StandardScaler returns NaN

env:

spark-1.6.0 with scala-2.10.4

usage:

// row of df : DataFrame = (String,String,double,Vector) as (id1,id2,label,feature)
val df = sqlContext.read.parquet("data/Labeled.parquet")
val SC = new StandardScaler()
.setInputCol("feature").setOutputCol("scaled")
.setWithMean(false).setWithStd(true).fit(df) 


val scaled = SC.transform(df)
.drop("feature").withColumnRenamed("scaled","feature")

Code as the example here http://spark.apache.org/docs/latest/ml-features.html#standardscaler

NaN exists in scaled, SC.mean, SC.std

I don’t understand why StandardScaler could do this even in mean or how to handle this situation. Any advice is appreciated.

data size as parquet is 1.6GiB, if anyone needs it just let me know

UPDATE:

Get through the code of StandardScaler and this is likely to be a problem of precision of Double when MultivariateOnlineSummarizer aggregated.