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https://issues.apache.org/jira/browse/SPARK-11302?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14974191#comment-14974191
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eyal sharon commented on SPARK-11302:
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Hi Sean ,
Thanks for reaching out.
For convenience, when added the Covariance matrix to the ticket I rounded
the numbers.
Below are the real values (should be organize in a 4x4 matrix ). The
covariance matrix, by math definition, is always *positive semi definite ( *and
not positive definite* )*
I checked this values in R with this function *
is.positive.semi.definite (*with
a tolerance levels of e-11,e-15,e-20) and it returns true for all cases .
401139.3599484815,387621.07664008765,73902.67897058972,314299.39550677023
,387621.07664008765,408594.15705509897,94234.19718534013,351268.39070671634
,73902.67897058972,94234.19718534013,969566.5912689088,125849.1446871119
,314299.39550677023,351268.39070671634,125849.1446871119,393043.68462620175
Best, Eyal
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> Multivariate Gaussian Model with Covariance matrix return zero always
> ------------------------------------------------------------------------
>
> Key: SPARK-11302
> URL: https://issues.apache.org/jira/browse/SPARK-11302
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Reporter: eyal sharon
> Priority: Minor
>
> I have been trying to apply an Anomaly Detection model using Spark MLib.
> As an input, I feed the model with a mean vector and a Covariance matrix.
> ,assuming my features contain Co-variance.
> Here are my input for the model ,and the model returns zero for each data
> point for this input.
> MU vector -
> 1054.8, 1069.8, 1.3 ,1040.1
> Cov' matrix -
> 165496.0 , 167996.0, 11.0 , 163037.0
> 167996.0, 170631.0, 19.0, 165405.0
> 11.0, 19.0 , 0.0, 2.0
> 163037.0, 165405.0 2.0 , 160707.0
> Conversely, for the non covariance case, represented by this matrix ,the
> model is working and returns results as expected
> 165496.0, 0.0 , 0.0, 0.0
> 0.0, 170631.0, 0.0, 0.0
> 0.0 , 0.0 , 0.8, 0.0
> 0.0 , 0.0, 0.0, 160594.2
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