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https://issues.apache.org/jira/browse/SPARK-11302?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14974382#comment-14974382
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eyal sharon commented on SPARK-11302:
-------------------------------------
Sure , I will try to elaborate more
MU is the mean vector of my data set.
Here is the basic flow of my code with function I used. Each function runs
over the data set arranged in a matrix
*1- Create a mu vector *
def createMU(mat: DenseMatrix): Vector = {
val columnsInArray = toArrays(mat,false)
Vectors.dense(columnsInArray.map(vector => vector.sum/vector.length ))
}
*2- create a cov matrix *
def createCovSigma(mat: DenseMatrix,mu: Vector) : DenseMatrix = {
val rowsInArray = toArrays(mat,true)
val sigmaSubMU = rowsInArray.map(row => {(row.toList zip
mu.toArray).map(elem=>elem._1-elem._2)}.toArray )
val checkArray = sigmaSubMU.flatMap(row=>row)
println("Matrix dimensions - rows: " + mat.numRows + ",cols: " +
mat.numCols)
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
}
* Note the I am using an auxiliary function toArrays, here is the definition:
def toArrays(mat: Matrix,byRow: Boolean): Array[Array[Double]] = {
val direction = if (byRow) mat.numCols else mat.numRows
mat.toArray.grouped(direction).toArray
}
*3- After having the mu and the sigma, I can no create an instance of the
gaussian *
val mg = new MultivariateGaussian(mu,sigma)
4- Now, I can create a projection using the PDF
E.g- d3=mg.pdf(Vectors.dense(629,640,1.7188,618.19))
The model returns zero for every data point
4- For validation, I ran a gaussian implantation on Matlab and the results
are:
- For the case of *non covariance* matrix, the two models yield same result
exactly
- For the case of *covariance*, Matlab yields good result but Mlib doesn't.
( note that I feed the two models with the same input, concretely, the same
MU and covariance matrix )
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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