[systemds] 01/02: [MINOR] Set default gmmPredict model type

[ba...@apache.org]

This is an automated email from the ASF dual-hosted git repository.

baunsgaard pushed a commit to branch main
in repository https://gitbox.apache.org/repos/asf/systemds.git

commit f4d9c2af97a6e2d1ea205d63d83e7734ae3d0edd
Author: baunsgaard <ba...@tugraz.at>
AuthorDate: Fri Dec 3 14:35:26 2021 +0100

    [MINOR] Set default gmmPredict model type
    
    Also build python gmm based on it.
---
 scripts/builtin/gmmPredict.dml                                    | 2 +-
 src/main/python/systemds/operator/algorithm/builtin/gmmPredict.py | 5 +++--
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/scripts/builtin/gmmPredict.dml b/scripts/builtin/gmmPredict.dml
index e054902..21a897b 100644
--- a/scripts/builtin/gmmPredict.dml
+++ b/scripts/builtin/gmmPredict.dml
@@ -44,7 +44,7 @@
 # compute posterior probabilities for new instances given the variance and mean of fitted data
 
 m_gmmPredict = function(Matrix[Double] X, Matrix[Double] weight,
-  Matrix[Double] mu, Matrix[Double] precisions_cholesky, String model)
+  Matrix[Double] mu, Matrix[Double] precisions_cholesky, String model = "VVV")
   return(Matrix[Double] predict, Matrix[Double] posterior_prob)
 {
   # compute the posterior probabilities for new instances
diff --git a/src/main/python/systemds/operator/algorithm/builtin/gmmPredict.py b/src/main/python/systemds/operator/algorithm/builtin/gmmPredict.py
index 23a6397..e4e556a 100644
--- a/src/main/python/systemds/operator/algorithm/builtin/gmmPredict.py
+++ b/src/main/python/systemds/operator/algorithm/builtin/gmmPredict.py
@@ -33,7 +33,7 @@ def gmmPredict(X: Matrix,
                weight: Matrix,
                mu: Matrix,
                precisions_cholesky: Matrix,
-               model: str):
+               **kwargs: Dict[str, VALID_INPUT_TYPES]):
     """
     :param X: Matrix X (instances to be clustered)
     :param weight: Weight of learned model
@@ -42,7 +42,8 @@ def gmmPredict(X: Matrix,
     :param model: fitted model
     :return: 'OperationNode' containing predicted cluster labels & probabilities of belongingness & for new instances given the variance and mean of fitted data 
     """
-    params_dict = {'X': X, 'weight': weight, 'mu': mu, 'precisions_cholesky': precisions_cholesky, 'model': model}
+    params_dict = {'X': X, 'weight': weight, 'mu': mu, 'precisions_cholesky': precisions_cholesky}
+    params_dict.update(kwargs)
     
     vX_0 = Matrix(X.sds_context, '')
     vX_1 = Matrix(X.sds_context, '')