[GitHub] [systemds] Baunsgaard commented on a change in pull request #1191: Cspline Builtin

[GitBox <gi...@apache.org>]

Baunsgaard commented on a change in pull request #1191:
URL: https://github.com/apache/systemds/pull/1191#discussion_r584551960



##########
File path: scripts/builtin/cspline.dml
##########
@@ -0,0 +1,63 @@
+#-------------------------------------------------------------
+#
+# 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.
+#
+#-------------------------------------------------------------
+#
+# THIS SCRIPT SOLVES CUBIC SPLINE INTERPOLATION
+#
+# INPUT PARAMETERS:
+# --------------------------------------------------------------------------------------------
+# NAME  TYPE           DEFAULT   MEANING
+# --------------------------------------------------------------------------------------------
+# X     Matrix[Double]  ---      1-column matrix of x values knots
+# Y     Matrix[Double]  ---      1-column matrix of corresponding y values knots
+# inp_x Double          ---      the given input x, for which the cspline will find predicted y.
+# Log   String          " "      Location to store iteration-specific variables for monitoring and debugging purposes
+#
+# tol   Double          0.000001 Tolerance (epsilon); conjugate graduent procedure terminates early if
+#                                L2 norm of the beta-residual is less than tolerance * its initial norm
+# maxi  Int             0        Maximum number of conjugate gradient iterations, 0 = no maximum
+# --------------------------------------------------------------------------------------------
+# OUTPUT: 
+# pred_Y Matrix[Double] ---      Predicted value
+# K      Matrix[Double] ---      Matrix of k parameters

Review comment:
       I think the submission is fine, but to make it practical to use we need the predict. This could be a new JIRA Task.




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