira/browse/MATH-1242
Project: Commons Math
Issue Type:
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https://issues.apache.org/jira/browse/MATH-1236?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Otmar Ertl updated MATH-1236:
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Attachment: patch
Improve Kolmogorov-Smirnov statistic for two samples
Otmar Ertl created MATH-1236:
Summary: Improve Kolmogorov-Smirnov statistic for two samples
Key: MATH-1236
URL: https://issues.apache.org/jira/browse/MATH-1236
Project: Commons Math
Issue Type:
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https://issues.apache.org/jira/browse/MATH-1220?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Otmar Ertl updated MATH-1220:
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Attachment: Zipf Rejection Inversion Sampling Notes.pdf
I attached my notes that should make it clearer
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Otmar Ertl commented on MATH-1220:
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Yes, my proposed method outperforms the rejection
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Otmar Ertl updated MATH-1220:
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Attachment: patch_v2
I have found some time to read the paper you mentioned:
http://epub.wu.ac.at/1176/.
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Otmar Ertl reopened MATH-1220:
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More efficient sample() method for ZipfDistribution
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Otmar Ertl commented on MATH-1220:
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Since we are sampling from a finite number of points,
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Otmar Ertl commented on MATH-1220:
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Caching generalizedHarmonic(numberOfElements,
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Otmar Ertl edited comment on MATH-1220 at 4/27/15 3:10 PM:
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Do
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Otmar Ertl commented on MATH-1220:
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Do you mean using a pre-calculated CDF tanle and
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Otmar Ertl commented on MATH-1220:
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The avg. number of iterations until a random value is
Otmar Ertl created MATH-1220:
Summary: More efficient sample() method for ZipfDistribution
Key: MATH-1220
URL: https://issues.apache.org/jira/browse/MATH-1220
Project: Commons Math
Issue Type:
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https://issues.apache.org/jira/browse/MATH-1220?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Otmar Ertl updated MATH-1220:
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Attachment: patch_v1
More efficient sample() method for ZipfDistribution
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Otmar Ertl commented on MATH-1220:
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The patch overrides the sample() method for
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Otmar Ertl updated MATH-990:
Attachment: patch
The performance can be further improved while still using Collections.sort by
avoiding the
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Otmar Ertl commented on MATH-1159:
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If multiple quantile values need to be computed
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Otmar Ertl commented on MATH-1158:
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I like your proposal. I think there is much more to
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Otmar Ertl commented on MATH-1154:
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I agree with Phil that my proposed patch does not
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Otmar Ertl commented on MATH-1124:
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What about the C++ STL design choice? The
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Otmar Ertl edited comment on MATH-1124 at 10/7/14 6:25 PM:
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What
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Otmar Ertl commented on MATH-1124:
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@Ole Ersoy: For some distributions it is quite common
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Otmar Ertl commented on MATH-1124:
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It is fairly easy to write a RandomGenerator mock
Otmar Ertl created MATH-1154:
Summary: Statistical tests in stat.inference package are very slow
due to implicit RandomGenerator initialization
Key: MATH-1154
URL: https://issues.apache.org/jira/browse/MATH-1154
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Otmar Ertl updated MATH-1154:
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Attachment: math3.patch
This patch demonstrates a fix using lazy initialization of default random
number
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