Mailing-List: contact dev-help@systemml.incubator.apache.org; run by ezmlm
Precedence: bulk
Reply-To: dev@systemml.incubator.apache.org
MIME-Version: 1.0
From: Ethan Xu <ethan.yifanxu@gmail.com>
Date: Wed, 19 Apr 2017 17:07:15 -0400
Message-ID: <CAGzHvLm3EJyZ9gbFbfMx+oroSvLq+=tR4GvofZiwqY1Ze8yP1w@mail.gmail.com>
Subject: Documents of SystemML Algorithms Reference
To: dev@systemml.incubator.apache.org
Content-Type: multipart/alternative; boundary=001a1140df48b13198054d8b68b9
archived-at: Wed, 19 Apr 2017 21:07:22 -0000

--001a1140df48b13198054d8b68b9
Content-Type: text/plain; charset=UTF-8

Hello,

I'm reading the documents on Multinomial Logistic Regression (
https://apache.github.io/incubator-systemml/algorithms-classification.html#usage)
with Scala API. It says

val model = lr.fit(X_train_df)
val prediction = model.transform(X_test_df)


The "Arguments" section below it says:

X: Location (on HDFS) to read the input matrix of feature vectors; each row
constitutes one feature vector.

Y: Location to read the input one-column matrix of category labels that
correspond to feature vectors in X. Note the following:...
The explanation of the arguments seem to correspond to the Hadoop and Spark
API.

Could someone please advise what are the specifications of `X_train_df` and
`X_test_df`? Are they the same as specified in the Python API? i.e.:

# X_train, y_train and X_test can be NumPy matrices or Pandas
DataFrame or SciPy Sparse Matrixy_test = logistic.fit(X_train,
y_train).predict(X_test)# df_train is DataFrame that contains two
columns: "features" (of type Vector) and "label". df_test is a
DataFrame that contains the column "features"

The explanation of arguments for Python/Scala seem to be missing for other
algorithms, too.

Thanks a lot,

Ethan

--001a1140df48b13198054d8b68b9--