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 <[email protected]> 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, '')
