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https://issues.apache.org/jira/browse/SPARK-11302?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14973325#comment-14973325
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eyal sharon commented on SPARK-11302:
-------------------------------------
Hi Sean,
Thanks for your reply. I will try to add more info
- I'm using a Multivariate Gaussian for anomaly detection. I'm using this
source from Mlib - MultivariateGaussian
<https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala>
This library enables to create a Gaussian instance and to feed it with new
data point (which is a dense vector ) to return the probability.
Now ,when I run my code over, it always returns zero
- I checked my code using this example implantation I have found on GIT example
for anomaly detection <https://github.com/vivanov/anomaly-detection>
Note the this example uses a *non covariance* matrix, If you run this code
with a full covariance matrix, the PDF function will always return zero.
To check to covariance case , here is a function which takes a data set
(mat) with features and a corresponding mean vector (mu) :
def createCovSigma(mat: DenseMatrix,mu: Vector) : DenseMatrix = {
val rowsInArray = mat.transpose.toArray.grouped(mat.numCols).toArray
val sigmaSubMU = rowsInArray.map(row => {(row.toList zip
mu.toArray).map(elem=>elem._1-elem._2)}.toArray )
val checkArray = sigmaSubMU.flatMap(row=>row)
val mat2 = new DenseMatrix(mat.numRows, mat.numCols,checkArray,true)
val sigmaTmp: DenseMatrix = mat2.transpose.multiply(mat2)
val sigmaTmpArray=sigmaTmp.toArray
val sigmaMatrix: DenseMatrix = new DenseMatrix(mat.numCols,
mat.numCols, sigmaTmpArray.flatMap(x=>List(x/mat.numRows)),true)
sigmaMatrix
}
If you need me to add more info I will
Thanks!
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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