[MINOR] Add ses Python autogenerated builtin

Author: Baunsgaard

src/main/python/systemds/operator/algorithm/__init__.py

@@ -162,6 +162,7 @@
 from .builtin.scaleApply import scaleApply 
 from .builtin.scaleMinMax import scaleMinMax 
 from .builtin.selectByVarThresh import selectByVarThresh 
+from .builtin.ses import ses 
 from .builtin.setdiff import setdiff 
 from .builtin.sherlock import sherlock 
 from .builtin.sherlockPredict import sherlockPredict 
@@ -340,6 +341,7 @@
  'scaleApply',
  'scaleMinMax',
  'selectByVarThresh',
+ 'ses',
  'setdiff',
  'sherlock',
  'sherlockPredict',

src/main/python/systemds/operator/algorithm/builtin/ses.py

@@ -0,0 +1,48 @@
+# -------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# -------------------------------------------------------------
+
+# Autogenerated By   : src/main/python/generator/generator.py
+# Autogenerated From : scripts/builtin/ses.dml
+
+from typing import Dict, Iterable
+
+from systemds.operator import OperationNode, Matrix, Frame, List, MultiReturn, Scalar
+from systemds.utils.consts import VALID_INPUT_TYPES
+
+
+def ses(x: Matrix,
+        **kwargs: Dict[str, VALID_INPUT_TYPES]):
+    """
+     Builtin function for simple exponential smoothing (SES).
+    
+    
+    
+    :param x: Time series vector [shape: n-by-1]
+    :param h: Forecasting horizon
+    :param alpha: Smoothing parameter yhat_t = alpha * x_y + (1-alpha) * yhat_t-1
+    :return: Forecasts [shape: h-by-1]
+    """
+
+    params_dict = {'x': x}
+    params_dict.update(kwargs)
+    return Matrix(x.sds_context,
+        'ses',
+        named_input_nodes=params_dict)