What I want to do
I have a trained a ml.classification.LogisticRegressionModel using spark ml package.

It has 3 features and 3 classes. So the generated model has coefficients in (3, 3) matrix and intercepts in Vector of length (3) as expected.

Now, I want to take these coefficients and convert this ml.classification.LogisticRegressionModel model to an instance of mllib.classification.LogisticRegressionModel model.

Why I want to do this
Computational Speed as SPARK-10413 is still in progress and scheduled for Spark 2.2 which is not yet released.

Why I think this is possible
I checked https://spark.apache.org/docs/latest/mllib-linear-methods.html#logistic-regression and in that example a multinomial Logistic Regression is trained. So as per this the class mllib.classification.LogisticRegressionModel can encapsulate these parameters.

Problem faced
The only constructor in mllib.classification.LogisticRegressionModel takes a single vector as coefficients and single double as intercept but I have a Matrix of coefficients and Vector of intercepts respectively.

I tried converting matrix to a vector by just taking the values (Guess work) but got 

requirement failed: LogisticRegressionModel.load with numClasses = 3 and numFeatures = 3 expected weights of length 6 (without intercept) or 8 (with intercept), but was given weights of length 9

So any ideas?