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Sean Owen commented on SPARK-11302: ----------------------------------- In R, it notes that your covariance matrix isn't positive definite. It isn't -- it has negative eigenvalues. It doesn't mean it's wrong, but it could. Are you sure this is the right input and isn't maybe a victim of precision problems or rounding? in any event, I don't think MLlib is the problem here, since R won't compute this either. (I glanced at the implementation and it looked like what I expect to see too.) > 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 -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org