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https://issues.apache.org/jira/browse/MAHOUT-1856?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15833722#comment-15833722
]
ASF GitHub Bot commented on MAHOUT-1856:
----------------------------------------
Github user andrewpalumbo commented on a diff in the pull request:
https://github.com/apache/mahout/pull/246#discussion_r97235704
--- Diff:
math-scala/src/main/scala/org/apache/mahout/math/algorithms/regression/OrdinaryLeastSquares.scala
---
@@ -0,0 +1,96 @@
+/**
+ * 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.
+ */
+
+package org.apache.mahout.math.algorithms.regression
+
+import org.apache.mahout.math.{Vector => MahoutVector}
+import org.apache.mahout.math.drm.RLikeDrmOps._
+import org.apache.mahout.math.drm.DrmLike
+import org.apache.mahout.math.scalabindings._
+import org.apache.mahout.math.scalabindings.RLikeOps._
+
+import scala.reflect.ClassTag
+
+/**
+ * import
org.apache.mahout.math.algorithms.regression.OrdinaryLeastSquares
+ * val model = new OrdinaryLeastSquares()
+ *
+ * model.calcStandardErrors = true
+ *
+ */
+class OrdinaryLeastSquares[K](hyperparameters: Map[String, Any] = Map(""
-> None)) extends LinearRegressor[K] {
+ // https://en.wikipedia.org/wiki/Ordinary_least_squares
+
+ var calcStandardErrors: Boolean =
hyperparameters.asInstanceOf[Map[String,
Boolean]].getOrElse("calcStandardErrors", true)
+ var addIntercept: Boolean = hyperparameters.asInstanceOf[Map[String,
Boolean]].getOrElse("addIntercept", true)
+
+ var summary = ""
+ def fit(drmFeatures: DrmLike[K], drmTarget: DrmLike[K]): Unit = {
--- End diff --
Continuing on from our discussion on Slack I would think that Fit may be a
more appropriate place for Hyperparameters eg:
```
fit(observed_independent: Drm[K], observerd_targets: Drm[K],
hyperparamters: Option[List[double]]): List[double]
```
I think that this may be a matter of Convention, so If you're following a
convention that I am not familiar with, this may be fine. However I feel that
this may be more robust.
> Create a framework for new Mahout Clustering, Classification, and
> Optimization Algorithms
> ------------------------------------------------------------------------------------------
>
> Key: MAHOUT-1856
> URL: https://issues.apache.org/jira/browse/MAHOUT-1856
> Project: Mahout
> Issue Type: New Feature
> Affects Versions: 0.12.1
> Reporter: Andrew Palumbo
> Assignee: Trevor Grant
> Priority: Critical
> Fix For: 0.13.0
>
>
> To ensure that Mahout does not become "A loose bag of algorithms", Create
> basic traits with funtions common to each class of algorithm.
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